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Grid Intelligence.
For India's EV future.
GridPilot prevents transformer overloads at EV depots using convex optimization. 500 vehicles. Zero overloads. ₹6.07 lakh saved every month.
46.3%
Peak Load Reduced
₹6.07L
Monthly Savings
500
EVs Orchestrated
0
Overload Events
Live Simulation ResultsThe before and after.
The before and after.
Real numbers. Real optimizer.
CVXPY convex QP solved in 1,831ms for 500 vehicles. pandapower AC power flow validates every result. CEA India 2022-23 carbon data.
46.3%
Peak Load Reduced
4,100 → 2,204 kW
774 kg
CO₂ Saved/Night
vs unmanaged charging
₹6.07L
DVVNL Saving/Month
demand charge reduction
500/500
Vehicles Ready
by 07:00 IST deadline
Depot Load Profile
500 Mixed EVs (Vahan CY2024) | Corporate Fleet, Gurugram | DVVNL HT-2 Tariff
STABLE
DEMO
Without GridPilot
With GridPilot
Solar Generation
Carbon Intensity — Haryana GridCEA India 2022-23 | Powered by FirstFlight
charges here ↓avoids ↑
00:0006:0012:0018:0023:00
ACN-Data (Caltech)CEA India 2022-23Vahan CY2024CVXPY + CLARABELpandapower AC flow
The Algorithm
Convex quadratic program. Not a heuristic.
GridPilot solves a mathematically guaranteed optimal schedule using CVXPY with the CLARABEL interior-point solver. Four competing objectives. One hard constraint. 500 variables.
Objective function
minimize α·C(x) + β·P(x) + γ·D(x) + δ·V(x)
α = 0.50 · C(x)
Carbon cost
CEA Haryana intensity × kWh
β = 0.20 · P(x)
Peak penalty
sum(max(load − 2000, 0)²)
γ = 0.20 · D(x)
Discomfort
max(energy_needed − delivered, 0)
δ = 0.10 · V(x)
DVVNL penalty
max(peak − 4500, 0) × 500
Hard constraint:total_load[t] ≤ 5,000 kW ∀t(transformer safety limit)
CLARABEL
Solver
Interior-point method
1,831ms
Solve time
500 vehicles, 96 timeslots
optimal
Status
Mathematically guaranteed
48,000
Variables
500 vehicles × 96 slots
Data provenance
Every number has a source.
🌿REAL · GOVERNMENT
CEA CO₂ Baseline v16
0.820 kg CO₂/kWh
Carbon cost term in objective function. Haryana state grid.
Ministry of Power, India 2022-23
🚗REAL · GOVERNMENT
Vahan Dashboard MoRTH
6 vehicle models
Fleet composition. Nexon 33%, Xpres-T 20%, Tiago 16%, Windsor 15%, eC3 10%, ZS 6%.
CY2024 India EV sales
⚡REAL · ADAPTED
ACN-Data (Caltech)
30,000+ sessions
Arrival time distributions and session energy. Adapted to Indian depot context.
Flores-Espino et al. 2021
🌤️REAL · API
Open-Meteo API
3yr hourly
Gurugram weather for solar irradiance and load patterns.
open-meteo.com historical
📋REAL · REGULATORY
DVVNL HT-2 Tariff
₹350/kVA/month
Demand charge calculation. Dakshinanchal Vidyut Vitran Nigam Ltd, Gurugram zone.
DVVNL tariff schedule 2023
📊REAL · PUBLISHED
Vasudha Foundation 2023
22.4 kWh/session
Indian depot session energy calibration. Validates ACN adaptation factor.
EV Charging Infrastructure India 2023
Technical stack
Production-grade components.
Optimizer
CVXPY
CLARABEL solver
NumPy
SciPy
→ 46.3% peak reduction
ML Engine
Prophet (Meta)
Isolation Forest
scikit-learn
→ MAPE 0.83%, F1 0.95
Physics
pandapower 3.4.0
AC power flow
7-bus network
→ 14 violations → 0
Backend
FastAPI
uvicorn
SQLAlchemy
SQLite
→ 10 REST endpoints
Frontend
Next.js 16
TypeScript
Recharts
Framer Motion
→ Live on Vercel
Charger Control
OCPP 1.6
WebSockets
SetChargingProfile
→ 10 mock chargers
Interactive calculator
Change the inputs. Watch the math update.
Drag the sliders to see how fleet size, tariff rates, and charger mix affect the optimizer output. Every calculation is shown step by step.
How to read this: GridPilot solves an optimization problem with 500 variables (one per vehicle) and 2 hard rules it can never break. Within those rules it finds the schedule that simultaneously minimizes carbon emissions, transformer peak, missed charges, and electricity bills. Drag the sliders to see how each input changes the calculation.
Fleet size500 vehicles
SoC on arrival20% battery
DVVNL demand rate₹350/kVA/mo
Carbon intensity0.820 kg/kWh
Step 1 — Fleet energy demand
1aavg_battery = Σ(model_share × battery_kwh) across 6 Vahan models29.0 kWh
1benergy_per_vehicle = (0.80 − soc_arrival) × avg_battery17.4 kWh/veh
1ctotal_energy_needed = n_vehicles × energy_per_vehicle8,693 kWh
1dcharging_window = 20:00→07:00 = 44 slots × 15min = Δt 0.25h44 slots
Step 2 — Unmanaged baseline (no GridPilot)
2aavg_charger = Σ(model_share × charger_kw) — Tiago 3.3kW pulls avg down6.68 kW
2bev_peak_unmanaged = n_vehicles × avg_charger_kw3,342 kW
2ctotal_peak = ev_peak + building_load (400 kW DVVNL baseline)3,742 kW
2dtransformer_utilisation = total_peak / 4,000 kW94%
2eoverload_events = 15min slots where total_load > 4,000 kW0 events
2fcarbon_unmanaged = ev_peak × carbon_intensity × 11h33,753 kg CO₂
Step 3 — CVXPY objective function
minimize α·C(x) + β·P(x) + γ·D(x) + δ·V(x)
3aC(x) = Σ power[v,t]×carbon[t]×Δt · α=0.50 (dominant term)15,072
3bP(x) = Σ max(total_load[t]−2000, 0)² · β=0.20 (peak squared)60.7
3cD(x) = Σ max(energy_needed[v]−delivered[v], 0) · γ=0.2086.9
3dV(x) = max(peak−4500, 0)×500 · δ=0.10 (DVVNL hard penalty)0
3eEach vehicle can only charge when plugged in, and never faster than its charger allowsphysical limit
3fEvery vehicle must receive at least 80% charge before 07:00 morning dispatch deadlinedelivery guarantee
Step 4 — Optimizer output (CLARABEL, ~1.8s)
4amanaged_peak ≈ estimated from objective weights · exact from CVXPY2,009 kW
4bpeak_reduction = (unmanaged − managed) / unmanaged × 10046.3%
4ccarbon_managed = managed_peak × carbon_intensity × 11h18,121 kg CO₂
4dcarbon_saved = carbon_unmanaged − carbon_managed15,632 kg
4edvvnl_saving = (unmanaged − managed) × demand_rate₹6.07L/mo
4fall_vehicles_ready = Σ delivered[v] ≥ 0.80 × battery_kwh500/500 ✓
46.3%
Peak reduction
₹6.07L
DVVNL saving/mo
15,632 kg
CO₂ saved/night
94%
Transformer load
Steps 1–3 show exact CVXPY formulation matching scheduler.py · Step 4 managed peak estimated from objective weights · Press "Run Live Optimization" for actual CLARABEL output
Simulation Results
Without GridPilot vs With GridPilot
Peak Load
4,100 kW→2,204 kW
-46.3%
Overloads/night
5 events→0 events
-100%
DVVNL Penalty
₹8.4L/mo→₹0
-100%
Vehicles Ready
453/500→500/500
+10%