Design and optimal energy management of community microgrids with flexible renewable energy sources

Nikita Tomin, Vladislav Shakirov, Aleksander Kozlov, Denis Sidorov, Victor Kurbatsky, Christian Rehtanz and Electo E.S. Lora

Renewable Energy, 2022, vol. 183, issue C, 903-921

Abstract: 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.

Keywords: Community microgrid; Biomass gasifier generator; Local market; LCOE; Bilevel programming; Reinforcement learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (27)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:183:y:2022:i:c:p:903-921

DOI: 10.1016/j.renene.2021.11.024

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