Shared energy storage configuration in distribution networks: A multi-agent tri-level programming approach

Shared energy storage has the potential to decrease the expenditure and operational costs of conventional energy storage devices. However, studies on shared energy storage configurations have primarily focused on the peer-to-peer competitive game relation among agents, neglecting the impact of network topology, power loss, and other practical factors on energy storage configuration. Additionally, they do not differentiate between various roles of agents, such as shared energy storage operators, electricity consumers, and distribution network operators. To address the challenges presented by the complex interest structures, diverse usage patterns, and potentially sensitive location associated with shared energy storage, we present a multi-agent model for shared energy storage services that takes into account the perspectives of different actors in distribution networks. We develop a tri-level programming model for the optimal allotment of shared energy storage and employ a combination of analytical and heuristic methods to solve it. A case study demonstrates that our model can attain effective allocation of shared energy storage, take into account the interests of multiple parties, and converge well. We examine the impacts of different energy storage service patterns on distribution network operation modes and compare the benefits of shared and non-shared energy storage patterns. By analyzing data on the cost of operating distribution networks, voltage stability, and distributed power consumption, we investigate the potential advantages of the multi-agent distributed shared energy storage service pattern in distribution networks. Our research provides valuable insights into implementing shared energy storage on a large scale in distribution networks. •A multi-agent model for distributed shared energy storage services is proposed.•A tri-level model is designed for optimizing shared energy storage allocation.•A hybrid solution combining analytical and heuristic methods is developed.•A comparative analysis reveals shared energy storage’s features and advantages.