multi-objective mean–variance–skewness model for generation portfolio allocation in electricity markets

this paper proposes an approach for generation portfolio allocation based on mean–variance–skewness (mvs) model which is an extension of the classical mean–variance (mv) portfolio theory, to dealwith assets whose return distribution is non-normal. the mvs model allocates portfolios optimally by considering the maximization of both the expected return and skewness of portfolio return while simultaneously minimizing the risk. since, it is competing and conflicting non-smooth multi-objective optimization problem, this paper employed a multi-objective particle swarm optimization (mopso) based meta-heuristic technique to provide pareto-optimal solution in a single simulation run. using a case study of the pjm electricity market, the performance of the mvs portfolio theory based method and the classical mv method is compared. it has been found that the mvs portfolio theory based method can provide significantly better portfolios in the situation where non-normally distributed assets exist for trading.