An optimal real-time pricing strategy for aggregating distributed generation and battery storage systems in energy communities: A stochastic bilevel optimization approach

The expansion of distributed electricity generation and the increasing capacity of installed battery storage systems at the community level have posed challenges to efficient technical and economic operation of the power systems. With advances in smart-grid infrastructure, many innovative demand response business models have sought to tackle these challenges, while creating financial benefits for the participating actors. In this context, we propose an optimal real-time pricing (ORTP) approach for the aggregation of distributed energy resources within energy communities. We formulate the interaction between a community-owned profit -maximizing aggregator and the users (consumers with electricity generation and storage potential, known as "prosumagers", and electric vehicles) as a stochastic bilevel disjunctive program. To solve the problem efficiently, we offer a novel solution algorithm, which applies a linear quasi-relaxation approach and an innovative dynamic partitioning technique. We introduce benchmark tariffs and solution algorithms and assess the performance of the proposed pricing strategy and solution algorithm in four case studies. Our results show that the ORTP strategy increases community welfare while providing useful grid services. Furthermore, our findings reveal the superior computational efficiency of our proposed solution algorithm in comparison to benchmark algorithms.