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 load oper-ating time window and order between them, is a key aspect in demand response. This paper aims to address constraints imposed on the operation schedule of appliances while also participating in demand response events. An innovative crossover method of genetic algorithms is proposed, implemented, and validated. The proposed solution considers distributed generation, dynamic pricing, and load shifting to minimize energy costs, reducing the electricity bill. A case study using real household workload data is presented, where four appliances are scheduled for five days, and three different scenarios are explored. The implemented genetic algorithm achieved up to 15% in bill reduction, in different scenarios, when compared to business as usual.

Automated summary

This paper proposes a flexible demand management solution that addresses constraints in residential load scheduling and demand response. By utilizing a crossover method in genetic algorithms, along with distributed generation, dynamic pricing, and load shifting, significant reductions in energy costs can be achieved.