Optimization of electric demand response based on users’ preferences

This paper addresses the challenge of integrating user preferences into residential demand response programs while maintaining a practical modeling approach with minimal monitoring needs. While user-centered approaches enhance demand response effectiveness, they often require extensive data collection, whereas simplified modeling approaches improve feasibility but lack user-specific flexibility. This work proposes a demand response optimization framework designed to schedule electric-consuming activities within an energy community, maximizing photovoltaic self-consumption and enabling peak-shaving strategies while considering users’ availability and willingness to shift activities. The framework uses forecasts of photovoltaic production and community electric demand, converted into categorical signals, to guide the scheduling. An agent-based structure facilitates the optimization and generation of demand response suggestions. Case studies include a simplified scenario and a scalability analysis considering up to 1000 activities, with varying user availability scenarios. Results demonstrate that the proposed electric optimizer can schedule a high percentage of electric-consuming activities during optimal periods (high photovoltaic production and reduced demand), with the specific percentage varying based on user availability and community size. The optimizer achieves a trade-off between scheduling activities in the most favorable times and distributing them to avoid demand peaks. The proposed electric optimizer offers a user-centered solution with limited data and monitoring requirements, contributing to shifting consumption within a community while respecting individual preferences. • The optimizer schedules electric-consuming activities in an energy community. • Activities are shifted to times with higher PV production and lower demand. • Users’ preferences for shifting activities during the day are included as constraints. • In large communities, a trade-off is set to reach balanced activity distribution. • The optimizer is able to schedule up to 79% of activities in the best period.