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Model Predictive Control for Multi-Objective Vehicle-to-Grid Dispatch: Jointly Optimizing Peak Shaving, Renewable Utilization, Battery Degradation, and Economic Revenue in Smart EV Infrastructures

スマートEVインフラにおける多目的V2Gディスパッチのモデル予測制御：ピークカット、再生可能エネルギー利用、バッテリー劣化、経済収益の同時最適化 (AI 翻訳)

Arif MAB, Deb S

Research Squareプレプリント2026-07-31#EV・輸送経営インパクト: コスト削減対象セクター: power
DOI: 10.20944/preprints202607.2314.v1
原典: https://doi.org/10.20944/preprints202607.2314.v1

🤖 gxceed AI 要約

日本語

本論文は、V2G技術を用いたEVフリートの分散型ストレージ活用において、価格追従ルールとモデル予測制御（MPC）の2つの戦略をIEEE 33バス系統で比較した。EV普及率10%、30%、50%で検証し、MPCは全ケースで系統ピークを18〜28%削減する一方、価格追従ルールは50%普及時にピークを23.7%悪化させることを示した。また、MPCの一部線路での過負荷リスクも報告している。

English

This paper compares price-following and MPC strategies for V2G dispatch on an IEEE 33-bus feeder at 10%, 30%, and 50% EV shares. MPC reduces system peak by 18-28% in all cases, while price-following worsens peak by 23.7% at 50% share. The study also reports higher worst-case line loading for MPC, providing transparent results.

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普及と系統安定化の両立が課題であり、V2Gの制御戦略は今後のスマートグリッド設計に示唆を与える。特に、再エネ導入拡大に伴う系統負荷対策として、MPCのような高度な制御が有効であることを示す点で、日本の電力システム改革や次世代送配電網構築に参考になる。

In the global GX context

Globally, V2G is seen as a key flexibility resource for integrating renewables. This paper provides a rigorous comparison of control strategies, highlighting the risks of naive price-following and the benefits of MPC, which is relevant for grid operators and policymakers designing smart charging incentives and grid codes.

👥 読者別の含意

🔬研究者:Provides a clear methodological framework for comparing V2G control strategies, with transparent reporting of trade-offs.

🏢実務担当者:Offers insights for EV fleet operators and grid operators on the importance of advanced control over simple price signals.

🏛政策担当者:Highlights the need for policies that encourage smart charging to avoid adverse grid impacts from uncoordinated V2G.

📄 Abstract（原文）

Vehicle-to-grid (V2G) technology lets a parked electric vehicle push power back to the network, so a fleet of cars can act as distributed storage. Many studies report large benefits from this, such as peak shaving and better use of renewable energy, but most describe their simulation only in words, name no test network, and compare a single charging behavior against a do-nothing case. It is then hard to separate what V2G delivers from what the control strategy delivers. This paper builds and compares two clearly defined strategies on one fully specified system. The first is a price-following rule: each vehicle charges when energy is cheap and discharges when it is dear, with no knowledge of the network. The second is a receding-horizon model predictive control (MPC) strategy that re-plans every hour and lowers the system peak, the energy cost, and the battery wear together. Both run on the IEEE 33-bus distribution feeder at low (10%), medium (30%), and high (50%) EV shares, and every hour is checked with a full AC power flow rather than an assumption. The central result is not a simple win for the smart controller. At a 50% share the price rule makes the system peak 23.7% worse than having no V2G at all, because the whole fleet reacts to one price signal at once, while the MPC cuts the peak by 18 to 28% in every case. The MPC also shows higher worst-case loading on some individual lines, and that is reported rather than hidden. Results are given as they came out of the model.

🔗 Provenance — このレコードを発見したソース

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