Modelling renewable energy communities: assessing the impact of different configurations, technologies and types of participants

Abstract

Abstract Background Energy communities (ECs) have emerged as a solution to support governments mitigating climate change and comply with decarbonization goals, while introducing end-users on the energy value chain. In this paradigm, citizens have an active role in reducing electricity demand from the utility grid, by generating, sharing and/or trading locally generated renewable energy, such as solar energy. However, the economic and environmental outputs of energy communities are dependent on a variety of factors, such as technology features (renewable energy generation, existence of flexible equipment and/or energy storage systems), types of participants (consumers and prosumers with different electricity intensity and load profiles), and electricity sharing/trading agreements. As such, assessing the impact these will have on delivering benefits to the energy community and its participants is of paramount importance. Methods This work models different energy communities’ design typologies in Lisbon, Portugal considering different types of consumers with heterogenous electricity demand profiles and willingness to participate, multiple technology deployment scenarios (solar systems installation, batteries, and electric vehicles), and electricity trading (collective self-consumption versus peer-to-peer trading). Results Results demonstrate community electricity cost savings are up to 42%, with self-sufficiency rate up to 12.5%, which is considerably low due to the participation of high demanding sectors (such as industry or retail). At participants’ individual level, electricity costs savings can reach 48% and 53%, for residential consumers and prosumers, respectively, while for high-demanding participants are slightly lower: 43% for hotel, 44% for retail, 13% for industry and 5% for university. Individual self-sufficiency rates register highest results for the residential prosumers (35% for PV prosumers, 28% for PV + electric vehicles and 54% with PV + batteries) while for other participants results fall between 6% (retail) and 26% (industry). Conclusions We conclude that for ECs deployment, individual PV self-consumption assets are not sufficient, thus greater PV sizes and higher adoption rates should be considered, according to consumer and prosumers shares. The share/trade of PV surplus, paired with competitive aggregation tariffs results in positive economic and environmental outputs, for the majority of both consumers and prosumers.