Abstract
This article introduces an application of the recently developed hunger games search (HGS) optimization algorithm. The HGS is combined with chaotic maps to propose a new Chaotic Hunger Games search (CHGS). It is applied to solve the optimal power flow (OPF) problem. The OPF is solved to minimize the generation costs while satisfying the systems’ constraints. Moreover, the article presents optimal siting for mixed renewable energy sources, photovoltaics, and wind farms. Furthermore, the effect of adding renewable energy sources on the overall generation costs value is investigated. The exploration field of the optimization problem is the active output power of each generator in each studied system. The CHGS also obtains the best candidate design variables, which corresponds to the minimum possible cost function value. The robustness of the introduced CHGS algorithm is verified by performing the simulation 20 independent times for two standard IEEE systems—IEEE 57-bus and 118-bus systems. The results obtained are presented and analyzed. The CHGS-based OPF was found to be competitive and superior to other optimization algorithms applied to solve the same optimization problem in the literature. The contribution of this article is to test the improvement done to the proposed method when applied to the OPF problem, as well as the study of the addition of renewable energy sources on the introduced objective function.
Highlights
Introduction published maps and institutional affilIn the broadest sense, the scope of electrical power systems is significantly attracting the interest of many researchers all over the world, as the electrical power system is a complicated and dynamic one
The chaotic hunger games search (CHGS) algorithm achieved a reduction in the cost by 3.9% compared with the genetic algorithm (GA), and by 2.48% when compared with the particle swarm optimization (PSO)
This paper has introduced an improved CHGS optimization algorithm to solve the optimal power flow problem
Summary
The first objective is to solve the basic OPF problem using the newly developed CHGS optimization method with fixed loads and without adding RESs to the systems. The second part of the optimization problem in this study is the optimal siting of the PV and wind energy sources to the studied systems using the CHGS optimization method. The optimal bus represents the bus that performs a minimal value of the fuel cost function when RESs are connected to it. The systems used in the OPF problem to evaluate the newly introduced CHGS optimization algorithms are the standard IEEE 57- and 118-bus systems. Some statistical analyses are provided at the end of the simulation results section, to verify the robustness of the newly developed CHGS optimization method
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