A probabilistic chance-constrained day-ahead scheduling model for grid-connected microgrid

Abstract

The forecast data of the renewable energy generation and loads cannot be exactly accurate because of their intermittence and fluctuation characteristics. To handle this problem, a probabilistic chance-constrained model for day-ahead scheduling is proposed in this paper. The proposed model is established not only by the aggregated scenarios but also by the eliminated ones which are used in chance constraints. The mixed integer linear programming algorithm is applied to solve the schedule problem efficiently. Finally, a grid-connected microgrid consisting of a photovoltaic system (PV), a wind turbine (WT), a micro turbine (MT), a diesel engine (DE), a fuel cell (FC), and a battery energy storage system (BESS) is studied, and the simulation results show the effectiveness of the probabilistic chance-constrained model.