Stochastic optimization for the scheduling of a grid-connected microgrid with a hybrid energy storage system considering multiple uncertainties

Abstract

The deployment of a microgrid with renewable energy sources (RESs) generally considers two aspects i.e., the need for the energy storage and the presence of many uncertainties due to the intermittent nature of several RESs and the load demand variability. The latter justifies the use of stochastic optimization for the scheduling purpose. Therefore, this paper aims to investigate the optimal stochastic scheduling and evaluate the expected performance of a microgrid in grid-connected mode with a hybrid energy storage system consisting of a battery and a supercapacitor. The proposed optimization approach is formulated as a stochastic mixed-integer linear programming (MILP) problem and applied to a modified ORNL-DECC microgrid test system. The system uncertainties are associated with wind power, solar power, and load demand, which are represented by Gaussian distributed scenarios with standard deviations of 10%, 5%, and 3%, respectively. To investigate the effect of supercapacitor addition on the scheduling stochastic performance, the power rating of supercapacitor is varied between 0 and 20 kW. The analysis is performed based on the results of scheduling using the deterministic and the stochastic optimization schemes. In the deterministic scheme, the addition of supercapacitor is reviewed based on the achieved total cost. In the stochastic optimization scheme, we use the expected value of perfect information (EVPI) to observe the stochastic performance in addition to the expected total cost. The results are beneficial when assessing the advantage and disadvantage of supercapacitor deployment in a microgrid scheduling.