Abstract

The aggregation of end-users who produce, consume, and exchange surplus energy within the borders of a shared geographical area manifests a new way of using renewable energy as represented by Energy Communities (EC). At that, both industrial and "residential" community microgrids can be such consumers, which act as a single controlled object in relation to the external power grid. The reasons and objectives of prosumers joining their forces to form an EC differ depending on their predominant type and nature (industrial enterprises, residential buildings, farms, etc.). These include consumers' need for energy autonomy, lowering of otherwise high electricity tariffs, improvement of energy supply resilience and reliability, minimization of urban pollutant emissions, more efficient use of renewable energy sources, etc. This study relies on a general methodology developed by the authors to assess key defining features of the establishing and operating ECs of industrial and community microgrids serving residential loads. We demonstrate how methods of multi-criteria decisionmaking and artificial intelligence can take into account these features in an efficient way so as to maximize the local and system-wide effects of different types of ECs.

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