Decision-making under uncertainty in short-term electricity markets

In the course of the energy transition, the share of electricity generation from renewable energy sources in Germany has increased significantly in recent years and will continue to rise. Particularly fluctuating renewables like wind and solar bring more uncertainty and volatility to the electricity system. As markets determine the unit commitment in systems with self-dispatch, many changes have been made to the design of electricity markets to meet the new challenges. Thereby, a trend towards real-time can be observed. Short-term electricity markets are becoming more important and are seen as suitable for efficient resource allocation. Therefore, it is inevitable for market participants to develop strategies for trading electricity and flexibility in these segments. The research conducted in this thesis aims to enable better decisions in short-term electricity markets. To achieve this, a multitude of quantitative methods is developed and applied: (a) forecasting methods based on econometrics and machine learning, (b) methods for stochastic modeling of time series, (c) scenario generation and reduction methods, as well as (d) stochastic programming methods. Most significantly, two- and three-stage stochastic optimization problems are formulated to derive optimal trading decisions and unit commitment in the context of short-term electricity markets. The problem formulations adequately account for the sequential structure, the characteristics and the technical requirements of the different market segments, as well as the available information regarding uncertain generation volumes and prices. The thesis contains three case studies focusing on the German electricity markets. Results confirm that, based on appropriate representations of the uncertainty of market prices and renewable generation, the optimization approaches allow to derive sound trading strategies across multiple revenue streams, with which market participants can effectively balance the inevitable trade-off between expected profit and associated risk. By considering coherent risk metrics and flexibly adaptable risk attitudes, the trading strategies allow to substantially reduce risk with only moderate expected profit losses. These results are significant, as improving trading decisions that determine the allocation of resources in the electricity system plays a key role in coping with the uncertainty from renewables and hence contributes to the ultimate success of the energy transition

The long-term impact of increased fossil fuel prices and market design on the market values of renewable generation

Following Russia\u27s invasion of Ukraine in 2022, European countries took significant steps to reduce their reliance on energy imports from Russia, particularly in the gas and coal sectors. At the same time, to import less primary energy in the future, some countries have adopted new renewable energy targets. The question is to what extent the increase in gas and coal prices can contribute to refinancing renewable energy on the electricity wholesale market. To investigate this, an agent-based approach is used to examine the market values of renewable energies in several European countries until 2040. It is shown that increased expansion targets have a more substantial negative impact on the market values of renewable energies than increased gas and coal prices have a positive effect. In addition, it is observed that the introduction of capacity markets does not significantly influence market values and wholesale electricity prices in the medium term. However, by 2040, lower electric

Steelmaking Technology and Energy Prices: The Case of Germany

We examine the relationship between the choice of steelmaking technology and energy prices in Germany using data beginning 1970. The analysis indicates that technology choice began to cointegrate with comparative energy prices in the early 90s. The short and long-run effects of energy prices are captured in a partial adjustment model; the ratio of electricity to coal prices is seen to exert sizeable influence on the short and long-term deployment of the electric arc furnace for secondary steelmaking. If current trends in energy prices continue, the share of secondary steelmaking in total steel production is expected to increase rather slowly

Forecasting of Frequency Containment Reserve Prices Using Econometric and Artificial Intelligence Approaches

The forecasting of control reserve prices is essential in order to participate reasonably in the auctions. Having identified a lack of related literature, we therefore deploy approaches based on auto-regressive and exogenous factors coming from econometrics and artificial intelligence and set up a forecasting framework. We use SARIMA and SARIMAX models as well as neural networks and forecast based on a rolling one-step forecast with re-estimation of the models. It turns out, that the combination of auto-regressive and exogenous factors yields the best results compared to approaches solely considering auto-regressive or exogenous factors. Further, the artificial intelligence approach outperforms the econometric approach in terms of forecast quality, whereas for the further use of the generated models, the econometric approach has advantages in terms of interpretability

Short-term Risk Management for Electricity Retailers Under Rising Shares of Decentralized Solar Generation

Electricity retailers face increasing uncertainty due to the ongoing expansion of unpredictable, distributed generation in the residential sector. We analyze how increasing levels of households\u27 solar PV self-generation affect the short-term decisionmaking and associated risk exposure of electricity retailers in day-ahead and intraday markets. First, we develop a stochastic model accounting for correlations between solar load, residual load and price in sequentially nested wholesale spot markets across seasons and type of day. Second, we develop a computationally tractable twostage stochastic mixed-integer optimization model to investigate the trading portfolio and risk optimization problem faced by retailers. Through conditional value-at-risk we assess retailers\u27 profitability and risk exposure to different levels of PV self-generation by assuming different retail tariff schemes. We find risk-hedging trading strategies and tariffs to have greater impact in Summer and with low levels of residual load in the system, i.e. when the solar generation uncertainty affect more the households demand to be served and the wholesale spot prices. The study is innovative in unveiling the potential of dynamic electricity tariffs, which are indexed to spot prices, to sustain a high penetration of renewable energy source while promoting risk sharing between customer and retailer. Our findings have implications for electricity retailers facing load and revenue risks in wholesale spot markets, likewise for regulators and policy-makers interested in electricity market design

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

On the Role of Risk Aversion and Market Design in Capacity Expansion Planning

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\u27 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 crossborder effects

Einsatz von Strommarktmodellen zur Untersuchung der Versorgungssicherheit in Europa

Versorgungssicherheit ist eine der Zieldimensionen bei der Ausgestaltung von Energiesystemen. Die Energiekrise in Europa im Jahr 2022 hat grundlegende Fragen aufgeworfen, inwieweit die Stromversorgung noch gesichert ist. Die vorliegende Arbeit soll analysieren, mit welchen Methoden Energiesysteme und Versorgungssicherheit in der Fachliteratur untersucht worden sind. Es wird unterschieden zwischen Arbeiten zu kurz- und mittelfristiger, sowie langfristiger Versorgungssicherheit. Dazu werden einschlägige Arbeiten aus dem Bereich der Energiesystemanalyse betrachtet und die unterschiedlichen Modellierungsansätze herausgearbeitet. Die Ergebnisse zeigen, dass Optimierungsmodelle am häufigsten in der Analyse langfristiger Versorgungssicherheit eingesetzt werden, obwohl agentenbasierte Simulationsmodelle signifikante Vorteile bei der Untersuchung einzelwirtschaftlicher Anreize in liberalisierten Märkten, insbesondere unter Berücksichtigung von Auswirkungen politischer Entscheidungen aufweisen

Investitionsplanung unter Unsicherheit – Ein agentenbasierter Ansatz für liberalisierte Strommärkte

In liberalisierten Strommärkten bilden umfassende Wirtschaftlichkeitsbewertungen die Basis für Investitionsentscheidungen. Unter anderem aufgrund der Kapitalintensität sowie langfristiger Investitionshorizonte verhalten sich Investoren dabei in hohem Maße risikoavers. In diesem Beitrag wird das agentenbasierte Strommarktmodell PowerACE um die Berücksichtigung von Unsicherheiten und dem damit verbundenen Risiko für die Ausbauplanung erweitert. Für die Generierung der Szenarios werden verschiedene Wetterjahre derart kombiniert, dass das Ausmaß der Volatilität der Residuallast repräsentativ über den gesamten Investitionshorizont abgebildet wird. Mithilfe der Szenarios wird eine Verteilung der Profitabilität abgeleitet, auf deren Basis für die Bewertung von Investitionsoptionen neben der erwarteten Profitabilität auch der Conditional Value-at-Risk in einem multikriteriellen Entscheidungskalkül berücksichtigt wird. Die Ergebnisse werden in Bezug auf die Entwicklung der europäischen Kraftwerkskapazitäten, der Day-Ahead Marktpreise sowie der Versorgungssicherheit ausgewertet. Bei einer Investitionsplanung unter Risikoaversion ergibt sich gegenüber dem risikoneutralen Fall länderübergreifend ein etwas niedrigeres Kapazitätsniveau. Dies wiederum führt zu negativen Auswirkungen auf die Versorgungssicherheit in Form häufigerer Knappheitssituationen sowie generell erhöhten Day-Ahead Marktpreisen. Diese Ergebnisse verdeutlichen die Relevanz einer geeigneten Abbildung der Risikoaversion von Investoren im Kontext der Diskussion um ein angemessenes Marktdesign für sehr hohe Anteile erneuerbarer Energien