Low-Carbon Economic Dispatch of Multi-Park-Level Integrated Energy Systems Considering Thermal Network Demand Response and Improved Shapley Value Method
マルチパークレベル統合エネルギーシステムの低炭素経済ディスパッチ：熱ネットワーク需要応答と改良シャープレイ値法を考慮して (AI 翻訳)
Xianwen Lu, Chengfu Wang, Hua Sun, Yong Wang, Chunling Liu, Chao Liu, Ya Su, Ruoxi Cheng
🤖 gxceed AI 要約
日本語
本論文は、複数パークの統合エネルギーシステム（PIES）の低炭素経済ディスパッチを目的とし、改良シャープレイ値法による利益配分を提案。GANによる不確実性考慮、排出権取引と需要応答を組み込み、炭素削減効果を検証した。
English
This paper proposes an improved Shapley value method for economic dispatch of multi-park integrated energy systems (PIES), considering carbon emissions, thermal network demand response, and benefit sharing. It uses GANs for scenario generation and incorporates emissions trading, demonstrating carbon reduction effectiveness.
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
日本のエネルギーシステムでは、複数事業者間の連携による低炭素化が進んでおり、本手法は利益配分や需要応答の設計に示唆を与える。排出権取引との統合も注目される。
In the global GX context
Globally, this work provides a cooperative game framework for benefit allocation in integrated energy systems with carbon trading, relevant for designing fair sharing mechanisms in multi-actor low-carbon dispatch.
👥 読者別の含意
🔬研究者:Cooperative game theory and GAN-based scenario generation for energy dispatch are novel contributions.
🏢実務担当者:The benefit-sharing method can be applied to design contracts and dispatch strategies for multi-park energy systems.
🏛政策担当者:The integration of carbon trading and demand response offers insights for policy design in energy markets.
📄 Abstract（原文）
This paper proposes an improved Shapley Value method for the economic dispatch of Multi-Park-Level Integrated Energy Systems (PIESs). The method considers carbon emissions, thermal network demand response, and the benefit-sharing problem, aiming to fully exploit the renewable energy scheduling potential and achieve low-carbon operation. Firstly, to address the high randomness and volatility of renewable energy output in PIES, a Generative Adversarial Network is employed to learn the probability distribution of historical data, generating renewable energy output sequences that reflect uncertainty and providing multiple potential scenarios. Secondly, a PIES dispatch strategy is proposed, considering the Emissions Trading System and thermal network user satisfaction. A thermal energy user satisfaction model is established from both energy usage methods and price incentives. Next, based on cooperative game theory with an improved Shapley value for benefit allocation, the errors in the generator and discriminator of the Generative Adversarial Network are quantitatively analyzed. Combining the marginal contributions of each PIES, energy sharing and benefit allocation between PIESs are realized. Finally, in the case studies, the effectiveness of carbon reduction after incorporating demand response in IES is verified for the proposed model and method, while also coordinating the complex benefit allocation among PIESs.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/tia.2025.3587628first seen 2026-05-15 17:05:47
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