Fast Convergence Real-Coded Genetic Algorithm for Short-Term Solar-Wind-Hydro-Thermal Generation Scheduling

Abstract

This article develops and recommends fast convergence real-coded genetic algorithm (FCRCGA) for solving solar-wind-hydro-thermal power generation scheduling with battery energy storage system (BESS). Genetic algorithm (GA) is based on inbred operation of human chromosomes. GA has the ability to establish the global or very close to the global optima. In this article, FCRCGA has been suggested to heighten convergence speed and solution quality. The efficacy of the suggested technique has been confirmed on two test systems and 15 benchmark functions. Simulation outcomes of the suggested technique have been matched up to those acquired by real-coded genetic algorithm (RCGA) technique. It has been observed from the comparison that the suggested FCRCGA technique has the ability to endow with superior solution.

Keywords:

• fast convergence real-coded genetic algorithm
• solar-wind-hydro-thermal system
• cascaded reservoirs
• valve point effect
• battery energy storage system

Additional information

Notes on contributors

Mousumi Basu

Mousumi Basu received the bachelor degree, master degree and Ph.D. degree from Jadavpur University, Kolkata, India, in 1991, 1993 and 2003 respectively. She is a professor at Power Engineering Department of Jadavpur University. Her research is focused on power system optimization, soft computing techniques and renewable energy sources.