A Multi-Objective Framework for Cost and Carbon-Optimal Vehicle Electrification Under Grid Constraints
グリッド制約下におけるコストと炭素最適な車両電化のための多目的フレームワーク (AI 翻訳)
K. J. Mpiana, Sunetra Chowdhury
🤖 gxceed AI 要約
日本語
本研究は、コストと炭素排出の両面で最適なEV電化を実現する多目的最適化フレームワークを提案。再生可能エネルギー比率とグリッド容量制約を考慮し、電化の実行可能領域を特定。結果は、統合計画なしでは電化が排出増やグリッド違反を招くことを示し、経済的に viable な範囲で削減を達成する条件を明らかにした。
English
This study develops a multi-objective framework to optimize vehicle electrification for both cost and carbon emissions, considering grid constraints and renewable energy share. It derives conditions for EVs to outperform ICE vehicles and identifies Pareto-optimal trade-offs. Results show that without integrated planning, electrification can increase emissions and violate grid limits, but with coordinated charging and renewable support, significant reductions are achievable within economic limits.
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
日本では再エネ比率と系統制約が電化推進の鍵。本フレームワークは、日本の電力系統とEV普及計画に即した電化戦略立案に活用可能。特に、FIT/FIPや系統混雑を考慮したEV充電制御の政策設計に示唆を与える。
In the global GX context
Globally, this framework informs grid-aware electrification planning under carbon constraints. It integrates carbon pricing and charging coordination, offering a decision-support tool for policymakers and utilities to set renewable energy thresholds and avoid counterproductive outcomes.
👥 読者別の含意
🔬研究者:Provides a formal multi-objective optimization approach and closed-form conditions for carbon-optimal electrification, advancing transport-energy system modeling.
🏢実務担当者:Offers a quantitative basis for fleet electrification decisions, including trade-off analysis and feasibility mapping for cost and emission targets.
🏛政策担当者:Highlights the critical role of renewable share and grid capacity in ensuring EV emission reductions, supporting cut-off-based policy design.
📄 Abstract（原文）
Electrification of road transport is widely promoted as a pathway to reduce greenhouse gas (GHG) emissions; however, its effectiveness depends critically on electricity carbon intensity, renewable energy share, charging behavior, and grid capacity constraints. This study develops a multi-objective analytical and optimization framework to evaluate cost and carbon-optimal electric vehicles electrification by jointly minimizing system cost and carbon emissions under coupled transport–energy system conditions. A closed form cut-off condition is derived to determine the minimum renewable electricity share required for electric vehicles to achieve lower emissions than internal combustion engine vehicles, and the formulation is extended to mixed fleets including battery electric and plug-in hybrid electric vehicles. The framework integrates fleet-level emissions, electricity demand, renewable capacity limits, charging losses, carbon taxation, and peak charging constraints to define a feasible electrification region. Feasibility mapping, Monte Carlo exploration, and evolutionary multi-objective optimization are employed to characterize trade-offs between CO2 emission and total system cost, and to identify Pareto-optimal and knee point solutions. The results show that electrification without sufficient renewable support or coordinated charging can increase emissions and violate grid limits, whereas integrated planning enables significant emission reduction within economically viable regions. These findings provide a quantitative and decision-oriented basis for cut-off-informed and grid-aware electrification planning in carbon-constrained power systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/wevj17060291first seen 2026-06-19 04:40:57
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