Multi-Objective Optimal Dispatch of Integrated Energy Systems Guided by Dynamic Carbon Emission Factors.
動的炭素排出係数に基づく統合エネルギーシステムの多目的最適運用 (AI 翻訳)
Yue Lin, Zhimin Lu, Huaiqing Qin, Zeming Liu, Xiaolong Li, Guimin Li, Qing Wang, Pingxin Wang, Jian Yang, Shunchun Yao
🤖 gxceed AI 要約
日本語
本論文では、地域統合エネルギーシステム（RIES）を対象に、動的炭素排出係数と時間帯別料金を需要応答信号として用いた二層多目的最適化モデルを提案。NSGA-IIとGUROBIを用いて運用コスト、炭素排出、負荷安定性を同時最適化し、コスト10.29%削減、炭素1.42%削減、電気負荷3.50%・熱負荷6.50%低減を達成。経済性と低炭素性のトレードオフを明示。
English
This paper proposes a bilevel multiobjective optimization model for regional integrated energy systems, using dynamic carbon emission factors and time-of-use pricing as demand response signals. The model achieves a 10.29% cost reduction and a 1.42% carbon decrease while improving load stability. It highlights trade-offs between economic, environmental, and stability objectives.
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
日本でも地域エネルギーシステムの最適化と脱炭素化が進む中、動的炭素排出係数を用いた本手法は、リアルタイムの排出管理やGX投資判断に示唆を与える。また、SSBJ開示におけるスコープ2排出量の時間的変動評価にも応用可能。
In the global GX context
This paper contributes to global energy transition research by demonstrating how dynamic carbon emission factors can guide operational dispatch in integrated energy systems. The explicit trade-off analysis between cost, carbon, and stability offers insights for corporate decarbonization and grid management, aligning with ISSB's emphasis on scenario analysis and carbon footprint reduction.
👥 読者別の含意
🔬研究者:The bilevel optimization framework with dynamic CEF and TOU pricing provides a replicable methodology for multi-objective energy dispatch studies.
🏢実務担当者:Energy managers can use the model to balance cost, emissions, and load stability in real-time operations, especially for integrated energy systems.
🏛政策担当者:The trade-off findings inform regulatory design for demand response programs and carbon pricing mechanisms in energy systems.
📄 Abstract（原文）
Regional integrated energy systems (RIES) represent a promising approach for the energy transition and sustainable development, leveraging the flexible, coordinated operation of a multienergy system. With the growing penetration of renewable energy sources and the increasing complexity of energy management, the optimization of RIES necessitates the integration of demand response (DR) mechanisms. The optimization problems are increasingly characterized by multiobjective optimization. This study proposes a novel bilevel multiobjective optimization model for RIES, designed to minimize operational costs, reduce carbon emissions, and enhance load stability simultaneously. The model utilizes dynamic carbon emission factors, derived from the carbon emission flow (CEF) calculation, and time-of-use (TOU) energy pricing as DR signals. The Pareto front for demand-side strategies is obtained using the NSGA-II algorithm, coordinated with an upper-level economic dispatch solved by GUROBI, thereby balancing system and user benefits and identifying the optimal trade-offs among these conflicting objectives. Validation through integrated case studies with a 30-bus power, 20-node gas, and 8-node heat system demonstrates that multiobjective optimization with DR significantly improves economic and environmental performance: achieving a 10.29% cost reduction and a 1.42% carbon decrease, load variation was effectively managed, with electric and thermal load reductions of 3.50% and 6.50%, respectively. However, maintaining system load stability comes at the expense of fully achieving economic and low-carbon objectives, highlighting the critical trade-offs inherent in multiobjective optimization.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1021/acsomega.5c11710first seen 2026-05-15 17:09:25
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