Optimized economic scheduling of demand response in integrated energy systems considering dynamic energy efficiency and dynamic carbon trading
動的エネルギー効率と動的炭素取引を考慮した統合エネルギーシステムにおける需要応答の最適経済スケジューリング (AI 翻訳)
Haoyu Mao, Qiyue Deng, Zihao Zhang, Xiaohui Yang
🤖 gxceed AI 要約
日本語
本論文は、統合エネルギーシステム（IES）の需要応答経済スケジューリングにおいて、動的エネルギー効率と動的炭素取引を導入した最適化モデルを構築。分散ロバスト最適化（DRO）とモデル予測制御（MPC）の協調フレームワークを提案し、季節別の炭素クォータ配分戦略を設計。シミュレーションにより、総コスト13.07%削減、炭素取引コスト11.57%削減を実証。
English
This paper constructs an optimization model for demand response economic scheduling in integrated energy systems (IES) incorporating dynamic energy efficiency and dynamic carbon trading. It proposes a distributed robust optimization (DRO)-model predictive control (MPC) collaborative framework and a tiered dynamic carbon quota allocation strategy. Simulation results show total cost reduction of 13.07% and carbon trading cost reduction of 11.57% compared to conventional approaches.
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
日本では炭素価格制度の本格化が進む中、IESの動的最適化手法は需要家のコスト削減と排出削減に貢献する可能性がある。特に季節変動を考慮した炭素クォータ配分は、日本の電力システムに応用できる示唆を与える。
In the global GX context
This paper advances the global discourse on carbon trading integration within energy system optimization by introducing dynamic efficiency and seasonal carbon quota allocation, which are relevant for regions implementing carbon pricing and smart grid technologies.
👥 読者別の含意
🔬研究者:Energy system modelers can adopt the DRO-MPC framework for robust scheduling under uncertainty.
🏢実務担当者:Energy managers in industrial parks or integrated energy networks can apply the dynamic carbon trading model to reduce costs.
🏛政策担当者:Policymakers designing carbon quota allocation mechanisms may consider seasonal and efficiency-based dynamic strategies.
📄 Abstract（原文）
Addressing uncertainties on the demand side caused by electricity price fluctuations during integrated energy system (IES) dispatch, modeling biases resulting from static assumptions about equipment energy efficiency, and cost redundancy issues stemming from unreasonable seasonal allocation of carbon quotas, this study constructs an electricity PDR economic dispatch optimization model incorporating dynamic energy efficiency and dynamic carbon trading. It proposes a “distributed robust optimization (DRO)-model predictive control (MPC)” collaborative framework and a tiered dynamic carbon quota allocation strategy accounting for seasonal output and efficiency variations of equipment, tailored to match carbon emission characteristics across different seasons. At the demand response level, an electricity price elasticity coefficient matrix is introduced to quantify the impact of real-time price fluctuations on load, integrating it into the MPC model to resolve the time-scale mismatch between day-ahead and intraday scheduling. Simulation results demonstrate: The coupled dynamic energy efficiency and carbon trading model reduces total system costs by 13.07% and carbon trading costs by 11.57% compared to the conventional approach. Regarding tracking error, the combination of rolling optimization and feedback correction improves tracking accuracy by 14.66% and 6.13% compared to cases without feedback correction and rolling optimization, respectively, while reducing total costs by 4.36% compared to the case without rolling optimization. This study provides a scientifically feasible optimization solution for low-carbon economic dispatch of IES under uncertainty.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1038/s41598-025-33497-3first seen 2026-05-05 22:56:08
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。