Residential load shifting in demand response events for bill reduction using a genetic algorithm

Flexible demand management for residential load scheduling, which considers constraints, such as operating time window and order between them, is a key aspect in response. This paper aims to address constraints imposed on the operation schedule of appliances while also participating response events. An innovative crossover method genetic algorithms proposed, implemented, validated. The proposed solution distributed generation, dynamic pricing, shifting minimize energy costs, reducing electricity bill. A case study using real household workload data presented, where four are scheduled five days, three different scenarios explored. implemented algorithm achieved up 15% bill reduction, scenarios, when compared business usual. • Load flexibility with Demand Response participation reduction. Usage DG based renewables, market appliances. Innovative Genetic Algorithm (GA) crossovers.