A multi-criteria approach to designing and managing a renewable energy community. (November 2022)

• Record Type: Journal Article
• Authors: Tomin, Nikita; Shakirov, Vladislav; Kurbatsky, Victor; Muzychuk, Roman; Popova, Ekaterina; Sidorov, Denis; Kozlov, Alexandr; Yang, Dechang
• Is Part Of: Renewable energy. Volume 199(2022)
• Page Start: 1153
• Page End: 1175
• Publication Date: 2022-11
• Subjects: Community -- Decision-making -- Learning -- Microgrid -- Optimization -- Renewables; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2022.08.151
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: The energy communities based on the integration of microgrids make it possible to gain economic, environmental, technical, and social benefits. The paper aims to propose a unified multi-criteria approach covering both the planning stage and the stage of managing the energy community, in the context of various interests of its participants. Planning stage should take into account the long-term goals of the community and possible changes in external conditions. Therefore, we suggest an approach relying on the multi-attribute value theory considering the uncertainty of decision makers' preferences. Interval estimators used to express preferences enable a choice of community configuration with robust performance under changing conditions within some limits. In the operation stage, the new multi-criteria model of an intelligent "energy community operator" is proposed. It is based on bi-level programming and reinforcement learning, implementing the structure of a fair local market for sustainable development of the community. To optimize the operation of individual microgrids within the community, the multi-objective Monte-Carlo Tree Search (MCTS) algorithm is used, which helps to improve the convergence in the Stackelberg game. The multi-criteria version of the MCTS algorithm allows implementing an adaptive local automation model to solve a multi-objective lower-level problem: minimize operating costs, risk of power shortage, and CO2 emissions; smooth load peaks, and optimize power exchange between microgrids. At the top level, a management strategy that will be beneficial to all members of the community is chosen to guarantee their long-term aggregation. The effectiveness of the proposed approach is demonstrated by the example of an energy community created for three remote villages located on the coast of the Sea of Japan. The natural and climatic conditions of the area allow the efficient use of wind, solar, and biomass resources. Building the community involves the consideration of three scenarios, in which priority is given to economic efficiency, environmental efficiency, or balanced development. Highlights: The practical multi-criteria approach for development a sustainable energy community. Energy community sizing under uncertainty of decision makers' preferences. Bi-level programming for a "fair" community operator model. Monte-Carlo Tree Search algorithm for multi-criteria community management.