With the continuous expansion of grid-connected clean energy sources such as wind and solar power, the proportion of power generation from conventional thermal power units has gradually decreased. This change indirectly affects the stability of power system frequency, making it a current research hotspot to find new frequency regulation methods to assist traditional generators in quickly suppressing fluctuations and improving the overall frequency regulation capability of the grid. Energy storage technology is currently recognized as one of the effective means to solve the problem of grid frequency regulation. As a key energy storage technology, battery-based energy storage, particularly lithium-ion energy storage cell systems, offers advantages such as fast response and high precision. This paper proposes a comprehensive control strategy that considers both the frequency stability of the power grid and the state of charge (SOC) of the energy storage cell to enhance its performance in primary frequency regulation.
This study addresses several shortcomings in existing research. First, many studies have not considered the impact of the SOC of the energy storage cell on the stability of primary frequency regulation, and their control methods are singular. Second, integrated control methods that consider SOC often fail to effectively combine different control strategies with an appropriate proportional relationship. Third, the dead-band range for the frequency regulation of the energy storage cell system should be smaller than that of traditional units to improve economic efficiency, a factor often overlooked in previous work. This paper develops a model for the power system and the battery energy storage system, examines virtual droop and virtual inertia control strategies, and introduces a comprehensive control strategy that adjusts the output power based on the SOC of the energy storage cell. The effectiveness of this proposed strategy is validated through simulation analysis.
Model Construction
Traditional Power System Primary Frequency Regulation Model
The traditional primary frequency regulation model of the power system consists of a steam turbine governor model, a steam turbine model, and a generator-load model. The transfer function of the steam turbine governor is:
$$ \Delta P_v(s) = \frac{1}{1+\tau_g s} \Delta P_n(s) $$
where \( \Delta P_v \) is the power increment corresponding to the steam valve change, \( \Delta P_n \) is the change in electromagnetic power, and \( \tau_g \) is the inertia time constant of the governor. The control signal is defined as:
$$ \Delta P_n(s) = \Delta P_c(s) – \frac{1}{R} \Delta f(s) $$
Here, \( \Delta P_c \) is the control signal of the power system, \( R \) is the droop coefficient of the governor, and \( \Delta f \) is the frequency deviation. The transfer function of the reheat steam turbine is:
$$ G_T(s) = \frac{\Delta P_m(s)}{\Delta P_v(s)} = \frac{1+K_r \tau_r s}{(1+\tau_T s)(1+\tau_r s)} $$
In this equation, \( G_T \) is the transfer function of the prime mover, \( \tau_T \) is the time constant of the prime mover, \( \tau_r \) is the reheat time constant, and \( K_r \) is the reheat coefficient. The generator-load transfer function is represented by the following block diagram, where the mechanical power change \( \Delta P_m \), the load change \( \Delta P_L \), the inertia constant \( M = 2H \), the damping coefficient \( D \), and the angular speed deviation \( \Delta \omega \) are key parameters. In a synchronous AC power system, the relationship between grid frequency and generator angular speed is \( f = \omega/(2\pi) \), leading to \( \Delta f = \Delta \omega \) in per-unit values.
Battery Energy Storage System Model
The model of the battery energy storage system includes a dead-band block, the transfer function of the storage system, and the SOC calculation block. To simplify the simulation without sacrificing accuracy, the energy storage cell is represented by a first-order inertia model, which is suitable for its fast charging and discharging response. The transfer function is:
$$ G_B(s) = \frac{1}{1+\tau_B s} $$
where \( \tau_B \) is the time constant of the energy storage cell system. This model effectively captures the dynamic behavior of the energy storage cell when participating in grid frequency regulation.
Control Strategy for Energy Storage Cell Participation in Primary Frequency Regulation
Virtual Droop and Virtual Inertia Control Strategies
When the load in the power system changes, the grid frequency fluctuates. The energy storage cell participates in primary frequency regulation by providing or absorbing power to compensate for frequency changes. The virtual droop control strategy mimics the droop characteristic of generators, with its power response expressed as:
$$ \Delta P_e(s) = -K_e \Delta f $$
Here, \( \Delta P_e \) is the droop output power, and \( K_e \) is the droop control coefficient. The virtual inertia control strategy simulates the inertial response of generators:
$$ \Delta P_{ine}(s) = -K_{ine} \frac{d\Delta f}{dt} $$
where \( \Delta P_{ine} \) is the inertia output power, \( d\Delta f/dt \) is the rate of change of frequency, and \( K_{ine} \) is the inertia control coefficient.
Primary Frequency Regulation Strategy Based on SOC
As the capacity of the energy storage cell system is much smaller than the power system, using fixed control coefficients can lead to overcharging or over-discharging, potentially causing oscillations. To prevent this, a variable control coefficient strategy based on SOC is designed. The SOC of the energy storage cell is divided into five intervals to adjust the droop coefficient. The charging and discharging droop coefficients, \( K_{e,c} \) and \( K_{e,f} \), are defined as piecewise functions of the SOC \( Q_{SOC} \).
$$ K_{e,c} = \begin{cases} K_{e,max} & Q_{SOC} \in [0, Q_3] \\ K_{e,max} \left( \frac{Q_{SOC} – Q_4}{Q_4 – Q_3} \right)^n & Q_{SOC} \in (Q_3, Q_4] \\ 0 & Q_{SOC} \in (Q_4, 1] \end{cases} $$
$$ K_{e,f} = \begin{cases} 0 & Q_{SOC} \in [0, Q_1] \\ K_{e,max} \left( \frac{Q_{SOC} – Q_1}{Q_2 – Q_1} \right)^n & Q_{SOC} \in (Q_1, Q_2] \\ K_{e,max} & Q_{SOC} \in (Q_2, 1] \end{cases} $$
In these equations, \( K_{e,max} \) is the maximum droop coefficient, and the normal operating range of the energy storage cell SOC is set to [0.1, 0.9]. The boundaries \( Q_1, Q_2, Q_3, Q_4 \) are set to 0.1, 0.3, 0.7, and 0.9, respectively, with n=2. The virtual inertia coefficients are defined similarly, with a ratio \( k_{ine/e} \) to ensure comparable output from both control strategies:
$$ \begin{cases} K_{ine,c} = k_{ine/e} K_{e,c} \\ K_{ine,f} = k_{ine/e} K_{e,f} \end{cases} $$
By combining virtual droop and virtual inertia control, the total power response of the energy storage cell is given by:
$$ \Delta P_B(s) = \lambda \Delta P_e(s) + (1-\lambda) \Delta P_{ine}(s) $$
To flexibly adjust the proportion of each control method based on frequency deviation, the output coefficient \( \lambda \) is defined as a function of \( |\Delta f| \):
$$ \lambda = \begin{cases} 0.25 + \frac{|\Delta f|}{4\Delta f_s} & 0 \le |\Delta f| \le \Delta f_s \\ 0.5 + \frac{|\Delta f|}{4\Delta f_s} & \Delta f_s < |\Delta f| \le 2\Delta f_s \\ 0.75 & |\Delta f| > 2\Delta f_s \end{cases} $$
Here, \( \Delta f_s \) is the set critical frequency deviation. When the deviation is small, the virtual inertia output is increased to reduce the rate of change, while for large deviations, the virtual droop output is increased to suppress the frequency excursion.
Frequency Deviation Control Strategy
To protect the energy storage cell from excessive cycling, a dead-band for frequency regulation is implemented. The dead-band for the energy storage cell is set to 60% of the conventional thermal power unit’s dead-band, which is typically [-0.033 Hz, 0.033 Hz]. Therefore, the dead-band for the energy storage cell is [-0.0198 Hz, 0.0198 Hz]. When the frequency deviation falls within this range, the energy storage cell does not act. When it exceeds this range, the energy storage cell charges or discharges accordingly.
Comprehensive Control Strategy
The proposed comprehensive control strategy for an energy storage cell participating in primary frequency regulation is structured as follows. First, if \( |\Delta f| \le 0.0198 \) Hz, the grid frequency is stable, and the energy storage cell remains inactive. If \( |\Delta f| > 0.0198 \) Hz, the energy storage cell assists the thermal power unit. The charging/discharging droop and inertia coefficients are calculated based on the SOC using the defined functions. The actual output power of the energy storage cell is then determined by the formula for \( \Delta P_B(s) \), with the proportion of virtual inertia and droop outputs adjusted according to the value of \( \lambda \), which depends on the frequency deviation magnitude.
Simulation Analysis
Simulations were conducted using Matlab/Simulink, modeled on a 1000 MW steam turbine from a power plant. The parameters of the energy storage cell were set to 10 MW/1 MWh to observe its impact on frequency regulation performance. The base values were 1000 MW and 50 Hz, with relevant parameters in per-unit values.
The simulation model parameters are listed in the table below:
| Parameter | Value | Parameter | Value |
|---|---|---|---|
| M | 10 | kine/e | 4 |
| D | 2 | τr (s) | 10 |
| R | 0.05 | τT (s) | 0.3 |
| Kr | 0.3 | τg (s) | 0.08 |
| Ke,max | 10 | Δfs (Hz) | 0.001 |
Step Load Disturbance Scenario
Under a step load disturbance of \( \Delta P_L = 0.05 \) p.u., the initial SOC of the energy storage cell was set to 0.5. The performance of the proposed integrated control strategy was compared with variable droop control, fixed droop control, and a no-storage scenario. The evaluation indices included the maximum frequency deviation \( \Delta f_{jy,max} \), the steady-state frequency deviation \( \Delta f_w \), and the change in SOC over 300 s, \( \Delta Q_{SOC} \).
The results indicate that under a large step disturbance, all control strategies with energy storage cell output reached the 10 MW limit, resulting in similar maximum frequency and steady-state deviations. However, these were significantly reduced compared to the no-storage case, with a reduction of 18.17% in maximum deviation and 15.68% in steady-state deviation. As simulation time progressed, the fixed droop control strategy, which did not account for SOC, led to complete discharge of the energy storage cell and a subsequent frequency drop. The proposed strategy showed a slower frequency drop due to the dampening effect of virtual inertia control.
The simulation results for SOC change are summarized in the table below:
| Control Strategy | Δfjy,max (p.u.) | Δfw (p.u.) | ΔQSOC |
|---|---|---|---|
| No Storage | 0.00578 | 0.00287 | – |
| Fixed Droop Coefficient | 0.00473 | 0.00242 | 0.5 |
| Variable Droop Coefficient | 0.00473 | 0.00242 | 0.376 |
| Proposed Integrated Strategy | 0.00473 | 0.00242 | 0.369 |
After 300 s of simulation, the SOC change under the proposed strategy was 0.369, which is 1.86% lower than that of the variable droop control strategy and 26.2% lower than the fixed droop control strategy. This demonstrates that the proposed strategy consumes the least amount of energy storage cell energy and provides the best SOC maintenance.
Continuous Load Disturbance Scenario
For continuous load disturbances ranging from [-0.05, 0.05] p.u., the evaluation indices included the peak-to-valley frequency deviation \( \Delta f_{lx,max} \), the root mean square (RMS) of frequency deviation \( \Delta f_{pj} \), and the average SOC over 300 s, \( SOC_{pj} \). The initial SOC was set to 0.5.
The results show that under continuous disturbances, the proposed integrated control strategy outperforms other methods. The peak-to-valley deviation was reduced by 13.4% compared to variable droop control, 30.9% compared to fixed droop control, and 28.6% compared to the no-storage scenario. The RMS frequency deviation was also the smallest, with reductions of 25.9%, 27.5%, and 1.96%, respectively.
| Control Strategy | Δflx,max (p.u.) | Δfpj (p.u.) | SOCpj |
|---|---|---|---|
| No Storage | 0.00532 | 0.000811 | – |
| Fixed Droop Coefficient | 0.00550 | 0.000829 | 0.495 |
| Variable Droop Coefficient | 0.00439 | 0.000613 | 0.503 |
| Proposed Integrated Strategy | 0.00380 | 0.000601 | 0.501 |
Furthermore, the average SOC under the proposed strategy (0.501) was closest to the initial set value of 0.5, indicating the best SOC maintenance. This helps extend the lifespan of the energy storage cell and improves operational economy.
Conclusion
This paper proposes a comprehensive control strategy for energy storage cell systems participating in primary frequency regulation of the power grid. The strategy effectively combines virtual droop and virtual inertia control by setting an appropriate output coefficient based on the frequency deviation. A variable coefficient approach based on the SOC of the energy storage cell prevents overcharging and over-discharging. Additionally, optimizing the frequency dead-band range enhances economic efficiency. Simulation results demonstrate that: (1) The strategy allows the energy storage cell to respond quickly and smoothly to frequency changes, avoiding secondary frequency fluctuations and contributing to grid stability. (2) By considering the SOC, the control strategy protects the energy storage cell from harmful operating conditions. (3) The proposed method achieves superior performance in both maintaining grid frequency stability and preserving the SOC of the energy storage cell, making it a viable reference for practical applications.