Resilient EV charging station network design using AI algorithms
AIアルゴリズムを用いたレジリエントなEV充電ステーション網の設計 (AI 翻訳)
Somasundaram, Deepa, Krishnamoorthy, N., Anand, J. Vijay, Priyanka, R., Krishnan, T. Santhana, Dhandapani, Kirubakaran
🤖 gxceed AI 要約
日本語
本論文は、LSTMによる時空間需要予測、GA-PSOによる多目的最適化、深層強化学習による適応的レジリエンスを統合したAI駆動型EV充電ステーション配置フレームワークを提案する。評価の結果、従来手法と比較してレジリエンス指数0.92、移動距離54%削減、設置コスト16%削減、ピークグリッド依存度18%低減を達成した。
English
This paper proposes an AI-driven framework for EV charging station placement integrating LSTM-based spatiotemporal demand forecasting, GA-PSO multi-objective optimization, and deep reinforcement learning for adaptive resilience. Evaluation achieves a resilience index of 0.92, travel distance reduction of 54%, installation cost reduction of 16%, and peak grid dependency reduction of 18% compared to conventional methods.
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普及目標（2035年までに新車販売100%電動化）に伴い、充電インフラの効率的配置が急務となっている。本提案は都市部のグリッド制約下でAI最適化を行うため、日本各地の自治体や電力会社が導入を検討する価値がある。
In the global GX context
As global EV adoption accelerates, optimal charging station placement is critical for grid stability and user convenience. This AI-driven framework offers scalable solutions for smart cities, integrating renewable energy and handling grid disturbances, directly relevant to ISSB and TCFD-aligned infrastructure planning.
👥 読者別の含意
🔬研究者:The hybrid AI framework combining LSTM, GA-PSO, and DRL provides a novel methodology for resilient infrastructure optimization that can be extended to other urban systems.
🏢実務担当者:The quantifiable reductions in installation cost (16%) and travel distance (54%) offer clear ROI for companies deploying EV charging networks.
🏛政策担当者:The demonstrated improvements in grid stability and renewable integration support policy goals for EV infrastructure and decarbonization.
📄 Abstract（原文）
This paper proposes an AI-driven resilient network design framework for optimal electric vehicle (EV) charging station placement under stochastic demand and dynamic grid constraints. The proposed approach uniquely integrates long short-term memory (LSTM) based spatiotemporal demand forecasting with a hybrid genetic algorithm–particle swarm optimization (GA–PSO) model for multi-objective station placement. In addition, a deep reinforcement learning (DRL) agent is incorporated to enhance adaptive resilience under real-time grid disturbances. The framework minimizes installation cost, reduces user travel distance, and improves grid stability while ensuring equitable accessibility. The model is evaluated under multiple scenarios, including peak demand, station outages, renewable intermittency, and grid capacity reduction. Results demonstrate that the proposed hybrid AI framework achieves a resilience index of 0.92, reduces travel distance by 54%, and lowers installation cost by up to 16% compared to conventional approaches such as linear programming (LP) and K-means clustering. The integration of renewable energy further reduces peak grid dependency by 18%. The proposed methodology provides a scalable and practical solution for designing sustainable and resilient EV charging infrastructure in smart urban environments.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20639680first seen 2026-06-12 04:18:59
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