Privacy-preserving energy management in local energy communities with EVs – An enhanced benders-like solution strategy

Abstract

Local Energy Communities (LECs) are collectives of prosumers collaborating to reach common goals, such as the reduction of energy procurement costs and the provision of ancillary services to the network operator. They use the flexibility of modern residential installations, including rooftop photovoltaic (PV) systems and controllable loads, such as the charging stations of electric vehicles (EVs). A central unit, called community manager, usually coordinates the actions of prosumers. However, the need for large information exchange in this multi-agent framework is a problem for the widespread adoption of such models. Data privacy concerns between prosumers and the manager may deter participation. This paper presents a novel energy management strategy for LECs with EV charging stations that protects privacy. The proposed approach only shares dual variables with the community manager, while all primal variables, such as power schedules, remain private. The method uses Benders decomposition to solve the day-ahead energy management problem, which has a separable structure. To speed up the process, a Benders-bundle algorithm has been developed, which is faster than the basic Benders method. The method also makes it easy to include network constraints, so the results can be implemented in the network without congestions or voltage problems. The method is tested on a case of a community with 14 prosumers connected to a 15-bus radial low voltage distribution network. Results show that the new proposal performs as well as a centralized approach and is characterized by a good balance between solution accuracy and privacy preservation compared to other distributed and decentralized methods.