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

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We consider using a battery storage system simultaneously for peak shaving and frequency regulation through a joint optimization framework which captures battery degradation, operational constraints and uncertainties in customer load and regulation signals. Under this framework, using real data we show the electricity bill of users can be reduced by up to 15\%. Furthermore, we demonstrate that the saving from joint optimization is often larger than the sum of the optimal savings when the battery is used for the two individual applications. A simple threshold real-time algorithm is proposed and achieves this super-linear gain. Compared to prior works that focused on using battery storage systems for single applications, our results suggest that batteries can achieve much larger economic benefits than previously thought if they jointly provide multiple services.Comment: To Appear in IEEE Transaction on Power System

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