Optimal Photovoltaic Energy Allocation in Residential Renewable Energy Communities
住宅用再生可能エネルギーコミュニティにおける太陽光発電の最適配分 (AI 翻訳)
Girona-Badia, Marc, Cipriano, Jordi, Luna, Alvaro, Laguna, Gerard
🤖 gxceed AI 要約
日本語
本論文は、住宅用再生可能エネルギーコミュニティ（REC）内で太陽光発電の最適配分を線形計画法で検討。事後配分方式が事前方式より優れ、2次コスト関数がコミュニティ全体の経済性と公平性のバランスに有効。スペインの実データで検証し、欧州のクリーンエネルギー移行に貢献。
English
This paper uses linear programming to optimize photovoltaic energy allocation in residential Renewable Energy Communities (RECs). A posteriori allocation outperforms a priori approaches, and quadratic cost functions best balance community savings, surplus reduction, and investment payback fairness. Validated with Spanish DSO data, it offers a scalable tool for RECs.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも地域再生可能エネルギーコミュニティが注目される中、FIT終了後の自己消費型モデルの設計に示唆を与える。ただし、本論文は欧州の規制・市場を前提としており、日本の電力システムへの直接適用には調整が必要。
In the global GX context
This study contributes to the global discourse on REC deployment by providing an optimization framework that balances economic efficiency and equity. It is relevant for regions advancing collective self-consumption under the EU's Clean Energy Package, but its methodology can be adapted to other regulatory contexts.
👥 読者別の含意
🔬研究者:Take the linear programming approach and cost function comparison as a foundation for extending REC optimization to include storage or dynamic pricing.
🏢実務担当者:Use the a posteriori allocation framework as a decision-support tool for designing equitable energy sharing rules in community solar projects.
🏛政策担当者:Consider the demonstrated benefits of a posteriori allocation and quadratic cost functions when designing regulations for RECs to maximize social welfare.
📄 Abstract（原文）
This paper investigates the optimal allocation of photovoltaic energy within Residential Renewable Energy Communities (RECs) to maximise the economic and operational benefits of collective self-consumption. Developed within the Horizon Europe DEDALUS project, the study addresses one of the key challenges limiting the widespread deployment of RECs: the absence of efficient and equitable methodologies for distributing locally generated renewable electricity among community members. The proposed approach seeks to improve energy sharing while reducing surplus energy exported to the grid and ensuring a fair return on investment for participating users. The proposed methodology builds upon linear programming optimisation techniques to determine optimal photovoltaic energy-sharing coefficients under both a priori and a posteriori allocation frameworks. Several cost functions are evaluated to balance competing objectives, including minimising community energy costs, reducing energy surplus, improving self-consumption, and ensuring an equitable distribution of economic benefits among REC members. The optimisation framework is validated using real electricity consumption, photovoltaic generation and market price data collected from a Spanish Distribution System Operator (DSO), considering multiple Renewable Energy Community sizes and regulatory scenarios. The results demonstrate that a posteriori energy allocation consistently outperforms a priori approaches by reducing forecasting uncertainty, increasing self-consumption and improving overall economic performance. Among the evaluated optimisation strategies, quadratic cost functions provide the best compromise between maximising community savings, limiting surplus energy exported to the grid and balancing investment payback across participants. The proposed optimisation framework offers a scalable decision-support tool for Renewable Energy Communities and contributes to the development of more efficient, fair and resilient collective self-consumption schemes capable of accelerating Europe's clean energy transition.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/21508853first seen 2026-07-24 04:16:28
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