Solving the multi-objective economic emission dispatch problems using Fast Non-Dominated Sorting TVAC-PSO combined with EMA

In this study, the Fast Non-Dominated Time-Varying Acceleration Coefficient-Particle Swarm Optimization (TVAC-PSO) combined with Exchange Market Algorithm (EMA) is proposed to solve the economic emission dispatch problems consisting of Combined Heat and Power Economic Emission Dispatch (CHPEED) and Dynamic Economic Emission Dispatch (DEED) multi-objective optimization problems considering operational constraints. A two-stage approach has been used to select the Best Compromise Solutions (BCSs), as the best solution which minimizes the operational cost and emission, simultaneously. For this purpose, at the first stage, applying Fuzzy Clustering Mean (FCM), the obtained Pareto Optimal Front (POF) is divided into several separated clusters. Then, using the Technique for Order of Performance by Similarity to Ideal Solution (TOPSIS), a single BCS is selected among each cluster. At first, the superiority of the proposed algorithm is evaluated on a number of benchmark functions, as well as the 48-unit CHPED test case. Then, to demonstrate the ability of the proposed algorithm in solving the multi-objective problem by finding the POF, the presented method has been applied to three case studies, and the results are compared with other algorithms in this field. Furthermore, a new test case is presented to confirm the proposed algorithm’s performance. The results verify the proposed method’s superiority over other available techniques in the literature. One of the most important novelty of this study is solving a multi-objective DEED problem considering the Ramp Rate Limits (RRLs), Valve Point Loading Effect (VPLE), power transmission loss impact, Spinning Reserve Requirements (SRRs), Prohibited Operating Zones (POZs) and Multiple Fuel Units (MFUs) simultaneously, for the first time.