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.

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.

In Europe, the ongoing renewable expansion and delays in the planned grid extension have intensified the discussion about an adequate electricity market design. Against this background, we jointly apply an agent-based electricity market model and an optimal power flow model to investigate the long-term impacts of splitting the German market area into two price zone. Our approach allows capturing long-term investment and short-term market behavior under imperfect information. We find strong impacts of a German market splitting on electricity prices, expansion planning of generators and required congestion management. While the congestion volumes decrease significantly under a market split in the short term, the optimal zonal configuration for 2020 becomes outdated over time due to dynamic effects like grid extension, renewable expansion and new power plant investments. Policymakers and regulators should therefore regularly re-assess bidding zone configurations. Yet, this stands in contrast to the major objective of price zones to create stable locational investment incentives.

Growing numbers of plug-in electric vehicles in Europe will have an increasing impact on the electricity system. Using the agent-based simulation model PowerACE for ten electricity markets in Central Europe, we analyze how different charging strategies impact price levels and production- as well as consumption-based carbon emissions in France and Germany. The applied smart charging strategies consider spot market prices and/or real-time production from renewable energy sources. While total European carbon emissions do not change significantly in response to the charging strategy due to the comparatively small energy consumption of the electric vehicle fleet, our results show that all smart charging strategies reduce price levels on the spot market and lower total curtailment of renewables. Here, charging processes optimized according to hourly prices have the strongest effect. Furthermore, smart charging strategies reduce electricity purchasing costs for aggregators by about 10% compared to uncontrolled charging. In addition, the strategies allow aggregators to communicate near-zero allocated emissions for charging vehicles. An aggregator's charging strategy expanding classic electricity cost minimization by limiting total national PEV demand to 10% of available electricity production from renewable energy sources leads to the most favorable results in both metrics, purchasing costs and allocated emissions. Finally, aggregators and plug-in electric vehicle owners would benefit from the availability of national, real-time Guarantees of Origin and the respective scarcity signals for renewable production.

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.

Investment decisions in competitive power markets are based upon thorough profitability assessments. Thereby, investors typically show a high degree of risk aversion, which is the main argument for capacity mechanisms being implemented around the world. In order to investigate the interdependencies between investors' risk aversion and market design, we extend the agent-based electricity market model PowerACE to account for long-term uncertainties. This allows us to model capacity expansion planning from an agent perspective and with different risk preferences. The enhanced model is then applied in a multi-country case study of the European electricity market. Our results show that assuming risk-averse rather than risk-neutral investors leads to slightly reduced investments in dispatchable capacity, higher wholesale electricity prices, and reduced levels of resource adequacy. These effects are more pronounced in an energy-only market than under a capacity mechanism. Moreover, uncoordinated changes in market design may also lead to negative cross-border effects.