With the increasing capacity and structural complexity of regional power grids, the low-inertia and low-damping characteristics of microgrids composed of high-penetration distributed energy sources (e.g., photovoltaics and wind power) significantly impact grid stability. Energy storage systems utilizing voltage-source inverters (VSIs) serve as controllable power nodes, enabling voltage/frequency regulation and grid support. This paper focuses on improving energy storage inverter control strategies to achieve grid-friendly integration and enhance service quality for power systems.
1. Fundamental Control Strategies
1.1 Droop Control
The droop control strategy emulates synchronous generator characteristics through power-frequency and voltage-reactive power decoupling. The fundamental equations are:
$$
\begin{cases}
\omega = \omega_0 – m(P_e – P_0) \\
E = E_0 – n(Q_e – Q_0)
\end{cases}
$$
where \(m\) and \(n\) represent active/reactive droop coefficients. The limitations of conventional droop control are demonstrated through simulation scenarios:
| Parameter | Value |
|---|---|
| DC bus voltage | 700V |
| Switching frequency | 10kHz |
| Filter inductance | 1mH |
| Reactive droop coefficient | 1×10⁻⁵ |
1.2 Virtual Synchronous Generator (VSG) Control
The VSG control strategy replicates synchronous generator dynamics through rotor motion equations:
$$
\begin{cases}
J\frac{d\omega}{dt} = P_m – P_e – D(\omega – \omega_N) \\
\frac{d\delta}{dt} = \omega – \omega_N
\end{cases}
$$
Key parameters include virtual inertia \(J\) and damping coefficient \(D\). The power-angle relationship under grid-connected conditions is expressed as:
$$
P_e = \frac{EU_g}{X}\sin\delta
$$
2. Adaptive Control Improvements
2.1 Adaptive Droop Control
An adaptive reactive current droop control strategy is proposed to enhance voltage regulation:
$$
n_i =
\begin{cases}
k_1\left|\frac{dE}{dt}\right| + n_{min}, & \left|\frac{dE}{dt}\right| < C_{st} \\
k_2, & \left|\frac{dE}{dt}\right| \geq C_{st}
\end{cases}
$$
The small-signal stability model confirms system stability with adaptive parameters:
$$
\begin{bmatrix}
\Delta\dot{\delta} \\
\Delta\dot{i}_d \\
\Delta\dot{i}_q
\end{bmatrix}
=
\begin{bmatrix}
0 & -\frac{m}{s} & 0 \\
\frac{U_g\sin(\delta-\theta)}{Z} & -\frac{\omega_c}{s} & 0 \\
\frac{U_g\cos(\delta-\theta)}{Z} & 0 & -\frac{\omega_c}{s}
\end{bmatrix}
\begin{bmatrix}
\Delta\delta \\
\Delta i_d \\
\Delta i_q
\end{bmatrix}
$$
2.2 Hysteresis-Based Angle Control
A hysteresis control strategy prevents power angle instability during voltage sags:
$$
\delta_{ref} =
\begin{cases}
\delta_N + \Delta\delta_{max}, & \delta > \delta_N + \Delta\delta_{max} \\
\delta_N – \Delta\delta_{max}, & \delta < \delta_N – \Delta\delta_{max} \\
\delta, & \text{otherwise}
\end{cases}
$$
3. Adaptive VSG Control and Energy Storage Configuration
3.1 Dynamic Parameter Adjustment
Adaptive virtual inertia and damping coefficients are designed as:
$$
\begin{cases}
J’ = J_0 + k_j\frac{d(\omega-\omega_N)}{dt} \\
D’ = D_0 + k_dJ’\frac{d(\omega-\omega_N)}{dt}
\end{cases}
$$
| Parameter | Value |
|---|---|
| Base inertia \(J_0\) | 0.4 kg·m² |
| Inertia adjustment \(k_j\) | 260 |
| Damping adjustment \(k_d\) | 3.15 |
3.2 Energy Storage Configuration
The power and energy constraints for energy storage inverters under different damping conditions are derived:
| Damping Type | Power Constraint | Energy Constraint |
|---|---|---|
| Underdamped | \(\Delta P_{eq/max}^* = \Delta P_{eq}(t_{peak})\) | \(E_{eq}^*(t) = \int_0^t \Delta P_{eq}^*(\tau)d\tau\) |
| Critical Damped | \(\Delta P_{el/max}^* = \Delta P_{el}(t_{peak})\) | \(E_{el}^*(t) = \int_0^t \Delta P_{el}^*(\tau)d\tau\) |
| Overdamped | \(\Delta P_{eg/max}^* = \Delta P_{eg}(t_{peak})\) | \(E_{eg}^*(t) = \int_0^t \Delta P_{eg}^*(\tau)d\tau\) |
4. Simulation Verification
Comparative simulations demonstrate the effectiveness of proposed strategies:
- Adaptive droop control reduces voltage deviation by 23.8% during 200kW load switching
- Hysteresis control limits power angle oscillation within ±2° during grid faults
- Adaptive VSG control decreases frequency overshoot by 41.7% compared to conventional VSG
The configuration requirements for energy storage inverters under different damping ratios are validated through time-domain simulations, confirming the accuracy of derived power/energy constraints.
5. Conclusion
This research presents comprehensive solutions for energy storage inverter control:
- Adaptive droop control with voltage-change-rate-based parameter adjustment enhances voltage regulation
- Hysteresis-based angle limitation ensures transient stability during grid faults
- Dynamic VSG parameters improve frequency regulation while reducing power oscillations
- Configuration constraints provide theoretical guidance for energy storage system design
The proposed strategies significantly improve the grid-supporting capability of energy storage inverters, demonstrating strong potential for practical applications in modern power systems with high renewable penetration.