Geert-Jan van der Zanden MSc EMP 2011

The Smart Grid in Europe:

The Impact of Consumer Engagement on the Value of the European Smart Grid

Smart grids?

Image: http://theasiacareertimes.com/wp-content/uploads/2011/06/smartgrid.jpg

Power (IEA): 17% of GHG

Smart grid (EC):European CO2 - 9% Domestic consumption - 10%

The smart grid – Consumer engagement

Main research questions:

• Why is the smart grid in Europe not developing as quickly as expected?

• How can the development of the smart grid in Europe be facilitated by maximizing consumer engagement with the smart grid?

Sub-questions:

• What is the projected value of the smart grid in Europe?

• What impact does consumer engagement have on the value of the smart grid in Europe and how can it be maximized?

Thesis structure

Qualitative

Drivers & barriers

Consumers

Value (Business Case)

Consumer

Quantitative

Smart Grid

Behavioral Change Theories• Theory of Regulatory Engagement

• Transaction Theory• Social Comparison Theory

• Theory of Diffusion of Innovation • Theory of Affordances (expanded)

Methodology

Empirical EvidenceReviews of demand response tests by:

• Faruqui• Darby

• Ehrhardt-Martinez• Myself

Recommendations

Literature Research

Interviews

Blog Discussion

Forecasted investment in smart grid technology in Europe

• Goldman Sachs: €187 billion in next 30 years

• Smart Energy Demand Coalition: €120 billion by 2030

• Booz & Company: €90 billion by 2020

Source: Van der Zanden, GTM Research

Distribution Automation

Systems management and data security ICT

Distributed and Renewable electricity generation (DG/RES)

Advanced energy storage

Energy Management Services / HAN

EV charging infrastructure

Advanced Metering Infrastructure AMI

Demand response (DR) systems Demand response (DR) systems

smart grid technologies

Source: Van der Zanden, adapted from IEA – Technology Roadmap: Smart Grids, 2011

Generation Transmissionn

Distribution Industrial Service ResidentialTransmission lines

Transmission substation

Distribution lines

Distributiion substation

Trans-former

Drivers and barriers

Drivers and barriers

Consumer engagementfeedback

absolute reduction

demand response

2%-8%€3.6 billion - €18.2 billion p.a.

consumer bill

peak demand reduction

5%-15%€3 billion - €9 billion p.a.

in deferred capex

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Source: Van der Zanden, GTM Research

• Not very obvious for utilities

• Very clear for society

• High impact from consumer response but high uncertainty

• Important role for regulators

Smart grid business case

Behavioral Change Theories

• Theory of Regulatory Engagement• Transaction Theory• Social Comparison Theory • Theory of Diffusion of Innovation • Theory of Affordances (expanded)

Socio-environmental stimuli

Actor’s motivation and experience

Intrinsic action

possibilities

Actor’s capabilities

Gibson (1997)

Norman (1999)

Van der Zanden (2011)

Design

Plans / Individual goals

Education / incentivesSocietal goals

Desired Action

Theory of Affordances (Expanded)

Empirical evidence

Faruqui (2009): • Higher P, lower consumption• Dynamic pricing most effective• ‘Opt out’ has higher adoption rate than ‘opt in’• Response depends on enabling technologies, consumer segment, pre-pay/credit.

Darby (2006, 2009):• Not AMI (technology), but feedback changes consumption. • Direct feedback more effective than indirect feedback, but depends on quality of

information / interface and context.• Historic comparison seems more effective than social comparison.• Frequent, accurate billing with guidance on average use disaggregated by end-use

can be almost as effective as real disaggregated feedback.• Online feedback requires more effort from consumers.• Smart meters as communication-hub to build utility-consumer relationship?

Ehrhardt-Martinez (2010)• Frequent or real-time,

disaggregated feedback generates the highest savings.

• Enhanced billing is a very cost effective option.

• Programs focused on overall reduction are more effective (-10%) than those focused on peak load reduction (-3%)

Empirical evidence

Own findings (review of results of 24 widely varying European studies)

• Response depends on target consumer and context.

• Without educated consumers, real time feedback seems only marginally more effective than enhanced billing.

Actionable advice seems more important than real-time feedback.

• Social feedback seems effective.• Regular reminders contribute to

habit formation.

Empirical evidence

10 Recommendations for utilities

1 Focus on technology or financials only is not going to generate engagement.

2 Lower transaction costs for consumers through education or set-and-forget technology.

3 Segment consumers, no one-size-fits all.4 Give consumers easy feedback, actionable advice and insight in the big

picture.5 Leverage social networks.6 Collaborate with third party providers.7 ‘Opt-out’ rather than ‘opt-in’ programs.8 Introduce pre-paid alongside on-credit schemes.9 Underline the hedonic aspects of electricity savings.10 Stimulate two- or three-way interaction between utility and customers.

10 Recommendations for regulators

1 Communicate societal goals and targets. Educate and encourage consumers.

2 Policies that align utility goals with societal and consumer goals.3 Not smart meters, but accurate, timely feedback and advice should

be mandatory.4 ‘Opt-out’ of dynamic pricing, rather than ‘opt-in’.5 Promote overall reduction rather then peak load.6 Invest in behavioral change and formation of norms and values

around energy efficiency.7 Promote behavioral change before ee-retrofits and ee-appliances.8 Ensure clear, stable and supportive regulatory conditions. 9 Apply the integration principle.10 Accelerate R&D and sharing of results.

Q & A

Image: GEImage: GE

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