Day-ahead optimization model for renewable energy communities considering load shifting, electric vehicles and vehicle-to-grid technology

Renewable Energy Communities (RECs) represent a promising approach to accelerate the energy transition by enabling collective self-consumption and local energy management. However, the intermittent nature of renewable generation and the increasing integration of Electric Vehicles (EVs) pose significant challenges for optimal energy scheduling. This paper proposes a day-ahead multi-objective optimization model for RECs that simultaneously considers load shifting, EV charging coordination, and Vehicle-to-Grid (V2G) technology to minimize operational costs while maintaining user comfort. The model is formulated as a Mixed Integer Linear Programming (MILP) problem and implements the epsilon-constraint method to generate Pareto-optimal solutions, revealing trade-offs between economic efficiency and user preferences. Load shifting is modeled using a Multiple Knapsack Problem (MKP) approach with penalty functions to account for deviations from preferred time slots. Results from a case study composed of three different objectives demonstrated that, in the best case, self-sufficiency can be increased from 17.06% to 99.27%, and a significant reduction from 8.54 € to −3.47 € can be achieved in a single day. • Day-ahead load shifting for optimizing renewable energy use in communities. • Integrates EVs, vehicle-to-grid, and time-of-use electricity rates. • Considers members’ flexibility to meet community goals. • Achieves up to 99.27% self-sufficiency in the best-case scenario. • Demonstrates a cost reduction from 8.54 € to −3.47 € per day.