Local energy trading for microgrids: Modeling human behavior, uncertainty and grid constraints

Electricity is one of the major drivers of today’s society and its rapid development, however, it also significantly contributes to the ongoing global warming. To reduce its impact, the energy transition aims to change the electricity production by switching from fossil fuels to more renewable and sustainable energy sources. In adition, the energy transition also addresses required changes in the heating and mobility sector.
These changes have a significant impact on the electricity system, and an intelligent (active) management of the electricity production and consumption is required. Throughout the last years, many approaches have been proposed to manage production and consumption. At the core of this thesis, we focus on three different aspects of such approaches.
1: We consider and analyze the impact of human behavior on the outcome of a local electricity market. Due to the direct participation of households, the question arises how human preferences and behavior affect the outcome of the market. Therefore, we translate a behavioral model from social science into a multi-objective optimization problem, which uses the personal preferences and motives of households and creates tailor-made bidcurves. We analyze the results on an household and the market level to derive implications for future maket design.
2: The shift in electricity production leads to increased uncertainties in the future energy system. To deal with such uncertainties, we focus on a joint energy management of a neighborhood. We apply ideas and techniques from robust optimization to deal with the uncertainty, and mainly focus on an approach combining static robust optimization with a rolling horizon framework. Hereby, we generalize the rolling horizon by allowing more flexible starting time slots and compare and analyze two such generalized rolling horizon versions.
3: The increased peaks due to the additional production and generation pose a serious burden to the current electricity grid. To ensure a safe operation, we focus on grid constraints in the context of a real-time control approach, which implements day-ahead and intraday market solutions. We use the planned solutions to guide the real-time decisions, and identify an interesting connection between day-ahead operations and their real-time realization.