Method and apparatus for regulating power of offshore wind farm energy storage system on basis of state of charge

Abstract

Disclosed in the present application are a method and apparatus for regulating the power of an offshore wind farm energy storage system on the basis of the state of charge. The method in the present application comprises: acquiring a power grid frequency and the state of charge of an offshore wind farm energy storage system, and on the basis of the power grid frequency and the state of charge, acquiring a power grid frequency deviation and a state-of-charge recovery demand, respectively; determining whether the power grid frequency deviation is within a preset power grid frequency dead band, if the power grid frequency deviation is within the power grid frequency dead band, in view of the power grid frequency deviation and the state-of-charge recovery demand, acquiring an inertia power regulation and control amount of the energy storage system by means of a fuzzy control algorithm combined with a preset adaptive inertia frequency control model, otherwise, in view of the power grid frequency deviation and the state-of-charge recovery demand, acquiring the inertia power regulation and control amount by means of the adaptive inertia frequency control model; and on the basis of the inertia power regulation and control amount, regulating and controlling the inertia power of the offshore wind farm energy storage system. By means of the present application, the level of refinement and accuracy of a regulation and control process for an energy storage system can be improved.

Description

Power regulation method and device for offshore wind farm energy storage system based on state of charge Technical Field

[0001] This application relates to the field of power grid system automation control, and in particular to a power regulation method and device for an offshore wind farm energy storage system based on state of charge. Background Technology

[0002] With the development of renewable energy technologies, a large number of renewable energy devices have been connected to the power grid system, resulting in a gradual reduction in the inertia of the power system.

[0003] To address these issues, existing technologies often use energy storage systems to simulate the inertial characteristics of synchronous generators, thereby improving rapid frequency response and enhancing grid frequency stability. However, existing technologies employ only fixed virtual inertia and damping coefficients in their frequency inertial control strategies, which can easily lead to unhealthy charging states in the energy storage systems. This affects the lifespan of equipment within the energy storage systems and increases the operational risks and costs of the grid system. Furthermore, in traditional grid system regulation techniques, when the frequency deviation is within the dead zone, the regulation system typically does not respond to avoid large frequency fluctuations. This can cause the grid frequency deviation to continue to increase until it exceeds the dead zone before triggering adjustment. This increases the losses and energy consumption of equipment within the grid, while also increasing the risks to the grid system.

[0004] Therefore, how to more precisely and dynamically adjust the inertial power of the energy storage system has become a technical problem that needs to be solved. Summary of the Invention

[0005] This application provides a power regulation method for offshore wind farm energy storage systems based on state of charge, in order to solve the technical problem of how to dynamically adjust the inertial power of the energy storage system to improve the precision and accuracy of the adjustment process.

[0006] To address the aforementioned technical problems, in a first aspect, embodiments of this application provide a power regulation method and apparatus for an offshore wind farm energy storage system based on state of charge, comprising:

[0007] The grid frequency and the state of charge of the offshore wind farm energy storage system are obtained, and the grid frequency deviation and charge recovery requirements are obtained based on the grid frequency and the state of charge, respectively.

[0008] Determine whether the grid frequency deviation is within a preset grid frequency dead zone; if it is within the grid frequency dead zone, combine the grid frequency deviation and the charge recovery requirement, and obtain the inertial power regulation amount of the energy storage system through a fuzzy control algorithm combined with a preset adaptive inertial frequency control model; otherwise, combine the grid frequency deviation and the charge recovery requirement, and obtain the inertial power regulation amount through the adaptive inertial frequency control model.

[0009] The inertial power of the offshore wind farm energy storage system is adjusted according to the inertial power adjustment amount.

[0010] Compared with the prior art, the embodiments of this application have the following beneficial effects: When the grid frequency deviation is in the grid frequency dead zone, by introducing fuzzy control, the small frequency deviation can be finely adjusted, solving the problem that the high-power regulation of the system cannot meet the fine adjustment requirements, and improving the stability of the grid system; In addition, by considering the current state of charge of the energy storage system based on the charge recovery requirements, adaptive adjustment is performed to avoid the unhealthy state of charge of the energy storage system caused by the fixed virtual inertia coefficient and virtual damping coefficient, thereby improving the safety and accuracy of the grid system.

[0011] In some embodiments of the first aspect of this application, the steps for building the adaptive inertia frequency control model include:

[0012] By combining the Sigmoid function and the charge constraint of the energy storage system, the discharge coefficient control function of the energy storage system is obtained;

[0013] The adaptive inertia frequency control model is obtained by combining the discharge coefficient control function and the inertial response equation of the offshore wind farm energy storage system.

[0014] Compared with the prior art, the above embodiments have the following beneficial effects: Since the Sigmoid function is generated exponentially in the initial stage and then gradually saturates and approaches an asymptote parallel to the x-axis, this characteristic is compatible with the charge constraint conditions of the energy storage system. The discharge system control function obtained by combining the two can improve the accuracy of the subsequent adaptive inertia frequency control model.

[0015] In some embodiments of the first aspect of this application, obtaining the discharge coefficient control function of the energy storage system by combining the Sigmoid function and the charge constraint of the energy storage system includes:

[0016] The discharge coefficient control function of the energy storage system is as follows:

[0017] Where, k sc and k sdThese represent the discharge coefficients of the energy storage system when the grid frequency is below the rated frequency and when the grid frequency is above the rated frequency, respectively; k s It is a constant; SOC min and SOC max , , represent the minimum and maximum states of charge of the energy storage system, respectively; e is the base of the natural logarithm; Δf is the grid frequency deviation; and SOC is the state of charge of the energy storage system.

[0018] Compared to existing technologies, the above embodiments have the following beneficial effects: As can be seen from the above formula, when the grid frequency deviation is less than 0, it indicates that the grid frequency is lower than the rated value. In this case, the energy storage device needs to discharge to compensate for the grid frequency difference. Conversely, when the grid frequency deviation is greater than 0, it indicates that the grid frequency is higher than the rated value. In this case, the energy storage device needs to be charged to absorb excess energy from the grid. However, in addition to considering the fluctuations in the grid frequency, the current state of charge of the energy storage system also needs to be taken into account. If the current state of charge of the energy storage system is at full load, even if the grid frequency is higher than the rated value, it is impossible to forcibly start the energy storage system for charging. Therefore, by combining the Sigmoid function, the power fluctuations of the energy storage device during charging and discharging can be smoothly controlled, improving the stability of the grid system.

[0019] In some embodiments of the first aspect of this application, obtaining the adaptive inertia frequency control model by combining the discharge coefficient control function and the inertial response equation of the offshore wind farm energy storage system includes:

[0020] The adaptive inertia frequency control model is specifically as follows:

[0021] Among them, P ess Inertial power provided to the energy storage system; k sc and k sd These are the discharge coefficients of the energy storage system when the grid frequency is below the rated frequency and when the grid frequency is above the rated frequency, respectively; J v and D v These are the virtual inertia coefficient and the virtual damping coefficient, respectively; Δf is the grid frequency deviation. Take the derivative of the grid frequency f with respect to time t.

[0022] Compared with existing technologies, the above embodiments have the following beneficial effects: The above formula combines the fixed inertial response equation with the discharge coefficient control function that adaptively adjusts the discharge coefficient. Based on the frequency fluctuations of the current power grid system, it adaptively adjusts the inertial power provided by the energy storage system, which solves the problem of unhealthy state of charge of the energy storage system caused by the fixed virtual inertial coefficient and virtual damping coefficient, thereby improving the safety of the power grid system.

[0023] In some embodiments of the first aspect of this application, obtaining the inertial power regulation amount of the energy storage system by combining a fuzzy control algorithm with a preset adaptive inertia frequency control model includes:

[0024] The grid frequency deviation and the charge recovery requirement are input into the fuzzy control algorithm to obtain the first inertial power regulation amount;

[0025] The grid frequency deviation and the charge recovery requirement are input into the adaptive inertia frequency control model to obtain the second inertial power regulation amount;

[0026] The inertial power control amount is obtained by combining the first inertial power control amount and the second inertial power control amount.

[0027] Compared with the prior art, the above embodiments have the following beneficial effects: Since the grid frequency deviation is in the grid frequency dead zone, the adaptive inertia frequency control model cannot meet the precision requirements of the power control of the energy storage system. At this time, the energy storage system can make fine adjustments to the small frequency deviation by introducing fuzzy control, thereby improving the stability of the grid system.

[0028] In some embodiments of the first aspect of this application, the step of inputting the grid frequency deviation and the charge recovery requirement into a fuzzy control algorithm to obtain a first inertial power regulation amount includes:

[0029] Based on the preset membership diagrams corresponding to the power grid frequency deviation and the charge recovery requirement, determine the first fuzzy variable and the second fuzzy variable corresponding to the power grid frequency deviation and the charge recovery requirement, respectively.

[0030] Based on the preset fuzzy rule table, and in combination with the first fuzzy variable and the second fuzzy variable, the third fuzzy variable corresponding to the first inertial power control amount is determined;

[0031] The area center corresponding to the third fuzzy variable is calculated using the area center method, and the area center is used as the first inertial power control quantity.

[0032] Compared with existing technologies, the above embodiments have the following beneficial effects: Fuzzy control is a control method based on fuzzy logic, which imitates the human decision-making process. It is especially suitable for complex systems that are difficult to describe with precise mathematical models in classical control theory. Since the existing adaptive inertia frequency control model cannot accurately describe the inertial response of the energy storage system when facing grid frequency fluctuations when the grid frequency deviation is in the grid frequency dead zone, by setting the corresponding fuzzy rule table and the membership graph of each variable, the continuous variables are discretized into fuzzy variables and decentered by the area center method, thereby making up for the problem of insufficient accuracy of the adaptive inertia frequency control model in a small dead zone and improving the accuracy of the energy storage system in adaptively adjusting the inertial power.

[0033] Secondly, embodiments of this application also provide a power regulation device for an offshore wind farm energy storage system based on state of charge, comprising: a first data acquisition module, an inertial power regulation quantity acquisition module, and an inertial power regulation module;

[0034] The first data acquisition module is used to acquire the grid frequency and the state of charge of the offshore wind farm energy storage system, and to acquire the grid frequency deviation and the charge recovery requirement based on the grid frequency and the state of charge.

[0035] The inertial power regulation acquisition module is used to determine whether the grid frequency deviation is within a preset grid frequency dead zone. If it is within the grid frequency dead zone, the module combines the grid frequency deviation and the charge recovery requirement with a fuzzy control algorithm and a preset adaptive inertial frequency control model to obtain the inertial power regulation of the energy storage system. Otherwise, the module combines the grid frequency deviation and the charge recovery requirement with the adaptive inertial frequency control model to obtain the inertial power regulation.

[0036] The inertial power control module is used to control the inertial power of the offshore wind farm energy storage system according to the inertial power control amount.

[0037] In some embodiments of the second aspect of this application, the steps for building the adaptive inertia frequency control model include:

[0038] By combining the Sigmoid function and the charge constraint of the energy storage system, the discharge coefficient control function of the energy storage system is obtained;

[0039] The adaptive inertia frequency control model is obtained by combining the discharge coefficient control function and the inertial response equation of the offshore wind farm energy storage system.

[0040] In some embodiments of the second aspect of this application, obtaining the discharge coefficient control function of the energy storage system by combining the Sigmoid function and the charge constraint of the energy storage system includes:

[0041] The discharge coefficient control function of the energy storage system is as follows:

[0042] Where, k sc and k sd These represent the discharge coefficients of the energy storage system when the grid frequency is below the rated frequency and when the grid frequency is above the rated frequency, respectively; k s It is a constant; SOC min and SOC max , , represent the minimum and maximum states of charge of the energy storage system, respectively; e is the base of the natural logarithm; Δf is the grid frequency deviation; and SOC is the state of charge of the energy storage system.

[0043] In some embodiments of the second aspect of this application, obtaining the adaptive inertia frequency control model by combining the discharge coefficient control function and the inertial response equation of the offshore wind farm energy storage system includes:

[0044] The adaptive inertia frequency control model is specifically as follows:

[0045] Among them, P ess Inertial power provided to the energy storage system; k sc and k sd These are the discharge coefficients of the energy storage system when the grid frequency is below the rated frequency and when the grid frequency is above the rated frequency, respectively; J v and D v These are the virtual inertia coefficient and the virtual damping coefficient, respectively; Δf is the grid frequency deviation. Take the derivative of the grid frequency f with respect to time t.

[0046] In some embodiments of the second aspect of this application, obtaining the inertial power regulation amount of the energy storage system by combining a fuzzy control algorithm with a preset adaptive inertia frequency control model includes:

[0047] The grid frequency deviation and the charge recovery requirement are input into the fuzzy control algorithm to obtain the first inertial power regulation amount;

[0048] The grid frequency deviation and the charge recovery requirement are input into the adaptive inertia frequency control model to obtain the second inertial power regulation amount;

[0049] The inertial power control amount is obtained by combining the first inertial power control amount and the second inertial power control amount.

[0050] In some embodiments of the second aspect of this application, the step of inputting the grid frequency deviation and the charge recovery requirement into a fuzzy control algorithm to obtain a first inertial power regulation amount includes:

[0051] Based on the preset membership diagrams corresponding to the power grid frequency deviation and the charge recovery requirement, determine the first fuzzy variable and the second fuzzy variable corresponding to the power grid frequency deviation and the charge recovery requirement, respectively.

[0052] Based on the preset fuzzy rule table, and in combination with the first fuzzy variable and the second fuzzy variable, the third fuzzy variable corresponding to the first inertial power control amount is determined;

[0053] The area center corresponding to the third fuzzy variable is calculated using the area center method, and the area center is used as the first inertial power control quantity. Attached Figure Description

[0054] Figure 1 is a flowchart illustrating a power regulation method for an offshore wind farm energy storage system based on state of charge, provided in some embodiments of this application.

[0055] Figure 2 is a flowchart illustrating another power regulation method for an offshore wind farm energy storage system based on state of charge, provided in some embodiments of this application.

[0056] Figure 3 is a membership diagram provided in some embodiments of this application for determining the corresponding fuzzy variables in a fuzzy control algorithm;

[0057] Figure 4 is a schematic diagram of the structure of a power regulation device for an offshore wind farm energy storage system based on state of charge, provided in some embodiments of this application. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0059] Example 1

[0060] Please refer to Figure 1, which illustrates a power regulation method for an offshore wind farm energy storage system based on state of charge, according to an embodiment of this application. The method includes steps S10 to S30, specifically:

[0061] S10: Obtain the grid frequency and the state of charge of the offshore wind farm energy storage system, and obtain the grid frequency deviation and charge recovery requirements based on the grid frequency and the state of charge.

[0062] Specifically, in some embodiments of this application, the aforementioned power grid frequency deviation and charge recovery requirement are obtained through the following steps:

[0063] S11: Real-time acquisition of the state of charge (SOC) of the energy storage system and the grid frequency (f) during real-time grid operation via sensors;

[0064] S12: Obtain the parameter: Rated frequency of the power grid f N And according to the rated frequency f N Obtain the power grid frequency deviation Δf, where Δf = ff N ;

[0065] S13: Obtain the reference value of the State of Charge (SOC) of the energy storage system when the grid frequency dead zone is in use. b And according to SOC b Obtain the charge recovery requirement ΔSOC, where ΔSOC = SOC b -SOC.

[0066] S20: Determine whether the grid frequency deviation is within a preset grid frequency dead zone; if it is within the grid frequency dead zone, combine the grid frequency deviation and the charge recovery requirement, and obtain the inertial power regulation amount of the energy storage system through a fuzzy control algorithm combined with a preset adaptive inertial frequency control model; otherwise, combine the grid frequency deviation and the charge recovery requirement, and obtain the inertial power regulation amount through the adaptive inertial frequency control model.

[0067] Furthermore, in some embodiments of this application, the steps for building the adaptive inertia frequency control model include:

[0068] By combining the Sigmoid function and the charge constraint of the energy storage system, the discharge coefficient control function of the energy storage system is obtained;

[0069] The adaptive inertia frequency control model is obtained by combining the discharge coefficient control function and the inertial response equation of the offshore wind farm energy storage system.

[0070] Since the Sigmoid function is exponentially generated in the initial stage and then gradually saturates and approaches an asymptote parallel to the x-axis, this characteristic is compatible with the charge constraint conditions of the energy storage system. The discharge system control function obtained by combining the two can improve the accuracy of the subsequent adaptive inertia frequency control model.

[0071] Preferably, in some embodiments of this application, the adaptive inertia frequency control model can be obtained through the following preferred implementation methods:

[0072] S211: Simulate the rotor motion equation of a synchronous motor. Based on the inertial response characteristics of the offshore wind farm energy storage system, obtain the following inertial response equation for the offshore wind farm energy storage system:

[0073] Among them, P ess The inertial power provided to the energy storage system; J v and D v These are the virtual inertia coefficient and the virtual damping coefficient, respectively; Δf is the grid frequency deviation. Take the derivative of the grid frequency f with respect to time t.

[0074] S212: Based on the charge constraint conditions and the Sigmoid function of the energy storage system, the discharge coefficient control function of the energy storage system is constructed through the following steps:

[0075] The charge constraint condition for the energy storage system is: SOC min ≤SOC≤SOC max

[0076] The Sigmoid function is:

[0077] Combining the above charge constraints and the Sigmoid function, and substituting the charge state of the energy storage system into the variable t in the Sigmoid function, the specific steps are as follows:

[0078] When Δf < 0, meaning the grid frequency is lower than the rated value and the energy storage device needs to discharge, the discharge coefficient control function of the energy storage system is:

[0079] When Δf > 0, meaning the grid frequency is higher than the rated value and the energy storage device needs charging, the discharge coefficient control function of the energy storage system is:

[0080] Where, k sc and k sd These represent the discharge coefficients of the energy storage system when the grid frequency is below the rated frequency and when the grid frequency is above the rated frequency, respectively; k s It is a constant; SOC min and SOC max , , represent the minimum and maximum states of charge of the energy storage system, respectively; e is the base of the natural logarithm; Δf is the grid frequency deviation; and SOC is the state of charge of the energy storage system.

[0081] S213: By combining the inertial response equation from step S211 with the discharge coefficient control function from S212, the following adaptive inertia frequency control model is obtained:

[0082] As can be seen from the formula in step S212, when the grid frequency deviation is less than 0, it indicates that the grid frequency is lower than the rated value. In this case, the energy storage device needs to discharge to compensate for the grid frequency difference. Conversely, when the grid frequency deviation is greater than 0, it indicates that the grid frequency is higher than the rated value. In this case, the energy storage device needs to be charged to absorb excess energy from the grid. However, in addition to considering the fluctuations in grid frequency, the current state of charge of the energy storage system also needs to be taken into account. If the current state of charge of the energy storage system is at full load, even if the grid frequency is higher than the rated value, it is impossible to forcibly start the energy storage system for charging. Therefore, by combining the Sigmoid function, the power fluctuations of the energy storage device during charging and discharging can be smoothly controlled, improving the stability of the grid system. As can be seen from the formula in step S213, by combining the fixed inertial response equation with the discharge coefficient control function that adaptively adjusts the discharge coefficient, the inertial power provided by the energy storage system is adaptively adjusted according to the frequency fluctuations of the current grid system. This solves the problem of unhealthy state of charge of the energy storage system caused by the fixed virtual inertial coefficient and virtual damping coefficient, thus improving the safety of the grid system.

[0083] Furthermore, in some embodiments of this application, obtaining the inertial power regulation amount of the energy storage system by combining a fuzzy control algorithm with a preset adaptive inertia frequency control model includes:

[0084] The grid frequency deviation and the charge recovery requirement are input into the fuzzy control algorithm to obtain the first inertial power regulation amount;

[0085] The grid frequency deviation and the charge recovery requirement are input into the adaptive inertia frequency control model to obtain the second inertial power regulation amount;

[0086] The inertial power control amount is obtained by combining the first inertial power control amount and the second inertial power control amount.

[0087] Since the grid frequency deviation is in the grid frequency dead zone, the adaptive inertia frequency control model cannot meet the precision requirements of power control of the energy storage system. In this case, the energy storage system can introduce fuzzy control to finely adjust the small frequency deviation and improve the stability of the grid system.

[0088] Preferably, in some embodiments of this application, referring to Figure 2, therein is an inertial power regulation method when the grid frequency deviation is in the grid frequency dead zone and when it is not in the dead zone, specifically:

[0089] After collecting the grid frequency and the state of charge (SOC) of the energy storage system, the grid frequency deviation and charge recovery requirement are calculated based on these parameters. Then, it is determined whether the grid frequency deviation is within the dead zone [-0.025, 0.025]. If it is, the inertial power regulation amount is obtained by combining the frequency inertia (i.e., the inertial response equation), adaptive control (i.e., the discharge coefficient control function), and fuzzy control algorithm. The frequency inertia and adaptive control are used to obtain the second inertial power regulation amount, and the fuzzy control algorithm is used to obtain the first inertial power regulation amount. The second and first inertial power regulation amounts are added together to obtain the final inertial power regulation amount. Otherwise, only the frequency inertia and adaptive control are used to obtain the second inertial power regulation amount as the final inertial power regulation amount.

[0090] Furthermore, in some embodiments of this application, the step of inputting the grid frequency deviation and the charge recovery requirement into the fuzzy control algorithm to obtain the first inertial power regulation amount includes:

[0091] Based on the preset membership diagrams corresponding to the power grid frequency deviation and the charge recovery requirement, determine the first fuzzy variable and the second fuzzy variable corresponding to the power grid frequency deviation and the charge recovery requirement, respectively.

[0092] Based on the preset fuzzy rule table, and in combination with the first fuzzy variable and the second fuzzy variable, the third fuzzy variable corresponding to the first inertial power control amount is determined;

[0093] The area center corresponding to the third fuzzy variable is calculated using the area center method, and the area center is used as the first inertial power control quantity.

[0094] Fuzzy control is a control method based on fuzzy logic that mimics human decision-making processes. It is particularly suitable for complex systems that are difficult to describe using precise mathematical models in classical control theory. Since the existing adaptive inertia frequency control model cannot accurately describe the inertial response of energy storage systems to grid frequency fluctuations when the grid frequency deviation is in the grid frequency dead zone, by setting up corresponding fuzzy rule tables and membership graphs of each variable, continuous variables are discretized into fuzzy variables and then decentered using the area center method. This compensates for the insufficient accuracy of the adaptive inertia frequency control model in a small dead zone and improves the accuracy of the energy storage system in adaptively adjusting inertial power.

[0095] Preferably, in some embodiments of this application, the step of obtaining the first inertial power control amount through the fuzzy control algorithm can be specifically performed by the following preferred implementation methods:

[0096] S221: In some embodiments of this application, a two-dimensional fuzzy controller is used, that is, the number of inputs to the fuzzy control algorithm is two, namely the grid frequency deviation Δf and the charge recovery demand ΔSOC; the output variable is set as the first inertial power regulation quantity, which is P here. ess The fuzzy sets for the input and output variables are set as {NB, NS, ZO, PS, PB}, representing the fuzzy variables {negative large, negative small, zero, positive small, positive large}, respectively. The membership diagram of each fuzzy variable is shown in Figure 3. From Figure 3, it can be seen that the universe of discourse for ΔSOC is set to [-0.6, 0.6], the universe of discourse for Δf is set to [-0.025, 0.025], and P... ess The universe of discourse is set to [-0.97, 0.97]. Based on the actual input and output variable values, the corresponding fuzzy variables are found in Figure 3; for example, if the input variables grid frequency deviation Δf and charge recovery demand ΔSOC are -0.015 and 0.25 respectively, then the input variables grid frequency deviation Δf and charge recovery demand ΔSOC correspond to the fuzzy variables NS and PS respectively.

[0097] S222: After obtaining the fuzzy variables corresponding to the input variables according to S221, find the fuzzy variables of the output variables from Table 1 below; where Table 1 is a fuzzy rule table determined according to the set fuzzy rules; for example, the fuzzy variables of the input variables grid frequency deviation Δf and charge recovery demand ΔSOC are NS and PS respectively, and according to Table 1 below, the output variable first inertial power control quantity P is known. ess The fuzzy variable corresponding to ZO is .

[0098] S223: The area center method is used to adjust the first inertial power control quantity P obtained from S222. ess The corresponding fuzzy variables are defuzzified. Among them, the area center method is a variant of the centroid method, which is particularly suitable for the Mamdani inference system. After calculating the area center of the fuzzy output set, the area center is used as the clear output value.

[0099] Table 1

[0100] Preferably, in some embodiments of this application, the above-mentioned fuzzy rule table can be obtained based on fuzzy rules set according to the following principles:

[0101] Principle 1: When ΔSOC>0, it indicates that SOC b When the SOC is greater than the SOC, and Δf is a positive value, the energy storage system quickly charges to restore the SOC value; conversely, when Δf is a negative value, the energy storage system does not operate.

[0102] Principle 2: When ΔSOC < 0, it indicates that SOC bWhen the SOC is less than the SOC, and Δf is negative, the energy storage system discharges rapidly to restore the SOC value; conversely, when Δf is positive, the energy storage system does not operate.

[0103] S30: Adjust the inertial power of the offshore wind farm energy storage system according to the inertial power adjustment amount.

[0104] In summary, it can be seen that the power regulation method for offshore wind farm energy storage system based on state of charge provided in this application has the following beneficial effects:

[0105] When the grid frequency deviation is within the grid frequency dead zone, fuzzy control can be introduced to finely adjust the minute frequency deviation, solving the problem that the high-power regulation of the system cannot meet the fine adjustment requirements and improving the stability of the grid system. In addition, by considering the current state of charge of the energy storage system based on the charge recovery requirements, adaptive adjustment can be performed to avoid the unhealthy state of charge of the energy storage system caused by fixed virtual inertia coefficients and virtual damping coefficients, thereby improving the safety and accuracy of the grid system.

[0106] Example 2

[0107] Referring to Figure 4, a power regulation device for an offshore wind farm energy storage system based on state of charge is provided in an embodiment of this application, including: a first data acquisition module 11, an inertial power regulation quantity acquisition module 12, and an inertial power regulation module 13.

[0108] In some embodiments of this application, the first data acquisition module 11 is used to acquire the grid frequency and the state of charge of the offshore wind farm energy storage system, and to acquire the grid frequency deviation and charge recovery requirement based on the grid frequency and the state of charge, respectively; the inertial power regulation acquisition module 12 is used to determine whether the grid frequency deviation is within a preset grid frequency dead zone; if it is within the grid frequency dead zone, the inertial power regulation amount of the energy storage system is acquired by combining the grid frequency deviation and the charge recovery requirement with a fuzzy control algorithm and a preset adaptive inertial frequency control model; otherwise, the inertial power regulation amount is acquired by combining the grid frequency deviation and the charge recovery requirement with the adaptive inertial frequency control model; the inertial power regulation module 13 is used to regulate the inertial power of the offshore wind farm energy storage system according to the inertial power regulation amount.

[0109] In some embodiments of this application, the steps for constructing the adaptive inertia frequency control model include: obtaining the discharge coefficient control function of the energy storage system by combining the Sigmoid function and the charge constraint of the energy storage system; and obtaining the adaptive inertia frequency control model by combining the discharge coefficient control function and the inertial response equation of the offshore wind farm energy storage system.

[0110] In some embodiments of this application, the step of combining the Sigmoid function and the charge constraint of the energy storage system to obtain the discharge coefficient control function of the energy storage system includes:

[0111] The discharge coefficient control function of the energy storage system is as follows:

[0112] Where, k sc and k sd These represent the discharge coefficients of the energy storage system when the grid frequency is below the rated frequency and when the grid frequency is above the rated frequency, respectively; k s It is a constant; SOC min and SOC max , , represent the minimum and maximum states of charge of the energy storage system, respectively; e is the base of the natural logarithm; Δf is the grid frequency deviation; and SOC is the state of charge of the energy storage system.

[0113] In some embodiments of this application, obtaining the adaptive inertia frequency control model by combining the discharge coefficient control function and the inertial response equation of the offshore wind farm energy storage system includes:

[0114] The adaptive inertia frequency control model is specifically as follows:

[0115] Among them, P ess Inertial power provided to the energy storage system; k sc and k sd These are the discharge coefficients of the energy storage system when the grid frequency is below the rated frequency and when the grid frequency is above the rated frequency, respectively; J v and D v These are the virtual inertia coefficient and the virtual damping coefficient, respectively; Δf is the grid frequency deviation. Take the derivative of the grid frequency f with respect to time t.

[0116] In some embodiments of this application, obtaining the inertial power regulation amount of the energy storage system by combining a fuzzy control algorithm with a preset adaptive inertia frequency control model includes:

[0117] The grid frequency deviation and the charge recovery requirement are input into the fuzzy control algorithm to obtain the first inertial power regulation amount;

[0118] The grid frequency deviation and the charge recovery requirement are input into the adaptive inertia frequency control model to obtain the second inertial power regulation amount;

[0119] The inertial power control amount is obtained by combining the first inertial power control amount and the second inertial power control amount.

[0120] In some embodiments of this application, the step of inputting the grid frequency deviation and the charge recovery requirement into the fuzzy control algorithm to obtain the first inertial power regulation amount includes:

[0121] Based on the preset membership diagrams corresponding to the power grid frequency deviation and the charge recovery requirement, determine the first fuzzy variable and the second fuzzy variable corresponding to the power grid frequency deviation and the charge recovery requirement, respectively.

[0122] Based on the preset fuzzy rule table, and in combination with the first fuzzy variable and the second fuzzy variable, the third fuzzy variable corresponding to the first inertial power control amount is determined;

[0123] The area center corresponding to the third fuzzy variable is calculated using the area center method, and the area center is used as the first inertial power control quantity.

[0124] In summary, it can be seen that the power regulation device for offshore wind farm energy storage system based on state of charge provided in this application has the following beneficial effects:

[0125] When the grid frequency deviation is within the grid frequency dead zone, fuzzy control can be introduced to finely adjust the minute frequency deviation, solving the problem that the high-power regulation of the system cannot meet the fine adjustment requirements and improving the stability of the grid system. In addition, by considering the current state of charge of the energy storage system based on the charge recovery requirements, adaptive adjustment can be performed to avoid the unhealthy state of charge of the energy storage system caused by fixed virtual inertia coefficients and virtual damping coefficients, thereby improving the safety and accuracy of the grid system.

[0126] Example 3

[0127] Based on the above embodiments of the power regulation method for offshore wind farm energy storage systems based on state of charge, another embodiment of this application provides a power regulation terminal device for offshore wind farm energy storage systems based on state of charge. This power regulation terminal device for offshore wind farm energy storage systems based on state of charge includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power regulation method for offshore wind farm energy storage systems based on state of charge of any embodiment of this application.

[0128] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the power regulation device of the offshore wind farm energy storage system based on state of charge.

[0129] The power regulation device for the offshore wind farm energy storage system based on state of charge can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The power regulation terminal device for the offshore wind farm energy storage system based on state of charge may include, but is not limited to, a processor and a memory.

[0130] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the state-of-charge (SOC)-based offshore wind farm energy storage system power regulation equipment, connecting various parts of the entire SOC-based offshore wind farm energy storage system power regulation equipment via various interfaces and lines. The memory can be used to store the computer programs and / or modules. The processor implements various functions of the SOC-based offshore wind farm energy storage system power regulation equipment by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, at least one application program required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0131] Example 4

[0132] Based on the above embodiments of the power regulation method for offshore wind farm energy storage system based on state of charge, another embodiment of this application provides a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, the device where the storage medium is located controls the execution of the power regulation method for offshore wind farm energy storage system based on state of charge of any embodiment of this application.

[0133] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0134] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A power regulation method for an offshore wind farm energy storage system based on state of charge, characterized in that, include: The grid frequency and the state of charge of the offshore wind farm energy storage system are obtained, and the grid frequency deviation and charge recovery requirements are obtained based on the grid frequency and the state of charge, respectively. Determine whether the grid frequency deviation is within a preset grid frequency dead zone; if it is within the grid frequency dead zone, combine the grid frequency deviation and the charge recovery requirement, and obtain the inertial power regulation amount of the energy storage system through a fuzzy control algorithm combined with a preset adaptive inertial frequency control model; otherwise, combine the grid frequency deviation and the charge recovery requirement, and obtain the inertial power regulation amount through the adaptive inertial frequency control model. The inertial power of the offshore wind farm energy storage system is adjusted according to the inertial power adjustment amount.

2. The power regulation method for an offshore wind farm energy storage system based on state of charge as described in claim 1, characterized in that, The steps for building the adaptive inertia frequency control model include: By combining the Sigmoid function and the charge constraint of the energy storage system, the discharge coefficient control function of the energy storage system is obtained; The adaptive inertia frequency control model is obtained by combining the discharge coefficient control function and the inertial response equation of the offshore wind farm energy storage system.

3. The power regulation method for an offshore wind farm energy storage system based on state of charge as described in claim 2, characterized in that, The process of combining the Sigmoid function and the charge constraint of the energy storage system to obtain the discharge coefficient control function of the energy storage system includes: The discharge coefficient control function of the energy storage system is as follows: Where, k sc and k sd These represent the discharge coefficients of the energy storage system when the grid frequency is below the rated frequency and when the grid frequency is above the rated frequency, respectively; k s It is a constant; SOC min and SOC max , , represent the minimum and maximum states of charge of the energy storage system, respectively; e is the base of the natural logarithm; Δf is the grid frequency deviation; and SOC is the state of charge of the energy storage system.

4. The power regulation method for an offshore wind farm energy storage system based on state of charge as described in claim 2, characterized in that, The adaptive inertia frequency control model is obtained by combining the discharge coefficient control function and the inertial response equation of the offshore wind farm energy storage system, including: The adaptive inertia frequency control model is specifically as follows: Among them, P ess Inertial power provided to the energy storage system; k sc and k sd These are the discharge coefficients of the energy storage system when the grid frequency is below the rated frequency and when the grid frequency is above the rated frequency, respectively; J v and D v These are the virtual inertia coefficient and the virtual damping coefficient, respectively; Δf is the grid frequency deviation. Take the derivative of the power grid frequency f with respect to time t.

5. The power regulation method for an offshore wind farm energy storage system based on state of charge as described in claim 1, characterized in that, The method of obtaining the inertial power regulation of the energy storage system by combining a fuzzy control algorithm with a preset adaptive inertia frequency control model includes: The grid frequency deviation and the charge recovery requirement are input into the fuzzy control algorithm to obtain the first inertial power regulation amount; The grid frequency deviation and the charge recovery requirement are input into the adaptive inertia frequency control model to obtain the second inertial power regulation amount; The inertial power control amount is obtained by combining the first inertial power control amount and the second inertial power control amount.

6. The power regulation method for an offshore wind farm energy storage system based on state of charge as described in claim 5, characterized in that, The step of inputting the grid frequency deviation and the charge recovery requirement into the fuzzy control algorithm to obtain the first inertial power regulation amount includes: Based on the preset membership diagrams corresponding to the power grid frequency deviation and the charge recovery requirement, determine the first fuzzy variable and the second fuzzy variable corresponding to the power grid frequency deviation and the charge recovery requirement, respectively. Based on the preset fuzzy rule table, and in combination with the first fuzzy variable and the second fuzzy variable, the third fuzzy variable corresponding to the first inertial power control amount is determined; The area center corresponding to the third fuzzy variable is calculated using the area center method, and the area center is used as the first inertial power control quantity.

7. A power regulation device for an offshore wind farm energy storage system based on state of charge, characterized in that, include: The module includes a first data acquisition module, an inertial power control quantity acquisition module, and an inertial power control module; The first data acquisition module is used to acquire the grid frequency and the state of charge of the offshore wind farm energy storage system, and to acquire the grid frequency deviation and the charge recovery requirement based on the grid frequency and the state of charge. The inertial power regulation acquisition module is used to determine whether the grid frequency deviation is within a preset grid frequency dead zone. If it is within the grid frequency dead zone, the module combines the grid frequency deviation and the charge recovery requirement with a fuzzy control algorithm and a preset adaptive inertial frequency control model to obtain the inertial power regulation of the energy storage system. Otherwise, the module combines the grid frequency deviation and the charge recovery requirement with the adaptive inertial frequency control model to obtain the inertial power regulation. The inertial power control module is used to control the inertial power of the offshore wind farm energy storage system according to the inertial power control amount.

8. The power regulation device for an offshore wind farm energy storage system based on state of charge as described in claim 7, characterized in that, The steps for building the adaptive inertia frequency control model include: By combining the Sigmoid function and the charge constraint of the energy storage system, the discharge coefficient control function of the energy storage system is obtained; The adaptive inertia frequency control model is obtained by combining the discharge coefficient control function and the inertial response equation of the offshore wind farm energy storage system.

9. The power regulation device for an offshore wind farm energy storage system based on state of charge as described in claim 8, characterized in that, The process of combining the Sigmoid function and the charge constraint of the energy storage system to obtain the discharge coefficient control function of the energy storage system includes: The discharge coefficient control function of the energy storage system is as follows: Where, k sc and k sd These represent the discharge coefficients of the energy storage system when the grid frequency is below the rated frequency and when the grid frequency is above the rated frequency, respectively; k s It is a constant; SOC min and SOC max , , represent the minimum and maximum states of charge of the energy storage system, respectively; e is the base of the natural logarithm; Δf is the grid frequency deviation; and SOC is the state of charge of the energy storage system.

10. The power regulation device for an offshore wind farm energy storage system based on state of charge as described in claim 8, characterized in that, The adaptive inertia frequency control model is obtained by combining the discharge coefficient control function and the inertial response equation of the offshore wind farm energy storage system, including: The adaptive inertia frequency control model is specifically as follows: Among them, P ess Inertial power provided to the energy storage system; k sc and k sd These are the discharge coefficients of the energy storage system when the grid frequency is below the rated frequency and when the grid frequency is above the rated frequency, respectively; J v and D v These are the virtual inertia coefficient and the virtual damping coefficient, respectively; Δf is the grid frequency deviation. Take the derivative of the power grid frequency f with respect to time t.

11. The power regulation device for an offshore wind farm energy storage system based on state of charge as described in claim 7, characterized in that, The method of obtaining the inertial power regulation of the energy storage system by combining a fuzzy control algorithm with a preset adaptive inertia frequency control model includes: The grid frequency deviation and the charge recovery requirement are input into the fuzzy control algorithm to obtain the first inertial power regulation amount; The grid frequency deviation and the charge recovery requirement are input into the adaptive inertia frequency control model to obtain the second inertial power regulation amount; The inertial power control amount is obtained by combining the first inertial power control amount and the second inertial power control amount.

12. The power regulation device for an offshore wind farm energy storage system based on state of charge as described in claim 11, characterized in that, The step of inputting the grid frequency deviation and the charge recovery requirement into the fuzzy control algorithm to obtain the first inertial power regulation amount includes: Based on the preset membership diagrams corresponding to the power grid frequency deviation and the charge recovery requirement, determine the first fuzzy variable and the second fuzzy variable corresponding to the power grid frequency deviation and the charge recovery requirement, respectively. Based on the preset fuzzy rule table, and in combination with the first fuzzy variable and the second fuzzy variable, the third fuzzy variable corresponding to the first inertial power control amount is determined; The area center corresponding to the third fuzzy variable is calculated using the area center method, and the area center is used as the first inertial power control quantity.

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