Reliability-based Multi-objective Optimization with Uncertain Wind Power

The unpredictability for wind power interprets the critical factor limiting its large-scale penetration in microgrids. Gaussian and Cauchy distributions describe both the arbitrary and vague characteristics of wind power to evaluate wind power unpredictability. Afterward, the wind power is presumed an undefined arbitrary variable, and a multi-objective optimization model concerning the credibility density function is developed to balance the economics and the reliability of microgrids. For the solution of the model, the NSGA-II algorithm is applied to seize the Pareto front. By fully deliberating the cognitive uncertainty of the dispatchers, the evidence-theory-based decision-making is employed to drive the eventual scheduling from the Pareto set. The emulation performances substantiate the appropriateness and efficacy of the proffered model with unpredictable wind power by providing a compromise scheme between economics and reliability.