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

Advanced Methods of Power Load Forecasting

This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load