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 decarbonization 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 analyzed. PV, wind, grid and battery are the sources to be effectively scheduled for uninterrupted power and cost minimization. Energy management controllers use optimization strategies for effective utilization of sources and cost minimization. The methodologies are detailed as optimization problems. Limiting the household energy cost is considered as objective, and the delivery ratio of power offered to the grid and utilized locally is treated as the optimization variable. In this paper, an improved genetic algorithm is proposed to solve the formulated nonlinear optimization 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.