Maximizing shared benefits in renewable energy communities: A Bilevel optimization model

Abstract

To respond to the global need for sustainable energy solutions and the imperative to combat climate change, Renewable Energy Communities (REC) have emerged as a promising solution to achieve energy transition goals. Of course, some optimization tools need to be developed to face the challenges related to their operational management and maximize their potential. In this context, this paper proposes a bilevel optimization approach for the optimal management of a REC, focusing on maximizing shared energy and economic benefits. The high-level models the problem of the Energy Community Manager (ECM), who aims at maximizing shared energy rewarded with incentives depending on the plants according to the new legislation; instead, the low-level problems focus on each Energy Community Participant (ECP) aiming to minimize individual costs. To solve this problem Karush-Kuhn-Tucker (KKT) conditions are exploited to convert low-level problems into constraints for the high-level problem. Two different approaches (MILP and NLP formulations) to approximate the high-level objective function are proposed and tested, and the best approach is applied to a case study involving ten ECPs. The scalability of the proposed approach is evaluated as well as the impact of the most influencing parameters. According to the results, each ECP would obtain an annual income for sharing energy, which could be significant, especially when proper pricing strategies are considered. Moreover, the proposed model is suitable for online operations as the runtime is quite low.