需給不確実性を考慮した統合エネルギーシステムの協調的多時間軸低炭素経済ディスパッチ戦略
Coordinated Multi-Time-Scale Low-Carbon Economic Dispatch Strategy for Integrated Energy Systems Considering Source-Load Uncertainties (原題)
Mu Li, Shouyuan Wu, Yuman Song
🤖 gxceed AI 要約
日本語
再生可能エネルギー普及に伴う需給両側の不確実性に対応するため、需給応答と多エネルギー連携を統合した多時間軸最適運用フレームワークを提案。日前・当日・リアルタイムの階層段階制御により、CHP発電40.70%増、廃熱冷房消費41.12%増を実現し、負荷偏差を0.17%以下に抑制。P2G導入でガス調達費12.70%削減、DRで電力購入費9.97%削減・EV充電費15%削減を達成した。
English
Proposes a multi-timescale optimal dispatch framework integrating demand response and multi-energy coupling to handle source-load uncertainties in integrated energy systems. Hierarchical day-ahead, intra-day, and real-time stages boost CHP generation by 40.70% and waste-heat cooling by 41.12%, holding load deviations below 0.17%. Power-to-gas cuts gas procurement costs 12.70% and demand response lowers electricity purchase costs 9.97% and EV charging costs 15%.
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、再エネ大量導入下での系統安定化と需要側柔軟性の確保が重要課題であり、本論文の多時間軸運用とDR設計は、電力・熱・冷の統合管理やVPP・DR市場設計を検討する日本企業・政策担当者に実務的示唆を与える。ただしSSBJや有報開示との直接接続はない。
In the global GX context
Globally, this work contributes to the operational layer of energy transition: multi-timescale dispatch and demand response are central to integrating variable renewables and managing source-load symmetry. It offers quantitative evidence on P2G economics and EV charging flexibility, relevant to grid operators and IES planners, though it does not engage with TCFD/ISSB disclosure frameworks.
👥 読者別の含意
🔬研究者:多時間軸最適化と需給不確実性の対称性を扱う定量的枠組みとして、IES運用研究の参考になる。
🏢実務担当者:P2GやDRを組み合わせた運用コスト削減・ピーク抑制の具体的数値は、エネルギー管理システム設計に活用できる。
🏛政策担当者:DRやEV充電制御の効果は、需要側柔軟性を促す制度設計やインセンティブ検討の根拠になり得る。
📄 Abstract（原文）
The growing penetration of renewable energy sources introduces significant uncertainties into integrated energy systems (IESs). Conventional single-timescale management strategies, typically designed for static power balance, fail to address the symmetry of source-load uncertainties arising from both supply and demand sides. To address this challenge, this paper proposes a multi-timescale optimal scheduling framework that integrates demand response (DR) and multi-energy flow coupling. The framework adopts a hierarchical progressive strategy across day-ahead, intra-day, and real-time stages. The day-ahead stage optimizes the economic baseline with an hourly resolution. The intra-day stage conducts rolling correction at 15 min intervals to activate slow-response equipment flexibility, boosting combined heat and power (CHP) generation by 40.70% and increasing waste-heat cooling consumption by 41.12%. The real-time stage employs energy storage at 5 min resolution to suppress fluctuations, maintaining electricity, heat, and cooling load deviations, respectively, at remarkably low levels of 0.17%, 0.10%, and 0.06%. Comparative results show that with power-to-gas (P2G) integration, the system purchases off-peak electricity for synthetic natural gas production, cutting gas procurement costs by 12.70% and reducing net carbon emissions from 5.14 t to 4.91 t. DR mechanisms enable a gas–electricity substitution strategy that lowers electricity purchase costs by 9.97%, reduces evening peak electric vehicle (EV) charging load by 8.32%, and decreases charging expenses by 15%.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/sym18091521first seen 2026-09-14 04:42:23
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