This work tackles a relatively new issue in power system operation, known as the Environmental/Economic Dispatch problem. For this purpose, the combination of two powerful heuristic algorithms, namely, the Exchange Market Algorithm (EMA) and Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO), was employed. Additionally, the Multiple Constraint Ranking (MCR) technique was used to address the system constraints such as prohibited operating zones and ramp rate limits. Furthermore, the mutation operator was used to improve the performance of the global search mechanism. The main purpose of combining these two algorithms was utilizing the EMA’s high performance to explore the global optimum and local exploitation ability of AIWPSO. The algorithm performance was evaluated on six standard benchmark functions and was scrutinized on several different test systems, including 6–40 units. By using the proposed method, the minimum values of the reduction in annual costs, with equal or less emissions, compared to other methods, were USD 17,520, 8760 and 10,801,080, respectively, for the 6-unit, 10-unit, and 40-unit test systems (assuming the same load profile throughout the year). Similarly, in the 14-unit test system for 1750, 2150, and 2650 (MW) load demands, these values were USD 229,879, 148,438, and 4483, respectively.

Abstract--This paper develops a novel multi-objective evolu-tionary algorithm (MOEA), namely an enhanced multi-objective exchange market algorithm (EMEMA), to solve the multi-objective problems in the presence of uncertainty. In this paper, Quasi-Monte Carlo (QMC) simulation is implemented for the first time to address the overestimation and underestimation caused by the uncertain wind power generation. In QMC, instead of pseudorandom numbers, some samples are generated to re-duce the computational burden using the Sobol sequences. In addition, a new procedure is introduced to increase the spread and maintain the diversity of solutions. The effectiveness of the EMEMA is tested on two well-known benchmark functions and Taiwans well-known 40-unit test system in two different scenarios, with and without considering the uncertainty. Also, four different measurements are employed to scrutinize the algorithm performance. The results prove the superiority of the proposed method compared to the state-of-art algorithms. Finally, the robustness of the EMEMA is studied using parametric and non-parametric statistical tests. The statistical analysis corroborates the robustness of the proposed method.