Energy consumption optimization of chiller plants with the genetic algorithm based GWO and JAYA algorithm in the dynamic pricing demand response

Abstract

Demand response is a program in the electrical system that is used to increase or decrease the energy demand of the consumers where the consumers can actively respond to the system requirements. We developed an energy scheduling system to schedule the consumer load in a dynamic pricing scheme to reduce the cost and lower the peak demand. The problem formulation is done to model the food storage chiller plant for cost minimization with the consumer constraint. The optimal schedule is to be generated in a real time varying electricity price scheme. To solve this optimization problem Grey wolf optimizer (GWO) algorithm and JAYA optimization algorithms are used. Besides, the Genetic algorithm (GA) based GWO and GA based JAYA algorithm are developed and simulated. The genetic algorithm improves the population diversity of the solutions. The simulation results show a 22 % cost reduction and 10 % energy consumption reduction as compared with normal control method and consumer constraints are satisfied. The algorithms GA-GWO and GA-JAYA give better results for optimal cost minimization in terms of the reduced cost, energy consumption, energy consumption shift to low electricity price hours and temperature maintained below the mean set temperature through the entire duration. • Mathematical load modelling of the chiller plants is done to minimize cost and generate optimal load schedule for the day. • Genetic algorithm based GWO and Genetic algorithm based Jaya algorithm are developed to solve the optimization problem. • The optimization performance of the GA-GWO and GA-Jaya algorithms are compared with the existing algorithms.