Demand Response Planning via Home Energy Management System for Residential Aggregators in a Virtual Power Plant

This paper proposes a demand-responsive virtual power plant framework to address the energy management issues in distribution networks, considering the lack of distributed energy resources among apartment residents. In the proposed interaction mechanism, a price-based demand response strategy is utilized to develop load scheduling for residential aggregators, thereby achieving the goal of peak shaving and valley filling of the power system. Bi-level optimization problems are solved via bilevel particle swarm optimization for aggregation agents and users, separately. With the help of agents, load schedules reduce the peak-valley difference. For evaluating the effectiveness of the proposed framework, experiments are carried out for 135 loads in 15 houses from a publicly available dataset. The results show load scheduling succeeds in demand response.