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Electricity markets in liberalised nations are composed primarily of private firms that make strategic decisions about how to secure competitive advantage. Energy transitions, driven by decarbonisation targets and technological innovation, will create new markets and destroy old ones in a re-configuration of the power sector. This research suggests that by 2050 up to 21bnGBP per year of new financial value is available in the UK electricity system, and that depending on scenario, these new values represent up to 31% of the entire electricity sector. To service these markets business model innovation and new firm strategies are needed in electric power provision. Energy scenarios can inform strategic decisions over business model adaptation, but to date scenario modelling has not directly addressed firm strategy and behaviour. This is due in part to neo-classical assumptions of firm rationality and perfect foresight. This research adopts a resource based view of the firm rooted in evolutionary economics to argue that quantifying the relative size of the markets created and destroyed by energy transitions can provide useful insight into firm behaviour and innovation policy.
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Applied Energy
journal homepage: www.elsevier.com/locate/apenergy
Valuing energy futures; a comparative analysis of value pools across UK
energy system scenarios
Marie-Sophie Wegner
a
, Stephen Hall
b,
,Jerey Hardy
c
, Mark Workman
d
a
Imperial College London, Energy Futures Lab, United Kingdom
b
University of Leeds, Sustainability Research Institute, United Kingdom
c
Imperial College London, Grantham Institute Climate Change and the Environment, United Kingdom
d
Energy Systems Catapult, United Kingdom
HIGHLIGHTS
By 2050 up to 21 bnGBP per year of new nancial value is available in the UK energy system.
Carbon pricing is critical across all studied scenarios.
Electric vehicle revenues are a key driver of sector growth.
Flexibility markets are volatile and require more innovation policy.
The value pool method is a useful tool for understanding rm strategy and innovation policy.
ARTICLE INFO
Keywords:
Electricity markets
Energy scenarios
Value pools
Evolutionary economics
ABSTRACT
Electricity markets in liberalised nations are composed primarily of private rms that make strategic decisions
about how to secure competitive advantage. Energy transitions, driven by decarbonisation targets and techno-
logical innovation, will create new markets and destroy old ones in a re-conguration of the power sector. This
research suggests that by 2050 up to 21 bnGBP per year of new nancial value is available in the UK electricity
system, and that depending on scenario, these new values represent up to 31% of the entire electricity sector. To
service these markets business model innovation and new rm strategies are needed in electric power provision.
Energy scenarios can inform strategic decisions over business model adaptation, but to date scenario modelling
has not directly addressed rm strategy and behaviour. This is due in part to neo-classical assumptions of rm
rationality and perfect foresight. This research adopts a resource based view of the rm rooted in evolutionary
economics to argue that quantifying the relative size of the markets created and destroyed by energy transitions
can provide useful insight into rm behaviour and innovation policy.
1. Introduction
Electricity systems in developed economies are undergoing funda-
mental transitions driven by international decarbonisation targets,
technological innovation, and new market entrants [1,2]. These tran-
sitions create deep uncertainty for investors and power utilities [3,4].
These uncertainties are due, in part, to the multiple pathways energy
systems can take to achieve environmental, social and economic goals
[5,6]. The existence of multiple pathways increases risk perceptions of
premature and even intentional asset stranding for power sector in-
vestments [7]. These challenges result in a lack of investor condence
[8],aect the markets ability to provide sucient price signals to in-
vestors [9,10], and may increase the cost of capital for low-carbon
energy transitions [11].
Existing energy system models address this uncertainty to some
degree, by using climate change commitments, technology prices, and
demand forecasts to set parameters for quantitative energy transition
scenarios. These models often analyse scenario costs within constraints;
i.e. no nuclear, high CCS, high decentralised renewables [12]. The
majority of these models focus on cost optimisation or near-optimisa-
tion [13] and report in terms of total investment cost[14].
Recent analysis has demonstrated that total investment needs
globally for the energy sector to 2040 are between $3.9 and $4.9 tril-
lion at 2015 prices [15]. Within this range there is a substantial re-
allocation of capital between sectors, and wide technological diversity
depending on scenario [14,15]. Cost optimisation models have proved
http://dx.doi.org/10.1016/j.apenergy.2017.08.200
Received 8 May 2017; Received in revised form 10 August 2017; Accepted 26 August 2017
Corresponding author at: University Academic Fellow, Sustainability Research Institute, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, United Kingdom.
E-mail address: s.hall@leeds.ac.uk (S. Hall).
Applied Energy 206 (2017) 815–828
0306-2619/ © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
MARK
useful for policy makers seeking to achieve multiple energy system
objectives within a credible cost framework [16]. However, energy
modelling and scenario development has, to date, done less to address
commercial imperatives and investment uncertainties created by mul-
tiple pathways.
2. Firm strategy and investment in energy transitions
Addressing the commercial uncertainties created by energy transi-
tions is important because, in many developed nations, power sectors
have been liberalised; they comprise private rms making decisions
about how to compete in energy markets for wholesale generation and
retail supply [17]. Contributors to this journal concerned with strategic
investment decision-making in the power sector, have demonstrated
how utility investment decisions aect wider energy markets [18], how
critical investor perceptions of return are to expanding low-carbon
generation [19], and the importance of adopting real options or other
approaches to account for uncertainty in utility scale investment deci-
sions [20,21]. Much subsequent research however, is aimed at under-
standing the performance of specic technologies or investments across
a range of scenarios or experimental conditions [2224]. Recently this
work has been expanded to take account of longer term rm decision
making over multi-technology portfolios, which better represents a
traditional utility business model [25]. This is an important step as
there is mounting evidence of multiple threats to utility rms which
require long term business model transition and adaptation to address
[26,27]. To date there has been a disconnect between the growing so-
phistication of the energy scenarios produced by cost-optimisation
modelling, and the strategic investment decision-making literature.
To bridge this gap it is important to clarify how rm strategy is
constructed, before asking how scenario modelling can inform strategic
decision-making. Firm strategy is driven by a rms perception of the
markets it can earn a commercially viable protin[28]. Grant [28]
argues that this depends upon the attractiveness of the industryin
which the rm is located and its ability to establish competitive ad-
vantageover rivals. The attractiveness of industriesis related to the
level of competition within a sector and the industriesprot structure
[29].Competitive advantagerelates to both the cost advantages rms
can realise through process innovation, scale economies, or resource
eciencies; and dierentiation advantages such as brand trust or pro-
duct performance [28]
.
Given a suitably attractiveindustry, and a
reasonable expectation of competitive advantage, a utility may choose
to enter a new market or pursue new values by adapting its business
model [26].
2.1. Understanding the rm in energy transitions
Most cost optimisation models do not address rm strategy or be-
haviour, because of two important assumptions; these are rm ration-
ality and perfect competition [13,30]. Under these assumptions the
resources of rms are mobile and their foresight is perfect. If one set of
rms (such as incumbent utilities) fail, other rms will acquire the
resources needed to exploit prot opportunities in the energy sector.
The relative size of the various markets created or destroyed by various
system pathways would not matter under neo-classical assumptions of
rm behaviour, because all prot pools would be competed away under
perfect competition. Following this, the behaviour of rms in energy
scenario work has often been unproblematically assumed. However,
insights from both evolutionary and institutional economics contest this
assumption of actor foresight perfect competition between sectors
[30,31].
Proponents of Resource Advantage theory argue that rms acquire
substantial nancial, physical, human, organisational, informational
and relational resources which lead to competitive advantages (or
disadvantages) over prospective rivals, particularly new entrants [28].
This resource based, rm theory in its evolutionary form [32], explains
both why rms in large complex systems can secure incumbent ad-
vantage by trading on routines and replicated capabilities [33], and
how these routines become a barrier to system change by creating path
dependency and lock in [34,35]. The multi-level perspective or MLP
has demonstrated how path dependency and lock in can be overcome
by fostering niche innovation [36]. The same school recognises that
although large incumbent rms will probably not be the initial leaders
of sustainability transitions, their involvement might accelerate the
breakthrough of environmental innovations if they support these in-
novations with their complementary assets and resources[37].
To summarise, utility investment decisions and return expectations
are key determinants of market composition. Firms make decisions on
which markets to enter based on size of market, ease of access, prot
structure and competition within dierent segments. However, not all
rms are equally able to enter all markets. Therefore, incumbent utility
rms are more likely able to exploit new value opportunities created by
energy transitions. The relative size of the markets created or destroyed
by dierent energy system scenarios matters, because energy rms
undertaking future market analysis will likely select strategies that are
compatible with their resource endowments, the potential size of the
market opportunity that might develop, and its robustness across sev-
eral possible futures. For the development of innovation policy, un-
derstanding future market size and ease of access can dene the timing
and strength of market pull[38] for system innovations and in turn
dene appropriate innovation policy instruments such as scal, reg-
ulatory or statutory interventions [39].
2.2. Mapping future values in energy transitions
It is from this starting point that we frame our research goal; to
determine how energy rms, incumbent or otherwise, can understand
potential prot structures and market sizes (industry attractiveness)
over multiple future energy scenarios.
One technique used for industry attractiveness analysis is the prot
pool approach [27,40,41]. The prot-pool-analysis is a strategy tool
[29] which aims to map the total prots earned in an industry at dis-
crete points along the industry's entire value chain [40]. Prot pool and
value chain mapping facilitates insight into industry structure by vi-
sualising the economic and competitive forces driving the distribution
of prots [29].
Eurelectric [42] adopt the framework to point out the declining
prot pools available in electric utilitiescore business, while further
analysing new growth areas to oset this decline such as large-scale
renewable energy sources (RES) and the emergence of new down-
stream value pools from the green agendaand decentralised genera-
tion and energy eciency[42]. Commercial energy consultancies
have used the prot pool approach to quantify the implications of en-
ergy transitions for utilities strategy [43]. For example McKinsey
adopted this methodology to explore EU power sector prot pools to
2020, by sizing the prots from performance improvements, capacity
remuneration, RES and new down-stream growth [43].
Van Beek et al. [44] take an additional, critical methodological step
by switching the terminology from protto valuepool. This work
recognises that a protpool analysis needs detailed understandings of
the business models being used. Energy transitions create pools of value
which can only be rendered into prot by business model innovation
[45]. Van Beek et al. apply the value pool concept to quantify new
nancial opportunities available in energy transitions, prior to ex-
ploring the business models compatible with capturing those opportu-
nities. However, while [42,43] use the EBIT as a measure, Van Beek
et al. [44] measure the nancial opportunity in new revenuesand
avoided cost, across six discrete pools. The three studies above all
analyse a pan-European geography, demonstrating the international
applicability of the method. To date however, the value pool approach
M.-S. Wegner et al. Applied Energy 206 (2017) 815–828
816
has only been used to analyse single future scenarios in energy transi-
tions; it has not yet been applied to investigate how each value pool is
created, modied, or destroyed in multiple energy system scenarios. To
address this, the authors conducted a value pool analysis on a discrete
selection of well-developed UK energy scenarios.
2.3. Study objectives
The research question was: what is the magnitude of dierent value
pools in the UKs energy transition under a range of system scenarios?The
purpose was twofold. First to demonstrate how a value pool approach
can add to our understanding of the commercial opportunities in spe-
cic future energy scenarios. Second, using scenarios in this way would
bridge the gap between scenario modelling and investment decision-
making communities, by using a novel method to better evaluate
market sizes across multiple, uncertain futures. The approach and
outputs have application in both public and private sector strategic
decision-making. For private sector decision making the value pool
approach contributes to measuring industry attractiveness and can in-
form rm strategy and investment decision-making. For the public
sector, the value pool approach can be used to show where there is
likely to be substantial market pull[38] for energy innovations, i.e. in
large pools, robust across several scenarios; and where more substantial
innovation support maybe necessary due to weak market pull, i.e. in
small value pools or those particularly vulnerable to future scenarios.
3. Methods
3.1. Selecting scenarios
TheauthorsselectedtheUKtoinvestigateastheUKhasoneofthemost
detailed and diverse range of energy system scenarios [12].Thismethodis
replicable in any nation with a liberalised or liberalising energy market, and
which has a diversity of energy scenarios to draw on which reference
common criteria. Energy scenarios for the study were selected against four
common criteria. First, the scenarios had to extend to at least the year 2040.
Second, each scenario needed to be created by credible institution. Third,
each scenario had to include: predictions of nal demand, capacities of
installed generation, peak demand, electric vehicle penetration, population,
separation of commercial and residential demand, and clear assumptions on
household numbers. Finally, each scenario (bar one) had to be climate
compatiblei.e. compatible with the UKs 2050 target of reaching an 80%
reduction in CO
2
-emissions compared to 1990 levels set by the Climate
Change Act 2008 [46]. Scenarios were also required to have a broad range
of governance and control logics[47] to capture the political economies of
dierent possible futures. National GridsNo Progressionscenario was
selected as the non-climate compatible scenario because it has low levels of
green ambition and low prosperity, leading society to prioritise aordability
over environmental ambitions [48] (see Table 1).
Headline characteristics of the scenarios in all analysis years (2030,
2040, & 2050) are reported in the supplementary information
accompanying this paper [49].Table 2 shows the headline character-
istics for scenarios in the year 2050.
3.2. Dening the value pools
The next step was to dene the value pool narratives and data
sources. The value pools for calculation were selected based on [44].
Further work may wish to identify other value pools beyond those used
by [44], which have been adopted with limited amendments
1
for this
analysis.
To understand each value pool in the UK context, 16 elite semi-
structured interviews were undertaken. The interviews included:
3 × energy consultancies, 3 x utility companies, 2 × energy nance
providers, 2 × consumer bodies, 1 × ocer of the regulator, 2 × civil
servants, 2 × technology companies, and 1 × sector membership or-
ganisation. These interviews explored how each of the value pools
presented by [44] might be altered by UK market conditions. The in-
terviews were also used to test authorsassumptions on individual value
pools such as: discount rates, technology costs, replacement rates, op-
erational expenditures and potential future eciencies. The results
from the semi-structured interviews were combined with an analysis of
grey and academic literature to source key model assumptions.
Critically, there is no single business as usualreference case in the
value pool approach. All energy futures, including those with little
climate ambition, comprise dierent generation eets, demand proles,
electric vehicle penetrations, exibility requirements etc. The approach
aims to understand the new revenuesor cost savingsavailable in the
system as it evolves through time. They represent the size of possible
future energy opportunities, not cost increases or deviations from a
single base year or counterfactual reference case. Model inputs and
structure is summarised in Fig. 1:
In the methodological supplementary information provided, further
detail is given on model inputs and structure [49]. In the model in-
formation provided [50] data sources, assumptions, calculations, and
numerical results are presented for each constituent value pool. The
narrative for each value pool is set out below before model results are
presented.
4. Results
This section is structured in three parts. The rst section denes the
future market size in each scenario, then explains and quanties each
value pool in turn for each scenario and time step. The second section
presents the cumulative size of all value pools across scenarios. The
third section reports on sensitivity analysis.
4.1. Value pools and market size
It is useful to compare the magnitude of new value pools in the UKs
energy transition against the projected size of the whole electricity
market in each scenario. To analyse the full market size for each sce-
nario the method applied was to multiply the projected domestic/
commercial commodity price (p/kW h) against a decomposed annual
nal demand (TW h) for each scenario and decadal time step. This data
is provided only as indicative context and does not include electrical
standing charges for domestic or commercial customers.
Table 1
Selected Scenarios according to the criteria.
Author Name of the scenario
DECC 2050 calculator (2010/2011) High renewables, higher energy
eciency
Higher nuclear, less energy eciency
Higher CCS, more bioenergy
National grid (2016) Gone Green
No Progression
Realising energy transition pathways
(2008)
Market rules
Central coordination
Thousand owers
1
Three name changes to the value pools were conducted: Plant and Portfolio
Eciencywas renamed to Plant Eciencyand Energy Demand Reductionto
Energy Service Provisionsince the services covered in this pool do not all trigger de-
mand reductions, for example electric vehicle services. Further, Carbon Capture and
Usewas renamed to Carbon Capture and Storagesince the use of the carbon is con-
ceptualised by Accenture but no revenue streams are assumed. Additionally, an inter-
viewee mentioned that the demand for carbon for industrial or agricultural purposes is
limited in the UK, hence the more traditional concept of Carbon Capture and Storage
was applied.
M.-S. Wegner et al. Applied Energy 206 (2017) 815–828
817
Fig. 2 shows that new revenues and avoided costs combined are a
maximum of 31% and a minimum of 14% of the future market size
across the scenarios. The higher percentages are in the RTP Thousand
Flower scenario, followed by National Grid Gone Green. The higher
percentage values are in system scenarios most dierent from today in
terms of both eet composition, green ambitionand consumer beha-
viour. Electric vehicle uptake is the overwhelming driver of new rev-
enues. The scenarios with the highest capacities of centralised elec-
tricity generation plant (such as nuclear power or large fossil fuel
thermal plants) achieve the highest cost savings. National GridsNo
Progressionscenario has the lowest cumulative value pools and is
closest in composition to the system of today.
4.1.1. Value pool #1 plant and portfolio eciency
The average conversion eciencies of the UK thermal generation
eet are below the current optimum of 58% achievable by best avail-
able technologies in CCGT generation [51,52]. UK thermal eciencies
of gas, nuclear, and coal in 2015 were 48%, 35.6%, and 39.1%
Table 2
Scenarios 2050: overview of the scenario data.
NGrid no
progression
a
DECC 2050
higher RE,
more EE
DECC 2050
high nuclear,
less EE
DECC 2050
higher CCS, more
bioenergy
NGrid
gone
green
b
RTP
market
rules
RTP central co-
ordination
RTP thousand
ower
Electricity demand TW h 309 490 555 461 361 504 402 301
Power generation (incl.
import)
TW h 349 530 610 556 454 573 464 370
Conventional generation
capacity (excl. CCS)
GW 49 0 0 0 18 15 5 0
CCS equipped generation
Capacity
GW 0 13 2 40 11 46 32 23
Low-carbon generation
capacity
GW 42 121 97 49 119 104 90 84
Number of electric
vehicles
mln. 3.9 24.2 31.0 24.4 9.7 25.2 25.2 25.2
a
The national grid future energy scenarios 2016 cover the time horizon till 2040, hence a stable system for the time horizon till 2050 was assumed.
b
The national grid future energy scenarios 2016 cover the time horizon till 2040, hence a stable system for the time horizon till 2050 was assumed.
Fig. 1. Value pools identied and model process map.
M.-S. Wegner et al. Applied Energy 206 (2017) 815–828
818
respectively [52]. Existing value pool studies [44] include plant and
portfolio eciency as an important conventional value pool for uti-
lities, one which is particularly susceptible to carbon pricing in the
absence of nuclear. The portfolio eciency improvements used in this
study are shown in Table 3. Estimates were based on thermal eciency
potentials obtained from [51] and interview responses on realistic re-
placement rates and eciency gains from utility executives.
For value pool #1 the variables aected by thermal eciency im-
provements are the portfolio OPEX (in GBP/MW h), the thermal e-
ciency of the plants, and the emission factors of the plants. The sum-
mary equation for value pool #1 is:
=
avoided Cost System Operating Cost
System Operating Cost
No Efficiency Improvement
with Efficiency Improvement
The model showed that electric utilities can potentially avoid costs
between 172880 mGBP in 2030, 306990 mGBP in 2040, and
751809 mGBP in 2050 through Plant Eciency(Fig. 3).
The highest values arise in DECC 2050-Higher CCSand RTP-
Market Rulesscenarios with comparably large capacities of fossil
thermal generation to 2050. From 2030 to 2040 a steady growth of the
avoided cost is visible across the scenarios. The signicant increase of
the span in 2050 can be explained by a 25 GW addition of CCS capacity
in the DECC-Higher CCSscenario and a 10 GW reduction of gas power
plants in the DECC-High Nuclear scenario between 2040 and 2050.
Low minimum value represent decommissioning of centralised gen-
eration eet, while high values denote the opposite.
4.1.2. Value pool #2 energy service provision
Energy eciency increases and consequential energy demand re-
ductions are a fundamental part of sustainable energy futures [53,54].
The UKsnal demand curve between 2011 and 2015 has been rela-
tively at, nal electrical consumption was 302 TW h in 2015 [52].A
decreased demand for grid electricity is harmful to current utilities
which operate on a units-sold business model [55]. Across the analysed
energy scenarios there is substantial variation in demand predictions to
2050: nal demand remains relatively constant in the Thousand
Flowersand No ProgressionScenarios (301 TW h and 309 TW h in
2050 respectively); demand increases substantially to 555 TW h in the
DECC High Nuclearscenario (Table 2). Increases in demand are driven
by the electrication of heat and transport in the scenarios, while ef-
ciency gains and demand reductions are largely industrial, buildings
based, or assume appliance eciency gains. Servicing the electrica-
tion of transport, along with charging for energy retrot services, are
new revenue streams that utilities can access and could, to an extent,
compensate for the revenue losses caused by demand reductions else-
where. Heat electrication is not included as a new revenue stream as it
is likely to cannibalise other utility revenues, evidenced by the pre-
dominance of dual fuel utility customers in the UK [56]. Heat value
pool mapping however has the potential to inform further work.
New revenue streams in the value pool are:
0.0 20.0 40.0 60.0 80.0 100.0 120.0
NGRID No Progression
DECC 2050 - Higher RE, more EE
DECC 2050 - High Nuclear, less Energy Efficiency
DECC 2050 - Higher CCS, more Bioenergy
Ngrid - Gone Green
RTP - Market Rules
RTP - Central Coordination
RTP - Thousand Flower
bnGBP
New Revenues Avoided Cost Market Size
Fig. 2. Comparison of indicative market size
against new revenues and avoided costs in 2050.
Table 3
Assumed thermal eciency gain potential for value pool #1 (see [50]).
Plant type Natural gas Oil Coal Nuclear CCS coal CCS gas
Power plant (thermal) eciency 2015, before and till 2019 50% 30% 35% 36% 33% 50%
Power plant (thermal) eciency 203039 55% 30% 36% 36% 40% 54%
Power plant (thermal) eciency 204049 56% 30% 37% 36% 41% 56%
Power plant (thermal) eciency 2050 and onwards 57% 30% 37% 36% 43% 58%
0
500
1000
1500
2000
2030 2040 2050
mGBP 2015
Fig. 3. Value Pool #1 - spread of avoided cost across the scenarios.
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
2030 2040 2050
mGBP 2015
Fig. 4. Value Pool #2: new energy service provision revenues across scenarios.
M.-S. Wegner et al. Applied Energy 206 (2017) 815–828
819
- energy eciency measures/appliances: installation fee
2
- home energy management system (HEMS): installation and annual
maintenance fees
- electric vehicle (EV) services: charging fee and charging unit in-
stallation fee
The aim of the value pool calculation is to estimate the maximum
potential for the UK. An important assumption for this value pool is,
that by 2050 all existing households install energy eciency measures
and have a HEMS. This level of ambition is matched by recent work on
eciency retrot needs in the UK, from both industry [57] and aca-
demic analysis [58]. Further, all new-build households are assumed to
install a HEMS, see [50] for assumed fees.
The number of electric vehicle charging units installed was de-
termined by assuming a share of new electric vehicles to be supplied
with a home charger each year. Due to the currently low penetration of
electric vehicles, at rst a signicant amount of car owners would opt
for acquiring a charging unit, later an EV replaces another EV therefore
new charge point installations reduce a share of 60% in 2030 reducing
to 50% in 2050 is assumed. The summary equation for value pool #2 is:
=+
+
R
evenues from Energy Service Provision
Revenues from EE Installations HEMS Revenues
Electric Vehicle Service Revenues
The results show that across climate compatible scenarios new en-
ergy service revenues are between 2.87.3 bnGBP in 2030,
5.07.3 bnGBP in 2040 and 5.19.4 bnGBP in 2050 through VP Energy
Service Provision(Fig. 4). DECC 2050-High Nuclearand DECC 2050-
Higher REscenarios provide the highest values. The No Progression
values are signicantly lower than the other scenarios.
Electric vehicle services contribute 7895% of new revenues in this
value pool by 2050. National GridsNo Progressionscenario has low
electric vehicle penetration and has the lowest new revenue potential,
(Fig. 5). HEMS and EE installation services contribute 517% and 15%
respectively.
4.1.3. Value pool #3 local low-carbon generation
Decentralised generation, particularly microgeneration, across the
EU and other OECD nations is problematic for both utilities and infra-
structure operators because it undermines core revenues by reducing
nal demand [59]. While these revenue losses are dicult to avoid,
there are related value opportunities in the services surrounding micro-
generation technologies. Homeowners require system design, installa-
tion, and servicing. These all represent value propositions from which
utilities, with their related expertise and customer relationships, may be
well placed to capture [60].
The services considered in this value pool are: distributed genera-
tion lease service (solar PV), solar PV installation, O & M and decom-
missioning service, and local power brokerage or platform services
(see
3
). Households are assumed to sign up for one or a maximum of two
of the services oered as brokerage services and trading on a local
power platforms are mutually exclusive. Subsidy payments of any type
are not included in value pool 3 as they are assumed to accrue to the
metered user (see Table 4).
The summary equations for value pool #3 are:
×
R
evenues from Leasing Service
Number of Installation monthly Leasing Fee months per yea
r
12
=
+
+
R
evenues from Solar PV Roof Top Services
Revenues from Solar PV Roof Top Installations
Revenues from Solar PV Roof Top O M
Revenues from Solar PV Roof Top Decommissioning
&
R
evenues from Local Power Brokerage for Solar Home System Owne
r
Number of Installations monthly Subscription Fee
0
2000
4000
6000
8000
10000
050204020302
mGB 2015
Year
RTP-Thousand Flower RTP-Central Coordination
RTP-Market Rules Ngrid - Gone Green
DECC 2050 -High CCS, mor Bioenergy DECC 2050 High Nuclear, less Energy Efficiency
DECC 2020 - Higher RE, more EE Ngrid No Progression
Fig. 5. Value Pool #2 disaggregated by sce-
nario.
Table 4
Assumptions made for the value pool calculations.
Value assumed
Share solar PV roof-top-installations among the installed solar
PV capacity
a
50%
Installation fee of investment for roof-top solar PV system
b
15%
Management fee for O & M roof-top solar PV as share of the
total OPEX
70%
a
According the latest national statistics released on solar PV deployment about 32% of
the installed capacity is sized below 50 kW. However, due to the reduced UK subsidies
regime for new large-scale plants as well as the emphasis on increased customer parti-
cipation as well as distributed resources a share of 50% Roof-Top-Installations was
adopted.
b
Assumption made by accenture for calculations in 43.
2
For the installation fee a value of 15% of the avoided electricity cost over 10 years is
assumed.
3
https://www.openutility.com/.
M.-S. Wegner et al. Applied Energy 206 (2017) 815–828
820
=
evenues from Local Power Plat form for all domestic Customers
Number of House holds x monthly Subscription Fe e
The maximum size of the value pool is the sum of the revenues from
the distributed lease service as well as from the platform services for
domestic customers Figs. 69.
4
The revenues from all solar PV services have a minimum value of
zero, since the DECC 2050-High Nuclearand DECC 2050-Higher
CCSscenarios incorporate no distributed solar capacity.
Electric utilities may generate new revenues up to 2.3 bnGBP in
2030 to 2.8 bnGBP in 2050 through distributed energy lease services
(Fig. 6). From the solar PV services revenues up to 0.39 bnGBP in 2030
and 0.28 bnGBP in 2050 are available (Fig. 7). This decrease in the
overall revenues is due to market saturation.
From platform services provided to households owning solar sys-
tems, new revenues are up to 650 mGBP in 2030 to 780 mGBP in 2050
(Fig. 8).
The provision of power platform services can generate new revenues
in of 0.21.8 bnGBP in 2030, 0.11.9 bnGBP in 2040 and 0.052 bnGBP
in 2050 (Fig. 9). The minimum values of the span are set by the DECC
2050-High Nuclearscenario with minimal local generation. The
combined value pool for the two most substantial services (leasing and
power brokerage) are between 42 mGBP and 4.6 bnGBP by 2050 de-
pending on scenario.
4.1.4. Value pool #4 large scale low carbon generation
The cost of large-scale renewable energies decreased signicantly
over the past half-decade [61] posing a challenge to policy makers in
matching subsidy requirements against changing levelised costs of
electricity [62]. Continuous cost reductions are expected [63] and large
scale low carbon generation sources are becoming competitive with
new construction of conventional alternatives [64]. To make these in-
vestments competitive with the existing generation eet active in the
present market, they must be supported by either direct subsidy or their
conventional competitors made more expensive, often through carbon
pricing. In the UK the intention of the carbon price oor in electricity
market reform is for a rising cost of carbon to £30/tonne by 2020 and
£70/tonne by 2030 [62]. However since this carbon price mechanism is
currently subject to a freeze [65], the model uses the Committee on
Climate Changes expected UK market prices to 2050 [66]. This value
pool is dened as a cost saving which may be available to rms
choosing to build large-scale low carbon generation instead of con-
ventional gas power plants (excl. CCS). The large-scale, low-carbon
generation value pool includes: onshore and oshore wind, hydro,
biomass, nuclear and solar PV. This value pool represents the cost dif-
ference between constructing and running the large-scale, low-carbon
generation and constructing and running the same net capacity and
generating the same amount of electricity via gas CCGT power plants.
Here a positive value denotes a real saving while a negative value de-
notes extra cost, i.e. value pools can identify negative cases for in-
vestment.
The most signicant cost dierence between most low-carbon
generation and thermal power plants is between CAPEX and OPEX re-
quirements. Subsidies will play a part in future value pools, however,
the level of subsidy per technology is volatile, and dierent technolo-
gies are in or out of favour dependent on political commitments [67].
Thus, low carbon energy subsidy is not included in the value pool
0
500
1000
1500
2000
2500
3000
2030 2040 2050
mGBP 2015
Fig. 6. Value Pool #3: local low-carbon electricity distributed energy lease service
new revenue.
0
500
1000
1500
2000
2500
3000
2030 2040 2050
mGBP 2015
Fig. 7. Value Pool #3: local low-carbon electricity solar PV services new revenue.
0
500
1000
1500
2000
2500
3000
2030 2040 2050
mGBP 2015
Fig. 8. Value Pool #3: local low-carbon electricity platform service/solar PV new
revenue.
0
500
1000
1500
2000
2500
3000
2030 2040 2050
mGBP 2015
Fig. 9. Value Pool #3: local low-carbon electricity platform service all domestic gen-
eration new revenue.
4
The model further veries if all households can be supplied by local decentralised
sources (solar PV, onshore wind, biomass, CHP) by comparing annual domestic demand
with annual distributed low-carbon generation. Should this not be the case, then the
number of subscribers for the platform will be limited to the number of households that
can be supplied with local power based on the average household demand.
M.-S. Wegner et al. Applied Energy 206 (2017) 815–828
821
calculation. The cost of carbon, however, is included. The rationale for
this is that RE subsidies uctuate annually, but the UKs carbon price
oor gives some certainty that a common rate can be applied across all
relevant power sector technologies. Future work may include subsidy as
part of this value pool. The summary equations for value pool #4 are
thus:
=+ + +Annual Cost per Technology LCOI OPEX OPEX CO Cos
t
fix variable 2
=
−−
Cost avoided Annual Cost all Low Carbon Technologies
Annual Cost Gas Power Annual Cost Coal Powe
r
LCOIis the levelised cost of investment. LCOI was used in place of
the traditional Levelised Cost of Electricity (LCOE) approach. The LCOE
method adds the initial investment as well as the discounted operation
expenditures over the lifetime and divides the sum by the discounted
power generation over the lifetime. The result is the cost of electricity
levelised over the expected operational lifetime. However, the installed
capacities and the load factors of plants uctuate signicantly between
the scenarios. Therefore, the authors chose to exclude the annualised
operational expenditures from the levelised cost approach and instead
calculated the levelised cost of investment (LCOI) per technology and
scenario year, see [49,50].
The results show that the large-scale low-carbon capacity does not
generate cost savings in all scenarios across the rst two periods under
consideration (Fig. 10).
Six of the eight scenarios display negative values, hence additional
cost through the deployment of large-scale low carbon generation in
2030. Only National Grid No Progression and DECC High RE display
positive values in 2030 due to low and cost optimal deployment of RE
respectively. These results are expected due to the higher cost of many
low carbon generation technologies coupled with the fact that the
model used the Committee on Climate Changes expected carbon prices
which are below their target consistent values. It therefore falls to
direct subsidy to support various forms of low carbon energy in the
absence of a sucient market based price signal [17], or an increase in
the carbon price within the price oor mechanism towards target
consisted values. In 2040, the scenarios with positive values are Na-
tional Grid No Progression, and DECC 2050 Higher RE More EEand
DECC 2050 - Higher CCS, more Bioenergy. This is largely driven by
these scenarios deploying lowest cost low-carbon generation rst. In
2050 the plant operators can achieve savings through the deployed
large-scale low-carbon capacity across all scenarios. The potential cost
savings are in the order of 0.45 to 2.9 bnGBP in 2030, 3.6 to
3 bnGBP in 2040 and 0.618.1 bnGBP in 2050. The authorsap-
proach demonstrates that cost reductions, subsidy support and carbon
pricing continue to be necessary for the deployment of large scale low
carbon generation technologies across all scenarios until 2050 where a
sucient carbon price renders the value pool positive, i.e. free of direct
subsidy in all scenarios. This underlines how important eective carbon
pricing is to support large scale low carbon generation.
4.1.5. Value pool #5 exibility optimisation
Flexibility services can serve a range of dierent purposes by either
providing exibility to the wholesale market, to a portfolio (supplier
and generator), or balancing services to balance demand and supply
and to ensure the security and quality of electricity supply across the
transmission system [68]. The value pool considers the cost avoided
through demand side response (DSR) moving consumption to o-peak
periods, as well as the revenue potential for batteries and DSR by the
provision of frequency response, reserve capacity provision, triad
avoidance, and energy price arbitration as applicable. Values for the
remuneration of battery storage and DSR, as displayed in Table 5 were
adopted for the calculation.
5
Individual value sources and assumptions
are available in the methods narrative and published model [49,50].
Key sources used were [6971]. Battery storage is used as the single
storage technology due to data availability and recent market pene-
tration, future work should consider the eect of other storage tech-
nologies on this value pool, such as ywheels, and compressed air.
The value pool calculates the theoretical available potential in the
market for new revenues and avoided costs. The theoretically shiftable
load is modelled by dividing the load incurred by domestic (incl. EV)
-6.00
-4.00
-2.00
0.00
2.00
4.00
6.00
8.00
10.00
bnGBP 2015
2030 2040 2050
Fig. 10. Value pool #4 large scale low-carbon generation cost savings
across scenarios.
Table 5
Overview on the assumed remunerations for batteries and DSR per revenue stream.
Remuneration Unit Value
Batteries Frequency response utilisation, battery GBP/MW h 1.25
Frequency response availability, battery GBP/MW/a 81,600
Reserve provision utilisation, battery GBP/MW h 130
Reserve provision availability, battery GBP/MW/a 7650
Embedded benets triad avoidance
a
GBP/MW 50,000
Energy price arbitrage GBP/MW h 30
DSR Reserve provision GBP/MW/a 50,000
Frequency response GBP/MW/a 50,000
Average wholesale electricity price spread GBP/MW h 30
a
Every year there are three Triads during which the battery can discharge to earn
revenues. According to the Investment Template for Batteries from Smarter Network
Storage (UK Power Networks, 2013) a number of two successful discharges per year was
assumed.
5
Assumption: the value of balancing services remains constant during the review
period due to sucient exible capacity to eectively provide competition in the ex-
ibility market, thus keeping remuneration stable.
M.-S. Wegner et al. Applied Energy 206 (2017) 815–828
822
and non-domestic customers into shiftable and non-shiftable load, as-
sumption values follow [72,73] (see Table 6).
As only a proportion of the theoretical maximum shiftable load is
likely to be realised as achieved load shift, the nal achieved load shift
was varied between pathways based on the level of consumer engage-
ment pre-supposed by each qualitative pathways narrative; see [49,50].
Variations in summer over winter shiftable loads were conducted
thus:
××
Shifted Load Annual Electricity Demand
Achieved Shift of Load Seasonality Facto
r
( /(365 24) )
(%)
Further the avoided cost was calculated by assuming load shifting
occurred for 5 peak-hours per day (e.g. 68 am and 58 pm):
6
×
×
×
×
avoided Wholesale Cost Shifted Load hrs days
Average Electricity Price Spread
Shifted Load hrs days
Average Electricity Price Spread
5 365/2
5 365/2
Summer
Winter
The summary equations applied for value pool #5 were:
=+new Revenues Revenues from Battery Operation Revenues from DS
R
=avoided Cost avoided Whole sale Cost from DS
R
Through the operation of battery storage technologies new revenue
streams in the range of 46565 mGBP in 2030, and 461040 mGBP in
2040 & 2050 can be accessed in the power and balancing market
(Fig. 11).
Though the market can provide revenue streams for the provision of
exibility of battery storage, the level of annual remuneration as found
in the literature and applied to the model is below the required level to
create a viable investment at the current cost of the technology. Across
the scenario suite the average annual remuneration is between 160 and
175 GBP
2015
/kW, while the IEA show battery costs above these po-
tential remunerations until at least 2025 [61] albeit conceding that
learning rates in battery technologies are highly uncertain [15]. This
underlines the need for direct subsidy in storage as it is a key system
enabler, but does not benet directly from higher carbon pricing.
Power rms can potentially can generate new revenues from DSR in
the balancing market between 160390 mGBP in 2030, 190550 mGBP
in 2040 and 210610 mGBP in 2050 (Fig. 12). While at the same time
DSR can avoid wholesale cost in the order of 115270 mGBP in 2030,
140375 mGBP in 2040 and 150410 mGBP in 2050 (Fig. 13). Com-
pared to other services across the dierent value pools DSR provides
small revenue and cost reduction volumes in all scenarios apart from
National GridsGone Greenscenario, in which exibility optimisation
is comparable in magnitude to Value Pool #3 (local low carbon gen-
eration) in other scenarios.
4.1.6. Value pool #6 carbon capture and storage
A possible option for continuous operation of dispatchable and
Table 6
Assumptions of share of shiftable load between domestic and I &C consumers.
Demand component Share of shiftable
load
Domestic (incl. EV) Heat 100%
Lighting & appliances 50%
Industry & commercial (I & C) Industrial demand 16%
Commercial heat 100%
Commercial
light & appliances
65%
0
200
400
600
800
1000
1200
2030 2040 2050
mGBP 2015
Fig. 11. Value Pool 5: new revenues from battery storage services.
0
100
200
300
400
500
600
700
2030 2040 2050
mGBP 2015
Fig. 12. Value Pool 5: new revenues from exibility optimisation.
0
50
100
150
200
250
300
350
400
450
2030 2040 2050
mGBP 2015
Fig. 13. Value Pool 5: avoided costs from demand side response exibility.
6
Seasonality Factor: Due to the lack of applicable literature values a factor of 0.8 for
summer and a factor of 1.2 for winter were assumed.
M.-S. Wegner et al. Applied Energy 206 (2017) 815–828
823
large-scale, low-carbon electricity provision is the retrotting and new
build of fossil fuel power plants with carbon capture equipment. The
carbon emissions released during the electricity generation process are
captured and stored in geological storage facilities [74]. Though esti-
mates vary, due to parasitic load and increased capital cost the power
production in CCS equipped plants is more expensive than in unabated
plants [75]. However, utilised sources assume emissions are reduced by
around 90% [76], though note this is contested [77], therefore the
incurred carbon costs are reduced and emissions are lowered towards
achieving the 2050 target. However, the installation of capture plants
will only reach commercial viability when the carbon costs saved out-
weigh the cost increases associated with the capture plant (notwith-
standing non-energy payments / direct subsidy). The objective of this
value pool is to evaluate the cost incurred by CCS equipped coal and gas
power plants compared to the costs of equivalent unabated generation.
The summary calculation for value pool #6 is therefore:
=+ + +Annual Cost per Technology LCOI OPEX OPEX CO Cos
t
fix variable 2
=
Cost avoided Annual Cost CCS Technologies
Annual Cost unabated Technologies
Across the scenarios selected the model shows CCS value pools are
between 997 mGBP and 72 mGBP in 2030, 739 mGBP and
372 mBGP in 2040, and 14 mGBP to 1669 mGBP in 2050, sum-
marised in Fig. 14. The costs are avoided through the equipment of
fossil power plants with CCS systems and avoiding carbon prices. The
no progressionscenario does not include any CCS power plants, ac-
cordingly the value pool is zero. The value pool is increasing over the
review period due to increasing power specic carbon prices and im-
provement of the CCS technology leading to a higher eciency and
reduced CAPEX/OPEX. The span of the value pool increases over time
due to the signicantly dierent level of CCS capacity across the sce-
narios - Fig. 14.
4.2. Cumulative value pools across scenarios
Returning to the specic research question: what is the magnitude
of dierent value pools in the UKs energy transition under a range of
system scenarios?Across the surveyed scenarios, the potential new
revenues in the UK energy system are up to £12.8bn per year in 2050
(Fig. 15). The cost savings potential is up to £9.7bn per year in 2050
(Fig. 16). New revenues and cost savings are present to greater or lesser
degrees across all scenarios analysed; there is no single base or re-
ference case. The following results demonstrate the relative volatility/
stability and magnitude of each value pool across the eight system fu-
tures.
Against the No Progressionscenario, each climate compatible
scenario presents substantial new revenue pools. For avoided costs only
the DECC High RE more EE,High CCS more Bioenergy, and RTP
market rulesscenarios present substantially higher cost savings than
the National Grid No Progressionscenario. The reason for those three
scenarios achieving substantially higher savings is due to the compo-
sition of electricity generation eets. While the DECC High RE has a
large wind eet which results in a large VP#4 the DECC High CCS has a
very large CCS +Wind eets which results in similarly large savings.
4.3. Sensitivity analysis
In addition to future system characteristics, the estimated size of the
value pools is aected by cost inputs. To evaluate sensitivity to cost
inputs, carbon pricing, fuel pricing, capital costs and discount rates
were individually varied by 50% to +50% sensitivity. The results
show that new revenue pools are less sensitive to cost input than the
avoided cost pools. Thus, the availability and accessibility of these
value pools is sensitive to the carbon price, and cost of capital as well as
the investment and operational cost for the generation technologies.
Following Newberry [17], the authors specically focussed on the fuel
and carbon price relations. Fig. 17 highlights that, by 2050, small
variations in the carbon price can dene if low carbon technology eet
can become cost competitive. In the National Grid Gone Green Scenario
a carbon price of just 30% below the assumed value of
65.75 GBP
2015
/t
CO2
in 2050 can turn avoided costs into extra costs. The
eect on VP4, large scale low carbon generation, showed that by 2050
carbon price sensitivity has more eect than fuel prices (Fig. 18),
though fuel price sensitivities have more impact in earlier years, when
the carbon price is lower.
For Value Pool #6, six out of the seven climate compatible scenarios
render CCS a positive value pool (i.e. commercially attractive) by 2050.
A 20% reduction in the carbon price would mean only three of the
seven climate compatible scenarios render CCS as a cost saving value
pool by 2050 (Fig. 19).
5. Discussion
These results show substantial variability across the six value pools
identied in the UK electricity sector to 2050. There are ve main
points to draw out in discussion. The rst is the importance of the
carbon price. Without direct subsidy the main driver for rms to con-
struct large scale low carbon generation (VP#4) are the cost dier-
entials between these and conventional technologies. Carbon pricing
and fuel price increase are the two factors likely to convert large scale
low carbon generation from an extra cost to an avoided cost and hence
an attractive rm investment. Across the scenarios analysed the carbon
price only reaches a suciently high value to render these value pools
positive in 2050. These points support previous analysis [17] in
-1500
-1000
-500
0
500
1000
1500
2000
2030 2040 2050
mGBP 2015
Fig. 14. Value Pool #6: CCS avoided cost across scenarios.
M.-S. Wegner et al. Applied Energy 206 (2017) 815–828
824
suggesting that linked long term subsidy contracts of low carbon gen-
eration alongside carbon pricing will continue to be necessary to deliver
required levels of large scale low carbon generation. Fossil fuel price
volatility (particularly gas price) also aects this value pool, however,
unlike carbon prices and subsidy levels, fossil fuel prices are not under
the control of national governments. The authors add to this analysis by
quantifying value pools across multiple scenarios to show how volatile
the CCS and large low carbon generation markets are. They are both
sensitive to the above variables and the scenario itself. This means that
for utility rms seeking attractive markets, the CCS market in particular
would present substantial risk and is likely to be selected as a rm
strategy by very few actors. This may have the eect of reducing the
competition to enter the industry due to high risks of asset stranding.
The second point is that the energy service provision value pool is
robust across all scenarios, and the dominant driver of new revenue is
the electric vehicle charging element. Across all climate compatible
scenarios there is a substantial commercial opportunity available in
electric vehicle service provision. Indeed electric vehicle services are
the single biggest element of new revenues available across all assessed
future energy scenarios including no-progression. In contrast to the
more volatile value pools (i.e. VP#6 CCS), energy rms may see the
energy services for EVs as a more attractive option, particularly because
it is an asset lightstrategy dependent on branding and tariinnova-
tion as opposed to incurring large CAPEX sunk costs [78].
The third point is that ve of the six value pools analysed become
either large value pools or are destroyed entirely depending on the
energy system scenario. VP#3, local low carbon electricity, is robust
across all scenarios tested (including no progression), apart from DECC
High Nuclear less energy eciency, where it is destroyed. This suggests
rm strategy formed to pursue this value pool would be a low risk in
0
2
4
6
8
10
12
14
RTP -
Thousand
Flower
DECC 2050 -
Higher RE,
more EE
Ngrid - Gone
Green
DECC 2050 -
High Nuclear,
less Energy
Eĸciency
RTP - Market
Rules
RTP - Central
CoordinaƟon
DECC 2050 -
Higher CCS,
more
Bioenergy
NGRID No
Progression
bn GBP 2015
Energy Service Provision Local Low-Carbon Electricity Fledžibility KƉƟmisaƟon
Fig. 15. Cumulative new revenues across system futures in 2050 by value pool.
0
2
4
6
8
10
12
DECC 2050 -
Higher RE,
more EE
DECC 2050 -
Higher CCS,
more
Bioenergy
RTP - Market
Rules
Ngrid - Gone
Green
RTP - Central
Coordination
DECC 2050 -
High Nuclear,
less Energy
Efficiency
RTP -
Thousand
Flower
NGRID No
Progression
bn GBP 2015
Plant Efficiency Large-Scale Low-Carbon Electricity Flexibility Optimisation Carbon Capture and Storage
Fig. 16. Cumulative avoided costs across system futures in 2050 by value pool.
0.67
6.0
1.6
0.61
-1.8
-0.7
0.64
2.1
0.5
-3
-2
-1
0
1
2
3
4
5
6
7
Plant Efficiency Large-Scale Low
Carbon
Generation
CCS
BnGBP
Fig. 17. Carbon price sensitivity: changes to avoided cost value pools through variation
of the carbon price by ±50% in the NG gone green scenario in 2050.
M.-S. Wegner et al. Applied Energy 206 (2017) 815–828
825
terms of complete asset stranding. In contrast, VP# 5 exibility opti-
misation, and VP #6 CCS, are scenario sensitive and hence uncertain.
The fourth point is that the value pool method is compatible with
the study objective, to understand how potential prot structures and
market sizes over multiple future energy scenarios can be analysed. The
identication and application of the value pool method across multiple
system pathways enables assessment of industrial attractivenessacross
uncertain futures. This is important for energy transitions in liberalised
nations because the attractivenessof industries such as energy service
provision and exibility will determine the risk perception of those
sectors, the future levels of competition within them, and therefore the
willingness of rms to allocate strategic resources to exploiting them.
For example, the data above suggests that electric vehicle service pro-
vision is a robust value pool at low risk across multiple futures. The
opposite is true for exibility optimisation. All things being equal, the
market pull for smart electric vehicle service would be much stronger
than that for Demand Side Response or CCS, owing to the likely level of
competition which will be created to acquire these new revenues. This
demonstrates how the value pool method can bridge the gap between
scenario modelling and investment decision-making under uncertainty,
by using scenario outputs to determine potential market sizes across
uncertain futures.
Returning to the resource based view of the rm and evolutionary
rm theory, this would likely result in high competition and low prot
margins in electric vehicle service provision, due to the relatively low
costs of developing compatible nancial, physical, human, organisa-
tional, informational and relational resources. These costs are low be-
cause servicing the electric vehicle value pool requires only tariin-
novation, some smart metering, and taribranding [78]. The
combination of low barriers to entry (for incumbents) and a robust,
sizeable value pool, suggests adapting utility business models to capture
this revenue would be an attractive option. This may lead to strong
market pulland suggests innovation policy in this space need focus
less on nancial incentive, and more on securing an enabling regulatory
environment. In contrast, one might expect low competition (or high
rm failure rates) and higher relative prots for grid connected battery
rms and CCS utilities. This is because it is a high risk strategy to ac-
quire the nancial, human, and other resources needed capture a new
value pool that is so sensitive to both carbon pricing and future sce-
narios. This suggests low market pull, and may warrant innovation
support including capital grants/subsidy. The exibility optimisation
value pool has low barriers to entry, however, the volatility of the
sector may result in lower competition and higher prot margins for
successful companies. Alternatively, exibility companies may fail at
increasing rates and render innovation support a critical component of
energy policy.
The fth and nal point is that these data and the evolutionary,
resource based view of the rm oer new and productive avenues of
research that can link the energy systems modelling community to a
more grounded and empirically realistic rm theory. This adds a new
dimension to research on energy systems that can forge common
ground between neo-classical models and more heterodox approaches
[79]. Recent contributions [13,14] demonstrate that there are oppor-
tunities to deviate from perfect rationality within systems modelling
and specically demonstrate the ability of system models to vary capital
costs/hurdle rates based on qualitative criteria [30]. An evolutionary
6.0
5.2
4.2
-1.8
-1.0
0.0
2.1 2.1 2.1
-3
-2
-1
0
1
2
3
4
5
6
7
Carbon Price Gas and Coal
Price
Gas Price
BnGBP
Fig. 18. Changes to VP#4 large-scale low carbon generation by varying carbon and fuel
prices by ± 50% in the NG gone green scenario in 2050.
-2000
-1000
0
1000
2000
mGBP 2015
CPR = 20% Carbon price reduction on model assumption
Business-as-usual: NGRID No Progression DECC 2050 - Higher RE, more EE
DECC 2050 - High Nuclear, less Energy Efficiency DECC 2050 - Higher CCS, more Bioenergy
Ngrid - Gone Green RTP - Market Rules
RTP - Central Coordination RTP - Thousand Flower
CPR DECC 2050 - Higher RE, more EE CPR DECC 2050 - High Nuclear, less Energy Efficiency
CPR DECC 2050 - Higher CCS, more Bioenergy CPR Ngrid - Gone Green
CPR RTP - Market Rules CPR RTP - Central Coordination
Fig. 19. The eect of a 20% reduction in carbon pricing on VP#6: CCS across scenarios.
M.-S. Wegner et al. Applied Energy 206 (2017) 815–828
826
resource advantage theory approach with a value pool method may be
able to oer more realistic assumptions of rm dynamics, based on a
more quantitative appreciation of future industry attractiveness and
systemic market risks.
6. Conclusion
This research has demonstrated that the value pool approach, when
combined with multiple future energy scenarios, is a valuable and in-
sightful methodology to conduct a commercially focussed assessment of
energy transitions. Cost optimisation or near optimisation models re-
porting in total investment costs are useful for energy and climate
change policy making. Using the output of such models to investigate
the creation and destruction of new values in energy transitions pro-
vides additional insight that could inform rm strategies, investor de-
cision-making tools, and innovation policy. As contemporary energy
systems in many developed nations comprise private rms seeking
prots in competitive markets, this approach provides new insights
applicable in a wide range of international and trans-national contexts,
i.e. nations with liberalised or liberalising energy markets.
This analysis took the output of quantitative systems models to in-
vestigate how dierent value opportunities are created, re-scaled and
destroyed by dierent energy futures. To address the rational actor
and perfect foresightissues of current models, this value pool ap-
proach could be linked to provide inputs into rm behaviour, speci-
cally by determining the levels of competition likely for each new op-
portunity created by energy transitions. A further extension of this work
would be to investigate the business model innovations necessary to
render future value propositions into revenue streams and ultimately
rm prots.
Acknowledgements
This work was supported by the Engineering and Physical Sciences
Research Council under grant Ref: EP/N029488/1 and the Economic
and Social Research Council under grant Ref: ES/M500562/1. Financial
support was also received by the Energy Research Partnership. No
funders inuenced study design or analysis.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, in the
online version, at http://dx.doi.org/10.1016/j.apenergy.2017.08.200.
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