Day-ahead profit-based reconfigurable microgrid scheduling considering uncertain renewable generation and load demand in the presence of energy storage

Due to the penetration of renewable energy sources (RESs) with probabilistic natures into microgrids (MGs), optimal scheduling and reconfiguration (RG) processes are associated with uncertainty. This paper presents the stochastic profit-based optimal day-ahead scheduling of a reconfigurable microgrid (RMG) as a new generation of the conventional microgrid. The proposed algorithm finds the optimal RMG's topology from the profit maximization point of view, the optimal hourly MG's unit set-points like micro-turbines (MTs) and energy storage, and power exchange with the main grid, simultaneously. The generated power of wind turbine (WT) and PV panel, as well as load demand are considered as uncertain parameters. To solve the profit maximization problem of RMG, time-varying acceleration coefficients particle swarm optimization (TVAC-PSO) algorithm is employed. Also, to ensure simulation accuracy in the presence of high-level uncertainties, the autocorrelation model is used based on actual data for the uncertainty of renewable power output. The feasibility and applicability of the proposed framework are demonstrated on a 69-bus radial RMG with various distributed generators in different cases. The results show the effectiveness of the proposed model.