Improved representation of investment decisions in the German energy supply sector: An optimization approach using the TIMES model

Energy Strategy Reviews 26 (2019) 100421

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Improved representation of investment decisions in the German energy supply sector: An optimization approach using the TIMES model Ali Tash *, Mohammad Ahanchian, Ulrich Fahl Institute of Energy Economics and Rational Energy Use, University of Stuttgart, Heßbrühlstr. 49a, 70565, Stuttgart, Germany

A R T I C L E I N F O

A B S T R A C T

Keywords: TIMES model generator Energy system optimization Supply sector Actor heterogeneity Investment decisions

The German energy supply sector is becoming diverse and dispersed due to the variety of actors investing in energy generation technologies and spatially variable renewable resources, which bring about increased het­ erogeneity in the investment decisions of actors. Therefore, the effective utilization of renewable resources to­ wards a cost-optimal achievement of the “Energy Transition” goals is becoming more complex. We argue that addressing these complexities requires a method, which, in addition to a fine technological and regional char­ acterization, takes into account the heterogeneity of the investment decisions of actors while optimizing the total system. This paper describes methodological improvements via the well-known energy system optimization model generator called The Integrated MARKAL-EFOM System (i.e. TIMES), which enhances the representation of the actors’ investment realities regarding wind and photovoltaic technologies applied to the case of the German supply sector. Firstly, the actors are disaggregated by their main economic features, including cost of capital, representing their different investment valuations and budget restrictions. Then, Germany is divided into four regions to reflect the spatially variable renewable resources and electricity demand affecting actors’ optimal decisions. Lastly, the grid development costs and losses are considered, especially for power transmission across the regions. The newly developed TIMES Actors Model (TAM) incorporating these improvements is then tested to separately study the impact of CO2 taxes as a policy instrument and a national renewable quota as a target for the sector. The results showed that CO2 taxes and renewable targets affect the system quite differently, specifically regarding the optimal role that actors are expected to play within regions to meet the objectives of energy transitions at least system costs as well as regarding the power transmission between the regions. By means of these findings, actors can be targeted more properly by actor- and region-specific policy instruments demon­ strating which actor should invest where and into which technology, so that the energy transition can take place more quickly and at lower system costs. A comparison of the improved versus original versions of the model reveals the potential contribution of improving the representation of actors that have been so far overlooked in the energy system modelling practice.

1. Introduction The German energy supply sector is increasingly becoming diverse and dispersed through the variable spatial distribution of renewable energy resources and the diversity of actors investing in various power and heat generation technologies. On the one hand, these factors facil­ itate the active participation of different types of investors including citizens in the supply sector as well as the effective utilization of renewable resources towards achieving the ambitious goals of the

German Energy Transition1, such as an 80% share of renewables in gross electricity consumption by 2050 [1]. On the other hand, generation capacity planning as well as designing policy instruments and incentives in order to cost-effectively and reliably realize the Energy Transition targets is quite challenging, particularly for such a complex system consisting of diverse actors located in different environments. Fig. 1 demonstrates the ownership share of different investors for conventional and renewable power generation technologies in 2016. As can be seen in the figure, 76% of conventional generators as well as 17% of renewable

* Corresponding author. E-mail addresses: [email protected] (A. Tash), [email protected] (M. Ahanchian), [email protected] (U. Fahl). 1 Widely known as Energiewende. https://doi.org/10.1016/j.esr.2019.100421 Received 22 December 2018; Received in revised form 7 August 2019; Accepted 8 October 2019 Available online 22 October 2019 2211-467X/© 2019 The Authors. Published by Elsevier Ltd. This is an open (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Energy Strategy Reviews 26 (2019) 100421

Switzerland respectively using TIMES models. However, these studies do not consider regional differences and actor diversity within this sector. Nonetheless, Ref. [25] introduced different hurdle rates through scenario analysis in order to model the effect of investors risk perception for new investments in the energy system. However, these hurdle rates are technology-specific and do not represent the differences of investors risk perception as well as return expectations reflected in their specific cost of capital5. Moreover, in Ref. [27] it is discussed and shown extensively on a European level how grid expansion costs can change the power generation technology portfolio in the future and how costly grid expansion could be, while Germany is seen as a single node with no internal regional differences, emphasizing the need for considering grid issues in the German supply sector. Typical optimization approaches within the supply sector usually disregard actor diversity and local energy system differences and re­ strictions. These approaches assume a homogeneous supply sector consisting of one “average actor” representing various actor groups, which has unlimited access to all existing technologies. This average actor is assumed to be located in an “average environment” with free access to all available (renewable) resources throughout the system (e.g. a country) and can deliver its generated power or heat to meet demands located anywhere, thus failing to represent the heterogeneous economic and geographic reality of suppliers as well as consumers and might lead to inaccurate policy advice. This paper provides a methodological approach to improve the representation of actor diversity in the German supply sector with a focus on new investments in wind (onshore þ offshore) and PV (rooftop þ utility scale) technologies by citizens, institutional investors and utilities as well as taking into account the regional differences and grid aspects in order to more realistically capture the heterogeneity of in­ vestment decisions. The objective of this paper is to obtain the costoptimal configuration of power generation technologies according to the heterogeneous economic characteristics of diverse investor groups while behaving completely rationally and with perfect foresight. Cost optimality is examined from the system point of view, meaning that the investment decisions of actors serve the system’s optimum.

Fig. 1. Ownership share of investor groups for conventional (left) [5] and renewable (right) [4] power generation capacities in Germany in 2016.

capacities are owned by the big 42 and municipal utilities. However, as much as 83% of renewable generators are owned by new investor groups such as citizens and institutional investors3, emphasizing the relevance of considering actor (i.e. investor) diversity in analyzing the optimized future configuration of the German supply sector. Thus, this paper seeks to discover “which actor group should invest into which generation tech­ nology and where in Germany so that the supply sector is steered towards achieving the targets of the energy transition at least system costs.” In order to ensure that the ambitious goals of the energy transition and climate targets are met in a cost effective and reliable manner, numerous energy system models have been developed to provide an indepth analysis of the optimized future structure of the energy system by representing the environmental, economic and technological di­ mensions of the integrated energy system [6–9]. A considerable number of energy system models have been developed using the TIMES (The Integrated MARKAL-EFOM System) model generator4 representing the energy system from a global (e.g. TIAM [10]) to continental (e.g. PanEU [11], JRC-EU-TIMES [12]), national (e.g. TIMES-DK [13], TIMES-Norway [14], UK TIMES [15], etc.) and local (e.g. CA-TIMES [8]) levels to identify the least-cost pathways of the energy system in focus. However, most energy system models are comprised of a few average actors representing large groups of decision-makers without incorpo­ rating their heterogeneous economic reality. Several researchers have recently attempted to improve the representation of consumers’ het­ erogeneity and endogenize the choice of service demand technologies to avoid the so-called “winner-takes-all” effect that occurs quite often in such energy optimization models. For instance, in Refs. [16,17] the authors improved the representation of consumers’ diversity and introduced competition across different modes of transport to endogenize modal choice implemented in the TIMES-DK model. In addition to that, Refs. [18,19] introduced consumer diversity based on economic limitations (e.g. budget restrictions for new investments) in the residential sector to investigate different preferences regarding de­ mand technologies carried out within UK TIMES and TIMES-Households models using surveys conducted in the UK and France respectively. Moreover, only a few national models represent the regional differ­ ences within the energy system of a single country like TIMES-Canada [20], the US FACETS [21] and TIMES-DK [13]. These bottom-up tech­ nology-rich models with clear sectoral segregation and a fine level of temporal and spatial resolution provide valuable and profound insight into the energy system under various scenario analyses. Furthermore, there is a substantial number of optimization models focused on a na­ tional energy supply sector, especially power generation. For instance, Refs. [22–24] investigate decarbonization pathways and the need for new investments in transmission capacities in South Korea, Portugal and

2. Methodology and data TIMES is a technology rich, bottom-up model generator, which uses linear-programming to produce a least-cost energy system, optimized according to a number of limited resource potentials under some envi­ ronmental policies across medium to long time horizons [28]. The end-use energy demands should be fulfilled across the modelling time horizon in all regions using a range of technologies utilizing renewable and non-renewable resources at least system costs subject to constraints that specify physical, technical and policy restrictions. Therefore, TIMES models typically compute a total net present value of the stream of annual costs for each region, discounted to a user selected reference year according to Eq. (1). These regional discounted costs are then aggre­ gated into a single total cost, which constitutes the objective function to be minimized by the model. The model optimizes the entire system with a perfect foresight approach, meaning that the whole objective function, consisting of all discounted annual costs across the modelling horizon, is optimized at once. Total discounted system costs ¼ XR X �REFYR y 1 þ dr;y � ANNCOSTðr; yÞ r¼1

(1)

y2YEARS

2

E.ON, RWE, EnBW and Vattenfall. According to Refs. [2–4] institutional investors are financially-oriented in­ stitutions such as funds and insurances actively looking for portfolio diversifi­ cation by investment in energy projects for the payments of their long-term liabilities. 4 TIMES is developed and maintained by the Energy Technology Systems Analysis Program (ETSAP), a Technology Collaboration Programme of the IEA [10]. 3

where:

5 The cost of capital is the return a company must promise in order to get capital from the market, either by debt or equity [26].

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Energy Strategy Reviews 26 (2019) 100421

R is the set of regions in the area of study; YEARS is the set of years in the horizon for which there are costs, plus past years (before the initial period) if costs have been defined for past investments, plus a number of years after the horizon where some investment and dismantling costs are still being incurred; dr;y is the global discount rate; REFYR is the reference year for discounting and ANNCOST(r,y) is the total annual cost in region r and year y.

represent actor diversity within TAM, the actors are disaggregated to utilities, institutional investors and citizens including individuals and energy cooperatives. These actors are further characterized by their different cost of capital (hurdle rate), and their budget restrictions for investments in new energy generation technologies in different regions. TAM uses these hurdle rates to discount future cash flows, including costs and reimbursements, back to the user-defined reference year. This disaggregation is adopted based on the extensive investigations of power generation ownership in Germany by Refs. [2,4]. The estimations of the hurdle rate for each of these actor categories are taken from Ref. [3], reflecting the actors’ investment expectations. The TIMES model provides the opportunity to set different hurdle rates for any technology using technology-specific discount rates. The technology-specific discount rate replaces the global discount rate in the TIMES objective function only for the cash flows of that specific tech­ nology. This is elaborated further in Eq. (2).

The newly developed TIMES Actor Model (TAM) is a technologyoriented, linear optimization TIMES model that represents the entire energy system from primary energy supply, through energy conversion to residential, agricultural, commercial, industrial and transport end-use sectors deploying the general structure of the TIMES-PanEU [29] in Germany. This study presents methodological improvements to better represent the reality of actors’ rational investment decisions for power generation towards engagement in the German energy transition within the supply sector. In addition to the minimization of all discounted en­ ergy system costs across the modelling horizon (i.e. 2013–2060), TAM demonstrates the following improvements in the supply sector: disag­ gregation of actors investing in different wind and PV technologies, regional division and introducing grid aspects. Fig. 2 shows the supply sector structure of TAM indicating these improvements described in the following subsections. TAM relies on the data from Eurostat and the German Federal Sta­ tistical Office as well as other German data sources. Since the focus of this paper is the energy supply sector, other energy sectors are consid­ ered using exogenous regional demand curves from a demand prognosis study [30] and are also explained further in the Appendix at the end of this paper. The data on the available domestic resources for fossil fuels are given to the model from Ref. [31]. The supply sector in this model consists of extraction, fuel processing and transportation technologies which transform the primary fuels to useable commodities for power and heat generation technologies represented by techno-economic specifications (such as efficiency, specific investment costs, yearly fixed and variable O&M costs, emission factors, technical lifetime, etc. given to the model exogenously using [32,33]). The prices of imported fossil fuels are taken from the international price development curves provided by Ref. [34] and shown in Table 4 in the Appendix. These prices are given to the model exogenously, while the prices of other fuels produced domestically through fuel technologies, as well as the final prices of heat and power, are calculated endogenously in the model. The yearly potential for all renewable resources (i.e., wind, solar, bioenergy, hydro and geothermal energy) and their availability for each region are given to the model using [35].

Total discounted system costs ¼ XR X X �REFYR 1 þ dr;y;t r¼1

y

� ANNCOSTðr; y; tÞ

(2)

y2YEARSt2TECHS

where: TECHS is the set of all existing technologies; In addition to that, a limited number of technology types can be at the disposal of each actor group, reflecting the existing reality that technologies are differently preferred by or accessible to actor groups. For example, it is assumed in this paper that citizens and institutional investors can only invest in the main renewable resources, i.e. wind and solar energy6 while utilities and institutional investors cannot invest in citizens’ rooftop PV potential. However, there are also some technologies available to several actor groups such as utility scale PV, onshore and offshore wind. For instance, three different versions of the onshore wind technology with the same technical specifications exist in the model for each actor, each with a different investor’s specific discount rate representing the respective actor’s hurdle rate. The levels of the different hurdle rates are depicted in Fig. 2 and explained further in the Appendix. There are also some technologies which do not generate electricity and hence do not belong to any of the existing actors, such as biomass fuel processing technolo­ gies or the grid. These technologies belong to the category of “the rest” and receive the original global discount rate. Eq. (3) shows the improved mathematical formulation of the objective function to be minimized, considering actors disaggregation. Total discounted system costs ¼ XR X � X �REFYR 1 þ dr;y;tA r¼1 y2YEARS tA 2TECHSA

2.1. Disaggregation of the actors

� ANNCOSTðr; y; tA Þ

X 1 þ dr;y;tB

þ

The rationale for disaggregating the actors is that actors investing in the energy system have different time preferences (hence different perceptions of the so-called “time value of money”) and various expec­ tations for the return of their investments since they obtain the required capital from various sources such as company creditors, equity holders or even banks. Therefore, the cost of capital varies from actor to actor which suggests different investment valuations and hence heteroge­ neous investment decisions. The less an actor’s cost of capital is, the more likely an investment is realized in capital expensive technologies, e.g. renewables, and vice versa. However, actors with a lower cost of capital do have budget restrictions and cannot invest unboundedly (see Appendix for more details on the financial concept). The different cost of capital of the actors are represented in the model by different actors’ specific hurdle rates (see Appendix for more details on the financial concept). By introducing these different hurdle rates in the objective function of the model, we can include the different in­ vestment valuations of the actors in calculating the total system costs, which affects the technology choice of the overall model. In order to

y

�REFYR

y

�REFYR

y

�REFYR

y

� ANNCOSTðr; y; tB Þ

tB 2TECHSB

X

1 þ dr;y;tC

þ tC 2TECHSC

X þ tR 2TECHSR

1 þ dr;y;tR

� ANNCOSTðr; y; tC Þ � � ANNCOSTðr; y; tR Þ (3)

where: TECHSA is the set of all technologies available for actor A (e.g. citi­ zens); TECHSB is the set of all technologies available for actor B (e.g. Institutional investors);

6 Citizens’ investments in offshore wind technology has not been realized yet [4] and is neglected in this study.

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Energy Strategy Reviews 26 (2019) 100421

Fig. 2. The supply sector structure in TAM.

TECHSC is the set of all technologies available for actor C (e.g. util­ ities); TECHSR is the set of all technologies which do not belong to any actor (the rest);

new capr;y;tA is the new capacity of a technology belonging to actor A built in year y in region r; MYNCAA is the maximum yearly new capacity allowance for actor A (e.g. citizens); MYNCAB is the maximum yearly new capacity allowance for actor B (e.g. institutional investors);

Budget restriction is also a key restricting factor which limits in­ vestments in capital-expensive renewable technologies by the actors with access to less expensive capital (see Appendix for more details on the financial concept). Therefore, in order to emulate this effect in a more realistic way, the budget restriction of the actors with a lower cost of capital should be reflected in the model. Within TAM, this is modelled implicitly via constraints on the yearly new capacity investment al­ lowances of the actors in terms of gigawatts calculated using data in Refs. [2,4] applied as an upper bound in the model in order not to overestimate the capability of actors. Therefore, the two types of actors with a lower cost of capital, i.e. citizens and institutional investors, cannot invest without limits. This is implemented using a user constraint on each actor group over the four existing regions in the model, reflecting the fact that actors can invest in other regions as well, which already is the case in the German supply sector and gives the model the flexibility to more efficiently deploy actor groups across Germany. Eqs. (4) and (5) show the mathematical formulation of the respective con­ straints in the optimization problem representing the budget re­ strictions. The maximum yearly new capacity allowances are depicted in Fig. 2. XR X X new capr;y;tA � MYNCAA (4) r¼1

2.2. Regional division and grid aspects The rationale for incorporating a regional division in TAM is due to the variable geographical distribution of renewable resources and electricity demand as well as inter-regional electricity exchanges. On the one hand, the levelized costs of electricity generation from renewables, especially wind and solar, are different due to meteorological differ­ ences (also shown in Refs. [36–41]), which provides different cash flows for actors with the same economic characteristics but located in different regions, leading to dissimilar investment decisions. Thus, the decision to invest in a particular technology is crucially dependent on the region as well. On the other hand, the available renewable potential in a region might not match the level of demand in that region. Therefore, this regional imbalance in the demand and renewable supply should also be addressed by imports and exports or investments in non-renewable technologies, perhaps equipped with carbon capture and storage to comply with environmental targets. Furthermore, the generation mix is different in Germany. For instance, nuclear power plants are located in the west and north and their phasing out impacts these regions the most. On the other hand, there is more generation from lignite in the east due to its historically different economic development. This results in the possibility that the same actor might decide differently if situated in a different environment. In TAM, Germany is divided into four regions each of which consisting of several entire federal states. Although it is possible to have a higher spatial resolution representing all states separately, these four regions (namely north, east,

y2YEARSt2TECHSA

XR r¼1

X

X new capr;y;tB � MYNCAB

(5)

y2YEARSt2TECHSA

where: 4

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Energy Strategy Reviews 26 (2019) 100421

south and west7) are assumed to be enough for demonstrating how regional differences can affect the energy supply sector. Another aspect that can impact diverse actors’ optimal investment decisions is grid connection. In a particular condition, where further renewable generation is more expensive in one region than in its neighboring regions, two alternatives exist: 1) investment in trans­ mission grid expansion to import cheaper renewable electricity from the neighboring regions or 2) investment in more expensive renewables within the region (discussed further in Ref. [42]). The optimal possi­ bility among all alternatives can only be discovered when the grid extension costs and grid losses are considered in the model. Within TAM, the existing inter-regional transmission grid capacities, their losses and the costs of new investments in power transmission between the regions are considered. Distribution grid losses and operational costs are also approximated. The techno-economic data for both transmission and distribution grid modelling are taken from Ref. [12]. Considering all the evidence, it appears that optimal actor invest­ ment performance varies in different environments and, yet again, a similar question arises as to where which specific actor should be incentivized to invest in which technology so that targets are met at lower costs? It can only be answered if the model includes regional differences in renewable energy sources and demand, and considers grid aspects to a sufficient extent.

uration of fuel input to power generation technologies and the corre­ sponding annual CO2 emissions across developed scenarios are further presented and compared. 3. Results 3.1. New investments in renewables: relative roles of actors Fig. 3 illustrates the actors’ cumulative new investments in renew­ able technologies in regions across the modelling horizon under two different environmental policies which could be captured thanks to the methodological improvements incorporated in TAM. This figure depicts the overall future roles that actors should accept for a cost-optimum transition of the German energy supply sector. The most interesting aspects of this graph are as follows: The results show that, among the multiple investment options that citizens are facing, the least cost pathway for the system is to encourage them to invest almost exclusively in solar rooftop potential regardless of environmental policies. The regional divisions, together with grid aspects, also provide a valuable insight: due to the higher population and hence the higher number of buildings and available roof area, and to avoid extra grid costs and losses, citizens in the west should exploit rooftop potential (playing a role as prosumer) more than in other regions. Moreover, the costeffective path for the system indicates that institutional investors should solely be targeted to undertake all of the expensive offshore wind investments in the north. The rest of the investment capacity of the institutional investors in other regions should then be dedicated to onshore wind and utility-scale solar, especially in the east and south. Finally, utilities will have to participate more actively and in a com­ plementary way in all regions to exploit the remaining onshore wind and utility-scale solar potentials that are not unlocked by other actors.

2.3. Scenario construction The business-as-usual (BAU) scenario represents a continuation of current conditions where there are neither environmental policies nor model improvements and serves as the reference scenario in our study. On top of BAU, there are four other scenarios developed in this study each of which has two components. The first component indicates the policies, including 1) achieving certain renewable quotas stipulated in the Renewable Energy Act and the coalition agreement of the German government for each milestone year of the modelling horizon (RES) and 2) imposing carbon emission taxes that increase through the milestone years of the modelling horizon (CO2). The second component in these scenarios is meant to examine the effect of model improvements, shown in Fig. 2, by comparing TAM with the original model without im­ provements (ORG). The enacted law of nuclear phase-out by the end of 2022 is included in all scenarios as well, including the BAU scenario. Table 1 summarizes the scenario construction developed in this study and analyzed by TAM. The level of carbon taxes, as well as the renewable targets used in the scenarios and how they are implemented in the model, are explained in the Appendix. In the results section, the actors’ regional investment in renewable technologies within TAM is captured and presented. Moreover, the regional share of renewable generation is presented, followed by the impact of RES and CO2 policy scenarios on the net electricity exchange between regions. The config­

80

Capacity [GW]

70

BAU RES_ORG RES_TAM CO2_ORG CO2_TAM

Renewable target/policy ✖ Renewable quota Renewable quota Carbon taxes Carbon taxes

50 40 30 20 10

North

Regional division ✖ ✖ ✔ ✖ ✔

Grid aspects ✖ ✖ ✔ ✖ ✔

Actors disaggregation ✖ ✖ ✔ ✖ ✔

East

South

CO2_TAM

RES_TAM

CO2_TAM

RES_TAM

CO2_TAM

RES_TAM

CO2_TAM

RES_TAM

0

Table 1 Summary of the scenarios. Acronym

60

West

Utilities Other

Utilities Wind_Off

Utilities Wind_On

Utilities Solar utility scale

Utilities Solar rooftop

Institutional investors Wind_Off

Institutional investors Wind_On

Institutional investors Solar utility scale

Citizens Wind_Off

Citizens Wind_On

Citizens Solar utility scale

Citizens Solar rooftop

Fig. 3. Actors’ cumulative new investments in renewable technologies in re­ gions across the modelling horizon8.

7 North: Bremen, Hamburg, Lower Saxony, Mecklenburg-Vorpommern, Schleswig-Holstein; East: Berlin, Brandenburg, Saxony, Saxony-Anhalt, Thur­ ingia; South: Baden-Württemberg, Bavaria; West: Hesse, North RhineWestphalia, Rhineland-Palatinate, Saarland.

8 Obviously much of these investments will already reach their technical lifetime and stop existing before the end of the modelling horizon. However, this is only to show the total investments by the actors and their cumulative roles.

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3.2. Share of renewables: regional development

3.4. Electricity generation: policy vs. investor implications

Table 2 presents the share of renewables in gross electricity con­ sumption throughout Germany and within different regions in 2030 and 2060. The regional division provides an opportunity to analyze the di­ versity of renewable shares for each region. It is evident from the table that there will be more renewable generation than local consumption across all scenarios in the northern region in 2060, which implies that excess electricity is delivered to other regions. This is also the case for the east in the RES_TAM scenario. The reason for more renewable gen­ eration than consumption in the north and even to some extent in the east is that the demand in these regions is relatively lower than the available renewable potential9. Therefore, these two regions should be targeted to play a key role in the decarbonization of the German supply sector. Furthermore, the regional renewable shares in all regions under RES_TAM are higher than CO2_TAM since, under RES, certain renewable quotas should be achieved on a national level. Nonetheless, it can be seen that the renewable share in the north is almost the same across scenarios in 2060 (146% and 143% in RES_TAM and CO2_TAM respectively). This demonstrates the highly important role of the northern region in the transition of the energy supply sector regardless of the adopted policy, particularly when investors with lower cost of capital are deployed there.

Fig. 5 compares the power generation mix across scenarios. In the BAU scenario, the fuel mix for power generation during the modelling horizon does not change significantly except for the nuclear phase-out from 2023 onwards, which is due to the enacted phase-out law. How­ ever, by imposing the renewable share targets (RES) policy scenario, hard coal is substituted by renewable sources while the system stays relatively inert against the phase-out of lignite until the end of the modelling horizon. Nonetheless, a gradual increase in renewable gen­ eration can be seen which only serves to meet the renewable targets. Therefore, in the RES_ORG and RES_TAM scenarios there is no driving force for the phase-out of lignite except the growth of renewable share. In the CO2 scenarios, natural gas replaces lignite and hard coal noticeably quickly as they are prohibitively costly to the system due to the imposed growing carbon tax. However, this is only an interim measure to evade high taxes in the short-term with relatively cheaper gas investments. Given the considerable amount of carbon emissions through natural gas as well as the steady growth of carbon taxes across the modelling horizon, carbon capture and storage (CCS) in combination with both natural gas and biomass technologies as well as other re­ newables gradually substitute most of the fossil fuels by 2060. Moreover, as could be expected, there is more renewable generation in the scenarios with an explicit renewable share in consumption, which has to be fulfilled in every period regardless of the emissions. There is 25% more renewable generation in RES_ORG and 13% in RES_TAM compared to CO2_ORG and CO2_TAM respectively. However, the last implication suggests that the slow uptake of renewables by CO2 taxes can be offset to some degree by considering actors’ diverse investment decision-making. A closer inspection of Fig. 5 shows that there is considerably more offshore wind generation and hence more total renewable generation in CO2_TAM than in CO2_ORG in almost all the milestone years (except for 2060, when the CO2 tax is at its highest). This is due to the fact that actors benefiting from a low weighted average cost of capital are considered in the latter scenario, which can bring about more generation from offshore technology in the north even in the presence of grid costs. Taking into account that adopting a renewable quota policy and imposing incentives on renewable generation, such as feed-in tariffs, are actually two sides of the same coin11, the following implication arises: CO2 taxes tend to phase out more polluting power generation technol­ ogies at first while renewable quotas of a similar rigor and their dual renewable incentives are apt to increase generation from renewables. A comparison of which of these two approaches is more suitable for longterm emission reduction is explored in Fig. 6. Finally, the trend of electricity generation shows a slight increase in TAM scenarios than ORG, reflecting more generation due to grid losses.

3.3. Electricity exchange: relative roles of regions Fig. 4 portrays the cumulative net electricity exchange across regions across the modelling horizon in order to identify the roles that each region should play in the future to shape a least-cost system. As expected from Fig. 4, the northern and eastern regions will have to play a more significant role in providing southern and western regions with renew­ able electricity. However, these roles strongly depend on environmental policies. In the RES_TAM scenario with certain renewable quotas, there is excessively higher renewable electricity generation in the north and east to meet the national targets. This extra generation is then exported to the south and west to meet demand which cannot be fully satisfied by local renewable resources10. However, excessive electricity exchanges might be confronted with acceptance issues by the public regarding the transmission grid extension [43–45]. This proves that designing policy instruments regardless of the existing heterogeneity in the energy sys­ tem leads to misleading measures. Nevertheless, in the CO2_TAM sce­ nario there is remarkably less electricity exchange between regions due to the absence of renewable share targets. Thus, the system is more flexible for avoiding high grid costs or losses and profits from the ad­ vantages of fuel substitution as well as local decentralized generation in the regions. Another noticeable finding from Fig. 4 is the prominent role of the eastern region in providing the southern states with electricity, which has been overlooked so far in the German energy transition. The western region is then supplied almost exclusively by the north. This is mainly due to lower transmission costs (mostly new investment costs) and losses on account of shorter distances between the exporting and importing regions. This is contrary to the current network development plan in Germany which requires massive power transmission from the north all the way down to the south and should definitely be considered.

3.5. CO2 emissions and system costs: policy vs. grid/investor implications Fig. 6 shows the yearly CO2 emissions (on the left axis) as well as the total annualized system costs (on the right axis) across scenarios. Carbon emissions decrease gradually across the horizon. The emitted CO2 under RES_TAM is more than in RES_ORG in all milestone years, which con­ tradicts the initial impression when considering actors with lower hur­ dle rates. This adverse effect is due to the consideration of grid losses in the RES_TAM scenario. Since in both scenarios the target is to reach a certain share of renewable generation in final power consumption (not total generation), these two factors stay the same in both scenarios. Therefore, grid losses are compensated by more generation from lignite

9 There is an abundance of wind energy in the north. However, in the east this is mainly due to the fact that the demand in this region is relatively lower than the rest of Germany because of historically different economic developments. 10 The reasons for excessive electricity exchanges from the east to the south in RES_TAM in addition to higher renewable potential than demand in the east are the phase-out of nuclear plants in the south as well as the prolongation of lignite generation in the east while still complying with national renewable targets.

11 Not only in the policy instrument design but also in the linear mathematical optimization model of the energy system. In energy system optimization the incentives are actually the shadow prices (dual variable) of the renewable quota constraints [10].

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Table 2 National and Regional share of renewable generation in gross electricity consumption in 2030 and 2060. Scenarios BAU RES_ORG RES_TAM CO2_ORG CO2_TAM

2030

2060

DE

North

East

South

West

DE

North

East

South

West

35% 65% 65% 53% 60%

– – 91% – 86%

– – 69% – 67%

– – 62% – 59%

– – 49% – 42%

43% 95% 95% 75% 85%

– – 146% – 143%

– – 113% – 100%

– – 88% – 78%

– – 64% – 52%

ultimate decarbonization of the supply sector. This policy is even more effective when actor diversity is considered as it can be seen from the figure that the emissions in CO2_TAM are less than in CO2_ORG. This implies that by targeting challengers12 more precisely and involving them in energy system investments, emissions might be reduced faster and at lower costs. The emission reduction potential of challenger actors is so considerable that it can even offset the additional emissions caused by the grid as grid losses are considered in the CO2_TAM scenario unlike CO2_ORG. Additionally, it is apparent from Fig. 6 that CO2_ORG and CO2_TAM are costlier than RES_ORG and RES_TAM respectively, resulting in higher electricity prices as well. This is due to the imposition of carbon taxes and hence investments in the more expensive CCS technologies. However, this difference lies under 15% in all milestone years and approaches zero with the gradual decarbonization of the supply sector. A closer inspection of Fig. 6 also illustrates that CO2_TAM and RES_TAM scenarios are costlier than CO2_ORG and RES_ORG respectively despite the presence of actors with a lower cost of capital in the former scenarios. A cost decomposition revealed that high grid costs, which are considered only in CO2_TAM and RES_TAM scenarios,

1600

Exchange [TWh]

1400 1200 1000 800 600 400 200 0 to South

to West

RES_TAM

to South

to West

CO2_TAM

from North

from East

Fig. 4. Cumulative net electricity exchange between regions across the modelling horizon (only in scenarios with a regional division and power transmission between the regions).

Fig. 5. Comparison of electricity generation from different energy carriers across scenarios.

(also visible in Fig. 5). This flaw in the renewable target policy could not be revealed without considering the grid aspects. What stands out in Fig. 6 is that the imposition of carbon taxes is much more effective than achieving a renewable quota without any supplementary policy in an

12 Non-conventional actors with lower expectations for financial returns. Here, like citizens and institutional investors.

7

350

120

300

100

250

80

200 60 150 40

100

2020

2030 Yearly CO2 emissions

BAU RES_ORG RES_TAM CO2_ORG CO2_TAM

2013

BAU RES_ORG RES_TAM CO2_ORG CO2_TAM

0

BAU RES_ORG RES_TAM CO2_ORG CO2_TAM

0

BAU RES_ORG RES_TAM CO2_ORG CO2_TAM

20

BAU RES_ORG RES_TAM CO2_ORG CO2_TAM

50

2040

2050

2060

Costs [bn. €]

Energy Strategy Reviews 26 (2019) 100421

Base-Year

Emission [1000 kt]

A. Tash et al.

Total annualized system costs

Fig. 6. Yearly CO2 emissions and total annualized system costs.

completely cancel out the lower investment costs in these scenarios, which ultimately results in higher total costs and hence slightly higher electricity prices.

an enormous database. The temporal resolution of the model is on annual level, which does not affect the specific objective of this study regarding heterogeneous investment decisions of diverse actors located in different environments. Another shortcoming of this study is the fact that the energyeconomy optimization models determine the optimal technology pro­ file based on the rational decision-making of actors with perfect fore­ sight on behalf of the central decision-maker. However, investment decisions are more complex in reality and actors are affected by several attributes. Due to the limitations of the TIMES model, the investment decisions of actors is modelled in a simplified way and this approach only attempts to improve the representation of actors. However, incor­ porating complex heterogeneous behavioral aspects of actors, which are not necessarily cost-driven, requires extensive research on capturing relevant data and implementing these in other simulation tools. Although investment decisions in this model are only based on few at­ tributes, to the authors’ knowledge, the methodology adopted for this study is state-of-the-art within bottom-up optimization energy system models. Overall, the methodology adopted for this study allows for an anal­ ysis of how capturing different determinants of “rational” investment decisions with a fine level of heterogeneity in modelling will help to better understand the dynamics of the system, accounting for the required transition time and assist policy makers to better identify and target actors towards the energy transition. The methodological framework developed within TAM is a step forward in transforming the central, global and system-wide decision-maker who carries out de­ cisions on behalf of average investors to diverse actor groups who make decisions in reality.

4. Discussion 4.1. Methodology insights The methodological improvements adopted for this study have several advantages compared to a typical TIMES method. Firstly, TAM is capable of improving the representation of investment decisions through disaggregating actors through economic characteristics and expectations. Secondly, the regional division provides an opportunity to consider the spatial distribution of renewable potentials and demand as well as the inter-regional import and export of electricity between the regions. Thirdly, by integrating the requirements of the system with actors’ economic perceptions, policy recommendations could better target those actors that can bring about the desired energy transition in a more cost efficient manner. Finally, TAM is able to identify the tech­ nology configuration of the sector at least system cost while satisfying energy transition and decarbonization targets via an enhanced consid­ eration of investors and geographical differences. However, our approach also has some limitations. The technologies available to actors with a lower cost of capital can be extended in order to have more realistic and accurate results. For example, citizens could have access to investments in offshore wind as well as biomass tech­ nologies. The investment options for these actors could even be expanded to the grid, so that it can be discovered which investment option is preferred from a system perspective, either a grid extension or renewable expansion. In addition to that, a finer actor disaggregation as well as new actor types could be introduced to TAM, such as the gov­ ernment as an actor in order to investigate its optimal role in new in­ vestments towards energy transitions. Moreover, the grid in TAM has been modelled in a rather simplified way, approximated by a simple process with a capacity and efficiency. However, the grid is more so­ phisticated in reality. It is possible to use the linear DC power flow modelling feature in TIMES to represent the grid behavior in a more precise manner like in Ref. [27]. Nonetheless, this improvement would need more data on the grid such as the reactance of the lines and the phase angles at grid nodes. Furthermore, the geographical division of regions could be improved, focusing on each federal state or grid region introduced by Ref. [46], which needs a much more detailed model with

4.2. Policy insights The analyses carried out within this study are meant to suggest to German policy makers which environmental policy levers should be first implemented on actor groups in order to approach the targets. In addition, this study also demonstrated what should be the actors’ pri­ ority for investments in renewables in each region so that the energy transition could be realized at least system-costs. This can greatly contribute to designing region- and actor-specific policies which can steer actors towards the system in an optimum manner. However, the question of how to incentivize the actors is not yet addressed and is something for further research. 8

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Energy Strategy Reviews 26 (2019) 100421

This study has analyzed the least-cost structure of the future German supply sector while renewable quota and carbon tax measures are implemented. We found out that citizens should be encouraged to exploit their untapped PV rooftop potential, especially in the west of Germany. However, institutional investors should solely be targeted to undertake all of the expensive offshore wind investments in the north and then invest in onshore wind and utility-scale solar in other regions, in particular the south and east. The utilities will then have to partici­ pate more actively and in a complementary way in all regions to exploit the remaining renewable potentials that are not used by other actors. In both renewable target and carbon tax scenarios, the north and, to some extent, the east should play a key role in generating renewable elec­ tricity and importing it to the south and west, where renewable potential is not enough for the demand. However, renewable targets without any supplementary policy might lead to perceiving a false need for excessive power exchange between the regions and hence be confronted with acceptance issues by the public regarding the transmission grid exten­ sion. Finally, CO2 taxes tend to phase out more polluting power gener­ ation technologies at first while renewable quotas of a similar rigor and their dual renewable incentives are apt to increase generation from re­ newables. However, carbon taxes reduce emissions quicker as they target the problem directly, though at the expense of a slightly more expensive generation mix containing costly local CCS technologies.

assessed under two different environmental policies, namely, achieving a certain renewable share in electricity consumption and the imposition of carbon taxes, in order to capture the optimal system configuration of diverse actor groups in different regions with regards to various tech­ nologies. The findings have provided new insights to learn which actor group should invest in which generation technology and where in Ger­ many so that the supply sector is steered towards achieving the targets of the energy transition at least system costs. These findings will eventually help in understanding the heterogeneity of the supply sector and designing more accurate region- and actor-specific policies. This study lays the groundwork for future research into analyzing actors’ investment decisions in energy systems by improving a meth­ odological approach, verifying that an enhanced representation of the German supply sector could provide a clearer and more useful insight into a cost-effective transition. The representation of the actors’ in­ vestment decisions was improved in three different aspects, namely economic characteristics of the actors, regional differences as well as grid costs and losses. Each of these improvements can be developed in more detail given that the data is available. In this regard, a survey which can capture the relevant investors’ economic characteristics and preferences will be of great assistance. A greater focus on designing individual policy instruments for different actors in different regions could produce interesting findings regarding the least cost energy sys­ tem transition pathways. It is to be discovered which incentive should affect which actor to what extent and where, in order to match the optimal local and individual solutions with the optimal system solution, i.e. the system’s optimum becomes contingent upon the actor’s opti­ mum. Finally, combining this methodology with an agent-based modelling approach to provide a better representation of complex het­ erogeneous behavioral aspects, the interaction of actors and subsequent feedback loops can be a promising further step for this research.

5. Conclusion and outlook This paper has argued that a simple representation of the energy system usually leads to unrealistic results and hence inaccurate policy recommendations. Therefore, a more sophisticated energy model is suggested to improve the representation of investment decisions in the case of the German energy supply sector, where the investors in wind (onshore þ offshore) and PV (rooftop þ utility scale) technologies are disaggregated into three different categories based on their different cost of capital and budget restrictions, namely citizens, institutional in­ vestors and utilities. The German energy supply sector, as the environ­ ment where these actors are located, is also divided into four regions: north, east, south and west with different renewable energy potentials available. Inter-regional grid development as well as grid operation costs and losses are also considered. Then the energy system was

Acknowledgement This article and latest model development efforts have been carried out within the “Dezentral” project funded by the German Federal Min­ istry for Economic Affairs and Energy. The authors would like to thank the anonymous reviewers for their valuable comments that greatly improved the manuscript.

Appendix Cost of capital and hurdle rate As proposed by Ref. [3], the investment decision-making rationale of the actors regarding energy supply, especially the capital expensive (CAPEX) renewable technologies, is mainly shaped by their individual cost of capital. There are several factors which can influence the level of the cost of capital for a certain investor investing in a particular project, such as the risk/return characteristics of previous investments as well as returns of alternative investment opportunities. These factors are different from investor to investor (i.e. actor). For instance, the return that utilities expect from investing in a certain power generation technology is higher than that of a pension fund, as an institutional investor [3]. Since utilities, usually with risky past investments, are accountable to their shareholders and creditors for their high return expectations, whereas a pension fund prefers long-term investments with limited risks, limited operational interaction and stable cash flows, matching its long-term liabilities. In contrast, citizens do not have many different investment opportunities and, for their capital, they mostly rely on their own savings or low interest bank loans. This means citizens and institutional investors have lower a cost of capital than utilities for investing in generation technologies [3]. The more an actor’s cost of capital is, the less likely an investment is realized in capital expensive technologies, e.g. renewables, and vice versa. This statement seems obvious. However, actors with a lower cost of capital do have budget restrictions and cannot invest unboundedly. Therefore, it is quite valuable to know which actor should invest where in which technology so that the energy transition can take place more quickly and at lower costs. Actors have different expectations for their investments because of the different ways they obtain the required capital for an investment in an energy project. For instance, a company’s creditors or equity holders do not finance it for free. They demand to be paid for delaying their own consumption and assuming investment risk and also to compensate for other investment opportunities they refuse to take, i.e. opportunity cost of capital. Thus, the company should return some profits on top of the money they borrow which is different from investor to investor. It applies to individual investors too because they borrow from a bank or when they have the capital from their savings they always have the opportunity to invest their money in other projects or simply in a bank. Therefore, it only makes sense for an investor to proceed with a new project if its expected revenues are larger than its expected costs, in other 9

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Energy Strategy Reviews 26 (2019) 100421

words, it needs to be profitable and the return should be at least equal or greater than the cost of capital. The discount rate makes it possible to estimate how much the project’s future cash flows would be worth in the present. One method is to discount all the future cash flows of an investment project back to the reference year using the minimum acceptable rate of return (MARR) that an investor expects from investing into a certain project. If the discounted amount is at least equal or greater than the initial investment, the investor decides to invest in that project. This investor’s specific discount rate, also called hurdle rate (the actor’s minimum acceptable discount rate for investment valuation), is equal to the final cost of capital of an investor considering the risks and opportunity costs13. Therefore, if we include these different hurdle rates in the objective function formulation of the model, we can actually include different investment valuations of the actors in calculating the system costs and hence in the model choices. Budget restrictions Actors with a high cost of capital tend to invest in less capital expensive technologies which usually have high future operational costs (OPEX) such as conventional technologies. By contrast, actors that have access to less expensive capital, i.e. a lower cost of capital, opt for more capital expensive technologies (CAPEX) with low future costs, like renewable technologies. However, they do not have access to unlimited inexpensive capital, otherwise the transition to a decarbonized energy system would be much cheaper. Therefore, in order to represent reality, the budget restriction of the actors with a lower cost of capital should be reflected in the model too. Demand Since the model of this study considers the supply sector only, the demand sectors are represented in a simplified manner by using demand curves and its development across the modelling horizon for each sector separately (i.e. residential, agricultural, industrial, commercial and transport). Therefore, the research [30] is used to derive the demand developments. This research considers three different pathways, namely the reference (business as usual), an intermediate level and a high level of decarbonization. For the BAU scenario in this paper, the demand development of the reference pathway is used, while for the rest of the scenarios the highly decarbonized pathway is taken. The population and GDP growth of the states provided by the Federal Statistical Office of Germany are used to calculate the demand development in each state. The demand development for each model region is then calculated by aggregating the states’ demands located in the respective region. Scenarios Table 3 shows the level of the targets for renewable share in final electricity consumption at each milestone year in the RES_XXXX scenarios as well as the carbon taxes for each ton of emitted CO2 in the CO2_XXXX scenarios.

Table 3 Overview of the scenario target/policy based on [1] for renewable targets and [34] for carbon taxes under 2� scenario (2DS) Acronym

RES_XXXX CO2_XXXX

Description

Renewable share (%)

Achieving renewable targets Imposing 2 � C carbon taxes

Carbon taxes (2015US$/tCO2)

2020

2030

2040

2050

2060

2020

2030

2040

2050

2060

�35 N/A

�65 N/A

�75 N/A

�85 N/A

�95 N/A

0 20

0 100

0 140

0 180

0 240

The renewable share in total electricity consumption is incorporated in the model using a user constraint defined over all regions. Therefore, the renewable target is applied on a national level as it is so in reality. Using emission factors for each type of fuel, the model can calculate the total emitted carbon dioxide. By putting a tax on each unit of emitted carbon dioxide in the model, the carbon taxes are considered in the system costs. Table 4 demonstrates the development of fossil fuel import prices under the Reference Technology Scenario (RTS) and 2 Degrees Scenario (2DS) taken from Ref. [34]. The price of Lignite, nuclear fuel, biomass, biogas, waste, etc. are formed endogenously since they are extracted or produced domestically.

Table 4 The development of fuel import prices based on [34] under 2� scenario (2DS) Fuel

Oil Hard Coal Natural Gasc

Unit

2015

2015 USD/barrel 2015 USD/ton 2015 USD/MBtu

51 64 7.0

Prices in BAU scenarioa

Prices in CO2 and RES scenariosb

2020

2030

2040

2050

2060

2020

2030

2040

2050

2060

79 72 7.1

111 83 10.3

124 87 11.5

137 90 12.2

148 92 12.6

73 66 6.9

85 64 9.4

78 57 9.9

72 55 10.2

67 53 10.5

The prices of the 2DS scenario are used for CO2_XXX and RES_XXX scenarios so that they match the environmental component of these scenarios. a RTS scenario in the reference. b 2DS scenario in the reference. c Europe import prices.

Transmission grid

Electricity can be exchanged between the regions only at high voltage. These exchanges are modelled using simple trade processes. The losses of transmission technologies are assumed to be 4%/1000 km. Therefore, to calculate the efficiency of each trade process between each two regions, the distance is estimated by the distance between the geographical centers of the two regions. An existing capacity is assumed for a trade process between each two regions with a technical life of 40 years which approximates the existing line capacities from the Open Energy Modelling Initiative14. In­ vestments in new transmission technologies are needed if more electricity exchange than the existing capacity is needed or [34] the technical life of the trade processes is reached. The new investment cost is assumed to be 400€/MWkm for overhead line technology. The same distance between geographical centers of the regions is used to approximate the specific investment costs of each trade process between each two regions in terms of €/MW. 13 14

Please refer to Ref. [3] for further details on investment valuation in energy generation projects. https://wiki.openmod-initiative.org/wiki/Transmission_network_datasets. 10

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Distribution grid There are three different voltage levels in the model, namely low, medium and high. Different generation technologies produce at different voltages. For example, a conventional lignite power plant generates at high voltage, onshore wind technology at medium voltage and a small biogas engine or PV rooftop technology at low voltage. The demands are also connected to the grid at different voltage levels. For example, residential demand is only connected at low voltage and industrial demand at all three voltages, however, with different exogenously given shares from Ref. [29]. There are costs and losses associated to voltage conversions in order to model distribution grid costs and losses. There are no existing capacity or new investment costs assumed for the distribution grid since the estimation of the capacity of the distribution grid is beyond the scope of this paper. The techno-economic data for both transmission and distribution grid modelling are taken from Ref. [12].

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