AI Microgeneration Management System Development

Developing AI System for Microgeneration Management

Microgeneration is solar panels, small wind turbines, and mini-CHP units at consumer premises. In Russia it has been permitted since 2021 (Federal Law 471): excess power can be sold to the grid. AI optimizes use of own generation and manages the grid connection point.

Forecasting Generation and Consumption

Microgeneration task:

Every 15 minutes the system must decide: sell excess to grid or charge BESS; buy from grid or discharge BESS. The decision depends on forecast of:

• Own generation in coming hours (sun/wind)
• Facility consumption in coming hours
• Tariff curve (differential rates: night/day/peak)

import numpy as np
import pandas as pd
from prophet import Prophet

class MicrogenerationForecaster:
    """Forecasting generation and consumption for facility with microgeneration"""

    def fit_solar_model(self, historical_generation, capacity_kw):
        """Solar generation forecast using Prophet"""
        df = historical_generation.reset_index()
        df.columns = ['ds', 'y']
        df['y'] = df['y'] / capacity_kw  # normalize to capacity → capacity factor

        model = Prophet(
            changepoint_prior_scale=0.05,
            seasonality_mode='multiplicative',
            yearly_seasonality=10,
            daily_seasonality=True,
            weekly_seasonality=False  # sun doesn't depend on day of week
        )
        model.fit(df)
        return model

    def predict_next_day(self, solar_model, load_model, weather_forecast):
        """Combined forecast for next 24 hours"""
        future = solar_model.make_future_dataframe(periods=96, freq='15min')

        # Add weather regressor
        future = future.merge(weather_forecast[['ds', 'ghi', 'temperature']],
                             on='ds', how='left')

        solar_forecast = solar_model.predict(future)
        load_forecast = load_model.predict(future)

        return pd.DataFrame({
            'timestamp': future['ds'],
            'solar_kw': solar_forecast['yhat'].clip(0) * self.capacity_kw,
            'load_kw': load_forecast['yhat'].clip(0),
            'net_kw': (solar_forecast['yhat'] - load_forecast['yhat']).clip(-self.max_load)
        })

Battery Energy Storage Optimization

Charge/discharge strategy:

Stochastic optimization problem with 24–48 hour horizon:

from scipy.optimize import linprog
import numpy as np

def optimize_bess_schedule(
    solar_forecast,   # kW per hour
    load_forecast,    # kW per hour
    tariff_grid_buy,  # rubles/kWh per hour (grid purchase)
    tariff_grid_sell, # rubles/kWh per hour (grid sale)
    bess_capacity_kwh=10,
    bess_max_power_kw=5,
    bess_soc_init=0.5,
    bess_soc_min=0.1,
    bess_soc_max=0.9,
    bess_efficiency=0.9
):
    """
    BESS schedule optimization.
    Variables: [p_charge_t, p_discharge_t, p_grid_buy_t, p_grid_sell_t] × 24h
    """
    T = len(solar_forecast)

    # Linear programming (LP)
    # Simplification: don't account for binary constraints on simultaneous charge/discharge
    # Variables: [charge[0..T], discharge[0..T], buy[0..T], sell[0..T], soc[0..T]]
    n_vars = T * 4 + T  # charge, discharge, buy, sell, SoC

    c = np.zeros(n_vars)
    # Minimize cost: purchase cost - sales revenue
    for t in range(T):
        c[2*T + t] = tariff_grid_buy[t]    # purchase — expense
        c[3*T + t] = -tariff_grid_sell[t]  # sale — revenue (negative)

    # Constraints (simplified)
    # SoC balance, power constraints, SoC bounds

    result = linprog(c, method='highs')  # HiGHS solver
    return result

Zero-Export Control Management

Zero-Export Control:

If grid sell tariff is unprofitable or absent — prevent grid injection:

• Forecast of excess → increase controllable load (water heater, pump, EV charging)
• When unable to absorb → reduce solar inverter power (curtailment)
• Fast control (100ms cycle): current measurement at connection point → PID control of inverter output power

Aggregation for Demand Response Programs

VPP aggregator:

Hundreds of private microgeneration units → aggregator combines into Virtual Power Plant:

• Forecast of total aggregated export
• Participation in balancing market
• Payment distribution among participants by contribution (Shapley Values)

Monitoring and Diagnostics

Panel degradation assessment:

• Monitor Performance Ratio (PR): expected vs. actual generation
• PR decline of 0.5%/year — normal; >1.5%/year — degradation or soiling
• I-V curve analysis: identification of shading, bypass diode failure

Development timeline: 2–4 months for microgeneration management system with forecasting, BESS optimization, and Zero-Export control.