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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. • 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. [ABSTRACT FROM AUTHOR]

Energy communities is a new, but already successful prosumer model of the local energy systems' construction. It is based on distributed energy sources and the electricity consumers' flexibility who are the members of the community. In search of the most effective ways to interact within themselves and with the external energy system, local energy communities become platforms for exciting experiments in the field of new energy practices including local markets for flexibility, building cooperative microgrids, achieving energy autonomy, and many others. This work aims to present a unified approach to building and optimally managing the community microgrids with an internal market, given the social, environmental, and economic benefits of a particular location of such a community. A new modeling framework is introduced, based on bilevel programming and reinforcement learning, for structuring and solving the internal local market of a community microgrids, composed of entities that may exchange energy and services among themselves. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator (Community EMS). We strengthen the traditional bilevel problem statement by the local energy management system (Local EMS) introduction based on Monte-Carlo tree search algorithm. Our approach makes it possible to enable interaction of the local control systems for microgrids with the community microgrid operator as part bilevel programming problem solution. Numerical results obtained on the real test case of the microgrid community for the settlements located in the Transbaikal National Park (Russia), which include various renewable energy sources (wind, solar power, biomass gasifiers) and storage devices, show reduction of the LCOE index from 20% to 40% and improving the quality of electricity supply to the analyzed settlements. • Methodology for economically optimised design a community microgrids based on RESs. • The concept of building green microgrids communities integrated with biomass gasifiers. • The concept of a fair local electricity market using bi-level optimization. [ABSTRACT FROM AUTHOR]