Coupled modeling of flexibility resources and power market trading mechanism in a new power system
新しい電力システムにおける柔軟性リソースと電力市場取引メカニズムの結合モデリング (AI 翻訳)
Yang Yu
🤖 gxceed AI 要約
日本語
本論文は、新電力システムにおける柔軟性リソースと電力市場取引メカニズムの結合関係を解明するため、PSO-LS-SVMに基づく日前市場価格予測モデルと、カーボン・グリーン証書・消費量を組み込んだ二層最適化モデルを構築。シミュレーションにより、経済性と環境性の多目的最適化を達成し、省エネ・排出削減効果を示した。
English
This paper develops a coupled model of flexibility resources and power market trading in a new power system. It integrates a PSO-LS-SVM price prediction model and a bi-level optimization model incorporating carbon, green certificates, and consumption. Results show effective day-ahead market price forecasting and multi-objective optimization balancing economic and environmental benefits.
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GX文脈では、カーボン・グリーン証書の市場統合という点で参考になるが、中国の電力市場構造に基づいており、直接適用は難しい。日本の需給調整市場や非化石価値取引との比較研究に活用できる。
In the global GX context
This paper demonstrates a technical approach for integrating carbon pricing and green certificates into power market clearing, relevant for global energy transition. It provides a modeling framework that could inform similar efforts in ISSB or TCFD-aligned disclosure of market-based instruments.
👥 読者別の含意
🔬研究者:Provides a modeling framework for coupling carbon and green certificates in power markets, useful for further research on multi-objective optimization in energy systems.
🏢実務担当者:Offers insights for power market operators or utilities interested in integrating environmental targets into market clearing mechanisms.
🏛政策担当者:Illustrates how carbon pricing and renewable certificates can be operationalized in day-ahead markets, informing policy design for energy transition.
📄 Abstract（原文）
In order to explore the trading mechanism between flexibility resources and power market in the new power system, the article firstly elaborates the characteristics of flexibility resources in the new power system, and constructs a power spot market price prediction model optimized based on PSO algorithm and LS-SVM algorithm. In order to clarify the coupling relationship between flexibility resources and power market trading mechanism, a two-layer optimization model of coupled carbon-green certificate-consumption volume of power market trading in the previous day is constructed, and an arithmetic example analysis is carried out to analyze the application effect of the model. The results show that the PSO optimization LS-SVM model proposed in this paper is acceptable for day-ahead market electricity price prediction, and its prediction error is much lower than that of other comparative models, which shows that it can make effective prediction for the electricity market. This paper solves the multi-objective optimization problem of system economic benefits, energy saving and emission reduction benefits, and obtains a total of 83,607,000 yuan of optimization costs for the previous day's clearing, which is 177,000 yuan more than the difference of 8,537,700 yuan of the system cost of considering the economic benefits objective alone, and the difference of the total cost is not significant, which indicates that the multi-objective optimization function constructed in this paper is able to ensure the economic benefits of the system operation, and achieve the environmental benefits on the basis of maximization, to achieve the effect of energy saving and emission reduction.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.65102/is2026841first seen 2026-05-15 17:12:40
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