Development of Strategies for the Optimal Rescheduling of the Household Load having RES Integration

Abstract

The cornerstone of an Energy Management System is the smart home, which strategically schedules household appliances to reshape the domestic load curve while avoiding additional costs. Domestic loads can be rescheduled by utilising the power supply's incentives, which take the form of slot-based rates. The availability of the renewable power in the system provides more flexibility. To effectively schedule home loads, this work uses a metaheuristic methodology, specifically a population-based Particle Swarm Optimisation algorithm. The main objective is to reduce the cost of power while taking into account the preferences of the consumer. Time-of-day pricing and Marginal Cost Pricing, two unique cost functions, are examined in detail in this study and are analysed independently during the scheduling process. Next, the particle swarm optimisation algorithm's effectiveness is contrasted with a traditional method to demonstrate its superiority in obtaining ideal load scheduling. This study shows how effective the particle swarm optimisation algorithm is in optimising residential load schedules to reduce costs and better suit customer preferences. The results demonstrate its advantages over conventional techniques, opening the door for smart home energy management systems that are more effective.