Thomas Stegen | Searcharxiv

arXiv2026

Explicit Reward Mechanisms for Local Flexibility in Renewable Energy Communities

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.

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arXiv2025

A Predictive Flexibility Aggregation Method for Low Voltage Distribution System Control

This paper presents a method for predictive aggregation of the available flexibility at the residential unit level into a flexibility chart that represents the admissible active and reactive powers, along with the associated flexibility value. The method is also combined with centralized optimization to design a predictive privacy-preserving control scheme to manage low-voltage distribution systems in real-time. Similarly to hierarchical control strategies, this approach divides the optimization horizon into a real-time stage, responsible for decisions in the current market period, and an operational planning stage, which deals with decisions outside of this interval. First, a multiparametric optimization problem is solved offline at the residential unit level. Then, an operational planning problem, also formulated as a parametric optimization problem, is solved to account for the forecasts. The method generates the desired flexibility chart by combining the results of these two problems with measurements. The resulting approach is compatible with real-time control requirements, as heavy computations are performed offline in a decentralized manner. By linking real-time flexibility assessment with energy scheduling, our approach enables efficient and cost-effective management of low-voltage distribution systems. We validate this method on a low-voltage network of 43 buses by comparing it with a fully centralized optimization formulation with perfect foresight and a future-agnostic aggregation method.

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