Analysis of approaches to integrating microgrids into energy communities

In this article, we set out to identify and analyze the key features of aggregating microgrids into energy communities, with a focus on the predominance of industrial or residential loads. Research methods included a literature review and meta-analysis in the field of planning, modelling and management of microenergy systems and their communities. In addition, a methodological approach combining multi-criteria decision-making methods and artificial intelligence was used. The efficiency of the approach was demonstrated by the establishment of two types of energy communities for remote settlements on the Sea of Japan coast, which integrated residential and industrial loads. The “Autonomous Operator” model, which involved a two-level optimization and reinforcement learning algorithm based on Monte Carlo tree search, was tested in order to determine the optimal economic management of operation modes of the potential energy community. At the lower level, the problem of finding market equilibrium was solved by minimizing the function of total operating costs. At the upper level, the management strategy that provides the optimal profit distribution among the community members was selected. Two scenarios of microgrid integration and operation in an energy community were studied: industrial and public types. The research demonstrated that operating settlements as energy communities is a more economically and ecologically advantageous approach than operating them individually. The results indicated that the levelized cost of electricity (LCOE) decreased more significantly when combining settlements in an industrial-type energy community (from 22 rub/kWh to 6 rub/kWh) compared to a public-type community (from 22 rub/kWh to 9 rub/kWh). The analysis of the above characteristics of different types of energy communities can help designers to determine the possibilities, features and consequences of aggregating microgrids of different types under various territorial and climatic conditions.

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