Market Design for the Transition to Renewable Electricity Systems

The research carried out in this thesis aims to shed light on the role of the European electricity market design in the transition to a target electricity system that combines sustainability, affordability, and reliability. While the ongoing expansion of fluctuating renewable electricity sources challenges the established structures and market mechanisms, governments across Europe have decided to phase-out certain conventional technologies like coal or nuclear power. Since traditional electricity systems rely on flexibility provided by controllable generation capacity, other flexibility options are needed to compensate for the decommissioned conventional power plants and support the system integration of renewables. Against this background, the dissertation extends an established large-scale agent-based electricity market model in order to account for the developments towards an integrated European electricity market and the characteristics of storage technologies. In particular, the representation of cross-border effects is enhanced by integrating approaches from the fields of operations research, non-cooperative game theory, and artificial intelligence in the simulation framework. The extended model is then applied in three case studies to analyze the diffusion of different flexibility options under varying regulatory settings. These case studies cover some central aspects of the European electricity market, most importantly capacity remuneration mechanisms, the interaction of day-ahead market and congestion management, and the role of regulation for residential self-consumption. Results of the case studies confirm that by designing the regulatory framework, policymakers and regulators can substantially affect quantity, composition, location, and operation of technologies – both, on the supply side and the demand side. At the same time, changes and amendments to market design are frequent and will continue to be so in the years ahead. Moreover, given the increasing level of market integration in Europe, the role of cross-border effects of national market designs will gain further in importance. In this context, agent-based simulation models are a valuable tool to better understand potential long-term effects of market designs in the interconnected European electricity system and can therefore support the European energy transition

Life cycle greenhouse gas emissions of residential battery storage systems: A German case study

Battery storage systems (BSSs) are popular as a means to increase the self-consumption rates of residential photovoltaics. However, their environmental impact is under discussion, given the greenhouse gas emissions caused by the production and the efficiency losses during operation. Against this background, we carry out a holistic environmental assessment of residential BSSs by combining a partial life cycle assessment for the production phase with a detailed simulation of 162 individual German households for the operational phase. As regards the production phase, we only find small differences between the carbon footprints of different cell chemistries. Moreover, we can show that the balance of plant components have a comparable impact on the global warming potential as the cell modules. In terms of the operational phase, our simulations show that BSSs can compensate at least parts of their efficiency losses by shifting electricity demand from high-emission to low-emission periods. Under certain conditions, the operational phase of the BSSs can even overcompensate the emissions from the production phase and lead to a positive environmental impact over the lifetime of the systems. As the most relevant drivers, we find the exact emissions at the production stage, the individual household load patterns, the system efficiency, and the applied operational strategy

On the Role of Electricity Storage in Capacity Remuneration Mechanisms

In electricity markets around the world, the substantial increase of intermittent renewable electricity generation has intensified concerns about generation adequacy, ultimately driving the implementation of capacity remuneration mechanisms. Although formally technology-neutral, substantial barriers often exist in these mechanisms for non-conventional capacity such as electricity storage. In this article, we provide a rigorous theoretical discussion on design parameters and show that the concrete design of a capacity remuneration mechanism always creates a bias towards one technology or the other. In particular, we can identify the bundling of capacity auctions with call options and the definition of the storage capacity credit as essential drivers affecting the future technology mix as well as generation adequacy. In order to illustrate and confirm our theoretical findings, we apply an agent-based electricity market model and run a number of simulations. Our results show that electricity storage has a capacity value and should therefore be allowed to participate in any capacity remuneration mechanism. Moreover, we find the implementation of a capacity remuneration mechanism with call options and a strike price to increase the competitiveness of storages against conventional power plants. However, determining the amount of firm capacity an electricity storage unit can provide remains a challenging task

On the role of electricity storage in capacity remuneration mechanisms

In electricity markets around the world, the substantial increase of intermittent renewable electricity generation has intensified concerns about generation adequacy, ultimately driving the implementation of capacity remuneration mechanisms. Although formally technology-neutral, substantial barriers often exist in these mechanisms for non-conventional capacity such as electricity storage. In this article, we provide a rigorous theoretical discussion on design parameters and show that the concrete design of a capacity remuneration mechanism always creates a bias towards one technology or the other. In particular, we can identify the bundling of capacity auctions with call options and the definition of the storage capacity credit as essential drivers affecting the future technology mix as well as generation adequacy. In order to illustrate and confirm our theoretical findings, we apply an agent-based electricity market model and run a number of simulations. Our results show that electricity storage has a capacity value and should therefore be allowed to participate in any capacity remuneration mechanism. Moreover, we find the implementation of a capacity remuneration mechanism with call options and a strike price to increase the competitiveness of storages against conventional power plants. However, determining the amount of firm capacity an electricity storage unit can provide remains a challenging task

Diffusion and System Impact of Residential Battery Storage under Different Regulatory Settings

Cost reductions of rooftop photovoltaics and battery storage, increasing retail electricity prices as well as falling feed-in remuneration provide strong incentives for many German households to engage in self-consumption. These developments may also affect the electricity system as a whole. Against this background, we jointly apply a prosumer simulation and an agent-based electricity market simulation in order to investigate the long-term impacts of a residential battery storage diffusion on the electricity market. We analyze different regulatory frameworks and find significant effects on the household level, yet only moderate system impacts. In the long run, the diffusion of residential battery storage seems difficult to govern, even under a restrictive regulation. In contrast, the way the batteries are operated may be easier to regulate. Policymakers and regulators should focus on this aspect, since a system-friendly battery operation supports the system integration of residential photovoltaics while having little impact on the households’ selfsufficiency

Diffusion and system impact of residential battery storage under different regulatory settings

Cost reductions of rooftop photovoltaics and battery storage, increasing retail electricity prices as well as falling feed-in remuneration provide strong incentives for many German households to engage in self-consumption. These developments may also affect the electricity system as a whole. Against this background, we jointly apply a prosumer simulation and an agent-based electricity market simulation in order to investigate the long-term impacts of a residential battery storage diffusion on the electricity market. We analyze different regulatory frameworks and find significant effects on the household level, yet only moderate system impacts. In the long run, the diffusion of residential battery storage seems difficult to govern, even under a restrictive regulation. In contrast, the way the batteries are operated may be easier to regulate. Policymakers and regulators should focus on this aspect, since a system-friendly battery operation supports the system integration of residential photovoltaics while having little impact on the households’ self-sufficiency

The Merge of Two Worlds: Integrating Artificial Neural Networks into Agent-Based Electricity Market Simulation

Machine learning and agent-based modeling are two popular tools in energy research. In this article, we propose an innovative methodology that combines these methods. For this purpose, we develop an electricity price forecasting technique using artificial neural networks and integrate the novel approach into the established agent-based electricity market simulation model PowerACE. In a case study covering ten interconnected European countries and a time horizon from 2020 until 2050 at hourly resolution, we benchmark the new forecasting approach against a simpler linear regression model as well as a naive forecast. Contrary to most of the related literature, we also evaluate the statistical significance of the superiority of one approach over another by conducting Diebold-Mariano hypothesis tests. Our major results can be summarized as follows. Firstly, in contrast to real-world electricity price forecasts, we find the naive approach to perform very poorly when deployed model-endogenously. Secondly, although the linear regression performs reasonably well, it is outperformed by the neural network approach. Thirdly, the use of an additional classifier for outlier handling substantially improves the forecasting accuracy, particularly for the linear regression approach. Finally, the choice of the model-endogenous forecasting method has a clear impact on simulated electricity prices. This latter finding is particularly crucial since these prices are a major results of electricity market models

Impact of Electricity Market Designs on Investments in Flexibility Options

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