In an ideal situation, the derived experience curves are implemented directly in the modeling code, using Eq. (4.2) as shown in Sect. 4.1.1. Implementing this equation directly would allow for endogenous modeling of technology cost development. This is however not feasible for every type of energy system model. Requirements are that the mathematical layout and optimization approach is compatible with implementation of the power law curve of Eq. (4.2), and that the model's geographical scope is global, as technological learning is assumed to occur on a global scale. Alternatively, the implementation can be exogenous, where experience curves are used to calculate cost trajectories using market deployment data from well-aligned (in terms of modeling scenario) global models.

In a direct, endogenous implementation, there is a feedback loop between deployment of technologies and the experience curve. Cumulative technology deployment is given as an input to the experience curve, and the resulting cost is applied in the model in the feedback loop. If the model does not directly produce the required input data (cumulative production), if possible, a conversion from the model outputs to cumulative production can be made, serving as input for the experience curve.

In an exogenous implementation, costs are derived on a time-basis.Market deployment of installed capacities of the respective technologies is taken from an external source, ideally a global energy system or integrated model analyzing a well-aligned modeling scenario. The cumulative technology deployments from this external model are used in conjunction with the experience curve to derive exogenous, time-based cost trajectories, which are fed to the original model.

### *4.3.2 Issues with Implementation of Experience Curves in Energy Models*

When implementing experience curves in energy models, a variety of issues can be encountered, such as inadequate geographical scope and technical model constraints. Here, several of these main issues are investigated.

Within the REFLEX energy system modeling framework, most models are limited in geographical scope. Assuming technological learning to occur at a global scale, and thus costs are a function of global cumulative production, this limited geographical scope prevents the models from being able to fully endogenously model costs using the experience curve.

A possible solution here is to derive the cumulative technology deployments for the countries outside the local model from an exogenous global model (Louwen et al. 2018). In this way, there can still be a feedback loop between costs and technology deployment in the local model. However, this means that depending on the geographical scale of the local model, the contribution to global cumulative deployments might be very limited, hence the effect of deployment within the local model on cost decline can be small. Furthermore, the global model should be compatible with this approach in terms of geographical subdivision, and should be modeling a well-aligned scenario. An alternative approach would be to determine global capacity developments fully exogenously in terms of small additions from within the local model, using for instance an S-curve approach (Fleiter and Plötz 2013; Schmidt et al. 2017).

The technical design of energy system models is sometimes also inhibitive to implementation of experience curves. Especially optimization models are not compatible with the nonlinear experience curve equation, requiring the implementation of a piece-wise approximation of the function (Barretto 2001; Heuberger et al. 2017). Also, optimization have a so-called 'perfect foresight', meaning technologies with high learning rates are preferred, while contrastingly simulation models avoid technologies with high initial costs, requiring certain incentives to push simulation models to deploy these technologies.

### *4.3.3 Description of Energy Models with Implemented Experience Curves*

Within the REFLEX project, experience curves are implemented in the models FORECAST, ELTRAMOD, PowerACE, ASTRA, and TE3 (cf. Chapter 3). The models are linked in what is called an Energy Model System (EMS), where the outputs of some models serve as input for the other models, in several feedback iterations. In this way, the complete European energy system is modeled with high levels of technological detail and including all energy demand and conversion sectors. In the paragraphs below, these models in the context of experience curve implementation are shortly described.

Within the REFLEX project, the FORECAST model is the key model that delivers energy demand figures to the EMS. FORECAST is a bottom-up simulation model with a high level of technological detail, and is primarily aimed to support strategic decisions in the context of long-term development of energy demand and greenhouse gas emissions, and covers the industry, residential and tertiary sectors (Jakob et al. 2012; Fleiter et al. 2018). Within REFLEX, the FORECAST model applied experience curves for heat pumps and alkaline electrolysis (Schreiber et al. 2020 and cf. Chapters 6 and 7), in a semi-endogenous manner, using a combination of technology deployment from within the model and technology deployments from an external integrated assessment model. These two technologies are seen as the most potential appliances for decarbonizing energy demand from buildings and industries.

ELTRAMOD, short for ELectricity TRAnsshipment MODel, is a bottom-up optimization model aiming to model cost-minimal investments and dispatch of electricity generation facilities, including storage and power–to–x technologies (Zöphel et al. 2019; Schreiber et al. 2020). The model covers all EU–27 member states, and additionally includes Norway, Switzerland, United Kingdom, and the Balkan countries. Since ELTRAMOD is an optimization model and is not compatible with the nonlinearity of the experience curve function, a procedure was developed to implement experience curves using a piece-wise approximation of the experience curve. However, the implementation of experience curves using this approximation induced significant increases in computation time, hence, given the multi-iterative modeling schedule of the REFLEX EMS, it was opted to include the experience curve data exogenously. In ELTRAMOD, the exogenous experience curves were implemented for carbon capture and storage equipped at a variety of electricity generation technologies, for utility scale battery storage systems (lithium-ion and redox-flow), for heat pumps, and for alkaline electrolysis (cf. Schreiber et al. 2020 and Chapter 10).

PowerACE is a bottom-up and agent-based simulation model that analyzes impacts of market design and policy measures on deployment of low-carbon technologies (cf. Fraunholz et al. 2020 and Chapter 11). Electricity generation companies and other electricity market actors are modeled as individual agents, including electricity demand and generation from renewable resources. The focus of the model is thus the electricity market, supply and demand on a high time-resolution, but investments in generation and storage capacity are also included and are planned in the model on an annual basis (Fraunholz and Keles 2019; Fraunholz et al. 2019). Given the limited geographical scope of the model, experience curves for PowerACE, are implemented exogenously for utility scale electricity storage (lithium-ion and redox-flow batteries) and electricity generation with CCS (Schreiber et al. 2020).

ASsessment of TRAnsport Strategies (ASTRA) is a system dynamics model aimed to provide strategic policy assessment in transport and energy. It covers all EU-27 members, and additionally includes Norway, Switzerland, and the United Kingdom (Fermi et al. 2014 and Chapter 7). ASTRA focuses on passenger and freight transport, including all forms of transport (road, rail, air, and water) and the active modes cycling and walking. It models the development of transport per mode, and can report a variety of indicators like fuel consumption and greenhouse gas emissions. Market diffusion of vehicle technologies is modeled based on a total cost of ownership (TCO) approach that considers among others vehicle prices, fuel or energy prices, and maintenance. In ASTRA, experience curves are implemented for the battery pack component of battery, plug-in hybrid and fuel cell electric vehicles. Although the geographical scope of ASTRA is not global, experience curves are implemented endogenously, as global market developments are supplied by another transport model within the REFLEX EMS, the global model TE3 (Heitel et al. 2020).

The Transport, Energy, Economics, Environment (TE3) model is the final model discussed here. TE3 models passenger road transport, is also a system dynamics model, and is used for policy analysis. It includes all major non-European markets like India, Japan, China, and the United States (Gómez Vilchez and Jochem 2019 and Chapter 5). Technology choice is influenced by several parameters including social dynamics, policy measures, and (available) infrastructure, and technology cost. Like in ASTRA, experience curves are used endogenously in TE3 to model the costs of the battery pack components of battery, plug-in hybrid and fuel cell electric vehicles.

### **4.4 State-of-the-Art Experience Curves and Modeling Results**

Within the REFLEX project, experience curve data was collected for a diverse set of technologies. An overview of the result for selected key technologies is given in Fig. 4.3. For all technologies listed, costs per functional unit are plotted against cumulative installation of each technology in MW.

**Fig. 4.3** Overview of experience curves for selected technologies. *Data sources* according to Schmidt et al. (2017), Williams et al. (2017), Louwen et al. (2018), Junginger and Louwen (2020). LR = learning rate. The *p*-values indicate the *p*-value for the F-test of overall significance of regressions

### *4.4.1 Overview of State-of-the-Art Experience Curves*

For PV modules, both crystalline silicon (cSi) and cadmium telluride (CdTe) based, learning rates of around 20% are observed, while for other technologies much more varied learning rates were found. Clearly observable in the dataset for PV (cSi) module prices, is a period of price stability, even a small increase in prices around installed capacity of 100 GW. This deviation from the overall experience curve trend was the result of a sharp increase in silicon prices, resulting from a shortage in silicon production. Subsequently, when silicon production capacity was greatly expanded and silicon prices declined rapidly, a sharp decline of PV (cSi) module prices occurred.

For heat pumps, learning rates of around 20% for the total system were observed, based on data from Switzerland, while the learning rate for the specific cost of energy supplied is in the range of 20-25% due to improvements in the technical performance of heat pumps (Jakob et al. 2020). Although the empirical basis for heat pump experience curves needs to be improved, the high learning rates observed indicate the potential for heat pumps to become cost competitive with natural gas boilers (Jakob et al. 2020).

For alkaline electrolysis, a learning rate of 14.5% is detected, although the spread of data points results in a low goodness of fit, with a poor *R*2 and a p-value of 0.064, indicating the regression is not significant at a confidence level of 0.05. For all other technologies, the regression was found to be significant at this confidence level.

For both utility scale and residential storage systems (respectively, Li-Ion Utility and Li-Ion Res) and for battery electric vehicle (BEV) battery packs, learning rates of around 15% are observed, while for hybrid electric vehicle (HEV) batteries, the learning rate was found to be much lower, at 11%. For BEV battery packs, significant variance is present in the dataset, leading to a relatively low *R*2 value.

For onshore wind, the data of Williams et al. (2017) are analyzed, which represents levelized cost of electricity (LCOE) for onshore wind systems, in a model that corrects for a variety of parameters, including wind quality of the wind farms that are included in the dataset. A learning rate of 12% is observed for LCOE as a function of cumulative production, although when the LCOE data is expressed as a function of cumulative electricity generation (rather than cumulative capacity), a higher *R*2 value is obtained, as this also includes a correction for the increase in capacity factors that are observed over time.

The interested reader is referred to Louwen et al. (2018), where detailed results of the project regarding the learning curve approach are presented.

### *4.4.2 Deployments and Cost Developments of Relevant Technologies*

Within the REFLEX project, experience curves have been implemented for a number of technologies. An overview of these technologies is given in Schreiber et al. (2020) and Heitel et al. (2020). In this section the diffusion of key technologies in the different models described above and in Chapter 3 are discussed, and cost trajectories for these technologies are presented.

Transport technologies are modeled within REFLEX with the models ASTRA and TE3. To ensure consistent diffusion of vehicle types, and since the geographical scales of the models are different, market diffusion of transport modes is aligned in the two models in the REFLEX model coupling system (Heitel et al. 2020). The model results (Heitel et al. 2020) indicate that EV battery prices decline to 89 EUR2018/kWh in 2030 and 59 EUR2018/kWh in 2050, down from prices of around 300 EUR2018/kWh in 2015, the starting year of the transport models, with cumulative global deployment of EV batteries (89% outside of the EU) approaching 30 TWh in 2050. As expected, the sensitivity analysis shows that especially the uncertainty in the derived learning rates can significantly affect the deployment of EVs. Lower learning rates result in a slower cost decline of vehicle prices, which in turn slows the diffusion of EVs, while with higher learning rates, costs decline faster and market diffusion is also sped up.

Within ELTRAMOD, the optimization model of electricity generation facilities, experience curves are applied exogenously to determine cost developments for battery storage, CCS and power-to-x technologies. As the cost trajectories are thus independent of technology deployment within different modeling scenarios, it is more interesting to discuss sensitivities of these cost trajectories to the applied learning rates. This sensitivity analysis shows that as higher learning rates are assumed for battery storage systems, the deployment of these technologies is increased. This furthermore influences the deployment of other technologies such as CCS and CCGT, which show slightly reduced investments. Similar results are obtained in PowerACE, where impacts of market design and policy measures on deployment of low-carbon technologies are modeled: high learning rates for battery storage systems result in strongly increased deployments of these technologies at the expense of deployments in CCGT and OCGT generation and a strong reduction of deployments of compressed-air energy storage.

Within FORECAST, technological learning is considered for several energy demand technologies. However, since FORECAST is actually a group of models describing different demand sectors separately, an iterative approach was applied to make sure technology deployments in one sector can influence technological learning in the other sectors and vice versa (Schreiber et al. 2020). Heat pumps represent a key technology in the heat demand sector in future energy scenarios, and for this technology a cost reduction of around 20% was observed up to 2050, compared to the minimal cost reductions examined without considering technological learning. However, it was found that the installation rates of especially heat demand technologies were less affected by cost developments and technological learning, and were to a larger extent the result of scenario constrains (Schreiber et al. 2020). As the technology cost of heat demand technologies only represents a minor share in overall heating systems costs in terms of building refurbishment and the cost differences between different heating technologies are small, energy price differences or the cost for building refurbishment measures have a higher impact on the overall cost structure (Harmsen et al. 2017).

### **4.5 Lessons Learned**

As illustrated in this chapter, a comprehensive effort was undertaken to collect data with which state-of-the-art experience curves could be derived and to implement these experience curves in a variety of energy models that are part of the REFLEX Energy Modeling System. Particular effort was devoted to the endogenous implementation of experience curves in the models which was not as straightforward as was anticipated. The main lessons learned during this research are briefly discussed below.

### *4.5.1 Methodological Issues*

Regarding general methodological issues, some limitations of the experience curves were identified. These relate to the defined technology system boundaries and functional unit of the experience curve, but also to exogenous parameters affecting cost developments of technologies. Multi-factor experience curves can be a valuable extension of the single-factor experience curves adopted in the REFLEX project, giving the possibility to include the effects of additional parameters such as R&D activities, commodity prices, and market dynamics. While these multi-factor experience curves could offer the opportunity to attain more accurate cost modeling results, further research is required to make a good comparison between single- and multi-factor experience curves, and to study model implementation of multi-factor experience curves, since model implementation of this type of experience curves is much more complex. Further research should thus investigate the optimal balance between the improved accuracy of results *vs.* the added modeling complexity.

An issue related to system boundaries and functional units is the topic of component-based experience curves. For several technologies evidence are identified that components of the technology system have different learning rates. To accurately represent the cost developments of these technologies on the long term, experience curves for the separate components should be implemented to avoid overestimation of future cost reductions. By using component-based experience curves, the issue of spillover effects across different industries could also be at least partly addressed. Especially for lithium-ion batteries, which are used in electronics, in residential and utility scale stationary electricity storage and in electric vehicles, a key recommendation is to investigate experience curves of the different components from battery cells to energy-management systems, although data availability might be prohibitive. As a matter of facts, the model implementation of such component-based experience curves, especially multi-factor version of them, drastically increases the complexity and the requirements of the model to produce input data for the experience curves, making further research on this topic a necessity.

### *4.5.2 Model Implementation Issues*

With regard to model implementation, one main issue is how to represent technologies for which there is a lack of empirical data due to their implementation readiness. For early stage technologies like carbon capture and storage (CCS), or energy storage types with small implementation numbers such as compressed-air energy storage, there is almost no empirical data that allows for the derivation of experience curves. This means that the cost developments of these technologies in energy modeling need to be represented either by using experience curves based on proxy technologies, or by some exogenous cost estimations. For high profile technologies like battery-based electricity storage, the available datasets that allow for derivation of experience curves are quite limited in terms of timeframe, hence there is little certainty on the longterm cost developments of these technologies. The implications of this with regard to modeling results should be explicitly discussed, especially in those cases, like the REFLEX project, where the analysis goes up to 30 years ahead. Therefore, a key recommendation is to focus on the creation of cost (and not price) databases that track the costs of these kinds of technologies, for instance by ensuring the assessment and publication of cost data for publicly supported pilot projects for CCS and other novel technologies.

Another theme related to the model implementation of experience curves is the geographical scope of the respective energy model, which is often limited. Assuming technological learning is a global process, some form of exogenous cumulative production data is required. This can be derived through a variety of approaches, such as by using results from external global models that analyze well-aligned scenarios, or alternatively by using the S-curve based approach. Within the REFLEX project, a combination of these measures was applied on a more or less ad hoc basis. Future research projects that aim to implement experience curves in a similar manner in geographically restricted energy models, should outline an explicit approach to deal with this problem, for instance by including a global energy or integrated assessment model in the modeling activities, ensuring a consistent scenario analysis across global and regional models.

There were also several technical issues identified with regard to model implementation. A typical problem is the fact that the power curve function of experience curves is not compatible with some models, requiring the implementation of a piecewise linear approximation of the original function. Also, models tend to have either perfect foresight (optimization models) or contrastingly, a myopic view (simulation models), which could result in over- or underrepresentation of technologies in the modeling outcomes. Finally, it was found in the REFLEX project that the modeling scenarios that are investigated can also be a barrier for the implementation of experience curves for certain technologies; this is the case where technology deployments are defined as scenarios inputs, for instance by means of targets for the deployment of renewable energy technologies.

### **4.6 Conclusions**

A final recommendation is to further develop research studies on applications of experience curve and technological learning mechanisms outside the economy sector. As part of the REFLEX project scientific literature review, several studies were identified that use experience curve to describe developments in energy efficiency of appliances and industrial processes (Jakob and Madlener 2004; Ramírez and Worrell 2006; Weiss et al. 2010; Brucker et al. 2014; Fleiter et al. 2017) as well as greenhouse gas and energy demand of renewable energy technologies (Bergesen and Suh 2016; Louwen et al. 2016). Furthermore, the concept of technological learning has been applied in the domain of social sciences to represent the effects of *social learning* on the market diffusion of battery electric vehicles (Edelenbosch et al. 2018). It would be therefore extremely interesting to expand the scope of application of experience curves in energy modeling so as to allow for the endogenous modeling of these additional parameters.

### **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.

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### **Chapter 5 Electric Vehicle Market Diffusion in Main Non–European Markets**

**Katrin Seddig, Patrick Jochem, and Wolf Fichtner**

**Abstract** Electric vehicles (i.e., battery and plug-in hybrid electric vehicles) are seen as one promising technology toward a sustainable transport system as they have the potential to reduce CO2 emissions. The forecast of their market penetration depends on various factors including the cost development of key components such as the electric battery. This chapter focuses on the impact of experience curves on the battery costs, and consequently on the electric vehicles' market penetration, which is simulated by coupling two system dynamics transport models: ASTRA, representing Europe, and TE3, representing key non-European car markets. The results of the TE3 model show that the consideration of global endogenous learning curves has an impact on the battery costs and therefore, the development of the electric vehicle stock ("feedback loop").

### **5.1 Introduction**

### *5.1.1 Motivation*

The transport sector has not yet contributed to greenhouse gas mitigation targets but increased its emissions in most countries. As user behavior stayed rather constant during the last decades and a further increase in motorized transport is being observed—mainly in developing countries—technological solutions come into the focus for enabling the transition toward transport sector decarbonization. Hence, electric vehicles (EV) (including battery EV (BEV) and plug-in hybrid EV (PHEV)) in combination with the already ongoing energy transition seem to be a possibility to reverse the increasing trend of producing greenhouse gas emissions even if the number of the global passenger car fleet might double until 2050 (Creutzig et al. 2015).

K. Seddig (B) · P. Jochem · W. Fichtner

Institute for Industrial Production (IIP), Karlsruhe Institute of Technology, 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\_5

In the vein of this book chapter this increase in EV registrations has two effects. Due to the increase in battery demand, learning effects (cf. Chapter 3) will lead to decreasing battery prices. This effect reduces the price of stationary storages for the electricity system and the prices for new EV, which in turn accelerates their market penetration and increases the flexibility of electricity consumption. The consideration of global battery demand is necessary for modeling this effect adequately as batteries are produced and traded internationally.

### *5.1.2 Related Research and Research Question*

Until now, mobile information and communications technology devices such as smart phones and laptops have dominated global battery demand. If the EV market gains momentum, the battery market will experience a tremendous acceleration. Hence, the development of global EV registrations over time is decisive and should be carefully analyzed.

In literature there is already a plenitude of studies which deal with this future market and show the related high uncertainties (Gnann et al. 2018). Also the variety in the underlying methods is manifold (Jochem et al. 2017). Many studies have a shorter time horizon than 2050 (IEA 2018; Tsiropoulos et al. 2018). Most studies focus on one dedicated market (e.g., on Japan in González Palencia et al. 2017 or on China in Liu and Lin 2017) and do therefore neglect the global feedback effect (i.e., each local market of EV is dependent on the price development of batteries, which are mainly resulting from the global EV production). Hence, global scenario analysis is a suitable approach to analyze these uncertainties of future markets. For Europe suitable studies already exist (cf. Chapter 7 or Pasaoglu et al. 2016). However, considering additionally the non-Europe countries and therefore a model coupling in particular is still underrepresented in the literature. Consequently, the research question of this section is on how the EV market develops over time in the main non-European car markets within our given scenario framework. These four countries (US, Japan, India, and China) were chosen as they have over 60% of global car sales in 2016 (OICA 2018) and are expected to have over 30 million EV in the next years as they are members of the EV Initiative and thereby showing public commitments toward the EV deployment (EVI 2016).

The structure of the chapter is the following. Section 5.2 presents a discussion on the development of EV, including the implementation of the experience curve in existing transportation models to foster global learning as well as the defined scenario framework. Section 5.3 outlines the results and discusses limitations and Sect. 5.4 concludes and sums up the chapter insights.

### **5.2 Considering Experience Curves in Market Diffusion Modeling and Scenario Definition**

### *5.2.1 The TE3 Model and Implementation of Experience Curves*

The TE3 (Transport, Energy, Economics, Environment) model supports policymaking analysis in the context of oil demand reduction and GHG mitigation from car travel (Gómez Vilchez 2019).1 This tool explores market developments of future car powertrain technologies and offers an international perspective by covering six main car markets. The time horizon is limited to the period from 2000 to 2050 and the underlying methodology is mixed, as the model contains elements of econometrics and system dynamics (SD).

The endogenously implementation of experience curves of batteries for EV in the TE3 model is based on SD core concepts, considering feedback loops as well as distinguishing between flow and stock variables (see e.g. Sterman 2000). For the implementation of the experience curve in the TE3 model the following Eq. (5.1) is used.

$$C(X) = C\_0 \cdot X^{\log\_2(1-l)}\tag{5.1}$$

The parametrization of this Eq. (5.1) is derived from Chapter 4. The battery costs *C(X)* result from the product of the battery costs in the first year C0 and the overall production of batteries *X* as well as the learning rate *l* (15.2%) and is measured in USD/kWh. The cumulated battery capacities include BEV and PHEV batteries with their assumed battery capacities of 30 kWh and 10 kWh per car, respectively. While the historical costs are taken from literature, the overall production of batteries relies on the EV stock of the previous modeling period. This includes results of TE3 for the non-European (i.e. China, India, Japan, and US) as well as of ASTRA for the European markets. In an iterative process, the coupled models add the battery capacities of the EV in countries considered by the other model to their cumulative production; i.e., the TE3 model provides the cumulative battery capacities of the considered non-European markets and ASTRA of the European markets. This may lead—according to the learning curve approach (cf. Eq. 5.1)—to adjusted battery costs, which in turn may influence the EV market penetration of new model runs of the two models. This iterative process represents the so-called "feedback loop" (cf. Chapter 3). In this way, the development of the European market as well as of the four key non-European ones is jointly considered. Through the implementation of the respective experience curves and the described linkage between the two transport models, the global development of the battery costs is modeled endogenously.

1A version of TE3 is available at www.te3modelling.eu and further details can be found in Gómez Vilchez (2019).

### *5.2.2 Framework of the Two Analyzed Scenarios for the Main Non-European Car Markets*

Many EV specific policies have been launched over the last years. Recently, some countries even announced sales bans for internal combustion engine vehicles (ICEV) (e.g. IEA 2017a; Plötz et al. 2019). As part of the REFLEX modeling framework, one Mod-RES and one High-RES scenario is introduced. In the High-RES scenario it is further distinguished between a centralized and decentralized world (cf. Chapter 2). For the four non-European countries the distinction between a centralized and decentralized world is not considered.

The implementation of the two developed scenarios (Mod-RES and High-RES) to the BEV case is outlined in the following, including assumptions with respect to the applied EV policy and the general development of the respective countries with respect to population and resulting overall passenger car stock. The Mod-RES scenario relates in principle to current policies and the High-RES scenario until 2030 as well. The latter includes a higher investment in infrastructure and lower emission standards and has therefore, a stronger EV increase. Both scenario represent possible outcomes, knowing that until 2050 there is a high uncertainty.

#### **<IP_ADDRESS> Mod-RES Scenario**

The current policy instruments aiming at sustaining EV diffusion in the US, Japan, China, and India resemble each other in main points.

Table 5.1 gives an overview over most of the support programs for EV. Each of these countries gives a tax break or another financial incentive on the purchase price of EV. Japan exempts quite many taxes for owners of an EV (e.g., acquisition tax and tonnage tax). All countries subsidize the charging infrastructure by providing attractive loans or by directly co-financing charging stations. Non-financial incentives are quite common in China and the US: EV users have a clear advantage over others while driving exclusively on specific lanes such as bus lanes or have access to dedicated inner-city areas. In the US, incentives may vary from state to state. California is the state where EV owners receive significant advantages due to the Clean Vehicle Rebate Project and the State's government has a challenging goal to reach five million EV in 2030 (Clean Vehicle Rebate Project 2019). With the "Energy Independence and Security Act of 2007" the US support the expanding, establishing or equipping manufacturing facilities in the US with direct loans to increase the production of clean renewable fuels (US Congress 2007). The US supported automobile manufacturers with already eight billion USD (Tesla, Ford, and Nissan) out of this loan program whereas 17.7 billion USD are still remaining (US Department of Energy 2019a). China makes clear requirements for all car manufacturers which sell cars in China. This includes fines if they do not produce a certain number of EV (Environmental and Energy Study Institute 2018). Furthermore, they focus on dedicated EV shares in overall sales. In India, the government tries to reduce dependency on oil

(continued)

imports and promote the adoption of alternative fuels through introducing the FAME (Faster Adoption and Manufacturing of Hybrid and EV) initiative. The assumption of a strong EV increase in India until 2030 is not included as the planned values are not fixed values by now. ICEV selling bans are not considered in any of the four key non-European countries in our modeling because it is not clear whether the policy will remain until that period.

The Mod-RES scenario is in principle related to these rather short-term policies like subsidizing charging infrastructure. As it is hard to tell how to extrapolate the policies until 2050 the assumptions were more restrained, e.g., no further purchase subsidies were assumed. In general this scenario shows one possible future development pathway for the different car technologies.

When it comes to the population development of the four considered countries the medium variant scenario of the United Nations is taken until the year 2050 (cf. United Nations, Department of Economic and Social Affairs, Population Division 2017). Accordingly, China's population increases until 2030 and decreases afterwards. Japan has an overall decreasing trend. India and the US are assumed to steadily increase their population numbers (cf. United Nations, Department of Economic and Social Affairs, Population Division 2017).

To assess the total car stock, car ownership ratios are assumed. China is the country with the strongest increase for this ratio, having in 2018 about 11% of car ownership (i.e., 110 passenger cars per 1,000 inhabitants). It is assumed to be at around 31% (i.e., 310 passenger cars per 1,000 inhabitants) in 2050. While also for India and Japan a positive trend of this ratio is expected, the US car ownership ratio is assumed to be stable. Back in 2015, China had still a car stock of 136 million passenger cars and India about 22.5 million passenger cars (OICA 2018). Hence, both car stocks will increase significantly over the next period.

#### **<IP_ADDRESS> High-RES Scenario**

While population and car stock have the same values as in the Mod-RES scenario, an even stronger increase in the charging infrastructure is assumed for the High-RES scenario as one policy measure. Different studies present various development plans for EV charging infrastructure in a range of eight million of up to 33 million units in 2030 for the whole world (cf. IEA 2017b). In comparison, one further assumption is that in the High-RES scenario ten million EV supply equipment (EVSE), i.e., charging stations, are installed by 2030, leading to about 33 million for 2050 for the four considered countries. According to the German National Platform Future of Mobility this is still a rather unsatisfactory low number because one EVSE outlet for 15 EV (i.e., 10 million EVSE per 150 million EV in 2030) might not provide a seamless operation of EV. Additionally, the average emission standards for new cars amount to 40 g CO2/km in 2050, whereas the values of the Mod-RES scenario are assumed to be 50 g CO2/km in 2050.

### **5.3 Results of Key Non-European Countries**

### *5.3.1 Effects on Cumulative Battery Capacity and Battery Costs*

Figure 5.1 presents the development of the cumulated battery capacity (solid lines) and the derived battery costs (dotted lines) for the four key non-European countries and Europe (provided through the ASTRA model, cf. Chapters 6 and 7) in the time span from 2015 to 2050 for the Mod-RES and High-RES scenarios. While initially both scenarios develop very similarly, there is a significant increase of the cumulated battery capacity after the year 2030 in the case of the High-RES scenario.

The development of the battery costs well reflects the learning curve behavior with a significant decrease by about 50% in 2030 and up to 70% in 2050 compared to the year 2020. It can be seen that in both scenarios the shape of the curve is similar and the battery costs differ in 2050 by only 4 USD/kWh. The battery costs of 68 USD/kWh in 2050 represent 18% of the costs in 2015; this is due to the development of cumulated battery capacities, which is driven by EV volumes.

The development of battery costs is analyzed in several studies. The spread of the costs varies from 40 EUR/kWh to 250 EUR/kWh by 2040. Most of the studies depict battery costs between 50 and 100 EUR/kWh in the long-term, in particular if they were conducted after 2015 (cf. Tsiropoulos et al. 2018). Hence, the values of the two considered scenarios in this book chapter (64 USD/kWh and 68 USD/kWh in 2050) are within this range.

Different studies for the development of the EV fleets show prospects ranging from 5 TWh up to 36 TWh for the global cumulated battery capacity in 2040 (Tsiropoulos

**Fig. 5.1** Development of cumulated battery capacity (solid lines) for Europe and the four non-European key-countries and battery costs (dotted lines) from 2015–2050 (*Source* Figure based on data according to own calculations)

et al. 2018). In the two scenarios, the cumulative battery capacities including all the sales up to then sum up to 14.6 TWh and 16.5 TWh in 2040, respectively. In the year 2050 values of about 26.7 TWh and 34.3 TWh are reached. It should be kept in mind that the first cumulated GWh was reached in the year 2011 and the first cumulated TWh in 2015.

### *5.3.2 Development of the Car Stock for the Four Main Markets in the Mod-RES and High-RES Scenario*

The resulting EV car stocks of the European and the four key non-European car markets increase in both scenarios from less than ten million EV to about 450 million EV (cf. Figure 5.2). The results of the Mod-RES scenario show around 80 million PHEV and 367 million BEV in 2050. If we combine the values with Europe, the numbers add up to 536 million EV. The most dynamic countries are China (211 million BEV) and India (109 million BEV). This dynamic is mainly driven by the significant incentives for investing in BEV rather than in PHEV. Consequently, the overall PHEV share is rather low. The highest national shares for PHEV are seen in Japan (17%) and the US (13%) of the overall car stock in 2050. The car sales ratio per year is in each country by at least 35%, except for the US (27%) in 2030. In 2050, each of the countries has sales values reaching from 40% (US) to 63% (China).

In the High-RES scenario the share of the PHEV drops even further to 55 million PHEV and the share of BEV increases to a value of 481 million BEV in 2050. Together with Europe, there are around 710 million EV by 2050. None of the four countries has a two-digit percentage for the PHEV penetration by 2050. The highest

**Fig. 5.2** Development of PHEV and country-specific BEV stock in key non-European car markets until 2050 for the Mod-RES and High-RES scenarios (*Source* Figure based on data according to own calculations)

share of BEV in 2050 will be in China with around 56% and the lowest one will be in the US with about 32%.

As mentioned above the main difference between the two scenarios is the higher investment in infrastructure in High-RES. Therewith the public charging infrastructure has a density of at least one EVSE outlet for 15 BEV. Apart from that, in particular the strong decrease of the battery costs to less than 100 USD/kWh by 2030 (cf. Figure 5.1) has a major impact on the increased overall penetration of the EV stock.

The EV share of the total car stock until 2050 is shown in Fig. 5.3. Car ownership ratio increases in China, Japan and India while it is quite stable in the US. The growth of the total car stock in the US is a result of the increasing number of inhabitants, which has a change from 320 million people in 2015 to 390 million inhabitants in 2050 forecasted (cf. United Nations, Department of Economic and Social Affairs, Population Division 2017). In contrast, the drivers in China and India are high shares of EV by 2050 (56 and 54%, respectively), particularly in the Mod-RES scenario.

For the High-RES scenario each of the EV shares increases, in particular the one for Japan. The modest development of BEV in the US has mainly two reasons: First, the higher purchase prices of EV compared to other countries (cf. the New Policies Scenario by the World Energy Outlook 2017). Purchase prices of EV will not undercut the one from ICEV in the US before 2025 (cf. IEA 2017a). The lower operating costs do still attract some, but not all customers. Second, not all member states in the US have announced strong EV plans. Therefore, the resulting market share of about 1/3 in 2050 of the total stock seems to be in line with other scenarios in literature.

The Mod-RES and High-RES scenarios are within the range of several other studies focusing on the development of EV until 2030 and 2040 (cf. Figure 5.4).

**Fig. 5.3** EV share of the total car stock until 2050 for the Mod-RES and High-RES scenarios (*Source* Figure based on data according to own calculations)

**Fig. 5.4** Comparison of the global EV stock development to the four key non-European car markets of China (CN), India (IN), Japan (JP), and the US (*Source* Figure based on data according to own calculations (solid line) and for the dotted lines data from the studies of BNEF (2018), IEA (2017), IEA (2018))

Nevertheless, a "rest of the world" factor would need to be included when comparing the REFLEX scenarios to global studies such as the one of the IEA which includes other countries like Russia, Canada, Thailand, or even continents like South America or Africa that will play a role by 2050 and have a total car stock of over half a billion cars.

### *5.3.3 Critical Review and Limitations*

Finally, some limitations should be addressed. First, the considered time period until 2050 is very long for such new technologies resulting in a lot of uncertainties. For instance, policy instruments can have a significant impact on market development in particular purchase subsidies, taxes or car bans. In this work sales bans for ICEV were not considered, however, this might have a major effect on the market diffusion of EV or other alternative powertrain technologies. The user behavior, including a change in the customer preference might be another factor, which needs to be reflected. Apart from that, factors influencing the car price, like fuel costs or resources for EV, can have an impact on the car stock as well.

Moreover, both assumed scenario developments for the non-European countries are close to each other and a stronger investment in the charging infrastructure could increase the ramp up of EV even further. Moreover, the effect of the European countries on the global learning and hence the battery costs is in comparison much smaller than the effect of all the non-European countries due to the EV numbers. For the Mod-RES scenario the values are for Europe 85 million EV and for the non-European countries 447 million EV.

A consideration of a "rest of the world" factor would increase the accuracy of the results. Furthermore, the average learning rate (of 15.2%) was chosen for the application. A change in that parameter has a significant impact. If sensitivities of ± 2.9% are considered, the range of the battery costs is between 128 USD/kWh and 40 USD/kWh (lower and higher learning rate, respectively) (cf. Heitel et al. 2020). Moreover, a variation in the experience curve parameters would directly have an impact on the battery cost estimate and hence, on the estimated deployment of the EV stock.

### **5.4 Summary and Conclusions**

This chapter outlined the development of EV until 2050 in four key non-European car markets (i.e. China, India, Japan, and the US). In both underlying scenarios, a significant increasing trend for the EV car stock can be seen resulting in total numbers of 447 million (Mod-RES) and 536 million (High-RES) EV for the four considered non-European markets in 2050. The PHEV share in the High-RES scenario is lower than in the Mod-RES scenario for all countries and the BEV shares have an increasing trend over time and between scenarios. Not surprisingly, China and India are seen as the main EV markets due to their ambitious policy goals.

The coupling of the two models, ASTRA (for the European market) and TE3 (for the main non-European car markets), in order to consider 34 countries in detail (and therewith more than 90% of current EV sales) allows analyzing the interrelations of global battery costs and the individual market shares endogenously. The battery costs drop from 380 USD/kWh in 2015 to below 100 USD/kWh by 2030 and decrease to values of 68 USD/kWh and 64 USD/kWh in 2050 for the two scenarios, Mod-RES and High-RES, respectively.

For further research, additional countries and policies like sales bans, higher CO2 standards or higher investments for EVSE might be included in the modeling.

### **References**

BNEF (2018) Long-term electric vehicle outlook 2018. EVO 2018

US Department of Energy (2019b) Alternative fuels data center: electricity laws and incentives in federal. https://afdc.energy.gov/fuels/laws/ELEC?state=US. Accessed on 20 May 2019

**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.

### **Part III Demand Side Flexibility and the Role of Disruptive Technologies**

### **Chapter 6 Future Energy Demand Developments and Demand Side Flexibility in a Decarbonized Centralized Energy System**

### **Andrea Herbst, Anna-Lena Klingler, Stephanie Heitel, Pia Manz, Tobias Fleiter, Matthias Rehfeldt, Francesca Fermi, Davide Fiorello, Angelo Martino, and Ulrich Reiter**

**Abstract** European final energy consumption mainly stems from five sectors: transport, households, industry, residential, and agriculture using fossil fuels as dominant energy carriers. In order to achieve the climate targets, emissions in the demand sectors must be drastically reduced. Due to different characteristics and challenges each sector needs its own strategy how to achieve such decarbonization until 2050. In the following chapter, the impacts of an ambitious mitigation scenario on future energy demand and CO2 emissions for transport, industry, residential, and tertiary are analyzed discussing sector specific decarbonization strategies and mitigation options. Implications of such strategies for demand-side flexibility and its future need are analyzed.

F. Fermi · D. Fiorello · A. Martino TRT Trasporti e Territorio, Milan, Italy

U. Reiter TEP Energy GmbH, Zurich, Switzerland

A.-L. Klingler

S. Heitel

A. Herbst (B) · A.-L. Klingler · S. Heitel · P. Manz · T. Fleiter · M. Rehfeldt Competence Center Energy Technology and Energy Systems, Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany e-mail<EMAIL_ADDRESS>
Business Area Mobility and Urban Systems Engineering, Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany

Competence Center Sustainability and Infrastructure Systems, Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany

### **6.1 Introduction**

European final energy consumption can be divided in five main sectors: transport, households, industry, residential, and agriculture. The transport sector accounts for 31% of EU final energy demand followed by the residential sector with 27%, the industry sector with 25% and the tertiary sector with 15% of final energy consumption in the year 2017. The dominant energy carriers currently used are oil & petroleum products (37%), natural gas (23%), and electricity (23%) as well as renewable energy sources & biofuels (10%) (Eurostat 2017). Within the sectors the energy carrier mix varies depending on the structure and the specific requirements of the sectors. Transport energy demand is substantially dominated by fossil fuels (oil, petroleum, and natural gas) covering about 95%, with minor use of biofuels (4%) and electricity (1%) (European Commission 2017). Residential demand mainly stems from residential heating purposes (80%) which are provided via the use of natural gas, fuel oil, and electricity. Electricity demand for appliances & processes as well as demand for heating and cooling purposes in buildings lead to an energy carrier mix in the tertiary sector, which is dominated by the use of electricity and natural gas. In industry, final energy demand is dominated by the use of natural gas, coal, oil, and electricity for energy-intensive production processes like steel, cement, or basic chemical production.

The European Commission's recently published long-term strategy (European Commission 2016) shows that emissions in the demand sectors must be drastically reduced in order to achieve the climate targets. However, as the sectors considered are very heterogeneous, so are the respective challenges for decarbonization. As the transport sector was not able to decrease GHG emissions within the last decades due to a strongly increasing transport activity, a combination of three main strategies seems required to meet the challenge of decarbonization: (i) increasing the efficiency of the transport system including a shift to lower emission transport modes, (ii) speeding up the deployment of low-emission alternative energy such as advanced biofuels, electricity, hydrogen and renewable synthetic fuels, and (iii) accelerating the transition toward zero-emission vehicles.

The residential and tertiary sector face large challenges due to the high need of thermal renovation of the existing building stock as well as efficient new construction. Main challenges in the industry sector include the mitigation of process emissions from chemical reactions within the productions process, high-temperature needs and technical restrictions in industrial furnaces for the use of RES as well as feedstock demands for the chemical industry (currently mainly fossil-based).

In the following, the impacts of an ambitious mitigation scenario on future energy demand and CO2 emissions for transport, industry, residential, and tertiary as well as its implications for demand-side flexibility are analyzed.

### **6.2 Scenario Assumptions and Model Coupling**

The demand-side aspects of an ambitious mitigation scenario "well below 2 °C" correspond to an 80% emissions reduction of the entire system excluding carbon sinks and can be translated into sectoral carbon reduction targets for transport (61–63%), residential (87–91%), tertiary (88–93%), and industry (75–85%) (European Commission 2018). The considered mitigation scenario reflects a centralized energy system, which is characterized by external supply of RES secondary energy carriers and centralized district heating systems. The High-RES centralized scenario is analyzed in comparison to a reference scenario (Mod-RES) reflecting a current policy case based on known policies and observed trends (cf. Chapter 2). Important sector specific scenario assumptions can be summarized as shown in Table 6.1.

The results and analysis shown in the following sections have been carried out using three different types of models:

A detailed description of the used models and scenario assumptions can be found in Chapters 2 and 3.

### **6.3 Future Energy Demand and CO2 Emissions**

To achieve an ambitious mitigation level, major changes in the demand sectors have to take place in the medium and long-term. In addition to energy efficiency, another important pillar for a low-carbon demand-side transformation is fuel switch from fossil fuels (e.g., coal, natural gas, oil) to renewable energy sources (e.g., biomass, solar). However, energy efficiency and switching to renewables is likely not sufficient to achieve deep decarbonization especially for industry and transport (European Commission 2018). These sectors need additional strategies for significant emission

**Table 6.1** Scenario characterization by sector

reductions, as for example the use of secondary energy sources such as electricity and hydrogen.

A possible pathway for decarbonizing European demand is reflected in the High-RES centralized scenario (Fig. 6.1). In 2050 electricity becomes the dominant energy due to high electrification in all sectors (e.g., electrification of passenger transport by diffusion of battery electric and plug-in hybrid vehicles, the use of electric furnaces in industry, and high penetration of heat pumps in the tertiary and residential sector), this leads to an overall increase in electricity demand by 36% compared to 2015, from 2,732 to 3,706 TWh in 2050, which has to be provided from renewable energy sources. In addition to electricity, hydrogen as another secondary energy carrier based

**Fig. 6.1** EU-27 and UK final energy demand by energy carrier and scenario in TWh (2015–2050) (*Source* Own illustration)

on a CO2-neutral production, e.g., via renewable energy sources, gains importance after 2030. Based on the model calculation, 845 TWh are needed in the transport in 2050 (418 TWh mainly for fuel cell electric trucks) and industry sector (427 TWh of which 384 TWh are used for feedstock production in the chemical industry). Biomass use is increasing by 269 TWh compared to 2015 caused by additional demand for biokerosene in aviation for the transport sector. Due to lower cost for centralized heat generation, the demand for district heating is increasing by 32% compared to 2015, from 549 to 723 TWh in 2050. The remaining demand for fossil fuels primarily stems from uses in the transport sector –1,205 TWh mainly from oil-based fuels—and the industry sector—470 TWh of which 299 TWh are natural gas. In the tertiary and residential sector 246 TWh of fossil fuels, mainly gas, are used in 2050.

This fuel switch is accompanied by high increases in energy efficiency in all sectors leading to an overall demand reduction of 28% compared to 2015, from 13,003 TWh to 9,376 TWh in 2050 (including the demand for feedstock in the chemical industry). Efficiency gains in the transport sector mainly stem from increased efficiency of vehicles, supported by modal shift of road transport demand to rail, public transport,

**Table 6.2** EU-27 and UK direct CO2 emissions by scenario and sector

*Source* Own illustration

and other modes. In the residential and tertiary sector higher building standards, increased compliance and financial incentives for alternative heating lead to a strong increase in renovation rates and energy efficiency improvements (e.g., old direct electric heating, water boilers, and night storage heaters are being replaced by more efficient heat pumps). In addition, new efficiency classes and the inclusion of more products in the Ecodesign Directive lead to further reductions in demand after 2025. Also in industry, an ambitious exploitation of remaining energy efficiency potentials takes place (e.g., near-net-shape casting in the steel industry, new drying techniques in the paper industry, and efficiency improvements in electric motor systems). However, potentials for further improvement in this sector are limited due to already efficient production technologies. The introduction of innovative production technologies (e.g., low-carbon cement, RES H2 direct reduced steel, RES H2 methanol, and ammonia) lead to significant reductions in industrial CO2 emissions.

The high level of ambition in the High-RES decentralized scenario implies an overall direct emission reduction of 74% compared to 2015 (Table 6.2). Remaining emissions in 2050 mainly stem from oil demand in the transport sector (319 Mt) as well as industrial process emissions (80 Mt) and natural gas demand (60 Mt).

### *6.3.1 Decarbonizing the Transport Sector*

The transport sector is currently responsible for about a third of final energy consumption in EU-27 countries and UK (European Commission 2017). Therein, fuel oil products are the dominant energy carrier, constituting around 93% of total energy demand for transport in 2015.

Transport and mobility demand are key to determine future energy consumption. ASTRA estimates an increase in transport demand until 2050, mainly caused by economic and population growth expected in the future decades. Passenger transport demand is expected to increase by 34% and freight transport by 60% in the period between 2015 and 2050.

In the Mod-RES scenario, in which only already implemented European regulations are considered, the EU-27 and UK total final energy demand is expected to decrease by modest 3% from 2015 to 2050, with fuel oil products still representing the main energy carrier (about 84%). The slight decrease in energy demand can be observed from 2020 on, due to the implementation of policies enhancing the efficiency standards for cars and vans (cf. Fig. 6.2).

In the High-RES centralized scenario, total energy demand is decreasing strongly, about 30% from 2015 to 2050, due to a modal shift from road transport to more efficient transport modes and substantial efficiency improvements in the vehicle fleet including new drive technologies. The composition of the energy carriers underwent a big change: the use of fuel oil represents about 40% of demand in 2050 while the remaining share is covered by electricity (22%), hydrogen (15%), and biofuels (18%).

**Fig. 6.2** EU-27 and UK final energy demand of the transport sector by energy carrier and scenario in TWh (2015–2050) (*Source* Own illustration)

The strong increase in electricity and hydrogen demand results from a technological change for road vehicles. The electrification of passenger transport by diffusion of battery electric (BEV) and plug-in hybrid (PHEV) vehicles leads to a total annual electricity demand of 600 TWh in 2050, thus being ten times higher compared to 2015. The hydrogen demand reaches about 420 TWh per year in 2050, driven by the market penetration of fuel cell electric trucks (80% of the hydrogen demand for transport) and spillover effects to the diffusion of fuel cell (FC) technology for cars, vans, and buses. The triplication of biofuel demand from 2015 to 2050 is triggered mainly by air transport. It is assumed that 43% of aviation fuel demand is covered by biokerosene in 2050 to reduce the high carbon footprint of the aviation sector, which is constantly growing in terms of transport activity, leading to about 220 TWh biokerosene demand.

### **<IP_ADDRESS> Modal Shift**

In general terms, rail transport, navigation, and public transport are more energy efficient compared to road transport (Faberi et al. 2015). Thus, shifts to these modes are preferable. Push and pull factors have to be combined, i.e., discouraging motorized road transport, e.g., by energy and vehicle taxation, parking space management, or road charging, and at the same time making non-road modes more attractive by investing in rail, cycling, and walking infrastructure, by improving public transport services and the availability of shared mobility, and by promoting multimodality with electronic information platforms and services that reduce waiting times, enable seamless electronic ticketing and real-time trip information.

Modal shifts for freight are difficult to obtain due to rare direct access to rail and waterways at origin and destination and thus required reloading operations. In the Mod-RES simulation, the modal share of road transport declines from 50% in 2015 by only 3% toward rail and ships to 47% in 2050. In High-RES, an unintended rebound effect was even induced in later decades: Policies pushing the diffusion of zero-emission technologies make road freight transport a clean and attractive alternative, so that road share regains to 49% in 2050. For passenger transport, a significant modal shift to public transport, car-sharing, and active modes like biking including e-bikes and walking was observed on the local level in the model.

### **<IP_ADDRESS> Energy Efficiency of Vehicles**

If motorized trips can neither be avoided nor shifted to more efficient transport modes, efficiency can be increased on the vehicle level. Efficiency improvements for road transport were achieved since 1990 with an average of −0.7% per year for cars and about −0.5% per year for trucks and light-duty vehicles (EEA 2019). The EU regulations on CO2 emission performance standards for cars and vans provide an indication of the expected efficiency improvements until 2030 (Regulation (EU) 2019/631, 2011/510 and 2009/443): until 2021, a target reduction of about 27% with respect to 2015 is required in terms of fuel efficiency for new car vehicles, while for 2025 and 2030 the manufacturers will have to comply with a further reduction target of 15% and 37% for new cars. Approaches for high efficiency of vehicles are reduced resistance, e.g., via aerodynamic designs, lightweight materials and small vehicles, brake energy recuperation, and improved traffic flow through traffic management, harmonized speeds, driver training, as well as respective effects of connected and autonomous driving (e.g., Helms et al. 2010; Krail et al. 2019).

#### **<IP_ADDRESS> Diffusion of Low-/Zero-Emission Vehicles**

Efficiency improvements of road vehicles contribute to the decarbonization process but are not enough to achieve the GHG emission reduction target. Therefore, a substantial change of energy carriers from fossil fuels to electricity and hydrogen is needed through the diffusion of alternative drive technologies, in particular, battery electric vehicles, plug-in hybrid electric vehicles, and fuel cell electric vehicles. Their diffusion can be accelerated through measures like investments in charging and hydrogen infrastructure, tax incentives, tighter fuel efficiency standards and sales bans of new vehicles with conventional internal combustion engines. Drivers of the diffusion and resulting impacts for the energy system will be described more comprehensively in the chapter about disruptive technologies (cf. Chapter 7).

#### **<IP_ADDRESS> Alternative Fuels**

Electricity-based synthetic fuels and biofuels are considered as a valuable decarbonization option for aviation and navigation that both lack mature alternative technologies and have long operation lifetimes of their crafts. Considering the relatively low efficiency of these alternative fuels from well-to-wheel as well as sustainability concerns of biofuels, increased use for road modes seems only reasonable in a transition phase. Biokerosene was approved for commercial use in 2011 and some airlines are experimenting with their use. Several studies confirm that the biokerosene blend can reduce air pollutant and GHG emissions (European Commission 2011). However, with current technologies, the target of halving the CO2 emissions by 2050 with respect to 2005 cannot be met (IATA 2015). Therefore, more advanced, sustainable biofuels of the second generation (i.e., not in competition with food crops for human consumption) and third-generation (based on algae) or electrolysis-based power-to-x fuels need to be further developed, tested and production capacities to be built up.

### **<IP_ADDRESS> Impact on CO2 Emissions**

Although the strategies modal shift, efficiency improvement of vehicles, diffusion of low- and zero-emission vehicles and alternative fuels are pursued in the businessas-usual scenario Mod-RES to a certain extent, their degree of implementation is far too low to meet the decarbonization target for the transport sector of -60% GHG emissions by 2050 compared to 1990 levels. The simulation of Mod-RES with ASTRA showed that only a small reduction of GHG emissions is achieved from 2015 levels (−9%), with even a net increase from 1990 levels (−7%), triggered by the consistently growing transport activity.

Therefore, a comprehensive set of policies and infrastructure deployment decisions acting on both technology diffusion and demand management are required to meet the target. Several combinations and forms of the presented strategies could be envisaged. With the measures assumed in the High-RES centralized scenario a decrease of transport CO2 emissions by -61% was achieved, thus meeting the decarbonization target. The gap in model results for Mod-RES 2050 was closed in High-RES by policies that accelerate the diffusion of low- and zero-emission vehicles (further −30% CO2 emissions compared to Mod-RES 2050), policies increasing efficiency improvements (−16%), biofuels (−14%), and modal shift (−14%). A sales ban of pure internal combustion engine cars in 2040 and a strong promotion of fuel cell electric trucks accelerate the technology change for road vehicles. Biofuels contribute in particular for decarbonization of aviation and navigation. Policies aiming at a modal shift to active modes, public transport, and sharing mobility take effect in particular on the local level. Thus, taking also efficiency improvements of vehicles into account, the largest tank-to-wheel CO2 reduction in 2050 compared to 2015 is obtained for road modes, with approximately −90% for vans, −80% for cars and buses, and −65% for trucks. Air passenger transport emissions are reduced by about −30% in the same period by efficiency improvements and the use of biofuels.

### *6.3.2 Decarbonizing the Residential and Tertiary Sector*

Today the residential and the tertiary sector are responsible for about half of the European final energy demand. The most important end-use in both sectors is the provision of space heating in buildings, which is currently dominated by the use fossil fuels such as natural gas and oil. The high share of electricity demand in the residential and tertiary sector mainly stems from the use of electric appliances and lighting as well as the demand for ventilation and cooling. However, electricity is also needed for domestic hot water generation (besides also gas and oil) and to a limited extent for the provision of space heating, e.g., via electric boilers.

To achieve ambitious CO2 mitigation levels, both sectors need to make substantial further efforts to increase building efficiency and switch to alternative heating technologies. A possible pathway for decarbonizing EU-27 and UK residential and tertiary final energy demand is shown in the High-RES centralized scenario (cf. Fig. 6.3).

**Fig. 6.3** EU-27 and UK residential and tertiary final energy demand by energy carrier and scenario in TWh (2015–2050) (*Source* Own illustration)

### **<IP_ADDRESS> Energy Efficiency**

In the tertiary and residential sector large efficiency gains already take place in the current policy Mod-RES scenario leading to a decrease in final energy demand in 2050 by 22% compared to 2015, from 4,913 TWh to 3,821 TWh. These improvements are mainly caused by the national implementations of existing building codes (e.g., EPBD Directive 2010/31/EU) and efficiency standards (e.g., Ecodesign Directive 2009/125/EC). The requirements of these building codes and directives are already very high but it remains partially open how strict their implementation will be. Given the cost curves for different refurbishment measures (Reiter et al. 2019), the implementation of dedicated refurbishment strategies is essential to achieve the required refurbishment rates indicated by the achieved results. Especially in the tertiary sector where building functions can often refer to multiple building code classes, this poses a challenge for the appropriate selection and application of the respective standards.

A higher adoption of efficiency classes for more products in combination with the introduction of new efficiency classes after 2025 lead, together with a deeper refurbishment depth complying to minimum standards, and the shorter lifetime of heating systems, to a further reduction in final energy demand by additional 6% compared to the Mod-RES scenario (−28% compared to 2015, from 4,913 TWh to 3,555 TWh).

The development of electricity demand until 2050 is strongly dependent on the efficiency increase and ownership rates of appliances, as well as the efficiency increase of buildings and the technology mix of heating technologies in the scenario. In the residential sector old direct electric heaters, water boilers, and night storage heaters are replaced by more efficient heat pumps leading to an increase in final electricity demand in both scenarios (18% in Mod-RES from 1,643 TWh to 1,708 TWh, 24% in High-RES to 1,684 TWh) having a slightly stronger penetration of heat pumps in the High-RES scenarios.

Appliances in the residential and tertiary sector are the main contributor of electricity demand in 2050. White goods like refrigerators, freezers, dishwashers, and washing machines as well as ICT appliances like laptops and TVs are regulated within the European Ecodesign Directive that sets mandatory requirements for energyrelated products. Labeling and classification into efficiency classes and minimum standards of the products leads to a strong increase of the efficiency. Contrasting to the Mod-RES scenario, where the efficiency increase stems from stock turnover replacement of old products with more efficient ones complying with the current efficiency classes, the High-RES scenario includes more efficient products after 2025. Especially for dryers and refrigerators, a further efficiency increase can be observed until 2050, while for ICT appliances like laptops and TVs, the electricity consumption stays almost constant or increases, respectively, in both scenarios. The reason for this is the increase of the size of appliances and performance progress. In the tertiary sector, especially cooling demand is expected to increase due to more surface area cooled, therefore, limiting the efficiency gains due to more advanced appliances.

#### **<IP_ADDRESS> Fuel Switch**

Although the absolute difference of final energy demand in the two scenarios is not very large, the energy carrier composition in the two scenarios is quite different. In the High-RES centralized scenario, additional support for district heating is implemented given the assumption that more district heating networks are reinforced and more buildings can be connected to it at lower costs than today. Thus, the district heating demand in the tertiary and residential sector increases by 61% compared to 2015 (from 332 to 534 TWh). In addition, incentives and installation subsidies for RES heating supply as well as the shorter lifetime of heating systems increase the demand for low-exergy sources like solar thermal by fourfold (from 34 to 170 TWh) until 2050. Due to competition with other sectors (e.g., transport and agriculture), the use of biomass is limited in the High-RES centralized scenario decreasing by 11% compared to today. The ban of oil boilers after 2030 in the residential sector, in combination with a fuel tax on gas and oil, leads to a significant reduction in fossil fuel demand for space heating. While coal is completely phase out until 2050 the demand for fuel oil (−95% compared to 2015) and natural gas (−87% compared to 2015) decreases significantly.

### **<IP_ADDRESS> Impact on CO2 Emissions**

Overall emission reductions in both sectors are 89% compared to 2050 in the High-RES centralized scenario (84% in tertiary and 92% in residential). Especially in buildings, space heating demand is a main contributor to GHG emissions. To reduce these emissions, combined efforts in refurbishment rates, depths, and technology changes are needed. Furthermore, refurbishment is a prerequisite for the deployment of RES and electricity-based heating technologies, which are the main contributors to emission reduction in those sectors. However, reaching the EU targets includes major efforts from all actors and stakeholders as well as additional regulatory framework, especially to increase the refurbishment rate beyond the current levels. EU-wide regulations for building standards in the buildings sector are already in force and a main driver to reduce heating and consequently CO2 emissions in the long-run until 2050. To allow for a more centralized provision of renewable heat, financial incentives as well as connection regulations and strategies are needed to tap the full potential. Heat pumps can play a certain role for decarbonizing heat demand in the tertiary and residential sector, but specific support measures such as geothermal potential zones need to be managed as well as further cost reductions achieved for tapping ground sources for ambient heat gains.

### *6.3.3 Decarbonizing the Industry Sector*

The industrial sector currently accounts for about a quarter of the EU-27 and UK final energy demand, mainly using fossil fuels such as gas, coal, and oil, but also electricity. Energy-intensive processes and products like, e.g., steel (iron and steel), cement (non-metallic minerals), ethylene (chemicals), and ammonia (chemicals) dominate demand and CO2 emissions in this sector. Even though, some sectors already use a high proportion of electricity and biomass (e.g., paper industry), industry needs to make significant further efforts to reduce the use of fossil fuels in the coming decades. A particular challenge is the reduction of process emissions, as these emissions can only be reduced by radical changes in the production process, product mix or the use of CO2 capture and storage. In terms of end-use, most industrial greenhouse gas emissions result from high-temperature process heat, either in the form of steam or hot water, or from direct firing of different types of furnaces. The high temperatures and the specific technological requirements limit the use of renewable energies to biomass or secondary energy (Fleiter et al. 2019; Herbst et al*.* 2018a, b). A possible pathway for decarbonizing EU-27 and UK industry is shown in the High-RES centralized scenario (Fig. 6.4).

**Fig. 6.4** EU-27 and UK industrial final energy demand by energy carrier and scenario in TWh (2015–2050) (*Source* Own illustration)

### **<IP_ADDRESS> Energy Efficiency**

The energy efficiency of technologies used today for the production of basic materials products like steel, cement, glass, or paper have been improved continuously over the past decades. The High-RES centralized scenario assumes a very ambitious exploitation of available energy efficiency potentials in combination with increases in material efficiency, material substitution and the recycling of materials—leading to a decrease in energy demand (incl. chemical feedstocks) in 2050 by 27% compared to 2015, from 4,243 TWh to 3,109 TWh. Still, the improvements realized in most processes are below 20%, indicating a limited potential for further process optimization in this sector (Herbst et al*.* 2018b). Exceptions are steel rolling, where new production technologies like near net shape casting or the paper production, where innovative new drying technologies can make a larger difference and materialize higher efficiency potentials. In addition to the energy-intensive basic materials processes, also cross-cutting technologies like electric motors and lighting account for a high share of total electricity demand, due to their widespread use. These systems are yet less optimized and remaining potentials are higher. Implementing remaining energy efficiency potentials by also optimizing the entire motor systems can further reduce demand until 2050 (Herbst et al*.* 2018b).

### **<IP_ADDRESS> Fuel Switch**

In the High-RES centralized scenario a comprehensive shift toward electricity as the dominant energy carrier in all sectors takes place in the model. This leads to an in increase in electricity demand by 37% compared to 2015, from 1,036 TWh to 1,422 TWh. Especially after 2030, the relevance of electricity as energy carrier increases significantly (46% in 2050 of total demand), due to an economic and regulatory framework which is in favor of electricity and the market entry of innovative electricity-based production technologies.

Switching to low-carbon fuels for the generation of process heat can be a major mitigation option, given that process heat today is mainly supplied by fossil fuels. Steam and hot water generation is used across all industries and covers a temperature range of up to 500 °C, which allows the use of combined heat and power (CHP) technologies. Another important field for fuel switching are industrial furnaces, which are very diverse and often work at high temperatures above 1000 °C, e.g., in the cement, glass, and steel production. Fuel switching is possible, but the use of energy carriers experiences higher technical restrictions and RES are sometimes difficult to integrate. In the High-RES centralized scenario it is assumed that in 2050 steel production from blast furnaces is substituted by direct reduction of iron based on RES electrolysis and RES H2-plasma steel production combined with an increasing share of electric arc furnace steel production. In the cement and glass industry production shifts to electric kilns and furnaces and in the paper industry and other steam using industries use high-temperature heat pumps well as electric steam boilers are used where applicable.

In addition, RES hydrogen is used in the chemical industry as feedstock for the production of methanol and ammonia (384 TWh in 2050) and consequently reduces CO2 emissions for ethylene using methanol-based olefin production.

### **<IP_ADDRESS> Impact on CO2 Emissions**

As best available technologies and conventional fuel switch are not sufficient to decarbonize the European industry sector until 2050, the strong emissions reduction in the High-RES centralized scenario reflects the high level of ambition implied in this scenario. Direct industrial emissions decrease by 74% compared to 2015, from 745 to 197 Mt in 2050. Remaining emissions in 2050 mainly stem from the use of natural gas (~30% of total direct emissions in 2050) and chemical reactions within the production process (process emissions: ~ 40% of total direct emissions in 2050). The main contributor of CO2 emissions in 2050 is the non-metallic minerals sector (42% in 2050) including emissions from smaller point sources (e.g., bricks, lime, ceramics) and process emissions in the cement and glass industry. Further emissions remain in the chemical industry (16%) either from the use of natural gas or from process emissions in production. Sectors that are currently very CO2-intensive today, such as the iron and steel industry or chemical feedstock production, become almost CO2-free in 2050.

Important enablers are innovative efficiency technologies and the direct and indirect use of electricity in the scenario. Examples are direct reduction of iron based on RES electrolysis, electric kilns and furnaces and the use of RES H2 as feedstock for methanol/ethylene and ammonia production. Innovative products like low-carbon cement sorts (using new binders) can reduce the specific energy- and process-related cement emissions by between −30 and −70%. The scenario shows that decarbonization of the industrial sector is possible—even without carbon capture and storage however, this requires process innovations, which currently strongly differ in maturity and distance to market, CO2-free secondary energy carriers, and innovations in material efficiency and circular economy. Besides overcoming the barriers to market entry and setting up a regulatory framework, a main challenge lies in the fast diffusion until 2050. I.e., this scenario assumes that in most energy-intensive processes, the transition is completed in 2050, reflecting a very ambitious assumption.

### **6.4 The Future Need for Demand Side Flexibility**

As explained before, efficiency improvements are likely to reduce electricity demand of end-uses. At the same time, the decarbonization of selected sectors (e.g., heating and transport) will be realized through a shift toward the use of carbon–neutral electricity, driving the diffusion of new electricity consumers. It is known that these changes in the electricity demand structure will not only affect the total amount of electricity that is consumed, but also change the shape of the system load curve1 (Boßmann and Staffell 2015; Boßmann et al. 2015). This change in the hourly electricity demand in combination with increasing levels of fluctuating electricity generation from renewable energy sources (RES), is highly likely to result in increasing level of fluctuation in the residual load (i.e., the difference between the system load and electricity generation from RES). If electricity generation from RES exceeds the actual system load, the residual load drops below zero, meaning that RES need to be curtailed in the absence of alternative flexibility options. Thus, increasing shares of RES and a transforming system load curve could drive the need for conventional generation capacities, while the utilization, and hence profitability, of new and existing RES capacities could decline. Consequently, the challenge is to find appropriate measures that increase flexibility in the electricity system and smooth the residual load in order to balance supply and demand in a cost-efficient and sustainable manner (Boßmann 2015).

1The system load curve is defined as the total hourly load of all electricity consumers in a country.

In this context, information is required about the electricity consumption behavior of the individual processes in order to properly represent the technological changes inside the system load curve. Depending on the type of process, electricity load profiles are available as either yearlong (i.e., 8,760 h) profiles, typical day profiles that vary between season (summer, winter, transition) and/or weekdays (Saturday, Sunday, weekday), or temperature-dependent profiles. The latter is particular relevant in case of heating and cooling processes. The applied load profiles in the eLOAD model stem from various public sources and from the internal database of the Fraunhofer Institute for Systems and Innovation Research ISI (for details, cf. Boßmann 2015). Temperature-dependent profiles are generally applicable for various countries, since they are adjusted using local weather data. However, if typical day load profiles are not available in a specific country, these types of profiles are transferred across countries via the information form time use surveys. Such surveys report the activity (such as working, sleeping, and food preparation) of a representative sample of individual persons based on time diaries (cf. Gershuny et al. 2014, Statistika Centralbyran 2014). In this study it is assumed, that for selected processes activities correlate with electricity consumption (e.g., the activity of working with the load profile for electric vehicle charging at work) and that countries, featuring the same activity pattern, can be characterized by similar load profiles.

The application of the described process-specific load profiles in the eLOAD model results in the projection of the system load curve into the future. Figure 6.5 summarizes the effects of individual load profiles and the net effect on the system load curve in the High-RES centralized scenario in 2050. In this scenario, hydrogen is produced off-site in central large-scale electrolyzers. Therefore, hydrogen is provided directly by the energy suppliers and not included in the sectoral electricity demand. In case of decentralized scenario world this would be different (cf. Chapter 7). Note that due to an easier readability, the individual industry processes are aggregated as well as the consumption in the individual demand-side subsectors regarding the lighting, cooling, and heating processes. The most important process clusters explaining structural changes in the future system load curve are labeled. Due to the increasing electrification, the electricity demand generally increases until 2050. In particular, in the midday and evening hours, the electricity demand increases due to the charging of electric passenger cars at home and at work. In the cold season, the electricity demand by heat pumps affect the future system load. The increasing electricity demand for heating purposes is compensated to some extend by decreasing demand for lighting purposes and the classical industry processes (such as lime milling, extrusion, etc.) due to efficiency gains. Overall the system load gains in fluctuation for all considered countries and increases in temperature sensitivity in most countries, due to an increase in demand of temperature dependent heating and cooling processes.

An example of the structural changes and increasing fluctuations in the system load is depicted in Fig. 6.6, which shows the average system load curve for typical days in the EU-27 + NO + CH + UK. Overall electricity demand increases by 36% between 2014 and 2050, while the peak load increases by 51%. The influence of electric vehicle charging, causing load peaks in the midday and evening hours particularly on weekdays, is clearly visible in 2050. The effect of heat pumps increasing the

**Fig. 6.5** Average load change 2050 vs. 2014 aggregation of the EU-27, Norway, Switzerland (The calculations for the hourly demand are done for these countries. Due to the lack of available data Malta is excluded from the hourly calculations), and the United Kingdom by process in summer (left) and winter (right)—High-RES centralized scenario (*Source* Own illustration)

average load on winter days is equally detectable, however, less pronounced due to the compensating effect of efficiency gains.

As explained earlier, increasing fluctuations drive the need for flexibility options. Furthermore, the fluctuation generation of electricity from RES leads to a highly fluctuating residual load curve that would have to be covered by conventional generation capacities if no flexibility options are available. Figure 6.7 illustrates the fluctuation in the residual load for the EU-27 + NO + CH + UK and shows the development of system load and residual load from 2020 (left part) to 2050 (right part).

Figure 6.8 shows the load changes of the EU-27 + NO + CH + UK's system load (left) and residual load (right) from one hour to the next as a load duration curve for the years 2014 to 2050. It is shown that the total electricity consumption changed by more than ±50 GWh/h from one hour to the next in 2050 in 382 h. In the years before, the maximum load change was +43 GWh/h (2014), +47 GWh/h (2030),

**Fig. 6.6** Development of the average system load in the EU-27 + NO + CH + UK in summer and winter on Sundays and weekdays for the years 2014 to 2050—High-RES centralized scenario (*Source* Own illustration)

**Fig. 6.7** Average system (solid line) and residual load (dashed line) of the EU-27 + NO + CH + UK in 2020 (left) and 2050 (right)—High-RES centralized scenario (*Source* Own illustration)

and +50 GWh/h (2040). The load changes in the residual load increase even more dramatically to 2,127 h with load changes of more than ±50 GWh/h (cf. Fig. 6.8).

Besides the effect of higher fluctuations in system and residual load curves, the system load curve increases in temperature sensitivity in the future in many EU countries. While energy efficiency decreases electricity demand on warm days due to more efficient ventilation and cooling systems, the increasing electrification of

**Fig. 6.8** Load duration curve for the EU28 + NO + CH's system load (left) and residual load (right) in the years 2014 to 2050—High-RES centralized scenario (*Source* Own illustration)

heating systems for space heating and hot water particularly in the residential and service sector leads to rising electricity consumption on cold days.

Overall the High-RES centralized scenario shows that changes in the residual and system load curve that are to be expected in a decarbonized RES energy system, which stresses the need for future demand-side flexibility. However, there is large uncertainty to which extent and at what point in time load management will be deployed throughout the EU countries. For a detailed analysis of demand-side flexibility in an ambitious mitigation scenario see Part III.

### **6.5 Conclusions**

The analyses carried out in this study show that decarbonization of all demand sectors is possible until 2050. However, this requires massive efforts in all sectors and a policy framework supporting the necessary changes as well as CO2-free secondary energy carriers. In a scenario without carbon capture and storage, renewable electricity becomes the most important energy carrier in all sectors in 2050 used either directly or indirectly, e.g., as renewable hydrogen.

In the transport sector, energy demand will change significantly if GHG emission targets are met by 2050. While electrification is a key solution for road transport, blends of biofuels or synthetic fuels will play a major role in aviation and navigation. A combination of several strong measures and policies is required to meet the big challenge of decarbonizing a consistently growing transport demand. The diffusion of low and zero-emission drive technologies and further efficiency improvements of vehicles can contribute substantially. A large shift to the more efficient non-road modes would be even more preferable but requires a change of habits and handling processes, which is harder to achieve and predict.

In the residential and tertiary sector, refurbishment is a prerequisite for the deployment of RES and electricity-based heating technologies. Major efforts among all stakeholders are needed to increase energy efficiency in buildings and raise the refurbishment rate beyond current levels. Ambitious EU-wide regulations for building standards are already in force and the main driver to reduce heating and consequently CO2 emissions in the long-run until 2050. Heat pumps can play an important role for decarbonizing heat demand in the tertiary and residential sector, but specific support measures such as geothermal potential zones need to be managed as well as further cost reductions achieved for tapping ground sources for ambient heat gains. A higher adoption of efficiency classes for more products in combination with the introduction of new efficiency classes will also be necessary to reduce electricity demand for appliances.

In industry, the analysis has shown that today's available technologies are not sufficient for deep decarbonization of the sector. Mitigation levels in industry of more than 80% can be achieved without the use of CCS, but needs the implementation of a variety of different mitigation options including energy efficient and low-carbon production innovations, renewable-based electricity, and hydrogen (also as feedstock for the chemical industry), a comprehensive circular economy and improvements in material efficiency. In order to achieve this, the current policy mix needs to be adjusted in order to effectively support R&D activities directed at the decarbonization of industrial production. In this context also public RD&I funding can play an important role. A European Emission Trading minimum price path will be needed to provide more long-term clarity and the certainty for investors. A CO2 tax could provide fuel switching incentives for companies outside the ETS. Boosting material efficiency and a circular economy approach along the value chain also requires a broad policy mix (e.g., measures to increase recycling rate, measures to keep CO2 price signals visible along the value chain) including targeted public procurement.

### **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.

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### **Chapter 7 Disruptive Demand Side Technologies: Market Shares and Impact on Flexibility in a Decentralized World**

### **Stephanie Heitel, Anna-Lena Klingler, Andrea Herbst, and Francesca Fermi**

**Abstract** Electricity demand is expected to increase strongly as electrification and the use of hydrogen are promising decarbonization options for the demand side sectors transport and industry. In a decentralized system with volatile renewable energy sources, flexibility potentials will play an important role for secure and costefficient electricity supply. On the demand side, decentralized PV-battery systems and electric vehicles as well as hydrogen production by electrolyzers could provide the necessary flexibility. Energy demand over time is calculated based on assumed and simulated market shares of these and other low-emission technologies. Impacts on the system and residual load are analyzed, with a focus on the contribution of load shifting as a demand-side measure. Results indicate that load shifting can contribute significantly to integrate RES electricity.

### **7.1 Introduction**

Decarbonization of the transport and the industry sector will most probably result in a much higher demand for electricity (cf. Chapter 6; European Commission 2018). To cope with the volatile electricity generation of renewable energy sources (RES) and consumption peaks, more flexibility on the supply and demand side is required for a secure and cost-efficient electricity supply (cf. Chapters 9–11).

S. Heitel (B)

A.-L. Klingler

A. Herbst

F. Fermi TRT Trasporti e Territorio, Milan, Italy

Competence Center Sustainability and Infrastructure Systems, Fraunhofer Institute for Systems and Innovation Research (ISI), Karlsruhe, Germany e-mail<EMAIL_ADDRESS>
Business Area Mobility and Urban Systems Engineering, Fraunhofer Institute for Industrial Engineering (IAO), Stuttgart, Germany

Competence Center Energy Technology and Energy Systems, Fraunhofer Institute for Systems and Innovation Research (ISI), Karlsruhe, Germany

This paper focuses on analyzing flexibility potentials that can be provided by the demand side. Analyses are conducted for a decentralized energy system, meaning that hydrogen is produced locally in closer proximity to its consumers (cf. Chapter 2). In this scenario, we assume that consumers will support this decentralized system by installing rooftop PV systems and by engaging in demand side management (DSM) to provide flexibility to the system for a secure and cost-efficient electricity supply. Demand side management measures in this study aim to flatten the system load curves by reducing demand in peak times and at minimizing residual loads by adapting electricity consumption to electricity generation. The residual load is defined as the difference between system load curve and renewable electricity generation. By definition, a positive residual load means that the amount of electricity demand cannot be covered by RES. Negative residual loads occur when the production of renewable electricity exceeds the demand so that the renewable electricity cannot be integrated into the electricity system at this point in time (cf. Boßmann 2015; Chapter 6). In consequence, the reduction of negative residual loads by using demand side measures is an indicator that RES are used more efficiently. Furthermore, high fluctuations in the residual load curve can be a challenge for existing baseload power plants and could thus drive the need for additional conventional generation capacities, while the utilization, and hence profitability, of new and existing RES capacities could decline.

Compared to classical DSM measures such as heat pumps and refrigeration (cf. Chapters 8 and 12), batteries, as part of residential photovoltaic (PV) systems and in electric vehicles, as well as hydrogen electrolyzers can have a stronger role in providing flexibility in a decentralized energy system. As several decarbonization options exist for transport and industry that will have different impacts on the amount of electricity demand and the flexibility potential, the options of these sectors will be briefly described in the following.

### *7.1.1 Strategies for Decarbonizing Transport*

For the decarbonization of the transport sector, several strategies are available and have to be combined to achieve the challenging greenhouse gas (GHG) reduction target: modal shift from road transport to more efficient transport modes like rail, inland waterways, public transport, and active modes, improvement of the vehicle efficiency, diffusion of low- and zero-emission technologies, and alternative fuels (cf. Chapter 6 for more details). Looking at the demand side flexibility of these options, only the vehicle technologies battery electric vehicles (BEV), plug-in hybrid vehicles (PHEV) and fuel cell electric vehicles (FCEV) can lead to demand side flexibility. BEV and PHEV constitute a promising technology in particular for cars and light duty vehicles. Although manufacturers are developing battery electric heavy duty trucks, hybrid trolley trucks and FCEV trucks appear to be more promising options for long-distances, as batteries for long distance trucks would be of large size and weight. It is not yet clear which of these technologies will prevail (cf. Plötz et al. 2018). Trolley trucks are more efficient from an overall energy perspective, however, they require a catenary system infrastructure on highways for which acceptance in the population might be an issue (cf. Wietschel et al. 2017). Concerning flexibility, trolley trucks need electricity while driving, whereas the production of hydrogen via electrolysis as fuel for FCEV offers load shifting potential.

### *7.1.2 Technologies for Decarbonizing Industry*

For the decarbonization of the industry sector until 2050, a variety of different technologies and mitigation options are of importance. As available technologies and conventional fuel switch will not suffice to meet the climate targets on GHG reduction, the industry sector needs to open up remaining energy efficiency potentials in combination with high financial support for RES to promote fuel switching to biomass, power-to-heat, and power-to-gas.

In terms of end-use, most industrial GHG emissions result from high-temperature process heat, either in the form of steam or hot water or from direct firing of different types of furnaces. The high temperatures and the specific technological requirements limit the use of renewable energies to biomass or secondary energy sources. In this context, two types of electricity demand can be distinguished: the direct use of electricity for mechanical energy and heating, and the indirect use via electrolysisbased secondary energy carriers such as hydrogen (cf. Chan et al. 2019). These radical changes in industrial process technologies will be necessary to foster fuel switch to electricity and hydrogen in the iron and steel industry, the chemical industry, and the non-metallic minerals sector for electric smelting furnaces (e.g., glass industry).

To substitute coal-based primary steelmaking with steelmaking based on the direct reduction of iron ore with hydrogen, large amounts of renewable hydrogen (e.g., produced with electricity from renewable resources with Proton Electron Membrane (PEM) electrolysis) have to be available at competitive prices. Currently, a variety of different projects within the EU are working to have this technology ready for the market: the HYBRIT-project,1 the SALCOS-project2 and the H2Future/SuSteelproject.3

Another option to reduce emissions from primary steelmaking is the direct electrolysis of iron ore (cf. Siderwin 2018). This option also requires high amounts of renewables electricity as alkaline electrolysis is used to produce direct reduced iron from iron ore using electrical energy to replace conventional blast furnaces (cf. Chan et al. 2019).

In the basic chemical industry, the use of renewable hydrogen as feedstock can substantially reduce GHG emissions in this sector (cf. Fleiter et al. 2019; Herbst et al. 2018). Conventional ammonia and methanol production based on fossil combustion

1The implementing companies are SSAB, LKAB, and Vattenfall, cf. Vogl et al. (2018).

2The implementing companies are Salzgitter and Vattenfall, cf. https://salcos.salzgitter-ag.com/.

3The implementing companies are Voestalpine and VERBUND, cf. www.h2future-project.eu. https://www.k1-met.com/forschungsprogramm/susteel/, Chan et al. (2019).

are replaced, using hydrogen-based production routes: H2 ammonia from electricity rather than through methane steam reformation and H2 methanol based on water electrolysis with electricity, followed by hydrogenation of CO2 as carbon source (cf. Chan et al. 2019; Dechema 2017).

Further emission reductions can be achieved by ethylene production via lowcarbon Methanol-to-Olefins (MTO) leading to a strong increase in methanol demand (Chan et al. 2019; Dechema 2017).

Further, radical process changes take place in the cement production, assuming the market entry of low-carbon cement sorts. In addition, a stronger switch to secondary production takes place in this scenario in the steel, aluminum, glass, and paper industry, as well as increasing efforts for material efficiency improvements and substitution.

Looking at the future flexibility potential, the largest potential in the industrial sector stems from on-site hydrogen electrolysis (if electrolyzers are installed as assumed in the scenario of this study).

### *7.1.3 Focus of this Study: Disruptive Technologies with Demand Side Flexibility*

This study focuses on the three technologies that are likely to be the bulk of demand side flexibility in the long-term: decentralized *batteries as part of residential PV systems and in electric vehicles*, as well as *electrolyzers* for the production of H2 as fuel for freight transport and as feedstock and fuel for the industry sector. The target is to analyze their potential impact via load shifting on the residual load as an indicator for RES integration.

In the next Sect. 7.2, these three technologies will be described in more detail including their current status and factors influencing their diffusion. Section 7.3 describes the detailed scenario assumptions for a decentralized world (based on the decentralized High-RES scenario cf. Chapter 2), the model coupling approach and the methods used to calculate and simulate the diffusion of the respective technologies. In Sect. 7.4, the results are presented comprising the installed battery capacities as part of PV systems, technology diffusion in the vehicle fleet, radical process improvements in industry and the resulting demand for electricity and hydrogen. Section 7.5 analyses the impacts of these technologies on demand side flexibility. Finally, the results are discussed and conclusions including policy recommendations are drawn.

### **7.2 Disruptive Technologies with Flexibility Potential**

### *7.2.1 Photovoltaic Systems and Stationary Batteries*

In terms of the economic performance of residential PV systems, the rate of on-site consumed electricity (so-called self-consumption) is becoming ever more important due to increasing electricity prices and decreasing feed-in remuneration. Selfconsumption is particularly profitable in markets with high electricity end-user prices and relatively low levelized costs of electricity for PV. Relatively low or no feed-in remuneration promote high self-consumption rates. In order to increase self-consumption, battery systems are available, which allow matching the electricity production of PV systems and the household's consumption (cf. Schill et al. 2017). Due to increasing production capacities and technological learning, prices for batteries are expected to decrease substantially (cf. Louwen et al. 2018; Schmidt et al. 2017; cf. Chapter 4). Lithium-ion batteries are currently the dominant technology (cf. Figgener et al. 2017).

Batteries for the enhancement of self-consumption are therefore widely discussed and are already selling in some local markets. In Germany, with its high end-consumer electricity prices, about every second rooftop PV system is already combined with a battery: In 2016, 45% of new PV installations under 30 kW in Germany included a battery (cf. Figgener et al. 2017). Moreover, Barbour and González (2018) find that 'PV + battery systems' are to become a better investment than 'PV only systems'. The expectation of a broad diffusion of stationary batteries sparks the hope that this technology will provide necessary flexibility for the future electricity system. Recent studies find that participating in the balancing power markets could potentially increase economic benefits for the battery owner (cf. Stahl et al. 2018; Sterner et al. 2015). Due to complicated legislation, this option is still at its very early stages but is seen as viable to fill the need for more flexibility in the system.

### *7.2.2 Battery Electric Vehicles*

Global electric car sales and market shares are rapidly growing (cf. Chapter 5). In 2018, the new registrations nearly doubled the registrations of the previous year. The global electric car fleet achieved over 5.1 million vehicles (cf. International Energy Agency 2019). These numbers comprise BEV, in which batteries are the only energy source, and PHEV, which still incorporate an internal combustion engine (ICE) and contain smaller batteries. Looking at the market shares of new electric cars, Norway is the global leader with 46%, followed by Iceland with 17% and Sweden with 8% (cf. International Energy Agency 2019). These numbers show that electric vehicles (EVs) are a real technological option to decarbonize road transport and reduce air pollutants. Declining battery prices due to global learning effects in production (cf. Chapter 5; Heitel et al. 2019; Schmidt et al. 2017) are an important driver for the diffusion. EVs may soon achieve cost-parity with conventional vehicles based on the total cost of ownership (TCO). Further drivers will be technological improvements, like raising durability and energy density that enables longer ranges, as well as policies that incentivize automobile manufacturers to build up an EV portfolio. A high EV uptake will increase electricity demand and can challenge the power system in case of uncoordinated charging in times of peak demand, causing local overloading of distribution networks and the need for additional electricity generation. In contrast, however, EVs can also contribute to flexibility with DSM. The provided flexibility potential depends on several factors, in particular, the available charging infrastructure and charging patterns (cf. Gnann et al. 2018), and the willingness to participate in load shifting so that the timing of charging can be postponed to low-demand periods.

Such a controlled charging mechanism provides a respective flexibility potential that could even be enhanced through bidirectional charging, which would transform EVs into a distributed electricity storage from which electricity can be fed back to the grid or home (cf. International Energy Agency 2019). Besides regulation, willingness to allow load shifting will depend on price incentives that should compensate for disadvantages like reduced spontaneity for taking the car or a faster degradation of the battery due to more frequent and rapid charging cycles.

### *7.2.3 Hydrogen Electrolysis*

The electrolysis of water into hydrogen and oxygen has the potential to become a key element in coupling the electricity, transport and industry sectors by providing fossilfree fuels and feedstock in a future sustainable world. Additionally, electrolyzers can provide flexibility to the electricity system: According to Buttler and Spliethoff (2018), the technological development of the last few years shows that electrolysis is on its way to large-scale flexible energy-storage applications.

Large-scale electrolysis systems, which are in the focus of this study, consist of several electrolyzers in parallel. Therefore, it is possible to vary the power consumption of the overall system over a wide range by switching off individual electrolyzers. In this manner, current state of the art systems allow for a load flexibility of 0–100% of the nominal load (this applies for PEM electrolysis, cf. Buttler and Spliethoff 2018). Additionally, 20% overcharging is possible without significant effects on the lifetime (alkaline electrolysis, cf. Gutiérrez-Martín et al. 2015).

Since the large-scale operation of electrolyzers for the decarbonization of the energy system is still an emerging market, the mode of operation, whether decentralized (local electrolyzers) or centralized (with infrastructure carrying H2) is uncertain and both can be assumed (cf. De Vita et al. 2018). On the demand side, the future requirement of H2 in the transport and industry sector is expected to drive the market uptake of this technology.

### **7.3 Scenario Assumptions and Methodology**

### *7.3.1 Scenario Assumptions for High-RES Decentralized*

For the High-RES decentralized scenario, several strong measures are assumed to accelerate the decarbonization in order to achieve the GHG reduction targets and specific characteristics of a decentralized energy system are considered.

To support decentralized PV electricity generation, it is assumed that all European countries allow feeding PV electricity into the public grid. The electricity can be sold to the current market prices, while feed-in tariffs and premiums are abolished. Self-consumed PV electricity is not burdened with any surcharges, taxes, or levies.

Policies and assumptions that accelerate the diffusion of BEV and PHEV comprise a strong expansion of charging stations, increased taxes for conventional fuels and for the registration of ICE vehicles, and a sales ban for new ICE cars as of 2040. In this scenario, fuel cells are pushed as the future technology for trucks by R&D expenditures, deployment of a hydrogen filling station infrastructure for trucks, and a decision against the installation of catenary systems for hybrid trolley trucks. In the decentralized world, hydrogen is produced on-site at the filling stations. As households with rooftop PV have a higher probability to buy an electric car (as indicated by some studies, cf., e.g., Scherrer et al. 2019) due to financial advantages by consumption of self-produced electricity and higher technical familiarity, the EV sales are further supported by strongly increasing PV installations. Besides, it is assumed that multi-modal transport is more accepted in a decentralized world which leads to increasing demand for car sharing vehicles that are predestinated for higher electric shares in their fleet (cf. International Energy Agency 2019).

Due to radical process improvements, a strong shift toward electricity and hydrogen takes place in the iron and steel industry (DR electrolysis, H2 plasma, DR H2 + EAF) and the glass industry (electric melting). Furthermore, the production of ammonia, methanol and consequently ethylene is no longer based on fossil sources (e.g., natural gas, naphtha) leading to a significant drop in demand for refinery products but increasing the need for RES hydrogen. Hydrogen is produced on-site for industrial purposes and consequently leading to a high on-site industrial electricity demand for hydrogen electrolysis.

Table 7.1 summarizes the main assumptions for the High-RES decentralized scenario that are characteristic for a decentralized energy system.

Furthermore, we assume that DSM measures will be stipulated in the near future to facilitate renewable integration on a local level. Therefore, ambitious shares of flexible technologies (referred to as "smart share") are assumed. New flexibility options, namely, *decentralized batteries* and *hydrogen electrolyzers*, are considered to be 100% DSM ready from the time of their installation. "Classic" flexible technologies, such as heat pumps, refrigeration, etc., gain the DSM option with their refurbishment. As hydrogen is produced on-site at the industrial plants and at the filling stations for transport, the electricity consumption and flexibility potential is

**Table 7.1** Main scenario assumptions for the High-RES decentralized scenario

**Table 7.2** Assumed participation in DSM (i.e., "Smart share") over time in percentage, based on the assumption of a stipulated participation

accounted for on the demand side. The smart shares for the tertiary, industry, and residential sectors as well as for batteries and electrolyzers are presented in Table 7.2.

### *7.3.2 Model Coupling Approach*

For the simulation of the High-RES decentralized scenario across sectors, several models were coupled by data exchange in two iterations to include feedback mechanisms like the changing electricity price with the diffusion of additional demand side technologies (cf. Chapter 3 for more details on the single models and the data exchange). In the following, only the main models, linkages, and outputs that are required for the analysis of flexibility provided by the demand side are briefly explained. The two models—FORECAST, covering the industry, tertiary and residential sectors, and ASTRA, representing the transport sector—simulate the development of the annual electricity and hydrogen demand until 2050 by technology and consumer type, such as specific industrial processes or vehicle categories. In eLOAD, this annual demand is then translated to hourly demand curves by using load profiles of various technologies and consumer types. The resulting hourly system load curves are then optimized using demand side management with the assumed smart shares, to flatten the system load, and to reduce the negative residual load. For the calculation of the residual load, intermittent RES electricity from wind and PV are calculated by geographically highly resolved data on land availability as well as hourly time series of RES generation based on weather data, as described in Zöphel et al. (2019) and Slednev et al. (2018). Methods addressing the diffusion of the relevant technologies are described in the following section.

### *7.3.3 Methods Used for Technology Diffusion*

### **<IP_ADDRESS> Calculation of PV and Battery System Diffusion**

The market diffusion of decentralized battery systems in households is calculated in five steps: first, the optimal (self-consumption maximizing) battery operation is calculated. In a subsequent step, the economic benefits of the battery system are assessed for batteries with different capacities, and the—on average—optimal battery capacity is selected with the assumption that feed-in premiums and tariffs are abolished, and the electricity produced by PV systems can be sold to the public grid at spot market rates. In the third step, Rogers' theory on the diffusion of innovation (cf. Rogers 2003) is applied to assess the share of battery adopters among PV system owners. Based on the share of adopters and the current and future population of PV system owners, the total population of battery adopters is calculated in a fourth step. In a final step, the installed battery capacity is given for the years 2016 to 2050. The calculations and assumptions are described in more detail in Klingler et al. (2019).

#### **<IP_ADDRESS> Diffusion of Alternative Drive Technologies for Vehicles**

In ASTRA, the technology choice is implemented based on an adapted TCO approach that considers the following factors: vehicle prices, costs for energy consumption, maintenance costs, taxes, insurance, road charges as well as fuel procurement costs which depend on the deployment of the charging and filling station infrastructure and the ranges of the vehicles. The costs of the two new technology components i) battery and ii) fuel cell stack as part of the vehicle prices develop via experience curves covering global learning. Further qualitative aspects of purchase decisions related to a certain technology (like the anxiety of gas explosions or insufficient ranges, limited available vehicle models, etc.) are covered in a "residual disutility" term. The diffusion of technologies in the road vehicle fleet is simulated separately for the vehicle categories private cars, commercial cars, light duty vehicles, heavy duty vehicles in four separate gross vehicle weight categories, urban buses, and coaches. A set of suitable technologies is available for each category. The technology share for new vehicle purchases in each category is finally estimated with a discrete choice approach per country. The methodology is described in more detail in Heitel et al. (2019) and Krail (2009).

#### **<IP_ADDRESS> Assumptions for Technology Shares in Industry**

The diffusion of new production processes is an exogenous assumption in FORE-CAST. Data on potential market entry and maximum diffusion rates were determined in previous projects by means of expert surveys and interviews (cf. Eichhammer et al. 2018) as well as literature analysis (cf. Chan et al. 2019). Therefore, the scenario can say little about the actual speed of process replacement and diffusion. It can, however, allow important conclusions on the overall direction of process change.

For the iron and steel industry, it has been assumed that oxygen steel will be replaced as far as possible with electric steel and that the remaining blast furnace route will be substituted with electrolysis-based direct reduction and hydrogen-based steel production routes (H2 plasma steel, DR H2 + EAF).

In the chemical industry, it is assumed that the production of ammonia, methanol, and consequently ethylene is no longer based on fossil sources (e.g., natural gas, naphtha) but fully substituted by renewable hydrogen and ammonia production as well as methanol-to-ethylene production in 2050.

### **7.4 Results: Diffusion of Technologies and Energy Demand**

### *7.4.1 Installed Battery Capacity*

In the mid- to long-term future, larger batteries are installed in residential PV selfconsumption systems, due to decreasing technology costs. Based on technological learning, the specific investment decreases substantially, from 1,250 EUR/kWh in 2017 to 346 EUR/kWh in 2050 (cf. Klingler et al. 2019). Apart from the country of Norway, where the low electricity prices inhibit the batteries to gain an economic case, this cost development allows for high diffusion and large systems. In 2050, over 80 million batteries are expected in the EU-27 + CH + NO + UK, i.e., over 90% of the households with a PV rooftop system own a battery.

Table 7.3 shows the resulting on average installed battery capacity and Table 7.4 the total installed battery capacity for selected EU countries and the entire EU covering EU-27 + CH + NO + UK.

**Table 7.3** Most economic battery capacity in kWh for the average household in selected countries in the years 2020, 2030, 2040, and 2050

*Source* Data based on own calculations

**Table 7.4** Expected installed battery capacity in MWh in Europe and in selected EU countries

*Source* Data based on own calculations

### *7.4.2 Vehicle Fleet Technology Composition and Resulting Energy Demand*

With the assumptions for the High-RES decentralized scenario, BEV, PHEV, and FCEV diffuse substantially in the car fleet, comprising over 80% of total passenger car stock of the EU-27 countries and the UK in 2050. Decreasing battery prices, stricter CO2 standards for cars, extended charging infrastructure, diverse financial measures, and an increasing number of households with rooftop PV systems lead to a visible diffusion of BEV and PHEV in the upcoming decades. The diffusion accelerates from the year 2035 onwards as sales of new conventional ICE cars (i.e., gasoline, diesel, liquefied petroleum gas [LPG], and compressed natural gas [CNG]) are banned from 2040 onwards, having effects on car manufacturer's vehicle portfolios and purchase decisions already in the previous years.

For road freight transport, diesel remains the dominant fuel for the next two decades. BEV and PHEV prevail for trucks of the lightest weight category as prices for batteries decline, the number of available vehicle models increases and a growing number of cities restricts the entry of ICE vehicles. With more ambitious policies that increase costs for diesel trucks (i.e., stricter CO2 standards, emission-based registration taxes, fuel taxes, and road tolls) and a reliable H2-refueling infrastructure for heavy-duty vehicles, FCEV diffuse in the truck fleet as of 2030, achieving a share of 36% of all trucks in 2050. In order to enable market-entry based on a TCO approach, R&D and subsidies for fuel cell technology and hydrogen supply are required initially. Over time, the related cost further decline via experience curve effects for the production of fuel cell stacks and electrolyzers.

Figure 7.1 visualizes the diffusion of the low- and zero-emission technologies in the main road vehicle fleets in the High-RES decentralized scenario.

The resulting demand for electricity and hydrogen is depicted in Fig. 7.2. While the final electricity demand for the transport sector stems mainly from trains in 2015,

**Fig. 7.1** Technology composition of the vehicle fleet in the High-RES decentralized scenario for cars and trucks for EU-27 + UK (*Source* Data based on model results from ASTRA)

**Fig. 7.2** Final electricity and hydrogen demand of the transport sector in High-RES decentralized for EU-27 + UK in TWh (*Source* Data based on model results from ASTRA)

the sector's electricity demand increases by ten times to 547 TWh in 2050 due to the diffusion of BEV and PHEV. Therein, electric passenger cars are the dominant consumers. They represent 88% of electricity demand from road transport and 14% with regard to total electricity demand.

The final hydrogen demand of the transport sector is strongly increasing from 2030 onwards with the diffusion of fuel cell trucks, reaching 380 TWh by 2050. Although hydrogen is also demanded by cars, light duty vehicles (LDV), and buses, 80% of the hydrogen required for the transport sector will be consumed by heavy duty vehicles (HDV).

### *7.4.3 Radical Process Improvements in Industry and Their Implications for Future Electricity Demand*

In the High-RES decentralized scenario, a significant reduction of direct emissions in the industry sector is achieved (73% compared to 2015). This high level of ambition leads to a significant increase in demand for RES electricity and RES hydrogen in 2050, making electricity the dominant energy carrier in 2050 (from 1,036 TWh to 1,469 TWh in 2050, cf. Figure 7.3). Process technologies in 2050 use electricity either directly (e.g., DR electrolysis in the steel industry) or indirectly (e.g., production of ethylene via H2-based methanol). Where possible, the direct use of electricity is preferred over the indirect use (e.g., electric kilns and furnaces). In general, electricity, ambient heat, and biomass substitute a large part of industry's demand for natural gas in this scenario.