This chapter analyzes the fundamental changes in the context of a decarbonized electricity sector with cross-sectoral interdependencies between different demand and supply sectors considering the deployment of RES. For the analysis of an optimal flexibility provision in different European least-cost decarbonization pathways, the fundamental electricity market model ELTRAMOD is applied and embedded in the EMS of the REFLEX project. The normative REFLEX scenario framework illustrates the complexity of the future energy system transformation in Europe with possible overlapping development pathways regarding the RES expansion, the level and implementation approaches for sector coupling as well as different levels of flexibility provision.

The results in this chapter highlight the crucial cross-sectoral interactions and their influence on optimal investment and dispatch decisions for different flexibility options from an electricity market perspective. The analysis shows, that the leastcost efficient capacity mix depends on different deployments of DSM appliances. It can be observed that flexible measures in the demand side sectors decrease the value of flexibility options in the electricity market. Since DSM smooths the residual load, electricity market-based storages decrease in value. Nevertheless, an extensive application of DSM, as in the High-RES decentralized scenario, can lead to significant reductions of positive residual load peaks, thus limiting the need for additional (conventional) back-up capacities. These results support the scientific literature (Müller and Möst 2018; Strbac et al. 2012).

Additionally to the interactions with DSM applications, storages are particularly affected by cross-sectoral electrification. Since an electricity demand increase lowers the frequency of low or negative residual loads, incentives for wholesale marketbased storage technologies are also reduced. At the same time, sector coupling in combination with less ambitious RES expansion, increases the required conventional power plant capacities. Of high importance regarding the optimal flexibility mix in the power system is also the implementation of power–to–x technologies. A marketbased dispatch of investment cost-intensive technologies (e.g., electrolyzers), tends to maximize full load hours of these technologies, and thus rather increase flexibility needs in the electricity sector, mainly provided by additional power plant capacities. In contrast, particularly low capital costs technologies, such as electric boilers and heat pumps in combination with the respective storages, enable a cross-sectoral flexibility provision.

In general, the modeling results indicate, that under the given scenario framework and the input from the model coupling within REFLEX, besides new flexibility options such as DSM and storages, back-up capacities are still necessary to provide system flexibility. The ELTRAMOD results underline the crucial role of high CO2 prices to achieve the emission reduction targets. Although the High-RES scenarios are characterized by relatively high shares of RES, CO2 prices higher than 70 EUR/tCO2 are required to enforce decarbonization by avoiding investments in carbon-intensive generation technologies in the presence of an increasing electrification of the demand side sectors. High fuel and CO2 prices can incentivize the competitiveness of low-carbon technologies like gas-fired CCS and flexibility options, such as storages and power-to-x. These results support existing analyses and policy recommendations, e.g., EU Roadmap (European Commission 2011a). The insights gained in this chapter call for the improvement of the EU Emission Trading Scheme (ETS) to enable such high CO2 prices, to provide more long-term clarity and certainty in price developments as well as to include more CO2 emitting sectors. Nevertheless, an even more ambitious RES expansion allows for additional significant reductions in fossil fuel-based electricity generation and at the same time increase the value for storage technologies. Only high shares of RES allow for a mix of various flexibility options in the electricity market in a cross-sectoral energy system.

To summarize, the installed flexibility option mix in the electricity market is strongly influenced by the electricity demand and RES feed-in as well as by the sector coupling of the demand side sectors. Differences occur regarding the optimal mix of flexible power plants between the Mod-RES and High-RES scenarios. The combinations of the interactions discussed above result in a rather similar power plant mix in the two High-RES scenarios, although the scenarios are defined by different feed-in characteristics of varying combinations of fluctuating generation from wind and photovoltaic. For the High-RES scenarios, the achievable emission reductions in the present scenario framework and EMS exceed the reduction targets of the European Commission, while the LCOE are increasing rather moderately compared to the Mod-RES scenario. As shown in this analysis, high CO2 prices, preferably above 70 EUR/tCO2, and high RES shares are a no-regret strategy to achieve the EU emission reduction targets of at least 80–95% in 2050 (compared to the level of 1990).5 Accordingly, the optimal combination of flexibility options gains in importance in order to facilitate a high integration of intermittent RES.

### **References**

Bertsch J, Growitsch C, Lorenczik S, Nagl S (2016) Flexibility in Europe's power sector—an additional requirement or an automatic complement? Energy Econ 53:118–131. https://doi.org/ 10.1016/j.eneco.2014.10.022

5The European Green Deal, published after the completion of this project, sets more ambitious targets for reducing CO2 emissions, and most likely the power sector will also be affected by these ambitious targets in the near future.

Zöphel C, Schreiber S, Herbst A, Klingler AL, Manz P, Heitel S, Fermi F, Wyrwa A, Raczy ´nski M, Reiter U, Möst D (2019) Report on cost optimal energy technology portfolios for system flexibility in the sectors heat, electricity and mobility. REFLEX project report

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

### **Chapter 11 Impact of Electricity Market Designs on Investments in Flexibility Options**

**Christoph Fraunholz, Andreas Bublitz, Dogan Keles, and Wolf Fichtner**

**Abstract** Against the background of several European countries implementing capacity remuneration mechanisms (CRM) as an extension to the energy-only market (EOM), this chapter provides a quantitative assessment of the long-term crossborder effects of CRMs in the European electricity system. For this purpose, several scenario analyses are carried out using the electricity market model PowerACE. Three different market design settings are investigated, namely, a European EOM, national CRM policies, and a coordinated CRM. The introduction of CRMs proves to be an effective measure substantially shifting investment incentives toward the countries implementing the mechanisms. However, CRMs increase generation adequacy also in the respective neighboring countries, indicating that free riding occurs. A coordinated approach therefore seems preferable in terms of both lower wholesale electricity prices and generation adequacy.

### **11.1 The European Debate on Electricity Market Design1**

Since the liberalization of the electricity markets in the 1990s, the prevailing market design in European countries has been the energy-only market (EOM), in which capacity providers are solely compensated for the amount of electricity they sell on the markets. In this market design, according to theory, scarcity periods lead to peak prices, which enables investors to cover their fixed and capital costs. In other regions of the world, e.g., in several US markets, so-called capacity remuneration mechanisms (CRMs) are a common extension of the EOM with the earliest implementations dating back to the late 1990s (Bublitz et al. 2019). These mechanisms typically aim to reduce the investment risks by offering capacity providers supplementary income on top of the earnings from selling electricity on the spot markets.

C. Fraunholz (B) · A. Bublitz · D. Keles · W. Fichtner

Institute for Industrial Production (IIP), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

1This introductory section was previously published in Fraunholz and Keles (2019).

The additional generation, storage, or demand side capacity may then in turn help to improve generation adequacy, i.e., avoid shortage situations. Following the classification of the European Commission (2016), six generic types of CRMs can be distinguished:

In recent years, several European countries seem to face threats in terms of the future generation adequacy and therefore have either already implemented some kind of CRM or are currently in the process of evaluating appropriate solutions (cf. Fig. 11.1). These developments can be attributed to a variety of factors including strongly increasing shares of fluctuating electricity generation from renewable energy sources (RES), decreasing wholesale electricity prices as well as recent phase-out decisions for certain technologies. Yet, the tendency toward applying CRMs to increase investment incentives contradicts the European Commission's preference for an EOM in order to trigger new investments and provide signals for decommissioning in case of overcapacities. Moreover, in a highly interconnected electricity system like the European one, the uncoordinated implementation of local mechanisms might lead to potentially adverse cross-border effects, which stands in strong contrast to the European Commission's goal of creating an internal electricity market in Europe (Bublitz et al. 2019).

This chapter therefore aims to provide a quantitative assessment of the long-term cross-border effects of CRMs in the European electricity system. The electricity market model PowerACE is applied to a region covering Central Western European

and some Eastern European countries as well as Denmark and Italy. Different longterm simulations up to 2050 are carried out for all three REFLEX scenarios (Mod-RES, High-RES decentralized, High-RES centralized) to derive insights regarding the impact of national and coordinated CRM policies on amount and location of new investments, the resulting technology mixes in the electricity sector as well as generation adequacy.

### **11.2 Research Design**

For the quantitative analyses on electricity market design carried out in the following, the agent-based simulation model PowerACE is applied. A brief overview of PowerACE is given in Chapter 3. Further model details can be found in the following references:

**Fig. 11.2** Overview of the market areas modeled in PowerACE and their respective market design in the **a** "European EOM" setting, **b** "National CRM policies" setting, **c** "Coordinated CRM" setting. In order to capture a variety of different design options and corresponding cross-border effects, the regional scope covers Central Western European and some Eastern European countries as well as Denmark and Italy. (*Source* own illustration)

All three REFLEX scenarios (Mod-RES, High-RES decentralized, High-RES centralized)2 are analyzed considering three different settings with regard to electricity market design (cf. Fig. 11.2):

The investment methodology in PowerACE depends on the respective market design. In market areas without an implemented CRM, the investment decisions are solely driven by the future electricity price expectations of the investors. Contrary, in market areas with an active central buyer mechanism, annual descending clock auctions are carried out in order to contract a specific amount of secured generation and storage capacity. For this purpose, the regulator first sets an arbitrary reserve margin, which controls the desired level of generation adequacy and defines the capacity to be procured in the auction.

2Detailed information on the different scenarios are provided in Chapter 2. For recapitulation: (i) the electricity demand grows moderately in Mod-RES and substantially higher in High-RES, (ii) significantly more intermittent renewables are assumed in High-RES than Mod-RES, (iii) decentralized solar power dominates High-RES decentralized, whereas higher shares of offshore wind power characterize High-RES centralized.

3Please note, that due to the similarities of the different types of CRMs on an abstract level, the French CRM is modeled using the central buyer implementation in PowerACE, although in reality, a de-central obligation mechanism is used in France.

In case of uncoordinated national CRMs, the reserve margin μnat *y* is set to 1.0, such that the residual load in the respective market area can always be covered by the available conventional generation and storage capacity, without depending on electricity imports. The calculation of the required capacity *c*nat *m*,*y* is shown in Eq. 11.1, where *d* denotes the electricity demand, *r* the renewable feed-in, *m* the market area, *y* the year, and *h* the hour of the year.

$$c\_{m, \mathbf{y}}^{\text{nat}} = \mu\_{\mathbf{y}}^{\text{nat}} \cdot \max\_{h} \left( d\_{m, \mathbf{y}, h} - r\_{m, \mathbf{y}, h} \right) \quad \forall m, \mathbf{y} \tag{11.1}$$

For the coordinated CRM, the national reserve margin is adjusted by the ratio between peak residual load across all market areas and the sum of the national peak residual loads (cf. Equation 11.2). This procedure is also applied by Bucksteeg et al. (2019). Cross-border synergies obviously lead to lower reserve margins under the coordinated CRM μcoor *y* than in the uncoordinated case.

$$\mu\_{\rm y}^{\rm corr} = \mu\_{\rm y}^{\rm nat} \cdot \frac{\max \sum\_{m} \left( d\_{m, \rm y, h} - r\_{m, \rm y, h} \right)}{\sum\_{m} \max \left( d\_{m, \rm y, h} - r\_{m, \rm y, h} \right)} \leq \mu\_{\rm y}^{\rm nat} \quad \forall \rm y \tag{11.2}$$

Contrary to the model-endogenous investment decisions, decommissioning of existing power plants is exogenously defined in PowerACE and based on the respective age and technical lifetime of the generation units, which remain unchanged for all scenarios. Consequently, the development of the future technology mix across the various scenarios strongly depends on the techno-economic characteristics of the different investment options (conventional power plants and storage technologies). Since the expansion of RES is an exogenously defined and scenario-specific input to PowerACE, no additional investments in renewable technologies are considered. Moreover, the learning curves for storage technologies developed within the REFLEX project (cf. Chapter 4) are implemented in PowerACE.

### **11.3 Development of the Conventional Generation Capacities and Wholesale Electricity Prices**

In the following, the simulation results for all three REFLEX scenarios are presented and discussed. The impact of the different market design settings on amount, location, and technology mix of new investments as well as the resulting wholesale electricity price developments is in the focus of the result presentation. The European EOM is used as a benchmark, to which the national CRM policies as well as the coordinated CRM setting are compared.

By imposing a certain capacity target and then offering payments to capacity providers additional to the income from selling electricity on the markets, CRMs tend to shift investment incentives in interconnected electricity markets toward the countries using such mechanisms. In the respective neighboring countries without an own CRM, investment incentives may stay stable, but could as well decrease due to the additional capacity from abroad, which also influences domestic price expectations of potential investors. Consequently, under national CRM policies, both positive and negative cross-border effects may be observed.

Contrary, in the case of a coordinated CRM, capacity targets are set for each country, which may result in stable investment incentives across all interconnected market areas. Moreover, as previously described, the total capacities required to secure generation adequacy are lower in a coordinated CRM, due to cross-border synergies and better balancing of fluctuating electricity production from RES.

With regard to wholesale electricity prices, according to theory, the introduction of CRMs should reduce the amount of scarcity situations and related peak prices and therefore result in lower electricity wholesale prices. However, under national CRM policies, suppressed investments in neighboring countries of those using a CRM may also lead to negative cross-border effects. Consequently, the implementation of a CRM might prove to be less effective as expected when considering only an isolated country.

A coordinated CRM should incentivize sufficient capacity to cover the electricity demand at all times and therefore reduce the wholesale electricity prices in all interconnected market areas. However, these savings come at the price of capacity payments for the additional capacity. These may to a certain extent compensate or even overcompensate the savings achieved by lower wholesale electricity prices. This effect—despite its high practical relevance—is however out of the scope of the work presented in this chapter and should be subject to future research.

Figures 11.3, 11.4, 11.5, 11.6, 11.7 and 11.8 present the development of the total conventional generation and storage capacities as well as resulting wholesale electricity prices for all three REFLEX scenarios and two exemplary countries.4 Since all of these figures are structured similarly, some general remarks are provided before discussing the obtained results in more detail.

In the top part of the figures, the respective total conventional generation and storage capacities throughout the simulation period of 2020–2050 are shown for each of the three market design settings (European EOM, National CRM policies, Coordinated CRM—from left to right). Furthermore, the respective yearly national peak residual load, excluding imports/exports and storage is depicted as a reference point. As previously mentioned, the capacity developments are based on exogenously predefined decommissioning, which is identical for all investigated settings, as well as on model-endogenous investment decisions for different technologies.

The bottom part of the figures shows the resulting impact on the development of the wholesale electricity prices. For this purpose, the European EOM is defined as a reference and the relative price difference *pm*,*y* is then computed as the mean yearly

4Due to space limitations, only results for France and the Netherlands are presented in this section. These countries are chosen as representative ones in terms of cross-border effects, since France is using a CRM under the national CRM policies, while the Netherlands rely on an EOM, but are surrounded by countries applying some kind of CRM.

price under the respective market design *p*∗ *m*,*y* divided by that under the European EOM *p*ref *m*,*y* as shown in Eq. 11.3, where *m* denotes the market area and *y* the year. Consequently, by definition, the relative price differences in the European EOM are always at 0% throughout the simulation period. For illustrative purposes, the linear trend of the yearly price differences is also included in the respective diagrams.

$$
\Delta p\_{m, \mathbf{y}} = \overline{p\_{m, \mathbf{y}}^\*} / \overline{p\_{m, \mathbf{y}}^{\text{ref}}} - 1 \quad \forall m, \mathbf{y} \tag{11.3}
$$

In the following, some specific results of Mod-RES, High-RES decentralized, and High-RES centralized will be presented and discussed.

### *11.3.1 Mod-RES Scenario*

In Mod-RES, some general trends across all modeled countries can be identified:

As presented in Fig. 11.3, substantially more investments in open cycle gas turbines (OCGT) are carried out in France under the national CRM policies than in the European EOM. This is a direct consequence of the French CRM, which successfully incentivizes additional peak capacity. To a lesser extent, this finding is also true under the coordinated CRM. Since the required reserve margin in a coordinated approach is lower than for a national CRM, less peak load capacity is built in this setting than under the national CRM policies.

The additional capacity has a direct impact on the development of the wholesale electricity prices, which decline under both the national CRM policies and the coordinated CRM as compared to the European EOM. Despite lower capacity levels in France, the price decline is more pronounced under the coordinated CRM. This effect is due to more capacity in the French neighboring countries under the coordinated CRM than under national CRM policies, from which also France seems to benefit.

In the Netherlands, which rely on an EOM, investment incentives are drastically reduced under the national CRM policies as compared to the European EOM. This finding can be attributed to negative cross-border effects caused by the Dutch neighboring countries using CRMs.

Consequently, substantially less investments in peak load capacity, i.e., OCGTs are carried out under the national CRM policies, which results in increasing wholesale electricity prices (cf. Fig. 11.4). Contrary, in the coordinated CRM, investment incentives are higher than in both other market design settings due to a capacity target also for the Netherlands. This leads to additional OCGTs and lower prices.

### *11.3.2 High-RES Decentralized Scenario*

Also in the High-RES decentralized scenario, some general trends across the modeled countries can be identified:

As shown in Fig. 11.5, similarly as in Mod-RES, substantially more investments in OCGTs are carried out in France under the national CRM policies and the coordinated CRM than in the European EOM. However, due to the significant increase in electricity demand and the substantial smoothing of the load curve through DSM measures, the French CRM also incentivizes additional combined cycle gas turbines (CCGT). As a result, the wholesale electricity prices are lower under both the national CRM policies and the coordinated CRM as compared to the European EOM.

Owing to cross-border effects of the CRMs in neighboring countries, the investment incentives in the Netherlands are reduced under the national CRM policies also in High-RES decentralized. Yet, contrary to Mod-RES, these effects are much less pronounced. This is due to the strongly increasing demand across all countries in High-RES decentralized, which leads to a relatively high number of running hours for new capacity and therefore CCGTs often being the more profitable investment option than additional OCGTs. Since CRMs mainly affect the allocation of peak load capacity, i.e., OCGTs, the amount of investments in countries without CRM is less affected by cross-border effects of the national CRM policies in High-RES decentralized than in Mod-RES.

5Although the CO2 prices are assumed to grow stronger in High-RES decentralized than in Mod-RES, some coal-fired generation remains in the market even in 2050. This is because decommissioning of power plants is only considered exogenously based on their respective age.

In the coordinated CRM, for similar reasons as in France, substantial amounts of additional investments in both OCGTs and CCGTs are carried out. These capacity developments also affect the wholesale electricity prices in the Netherlands (cf. Fig. 11.6). Due to the small reduction in domestic capacities under the national CRM policies and the additional capacity in the neighboring countries, prices decrease even under this setting in the long run as compared to the European EOM. This effect is even more pronounced under the coordinated CRM.

### *11.3.3 High-RES Centralized Scenario*

The general trends regarding fuel switch and profitability of CCS and storage technologies, which could be identified in the results of High-RES decentralized, also apply to the High-RES centralized scenario. Regarding the development of capacity levels and wholesale electricity prices, the patterns in the High-RES centralized scenario are quite similar to those of High-RES decentralized in France. However, due to the significantly lower amount of DSM measures in the High-RES centralized scenario (cf. Chapters 2, 6, 7 and 8), more OCGTs and less CCGTs are built (cf. Fig. 11.7).

In the Netherlands, less CCGTs and more OCGTs are built under the national CRM policies than in the European EOM (cf. Fig. 11.8). This is likely due to a reduced number of running hours for new capacity caused by the additional capacity incentivized in the Dutch neighboring countries due to their CRMs. In the coordinated CRM, the patterns are very similar to those in the High-RES decentralized scenario. Regarding the development of the wholesale electricity prices, no clear trend can be identified under the national CRM policies, while prices decline in the coordinated CRM as compared to the European EOM.

### **11.4 Impact on Generation Adequacy**

Generation adequacy can be defined as the ability of an electricity system to provide sufficient dispatchable generation, storage, and flexible demand side capacity to cover the residual load at any time. Since the electrical grid is not modeled in PowerACE apart from the simplified consideration of maximum cross-border transmission capacities—grid restrictions are not considered in the evaluation of the generation adequacy presented in the following. Yet, the focus of this work is rather on the ability of different electricity market designs to provide adequate investment incentives to achieve a sufficient capacity level under consideration of the respective cross-border effects.

In order to assess and compare generation adequacy across the various settings and for all modeled countries, a simple, but straightforward indicator is applied. In the investment methodology of PowerACE, no restriction to cover the demand at all times is implemented, but the expansion planning rather emerges from the individual actors' decisions (cf. Chapter 3). Thus, situations may occur in which the day ahead market cannot be cleared due to an insufficient level of dispatchable generation and storage capacity, leading to the maximum day ahead market price of 3,000 EUR/MWh. In reality, reserve capacity would likely be activated, such that even in these scarcity situations, load shedding would not necessarily occur. Yet, the mean number of yearly hours with no successful clearing of the day ahead market in a given market area is a suitable proxy to measure generation adequacy.

Figure 11.9 provides a concise overview of the generation adequacy levels in all scenarios, market design settings, and countries. Across all scenarios, the unilateral implementation of CRMs under the national CRM policies rather obviously increases generation adequacy in the respective countries. However, also neighboring countries of those using a CRM seem to benefit from the additional capacity and face an increase in their generation adequacy levels. This finding indicates that free riding occurs. The effect is most pronounced in High-RES decentralized. As described before, the strongly growing electricity demand in this scenario combined with the extensive use of DSM measures leads to CCGTs often being more profitable than OCGTs. However, since CRMs mainly shift investment incentives for peak load capacity, investments in countries without a CRM barely decline in this scenario. Thus, these countries benefit from an almost unchanged level of domestic capacity plus the additional capacity of their neighbors with CRMs.

Interestingly, generation adequacy increases even further under a coordinated CRM, even in the countries that already use a CRM under the national CRM policies. Apparently, cross-border synergies, better balancing of fluctuating electricity production from RES as well as reduced free riding by neighboring countries without an own CRM, outweighs the impact of lower domestic capacity levels in the respective countries under the coordinated approach.

### **11.5 Summary and Conclusions**

In this chapter, the electricity market model PowerACE was applied to a region covering multiple interconnected European market areas with different electricity market designs. Several long-term simulations up to 2050 were carried out for all three REFLEX scenarios (Mod-RES, High-RES decentralized, High-RES centralized) in order to quantitatively assess the long-term cross-border effects of CRMs in the European electricity system. In this context, three different settings with regard to electricity market design were analyzed. Firstly, a European EOM, which served as a benchmark. Secondly, national CRM policies, including the unilateral introduction of CRMs as currently planned or already implemented in reality. Thirdly, a coordinated CRM as an approach potentially standing better in line with the goals of creating an internal electricity market in Europe than unilateral CRMs. By comparing the different settings, valuable insights regarding the impact of national and coordinated CRMs on amount and location of new investments, the resulting technology mixes

**Fig. 11.9** Mean yearly hours with no successful clearing of the day ahead market, i.e., the maximum day ahead market price of 3,000 EUR/MWh due to a shortage of dispatchable generation and storage capacity. All values are averaged over the years 2020 to 2050 and given in h/a. Across all scenarios, the implementation of CRMs does not only increase generation adequacy in the countries using the CRMs, but also in their neighboring countries. (*Source* own calculation)

in the electricity sector, the development of the wholesale electricity prices as well as generation adequacy could be derived.

In terms of the future technology mix, across all investigated scenarios and market areas, a strong fuel switch toward gas-fired power plants can be observed as a result of the assumed CO2 price development. Due to the more extreme assumptions with regard to CO2 prices in the High-RES scenarios, CCS technologies turn out to be profitable toward 2050, while this is not the case in the Mod-RES scenario. Furthermore, in all scenarios, storage technologies only play a minor role under the assumptions made. This finding is related to the moderately ambitious shares of renewable electricity generation, even in the High-RES scenarios due to the strongly increasing electricity demand. Besides, the applied electricity load curves are already smoothed by DSM measures prior to their implementation in PowerACE.

The unilateral introduction of CRMs proves to be an effective measure substantially shifting investment incentives toward the countries implementing the mechanisms. The additional generation capacity in these countries in turn reduces both the average wholesale electricity prices and the amount of scarcity situations. Depending on the specific setting, neighboring countries of those implementing a CRM may face both positive and negative cross-border impacts.

In the Mod-RES scenario, which is characterized by a moderate growth of electricity demand, OCGTs often prove to be the most profitable investment option. However, building more peak load capacity in countries with an active CRM drastically reduces investment incentives in neighboring countries without an own CRM, leading to increasing wholesale electricity prices in these countries.

Contrary, in the High-RES scenarios, where the electricity demand grows stronger over time, investments in CCGTs are often economically preferable over peak load capacity. Yet, in contrast to OCGTs, the profitability of CCGTs in countries without an own CRM is less affected by additional investments in neighboring countries with CRMs. Consequently, in the long run, the average wholesale electricity prices may decrease also in countries without an own CRM.

Despite the distortion of investment incentives, across all scenarios, CRMs generally increase generation adequacy not only in the country implementing the mechanism, but also in the neighboring countries, indicating that free riding occurs.

In all three scenarios, a coordinated CRM, in which capacity targets are set for each individual country, provides adequate investment incentives in all countries. Although individual capacity requirements are lower than in case of an unilateral introduction of a CRM, all countries benefit in terms of lower wholesale electricity prices and increased generation adequacy levels. This is also true for countries that already use a CRM under the national CRM policies setting. Apparently, reduced free riding by neighboring countries without an own CRM outweighs the impact of lower domestic capacity levels on wholesale electricity prices in the respective countries under the coordinated approach. However, the savings in terms of wholesale electricity prices come at the price of capacity payments for the additional capacity. These may to a certain extent compensate or even overcompensate the savings achieved by lower wholesale electricity prices. This effect was not considered in this chapter, but should be subject to future research in order to get a holistic picture.

Summing up, whether positive or negative cross-border effects of unilateral CRMs prevail, depends on a variety of factors, including the future development of electricity demand and renewable electricity generation as well as the geographical location of a given country. A coordinated approach generally seems preferable in terms of wholesale electricity prices and generation adequacy. The European Commission should therefore continue to assess potential CRMs carefully prior to allowing their real-world implementation and consider a coordinated European CRM as an alternative market design solution potentially standing better in line with the goals of creating an internal electricity market in Europe.

Although the analyses presented in this chapter provide valuable insights regarding long-term cross-border effects of CRMs in the European electricity system, open questions for future research remain. Two aspects of particular relevance are as follows. Firstly, the role of electricity storage would likely become more visible in a modeling approach where DSM measures simultaneously compete with storage technologies rather than smoothing the electricity load curves prior to their implementation in an electricity market model like PowerACE. DSM measures could then also participate in the CRMs in the same fashion as storage technologies. Secondly, in a real-world setting, also interconnector capacities are typically allowed to participate in CRMs of neighboring countries. Considering this aspect would probably reduce the cross-border effects of unilateral CRMs as presented in this chapter and therefore bring the situation closer to that of a coordinated European CRM, yet at a lower administrative burden.

### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

### **Chapter 12 Optimal Energy Portfolios in the Heating Sector and Flexibility Potentials of Combined-Heat-Power Plants and District Heating Systems**

### **Maciej Raczy´nski, Artur Wyrwa, Marcin Pluta, and Wojciech Suwała**

**Abstract** This chapter examines the role of centralized district heating (DH) systems in context of energy system flexibility and decarbonization. The analysis is performed by applying the model TIMES-Heat-EU. Capacity expansion and operation of the district heating generation units is mainly driven by the evolution of the district heating demand, which varies between the REFLEX scenarios. In all scenarios fuel and technology switches toward bioenergy and natural gas leading to CO2 emission reduction. Since the total amount of energy produced (both heat and electricity) is the highest in the High-RES centralized scenario, the corresponding CO2 emissions for district heating are the highest as well. The CO2 emissions can be reduced by ∼60% in 2050 compared to 2015. Furthermore, the role of thermal energy storage and power-to-heat technologies is examined.

### **12.1 Introduction**

At present about half of the final energy consumption in the EU is associated with heating and cooling purposes (European Commission 2016). These energy services are also expected to have a significant share in future energy consumption. In many EU countries, in particular in Scandinavia, Central, and Eastern Europe, a significant proportion of the heat demand in high-density urban areas is covered by district heating networks in which pressurized hot water is used as heat carrier at temperatures below 100° C (Lund et al. 2014). District heating (DH) has the benefits of integrating local heat resources, including waste heat and renewables, and of improved emission control (especially local). Supplying the heat produced in combined heat and power plants (CHPs) not only generates higher overall efficiency but also increases the flexibility of local power systems. Initiatives, such as "District Heat Atlas" (Möller et al. 2018) or "Urban Heat Demand Map" (Wyrwa and Chen 2017) are useful for the

M. Raczy ´nski · A. Wyrwa (B) · M. Pluta · W. Suwała

AGH University of Science and Technology, Cracow, Poland

M. Raczy ´nski e-mail<EMAIL_ADDRESS>
development of heat supply strategies and plans for different spatial scales such as national, regional, or local. District heating development requires high initial investments, and its economic feasibility is often constrained by the size of the local market. Therefore, various support mechanisms have been implemented by the EU member states to promote district heating and the development of cogeneration, including tax advantages, feed-in-tariffs, certificates, grants, and other kinds of additional support. For instance, Moya (2013) analyzes how to incentivize the growth of CHPs capacities, how to create attractive economic conditions for investors, as well as how to overcome existing barriers, e.g., complexity of the law. Although, there is no clear evidence that the countries which have applied CHP dedicated support mechanisms have been more effective in promoting the development of cogeneration than other countries, the results show that countries where cogeneration plays a significant role are sensitive to the presence of support mechanisms. The analysis carried out in Moya (2013) shows that none of the identified barriers is decisive in preventing the development of cogeneration. The obtained results also indicate that the possible overlap between RES and CHP support programs does not significantly affect the promotion of the CHP development. The report published by Cogen Europe (2011) underlines that the objective of ensuring high efficiency and sustainable development of the European energy system can be achieved through, *inter alia*, the increased use of cogeneration and RES. It is pointed out that the key elements are—firstly, the transition from fossil fuels to RES in electricity and heat production and secondly, the increase in energy efficiency, e.g., through the development of cogeneration. Still in the 2000s district heating has been produced in large extent based on fossil fuels such as gas, coal, or oil. Regardless, the transformation toward a low-carbon (or even carbon-free) district heating systems has already begun and district heating systems are in the focus of a sustainable energy system. Such transformation is possible by enabling a technology switch from fossil fuel to RES (e.g., Sweden is the world bioenergy leader as bioenergy accounts for 33% of the national final energy consumption, cf. World Energy Council (2016)), but also requires further integration of district heating systems into the power system to enlarge district heating flexibility.

Different modeling studies have been performed to analyze potential pathways for future development of the district heating sector. For instance, in Connolly et al. (2014) a methodology based on the combination of geographical information systems (GIS) and the energy system model EnergyPLAN is applied to determine the potential for heat networks and to elaborate plausible district heating development scenarios that would help to further decarbonize the EU energy system. In this chapter the TIMES-Heat-EU model is applied to explore the development of the district heating generation mix for the EU member states in the REFLEX scenarios (cf. Chapter 2).

### **12.2 TIMES-Heat-EU Model**

TIMES-Heat-EU model has been developed to assess the transition pathways toward more sustainable district heating supply and to analyze the role of district heating systems in context of energy system flexibility. The model was formulated with the help of the TIMES generator (Loulou 2008) and belongs to the class of integrated capacity expansion and dispatch planning models. TIMES-Heat-EU is dedicated to model the centralized heat supply by heat-only plants (HOPs) as well as combined heat and power plants (CHPs). The district heating demand is divided into three end-use sectors: the residential, tertiary, and industry sector.

The model uses a bottom-up approach, in which CHPs and HOPs are aggregated into main types according to the fuel used and type of installed turbine (cf. Fig. 12.1). The model considers main types of thermal energy storage (TES) in a short-term and seasonal perspective. The application of thermal energy storage enables the decoupling from power generation and heat generation. The operation of CHPs is influenced by electricity price signals. Power-to-heat (PtH) technologies, such as large electric heaters and heat pumps, can use electricity that would be otherwise curtailed (e.g., RES surpluses). The geographical coverage of the model considers the member states of the EU-27 and the United Kingdom. The time horizon covers the time period from 2015 to 2050 with five years' time steps. Each modeling year is further divided into 224 time-slices derived by aggregating the data every three hours in seven days for four seasons (8 x 7 x 4). The model is calibrated for 2015 based on the EUROSTAT data (Eurostat 2017a; Eurostat 2017b; Eurostat 2017c).

TIMES-Heat-EU solves the linear programming problem of district heating supply. District heat producers, represented by heat-only-plants, CHPs, and PtH, are maximizing their surplus. The optimization is constrained by a set of equation and inequalities. The main equations include: (i) commodity balance equations e.g., for district heating and electricity, (ii) CHP annual overall efficiency requirements

**Fig. 12.1** Schematic illustration of the district heating supply model—TIMES-Heat-EU (*Source* Own illustration)

in compliance with the EU legislation, (iii) required share of electricity generated in highly efficient cogeneration, (iv) ramping constraints for the operation of units.

The main output is the mix of heat generating technologies and their dispatch as well as the district heating prices. For a given country the district heating price is calculated as the weighted average (by heat production level) of district heating generation costs of individual CHPs and heat-only-plants including costs of purchasing CO2 allowances under consideration of the EU ETS. The results of TIMES-Heat-EU underline the economic feasibility of more flexible and RES-oriented cogeneration.

### **12.3 Developments in the District Heating Sector**

One of the most important input parameters influencing the development of district heating systems is the change in the future district heating demand, which is exogenous input parameter for TIMES-Heat-EU provided by the FORECAST model (cf. Chapters 3, Chapter 6 and 7).

As illustrated in Fig. 12.2, the demand for district heating in 2050 is expected to be lower than today in the Mod-RES and High-RES decentralized scenario, mainly due to progressive implementation of low-energy and refurbished buildings. The more significant drop in the High-RES decentralized scenario is due to the fact that, on top of that, central heating systems play a more important role in this scenario. Only the High-RES centralized scenario assumes an increase in the future DH demand mainly because of supporting measures introduced in this scenario in the FORECAST model, such as reinforcing district heating network to realize a more viable heating infrastructure (cf. Chapter 6).

**Fig. 12.2** Development of district heating demand in REFLEX scenarios across all modeled countries (*Source* Data according to model results from FORECAST)

produced in cogeneration will be the same as today, i.e., approximately 12%. This assumption is in line with the results of the EU28 Reference Scenario 2016 (cf Capros et al. 2016) derived by applying the PRIMES model. Such constraint is not imposed in the High-RES decentralized scenario due to infeasibility of the solution.1

Others factors influencing the model results are: prices of CO2 emission allowances, techno-economic parameters of processes employed in DH systems as well as potentials and costs of fuel and energy carriers. The coupling of the power and heat sectors is introduced by enabling CHPs having income from both, heat as well as electricity sales. At the same time, the application of power-to-heat technologies requires on the one hand, purchased electricity from the market and, on the other, generates income from heat sales. The income from heat sales is based on the average annual DH price calculated by TIMES-Heat-EU, whereas the income from electricity sales is based on the wholesale electricity prices calculated by ELTRAMOD (cf. Chapters 3 and 10). Thus, the price signals on the wholesale market derived in an iterative model coupling process with ELTRAMOD play a crucial role not only for dispatch decisions regarding CHPs, heat storage systems, and power-to-heat technologies, but also for investment decisions regarding new generation capacities. Moreover, the obtained results consider competition between existing actors (DH generation technologies), which is not always straightforward. For instance, reacting to the low electricity prices during some periods (time-slices), power-to-heat technologies have an incentive to produce district heating and thus rise the residual load contributing to upward flexibility (i.e., increasing electricity demand). However, with limited overall district heating demand, this heat could not be any longer produced in CHPs and thus is a lost opportunity to gain income from district heating and electricity sales. It has also consequences on the general activity of CHPs due to the efficiency requirements imposed on electricity from CHPs. As stated by the Directive 2012/27/EU electricity is considered as produced in high efficiency cogeneration only if the total annual efficiency of the unit is greater than 75 or 80% (depending on the technology employed).

### *12.3.1 Scenario Results*

Figure 12.3 depicts the development of electricity generating capacities of CHPs in the different REFLEX scenarios. In general, a switch toward natural gas and bioenergy-fueled plants can be observed. In the Mod-RES scenario some coal-fired capacities exist, but these are plants that are decommissioned and thus ending their operation in 2045 (cf. fuel input in Fig. 12.8).

1TIMES-Heat-EU contains a constraint to enforce new CHP plants to work as high-efficiency cogeneration units. This means that the ratio of energy output (heat and electricity) to fuel input has to be greater than the given efficiency threshold (e.g., 80% for CCGT). This efficiency requirement cannot be achieved with low DH demand while enforcing CHP plants to have a 12% share in total electricity generation—as it is in High-RES decentralized scenario (cf. Fig. 12.2). The actual shares of electricity generated by CHP units for each scenario are given in Table 12.1.

**Fig. 12.3** Overall electric capacity of CHP plants in the REFLEX scenarios across all modeled countries (*Source* Data according to TIMES-Heat-EU model results)

In the case of the High-RES scenarios, due to increasing CO2 prices no other fossil fuels than natural gas-fired power plants are installed from 2030 onwards. The capacity expansion of CHPs is mainly driven by the evolution of the district heating demand. That is why the highest capacity expansion can be observed in the High-RES centralized scenario. In the decentralized case, electricity is generated to a lesser extent in cogeneration.

In the case of heat-only plants (HOPs) the existing thermal capacities are decommissioned until 2030. In general, heat-only-plants are losing competition with CHPs as they can profit only from heat sales, whereas plants operating in high efficient cogeneration can generate profit from both: electricity and heat sales. New capacity installations of heat-only technologies consist mainly of large solar thermal plants, this is especially true for the centralized High-RES scenario (cf. Fig. 12.4).

**Fig. 12.4** Overall thermal capacity of heat-only plants (HOPs) in the REFLEX scenarios across all modeled countries (*Source* Data according to TIMES-Heat-EU model results)

PtH technologies in TIMES-Heat-EU include electric boilers and large-scale heat pumps. Their operational pattern is different. Both PtH types are consuming electricity purchased on the wholesale electricity market. Heat pumps are assumed to operate constantly within seasons and do not actively react to the changes of the electricity price. They are more capital intensive but have higher efficiencies (the minimum value of COP is set to 3). In contrary, electric heaters serve as peak load units that actively respond to electricity price variations by generating heat that can be stored.

Figure 12.5 presents the installed thermal capacities of PtH. The greatest capacity expansion, mainly electric heaters, can be observed in the High-RES centralized scenario, in which electricity price variations are higher compared to the other scenarios (there are many time-slices with low electricity price). In the centralized scenario lower incentives for DSM actions are assumed in the eLOAD model (cf. Chapters 6 and 7), that is used to generate hourly electricity demand profiles (cf. Chapter 3).

The TIMES-Heat-EU results for power-to-heat have to be interpreted differently than those of ELTRAMOD as both models represent different PtH modeling approaches: TIMES-Heat-EU is focused on the district heating sector and largescale PtH technologies. ELTRAMOD focuses more on small scale PtH technologies in residential and tertiary individual heating systems.

In TIMES-Heat-EU, thermal energy storages (TES) allow for short-term and seasonal storage, helping to balance heat demand and supply. Each scenario assumes the same relative split of annual district heating demand into individual time-slices (based on the outdoor temperature data). However, in absolute values, the district heating demand differs in time-slices due to the differences in annual district heating demands (which is the highest in High-RES centralized and the lowest in the decentralized scenario). It can be distinguished between three kinds of thermal energy storage systems, i.e., sensible thermal energy storage (STES), latent heat storage by phase-changing materials (PCM), and thermal-chemical storage (TCS). TIMES-Heat-EU considers only sensible thermal energy storage, as phase-changing materials and thermal-chemical storage are still at a research and development stage and

**Fig. 12.5** Overall heat generation capacity of power-to-heat technologies in the REFLEX scenarios across all modeled countries (*Source* Data according to TIMES-Heat-EU model results)

therefore require high investments (the advantage lies in higher storage capacity). More specifically, water tanks are selected for short-term storages, whereas borehole thermal energy storages are assumed for the seasonal storages. Figure 12.6 presents the overall amount of heat that flows out of thermal energy storages in the different REFLEX scenarios. The highest flows from inter-seasonal storages can be observed in the High-RES centralized scenario because district heating demand varies most (in absolute values) between seasons in this scenario. Short-term storages depend on the variations of DH demand between time-slices as well as on the changes in electricity prices, which influence the operation of PtH technologies.

Seasonal storages are employed mainly by large solar thermal plants and electric heaters. In some time-slices, in particular during summer time, RES electricity is still curtailed to some extent. This curtailed electricity could have been used by PtH technologies and stored in thermal energy storages, if sufficient capacities of PtH and TES had been available. However, decisions in terms of investments into new capacities are results of the economic optimization. Results show that only limited, economically viable investment in TES and PtH are made for which the costs are outweighed with the profits from the sales of district heat at a later date.

TIMES-Heat-EU calculates the weighted average annual district heating generation costs (WA-DH generation costs). In a first step, the unit district heating generation costs are calculated for each heat generation technology. These costs include fuel, fix and variable operation and maintenance costs, annualized investments as well as costs of CO2 emission allowances. In a second step, the total costs are divided by the amount of heat produced to calculate unit costs of heat generation by the given technology. This calculation is straightforward in case of heat-only-plants. In case of CHPs, the total costs are split into two parts and assigned to power and heat generated. Finally, the unit generation costs are weighed by heat production to deliver weighted average annual district heating generation costs. The development of district heating costs for selected countries (with the highest DH demand) and EU-27 + UK average is presented in Fig. 12.7. The average district heating generation costs are, in general,

**Fig. 12.6** Heat flow out of thermal energy storage in the REFLEX scenarios across all modeled countries (*Source* Data according to TIMES-Heat-EU model results)

**Fig. 12.7** Average district heating generation costs in the REFLEX scenarios for selected countries (*Source* Data according to TIMES-Heat-EU model results)

lower than the reported overall district heating price for the end-users (usually local DH operators are adding distribution and other fees, cf. Euroheat and Power 2017).

The weighted average annual district heating generation costs are increasing compared to the base-year in most of the EU-27 member states and the United Kingdom. This is mainly due to: (i) investments in new capacities, (ii) rising prices of CO2 emission allowances, and (iii) higher operational costs. Regarding (i), it should be mentioned, that investments for units that already exist in 2015 are considered in TIMES-Heat-EU as sunk costs. In the future the existing heat-only plants are replaced with CHP plants. This also has an impact as CHPs total costs are only partly assigned to heat. In reference to (ii), the weighted average annual district heating generation costs depend on the carbon intensity of the district heating generation mix. This in turn, depends on the potentials of the renewable resources and their exploitation. For instance, in the High-RES centralized scenario the bioenergy potential is fully exploited due to high district heat demand and therefore more gas-fired units have to be utilized, what is not the case in High-RES decentralized scenario. Finally, with regard to (iii), also the operational costs including mainly fuel costs have an impact on WA-DH generation costs.

As presented in Fig. 12.8, in all scenarios there are fuel and technology switches toward bioenergy (mainly biomass) and natural gas as well as toward heat production in cogeneration. Clearly, bioenergy-based CHP units are replacing existing solid fuelfired heat-only-plants and CHPs. Natural gas units are utilized in countries with low bioenergy potentials.

Figure 12.9 presents the amount of electricity produced in cogeneration for the REFLEX scenarios. As mentioned before, in case of the Mod-RES and High-RES centralized scenario the constraint to maintain about 12% of the total electricity production by CHP plants is assumed, which is not the case in the High-RES decentralized scenario. Thus, the highest electricity production occurs in the High-RES centralized scenario. In the High-RES decentralized case, the amount of electricity

**Fig. 12.8** Fuel input for DH generation in the REFLEX scenarios across all modeled countries (*Source* Data according to TIMES-Heat-EU model results)

**Fig. 12.9** Electricity generation of CHPs in the REFLEX scenarios across all modeled countries (*Source* Data according to TIMES-Heat-EU model results)

produced is associated with the DH demand and is limited by the annual efficiency requirement of cogeneration units.

### *12.3.2 CO2 Emissions in the Heating Sector*

The switch in the district heating generation mix toward renewables and cogeneration results in decreasing CO2 emissions in all scenarios as depicted in Fig. 12.10.

Since the total amount of energy produced (both heat and electricity) is the highest in the High-RES centralized scenario, the corresponding CO2 emissions are also the highest in this scenario. However, as compared to the Mod-RES scenario with much lower energy (district heating and electricity) demand, the CO2 emission factor per

**Fig. 12.10** CO2 emissions from district heating generation in the REFLEX scenarios across all modeled countries (*Source* Data according to TIMES-Heat-EU model results)

total energy output in 2050 in the High-RES centralized scenario is only slightly higher, as presented in Table 12.1. The reason for this is that in High-RES centralized scenario all CO2 neutral fuels (e.g., bioenergy) are used up to their supply limits, what implies the necessity to use some fossil fuels. This explains why in the High-RES decentralized scenario this emission factor is the lowest (cf. fuel input structure in Fig. 12.8). Table 12.1 summarizes the results of TIMES–Heat–EU. Specific CO2 emissions in the Mod-RES scenario are higher than in High-RES decentralized because more natural gas is used in this scenario, i.e., 33 and 20%, respectively. Both scenarios have similar district heating demands, but more electricity needs to be generated in the Mod-RES scenario, due to the constraint enforcing a 12% share of CHP plants in electricity generation.

### *12.3.3 Sensitivity Analysis*

One of the most important parameters influencing the results is the bioenergy supply (biomass and biogas). The available bioenergy potential has been estimated based on (Elbersen et al. 2012; Ruiz et al. 2015) and it was assumed to be 10% higher in 2050 as compared to 2015. Bioenergy consumption increases in every scenario, reaching the limit (available potential) in the High-RES centralized scenario. A sensitivity analysis was carried out, in which the bioenergy potential in 2050 was modified in a range from −100% to +80%. Therefore, in 2050, according to the former the bioenergy potential was equal to zero, while for the latter it was increased by 80% (which in absolute terms gave a number about four times higher than total bioenergy consumption in 2015). Potentials available in intermediate years were interpolated linearly between 2015 and 2050.

The following discussion on the impact of bioenergy does not consider results of the Mod-RES scenario, as the High-RES decentralized and centralized scenario represent two extreme cases in terms of electricity and district heating demand. Figure 12.11 presents renewable energy share (mainly bioenergy but also other RES) in primary energy consumption in the DH generation sector as function of bioenergy

 as a ratio between total electricity and heat output (including PtH) to fuel

**Fig. 12.11** Share of renewable energy in primary energy consumption in the district heating generation sector as function of bioenergy potentials in the High-RES centralized (left) and High-RES decentralized scenario (right) (*Source* Data according to own calculations)

potentials. Note, that in the future years it would be much easier to achieve greater RES shares in the primary energy consumption (PEC) in the High-RES decentralized scenario. This is because the overall PEC is much lower in the district heat sector in this scenario due to lower DH demand (cf. Fig. 12.2). In both scenarios the increase of bioenergy potentials leads to higher RES shares. However, in the High-RES decentralized scenario the curves flatten more in the range from 20 to + 80%, indicating that greater availability of bioenergy is not influencing the results as strong as in the centralized scenario. This has two reasons. Firstly, the RES share is already very high in the baseline situation (ca. 70 and 76% in 2040 and 2050, respectively). Secondly, some peak load capacities exist (e.g., gas-fired or electric boilers) which are not viable or even sometimes technically impossible to be replaced by RES-based technologies.

The main insight of this sensitivity analysis is that if the EU member states are following the pathway described in the High-RES decentralized scenario, then the existing bioenergy potential is sufficient to fulfill the future needs for the district heating generation sector. In contrary, in case of the High-RES centralized scenario, which foresee the growth of district heating demand, the current biomass potential limits the growth and new bioenergy supply sources are required to increase the RES share.

### **12.4 Conclusion**

The future district heating demand varies according to the considered REFLEX scenarios. The district heating demand is the lowest in High-RES decentralized and the highest in the High-RES centralized scenario. In case of the High-RES centralized scenario, the increased district heating demand has to be associated with developments of new district heating systems.

The presented results show that in the future existing heat only plants are being replaced by CHP plants. Bioenergy (mainly biomass) based CHP capacities are increasing (most significantly in the High-RES centralized scenario). This increase, however, can be constrained by the limited biomass potential. Biomass can play an important role in substituting fossil fuels in district heating generation, in particular in the EU member states where the district heating networks are already well developed. Therefore, the transition toward higher use of bioenergy (mainly biomass) requires sustainable organizational (logistic) solutions that minimize energy and CO2 emissions embedded in processing and transportation. Natural gas is still used to some extent. These results are in line with the outcomes of the "high efficiency" scenario presented in (Cogen Europe 2011) in which RES (mainly bioenergy) constitute two third in primary energy consumption in 2050, and one third is provided by natural gas. Seasonal heat storages and short-term heat storages help to smooth generation profiles and increase the heat production in summer time. Power-to-heat technologies in the TIMES-Heat-EU model include large-scale heat pumps and electric boilers. The former operate more constantly within seasons, whereas the latter actively respond to electricity price variations and generate district heating that can be stored. The use of PtH technologies helps to manage RES electricity surpluses that otherwise would be curtailed.

With decreasing district heating demand on the one hand and with a simultaneous increase in electricity demand on the other—as in case of High-RES decentralized scenario—it is impossible to maintain the current relative share of electricity produced in cogeneration while meeting the cogeneration efficiency requirement. In fact, in this scenario this share decreases from the current 12 to 7% in 2050. In general, district heating costs are increasing in future years. This is mainly due to the investments in new capacities, rising prices of CO2 emission allowances, and increasing fuel prices. Therefore, it is necessary to maintain the existing or newly implemented policy measures that will guarantee necessary profits for generators and keep the district heating end-user prices at competitive levels (in particular in member states where cogeneration plays a significant role). Only then, it will be possible to have an increase in district heating demand as shown in the High-RES centralized scenario. With the development of low-energy buildings, district heating networks should be expanded in regions where sufficient spatial heat density exist, in order to maintain the current district heating demand. Otherwise with decreasing district heating demand, as e.g., in case of the Mod-RES and High-RES decentralized scenario, CHPs are exposed to lower district heating and electricity sales, what leads to less favorable economic conditions for investors.

12 Optimal Energy Portfolios in the Heating … 233

### **References**

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### **Part V Analysis of the Environmental and Socio-Impacts beyond the Greenhouse Gas Emission Reduction Targets**

### **Chapter 13 Unintended Environmental Impacts at Local and Global Scale—Trade-Offs of a Low-Carbon Electricity System**

**Maryegli Fuss and Lei Xu**

**Abstract** The focus on expanding the sector coupling and binding the electricity system and end-user sectors like the transport and industry bring attention to environmental trade-offs. Otherwise, unintended environmental impacts could potentially impede the transformation process. Given that, this paper aims to identify and discuss environmental burdens that should require government attention. For that, the approach of coupling Life Cycle Assessment with the electricity market model (ELTRAMOD) is presented. Results show that the large impact on land use occupation as a regional issue requires attention due to diversified permitting mechanisms and eligibility criteria for solar fields among European member states. Metal and ozone depletion bring the challenge that transformation processes need attention on global limits related to finite resources and fugitive losses of anthropogenic substances.

### **13.1 Introduction**

Fossil-based electricity systems meet, among other criteria, the security conditions of a power outage for many energy-intensive sectors (e.g., residential and tertiary) in Europe for many years. The transition toward low-carbon energy sources (European Commission 2018c) focuses on expanding sector coupling. Now the critical issue is to bind the electricity system and the two largest fossil fuel combustion sectors, transport, and industries (cf. Part III and IV). The challenge going forward is to maintain the security of supply of the electricity sector (including transport and industry sectors) and at the same time to achieve specific decarbonization targets (cf. Chapter 2).

The embedded carbon emissions are becoming more and more discussed between researchers. For instance, there is a debate about the biases of a clean conceptual approach for greener mobility through electric vehicles when the energy in-use source is an oil or coal-based power plant (Clarke 2017; Egede et al. 2015; Holloway 2019).

M. Fuss (B) · L. Xu

Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany e-mail<EMAIL_ADDRESS>
© The Author(s) 2021 D. Möst et al. (eds.), *The Future European Energy System*, https://doi.org/10.1007/978-3-030-60914-6\_13

Indeed, there is no doubt that the electricity system has to undergo a radical transformation as the leading sector to reshape the entire European energy system (European Commission 2018c). Under these circumstances, the electricity system became the core sector that requires an in-depth analysis in the REFLEX project.

Among the REFLEX scenarios (cf. Chapter 2 and Poganietz et al. 2017; Zöphel et al. 2019), combustion emissions affecting climate change such as CO2, CH4, N2O, and so on, have the potential to be reduced significantly, if wind and solar technologies are deployed on a very large-scale in Europe. Moreover, decarbonizing the electricity sector will facilitate significant reductions in carbon emissions due to industry, transport, residential, and tertiary sectors. Part III and Part IV of this book describe how carbon targets could be achieved within the scope of the REFLEX scenarios.

Developments aiming at reducing greenhouse gas emissions from the electricity sector may nevertheless cause increases in other kinds of environmental impacts. Costa-Campi et al. (2017) also stress that energy policies usually focus on the political economy dimension, which could bring conflicts with the goal of environmental protection. It is necessary to be aware that the transition of the energy system depends on many techno-economic features such as the massive penetration of renewable technologies that are still not in place in some countries and the need for low-carbon emission fuels like natural gas (cf. Chapters 10 and 11). All these developments could drive critical environmental impacts on the European scale (i.e., local effects), but also globally due to the reach of supply chains. Although discussions at the political level have expanded to include broader sustainability-related considerations, imposing protection conditions for ecosystems and human health, for example, Europe still gears its policy toward carbon-based benchmark regulation exclusively (European Commission 2019a).

The objective of the present Chapter is to identify and discuss unintended environmental burdens based on the envisaged REFLEX scenarios that require further policy action. Unintended environmental burdens are impacts that are driven by a critical problem that could impede a smooth transition to the decarbonized European energy system. Based on the methodological framework developed in the REFLEX project (Brown et al. 2017; Brown et al. 2019), this paper applies the model coupling approach (cf. Chapter 3). It focuses on the process of coupling Life Cycle Assessment (LCA) with the bottom-up electricity market model (ELTRAMOD), the chosen energy system model for this study. The model coupling between LCA and ELTRAMOD brings the advantage of quantifying a full range of environmental impacts and therefore the possibility to discuss environmental trade-offs of the three REFLEX scenarios, Mod-RES, High-RES decentralized, and High-RES centralized (cf. Chapter 2 and 10).

The structure of this paper is therefore as follows:

• Describing the model coupling approach to analyze scenarios for the electricity system (Sect. 13.2);

### **13.2 Developing the Model Coupling Approach to Identify Environmental Trade-Offs**

For the REFLEX project, three fundamental aspects are taken into consideration in electricity production systems:

LCA and ELTRAMOD have a different objective function. While LCA is focused on environmental impacts modeling, ELTRAMOD is an electricity market model (cf. Chapter 10). This study follows the detailed procedures described by Xu et al. (2019). Firstly, inputs for the LCA model are based as closely as possible to the REFLEX scenarios (Chapter 2) in line with (1) and (2). Secondly, the LCA model is adapted to the technology groups used by ELTRAMOD. ELTRAMOD considers conventional and renewable technologies and a low-carbon group representing installed capacities of fossil fuel-fired carbon capture and storage (CCS) technologies (cf. Chapter 10), bringing attention to a policy aimed at securing fuel supply (3).

This study recognizes several challenges and barriers to producing LCA results that match public policy development (Hellweg and Milà i Canals 2014; Seidel 2016). For instance, transparency and accurate inventories are often questionable among non-practitioners. To establish a common understanding, the most significant inputs and adjustments for the model coupling are presented below.

### *13.2.1 Describing Relevant Input Parameters for the LCA Model in Context of the REFLEX Scenarios*

Transparency of input data for the LCA model is important to support robust conclusions. In the REFLEX project, identification of specific technology types is the starting point for ensuring that future supply needs (e.g., resources and fuels) are considered. The challenge is to include scenario-based considerations in establishing life cycle inventory for future energy technologies over their entire lifetime (including extraction of raw materials, manufacturing of new technologies, transportation and installation, generation of electrical power to the grid and end-of-life).

The future market share of innovative and conventional technologies is a parameter directly connected to the REFLEX scenarios. The study of Viebahn et al. (2015) provided a technological roadmap and bandwidth extrapolations based on a search of all renewable energy technologies existing in energy scenarios in Germany. Additionally, this study analyzed possible future developments of renewable energy technologies based on literature and expert knowledge. However, the market share parameter defined by Viebahn et al. (2015) is only applied in this study for wind and solar technologies that are currently accepted or emerging on the market. Technologies on the laboratory scale are not included. Wind power technologies are represented in the LCA model by four different turbine types (asynchronous generators, electrically excited direct drive, permanent magnet, and superconductor high-temperature) and solar power is represented by two types of photovoltaic cells (crystalline cells and thin-film cells).

The LCA model and ELTRAMOD have common techno-economic parameters. The approach for parameter harmonization in REFLEX (Fuss et al. 2018) required coordination of techno-economic characteristics of technologies (e.g., installed capacity, lifetime, and efficiency) to increase the consistency among the models. Techno-economic parameters provide, for example, the possibility to quantify resource demands for each technology according to the capacity expansion paths assumed in ELTRAMOD.

Figure 13.1 shows generic inputs and outputs for the transition of the electricity system in Europe. Although the goal is to make the electricity sector cleaner and provide better environmental conditions in Europe, attention should be paid to consequences at a global scale too. It should be noted that outputs of the transition should achieve goals according to the European commitment within the sustainable development goals (European Commission 2019c) such as to safeguard life on land and secure good health and well-being.

Figure 13.1 also shows fuels, resources, and entire technologies imported to Europe from the global market. This study assumes that these resources, fuels and technologies are produced in the global market. This assumption is based on the fact that the EU is in favor of free trade and does not make exceptions to this for energy technologies and related markets (European Commission 2018b). With respect to fuels, the LCA model considers the exploration (or cultivation for biomass) and production at supplier location as a first stage. The supplier countries commitment

**Fig. 13.1** Example of inputs and outputs required for electricity production (*Source* Own illustration)

ends after the transportation of the fuel to local and large-scale power plant distribution in Europe, the second stage considered in the LCA modeling. Due to significant uncertainties about future developments, it is assumed that the share of the global suppliers (i.e., European supplier leading countries) does not change in any of the REFLEX scenarios.

The entire inventory analysis makes use of the ECOINVENT 3.3 database, which provides well-documented foreground process data (Wernet et al. 2016). For each emerging technology, a new inventory database is established based on resource inputs and electricity demand for production. Details of the life cycle inventories for all technologies considered and the respective ECOINVENT data processes used are available in Brown et al. (2019).

### *13.2.2 Coupling the Results of ELTRAMOD and the LCA Model to Determine Policy Implications*

Figure 13.2 illustrates the procedure used in this study from coupling the results of ELTRAMOD through to the identification of relevant policy implication. Step 1 consists of coupling the described LCA model (Sect. 13.2.1) with results for electricity generation mix generation produced by ELTRAMOD. Equation 13.1 summarizes the model coupling calculation method:

**Fig. 13.2** Systematized procedures for extracting LCIA results for policy guidance (*Source* Own illustration)

$$Z\_{n,l} = \sum\_{T=1}^{n} LCIA\_{T,n,l} \* ET\_{T,l} \tag{13.1}$$

where *Zn* is the total environmental impact in category *n* over the life cycle of an energy technology group of all modeled countries, i.e., EU-27, Norway, Switzerland, United Kingdom, and Balkan countries in period *t* measured in the respective unit, e.g., in case of metal depletion, *t Feeq* (ton iron equivalent), *LCI AT,n,t* is the environmental impact of an energy technology group in period *t* measured in the respective unit, e.g., *t Feeq* , per TWh. *E TT,t* equals the electricity generation of the energy technology group in period *t* in TWh. For instance, *E Twind* represents the group of specific technologies *T* as previously described (e.g., synchronous generators, and asynchronous generators).

Step 2 consists of normalization to evaluate 18 different impacts categories according to the RECIPE life cycle impact assessment (LCIA) method (Goedkoop et al. 2012) that is used in the REFLEX project. In this step, the calculated results in each scenario and the respective temporal cases are divided by the base year (2014). In this way, it becomes possible to screen for the most relevant impacts for each scenario. The screening analysis aims to identify those environmental impacts that overtook the threshold defined by the modelers. The threshold of 80% reduction or increase was chosen. The value of the index is comparable to the climate target. Hence, unintended environment burdens are identified when the environmental impacts increase significantly (by at least 80%).

In-depth analysis of unintended environmental burdens is performed in step 3. The largest contributors (drivers) to these impacts are analyzed in terms of the most critical technology from the electricity production mix, life cycle stage, processes, and flows.

The level of uncertainty is one of the barriers to trust in LCA results, as mentioned by non-practitioners and decision makers (Seidel 2016). Sensitivity analyses, mapping characterization, and classification factors applied or not in the RECIPE method, double-checking with different LCIA methods were conducted as part of step 4 of the method (i.e., consistency checks).

Up to step 4, the used procedure follows the usual procedures that are taken in LCA modeling to derive robust conclusions (Zampori et al. 2016). Step 5 is added exclusively for this study. The objective of step 5 is to assess the findings of the LCA model from the perspective of current policies and national and industrial priorities as considered in the literature.

A comparison with current policies supports the discussion of identified drivers (e.g., technology or flow) is the trade-off that can impede the achievement of the climate target if no intervention is made.

The policy implications should be seen as precautionary information aiming to raise awareness about potential trade-offs and related environmental consequences without further policy intervention.

### **13.3 Unintended Environmental Consequences of the European Low-Carbon Electricity System**

According to the 2014–2050 REFLEX scenarios, results are presented for the total environmental impacts due to the total electricity generation in EU-27, Norway, Switzerland, United Kingdom, and Balkan countries (Chapters 2 and 10 and Zöphel et al. 2019) for a given temporal case. The absolute environmental impacts are presented (instead of the normalized results) for better comprehension of the findings discussed. They are introduced at an aggregate level as a final output of the schematic workflow, instead of describing the findings for each step (c.f. Figure 13.2) and each generation technology (Chapter 10). The overall impacts due to the REFLEX scenarios (step 1 and 2) are presented in Brown et al. (2019).

### *13.3.1 Environmental Impacts at Local Scale and the Challenges for European Member States*

Land use is an environmental trade-off arising for the low-carbon electricity systems in the REFLEX scenarios. Figure 13.3 shows the absolute land use for the development of the overall electricity generation according to the envisaged scenarios.

Increasing demand for ground-mounted photovoltaic (PV) is responsible for increases in land use for all scenarios. According to Fig. 13.3, High-RES scenarios show a dramatic increase in land use, by a factor of nearly five over the baseyear, reaching 32,630 km2·a (High-RES cen) and 33,724 km2·a (High-RES dec). In particular, between the years 2020 to 2040, the impact on land use occupation is a consequence of the changing mix of technologies used for electricity generation. While coal-based generation is replaced (among other technologies) by the increasing capacities of ground-mounted PV (and wind energy), land use impacts

**Fig. 13.3** Major technologies contributing to the land use impact of the overall electricity generation in EU-27, Norway, Switzerland, United Kingdom, and Balkan countries (*Source* Own illustration)

increase. In the period 2040–2050, land use impact doubles due to measures aiming to achieve the target to electrify the transport and industry sectors.

For comparison: the direct land use for coal-based generation is 4.5 km2/TWh compared to an average of 47.8 km2/TWh for ground-mounted PV. Indirect impacts due to coal surface mining are not taken into account. In contrast, the estimated land use of 2.5 km2/TWh for wind technologies is relatively low.

In the RECIPE impact assessment method, the land use category "land use occupation" avoids any other trade-offs for land used for food and the conservation of the ecosystem. The results above represent this category. It is assumed that no land transformation from one status (e.g., forest) to the other (e.g., agricultural land) will occur. Therefore, competitions, which still might happen as a consequence of land used for urban expansion or even a delay of restoration to the prior nature conditions, cannot be excluded (Goedkoop et al. 2012).

Although land use seems not to be a significant environmental problem, the results indicate that 28,864 km2 of land (equivalent to over 70% of the total area of Switzerland) would be required for solar fields in 2050 in the High-RES decentralized scenario. The requirement is similar for the High-RES centralized scenario, as shown in Fig. 13.3. This is because the electricity mix is very similar in both scenarios (Zöphel et al. 2019). In any of the cases (centralized or decentralized), it should be noted that 56% of the total area requirement for solar electricity generation is potentially aimed for solar fields between 2040 and 2050.

As part of the European SET-Plan, the deployment of ground-mounted PV will increase in European member states (European Commission 2018c). Italy, France, and Spain are the EU countries with the highest potential for technology due to proper climate conditions, among other criteria. The European standard classification, defines land use division into agricultural land, forest and "other" (this category includes built-up areas, roads and other transportation features, barren land, or wasteland) (CIA World Factbook 2018). If the installed capacity (demand) are taken into account according to High-RES decentralized scenario, it will require over 9% of the "other" category of land in Italy, 12% in Spain, and 4% in France. Those three countries have diversified permitting mechanisms and eligibility criteria for ground-mounted PV plants. In France, implementation conditions for groundmounted PVs are under discussion where bonus could be accepted if wasteland (e.g., landfills, industrial brownfield, and polluted area) would be taken (Bozonnat 2018). However, the French Act on Energy Transition (France 2015) stands only on the market segment for PV rooftop installations. Meanwhile, in Italy, large solar fields require a process authorization that is still not clearly defined. Within the authorization process, the Italian government aims to minimize agriculture land losses and guarantee their aesthetic green power system (Bellini 2019). Ground-mounted PV fields are well accepted in Spain. The country eliminated all constraints that could impede the deployment of renewable technologies in the national legislation since 2014 (Morales 2018). The free-deployment legislation could be attractive for investors. Therefore, Spain is facing the highest urban growth rate in the EU (URBACT 2019). Competition in Spain between urban development and space for ground mounted solar PV could be a future challenge, especially since there is only a small amount of land in the category "other."

However, Italy, Spain, and France are examples of countries where regulations could impede the transition to renewable energy based on ground-mounted PV plants.

### *13.3.2 Resource Depletion in REFLEX Mitigation Scenarios as a Backdrop of Global Trade Uncertainty*

Metal depletion, owing to the research focus on innovative technologies (cf. Sect. 13.2), calls for in-depth analysis of the availability of finite resources. At the same time, the shift to natural gas-based carbon capture and storage electricity generation requires attention to ozone depletion due to emissions of ozone-depleting substances in natural gas supply chains. In light of these considerations, metal and ozone depletion impacts are presented in the following sections.

#### **<IP_ADDRESS> Metal Depletion**

Another consequence of the electrification of the industry and transport sectors to reach the climate targets (cf. Chapter 6, 7 and 10), is the trade-off on metal depletion. Figure 13.4 shows that regardless of the scenario or year, metal depletion increases due to increased solar and wind energy generation. The high share of wind and solar electricity generation in the High-RES decentralized scenario (to achieve, for instance, the demand for hydrogen production in industry and transport in 2050, cf. Zöphel et al. 2019) increases the metal depletion impact to over 290% compared to the year 2014. Meanwhile, in the Mod-RES and High-RES centralized scenario, the growth is about 75 and 235% for the same period, respectively.

Metal depletion is estimated to be 19,530 tFeeq/TWh for wind onshore, 25,900 tFeeq/TWh for PV rooftop compared to 4,140 MtFeeq/TWh for nuclear power plants. Chromium steel, low-alloyed steel, reinforced steel (for towers, rotors, and nacelles) as well as copper for connecting wires together contribute to over 90% of the metal depletion for onshore wind generation. In spite of the fact that industries are seeking to minimize the demand for silver in crystalline PV manufacturing (ITRPV 2018), rooftop PV generation indicates the highest metal depletion impact where the major contributors are copper (58%), gold (7%), steel (4%), and silver (1%). Among those metals, recycling activities are intensively done only for steel.

In a sensitivity analysis, the possibility of steel recycling was taken into account. The results shown in Fig. 13.4 are based on the assumption that all metals are primary resources, i.e., obtained directly from mining activities. Nowadays, about 80% of steel is recycled globally (BIR 2018). With a hope that steel recycling could reach 90% until 2050, the burdens of metal depletion for electricity production in the High-RES centralized scenario can be reduced by 40% according to the sensitivity

**Fig. 13.4** Major technologies contributing to metal depletion impacts due to total electricity generation in the EU-27, Norway, Switzerland, United Kingdom, and Balkan countries. Abbreviations: CCGT (combined cycle gas turbine), PSP (pumped-storage power plants), Gas CCS (gas carbon capture and storage) (*Source* Own illustration)

analysis. It should be noted that recycling indexes for other metals are lower than 50% (e.g., copper) or even non-existent in the EU (e.g., silver) due to small quantities in electronic facilities (Hagelüken et al. 2016).

The challenge regarding metal depletion is that metals are non-renewable resources comparable to fossil fuels. The environmental impact indicates the reduction of availability of a specific group of resources (commodities). The assumption lies in the mining costs as the dominant and consistent factor between dissimilar metals in the RECIPE method (Goedkoop et al. 2012).

In the model applied, innovative wind and solar technologies (Sect. 13.2.1) are distinguished from the conventional ones according to the new resources required for their manufacture. Ten metals are additionally included for these technologies in this study, namely, neodymium, praseodymium, dysprosium, gadolinium, and cobalt for emerging wind turbines and cadmium, selenium, tellurium, gallium, and indium for thin-film solar cells. In the consistency checks (step 4, cf. Figure 13.2), it is observed that those metals were not assessed in the results presented in Fig. 13.4. The main reason is the missing characterization factors in the RECIPE method.

Following the same assumptions as presented in the RECIPE method, for example, the weighting factor of platinum is taken for neodymium. Platinum represents the highest mining costs with over 11,000 USD/kg in the RECIPE method (Goedkoop et al. 2012). Neodymium is rare and often hard to find, which would therefore be comparable to platinum. It is a rare earth metal belonging to a group of 15 different elements which together represent 5–10% of a mineralized rock (monazite). It is extracted from open-pit mining mainly located in China. Nowadays, due to several challenges such as environmental issues, illegal mining, and export, the Chinese government are implementing policies such as mining quotas (Mancheri et al. 2019). Neodymium is nevertheless an essential resource for wind turbines with high power output and large blade sizes (Vestas 2019). Taking the most ambitious REFLEX scenarios (High-RES scenarios) as an example, considering the framework conditions, the demand for neodymium alone would contribute to 95% of the total metal depletion for wind power. If neodymium would be included in the RECIPE method, it would lead to the greatest single inventory contribution in the metal depletion impact category.

Although the results for metal depletion are highly dependent on the technologies (Sect. 13.2.1) and resources previously described, there is no doubt that wind and solar technologies are highly competitive in the global market. Many European companies are strongly recognized on the market for wind technologies, such as Denmark's Vestas and Spain's Siemens Gamesa, which are behind China's Goldwind, the world's largest wind turbine companies (Froese 2019). Meanwhile, Chinese industries have taken the lead in photovoltaic manufacturing due to strengthened innovation efforts in the country (Gandenberger 2018). Thus, availability and access to the resources considered in this study will be a major factor to ensure the necessary commercial development and to achieve the envisaged targets for the transformation of the electricity system.

Secure access to resources and resource efficiency are objectives identified in the EU 2020 strategy (European Commission 2011). Nevertheless, metal depletion is a trade-off for climate targets as problems arise due to high metal demand (e.g., steel) and the high amount of non-recyclable metals.

#### **<IP_ADDRESS> Ozone Depletion**

The High-RES scenarios show how high CO2 prices will potentially require lowcarbon energy sources (e.g., nuclear power plants) and accelerating the deployment of the natural gas generation with carbon capture and storage (CCS) from 2040 onwards (cf. Chapter 10). An outcome of these developments is an increase in ozone depletion impact, as shown in Fig. 13.5.

Ozone depletion brings consequences for humans globally (e.g., excess skin cancer incidence) because of ozone layer destruction through fugitive losses of anthropogenic substances (Goedkoop et al. 2012; Velders et al. 2000). One of the

**Fig. 13.5** Major technologies contributing to ozone depletion in overall electricity generation in the EU-27, Norway, Switzerland, United Kingdom, and Balkan countries (*Source* Own illustration)

global responsibilities assumed by the EU is the Montreal Protocol that aims to protect the ozone layer by identifying hotspots where ozone-depleting substances might occur (EEA 2018). With this consideration in mind, two aspects of the ozone depletion results from the analysis of the REFLEX scenarios are presented (cf. Figure 13.5):

The transportation of natural gas, as fuel for CCGT and natural gas CCS generation, is the specific activity contributing to ozone depletion according to the life cycle phases connected to the supply chain. The identified problem arises due to leaks of methane and hydrocarbons in the pipelines from Russia, as the major natural gas supplier for Europe. Europe imports 40% of natural gas from Russia, 38% from Norway, 11% from Algeria, 4% from Qatar, and others (Eurostat 2019).

Transport of natural gas releases leakage nowadays (ICLG 2019; Healing 2018; Nasralla 2020). Pipeline leaks are a complex environmental problem for local supplier countries. Leakage of natural gas consists primarily of methane and ethane, and both are drivers for photochemistry reactions and non-linear interaction in the stratosphere-troposphere (Portmann et al. 2012; Velders 1997; Velders et al. 2000). Bromochlorodifluoromethane (Halon 1211) and Chlorodifluoromethane (HCFC-22) are two critical substances identified in the modeling analysis. These two substances are released in the stratosphere-troposphere due to other past and current industrial processes (Portmann et al. 2012). The concentrated amount of natural gas leakage may attack chlorine and bromide from Bromochlorodifluoromethane (Halon 1211) and Chlorodifluoromethane (HCFC-22) (Velders 1997; Portmann et al. 2012). Such reactions impede the future evolution of the ozone (Velders 1997; Portmann et al. 2012).

Additionally, the increasing demand for natural gas and continuous leakage problems can degrade regional air quality and are bad for human health (cf. Chapter 15, Velders et al. 2000). Moreover, the effects imposed on the supply chain of natural gas already bring new awareness that is connected to the problem of pipelines identified in this study. Among several issues, geopolitical changes are influencing the natural gas market (Correljé 2016) and can slow the pace of climate targets in Europe, if the electricity mix will be dependent on CCGT or gas CCS technologies. Furthermore, many Russian pipelines have exceeded their operational lifetime (Nasralla 2020). Thus, significant investments are needed to comply with new regulations, such as the installation of electromechanical corrosion protection of pipelines, technological communication facilities, leak identification, and warning systems (ICLG 2019; IEA 2006). Meanwhile, Norwegian companies are working on better sealing technologies because leakages are not visible in the stormy and cold North Sea by monitoring systems which leads to problems for the supply chain (Healing 2018). However, an environmental challenge exists for many European countries, especially Germany, that needs to import more natural gas as a substitute for the coal-based electricity generation.

Although industries and nations are already facing the challenge, it is still not clear how countries (exporter or importer) will divide the responsibility for judging the compliance to diminish pipeline leaks. Until no bilateral commitment is aimed, great demand for natural gas has the potential to enhance ozone depletion causing regional effects and trade-offs for the achievement of climate targets.

### **13.4 Conclusions and Policy Implications**

The electrification of end-user sectors and the simultaneous decarbonization of the electricity system are ambitious targets for the European Union. This sector coupling approach is a key element in the transition to a fossil-free economy. From an environmental perspective, a successful sector coupling is highly dependent on the development of the electricity system. Moreover, the reduction of greenhouse gas emissions by 80% for the electricity sector requires, among other things infrastructure planning, availability of technologies that are ready at a convenient time and environmentally friendly low-carbon emission fuels. Otherwise, three environmental impacts, identified in this paper, could impede the transition process for a low-carbon electricity system. Land use requires infrastructure planning among EU member states with different regulations. The results concerning the assessment of metal depletion demonstrate that the transformation process to a low-carbon electricity system is highly dependent on the availability of metals (finite resources) to produce technologies for intermittent electricity generation. The outcomes of the analysis regarding ozone depletion highlight the potential effects of fugitive losses that affect anthropogenic substances (e.g., Halon 1211 and HCFC-22 identified in this study) and are an issue for global suppliers of natural gas. This impact additionally leads to the demand for further international agreements (such as the Montreal Protocol) to protect the ozone layer.

The results are created through the schematic work and method described in this study. They build upon the model coupling of LCA and ELTRAMOD to assess the REFLEX scenarios. The outcomes are highly interconnected to the assumptions made and the LCA modeling. Therefore, this study includes a methodological description to promote transparency and robustness to the results. However, the analysis of potential environmental impacts of the energy transition increase awareness and identify new research areas.

Through the LCA modeling, it is identified that the deployment of groundmounted PV technologies has a crucial environmental impact. Land use for groundmounted PV in the base year (2014) amounts to only 3% of that required in 2050 for the High-RES decentralized scenario—950 km2 compared to 28,864 km2 (cf. Figure 13.3). Countries with best weather conditions for ground-mounted PV power plants (like Italy, Spain, and France) consider restrictions concerning aesthetic requirements as well as potential resident opposition (NIMBY—not in my back yard). Although data show, the availability of other lands for ground-mounted PV installation (cf. Section 13.3.1), society and legal systems could impede such developments. Considering these constraints would require a break-down of regulations and infrastructure planning among EU member states and continuous investment in research at local scales. Investment in research and innovation opens opportunities for solar energy. Hoffacker et al. (2017) present how PV systems enable techno-ecological synergies in the United States, for instance, the utilization of a reservoir power plant for solar electricity generation ("floatovoltaics") and PV systems integrated in agricultural landscapes (agro-photovoltaics). In Europe, few agro-photovoltaic projects have been implemented and still need to deal with agronomic challenges like the field management on crop production (Weselek et al. 2019).

A more tightly integrated Europe, not in political terms but rather with respect to technology development and supply markets, would help to address the challenges related to metal and ozone depletion. For instance, the transformation of the entire European electricity system depends mostly on the commitment of member states, where some countries will probably set up more ambitious targets than others. Consequently, these members will require a higher commitment to deploy innovative renewable technologies similar to those studied in this chapter, such as permanent magnet and superconductor high-temperature wind turbines. Contrary to the current free-market orientation on the openness of trade (European Commission 2018b), the proximity of renewable technology industries with the EU could bring advantages for both sides. In this regard, the EU could support even more local industries to address the fluctuation of resource prices and trade import tariffs, although Europe has one of the lowest trade import tariffs in the world (European Commission 2019b). On the other hand, industries could provide more information about how the required resources are affecting the global environment. Because the supply chain for crucial resources of wind technologies involves intense mining activities causing strong vulnerability due to regional impacts on the environment (Ballinger et al. 2020).

The assessment results presented in this study confirm that natural gas will be the most significant contributor to European energy security in the upcoming future, replacing oil and coal energy sources (Correljé 2016). Germany, for instance, has assumed a National Preventive Action Plan and Emergency Plan with regard to natural gas and has taken the responsibility to be the European business location for natural gas (European Commission 2018a). However, stockpiling of natural gas could be the most attractive alternative to guarantee desired fuel stocks, along the lines of a similar procedure adopted by Germany for petroleum (European Commission 2018a). Therefore, further commitments by supply and demand countries regarding global impacts (ozone depletion) would additionally create incentives between the natural gas sectors to promote dialogues and foster investments (e.g., renovation, research, and innovation) for an environmental friendly low-carbon fuel.

This study identifies and discusses policy implications related to three environmental trade-offs in the context of a low-carbon electricity system: land use occupation, metal depletion, and ozone depletion. Many contributing factors to the unintended environmental burdens (trade-offs) are technically known. However, the factors are still not discussed as an obstacle for the European electricity transition. The development of any strategy to deal with these environmental trade-offs should bring local industries, member states, and the EU together.

### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

### **Chapter 14 Assessing Social Impacts in Current and Future Electricity Production in the European Union**

**Nils Brown and David Lindén**

**Abstract** In this chapter social impacts of European electricity production are compared between the current situation and the REFLEX scenarios for 2050 from a life cycle perspective using the SOCA tool. The analyses indicate that for a limited number of social impact categories the SOCA add-on tool can identify geographic locations where improvement in social performance may non-negligibly improve the social impacts for future energy systems. Results show that gas supply from Russia is a major cause of social impact for all future scenarios in the subcategory "fair salary" due to the fact that the minimum wage is below the living wage in the country. The specific process for electricity generation in Europe contributes to social impacts in the same category to a lesser extent.

### **14.1 Introduction**

The development of environmental life cycle assessment (eLCA) as currently practiced is often traced back to the early 1990s. Meanwhile, only in the past decade interest has been directed toward using a life cycle perspective to perform social assessments—so-called social life cycle assessment (sLCA).

A major step in the methodology's development was the publication of the sLCA Guidelines (UNEP 2009). The technical framework for sLCA is based on the structure of the eLCA standards (ISO 2006a, 2006b), considering goal and scope definition, life cycle inventory analysis, life cycle impact assessment and life cycle interpretation (UNEP 2009). Among other things, the Guidelines identify a set of stakeholder categories each of which cluster a group of different stakeholders that "are expected to have shared interests due to their similar relationship to the investigated product

N. Brown (B)

SCB Statistics Sweden, Stockholm, Sweden e-mail<EMAIL_ADDRESS>
D. Lindén Ramboll, Stockholm, Sweden

systems" (UNEP 2009, p. 46). The stakeholder categories so identified are workers, local community, society (national and global), consumers and value chain actors.

The years since the publication of the Guidelines have seen a certain amount of research activity in the field, as testified by recent review papers (Arcese et al. 2018; Dubois-Iorgulescu et al. 2018; Zanchi et al. 2018). Databases have also been developed that collect social performance data for several products, product systems and functions for a wide variety of social contexts, where leading examples are the social hotspots database (Benoit-Norris et al. 2012) and the product social life cycle assessment (PSILCA) database (Ciroth and Eisfeldt 2016). The SOCA database addon (Eisfeldt 2017) meanwhile connects the social inventory and impact assessment methods used by PSILCA with the ubiquitous environmental LCI database ECOIN-VENT (Wernet et al. 2016). Data in the SOCA tool aim to support social assessment from the perspective of four stakeholder categories (workers, value chain actors, local community and society) and 37 connected social impact categories established according to the Guidelines (UNEP 2009). The stakeholder category workers are well-represented in SOCA, covering 18 separate indicators. Having first been made available in 2017, the tool is still quite new and there exist few if any examples of its application in literature, a gap that is aimed to be filled by the work carried out here.

Meanwhile, the aim of the REFLEX project is to analyze and evaluate the development toward a low-carbon energy system in the EU up to the year 2050 to support a better system integration of RES. The central approach in achieving this objective is to perform scenario-based energy economic system modeling (cf. e.g. E3MLab 2016; Fichtner et al. 2013; Fragkos et al. 2017; Herbst et al. 2012; IEA/OECD 2016; Schade et al. 2010 for examples of previous work in the same field).

Though the energy systems modeling performed in REFLEX provides important insights to support the path to carbon neutrality and beyond, the approach in general and REFLEX models specifically do not aim to cover in particular a range of social issues. Nevertheless, EU energy policy is guided by three fundamental principles security of supply, competitiveness and sustainability (European Commission 2016). Furthermore, the EU is committed to implement the Sustainable Development Goals (SDGs) in internal and external policies (European Commission 2019), including notably significant social issues such as good health and well-being, the elimination of poverty, labor rights, safety at work and fair wages. In light of the intersection of EU energy policy and development policy through the lens of sustainability, it is important to understand how an envisaged future energy system may affect the potential to achieve such goals as exemplified in the SDGs.

Considering the aforementioned ongoing development of methodologies, tools and databases for sLCA and since EU policy is guided simultaneously by the need for transition to a low-carbon energy system and to fulfill the SDGs, the main aim of this chapter is to compare social impacts for EU electricity production between the current situation and the REFLEX scenarios for 2050 from a life cycle perspective using the SOCA tool. Through fulfilling this aim it is intended to provide an example of the application of the SOCA add-on for social assessment and also to formulate policy recommendations for achieving improved social outcomes in the energy system transition.

### **14.2 Method**

### *14.2.1 Background to the SOCA Add-on for Social Life Cycle Assessment*

The SOCA add-on tool provides a quantity of worker-hours (the activity variable in social LCA, cf. UNEP 2009) and a social risk profile for each ECOINVENT unit process (as defined by a reference flow and functional unit). This is done in a few steps. Firstly, the sectoral allocation (according to International Standard Industrial Classification, United Nations 2008) and geographical allocation (cf. Mutel 2014) for each ECOINVENT unit process are mapped to the corresponding sectoral and geographical allocations in the PSILCA database, which is in turn based on the EORA database (cf. Ciroth and Eisfeldt 2016). Afterwards, each ECOINVENT unit process can be assigned a social risk profile in terms of four stakeholder categories (workers, value chain actors, local community and society) and 37 connected social impact categories established according to the Guidelines (UNEP 2009). In a second step, cost data given for each ECOINVENT unit process is multiplied by the labor intensity for the sector to which it has been mapped in PSILCA (in worker hours per unit monetary output) to yield an activity variable in worker-hours for each ECOINVENT unit process.

The final impact assessment step is performed by assigning the raw value for a given social indicator a *qualitative* level of risk (varying from no risk, very low risk, low risk, medium risk, high risk and very high risk) each of which is assigned a *quantitative* impact factor. As an example for the purposes of understanding, Table 14.1 shows the procedure for social impact assessment for the social indicator "Disability adjusted life years (DALYs) due to indoor and outdoor air pollution". The left hand column shows the intervals for the raw indicator values for each qualitative level of risk. For example, an ECOINVENT process with a social performance 7.8 DALYs per 1,000 inhabitants (i.e. the raw value of the social indicator) falls between 5 and 15 DALYs per 1,000 inhabitants and is therefore assigned a qualitative level "low risk" according to the central column in Table 14.1. Finally, the qualitative risk level

**Table 14.1** Example of semi-quantitative risk assessment for indicator "DALYs due to indoor and outdoor air and water pollution" (Ciroth and Eisfeldt 2016)

is assigned a quantitative social impact factor. Following the previous example, the unit process with a qualitative level "low risk" is assigned a social impact factor of 0.1. It should be noted in Table 14.1 that the impact assessment scheme is set up so that each increase in qualitative risk level causes an increase in quantitative risk factor by a *factor of 10.* Therefore, there is an exponential increase, and the level "very low risk" with a risk factor of 0.01 is ten thousand times less than the level "very high risk" with a risk factor of 100. The final social impact for a given ECOIN-VENT process (defined in terms of a reference flow and accompanying functional unit) is then calculated as the product between the worker-hours for that process's reference flow and the quantitative social impact factor for that process. Reasoning about the setting of the scales for qualitative risk levels and the values for the quantitative social impact factors are presented further in the PSILCA manual (Ciroth and Eisfeldt 2016).

### *14.2.2 Establishing the Life Cycle Model for Social Assessment*

Starting points for the assessment follow those of the environmental assessment. In line with the goal of the study, the four temporal cases considered in the social assessment are as those used for the environmental assessment, namely the base year (2014), the year 2050 according to the REFLEX Mod-RES scenario (hereafter 2050 Mod-RES), the year 2050 according to the REFLEX High-RES centralized scenario (hereafter 2050 High-RES centralized scenario) and finally the year 2050 according to the REFLEX decentralized High-RES scenario (hereafter 2050 High-RES decentralized scenario). For each temporal case the quantity of 1 kWh of grid electricity production is studied.

For each temporal case it is thus intended to assess EU electricity production from a social life cycle perspective. The electricity system as considered includes all capital and consumed material from the stage of raw material extraction up to the final production of electricity for delivery to the grid. For capital goods, only dismantling, demolition and disposal processes are considered for the end-of-life stage. No credits are considered for any potential recycling of capital materials.

Data on total electricity generation disaggregated by generation technology for each temporal case is taken from the REFLEX model ELTRAMOD (cf. Chapter 10). In order to reduce data quantities, a cut-off requirement was set that only generation technologies contributing to over 1% of total generation in any given temporal case was considered. It should be noted that after applying this cut-off criteria, over 99% of total generation was included in the system in all temporal cases.

Starting from the ELTRAMOD data for electricity generation, the life cycle inventory is developed for one unit of electricity production for each generation technology in each temporal case. A process flow diagram in Fig. 14.1 shows the

**Fig. 14.1** Generic process flow diagram for social life cycle inventory for generation of 1 kWh electricity with a specific generation technology (*Source* Own illustration)

generic process stages included when gathering inventory. Where available, background data is taken from relevant ECOINVENT processes for electricity generation in Germany. This is because electricity generation in ECOINVENT is generally modeled for specific countries and there are many different generation technologies in the database modeled for German conditions. For example, for electricity generation with natural gas combined cycle gas turbine, the background process is taken to be "electricity production, natural gas, combined cycle power plant | electricity, high voltage | cut-off, U—DE". Such processes cover all of the stages shown in Fig. 14.1. However, this approach provides a sound generic starting point for the assessment. It is necessary to develop system specific data for the models used, as described further below.

### **<IP_ADDRESS> Capital Goods**

In general, activity variables and social risk profile for capital goods production for each generation technology are based on the background ECOINVENT processes used to model each generation technology, and they are kept constant for all temporal cases.

However costs for wind (onshore and offshore) and photovoltaic (ground mounted and rooftop) power plants are currently changing rapidly (cf. Chapter 4 and e.g. Louwen et al. 2018). Therefore, activity variables connected to the entire "capital goods" processes for wind power and photovoltaic technologies in the assessment are

**Table 14.2** Sector and geographical region for social risk profiles for "onsite power plant production" and "electricity production, plant operation". Table according to United Nations (2008) and ECOINVENT Centre (2015)

changed for the current case compared to the background ECOINVENT processes according to recent capital investment data for each technology (IRENA 2018a; IRENA 2018b; Louwen et al. 2018). Furthermore, since costs for these technologies are further expected to decrease significantly up to 2050, updated activity variables are calculated for the 2050 scenarios using a learning curve approach based on an estimated global installed capacity for each technology (Brown et al. 2019).

A specific process is developed to model "onsite power plant construction" according to average European conditions (cf. Table 14.2). This is done since it is considered that the German conditions used for each background process are not sufficiently representative for the European case. Table 14.2 also shows the specific sector which this process is assumed to belong to. Other than the inputs noted above, inventory data is used directly from the selected ECOINVENT background process.

#### **<IP_ADDRESS> Fuel Supply**

For coal and gas-based generation technologies, the countries of origin for respective fuels (i.e. "fuel supply" in Fig. 14.1) are modeled in the base year according to data for supply to the entire EU according to Eurostat (2019). For nuclear generation, the generic global fuel supply considered in the background ECOINVENT process is used for the current case. This is considered relevant in light of the global nature of nuclear fuel supply to the EU—from Canada, Russia, Kazakhstan, Niger and Australia (World Nuclear Association 2019). Meanwhile, for biomass-based generation a mixture of EU-sourced wood chips and globally sourced wood pellets are assumed in the current year.

It is also assumed that the countries of origin for these fuels are the same in the 2050 scenarios as for the current case. This is considered reasonable considering the significant uncertainty in the future development of countries of origin.

Other than the inputs noted above, inventory data is used directly from the selected ECOINVENT background process. Of course, countries of origin may (or will most likely) change until 2050, however, due to the lack of knowledge with regard to the contribution of the countries of origin, the assumption to use todays countries of origin is still the best guess.

### **<IP_ADDRESS> Electricity Production and Plant Operation**

A process is also specifically developed to model "electricity production, plant operation" according to European conditions. The economic sector and geographical region according to ECOINVENT to model this process are shown in Table 14.2. The activity variable (i.e. number of worker-hours) for this stage is calculated based on the labor intensity of the specific sector and geographical location shown in Table 14.2 and generic cost data in ECOINVENT for the specific technology in question. Note that this is done for all generation technologies considered.

### *14.2.3 Social Impact Categories*

The assessment focuses on the stakeholder category workers since this is judged to be a category directly connected to and affected by supply chains for electricity production. From the indicators available to assess the worker stakeholder category, 12 are selected as shown in Table 14.3 below. Table 14.3 also shows how the selected indicators are related to the UN Sustainable Development Goal 8 Decent work and economic growth.

### *14.2.4 Calculation Method*

Firstly, the total quantity of worker-hours for 1 kWh of electricity generation for each generation technology in each temporal case is calculated using the LCA software tool open LCA. These data are extracted to excel. This gave the pure activity variable for production by each technology. The quantity of worker-hours required for a given generation technology in a given temporal case is then calculated as:

$$X\_{T,Y} = \chi\_{T,Y} P\_{T,Y} \tag{14.1}$$

where *XT,Y* is the total number of worker-hours due to generation technology *T* in temporal case *Y*, *xT,Y* is the number of work hours required to generate 1 kWh of electricity with generation technology *T* in scenario *Y* and *PT,Y* is the quantity of electricity generated in with technology *T* in temporal case *Y* in kWh. The total amount of worker-hours required for electricity generation for all n generation technologies in a given temporal case *XT ot,Y* can then be calculated as:

$$X\_{Tot,Y} = \sum\_{T=1}^{n} X\_{T,Y} \tag{14.2}$$

**Table 14.3** Subcategories and indicators for social risk used in this assessment, also showing the connection to UN Sustainable Development Goal 8 Decent work and economic growth

Secondly, the *social impact* for 1 kWh of electricity production for each generation technology in each temporal case is calculated in openLCA and extracted to excel. See the earlier Sect. 14.2.1 called "Background to the SOCA add-on for social LCA" for how this latter step is performed. The indicators and subcategories for which social impacts are calculated are given in Table 14.3. The calculation of the social impact due to electricity generation for a specific technology in a given temporal case is performed according to the method summarized by Eq. 14.1. The calculation of the total social impact due to electricity generation in a given temporal case then follows the format shown in Eq. 14.2.

Quantitative social impact factors (according to the scale shown in the right hand column in Table 14.1) for each temporal case and for each social subcategory considered is then calculated as the social impact calculated in the subcategory (see paragraph above) divided by the amount of worker-hours (see earlier in the Sect. 14.2.4 and Eq. 14.2).

### *14.2.5 Contribution Analysis*

The first step in the contribution analysis is to identify the electricity generation technologies making a *significant contribution* to any social risk subcategory considered in any the temporal case. This group included coal generation (in the base year only), natural gas-based technologies (in all temporal cases, mainly combined cycle gas turbine with and without carbon capture and storage technology), nuclear generation (mainly in the base year), wind power (mainly in 2050 High-RES scenarios) and solar power (mainly in 2050 High-RES scenarios).

The second step in the contribution analysis is to assess the percentage-wise contribution of different parts of the supply chain (according to the process flow diagram shown in Fig. 14.1) for each generation technology using the contribution tree function in openLCA. Finally, all unit processes in the supply chain for a given generation technology are arranged in descending order of their social impact contribution for each subcategory. According to this analysis, specific regions (or countries) and sectors making significant contributions to social impacts in each temporal case could be identified.

### **14.3 Results**

Figures 14.2, 14.3 and 14.4 shows results from the social assessment of electricity production in the EU. Comparing Fig. 14.2 with Fig. 14.3 it can be deduced that increased labor intensity (by about 10% comparing High-RES decentralized scenario with the current case) is responsible for a certain portion of the increase in social impacts seen in Fig. 14.2. Having said that, the large increase in calculated impacts seen in for example fair salary or forced labor show that an increase in risk levels (cf. Table 14.1) is also responsible for the observed increase in social impacts. The contribution analysis shows that the increases in social impacts in future scenarios observed in Fig. 14.2 (for all impact subcategories except for health and safety) is due to the increasing share of gas-based generation in the future. Gas technologies have higher than average impact considered per unit electricity generation.