Elastic Reserve Strategy for Probabilistic Energy Management with Interval Predictors in Microgrids

In this paper, we present a Model Predictive Control (MPC) based energy management method for isolated microgrid systems. An elastic reserve strategy is proposed to handle the probabilistic predictions of loads and Renewable Energy Sources (RES) given in the form of intervals so that the state information of systems can be better utilized in the scheduling. Scenario-selected optimizers apply the MPC framework and the mixed-integer linear programming (MILP) model to find optimal dispatches for isolated microgrids. Simulations are conducted to analyze the economic and stability performances of our proposed method and make comparisons with other methods. Numerical results show that the proposed approach is more adaptive and has better performance in terms of both economic efficiency and stability.