Residential high-resolution electricity demand optimization with a cooperative PSO algorithm

Abstract Electricity use in residential sector accounts for almost a quarter of the total. Occupant’s indoor activities will highly affect the electricity use pattern. This paper presents a cooperative heuristic optimization algorithm for residential schedules of controllable appliances considering user’s indoor activities. First of all, Markov Chains are employed to model occupant’s behaviors, in which transition matrices are generated by using American Time Use Survey data. Then, engineering models of electric appliances are established and electricity consumption is able to be simulated by corresponding people’s activities to common electricity usage. Finally, a cooperative PSO algorithm is employed to find optimal schedules for these appliances. Experiments are conducted. The results indicate that peak loads can be shaved, electricity bills can be decreased by 3.1%, and a reduction of 5.9% of total power can be reached by optimizing high-resolution residential demand.