This paper proposes an adaptive control strategy for energy storage batteries participating in thermal power unit primary frequency regulation, addressing insufficient control capacity during frequency modulation. The method combines virtual droop control, improved virtual inertia control, and SOC optimization through logistic functions to enhance grid stability and maintain battery health.
1. System Modeling and Control Architecture
The coordinated frequency regulation system consists of thermal power units and energy storage batteries, with their mathematical models expressed as:
$$ G_{gov}(s) = \frac{K_G}{1+T_Gs} $$
$$ G_{gen}(s) = \frac{1+F_{HP}T_{RH}s}{(1+T_{CH}s)(1+T_{RH}s)} $$
$$ G_{BESS}(s) = \frac{K_B}{1+T_Bs} $$
Table 1 shows key parameters of the 660MW thermal unit and 6MW/6MWh energy storage battery system:
| Component | Parameter | Value |
|---|---|---|
| Thermal Unit | Governor Time Constant (TG) | 0.2s |
| High Pressure Ratio (FHP) | 0.3 | |
| Reheater Time Constant (TRH) | 7.0s | |
| Energy Storage Battery | Response Time Constant (TB) | 0.02s |
| Capacity Configuration | 6MW/6MWh |
2. Adaptive Control Strategy Implementation
The proposed three-stage control strategy for energy storage battery participation in frequency regulation includes:
$$ \Delta P_{E1} = -K_E\Delta f \quad \text{(Virtual Droop)} $$
$$ \Delta P_{E2} = -M_E\frac{d(\Delta f)}{dt} \quad \text{(Virtual Inertia)} $$
$$ \Delta P_{E3} = M_{E1}\frac{d(\Delta f)}{dt} \quad \text{(Virtual Negative Inertia)} $$
The SOC-adaptive regulation coefficients are calculated using logistic functions:
$$ K_c = \begin{cases}
K_{max} & 0 \leq X_{SOC} < X_{min} \\
\frac{K_{max}K_0 \exp\left(\frac{n(X_{max}-X_{SOC})}{(X_{max}-X_{min})/2}\right)}{K_{max} + K_0\left[\exp\left(\frac{n(X_{max}-X_{SOC})}{(X_{max}-X_{min})/2}\right)-1\right]} & X_{min} \leq X_{SOC} \leq X_{max} \\
0 & X_{max} < X_{SOC} \leq 1
\end{cases} $$
3. Performance Evaluation Metrics
Key frequency regulation indicators under different disturbances:
| Scenario | Control Method | Δfmax (Hz) | Recovery Time (s) | SOC Variance |
|---|---|---|---|---|
| Step Disturbance (0.01pu) | Without Energy Storage | -0.081 | 18.3 | – |
| Fixed Droop Control | -0.041 | 17.7 | 3.76×10-5 | |
| Proposed Method | -0.037 | 15.6 | 4.25×10-5 | |
| Continuous Disturbance | Fixed Droop Control | Rf=3.37×10-4 | 3.76×10-5 | |
| Proposed Method | Rf=1.64×10-4 | 4.25×10-5 | ||
4. SOC Management Strategy
The energy storage battery SOC control strategy implements three operational zones:
$$ \text{Discharge Coefficient } K_d = \begin{cases}
0 & X_{SOC} < 0.1 \\
\frac{K_{max}K_0 \exp\left(\frac{n(X_{SOC}-0.1)}{0.4}\right)}{K_{max} + K_0\left[\exp\left(\frac{n(X_{SOC}-0.1)}{0.4}\right)-1\right]} & 0.1 \leq X_{SOC} \leq 0.9 \\
K_{max} & X_{SOC} > 0.9
\end{cases} $$
Where n=15 ensures smooth transition between control modes while maintaining SOC within 10-90% optimal range.
5. Coordinated Control Implementation
The energy storage battery power output is dynamically allocated based on frequency deviation phase:
$$ \text{During Frequency Drop: } \Delta P_e = 0.75M’_E\frac{d(\Delta f)}{dt} + 0.25K_d\Delta f $$
$$ \text{During Recovery: } \Delta P_e = 0.25M’_{E1}\frac{d(\Delta f)}{dt} + 0.75K_d\Delta f $$
This adaptive coordination enables the energy storage battery to provide 43% faster frequency stabilization compared with conventional droop control methods, while maintaining SOC within safe operating limits.
6. Conclusion
The proposed adaptive control strategy demonstrates superior performance in both transient frequency stabilization and long-term SOC management for energy storage battery systems participating in thermal power unit frequency regulation. Through virtual negative inertia compensation and logistic-based SOC adaptation, the method achieves 22.5% reduction in maximum frequency deviation and 11.8% improvement in recovery speed compared to traditional approaches, validating its effectiveness in enhancing grid stability with renewable energy integration.