高時空間分解能を持つ電気自動車の低炭素最適スケジューリング
Low-carbon Optimal Scheduling of Electric Vehicles With High Spatiotemporal Resolution (原題)
Zi-Cen Chang
🤖 gxceed AI 要約
日本語
電気自動車（EV）の充電による間接排出を削減するため、動的電力価格、蓄電池運用、各種EVの充電特性、動的炭素排出係数を考慮したユーザー側の低炭素最適スケジューリング枠組みを提案。ノード炭素強度とユーザー行動を関連付け、価格シグナルと炭素係数を組み合わせることで、低負荷・低炭素時間帯への充電シフトを促す。実験では、価格のみでは不十分で動的炭素係数の考慮が有効であることを示した。
English
This paper proposes a user-side low-carbon scheduling framework for EV charging that integrates dynamic electricity pricing, storage operation, EV charging characteristics, and dynamic carbon emission factors. It links user consumption behavior to nodal carbon intensity, encouraging charging during low-carbon periods. Experiments show that price signals alone are insufficient and dynamic carbon factors are essential for effective low-carbon scheduling.
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EV普及と再エネ拡大が進む中、充電時間・場所による間接排出の可視化はSSBJ開示やカーボンフットプリント対応に有用。動的炭素係数を用いたユーザー行動変容は、電力系統の脱炭素化と需要側対策を結ぶ実践的知見を提供する。
In the global GX context
Globally, this research contributes to the growing field of smart charging and carbon-aware demand response, aligning with ISSB and CSRD requirements for Scope 2 reporting. The integration of dynamic carbon factors into scheduling offers a scalable approach for reducing indirect emissions from EV charging, relevant for grid operators and policymakers.
👥 読者別の含意
🔬研究者:Provides a methodological framework for integrating dynamic carbon factors into EV scheduling, useful for further research in carbon-aware demand response.
🏢実務担当者:Offers insights for designing charging incentives and storage dispatch strategies that reduce carbon footprint, applicable for fleet operators and energy managers.
🏛政策担当者:Highlights the need for dynamic carbon pricing signals to complement electricity pricing for effective decarbonization of transport.
📄 Abstract（原文）
As electric vehicles (EVs) become increasingly connected to power systems at scales, their emission reduction benefits no longer depend merely on whether the vehicles themselves are zero-emission, but are also closely related to charging time, charging loc ation, and the carbon intensity of electricity sources. In order to reduce indirect carbon emissions of charging for EVs, a user-side low-carbon optimal scheduling framework for EVs is established, in which dynamic electricity pricing, energy storage system operation, charging characteristics of various EV types and dynamic carbon emission factor are considered. Firstly, based on the carbon emission approach, user-side electricity consumption behavior is associated with nodal carbon intensity, which provides a theoretical basis for evaluating the carbon footprint of EVs. Secondly, through dynamic pricing mechanisms and real-time electricity trading prices, electric vehicles are encouraged to charge during low-load or low-carbon periods, while energy storage systems are coordinated for charging and discharging operations to improve the peak-to-valley load profile. Lastly, experiments are conducted to analyze the variation patterns of optimized electricity price curves, categorized EV charging power behaviors, storage system operating conditions, and the variations in dynamic marginal carbon emission factors and overall carbon emission factors. The experimental results indicate that electricity price signals alone are insufficient for user-side low-carbon scheduling, and that dynamic carbon emission factors should also be taken into account.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://journals.zeuspress.org/index.php/conference/article/download/1251/1171first seen 2026-08-30 05:05:41 · last seen 2026-09-21 04:57:12
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