Explicit Reward Mechanisms for Local Flexibility in Renewable Energy Communities

TL;DR

This paper introduces an iterative decentralized method for flexibility planning in renewable energy communities, balancing individual incentives with global optimization, and demonstrating near-optimal results in a case study.

Contribution

It presents a novel iterative resolution procedure for decentralized flexibility coordination that ensures individual optimality and converges to a global optimum.

Findings

Decentralized scheme achieves within 3.5% of centralized scheme in bill savings.

Method effectively balances privacy concerns with optimization accuracy.

Validated on a 20-member domestic energy community case study.

Abstract

Incentivizing flexible consumption of end-users is key to maximizing the value of local exchanges within Renewable Energy Communities. If centralized coordination for flexible resources planning raises concerns regarding data privacy and fair benefits distribution, state-of-the-art approaches (e.g., bi-level, ADMM) often face computational complexity and convexity challenges, limiting the precision of embedded flexible models. This work proposes an iterative resolution procedure to solve the decentralized flexibility planning with a central operator as a coordinator within a community. The community operator asks for upward or downward flexibility depending on the global needs, while members can individually react with an offer for flexible capacity. This approach ensures individual optimality while converging towards a global optimum, as validated on a 20-member domestic case study for which the gap in terms of collective bill is not more than 3.5% between the decentralized and centralized coordination schemes.