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Modelling the Adoption of Renewable Energy Communities in Urban Districts: An Agent-Based Approach

都市地区における再生可能エネルギーコミュニティの採用のモデル化:エージェントベースアプローチ (AI 翻訳)

Francesca Vecchi, Simona Semeraro, Roberto Stasi, Umberto Berardi

Springer Link (Chiba Institute of Technology)📚 査読済 / ジャーナル2026-06-09#エネルギー転換Origin: EU対象セクター: real_estate
DOI: 10.1051/e3sconf/202671604019/pdf
原典: https://doi.org/10.1051/e3sconf/202671604019/pdf

🤖 gxceed AI 要約

日本語

本研究は、再生可能エネルギーコミュニティ(REC)の導入をエージェントベースモデルでシミュレーション。イタリア・バーリの混合用途地区を対象に、20年間の建物のREC参加動態を分析。結果、非住宅建物が主に参加し、居住用は経済性で個人プロシューマーが優位となる場合が多い。金融・エネルギー要因が初期に支配的だが、徐々にピア影響が重要に。政策への示唆を提供。

English

This study develops an agent-based model to simulate the adoption of Renewable Energy Communities (RECs) in urban districts, using a mixed-use neighborhood in Bari, Italy, over a 20-year horizon. Results show REC membership grows mainly in the first 5 years, reaching 33% of buildings, with non-residential buildings benefiting more. Financial and energy motivations dominate early, while peer influence becomes important later. The framework supports policymakers in designing targeted incentives.

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

日本ではREC普及は初期段階にあり、本モデルは地域特性に応じたインセンティブ設計の重要性を示す。特に、非住宅と住宅の参加率格差は、日本のFIT後の制度設計にも示唆を与える。

In the global GX context

This paper contributes to global energy transition literature by modeling REC adoption dynamics with agent-based methods. The multi-criteria utility framework and peer influence calibration are applicable to other regions, informing policy design for decentralized energy communities.

👥 読者別の含意

🔬研究者:The agent-based model with multi-criteria utility and peer influence provides a framework for further study of REC adoption dynamics.

🏢実務担当者:Utilities and urban planners can use insights on building-type participation and temporal adoption patterns to design effective REC initiatives.

🏛政策担当者:The model highlights the need for differentiated incentives and social influence strategies to boost REC enrollment, especially among residential buildings.

📄 Abstract(原文)

The transition to decentralized energy systems has introduced new configurations as Renewable Energy Communities (RECs), where individual prosumers collaborate to maximize collective energy and access to collective market energy mechanisms. Adoption dynamics within urban environments and customers' response remain poorly investigated, especially in terms of how technical, economic and social factors influence decision-making. This study develops an agent-based model to simulate the spatial and temporal enrolment of buildings in a REC at district scale over a 20-year horizon, using a mixed-use neighborhood in Bari (Italy) with rooftop photovoltaic systems as the case study. A multi-criteria utility framework is implemented for each building agent, combining financial, energy, environmental, and peer-influence components. The associated weights evolve as a function of contextual factors (i.e. tariffs, REC revenues, and awareness) and local social dynamics. Weights are calibrated against observed regional PV adoption data. At each monthly step, agents first pass a discounted payback-time filter for individual PV investment and, if feasible, compare the utilities of remaining single prosumers or joining a REC through a probabilistic decision rule. Results from 20 simulation runs indicate that REC membership grows mainly between years 1 and 5, reaching 39 buildings (about 33% of agents) and then stabilizing. Non-residential buildings with higher surplus generation systematically benefit from joining the REC, while only 14-32% of residential buildings enroll across runs, as individual prosumer configurations often remain economically more attractive under the current Italian REC remuneration scheme. Financial and energy motivations dominate in early years, whereas peer influence gradually becomes the most important weight. Despite the model assumptions, this new framework support policymakers in identifying targeted incentives, enhancing the effectiveness and inclusivity of energy transition policies.

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