Energy Profiling and Residential Load Shifting Mechanism with Cost Reduction using Genetic Algorithm

Abstract

A growing number of residential consumers despite high electricity costs in the provinces largely impart to the overall power market situation in the Philippines resulting in high emission generating units adding harm to the environment, coal dependency and supply shortage, especially during summer. Demand Side Management (DSM) aims to encourage consumers to use less energy during peak times. Demand Response (DR) is a type of DSM towards conserving the use of energy to reduce system peak demand and operational cost. This paper proposed a metaheuristic demand response mechanism for residential consumers to reduce consumers’ peak demand and minimize electricity cost via Genetic Algorithm load shifting without affecting the consumers’ conveniences. Further, the paper assumed that the energy market is existing and published hourly energy prices a day ahead and that the hourly demand of household consumers is known through a load forecast using Weighted Least Square. Furthermore, the flat iron and washing machine are the identified appliances the consumers’ willing to use during non-peak hours. The process was simulated through MATLABr2018a in generating the best-fit combinations for load shifting.