iBuild Working Paper: A co-evolutionary framework for understanding infrastructure business models for local delivery

Stephen Hall1, Catherine Bale2, Jonathan Busch1, Timothy J Foxon1,2, Katy Roelich1,3 and Julia Steinberger1

1 Sustainability Research Institute, School of Earth and Environment University of Leeds

2 Centre for Integrated Energy Research, University of Leeds

3 Institute for Resilient Infrastructure, School of Civil Engineering, University of Leeds

Draft Working paper on WS4: Integrative Case Study: Low Carbon Technologies - Heat Networks and Smart Grids

1.0 Introduction.

This Working Paper sets out initial thinking on the conceptual framework to be used for the Integrative Case Study on Low Carbon Technologies - Heat Networks and Smart Grids. The framework builds on previous work by the case study team and will be further developed in the application to the study, but it may be helpful to share these early ideas with other members of the iBUILD team.

This Integrative Case Study is examining the challenges relating to the development of local low carbon energy networks, and analysing alternative business models for how such networks can be used to efficiently deliver energy services to residential and commercial customers. This aims to:

characterise a range of business models applicable to low carbon energy networks, and

integrate these with business model analysis in WP1.3;

use empirical case studies to compare the potential benefits of the most promising business

models;

develop qualitative models with agent interactions and heuristics, incorporating insights from

the case studies, to test business models and inform development of quantitative dynamic

models.

The paper is structured as follows. Section 2 briefly summarises why heat networks and smart grids are interesting cases for which new business models for local infrastructure delivery are needed, and the particular challenges that these cases raise. Section 3 summarises the coevolutionary framework previously developed by one of the team (Foxon, 2011) for analysing the complex systemic interactions needed for a low carbon transition. Section 4 summarises the business model canvas that provides one useful characterisation of a business model that has been applied previously in conjunction with the coevolutionary framework (Hannon et al., 2013). Section 5 sets out initial ideas on how this busines model canvas could be extended to incorporate a wider range of social values beyond the direct financial value to the business.

2.0 Empirical context of case study 2.1 What is a Smart Grid? If the National Grid is the motorway of the electricity transmission system then the Distribution Grids are the regional A roads and B roads of our electricity transmission system. This network of A roads and B roads is managed as a regulated monopoly in the UK by Distribution Network Operators or ‘DNOs’. There are 14 regional distribution networks in Great Britain, currently operated by 6 DNOs.

The National Grid is already pretty ‘smart’ but the DNO’s grids are currently predominantly ‘dumb’ in that they enable very little intelligence to be gathered remotely and very little grid management beyond on/off states. There are particular hard engineering additions, ICT upgrades, business model innovations and institutional changes that enable a DNO grid to become ‘smart’ (Beard, 2010; Anaya and Pollitt, 2013; Giordano et al, 2013). The benefits of adopting the necessary elements of a smart grid are: Being able to reduce peak loads, enable more low carbon and distributed generation, reducing the cost of maintaining the system, better use of existing generation assets and reduced ‘downtime’ in the form of brownouts or blackouts (Beard, 2010). Relatively ‘smart’ grids can exist without distributed generation but truly smart grids incorporate distributed low carbon generation. Investing in distributed generation can achieve traditional financial returns, economic development benefits, social redistribution benefits and can achieve ecological improvements. 2.2 What is a heat network?

District heating (DH), also known as a heat network, is an infrastructure technology made up of a series of highly insulated pipes that transport heat from a heat source to a heat demand using hot water or steam. The source of heat can be flexible and can be tailored to make use of local resources such as waste heat from industrial processes or waste incinerators. Alternatively, the network can be linked up to a purpose built heat source such as a gas or biomass boiler or a combined heat and power (CHP) plant. The majority of existing UK heat networks use gas-fired CHP which can reach in excess of 80% efficiency by generating both electricity and heat products (BRE et al., 2013). As a result, DH systems can be more energy efficient than conventional building-level gas or electric heating systems and, therefore offer reduced carbon emissions and potential fuel bill savings.

For this project we will use the definition as used by DECC (Department of Energy and Climate Change, 2013); a district heating network is either:

Two or more distinct buildings connected to a single heat source; or,

One building in which there are more than ten individual customers connected to a single heat source.

Heat networks can be categorised further as:

Large networks – 500 or more residential properties and /or more than 10 non-domestic users.

Medium networks – between 100 and 500 residential properties and /or between 3 and 10 non-domestic users.

Small networks – less than 100 residential properties and /or less than 3 non-domestic users.

There are 1765 individual district heating networks identified in the DECC database.

2.3 Barriers to development of heat networks and smart grids

At present the UK meets only 2% of its heat demand via DH (DECC, 2013), However, the infrastructure plays a much larger role in other countries such as Denmark, where 61% of heat is delivered through heat networks (Danish Energy Agency, 2011) or Finland, which delivers 75% with DH (Hawkey, 2011).

The delivery of district heating projects in the UK is hindered by a set of wide-ranging barriers (BRE et al., 2013). These barriers pose different challenges to household-level heating systems that we are predominantly accustomed to in the UK. Projects ideally require a dense heat demand with a constant heat demand profile to ensure technical and economic viability. Often, disparate stakeholders need to work together to enable projects to become a reality. Local authorities are seen as playing a crucial local coordinating role to facilitate partnerships between the necessary

stakeholders, and in some cases developing their own projects. Modelling tools such as heat demand mapping are being developed to support identification of potential development sites with the aim of facilitating new projects and overcoming many of the barriers to district heating.

DECC commissioned a study into the barriers of deploying district heating from the perspective of local authorities and also of property developers(BRE et al., 2013). DH barriers for local authorities primarily centre on lack of resource. Staff resources are limited, particularly for those with knowledge and understanding of DH. Financial resources are also limited for procuring feasibility studies and other consultancy services, legal advice, and of course, the significant upfront capital costs of networks. Other barriers include the variable pricing of heat, lack of contract mechanisms and procurement skills and some customer scepticism of the technology (BRE et al., 2013).

Table 1: Main barriers to establishing a district heat network (and the corresponding impact of the barriers)

*** Big impact: potential to stop the project ** Medium impact: likely to lead to sub-optimal outcomes and/or significantly slow progress * Modest impact: likely to slow progress

Local Authority Led Property Development Led

Objective setting and mobilisation

Identifying internal resources to instigate scheme and overcome lack of knowledge (**)

Customer scepticism of technology (*)

Persuading building occupants to accept communal heat (mandated by the planning authority) (*)

Technical Feasibility and Financial Viability

Obtaining money for feasibility/viability work (***)

Identifying and selecting suitably qualified consultants (**)

Uncertainty regarding longevity and reliability of heat demand (*)

Uncertainty regarding reliability of heat sources (*)

Correctly interpreting reports prepared by consultants (*)

Selecting suitably qualified consultants (**)

Uncertainty regarding longevity and reliability of heat demand e.g. lack of heat demand in new buildings (*)

Uncertainty regarding reliability of heat sources (*)

Implementation and Operation

Paying the upfront capital cost (***)

Obtaining money for independent legal advice (***)

Lack of generally accepted contract mechanisms (**)

Inconsistent pricing of heat (**)

Up-skilling LA procurement

Concluding agreement with energy services provider including obtaining a contribution to the capital cost (**)

Lack of generally accepted contract mechanisms (**)

Inconsistent pricing of heat (**)

Source: BRE et al., 2013, page 5.

Understanding of the barriers to smart grids is at this stage perhaps less well developed. At present work by DECC in partnership with Ofgem and the Smart Grid Forum (an industry group) is underway

to understand the barriers to smart grid development (Department of energy and Climate Change, 2012). They note that ‘As well as the technical evolution of smarter networks there could be changes in the roles of, and interactions between, key players in the networks industry and it will be important to remove any barriers to the development of these new interactions and associated commercial frameworks’.

The integrative case studies in this work stream will deepen understandings of these commercial frameworks, and barriers by characterising the business models these infrastructures are delivered within and linking them to the wider elements of infrastructure systems in two empirical cases. We intend to do this within specific theoretical and conceptual frameworks.

3.0 Co-evolutionary framework for analysing a low carbon transition

In order to understand these barriers to the adoption of novel local energy infrastructures, it is helpful to consider these changes as part of a systemic transition to a low carbon energy system. Under the Climate Change Act (2008), the UK is committed to reducing its carbon (greenhouse gas) emissions by 80% by 2050, from a 1990 baseline. In addition, the UK Government is required to adopt and meet 5-yearly carbon budgets towards this target, based on the recommendations of the independent Committee on Climate Change. The 4th Carbon Budget, adopted by the Government in 2011, requires a 50% reduction from 1990 levels by the period 2023-2027, equivalent to around an additional 30% reduction on current levels. This target applies to carbon emissions from all economic activities within the UK national boundary (except for the UK’s contribution to international aviation and shipping), and so has implications for all infrastructure investment. The Carbon Plan (HM Government, 2011) published by the Government in December 2011 sets out the policies and programmes to meet the 4th Carbon Budget target. However, the main policy measures enacted so far focus on incentivising reductions in carbon emissions from large-scale centralised electricity generation, notably the Electricity Market Reform programme in the Energy Bill currently going through Parliament. This forms the basis for the recent announcement of a guaranteed price of at least £89/MWh for 35 years (roughly double the current wholesale electricity price) for the 2 reactor new nuclear power station to be built at Hinckley Point by French energy company EDF. Support in the form of feed-in tariffs for local distributed generation, such as solar PV, has been in place since 2010, though the short-notice reduction of tariffs in 2012 was criticised by the renewables industry. Meanwhile the full introduction of the corresponding support mechanisms for renewable heat, the Renewable Heat Incentive, has been delayed until 2014. The debates around the form and size of these incentives reflect the so-called energy ‘trilemma’ of the need to reduce carbon emissions whilst maintaining security of energy supply and affordability of energy for domestic and industrial consumers.

In order to conceptualise the systemic interactions involved in a transition to a low carbon economy, a coevolutionary approach was proposed by Foxon (2011). This provides a framework for analysing how ecosystems, technologies, institutions, business strategies and user practices coevolve to produce large sociotechnical systems that display attributes of path dependence and lock in, but have the potential to enable a transition to lower carbon economies. In this Integrative Case Study, we propose to apply this framework to analysis of infrastructure transitions. We argue that a coevolutionary approach is well placed to:

undertake detailed empirical analysis of innovation adoption in existing infrastructure provision systems;

frame and explain the multi-level interactions of socio-technical elements within infrastructure systems; and

understand the potential for managing transitions to alternative systems of infrastructure provision, which may prioritise economic, environmental or social attributes, not currently being delivered by incumbent models.

The utility of a coevolutionary framework is to provide a focussed framework for understanding the elements of a system, which co-evolve to produce particular configurations of systems of infrastructure provision. Foxon (2011) argues that long term socio-technical and techno-economic change can be understood by developing understanding the coevolution of ecosystems, technologies, institutions, business strategies and user practices (Figure 3.1). These elements of the system coevolve because they have significant causal impact on each other’s ability to persist (Murman 2003, Foxon 2011). A further simple example is how the growing demand for electricity (particularly at peak times) due to evolving user practices has structuring effects on the technological requirements of the distribution grid, the grids ‘ability to persist’ in its current form is threatened by peak load and as such variation of the technologies is driven by evolving demand elsewhere in the framework.

Figure 3.1 Coevolutionary framework for socio-technical systems change

Source: Foxon (2011 p.2262)

We propose an extended business model canvas approach (below) can be used to most usefully describe the ‘business strategies’ element within this system for infrastructure provision.

For definitions of each element of the system see Foxon (2011). In this framework each of these five systems is able to evolve under its own dynamics, but this evolution influences and is influenced by the dynamics in the other systems. The power of a coevolutionary approach is not only to explain each individual system, but to focus attention on the causal influences between systems. As such, investigating only the business models element of infrastructure systems is useful in and of its self, but for greater insight, we need to appreciate how these business models (‘business strategies’ in the framework) influence and are influenced by the other components of the system. An example of this approach is outlined below.

4.0 Business Model Canvas approach

To apply the coevolutionary framework, a characterisation of ‘business strategies’ is needed. Here, a business strategy is taken to mean how a business or other socio-economic organisation characterises its activities in order to achieve its goals of profit-making or other objectives. We take this to be synonymous with a ‘business model’, though some authors make a distinction between the two terms.

In particular, a new business model will have to compete with existing business models operating within the same space, under the systemic influences of existing technologies, institutions and user practices. In turn, the adoption of a new business model could create opportunities for adoption of new technologies, institutions and user practices. Thus, a characterisation of business models

combined with the coevolutionary framework provides an approach for analysing the interactions between business model adoption and wider systems change.

An example of this was undertaken in the course of Matthew Hannon’s PhD research, under Foxon’s supervision, into the systemic interactions relating to the adoption of the alternative Energy Service Company (ESCo) business model in the UK (Hannon et al. 2013). This work examined the competition between the Energy Service Company (ESCo) business model and the dominant Energy Utility Company (EUCo) business model, in the context of wider coevolutionary interactions. Under the EUCo business model, revenue increases with the number of energy units sold, and so incentives are based on minimising energy unit cost and maximising units sold, rather than incentivising customers to reduce their energy demand (Steinberger et al., 2009). Under the ESCo business model, firms supply energy services, such as useful heat or light, and so are not dependant on increasing units of energy consumed to increase revenues. Clearly the ways in which the business model (business strategy in figure 3.1) is configured has a structuring effect on the incentives of the firm, and subsequently, on the nature of its relationship with the customer, from whom increased consumption is now divorced from increased revenue. Hannon et al. (2013) characterise the historical path dependency that led to the dominance of the EUCo model over the ESCo model before outlining an analytical framework to understand the particular contribution ESCos might make to transitions to low carbon energy systems. Given their focus on the configuration of the business model system, Hannon et al. (2013) centralise their analysis here, characterised by figure 4.1.

Figure 4.1: Centralising the business model within the coevolutionary framework.

Source: Hannon et al. (2013) p.1034)

One utility of the coevolutionary approach is that it can accommodate a range of theoretical approaches to explain the analytical categories of ecosystems, institutions, user practices, business models and technologies. In the case of Hannon et al. (2013), the unit of analysis is the comparative analysis of business models for energy provision. Thus Hannon et al. are able to adopt a framework to enable analysis of the various components of business models. They adopt a particular characterisation of a business model from the business management literature by Osterwalder and Pigneur’s (2010), based on 9 building blocks (figure 4.2) which consist of key partners, key activities, key resources, customer value proposition, customer relationships, channels, customer segments, cost structure and revenue stream.

Figure 4.2: The nine building blocks of a business model

Source: Osterwalder and Pigneur (2010 in; Hannon et al, 2013 p.1034)

Hannon et al then go on to utilise this framework for their comparative analysis before situating this new understanding of the ESCo “vs” EUCo models within their coevolutionary interactions in the framework. In so doing Hannon et al are able to conclude that, despite the advantages of the ESCo model they have yet to proliferate more widely (selection/retention) due to a hostile selection environment, attributes of which include but are not limited to: the institutional structures favouring incumbent EUCo models, such as the regulatory regimes that favour integrated utilities that own both large-scale generation and supply businesses, and the technological systems that are only beginning to be able to accommodate distributed generation and dynamics. Clearly this approach is of particular interest when discussing infrastructure business models for local delivery. As such the following section links this approach to our work within iBuild on the integrative case study; ‘Low Carbon Technologies – Heat Networks and Smart Grids. We extend the business model canvas and place it within the coevolutionary framework. 5.0 Application to Integrative Case Studies and Development of Extended Business Model Canvas

Significant investment is needed in low carbon, climate resilient energy infrastructure at the urban scale. These investments include enhancements to existing electricity distribution networks, potentially new ‘private wire’ networks which service consumers before interacting with the wider grid and further new infrastructure for meeting demands for urban space heating through district heat networks. These investments will need to handle distributed energy generation and operate in concert with existing energy systems. This project examines the challenges relating to the development of innovative business models for heat networks and smart grids in two English cities by building on the coevolutionary approaches identified above and constructing new tools for understanding the deployment of these business models in an empirical context. Our approach to these case studies falls into three sections which are developing iteratively. Firstly we are utilising secondary sources to develop long lists of smart grid and heat projects (extracts from

which are shown in tables 5.1a, b and c). Secondly we are using the business model canvas approach to understand the components of each type of infrastructure business model. Thirdly we are building our analysis based on case study data. We are using a common format to characterise the long lists of empirical projects from the smart grid, distributed generation and heat network sectors in the UK. This is enabling a scanning exercise in which we can begin to characterise typologies of projects in each case. This will enable analysis of the main alternative business models being used in these projects and the common barriers that they face to adoption. Distributed generation, i.e. generation that connects to the distribution network rather than the national grid transmission network, is included as it enables both the smart grid business models and heat networks. Table 5.1a: Project characterisation examples — Smart Grids

Table 5.1b: Project characterisation examples — Distributed Generation

Table 5.1c: Project characterisation examples — Heat networks

The second element of our methodology is drawing out specific cases from each list and applying Osterwalder and Pigneur’s (2010) 9 building blocks to build our own ‘business model canvasses’ to understand the components of each model and interrogate it’s suitability for understanding infrastructure business models. An illustrative example of this is given in Figure 5.2. Figure 5.2: Using the basic framework for distributed generation.

The third aspect of our methodology is our primary data collection, using the business model canvasses above to begin to understand how the business models interact with other elements of the coevolutionary framework. This involves in depth semi structured interviewing of stakeholders who interact with the institutions, technologies, user practices and ecological aspects of the system at specific urban scales.

For example within the heat network section of this study the ‘Newcastle Energy Master Planner’ is acting as a ‘gatekeeper’ contact (Valentine 2005), who orchestrates stakeholders with intimate knowledge of each system to bring begin the transition towards local heat infrastructures in this specific urban context. In Leeds, the economic partnership is developing a ‘pipeline’ of ‘investment ready’ distributed generation projects which will both contribute towards the energy security and low carbon aspirations of the city at the same time as challenging the existing distribution systems. We are working with the economic partnership to investigate the business models available to deliver these investments. Early results, particularly from the more advanced work on heat networks, is demonstrating both the initial utility and inherent limitations of a business model canvas approach based on traditional assumptions of value creation and capture. In order to characterise the wider range of values that could be associated with infrastructure investment, an extension of Osterwalder and Pigneur’s approach is needed to create business model canvasses that are more suited to understanding different treatments of value. 5.2 Extending the business model framework for infrastructure Our empirical work is beginning to demonstrate the limitations of the ‘business model canvas’ analytical framework due to its inability to fully capture value concepts beyond consumption and revenue exchanges. Using our empirical data, with insights from the following brief theoretical review, we are proposing an extended version of the business model canvas which will deepen our understanding of the integrative case studies. By its nature infrastructure exhibits a number of traits which set it apart from other goods and services which can be delivered within the business model canvas approach. The exclusion of ‘infrastructure’ from traditional private provision (as captured by the canvasses) was historically based on the broad consensus, which held until the 1970’s in developed western contexts, that infrastructure networks required high degrees of state involvement, especially in the early stages. This was due to three particular features identified by Graham and Marvin, 2001; Chan et al, 2009.

Most infrastructure networks are best managed as natural monopolies; this is due to high capital requirements for entry as a direct investor (militating against ‘variation’) and infrastructure’s geographic fixity, meaning consumers must either move geographically in order to access alternatives, or quasi-market structures must be created by some level of the state.

It is very difficult to capture spill over benefits in traditional ways and prevent free riding, leading collective provision to be undersupplied by individual companies.

Lastly externalities can occur from the operation or construction of infrastructure (traffic fumes, sulphur dioxide from power stations, effluent from sewage treatment) that are equally difficult to price or directly compensate. Each of these three traits presages a closer mediation of infrastructures by the state than in other sectors.

Utilising these understandings for infrastructure we need to extend the business model canvas approach, thus the value proposition within the business model canvas is inherently more complex for infrastructure than in the current formation. This then goes beyond the approach of Osterwalder and Pigneur, whose approach was to characterise business models within a more traditional context. Their business model canvas can be usefully extended to characterise the elements that make up an infrastructural business model. Since the conditions above alter the value proposition and value capture possibilities, we propose the following working model.

The central component; the ‘value proposition’ could be split into four sections, four ‘propositions’: direct consumption value, economic development value, ecological value and social value. An example of the utility of this extension is how it would enable the deployment of general taxation to deliver heat network infrastructure where the value proposition is ‘alleviation of fuel poverty’ but the customer base is unable to provide sufficient ‘fiscal revenue stream’ to repay the initial capital investment needs, and where the infrastructure is fixed (a CHP plant in social housing) but the customer base (tenants) are mobile. The ‘revenue stream’ component of the canvas needs to be extended to include non-fiscal value capture. As such we can be more alert to the particularities of infrastructure provision. We extend ‘revenue stream’ to ‘value stream’ which can include fiscal revenues, but can also capture the non-monetary values outlined in the value proposition. As such the ‘value stream’ can parallel ‘value proposition’ and be extended to include wider economic development values as ‘stimulus’, ecological values and social values. A simple example of this could be a city investing in a smart grid system which not only enables the distribution network operator to capture value from capital expenditure avoided and maintenance savings, but also promotes economic development in the city in the form of clean energy start-ups and ecological benefits from lower carbon grid electricity. Clearly both of these examples are over-simplifications, by working through these integrated case studies we will be able to provide real world examples how these value streams are being proposed, understood, measured, delivered and captured. Figure 5.3 below represents our first attempt at extending the model to an ‘infrastructure business model canvas’. In so doing we will be better able to understand the infrastructure business models for local delivery in the two integrative cases and develop a useful device to integrate with other elements of the system in the coevolutionary framework. Finally, in order to examine the systemic influences on the adoption of new business models, the extended canvas is integrated into the coevolutionary framework (Figure 5.4)

Figure 5.3: Infrastructure business models for local delivery canvas

Figure 5.4: The extended canvas in the coevolutionary framework

6.0 Scaling up and Diversifying out — Links with further Work streams and the iBuild objectives

Early discussions have highlighted particular complementary themes within this integrative case study and WP3 ‘Issues of Scale in Local Infrastructure Delivery’. The characterisation of smart grid and district heating business models will contribute to work being undertaken in WP3.1 “Scaling infrastructure business models”. The adapted business model canvass could underpin the development of a framework of attributes of the scalability of particular models. This will be used in combination with the business model typologies and additional case study work to produce a specification for a complex systems model. This model will support analysis of the potential contribution of local business models to infrastructure provision and the implications of scaling up local delivery for national infrastructure business models.

The case studies selected for detailed analysis are limited to the energy sector, for the purposes of demonstrating the approach, however, this approach could be applicable beyond the energy sector into other infrastructure categories.

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