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<p>India’s transition to electric mobility demands charging infrastructure that is cost-efficient, grid-compatible, and capable of integrating solar generation. Existing studies typically examine demand forecasting, PV utilisation, charging-topology behaviour, and economic viability in isolation, limiting their relevance for large-scale deployment. This work proposes a unified co-design framework that jointly optimises charging-station siting, charger sizing, PV allocation, and operational economics under India’s tariff structure. Hourly EV demand is predicted using a hybrid forecasting model that combines Temporal Fusion Transformers with Graph Neural Networks to capture spatial and temporal variations. Solar-generation modelling, topology-based charger efficiencies, and distribution-grid constraints are incorporated into a techno-economic formulation. A multi-objective optimisation approach (NSGA-II) identifies configurations that minimise cost, reduce peak grid loading, and maximise solar utilisation. The framework is demonstrated using a representative mixed urban–highway region. Results show a 28–35% reduction in peak grid load, a 40–70% improvement in utilisation, and a 12–18% decrease in the levelised cost of charging compared with non-optimised deployments. The findings highlight the importance of integrated planning that aligns solar availability, demand behaviour, and tariff incentives. The proposed methodology offers a scalable decision-support tool for policymakers, utilities, and private developers planning future EV charging networks in India.</p>

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Techno economic integrated planning of solar integrated electric vehicle charging infrastructure in India using an AI enabled multi objective planning framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月27日

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  • Rahul Wilson Kotla,
  • Nimmakanti Anil,
  • Jayavani Lagudu,
  • T. Dinesh,
  • Bolla Kavya

摘要

India’s transition to electric mobility demands charging infrastructure that is cost-efficient, grid-compatible, and capable of integrating solar generation. Existing studies typically examine demand forecasting, PV utilisation, charging-topology behaviour, and economic viability in isolation, limiting their relevance for large-scale deployment. This work proposes a unified co-design framework that jointly optimises charging-station siting, charger sizing, PV allocation, and operational economics under India’s tariff structure. Hourly EV demand is predicted using a hybrid forecasting model that combines Temporal Fusion Transformers with Graph Neural Networks to capture spatial and temporal variations. Solar-generation modelling, topology-based charger efficiencies, and distribution-grid constraints are incorporated into a techno-economic formulation. A multi-objective optimisation approach (NSGA-II) identifies configurations that minimise cost, reduce peak grid loading, and maximise solar utilisation. The framework is demonstrated using a representative mixed urban–highway region. Results show a 28–35% reduction in peak grid load, a 40–70% improvement in utilisation, and a 12–18% decrease in the levelised cost of charging compared with non-optimised deployments. The findings highlight the importance of integrated planning that aligns solar availability, demand behaviour, and tariff incentives. The proposed methodology offers a scalable decision-support tool for policymakers, utilities, and private developers planning future EV charging networks in India.