Development of the Smart Grid AI system
The Smart Grid transforms the power grid from a unidirectional system (from generator to consumer) into a bidirectional interactive system. AI manages balancing, prevents accidents, and optimizes power flows in real time.
Intelligent Metering and Analytics (AMI)
Advanced Metering Infrastructure:
Smart meters (ASKUE) transmit readings every 15–30 minutes. For a network of 1 million meters - 2–4 million measurements per hour. ML on this thread:
Non-Technical Loss (NTL) Detection – identifying electricity theft:
import numpy as np
import pandas as pd
from sklearn.ensemble import IsolationForest, RandomForestClassifier
class NTLDetector:
"""Выявление нетехнических потерь (хищений) по данным smart-счётчиков"""
def extract_features(self, meter_data, window_days=90):
"""
meter_data: 15-минутные показания счётчика за 90 дней
"""
df = meter_data.copy()
df['hour'] = df.index.hour
df['dayofweek'] = df.index.dayofweek
features = {
# Паттерны потребления
'avg_consumption': df['kwh'].mean(),
'std_consumption': df['kwh'].std(),
'night_to_day_ratio': (df[df['hour'].between(1,5)]['kwh'].mean() /
(df[df['hour'].between(9,17)]['kwh'].mean() + 1e-6)),
# Аномальные признаки хищения
'zero_consumption_days': (df.resample('D')['kwh'].sum() < 0.1).sum(),
'sudden_drop': self._detect_sudden_drop(df['kwh']),
'meter_bypass_indicator': self._check_phase_imbalance(df),
# Корреляция с соседями (anomaly relative to cluster)
'vs_cluster_zscore': 0 # заполняется при сравнении с кластером
}
return features
def _detect_sudden_drop(self, consumption_series):
"""Резкое падение потребления = возможное обходное подключение"""
monthly = consumption_series.resample('M').sum()
if len(monthly) < 3:
return 0
recent_drop = (monthly.iloc[-1] / (monthly.iloc[:-1].mean() + 1e-6))
return float(recent_drop < 0.5) # упало более чем вдвое
Load Disaggregation (NILM):
From the overall consumption profile, identify the inclusion of specific devices: - Electric boiler: characteristic step-change in load (3–6 kW) - Washing machine: cyclic pattern 1–2 hours - EV charging: flat load 7–22 kW for 4–8 hours
Application: Understanding demand structure for Demand Response management.
Optimization of Power Flows (OPF)
Optimal Power Flow:
The OPF task is to control generators and compensating devices to minimize losses while maintaining constraints (currents, voltages, powers):
- Classic OPF: Interior Point Method (MATPOWER, PowerModels.jl) - ML-OPF: neural network trained on thousands of OPF solutions → predicting near-optimal solutions in milliseconds (for RT balancing)
import torch
import torch.nn as nn
class NeuralOPF(nn.Module):
"""
Нейросетевое приближение решения OPF для Real-Time управления.
Вход: вектор нагрузок по узлам (P, Q) → Выход: уставки генераторов
"""
def __init__(self, n_buses, n_generators):
super().__init__()
self.net = nn.Sequential(
nn.Linear(n_buses * 2, 512),
nn.LayerNorm(512), nn.ReLU(),
nn.Linear(512, 256),
nn.LayerNorm(256), nn.ReLU(),
nn.Linear(256, 128), nn.ReLU(),
nn.Linear(128, n_generators * 2) # P_gen, Q_gen для каждого генератора
)
def forward(self, load_profile):
return self.net(load_profile)
Voltage and reactive power control
Volt/VAR Optimization (VVO):
Maintaining voltage in the range of 0.95–1.05 pu across all grid nodes is a key task for distribution networks with renewable energy sources. With high solar generation, overvoltage in distribution networks can occur due to: - Control of OLTC (On-Load Tap Changers) of transformers - Reactive power of solar power plant inverters (Q-capability) - Voltage regulators (capacitor banks, SVRs)
The RL agent controls all VVO devices in real time: reward = loss minimization + penalty for exceeding voltage limits.
Microgrid Management
Autonomous Microgrid:
Industrial microgrid (factory or campus) with its own sources (PVS + DGU + BESS): - Island mode: when disconnected from the main grid, balancing is performed within the microgrid - MPC: within 5–15 minutes, the horizon decides how much power to take from the grid, BESS, DGU - Cost optimization: night/day tariff + peak power charge
Energy Sharing (P2P trading):
Direct electricity trading between prosumers (producer + consumer): - Blockchain for settlements (Ethereum, Hyperledger) - Double-Auction mechanism + ML forecast of generation/consumption - Pilot projects are already operating in Germany and Australia
Smart Grid Cybersecurity
ICS/SCADA security:
MITRE ATT&CK for ICS — a library of attacks on industrial systems: - Anomaly detection: atypical SCADA commands → ML Isolation Forest - FDI (False Data Injection) attack detection: faking sensor readings - Network segmentation: OT/IT separation + traffic monitoring
Development time: 8-14 months for a full-fledged Smart Grid AI platform with AMI analytics, OPF, VVO, and Microgrid management.