AI Energy Grid Digital Twin System

AI-powered digital twin of the energy grid

A digital twin of the power grid—from the local substation to the regional control center—enables modeling of operating modes, failure prediction, and real-time load optimization. The result: reduced failure rates, shorter restoration times, and savings on load balancing.

Twin Architecture for the Power Grid

Modeling levels:

• Topology model: network graph (nodes = buses, edges = lines/transformers) with electrical parameters
• State estimation: actual network state from SCADA telemetry
• Power flow model: calculation of currents and voltages for the current configuration
• Predictive layer: forecasting load, renewable energy generation, and failure probability

Real-time data:

grid_telemetry = {
    # SCADA каждые 4-15 секунд
    'voltage_kv': {bus_id: voltage for bus_id in buses},
    'current_a': {line_id: current for line_id in lines},
    'active_power_mw': {node_id: p for node_id in nodes},
    'reactive_power_mvar': {node_id: q for node_id in nodes},
    'transformer_load_pct': {trafo_id: load_pct for trafo_id in transformers},
    'breaker_status': {breaker_id: 'open'/'closed' for breaker_id in breakers}
}

State Estimation (SE): The classic algorithm (WLS — Weighted Least Squares) corrects SCADA measurements with errors → optimal network state assessment. ML addition: detection of bad data points (telemetry from faulty sensors).

Power Flow and performance analysis

Gauss-Seidel / Newton-Raphson Power Flow: Python libraries used:

• pandapower: an open source tool for analyzing distribution networks
• PyPSA (Python for Power System Analysis): for planning and optimization
• GridLAB-D: detailed distribution grid simulation

import pandapower as pp

net = pp.from_json('grid_topology.json')

# Обновление данных из SCADA
for gen_id, p_mw in scada_generation.items():
    net.gen.at[gen_id, 'p_mw'] = p_mw
for load_id, p_mw in scada_loads.items():
    net.load.at[load_id, 'p_mw'] = p_mw

# Расчёт потоков мощности
pp.runpp(net, algorithm='nr')  # Newton-Raphson

# Результаты
overloaded_lines = net.res_line[net.res_line['loading_percent'] > 100]
undervoltage_buses = net.res_bus[net.res_bus['vm_pu'] < 0.95]

Load and Generation Forecasting

Short-Term Load Forecasting (STLF):

from statsforecast.models import AutoARIMA, AutoETS
from sklearn.ensemble import GradientBoostingRegressor

# Горизонт 15 мин - 48 часов
load_features = {
    'hour_sin': np.sin(2 * np.pi * hour / 24),
    'hour_cos': np.cos(2 * np.pi * hour / 24),
    'weekday': day_of_week,
    'temperature': weather_forecast['temp'],
    'humidity': weather_forecast['humidity'],
    'holidays': is_holiday,
    'load_lag_24h': load_24h_ago,
    'load_lag_7d': load_7d_ago
}

# Ensemble: ARIMA + GBT + LSTM
forecast_ensemble = (
    arima_forecast * 0.3 +
    gbt_forecast * 0.4 +
    lstm_forecast * 0.3
)

Solar Power Generation Forecast:

def solar_forecast(irradiance_forecast, pv_capacity_mw, temperature_forecast):
    """
    Модель: физическая деградация при высокой температуре панелей
    PR (Performance Ratio) снижается ~0.4% на °C выше 25°C
    """
    temperature_coeff = 1 - 0.004 * max(0, temperature_forecast - 25)
    pv_generation = irradiance_forecast * pv_capacity_mw * pr_baseline * temperature_coeff
    return pv_generation

Wind Power: ML power curve wind → generation. Nonlinear relationship with cut-in (3-4 m/s), rated (12-15 m/s), and cut-out (25 m/s) speeds.

Predictive equipment analytics

Transformers are a hot spot:

def transformer_health_index(oil_diagnostics, load_history, age_years):
    """
    Метод МЭК 60422 + ML-дополнение
    Газовый анализ масла (DGA): H₂, CH₄, C₂H₂, C₂H₄, CO
    Rogers ratio / Duval triangle → классификация дефекта
    """
    # Классические диагностические признаки
    duval_zone = classify_duval_triangle(
        oil_diagnostics['c2h2'], oil_diagnostics['c2h4'], oil_diagnostics['ch4']
    )

    # ML-модель на временных рядах DGA + термин + нагрузка
    rul_prediction = lstm_transformer_model.predict(
        np.column_stack([dga_history, load_history, temperature_history])
    )

    return {
        'health_index': rul_prediction['health_score'],
        'defect_type': duval_zone,
        'predicted_remaining_life_years': rul_prediction['rul_years'],
        'recommended_action': get_recommendation(rul_prediction)
    }

High voltage switches:

• Shut-off time (operating time) is an indicator of mechanism degradation
• Contact wear by number of disconnections × current level
• SF6 pressure monitoring – gas leaks

Cable lines: Partial discharge is an early sign of insulation degradation. Monitoring of high-frequency current transformers.

Operational management and optimization

OPF (Optimal Power Flow): Minimizing network losses while observing restrictions (voltage, line loading):

import pyomo.environ as pyo

def optimal_power_flow(network, generation_costs, load_forecast):
    model = pyo.ConcreteModel()

    # Переменные: активная и реактивная мощность генераторов
    model.p_gen = pyo.Var(generators, domain=pyo.NonNegativeReals)
    model.q_gen = pyo.Var(generators, domain=pyo.Reals)

    # Целевая функция: минимизация стоимости генерации + потерь
    model.obj = pyo.Objective(
        expr=sum(generation_costs[g] * model.p_gen[g] for g in generators),
        sense=pyo.minimize
    )

    # Ограничения: баланс мощности, ограничения по напряжению и токам
    # ...
    solver = pyo.SolverFactory('ipopt')
    result = solver.solve(model)
    return model

Automatic Configuration Management (Reconfiguration): In case of line overload or failure, an algorithm searches for the optimal reconfiguration (radial topology without island sections). Graph algorithms + constrained optimization.

Demand Response: Flexible load management (industrial consumers) for peak balancing. Optimization: who and when to send a DR signal, taking into account responsiveness and economic incentives.

Integration with the control center

SCADA/EMS (Energy Management System): Two-way API: data from SCADA → Digital Twin (network status), recommendations from Twin → dispatcher displays.

ICCP (Inter-Control Center Communications Protocol): Standard for data exchange between control centers of different levels (PAO FGC UES, regional RGCs).

Compliance:

• NERC CIP (North America) / ENTSO-E (Europe): Cybersecurity and Reliability Requirements
• Russian standards: PUE, GOST R for relay protection and automation

Timeframe: SCADA connection, topology model, state estimation, and basic power flow — 6-8 weeks. Short-term load/generation forecast, predictive transformer maintenance, and OPF optimizer — 4-5 months. Full Digital Twin with DR, reconfiguration, cable diagnostics, and EMS integration — 7-10 months.