Engineering Local Electricity Markets for Residential Communities

In line with the progressing decentralization of electricity generation, local electricity markets (LEMs) support electricity end customers in becoming active market participants instead of passive price takers. They provide a market platform for trading locally generated (renewable) electricity between residential agents (consumers, prosumers, and producers) within their community. Based on a structured literature review, a market engineering framework for LEMs is developed. The work focuses on two of the framework\u27s eight components, namely the agent behavior and the (micro) market structure. Residential agent behavior is evaluated in two steps. Firstly, two empirical studies, a structural equation model-based survey with 195 respondents and an adaptive choice-based conjoint study with 656 respondents, are developed, conducted and evaluated. Secondly, a discount price LEM is designed following the surveys\u27 results. Theoretical solutions of the LEM bi-level optimization problem with complete information and heuristic reinforcement learning with incomplete information are investigated in a multi-agent simulation to find the profit-maximizing market allocations. The (micro) market structure is investigated with regards to LEM business models, information systems and real-world application projects. Potential business models and their characteristics are combined in a taxonomy based on the results of 14 expert interviews. Then, the Smart Grid Architecture Model is utilized to derive the organizational, informational, and technical requirements for centralized and distributed information systems in LEMs. After providing an overview on current LEM implementations projects in Germany, the Landau Microgrid Project is used as an example to test the derived requirements. In conclusion, the work recommends current LEM projects to focus on overall discount electricity trading. Premium priced local electricity should be offered to subgroups of households with individual higher valuations for local generation. Automated self-learning algorithms are needed to mitigate the trading effort for residential LEM agents in order to ensure participation. The utilization of regulatory niches is suggested until specific regulations for LEMs are established. Further, the development of specific business models for LEMs should become a prospective (research) focus

Reinforcement learning in local energy markets

Local energy markets (LEMs) are well suited to address the challenges of the European energy transition movement. They incite investments in renewable energy sources (RES), can improve the integration of RES into the energy system, and empower local communities. However, as electricity is a low involvement good, residential households have neither the expertise nor do they want to put in the time and effort to trade themselves on their own on short-term LEMs. Thus, machine learning algorithms are proposed to take over the bidding for households under realistic market information. We simulate a LEM on a 15 min merit-order market mechanism and deploy reinforcement learning as strategic learning for the agents. In a multi-agent simulation of 100 households including PV, micro-cogeneration, and demand shifting appliances, we show how participants in a LEM can achieve a self-sufficiency of up to 30% with trading and 41,4% with trading and demand response (DR) through an installation of only 5kWp PV panels in 45% of the households under affordable energy prices. A sensitivity analysis shows how the results differ according to the share of renewable generation and degree of demand flexibility