Energy storage participation power grid frequency modulation method, simulation system and equipment

Abstract

The invention discloses an energy storage participation power grid frequency modulation method, a simulation system and equipment, and relates to the field of power grid frequency modulation. The method comprises the following steps of: establishing an energy storage optimization model which takes the charge-discharge loss cost of the energy storage system and the minimum charge state differentiation degree between energy storage batteries in the energy storage system as an optimization target; when the frequency of the power system is changed, active power compensation required by frequency modulation is determined; according to the direct current of each energy storage battery at the current moment, the charge and discharge loss cost and the charge state differentiation degree are determined respectively; when an active power output instruction of the energy storage battery is calculated by adopting a model prediction control method, obtaining an active power increment of each energy storage battery in a prediction time domain based on an energy storage optimization model by utilizing the output power and the direct current voltage of the energy storage battery at the current moment, and distributing the active power increment to the energy storage battery; and the operation of the energy storage battery is close to the control requirement of the energy storage optimization model. The frequency modulation active power in the energy storage power station can be reasonably distributed, and the service life of the energy storage system is prolonged.

Description

Technical Field

[0001] The present application relates to the field of power grid frequency regulation, and in particular to a method, simulation system and equipment for energy storage participating in power grid frequency regulation. Background Art

[0002] As the largest power source in the power system, thermal power units undertake the main peak-shaving and frequency-regulating tasks. As a large number of new energy sources such as wind and solar power with different characteristics are connected to the power system through power electronics, they continue to replace the installed capacity of thermal power units, which brings difficulties and challenges to the peak-shaving of the power grid and the consumption of new energy.

[0003] As the proportion of new energy continues to increase, the system's rotational inertia and frequency regulation capabilities continue to decline, which puts pressure on the grid frequency regulation. At this time, using energy storage for inertia support and frequency regulation during the non-peak regulation phase can not only enhance the frequency stability of the system, but also greatly improve the utilization rate of the energy storage system. At the same time, the energy storage system also has the characteristics of fast response speed, high input and output controllability, and flexible configuration. Therefore, studying the optimal scheduling of high-proportion new energy power systems taking into account the energy storage frequency regulation capabilities is a practical problem that needs to be solved urgently.

[0004] Most of the existing energy storage frequency regulation methods analyze the inertial response and primary frequency regulation mechanism of electrochemical energy storage, study the frequency response capability of electrochemical energy storage, determine its basic frequency regulation capacity in the non-peak regulation stage, and then unify the energy storage peak regulation and primary frequency regulation to the same time scale for scheduling in the form of frequency response reserve capacity. This method of determining frequency regulation capacity is relatively conservative and cannot fully utilize the regulation capacity of energy storage. The overall utilization rate is low, and it does not fully consider the loss cost control of the energy storage system and the SOC balance of the energy storage battery. Therefore, it is urgent to propose a frequency regulation method that comprehensively considers the frequency regulation capacity of each energy storage power station and the cycle life of energy storage, so as to achieve a reasonable distribution of frequency regulation power among each energy storage battery and extend the service life of the energy storage system. Summary of the invention

[0005] The purpose of this application is to provide a method, simulation system and equipment for energy storage to participate in grid frequency regulation, which can achieve reasonable allocation of frequency regulation active power in energy storage power stations and extend the service life of energy storage systems.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In the first aspect, the present application provides a method for energy storage to participate in grid frequency regulation, including: constructing a spatial state model of the energy storage system; establishing an energy storage optimization model with the optimization objectives of minimizing the charging and discharging loss cost of the energy storage system and the degree of charge state differentiation between energy storage batteries in the energy storage system; when the frequency of the power system changes, collecting the measured frequency of the power system, and determining the active power compensation required for frequency regulation based on the measured frequency; obtaining the output power, DC current and DC voltage of each energy storage battery in the energy storage system at the current moment; and determining the active power compensation required for frequency regulation based on the DC current of each energy storage battery in the energy storage system. The invention relates to a method for calculating the active power increment of each energy storage battery in the energy storage system within a prediction time domain by adopting a model predictive control method based on the active power compensation required for frequency modulation, the output power and DC voltage of each energy storage battery in the energy storage system at the current moment, the charge and discharge loss cost of each energy storage battery in the energy storage system, and the difference degree of charge state between each energy storage battery in the energy storage system, based on the spatial state model and the energy storage optimization model; and the active power increment is distributed to each energy storage battery in the energy storage system as an active output instruction.

[0008] In the second aspect, the present application provides a system for simulating energy storage participating in grid frequency regulation, which applies the above-mentioned method for simulating energy storage participating in grid frequency regulation for simulation, and the system for simulating energy storage participating in grid frequency regulation includes: a power system, an inertial controller, an energy storage charging and discharging loss calculation module, an energy storage battery state of charge differentiation degree calculation module, an MPC controller and an energy storage system.

[0009] The inertial controller is used to collect the measured frequency of the power system when the frequency of the power system changes, and determine the active power compensation required for frequency modulation based on the measured frequency; the energy storage charging and discharging loss calculation module is used to calculate the charging and discharging loss cost of each energy storage battery in the energy storage system according to the DC current of each energy storage battery in the energy storage system at the current moment; the energy storage battery state of charge differentiation degree calculation module is used to calculate the degree of charge differentiation between each energy storage battery in the energy storage system according to the DC current of each energy storage battery in the energy storage system at the current moment; the MPC controller is used to calculate the charge state differentiation between each energy storage battery in the energy storage system according to the DC current of each energy storage battery in the energy storage system at the current moment. The output power and DC voltage at the previous moment, the active power compensation required for frequency modulation, the charging and discharging loss cost of each energy storage battery in the energy storage system, and the degree of difference in the state of charge between each energy storage battery in the energy storage system are calculated based on the spatial state model of the energy storage system and the energy storage optimization model. The model predictive control method is adopted to calculate the active power increment of each energy storage battery in the energy storage system in the prediction time domain; and the active power increment is distributed to each energy storage battery in the energy storage system as an active output instruction; the energy storage optimization model takes the charging and discharging loss cost of the energy storage system and the degree of difference in the state of charge between the energy storage batteries in the energy storage system as the optimization goal.

[0010] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for energy storage to participate in grid frequency regulation.

[0011] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0012] The present application provides a method, simulation system and equipment for energy storage to participate in grid frequency regulation, and establishes an energy storage optimization model with the optimization objectives of minimizing the charging and discharging loss cost of the energy storage system and the degree of differentiation of the state of charge between the energy storage batteries in the energy storage system. When the model predictive control method is used to calculate the active output instruction of the energy storage battery, the output power and DC voltage of the energy storage battery at the current moment are used. Based on the energy storage optimization model, the active power increment of each energy storage battery in the energy storage system in the predicted time domain is obtained, and distributed to each energy storage battery, so that the operation of the energy storage battery is close to the control requirements of the energy storage optimization model. Not only is the full utilization of the energy storage frequency regulation capability achieved, but also the frequency regulation active power in the energy storage power station can be reasonably allocated, thereby reducing the loss of the energy storage system and extending the service life of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments 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.

[0014] Figure 1 This is a flow chart of a method for energy storage to participate in power grid frequency regulation in one embodiment of the present application;

[0015] Figure 2 A schematic diagram of a system for simulating energy storage participating in power grid frequency regulation provided in one embodiment of the present application;

[0016] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions 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.

[0018] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0019] In an exemplary embodiment, Figure 1 As shown, the present application provides a method for energy storage to participate in grid frequency regulation, including the following steps 101 to 107. Among them:

[0020] Step 101: Construct a spatial state model of the energy storage system.

[0021] Step 102: Establish an energy storage optimization model with the optimization objectives of minimizing the charging and discharging loss cost of the energy storage system and the degree of difference in the state of charge between the energy storage batteries in the energy storage system.

[0022] Step 103: When the frequency of the power system changes, the measured frequency of the power system is collected, and active power compensation required for frequency modulation is determined according to the measured frequency.

[0023] Step 104: Obtain the output power, DC current and DC voltage of each energy storage battery in the energy storage system at the current moment.

[0024] Step 105: According to the DC current of each energy storage battery in the energy storage system at the current moment, respectively determine the charge and discharge loss cost of each energy storage battery in the energy storage system and the degree of difference in the state of charge between each energy storage battery in the energy storage system.

[0025] Step 106: Based on the active power compensation required for frequency modulation, the output power and DC voltage of each energy storage battery in the energy storage system at the current moment, the charge and discharge loss cost of each energy storage battery in the energy storage system, and the degree of differentiation of the state of charge between each energy storage battery in the energy storage system, the active power increment of each energy storage battery in the energy storage system in the prediction time domain is calculated using a model predictive control method based on the spatial state model and the energy storage optimization model.

[0026] Step 107: Distribute the active power increment as an active output instruction to each energy storage battery in the energy storage system.

[0027] By implementing the above steps 101 to 107, an energy storage optimization model is established with the charge and discharge loss cost of the energy storage system and the minimum degree of charge state differentiation between the energy storage batteries in the energy storage system as the optimization target. When the model predictive control method is used to calculate the active output command of the energy storage battery, the output power and DC voltage of the energy storage battery at the current moment are used. Based on the energy storage optimization model, the active power increment of each energy storage battery in the energy storage system in the predicted time domain is obtained and allocated to each energy storage battery, so that the operation of the energy storage battery is close to the control requirements of the energy storage optimization model. Not only is the full utilization of the energy storage frequency regulation capability achieved, but also the frequency regulation active power in the energy storage power station can be reasonably allocated, thereby reducing the loss of the energy storage system and extending the service life of the energy storage system.

[0028] The method of the present application can be applied to energy storage power stations that require multiple PCS (Power Conversion System, energy conversion system) control.

[0029] In another exemplary embodiment of the present application, in order to construct a spatial state model of the energy storage system, the above step 101 is replaced by the following steps 201 to 208:

[0030] Step 201: Establish the state of charge expression of the energy storage battery:

[0031]

[0032] Among them, SOC is the state of charge of the energy storage battery. 0 is the initial state of charge of the energy storage battery, Q c is the energy storage battery capacity, i D is the output current of the energy storage battery. The state of charge expression is the mathematical model of the energy storage battery. The SOC of the energy storage battery is expressed by the DC current iD Expression.

[0033] Step 202: Establish the DC current increment expression of the energy storage battery as follows:

[0034]

[0035] Among them, Δi D is the DC current increment of the energy storage battery, T id is the time constant of the active power control loop, k pd is the proportional gain of the PI controller of the DC current control loop of the energy storage battery, k id is the integral gain of the PI controller of the DC current control loop of the energy storage battery, is the reference power of the energy storage battery, is the active power increment of the energy storage battery, and s is a complex frequency domain variable.

[0036] Step 203: Establish the active power increment expression of the energy storage battery as follows:

[0037]

[0038] Among them, T fd is the time constant of the energy storage battery control loop, U D is the DC voltage of the energy storage battery.

[0039] It can be seen from step 202 and step 203 that the dynamic characteristics of the control loop of the active power and direct current of the energy storage battery are represented by a first-order lag function.

[0040] Step 204: Introduce the state function as:

[0041]

[0042] Among them, ΔP int is the state function.

[0043] Step 205: Based on the state of charge expression, the DC current increment expression, the active power increment expression and the state function, a state space model matrix of the energy storage battery is constructed in the form of:

[0044]

[0045]

[0046] in, is the first-order derivative of the state variable increment of the energy storage battery, Δx E is the state variable increment of the energy storage battery, Δu E is the control variable increment of the energy storage battery, A Eis the coefficient matrix of the energy storage battery, B E is the control matrix of the energy storage battery, E E is the initial measurement matrix of the energy storage battery; Δy E is the variable increment of the energy storage battery, C E is the output state coefficient matrix of the energy storage battery; ΔSOC is the change in the state of charge of the energy storage battery.

[0047] Step 206: According to the state space model matrix form of the energy storage battery, the continuous state space model of the energy storage system is obtained as follows:

[0048]

[0049] in, is the first-order derivative of the energy storage system state variable increment, Δx is the energy storage system state variable increment, u is the input variable in the continuous state space model formula, A is the energy storage system coefficient matrix, B is the energy storage system control matrix, E is the energy storage system initial measurement matrix; Δy is the energy storage system variable increment, C is the energy storage system output state coefficient matrix; Δx E1 , Δx E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The state variable increment of the energy storage battery, Δu E1 , Δu E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The control variable increment of each energy storage battery, Δy E1 , Δy E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The variable increment of the energy storage battery, A E1 , A E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The coefficient matrix of the energy storage battery, B E1 , B E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The control matrix of the energy storage battery, E E1 、E E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The initial measurement matrix of the energy storage battery, C E1 , C E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The output state coefficient matrix of the energy storage battery, diag represents a diagonal matrix.

[0050] Step 207: According to the continuous state space model of the energy storage system, a discrete state space model with sampling time is obtained:

[0051]

[0052] Among them, Δx(k) is the increment of the state variable of the discrete state space model at time k, Δx(k+1) is the increment of the state variable of the discrete state space model at time k+1, u(k) is the control variable of the discrete state space model at time k, G is the output state coefficient matrix of the discrete state space model, H is the control matrix of the discrete state space model; y(k+1) is the increment of the output variable of the discrete state space model at time k+1 with u(k) as the control variable; ΔT p is the sampling time, and τ is the time integration variable.

[0053] Step 208: Using the discrete state space model with sampling time as the space state model of the energy storage system.

[0054] In another exemplary embodiment of the present application, the energy storage optimization model in step 102 above considers two control objectives. The first control objective is to reduce the charging and discharging loss cost of the energy storage system, and the second control objective is to reduce the degree of differentiation of the state of charge between the energy storage batteries in the energy storage system. Then, the objective function of the energy storage optimization model in step 102 is:

[0055]

[0056] Among them, f L,j is the charge and discharge loss cost of the jth energy storage battery, S soc,j is the degree of charge state differentiation of the jth energy storage battery, N P is the total number of energy storage batteries, |||| represents the norm, is the objective function of the charging and discharging loss cost of the energy storage system, is the objective function of the degree of charge state differentiation among the energy storage batteries in the energy storage system, λ A and λ B are all weight coefficients.

[0057] In another exemplary embodiment of the present application, considering that the energy storage system should comply with its power limit when operating in the charge and discharge mode, and the power increased or decreased by the energy storage system should be equal to the active power compensation, the constraint conditions of the energy storage optimization model in step 102 are:

[0058]

[0059] in, is the initial power of the jth energy storage battery, is the active power increment issued by the dispatch command, The upper limit of the charging power of the energy storage battery. is the upper limit of the energy storage battery discharge power; ΔP ref The active power compensation required for frequency regulation. Equation (13) reflects the power limit that the energy storage system should comply with when operating in the charging and discharging mode, and Equation (14) reflects that the power increased or decreased by the energy storage system should be equal to the active power compensation.

[0060] In another exemplary embodiment of the present application, when the frequency of the power system changes, the active power compensation required for frequency regulation can be calculated based on the measured frequency collected from the power system model. Then the calculation formula for the active power compensation required for frequency regulation in the above step 103 is:

[0061] Δf=f ref -f m (15)

[0062]

[0063] Where Δf is the frequency deviation, f ref is the preset rated frequency, f m is the measured frequency of the power system; ΔP ref The active power compensation required for frequency modulation, K p is the proportional gain coefficient, K d is the integral gain coefficient, and t is the time.

[0064] In another exemplary embodiment of the present application, the calculation formula for the charge and discharge loss cost of each energy storage battery in the energy storage system in step 105 is:

[0065]

[0066] Among them, f L,j is the charge and discharge loss cost of the jth energy storage battery, is the charging power of the jth energy storage battery, is the discharge power of the jth energy storage battery, μ ESS is the cost coefficient of energy storage, γ ES S is the rated life of energy storage, T c To control the time in the time domain, α1, α2, α3 and α4 are the life cycle coefficients of energy storage, T ESS,j is the temperature of the jth energy storage battery, E R,j is the capacity of the jth energy storage battery; SOC j is the current state of charge of the jth energy storage battery, is the initial state of charge of the jth energy storage battery, Q c is the energy storage battery capacity, i D,j is the output current of the jth energy storage battery.

[0067] The prediction horizon is the period of time during which the system predicts its future behavior. The control horizon is the period of time during which the controller optimizes and implements control actions within the prediction horizon. In most cases, the control horizon is shorter than the prediction horizon, and the controller optimizes the control input only during the control horizon, and then assumes that the control input remains constant.

[0068] In another exemplary embodiment of the present application, the calculation formula for the degree of difference in the state of charge between the energy storage batteries in the energy storage system in step 105 is:

[0069]

[0070] Among them, S soc,j is the degree of charge differentiation of the jth energy storage battery, SOC j is the current state of charge of the jth energy storage battery, is the current average state of charge of all energy storage batteries, N P is the total number of energy storage batteries; is the initial state of charge of the jth energy storage battery, Q c is the energy storage battery capacity, i D,j is the output current of the jth energy storage battery. SOC j Please refer to formula (18) for the calculation formula of .

[0071] From equations (17) to (19), we can see that first, according to the DC current of each energy storage battery in the energy storage system at the current moment, the state of charge of the energy storage battery is calculated using equation (18), and then the state of charge of the energy storage battery is further used to calculate the charging and discharging loss cost and the degree of differentiation of the state of charge.

[0072] In another exemplary embodiment of the present application, the model predictive control (MPC) method adopted in step 106 converts the matrix equations of the discrete state space model, objective function and constraint conditions into a quadratic optimization function, solves it through a solver, calculates the active power increment of the energy storage system, and distributes it to each energy storage battery. After entering the next moment, the above steps are repeated with the new state of charge, active power, DC current and DC voltage as inputs, and prediction and optimization are performed again from the new state. Through continuous rolling optimization, the optimization goal of reducing the energy storage charging and discharging loss and the difference in the SOC of the energy storage battery is achieved, thereby reducing the frequency modulation cost and increasing the service life of the energy storage battery.

[0073] A unique feature of the MPC method is that it needs to predict the future system state. The general process of the MPC method is: First, the system state in the prediction time domain is predicted in turn through the discrete state space model. Secondly, the control variable output in the prediction time domain is solved. The goal of solving the control variable output in the prediction time domain is to find the optimal control variable so that the state vector in the prediction time domain is as close to the reference value as possible. The energy storage optimization model is solved by the solver to obtain the control variable output in the prediction time domain, which is the active power increment of each energy storage battery in the energy storage system in the prediction time domain of this application.

[0074] Take moment k as the current moment. If the prediction time domain is 10 moments, the moments in the prediction time domain are moment k+1, moment k+2, ..., moment k+10, respectively. The MPC method integrates the system state at each moment in the prediction time domain into a matrix form. Through these matrices, combined with the objective function and constraints, the problem is transformed into a quadratic optimization problem, and the control variables in the prediction time domain are solved by the solver. In this application, it is the active power increment of each energy storage battery in the energy storage system in the prediction time domain (also referred to as the change in the reference power of each energy storage battery). After obtaining these predicted values, the predicted values ​​are input as new control variables, and then the new state variables at this moment are input, and the next round of prediction is continued to obtain the next new control variable, and so on.

[0075] Based on the same inventive concept, the embodiment of the present application also provides an energy storage participating in grid frequency regulation simulation system for implementing the energy storage participating in grid frequency regulation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more energy storage participating in grid frequency regulation simulation system embodiments provided below can refer to the limitations of the energy storage participating in grid frequency regulation method above, and will not be repeated here.

[0076] In an exemplary embodiment, Figure 2 As shown, a simulation system for energy storage participating in grid frequency regulation is provided, including: a power system, an inertia controller, an energy storage charging and discharging loss calculation module, an energy storage battery charge state differentiation degree calculation module, an MPC controller and an energy storage system.

[0077] The inertial controller is used to collect the measured frequency of the power system when the frequency of the power system changes, and determine the active power compensation required for frequency modulation based on the measured frequency.

[0078] The energy storage charge and discharge loss calculation module is used to calculate the charge and discharge loss cost of each energy storage battery in the energy storage system according to the DC current of each energy storage battery in the energy storage system at the current moment.

[0079] The energy storage battery state of charge differentiation degree calculation module is used to calculate the degree of difference in the state of charge between each energy storage battery in the energy storage system according to the DC current of each energy storage battery in the energy storage system at the current moment.

[0080] The MPC controller is used to calculate the active power increment of each energy storage battery in the energy storage system within the prediction time domain based on the output power and DC voltage of each energy storage battery in the energy storage system at the current moment, the active power compensation required for frequency modulation, the charge and discharge loss cost of each energy storage battery in the energy storage system, and the degree of difference in the state of charge between each energy storage battery in the energy storage system, based on the spatial state model of the energy storage system and the energy storage optimization model; and distribute the active power increment as an active output instruction to each energy storage battery in the energy storage system; the energy storage optimization model takes the charge and discharge loss cost of the energy storage system and the degree of difference in the state of charge between the energy storage batteries in the energy storage system as the optimization goal.

[0081] Figure 2 Where Δf is the frequency deviation, ΔP ref is the active power compensation, f L is the energy storage charging and discharging loss, S SOC is the SOC differentiation degree of energy storage, is the reference power of the jth energy storage battery, is the output power of the jth energy storage battery, i D,j is the output current of the jth energy storage battery, U D,j is the output voltage of the jth energy storage battery, SOC j is the SOC of the jth energy storage battery. Figure 2 The batteries in are energy storage batteries, and batteries 1, ..., battery n together constitute an energy storage system.

[0082] As an optional implementation method, a simulation system for energy storage participating in grid frequency regulation is built on the MATLAB / SIMULINK platform. Figure 2As shown, the power system is replaced by a motor model, which is used to input the frequency information after the active disturbance into the model. The inertial controller is used to calculate the active compensation required for frequency modulation based on the preset grid rated frequency and the measured frequency after the power system is disturbed. The energy storage charge and discharge loss calculation module calculates the charge and discharge loss of the energy storage system based on the preset cost coefficient, rated life, initial state of charge, temperature, capacity life cycle coefficient of the energy storage battery, and the current state of charge of each battery, and inputs it to the MPC controller. The energy storage battery SOC differentiation degree calculation module calculates the SOC differentiation degree of the energy storage system based on the current state of charge of each energy storage battery, and inputs it to the MPC controller. The MPC controller allocates active output to each energy storage battery in each prediction cycle based on the spatial state model of the energy storage system, the input active compensation, energy storage charge and discharge loss, and the SOC differentiation degree of the energy storage battery, and then according to the objective function and constraints. The energy storage system is used to receive the active power instructions of the MPC controller and feed back the battery voltage, current, power and SOC to the energy storage charge and discharge loss calculation module and the energy storage battery SOC differentiation degree calculation module.

[0083] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the active power increment of each energy storage battery in the energy storage system in the predicted time domain. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for energy storage to participate in grid frequency regulation is implemented.

[0084] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0085] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for energy storage to participate in power grid frequency regulation, characterized in that: include: Construct a spatial state model of the energy storage system; Establish an energy storage optimization model with the optimization objectives of minimizing the charging and discharging loss cost of the energy storage system and the degree of difference in the state of charge between the energy storage batteries in the energy storage system; When the frequency of the power system changes, collecting the measured frequency of the power system, and determining the active power compensation required for frequency modulation according to the measured frequency; Obtain the current output power, DC current and DC voltage of each energy storage battery in the energy storage system; According to the DC current of each energy storage battery in the energy storage system at the current moment, the charge and discharge loss cost of each energy storage battery in the energy storage system and the degree of difference in the state of charge between each energy storage battery in the energy storage system are determined respectively; According to the active power compensation required for frequency modulation, the output power and DC voltage of each energy storage battery in the energy storage system at the current moment, the charge and discharge loss cost of each energy storage battery in the energy storage system, and the degree of differentiation of the state of charge between each energy storage battery in the energy storage system, based on the spatial state model and the energy storage optimization model, a model predictive control method is used to calculate the active power increment of each energy storage battery in the energy storage system in the prediction time domain; The active power increment is distributed as an active output instruction to each energy storage battery in the energy storage system.

2. The method for energy storage participating in power grid frequency modulation according to claim 1, characterized in that: Construct the spatial state model of the energy storage system, including: The state of charge expression of the energy storage battery is established as: Among them, SOC is the state of charge of the energy storage battery. 0 is the initial state of charge of the energy storage battery, Q c is the capacity of the energy storage battery, i D is the output current of the energy storage battery; The DC current increment expression of the energy storage battery is established as: Among them, Δi D is the DC current increment of the energy storage battery, T id is the time constant of the active power control loop, k pd is the proportional gain of the PI controller of the DC current control loop of the energy storage battery, k id is the integral gain of the PI controller of the DC current control loop of the energy storage battery, is the reference power of the energy storage battery, is the active power increment of the energy storage battery, s is a complex frequency domain variable; The expression of active power increment of energy storage battery is established as: Among them, T fd is the time constant of the energy storage battery control loop, U D is the DC voltage of the energy storage battery; The introduced state function is: Among them, ΔP int is the state function; Based on the state of charge expression, DC current increment expression, active power increment expression and state function, the state space model matrix of the energy storage battery is constructed as follows: in, is the first-order derivative of the state variable increment of the energy storage battery, Δx E is the state variable increment of the energy storage battery, Δu E is the control variable increment of the energy storage battery, A E is the coefficient matrix of the energy storage battery, B E is the control matrix of the energy storage battery, E E is the initial measurement matrix of the energy storage battery; Δy E is the variable increment of the energy storage battery, C E is the output state coefficient matrix of the energy storage battery; ΔSOC is the change in the state of charge of the energy storage battery; According to the state space model matrix form of the energy storage battery, the continuous state space model of the energy storage system is obtained as follows: in, is the first-order derivative of the energy storage system state variable increment, Δx is the energy storage system state variable increment, u is the input variable in the continuous state space model formula, A is the energy storage system coefficient matrix, B is the energy storage system control matrix, E is the energy storage system initial measurement matrix; Δy is the energy storage system variable increment, C is the energy storage system output state coefficient matrix; Δx E1 , Δx E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The state variable increment of the energy storage battery, Δu E1 , Δu E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The control variable increment of each energy storage battery, Δy E1 , Δy E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The variable increment of the energy storage battery, A E1 , A E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The coefficient matrix of the energy storage battery, B E1 , B E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The control matrix of the energy storage battery, E E1 、E E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. P The initial measurement matrix of the energy storage battery, C E1 , C E2 and They are the first energy storage battery, the second energy storage battery and the Nth energy storage battery. p The output state coefficient matrix of the energy storage battery, diag represents a diagonal matrix; According to the continuous state space model of the energy storage system, the discrete state space model with sampling time is obtained as follows: Among them, Δx(k) is the increment of the state variable of the discrete state space model at time k, Δx(k+1) is the increment of the state variable of the discrete state space model at time k+1, u(k) is the control variable of the discrete state space model at time k, G is the output state coefficient matrix of the discrete state space model, H is the control matrix of the discrete state space model; y(k+1) is the increment of the output variable of the discrete state space model at time k+1 with u(k) as the control variable; ΔT p is the sampling time, τ is the time integration variable; The discrete state space model with sampling time is used as the space state model of the energy storage system.

3. The method for energy storage participating in grid frequency modulation according to claim 1, characterized in that: The objective function of the energy storage optimization model is: Among them, f L,j is the charge and discharge loss cost of the jth energy storage battery, S soc,j is the degree of charge state differentiation of the jth energy storage battery, N P is the total number of energy storage batteries, || || represents the norm, is the objective function of the charging and discharging loss cost of the energy storage system, is the objective function of the degree of charge state differentiation among the energy storage batteries in the energy storage system, λ A and λ B are all weight coefficients.

4. The method for energy storage participating in grid frequency modulation according to claim 3 is characterized in that: The constraints of the energy storage optimization model are: in, is the initial power of the jth energy storage battery, is the active power increment issued by the dispatch command, The upper limit of the charging power of the energy storage battery, is the upper limit of the energy storage battery discharge power; ΔP ref To compensate for the active power required for frequency modulation.

5. The method for energy storage participating in grid frequency modulation according to claim 1, characterized in that: The calculation formula for active power compensation required for frequency modulation is: Δf=f ref -f m ; Where Δf is the frequency deviation, f ref is the preset rated frequency, f m is the measured frequency of the power system; ΔP ref The active power compensation required for frequency modulation, K p is the proportional gain coefficient, K d is the integral gain coefficient, and t is the time.

6. The method for energy storage participating in grid frequency regulation according to claim 1, characterized in that: The calculation formula for the charge and discharge loss cost of each energy storage battery in the energy storage system is: Among them, f L,j is the charge and discharge loss cost of the jth energy storage battery, is the charging power of the jth energy storage battery, is the discharge power of the jth energy storage battery, μ ESS is the cost coefficient of energy storage, γ ESS is the rated life of energy storage, T c To control the time in the time domain, α1, α2, α3 and α4 are the life cycle coefficients of energy storage, T ESS,j is the temperature of the jth energy storage battery, E R,j is the capacity of the jth energy storage battery; SOC j is the current state of charge of the jth energy storage battery, is the initial state of charge of the jth energy storage battery, Q c is the capacity of the energy storage battery, i D,j is the output current of the jth energy storage battery.

7. The method for energy storage participating in grid frequency regulation according to claim 1, characterized in that: The calculation formula for the degree of difference in the state of charge between each energy storage battery in the energy storage system is: Among them, S soc,j is the degree of charge differentiation of the jth energy storage battery, SOC j is the current state of charge of the jth energy storage battery, is the current average state of charge of all energy storage batteries, N P is the total number of energy storage batteries; is the initial state of charge of the jth energy storage battery, Q c is the capacity of the energy storage battery, i D,j is the output current of the jth energy storage battery.

8. A system for simulating energy storage participating in power grid frequency regulation, characterized in that: The energy storage participating in the grid frequency regulation simulation system applies the energy storage participating in the grid frequency regulation method described in any one of claims 1 to 7 for simulation, and the energy storage participating in the grid frequency regulation simulation system comprises: a power system, an inertial controller, an energy storage charging and discharging loss calculation module, an energy storage battery charge state differentiation degree calculation module, an MPC controller and an energy storage system; The inertial controller is used to collect the measured frequency of the power system when the frequency of the power system changes, and determine the active power compensation required for frequency modulation according to the measured frequency; The energy storage charge and discharge loss calculation module is used to calculate the charge and discharge loss cost of each energy storage battery in the energy storage system according to the DC current of each energy storage battery in the energy storage system at the current moment; The energy storage battery state of charge differentiation degree calculation module is used to calculate the degree of difference in the state of charge between each energy storage battery in the energy storage system according to the DC current of each energy storage battery in the energy storage system at the current moment; The MPC controller is used to calculate the active power increment of each energy storage battery in the energy storage system within the prediction time domain based on the spatial state model of the energy storage system and the energy storage optimization model, using a model predictive control method according to the output power and DC voltage of each energy storage battery in the energy storage system at the current moment, the active power compensation required for frequency modulation, the charge and discharge loss cost of each energy storage battery in the energy storage system, and the degree of charge state differentiation among each energy storage battery in the energy storage system; and distribute the active power increment as an active output instruction to each energy storage battery in the energy storage system; the energy storage optimization model takes the charge and discharge loss cost of the energy storage system and the degree of charge state differentiation among the energy storage batteries in the energy storage system as the optimization goal.

9. The energy storage participating in the grid frequency regulation simulation system according to claim 8 is characterized in that: The electric power system adopts a motor model.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for energy storage to participate in grid frequency regulation as described in any one of claims 1 to 7.

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