Risk Aware Robust Decision Making in Power Systems with Renewable Resources

The increasing penetration of renewable generation poses significant risks to the reliable operation of power systems, mainly due to the variable and uncertain nature of the output of wind and solar resources. This dissertation presents a robust optimization based decision making framework in future power systems with high penetration of variable renewable resources. The first part of this dissertation involves the modeling and analysis of a robust optimization based bidding strategy for the combination of a wind farm and an energy storage device participating in a deregulated electricity market. The selection of the uncertainty set for the robust optimization problem, based on the decision maker’s risk preference, is also discussed. From the market participant’s point of view improved utilization of the renewable resource, through storage enabled energy arbitrage, can lead to better economic performance. The storage device can provide firming power to the output of the wind farm, enabling the renewable resource to participate in the electricity market. The robust optimization based approach is compared to a deterministic optimization based approach through a numerical example. The second part of this dissertation investigates the metric and the dispatch method needed for a more robust real-time market operation. A novel metric for evaluating system-wide ramp flexibility is proposed. A robust framework to ensure the reliable dispatch of generators is presented and analyzed. The robust model is compared to both the conventional economic dispatch as well as a proposed industry approach to managing system flexibility called the look-ahead dispatch. Furthermore, the formulation for a robust multi-zonal dispatch model is presented. The proposed robust model and flexibility index is demonstrated through a numerical on a modified IEEE 24 Bus Reliability Test System

Robust self-scheduling of a price-maker energy storage facility in the New York electricity market

Recent progress in energy storage raises the possibility of creating large-scale storage facilities at lower costs. This may bring economic opportunities for storage operators, especially via energy arbitrage. However, storage operation in the market could have a noticeable impact on electricity prices. This work aims at evaluating jointly the potential operating profit for a price-maker storage facility and its impact on the electricity prices in the New York state market. Based on historical data, lower and upper bounds on the supply curve of the market are constructed. These bounds are used as an input for the robust self-scheduling problem of a price-maker storage facility. Our computational experiments show that the robust strategies thus obtained allow to reduce significantly the loss exposure while maintaining reasonably high expected profits

Managing Intermittent Renewable Generation with Battery Storage using a Deep Reinforcement Learning Strategy

Most of Germany’s existing wind and solar plants have been losing their subsidies after 20 years of operation since 2020. Without support schemes, the challenges for the renewable operators are the intermittent generation and the fluctuating power prices. Consequently, lower-than-expected revenues and high revenue variability make it more difficult for the renewable operators to be active on power markets. Therefore, the renewable operators have to be profit effective as well as cope with the high variability of their revenue. This paper proposes a deep reinforcement learning (DRL) based model to adjust the renewable operators’ short-term energy supply using a battery storage strategy. The simulative empirical evaluation shows that the renewable operators can be profitable on the market and improve their revenue stability using the proposed DRL based battery storage strategy

Optimising investments in battery storage and green hydrogen production

Energy systems are undergoing a transition towards low-carbon alternatives, but intermittent renewable sources like wind and solar pose challenges. Battery storage and hydrogen technologies, offer potential solutions with numerous benefits. They can enhance grid stability, improve power quality, and decarbonise industries like heavy manufacturing, heating, and shipping. Both batteries and hydrogen complement renewables by storing excess power and using curtailed energy. To drive the widespread adoption of low-carbon energy technologies, it is crucial to establish its economic viability. This research focuses on optimising the revenues of low-carbon energy investments, specifically battery storage and green hydrogen production. It explores three key areas: determining the optimal usage of these technologies, identifying the best deployment locations, and addressing uncertainties. In terms of usage, the research analyses various case studies and modelling techniques. It applies optimisation models to energy markets, examines community-owned battery projects, and combines machine learning with optimisation models to maximise battery revenues across different market segments. Additionally, the research explores the optimal investment and usage of PEM electrolysers within wind farms to produce green hydrogen, using optimisation models and real options analysis

The Effects of Battery Storage on Risk and Cost of Capital of Wind Park Investments

To reach the defined reduction goals for green house gas emissions, an increasing share of renewables and especially wind power is necessary. However, these generation technologies are intermittent and progressively exposed to market risks as a consequence of declining financial support in the future. To reduce revenue volatility, in this thesis, a wind farm is combined with a battery storage. The study emphasizes the battery’s effect on the investment risk and the accompanying cost of capital. In order to assess this effect, I develop a deterministic optimization model based on historic wind farm and market price data in order to maximize cash flows. Monte Carlo scenarios are generated to evaluate the impact on risk by using the Value-at-Risk as risk criterion. I find that batteries can indeed reduce revenue risk in a case without subsidies. Furthermore, the link to cost of capital is made. The latter, as well as the battery prices, need to be reduced by a certain amount to make the application of a battery economically reasonable. To reach the defined reduction goals for green house gas emissions, an increasing share of renewables and especially wind power is necessary. However, these generation technologies are intermittent and progressively exposed to market risks as a consequence of declining financial support in the future. To reduce revenue volatility, in this thesis, a wind farm is combined with a battery storage. The study emphasizes the battery’s effect on the investment risk and the accompanying cost of capital. In order to assess this effect, I develop a deterministic optimization model based on historic wind farm and market price data in order to maximize cash flows. Monte Carlo scenarios are generated to evaluate the impact on risk by using the Value-at-Risk as risk criterion. I find that batteries can indeed reduce revenue risk in a case without subsidies. Furthermore, the link to cost of capital is made. The latter, as well as the battery prices, need to be reduced by a certain amount to make the application of a battery economically reasonable.  Keywords: Renewable energy, Energy markets, Battery storage, Wind investment, Energy investment ris

Energy Storage and Green Hydrogen Systems in Electricity Markets: A Modelling and Optimization Framework with Degradation and Uncertainty Considerations

Mención Internacional en el título de doctorThe increasing penetration of renewable energy in electrical systems requires advances in increasing their controllability. Energy Storage Systems (ESSs) are one of the solutions, since they allow the management of generated energy. Green hydrogen production systems, on the other hand, can utilize electricity to produce hydrogen. This energy carrier which can be sold for revenue generation and can be produced using Alkaline Electrolyzers (AELs). To coordinate these systems in renewable energy plants, advanced control techniques are needed. Complex processes such as degradation, partial loading and the effect of uncertainties must be considered. These considerations add to the complexity, which can obstruct control process, hence a simplistic formulation is required. This dissertation addresses this issue by implementing the effect of both ESS and AEL degradation into short-term planning keeping a linear formulation. Moreover, electrolyzer partial loading effect and operational states are also considered. Novel approaches in their inclusion into short-term planning for electricity market participation are proposed, analyzing their long-term economical significance. Due to the nature of spot electricity markets, which require the commitment of energy delivery beforehand, the uncertainty of renewable source and electricity prices may affect the performance of the system. Various stochastic approaches for short-term optimization are evaluated, with the proposal of novel strategies. The long-term impact of including risk-aware strategies is also analyzed in a simulation framework, whose results indicate that conservative approaches do not necessarily yield better outcomes. The present study commences with the modelling and formulation of a standalone ESS participating in the day-ahead market. A renewable energy source is incorporated into this model, creating a Hybrid Farm (HF) for multi-market participation. Lastly, a green hydrogen production system is also integrated, allowing the involvement in the hydrogen market. A novel algorithm for operation under uncertainties is proposed, which has been found to outperform a classical Montecarlo approach. Throughout the research, Python was employed as the programming language of choice. The generated code has been uploaded to a public repository. Real historical data was used to validate the findings and provide a more realistic representation of the systems under study.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Mónica Chinchilla Sánchez.- Secretario: Joaquín Eloy-García Carrasco.- Vocal: Pedro Vicente Jover Rodrígue