Real-Time Grid Balancing with Renewable Integration

Module: Sustainable Tech | Difficulty: Premium

Frequency Dynamics

AGC

Comparison

Python Implementation

import numpy as np
from scipy.integrate import odeint

class GridBalancer:
    def __init__(self, inertia=5, damping=1):
        self.H = inertia
        self.D = damping

    def frequency_dynamics(self, state, t, p_mech, p_elec):
        dw = (p_mech - p_elec - self.D * state[0]) / (2 * self.H)
        return [dw]

    def optimize_storage_dispatch(self, renewable, demand, storage_capacity, soc_init):
        n = len(renewable)
        soc = np.zeros(n + 1)
        soc[0] = soc_init
        dispatch = np.zeros(n)
        for t in range(n):
            surplus = renewable[t] - demand[t]
            if surplus > 0:
                charge = min(surplus, storage_capacity - soc[t])
                dispatch[t] = -charge
                soc[t+1] = soc[t] + charge
            else:
                discharge = min(-surplus, soc[t])
                dispatch[t] = discharge
                soc[t+1] = soc[t] - discharge
        return dispatch, soc

Research Insight: Virtual power plants can provide grid balancing at 40-60% lower cost than traditional peaker plants.