A data-driven stochastic energy sharing optimization and implementation for community energy storage and PV prosumers

Abstract

The development of energy sharing community is demanded for the on-site consumption of distributed generations and efficient utilization of renewable energy. This paper proposes a two-stage stochastic energy sharing model considering the photovoltaic (PV) power uncertainties to minimize the social cost of PV prosumers and community energy storage (CES) in community. The proposed optimization model derives the optimal schedule of community under the worst case in the first stage. The regulation model for PV prosumers is formulated in the second stage to address the PV uncertainties including three regulation methods: the demand response, the charging/discharging schedule, and the transactive energy schedule. Particularly, a modeling approach based on the data-driven uncertainty set is introduced to find the worst probability distribution. The formulated two-stage problem can be solved by the column-and-constraint generation algorithm, where the coupled model is decomposed into a main problem and a sub problem and solved by iteration. Numerical simulation results show the effectiveness of the proposed energy sharing optimization model to decrease the energy cost. Experimental results demonstrate the feasibility of multi-ports energy hub used in energy sharing community.