The rapid integration of renewable energy sources into power grids has intensified the demand for advanced control strategies to ensure grid stability. Among these strategies, droop control for energy storage inverter has emerged as a critical solution for frequency and voltage regulation. However, traditional droop control methods face challenges such as excessive frequency change rates during disturbances and oscillations introduced by low-pass filters. This paper proposes an adaptive filtering time constant-based droop control strategy to address these limitations, enhancing the dynamic performance and stability of energy storage inverter.
1. Background and Challenges
Energy storage inverter play a pivotal role in modern power systems by enabling bidirectional power flow and grid support functionalities. Traditional droop control mimics the frequency/active power (P−ω) and voltage/reactive power (Q−V) characteristics of synchronous generators. The governing equations are:ω−ω0=−Kp(P−Pref)(1)V−V0=−Kq(Q−Qref)(2)
Here, Kp and Kq represent droop coefficients, while ω0 and V0 denote nominal frequency and voltage. Although effective in steady-state conditions, traditional droop control lacks inertia, leading to rapid frequency deviations during disturbances. To address this, low-pass filters are often integrated into droop control loops to introduce virtual inertia:ω−ω0=−τs+1Kp(P−Pref)(3)
Here, τ is the filter time constant. While this modification improves inertia, it introduces oscillatory dynamics due to the second-order system response.
2. Small-Signal Modeling and Stability Analysis
The closed-loop transfer function of the system with a low-pass filter is derived as:ΔPrefΔω=τs2+s+KpKδKps(4)
where Kδ=XlineVVg represents the power transmission coefficient. Root locus analysis (Figure 1) reveals that increasing τ moves the poles closer to the imaginary axis, amplifying oscillations. A fixed τ results in persistent underdamped behavior, compromising stability.
3. Adaptive Filtering Time Constant Strategy
To mitigate oscillations while preserving inertia, an adaptive filtering time constant (τ) is proposed. The time constant dynamically adjusts based on frequency deviation (Δω=ω−ω0) and its rate of change (dω/dt):τ={τ0,τ0+kΔωdtdω,∣Δω∣≤m∣Δω∣>m(5)
Here, τ0 is the nominal time constant, m is the deviation threshold, and k is the adaptation gain. Table 1 summarizes the adaptive behavior under different operating conditions.
Table 1: Adaptive Time Constant Adjustment
| Condition | Δω | dω/dt | System State | τ Adjustment |
|---|---|---|---|---|
| 1 | >0 | >0 | Approaching ω0 | Increase τ |
| 2 | >0 | <0 | Diverging | Decrease τ |
| 3 | <0 | >0 | Diverging | Decrease τ |
| 4 | <0 | <0 | Approaching ω0 | Increase τ |
4. Parameter Design Guidelines
4.1 Virtual Inertia and Damping
The virtual inertia (Jv) and damping (Dv) are related to τ and Kp:Jv=Kpτ,Dv=Kp1(6)
To ensure stability, τ0 is designed using the maximum permissible frequency gradient (max[dω/dt]) and power rating (Pmax):τ0=Kpmax[dtdω]Pmax(7)
4.2 Adaptation Gain (k)
The adaptation gain k is bounded by the minimum (τmin) and maximum (τmax) time constants:Δωdtdωmaxτmin−τ0≤k≤Δωdtdωmaxτmax−τ0(8)
4.3 Damping Ratio and Phase Margin
The damping ratio (ξ) and phase margin (γ) are critical for dynamic performance:ξ=2τKpKδ1(9)γ=arctan1+4ξ4−2ξ22ξ(10)
A damping ratio of ξ∈[0.8,1] and γ≥45∘ are recommended.
5. Simulation and Validation
A MATLAB/Simulink model of a grid-connected energy storage inverter was developed to validate the proposed strategy. Key parameters are listed in Table 2.
Table 2: Simulation Parameters
| Parameter | Value | Parameter | Value |
|---|---|---|---|
| Vdc | 800 V | ω0 | 100π rad/s |
| V0 | 310 V | τ0 | 0.24 s |
| Pref | 2 kW | k | 0.3 |
| Kp | 0.0005 rad/W | Lf | 3 mH |
| Kq | 0.0004 V/var | Cf | 30 µF |
5.1 Traditional Droop Control
Under a 4 kW step change in Pref, the traditional method exhibits a frequency spike of 0.32 Hz (Figure 2a) and rapid active power settling (Figure 2b).
5.2 Low-Pass Filter-Enhanced Droop Control
With fixed τ, oscillations emerge (Figure 3a), and the frequency deviation reduces to 0.08 Hz, but power overshoot reaches 2470 W (Figure 3b).
5.3 Adaptive Filtering Strategy
The proposed method reduces power overshoot to 600 W (Figure 4a) and limits frequency deviation to 0.06 Hz (Figure 4b), demonstrating superior damping and stability.
6. Conclusion
This paper presents an adaptive filtering time constant-based droop control strategy for energy storage inverter. By dynamically adjusting τ, the method balances inertia and damping, mitigating frequency gradients and oscillations. Simulation results confirm its effectiveness in enhancing grid stability and reducing the risk of distributed energy resource disconnection. Future work will explore real-time implementation and multi-inverter coordination.