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.

翻译：激励终端用户的灵活消费是最大化可再生能源社区内本地交换价值的关键。如果集中式协调用于灵活资源规划会引发数据隐私和公平利益分配的担忧，那么现有先进方法（例如双层优化、ADMM）常面临计算复杂性和凸性挑战，从而限制了嵌入式灵活性模型的精度。本研究提出一种迭代求解程序，以解决社区内由中央运营商作为协调者的去中心化灵活性规划问题。社区运营商根据全局需求请求上调或下调灵活性，而成员可独立响应并提供灵活容量报价。该方法在保证个体最优性的同时收敛至全局最优解，这一点在一个包含20户家庭的案例研究中得到验证，其中去中心化与集中式协调方案在集体电费方面的差距不超过3.5%。