Black Widow Optimization Based Novel Approach for Enhancing Economic and Environmental Performance of Power System

Abstract

Economic Dispatch is a key optimization problem in power systems, aiming to distribute generation from sources to meet demand at the lowest cost while ensuring operational constraints are met. With a growing focus on reducing greenhouse gas emissions, Combined Economic Emission Dispatch (CEED) methods have emerged to integrate economic and environmental goals. These methods optimize power generation allocation to minimize fuel costs and emissions, striking a balance between efficiency and environmental impact, which is crucial for cleaner power generation. In earlier works, various optimization algorithms have been proposed to solve CEED, with classical methods like Linear Programming, Lambda-Iteration, and Newton Raphson having drawbacks such as converging towards local optimums and limitations in dealing with non-smooth cost functions. In recent years, nature-inspired optimization algorithms like Black Widow Optimization (BWO) have gained popularity for complex optimization problems like CEED. This research aims to improve the economic and environmental efficiency of power systems by combining chaotic mapping with the BWO algorithm. By considering a fitness function that accounts for both fuel costs and emissions, the proposed method is applied to three test systems. For test system 1, Chaotic Black Widow Optimization (CBWO) achieved the lowest best fitness value at $94,880 per hour, surpassing the second-best BWO's best of $95,505 per hour. For test system 2, CBWO recorded the lowest best fitness value at $166,210 per hour, which is comparatively better than the others. For test system 3, CBWO reached a best fitness of $42,995 per hour, significantly better than the others. The selection of comparative algorithms—BWO, Whale Optimization Algorithm (WOA), Ant Lion Optimizer (ALO), Grasshopper Optimization Algorithm (GOA), and Moth-Flame Optimization (MFO)—is based on their unique characteristics and proven efficacy in complex optimization scenarios. To validate the results and ensure the robustness and reliability of the proposed method, it was necessary to compare them with previously published works. This comparison included three additional test systems, with test system 6 specifically considering all kinds of emissions, including NOx, SOx, and COx. CBWO demonstrated superior performance in these test systems as well, outperforming other algorithms, demonstrating its effectiveness. This comprehensive validation underscores CBWO's potential to enhance the efficiency and sustainability of power systems by effectively balancing economic and environmental objectives.