Presenting a Novel Evolutionary Method for Reserve Constrained Multi-Area Economic/Emission Dispatch Problem

Economic dispatch (ED) attempts to find the most cost-effective combination of power generation units while meeting operational constraints. Another problem that can't be resolved by standard economic dispatch problems is figuring out the method of generating dispatch that would be most cost-effective in meeting the local demand without exceeding the tie-line capacity. Making a trade-off between fuel costs and environmental concerns, a contentious problem in industrialized countries, seems essential. As a result, this study introduces a multi-objective approach for different ED problems, such as multi-area emission economic dispatch (MAEED) and reserve constrained multi-area emission economic dispatch (RCMAEED), when there are real-world restrictions present, like the valve point effect (VPE), prohibited operating zones (POZs), multi-fuel operation (MFO), and ramp-rate (RR) restrictions. In this study, the generation cost and emissions are taken into consideration as objective functions. Since the MAED problem in the power system is inherently nonlinear, adding the aforementioned restrictions makes the problem even more challenging. To address the complexity of the multi-objective optimization problem, the modified grasshopper optimization (MGO) algorithm, based on the chaos mechanism, is proposed in this paper. The proposed method has been tested on a four-area power system with sixteen electrical generators, and the results are contrasted with those of previous evolutionary techniques. Based on the results, it can be concluded that using the proposed MGO method to solve the MAED and RCMAED problems will result in generation costs that are around $300 and $600 less than using the MPSO and PSO methods, respectively. Also, the proposed MGO method has reduced emission levels by roughly 30% as compared to the GO method in order to solve the RCMAEED problem.