An Improved Non-dominated Sorting Genetic Algorithm for the Optimal Economic Emission Dispatch Problem with Wind Power Sources

This study proposes a novel multi-objective technique for the Stochastic Economic Emission Dispatch Problem (SEEDP) integrating wind energy sources. To do this, the SEEDP is first formulated as a Chance Constrained Programming (CCP) problem where the randomness of the Wind Power (WP) output is obtained with the Weibull distribution function. Nevertheless, the chance constraint is employed to describe the fulfillment of the power balance constraint. In fact, after applying the probability theory, the proposed CCP issue is converted into a deterministic optimization problem. Moreover, the impact of WP penetration on the optimal solutions is investigated. To resolve the proposed multi-objective approach, the second version of the Non-dominated Sorting Genetic Algorithm (NSGAII) is applied. Moreover, to test the robustness of the proposed strategy, a ten-unit system is used and the acquired results are compared with those of other optimization techniques.