Multi-Energy Cooperative Primary Frequency Regulation Analysis of a Hybrid Plant Station for Wind Power and Hydrogen Production Based on Ensemble Empirical-Mode Decomposition Algorithm

Abstract

Wind curtailment and inadequate grid-connected frequency regulation capability are the main obstacles preventing wind power from becoming more permeable. The electric hydrogen production system can tackle the wind curtailment issue by converting electrical energy into hydrogen energy under normal operating circumstances. It can be applied as a load-reducing method during frequency regulation to help the system restore the power balance. First, this study proposes the concept of a hybrid plant station that combines the production of hydrogen and wind energy. This plant station will be referred to as a hybrid station with centralized hydrogen production and distributed energy storage. By mimicking the synchronous generator’s frequency control features, the primary frequency regulation mechanism of a hybrid plant station is examined. Secondly, due to the frequency regulation requirements of the power grid’s full-time domain hybrid power station, this paper proposes a hybrid plant station control strategy based on the EEMD variable parameter control algorithm. In frequency regulation power, the electric hydrogen production device load reduction responds to the low-frequency component, and the supercapacitor responds to the high-frequency component. The impact of the dynamic characteristics of alkaline electrolyzers on the frequency regulation effect is analyzed in this article, along with a comparison of the matching of various energy storage devices and electrolyzers in power grid frequency regulation. Finally, the feasibility and soundness of the proposed control strategy are confirmed by creating a simulation model representing a hybrid plant station involved in primary frequency management under various operational scenarios.

1. Introduction

In 2021, China’s government work report stated that in order to meet the goal of “carbon peaking • carbon neutrality” [1], the energy system will be more stable and cleaner. The new power system will be dominated by clean energy, and it is anticipated that the total installed capacity of solar and wind power will exceed 1.2 billion kW [2]. However, because wind turbines do not have a coupling relationship with system frequency [3], they do not possess the ability to regulate grid-connected frequency, which is similar to synchronous generators [4]. As a result, large-scale grid-connected wind turbines will decrease frequency stability, which will have an impact on the power system’s ability to operate safely and steadily [5]. In order to address the issue of weak grid-connected frequency regulation capabilities of wind turbines, China’s power grid laws stipulate that grid-connected wind turbines should possess the same frequency control capabilities as synchronous generators [6].

Previous research has shown that wind turbines are coupled with the system frequency through additional control and have frequency regulation capabilities [7,8]. References [9,10] suggested a way for wind turbines to react to variations in system frequency by adjusting the rotor kinetic energy and simulating the frequency regulation features of synchronous generators. However, this control method is affected by the status of the wind turbine and cannot provide stable power for frequency regulation. References [11,12] by adjusting the rotor speed and pitch angle, reserve power for system frequency management; nevertheless, this increases the system wind desertion rate at the expense of the MPPT state. A multi-machine collaborative frequency regulation control strategy was proposed by [13,14]. The control system is complicated, and the wind turbine operating status may be more consistent given the wind power aggregation effect.

Simultaneously, existing research has suggested using wind turbine energy storage devices as power absorption elements to fulfill the system’s frequency regulation needs [15]. The distributed energy storage architecture is provided by [16]. The DC–DC converter connects the energy storage device to the wind turbine DC bus. References [17,18] examine the collaborative wind turbine energy storage device’s frequency regulation features and suggest a wind storage combination control technique for frequency regulation. Reference [19] suggests using the energy storage device in conjunction with the extra control of the wind turbines to aid in the recovery of the system frequency. On the other hand, an energy storage device alone has a large frequency regulation capacity at a high investment cost.

Wind electricity hydrogen production provides a new reference for wind turbine grid-connected frequency regulation [20]. Under normal working conditions, the power-to-hydrogen (P2H) device consumes electricity and hydrogen production to improve the absorption capacity of wind power, and the hydrogen generated can supply the hydrogen downstream industry and improve the system economy [21]. In frequency regulation, P2H devices can be used as a large-capacity, controllable load to participate in system frequency regulation through load reduction [22]. The energy storage device quickly compensates for the power when the load changes abruptly and assists in the frequency recovery of the system [23]. The synergistic effect of electric hydrogen production devices and energy storage devices becomes a new way for wind power stations to respond to system frequency regulation [24]. Reference [25] analyzes the inertia response mechanism of the synchronous generator. The paper proposes to use the power throughput of energy storage devices to enable wind power stations to have inertial response capabilities. However, the primary frequency regulation is not considered. Reference [26] proposed a power fluctuation suppression method based on the active release of wind turbine rotor kinetic energy and an energy storage device, but did not consider grid frequency changes. References [27,28] establish the mathematical model of the electric hydrogen production device and analyze its operating mechanism. The analysis shows that the electric hydrogen production device can respond to system frequency changes by reducing load. Still, the impact of its dynamic characteristics on the frequency regulation effect is ignored. Reference [29] proposed a power allocation model to coordinate the control strategies of energy storage devices and electric hydrogen production devices. However, the article does not explain the connection between system frequency and power commands. References [30,31] construct a micro-grid architecture including wind power, energy storage, and controllable load, and a hierarchical collaborative frequency regulation method based on the model prediction algorithm is raised. However, the frequency regulation advantages of each unit and the reasonable distribution of power instructions are not considered.

This research examines the main operational mechanism for frequency management in a hybrid plant station in order to address the aforementioned issues. It suggests a variable-parameter ensemble empirical-mode decomposition (EEMD) algorithm-based control technique for primary frequency regulation in a hybrid plant station.

(1)

A hybrid plant station, which is based on a synchronous generator, combines energy storage, electric hydrogen production, and wind power generation. It participates in the primary frequency regulation characteristic analysis and can support system frequency recovery in the same way as a traditional power station.

(2)

After establishing a mathematical model of the energy storage device and the electric hydrogen production device, the two were selected based on their respective economies and dynamic properties, as well as their effects on the system. An analysis is conducted on the capabilities of hybrid power plants to regulate frequency.

(3)

The influence of various EEMD parameters on the power distribution results is analyzed, and a multi-energy cooperative frequency regulation control strategy based on a variable parameter control EEMD algorithm is raised. This is all based on the EEMD algorithm to achieve a rational distribution of frequency regulation power. The main frequency regulation simulation model for a hybrid power station under varying wind speeds is developed, and the simulation’s outcomes confirm that the suggested control strategy is effective.

2. Analysis of the Operating Mechanism of a Hybrid Plant Station Primary Frequency Regulation

Figure 1 shows the structure of the proposed hybrid power station. Module I is the wind turbine unit, composed of the wind-storage integrated doubly-fed wind turbine unit shown in module II. The energy storage device is connected to the DC bus of the wind turbine through the DC–DC converter. The wind turbine blade captures the wind energy. It converts it into its rotational kinetic energy, which is then converted into electric energy by the double-fed induction generator and collected in the AC bus. Part of the electric energy is sent to the distribution network shown in module Ⅲ for grid-connected power generation, and the other part flows to module IV for hydrogen production by wind curtailment. The wind curtailment to produce hydrogen modules includes the electric hydrogen production system and the hydrogen downstream industry. The downstream industry of hydrogen consumption shown in module V includes hydrogen compression, which is used in the hydrogen industry through transmission pipelines and the raw material supply of hydrogen energy vehicles. On the other hand, hydrogen can be used as a raw material to synthesize natural gas with carbon dioxide and then sold directly to the natural gas network to obtain economic benefits.

A hybrid plant station’s wind turbines run in accordance with the maximum wind power curve, and because the rotor speed is totally disconnected from the system frequency, it is unable to react to changes in the frequency of the system. Through the rotor speed-regulating mechanism, there exists a coupling relationship between the system frequency and the synchronous generator, which can enhance the power output in response to frequency variation. Therefore, the primary frequency regulation characteristics of a hybrid plant station combining energy storage, hydrogen production, and wind power generation are analyzed based on the primary frequency regulation method of the traditional synchronous set.

The inertia response of the synchronous generator inhibits the frequency change rate. The drop characteristic reduces the frequency change value, and the frequency regulation mechanism is shown in Appendix A. Through frequency regulation, the synchronous generator restores the frequency to a reasonable range [6] (domestic technical indicators require a frequency fluctuation range of 47 Hz to 52 Hz).

This analysis focuses on the power compensation technique of a hybrid plant station under the situation of frequency drop since a sudden increase in load typically produces a system frequency abrupt shift.

Three processes are involved in the wind turbine simulated synchronous generators’ response to changes in system frequency, as depicted in Figure 2. (1) The synchronous generators’ inertia response; (2) The synchronous generators’ inertia response and droop characteristics; (3) The synchronous generators’ droop characteristics are simulated.

(1)

The inertia response energy source of the synchronous generator is the rotor kinetic energy. Assuming that the system frequency drops from 50 Hz to f₁, the per-unit value of speed change is f₁/50~1 pu, and the kinetic energy of the rotor released by the synchronous generator is as shown in Equation (1) [25]:

$$\Delta E_{\mathrm{k}\_\max }=\frac{1}{2}J\left(1^2-{\left(f_1/50\right)}^2\right){\omega }_{\mathrm{S}}^2$$

(1)

In the equation, J is the moment of inertia of the generator; and ω_s is the rated speed of the generator.

A hybrid plant station should release energy equal to that of the synchronous generator in order to guarantee that the inertia response effect is the same as that of the synchronous generator, as indicated by Equation (2):

$$\Delta E_{\mathrm{W}}=\Delta E_{\mathrm{k}\_\max }=P_{\mathrm{W}}\times \Delta t=\frac{2500-f_1^2}{5000}{P}_{\mathrm{N}}{T}_{\mathrm{J}}$$

(2)

In the equation, P_N is the rated power of the wind turbine; and T_J is the inertia time constant of the synchronous generator. Assuming that the time to complete the inertia response of a hybrid plant station is equivalent to that of the synchronous generator, that is, T_J = $\Delta t$, then the compensation power required by a hybrid plant station in the process (1) as shown in Equation (3):

$$P_1=\frac{2500-f_{\mathrm{a}}^2}{5000}{P}_{\mathrm{N}}$$

(3)

In the equation, f_a is the system frequency at the beginning of a hybrid plant station’s simulated drop characteristic.

(2)

The system frequency is regulated by the inertia response and drops characteristics of the simulated synchronous generators of a hybrid plant station when the system frequency lowers from f_a to the lowest point f_b.

The synchronous generator’s power frequency characteristic curve indicates that when the system frequency falls to f₁, the drop characteristic provides a power supply, as indicated by Equation (4):

$$P_{\mathrm{V}}=K\times \left(50-f_1\right)$$

(4)

In the equation, K is the drop coefficient of the generator.

Therefore, the power required by a hybrid plant station in the process (2), as shown in Equation (5),

$$P_2=\frac{f_{\mathrm{a}}^2-f_{\mathrm{b}}^2}{5000}{P}_{\mathrm{N}}+K\times \left(f_{\mathrm{a}}-f_{\mathrm{b}}\right)$$

(5)

(3)

Damping the system’s frequency change represents the synchronous generator’s inertia response process. Thus, as demonstrated by Equation (6), a hybrid plant station only simulates the drop characteristic of the synchronous generator and the needed power as the system frequency rises from the lowest point f_b.

$$P_3=K\times \left(f_{\mathrm{c}}-f_{\mathrm{b}}\right)$$

(6)

In the equation, f_c is the system frequency when a hybrid plant station completes the primary frequency regulation.

In summary, when the hybrid plant station’s simulated synchronous generator engages in primary frequency regulation, the total power that must be adjusted in the process is displayed in Equation (7):

$$P_{\mathrm{WSP}}=\frac{2500-f_{\mathrm{b}}^2}{5000}{P}_{\mathrm{N}}+K\times \left(f_{\mathrm{a}}+f_{\mathrm{c}}-2f_{\mathrm{b}}\right)$$

(7)

As shown in Equation (7), the compensation power required by a hybrid plant station when the system frequency drops is P_WSP, and the electric hydrogen production load reduction and the power release of the energy storage device respond to the frequency regulation power. Therefore, the dynamic characteristics of each unit of the hybrid power plant are analyzed below.

3. Model Establishment and Dynamic Characteristics Analysis of the Wind-Energy Storage-Hydrogen System

3.1. Mathematical Model and Dynamic Characteristics Analysis of the Electric Hydrogen Production Device

There are three main types of electrolysis cells, including alkaline electrolytic cells, which are suitable for large-scale engineering applications. Solid oxide electrolytic cells are still in the laboratory research stage, while proton exchange membrane electrolytic cells offer good technical indications but high pricing. Alkaline electrolytic cells have strong economics and a high level of technological maturity, making them appropriate for hybrid plant stations. The following is the mathematical model used to create an alkaline electrolytic cell [32].

The single alkaline electrolytic cell voltage expression is shown in Equation (8):

$$\begin{array}{c}{U}_{\mathrm{cell}}=U_{\mathrm{rev}}+\frac{r_1+r_2{T}_{\mathrm{el}}}{A_{\mathrm{el}}}{I}_{\mathrm{el}}+\left(s_1+s_2{T}_{\mathrm{el}}+s_3{T}_{\mathrm{el}}^2\right)\times \\ \lg \left(\frac{t_1+\frac{t_2}{T_{\mathrm{el}}}+\frac{t_3}{T_{\mathrm{el}}^2}}{A_{\mathrm{el}}}{I}_{\mathrm{el}}+1\right)\hspace{1em}\hspace{1em}\hspace{1em}\end{array}$$

(8)

In the equation, U_cell is the electrolytic cell voltage; I_el is the electrolytic cell current; U_rev is the reversible voltage; r₁, r₂ are the ohmic parameters of the electrolyte; s₁, s₂, s₃, t₁, t_2, and t₃ are the electrode overvoltage parameters; A_el is the electrode area; and T_el is the electrolyte temperature.

The voltage of the electric hydrogen production device composed of the alkaline electrolyzer in series is shown in Equation (9):

$$U_{\mathrm{el}}=N_{\mathrm{el}}{U}_{\mathrm{cell}}$$

(9)

In the equation, N_el is the electrolytic cell number in series, and U_el is the voltage value of the electric hydrogen production device.

The operating state and dynamic properties of the electric hydrogen generation device govern how it responds to the frequency regulation power. The dynamic properties of the alkaline electrolytic cell are analyzed using appropriate parameters [33], and the analysis’s findings are displayed in

Table 1. Main technical indexes of the alkaline electrolytic cell.

Main Technical Indicators		Indicators Value
Start-stop	Start-stop delay α/h	0
	Start-stop capability $Y_{\max }$/$Z_{\max }$	1/1
Power	Work scope [$P_{\min }$, $P_{\max }$]	[25%$P_{\mathrm{el}\_\max }$, 100%$P_{\mathrm{el}\_\max }$]
	Gradeability $\Delta P_{\max }$	<(1/7200)$P_{\mathrm{el}\_\max }$/s

:

Table 1 shows that although the alkaline electrolyzer can be swiftly started, it cannot be repeatedly started and stopped in a single day. The alkaline electrolyzer has a restricted operating range. The alkaline electrolyzer will shut off if the input power drops below 1/4 of the rated power, and its working characteristics will limit its capacity to climb power. Equation (10) illustrates the frequency regulation power command that the alkaline electrolyzer can reply to, taking into account its operational characteristics:

$$\frac{1}{4}{N}_{\mathrm{el}}{P}_{\mathrm{el}\_\max }\le P_{\mathrm{P}2\mathrm{H}}\le N_{\mathrm{el}}{P}_{\mathrm{el}\_\max }$$

(10)

In the equation $P_{\mathrm{el}\_\max }$ is the rated power of the electrolyzer.

Furthermore, within the restrictions of its dynamic properties, the alkaline electrolyzer’s frequency regulation power must meet the following power climbing capacity requirements:

$$\left|P_{\mathrm{el},\mathrm{t}}-P_{\mathrm{el},\mathrm{t}-1}\right|\le \Delta P_{\max }$$

(11)

In Equation (11), P_el,t is the power command the electrolyzer needs to undertake at moment t; $\Delta P_{\max }$ is the power change threshold per unit time.

In summary, because of its operating and dynamic features, the electric hydrogen generation device cannot fully respond to the frequency regulation power when it participates in the system’s frequency regulation. The electric hydrogen generation and energy storage devices complement each other regarding frequency regulation features. Facilitate the recovery of system frequency by using rapid power correction.

3.2. Comparative Analysis of the Electrical Characteristics of Different Energy Storage Devices Combined with the P2H Device

Select from three distinct categories of energy storage apparatuses. Relevant research findings on flywheel energy storage, batteries, and supercapacitor energy storage systems (SCESS) are included in Appendix B, along with projections for potential future development scenarios. To improve frequency control effects in the hybrid power station, choose the right kind of energy storage device based on the information in the figure and the electrical properties of each energy storage device [34]. A schematic depiction of the findings comparing different energy storage systems can be found in Figure 3.

The electric hydrogen generation device has great power control capabilities on medium and long time scales, as demonstrated in Figure 3a. It also boasts a good capacity economy. However, the flexibility and performance of short-time high-frequency power regulation are lacking. The storage battery’s electric properties indicate a medium level, meaning it cannot work with the electric hydrogen production unit to produce complementary advantages. Flywheels and supercapacitors have a high charging and discharging economy and can adapt flexibly to short-term high-frequency power demands. As shown in Figure 3b, the energy storage devices, both flywheel and supercapacitor, can make up for the deficiency of dynamic characteristics of electric hydrogen production devices. After combining the two devices with electric hydrogen production devices, the system presents higher electrical parameter indices. However, flywheel energy storage is still in the early commercialization stage and has yet to be applied in large-scale engineering. Therefore, the supercapacitor is combined with the electric hydrogen production device to assist a hybrid plant station in participating in the primary frequency regulation.

3.3. Influence of the P2H and SCESS Dynamic Characteristics on the Frequency Regulation Effect of the Hybrid Plant Station

The P2H device and the SCESS are the power sources of a hybrid plant station that is taking part in the system frequency regulation; so, the frequency regulation outcomes will be impacted by the dynamic response characteristics of the two. Taking into account the P2H device’s operating characteristics, when a hybrid plant station engages in the primary frequency regulation of the system, the P2H device can assume the following frequency regulation power through load shedding:

$$\Delta P=P_{\mathrm{P}2\mathrm{Hreal}}-N\times P_{\min }$$

(12)

Among them, P_P2Hreal is the operating state of the P2H device; N is the number of electrolyzers opened; and P_min is the minimum working limit of the electrolyzer.

The actual load-shedding power of electric hydrogen production is limited by climbing ability, as shown in Equation (13):

$$\underset{\Delta t\to 0}{\lim }\frac{P_{\mathrm{P}2\mathrm{H}}\left(t\right)-P_{\mathrm{P}2\mathrm{H}}\left(t-\Delta t\right)}{\Delta t}\le N\times \Delta P_{\max }$$

(13)

Therefore, when $\Delta P$ = P_wsp, the SCESS needs to compensate for the energy as shown in Equation (14):

$$E_{\mathrm{SC}}={\displaystyle {\int }_0^{t_1}\left(\Delta P-N\Delta P_{\max }t\right)dt}$$

(14)

In the equation, t₁ is the time when the actual load reduction power of the P2H device reaches $\Delta P$.

Equations (12)–(14) show that the number of electrolyzers opened has an impact on the P2H device’s load-shedding power. The P2H device has more load reduction power when the N value is smaller; however, because of the limited climbing power, the actual load shedding power of P2H per unit period is lower, and the SCESS must assume excessive frequency regulation power. The maximum load-shedding power curve of P2H and the various electrolyzer opening numbers (N1 > N2 > N3) are displayed in Figure 3.

As seen in Figure 4, the maximum load-shedding power value reduces as more electrolyzers are opened, but the load-shedding rate increases. However, additional load-shedding power is possible when many electrolyzers are opened. However, it restricts the rate of load-shedding and results in excessive energy release from the SCESS, making it unable to take part in the next frequency regulation operation.

The N value should be adjusted depending on the hydrogen production state to achieve a better frequency regulation effect. In the low hydrogen production state, a smaller N value should be used to obtain more load-shedding power and meet the frequency regulation requirements. Conversely, in the high hydrogen production state, a larger N value should be used to increase the actual load reduction rate of P2H while still satisfying the frequency regulation requirements.

4. Primary Frequency Regulation and Control Strategy of the Hybrid Plant Station Based on the EEMD Power Distribution Algorithm

In the first two chapters, we researched the primary frequency regulation operation mechanism of a hybrid plant station and analyzed the dynamic characteristics of each unit. It is determined that the P2H device and the SCESS jointly assume the frequency regulation power. This chapter proposes a multi-energy cooperative frequency regulation control strategy for a hybrid plant station based on the EEMD algorithm. This strategy rationally allocates the primary frequency regulation power instructions of P2H equipment and SCESS equipment by analyzing the EEMD algorithm.

4.1. Research on EEMD Algorithms Suitable for Power Frequency Division

Empirical Mode Decomposition (EMD) is a new time domain analysis method of signal decomposition that decomposes the signal adaptively according to its intrinsic properties. The intrinsic-mode functions (IMFs) with frequencies from high to low are obtained. EEMD is an improvement on EMD. It improves the mode aliasing problem of EMD by adding a white noise processing signal. The addition of white noise can provide a relatively consistent reference scale distribution for EMD and ensure the time-domain continuity of each mode function to reduce mode aliasing. At the same time, compared with the short-time Fourier transform, EEMD is more suitable for processing non-stationary data. Compared with wavelet transform, EEMD has better adaptability and is a frequency division algorithm that is very suitable for the application scenarios of this article. The steps of the EEMD algorithm to achieve power instruction decomposition are as follows:

(1)

Add m random white noise sequences n_i(t) with zero mean and equal variance to the power instruction signal p(t), and set their sum as P_i(t):

$$P_i\left(t\right)=p\left(t\right)+n_i\left(t\right)\hspace{1em}\left(i=1,2,\dots ,m\right)$$

(15)

(2)

The EMD algorithm decomposes P_i(t). The specific process is as follows:

• All local maximum values and local minimum values are connected by the cubic spline interpolation method to form upper and lower envelope curves y_max(t) and y_min(t) of the signal, and the average y_j(t) is calculated.

  $$y_j\left(t\right)=\frac{y_{\max }\left(t\right)+y_{\min }\left(t\right)}{2}$$

  (16)
• Subtract the average value y_j(t) from the power instruction signal to be decomposed P_i(t):

  $$h_{ij}\left(t\right)=P_i\left(t\right)-y_j\left(t\right)$$

  (17)
• The calculated difference h_j(t) is taken as the data to be decomposed, and steps 1 and 2 are repeated until the standard deviation of the calculated difference is less than the pre-set value σ:

  $$SD={\displaystyle \sum _{t=0}^T\left[\frac{{\left|h_{i\left(j-1\right)}\left(t\right)-h_{ij}\left(t\right)\right|}^2}{h_{i\left(j-1\right)}^2\left(t\right)}\right]}<\sigma$$

  (18)
• At this point, the first IMF component of P_i(t) is obtained, and the high-frequency component h_i1(t) is separated from P_i(t) to obtain the remaining signal.

  $$r_{i1}\left(t\right)=P_i\left(t\right)-h_{i1}\left(t\right)$$

  (19)

r_i1(t) is taken as the original signal, and steps 1 to 2 are repeated to obtain the second IMF component h_i2(t) of P_i(t) and the remaining signal r_i2(t). This cycle is repeated to obtain n IMF components of frequency from high to low and a residual signal r_in(t) representing the center trend of P_i(t). The power instruction signal can be expressed as:

$$P_i\left(t\right)={\displaystyle \sum _{j=1}^n{h}_{ij}\left(t\right)}+r_{\mathrm{in}}\left(t\right)$$

(20)

(3)

Repeat step (2) for the EMD decomposition of the power instruction signal of each white noise sequence P_i(t) to obtain the IMF signal of the m group and the margin of the m group:

$$H=\left[\begin{array}{cccc}{h}_{11}& h_{12}& \cdots & h_{1n}\\ h_{21}& h_{22}& \cdots & h_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ h_{m1}& h_{m2}& \cdots & h_{mn}\end{array}\right],\hspace{1em}r=\left[\begin{array}{c}{r}_{1n}\\ r_{2n}\\ ⋮\\ r_{mn}\end{array}\right]$$

(21)

(4)

Calculate the average value of the IMF component of the m group and the margin of the m group:

$$\left\{\begin{array}{c}{H}_1=\frac{1}{m}{\displaystyle \sum _{i=1}^m{h}_{i1}}\\ H_2=\frac{1}{m}{\displaystyle \sum _{i=1}^m{h}_{i2}}\\ ⋮\\ H_n=\frac{1}{m}{\displaystyle \sum _{i=1}^m{h}_{i\mathrm{n}}}\end{array}\right.,R=\frac{1}{m}{\displaystyle \sum _{i=1}^m{r}_{\mathrm{in}}}$$

(22)

Step (4): Eliminate the influence of the white noise sequence added at the beginning, and then the original signal p(t) can be expressed as:

$$p\left(t\right)={\displaystyle \sum _{i=1}^n{H}_i}+R$$

(23)

The primary frequency regulation power command signal is divided into n Intrinsic Mode Function (IMF) components with frequencies ranging from high to low and a residual component using the EEMD algorithm. Equation (23) illustrates that the top a IMF component with a higher frequency is assumed by the SCESS, while the P2H device assumes the rest.

In the EEMD algorithm, the parameters that have an impact on the power frequency division result include the random white noise sequence n_i(t), the pre-set standard deviation σ (in simulation, the parameter is set as Nstd, which is the ratio of additional noise standard deviation to input data standard deviation), the number of white noise groups m, and the number of high-frequency IMF components undertaken by the SCESS during power component recombination a. Where white noise sequence n_i(t) is a random parameter, the influence of other parameters Nstd, m, and a of EEMD is shown in Figure 4.

The EEMD algorithm assigns the frequency regulation power instruction (a) to the P2H device P_P2H and the SCESS P_SCESS, as depicted in Figure 5. Various parameter combinations lead to drastically diverse frequency division processing outcomes from EEMD, which in turn provide distinct frequency regulation effects. Of them, parameter m mostly influences signal amplitude in EEMD signal processing, while parameter a primarily influences signal frequency. To achieve better results, distinct EEMD parameters should be chosen for various frequency regulation requirements.

4.2. Multi-Energy Cooperative Frequency Regulation Control Strategy Based on the EEMD Algorithm

Based on the EEMD algorithm, this chapter researches the control strategy of the P2H device and SCESS, the multi-energy cooperative frequency regulation control strategy of a hybrid plant station based on the EEMD algorithm proposed.

Considering that the electric hydrogen production device is affected by its own operating status and dynamic characteristics, its control strategy is established as follows: when the system frequency drops, the compensated power of a hybrid plant station is P_WSP, and the electric hydrogen production device cannot independently undertake the power command. Therefore, after the total frequency regulation power command P_WSP is processed by the EEMD algorithm, the electric hydrogen production device can assume the low-frequency power component P_P2H. It is divided by the terminal voltage of the electric hydrogen production device U_el to obtain the current reference value I_elref. I_elref subtracts the current I_el of the electric hydrogen production device and generates D_el through the PI link, which then generates a PWM wave and acts on the switching element to realize power control of the electric hydrogen production device. The supercapacitor absorbs/compensates the high-frequency components of the P_WSP processed by the EEMD algorithm. The high-frequency power component P_sc and the voltage U_sc of the supercapacitor are divided to obtain the current reference value I_scref. After the subtraction of the I_scref and the feedback current I_sc, the D_sc is generated through the PI link. Then, the PWM wave is generated to act on the switching element to realize the power control of the supercapacitor.

Combined with the above control strategies for each unit of a hybrid plant station, a primary frequency regulation control strategy based on the EEMD algorithm of a hybrid plant station is established. The control flow chart is shown in Figure 6.

As shown in Figure 6, the change in load power causes the system frequency to change suddenly. The phase-locked loop first locks the angular velocity of the grid and then obtains the real-time value of the system frequency. The grid frequency has a reasonable fluctuation range, and 0.03 Hz is the system frequency regulation dead zone. Hybrid plant stations within this range do not participate in system frequency regulation. When the system frequency decreases, the control strategy of the hybrid power station is as follows: When a sudden increase in load causes a system power imbalance, the compensation power P_WSP required by the hybrid power station is calculated by Equation (7). This power command is used as the output signal of the EEMD algorithm. Frequency division processing Finally, an IMF component with a lower frequency is used as the power response signal (P_P2H) for electric hydrogen production. The power control link controls the electric hydrogen production unit load reduction in response to the system frequency regulation. The other high-frequency power components constitute the power response signal P_sc of the supercapacitor. Through the power control link of the supercapacitor, the supercapacitor is controlled to release power to realize power compensation quickly. The electric hydrogen production device and energy storage device cooperate to assist the frequency recovery of the system so that a hybrid plant station has the same frequency regulation capacity as the traditional power station.

5. Verification of the Simulation

Using MATLAB2020b Simulink software, a simulation model of the hybrid plant station involved in the system’s principal frequency regulation was created. In Figure 7, the model architecture is displayed. Three sets of wind turbines, each consisting of three doubly fed induction generators, an electric hydrogen production system, a group of synchronous power generators (SGs), a fixed load (L1), and an adjustable load (L2), are all included in the simulation model.

To better mimic real-world settings, Figure 7 depicts three groups of wind turbines with a 7.5 MW rating operating under various wind speed conditions. The DC bus’s two ends are connected to a supercapacitor. Power is produced and gathered in the AC bus as the wind turbine harnesses wind energy. The remainder of this electricity goes to the electric hydrogen production system, with a portion being boosted to power loads L1 and L2. Each wind turbine has a 2.5 MW peak power output operating at maximum capacity. After a specific margin was determined, 100 electric hydrogen production devices were ultimately chosen, taking into account the remaining capacity, preventing wind abandonment, and the device’s full load. This electric hydrogen production system is connected to the AC bus using an AC/DC converter. A 100 MW synchronous generator set was added to the model to simulate the power system conditions. See Appendix C for the simulation parameters of wind turbines and synchronous generators.

To make sure the model has good adaptability, a simulation analysis of hybrid plant stations under various wind speed situations is carried out. The wind turbine’s output power of 0.53 MW at an input wind speed of 7 m/s is insufficient to meet grid connection requirements. while the wind turbine is operating at its 2.5 MW rated output power and the wind speed is 12 m/s. The wind speed input is thus set to 8 m/s, 10 m/s, and 12 m/s after the wind turbine’s operating status was examined under various wind speed inputs.

When the wind turbine is set to work under different input wind speeds and the number of switches in different electrolyzers N, the maximum system frequency drop value, frequency regulation parameters of the electric hydrogen production device, and SOC reduction in the supercapacitor in a hybrid plant station with a sudden increase of 15 MW are shown in

Table 2. System parameter index under different numbers of N.

Wind Speed/m/s	N	$\mathbf{\Delta }\mathit{P}$/MW	$\mathbf{\Delta }\mathbf{SOC}$	Max(Δf)/Hz
8	5	0.75	100%	0.36
	8	1.2	93%	0.34
	11	1.05	100%	0.35
10	40	6	59%	0.32
	50	6.1	42%	0.32
	60	5.6	34%	0.32
12	80	12	20%	0.32
	90	12.4	16%	0.32
	100	11.9	13%	0.32

.

Table 2 illustrates that the maximum drop value of the system frequency is 0.36 Hz, 0.34 Hz, and 0.35 Hz, respectively, when the wind speed input is 8 m/s and the N value is 5, 8, and 11, respectively. The electric hydrogen production system’s maximum load reduction power is constrained, and the supercapacitors’ energy deficit prevents them from contributing to frequency regulation. To improve the frequency regulation impact, a suitable N value should be chosen at low wind speeds to boost the load reduction power of electric hydrogen production. At medium and high wind speeds, the electric hydrogen production device is mainly limited by climbing power, so the N value should be increased to reduce the output of the SCESS and leave more power margin.

The load power is set at a sudden increase of 15 MW. The system frequency adjustment curves of the fixed-parameter EEMD algorithm and the variable-parameter control EEMD algorithm under different input wind speeds are shown in Figure 8 and Figure 9.

The variable parameter EEMD algorithm is used, as seen in Figure 8 and Figure 9. The highest system frequency drop values under various input wind speeds are 0.34 Hz, 0.32 Hz, and 0.315 Hz, in that order. The system’s greatest frequency drop values with the fixed parameter EEMD algorithm are 0.36 Hz, 0.32 Hz, and 0.315 Hz. This is due to the limited load-reduction power of electric hydrogen production at low wind speeds. Suppose the power is distributed according to the EEMD algorithm at medium and high wind speeds. In that case, the electric hydrogen production device cannot assume the frequency regulation power instruction, resulting in a poor frequency regulation effect. The relevant parameters of the EEMD algorithm should be adjusted under different wind speeds to reasonably allocate the frequency regulation power instructions for electric hydrogen production and supercapacitors.

Following is the selection of the ideal number of switches for electric hydrogen production using the variable parameter control strategy of the proposed EEMD algorithm. The main frequency regulation of a hybrid plant station is simulated and examined while taking into account various wind speed input conditions and the wind power plant’s polymerization effect.

Every wind turbine is configured to run at 8 m/s, which is the low wind speed input state. Figure 10 and Figure 11 display the running status of each unit of a hybrid plant station as well as the system frequency regulation curve under various sudden load power increase degrees (10, 15, and 20 MW).

As shown in Figure 10 and Figure 11, the output power of the wind turbine unit is low under low wind speed input. The initial power of the electric hydrogen production operation is low, and the load reduction capacity is limited. In this case, EEMD parameters are adjusted so that the electric hydrogen production device can maximize its participation in frequency regulation, and the supercapacitor assumes the remaining frequency regulation power. When the sudden load power increases by 10 MW and 15 MW, the maximum frequency drop of the system is 0.23 Hz and 0.34 Hz, respectively. At this time, by changing the parameters of the EEMD algorithm and controlling the electric hydrogen production device to maximize the load reduction, the supercapacitor still has a power margin. When the load power suddenly increases to 20 MW, the supercapacitor state of charge drops to 0 at 3.8 s. Due to the energy deficit, the supercapacitor cannot respond to the power instruction, and the maximum frequency drop of the system is 0.45 Hz. The wind turbine runs in a sub-synchronous state when low wind speed is input. The power flowing through the rotor-side converter is 0.12 MW, and the compensating power of the supercapacitor flows to the AC bus through the grid-side converter, so the power flowing through the grid-side converter (GSC) is the difference between the two. When the wind speed is low, by adjusting the EEMD parameters to allocate the power order reasonably, a hybrid plant station has a good frequency regulation effect.

The wind speed is 10 m/s, and the wind turbines are configured to maintain an input state of medium wind speed. The hybrid plant station’s system frequency regulation curve and the operational state of each unit under varying degrees of unexpected demand power increase (10, 15, and 20 MW) are depicted in Figure 12 and Figure 13.

Figure 12 and Figure 13 show that the wind turbine unit output power increases under medium wind speed input. In this case, the power flowing to the electric hydrogen production system is greater, and the load reduction power of the electric hydrogen production device is more significant; therefore, by adjusting the EEMD parameters, the electric hydrogen production device load reduction assumes more frequency regulation power. The supercapacitor frequency regulation power instruction is reduced compared to low wind speed, and the state of charge is high after frequency regulation. When the medium wind speed is input, the wind turbine runs in the super-synchronous state, and the converter power flowing through the rotor side is 0.128 MW. The compensation power of the supercapacitor is inconsistent under different sudden load power increase states, so the power of the converter flowing through the network is also different. When a hybrid plant station participates in frequency regulation, the maximum value of system drop is 0.21 Hz, 0.32 Hz, and 0.43 Hz, respectively; the frequency regulation effect is ideal.

All wind turbines are set to operate at a high wind speed input state of 12 m/s. Figure 14 and Figure 15 show the system frequency regulation curve and the running status of each unit in a hybrid plant station under different sudden load power increase degrees (10, 15, and 20 MW).

The wind turbine output power is rated under high wind speed input, as Figure 14 and Figure 15 demonstrate. The EEMD parameters can be changed so that the electric hydrogen production device serves as the hybrid plant station’s central frequency regulation component and that the supercapacitor’s output is minimal, leaving sufficient power for the subsequent frequency regulation tasks. Currently, the highest value of the system frequency drop under varying degrees of load power disturbance is 0.21 Hz, 0.32 Hz, and 0.43 Hz, respectively. For high wind speed input, the power flowing through the rotor-side converter is −0.587 MW, and the power flowing through the grid-side converter is the difference between its power and the compensation power of the supercapacitor.

Three sets of wind turbines are configured to operate under different wind speed input conditions in order to account for the hybrid power station’s aggregation impact. At two seconds, a sudden 15 MW power load is introduced to the system. Figure 16 shows the system frequency adjustment curve when the hybrid power station’s multi-energy collaborative control technique and the other three control methods are used.

Figure 16 shows that the system’s initial state power is balanced and that its frequency fluctuates pretty close to the 50 Hz power frequency. An abrupt rise in load power disrupts the system’s power balance, and the hybrid power plant reacts swiftly. The lowest value of the system frequency is decreased to 49.69 Hz using the multi-energy collaborative control strategy of the EEMD algorithm, taking into account the dynamic characteristics of each unit of the hybrid power station and rationally allocating frequency regulation power. The lowest value of the frequency drop is 49.68 Hz when using the EMD allocation algorithm to allocate frequency regulation power instructions. The maximum frequency of the system is lowered to 49.66 Hz when the electric hydrogen production device is utilized to respond to the frequency regulation power independently. This is due to the operational characteristics of a single energy storage device. The system frequency decreases to 49.67 Hz when the SCESS is independently frequency-regulated. The control strategy suggested in this article has the best frequency regulation impact and the least frequency change when analyzed based on the system frequency. The operational power curves for the supercapacitor and electric hydrogen production systems are shown in Figure 17.

It can be seen from Figure 17 that when the control strategy proposed in this article is adopted, the load reduction in the electric hydrogen production device is larger, which is the main body in the frequency regulation process. The use of EMD algorithm distribution makes the power curve of the electric hydrogen production device smoother, but the overall output of the electric hydrogen production device is less, and the supercapacitor bears a large part of the frequency regulation power. The independent frequency regulation of the electric hydrogen production device has the largest load reduction, but the frequency regulation effect is not ideal due to the adjustment limitations of the electric hydrogen production device itself. Supercapacitors are limited by their own capacity and SOC when independent frequency regulation occurs, and the frequency regulation effect is poor. Figure 18 shows the supercapacitor SOC curve.

As shown in Figure 18, the control strategy proposed in this article reduces the supercapacitor SOC and does not cause energy discharge. The supercapacitor exits frequency regulation due to energy discharge at 2.85 s and 2.84 s, respectively, by EMD distribution and supercapacitor-independent frequency regulation, resulting in the frequency regulation effect of the two not being ideal. At the same time, when the EMD algorithm is used for distribution, the supercapacitor exits the frequency regulation, causing the system frequency to fluctuate again at 2.85 s. The overall frequency regulation effect is not ideal. The following table records the lowest value of system frequency drop when using EEMD and EMD algorithm allocation.

It can be seen from the

Table 3. System frequency deviation.

Working Conditions	Exploding Load/MW	System Frequency Drops to the Lowest Value
		EEMD	EMD
Consider wind power aggregation effects	10	49.79	49.77
	15	49.69	49.68
	20	49.57	49.54

that the lowest value of the system frequency drop when using the EEMD algorithm is generally higher than the result when using the EMD algorithm for distribution. Under operating conditions that consider the wind power aggregation effect, when the system load suddenly increases by 10 MW, 15 MW, and 30 MW, the lowest frequency drop value of the EEMD algorithm is 0.02 Hz, 0.01 Hz, and 0.03 Hz higher than that of the EMD algorithm, respectively. Compared with the EEMD algorithm, the EMD algorithm lacks the step of adding white noise, so EMD cannot have the same precise distribution effect as EEMD, and it is difficult to obtain a more reasonable power distribution when reconstructing the IMF component. Therefore, EEMD has a better frequency regulation effect and working condition universality.

6. Conclusions

Due to its dynamic response ability and operating condition, the electric hydrogen production device in a hybrid plant station is unable to fully respond to the frequency regulation power of the system, as the wind turbines are unable to adapt to changes in the system’s frequency. The following results are reached by the control method of collaborative frequency regulation proposed in this research between the electric hydrogen production device and the super-capacitor, which is based on the variable parameter EEMD algorithm for power allocation.

(1)

Using the reasonable number of electrolyzers opened and the variable parameter ensemble empirical-mode decomposition algorithm, considering the difference in dynamic characteristics of electric hydrogen production devices under different wind speeds, the system has a favorable frequency regulation effect under low, medium, and high wind speeds through a reasonable distribution strategy.

(2)

The frequency regulation control strategy based on the variable parameter EEMD algorithm is adopted for the hybrid plant station under different sudden load power increase degrees (10, 15, and 20 MW); the maximum frequency drop values are 0.23, 0.34, and 0.45 Hz under low wind speed input; in the case of medium wind speed input, the maximum frequency drop values are 0.21, 0.32, and 0.43 Hz; in the case of high wind speed input, the maximum frequency drop values are 0.21, 0.32, and 0.43 Hz. It can be seen that with a sudden increase in load power, the power response of the electric hydrogen production device and the energy storage device in the hybrid plant station, the frequency drop value of the system is limited within a reasonable range.

(3)

Taking the hybrid power plant’s aggregation impact into consideration. The highest frequency drop values of the system are 0.31 Hz, 0.32 Hz, 0.34 Hz, and 0.33 Hz, respectively, when utilizing the hybrid power station frequency regulation control strategy based on the variable parameter control EEMD algorithm, the EMD distribution strategy, and two single energy control strategies. This is because the electric hydrogen production device’s dynamic response capabilities and operational status limit its capacity to regulate frequency independently, and the supercapacitor’s ability to regulate frequency independently is limited by its charging state. The variable parameter EEMD allocation algorithm has a more exact allocation impact than the EMD allocation algorithm. The frequency regulation control strategy of the hybrid power station is based on the variable parameter EEMD algorithm, which realizes the reasonable dispatch of multiple energy sources and enhances the frequency regulation capability of the hybrid power station. In summary, by building a simulation model of a hybrid plant station participating in the primary frequency regulation, this paper verifies that the proposed multi-energy cooperative frequency regulation control strategy based on the variable parameter control EEMD algorithm can assist the system frequency recovery and has a favorable frequency regulation effect, which provides a reference value for improving the wind power penetration capacity and promoting the wind power abandoning hydrogen production technology.