Optimizing battery storage for sustainable energy communities: A multi-scenario analysis

Feng Guo, Luis Gomes, Ling Ma, Zhiyong Tian, Zita Vale, ShiYuan Pang

Abstract

The Energy Community (EC) is expanding worldwide, with Solar Photovoltaic (PV) systems as the primary Renewable Energy Source (RES). However, "solar curtailment" challenges the European Union's 2030 Target of achieving 45 % renewables in final energy consumption. Peer-to-peer (P2P) energy sharing and Battery Energy Storage Systems (BESS) sharing can improve the RES share more effectively, but they face obstacles like high costs and low utilisation rates. This research develops a hybrid model that combines P2P energy sharing with BESS, optimised using Mixed Integer Linear Programming (MILP). A new pricing mechanism for P2P transactions and refined Shapley value calculations are introduced. Simulations conducted over 672 h across four seasons and 21,147 scenarios demonstrate that all proposed models effectively increase the renewable energy share. SBES-S and OCBES-S models are the most economical in spring and summer, with EC costs of €-13.028 and €-55.578. The OCBES-S model is the most cost-effective for autumn and the year, costing €92.998 and €174.101, respectively. Compared to the scenario without BESS and P2P sharing, it reduces annual electricity costs by 16.34 %. This study provides all EC stakeholders with a tool for option comparison and economic analysis, supporting the sustainable development of EC and renewable energy.

Keywords

Energy community;Battery energy storage systems;Peer to peer

Mixed integer linear programming;Renewable energy;Sustainable development

https://www.sciencedirect.com/science/article/pii/S2210670724008527