Implementation of multi‐objective chaotic mayfly optimisation for hydro‐thermal‐ solar‐wind scheduling based on available transfer capability problem

• Published in: International transactions on electrical energy systems Vol. 31; no. 11
• Main Authors: Majumdar, Kingsuk, Roy, Provas Kumar, Banerjee, Subrata
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi Limited 01.11.2021
• Subjects: Algorithms; available transfer capability (ATC); chaotic mayfly algorithm (C‐MAP); Cost function; Electric power; Electric power demand; Electric power generation; Evolutionary algorithms; Grey wolf optimiser (GWO); hydro‐thermal‐solar‐wind scheduling (HTSW); mayfly algorithm (MA); Natural resources; Objective function; Optimization; optimum power flow (OPF); particle swarm optimisation (PSO); Power flow; Power transfer; Scheduling; Thermal power; Thermal power plants; Thermoelectricity; Wind
• ISSN: 2050-7038; 2050-7038
• DOI: 10.1002/2050-7038.13029

Summary The electrical power generation from conventional thermal power plants needs to be interconnected with natural resources like solar, wind, hydro units with all‐day planning, and operation strategies to save mother nature and meet the current electricity demand. The complexity and size of the power network are increasing rapidly day by day. The enhanced power transfer from one section to another section in the existing grid system is the subject of available transfer capability (ATC), which is the modern power system's critical factor. In this paper, the minimisation of power generation cost of the thermal power units is achieved by incorporating renewable sources, says hydro, winds, and solar plants for 24 hours scheduled, and ATC calculation is the prime objective. In recent literature, the Mayfly algorithm (MA) optimisation approach, which combines the advantages of evolutionary algorithms and swarms intelligence to attend better results, is successfully implemented. In this article, optimum power flow (OPF) based ATC is enforced under various conditions with hydro‐thermal‐solar‐wind scheduling concept on the IEEE 9, IEEE 39, and IEEE 118 test bus systems to check the performance of the proposed chaotic MA. The chaotic MA is a hybridised format of the MA and chaotic map (C‐MAP) method with opposition based learning. It is noted from the simulation study that the suggested hybrid C‐MAP approach has a dominant nature over other well‐established optimisation algorithms. In case of single objective function, the optimum value of the cost function is better than 13% to its nearest competitor approach. For multi‐objective, it is improved by more than 19% and ATC value is enhanced by near about 56% and more. In this article multi‐objective, chaotic mayfly optimisation algorithm along with hydro‐thermal‐solar‐wind scheduling concept is used to calculate available transfer capability. The performance of the proposed method is tested on IEEE 9, IEEE 39, and IEEE 118 test case systems for 24‐hour time horizon.