Dynamic Carbon-Aware Scheduling for Electric Vehicle Fleets Using VMD-BSLO-CTL Forecasting and Multi-Objective MPC
VMD-BSLO-CTL予測と多目的MPCを用いた電気自動車フリートの動的カーボンアウェアスケジューリング (AI 翻訳)
Hongyu Wang, Zhiyu Zhao, Kai Cui, Zixuan Meng, Bin Li, Wei Zhang, Wenwen Li
🤖 gxceed AI 要約
日本語
本論文は、電気自動車（EV）フリートの低炭素充電を実現する「予測-最適化」閉ループフレームワークを提案する。まず、VMDとCNN-Transformer-LSTMを統合したハイブリッド予測モデル（VMD-BSLO-CTL）を構築し、英国ナショナルグリッドのデータで有効性を検証した。次に、多目的MPC戦略により、ユーザーの経済性と系統安定性を両立する充電スケジューリングを実現。シミュレーションの結果、経済コスト4.17%削減、炭素排出8.82%削減、ピークバレー差6.46%低減、負荷分散11.34%低減を達成した。最後に、クラウドエッジ連携展開スキームを示し、次世代低炭素エネルギー管理への工学的可能性を示唆する。
English
This paper proposes a 'Prediction-Optimization' closed-loop framework for low-carbon EV fleet charging. A hybrid forecasting model (VMD-BSLO-CTL) integrating VMD and CNN-Transformer-LSTM is built and validated on UK National Grid data. A multi-objective MPC strategy trades off user economy and grid stability. Simulations show 4.17% cost reduction, 8.82% carbon emission reduction, 6.46% peak-valley difference reduction, and 11.34% load variance reduction. A cloud-edge collaborative deployment scheme demonstrates engineering potential for next-generation low-carbon energy management.
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普及とカーボンニュートラル目標が進む中、本フレームワークは需要側応答の高度化に寄与する。日本独自の系統データを用いた適用が期待されるが、本研究は英国データに基づくため、日本の電力構成や炭素強度特性への適合検証が必要。
In the global GX context
This work advances carbon-aware scheduling for EV fleets, directly supporting global climate disclosure frameworks (ISSB, TCFD) by providing quantifiable emission reductions. It also aligns with transition finance by demonstrating cost-effective decarbonization in the transport sector.
👥 読者別の含意
🔬研究者:The hybrid forecasting model (VMD-BSLO-CTL) and multi-objective MPC approach offer novel methods for spatio-temporal carbon intensity forecasting and EV fleet optimization.
🏢実務担当者:EV fleet operators and grid managers can adopt the framework to reduce both costs and carbon emissions while improving grid stability.
🏛政策担当者:The study provides evidence that smart charging policies can achieve significant emission reductions without compromising user economy.
📄 Abstract（原文）
Accurate perception of dynamic carbon intensity is a prerequisite for low-carbon demand-side response. However, traditional grid-average carbon factors lack the spatio-temporal granularity required for real-time regulation. To address this, this paper proposes a “Prediction-Optimization” closed-loop framework for electric vehicle (EV) fleets. First, a hybrid forecasting model (VMD-BSLO-CTL) is constructed. By integrating Variational Mode Decomposition (VMD) with a CNN-Transformer-LSTM network optimized by the Blood-Sucking Leech Optimizer (BSLO), the model effectively captures multi-scale features. Validation on the UK National Grid dataset demonstrates its superior robustness against prediction horizon extension compared to state-of-the-art baselines. Second, a multi-objective Model Predictive Control (MPC) strategy is developed to guide EV charging. Applied to a real-world station-level scenario, the strategy navigates the trade-offs between user economy and grid stability. Simulation results show that the proposed framework simultaneously reduces economic costs by 4.17% and carbon emissions by 8.82%, while lowering the peak-valley difference by 6.46% and load variance by 11.34%. Finally, a cloud-edge collaborative deployment scheme indicates the engineering potential of the proposed approach for next-generation low-carbon energy management.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/en19020456first seen 2026-05-15 17:24:29
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