Optimal Load Scheduling Using Genetic Algorithm to Improve the Load Profile

TL;DR

This paper presents a genetic algorithm-based method for optimal load scheduling in smart grids, aiming to reduce energy costs and improve grid stability by shifting peak loads to off-peak hours.

Contribution

It introduces a novel application of genetic algorithms for load scheduling using real-time pricing signals to optimize energy use and reduce peak demand in smart grids.

Findings

GA effectively reduces energy costs.

Peak to average ratio is improved.

Load profile is optimized for stability.

Abstract

Stability and protection of the electrical power systems are always of primary concern. Stability can be affected mostly by increase in the load demand. Power grids are overloaded in peak hours so more power generation units are required to cope the demand. Increase in power generation is not an optimal solution. With the enlargement in Smart grid (SG), it becomes easier to correlate the consumer demand and available power. The most significant featutre of smart grid is demand response (DR) which is used to match the demand of available electrical energy and shift the peak load into off peak hours to improve the economics of energy and stability of grid stations. Presently we used Genetic algorithm (GA) to schedule the load via real time pricing signal (RTP). Load is categorized depending on their energy requirement, operational constraint and duty cycle. We conclude that GA provides optimal solution for scheduling of house hold appliances by curtailing overall utilized energy cost and peak to average ratio hence improving the load profile.