Improved GA based power and cost management system in a grid-associated PV-wind system

Abstract

Renewable hybrids play an essential part in assisting India with quickening the decarbonisation of power production and lowering power production expense in the medium term. PV and wind energy are complementary to each other, making the system to generate electricity almost throughout the year. In this paper, a grid-associated PV-wind energy system tied with a battery is analysed. PV, wind, grid and battery are the sources to be effectively scheduled for uninterrupted power and cost minimisation. Energy management controllers use optimisation strategies for effective utilisation of sources and cost minimisation. The methodologies are detailed as optimisation problems. Limiting the household energy cost is considered as objective, and the delivery ratio of power offered to the grid and utilised locally is treated as the optimisation variable. In this paper, an improved genetic algorithm is proposed to solve the formulated nonlinear optimisation problems. The time-of-use tariff is becoming popular in India; therefore, this article analyses the improved genetic algorithm based intelligent power and cost management system under time-of-use tariff. Using MATLAB, the proposed approach's performance is presented with the comparative analysis of conventional self-made for self-consumed and rest for sale mode and genetic algorithm-based energy management controller.

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

“…The genetic algorithm is a Darwinian developmental simulation of natural selection and the process computational model of the genetic mechanism of biological evolution proposed by [22]. It is an extensive simulation of the natural evolution method of optimal solutions to some complex problems.…”