Bilevel Stochastic Low-Carbon Operation Optimization of Integrated Energy Systems Based on Dynamic Mean–Conditional Value at Risk (CVaR) and Stepwise Carbon Trading Mechanism
動的平均条件付きバリュー・アット・リスク（CVaR）と段階的炭素取引メカニズムに基づく統合エネルギーシステムの二段階確率的低炭素運転最適化 (AI 翻訳)
Jing Zhang, Xinyi He, Jianfei Li, Diyu Chen, Yingang Ye, Shumei Chu, Xinhong Cheng, Fei Zhao
🤖 gxceed AI 要約
日本語
本論文は、多様な不確実性下で統合エネルギーシステム（IES）の低炭素運用を改善するため、動的平均CVaRリスクモデルと段階的炭素価格メカニズムを組み込んだ二段階確率最適化フレームワークを提案する。上層ではNSGA-IIを用いて容量計画を最適化し、下層では時間変動するリスク回避係数でシナリオベースの運用評価を行う。段階的炭素価格関数とキャップ付き炭素収入メカニズムは実際の炭素市場を反映する。シミュレーションにより、従来の静的リスクモデルと比較してコストと排出の変動を低減し、経済性と低炭素性能のバランスが向上することを示した。
English
This paper proposes a bilevel stochastic optimization framework with a dynamic mean-CVaR risk model and a tiered carbon pricing mechanism to improve low-carbon operation of integrated energy systems under multi-source uncertainties. The upper level uses improved NSGA-II for capacity planning, while the lower level performs scenario-based operation evaluation with a time-varying risk aversion coefficient. A stepwise carbon price function and a capped carbon revenue mechanism reflect real carbon market regulations. Simulation results show reduced cost and emission volatility and a better trade-off between economy and low-carbon performance compared to conventional static-risk models.
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
日本でも炭素価格の段階的導入や再エネ統合が進む中、本論文の動的リスクモデルと段階的炭素取引メカニズムは、日本の地域エネルギーシステムの低炭素運用最適化に示唆を与える。特に、時間変動するリスク回避係数は、日本特有の気象変動や需要パターンに対応した運用戦略に応用可能である。
In the global GX context
This paper contributes to the global GX context by integrating dynamic risk modeling with realistic carbon pricing mechanisms for multi-energy system optimization. Its stepwise carbon trading and cap mechanisms reflect real-world carbon market designs, such as those in the EU ETS and regional markets, and offer insights for system operators and policymakers seeking to balance economic and environmental objectives under uncertainty.
👥 読者別の含意
🔬研究者:Energy system optimizers can leverage the dynamic CVaR and bilevel framework for robust low-carbon planning under uncertainty.
🏢実務担当者:Corporate energy planners can use the proposed model to optimize integrated energy systems with carbon cost considerations and risk management.
🏛政策担当者:Carbon market designers can learn from the stepwise pricing and revenue cap mechanisms to improve market efficiency and emission reduction incentives.
📄 Abstract（原文）
To enhance the low-carbon operational performance of integrated energy systems (IESs) under multi-source uncertainties, this study proposes a bilevel stochastic optimization framework incorporating a dynamic mean–CVaR risk model and a tiered carbon pricing mechanism. The upper level adopts an improved NSGA-II to jointly optimize economic cost, carbon emissions, and system flexibility through capacity planning decisions. The lower level performs scenario-based operation evaluation with a time-varying risk aversion coefficient, enabling differentiated risk responses across operating periods. A stepwise carbon price function and a capped carbon revenue mechanism are introduced to represent real carbon market regulations and avoid excessive emission reduction benefits. Multidimensional uncertainty scenarios—covering renewable variability, load fluctuations, and market price disturbances—are generated for risk-aware evaluation. Simulation results show that the proposed approach effectively reduces cost and emission volatility and achieves a more balanced trade-off between economy and low-carbon performance compared with conventional static-risk models. Sensitivity analyses further reveal that increased risk aversion shifts system operation strategies from economy-oriented to robustness-oriented modes, highlighting the importance of dynamic risk modeling and carbon policy design for future low-carbon multi-energy systems.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/en19061421first seen 2026-05-05 22:47:16
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。