Abstract
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.
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Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Abbreviations
- t:
-
Time index (h)
- i:
-
Node/location index
- PEV(t):
-
EV charging demand power delivered to vehicles
- EEV(t):
-
Energy delivered to EVs during interval (t)
- Pin(t):
-
Charger AC-side input power
- Psolar(t):
-
On-site PV generation available at time (t)
- Pgrid(t):
-
Net real-power drawn from the distribution grid
- Pbase,i(t):
-
Non-EV base electrical demand at node (i)
- PEV,i(t):
-
EV-charging load at node (i)
- Psolar,i(t):
-
PV contribution at node (i)
- APV :
-
Installed PV array capacity (rated)
- G(t):
-
Incident solar irradiance
- ηsys :
-
Aggregate PV system efficiency
- ηk(P):
-
Part-load efficiency of charger topology (k)
- ηk :
-
Rated efficiency of topology (k)
- αk :
-
Curvature factor for part-load efficiency
- ΔVi(t):
-
Voltage deviation at node (i)
- Ri :
-
Equivalent feeder resistance
- Pmax, transformer,i :
-
Maximum permissible transformer loading
- ΔVlimit :
-
Allowable voltage-deviation limit
- CAPEX:
-
Total capital expenditure
- Chardware :
-
Charger and converter hardware cost
- CEBOS :
-
Electrical balance-of-system cost
- Ccivil :
-
Civil-works cost
- EPV,annual :
-
Annual energy generated by the PV system
- Egrid,annual :
-
Annual energy imported from the grid
- Cinstall :
-
Installation and commissioning cost
- OPEXannual :
-
Annual operating expenditure
- Eannual, delivered :
-
Annual energy delivered to EVs
- CRF:
-
Capital recovery factor
- NPV:
-
Net present value over project lifetime
- Revenuey :
-
Annual charging-service revenue in year (y)
- ACoS:
-
Annualised cost of supply; reference tariff parameter used
- Solar-hour tariff window:
-
Reduced tariff multiplier (0.7 × ACoS; 09:00–16:00)
- Non-solar tariff window:
-
Higher tariff multiplier (1.3 × ACoS) outside solar hours
- F1 :
-
Cost-minimisation objective
- F2 :
-
Peak-grid-load minimisation objective
- F3 :
-
Solar-utilisation maximisation objective
- S:
-
Candidate planning configuration
- xi(t):
-
Historical demand feature sequence at node (i)
- \(\hat{\user2{P}}_{{{\text{EV}},{\text{i}}}} \left( {\text{t}} \right)\) :
-
Forecast EV-charging demand at node (i)
- CNN:
-
Convolutional feature-extraction stage
- LSTM:
-
Temporal sequence-learning stage
- LCOC:
-
Levelised cost of charging
- Mmaint :
-
Annual maintenance expenditure
- Cland :
-
Land/site-development cost component
- T:
-
Project lifetime (years)
- r:
-
Discount rate
- Nc :
-
Number of chargers installed at a given station
- Prated :
-
Rated charging power of an individual charger
- Lpeak,i :
-
Peak grid loading observed at node (i)
- CFPV :
-
Capacity factor of the PV installation
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Kotla, R.W., Anil, N., Lagudu, J. et al. Techno economic integrated planning of solar integrated electric vehicle charging infrastructure in India using an AI enabled multi objective planning framework. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37080-2
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DOI: https://doi.org/10.1038/s41598-026-37080-2
