An integrated stochastic programming model for the fair and efficient operation of renewable energy communities

This paper presents a two-phase framework for the optimal operation of renewable energy communities that balances operational efficiency with equitable benefit distribution. We propose a two-phase stochastic programming framework: the first phase maximizes internal energy exchange, and the second ensures fair cost-benefit allocation. This integrated approach addresses both operational efficiency and equity under uncertainty. Our methodology addresses uncertainty in renewable generation and demand through stochastic programming, incorporating flexible loads and energy storage to enhance system performance. A key innovation is the fairness mechanism that guarantees minimum benefits for each participant, promoting community cohesion and long-term viability. Computational experiments, carried out on a realistic test case, validate the advantages of our approach, demonstrating improved efficiency through uncertainty management and equitable distribution of community benefits. The results show that our framework successfully navigates the technical and social dimensions of community energy systems, offering a practical solution for sustainable energy communities development that aligns individual interests with collective goals. • Novel stochastic program optimizes Renewable Energy Community (REC) operations fairly. • Two-phase methodology: novel energy exchange first, then fair cost-benefit split. • Models uncertainty via stochastic scenarios for robust, novel REC decisions. • Integration of flexible loads & energy storage to boost efficiency & sustainability. • Fair pricing mechanism to promote inclusivity and long-term community trust.