A wind farm voltage control method, device, equipment and storage medium

Abstract

The present application provides a method, device, equipment and storage medium for controlling voltage in a wind farm, which relates to the field of wind power technology. Through the Jason wake model, the available wind power of the wind turbine is calculated, the node admittance matrix of the wind farm is constructed, and based on the terminal voltage of each node, the voltage sensitivity matrix is ​​calculated, and a model predictive control framework is constructed. The control problem in the model predictive control framework is converted into a quadratic programming problem. By solving the problem, the optimal power injection strategy of the wind farm that meets the grid operation requirements is obtained. By calculating the wind power based on the Jason wake model, the available wind power of each wind turbine can be more accurately evaluated, and the power loss caused by wind speed fluctuations and wake effects can be reduced. The energy storage system and the static VAR generator are modeled to coordinate and maintain the terminal voltage of all wind turbines within a feasible range, which plays a role in peak shaving and valley filling to reduce wind energy waste and improve the economy and operational reliability of the wind farm.

Description

Technical Field

[0001] The present application relates to the field of wind power technology, and in particular to a method, device, equipment and storage medium for controlling voltage in a wind farm. Background Art

[0002] With the growing global demand for renewable energy, wind power, as one of the clean and renewable energy forms, has shown a rapid growth trend in installed capacity and power generation. However, the operation and control of wind farms still face many challenges. On the one hand, the randomness and intermittency of wind speed lead to large fluctuations in wind power output, which is difficult to accurately predict and stably control; on the other hand, wind farms usually contain a large number of wind turbines, and the mutual influence between these units (such as wake effect) increases the complexity of system control. In addition, wind farms also need to interact efficiently and stably with the power grid to meet the dispatching needs and operational stability requirements of the power grid.

[0003] Traditional wind farm control methods are often based on simple feedback control or open-loop control strategies, which are difficult to achieve efficient and stable operation of wind farms in complex and changing operating environments. Therefore, there is an urgent need for a new wind farm control method that can improve the power generation efficiency and response speed of wind farms while ensuring system stability, so as to better meet the dispatching needs of the power grid and the development goals of renewable energy. Summary of the invention

[0004] In view of the above problems, the present application provides a wind farm voltage control method, device, equipment and storage medium, including the following contents:

[0005] In a first aspect, the present application provides a wind farm voltage control method, the method comprising:

[0006] Collect wind speed and direction data of wind farms;

[0007] Based on the Jason wake model, the available wind power of each wind turbine is calculated;

[0008] Construct the node admittance matrix of the wind farm and calculate the voltage sensitivity matrix based on the terminal voltage of each node to establish the relationship between bus voltage and power injection;

[0009] Construct the state space equations of wind turbine, energy storage system and static VAR generator respectively;

[0010] A model predictive control framework is constructed, and the control problem in the model predictive control framework is converted into a quadratic programming problem. The quadratic programming problem includes upper and lower bound constraints on the voltage of each node in the wind farm and upper and lower bound constraints on the power output of the power source. By solving the quadratic programming problem, the optimal power injection strategy of the wind farm that meets the grid operation requirements is obtained.

[0011] Optionally, the calculation of the available wind power of each wind turbine based on the Jason wake model includes:

[0012] The Jason wake model formula is used to describe the influence of the upstream wind turbine WTi on the wind speed of the downstream wind turbine WTj:

[0013] ;

[0014] where v i is the wind speed of WTi, C i is the thrust coefficient of WTi, k is the wake expansion coefficient, D ij is the distance between WTi and WTj along the wind direction, R is the rotor radius of the wind turbine, A0 is the rotor area, is the wake shadow area of ​​WTi caused by WTj;

[0015] ;

[0016] Calculate the impact of the upstream fan on the wind speed of each downstream fan, where I i is the set of upstream wind turbines that produce wake effects on downstream wind turbines, v0 is the free stream wind speed;

[0017] The actual available wind power calculation formula for each wind turbine under the wake effect is:

[0018] ;

[0019] ρ represents the air density, C p max It represents the maximum wind energy utilization coefficient under the influence of the upstream fan on the wind speed of the downstream fan, η represents the fan efficiency; A0 is the rotor area; v is the wind speed of the fan after considering the wake effect.

[0020] Optionally, the voltage sensitivity matrix is:

[0021] ;

[0022] ;

[0023] In the wind farm, the original topology is regarded as a network consisting of 1 slack bus S and N PQ buses; where S∪N={1,2,…,N+1}; represents the sensitivity of the voltage of bus j to the active power injected into bus l; represents the sensitivity of the voltage of bus j to the reactive power injected by bus l; Indicates plural, represents the conjugate of a complex number; represents the apparent power output by node i; , denote the conjugate of the voltage at node i and the voltage at node j respectively; is the element in the i-th row and j-th column of the node admittance matrix;

[0024] ;

[0025] ;

[0026] in, is the sensitivity of the voltage amplitude of bus i to the active power injected on bus l, is the sensitivity of the voltage phase angle of bus i to the active power injected on bus l, is the sensitivity of the voltage amplitude of bus i to the reactive power injected on bus l, is the sensitivity of the voltage phase angle of bus i to the reactive power injected on bus l.

[0027] Optionally, the state space equation of the fan is:

[0028] ;

[0029] in, and Respectively represent the increment of the active and reactive power output reference value of the wind turbine, ΔP wt and ΔQ wt are the increments of active and reactive power output of the wind turbine respectively; T P and T Q are active and reactive time constants respectively; and are the derivatives of the wind turbine’s active power and reactive power output increments, respectively.

[0030] Optionally, the state control equation of the energy storage system ESS is:

[0031] ;

[0032] Among them, x c and x s They represent the charging and discharging power of ESS at time t; u c and u s are binary variables that define the charging and discharging states of ESS; η c and η s are the efficiency coefficients of ESS during charging and discharging, both set to 0.9; SOC is the state of charge, ε is the minimum storage energy of ESS, set to 0.1; E t represents the storage energy of ESS over time, E cap is the maximum capacity of the ESS.

[0033] Optionally, the state space equation of the static VAR generator SVG is:

[0034] ;

[0035] in, , and T svg They represent the increment of the SVG output reactive power reference value, the increment of the SVG output reactive power and the reactive time constant respectively; where, It is the derivative of the SVG output reactive power increment.

[0036] Optionally, the model predictive control framework includes a sensitivity matrix and a state space equation:

[0037] ;

[0038] Where ΔV, Δθ, ΔP and ΔQ are the increments of voltage amplitude, voltage phase angle, active power and reactive power respectively; , , and They are the voltage to power sensitivity matrices, which are used to linearize the wind farm model;

[0039] Continuous state space equations for a wind farm:

[0040] ;

[0041] in:

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] Where ΔP ess Represents the increase in active power of the energy storage system, ΔP wt Indicates the increase in active power of the fan;

[0047] ΔQ svg Represents the increment of reactive power of static var generator, ΔQ wt Indicates the increase in reactive power of the fan;

[0048] , They respectively represent the increment of the active power output reference value of the energy storage system and the increment of the reactive power output reference value of the static VAR generator;

[0049] , Respectively represent the increment of the reference value of the active and reactive power output of the wind turbine;

[0050] represents the active time constant of the energy storage system, Represents the reactive time constant of the static reactive generator;

[0051] , Respectively represent the active and reactive time constants of the fan.

[0052] Optionally, the upper and lower bound constraints of the voltage of each node in the wind farm and the upper and lower bound constraints of the power output of the power source are:

[0053] ;

[0054] Represents the voltage constraint, which is used to ensure that the voltage at the wind turbine terminal remains within the feasible range and does not exceed the upper and lower limits allowed;

[0055] ;

[0056] Represents the active power constraint, which is used to limit the active power output of the wind turbine to ensure that the maximum available wind power is not exceeded;

[0057]

[0058] Represents reactive power constraint, which is used to limit the reactive power output of the wind turbine to ensure that the power output does not exceed the capacity of the wind turbine.

[0059] Optionally, obtaining the optimal power injection strategy of the wind farm under the condition of meeting the grid operation requirements by solving the quadratic programming problem includes:

[0060] The objective function of reducing the average voltage deviation of the wind turbine is established, and the objective function of minimizing the average voltage deviation at the wind turbine end is expressed as:

[0061] ;

[0062] According to the sensitivity, the terminal voltage deviation ΔV of each fan at time k is calculated. wt (k) is:

[0063] ;

[0064] is the terminal voltage of each wind turbine at the initial moment, ΔQ(k) is the reactive power increment of the wind turbine at moment k, N is the terminal voltage reference value of the fan; Tis the set of wind turbine nodes;

[0065] After obtaining the optimal power injection strategy of the wind farm under the condition of meeting the grid operation requirements, the method further includes:

[0066] According to the optimal power injection strategy, the power generation of the wind turbine is dynamically adjusted to ensure that its output meets the instructions of the transmission system operator TSO,

[0067] ;

[0068] in is the active power instruction issued by TSO, is the active power reference value of wind turbine i.

[0069] In a second aspect, the present application provides a device, the device comprising:

[0070] A collection unit, used to collect wind speed data and wind direction data of the wind farm;

[0071] A first calculation unit, used for calculating the available wind power of each wind turbine based on the Jason wake model;

[0072] The second calculation unit is used to construct a node admittance matrix of the wind farm and calculate a voltage sensitivity matrix based on the terminal voltage of each node to establish a correlation between the bus voltage and the power injection;

[0073] A model building unit, used for building state space equations of the wind turbine, the energy storage system and the static VAR generator respectively;

[0074] The model building unit is also used to build a model predictive control framework, convert the control problem in the model predictive control framework into a quadratic programming problem, the quadratic programming problem includes upper and lower bound constraints on the voltage of each node in the wind farm and upper and lower bound constraints on the power output of the power source, and by solving the quadratic programming problem, the optimal power injection strategy of the wind farm that meets the grid operation requirements is obtained.

[0075] Optionally, the calculation of the available wind power of each wind turbine based on the Jason wake model includes:

[0076] The Jason wake model formula is used to describe the influence of the upstream wind turbine WTi on the wind speed of the downstream wind turbine WTj:

[0077] ;

[0078] where v i is the wind speed of WTi, C i is the thrust coefficient of WTi, k is the wake expansion coefficient, D ijis the distance between WTi and WTj along the wind direction, R is the rotor radius of the wind turbine, A0 is the rotor area, is the wake shadow area of ​​WTi caused by WTj;

[0079] ;

[0080] Calculate the impact of the upstream fan on the wind speed of each downstream fan, where I i is the set of upstream wind turbines that produce wake effects on downstream wind turbines, v0 is the free stream wind speed;

[0081] The actual available wind power calculation formula for each wind turbine under the wake effect is:

[0082] ;

[0083] ρ represents the air density, C p max It represents the maximum wind energy utilization coefficient under the influence of the upstream fan on the wind speed of the downstream fan, η represents the fan efficiency; A0 is the rotor area; v is the wind speed of the fan after considering the wake effect.

[0084] Optionally, the voltage sensitivity matrix is:

[0085] ;

[0086] ;

[0087] In the wind farm, the original topology is regarded as a network consisting of 1 slack bus S and N PQ buses; where S∪N={1,2,…,N+1}; represents the sensitivity of the voltage of bus j to the active power injected into bus l; represents the sensitivity of the voltage of bus j to the reactive power injected by bus l; Indicates plural, represents the conjugate of a complex number; represents the apparent power output by node i; , denote the conjugate of the voltage at node i and the voltage at node j respectively; is the element in the i-th row and j-th column of the node admittance matrix;

[0088] ;

[0089] ;

[0090] in, is the sensitivity of the voltage amplitude of bus i to the active power injected on bus l, is the sensitivity of the voltage phase angle of bus i to the active power injected on bus l, is the sensitivity of the voltage amplitude of bus i to the reactive power injected on bus l, is the sensitivity of the voltage phase angle of bus i to the reactive power injected on bus l.

[0091] Optionally, the state space equation of the fan is:

[0092] ;

[0093] in, and Respectively represent the increment of the active and reactive power output reference value of the wind turbine, ΔP wt and ΔQ wt are the increments of active and reactive power output of the wind turbine respectively; T P and T Q are active and reactive time constants respectively; and are the derivatives of the wind turbine’s active power and reactive power output increments, respectively.

[0094] Optionally, the state control equation of the energy storage system ESS is:

[0095] ;

[0096] Among them, x c and x s They represent the charging and discharging power of ESS at time t; u c and u s are binary variables that define the charging and discharging states of ESS; η c and η s are the efficiency coefficients of ESS during charging and discharging, both set to 0.9; SOC is the state of charge, ε is the minimum storage energy of ESS, set to 0.1; E t represents the storage energy of ESS over time, E cap is the maximum capacity of the ESS.

[0097] Optionally, it is characterized in that the state space equation of the static VAR generator SVG is:

[0098] ;

[0099] in, , and T svg They represent the increment of the SVG output reactive power reference value, the increment of the SVG output reactive power and the reactive time constant respectively; where, It is the derivative of the SVG output reactive power increment.

[0100] Optionally, it is characterized in that the model predictive control framework includes a sensitivity matrix and a state space equation:

[0101] ;

[0102] Where ΔV, Δθ, ΔP and ΔQ are the increments of voltage amplitude, voltage phase angle, active power and reactive power respectively; , , and They are the voltage to power sensitivity matrices, which are used to linearize the wind farm model;

[0103] Continuous state space equations for a wind farm:

[0104] ;

[0105] in:

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] Where ΔP ess Represents the increase in active power of the energy storage system, ΔP wt Indicates the increase in active power of the fan;

[0111] ΔQ svg Represents the increment of reactive power of static var generator, ΔQ wt Indicates the increase in reactive power of the fan;

[0112] , They respectively represent the increment of the active power output reference value of the energy storage system and the increment of the reactive power output reference value of the static VAR generator;

[0113] , Respectively represent the increment of the reference value of the active and reactive power output of the wind turbine;

[0114] represents the active time constant of the energy storage system, Represents the reactive time constant of the static reactive generator;

[0115] , Respectively represent the active and reactive time constants of the fan.

[0116] Optionally, the upper and lower bound constraints of the voltage of each node in the wind farm and the upper and lower bound constraints of the power output of the power source are:

[0117] ;

[0118] Represents the voltage constraint, which is used to ensure that the voltage at the wind turbine terminal remains within the feasible range and does not exceed the upper and lower limits allowed;

[0119] ;

[0120] Represents the active power constraint, which is used to limit the active power output of the wind turbine to ensure that the maximum available wind power is not exceeded;

[0121] ;

[0122] Represents reactive power constraint, which is used to limit the reactive power output of the wind turbine to ensure that the power output does not exceed the capacity of the wind turbine.

[0123] Optionally, the solving unit obtains the optimal power injection strategy of the wind farm under the condition of meeting the grid operation requirements by solving the quadratic programming problem, including:

[0124] The objective function of reducing the average voltage deviation of the wind turbine is established, and the objective function of minimizing the average voltage deviation at the wind turbine end is expressed as:

[0125] ;

[0126] According to the sensitivity, the terminal voltage deviation ΔV of each fan at time k is calculated. wt (k) is:

[0127] ;

[0128] is the terminal voltage of each wind turbine at the initial moment, ΔQ(k) is the reactive power increment of the wind turbine at moment k, N is the terminal voltage reference value of the fan; T is the set of wind turbine nodes;

[0129] After obtaining the optimal power injection strategy of the wind farm under the condition of meeting the grid operation requirements, the method further includes:

[0130] According to the optimal power injection strategy, the power generation of the wind turbine is dynamically adjusted to ensure that its output meets the instructions of the transmission system operator TSO,

[0131] ;

[0132] in is the active power instruction issued by TSO, is the active power reference value of wind turbine i.

[0133] In a third aspect, the present application provides a device comprising a memory and a processor, wherein the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device performs the method introduced in any implementation of the first aspect.

[0134] In a fourth aspect, the present application provides a computer-readable storage medium, in which codes are stored. When the codes are executed, a device executing the codes implements the method described in any implementation of the first aspect.

[0135] The present application provides a method for controlling voltage of a wind farm, which includes collecting wind speed data and wind direction data of the wind farm, calculating the available wind power of each wind turbine based on the Jason wake model, constructing a node admittance matrix of the wind farm, and calculating a voltage sensitivity matrix based on the terminal voltage of each node to establish a relationship between bus voltage and power injection, respectively constructing state space equations of wind turbines, energy storage systems and static reactive generators, constructing a model predictive control framework, and converting the control problem in the model predictive control framework into a quadratic programming problem, wherein the quadratic programming problem includes upper and lower bound constraints of the voltage of each node in the wind farm and upper and lower bound constraints of the power output of the power source, and by solving the quadratic programming problem, the optimal power injection strategy of the wind farm under the condition of meeting the grid operation requirements is obtained. The method can more accurately evaluate the available wind power of each wind turbine and reduce the power loss caused by wind speed fluctuations and wake effects by calculating wind power based on the Jason wake model. At the same time, the energy storage system and static VAR generator are combined through modeling to coordinate and maintain the terminal voltage of all wind turbines within a feasible range, which plays the role of peak shaving and valley filling to reduce wind energy waste and shortage, thereby improving the economy and operational reliability of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0136] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0137] Figure 1 A flow chart of a wind farm voltage control method provided in an embodiment of the present application;

[0138] Figure 2A graph showing changes in reactive output power of a static VAR generator and active output power of a wind farm over time provided in an embodiment of the present application;

[0139] Figure 3 A graph showing the SOC variation over time of an energy storage element provided in an embodiment of the present application;

[0140] Figure 4 A schematic structural diagram of a wind farm voltage control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0141] As described in the background of this application, the wake effect in a wind farm reduces the wind speed of downstream wind turbines and increases turbulence, resulting in a total power generation loss of about 10%-20%. However, due to the inaccuracy of the collected wind power data and the possible data loss during information transmission, wind farms will face more complex problems in following the instructions of the transmission system operator while maintaining the voltage stability of the wind turbine terminal.

[0142] Voltage control of wind farms is crucial to the stable operation and reliability of power systems. However, energy storage devices and static VAR generators, as flexible power electronic devices, are currently not used to participate in voltage control of wind farms, which will cause waste or shortage of wind energy.

[0143] To meet these challenges, this application proposes voltage control based on model prediction to optimize the stability of the wind turbine terminal voltage and ensure that the wind turbine can effectively respond to the system TSO command. By using the Jason wake model to accurately calculate the power boundary of the wind turbine and introducing coordinated control through energy storage devices and static VAR generators, the stability and reliability of the wind turbine can be improved.

[0144] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0145] Figure 1 A flow chart of a wind farm voltage control method provided in an embodiment of the present application. Figure 1 As shown, the wind farm voltage control method provided in the embodiment of the present application may include:

[0146] S101. Collect wind speed data and wind direction data of a wind farm.

[0147] Wind speed data: refers to the wind speed measurement at each point or specific location in the wind farm, usually in meters per second (m / s). Wind speed is an important parameter for evaluating wind energy resources and predicting wind turbine power generation.

[0148] Wind direction data: refers to the direction of wind flow, usually expressed in degrees (such as 0° for north, 90° for east, 180° for south, 270° for west, etc.). Wind direction affects the layout efficiency and output power of the fan because it determines the direction of the fan blades.

[0149] S102. Calculate the available wind power of each wind turbine based on the Jason wake model.

[0150] The Jason Wake Model is a mathematical model used to simulate the wind speed reduction (wake effect) caused by the interaction between wind turbines in a wind farm. The model takes into account the distance between wind turbines, wind direction, wind speed, and the size and power characteristics of the wind turbines to calculate the actual wind speed received by each unit and the resulting usable wind power.

[0151] Available wind power: refers to the maximum electrical power that a wind turbine can convert under given wind speed and direction conditions. It is affected by many factors such as wind turbine design, wind speed, wind direction, and wake effect. When using the Jason wake model to calculate the available wind power of each wind turbine, it is only necessary to measure the wind speed and wind direction of the entire wind farm. There is no need to measure the wind speed of each wind turbine separately, nor is there a need to send the specific available power data of each wind turbine to the central controller.

[0152] The calculation of the available wind power of each wind turbine based on the Jason wake model includes:

[0153] The Jason wake model formula is used to describe the influence of the upstream wind turbine WTi on the wind speed of the downstream wind turbine WTj:

[0154] ;

[0155] where v i is the wind speed of WTi, C i is the thrust coefficient of WTi, k is the wake expansion coefficient, D ij is the distance between WTi and WTj along the wind direction, R is the rotor radius of the wind turbine, A0 is the rotor area, is the wake shadow area of ​​WTi caused by WTj;

[0156] ;

[0157] Calculate the impact of the upstream fan on the wind speed of each downstream fan, where I i is the set of upstream wind turbines that produce wake effects on downstream wind turbines, v0 is the free stream wind speed;

[0158] The actual available wind power calculation formula for each wind turbine under the wake effect is:

[0159] ;

[0160] ρ represents the air density, C p max It represents the maximum wind energy utilization coefficient under the influence of the upstream fan on the wind speed of the downstream fan, η represents the fan efficiency; A0 is the rotor area; v is the wind speed of the fan after considering the wake effect.

[0161] S103: construct a node admittance matrix of the wind farm, and calculate a voltage sensitivity matrix based on the terminal voltage of each node to establish a relationship between the bus voltage and the power injection.

[0162] Node admittance matrix: In power system analysis, a matrix used to describe the relationship between current and voltage between network nodes. In a wind farm, it reflects the electrical connection between nodes within the wind farm.

[0163] Voltage sensitivity matrix: A matrix that represents the sensitivity of system node voltage changes to power injection changes. By calculating the voltage sensitivity matrix, we can understand the impact of different node injection power changes on system voltage, providing a basis for optimization control. The calculation of the voltage sensitivity matrix is ​​based on the network topology of the power system. Through this matrix, the bus voltage of the entire wind farm can be dynamically adjusted and optimized by directly controlling the power injection situation in the power grid.

[0164] Bus voltage: refers to the voltage value of each node (bus) in the power system, and is one of the important indicators for the stable operation of the power system.

[0165] The voltage sensitivity matrix is:

[0166] ;

[0167] ;

[0168] In the wind farm, the original topology is regarded as a network consisting of 1 slack bus S and N PQ buses; where S∪N={1,2,…,N+1}; represents the sensitivity of the voltage of bus j to the active power injected into bus l; represents the sensitivity of the voltage of bus j to the reactive power injected by bus l; Indicates plural, represents the conjugate of a complex number; represents the apparent power output by node i; , denote the conjugate of the voltage at node i and the voltage at node j respectively; is the element in the i-th row and j-th column of the node admittance matrix;

[0169] ;

[0170] ;

[0171] in, is the sensitivity of the voltage amplitude of bus i to the active power injected on bus l, is the sensitivity of the voltage phase angle of bus i to the active power injected on bus l, is the sensitivity of the voltage amplitude of bus i to the reactive power injected on bus l, is the sensitivity of the voltage phase angle of bus i to the reactive power injected on bus l.

[0172] S104. Construct state space equations of the wind turbine, energy storage system and static VAR generator respectively.

[0173] State-space equations: A set of mathematical equations that describe the dynamic behavior of a system, usually including the system's state variables, input variables, and output variables. In a wind farm, the state-space equations of wind turbines, energy storage systems, and static VAR generators describe their operating characteristics and dynamic responses, respectively.

[0174] Wind turbine: A mechanical device that converts wind energy into electrical energy. Its state-space equation reflects the relationship between variables such as wind speed, rotor speed, and generator output power.

[0175] Energy storage system: A device used to store and release electrical energy, such as batteries, supercapacitors, etc. Its state-space equations describe the charging and discharging process of energy storage elements and their interaction with the power grid.

[0176] Static VAR generator (SVG): A power electronic device used to regulate reactive power in power systems. Its state space equation describes the control strategy of SVG and the regulation process of output reactive power.

[0177] The state space equation of the fan is:

[0178] ;

[0179] in, and Respectively represent the increment of the active and reactive power output reference value of the wind turbine, ΔP wt and ΔQ wt are the increments of active and reactive power output of the wind turbine respectively; T P and T Q are active and reactive time constants respectively; and are the derivatives of the wind turbine’s active power and reactive power output increments, respectively.

[0180] The state control equation of the energy storage system ESS is:

[0181] ;

[0182] Among them, x c and x s They represent the charging and discharging power of ESS at time t; u c and u s are binary variables that define the charging and discharging states of ESS; η c and η s are the efficiency coefficients of ESS during charging and discharging, both set to 0.9; SOC is the state of charge, ε is the minimum storage energy of ESS, set to 0.1; E t represents the storage energy of ESS over time, E cap is the maximum capacity of the ESS.

[0183] The state space equation of the static VAR generator SVG is:

[0184] ;

[0185] in, , and T svg They represent the increment of the SVG output reactive power reference value, the increment of the SVG output reactive power and the reactive time constant respectively; where, It is the derivative of the SVG output reactive power increment.

[0186] S105, constructing a model predictive control framework, converting the control problem in the model predictive control framework into a quadratic programming problem, and obtaining an optimal power injection strategy for the wind farm that meets the grid operation requirements by solving the quadratic programming problem.

[0187] The quadratic programming problem includes upper and lower bound constraints on voltages of nodes in the wind farm and upper and lower bound constraints on power output of power sources.

[0188] Model Predictive Control (MPC): An advanced control strategy that achieves effective control of the system by predicting the future behavior of the system and optimizing the control input. In wind farm control, MPC can be used to optimize the power output and voltage stability of the wind farm.

[0189] Quadratic programming problem: a mathematical optimization problem in which the objective function is quadratic and the constraints are linear. In wind farm control, by converting the MPC control problem into a quadratic programming problem, the optimal control strategy can be solved using mature optimization algorithms. Upper and lower bound constraints on voltage: refers to the maximum and minimum values ​​allowed for the voltage at each node in the wind farm, which are used to ensure the stable operation of the system and prevent equipment damage. Upper and lower bound constraints on the power output of the power source: refers to the allowable range of output power of power sources such as wind turbines and energy storage systems, which are used to limit the operating conditions of the equipment and protect the safety of the equipment.

[0190] By converting the control problem into a quadratic programming problem and solving the optimization problem, accurate control of the wind farm voltage and effective utilization of wind power resources can be achieved. The control instructions obtained by solving the quadratic programming problem will be directly applied to the control systems of wind turbines, energy storage systems and static VAR generators to achieve overall optimized operation of the wind farm.

[0191] The model predictive control framework includes the sensitivity matrix and state space equations:

[0192] ;

[0193] Where ΔV, Δθ, ΔP and ΔQ are the increments of voltage amplitude, voltage phase angle, active power and reactive power respectively; , , and They are the voltage to power sensitivity matrices, which are used to linearize the wind farm model;

[0194] Continuous state space equations for a wind farm:

[0195] ;

[0196] in:

[0197] ;

[0198] ;

[0199] ;

[0200] ;

[0201] Where ΔP ess Represents the increase in active power of the energy storage system, ΔP wt Indicates the increase in active power of the fan;

[0202] ΔQ svg Represents the increment of reactive power of static var generator, ΔQ wtIndicates the increase in reactive power of the fan;

[0203] , They respectively represent the increment of the active power output reference value of the energy storage system and the increment of the reactive power output reference value of the static VAR generator;

[0204] , Respectively represent the increment of the reference value of the active and reactive power output of the wind turbine;

[0205] represents the active time constant of the energy storage system, Represents the reactive time constant of the static reactive generator;

[0206] , Respectively represent the active and reactive time constants of the fan.

[0207] The upper and lower bounds of the voltage of each node in the wind farm and the upper and lower bounds of the power output of the power source are:

[0208] ;

[0209] Represents the voltage constraint, which is used to ensure that the voltage at the wind turbine terminal remains within the feasible range and does not exceed the upper and lower limits allowed;

[0210] ;

[0211] Represents the active power constraint, which is used to limit the active power output of the wind turbine to ensure that the maximum available wind power is not exceeded;

[0212] ;

[0213] Represents reactive power constraint, which is used to limit the reactive power output of the wind turbine to ensure that the power output does not exceed the capacity of the wind turbine.

[0214] The optimal power injection strategy of the wind farm that meets the grid operation requirements is obtained by solving the quadratic programming problem, including:

[0215] The objective function of reducing the average voltage deviation of the wind turbine is established, and the objective function of minimizing the average voltage deviation at the wind turbine end is expressed as:

[0216] ;

[0217] According to the sensitivity, the terminal voltage deviation ΔV of each fan at time k is calculated. wt (k) is:

[0218] ;

[0219] is the terminal voltage of each wind turbine at the initial moment, ΔQ(k) is the reactive power increment of the wind turbine at moment k, N is the terminal voltage reference value of the fan; T is the set of wind turbine nodes;

[0220] After obtaining the optimal power injection strategy of the wind farm under the condition of meeting the grid operation requirements, the method further includes:

[0221] According to the optimal power injection strategy, the power generation of the wind turbine is dynamically adjusted to ensure that its output meets the instructions of the transmission system operator TSO,

[0222] ;

[0223] in is the active power instruction issued by TSO, is the active power reference value of wind turbine i.

[0224] In order to verify the effectiveness of the above-mentioned wake wind farm voltage control method, this application also designed an experiment to prove it. Specifically, a wind farm model was created in MATLAB / Simulink, which includes 32 5MW DFIG-based wind turbines. Each wind turbine is connected to the feeder through a 0.9 / 33kV transformer. Four feeders are connected to the medium voltage (MV) bus, and there are 8 wind turbines every 4 kilometers on each feeder. All wind turbines are gathered at the connection point (POC) and transmitted to the external power grid through a 155 / 380 kV main transformer. The rated power of the wind farm is 160MW, and the rated power of each wind turbine is 5 MW. The resistance, inductance and capacitance of the 33kV cable are 0.108Ω / km, 0.3915mH / km and 0.13μF / km, respectively. Similarly, the resistance, inductance and capacitance of the 155kV cable are 0.158Ω / km, 0.47mH / km and 0.13μF / km, respectively. The rated capacity, resistance and reactance of the 0.9 / 33kV transformer are 6.25MVA, 0.008pu and 0.06pu respectively; the rated capacity, resistance and reactance of the 33 / 155kV transformer are 100MVA, 0.01pu and 0.06pu respectively; the rated capacity, resistance and reactance of the 155 / 380kV transformer are 500MVA, 0.01pu and 0.06pu respectively. The central controller records the terminal voltage of the local wind turbine, as well as the active power and reactive power output by the wind turbine, the active power output by the energy storage device and the reactive power output by the static VAR generator. The central controller measures the wind speed and direction of the wind farm, then calculates the wind power of each wind turbine and the voltage sensitivity matrix of the system, and determines the optimal output power of the wind turbine and multiple power sources. According to observations, the configured energy storage device capacity and static VAR generator capacity are both 30% of the wind farm capacity.

[0225] Figure 2 A graph of the reactive output power of a static VAR generator and the active output power of a wind farm over time provided in an embodiment of the present application shows the reactive output power of the static VAR generator and the active output power of the wind farm. When all wind turbines are running at maximum power, the output power of the wind farm is 400MW. However, due to factors such as wake effect and wind speed fluctuation, the output energy of the wind farm is inevitably lower than the critical value of 400MW. In the initial stage of 1 to 100 seconds, the output power of the wind farm is stable at about 230MW, and SVG contributes about 16MVar of reactive power. Subsequently, during the period from 100 seconds to 180 seconds, the available output power of WF gradually increases. Therefore, TSO raises the active power instruction accordingly. Therefore, the output power of the wind farm gradually increases. In order to reduce the impact of the increase in active power on the voltage at the wind turbine terminal, the static VAR generator gradually increases the reactive power output. This adjustment ensures a smooth transition of the overall operation of the system.

[0226] Figure 3 A graph of SOC variation over time of an energy storage element provided in an embodiment of the present application, wherein the function of the energy storage element is to shave peaks and fill valleys. When the available power of the wind farm is greater than the TSO instruction, the energy storage element absorbs energy. At the same time, when the available power of the wind farm is less than the TSO instruction, the energy storage element releases energy. During the period of 1-100 seconds, the state of charge of the energy storage element shows an upward trend, while during the period of 100-180 seconds, it shows a downward trend. This dynamic behavior highlights the role of energy storage elements in maintaining grid stability by balancing fluctuations between renewable energy generation and demand.

[0227] The above are some specific implementations of a wind farm voltage control method provided in the embodiment of the present application. Based on this, the present application also provides a corresponding device. The device provided in the embodiment of the present application will be introduced from the perspective of functional modularization.

[0228] Figure 4 A schematic diagram of the structure of a wind farm voltage control device provided in an embodiment of the present application. Figure 4 As shown, the wind farm voltage control device provided in the embodiment of the present application includes:

[0229] The collection unit 400 is used to collect wind speed data and wind direction data of the wind farm;

[0230] A first calculation unit 410 is used to calculate the available wind power of each wind turbine based on the Jason wake model;

[0231] The second calculation unit 420 is used to construct a node admittance matrix of the wind farm and calculate a voltage sensitivity matrix based on the terminal voltage of each node to establish a correlation between the bus voltage and the power injection;

[0232] A model building unit 430, used to build state space equations of the wind turbine, the energy storage system and the static VAR generator respectively;

[0233] The model building unit is also used to build a model predictive control framework, convert the control problem in the model predictive control framework into a quadratic programming problem, the quadratic programming problem includes upper and lower bound constraints on the voltage of each node in the wind farm and upper and lower bound constraints on the power output of the power source, and by solving the quadratic programming problem, the optimal power injection strategy of the wind farm that meets the grid operation requirements is obtained.

[0234] In an implementation of the embodiment of the present application, the first calculation unit calculates the available wind power of each wind turbine based on the Jason wake model, including:

[0235] The Jason wake model formula is used to describe the influence of the upstream wind turbine WTi on the wind speed of the downstream wind turbine WTj:

[0236] ;

[0237] where v i is the wind speed of WTi, C i is the thrust coefficient of WTi, k is the wake expansion coefficient, D ij is the distance between WTi and WTj along the wind direction, R is the rotor radius of the wind turbine, A0 is the rotor area, is the wake shadow area of ​​WTi caused by WTj;

[0238] ;

[0239] Calculate the impact of the upstream fan on the wind speed of each downstream fan, where I i is the set of upstream wind turbines that produce wake effects on downstream wind turbines, v0 is the free stream wind speed;

[0240] The actual available wind power calculation formula for each wind turbine under the wake effect is:

[0241] ;

[0242] ρ represents the air density, C p max It represents the maximum wind energy utilization coefficient under the influence of the upstream fan on the wind speed of the downstream fan, η represents the fan efficiency; A0 is the rotor area; v is the wind speed of the fan after considering the wake effect.

[0243] In one implementation of the embodiment of the present application, the voltage sensitivity matrix is:

[0244] ;

[0245] ;

[0246] In the wind farm, the original topology is regarded as a network consisting of 1 slack bus S and N PQ buses; where S∪N={1,2,…,N+1}; represents the sensitivity of the voltage of bus j to the active power injected into bus l; represents the sensitivity of the voltage of bus j to the reactive power injected by bus l; Indicates plural, represents the conjugate of a complex number; represents the apparent power output by node i; , denote the conjugate of the voltage at node i and the voltage at node j respectively; is the element in the i-th row and j-th column of the node admittance matrix;

[0247] ;

[0248] ;

[0249] in, is the sensitivity of the voltage amplitude of bus i to the active power injected on bus l, is the sensitivity of the voltage phase angle of bus i to the active power injected on bus l, is the sensitivity of the voltage amplitude of bus i to the reactive power injected on bus l, is the sensitivity of the voltage phase angle of bus i to the reactive power injected on bus l.

[0250] In one implementation of the embodiment of the present application, the state space equation of the wind turbine is:

[0251] ;

[0252] in, and Respectively represent the increment of the active and reactive power output reference value of the wind turbine, ΔP wt and ΔQ wt are the increments of active and reactive power output of the wind turbine respectively; T P and T Q are active and reactive time constants respectively; and are the derivatives of the wind turbine’s active power and reactive power output increments, respectively.

[0253] In one implementation of the embodiment of the present application, the state control equation of the energy storage system ESS is:

[0254] ;

[0255] Among them, x c and x s They represent the charging and discharging power of ESS at time t; u c and u s are binary variables that define the charging and discharging states of ESS; η c and η s are the efficiency coefficients of ESS during charging and discharging, both set to 0.9; SOC is the state of charge, ε is the minimum storage energy of ESS, set to 0.1; E t represents the storage energy of ESS over time, E cap is the maximum capacity of the ESS.

[0256] In one implementation of the embodiment of the present application, the state space equation of the static VAR generator SVG is:

[0257] ;

[0258] in, , and T svg They represent the increment of the SVG output reactive power reference value, the increment of the SVG output reactive power and the reactive time constant respectively; where, It is the derivative of the SVG output reactive power increment.

[0259] In one implementation of the embodiment of the present application, the model predictive control framework includes a sensitivity matrix and a state space equation:

[0260] ;

[0261] Where ΔV, Δθ, ΔP and ΔQ are the increments of voltage amplitude, voltage phase angle, active power and reactive power respectively; , , and They are the voltage to power sensitivity matrices, which are used to linearize the wind farm model;

[0262] Continuous state space equations for a wind farm:

[0263] ;

[0264] in:

[0265] ;

[0266] ;

[0267] ;

[0268] ;

[0269] Where ΔP ess Represents the increase in active power of the energy storage system, ΔP wt Indicates the increase in active power of the fan;

[0270] ΔQ svg Represents the increment of reactive power of static var generator, ΔQ wt Indicates the increase in reactive power of the fan;

[0271] , They respectively represent the increment of the active power output reference value of the energy storage system and the increment of the reactive power output reference value of the static VAR generator;

[0272] , Respectively represent the increment of the reference value of the active and reactive power output of the wind turbine;

[0273] represents the active time constant of the energy storage system, Represents the reactive time constant of the static reactive generator;

[0274] , Respectively represent the active and reactive time constants of the fan.

[0275] Optionally, the upper and lower bound constraints of the voltage of each node in the wind farm and the upper and lower bound constraints of the power output of the power source are:

[0276] ;

[0277] Represents the voltage constraint, which is used to ensure that the voltage at the wind turbine terminal remains within the feasible range and does not exceed the upper and lower limits allowed;

[0278] ;

[0279] Represents the active power constraint, which is used to limit the active power output of the wind turbine to ensure that the maximum available wind power is not exceeded;

[0280] ;

[0281] Represents reactive power constraint, which is used to limit the reactive power output of the wind turbine to ensure that the power output does not exceed the capacity of the wind turbine.

[0282] In an implementation of the embodiment of the present application, the solving unit obtains the optimal power injection strategy of the wind farm under the condition of meeting the grid operation requirements by solving the quadratic programming problem, including:

[0283] The objective function of reducing the average voltage deviation of the wind turbine is established, and the objective function of minimizing the average voltage deviation at the wind turbine end is expressed as:

[0284] ;

[0285] According to the sensitivity, the terminal voltage deviation ΔV of each fan at time k is calculated. wt (k) is:

[0286] ;

[0287] is the terminal voltage of each wind turbine at the initial moment, ΔQ(k) is the reactive power increment of the wind turbine at moment k, N is the terminal voltage reference value of the fan; T is the set of wind turbine nodes;

[0288] After obtaining the optimal power injection strategy of the wind farm under the condition of meeting the grid operation requirements, the method further includes:

[0289] According to the optimal power injection strategy, the power generation of the wind turbine is dynamically adjusted to ensure that its output meets the instructions of the transmission system operator TSO,

[0290] ;

[0291] in is the active power instruction issued by TSO, is the active power reference value of wind turbine i.

[0292] The embodiments of the present application also provide corresponding devices and computer storage media for implementing the solutions provided by the embodiments of the present application.

[0293] The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the method described in any embodiment of the present application.

[0294] The computer storage medium stores codes, and when the codes are executed, a device executing the codes implements the method described in any embodiment of the present application.

[0295] Through the description of the above implementation methods, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0296] It should be noted that each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and apparatus embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments. The device and apparatus embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0297] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A wind farm voltage control method, characterized in that: The method comprises: Collect wind speed and direction data of wind farms; Based on the Jason wake model, the available wind power of each wind turbine is calculated; Construct the node admittance matrix of the wind farm and calculate the voltage sensitivity matrix based on the terminal voltage of each node to establish the relationship between bus voltage and power injection; Construct the state space equations of wind turbine, energy storage system and static VAR generator respectively; Constructing a model predictive control framework, converting the control problem in the model predictive control framework into a quadratic programming problem, wherein the quadratic programming problem includes upper and lower bound constraints on the voltage of each node in the wind farm and upper and lower bound constraints on the power output of the power source, and solving the quadratic programming problem to obtain the optimal power injection strategy of the wind farm under the condition of meeting the grid operation requirements; The model predictive control framework includes the sensitivity matrix and state space equations: ； Where ΔV, Δθ, ΔP and ΔQ are the increments of voltage amplitude, voltage phase angle, active power and reactive power respectively; , , and They are the voltage to power sensitivity matrices, which are used to linearize the wind farm model; Continuous state space equations for a wind farm: ； in: ； ； ； ； Among them, ΔP ess Represents the increase in active power of the energy storage system, ΔP wt Indicates the increment of active power of the fan; ΔQ svg Represents the increment of reactive power of static var generator, ΔQ wt Indicates the increase in reactive power of the fan; , They respectively represent the increment of the active power output reference value of the energy storage system and the increment of the reactive power output reference value of the static VAR generator; , Respectively represent the increment of the reference value of the active and reactive power output of the wind turbine; represents the active time constant of the energy storage system, Represents the reactive time constant of the static reactive generator; , Respectively represent the active and reactive time constants of the fan; The state space equation of the energy storage system ESS is: ； Among them, x c and x s They represent the charging and discharging power of ESS at time t; u c and u s are binary variables that define the charging and discharging states of ESS; η c and η s are the efficiency coefficients of ESS during charging and discharging, both set to 0.9; SOC is the state of charge, ε is the minimum storage energy of ESS, set to 0.1; E t represents the storage energy of ESS over time, E cap is the maximum capacity of the ESS.

2. The method according to claim 1, characterized in that The calculation of the available wind power of each wind turbine based on the Jason wake model includes: The Jason wake model formula is used to describe the influence of the upstream wind turbine WTi on the wind speed of the downstream wind turbine WTj: ； where v i is the wind speed of WTi, C i is the thrust coefficient of WTi, k is the wake expansion coefficient, D ij is the distance between WTi and WTj along the wind direction, R is the rotor radius of the wind turbine, A0 is the rotor area, and is the wake shadow area of ​​WTi caused by WTj; ； Calculate the impact of the upstream fan on the wind speed of each downstream fan, where I i is the set of upstream wind turbines that produce wake effects on downstream wind turbines, v0 is the free stream wind speed; The actual available wind power calculation formula for each wind turbine under the wake effect is: ； ρ represents the air density, C p max It represents the maximum wind energy utilization coefficient under the influence of the upstream fan on the wind speed of the downstream fan, η represents the fan efficiency; A0 is the rotor area; v is the wind speed of the fan after considering the wake effect.

3. The method according to claim 1, characterized in that The voltage sensitivity matrix is: ； ； In the wind farm, the original topology is considered as a network consisting of 1 slack bus S and N PQ buses; Where S∪N={1,2,…,N+1}; represents the sensitivity of the voltage of bus j to the active power injected into bus l; represents the sensitivity of the voltage of bus j to the reactive power injected by bus l; Indicates plural, represents the conjugate of a complex number; represents the apparent power output by node i; , denote the conjugate of the voltage at node i and the voltage at node j respectively; is the element in the i-th row and j-th column of the node admittance matrix; ； ； in, is the sensitivity of the voltage amplitude of bus i to the active power injected on bus l, is the sensitivity of the voltage phase angle of bus i to the active power injected on bus l, is the sensitivity of the voltage amplitude of bus i to the reactive power injected on bus l, is the sensitivity of the voltage phase angle of bus i to the reactive power injected on bus l.

4. The method according to claim 1, characterized in that: The state space equation of the fan is: ； in, and Respectively represent the increment of the active and reactive power output reference value of the wind turbine, ΔP wt and ΔQ wt are the increments of active and reactive power output of the wind turbine respectively; T P and T Q are active and reactive time constants respectively; and are the derivatives of the wind turbine’s active power and reactive power output increments, respectively.

5. The method according to claim 1, characterized in that The state space equation of the static VAR generator SVG is: ； in, , and T svg They represent the increment of the SVG output reactive power reference value, the increment of the SVG output reactive power and the reactive time constant respectively; where, It is the derivative of the reactive power increment of SVG output.

6. The method according to claim 1, characterized in that The upper and lower bounds of the voltage of each node in the wind farm and the upper and lower bounds of the power output of the power source are: ； Represents the voltage constraint, which is used to ensure that the voltage at the wind turbine terminal remains within the feasible range and does not exceed the upper and lower limits allowed; ； Represents the active power constraint, which is used to limit the active power output of the wind turbine to ensure that the maximum available wind power is not exceeded; ； Represents reactive power constraint, which is used to limit the reactive power output of the wind turbine to ensure that the power output does not exceed the capacity of the wind turbine.

7. The method according to claim 1, characterized in that The optimal power injection strategy of the wind farm that meets the grid operation requirements is obtained by solving the quadratic programming problem, including: The objective function of reducing the average voltage deviation of the wind turbine is established, and the objective function of minimizing the average voltage deviation at the wind turbine end is expressed as: ； According to the sensitivity, the terminal voltage deviation ΔV of each fan at time k is calculated. wt (k) is: ； is the terminal voltage of each wind turbine at the initial moment, ΔQ(k) is the reactive power increment of the wind turbine at moment k, N is the terminal voltage reference value of the fan; T is the set of wind turbine nodes; After obtaining the optimal power injection strategy of the wind farm under the condition of meeting the grid operation requirements, the method further includes: According to the optimal power injection strategy, the power generation of the wind turbine is dynamically adjusted to ensure that its output meets the instructions of the transmission system operator TSO, ； in is the active power instruction issued by TSO, is the active power reference value of wind turbine i.

8. A wind farm voltage control device, characterized in that: The device comprises: A collection unit, used to collect wind speed data and wind direction data of the wind farm; A first calculation unit, used for calculating the available wind power of each wind turbine based on the Jason wake model; The second calculation unit is used to construct a node admittance matrix of the wind farm and calculate a voltage sensitivity matrix based on the terminal voltage of each node to establish a correlation between the bus voltage and the power injection; A model building unit, used for building state space equations of the wind turbine, the energy storage system and the static VAR generator respectively; The model building unit is also used to build a model predictive control framework; A solving unit is used to convert the control problem in the model predictive control framework into a quadratic programming problem, wherein the quadratic programming problem includes upper and lower bound constraints on the voltage of each node in the wind farm and upper and lower bound constraints on the power output of the power source, and obtain an optimal power injection strategy for the wind farm under the condition of meeting the grid operation requirements by solving the quadratic programming problem; The model predictive control framework includes the sensitivity matrix and state space equations: ； Where ΔV, Δθ, ΔP and ΔQ are the increments of voltage amplitude, voltage phase angle, active power and reactive power respectively; , , and They are the voltage to power sensitivity matrices, which are used to linearize the wind farm model; Continuous state space equations for a wind farm: ； in: ； ； ； ； Among them, ΔP ess Represents the increase in active power of the energy storage system, ΔP wt Indicates the increment of active power of the fan; ΔQ svg Represents the increment of reactive power of static var generator, ΔQ wt Indicates the increase in reactive power of the fan; , They respectively represent the increment of the active power output reference value of the energy storage system and the increment of the reactive power output reference value of the static VAR generator; , Respectively represent the increment of the reference value of the active and reactive power output of the wind turbine; represents the active time constant of the energy storage system, Represents the reactive time constant of the static reactive generator; , Respectively represent the active and reactive time constants of the fan; The state space equation of the energy storage system ESS is: ； Among them, x c and x s They represent the charging and discharging power of ESS at time t; u c and u s are binary variables that define the charging and discharging states of ESS; η c and η s are the efficiency coefficients of ESS during charging and discharging, both set to 0.9; SOC is the state of charge, ε is the minimum storage energy of ESS, set to 0.1; E t represents the storage energy of ESS over time, E cap is the maximum capacity of the ESS.

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