New Stochastic Bi-Objective Optimal Cost and Chance of Operation Management Approach for Smart Microgrid

This paper develops a new probabilistic optimization framework based on chance constrained programming for bi-objective optimal energy management in microgrids considering intermittent characteristics of wind and photovoltaic power, and customers' load profile. The proposed approach uses a method based on jointly distributed random variables to calculate the chance of meeting the load requirements while maintaining the operation cost below a present value. The framework benefits from the new improved hybrid artificial bee colony and differential evolution algorithm as the optimization technique to solve the optimal energy management of a grid-connected microgrid. A sample average approximation approach is used to verify the results of the proposed method in comparison with the scenario-based and the Monte Carlo-based stochastic programming.