Optimization of the composition of residential buildings in a renewable energy community based on monitored data

Abstract Energy communities (ECs) are a promising solution to integrate renewable local production with buildings’ systems and services. To exploit renewable energy sources, ECs should be carefully designed, identifying an appropriate mix of prosumers and consumers. In this research, the electrical energy loads of eight dwellings have been monitored for a year. Then, each dwelling is evaluated either as a mere consumer, maintaining its monitored electrical consumption profile as it is, or as a prosumer, thus simulating a photovoltaic system on the roof, sized to provide a given fraction of its energy needs and sharing the surplus with other EC participants. Genetic optimization is employed to seek the optimal mix of consumers and prosumers within the community to optimize the shared energy within the EC. Results show that dwellings with night-time energy requirements are included as prosumers to maximize photovoltaic power sharing during daylight time, and dwellings with regular daily loads are included as consumers.