大規模電気自動車の時空間スケジューリングを考慮したパーク型統合エネルギーシステムの低炭素経済最適化
Low-Carbon Economy Optimization of Park-Level Integrated Energy Systems Considering Temporal and Spatial Scheduling of Large-Scale Electric Vehicles (原題)
Yudong Wang, Huijuan Huo, Shuo Wang, Tianqiong Chen, Weiwei Li, Rui Zhao, Cheng Xin, Jing Duan, Bingkang Li, Nianduo Li
🤖 gxceed AI 要約
日本語
パーク型統合エネルギーシステム（PIES）において、大規模EV fleetの時空間スケジューリングを同時最適化する二層モデルを構築。上層は運用コスト最小化と炭素削減最大化の多目的最適化、下層は配電網損失最小化のEV充放電配置を行う。炭素排出強度指標（CEI）を導入し炭素制約を課す。IEEE33ノード系での検証で、無秩序充電比べ総コスト18.2%減、純炭素排出42.1%減、網損失17.4%減を達成。炭素価格とEV普及率が経済・低炭素性能に強く影響することを示す。
English
This paper builds a bi-layer scheduling model for park-level integrated energy systems that jointly optimizes temporal and spatial scheduling of large EV fleets. The upper layer runs multi-objective optimization (min operating cost, max carbon abatement) with a carbon emission intensity index imposing system carbon constraints; the lower layer minimizes distribution losses via EV charge/discharge allocation. On a modified IEEE 33-node system, the scheme cuts total cost 18.2%, net carbon emissions 42.1% and network losses 17.4% versus disordered charging. Carbon price and EV penetration strongly affect outcomes.
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
中国のパーク型統合エネルギーシステムとEV fleet最適化の知見は、日本の産業団地・業務用施設におけるEV活用型需給調整やカーボンニュートラルポート構想、SSBJ開示におけるScope1・2削減策の実装設計に示唆を与える。炭素価格感度分析は国内カーボンプライシング議論にも参照価値がある。
In the global GX context
Adds quantitative evidence on how EV fleet flexibility and carbon pricing interact in integrated energy systems, relevant to global transition-finance and corporate decarbonization planning under ISSB/CSRD where Scope 1-2 abatement pathways and internal carbon pricing are disclosed. The CEI-based carbon constraint offers a modeling template for linking operational dispatch to disclosure-grade emissions accounting.
👥 読者別の含意
🔬研究者:EV fleetの時空間柔軟性と炭素制約を統合した二層最適化の定式化と、炭素価格感度の定量結果が参考になる。
🏢実務担当者:産業・業務パークでのEV充放電マネジメントと炭素制約を組み合わせた運用コスト・排出削減設計に活用できる。
🏛政策担当者:炭素価格水準とEV普及率が低炭素運用の経済性を左右する点は、カーボンプライシング設計の参考になる。
📄 Abstract（原文）
Park-level integrated energy systems (PIESs) realize coordinated carbon reduction via multi-energy coupling and multi-agent flexibility, yet few studies jointly optimize the temporal and spatial scheduling of large fleets of electric vehicles (EVs) for low-carbon economic dispatch. This paper develops a bi-layer scheduling model for PIESs integrated with large EV fleets. The upper layer adopts multi-objective optimization to minimize operating costs and maximize carbon abatement, coordinating CHP, boilers, energy storage, PV and aggregate EV power across a 24 h horizon. A carbon emission intensity index (CEI) is defined to trace embodied carbon flows and impose system carbon constraints. The lower layer minimizes distribution network power loss by rationally assigning EV charging/discharging loads to network nodes subject to voltage, line capacity and charging pile limits. NSGA-II with fuzzy decision-making solves the upper multi-objective MINLP, and Gurobi addresses the lower MILP power flow model. Simulations on a modified IEEE 33-node park system with four comparative cases validate the proposed strategy. Results show that, compared with disordered EV charging, the bi-layer scheme cuts total cost by 18.2%, net carbon emissions by 42.1% and network losses by 17.4%. Sensitivity analysis proves that the carbon price and EV penetration remarkably affect economic and low-carbon performance. This two-stage spatio-temporal optimization framework provides an effective tool to harness the low-carbon flexibility potential of large-scale EV fleets in industrial and commercial parks.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/pr14213525first seen 2026-11-05 05:10:22 · last seen 2026-11-06 04:52:07
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