Multi-objective mean-variance-skewness model for nonconvex and stochastic optimal power flow considering wind power and load uncertainties

This paper presents a multi-objective mean-variance-skewness (MVS) model for reliably assessing the problem of nonconvex and stochastic optimal power flow (NSOPF), considering valve-point effect, stochastic load and uncertain integrated wind power. The MVS model aims to maximize both the expected return and skewness while simultaneously minimizing the risk, which is formulated as a competing and conflicting three-objective optimization problem. Based on such a model, we propose a multi-objective optimization algorithm, multiple preys based evolutionary predator and prey strategy (MPEPPS), to provide Pareto solutions, which show the trade-off relationship among the expected return, the skewness and the risk of the dispatching objective. Subsequently, a multi-criteria decision making method, the technique for order preference by similarity to an ideal solution (TOPSIS), is applied for determining the final dispatch solution. The objective of this paper is to develop a reliable model to assess the NSOPF from the perspective of economics and reliability of power system operation, and propose an efficient algorithm to obtain a solution that considers all of the possible load and wind power simultaneously. Simulation results based on a modified IEEE 30-bus power system demonstrate the reliability and effectiveness of the MVS and MPEPPS in solving NSOPF.