Explicit Reward Mechanisms for Local Flexibility in Renewable Energy Communities

2026-01-09

arXiv.org

AI Summary

This work proposes an iterative decentralized coordination mechanism to incentivize prosumers in renewable energy communities to participate in local flexibility scheduling while preserving data privacy and ensuring fair remuneration. The community operator initiates flexibility requests based on aggregate demand, and members autonomously submit capacity bids. Through explicit reward allocation and iterative negotiation, the mechanism converges toward a near-global optimum without exposing individual private information. In contrast to conventional bilevel or ADMM-based approaches, the proposed method effectively circumvents issues of non-convexity and high computational complexity. Evaluated on a 20-household case study, the scheme achieves collective electricity costs within 3.5% of those under a centralized benchmark, demonstrating near-optimal economic performance alongside efficient and privacy-preserving coordination.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

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.

Problem

• Renewable Energy Communities
• Local Flexibility
• Decentralized Coordination
• Flexible Consumption
• Privacy-Preserving Incentives

Innovation

• decentralized flexibility
• iterative coordination
• renewable energy communities
• flexible consumption
• distributed optimization