AI for Energy Transition & Sustainability

Energy Mix Modeling: India's Path to 500 GW Non-Fossil by 2030

India's energy transition is not a decarbonization story — it is a growth-plus-decarbonization story. Total electricity demand will double from ~1,500 TWh (2023) to ~3,000 TWh (2032). The non-fossil target (500 GW by 2030, including 280 GW solar + 140 GW wind + 15 GW small hydro + 10 GW biomass + nuclear/large hydro) must be achieved while simultaneously adding 40-50 GW of thermal capacity to meet baseload growth.

Open data/energy-mix-projections.json — scenario-based energy mix projections from 2024 to 2035 under different policy, cost, and demand assumptions.

Capacity Expansion Optimization

The fundamental optimization: what mix of generation, storage, and transmission to build to meet projected demand at minimum system cost while meeting emission and renewable targets.

Decision variables (per year, per region):
  new_solar_gw, new_wind_gw, new_thermal_gw
  new_battery_storage_gwh (4-hour, 8-hour)
  new_pumped_hydro_gw
  new_transmission_gw_km (inter-regional corridors)
  thermal_retirement_gw

Objective: minimize total system cost (capital + fuel + O&M + transmission)

Constraints:
  meet demand in each time block (8,760 hours/year)
  renewable purchase obligation (RPO) targets
  emission intensity targets (CEA trajectory)
  land availability for solar/wind (state-wise limits)
  coal supply constraints (CIL production, rail logistics)
  grid integration limits (25% instantaneous renewable penetration → increasing)

Traditional capacity expansion uses MILP (Mixed Integer Linear Programming) with representative days. AI enhances this in two ways:

Regional Imbalance: RE-Rich vs Load-Rich

India's renewable resources and load centres are geographically mismatched:

AI models optimize inter-regional power flow to minimize transmission losses and congestion costs. The Green Energy Corridor Phase II (₹12,031 crore) adds 10,753 ckm of transmission specifically for RE evacuation — the routing and capacity decisions benefit from load flow optimization that accounts for variable RE generation patterns.

Green Hydrogen Economics: Electrolyzer Optimization

India's National Green Hydrogen Mission (₹19,744 crore) targets 5 MMTPA green hydrogen production by 2030. Current economics: green hydrogen costs ₹350-450/kg in India versus ₹150-200/kg for grey hydrogen (SMR with Indian natural gas prices). The gap must close to ₹250/kg for industrial adoption.

Open data/green-hydrogen-data.csv — electrolyzer performance data: stack voltage, current density, hydrogen output, efficiency, water consumption, and degradation metrics over time.

Electrolyzer Stack Optimization

Alkaline and PEM electrolyzers have nonlinear efficiency curves — efficiency peaks at 30-70% of rated capacity and drops at both extremes. When powered by variable renewable energy, the operating point fluctuates continuously.

Electrolyzer optimization model:
  Inputs:
    renewable_power_available_kw (15-minute intervals)
    electricity_price_inr_per_kwh (if grid-connected)
    hydrogen_storage_level_kg
    hydrogen_demand_schedule_kg_per_hour
    stack_temperature, stack_voltage, current_density
    stack_age_hours (degradation tracking)

  Decision variables:
    electrolyzer_power_setpoint (per 15-min interval)
    grid_import_export_kw (if hybrid RE + grid)
    hydrogen_production_vs_storage_dispatch

  Objective: minimize levelized cost of hydrogen (₹/kg)

  Constraints:
    meet hydrogen demand schedule
    stack temperature limits (60-80°C for alkaline)
    minimum turndown ratio (20% for PEM, 40% for alkaline)
    maximum ramp rate (°C/min thermal constraint)
    stack efficiency > minimum threshold

Cost Curve Analysis

The levelized cost of green hydrogen depends on three factors in roughly equal proportion:

LCOH breakdown (Indian conditions, 2024):
  Renewable electricity: ₹150-180/kg (at ₹2.5-3.0/kWh, 55 kWh/kg)
  Electrolyzer CAPEX: ₹100-150/kg (at $600-800/kW, 4000h/year CUF)
  BOS + water + O&M: ₹50-80/kg
  Total: ₹300-410/kg

Sensitivity analysis (ML-generated scenarios):
  RE cost ₹2.0/kWh + electrolyzer $400/kW + 5000h CUF → ₹200/kg (achievable by 2028)
  RE cost ₹1.5/kWh + electrolyzer $250/kW + 6000h CUF → ₹140/kg (target for 2030)

For Reliance's green hydrogen initiative (targeting refinery hydrogen replacement at Jamnagar) and Adani's green hydrogen export plans (Mundra), the optimization is site-specific: co-locating electrolyzers with hybrid solar-wind achieves higher CUF (5,000-6,000 hours vs 2,000-2,500 for solar-only) and lower LCOH.

AI models optimize the RE-electrolyzer sizing ratio, storage capacity (buffer tank vs compressed storage vs pipeline), and operating strategy to minimize LCOH under site-specific RE resource profiles.

ESG Reporting: BRSR, CDP, SBTi, TCFD for Indian Energy Companies

Indian energy companies face a reporting burden that is escalating rapidly:

Open data/esg-reporting-metrics.json — ESG data collection framework: emission scopes 1/2/3, water consumption, waste generation, energy intensity, safety metrics, and social indicators mapped to BRSR, CDP, and GRI standards.

Automated Emission Calculation

Scope 1 and 2 emissions are conceptually straightforward but operationally complex at scale. A refinery has 200+ emission sources (process heaters, flares, fugitives, wastewater treatment, storage tanks). A power utility has 50+ generating units across multiple fuels.

Emission automation pipeline:
  Data sources:
    DCS/SCADA (fuel consumption, process data — real-time)
    Fuel quality lab (GCV, carbon content — daily/per-shipment)
    CEMS (continuous emission monitoring — real-time for SO2/NOx/PM)
    Procurement records (Scope 2: grid electricity, Scope 3: purchased fuels)

  Calculation engine:
    IPCC emission factors × activity data (default method)
    Mass balance approach (for process emissions — refineries, fertilizers)
    Direct measurement (CEMS data for large sources)

  ML contributions:
    Gap filling: predict emissions during CEMS downtime from DCS data
    Reconciliation: flag inconsistencies between fuel input and emission output
    Forecasting: predict quarterly emissions from production plan
    Scope 3 estimation: supply chain emissions from procurement data + industry averages

Climate Scenario Analysis (TCFD)

TCFD requires scenario analysis under at least two climate scenarios. For Indian energy companies, the relevant scenarios are:

ML models quantify financial impact under each scenario:

For NTPC (example):
  Revenue impact: f(electricity_demand, RE_share, carbon_price, thermal_retirement_schedule)
  Asset impairment: NPV of thermal fleet under each carbon price trajectory
  Transition opportunity: RE + storage + green hydrogen revenue potential
  Physical risk: plant-level exposure to water stress, heat waves, flooding

For NTPC's green pivot (targeting 60 GW RE by 2032), the scenario analysis quantifies the stranded asset risk of the existing 56 GW thermal fleet versus the growth opportunity in RE, storage, and green hydrogen — enabling board-level investment decisions with quantified climate risk.

Grid Integration Challenges for Variable Renewable Energy

As India approaches 25-30% instantaneous RE penetration, grid management challenges intensify:

Ramp Rate Management

The "duck curve" is already visible in southern and western grids. Evening ramp rates (solar generation drops while demand rises) exceed 10 GW/hour in some scenarios. Thermal plants designed for baseload operation cannot ramp faster than 1-3% per minute.

AI-based dispatch optimization balances:

Frequency Regulation

India's grid frequency target is 50.00 Hz ±0.05 Hz. Variable RE generation introduces sub-second frequency perturbations that conventional AGC (Automatic Generation Control) cannot track. ML-based frequency prediction (5-30 second ahead) enables predictive ancillary service dispatch — activating battery or hydro response before the frequency deviation exceeds the dead band.

Key Takeaways