Multi-Energy Collaborative Pricing Mechanism of Virtual Power Plants Under Carbon Trading Regulation
炭素取引規制下における仮想発電所のマルチエネルギー協調価格設定メカニズム (AI 翻訳)
Ru Wang, Junxiang Li, Ziyin Yang
🤖 gxceed AI 要約
日本語
本論文は、炭素取引規制を考慮した仮想発電所（VPP）のマルチエネルギー協調価格メカニズムを提案する。二段階最適化モデルを用いてユーザー効用と供給者コストを同時に最適化し、粒子群最適化（PSO）アルゴリズムで求解する。ケーススタディにより、提案手法がピークシフトと需給バランスを実現し、運用効率と経済性を向上させつつ炭素排出を削減することを示す。
English
This paper proposes a multi-energy collaborative pricing mechanism for virtual power plants (VPPs) under carbon trading regulation. It uses a bi-level optimization model to simultaneously maximize user utility and minimize supplier costs, considering energy, O&M, and carbon emission costs. A particle swarm optimization algorithm solves the model. A case study shows the mechanism effectively achieves peak shaving, supply-demand balance, and reduces carbon emissions while improving operational efficiency.
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
本論文は、カーボンプライシングとVPPの統合に関する実践的モデルを提供しており、日本のエネルギー市場改革やカーボンニュートラル目標に資する。特に、排出量取引制度下の需給調整メカニズムは、日本の地域エネルギー管理に示唆を与える。
In the global GX context
This paper contributes to the global literature on carbon trading and virtual power plants by presenting a bi-level optimization approach that integrates carbon costs into pricing. It offers insights for designing demand response programs and carbon pricing mechanisms that enhance renewable integration and system efficiency.
👥 読者別の含意
🔬研究者:Researchers can utilize the bi-level optimization framework for further studies on carbon-constrained energy systems.
🏢実務担当者:Practitioners in energy trading and VPP operations can apply the pricing mechanism to enhance economic efficiency and reduce emissions.
🏛政策担当者:Policymakers can consider the model's insights for designing carbon trading regulations that incentivize demand response and renewable integration.
📄 Abstract（原文）
In response to global climate change, virtual power plants (VPPs) have emerged as critical entities for integrating distributed energy resources and enabling demand response. However, the design of multi-energy collaborative pricing mechanisms for VPPs remains a significant challenge, particularly under carbon trading regulation. This paper addresses this gap by proposing a bi-level optimization model that captures the real-time interactions between users and energy suppliers. The model is designed to simultaneously maximize user utility and minimize supplier costs, explicitly accounting for energy costs, equipment operation and maintenance (O&M) costs, carbon emission costs, and power generation structure constraints. A particle swarm optimization (PSO) algorithm is employed to solve the formulated problem. The results of a case study demonstrate that the proposed mechanism effectively guides users toward peak shaving and valley filling, achieving a real-time balance between supply and demand. Furthermore, the simulation results indicate that the model significantly enhances power system operational efficiency and economic benefits while reducing carbon emissions. This work offers a practical approach for improving renewable energy integration and overall system performance within a carbon-constrained environment.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/superintelligence1010002first seen 2026-05-05 22:48:03
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