Delivering the Benefits of Smart Appliances EA Technology A research report completed for the Department for Environment, Food and Rural Affairs

(September 2011)

Published by the Department for Environment, Food and Rural Affairs Department for Environment, Food and Rural Affairs Nobel House 17 Smith Square London SW1P 3JR Tel: 020 7238 6000 Website: www.defra.gov.uk © Queen's Printer and Controller of HMSO 2011 This publication is value added. If you wish to re-use this material, please apply for a Click-Use Licence for value added material at: http://www.opsi.gov.uk/click-use/value-added-licence-information/index.htm Alternatively applications can be sent to Office of Public Sector Information, Information Policy Team, St Clements House, 2-16 Colegate, Norwich NR3 1BQ; Fax: +44 (0)1603 723000; email: hmsolicensing@cabinet-office.x.gsi.gov.uk Information about this publication is available from: SCP&W Evidence Base Defra Zone 5D, 5th Floor, Ergon House c/o Nobel House, 17 Smith Square London SW1P 3JR Email: scpevidence@defra.gsi.gov.uk

Delivering the Benefits of Smart Appliances SPMT10_043

Final Report to the Department for Environment, Food and Rural Affairs September 2011 This research was commissioned and funded by Defra. The views expressed reflect the research findings and the authors’ interpretation; they do not necessarily reflect Defra policy or opinions. EA Technology Capenhurst Chester CH1 6ES

TABLE OF CONTENTS

Executive Summary

1 Introduction and Objectives .....................................................................................1

2 Drivers to manage customer demands ...................................................................3

3 Smart Appliances – An Overview...........................................................................10

4 Smart Appliance Experiences ................................................................................13

5 Stakeholder Views and Experiences .....................................................................18

6 Barriers to Deployment and Uptake of Smart Appliances in the UK..................28

7 Impact of Selected Smart Appliances....................................................................35

8 Roadmap for the deployment of Smart Appliances in the UK ............................40

9 Concluding Remarks ...............................................................................................46

Appendix A: End Use Categories.................................................................................A-1

Appendix B: Categorisation of Appliances.................................................................A-4

Appendix C: Smart Appliance Experiences ................................................................A-5

Appendix D: Organisations/ Persons Engaged with for Stakeholder Dialogue....A-26

Appendix E: Case Studies...........................................................................................A-27

Glossary

AC Alternating Current

DC Direct Current

DD Dynamic Demand

DECC Department of Energy and Climate Change

Defra Department for Environment, Food and Rural Affairs

DHW Domestic Hot Water

DNO Distribution Network Operator

DPCR Distribution Price Control Review

DSM Demand Side Management

EV Electric Vehicle

FIT Feed in Tariff

GB Great Britain

GB-TSO Great Britain Transmission System Operator

HV High Voltage

L1 Level 1 (as used in Smart Appliance case studies)

L2 Level 2 (as used in Smart Appliance case studies)

LCNF Low Carbon Network Fund

LV Low Voltage

MTP Market Transformation Programme

RHI Renewable Heat Incentive

SBP System Buy Price

STOR Short Term Operating Reserve

SSP System Sell Price

ToU Time of Use

UK United Kingdom

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Executive Summary

Smart Appliances are one of the suite of measures that can help the UK meet its policy objectives in terms of carbon emissions reduction, the facilitation of the uptake of renewable energy sources and in reducing overall energy consumption. The development of Smart Appliances is complementary to the ongoing work on Smart Meters and Smart Grids. In many respects the successful deployment of Smart Appliances is essential for Smart Grids and Smart Metering to deliver the full range of anticipated benefits, particularly those in relation to end-use energy efficiency. Recognising the importance of Smart Appliances in enabling end-users to adjust their pattern, and to a lesser extent, their total energy consumption, AEA Technology acting on behalf of Defra commissioned a study to investigate the potential benefits of Smart Appliances in terms of energy and carbon savings and enhanced electricity system operation. This report presents the outcome of this study. Drivers for Smart Appliances in the UK Consideration of the main drivers to manage the pattern of demand in the UK shows that the generic requirements for Smart Appliances fall into two main categories, namely:

A- Shorter term measures of up to half an hour, which can be used to provide balancing services (particularly frequency response) to National Grid. This could also include short duration (i.e. up to half an hour) interruptions to assist with the avoidance of peak prices.

B- Longer term measures that can be used for a wider range of purposes including the management of network constraints or the provision of reserve services to National Grid. This could also include the integration of renewables, for example during extended periods of low wind.

Smart Appliance Experiences A review of selected trials and pilots of Smart Appliances highlights that a range of technologies have been evaluated over a number of years (for example one of the UK trials dates back to the late 1990s). However, Smart Appliances are still very much an emerging concept. To date, the majority of trials and pilots have targeted large single end-uses of energy that contribute towards the peak demands they are being used to alleviate. The review demonstrates the importance of Standards to ensure to allow demand response to be implemented using standard operational instructions and communications. Such a requirement, as yet, does not exist in the UK. Another important feature of a number of the trials is the impact of customer engagement on the project outcome. Some of the smaller trials demonstrate a high degree of customer involvement, and as such high levels of peak load reduction were achieved. However, this is not always the case for larger trials. In addition, the review suggests that not all customers are willing to install control technologies that

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enable their end use loads to be remotely or automatically controlled. This demonstrates that customer willingness to participate is an important factor in evaluating the potential impact of a particular measure. Stakeholder Dialogue In addition to consumers, technology developers, appliance manufacturers and appliance suppliers are key stakeholders in the development and deployment of Smart Appliances. They can be expected to understand the requirements of consumers and have direct knowledge of the impact of external factors on their businesses. Therefore a number of interviews were undertaken with targeted industry stakeholders to explore their views and opinions of Smart Appliances. Other industry experts with a specific interest in Smart Appliances and customer behaviour were also selected. As might be expected, the stakeholders provided a diverse range of views and opinions, and in most cases there was little consensus in the responses to the questions posed. However, key issues raised included:

- The lack of customer awareness of the benefits of Smart Appliances is regarded as a major obstacle to the deployment of Smart Appliances;

- The difficulty of ensuring benefits can be properly allocated between consumers, Energy Suppliers and other stakeholders is viewed as another important factor;

- The need for appliance Standards is considered to be a pre-requisite for the development of Smart Appliances, particularly in terms of ensuring interoperability and the provision of ‘plug and play’ solutions;

- The role of Time of Use (ToU) tariffs is generally considered to be an essential motivator for the development and deployment of Smart Appliances by those interviewed. Without ToU, many of the stakeholders believe that there will be little stimulus for the development and deployment of Smart Appliances.

Smart controls were also recognised in having a role to play in helping to ensure that heating systems are operating efficiently, i.e. not over or under-heating the occupied space. Barriers to Deployment and Uptake in the UK There are a number of barriers that impact on the potential deployment and uptake of Smart Appliances in the UK. These include technology related barriers, market related barriers and customer related barriers. A summary of these is provided overleaf. Technology Related Barriers Not all Smart Appliances respond to inherent changes in electricity network properties. Those that don’t, rely on a communications system to implement changes to their pattern of operation. The Smart Meter provides a communications link directly to the home, thereby providing an opportunity to directly control end-use loads. Therefore, the readiness of the electricity network to interface with Smart

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Appliances is regarded to be an important factor. The wide scale adoption of Smart Appliances is unlikely to happen until Smart Meters and Smart Grids are in place. A number of aspects of the rollout of Smart Meters have yet to be finalised. Whilst it has been decided that data collected from Smart Meters will be channelled through a central communications hub known as DataCommsCo (DCC), it is not yet clear how parties such as Energy Suppliers and Network Operators will interact with the DCC to remotely control end-use loads via the Home Area Network. This is regarded as a crucial element in the implementation of Smart Appliances, as it enables the existing two-way communications system for the Smart Meter to be utilised to control the operation of appliances. Market Related Barriers There are a number of market related barriers that impact on the deployment of Smart Appliances, many of these relate specifically to the structure of the UK electricity market and the rules governing the way electricity is traded. ToU tariffs are generally recognised as an important stimulus for the development and deployment of Smart Appliances. In the UK, the electricity supply market is fully de-regulated, and it is up to Energy Suppliers themselves to design their tariffs. Therefore, it is difficult to determine the extent to which innovative ToU tariffs will be offered by Energy Suppliers in the UK. However, it is important to note that other incentive mechanisms can be put in place. Alternatives to ToU tariffs might include one-payments (suitable for all types of DSM measures) or discounts from the Energy Tariff (applicable only for Supplier led DSM measures). The UK electricity market is fully unbundled, i.e. the competitive and non-competitive businesses are fully separated even where they are in common ownership. Thus, the benefits of Smart Appliances are spread amongst a number of stakeholders, rather than within a single, vertically integrated utility company. As a result, it is difficult to ensure that the costs and benefits are shared appropriately between the relevant stakeholders. This is especially pertinent from the perspective of Distribution Network Operators, who do not routinely engage directly with end-use consumers. The profiling settlement system used to determine the pattern of consumption of households and other (selected) small users means that it is difficult for Energy Suppliers to capture the value associated with load shifting. The introduction of Smart Meters means that it will be possible for all electricity consumption to be settled according to the actual profile, rather than the ‘deemed’ profile. However, there is no requirement on Energy Suppliers to move away from the use of the current profiling system, although they are able to do so if they wish. Customer Related Barriers Customer attitudes towards Smart Appliances represents one of the most significant barriers to the widescale deployment of these devices. In a situation where ‘competitive market forces’ dominate, then the customer is the ultimate decision maker. If customers do not want the appliances, or if developers do not envisage a market where customers will want the appliances, then it is evident that the market for such products will not develop.

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Impact of Selected Smart Appliances A number of case studies have been used to better understand the potential impact of a selection of Smart Appliances and the specific barriers to their uptake. Five appliances that contribute most to demand during peak periods (both now and in future years) and which are suitable for demand response were selected. The selected appliances are heat pumps, air conditioners, refrigeration, washing machines and tumble dryers. The control of the charging of Electric Vehicles is recognised as an important area, however this was outside the scope of the current study. The case studies were used to explore the benefits associated with two levels of Smart Appliances with a ‘Business As Usual’ baseline and to explore the main barriers to their uptake. Under Level 1 only minimal design changes are required to existing appliance designs, whilst Level 2 represents more complex options, as summarised below.

- For both heat pumps and air conditioning, Level 1 does not require any changes to the design of the standard system other than the means of remotely switching the appliance off for short periods, whilst Level 2 requires design changes to incorporate thermal storage.

- For refrigeration, Level 1 appliances include the capability to provide dynamic frequency response services to National Grid by automatically adjusting to changes in system frequency. Level 2 appliances require further developments to the dynamic demand concept to allow extended interruptions of, say, half an hour.

- For washing machines and tumble dryers, Level 1 does not require any changes to the design of the existing appliances, but rather relies on changes in human behaviour to minimise usage during certain periods. Level 2, then requires design changes to enable these appliances to be automatically scheduled to operate during prescribed periods. Alternative designs that allow the operating cycles to be interrupted or to defer the heating load are also briefly considered.

Delivering the Benefits of Smart Appliances – the Roadmap The results of the case studies were used to produce a Priority Matrix which compares the relative impact and ease of implementation of a range of measures. This approach is useful in identifying the ‘quick wins’, (i.e. measures that have a high impact and are easy to implement) and those that should be avoided (i.e. those with a low impact and are difficult to implement).

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Key:L1A

L1B

L2A & B

Low HighEase of Implementation

Hig

hLo

wIm

pa

ct

L1B

L2A

L2B

L1A

L2A

L2 B

L1A

Heat Pumps

Washing Machines / Tumble Dryers

Refrigeration

Air-conditioners

L1B

L1A

L1B

L2A & B

L1 - Level 1 smart appliance

L2 - Level 2 smart appliance

A - Short term interruptions

B - Longer term interruptions

Priority Matrix

In the diagram above, L1A refers to an appliance with a Smartness Level of 1 applied to a short term measure; L2B refers to an appliance with a Smartness Level of 2 applied to a longer term measure. The Priority Matrix shows that of the appliances considered, heat pumps and air conditioners that provide short term interruptions (L1A) represent ‘quick-wins’, i.e. can be implemented with relatively high ease with reasonable impact. However, it is important to note that the simple on/off control of heat pumps and air conditioners are not believed to represent the measures that have the potential to deliver the highest impact. This is attributed to the provision of longer duration interruptions by these appliances (L2A heat pumps and air conditioners). However, it is not known how much more valuable are the benefits arising from these more complex designs compared to the simpler “quick wins”. The Priority Matrix was used to establish a prioritised list of actions to enable the high impact / high ease of implementation measures to be put into action as soon as possible. These include generic measures that are applicable to all types of Smart Appliances, and specific measures for heat pumps and air conditioners.

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Conclusions The expected increase in the uptake of heat pumps and, perhaps to a lesser extent, air conditioning is expected to lead to significant increases in electricity consumption and peak demands. Thus, although these loads do result in increased peak demands, they could also be potential solutions. Both air conditioners and heat pumps provide “quick wins” through simple remote on/off controls. However, it is believed that these may only provide limited benefits (short term interrupts) whilst it is the application of more complex designs that have the potential to provide greater benefits (both short term and longer term interrupts). However, there is a risk that focusing on the ‘quick wins’ in the short term may make it less likely for the more desirable solutions to be progressed at a later time. Therefore, further analysis is recommended to quantify the relative benefits to the UK of different types of DSM measures. It is also considered that thought should be given to introducing minimum design Standards for heat pumps in the UK. These Standards could, for example, prohibit the use of heat pumps incorporating an electric flow boiler, or alternatively ensure that such heat pumps are required to include the provision for remote or automatic on/off control of the flow boiler. However, due to the single market rules, such Standards would need to be delivered at the European level. This would be difficult unless there is widespread support across Europe for such Standards. Additionally, consideration needs to be given to the potential role of bivalent heat pumps (i.e where the peak heating load is met by an auxiliary heating system, usually a gas or oil boiler) which have the potential to make the implementation of DSM easier to implement, but could reduce the carbon benefit of heat pumps. The provision of dynamic frequency response by cold appliances represents the most developed demonstration of a new1 Smart Appliance in the UK. It is currently undergoing extensive field trial evaluations in several hundred homes. However, the major obstacle to the role out of the technology (and thus the reason this is not considered to be represent a “quick win”) is the lack of any financial mechanism to encourage the uptake of these appliances by end-users. Therefore, there could be merit in investigating whether any appropriate financial mechanisms could be put in place to incentivise customers to purchase appliances with dynamic frequency response capability. This would also be of relevance to other end use loads suitable for dynamic frequency response, such as air conditioning and hot water heaters. Probably one of the most important factors affecting the uptake of Smart Appliances is customer awareness and acceptance. If customers do not want to allow 3rd parties to remotely control their appliances or to employ technologies that can automatically schedule the operation of their appliances, then it is unlikely that Smart Appliances will be able to deliver any of their potential benefits - even if all the measures identified here are addressed. Therefore raising awareness of the importance of the pattern of energy consumption as well as of the overall amount of energy consumption is considered to be important. Here, it is believed that behavioural science and behavioural economics can provide useful tools to ensure such information can be effectively communicated to consumers.

1 The radio teleswitch control of night storage heating is a well established application of a Smart Appliance in the UK used

over a number of years.

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1 Introduction and Objectives

1.1 Background

The current pace of change within the electricity industry is unprecedented in recent times, both within the UK and worldwide. The principal drivers for change relate to the wide ranging measures being implemented to reduce greenhouse gas emissions. In particular, the increasing move towards the wide-scale deployment of time variable renewable generation, particularly wind generation, presents a number of challenges in relation to the balance of supply and demand. No longer is it considered viable for electricity to be provided ‘on demand’ in response to the requirements of end-users. Rather, a co-ordinated approach is required whereby energy production and demand become integrated to ensure the use of renewables can be optimised whilst also minimising the use of fossil fired generation. Smart Appliances are one of a suite of measures that could help the UK meet its policy objectives in terms of reducing energy consumption, reducing carbon emissions and the provision of energy from renewable sources. They provide consumers with the capability to adapt their pattern of energy consumption, for example, to:

- Optimise the use of electricity from renewable resources - Reduction of peak demand for electricity, thereby reducing the need for peak

generation plant (usually fossil fired generation), used for only a few hours per year;

- Assist with the balancing of supply and demand to maximise the use of existing network assets; and

- Provide support services to the System Operator to maintain the quality and security of supply.

The development of Smart Appliances is complementary to the ongoing work on Smart Meters and Smart Grids. HM Government has committed to the roll-out of Smart Meters to all UK households by 2019, and it is envisaged that technologies to enable the uptake of Smart Grids could be available by 20152. This project, therefore, provides an overview of existing experiences of Smart Appliances, explores the main barriers to their uptake and sets out a roadmap that addresses these barriers to ensure their successful deployment.

2 Investing in a Low Carbon Britain, Joint Publication from BERR, DECC and DIUS, April 2009

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The report is structured as follows: Section 2 provides an overview of the main drivers for the management of

customer demands in the UK, and the generic requirements for a range of demand management measures that can be met through the deployment of Smart Appliances;

Section 3 provides an overview of Smart Appliances and describes how they can help with the management of customer demands;

Section 4 summarises a selection of trials and pilots of Smart Appliances, and highlights any key learning points relevant to the UK context. Further details of these trials are found in the Appendices;

The views and experiences of a selection of key stakeholders in relation to the key benefits and barriers associated with the uptake of Smart Appliances are summarised in Section 5;

The generic barriers to the uptake of Smart Appliances in the UK are described in Section 6;

The relative merits of implementing a selection of Smart Appliances are considered in Section 7 in terms of the relative magnitude of the potential benefits and the ease of implementation; and

Section 8 sets out a roadmap to facilitate the successful deployment of Smart Appliances in the UK.

Note – UK and GB This report focuses on the benefits of Smart Appliances in the UK context. However, the electricity industry in the UK comprises the Great Britain (GB) wholesale market, whilst Northern Ireland is part of the Single Wholesale Electricity Market of Ireland and Northern Ireland. As such, there are a number of differences between the market arrangements in place in Northern Ireland and in GB. Despite these differences, the findings of this report are, in general, applicable to the UK as a whole, and the terms GB and UK are used interchangeably. The Smart Metering Implementation Programme referred to in this report applies only to GB.

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2 Drivers to manage customer demands

Interest in the potential for Demand Side Management (DSM) has increased significantly over recent years, driven by the UK’s commitment to significantly reduce greenhouse gas emissions over the coming years. DSM is now widely regarded as one of the suite of measures that will help towards the achievement of these goals. A more active demand side is an important mechanism for addressing the issues of improving the overall balance between supply and demand, reducing the reliance on fossil fuel generation particularly at peak times and increasing the utilisation of renewable energy sources with variable output. Households and small commercial buildings account for a significant proportion of the UK’s electricity consumption, therefore, these sectors have the potential to make an important contribution towards meeting the UK’s carbon emissions reduction target. This Section provides an overview of the main drivers for managing customer demands in the UK. DSM refers to any action that influences the quantity or pattern of energy consumed by end-users, as highlighted in the schematic shown below.

Cogeneration

Standby Generation

Efficiency RetrofitsOptimised ControlsEfficient Motors & ChillersEfficient LightingOffice Equipment

Energy Efficiency Measures

Off-peak Hot Water‘Cool’ StoragePower Factor Correction

Static Load Management

Elec to Gas Hot Water/Heat

Solar Hot WaterPhotovoltaics / Small Wind

Industrial BiomassWood Stoves

Local Generation &

Fuel Switching

Influencing Customer End Use Energy

Influencing Customer Primary Energy

Interruptible LoadsScheduled Loads

Dynamic Load Management

Cogeneration

Standby Generation

Efficiency RetrofitsOptimised ControlsEfficient Motors & ChillersEfficient LightingOffice Equipment

Energy Efficiency Measures

Off-peak Hot Water‘Cool’ StoragePower Factor Correction

Static Load Management

Elec to Gas Hot Water/Heat

Solar Hot WaterPhotovoltaics / Small Wind

Industrial BiomassWood Stoves

Local Generation &

Fuel Switching

Influencing Customer End Use Energy

Influencing Customer Primary Energy

Interruptible LoadsScheduled Loads

Dynamic Load Management

Source: Demand Side Management, Benefits to Industry and the Community, Richard Lee, Energy SA, October 2001

Figure 2.1 Demand Side Management

Thus, DSM encompasses a wide range of measures such as:

- Energy efficiency measures, targeting the reduction of energy consumption in general or at specific times (such as during network peaks);

- Fuel switching and local generation, that encourage end-users to make use of alternative primary energy sources, either over the long or short term;

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- Load shifting to reduce peak demands, either on a short term flexible basis (often referred to as demand response) or on a more permanent basis (such as the move from direct electric heating to night-storage heating).

Smart Appliances have the potential to influence the full suite of DSM measures, as summarised below.

- Energy Efficiency – through improved awareness of energy consumption and energy costs, and avoided energy wastage;

- Dynamic load management – via direct load control or automatic response to Critical Peak Pricing or other dynamic tariffs that deliver short term changes to the pattern of energy consumption;

- Static load management – automatic response to Time of Use (ToU) tariffs to encourage users to make a permanent change to their pattern of energy consumption;

- Fuel switching – by way of the automatic scheduling of on-site generation to avoid/reduce electricity imports from the network at times of high electricity prices, to minimise exports onto the network at times of system ‘stress’ or to maximise the utilisation of on-site generation.

The specific drivers for such changes to the pattern of demand consumption are highlighted below.

2.1 Management of network constraints

The capacity of electricity networks is determined by the peak demand (MW) for electricity, and not by the total energy requirement (MWh). Therefore, the larger the peak demand, the larger the infrastructure needed to meet that demand. Consequently, the pattern of electricity consumption is as important as the amount of electricity consumed. Thus measures to reduce peak demand provide network operators with an alternative to reinforcing the networks. Figure 2.2 below shows the summer and winter profile for electricity on a range of example days, including the winter peak day, a typical winter day and a typical summer day. It shows that peak demand occurs during the winter and extends over a period of about 3 hours between 4pm and 7pm.

Figure 2.2 GB Summer and Winter Daily Demand Profiles in 2009/10

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Measures that move demand away from the peak or reduce the peak demand have the potential to defer or avoid network reinforcement measures. In this example, the measures would need to be in place over consecutive days over the winter period. Thus, Smart Appliances that respond automatically to ToU tariffs with high prices over the winter evening peak, could help reduce network peak demands. Alternatively, customers could simply avoid using their appliances during this period in order to reduce their energy costs. Thus appliances that provide customers with information on the cost associated with their operation at any given time could help motivate customers to reduce demand at the time of the peak. In the future, as electric vehicles and heat pumps become more prevalent, such measures could become particularly important in helping to mitigate against significant increases in peak demand. In addition to the winter peak load reduction, demand side management measures can also help Distribution Network Operators (DNOs) manage their networks in event of the failure of an asset. Networks are designed such that they have inherent redundancy, often referred to as the (N-1) criteria, such that the failure of any one asset does not cause an outage. As a result, assets typically run in pairs such that they operate at 50% of their rated capacity at peak demand. Thus, assets typically run at well below their rated capacity, except in a post fault situation. Thus measures that reduce demand following a network failure, could enable DNOs to increase the loading on their existing assets above the 50% limit, provided that they were certain that demand side measures could be implemented when required. In this case, demand measures would only be required in post fault situations, which would typically be 1 or 2 times a year, although they may need to be sustained over a period of 1 to 2 weeks whilst repairs are undertaken.

2.2 Provision of balancing services to National Grid

National Grid, the Great Britain Transmission System Operator (GB-TSO), procures a range of services to balance demand and supply and to ensure the security and quality of electricity supply. These services include:

- Frequency response services; - Reserve services; - Reactive power; - System security services; - Energy related services;

In general terms, these services require flexibility, i.e. they need to be able to deliver within a tightly defined timeframe. They also need to be dependable, i.e. they need to be able to deliver whenever the service is required. For this reason, the provision of these services is largely dominated by fossil fired power stations, either inefficient peaking plant that can be quickly brought on line or by plant operating inefficiently in part load conditions. Thus, the use of demand side measures offers the opportunity to reduce the CO2 emissions associated with the provision of these services. The services most suited to demand response are frequency response and reserve services, which are described below.

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Frequency Response. Under the terms of its licence, National Grid is required to maintain the system frequency within prescribed limits. In order to do so, generation and demand resources are held in automatic readiness, so that they can react to the failure of a generator or a sudden increase in demand. There are two types of frequency response: dynamic and non dynamic response. Dynamic frequency response is a continuously provided service used to manage the normal second by second changes on the system. Non dynamic frequency response is usually a discrete service triggered at a defined frequency deviation. The demand side has a well-established history of providing non dynamic frequency response, and cement manufacturers and steel works have provided support to National Grid over a number of years. The Table below describes the characteristics required for non dynamic frequency response services provided by demand customers.

Table 2.1 Frequency Response Characteristics (for demand customers)3

Minimum capacity 3MW (but can be aggregated over a number of sites) Delivery within 2 seconds (maximum) Delivery for 30 minutes (minimum) Availability 24 hours per day Reserve Services National Grid requires additional resources in order to deal with unforeseen demand increases and/or generator unavailability. These resources are provided both by generators and by the demand side. There are a number of specific services that fall within this category, but Short Term Operating Reserve (STOR) is the main area where the Demand Side is regarded as being able to contribute. The table below describes the characteristics required STOR.

Table 2.2 STOR Characteristics4

Minimum capacity 3MW (but can be aggregated over a number of sites) Delivery within 4 hours (maximum) Delivery for 2 hours (minimum) Availability Within prescribed windows

3 Frequency Response by Demand Management, Major Technical Requirements available http://www.nationalgrid.com

accessed 20 May 2011

4 Short Term Operating Reserve, General Description of the Service, 6th May 2011, National Grid Publication available http://www.nationalgrid.com

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2.3 Energy Peak Price Avoidance

Energy Suppliers purchase energy in half-hourly blocks to cover the needs of their customers. If the amount purchased does not exactly match the amount consumed, they face imbalance charges. If a Supplier’s own imbalance position assists with the overall system balancing, for example if the system is long5 a Supplier that is short6 will receive a payment equivalent to the System Sell Price (SSP). However, if a Supplier is short when the system is short, the Suppliers much purchase their top-up electricity at the System Buy Price (SBP). The imbalance charges are determined by the weighted average value of the bids and offers into the Balancing Mechanism. The diagram below shows the imbalance prices faced by Energy suppliers on 7th December 2010, the day with the highest peak demand over the winter period 1 November 2010 to 28 February 20117.

0

50

100

150

200

250

300

350

400

450

500

0 6 12 18 24 30 36 42 48

Settlement Period

£/M

Wh

SSP (£/MWh)

SBP (£/MWh)

Figure 2.3 System Buy Prices (SBP) and System Sell Prices (SSP) for 7th December 2010

In this case, the System Buy Price and System Sell prices coincide with the time of peak demand, and the high prices prevail for a period of around 3 hours. However, price peaks can occur at any time of the day. Thus demand management provides Energy Suppliers with the opportunity to minimise their exposure to high imbalance prices. Energy Suppliers with demand flexibility are also able to bid directly into the Balancing Mechanism, and thus provide demand reduction services in return for a direct payment in order to help the System Operator maintain the overall balance between supply and demand. As is the case in Figure 2.3, the peak prices are likely to coincide with the time of peak demand. However, as discussed in Section 2.4 below, this may not necessarily be the case in the future as the proportion of electricity generated using renewable resources increases.

5 The system is ‘long’ when generation exceeds demand overall. A supplier is ‘long’ when the amount of generation

purchased exceeds the amount of energy consumed by their customers, or if consumers’ demand is less than anticipated.

6 i.e. does not have enough contracts in place to cover the energy consumed by its customers

7 Data published by Elexon, and available via http://www.bmreports.com (Accessed 20 May 2011)

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2.4 Integration of Renewables

UK’s Climate Change Act (2008) established a target of reducing greenhouse gas emissions by 80% by 2050, with an interim target of a reduction of at least 26% by 20208. In addition, the UK has signed up to the EU Renewable Energy Directive which includes a UK target of 15% of energy from renewables by 2020. In practice, this will require that around a third of electricity to be generated from renewable resources. This decarbonisation of the electricity generation sector combined with other policy measures is expected to lead to a significant increase in the demand for electricity, particularly for heating and transport. The integration of renewable generation resources, particularly wind generation where the output varies according to the prevailing conditions, poses a number of challenges in terms of maintaining the balance between supply and demand. As has been highlighted in Sections 2.1 to 2.3, there are a number of ways that the demand side can help to assist with the integration of renewable electricity generation. The following, therefore, discusses how renewables will increase the need for these services. Provision of balancing services to National Grid The provision of support services to National Grid will become increasingly important as Great Britain moves towards a generation mix dominated by variable output renewables and inflexible nuclear generation. These generation sources are not well suited to the provision of balancing services, and therefore the demand side provides a valuable opportunity to reduce the reliance on fossil fired generation. Management of network constraints The move to renewable energy will see a shift towards electrification of certain energy requirements, particularly heating and transport. Unless the pattern of these new demands and existing demands are optimised to maximise the use of the existing assets, the widespread uptake of heat pumps and electric vehicles, could lead to the requirement for significant investment in infrastructure assets9. Energy peak price avoidance In 2009, Pöyry10 conducted a study to investigate the potential impact of renewable energy on market prices. The study concluded that the dispatch pattern of the different types of generation plant will be very much different to that in the current market. This is particularly pertinent for Combined Cycle Gas Turbines (CCGTs) which are predicted to move from a pattern of demand following (i.e. running during periods of peak demand) to operating only when there is no wind. As a result, prices during low wind periods could reach extremely high levels of up to £500/MWh, which could be sustained over several hours and/or days. More importantly, prices could

8 http://www.legislation.gov.uk/ukpga/2008/27/contents

9 Benefits of Advanced Smart Metering for Demand Response based Control of Distribution Networks, Summary Report Version 2.0, Joint publication by Centre for Sustainable Electricity and Distributed Generation Imperial College, Centre for Transport Studies, Imperial College and Energy Networks Association

10 Impact of Intermittency: How Wind Variability Could Change the Shape of the British and Irish Electricity Markets, Summary Report, Pöyry Energy (Oxford) Ltd, July 2009

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become increasingly volatile, and in such circumstances the demand side has the potential to play an increasingly important role in helping Energy Suppliers avoid peak prices. The demand side has the potential to play an important role by adjusting its pattern of consumption in order to maximise the use of renewable resources. For example, ensuring that any schedulable processes coincide with the availability of wind generation. The Pöyry study showed that there could be instances where energy prices become very much lower than today, or even negative, coinciding with high wind resources. During such periods, it would clearly be advantageous to be able to bring energy consumption forwards to make use of available generation surplus, and by doing so reduce energy costs for consumers.

2.5 General requirements for customer demand management

The preceding Sections describe the various drivers for managing customer demand for electricity in the UK. This information is summarised in the following Table, which indicates the generic requirements for demand measures meeting the categories discussed in Sections 2.1 to 2.4.

Table 2.3 Generic requirements for demand management measures

How long? How often? When required

Management of network constraints

3 hours

Regularly during the winter, or occasionally for a few weeks (following a network fault)

Early evening peak, during the winter

Balancing Services

30 mins up to few hours

Continually (for dynamic frequency response), to occasionally for other services

Any time of day / year

Energy peak price avoidance

In half-hourly blocks, with some consecutive half-hours

Occasionally, although more so as wind generation increases

Any time of day / year

Renewable integration(i)

Up to several days (for example during extended periods of low wind output)

Occasionally Any time of day / year

(i) In the context of this study, renewables integration specifically relates to matching demand to the wind generation on a ‘macro’ scale, i.e. to bring demand forward when wind generation levels are high or to delay demand when wind generation levels are low.

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3 Smart Appliances – An Overview

There is no industry wide definition of what constitutes a Smart Appliance. However, in general, the term is used to refer to an “appliance or device that is capable of modifying its operation in response to signals received from the electricity network or a communications system.” In the context of this study, Smart Appliances refer to electrical appliances only and the study is not concerned with other utilities (gas, water and telecommunications). The project focuses on Smart Appliances in both domestic and small commercial locations. Thus, Smart Appliances specifically suitable for industrial and transport sectors are excluded from the scope of the study. Electric vehicle (EV) chargers have the potential to represent a large (in the order of 9kWh per vehicle per day11) load in the residential context. The subject of controlling this charging via the integration of Smart Meters and the Smart Grid is extensive. There is a large potential for EV chargers to act as Smart Appliances, ensuring charging takes place either in periods of low demand (overnight) or when surplus renewable energy is available. The Committee on Climate Change has recommended that 1.7 million electric vehicles are needed by 2020 to deliver "dramatic improvements" in the carbon efficiency of transport12. The development of Smart charging infrastructures for these new electric vehicles is an essential component for ensuring that these new electric vehicles do not impact on network peak demands. Whilst this is recognised as an important area, EVs are outside the scope of the current study. The commercial sector covers a vast range of building types and functions, from small corner shops and small offices to vast office buildings and hypermarkets. The use of sub-metering and building management systems within larger commercial buildings (i.e. those with maximum demands over 100kW) already provides the opportunity for these customers to understand and manage their patterns of energy consumption. Therefore, this project focuses on small commercial properties, i.e. those with a maximum demand below 100kW and currently falling within the non half-hourly metering market. The roll-out of advanced meters to this sector offers the potential to investigate potential synergies for Smart Appliances within the household sector. A breakdown of end-use consumption for the domestic and commercial sectors is found in Appendix A.

11 Based on a vehicle consumption figure of 0.16kWh/km (Cenex, 2008), an average daily mileage of 51km (DfT) and a

charging efficiency of 90%.

12 http://www.theccc.org.uk/sectors/surface-transport/electric-cars

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3.1 Smart Appliances - functionality

As mentioned previously, a ‘Smart Appliance’ can be defined in general terms as follows:

“An appliance or device that is capable of modifying its operation in response to signals received from the electricity network or a communications system.”13

These appliances can be divided into categories based on the signals to which they respond, although it should be noted that the two groups are not mutually exclusive:

1. Appliances which respond to inherent changes in electricity network properties without any consumer intervention. These appliances act independently of consumers, Smart Meters and Smart Grid devices. Examples include:

o dynamic demand controllers whereby software within the appliance’s control unit measures signals from the electricity supply (e.g. mains frequency) and adjusts power consumption automatically to compensate14; and

o voltage controllers that respond to network voltage fluctuations and maintain an optimum voltage level into the household, which can lead to energy savings in certain appliances15.

2. Appliances which have an integrated communications device and therefore provide information to consumers, Suppliers and network operators and can respond to communication signals sent by these parties (i.e. signals distinct from the properties of the electricity supply). These appliances can utilise Smart Meter and Smart Grid technology to allow the integration of communication signals within the network, or otherwise include dedicated communication and controls.

Appliances can be further categorised according to their technical suitability for different types of demand management actions, i.e: whether they are suitable for interruption at little or no notice, whether they are more suited to rescheduling their operation from one time to another, or a combination of the two, as shown in Appendix B.

13 SPMT10_043 Delivering the Benefits of Smart Appliances, Issue 0.1 15.10.10

14 See http://www.rltec.com/ for a description of the dynamic demand technology.

15 See http://www.vphase.co.uk/default.aspx for a description of the VPhase device

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3.2 Smart Appliances – Scope of Current Study

Thus in summary the appliances to be considered within this study are as described below;

Electrical appliances in the residential and small commercial (<100kW) sector;

The primary focus will be on individual appliances which are either interruptible, schedulable or hybrid (see Appendix B), or ‘add-on’ devices that enable the pattern of consumption of individual appliances to be optimised in response to certain external stimuli. As such, devices such as voltage controllers that optimise the overall supply into a property, rather than optimise the usage pattern of individual devises, are not considered here;

Smart Appliances are those that fall into the categories described below; o Those that respond automatically to inherent changes in electricity

network properties without any consumer intervention. These appliances act independently of consumers, Smart Meters and Smart Grid devices; and/or

o Appliances which have an integrated communications device and therefore provide information to consumers, suppliers and network operators and can respond to communication signals sent by these parties (i.e. signals distinct from the properties of the electricity supply).

Air conditioning, electric space and water heating will be included within the scope of the project; and

The charging of electric vehicles is excluded.

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4 Smart Appliance Experiences

This Section of the Report provides an overview of a selection of trials and pilots of Smart Appliances to explore their impact on energy consumption and to highlight issues affecting the deployment of Smart Appliances in the UK. This Section does not attempt to capture all trials and pilots that have been undertaken, rather a relatively small number of examples have been selected to highlight key learning points relevant to the UK context. Full information on the pilots and trials reviews is found in Appendix C. Table 4.1 focuses on UK experiences whereas Table 4.2 highlights selected overseas examples of Smart Appliance trials. The information summarised in these Tables highlights that, in the vast majority of cases, the pilots and trials that have been conducted to date have focused on large single end-uses of energy that contribute significantly to the peak loads that they are helping to alleviate, i.e. air conditioning and electric heating loads. The main exception to this is the trial of cold appliances with dynamic frequency response in the UK. One of the important learning points from this trial is that demonstrating the technical viability of a concept and ensuring that the benefits outweigh the costs are not sufficient for the successful deployment of a technology. In this particular example, the key barrier is the inability to establish the route to market. There is no financial mechanism to appropriately allocate the costs and benefits between stakeholders and the consumers themselves, particularly within the UK context. The direct load control programme implemented by ETSA utilities, Australia, demonstrates the importance of product Standards. The programme discovered that not all air conditioners could be controlled using the communication device being trialled, and as such the scale of the trial had to be reduced from 2,400 sites to 750 sites. This has led to a requirement for all new air conditioners sold in Australia to incorporate a standard interface to allow demand response to be implemented using standard operational instructions and communications. Although a specification16 has been developed at a European level that sets out the requirements that devices, objects and systems must comply with in order for them to be capable of interoperability, it is not an official Standard. Thus, there is no Standard in place in the UK to ensure that demand response can be implemented using standard operational instructions and communications. Any Standard would need to be developed at the European level due to single market rules, and would require widespread support across Europe.

16 An ‘Interoperability framework requirements specification for service to the home (IFRS)’, A CENELEC Workshop

Agreement, published June 2010.

14

Another important feature of a number of the trials includes the impact of customer engagement. Some of the smaller trials demonstrate a high degree of customer involvement, where-as this is not always the case for larger trials. For example, in the California Statewide Pricing Programme, around one-third of the customers in the larger demand group (> 20 kW) declined the offer of a free Smart thermostat to enable their air conditioning load to be automatically controlled during high price periods. As a result, the demand reduction of these customers was very much less (around half) than that for customers who did take up the offer. Therefore, the resulting impact of Smart Appliances is highly dependent upon customer attitudes and behaviours.

15

Table 4.1 Summary of selected UK Experiences of Smart Appliances

Trial Outcomes Key learning points

npower and RLTec Dynamic Demand Trial (domestic cold appliances) (up to 3000 households)

Trial still underway however analysis suggests that: Each fridge will save between £0.70 and

£5.60 per year Each device costs £4 per appliance A potential carbon saving of between 17 and

44kg of CO2 per fridge per annum is estimated

Trial is still underway however: A large scale adoption of the technology would be necessary to access

system balancing value, however the technology could be deployed in a greater range of appliances.

No financial mechanism to encourage consumers to purchase appliances that provide dynamic frequency response technology.

Legislating for the inclusion of this technology in all new products not practicable because appliance Standards set at EU level. Frequency control does not pose the same challenges across the mainland Europe that it does within the UK and Ireland.

Further investigation required on the effect on appliance efficiency and general wear and tear.

Sustainable Blacon (50 households)

Trial sill underway – no results yet available Trial still underway

Energy Wastage Study

Survey of 2,000 households shows extent to which customers leave unattended appliances switched on, either deliberately or accidentally.

Potential opportunity for Smart Appliances to avoid energy wastage, but there is a lack of reliable and cost effective occupancy detection sensors.

ETHOS Multimedia Energy Management System (100 households)

25% reduction in peak demand on the relevant section of the distribution network.

8% reduction in average electricity consumption in participating households.

Increased customer comfort.

Focus on electric space and water heating to reduce localised network peak demands.

Technology readiness at the time of the trial (1996-1998) was an issue. Introduction of profile settlement system considered to be a barrier to roll-out

at the time of the trial. Wattbox Heating Control (control of gas and electric space heating)

Energy consumption decreased by 14% in trials

Many households do not cope well with conventional heating controls. There could be an opportunity to link this sort of device to Smart Meters to

further optimise heating controls based on price signals.

Early Adopters Research Results of focus groups undertaken for three

water authorities gauged customer receptivity to Smart Home concept.

Results indicate an interest in simple, optional controls to allow appliances to be operated when electricity is cheaper.

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Table 4.2 Summary of selected Overseas Experiences of Smart Appliances

Country Trial Outcomes Key learning points California State Wide Pricing Program (Commercial and Industrial)

Customers with Smart thermostats reduced peak period energy use by 13% more than those without

Air conditioning loads targeted via range of ToU tariffs. Enabling technologies helped customers to be more price responsive. Not all customers took up offer of a free Smart thermostat to enable

air conditioners to respond automatically.

California State Wide Pricing Program (Residential)

Customers with enabling technologies saved 18% more than those without, and on Super Peak days this increased to 26%.

Air conditioning loads, electric water heating and pool pumps targeted via range of ToU tariffs.

Recent blackout in California had made householders aware of system constraints and potential supply shortages

Time of Use pricing is already used in many parts of the USA and is therefore a familiar concept

Householders were able to automate their own electricity loads after being given future price tariff information

US

A

LIPAedge (direct load control of 23,400 air conditioners)

16,067MW average load reduction, (680kW/air conditioning unit)

Cost of US$515 per customer (approximately £320 per customer) yielding a combined average cost of US$487/kW (approximately £300/kW) of demand reduction17.

Householders have ability to override technology under some circumstances

Number of interruptions was limited Long Island Power Authority (LIPA) is a non-profit making municipal

company with different drivers to UK companies LIPA run the supply, transmission and distribution company on Long

Island, amalgamating benefits in one company and providing a more extensive relationship with the customer

Aus

tral

ia

ETSA Utilities Air Conditioning Direct Load Control Program (15 households increasing to 750)

Reduction in peak load Change in appliance Standards so that

enabling technologies can be easily fitted to applicable devices

Much of peak load growth attributable to growth in air conditioning. Careful initial research undertaken to monitor customer comfort

levels. Small initial trial to gain experience with technology. For some householders air conditioning was still a novelty. A marketing trial was undertaken to explain the trial. It successfully

helped to changed public perceptions of electricity. A strong regulator encouraged DNOs to undertake such trials.

17 Based on an exchange rate of US$1 = £0.62

17

Country Trial Outcomes Key learning points A

ustr

alia

(c

ontin

ued

) Western Sydney Interruptible Air Conditioning Rebate Trial (90 households)

Technology worked correctly Customers preferred shorter more

frequent off cycles rather than prolonged interruptions

High administration cost High cost of metering Billing was problematic

Long history of direct load control (for DHW), including strong customer support framework for DHW scheme, and high price returns for customers on DHW scheme

DNO has direct relationship with customers - also their energy supplier.

Billing of a similar scheme in the UK would be difficult until Smart Meters are rolled out.

The unbundling of the UK energy market has resulted in financial incentives for such a scheme being thinly spread across multiple stakeholders.

Den

mar

k

Elkraft (control of electric heating load) (25 households)

Customer set own preferences for interruptions.

40% used the facility to stop an interruption.

41% of interrupted consumption was used afterwards to bring temperature up to required level.

24% of householders set a maximum interruption length.

Installation costs were about 800€ per house.

Participants’ electric heating loads were greater than 16,000kWh/year.

High degree of customer satisfaction (participants would recommend the system to friends).

Very small scale trial. Customers happy to suggest appliances that could also be included

in the scheme Most were “satisfied” or “nearly satisfied” with the financial incentive

paid. Most customers were happy with three hour interruptions to their

heating. Need for interruptions to be synchronised with meter settlement

periods identified.

Nor

way

Oslo ‘Ebox’ (control of domestic hot water loads) (20 households)

Peak load reduction of up to 15% (of maximum metered peak load in participating households)

Very small trial. Significant potential to manage hot water loads. Inconvenience to residents kept to a minimum.

New

Z

eala

nd Orion Network DSM

Project (control of domestic hot water loads)

High prevalence of electric water heating resulted in a large peak reduction, resulting in a financial benefit of NZ$12m/yr year (approx £6m/yr) for DNO and NZ$6m/yr (approx £3m/yr)for transmission18.

High consumer price benefits. Strong consumer support network. Long standing program. High prevalence of electric water heating in New Zealand. Strong financial incentive on DNO to reduce peak demand.

18 Based on an exchange rate of NZ$1 = £0.50

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5 Stakeholder Views and Experiences

Technology developers, appliance manufacturers and appliance suppliers are key stakeholders in the development and deployment of Smart Appliances. They can be expected to understand the requirements of consumers and have direct knowledge of the impact of external factors on their businesses. Therefore, a number of interviews were undertaken with targeted industry stakeholders to explore their views and opinions of Smart Appliances. The stakeholders were selected from the following two groups:

- Equipment and device developers, manufacturers and suppliers (from here-on referred to as ‘manufacturers’); and

- Other industry experts, with a specific interest in Smart Appliances particularly from the consumer perspective (from here-on referred to as ‘industry experts’).

Pro-formas were used as the basis for telephone interviews with these two stakeholder groups. The findings of the interviews were captured in the pro-forma, and participants were invited to review the findings. None of the responses are attributed to individual participants. However, a list of those interviewed is found in Appendix D. The following sections provide an overview of the comments provided.

5.1 Definition of a Smart Appliance

All respondents were asked at the outset to clarify their understanding of the definition of a Smart Appliance. As would be expected from a group of individuals targeted for their knowledge and experience of Smart Appliances, there was a high level of consistency amongst the respondents from both stakeholder groups. All respondents agreed that Smart Appliances provide a higher level of connectivity and communication. One of the respondents specifically referred to the use of bi-directional communication. Some, but not all, identified that the additional connectivity would allow increased energy efficiency through measures such as helping users to learn how to operate their appliances more energy efficiently, allowing appliances to respond automatically to external signals, or help users to make more informed decisions. The importance of the additional connectivity in being able to offer additional services such as remote control, diagnostic tools and extra programmable functionality was highlighted as an important factor. In addition to exploring their understanding of Smart Appliances, respondents were asked to comment on which appliances were thought to offer the greatest potential to customers in terms of the anticipated benefits that could be delivered. Responses were sought for domestic customers and commercial customers, as highlighted below.

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Domestic Customers There was no clear consensus from the respondents on which appliances could provide the greatest potential benefits to customers. Responses focussed very much on the types of loads, rather than on specific end-uses. Thus, it was thought that appliances that provide flexibility of use, the ability to reduce energy consumption and include the capability to be remotely controlled were seen as having the greatest potential for domestic customers. Some respondents cited specific end-uses of energy, i.e. those with the highest energy demand or those most suited to adjusting their pattern of consumption, as likely to have the greatest potential (both for energy efficiency and load shifting). Heating, ventilation, air conditioning systems and tumble dryers were identified. Energy consumption was identified as being a longer term goal for Smart Appliances, dependent on market conditions such as ToU tariffs, while in the shorter term customer care solutions that provide flexibility of use may be more predominant. It was also suggested that appliances which could be remotely controlled have the greatest potential. The respondents were split in their opinions on the type of appliances that consumers are most likely to favour. Some thought that it would be appliances that assisted their lifestyle either though time efficiencies or making a task easier, while others suggested appliances that assisted the consumer in energy efficiency or load shifting may be attractive. However, it is interesting to note that some of the industry experts questioned whether consumers were interested in reducing or moving their energy loads or indeed if there were any domestic loads worth shifting. The respondents were also split on the benefits of Smart Appliances for load shifting and energy reduction. One suggested that the benefits would be in appliances with the largest energy consumption such as electric vehicles, freezers and fridges. Another highlighted that presently, consumers have no understanding of why they should participate in load shifting so education is necessary, combined with some form of (probably financial) incentive. Another proposed that experience suggests that consumers will not participate in load shifting because Energy Companies will not share any benefits with them. It was also suggested that the benefits of Smart Appliances would depend on the context of the customer. For example, people living in flats may be limited by the extent to which they are able to shift loads due to the impact on their neighbours, e.g. the noise from washing machines operated overnight could cause represent a nuisance for neighbours. However, this could be equally true for all households regardless of the type of property, i.e. householders may not wish to operate their appliances at night because they themselves are affected by the noise. Commercial Customers In terms of commercial customers, the greater energy savings (in absolute terms) that could be made by individual users was cited as an important factor, potentially making this market more important than the domestic market. The fact that appliances sold to this sector were subject to fewer regulations and Standards, i.e. the energy labelling of appliances, was also viewed as being a significant factor in making this a potentially attractive market for Smart Appliances.

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The ability for customers to be able to remotely monitor the performance of individual appliances or processes was seen as a key advantage for this customer segment. For example, the ability to check that overnight processes are occurring according to plan could highlight inappropriate control implementation. Air conditioning units were identified as being the most attractive appliance for load shifting amongst commercial customers. Additionally, commercial lighting and air conditioning units were thought to represent key applications for ‘Smart Goods’ because of their high power use, especially when facilities are unoccupied during the evening peak. Thus, in this context, the ‘Smart’ features would focus on avoiding energy wastage in unoccupied premises. Smart controls were also recognised in having a role to play in helping to ensure that heating systems are operating efficiently, i.e. not over or under-heating the occupied space. Respondents drew attention to devices that could increase productivity, move loads or increase convenience. It was also suggested that the commercial sector may be a good place to start introducing Smart Appliances because of the large energy and financial savings available. In addition, the roll-out of Smart Appliances to this sector could help to reassure the domestic sector about the additional benefits of Smart Appliances.

5.2 Development of Smart Appliances

Respondents were asked to comment on the main drivers for the development of Smart Appliances. In addition, insights into the current developments by the respondent’s own organisations were sought. Not all the manufacturers interviewed were developing products in the Smart Appliance arena. Of those that were developing new products, the main focus of their activities was on the development of communications platforms, Smart water and storage heaters and household appliances. Factors driving these developments included UK energy policy goals, international (particularly North American) regulations, the price of energy, maintenance savings and customer demand for increasing functionality in their new appliances. The timing of the emergence of these new appliances ranged considerably. Some manufacturers had prototypes that would be piloted later this year, while others spoke about achieving full market readiness in up to ten years. Important factors for encouraging the development for new products included:

- Ensuring that customers are aware of the potential benefits to them from Smart Appliance ownership;

- Ensuring customers are in a position to take advantage of these benefits; and - Preventing a consumer backlash.

In general, it was noted that manufacturers develop products for a global market although some develop products specifically for individual geographical regions. It was suggested that global Standards, rather than for example just EU Standards, were required to create the volumes and prices required to drive prices for Smart Appliances to an effective level. Other respondents focused more on the need for

21

EU wide Standards, particularly on the need for Standards applicable across a wide range of appliances, for example for both white and brown goods. The industry experts had a divided opinion about who would be driving the push towards Smart Appliances. Some thought that manufacturers were seen as having the greatest motive, although they would be more interested in developing remote diagnostic functions, learning more about how products are used and integrating appliances into communication networks for the consumers’ convenience. Being able to integrate appliances with renewable generation is also highlighted as a potential driver. It was proposed that the organisations with most interest in Smart Appliances would be Network Operators, as they provide them an opportunity to control loads to improve energy security and reduce CO2 emissions. This has driven device development in North America and Australia and was thought likely to become more important here as the installed capacity of wind increases. However, in the UK, it is the Energy Suppliers, and not Network Operators, that lead the roll-out of Smart Meters to consumers.

5.3 Barriers to the development of Smart Appliances

The experts provided a wide range of views on the barriers to the development of Smart Appliances. Issues raised focused on the following aspects: The high capital cost associated with product development; Energy companies not yet agreeing on how they want appliances integrating, or

not yet being in a position to utilise them; A lack of government policy and regulation for Smart Appliances, including the

impact of building regulations on the promotion of electric heating; Customer attitudes to Smart Appliances. Lack of consistent appliance Standards; The costs of developing appliances are not necessarily borne by those who

benefit from their use; The long timescale associated with the roll-out of Smart Appliances due to the

infrequency of appliance replacement; Different manufacturers having different communication platforms; The requirement for an override function to make Smart Appliances acceptable

to consumers; and A lack of consumer confidence that they will be able to use and understand the

technology. One interviewee identified the inefficiencies converting between AC (alternating current) and DC (direct current) power supply through the energy supply chain, especially within houses with microgeneration as a barrier, although this is not specifically an issue relating to Smart Appliances. Measures suggested for overcoming these potential barriers included: The need for consistent international and national appliance Standards to be

implemented; Trials to prove the business case for Smart Appliances; Making appliances useful to householders; A greater awareness of the consequences of personal energy use,

22

Consumer awareness of override functions; and Ensuring appliances are as easy to use as ‘non smart’ appliances, and are able

to communicate with other appliances. A coordinated approach, addressing all issues and stakeholders was seen to be important. The importance of Energy Companies considering the needs of consumers was also highlighted. The need for appliances to be developed for consumers with microgeneration, to enable them to maximise the use of their on-site generation, was identified. One of the industry experts also pointed out that the development of appliances that did what the consumers themselves wanted would overcome many of the barriers to the uptake of Smart Appliances.

5.4 Key factors influencing uptake of Smart Appliances

The stakeholders were asked to comment on the key factors affecting the decision to purchase Smart Appliances. The manufacturers were split about the importance of cost and economic returns to consumers buying products. Some saw this as being a big barrier while others thought that other benefits of Smart Appliances would make cost less of a barrier. Interestingly, the green credentials of appliances were seen as being less important by most of the interviewees. Manufacturers also suggested that it was important to appropriately pitch the appliance cost, i.e. so that the technology is taken seriously but the appliance is not too expensive. A number of other key factors that may influence the uptake of Smart Appliances were raised. One of the most important was the need for appliances to be ‘plug and play’, so that they could be easily connected and operated by users. It was generally regarded by the majority of respondents that Smart Appliances should provide additional benefits to consumers over and above ‘non smart’ products. Appliances should be equal in all other respects compared to ‘non smart’ version, i.e. appliances should provide the same service to customers, but at a lower energy cost. Alternatively, products should provide additional features that are valued by the end-users themselves, e.g. they provide time saving features, make things easier for consumers or improve quality of life. A system that allowed users to link all their household appliances to a single control system may be seen as an advantage by certain users. Effective product labelling, so that consumers can readily understand the benefits of Smart Appliances, was highlighted as a key factor in encouraging the uptake of these appliances. This should include both information on the kWh/year savings or the annual energy cost savings (compared to their overall energy costs). Consumers need to have the confidence that they will be able to operate the technology effectively, before they will make the decision to purchase. Thus education is regarded to be an important factor in helping consumers understand the benefits of Smart Appliances. However, it was recognised that the focus here should be on explaining the benefits of Smart Appliances to the consumers themselves,

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rather than in terms of societal benefits. In particular the benefits to the individual need to outweigh any extra costs associated with the purchase of Smart Appliances. Whilst a desire to have the ‘latest’ technology could stimulate certain users to purchase Smart Appliances, it was suggested that Smart Appliances should be aimed at the mass market as opposed to being seen as ‘geeky’. For commercial customers, manufacturers and industry experts agree that quality, reliability, return on their investment and the ability to reduce costs are important. They have a greater potential for energy and financial savings and potentially rewards from load shifting. Environmental benefits are a low priority according to a number of the respondents, however others disagreed with this. Running costs and capital costs While initial costs are an important part of the decision to purchase an appliance, running costs were seen as being of increasing, if not equal importance. This could be particularly relevant as energy prices rise. While one appliance manufacturer pointed out that their green appliances had sold well, most of those interviewed suggested that environmental concerns were important only in so far as reducing energy consumption resulted in lower bills. One respondent pointed out that running costs are of less importance to the buyers of high end products, which is the part of the market that Smart Appliances are being targeted at currently. These consumers want an improved lifestyle. The difficulty in estimating the running costs of an appliance for an individual user due to the diversity of energy tariffs available was highlighted. It was suggested that running costs may be more of an issue for items such as electric forms of heating and electric vehicles. Running costs of smaller items were not considered to be important at the current time. However, as users become more aware of the cost of running individual appliances, this may also be a consideration for these appliances in the coming years. A number of respondents also commented that better labelling of an appliance’s energy rating and running cost would help consumers understand the reduced cost of running Smart Appliances. Environmental Impact Manufacturers generally thought that environmental concerns were less important to consumers than saving money. However, it was acknowledged that existing, environmental products do sell well. Local issues may also make environmental concerns more important. The industry experts agreed that while consumers liked the feeling that they were helping the environment, this is not regarded to be a main driver for the majority of householders. Environmental concerns may influence commercial decisions more because of Government policies and customers examining companies’ ‘green’ credentials.

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Awareness Respondents were in agreement that customers, both domestic and commercial, had little awareness of Smart Appliances and Smart Grids. However, it was thought that consumers on Economy 7 tariffs are better placed to understand the concept. Some industry experts suggested that little work had been done to investigate the extent to which consumers were aware of Smart Appliances. Greater dialogue may also help manufacturers to understand applications that customers may find useful. It was thought that the introduction of Feed in Tariffs (FITs) had the potential to help users with microgeneration understand the benefits of using electricity at particular times, i.e. to minimise exports, and thus users understand the benefits of Smart Appliances. It was suggested that a change in the relationship between consumers and their energy company would be required before consumers could fully understand the benefits (expressed in simple terms) of Smart Appliances. There was also the view that consumers would need to change their attitudes towards energy consumption in general, before the impact of Smart Appliances (i.e. the impact of the pattern of consumption) could be fully appreciated. Manufacturers suggested a number of reasons why purchasers may choose a Smart Appliance ahead of a non smart one. These included saving money, extra functionality, desire to keep up with the latest features because appliances last so long (i.e. future proofing is important), lifestyle improvements and rebates for participating in schemes. More basic but appealing models may initially be useful while consumers get used to the concept. The electricity network must also be ready to utilise the flexibility of appliances, and the savings proven using real data. Local arguments, for example, not having to build a new power station may help to influence consumer decisions. Prices of the appliances must also be realistic in order to encourage consumers to purchase them in preference to other ‘non smart’ products. Several of the industry experts suggested that rather than letting householders have a choice between Smart and conventional appliances, all appliances should be sold ‘Smart ready’, in much the same way that TVs are currently sold HD ready. It was suggested that Smart features may only be appeal to a minority of the market. The importance of an override function, allowing users to have ultimate control over how their appliances operate, was seen as a pre-requisite. It was also suggested that consumers’ relationship with their Energy Supplier and attitude towards energy needs to change drastically before they would actively choose Smart Appliances. Financial savings may also be imperative. Others said that a ‘social revolution’ in householders’ attitude towards energy was necessary. Manufacturers suggested that a holistic education programme is required to promote the benefits of Smart Appliances and associated technologies to the consumer. Government, energy companies and manufacturers were all identified as having a role in improving awareness. According to industry experts, Smart Appliances could be better appreciated by consumers through improved product labelling and better communication of the benefits. It was recognised that certain appliances appeal to a certain segments of the population. One suggestion was also made for making

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Smart Appliances the ‘default’, but provide consumer with the facility to ‘opt out’, i.e. with an override function. The importance of trust (i.e. that consumers trust that Smart Appliances are providing benefits) was highlighted. The role of organisations such as Chambers of Commerce, magazines (such as Which?, Radio Times, Women’s and DIY magazines) were recognised has having the potential to play an important role with regards to promoting trust. The need for change to the current settlement system (the mechanism by which energy trades are settled for each half-hourly trading period) was identified as a key factor to unlocking the benefits of Smart Appliances, particularly for matching supply to demand. Similarly, time-of-use tariffs were viewed as an important mechanism to encourage changes to the pattern of consumption of electricity, and thus stimulate the uptake of Smart Appliances. Other benefits Respondents were asked to comment on additional benefits associated with Smart Appliances that could be an important factor in promoting their uptake. Suggestions included:

- communication and connectivity features that can help improve lifestyle as well as providing energy saving features. They may also be valued for helping customers save money.

- a remote control function (that allows the users to remotely control their own appliances);

- appliances that integrate into a domestic renewable system to optimise a households’ power usage and thus optimise income from FITs.

However, it was also emphasised that Smart Appliances must offer the same or better functions than normal or ‘non smart’ appliances. They also need to provide demonstrable energy savings. Role of Smart Meters Some of the manufacturers’ representatives identified the rollout of Smart Meters as being essential for the later adoption of Smart Appliances. This is because they provide both energy consumption and cost information to households or because of the reliance on the Home Area Network for the successful operation of Smart Appliances. However, others suggested Smart Meters were not essential, with Smart Appliances able to operate independently of Smart Meters. However, it was generally agreed that Smart Meters that provide real time energy information were important as an educational tool to allow householders to understand the impact of individual appliances on their electricity load. Smart Appliances as premium products The manufacturers’ representatives nearly all agreed that Smart Appliances will become ‘the norm’. Some will have additional functionality and be classed as premium within the classification. Some suggested that support may be required initially to help this happen however others suggested that widespread adoption could happen without incentives. One suggestion was that Smart Plugs could be

26

provided by Energy Suppliers at a low cost to familiarise consumers with the concept of Smart Appliances. Some industry experts suggested that Smart Appliances should have enough value without being incentivised. Another suggested that all appliances should be Smart and ToU tariffs used to incentivise off peak electricity usage. It was also pointed out that those people who need to save money the most may not be able to afford Smart Appliances and that schemes may be required to help them. Time of Use (ToU) Tariffs Some manufacturers thought that ToU tariffs would be beneficial for helping to promote more widespread adoption of Smart Appliances. All respondents suggested that the products they would develop would have the functionality to be able to decide when to run if ToU tariffs were introduced, although the importance of an over ride function was stressed by one representative. One representative suggested that ToU tariffs would be more acceptable to consumers if they were predictable although this would be less cost effective to the utilities. Industry experts point out that the public is familiar with Economy 7 so understand that electricity can be more expensive at certain times, however some thought that it would be quite a jump from this to understanding ToU pricing. Most suggested that for ToU tariffs to work a substantial incentive may be required to reward consumers. One respondent suggested that customers would need evidence that ToU tariffs were worth the risk and inconvenience. Another suggested that they may assist with improving the understanding of how much energy consumers are using, especially if they receive financial benefits. It was generally thought that a far better understanding of household’s energy use and attitudes towards energy is required. Another suggested that attention should not be drawn away from reducing demand rather than just moving it.

5.5 Barriers to the uptake of Smart Appliances

A range of issues were identified as representing potential barriers to the uptake of Smart Appliances by users. Although none of the users were asked to comment on how the costs of Smart Appliances would compare to ‘non smart’ products, there was a general consensus that Smart Appliances would be more expensive and that the additional costs could prove a barrier to uptake. The ease with which Smart Appliances can be purchased was also cited as another factor that clearly influences uptake, e.g. are they readily available from a range of outlets or only from specialist retailers. The time pressures on commercial customers require that products must be easy to use. Users must also be willing to change their energy use patterns. Not all Smart Appliances respond to inherent changes in electricity network properties. For those that don’t, a communications system is required to implement changes to their pattern of operation. The Smart Meter provides such a communications link directly to the home, thereby providing the opportunity to directly control end-use loads. Other options include the installation of a dedicated communications system or use of internet and WiFi enabled devices. However, the readiness of the electricity network to interface with Smart Appliances is regarded to be an important factor. The wide scale adoption of Smart Appliances is unlikely to happen until Smart Meters and Smart Grids are in place. The need for an operating

27

platform into which appliances can be readily connected was highlighted, together with interoperable appliances. Concerns over data privacy, security and access to data are seen as a major barrier to the implementation of Smart Appliances, particularly in terms of control by 3rd parties. One respondent thought that trials and reports highlighting the advantages to consumers may overcome some of these attitudes. Monetary incentives could be used to stimulate the market through a transition period, although it was recognised that users would require time to get used to the idea of control by 3rd parties. One respondent suggested that a surcharge could be imposed for opting out of 3rd party appliance control. The term ‘control’ was regarded as unhelpful and that the decision must always remain with the consumer as to whether to respond – if this option is removed then consumers will not like the concept. It was generally agreed that consumers may not be happy to cede control of their appliances to 3rd parties and safeguards would be required, for example, to limit the maximum temperature in remotely controlled refrigerators, or to ensure that electric cars were fully charged by a certain time. The intrusion may also be unwelcome. Clear agreements about data use and data privacy would be required and customers would have to be involved in any agreement between Energy Companies and 3rd Parties. Some form of personalised feed-back may also make it more acceptable. It was highlighted that in the UK, domestic electricity loads are not dominated by single large loads as much as in, for example, regions of the USA where air conditioning units cause a problem and can be readily targeted for load control. Simple understandable systems may also be advantageous. It was also suggested that the concept of load control by 3rd parties may be problematical because any advantage is to the Energy Suppliers and Network Operators and not the consumer.

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6 Barriers to Deployment and Uptake of Smart Appliances in the UK

There are a number of barriers that impact on the potential deployment and uptake of Smart Appliances in the UK. Some are generic barriers that apply to all types of Smart Appliances whilst others are specific to particular appliance groups. This Section describes the generic barriers, whilst those specific to particular appliance groups are considered in Section 7. The barriers to the successful deployment of Smart Appliances can, generally, be classified into the following three categories: Technology Related Barriers, i.e. those relating to the Smart Appliance itself

and any related infrastructure required to deliver the associated ‘Smart functionality’;

Market Related Barriers, i.e. those relating to the structure of the electricity market, the rules governing the way electricity is traded and the way that market stakeholders interact with one another; and

Customer Related Barriers, i.e. those relating to customer attitudes towards Smart Appliances and the way that customers interact and use their Smart Appliances.

These are considered in further detail in the following Sections.

6.1 Technology Related Barriers

With the notable exception of night storage heaters remotely controlled using teleswitching signals sent via the national radio broadcasting infrastructure, Smart Appliances are not, as yet, mainstream products. However, devices are now beginning to emerge that enable customers to remotely control individual appliances and end use loads. A number of manufacturers have announced their intentions to launch Smart products in the UK in the near future, but these are not yet readily available. Importantly, no clear consensus has yet emerged on the level of functionality that will be offered by Smart Appliances. For example, Smart Appliances could comprise any or all of the following features:

- An appliance that can respond automatically to tariff signals sent by a Smart Meter, such that the usage of these appliances is prevented or reduced during high price periods, or rescheduled to coincide with low price periods;

- An appliance that can be remotely disconnected by a third party for a limited period of time;

- An appliance that can schedule its operation to optimise the use of on-site generation;

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- An appliance that can automatically detect the changes in the network, for example frequency or voltage changes and respond automatically;

The term could equally describe appliances that provide non energy related benefits to consumers. For example, appliances that can be remotely controlled by users for added convenience. Without an industry wide consensus on what constitutes a Smart Appliance, it is not possible for customers to compare the functionality of one Smart Appliance with another. For example, the term ‘Smart’ could be used to describe an appliance that provides additional benefits over an above ‘standard’ models, i.e. a ‘Smart’ washing machine may be one that provides customers with the ability to remotely update washing cycle programs over the internet, rather than specifically relating to ‘Energy Smart’ features. Similarly, there are currently no criteria that define the minimum functionality that must be achieved in order for an appliance to be classified as ‘Smart’. For example, the Energy Saving Trust Recommended energy labelling scheme19 permits products that meet strict criteria on energy efficiency to carry the ‘Energy Efficiency Recommended’ logo, and a similar scheme could be applied to Smart Appliances. The dialogue with key industry stakeholders highlighted that the readiness of the electricity network to interact to Smart Appliances was seen as an important factor; until Smart Meters and Smart Grids are in place it is difficult to foresee the wide scale adoption of Smart Appliances that can be fully utilised. There is a need for a standard operating platform into which appliances can be easily connected, rather like the ‘plug and play’ platform adopted by Microsoft and the ‘apps’ used on Smart Phones. The most recent proposals for the rollout of Smart Meters in the UK are contained in a suite of documents published by DECC on 18 August 201120. These include a consultation on the obligation of Suppliers to rollout Smart Meters and on the specification of metering equipment in the home. It has been decided that the data collected from domestic Smart Meters will be channelled through a central communications hub known as DataCommsCo (DCC). This hub will provide two way communications with the Smart Meter. Parties such as Energy Suppliers and Network Operators will be able to access data from Smart Meters, as required to meet their licence obligations, or as authorized by individual customers. The data flow is represented in Figure 6.1.

19 http://www.energysavingtrust.org.uk 20 Accessed via http://www.decc.gov.uk/en/content/cms/consultations/cons_smip/cons_smip.aspx

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Figure 6.1 Data flow in proposed UK Smart Metering model

In addition to accessing data, the Smart Meters include the functionality to send pricing or other messages to the customer premises. This will be facilitated through a Home Area Network, as indicated in Figure 6.2 below. However, the mechanism by which network operators and other third parties implement the control of end-use loads such as Smart Appliances has yet to be defined.

Source: Smart Metering Implementation Programme: Statement of Design Requirements, 27 July 2010

Figure 6.2 Home Area Network

Thus, the Home Area Network is regarded as a crucial element in the implementation of Smart Appliances, as it enables the existing two-way communications system for the Smart Meter to be utilised to control the operation of appliances. In this way, the appliances themselves may not therefore need to be ‘Smart’, but rather the decision making is undertaken within the Home Area Network. However, the details of the Home Area Network have yet to be defined. Thus, the specification of the Home Area Network is seen as a priority in terms of stimulating the development and deployment of Smart Appliances.

6.2 Market Related Barriers

There are already a number of mechanisms in place that incentivise customers to provide demand side services, for example frequency response and reserve services to National Grid. Whilst these could include demand side services implemented using Smart Appliances, there are a number of market related barriers that impact on

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their deployment. Many of these relate specifically to the structure of the electricity market and the rules governing the way electricity is traded. Current thinking about the Smart Meter rollout strategy is contained in ‘Smart Metering Implementation Programme: Rollout Strategy’21. It has been decided that the rollout of Smart Meters will be led by Energy Suppliers. Once Smart Meters are in place, the potential exists for Suppliers to offer a much wider range of electricity tariffs than is currently available. This could include:

- Static ToU tariffs: to reflect the varying costs of providing electricity at different times of the day (evening, peak, night etc);

- Real time (or day ahead tariffs), where prices vary half-hourly in line with market price fluctuations; and

- Critical peak pricing, whereby the high prices are applied during specified hours on a certain number of days, although the timing of these ‘critical days’ are not fixed in advance.

Energy retailing in the UK is a fully competitive market, with Energy Suppliers free to decide the level and the structure of the energy tariffs they offer. Therefore, it is difficult to predict how such tariffs will evolve as Smart Meters are rolled out. However, dynamic tariffs (either as real time day ahead prices or critical peak pricing) are regarded as a major stimulus for the deployment of Smart Appliances: without some form of financial incentive it is unlikely that there will a significant demand for Smart Appliances. It is, however, important to note that static ToU, i.e. tariffs that do not vary on a day by day basis (other than perhaps seasonally) do not require ‘Smart’ features, and instead require more simple behavioural changes or simple timer functions. It is important to note that ToU tariffs are not a prerequisite for the implementation of Smart Appliances. Other incentive mechanisms can be put in place. Alternatives might include one-off payments (suitable for all types of DSM measures) or discounts from the Energy Tariff (applicable only for Supplier led DSM measures). As discussed previously, as the proportion of wind generation increases over the medium to longer term, it is likely that Suppliers could see wholesale electricity prices fluctuate considerably, reflecting the availability of wind. Under such conditions, Suppliers could face huge price risks and therefore, may be more likely to introduce dynamic tariffs. However, in the short term, it is difficult to determine the extent to which these tariffs will be offered by Energy Suppliers. It is worth noting here that HM Government has recently published a White Paper focused on reforms to the electricity market22. The Paper identifies a need for the introduction of capacity payments to encourage investment in flexible reserve plant. The introduction of such payments could reduce the ‘peakiness’ of electricity prices, and hence on one of the drivers for Smart Appliances. However, the payments would be equally available to demand side participants, thus providing an incentive to implement demand side programmes. 21 Smart Metering Implementation Programme, A consultation on draft licence conditions and technical specifications for the

roll-out of gas and electricity smart metering equipment, August 2011 22 Planning our electric future: a White Paper for secure, affordable and low carbon electricity, July 2011

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As discussed in Section 2, Smart Appliances have the potential to assist Network Operators manage network constraints, and thus defer or avoid network reinforcement. However, the unbundling of the electricity industry means that DNOs no longer have any direct engagement with end use customers. The charges levied by DNOs to recover the costs associated with distributing electricity to end-users (known as Distribution Use of System (DUoS) charges) are collected by Energy Suppliers rather than directly via the DNOs. Therefore, it is it is up to the Energy Suppliers how these charges are bundled together with the energy tariff for HV and LV customers. Whereas DNOs have introduced ToU distribution charging for HV and LV customers with half-hourly metering (i.e. with maximum demands above 100kW), such charging is not in place for domestic and small commercial users. These customers are currently charged on a flat unit rate, or a two rate tariff for those on an off peak tariff. Therefore, DNOs are faced with making direct contractual arrangements with end-users, or third party aggregators. The details of these arrangements are yet to be fully established, and are currently being investigated as part of a number of the Low Carbon Network Fund trials that are currently underway. In an unbundled electricity system, the benefits of Smart Appliances are spread amongst a number of stakeholders, rather than within a single, vertically integrated utility company. Thus it is difficult to ensure that the costs and benefits are shared appropriately between the relevant stakeholders. For example, the benefits of peak load reduction could be shared amongst:

- the Energy Supplier (via reduced purchase costs of electricity at the peak); and

- the DNO (via deferred or avoided network investment) The profiling settlement system23 that is currently used to allocate the total annual consumption of households and other small users24 into half-hourly blocks means that it is difficult for energy suppliers to capture the value associated with load shifting. Under the current arrangements, the pattern of consumption of customers is determined according to a profile, and total consumption is allocated to specific half-hours according to these profiles. Customers are classified into one of eight profile classes. However, within these classes, customers with demand management are treated in the same way as customers without demand management. This is regarded to have been a major barrier to the introduction of innovative tariffs or direct load control schemes since the system was introduced in 1998. The introduction of Smart Meters means that it will be possible for all electricity consumption to be settled according to their actual profile, rather than a ‘deemed’ profile. However, there is no requirement on Energy Suppliers to move away from the use of the current profiling system for domestic customers and some small non-domestic customers25, although they are able to do so if they wish.

23 For a description of the load profiles and their use in electricity settlement see

http://data.ukedc.rl.ac.uk/browse/edc/Electricity/LoadProfile/doc/Load_Profiles.pdf 24 With a maximum demand less than 100kW 25 A proposal to introduce half-hourly settlement for all customers currently in profile classes 5 to 8 is pending, but no such

requirements are yet proposed for customers in profile classes 1 to 4 which includes domestic customers.

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6.3 Customer Related Barriers

Customer attitudes towards Smart Appliances represents one of the most significant barriers to the widescale deployment of these devices. In a situation where ‘competitive market forces’ dominate, then the customer is the ultimate decision maker. If customers do not want the appliances, or if developers do not envisage a market where customers will want the appliances, then it is evident that the market for such products will not develop. As outlined in Section 2, there are a number of clear drivers for Smart Appliances. However, the ‘business case’ for their deployment in the UK is far from clear. In particular, it is difficult to quantify the impact (i.e. MWh of electricity saved, MW of peak demand reduction and tonnes of CO2 saved) or to quantify the implementation costs (i.e. development costs, capital costs and on-going operational costs). The case studies summarised in Section 7 go some way toward exploring some of the issues and demonstrate the difficulty of quantifying the benefits. Whilst a number of pilots and trials of Smart Appliances have been undertaken, many of these have taken place overseas and have focussed on large single end-use applications such as heating or air conditioning. No detailed studies have been undertaken to quantify the potential magnitude of savings in the UK and whether the costs of achieving these savings are economic. The dialogue with key industry stakeholders highlights the importance of increasing customer awareness of Smart Appliances. However, the issue of motivating customers to become more energy aware must not be underestimated. The use of behavioural economics is now becoming seen as increasingly important in the role of encouraging energy efficiency. For example, it is becoming increasingly evident that customers do not make decisions on a purely rational economic basis26, and therefore, a purely economical argument that customers will save money if they adopt Smart Appliances is unlikely to be sufficient to promote their adoption. Similarly, whilst it is widely recognised that customers need to be made aware of the benefits of Smart Appliances, there is growing evidence to show that the way information is presented to customers is an important factor. A good example of this is provided in Nudge27, which suggests that presenting energy information in terms of how much money customers have just lost by not pursuing a certain action may be far more effective that presenting information on how much could be saved. Similarly, a recent report from Consumer Focus28 recognises the importance of the role of information in changing energy related behaviours. It seems reasonable to suggest that such arguments will apply equally to Smart Appliances as they do to energy efficiency. Consumers are likely to need reassurances before they will permit third parties to remotely control their appliances. This will require understanding of customers own views and concerns regarding Smart Appliances. Also, as highlighted in Section 5,

26 Predictably Irrational, Dan Ariely 27 Nudge – improving decisions about health, wealth and happiness, Thaler and Sunstein 28 Missing the Mark, Consumers, Energy Bills, Annual Statements and Behaviour Change, Hannah Mummery and Gillian

Cooper, Consumer Focus, June 2011

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close liaison with customer representatives who are well placed to solicit the view of customers, as well as being seen as trusted sources of information, will be useful in terms of allaying customer concerns. This could be particularly relevant where there is conflicting advice from different organisations. For example, a campaign by Tayside Fire Department recommended that customers do not operate appliances at night or when their properties are unoccupied because “more house fires are caused by electrical appliances than anything else”29.

29 http://www.taysidefire.gov.uk/page/Switch_Off_173.html accessed 1 June 2011

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7 Impact of Selected Smart Appliances

Smart Appliances enable consumers to modify the amount of energy they consume and/or the pattern of their consumption in response to certain external stimuli. Although a project is currently underway to monitor the end-use consumption patterns in a group of UK households, no data is currently available from this trial30. However, a number of illustrative load profiles were produced for a range of European countries as part of a research project to investigate the potential of Smart Appliances. The data for an average UK household is shown in Figure 7.1, and shows a break-down of the pattern of consumption by different electricity end uses for domestic customers, both now and for 2025. These profiles do not include lighting, consumer electronics and entertainment loads. Although electric heating loads are included, the profiles are believed to represent the average heating load profile across all households (i.e. those with and without electric heating). Thus, the profiles below do not provide a good representation of the load profile of homes with electric heating only.

2010 2025

WH = Water heating

A/C = Air conditioning

OS = Oven / stove

TD = Tumble dryer

WM = Washing Machine

CP = Circulating Pumps

DW = Dishwasher

RF = Refrigeration

FR = Freezers

EH = Electric heating Source: Synergy Potential of Smart Appliances, D2.3 of WP 2 of from the Smart-A Project

Figure 7.1 Estimated Daily Load Curve of an Average UK Household

30 A Study of the Energy Consumption of Domestic Products in UK Households on Behalf of the Department of the

Environment, Food and Rural Affairs, the Department for Energy and Climate Change and the Energy Saving Trust, AEA/TEN/244

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As discussed in Section 3, many end-uses are not well suited to demand management, namely lighting and consumer electronics. Thus, of those loads shown in Figure 7.1 that can be interrupted or rescheduled (see Appendix B for details of the classification of loads for DSM), those that contribute most to system peak demands, both now and in the future are: Refrigeration Central heating circulating pumps Washing machines Tumble dryers It is interesting to note that central heating circulating pumps (circulators) represent a significant end-use of electricity in domestic properties. EcoDesign31 requirements, introduced by the EU Commission, impose energy efficiency Standards on a range of products to reduce their environmental impact. The Ecodesign requirements for circulators are set out in the relevant legislation32: The efficiency requirements will become mandatory in January 2013, and once in force it would be expected that the energy efficiency of circulating pumps will increase significantly. Therefore, the role of Smart circulators is not considered within the scope of this study. The decarbonisation of the electricity generation sector will see significant growth in new electric loads, particularly heating and transport. As mentioned previously, electric vehicles are not within the scope of this current study. In terms of the growth in electric heating, this is likely to be dominated by heat pumps rather than direct electric heating or electric storage heating. For example, a number of potential pathways that lead to an 80% reduction in carbon emissions by 2050 have been considered by the Department of Energy and Climate Change (DECC) in its ‘2050 Pathways Analysis’33. Although the pathways do not attempt to forecast particular outcomes, the six illustrative pathways that were developed demonstrate an expectation that heat pumps will be the main form of heating for both the domestic and commercial sectors in future years. Consequently heat pumps are likely to contribute significantly to increases in both overall electricity consumption and peak demand within both domestic and commercial buildings in the future. However, they are also well suited to demand management and are therefore a potentially valuable resource to help mitigate against increases in peak demand attributed to their uptake. It is important to note that, although not specifically considered here, direct electric heating and storage heating are also well suited to demand management. For example, electric storage heating is a well established DSM measure in the UK, developed specifically to optimise the use of low cost electricity from nuclear generation during the off-peak period. However, as it is generally anticipated that growth in electric heating will be attributable to heat pumps, only this form of electric heating is considered in the case studies.

31 http://www.eup-ecodesign.com/ 32 http://ec.europa.eu/enterprise/policies/sustainable-business/documents/eco-design/legislation/implementing-

measures/index_en.htm 33 2050 Pathways Analysis, available from http://www.decc.gov.uk/

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Very little is known on the breakdown of energy consumption patterns within the small commercial sector, with even less known on the pattern of electricity consumption within this sector. Therefore, it is difficult to identify particular end uses that are well suited to demand management within small commercial buildings. However, as highlighted in Appendix B, air conditioning currently represents around 10% of electricity end-use consumption in all commercial buildings, and there is an expectation that air conditioning loads will increase in future years. In addition, air conditioning was highlighted as a key area for Smart Appliance development in the commercial sector during the dialogue with the key Stakeholders. Therefore, air conditioning is also considered here.

7.1 Case Studies

A number of case studies have been used to better understand the potential impact of a selection of Smart Appliances and the associated barriers to their uptake. The five appliance types considered are as follows: Heat pumps; Air conditioners. Refrigerators (cold appliances); Washing machines; and Tumble dryers; Each case study considers the potential impact of each of the selected appliances under three scenarios as described below; Business As Usual. This is the baseline against which the Smart Appliance

options are compared. It represents what is likely to happen in the future if no specific measures are put in place to encourage the implementation of Smart Appliances over and above the measures that are currently in place or planned. Thus, the Business As Usual incorporates the role of Smart Meters to all consumers by 2019 and assumes that customer attitudes to energy consumption do not change significantly over the period. This scenario does, however, incorporate improvements in appliance efficiencies arising through an ambitious and feasible programme of policy measures34.

Smart Appliances – Level 1: This scenario explores the potential impact of Smart Appliances that incorporate a ‘minimal’ degree of smartness, and requires relatively modest developments to existing appliance designs.

Smart Appliances – Level 2: This scenario explores what could be achieved through a greater degree of smartness, and considers the extent to which Smart Appliances could enable consumers to modify their energy consumption.

The following provides a summary description of the Level 1 and Level 2 for each of the Smart Appliances under consideration. Detailed descriptions of each of the case studies can be found in Appendix E.

- For both heat pumps and air conditioning, Level 1 does not require any changes to the design of the standard system other than the means of

34 The Market Transformation Programme’s Policy Scenario

38

remotely switching the appliance off for short periods. Level 2 requires design changes to incorporate thermal storage.

- For refrigeration, Level 1 appliances include the capability to provide dynamic

frequency response services to National Grid by automatically adjusting to changes in system frequency. Level 2 appliances require further developments to the dynamic demand concept to allow extended interruptions of, say, half an hour.

- For washing machines and tumble dryers, Level 1 does not require any

changes to the design of the existing appliances, but rather relies on changes in human behaviour to minimise usage during certain periods. Level 2, then requires design changes to enable these appliances to be automatically scheduled to operate during prescribed periods. Alternative designs that allow the operating cycles to be interrupted or to defer the heating load are also briefly considered.

7.2 Case Study Findings

As described in Section 2, there are a number of drivers to manage the pattern of demand in the UK, including:

- The provision of balancing services to National Grid; - The management of network constraints; - The avoidance of peak energy prices; and - The integration of renewable generation resources.

Smart Appliances enable customers to adjust their pattern of electricity consumption in a coordinated and controlled manner so that the modified pattern of demand can be relied upon for the purposes described above. As such, Smart Appliances are one of the suite of measures that could help the UK meets its policy objectives in terms of carbon emissions reduction, the facilitation of the uptake of renewable energy sources and in reducing overall energy consumption. The generic requirements for demand management are described in Section 3, which highlights how requirements vary depending upon the purpose for which the demand response is to be used. Thus, not all end-use loads are suitable for all purposes. The case studies outlined in Appendix E describe the potential impacts associated with the deployment of the selected Smart Appliances, and the specific barriers to the uptake of each technology. This information has been used to compare the benefits and barriers associated with the selected Smart Appliances. The comparison is presented in Table 7.1, which provides a qualitative assessment of the Level 1 and Level 2 scenarios compared to the Business As Usual baseline.

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Table 7.1 Overview of Case Study Findings

Appliance

Heat Pumps*

Air Conditioners

Cold Appliances

Washing Machines

Tumble Dryers

Smartness Level

1 2 1 2 1 2 1 2 1 2

Network constraints management

Ancillary Services

Energy peak price avoidance

Ben

efit

s

(Rel

ativ

e Im

pac

t co

mp

ared

to

B

usi

nes

As

Usu

al)

Renewables integration

Technology

Market

Bar

rier

s (E

ase

of

Imp

lem

enta

tio

n)

Customer

* Bi-valent and fuel substitution options are not included under heat pumps (see Appendix E for discussion) but would, in principle, provide significant benefits and relative ease of implementation

Key: Benefits

Benefits compared to the ‘Business As Usual’ baseline are potentially significant

Potential benefits compared to the ‘Business As Usual’ baseline, but these are not regarded to be significant

Minimal or no benefits compared to the ‘Business As Usual’ baseline

Barriers

Minimal barriers to deployment

Some barriers to deployment

Significant barriers to deployment

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8 Roadmap for the deployment of Smart Appliances in the UK

As described in the preceding Sections, there are a wide range of barriers to the uptake of Smart Appliances. In order to identify specific actions to overcome these barriers, and the timescales over which they these should be undertaken, a number of case studies were developed (see Section 7). These provide an overview of the potential impact of Smart Appliances compared to a ‘Business As Usual’ scenario and consider the ease with which the various measures can be implemented in a UK context. This information has been used to produce a Priority Matrix, as illustrated Figure 8.1. This provides a useful framework for prioritising activity by comparing the relative impact and ease of implementation of a range of measures. It is particularly useful in identifying the ‘quick wins’, (i.e. measures that have a high impact and are easy to implement) and those that should be avoided (i.e. those with a low impact and are difficult to implement).

Imp

act

Ease of Implementation High

High

Low

Low

High ImpactHigh Ease of Implementation

“quick wins”

Low ImpactLow Ease of Implementation

“avoid”

Low ImpactHigh Ease of Implementation

“possible”

High ImpactLow Ease of Implementation

“review”

Figure 8.1 Illustrative Priority Matrix

Therefore Figure 8.2 illustrates the relative merits (i.e. the impact) and the ease of implementation of two levels of Smart Appliances (Level 1 and Level 2 as described in Section 7) compared to a non smart version (i.e. the baseline or business as usual). In this context, easy to implement includes measures that require little to no changes to existing appliance design and/or measures that have no significant barriers to entry. It is important to note, however, that the case studies presented in Section 7 are not based on extensive modeling to quantify the relative impacts of different approaches and the ease of implementation. Rather, they represent a largely qualitative approach that considers the situation for each of the appliances considered, e.g. does a Smart washing machine offer significantly improved benefits compared to a

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non-Smart appliance. In addition, the case studies do not explore the potential impact of the level of penetration of Smart Appliances. Therefore, further work may be required to establish a detailed comparison between different appliances for varying penetration levels. Thus, the comparison shown in Figure 8.2 provides a qualitative assessment of the level of demand response and the associated ease of implementation that could be reasonably predicted to be achievable for the case studies under consideration. In each case, the impact is considered in terms of specific applications for the resulting demand response. The impact for each appliance (and for each Level of Smartness) is considered for the following two options:

A. For shorter term measures, i.e. balancing services (primarily frequency response services lasting typically up to half and hour). This could also include short duration (i.e. up to half an hour) interruptions to assist with the avoidance of peak prices.

B. For longer term measures, i.e. managing network constraints (up to three hours), and renewables integration (action over several days). Also included within this category is the provision of reserve services, typically required for a period of 2 hours.

Thus, in Figure 8.2, L1A refers to an appliance with a Smartness Level of 1 applied to a short term measure; L2B refers to an appliance with a Smartness Level of 2 applied to a longer term measure.

Key:L1A

L1B

L2A & B

Low HighEase of Implementation

Hig

hLo

wIm

pa

ct

L1B

L2A

L2B

L1A

L2A

L2 B

L1A

Heat Pumps

Washing Machines / Tumble Dryers

Refrigeration

Air-conditioners

L1B

L1A

L1B

L2A & B

L1 - Level 1 smart appliance

L2 - Level 2 smart appliance

A - Short term interruptions

B - Longer term interruptions

Figure 8.2 Impact / Ease of Implementation Matrix for Selected Smart Appliances

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8.1 Actions Required to Deliver the Benefits of Smart Appliances

High Impact / High Ease of Implementation (“quick wins”) Those appliance groups falling within the ‘high impact, high ease of implementation’ area are:

- Heat pumps, L1A: simple on/off control - Air conditioners, L1A: simple on/off control

Thus, actions to be considered over the short term include that relate specifically to the uptake of heat pumps and air conditioners are:

- A better understanding of the level of interrupt that is acceptable to householders and commercial customers is needed, i.e. for how long can these appliances be switched off without comfort levels being significantly affected, and what the system impacts might be of the post-interrupt restart.

- An investigation into the relative value (to the UK) of the capability of heat pumps and air conditioners to deliver short term interrupts compared to the value of delivering longer term interrupts is required. As seen in Figure 8.2, none of the appliance measures considered in Section 7 are positioned in the far top-right of the ‘quick wins’ quadrant. The measures with the highest impact sit in the top-left quadrant, i.e. high impact / low ease of implementation. Therefore, it is considered essential to obtain a more detailed understanding of the relative benefits of different DSM measures to the UK, i.e. how much more valuable are Level 2 heat pumps and air conditioners compared to Level 1 versions. If the benefits are significantly greater, then there may be merit in focusing on the higher impact solutions rather than just on the quick wins. In particular, there is the possibility that focusing on the ‘quick wins’ in the short term may make it less likely for the more desirable solutions to be progressed at a later time.

- Even though the measures here involve the simple on/off control of heat pumps and air conditioners, experience from international field trials show that minimum appliance Standards will be required particularly in terms of the interface between the appliance and (in the UK context) the Home Area Network.

In addition to the appliance specific measures listed above, there are a number of more generic actions required to facilitate their uptake in the UK. Thus actions applicable to all Smart Appliances are:

- The role of the Smart Meter is a crucial element in providing a cost effective communications interface with the end-user. Therefore, the mechanism by which Energy Suppliers and other 3rd parties issue instructions to these appliances via the Smart Meter and the Home Area Network needs to be established. In particular, the role of the Data Communications Company (DCC) in handling these communications needs to be defined, including the requirements for the speed of communications (i.e. how quickly can messages be sent), the prioritisation of messages and the frequency of communications (i.e. how often can messages be sent).

43

- The protocols for communication with Smart Appliances need to be agreed, in the form of either voluntary or mandated Standards. These Standards will need to be developed within an EU context as appliances are not generally produced specifically for a UK market. However, it will be essential to ensure that the specific requirements of the UK market structure and the Smart Meter specification currently being finalised for the UK roll-out are addressed.

- A better understanding is required of the financial mechanisms that need to be put in place to reward customers for allowing their appliances to be remotely controlled by 3rd parties. This should include a review of the role of ToU tariffs and other financial rewards.

- Whilst ToU tariffs can be implemented within the current settlement arrangements, the use of standard profiles for households and selected smaller non-domestic customers35 could limit the ability to capture the benefits of modifying the load profile for these customers. Therefore, consideration needs to be given to the potential implementation of half-hourly settlements for all customers once Smart Meters are rolled out.

- Customer engagement and awareness is a key factor in ensuring the successful uptake of Smart Appliances. Therefore, a better understanding of customer acceptance of 3rd party control of appliances is regarded to be an essential prerequisite. Otherwise, there is a risk that products and services are developed without due regard for the concerns of the customers themselves.

High Impact, Low Ease of Implementation (“review”) Those appliances with the potential to provide a high impact, but with a lower ease of implementation compared to those listed above include:

- Heat Pumps, L2A & L2B: fleet operation or incorporation of storage to allow maximum flexibility for the full range of DSM measures

- Air conditioners, L2A & L2B: incorporation of storage - And Refrigeration (cold appliances) L1A and L2A: short-term interrupts using

varying levels of appliance control complexity. Many of the actions highlighted above (under “quick wins”) apply equally to the appliance groups falling in the “review” quadrant. Therefore, additional actions to be considered over a longer time horizon to facilitate the uptake these potentially desirable measures are:

- Further investigation and research into the role of thermal storage in providing heat pumps with more flexibility for DSM measures. This includes obtaining a better understanding of the advantages of thermal storage versus the increase capital costs and the impact on space requirements. This also applies to air conditioning, although arguably to a lesser extent that for heat pumps as space limitations maybe less onerous in a commercial building context.

35 Non maximum demand customers (<100kW) settled according to profile classes 3 and 4

44

- The benefits associated with the provision of dynamic frequency response services to National Grid by cold appliances is well understood, and appliances incorporating this technology are currently undergoing extensive field trial evaluations. However, the major obstacle to the role out of the technology (and thus the reason this has been placed in the “review” quadrant rather than the “quick wins” quadrant) is the lack of any financial mechanism to encourage the uptake of these appliances by end-users. Therefore, there could be merit in investigating whether any appropriate financial mechanisms could be put in place to incentivise customers to purchase appliances with dynamic frequency response capability. This is especially relevant given that the annual benefits amount to a few pounds per customer per year.

Low Impact, High Ease of Implementation (“possible”) Those appliances falling within the ‘low impact, high ease of implementation’ area include:

- Heat Pumps, L1B: simple on/off control; - Air conditioners, L1B: simple on/off control;

As the impact associated with these measures is considered to be low, no specific actions are considered appropriate at this time. However, there may be merit in revisiting this area once all other actions have been implemented and the impacts measured. Low Impact, Low Ease of Implementation (“avoid”) Those appliances falling within the ‘low impact, low ease of implementation’ area include:

- Washing Machines & Tumble Dryers, L1A and L1B: user education - Washing Machines & Tumble Dryers, L2A and L2B: enhanced operational

capability to allow washing machine and tumble dryer loads to be automatically scheduled (B) and/or interrupted mid cycle (A);

- Cold Appliances, L1B and L2B In all of these cases, the low impact reflects the relatively small average energy use of the appliances concerned. As both the impact and the ease of implementation associated with these measures are considered to be low, no specific actions are considered appropriate at this time. However, raising consumer awareness of the importance of the pattern of energy consumption as well as of the overall amount of energy consumed is nevertheless considered to be important. Here, behavioural science and behavioural economics may provide useful tools for how this information can be best communicated to consumers.

8.2 Timelines for Implementation

Figure 8.3 provides a suggested timeline for the implementation of the actions described in Section 8.1, which also includes key dates in the Smart Meter Implementation Programme36.

36 Smart Metering Implementation Programme, Response to Prospectus Consultation, Overview Document, March 2011

45

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 ….. Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Smart Metering Implemention Programme

Draft technical specificationDraft technical specification complete EU Notification period for technical specification

Regulatory Obligations on Suppliers come into force - Tranche 1(*) (*) Mandated roll-out completion date, Installation code of practice

DCC licence application process DCC licence awarded DCC service providers appointed

Smart' change of supplier arrangements become standard

Mass roll-out

Delivering the Benefits of Smart AppliancesGeneric actionsProcedures for 3rd party communication with Smart Appliances via DCC and HAN

Standard protocols for communicating with Smart Appliances

Review of profile settlement arrangements

Customer engagement

Appliance specific actionsRelative value of short vs long interuptions for heat pumps, air conditioning loads to UK.

Level of interupts acceptable to customers (heat pumps, air-conditioning loads)Define programme of workUndertake analysis/trials (two heating/cooling seasons)Interim findings Delivery of findings

Design specification for heat pumps

2011 2012 2013 2014 2019 20202015

Figure 8.3 Timeline for Implementation

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9 Concluding Remarks

This report highlights that Smart Appliances represent one of a suite of measures that could help the UK meet its goals for reducing energy consumption, reducing carbon emissions and the provision of energy from renewable sources. However, as have been cited throughout this document, there are wide ranging barriers to their uptake in the UK. Many of these barriers are generic to all types of Smart Appliances, whilst others relate to issues particular to specific Appliances. All are equally important – unless they are addressed it will be unlikely that the use of Smart Appliances will become widespread. The roadmap presented in Section 8 demonstrates the relative merits of a range of Smart Appliances in terms of their impact (in providing certain DSM measures) and the ease with which they can be implemented. This shows that of the appliances considered, heat pumps and air conditioners that provide short term interruptions represent ‘quick-wins’, i.e. can be implemented with relatively high ease with reasonable impact. In order for these to be implemented, therefore, the priority actions to be completed are as summarised below:

- A better understanding of the level of interrupt that is acceptable to householders and commercial customers is needed, i.e. for how long can these appliances be switched off without comfort levels being significantly affected, and what the system impacts might be of the post-interrupt restart.

- Even though the measures here involve the simple on/off control of heat pumps and air conditioners, experience from international field trials show that minimum appliance Standards will be required particularly in terms of the interface between the appliance and (in the UK context) the Home Area Network.

- The role of the Smart Meter is a crucial element in providing a cost effective communications interface with the end-user. Therefore, the mechanism by which Energy Suppliers and other 3rd parties issue instructions to these appliances via the Smart Meter and the Home Area Network needs to be established. In particular, the role of the Data Communications Company (DCC) in handling these communications needs to be defined, including the requirements for the speed of communications (i.e. how quickly can messages be sent) and the frequency of communications (i.e. how often can messages be sent).

- The protocols for communication with Smart Appliances need to be agreed, in the form of either voluntary or mandated Standards. These Standards will need to be developed within an EU context as appliances are not generally produced specifically for a UK market. However, it will be essential to ensure that the specific requirements of the UK market structure and the Smart Meter specification currently being finalised for the UK roll-out are addressed.

47

- A better understanding is required of the financial mechanisms that need to be put in place to reward customers for allowing their appliances to be remotely controlled by 3rd parties. This should include a review of the role of ToU tariffs and other financial rewards.

- Whilst ToU tariffs can be implemented within the current arrangements, the current use of standard profiles for households and selected smaller non-domestic customers37 could limit the ability to capture the benefits of modifying the load profile for these customers. Therefore, potential implementation of half-hourly settlements for all customers once Smart Meters are rolled out needs to be given consideration.

- Customer engagement and awareness is a key factor in ensuring the successful uptake of Smart Appliances. Therefore, a better understanding of customer acceptance of 3rd party control of appliances is regarded to be an essential prerequisite. Otherwise, there is a risk that products and services are developed without due regard for the concerns of the customers themselves.

It is important to note that the simple on/off control of heat pumps and air conditioners are not believed to represent the measures that have the potential to deliver the highest impact. This is attributed to the provision of longer duration interruptions by these appliances. The expected increase in the uptake of heat pumps and, perhaps to a lesser extent, air conditioning is anticipated to lead to significant increases in electricity consumption and peak demands. Thus, although these loads do result in increased peak demands, they could also provide part of the solution. Both air conditioners and heat pumps provide “quick wins” through simple remote on/off controls. However, it is believed that these may only provide limited benefits (short term interrupts) whilst it is the application of more complex designs that have the potential to provide greater benefits (both short term and longer term interrupts). A crucial question is thus: how “valuable” are the benefits arising from these more complex designs compared to the simpler “quick wins”. It is therefore considered essential to obtain a more detailed understanding of the relative benefits of different DSM measures to the UK, i.e. how much more valuable are short term interruptions than longer term interruptions. If the benefits are significantly greater, then there may be merit in focusing on the higher impact solutions rather than just on the “quick wins”. There is a risk that focusing on the ‘quick wins’ in the short term may make it less likely for the more desirable solutions to be progressed at a later time. Therefore, further investigation and research into the role of thermal storage in providing heat pumps and air conditioning more flexibility for DSM measures could be considered as a priority activity over the short term. This includes obtaining a better understanding of the advantages of thermal storage versus the increase capital costs and the impact on space requirements.

37 Non maximum demand customers (<100kW) settled according to profile classes 3 and 4

48

Consideration should be given to introducing minimum design Standards for heat pumps in the UK. These Standards could, for example, prohibit the use of heat pumps incorporating an electric flow boiler, or alternatively ensure that such heat pumps are required to include the provision for remote or automatic on/off control of the flow boiler. Due to the single market rules, such Standards would need to be delivered at the European level. This would be difficult unless there is widespread support across Europe for such Standards. Additionally, consideration needs to be given to the potential role of bivalent heat pumps (i.e where the peak heating load is met by an auxiliary heating system, usually a gas or oil boiler38), which have the potential to make the implementation of DSM easier to implement, but could reduce the carbon benefit of heat pumps. The provision of dynamic frequency response by cold appliances represents the most developed demonstration of a new39 Smart Appliance in the UK, and is currently undergoing extensive field trial evaluations. However, the major obstacle to the role out of the technology (and thus the reason this is not considered to be represent a “quick win”) is the lack of any financial mechanism to encourage the uptake of these appliances by end-users. Therefore, there could be merit in investigating whether any appropriate financial mechanisms could be put in place to incentivise customers to purchase appliances with dynamic frequency response capability. This would also be of relevance to other end use loads suitable for dynamic frequency response, such as air conditioning and hot water heaters. Probably one of the most important factors affecting the uptake of Smart Appliances is customer awareness and acceptance. If customers do not want to allow 3rd parties to remotely control their appliances or to employ technologies that can automatically schedule the operation of their appliances, then it is unlikely that Smart Appliances will be able to deliver any of their potential benefits - even if all the measures identified here are addressed. Therefore raising awareness of the importance of the pattern of energy consumption as well as of the overall amount of energy consumption is considered to be important. Here, it is believed that behavioural science and behavioural economics can provide useful tools to ensure such information can be effectively communicated to consumers.

38 - see Appendix E – Heat Pump Case Study for a description of bivalent heat pumps

39 The radio teleswitch control of night storage heating is a well established application of a Smart Appliance in the UK used over a number of years.

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Appendix A: End Use Categories

An appliance can be very loosely defined as:

“A device or piece of equipment designed to perform a specific task”40 In the context of energy consumption, the term ‘appliance’ generally refers to the following specific domestic energy end-uses:

Cold appliances, such as fridges and freezers; Wet appliances, such as dishwashers, washing machines and tumble

driers; ICT; such as home computing, mobile phones and home printing; Entertainment, such as TVs, digital receivers, music systems; and Cooking; including microwaves, toasters and kettles.

As indicated in Figure A.1 below, electric space and hot water heating account for around a quarter of the electricity used by all UK households. These end-uses can respond to Time of Use (ToU) price signals and/or be controlled remotely by third parties and therefore represent potentially valuable demand resources.

Space heating13%

Hot Water14%

Cooking6%

Lighting & Appliances67%

Figure A.1 Domestic Electricity Consumption, 200841

Electric heating is ideally suited to demand management, as it can be interrupted with little or no notice and often with minimal short term impact on end-users. 40 Oxford Dictionairies Online. Accessed 25/01/11 from:

http://oxforddictionaries.com/view/entry/m_en_gb0035470#m_en_gb0035470 41 Energy consumption in the United Kingdom, Domestic Data Tables, available at

http://www.decc.gov.uk/en/content/cms/statistics/publications/ecuk/ecuk.aspx

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However, a significant proportion of this load currently occurs during the off-peak period and thus its management would have little impact on peak demand for electricity. However, as the amount of electricity generated from wind increases over the coming years, electric heating provides a valuable resource to help optimise the use of this variable generation resource during the off-peak period. Additionally, the de-carbonisation of the electricity generation sector over the coming years, and the move towards zero carbon homes, will see an increase in the number of homes heated with electricity, with heat pumps seen as a key technology. As such, electric heating is considered to be a valuable resource for helping to manage the demand pattern of households. Similarly, it is suggested that hot water heating also be included within the scope of the project. Whilst air conditioning does not represent a significant demand for electricity within the domestic sector, it does account for a sizeable proportion of electricity consumed in the commercial sector, as indicated in Figure A.2 below. Therefore, it is suggested that the control of air conditioning loads, for example via the implementation of ‘Smart thermostats’ be included within the scope of this study.

0% 25% 50% 75% 100%

Commercial Offices

Communication andTransport

Education

Government

Health

Hotel and Catering

Other

Retail

Sport and Leisure

Warehouses

Catering

Computing

Cooling and Ventilation

Hot Water

Heating

Lighting

Other

Figure A.2 Electricity Consumption by End-Use in the Commercial Sector, 200842

It is worth noting here that there is a general lack of data on the energy consumption categorised by different size of end-consumer (defined in terms of annual energy consumption). This will inevitably impact on the results of this project. The data in Figure A.2 encompasses the whole of the commercial sector, and therefore includes a wide range of premises from small shops and offices up to large offices and hypermarkets. Therefore the cooling loads shown above will include large central air

42 Energy Consumption in the UK Service sector data tables 2010 update, Department of Energy and Climate Change, July

2010, URN 10D/799

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conditioning and refrigeration systems, as well as the split type air conditioning units more suited to smaller properties and which therefore fall within the scope of this project. Therefore, within the context of this study, the term ‘Smart Appliances’ has been expanded to also include heating, hot water and air conditioning.

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Appendix B: Categorisation of Appliances

Definition Loads can be interrupted with minimal or no notice.

Consumers are more interested in a level of service and are less time sensitive as to when this service or process occurs. For example, for electrical space heating, providing storage heaters provide sufficient heat at the correct time, the consumer is not concerned when the heaters are ‘charged’, or if this load is interrupted.

Inte

rru

pti

ble

�

Typical end-uses Typical end uses are electric space and water heating, and air conditioning. The inclusion of some form of thermal storage within the system may be required to facilitate this.

Definition These loads are more problematical to interrupt. For example, for washing machines, it may be possible to advance or delay the start of a cycle, but it not advised to halt a cycle during the washing process due to potential damage to clothes. A more significant period of notice is required (anticipated to be in the region of 4 to 6 hours). They are usually relatively significant loads, especially within small consumers’ sites.

Sch

edu

lab

le�

Typical end-uses Types of end uses include wet appliances. Definition These are loads that can be interrupted, but there is a limit to

how often or for long they can be interrupted. Scheduling interruptions may increase the time that the loads can be interrupted for.

Hyb

rid

� Typical end-uses Types of end uses are cold appliances. These can be interrupted but, presently, only for a limited period without impacting on food quality. Although outside the scope of this present study, electric vehicle (EV) charging would also be included within this category. EVs must have sufficient charge available at the start of each journey. Within this requirement charging can either be interrupted or re-scheduled.

Definition These are loads that are associated with either the comfort or convenience of the consumer. Interruptions are unwelcome, unless chosen by the consumer. Consumers may change their behaviour in response to a price signal but are unlikely to be willing to accept direct interventions by other parties.

Co

nve

nie

nce

�

Typical end-uses Types of end uses are lighting, cooking, computing and consumer appliances.

The primary focus of this study is on the first three of these categories, namely, interruptible, reschedulable and hybrid loads.

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Appendix C: Smart Appliance Experiences

C.1 UK Experiences

Following the establishment of the Low Carbon Network Fund (LCNF)43 during the latest Distribution Price Control Review (DPCR5) for the Distribution Network companies, there has been a significant increase in interest in the control of customer loads, particularly those of domestic customers. The Fund allows up to £500m support to projects sponsored by the DNOs to try out new technology, operating and commercial arrangements to help DNOs meet the challenges faced in a low carbon future. There are two tiers of funding available; Tier 1 focuses on small scale projects, while larger projects fall within Tier 2 and are funded through an annual competition. In the first year (2010/11), four projects were awarded Second Tier funding and eleven projects were registered under the First Tier. A number of these projects are looking specifically at the role of the customer in helping the DNOs to operate and manage their assets more effectively, including demand side response and Smart Appliances. No results are yet available from these projects. Other examples of trials and applications of Smart Appliances within the UK are highlighted below.

C1.1 The Potential for Dynamic Demand. Prepared for The Department of Energy and Climate Change (DECC). November 2008.44

This report provides an overview of the potential for Dynamic Demand in the UK. It defines Dynamic Demand technology as that which would allow non-time critical electronic appliances such as domestic refrigerators to play a role in system balancing (i.e. by automatically switching off when frequency is low, and on when frequency is high). An additional report was prepared by the UK Centre for Sustainable Energy and Distributed Generation which considered the economic and environmental impact of dynamic demand, relating specifically to domestic refrigerators, under a number of alternative future generation development scenarios. The conclusions of the report were as follows:

Annual energy cost savings between £0.70 and £5.60 per fridge per annum. This reflects cost savings in power stations resulting from a reduction in the number of “part loaded” generation units currently providing Frequency Response Services. The savings from the consumer’s point of view are less clear.

Outside of the development costs, Dynamic Demand control increases component costs by around £4 per appliance45.

43 http://www.ofgem.gov.uk/NETWORKS/ELECDIST/LCNF/Pages/lcnf.aspx

44 Available from: http://www.supergen-networks.org.uk/filebyid/50/file.pdf (Accessed 14/02/11)

45 BNXS41: Dynamic Demand Control of Domestic Appliances. http://efficient-products.defra.gov.uk/spm/download/document/id/658 (Accessed 17/02/11)

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The use of Dynamic Demand refrigerators has a potential annual carbon saving of between 17 and 44kg of CO2 per fridge per annum, dependent on the generation mix.

A number of issues were raised as worthy of further investigation, as follows:

o The effect on the overall efficiency of appliances operation including wear and tear and associated costs as a result of providing Dynamic Demand services; and

o The impact of the pattern of use of fridges (i.e. door opening) on the value of Dynamic Demand which could be provided.

In November 2008 Ofgem approved an application under the Carbon Emissions Reduction Target (CERT) Scheme by npower to carry out a ‘demonstration action’ using refrigerators fitted with Dynamic Demand. Indesit provided and distributed the fridge-freezers with RLTec Dynamic Demand technology installed for the trial46. The first tranche of fridge-freezers were delivered in May 2010 to residents in the West Midlands. A further three hundred Smart fridges were scheduled to be part of the initial stage of the trial, with up to three thousand to be included in the later stages.47 The benefits associated with the Dynamic Demand concept applied to domestic refrigeration were demonstrated to be significant, as evidenced by the theoretical study and the field trials, which are believed to support the theoretical analysis. In addition, the benefits have been shown to outweigh the additional costs. However, the benefits accrue to the Transmission System Operator (National Grid), whilst the costs accrue to customers. Therefore, there is no incentive for customers to purchase a ‘Smart’ product rather than a conventional ‘non smart’ device, as the benefits are effectively shared amongst all network users through reduced energy balancing costs. Although it would be possible for National Grid to provide a financial incentive to customers to reward them for purchasing a Smart Appliance, a payment of up to £5.60 per household per annum is unlikely to prove attractive. In addition, it would seem likely that the costs of administering such a reward could outweigh the incentive itself. Thus despite a strong economic argument in support of the concept (i.e. benefits greater than costs), there are significant barriers to the implementation of Dynamic Demand refrigeration equipment. One solution that has been suggested is the mandating of the Dynamic Demand technology in all cold appliances sold in the UK. However, as is highlighted in Section 1, manufacturers do not generally develop products specifically for the UK market, but rather for the wider European market. Consequently, the mandate would need to apply to all products sold within the EU, rather than products sold within the UK. Frequency control does not pose the same challenges across the mainland Europe that it does within the UK, which as an island experiences a wider range in frequency.. As such, frequency response refrigeration products are specific to the needs of the UK. Thus, here it is essentially the ‘energy market barriers’, pertaining to the UK energy market structure that represent the main barrier to the implementation of this concept.

46 http://www.renewableenergyfocus.com/view/5041/indesit-rltec-and-npower-dynamic-demand-smart-grid-technology/

(Accessed 16/02/11)

47 http://www.npowermediacentre.com/Press-Releases/The-cool-way-to-cut-CO2-Europe-s-first-smart-fridges-trial-starts-in-UK-dcd.aspx (Accessed 16/02/11)

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Summary of Dynamic Demand Trial Scale of planned trial: 300 hundred refrigerators in the first stage, 3,000 in the

later stages. Drivers for the device development: ability to play a role in system balancing Solutions facilitating the deployment of Smart Appliances: this technology does

not require additional communication technology and operates based on frequency of supply only

Outcomes: Estimated financial benefit of between £0.70 and £5.60 per annum. Carbon dioxide savings of between 17 and 44kg of CO2 per fridge per annum

Key Learning Points: o Further investigations required in relation to the overall effect on

operational efficiency and wear and tear on appliances. o In order for this type of appliance to be adopted by a large number of

consumers they would need to be able to access the value from system balancing, potentially through an aggregating company

o This type of technology could potentially be expanded to a greater range of appliances (see RLtec Smart Grid Solutions, below)

C1.2 RLtec Smart Grid Solutions

The DECC report outlined above has highlighted a requirement for increased Dynamic Demand services. In addition to the trial of Smart fridge-freezers with npower and Indesit, RLtec have also received research and development funding from the Carbon Trust48 for other appliances. This grant funding has been used to enable RLtec to test Dynamic Demand technology for data centre air conditioning units, in a trial hosted by Department for Environment, Food and Rural Affairs (Defra). The company also received a £260,000 grant from DECC’s Smart Grid Demonstration Fund. The Carbon Trust funded project seeks to transfer the Dynamic Demand technology outlined for fridge-freezers above to the cooling, humidification and de-humidification needs of a data centre. This trial illustrates how Dynamic Demand has the potential to be expanded to other technology areas, although this specific application is likely to be outside the scope of this study. Results from the trial are not publically available at the time of writing.

48 http://www.rltec.com/sites/default/files/Carbon%20Trust%20Case%20Study%20-%20CTS248.pdf (Accessed 16/02/11)

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C1.3 Sustainable Blacon49

Blacon, a suburb near Chester in Cheshire, is one of the many communities participating in DECC’s Low Carbon Communities Challenge. As part of its activities to reduce carbon emissions, a trial of 150 households is currently underway to actively manage household electricity consumption. The trial does not involve Smart Appliances; rather it is evaluating the impact of an energy management system which controls individual appliances via the use of ‘Smart plugs’. The system being trialled is the AlertMe50 system, although a number of other similar systems are available. The trial involves: 50 ‘active' homes equipped with the AlertMe system, 50 ‘passive' homes using systems which will allow passive monitoring of energy usage, and 50 ‘control' homes. In addition, all three groups will take part in a community based education programme. No results are yet available from this trial.

C1.4 ETHOS Project Trial of Multimedia Energy Management Systems51

This trial was designed to test whether multimedia energy management systems could be used to achieve demand management outcomes. The trial involved 100 residential customers of the (former) South Wales Electricity Company (SWALEC), between 1996 and 1998. The aim was to reduce the peak load on the distribution network. Two systems were trialled - Low Cost CELECT (LC-CELECT) and Credanet. The LC-CELECT system controlled space heating, utilising customer settings, room temperature and electricity cost information to meet the required temperature at minimum cost. A single zone with one temperature setting could be controlled by the LC-CELECT system. The Credanet system used dedicated heaters with integral transceivers but did not include data logging equipment, therefore individual temperature recorders were used to collect room temperature information. However, the Credanet system had three zones and allowed different temperatures to be set for each zone (e.g. one temperature for occupied rooms, and a lower temperature elsewhere). All houses in the trial were also provided with prototype DICE water heater controllers in order to meet the customer’s requirements for hot water at minimum cost. It was not possible to proceed with a planned trial of direct load control dishwashers and clothes washer/dryers due to the unavailability of controllable models. Overall, the trial achieved a 25% reduction in peak demand on the relevant section of the SWALEC distribution network. LC-CELECT improved comfort for customers and achieved an average reduction in electricity consumption of 8%. The Credanet heating system and DICE water heater controllers were also successful in reducing electricity consumption.

49 http://www.sustainableblacon.org.uk/

50 http://www.alertme.com/

51 Worldwide Survey of Network-driven Demand-Side Management Projects. Task XV of the IEA DSM Programme. Oct 2008. Available from: http://www.ieadsm.org/ (Accessed 17/02/11)

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The introduction of electricity supply competition for domestic customers in 1999 is believed to have contributed to the decision to not pursue the concept beyond the trials noted here. The unbundling of supply and distribution companies meant that the benefits of load shifting were split between two companies, rather than to a single vertically integrated utility. In addition, and possibly more importantly, the introduction of the profiling settlement system meant that it was not possible for energy suppliers to capture the value associated with load shifting. Under the load profiling arrangement, the energy profile of customers is determined according to a profile, and total consumption is allocated to specific half-hours according to these profiles. Thus, customers with demand management are treated in the same way as customers without demand management. Summary of ETHOS Trial Scale of scheme: 100 residential customers in South Wales Drivers for the study: to determine if multimedia energy management systems

could be used to achieve demand management outcomes Outcomes: 25% reduction in peak demand Key Learning Points:

o Controlling electric space and water heating in the UK can be used to achieve a reduction in peak demand

o In this study, the unavailability of appliances was a barrier

C1.5 Energy Waste Due to Leaving Electrical Appliances Switched On When Not in Use. Prepared for Logicor. 2010.52

This survey was carried out to determine which appliances are routinely left switched on and unattended, both deliberately and accidentally. Research was also conducted into the average power consumption of these appliances. Although this survey does not relate to, or consider Smart appliances specifically, this ‘energy wastage’ is something which could be minimised through Smart appliance design, i.e. appliances that could turn themselves off when they are no longer required. A recent study53 has, however, shown that there is a lack of reliable and cost effective occupancy detection sensors to enable appliances to be turned off. The Table below shows the results from the survey of 2,051 respondents.

52 Available from: http://www.logicor.co.uk/wasted.pdf Accessed 14/02/11 53 Smart Energy Management Solutions: Smart Metering, Smart Appliances and Interoperability, Presentation by Professor

Philip Moor, Director of Research De Montfort University and TAHI Board Member, AMDEA workshop, London, 11th May 2011

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Table C.1 Appliance Usage Patterns

Appliance Power (W) Average Time (minutes) left on Electric Kettle 2100 9.0 Electric Blanket 150 3.6 Lamp 240 99.3 Tumble Dryer 3750 16.4 Microwave 1050 5.6 Immersion/ Water Heater 5000 73.0 Modem 10 289.5 Computer 100 172.6 Computer Screen 150 122.7 CD Player/ Hi-Fi 30 33.0 Set-Top Box 22 189.1 TV 110 92.3 Iron 1400 0.6 Electric Portable Heater 1125 5.3 Hair Care Appliance 1440 1.8 Electric Hob 4600 1.5 Mobile Phone Charger 4 97.8 Printer 35 83.4 Summary of Energy Wastage Survey Scale of scheme: survey of 2,051 respondents Drivers for the study: estimating the potential benefits for a device which could

reduce energy consumption from leaving appliances switched on when not in use Outcomes: The survey calculated that households could save around 1,300 kWh/

year, equivalent to approximately £200 Key Learning Points:

o It may be possible for ‘Smart Appliances’ to include some elements which eliminate this wastage of energy, thereby providing a financial benefit to the consumer

C1.6 Wattbox Heating Controller

The Wattbox is an alternative design of heating controller for both residential and small commercial premises. The system controls space and water heating (both gas and electric) and reduces energy consumption by monitoring and learning occupant behavior patterns and temperature preferences to program the controller accordingly, whilst minimising energy consumption. Adjustments can be made by the user using simple commands- “more heat”, “less heat” and ”more hot water”54. This monitoring and learning process can replace manual programming of the controller which the manufacturer suggests 30% of householders “do not cope well with”55. The controller can operate with a range of heating appliances, including new technologies such as micro combined heat and power (micro-CHP) units. The energy savings as a result of using a Wattbox controller are variable, depending on 54 Wattbox Technical Description. Available from:

http://www.wattbox.com/Wattbox/Products_and_Services_files/Wattbox%20Technical%20Description.pdf (Accessed 17/02/11) 55 http://www.wattbox.com/Wattbox/Home.html Accessed 17/02/11.

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the effectiveness of the original controller in avoiding over or under heating of the occupied space. Savings of between 9 and 22% were observed from computer modelling, whereas the trial results showed a decrease in energy consumption of 14%56. When the product is launched commercially, Wattbox are aiming for a retail cost of around £200 (in-line with existing time-clock controllers in the UK). The Wattbox is currently undergoing trials as part of the Technology Strategy Board ‘Retrofit for the Future’ project, supplying 28 controllers.57 Wattbox has the functionality to work alongside Smart Meters and other home automation products56. Summary of Wattbox Controller Drivers for the device development: reducing energy demand through optimised

automatic programming of space and water heating Outcomes: Decreased energy consumption of 14% shown in trials Key Learning Points:

o There could be an opportunity to link such a heating controller with information provided by a Smart Meter to further optimise heating controls based on price signals

C1.7 Early Adopters Research- Summary Report. Prepared for Three Water Authorities. November 2001

This report58 summarised the results of a number of focus groups undertaken for three water authorities (East of Scotland Water Authority, North of Scotland Water Authority and the West of Scotland Water Authority). The aims of the focus groups were to understand customer demand and expectations for the future use of technology in the home, gauge customer reactions to the “Smart home” concept and test the appeal, benefits and drawbacks of various services. It should be noted that this work was carried out in 2001 and therefore some of the services offered have now become commonplace or have been superseded. The key findings of relevance to Smart Appliances were as follows:

In terms of White Goods Monitoring (i.e. the potential for an email to be generated when the system detects that white goods have developed a fault), members of the focus groups welcomed “ideas which are likely to lead to savings both in terms of personal finance and the environment and the idea of a ‘green button’ on appliances, which switches them on when electricity is cheapest was well received”.

Focus groups also indicated that “the possibility of saving money via more accurate heating and best use of cheaper electricity tariffs is seen as a tangible benefit”.

Summary of Early Adopters Research

56 Wattbox Technical Description. Available from:

http://www.wattbox.com/Wattbox/Products_and_Services_files/Wattbox%20Technical%20Description.pdf (Accessed 17/02/11)

57 http://www.wattbox.com/Wattbox/RfF_Results.html (Accessed 17/02/11.)

58 Early Adopters Research – Summary Report, Prepared for The Three Water Authorities by George Street Research Limited, November 2001, Ref: 2963

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Drivers for the focus group: to understand customers demands and expectations for the future use of technology in the home

Key Learning Points: o In 2001, consumers were interested in some form of optional control

(‘green button’) to switch appliances on when electricity was cheaper. This feature could be provided through Smart Appliances

C.2 Overseas Experiences

C2.1 USA

The electricity market in the United States of America is split into distinct regions, with each having regulation and balancing responsibility. Each region has particular issues. The Section below captures some of the experiences from a small sample of the pilot trials and schemes involving Smart appliances in the USA. California State-Wide Pricing Program (Commercial and Industrial)59. Following the “energy crisis” in California in 2000 and 2001 that resulted in short term energy shortages, combined with fears about long-term generation and transmission capacity, California Public Utilities Commission sanctioned several trials of automated demand response systems. Two elements of this program have been reported in relation to Demand Response. The first, which is described here, was a trial undertaken in the summer of 2004 and 2005 and related to the introduction of ToU and Critical Peak Period (CPP) pricing schemes. Smart thermostats for adjusting air conditioning units were used by some participants in this trial. During this pilot ToU and CPP pricing was introduced for small commercial and industrial customers in the Southern California Edison service territory. The critical peak price was around $1.00/kWh compared to a normal peak price of $0.17/kWh for customers with demand less than 20kW. Customers were divided into the following two groups:

- LT20, customers with demands less than 20kW; and - GT20, customers with demands greater than 20kW

All customers were offered free Smart thermostats as part of the trial to automatically adjust their air conditioning settings during critical peak pricing periods. Around one third of the LT20 group accepted the free Smart thermostat, compared to around 60% of the GT20 customers. The key results in terms of the impact of enabling technology (Smart thermostats) in conjunction with Critical Peak Pricing were as follows:

LT20 customers were not price responsive on normal weekdays, regardless of whether they had enabling technology installed or not.

59 California’s Statewide Pricing Pilot: Commercial & Industrial Analysis Update, June 2006

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LT20 customers without enabling technology were also not price responsive on Critical Peak Pricing days.

LT20 customers with enabling technology reduced peak-period energy use on critical days by more than 13% across two summers.

There was no statistically significant difference in price response on normal weekdays and critical days for GT20 customers without enabling technology

On critical days, GT20 customers with enabling technology were roughly twice as price responsive as customers without technology.

California State-Wide Pricing Program (Residential) The California Public Utilities State-Wide Pricing Program was a pilot scheme started in 2003 to assess different time-based tariff schemes. Residential customers were provided with a free advanced digital electricity meter to provide energy information and facilitate energy management. They also had access to an online tool to check their energy usage. A free Honeywell programmable thermostat was also provided. It is not clear if any direct link was provided between the operation of the thermostat and pricing signals, or whether consumers were required to interpret the ToU tariffs when programming the thermostat in order to make cost savings. Participants were randomly enrolled into one of the three tariff groups listed below:

ToU Pricing (peak and off-peak price) Critical Peak Pricing (peak, off-peak and super-peak price) Information Only (no price change but received all the programme

information) A small number of enrolled customers were also provided with additional equipment including communication links with electric water heating, the pool pump and thermostat. These customers could view real time interval demand and trends in historical consumption via a web portal and could then alter climate control and pool runtime preferences. These customers saved up to 18% more energy than other pilot customers who had not been issued with this equipment. On super peak days they used 26% less peak energy than other pilot customers60. This demonstrates the importance of customers having access to information (and education with regard to how to use this information) from their Smart Meters, and appliances which allow them to alter their behavior. Summary of Californian Trials Drivers for the study: to assess different time-based tariff schemes Solutions facilitating the deployment of Smart Appliances: Smart Appliances were

used in conjunction with ToU pricing and/ or additional information Outcomes: 18% additional energy saving for customers with additional Smart

Appliances compared to those without. On super peak days this group used 26% less energy than other pilot customers.

Key Learning Points:

60 State-Wide Pricing Results, Overview and Results, http://www.nwcouncil.org/energy/dr/library/drrc_presentation.pdf

accessed 16th March 2011 and A Scoping Study: Demand Side Measures for Small Business and Residential Customers on Ireland’s Electrical System, Kema Ltd and UK Department of Trade and Industry, November 2005, p25-27

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o Householders were given information about future price tariff periods and were allowed with this information to automate their household electricity loads

o Time of use pricing already exists in parts of the United States so the concept is already understood,

o Recent blackouts in California had made householders aware of the system constraints and potential supply shortages

The use of ToU and critical peak tariffs could be a potential barrier to the roll-out of this model in the UK as these do not exist in the UK and are thought be to unpopular with consumers. Such tariffs are therefore unlikely to be introduced by customer led electricity supply companies Long Island Power Authority LIPAedge- Sponsoring Organisation: Long Island Power Authority Long Island Power Authority is a non-profit making municipal electricity provider who own and operate the distribution and transmission networks on Long Island, New York State. They also act as an electricity supplier. They do not operate any generation plants. LIPAedge was initially introduced in 2001 to reduce peak demand where generation was insufficient or where the network was constrained. The scheme was the largest residential direct load control program in the USA using two way communications. In July 2003 the scheme was closed to new participants because it had sufficient air conditioning units under direct control. Scheme participants agree to have their central air conditioning system adjusted for a maximum of seven days over a period of four summer months, between the hours of 2pm and 6pm. Participant households were given US$25 and a Comfort Choice programmable remotely accessible internet thermostat. Participants can override events, although the system operator can block overrides if necessary. The system communicates with the air conditioning unit using the SkyTel pager network. The network operator has flexibility to address all, one of a group of 15 or just a single air conditioning unit, requesting it to change its set point between particular hours, by cycling air conditioning compressors for a portion of each hour or to curtail immediately. The scheme operators expected that a 24.9MW peak reduction was possible from the full 23,400 controlled air conditioners. Response was found to be highly dependent on temperature, day of the week and time of the day. Over three curtailment events during the summer of 2002, the average load reduced was 16,067MW (equivalent to around 680kW per air conditioning unit), and the average energy saving during each curtailment event was 66,613MWh. The program cost US$515 per residential customer. It yielded a combined average cost of US$487/kW of demand reduction.

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Summary of LIPAedge Scheme Scale of scheme: 23,400 controllable air conditioners Drivers for the study: reduction in peak demand Solutions facilitating the deployment of Smart Appliances: two-way

communications with programmable thermostat, override option and financial incentive

Outcomes: Average load reduction of 16,067MW. Cost of US$515 per customer. Key Learning Points:

o Householders were given remote control thermostats o The number of annual interruptions that each household would be subject

to was limited, potentially increasing the willingness of consumers to enter the scheme

o Households could override events under some circumstances o The relatively low incidence of households with air conditioning in the UK

could be a barrier to the success of similar scheme in the UK– this model may be more applicable to commercial premises

o Long Island Power Authority is run as a non-profit making municipal company and therefore has different drivers to companies in the UK which may limit the possibilities to conduct such a scheme in the UK

o Long Island Power Authority runs a supply company and transmission and distribution on Long Island, this may allow them to conduct such a scheme, which may be more difficult in the UK

C2.2 Australia

The Australian electricity system consists of two markets, one covering the Western and Northern States, and the other covering Queensland, New South Wales, Victoria, Tasmania and Australian Capital Territory. The electricity market is not un-bundled, and some network companies are state owned. Load increase, especially peak load, has become a growing problem because of urban growth and the increased ownership of air conditioning units. ETSA Utilities Air Conditioner Direct Load Control Programme, Adelaide The electricity load profile in Southern Australia is especially ‘peaky’. This is attributed to a high incidence of domestic air conditioning. The peak network demand in this region was 2,563MW on 16 January 2007. The local distribution operator, ETSA Utilities, estimate that the peak load on a very hot day over the summer of 2006/07 was approximately 1,000MW greater than the average daily peak demand. In 2003 Essential Services Commission of South Australia, the industry regulator, introduced a regime to encourage ETSA to investigate and implement DSM options61. An initial trial of the remote management of domestic air conditioners of 20 customers was carried out over the summer of 2005/06. They were paid AU$100 to

61 Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the

International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr. David Crossley, pp 71-72

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participate. ETSA’s radio network was used as the chosen communication medium. The trials aims were to:

Determine customer perception of changes in comfort Determine the impact on aggregated demand Gain installation and operational experience of the control technology Test the performance of the control technology Gain experience in quantifying change

The initial trial customers were given a high degree of support, for example by being given a named ETSA staff contact to report any adverse impacts or problems to, and their experiences and perceptions were thoroughly monitored. A selection of cycling strategies was also tested62. The following summer a much larger trial was implemented. Following a large marketing campaign 1,700 domestic and 700 commercial air conditioning systems were identified to participate in the scheme. However it transpired that the communication device was unsuitable for use on some newer generation air conditioners. This reduced involvement to 750 sites although a subsequent trial was conducted with the most suitable of the new generation air conditioners. As part of the larger trial, ninety randomly selected sites were monitored at the site level, while the remaining sites were monitored at either street or transformer level. A variety of switching periods were employed62. An important lesson drawn from the pilot program was that a random overlapping of switching programs is required, otherwise, if the overlap is none random, a “saw tooth” demand profile is resultant. The figure below shows the aggregated demand from the 68 properties involved in the initial trial on 11 March 2006. The area highlighted in red shows the period when the air conditioning units were under direct load control for 15 minutes of every 30 minute period. A clear reduction in load can be observed.

62 Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the

International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr. David Crossley

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Figure C.1 Aggregated Demand on 11 March 2006 for the Houses Involved in the ETSA Utilities Initial Trial63

A further trial was initiated in three different suburbs of Adelaide to gain more cost benefit data on the direct load control of larger air conditioners, investigate effects on the distribution network, gain more customer participation data and to compare the impact of direct load control relative to other geographical trial areas. The data from these trials was used to create estimates of the load reduction that could be expected from an event under similar circumstances to the trial conditions64.

Cost benefit analysis of these schemes revealed that the benefits accrued primarily to customers, energy retailers, and the transmission company whilst the schemes costs fell on the distributor and energy generator65. As a result of the problems installing the DRED Peakbreaker technology on some models of air conditioners a voluntary compliance Standard was adopted in December 2009, namely “AS4755 Demand response capabilities and supporting technologies for electrical products Part 3.1 Interaction of demand response enabling devices and electrical products – Operational instructions and connections for air conditioners”. This Standard requires all air conditioning units sold in Australia to have a standard interface. The Australian Standards committee is investigating making similar requirements for pool pumps and hot water controllers and investigating Smart Meter interoperability.65

63 Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the

International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr. David Crossley,p78

64 Demand Management Program Interim Report No. 3, June 2010, ETSA Utilities, p27-28, accessed from http://www.etsautilities.com.au/centric/our_network/demand_management.jsp, June 2011

65 Demand Management Program Interim Report No. 3, June 2010, ETSA Utilities, p30

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Summary of ETSA Direct Load Control Programme Scale of scheme: initial trial of 15 customers followed by larger trial of 750 sites. Drivers for the study: evaluating the technology, its impact on the customer and

potential impact on aggregated demand How trial participants were recruited: Participants in the larger trial were recruited

via a marketing campaign. Residents demonstrated a new attitude towards electricity and were happy to “do their bit” to help their community.

Outcomes: Reduction in peak load Key Learning Points:

o Careful initial research was carried out to determine customer perception of comfort changes which may have contributed to the success of the model;

o Carrying out a small initial trial to test the system and gain operational experience of the communications system allowed installers to familiarise themselves with the technology;

o As air conditioning was generally only recently installed and therefore not an established essential, customers may be willing to accept the change in its operation;

o A large marketing trial was undertaken to explain the reasons for the trial and to recruit volunteers;

o A strong regulator encouraging a change of behaviour within the distribution network operator may have contributed to the success of the trial;

o The relatively low incidence of households with air conditioning in the UK could be a barrier to the success of similar scheme in the UK– this model may be more applicable to commercial premises;

o Load reduction was dependent on location because of different build types and ages and climatic conditions; and

o Much of the load peak can be attributed to a single appliance group (air conditioning) however this is not true in the UK which may limit the impact of such as scheme.

Western Sydney Interruptible Air Conditioning Rebate Trial Integral Energy distributes electricity to 2.1million households and businesses across Western Sydney, Illawarra and the Southern Highlands. They also hold licences to retail electricity across all the regions of Australia covered by the National Electricity Market. Integral is motivated by a number of factors to engage in demand management:

Combined customer maximum demand, usually driven by weather patterns A high growth in demand due to urban consolidation Demand growth, mainly driven by air conditioning

Integral has been involved in two forms of residential load management.

Domestic hot water Air conditioning66

66 Submission to the Legislative Standing Committee On Public Works – Inquiry into Energy Consumption in Residential

Buildings, Integral Energy, August 2003, p3

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Domestic Hot Water Prior to the formation of Integral in 1995, its predecessor organisations had experience of direct load control of hot water. The control of this load was useful to network operation because historically it coincided with the winter evening peak. Ripple signals carried on the 11 kV or lower voltage systems are used at a variety of different frequencies to stagger channels of hot water systems to avoid the daily peak. The hot water is on a separate circuit and meter. A large price incentive is offered to customers in return for surrendering control of their hot water system. If a customer runs out of hot water, a twenty four hour helpline and emergency help crew can be called upon to rectify the problem. In 2003, Integral had an estimated 1,556MW of domestic hot water load under this form of control, reducing peak demand by 389MW 67. Air Conditioning In summer 2001 Integral sponsored a trial to investigate the technical and commercial feasibility of directly controlling residential air conditioning. The motives for this trial were:

To resolve network issues and defer expenditure To resolve retail issues caused by exposure to high pool prices To test the reliability and response time of the equipment To investigate the impact of cycling on load profile To gauge customer experience and acceptance

The trial involved 90 participants. A proportion of the participants were used as a control sample and experienced no load cycling of their air conditioning. Two control methods were trailled, namely pager and ripple technology. Customers received a $150 incentive payment upon completion of a final survey at the end of the trial. The following conclusions were drawn from the trial:

The technology worked correctly apart from on two occasions when the pager did not re-activate the air conditioning systems automatically

Customers preferred shorter, more frequent, off cycles rather than prolonged interruptions

The program had high administrative costs due to customer inquiries and information gathering

Electronic metering costs were higher than expected, and Billing was problematic because of complications due to the rebate as well as

the need to estimate bills during an unusually hot summer67.

67 DM Programs for Integral Energy, Charles River Associates (Asia Pacific)Pty Ltd, August 2003

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Summary of Western Sydney Interruptible Air Conditioning Trial Scale of scheme: 90 participants in the air conditioning trial Drivers for the study:

o Domestic Hot Water: winter peak load reduction o Air Conditioning: Resolving network issues and deferring expenditure,

reduce exposure to peak pool prices, evaluating the performance of the technology, assessing impact on consumers

Outcomes: o Domestic Hot Water: 389MW reduction in peak demand o Air Conditioning: technology operated correctly in the majority of cases,

customers preferred shorter, more frequent interruptions than longer ones, and high administrative costs were experienced

Key Learning Points: o The scheme may have been successful due the long history of direct load hot

water control o A strong support framework for the hot water scheme may have increased its

success as consumers were confident that support would be provided if necessary

o The high level of price return for households on the hot water scheme may have increased its level of success

o Integral Energy has relationships with customers as both their distribution operator and supplier. Market barriers in the UK prevent this occurring

o The unbundling of the UK electricity sector means that the costs of high electricity pool prices and the high cost of network reinforcement are split across different industry participants. Financial incentives are therefore spread over multiple players and less acute limiting the replicability of such a scheme in the UK.

o Billing for such a scheme in the UK may be difficult until the roll out of Smart Meters is complete

C2.3 Scandinavia

The electricity markets in the three Scandinavian countries of Denmark, Norway and Sweden are highly interconnected. There is a high potential for demand management because of the prevalence of electric heating in the region. A number of pilot trials that included a Smart Appliance element have been undertaken in the Scandinavian countries. A selection of these are summarised in the Table below.

Table C.2 Scandinavian trials of Smart Appliances

Country Size of trial Control technology

Appliance controlled

Denmark 25 Internet and mobile phone technology

Electric heating

Norway (Oslo)

20 Internet, wireless radio and an “Ebox”

Electric heating

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Denmark Liberalisation of the electricity market in Denmark began in the 1990s. Supply and distribution companies have been separated and supply competition now exists, however switching electricity supplier is rare amongst Danish households. The Transmission System Operator (Energinet.dk) is state owned. Most generation is owned by one of two companies. The wholesale electricity market is integrated into the Nordic power market The Danish pilot trial in 2003/04 was sponsored by Elkraft Sytem, Eltra, Seas and Energy Piano. It was undertaken as part of the EFFLOCOM (Energy Efficient and Load Curve Impact of Commercial Development in Competitive Markets) project. The houses included in the trial each had electric heating, with an annual consumption greater than 16,000kWh. Trial participants were able, via the internet, to set limits for the duration of any interruption for up to five zones in their home for different periods of the day. They could also end any interruption if required. They received a report on their electricity consumption and the bonus they had saved. The savings as a result of demand response were reported separately, increasing the visibility of the benefits to consumers68. One hundred hours of high prices was simulated over the winter with bonuses varying between 0.13€ and 0.40€69 per kWh. Demand response prices were applied during two time periods- 06:00-11:00 and 16:00-19:00 The communication configuration used can be seen in the Figure below.

Figure C.2 Overview of Danish Pilot69

Electricity consumption for heating was recorded and stored every quarter hour. This quarter hour consumption was collected via daily remote meter reading using GPRS. Customers were able to set preferences for the duration of interruptions

68 Energy efficiency and load curve impacts of commercial development in competitive markets. Results from the

EFFLOCOM Pilots. Available via http://www.efflocom.com/pdf/EFFLOCOM%20report%20no.%207%20Pilot%20Results.pdf (Accessed 24/03/11)

69 IEA DRR Task XIII Subtask 5 – DK technology database, DK Tech Case #1: EFFLOCOM pilot: DR offered by households with direct electric heating, Casper Kofod

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based on the location (five zones), time of interruption (06:00-11:00 and 16:00-19:00) and level of bonus68. The following lessons were drawn from the trial:

The installation costs were about 800€ per home (for 1,000 houses) Only 40% of customers used the facility to stop an interruption Customers didn’t change the level of interruption that was acceptable based

on the level of bonus 24% of houses used the facility to set the maximum duration of interruption Total customer savings/benefits were about 120€ per year, 80€ bonus from

demand response, and 40€ from reduced consumption during interruptions Only 41% of interrupted consumption was used afterwards to bring the

temperature back to the required level Interruptions and meter times need to be synchronised Failsafe messages were introduced

A customer survey was carried out following the trial. The main findings of this were68:

Customers were interested in both the graphs and figures of consumption over time and the reports of bonuses received from demand response

Most customers were either “satisfied” or “nearly satisfied” with the level of bonus received from participating in the project

Some customer suggested that dishwashers, washing machines and tumble driers could also be included in the management

Nearly all customers stated that a three hour interruption was acceptable No customers in the trial used additional portable electric heating during

interruptions All customers would recommend the system to other users and would like to

continue participating in the demand response scheme Oslo The electricity sector in Norway is de-regulated, however many of the participating companies are state-owned. A single state owned company, Stattnet, operates the Transmission Network, while small local companies operate the local distribution networks. Retail is competitive. Norway is a member of the Nordic wholesale power market The objective of this trial was to avoid or defer reinforcement of the electricity network and to learn more about consumer behaviour. It consisted of two parts, the first of which was for the electricity network company to control hot water production for an apartment block. This was achieved with no impact on hot water consumption for the residents. The second part of the trial involved residents using an “Ebox” to programme the heating schedule for their property. The network company was also able to control the “Ebox” and override the householders programmed schedule according to the load at a local overloaded transformer. The reaction of the participants to the technology was monitored. The combined result of the two trials

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was a reduction in peak usage by up to 15% (of the metered maximum demand of the households).70 Summary of Scandinavian Trials Scale of scheme: a single apartment block Drivers for the study: to avoid/ defer network investment and to learn more about

consumer behavior Outcomes: Reduction in peak load by 15% Key Learning Points:

o The initial trial may have been particularly successful as there was a large potential to manage hot water systems in blocks of flats while limiting inconvenience to residents to a minimum

C2.4 New Zealand

New Zealand has a competitive electricity supply and generation market, with regulation imposed upon the natural monopoly transmission and distribution elements. Some of the companies operating in the market are state or trust owned while the rest are privately owned. The electricity retail market uses a locational pricing auction, calculating prices at each transmission node. Some retailers and industrial users bid to reduce their capacity. All the major generating companies have energy retail businesses. Full retail competition exists allowing consumers the freedom to choose their electricity supplier. Orion Network DSM Project Orion New Zealand Ltd. is the state owned distribution operator for the Christchurch region of New Zealand. The New Zealand transmission operator, Transpower, charges distribution operators based on an average of the twelve highest peaks, by half hour, at the transmission grid exit points. This creates an incentive to manage peak demand, which Orion has been doing since 1990. This is being done with direct load control of hot water using ripple control. Two control regimes are employed71:

Peak Control Water Heating Night only Water Heating

In 1996, 88% of hot water systems in New Zealand were electric. A study published in 2006 suggests that a large proportion of residential electricity (31%) is used to provide hot water – this represents 8% of all electricity use.72 Thus, the focus is on the control of a large, single load group that can be readily interrupted with minimal or no impact on the end-users, and which contributes significantly towards system peak demands. 70 Scoping Study: Demand Side Measures for Small Business and Residential Customers on Ireland’s Electrical System,

Sustainable Energy Ireland, November 2005, p32-33

71 Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr. David Crossley, pp 91-93

72 Hot Water Over Time – The New Zealand Experience, Conference Paper No 132 (2007), Nigel Isaacs, Dr Michael Camilleri, Lisa French, Branz 2008, ISSN: 0111-7505, p7

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Peak Control Water Heating This system is generally employed by households with smaller water tanks or consumers who require larger quantities of hot water. The ripple system is used to switch off water heating elements during peak periods and to rotate through sixteen groupings of water heating load. Orion has set service levels so that it aims to have individual systems turned off for no more than seven hours in any day, and no more than four hours in a seven hour period. Between 2005 and 2007 this system was used to switch water heating for an average of 54 hours a year, usually over the winter. When demand approached a threshold (570MW in 2007), water heaters were switched off in groups. These groups are cycled to limit customer impact. Night Only Water Heating This system is generally used by customers with bigger water tanks. The ripple system is used to turn on their system overnight. The system is so successful that the tank deployment has to be staggered to avoid creating night-time peaks. An afternoon boost period is also an available option to customers on this regime73. These regimes are made attractive using pricing regimes – night only hot water customers pay only about 50% of the Anytime Use tariff for night time hot water heating and Controlled Load tariff save about 10% on the Anytime Use tariff.

Table C.3 Tariff schedule for Electricity Retail Contract (May 2008)

Tariff type Variable price* Anytime use tariff 19.267 cents/kWh

Controlled load tariff 17.129 cents/kWh Day/night – day tariff Day/night – night tariff

21.765 cents/kWh 9.256 cents/kWh

* All tariffs pay a fixed tariff of 60.53 cents per day in addition About 90% of water heating is controlled using these regimes in Orion’s’ distribution area. The result of these schemes is demonstrated in the Figure below. The yellow area shows the percentage of load shed during the reduction period. The red line shows the expected load had load reduction not been employed, and the blue line shows the actual load.

73 Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the

International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr. David Crossley, pp 93-94

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Figure C.3 Orion Daily Load Management Summary, Thursday 28 June 200774

Based on estimations, Orion suggest that without this scheme, delivering peak load would cost an additional NZ$12 million per year for distribution and NZ$6 million for transmission. Summary of Orion DSM Project Drivers for the study: reducing peak demand due to charging of DNOs based on

an average of the twelve highest peaks by half hour at the transmission grid exit points

Outcomes: Reduction in peak load with a total estimated financial benefit of NZ$12 million per year for distribution and NZ$6 million for transmission

Key Learning Points: High consumer price benefits, with limited inconvenience to the customer and

a strong support network are likely to have contributed to the success of the scheme

The program has been in operation since 1990 and is therefore well known to consumers

The benefits experienced in New Zealand are relatively high due to the high prevalence of electric heated water storage systems. This may not be applicable in the UK where there is currently a relatively low incidence of households with electric heated water storage systems

There is a strong financial incentive on DNO to limit peak demand in New Zealand, which may have contributed to a successful peak load limiting system being developed. This is not the case in the UK so it may be more difficult to replicate such a system.

74 Worldwide Survey of Network-Driven Demand-side Management Projects, Research Report No 1 Task XV of the

International Energy Agency Demand Side Management Programme, Second Edition, October 2008, Operating Agent: Dr. David Crossley, pp 93-98

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Appendix D: Organisations/ Persons Engaged with for Stakeholder Dialogue

Person Organisation Information

Conor Mullaney Glen Dimplex Manufacturer of electrical heating (storage heaters and heat pumps)

Elisabetta Bari & Vanessa Butani

Electrolux Manufacturer of white appliances.

Marc Stanton Cool Power Products

Developers of a device to match demand to local generation

Victor Mackaij Echelon Networking and Smart Grid software and hardware solutions.

Gale Horst Electric Power Research Institute

Project manager of Smart Grid demonstration project (and formerly from Whirlpool)

Hamish Neale Control 4 Supplier and developer of home automation products

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Dan Taylor Centrica Energy supplier, active in Smart Metering and Smart Home technologies

John Counsell University of Strathclyde

Professor, Chair of BRE Centre at Strathclyde University, with interest in electric heating, advanced controls and energy efficiency.

Sarah Darby

Environmental Change Institute; University of Oxford;

Research Fellow focusing on what householders are able to learn from developments in energy metering, billing and displays and how effective these developments may be in reducing and managing demand.

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s”

Ritsuko Ozaki Imperial College London

Senior Research Fellow, with a focus on energy consumption by households. Currently working with UK Power Networks on the Low Carbon Network Fund looking how householders' understanding of their own energy use patterns (time of day) and levels (actual usage) can help change their behaviours.

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Appendix E: Case Studies

E.1 Heat Pumps

The following is written primarily from the perspective of space heating for domestic properties, although it is also applicable to heating other buildings, and producing domestic hot water. There are a wide variety of heat pump systems, some of which are more suited to DSM than others. Common system types are:

Air to air Air to water Ground to water Water to water

Where, in each case, the first term refers to the source of heat and the second to the type of heat sink (or heat distribution system). In addition to these basic types, the presence of any supplementary heating is also important, both to the need for DSM (as will be explained below) and to the ease with which DSM can be implemented. The following terms are often used to describe the type of supplementary heating.

Mono-valent – heat pump with no integrated supplementary heating Bi-valent – two fuel (i.e. electricity to drive the heat pump and a fossil fuel fired

boiler as the back-up system) Mono-energetic – a single form of energy supply driving the heat pump and

providing any supplementary heat (i.e. an electrically driven heat pump with an integral electric flow boiler)

Most UK designs of heat pump are mono-valent (e.g. Kensa75, Calorex76) and many overseas designs are also supplied in this mode. Mono-valent heat pumps can be utilised as a part of a bivalent system – typically by running the heat pump parallel with a pre-existing fossil fuel fired boiler. Many European designs are mono-energetic (both Scandinavian and German makes are very common in the UK). Here the design philosophy is to size the heat pump to cope with 80 or 90% of the design day heat load of the property and bring on the electric flow boiler to increase the system output on the few days of the year when more heat is required than the heat pump alone can deliver. This approach clearly adds to the maximum demand on the electricity system (both in terms of network capacity and generation capacity) – hence increasing the need for DSM. However, switching the flow boiler off in times of network or supply constraints is a simple DSM measure.

75 Kensa Heat Pumps, http://www.kensaengineering.com/

76 Calorex Heat Pumps, http://www.calorex.com/

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The heat source has little bearing on the DSM capability. However, air source systems are more likely to require back-up or supplementary heating as their heating capacity falls more rapidly with falling ambient temperature than do either water or ground source heat pumps, and thus may have a ready means for achieving some DSM capability. Water distributed heating systems (radiators, under-floor heating, fanned radiators) offer a much wider range of DSM capabilities than do air distributed systems, simply because the water provides a natural buffering or thermal storage capability. Thermal storage can take the form of:

The building fabric The heating system (e.g. water content of radiators) Separate storage vessels or buffer tanks.

It is currently common practice to install buffer tanks with water distributed systems to reduce the prevalence of on/off cycling of the heat pump (particularly for the fixed speed, UK and European, designs). The following explores a number of DSM options and matches them to the types of heat pump, first exploring simple options requiring little or no changes to current standard design practices and then exploring more complex options or the use of alternative fuel sources. Table E.1summarises a number of options for implementing DSM that do not require any changes to standard heat pump design practices. Rather, they require changes to the way the systems are controlled and operated. However, there may well be a need to change current industry advice (and Renewable Heat Incentive (RHI) provisions) to encourage their adoption. Table E.2 summarises a number of more complex options for implementing DSM that require changes to standard heat pump design practices. In many ways, the bi-valent heat pump with secondary gas boiler operation provides the simplest and most effective DSM technique (indefinite time interruptions). However, there are good arguments against the use of these systems:

The aim in using heat pumps is to move away from fossil fuels; Maintaining two systems adds to the running costs (one of the often cited

advantages of heat pumps is their low maintenance requirement compared to, say, gas or oil boilers).

On the other hand, liquid / gaseous fuels have very high energy storage densities. A long term, non-fossil fuel option is hydrogen (or derivates thereof), which can be produced by electrolysis from zero / low carbon sources of electricity. An integrated heat pump with, for example, bottled gas (c.f. Calor) to produce a single package that would only require one maintenance visit, is conceivably a viable product and one that would give very good DSM capabilities. In a similar vein, a Canadian design of air-source heat pump with an integral mains gas burner was on the market in North America – allowing the use of air-source heat pumps in very cold climates.

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Table E.1 Simple DSM Options

Option Heat distribution system type

DSM implementation approach

Limitations / Issues Interruption duration

Mono-valent heat pump with fabric thermal storage

Air and water Raise / lower set point, via Smart thermostat

Only realistic option for air-distributed systems. Longer interrupts are possible with buildings designed to give high fabric thermal storage (for example the Termodeck77 system)

Short (possibly up to 1 hour)

Mono-valent heat pump with underfloor heating with screed

Water Raise / lower set point, via Smart thermostat

Only limited use of underfloor heating systems in UK homes.

Medium (potentially several hours)

Mono-valent heat pump with buffer tanks

Water Switch on/off heat pump, whilst allowing circulating pump to continue operating

Generally only provides short terms storage, as existing buffer tank designed to reduce on/off heat pump cycling

Short (up to 1 hour)

Mono-energetic heat pump with electric flow boiler

Water Switch on/off of electric flow boiler whilst allowing heat pump to continue operating

Whilst this does give impression of DSM capability, it is essentially just providing a solution to a self-inflicted problem.

Short (up to 1 hour)

Bi-valent heat pump with secondary gas boiler

Water Fuel substitution – i.e. only operate boiler during ‘DSM events’

Requires the controls of the boiler and the heat pump to be integrated. System must be carefully designed to maintain low flow temperatures for heat pump use.

Long (several hours, maybe even days provided boiler is sized appropriately)

Bi-valent heat pumps with secondary heating system

Air and water Use of secondary heating – i.e. use of wood burning stoves during ‘DSM events’.

Advanced warning of high prices could enable householders to use the secondary form of heating on days with high electricity costs. UK households do not currently require the installation of a secondary heating system.78

Long (several hours, maybe even days provided boiler is sized appropriately)

77 Termodeck has been used in a limited number of large non-domestic building in the UK. It uses hollow concrete slabs to deliver heated fresh air to the building

http://www.tarmacbuildingproducts.co.uk/products_and_services/termodeck.aspx

78 It is compulsory for electrically heated households in Finland to have some form of reserve heating, such as a heat storing fireplace (typically a wood-burning stove surrounded by stone that stores the heat and releases it slowly over time).

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Table E.2 Complex DSM Options

Option Heat distribution system

DSM implementation approach Limitations / Issues Interruption durations

Mono-valent heat pump with large buffer tanks

Water

Heat pump can then be switched off during ‘DSM events’ and heating requirements met by the thermal storage capacity within the buffer tank.

If heat pump used to charge up the thermal store, the storage temperature will be limited, thus requiring potentially large storage volumes. If off-peak direct electricity used to store energy in the buffer tank, higher storage temperatures can be utilised, thus increasing storage capacity for a given volume, but could impact on heat losses.

Several hours (depending upon capacity of buffer tank).

Mono-valent heat pump with large buffer tanks using phase change materials (PCMs)

Water As above, but use of PCMs allows for increase energy storage density within buffer tank

Energy storage using PCMs for this application is largely unproven79.

Several hours (depending upon capacity of buffer tank).

Mono-energetic heat pump with electric storage heating

Water

Utilise storage heater technology (thermal bricks) to provide heat directly to a water distributed system, thus allowing heat pump to be switched off during ‘DSM events’

Unproven technology. A similar system was developed in the 1970s / 1980s but never reached commercialisation. Potentially greater energy storage density than water or PCM systems.

Several hours (depending upon capacity of thermal storage bricks).

Mono-valent heat pump with local electricity storage

Water and Air During ‘DSM events’, the local electricity store is used to operate the heat pump.

Not a Smart appliance solution, but included for completeness.

Several hours (depending upon capacity of buffer tank).

Mono-valent heat pump with longer term thermal storage

Water Use of seasonal energy storage to allow heat pump to be turned off during ‘DSM events’.

Range of options currently being researched, although none are considered to be proven technologies. Options include: Adsorption systems (e.g. zeolites) Gravel beds Large (buried) PCM stores

Several days (depending upon capacity of buffer tank).

79 Ice storage has been widely used for shifting the electrical load of air conditioning systems, other PCMs are available with a range of phase change temperatures suitable for heating applications.

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Heat Pump DSM Scenarios The following discussion is based on the 3 scenarios introduced in Section 7, i.e. Business as Usual, Smart Appliances – Level 1 and Smart Appliances - Level 2. Business as Usual If current predictions for the uptake of heat pumps, as part of an overall strategy to decarbonise the supply of space heating, are correct, then “Business as Usual” will lead to large increases in demand for electricity in domestic properties. In particular winter demands, both through the day and peak demands, will increase substantially. The degree of increase will depend on:

Heat load of properties Performance of the heat pump (winter coefficient of performance) Operating pattern (i.e. 24 / 7 operation or intermittent heating) Penetration level (the numbers of houses with and without heat pumps in a

given geographic area) The following Figure provides an illustrative example of the possible effect – this for 24 / 7 operation of the heat pump. It shows the additional demand of the heat pump imposed on the standard domestic electricity profile. This relatively small increase (~33% in maximum demand) can be regarded as the increase for 100% penetration of heat pump use in well insulated properties (or lower penetration levels in less well insulated properties).

Half Hourly Power Demand With and Without Heat Pump (January Weekday)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00

Time

Power Dem

and (kW

)

Without HP Elec Demand (kW)

With Heat Pump Power Demand (kW)

Figure E.1 Daily Demand Profile for a Household with a Heat Pump

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Smart Appliances – Level 1 Here we assume minor changes to control or system design, so that simple DSM measures can take place. Typically these will take the form of responses to signals to turn the heat pump off for short periods. Alternatively, with a slight increase in sophistication, a Smart thermostat could be used to raise and lower the set points either side of a call to interrupt supply. How useful this will be depends on the particular DSM driver. If we consider reducing the maximum demand as the driver and calling for a short interrupt of all heat pumps during the period of maximum demand, then, modifying Figure E.1 produces the profile shown in Figure E.2.

Electricity Demand for a January Day

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00

Time

Electricity Dem

and (kW

)

Simple Measure with HP Electricity demand(kW)

BaU With HP Electricity Demand (kW)

Base Electricity Demand (kW)

Figure E.2 Illustrative Demand Profile - Short Interruption to Heat Pump

Here we see a large reduction in demand for a short period, but, perhaps counter intuitively, very little change in overall maximum demand. Thus simple interrupts, such as shown here, will be of little benefit to some types of DSM driver (maximum demand). However, they may well still be useful to some of the other DSM drivers (such as the Frequency Response service required by National Grid).

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Smart Appliances – Level 2 Significant improvements can be made to the situation shown in Figure E.2 by redesigning the system to include significant levels of thermal storage. Such a scenario can be illustrated by Figure E.3.

Half Hourly Power Demand With and Without Heat Pump (January Weekday)

0.000

0.200

0.400

0.600

0.800

1.000

1.200

1.400

00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00

Time

Power Demand (kW

)

Without HP Elec Demand (kW)

With Heat Pump Power Demand (kW)

Figure E.3 Illustrative Demand Profile - Peak Lopping to Heat Pump

Here we see the heat pump continuing to operate during the peak hours, but load sharing with output from a thermal storage that has been charged in an earlier period of the day. This form of operation can result in load being capped and held at the maximum allowable level for several hours, provided that storage levels are sufficient. An alternative that would achieve a similar profile to that shown here, would be to operate many heat pumps as a fleet – staggering their off periods. Thus each individual heat pump would have a profile akin to that shown in. Figure E.2 but each with a different interrupt time, with the resulting overall network profile as shown in Figure E.3 Thus we see that the Smart Appliance Scenario Level 2 can be achieved with either:

Complex heat pump designs (Table E.2), or Simple (mono-valent and mono-energetic) heat pumps (Table E.1) with

complex, fleet-operated, network controls. Key to the choice of approach is the level of simple interrupt that is acceptable to the householder (i.e. how long can a heat pump, with little or no in-built storage, be switched off for without comfort levels being significantly affected. This will depend on householder preferences, type of property (thermal mass) and, probably,

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operating regime (interrupts may be more acceptable if heat is supplied continuously throughout the day, and hence the building has achieved more of a steady state condition, than the case where heat is supplied intermittently and the central heating may only have been on for a few hours). This is an area where more information is required and one that deserves further research. The choice will also depend on the time periods for the DSM operation and the level of increase in the “Business as Usual” profile over the current profile. The greater this increase, the more storage will become the preferred DSM approach. Whilst thermal storage would appear to give more flexibility for DSM measures, it does have significant disadvantages in terms of additional capital cost and physical space requirements. Further information and research is required in this area. Smart Appliances – the bi-valent option Deserving of separate mention is the question of fuel substitution and bi-valent operation. Unlike all other appliances, heating is the one area where the appliance (the heat pump) can be enhanced or substituted with another fuel source. Although this is a relatively simple measure, and hence included as simple DSM options in Table E1, it can meet the full range of DSM drivers, including the extreme case of interruptions lasting several days (e.g. matching demand to periods of low wind generation). Of course how acceptable this is depends on how the energy supply market develops, and on Government policy and incentives. Clearly the concern with bi-valent and other fuel substitution approaches is that the secondary fuel gets used more than the heat pump, hence defeating the reasons for installing the heat pump in the first place. Good price signals (or carbon based signals) feeding into automated control systems should avoid this possibility.

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E.2 Air conditioning

The following will focus on air conditioning for relatively small offices. At present the uptake of air conditioning in domestic properties is very low. Whether this will change is difficult to assess - there being conflicting opinions arising from the need to minimise energy use on the one hand, and the expectation of rising summer temperatures on the other. For now (at least in the domestic sphere) cooling is still widely seen as something that can be lived without, rather than being regarded as an essential in the way that heating is. There are many similarities between the opportunities for using DSM with heat pumps, discussed above in Appendix E.1, and those for air conditioning. Short term interrupts may be feasible with little or no changes to the design and operation of the air conditioning equipment, whilst longer term interrupts are possible through the use of (negative) energy storage, or “coolth” storage as it is sometimes known. The main difference is that the range of possibilities is more limited with air conditioning than is the case with heat pump as will be discussed below. Air conditioning utilises a similar range of technologies to heat pumps (apart from there being no equivalent to the bi-valent and mono-energetic designs of heat pump). Gas fired / heat driven absorption air conditioning is possible and does find uses (e.g. heat driven air conditioning as part of CHP schemes) but they are not used as a back-up / bi-valent alternatives to electric driven air conditioning. The most common designs of electrically driven air conditioning are air-to-air systems. These are often reversible, providing cooling in summer and heating in winter (i.e. they act as heat pumps during winter). Such systems usually comprise an indoor unit and an outdoor unit, connected together by pipe-work through which refrigerant is circulated (so called “split” and “multi-split” system – the latter having several indoor units fed by a single outdoor unit). Similar in effect are small local units (through-the-wall, window units, temporary / ad-hoc units), which also rely on air-distribution of the cooling. Water distributed systems are also widespread – particularly in larger sizes where they are often referred to as chillers. In very large sizes, hybrid water / air systems are common, with a single, central, chiller feeding several air-handling units. Designing out the need for cooling is a particularly important option (the energy efficiency option for DSM – see Figure 2.1) – a case of smart designs rather than smart appliances:

low energy appliances (e.g. laptop performance rather than desk-pc performance);

local task lighting, low energy lighting; window shading; local air movement (comfort cooling for people); dehumidification (comfort cooling for people); heat tolerant machines (e.g. separate server / data base rooms);

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There are also a number of low energy cooling strategies such as night cooling options (e.g. Cool-phase from Monodraught80) and “free” cooling by ground source heat pumps (i.e. using the ground loop water directly with, for example, chilled beams - the only energy use then being the relatively small amount required for the water circulation pump). Accepting that air conditioning cannot be designed out / avoided in all cases and that there will inevitably be some electrically driven air conditioning, then there are a number of DSM options. Table E3 summarises a number of the simpler options for implementing DSM that do not require any changes to standard air conditioning designs and are, perhaps, particularly appropriate for air to air systems. Table E4 summarises a number of more complex options for implementing DSM that require changes to standard air conditioning designs. These are generally more suited to water distributed systems, with the exception of enhanced fabric-storage designs, such as the Termodeck81, which often use air distribution as an integral part of the design. The more complex DSM options will generally result in longer interrupt periods than are possible with the simpler systems.

80 See www.cool-phase.com

81 Termodeck has been used in a limited number of large non-domestic building in the UK. It uses hollow concrete slabs to deliver heated fresh air to the building http://www.tarmacbuildingproducts.co.uk/products_and_services/termodeck.aspx

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Table E.3 Simple DSM Options

Option Cooling distribution system type

DSM implementation approach

Limitations / Issues Interuption durations

Simple interrupt e.g. off for 15min in each 30 min DSM period

Impact on comfort – unknown – some studies in e.g. Australia (see Appendix C) Staggering of off periods required to smooth demand

Circa 30 min

Prioritise cooling zones

A sort of cooling rationing – only allowing it in critical areas of the building at certain periods of the day.

Possibly > 30 min (of partial load reduction)

Air to air Splits & multi-splits or chillers

Refrigerant / air water

Raise / lower set point, via smart thermostat

Length of interrupts achievable are unknown, but, as for heating-only heat pumps, will probably be quite limited

Circa 30 min

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Table E.4 Complex DSM Options

Option Cooling distribution system

DSM implementation approach Limitations / Issues Interuption durations

Chillers with water buffer tanks

Water Air conditioning equipment can be switched off during ‘DSM events’ and the cooling requirements met by the thermal storage capacity within the buffer tank.

Limited capacity – low DT within store – hence large volumes per kWh of “coolth”

Limited

Chillers with PCM stores (including ice)

Water Similar to water tanks, but large capacity per unit volume (kWh / m3) due to latent heat capacity of PCM

Energy efficiency will be related to the temperature of phase change. Ice is a low temperature (and hence low energy efficiency) storage means but will have the highest energy storage density.

Several hours

Enhanced fabric storage

Air Longer interrupts are possible with buildings designed to give high fabric thermal storage (e.g. Termodeck)

Several hours

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Air conditioning DSM Scenarios The following discussion is based on the 3 scenarios introduced in Section 7 - (Business as Usual, Smart Appliances – Level 1 and Smart Appliances - Level 2). Business as Usual Under “Business as Usual” there is expected to be a growth in the demand for air conditioning and hence an increase in use of electricity, particularly in the summer months. It is likely that some parts of the electricity network will become summer peaking – indeed this is already happening in some major cities. One potential mitigating factor is the obvious synergy between air conditioning and building mounted PhotoVoltaics (PV). However, demand on the network may well still increase leading to potential supply and network constraints. Smart Level 1 As discussed earlier in Appendix C2.2 (Overseas Experiences, Australia), there is evidence from overseas that very simple DSM measures can be used to provide significant benefits. The simplest is simply a remote call for air conditioning to be interrupted at certain times of the day, although, as noted in the Australian example, staggering of the switch-off and switch-back-on times is desirable. As with the Level 1 heat pump case study, there is the likelihood that simple interrupts will not have a large impact on system maximum demand, simply altering the time at which this occurs. Smart Level 2 As with the heat pump Level 2 example, storage greatly increases the ability for the Demand Side to deliver system benefits for all DSM Drivers. This includes benefits requiring both short-term interrupts (network / ancillary services) and longer term interrupts (network constraints, peak demand reduction and renewables integration).

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E.3 Refrigeration

UK households typically have a combination of either a separate fridge (undercounter or tall designs) and freezer (undercounter, tall or chest designs), or a combined fridge-freezer. The small commercial premises under consideration in this study may have similar sizes of fridges for the use of employees. Small shops with demand less than 100kW (i.e. within the scope of this study) may also have refrigeration units for products, for example small convenience stores. Data from the Market Transformation Programme82 estimates both ownership rate (% of households owning a given appliance) and lifespan (years). The data for 2010 is shown in the Table below.

Table E.5 Ownership and Lifespan of Cold Appliances

Appliance Ownership Rate (% of households) Lifespan (years)Fridge Freezer 65% 17.5 Chest Freezer 16% 16.7 Upright Freezer 30% 15.5 Fridge (only) 43% 12.8

Refrigerators and freezers operate cyclically- i.e. they operate a cooling cycle and maintain a low temperature between cycles through the use of good insulation and the thermal mass of the contents of the unit. It is estimated that an appliance is ‘on’ for 33% of the time. The length of time the unit is cycling ‘on’ is increased by consumer activity such as opening the door and adding warm food to the refrigerator. Analysis as part of the European Smart-A project has suggested that this activity accounts for 25% of the total energy consumption of the appliance. It could therefore be suggested that there are more cycles in the evening, when these activities are more likely to take place.

82 BNC08: Assumptions underlying the energy projections for domestic cold appliances. Version 3.1. 15/01/2008. Available

from: http://www.greenandeasy.co.uk/upload/file/Fridges_Freezers_2008January15.pdf Accessed 10/06/11.

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DSM Options for Refrigeration There a number of potential ‘Smart’ solutions which could be developed for cold appliances, set out as follows:

Business-as-Usual: consumers are encouraged to purchase energy efficient appliances through energy labelling. It may also be possible to encourage consumers to buy a suitable size of appliances with a minimal number of features to decrease energy consumption, although this may be at odds with a tendency for manufacturers/ retailers to ‘up-sell’ (i.e. persuade the consumer to choose a more expensive model with additional features).

Smart Level 1: A dynamic demand technology is used to provide frequency response services to the System Operator (National Grid).

Smart Level 2: The cycling of appliances could be interrupted for extended periods with more complex monitoring of the temperature set point (e.g. through a Smart thermostat). The use of Smart thermostat could reduce the temperature of the unit prior to an event (e.g. the half hour of highest demand) and then stop cycling during the event. A potential alternative, or complementary, solution could make use of phase change materials as in-built storage.

The following Sections will provide further details of each of these scenarios and the benefits of each. Business As Usual Energy efficiency labelling for appliances was introduced in 1992, using classes A-G. Additional classes were introduced in 2004 (A+ and A++)83. The Market Transformation Programme (MTP) have estimated the stock (i.e. number of appliances within UK homes) of different groups of appliances for each year from 2004 to 2030 for chest freezers, refrigerators, fridge freezers, and upright freezers. The total energy consumed per year (GWh/ year) of each class of appliances is also estimated. From this consumption and total stock data it is possible to estimate the energy consumption for each type of appliance by energy labelling band for a number of reference years. The case for 2010 and 2020 is illustrated in the Table below.

Table E.6 Energy Consumption (kWh/year) in 2010 and 2020 for cold appliances by energy label category

Chest Freezer Fridge Freezer RefrigeratorUpright Freezer

Energy Label

2010 2020 2010 2020 2010 2020 2010 2020 A+++ N/A 83 N/A 156 72 72 N/A 100 A++ 123 104 195 195 91 90 140 125 A+ 170 146 268 266 128 123 196 172 A 203 195 372 363 170 165 226 225 B 254 258 431 422 211 211 298 304 C 309 309 529 531 244 243 380 374 D 539 546 541 523 257 259 387 385 Fleet Average

323 183 446 286 200 124 316 189

83 http://www.energylabels.org.uk/eulabel.html (Accessed 13/06/11)

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This illustrates the decreasing total energy consumption under each class of appliance, and the introduction of A+++ Standard. The power demand from any single appliance over a 24 hour period is a function of the cycling of the appliance. This is related to the ‘normal’ operation of the appliance and additional ‘events’ such as the door opening and warmer food being placed in the appliance. The Smart-A project has estimated total energy consumption of 404kWh per year (considerably less efficient than the figures for the MTP above), with an average power demand of 138W, assuming the appliance is ‘on’ (cycling) for a third of the time84. For the energy efficiency standards above, a 2010 A+ rating is equivalent to a power demand of 45W, and a D rating 87W when cycling. The timing of the load (cycling) is dependant on the thermal properties of the cold appliance, its surroundings, and the consumer behaviour. A University of Bonn study, cited within the Smart-A project has correlated energy demand to the frequency of door opening events. It is assumed that 25% of the total energy consumption of the fridge is due to consumer behaviour. The resulting curve is shown below for the Smart-A appliance with a total annual energy consumption of 404kWh per year. This takes into account the increased energy consumption during periods when the door is most likely to be opened. It should be noted that this results in a peak in the early evening, coincident with the existing peak in overall network demand.

Figure E.4 General Pattern of a daily load curve of a refrigerator in an average European household84

If it is assumed that all types of cold appliance follow the same profile of door opening, but the their total energy consumption differs, then a series of curves could be plotted for A+++ to D rated chest freezers, refrigerators, fridge freezers and

84 Synergy Potential of Smart Appliances. D2.3 of WP2 from the Smart-A project. November 2008. Prof. Dr. Rainer

Stamminger, Rheinische Friedrich-Wilhelms-Unversität Bonn. Available from: http://www.smart-a.org/WP2_D_2_3_Synergy_Potential_of_Smart_Appliances.pdf (Accessed 13/06/11)

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upright freezers. An example pair of curves is shown below for the fleet average fridge freezers in 2010 and 2020 on the following Figure.

Figure E.5 Business as Usual Fridge Freezer Load 2010 and 2020

In the Business as Usual scenario, consumers would be encouraged to purchase the most energy efficient appliances through labelling. This could increase the size of the energy reduction between 2010 and 2020 but would not alter the shape of the power demand curve. Smart Level 1 A technology has been developed by RLtec85 which can be integrated into refrigerators. The control unit senses the frequency of electricity supplied to the appliance and adjusts the power demand accordingly- i.e. using power when demand is low and frequency is high, and vice-versa when frequency is low. It applies this method within a widened ‘band’ of temperature set point, such that the maximum temperature is increased when frequency is low, and decreased when frequency is high. This ensures that although the fridge/ freezer load is increased or reduced, the appliance remains at a safe temperature to maintain food safety. By aggregating the action of a number of these devices (“several thousands or millions of appliances” to emulate the current Frequency Response Services procured by National Grid86) it would be possible to provide balancing services to the National Grid. Initial field trials of the technology have focused on cold appliances, but Rltec

85 http://www.rltec.com/ Accessed 13/06/11

86 The Potential for Dynamic Demand. Department of Energy and Climate Change. URN 08/1453. November 2008. Available from: http://www.supergen-networks.org.uk/filebyid/50/file.pdf (Accessed 13/06/11)

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suggest the technology could be extended to air conditioning and hot water heaters87. DECC commissioned a study to assess the potential for Dynamic Demand (DD). The study was completed by the Centre for Sustainable Energy and Distributed Generation86. The objectives for the study were to:

To assess the potential for DD to reduce the requirement for the System Operator to commission Balancing Services;

To estimate DD carbon saving potential, both in current electricity generation scenario and in scenarios featuring greater integration of renewable energy generation; and

To estimate the potential carbon savings on a per-appliance basis. The study is suitable to estimate the benefits of using such a technology as ‘Smart Level 1’ within this work. The following diagram illustrates the ‘normal’ and ‘Dynamic Demand’ operation of the refrigerator.

State representation Refrigerator temperature and compressor state

Figure E.6 ‘Normal’ operation of a refrigerator88

87 Background Information. RLtec. Available from: http://www.rltec.com/sites/default/files/090427_product_background_0.pdf

(Accessed 13/06/11)

88 Economic and Environmental Impact of Dynamic Demand. Centre for Sustainable Electricity and Distributed Generation. November 2008.

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Figure E.7 DD Refrigerator Behaviour with a Step Change in System Frequency88

In the ‘Dynamic Demand’ scenario, the duty cycle length is changed in proportion to the change in system frequency. This is illustrated in the figure above, where the grid frequency changes from 50Hz to 49.5Hz at the 2 hour point. After this point the shortened duty cycle and higher minimum temperature of the refrigerator can be seen. This shortened duty cycle leads to a lower average power demand. The power relieved for response per appliance under various scenarios is summarised in the following table:

Table E.7 Power potentially relieved by refrigerators while delivering frequency response

Power relieved for response (W per appliance) In fed Loss (MW)(*) 1320 1800

System Demand (GW)

25 40 60 25 60

100 19 21 18 29 24

75 19 25 20 29 27

50 19 28 23 28 27

DD

Pen

etra

tio

n (

%)

25 20 29 26 28 28

(*) In fed loss – sudden loss of a single generator

This illustrates that in the most extreme scenario- i.e. the loss of the largest possible generation unit (1800MW), at the period of highest demand, when levels of DD

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penetration are low, each appliance would provide 28W of relieved power, relative to a rating of 150W. The analysis assumed that the appliances would be able to provide this level of response for a sufficient period of time (30 minutes) and that all refrigerators would instantly recover their temperature. The Figures in the table above are therefore at the upper limit of what could be provided. The other main conclusions of the study are summarised below:

The simplified modelling approach showed that Dynamic Demand refrigerators could contribute between 728 and 1174MW of Frequency Response Services, based on 40 million appliances under different generation scenarios.

The potential value of annual fuel savings per appliance were estimated to be between £0.70 and £5.60, dependent upon the precise mix of coal, gas, nuclear and wind generation and demand levels. These savings are as a result of reduced fuel costs by reducing the number of ‘part-loaded’ and therefore less efficient generators running. It is not clear what proportion of this cost saving could be passed onto consumers and through what mechanism. Assuming a ten year life and a discount rate of 10% the capitalised present value of the technology is between £4.40 and £34.10.

By reducing the use of part loaded and therefore less efficient generation, the DD technology could save between 17 and 44kg of CO2 per refrigerator per year, depending upon the generation mix.

The following issues were identified for further study:

The results are sensitive to modelling technique used and further insight is required into:

o Whether the resource could provide sufficient balancing services over the time period required (presently 30 minutes);

o Whether increased reserve requirements would be required once the refrigeration load is reconnected, as their power demands may be greater as the temperature would rise during the ‘off’ cycle. The SEDG report highlights the importance of this research as follows: “This is clearly of fundamental importance for the value of DD and further research in this area is essential”88; and

o The impact of alternative control algorithms for DD on providing frequency response services.

A demonstration project was identified as necessary, to understand practical issues such as:

o The impact of DD on the efficiency of appliances and ‘wear and tear’ costs;

o How much affect the pattern of use of the appliance (e.g. door openings) has on the potential value of DD; and

o The effect of future changes in appliance design on DD potential. A trial of this technology was approved by Ofgem under CERT legislation as a ‘demonstration action’ in November 2008. The trial was originally scheduled to begin in 200986. This was delayed, with the first tranche of fridge-freezers being delivered in May 2010 to residents in the West Midlands. A further three hundred Smart

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fridges were delivered in the initial stage of the trial, with up to three thousand to be included in the later stages of the trial over the next two years89. However, the major obstacle to the role out of the technology is the lack of any financial mechanism to encourage the uptake of these appliances by end-users. Although demonstrated to be cost effective (i.e. benefits outweigh the costs), mandating the inclusion of dynamic frequency response within these appliances is not practicable. This is due to the fact appliance Standards need to be implemented on a EU-wide basis, rather than for the UK region, and the benefits of the technology relate specifically to the UK region. Smart Level 2 Smart Level 2 would represent an extension of Level 1, such that an extended interruption (time of reducing the proportion of time which the appliance is cycling for) could be acceptable. This would extend the dynamic demand concept to enable the appliance to avoid peak pricing periods, or provide services to the System Operator for a longer period time. This could potentially extract a higher value than Smart Level 1. One potential method of extending this interruption would be to provide a communications link to the Smart cold appliance to provide a warning of a future interruption. This would enable the unit to cool to a lower temperature before an interruption, extending the period of time for which the cycle could be interrupted before a maximum temperature is reached. The graph in Figure E.7 illustrated the potential for a shortened duty cycle without pre-cooling. Figure E.8 below shows show refrigerator duty cycles- the first for Smart Level 1 (a reproduction of Figure E.7) and the second for Smart Level 2.

Figure E.8 Refrigerator Duty Cycles for Smart Level 1 and 2

89 http://www.npowermediacentre.com/Press-Releases/The-cool-way-to-cut-CO2-Europe-s-first-smart-fridges-trial-starts-in-

UK-dcd.aspx (Accessed 14/06/11)

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This shows the lengthening of the duty cycle prior to the interruption at hour 2, and shorter duty cycles. The effect on the temperature of the cold appliance would require further consideration. For refrigerators there is a minimum suitable temperature, below which food can be damaged. The temperature should not be less than this value during the pre-cooling. During the interruption, the maximum temperature must still stay below the maximum acceptable value. Further investigations would be required to determine if pre-cooling would meet these criteria. Pre-cooling clearly relies on prior warning of an event. This event could be a requirement to provide services to the System Operator such as frequency response, or a peak price. Under some conditions, such as the unforeseen failure of a generator requiring frequency response services, it would not to be possible to pre-cool the appliances. This could limit the potential benefit from the pre-cooling functionality. The size of the benefit is likely to be of a similar magnitude to Smart Level 1 (i.e. tens of Watts per appliance). The potential benefits could be slightly higher as a longer interrupt could be provided. The length of this interruption which is achievable and the likelihood of the necessary warning for pre-cooling would require greater investigation. An assessment could then be made of the additional benefits and costs involved in Level 2 relative to Level 1.

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E.4 Washing Machines

Washing machines are owned by around 80% of UK households90 and consume approximately 4.4 TWh of UK electricity per year, as indicated in Table E.8 below.

Table E.8 Ownership and Energy Consumption of washing machines in the UK

2010 2020 2030 Number of UK Households ('000s) (i) 26,795 29,458 32,021

Households with a washing machine ('000s) (ii)

21,082 23,566 25,617

% ownership 79% 80% 80%

Energy Consumption (GWh) of washing machines (iii)

4,369 4,564 4,692

Average household consumption (kWh/year) 207.3 193.7 183.2

(i) BNXS25: UK Household and Population Figures 1970 – 2030, Version 3.0, Market Transformation Programme

(ii) BNW01: Combined Laundry: Government Standards Evidence Base 2009: Key Inputs Version 1.1, Market Transformation Programme

(iii) Market Transformation Programme

It is not generally regarded to be practicable to interrupt washing machines mid-cycle due to the potential impact on clothing. Therefore, DSM options are predominately limited to scheduling the use of the appliances to avoid consumption during certain periods of the day. Examples include:

- Lifestyle changes by householders to limit washing machine usage during certain times of the day.

- Utilisation of ‘in-built’ timer functions to delay the start of washing machine cycles, thus avoiding peak-time consumption.

These are relatively simple options that could be implemented with existing washing machine designs. Alternative options, that require changes to washing machine designs could include:

- Interrupting the hot water heating cycle for a short time (a few minutes) - Integration of the washing machine with a Home Area Network to enable

washing machine usage to be adapted to dynamic tariffs - In-built hot water storage91 to enable hot water heating load to be deferred,

without impacting on the customer. Table E.9 summarises a number of options for implementing DSM for washing machines.

90 Excludes households with washer driers, which account for a further 16% of households

91 For example, use of Phase Change Materials which can be heated using low cost electricity, and then used to pre-heat the incoming cold water supply.

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Table E.9 DSM Options for Washing Machines

DSM implementation approach Limitations / Issues Interuption durations Change of user behaviour, to avoid usage during peak periods. Range of approaches possible, including feedback from In-Home Displays or awareness campaigns.

No technology requirements – can be implemented with all existing appliances. Suitable for reducing use during a pre-defined period. Therefore only suitable to avoid network peaks demands.

Up to a few hours

Sim

ple

Op

tio

ns Change of user behaviour, whereby users

delay the start of their wash cycle using the ‘in-built’ timer function. Raise awareness of potential role of ‘in-built’ timers

Not all washing machines have ‘in-built’ timer function. Customer perception of risks of fire or flood damage if appliances turned on during night or when house in unoccupied Suitable for reducing use during a pre-defined period. Therefore only suitable to avoid network peaks.

Up to a few hours

Interruption to hot water heating cycle. Would require one-way communication to the washing machine, possibly via the Smart Meter and Home Area Network

Suitable for reducing use during very short periods only (a few minutes), therefore limited only to balancing services applications. No suitable technology currently available.

Short intervals (few minutes)

Optimisation of washing machine usage with remote signals (e.g. dynamic tariffs) via the Smart Meter / Home Area Network.

Suitable for reducing use during more flexible range of timings. No Standard protocols exist for way that remote signals are issued via the Home Area Network.

Up to a few hours

Co

mp

lex

Op

tio

ns

In-built hot water storage facility to allow electrical load for hot water heating element to be deferred, with no impact on customer.

Suitable for reducing use during more flexible range of timings. No suitable technology currently available.

Up to a few hours

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Washing Machine DSM Scenarios The following discussion is based on the three scenarios introduced in Section 7, i.e. Business as Usual, Smart Appliances – Level 1 and Smart Appliances - Level 2. It was not possible within the scope of this study to undertake detailed studies to evaluate the potential impact of washing machines on managing network constraints, providing balancing services to National Grid or integration of renewables. Therefore, the following Sections explore the potential magnitude of savings that might be achievable with Smart Appliances under two potential scenarios (Level 1 and Level 2) compared to the ‘Business as Usual’ baseline. Business as Usual As indicated in Table E.8, “Business as Usual” will lead to a modest growth in energy consumption by washing machines from 4.4TWh to around 4.7 TWh over the period 2010 to 2030. This growth is driven by increased appliance ownership, which is offset to a large extent by improvements in energy efficiency. Analysis of around 2,500 households across 10 European countries conducted for the Smart-A project determined the general pattern of energy consumption of washing machines, as indicated in Figure E.9 below. Whilst the pattern will undoubtedly differ from household to household, it is nevertheless a useful measure across a group of households.

Source: Synergy Potential of Smart Appliances, D2.3 of WP2 from the Smart-A project,

Figure E.9 Daily Pattern of Consumption of Washing Machines

The data presented in Figure E.9 is based on an average annual consumption across Europe of 150kWh per year per household, and around 170 wash cycles per year (i.e. around 0.89kWh/cycle). The equivalent data for UK households is shown in Table E.10 below.

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Table E.10 Average Energy Consumption by Washing Cycle for the UK, 2010 to 2030

2010 2020 2030

Average household consumption (kWh/year) 207.3 193.7 183.2

Average number of cycles 260 260 260

Average consumption per cycle (kWh/cycle) 0.80 0.74 0.70 Source: Data provided by Market Transformation Programme

Therefore, adjusting the data shown in Figure E.9 to reflect UK average usage information provides the following estimate of the pattern of consumption for washing machines. This is taken to be the ‘Business As Usual Scenario’ against which the Smart options will be later considered.

0

10

20

30

40

50

60

00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00

Time of Day

Lo

ad (

W)

2010 2020 2030

Figure E.10 Business As Usual Average Energy Consumption Pattern of Washing Machines in UK Households

As shown in Figure E.10, the average washing machine demand at the time of the system peak (i.e. during the early evening) is currently around 43W per household, falling to around 38W per household by 2030. Thus, based on the pattern of consumption determined in the Smart-A project, washing machines account for around 0.9GW of UK demand at the time of the system peak, i.e. around 1.5% of current total demand. If it is assumed that households with washer driers have a similar pattern of consumption, then total demand due to clothes washing in households would represent around 2% of total demand.

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Smart Level 1 As discussed previously, simple options for washing machines involve changes to customer behaviour that lead to reduced washing machine usage during peak periods, and thus in themselves do not rely upon ‘Smart Appliances’. The willingness of customers to be more flexible in their usage of certain appliances was explored in a recent survey commission by Ofgem92. The survey of 2,000 households gauged the level of interest in moving specified end-uses of electricity, and the results are summarised below.

Table E.11 Customer receptivity to moving end-uses of electricity

Measure % of respondents very/fairly likely to adopt measure

Heat water at different times of day 56% Use certain appliances after midnight 51% Install technology to turn off appliances when prices are high

47%

Carry out chores or cook meals in cheaper periods (e.g. after 7pm)

41%

Use electric storage heaters 35% It would seem unlikely that 50% of electrical load for washing machines could be avoided through behavioural changes alone. Therefore, if is assumed that 10% of households would be willing and able to significantly reduce their washing machine usage between the hours of 17:00 and 20:00, network peak demand could be reduced by up to 0.1GW (i.e. 10% of 0.9 GW peak demand attributable to washing machines). Smart Level 2 The results of a study on the impact of tariff structure and technology on the level of demand response achieved in almost 70 pilots and trials showed that93, in general, dynamic pricing combined with automatic control technologies lead to greater levels of demand response. Therefore, it would be expected that the Level 2 scenario would contribute more peak load reduction compared to Level 1 scenario. It is not possible to confirm whether this would be the case for this particular example, nor is it possible to quantify the additional benefits compared to the Level 1 scenario. However, the benefits, in absolute terms are likely to be modest. For example, even if the impact on peak load could be doubled compared to the Level 1 scenario, the impact would represent a reduction of around 1.8 GW. It is however important to note that peak demand is expected to grow significantly in future years as the electricity supply sector is decarbonised. Therefore, as a percentage of total growth, it is unlikely that washing machines would be able to have a significant impact on overall demand.

92 Demand Side Response, a discussion paper, 15 July 2010, Ref82/10 Ofgem

93 Rethinking Prices, The changing architecture of demand response in America, Ahmad Faruqui, Ryan Hledik and Sanem Sergici, Public Utilities Fortnightly, January 2010

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E.5 Tumble Dryers

Tumble driers are owned by just under half of UK households94 and consume approximately 4.4 TWh of UK electricity per year, as indicated in Table E.12 below.

Table E.12 Ownership and energy consumption of tumble dryers in the UK

2010 2020 2030

Number of UK Households ('000s) (i) 26,795 29,458 32,021

Households with a tumble dryer ('000s) (ii) 11,860 13,348 14,509

% ownership 44% 45% 45%

Energy Consumption (GWh) of tumble dryers (iii)

4,405 4,470 4,125

Average household consumption (kWh/year)

371.5 334.9 284.3

(iv) BNXS25: UK Household and Population Figures 1970 – 2030, Version 3.0, Market Transformation Programme

(v) BNW01: Combined Laundry: Government Standards Evidence Base 2009: Key Inputs Version 1.1, Market Transformation Programme

(vi) Data provided by Market Transformation Programme

Whilst it is not generally regarded to be feasible to turn off tumble dryers mid-cycle, it would be possible to either interrupt the heating cycle (whilst continuing to tumble the clothes) or reschedule the cycle, thereby providing a range of DSM options similar to those discussed for washing machines in the previous Section. Thus, rescheduling of tumble dryer loads could be achieved with existing appliances in the following ways:

- Lifestyle changes by householders to limit washing machine usage during certain times of the day; and

- Utilisation of ‘in-built’ timer functions to delay the start of washing machine cycles, thus avoiding peak-time consumption.

Alternative options, that would require development to existing appliance designs include:

- Interrupting the heating element for a short time (a few minutes); - Integration of the tumble dryer with a Home Area Network to enable its usage

to be adapted to dynamic tariffs; - In-built heat storage95 to enable heating load to be deferred, without impacting

on customer. Table E.13 summarises these options and the issues associated with their deployment.

94 Excludes households with washer driers, which account for a further 16% of households

95 For example, use of Phase Change Materials which can be heated using low cost electricity, and then used to pre-heat the incoming cold water supply.

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Table E.13 DSM Options for Tumble Dryers

DSM implementation approach Limitations / Issues Interuption durations Change of user behaviour, to avoid usage during peak periods. Range of approaches possible, including feedback from In-Home Displays or awareness campaigns.

No technology requirements – can be implemented with all existing appliances. Suitable for reducing use during a pre-defined period. Therefore only suitable to avoid network peaks demands.

Up to a few hours

Sim

ple

Op

tio

ns Change of user behaviour, whereby users

delay the start of their wash cycle using the ‘in-built’ timer function. Raise awareness of potential role of ‘in-built’ timers

Not all tumble dryers have ‘in-built’ timer function as standard. Customer perception of risks of fire or flood damage if appliances turned on during night or when house in unoccupied Suitable for reducing use during a pre-defined period. Therefore only suitable to avoid network peaks.

Up to a few hours

Interruption to heating element. Would require one-way communication to the washing machine, possibly via the Smart Meter and Home Area Network

Suitable for reducing use during very short periods only (a few minutes), therefore limited only to balancing services applications. No suitable technology currently available.

Short intervals (few minutes)

Optimisation of washing machine usage with remote signals (e.g. dynamic tariffs) via the Smart Meter / Home Area Network.

Suitable for reducing use during more flexible range of timings. No standard protocols exist for way that remote signals are issued via the Home Area Network.

Up to a few hours

Co

mp

lex

Op

tio

ns

In-built heat storage facility to allow electrical load for heating element to be deferred, with no impact on customer.

Suitable for reducing use during more flexible range of timings. No suitable technology currently available.

Up to a few hours

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Tumble Dryer DSM Scenarios The following discussion is based on the three scenarios introduced in Section 7, i.e. Business as Usual, Smart Appliances – Level 1 and Smart Appliances - Level 2. It was not possible within the scope of this study to undertake detailed studies to evaluate the potential impact of tumble dryers on managing network constraints, providing balancing services to National Grid or integration of renewables. Therefore, the following Sections explore the potential magnitude of savings that might be achievable with Smart Appliances under two potential scenarios (Level 1 and Level 2) compared to the Business as Usual baseline. Business as Usual As indicated in Table E.12, “Business as Usual” will lead to a modest decrease in energy consumption by tumble dryers from 4.4TWh to around 4.1 TWh over the period 2010 to 2030. Although the number of appliances increases over this period, advances in energy efficiency lead to an overall reduction in consumption. Analysis of around 2,500 households across 10 European countries conducted for the Smart-A project determined the general pattern of energy consumption of tumble dryers, as indicated in Figure E.11 below. The extent to which the pattern of consumption varies between countries is not known, however, in the absence of UK specific data it does provide a useful measure of the average pattern across a group of households.

Source: Synergy Potential of Smart Appliances, D2.3 of WP2 from the Smart-A project,

Figure E.11 Daily Pattern of Consumption of Tumble Dryers

The data presented in Figure E.11 is based on an average annual consumption across Europe of 251kWh per household, and around 102 wash cycles per year (i.e. around 2.46 kWh/cycle). The equivalent data for UK households is shown in Table E.14 below.

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Table E.14 Average Energy Consumption by Tumble Dryer Cycle for the UK, 2010 to 2030

2010 2020 2030

Average household consumption (kWh/year) 371.5 334.9 284.3

Average number of cycles 140 140 140

Average consumption per cycle (kWh/cycle) 2.65 2.39 2.03 Source: Data provided by Market Transformation Programme

Therefore, adjusting the data shown in Figure E.11 to reflect UK average usage information provides the following estimate of the pattern of consumption for washing machines. This is taken to be the ‘Business As Usual Scenario’ against which the Smart options will be later considered.

0

10

20

30

40

50

60

70

00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00

Time of Day

Lo

ad (

W)

2010 2020 2030

Figure E.12 Business As Usual Average Energy Consumption Pattern of Tumble Dryer in UK Households

As shown in Figure E.12, the average tumble dryer demand at the time of the system peak (i.e. during the early evening) is currently around 50W per household, falling to around 35W per household by 2030. Thus, based on the pattern of consumption determined in the Smart-A project, washing machines account for around 0.6GW of UK demand at the time of the system peak, i.e. around 1% of current total demand. If it is assumed that households with washer driers have a similar pattern of consumption for clothes drying, then total demand due to clothes drying in households would represent around 1.5% of total demand. However, the assumptions here assume that tumble dryer usage is approximately constant throughout the year. In practice, demand will be higher during the winter.

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Smart Level 1 As discussed previously, simple options for tumble dryers involve changes to customer behaviour that lead to reduced washing machine usage during peak periods, and thus in themselves do not rely upon ‘Smart Appliances’. Using a similar approach to that used for washing machines, if it is assumed that 10% of households would be willing and able to significantly reduce their tumble dryer usage between the hours of 17:00 and 20:00, network peak demand could be reduced by less than 0.1GW (i.e. 10% of 0.6 GW peak demand attributable to tumble dryers). Smart Level 2 Here it is assumed that the Level 2 scenario could contribute more peak load reduction compared to Level 1 scenario. However, it is important to stress that no evidence does exist to substantiate this assertion. Nevertheless, assuming a 20% impact at the time of system peak still represents a modest reduction in peak demand of around 0.1 GW. It is however important to note that peak demand is expected to grow significantly in future years as the electricity supply sector is decarbonised. Therefore, as a percentage of total growth, it is unlikely that tumble dryers would be able to have a significant impact on the overall pattern of demand.