Development of an AI System for Renewable Energy Management
Renewable generation—sun and wind—is non-stationary: depends on weather. An AI system solves three tasks: generation forecasting, energy storage optimization, and grid balancing. Accurate forecasting reduces balancing cost and imbalance by 20-40%.
Solar Panel Generation Forecasting
Physical model:
P_solar = G_poa × η × A × (1 - β × (T_module - T_ref))
G_poa—irradiance in panel plane, η—efficiency, β—temperature coefficient.
Irradiance from NWP: GHI (Global Horizontal Irradiance) from ECMWF/GFS → conversion to POA (Plane of Array) considering panel tilt and orientation.
ML model for residuals: Physical model + ML correction = hybrid approach. LightGBM learns from:
- Cloud cover (Cloud Cover Index from satellite: EUMETSAT MSG)
- Aerosols (AOD—Aerosol Optical Depth)
- Panel temperature (not air!)
- Degradation/soiling (seasonal factor)
Accuracy: MAPE 5-8% for daily forecast.
Wind Generation Forecasting
Nonlinearity: wind turbine power ∝ V³ in operating range. 10% wind speed forecast error → 30% power forecast error.
Power Curve Model:
# Standard P-V curve from manufacturer—simplification
# ML refines: actual efficiency depends on turbulence, ramping, ice accretion
def wind_power_ml(wind_speed, wind_direction, temperature, air_density, turbulence_intensity):
features = np.array([wind_speed, np.sin(np.deg2rad(wind_direction)),
np.cos(np.deg2rad(wind_direction)), temperature,
air_density, turbulence_intensity])
return power_curve_model.predict(features.reshape(1, -1))[0]
Ensemble NWP: Multiple NWP models (ICON, GFS, ECMWF) provide different wind forecasts. BMA (Bayesian Model Averaging) for combination with uncertainty estimation.
Energy Storage Systems Management (BESS)
Battery Energy Storage System—key tool for smoothing renewable variability.
RL agent for BESS control:
State: SOC (State of Charge), forecast_solar, forecast_wind,
grid_price, demand_forecast, regulation_signal
Action: charge_rate [-1, 1] (discharge → charge)
Reward: revenue_from_arbitrage - battery_degradation_cost -
imbalance_penalty + ancillary_service_revenue
PPO or SAC for continuous action space.
BESS Tasks:
- Energy arbitrage: charge at low price → discharge at high price
- Peak shaving: cut load peaks (reduce grid power for tariff)
- Frequency regulation: FCR/aFRR (Frequency Containment Reserve)
- Smoothing renewable output: reduce variability
VPP (Virtual Power Plant)
Aggregation of many small renewable installations, BESS and flexible loads into single virtual market participant:
VPP Components:
- Rooftop solar panels of residential buildings (100 kW × 5000 objects = 500 MW)
- EV batteries (V2G—Vehicle to Grid)
- Heat pumps as controllable load
- Industrial BESS
Dispatch Optimization: Mixed-Integer Linear Programming (MILP):
Minimize: cost_of_generation + grid_cost - revenue_from_market
Subject to: power_balance, storage_constraints, ramp_rate_limits
Gurobi / CPLEX / OR-Tools for real-time solving.
Electricity Market Participation
Day-Ahead Market (DAM): Submit buy/sell bids for next day. Generation forecast → bid. Imbalance = penalty.
Intraday Market: Adjust bids when renewable forecast updates. Buy/sell decision based on expected balance.
Ancillary Services (RCM / ASVGO): BESS participates in reserve market: FCR (seconds), aFRR (minutes), mFRR (hours). ML model determines optimal bid volume per product.
Integration:
- SO UES (system operator): API for OREM bid submission
- SCADA WPP/SPP: Modbus/IEC 61850 for equipment control
- BESS BMS: CAN/Modbus protocol for charge/discharge management
Metrics:
- Forecast MAPE: < 8% for daily WPP/SPP forecast
- Imbalance reduction: 30-50% reduction vs. without ML
- Revenue uplift: additional revenue from arbitrage and reserves
Timeline: WPP/SPP generation forecast + basic BESS management—6-8 weeks. VPP with RL dispatch and market trading—5-7 months.