Cross entropy optimization based on decomposition for multi-objective economic emission dispatch considering renewable energy generation uncertainties

Due to the increasing deterioration of environmental problem, combined economic emission dispatch (CEED) problem has become one of the active research areas in recent years. However, with sustained growth of intermittent power supplies connected to power system, their randomness and volatility will pose new challenges to power system optimization dispatch. For dealing with this problem, in this study, a novel Pareto optimization algorithm, called multi-objective cross entropy algorithm based on decomposition (MOCE/D), is proposed to solve a multi-objective optimization model for wind/hydro/thermal/photovoltaic power system by considering the uncertainties of intermittent power supplies and various practical constraints. Then, a hyper-plane-based decision-making strategy is introduced to identify the best compromise solution for the obtained Pareto frontiers. The overall performance of the proposed MOCE/D algorithm have been comprehensively investigated on the modified IEEE 30-bus and 118-bus systems. The statistical simulation results demonstrated that the proposed power system structure effectively reduces the operational cost as well as hazardous emissions; the proposed MOCE/D exhibits more competitive performance than the other state-of-the-art optimization algorithms, and therefore the obtained optimized operation strategy can provide a better trade-off between all objectives considered in this study.