MULTI-OBJECTIVE CHAOTIC MAYFLY OPTIMIZATION FOR SOLAR-WIND-HYDROTHERMAL SCHEDULING BASED ON ATC PROBLEM

Abstract

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 minimization of power generation cost of the thermal power units is achieved by incorporating renewable sources, says hydro, winds, and solar plants for 24 h scheduled, and ATC calculation is the prime objective. In recent literature, the Mayfly algorithm (MA) optimization approach, which combines the advantages of evolutionary algorithms and swarms intelligence to attend better results, is successfully implemented. In this article, optimum power flow-based ATC is enforced under various conditions with hydro-thermal-solar-wind scheduling concept on the IEEE 9 test bus system to check the performance of the proposed chaotic MA. The chaotic MA is a hybridized format of the MA and chaotic map (CHMA) method. It is noted from the simulation study that the suggested CHMA approach has a dominant nature over other well-established optimization algorithms. In case of single objective function, the value of the cost function is improved by 14% and that of for multi-objective, it is improved by more than 20% and ATC value is enhanced by near about 55% and more.