Co-Optimized Scheduling of a Multi-Microgrid System Based on a Reputation Point Trading Mechanism
レピュテーションポイント取引メカニズムに基づくマルチマイクログリッドシステムの協調最適スケジューリング (AI 翻訳)
Jiankai Fang, Dongmei Yan, Hongkun Wang, Hui Deng, Xinyu Meng, Hong Zhang
🤖 gxceed AI 要約
日本語
本論文は、信頼リスクを考慮したマルチマイクログリッドシステムの協調最適スケジューリングモデルを提案する。RMSE、MAE、DTWを用いた評判評価フレームワークにより不正取引を検出し、シャプレイ値に基づく動的ネットワーク料金と段階的カーボン取引制度を統合する。シミュレーションでは、総炭素排出量を49.6トン削減し、各事業者の収益を4%〜33%向上させることを実証した。
English
This paper proposes a co-optimized scheduling model for multi-microgrid systems that incorporates a reputation evaluation framework using RMSE, MAE, and DTW to detect fraudulent behavior. It integrates a Shapley value-based dynamic network pricing and a step-type carbon trading scheme. Simulation results show a reduction of 49.6 tons in carbon emissions and revenue increases of 4.08% to 33.00% for stakeholders.
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
本論文の提案する信頼リスクを考慮したカーボン取引メカニズムは、日本のスマートシティや分散型エネルギーシステムにおける信頼性向上と脱炭素の両立に示唆を与える。特に、SSBJやカーボンプライシング政策との連動が期待される。
In the global GX context
This paper is relevant to global efforts in designing trustworthy decentralized energy markets and carbon pricing mechanisms. It provides a practical solution for integrating social, economic, and physical dimensions, aligning with trends in smart city development and emissions reduction targets.
👥 読者別の含意
🔬研究者:Researchers in energy systems and carbon markets can learn from the novel reputation-based trust framework and its integration with carbon trading.
🏢実務担当者:Practitioners in microgrid operations can apply the co-optimized scheduling model to enhance profitability and reduce emissions.
🏛政策担当者:Policymakers can consider the step-type carbon trading scheme as a model for promoting fair and effective carbon pricing in localized markets.
📄 Abstract（原文）
With the rapid integration of distributed energy resources, achieving a balance between economic efficiency and environmental sustainability in multi-microgrid (MMG) systems is critical. However, existing studies typically treat microgrid operators as fully compliant entities. They often neglect the “trust-risk” dimension along with potential default behaviors in decentralized markets. This paper proposes a novel co-optimized scheduling model for urban MMG systems, centered on a unified “Social–Economic–Physical” coupling framework. To ensure transaction integrity, a robust reputation evaluation framework is developed using Root Mean Square Error (RMSE), mean absolute error (MAE), plus Dynamic Time Warping (DTW). This framework effectively identifies fraudulent data or contractual breaches. Furthermore, to enhance fairness while promoting decarbonization, the model integrates a dynamic network pricing strategy based on the Shapley value. It works alongside a reputation-weighted reward–penalty step-type carbon trading scheme. The proposed model is formulated as a mixed-integer linear programming (MILP) problem and solved using MATLAB R2025b with CPLEX 12.10. Simulation results demonstrate that the integrated approach significantly optimizes system performance. Total carbon emissions are reduced by 49.6 tons. Meanwhile, revenues for the MMG Alliance, individual microgrids, and shared energy storage operators increase by 4.08% to 33.00%. The proposed framework provides a practical governance solution for Smart City multi-microgrid systems, effectively addressing the “trust-risk” challenge in decentralized urban energy markets. The findings validate that the proposed mechanism effectively fosters a trustworthy trading environment, achieving a “win-win” outcome for economic profitability and urban energy resilience.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/smartcities9040069first seen 2026-05-05 22:55:25
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