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分散型太陽光発電を活用した持続可能なエネルギー管理のための電気自動車の双方向充電

Bi-directional charging of electric vehicles for sustainable energy management with distributed solar generation (原題)

Shang, Wen-Long, cheng, haibo

Zenodoプレプリント2026-08-26#エネルギー転換Origin: Global経営インパクト: コスト削減対象セクター: power
DOI: 10.1016/j.apenergy.2026.128478
原典: https://zenodo.org/records/22114230
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🤖 gxceed AI 要約

日本語

本研究は、電気自動車（EV）の双方向充電（V2G）と分散型太陽光発電（屋根置きPV、車両一体型PV）を統合したピーク需要最小化モデルを開発。英国のスマートメーターデータとIEEE欧州テスト配電系統を用い、V2G単独ではピーク需要を5.72%削減するのに対し、太陽光と組み合わせると14.62%削減し、配電損失も低減することを示した。不確実性分析でもPV対応V2Gが最も低い平均ピーク需要（21.73kW）を達成。

English

This study develops a peak-minimization model integrating bidirectional EV charging (V2G) with distributed solar (rooftop and vehicle-integrated PV). Using UK smart-meter data and the IEEE European test feeder, it shows V2G alone cuts peak demand by 5.72% at 50% EV penetration, while coupling with solar achieves 14.62% reduction and lowers feeder losses. Uncertainty analysis confirms PV-aware V2G yields the lowest mean peak (21.73 kW) and largest average reduction (11.58%).

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、再エネ導入拡大とEV普及に伴い、配電系統のピーク対策が課題。本研究成果は、V2Gと太陽光の協調制御による系統負荷平準化の定量的根拠を提供し、今後のスマートグリッド政策や系統運用に示唆を与える。

In the global GX context

Globally, this research contributes to the growing literature on V2G and distributed energy resources, offering a robust framework for peak management and loss reduction in low-voltage networks. It supports the integration of renewables and EVs, aligning with international efforts to decarbonize transport and electricity systems.

👥 読者別の含意

🔬研究者:Provides a validated optimization model and quantitative evidence for PV-aware V2G benefits, useful for further research on grid integration and uncertainty analysis.

🏢実務担当者:Offers practical insights for utilities and energy managers on coordinating EV charging with solar to reduce peak demand and losses, potentially informing demand response programs.

🏛政策担当者:Highlights the value of incentivizing V2G and solar integration in distribution networks, supporting policies for renewable energy and EV infrastructure.

📄 Abstract（原文）

Electrifying road transport is essential for net-zero transitions, yet large-scale residential EV charging can intensify peaks in residential low-voltage distribution networks and increase technical losses. Prior studies show that bidirectional charging (V2G) can reduce peak demand and that distributed solar generation can offset local electricity use, but these resources are often analysed separately and rarely within an integrated framework that jointly quantifies peak impacts and converter-side and network-side efficiency penalties. This study develops a peak-minimisation optimisation model that coordinates EV charging and discharging while coupling V2G with distributed solar generation, including rooftop PV and vehicle-integrated PV. The model incorporates plug-in availability, daily mobility energy requirements, state-of-charge bounds, charging/discharging power limits, UK smart-meter household demand profiles, travel-behaviour-informed EV energy needs, home-availability patterns, and the IEEE European low-voltage test feeder. Four scenarios are compared: unidirectional charging, V2G, V2G with a grid-renewable setting, and V2G with distributed solar. Results show that V2G alone reduces peak demand by 5.72% at 50% EV penetration, whereas coupling V2G with distributed solar achieves a larger reduction of 14.62% and lowers feeder I 2 R losses, despite higher conversion losses from increased energy shifting. A Monte Carlo-based uncertainty analysis further confirms that these findings remain valid under combined weather variability and stochastic EV user behaviour. Under uncertainty, the PV-aware V2G scenario achieves the lowest mean peak demand, 21.73 kW, and the largest average peak reduction, 11.58%. These findings support PV-aware coordinated charging as a practical option for peak management, feeder-loss reduction and resilient residential low-voltage energy management.

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