Method and device for energy storage participating in emergency control of power grid frequency under wind power low voltage ride through

Abstract

The application relates to the technical field of power systems, in particular to a method and device for participating in emergency control of a power grid frequency by energy storage under wind power low-voltage ride-through, wherein the method comprises the following steps: acquiring online operation data of a power system, a real-time emergency control resource pool and a real-time estimated emergency control amount under a predicted fault condition; judging whether the power system meets a preset starting emergency control condition in the case of a fault of the power system; if the power system meets the preset starting emergency control condition, inputting the real-time emergency control resource pool and the real-time estimated emergency control amount into a pre-established energy storage output distribution optimization model based on the online operation data, generating an online emergency control optimization strategy by solving, and controlling the power system to execute the online emergency control optimization strategy. The application can configure energy storage in the system, coordinate and distribute the support capacity of each energy storage, optimize the system frequency response characteristics of the power system, and avoid triggering a low-frequency load shedding action or a transient high-frequency problem.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and particularly relates to a method and device for energy storage participating in emergency control of power grid frequency under wind power low-voltage ride-through. BACKGROUND

[0002] With the increasing penetration of new energy such as wind power, a large number of conventional units are replaced, the system inertia is greatly reduced, and the low disturbance resistance of new energy also brings great impact on the safe and stable operation of the power system. For example, a large number of wind turbines enter low-voltage ride-through, causing the power system frequency to rapidly drop under extremely low inertia, and then inducing system splitting. Therefore, it is urgent to study the power grid safety and stability problems and control measures that may be caused by new energy fault ride-through.

[0003] In the related art, research is mainly carried out on the operating characteristics and influence of wind turbine fault ride-through. For example, auxiliary devices such as crowbar circuit and chopper resistance are added to the inverter, so as to improve the low-voltage ride-through capability of the wind turbine.

[0004] However, the low-voltage ride-through solution in the related art is limited by the limited support capability of new energy units, and multiple new energy units themselves are emphasized for support, so as to cause the inability to cope with the frequency stability problem caused by low-voltage ride-through, poor flexibility of control measures, and excessive control cost. When large-area wind power low-voltage ride-through brings a large short-time power impact, the system regulation capability is insufficient to maintain the stability of the power grid frequency, and a series of chain problems may be caused, and the frequency safety and stability problem is aggravated, which needs to be solved urgently. SUMMARY

[0005] The present application provides a method and device for energy storage participating in emergency control of power grid frequency under wind power low-voltage ride-through, to solve the problems in the related art that the low-voltage ride-through solution is limited by the limited support capability of new energy units, multiple new energy units themselves are emphasized for support, so as to cause the inability to cope with the frequency stability problem caused by low-voltage ride-through, poor flexibility of control measures, and excessive control cost. When large-area wind power low-voltage ride-through brings a large short-time power impact, the system regulation capability is insufficient to maintain the stability of the power grid frequency, and a series of chain problems may be caused, and the frequency safety and stability problem is aggravated.

[0006] The first aspect embodiment of the application provides a method for energy storage to participate in emergency control of power grid frequency under wind power low voltage ride through, comprising the following steps: obtaining online operation data of a power system, a real-time emergency control resource pool and a real-time estimated emergency control amount under a predicted fault condition; in the case of a fault of the power system, judging whether the power system meets a preset starting emergency control condition; if the power system meets the preset starting emergency control condition, inputting the real-time emergency control resource pool and the real-time estimated emergency control amount into a pre-established energy storage output distribution optimization model based on the online operation data, generating an online emergency control optimization strategy by solving the energy storage output distribution optimization model, and controlling the power system to execute the online emergency control optimization strategy.

[0007] Optionally, in an embodiment of the application, in the case of a fault of the power system, judging whether the power system meets a preset starting emergency control condition comprises: obtaining active power characteristic curves of different wind power plants under low voltage ride through based on the online operation data; performing equivalent fitting processing on the active power characteristic curves to obtain a system equivalent short-time power impact curve, and inputting information of the system equivalent short-time power impact curve into a pre-established system frequency response model to obtain an estimated system frequency minimum point of the power system; in the case of triggering action of a low-frequency load shedding device at the estimated system frequency minimum point, determining that the power system meets the preset starting emergency control condition.

[0008] Optionally, in an embodiment of the application, before inputting the real-time emergency control resource pool and the real-time estimated emergency control amount into the pre-established energy storage output distribution optimization model, further comprising: optimizing emergency control power of energy storage of the power system based on a target demand of the system frequency minimum point to obtain optimized emergency control power; and establishing an emergency control resource optimization distribution mathematical model based on the optimized emergency control power to optimize the energy storage output distribution optimization model according to the emergency control resource optimization distribution mathematical model.

[0009] Optionally, in an embodiment of the application, the constraint conditions of the emergency control resource optimization distribution mathematical model comprise power balance constraints, emergency control total amount equality constraints, energy storage adjustment range constraints, and node bus frequency inequality constraints.

[0010] Optionally, in an embodiment of the application, the function of the target demand of the system frequency minimum point is:

[0011] max(F1(p1)+F2(p2)+......+F n (p n )-n*F(0))

[0012] wherein, p1, p2,..., pn represent the output of each energy storage, n represent the output of each energy storage, F i (p i ) represents the lifting effect of the system frequency minimum point, F(0) represents the minimum point when the energy storage does not output, and n represents the number of energy storages.

[0013] The second aspect embodiment of the application provides a device for participating in emergency control of a power grid frequency by an energy storage under wind power low-voltage ride-through, comprising: an acquisition module, configured to acquire online operation data of a power system, a real-time emergency control resource pool, and a real-time estimated emergency control amount under a predicted fault condition; a judgment module, configured to judge whether the power system meets a preset starting emergency control condition in the case of a fault of the power system; and a control module, configured to, in the case that the power system meets the preset starting emergency control condition, input the real-time emergency control resource pool and the real-time estimated emergency control amount into a pre-established energy storage output distribution optimization model based on the online operation data, generate an online emergency control optimization strategy by solving the energy storage output distribution optimization model, and control the power system to execute the online emergency control optimization strategy.

[0014] Optionally, in an embodiment of the application, the judgment module comprises: an acquisition unit, configured to acquire active power characteristic curves of different wind power plants under low-voltage ride-through based on the online operation data; a processing unit, configured to perform equivalent fitting processing on the active power characteristic curves to obtain a system equivalent short-time power impact curve, and input information of the system equivalent short-time power impact curve into a pre-established system frequency response model to obtain an estimated system frequency minimum point of the power system; and a judgment unit, configured to determine that the power system meets the preset starting emergency control condition in the case that the estimated system frequency minimum point triggers action of a low-frequency load shedding device.

[0015] Optionally, in an embodiment of the application, further comprising: a first optimization module, configured to, before inputting the real-time emergency control resource pool and the real-time estimated emergency control amount into the pre-established energy storage output distribution optimization model, optimize emergency control power of the energy storage of the power system based on a target demand of the system frequency minimum point to obtain optimized emergency control power; and a second optimization module, configured to establish an emergency control resource optimization distribution mathematical model based on the optimized emergency control power, so as to optimize the energy storage output distribution optimization model according to the emergency control resource optimization distribution mathematical model.

[0016] Optionally, in an embodiment of the present application, the constraint conditions of the emergency control resource optimization allocation mathematical model include power balance constraints, emergency control total amount equality constraints, energy storage adjustment range constraints, and node bus frequency inequality constraints.

[0017] Optionally, in an embodiment of the present application, the function of the target demand of the system frequency minimum point is:

[0018] max(F1(p1)+F2(p2)+......+F n (p n )-n*F(0))

[0019] Wherein, p1, p2,......, p n represent the output of each energy storage, F i (p i ) represents the lifting effect of the system frequency minimum point, F(0) represents the minimum point when the energy storage does not output, and n represents the number of energy storages.

[0020] The third aspect embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the wind power low voltage ride through under energy storage participating in grid frequency emergency control method as described in the above embodiments.

[0021] The fourth aspect embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the wind power low voltage ride through under energy storage participating in grid frequency emergency control method as described above.

[0022] The fifth aspect embodiment of the present application provides a computer program product, comprising a computer program, and the computer program is executed to implement the wind power low voltage ride through under energy storage participating in grid frequency emergency control method as described above.

[0023] The embodiment of the application can generate an online emergency control optimization strategy under the condition of low-voltage ride-through failure of the power system based on the operation data of the power system, the real-time emergency control resource pool and the real-time estimated emergency control amount under the condition of the expected failure, thereby realizing the addition of the energy storage to the frequency emergency control method of the power system under the condition of low-voltage ride-through failure, playing the dynamic active support performance, significantly optimizing the system frequency response characteristics of the power system by coordinating the support capacity of each energy storage, effectively avoiding the triggering of the low-frequency load shedding action or the occurrence of the transient high-frequency problem, and timely responding to the low-voltage ride-through failure of the power system, thereby guaranteeing the stable operation of the power system. Therefore, the low-voltage ride-through solution in the related art is limited by the limited support capacity of the new energy unit, and the support of the new energy itself is emphasized, which leads to the inability to cope with the frequency stability problem caused by low-voltage ride-through, poor control measure flexibility and large control cost. When large-area wind power low-voltage ride-through causes a large short-time power impact, the system regulation capacity is insufficient to maintain the frequency stability of the power grid, and a series of chain problems may be caused, thereby aggravating the frequency safety and stability problem.

[0024] Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and / or can be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0025] The above and / or additional aspects and advantages of the application will become apparent and be readily appreciated from the following description, including the accompanying drawings, in which:

[0026] Figure 1 A flowchart of a method for energy storage participating in grid frequency emergency control under wind power low-voltage ride-through according to an embodiment of the application is provided.

[0027] Figure 2 A flowchart of a method for energy storage participating in grid frequency emergency control according to an embodiment of the application is provided.

[0028] Figure 3 A schematic diagram of a 39-node system structure according to an embodiment of the application is provided.

[0029] Figure 4 A simulation schematic diagram of emergency power support output of energy storage according to an embodiment of the application is provided.

[0030] Figure 5 A comparison schematic diagram of system frequency deviation under emergency control according to an embodiment of the application is provided.

[0031] Figure 6 A comparison schematic diagram of improvement effect under emergency control of energy storage at different access locations according to an embodiment of the application is provided.

[0032] Figure 7A BUS2 energy storage-frequency minimum point characteristic curve fitting effect schematic diagram of one embodiment of the present application;

[0033] Figure 8 A frequency minimum point schematic diagram of each node under two energy storage output distribution schemes of one embodiment of the present application;

[0034] Figure 9 A minimum emergency control amount schematic diagram under two energy storage output distribution schemes of one embodiment of the present application;

[0035] Figure 10 A structure schematic diagram of a device for energy storage participating in grid frequency emergency control under wind power low voltage ride through according to an embodiment of the present application;

[0036] Figure 11 A structure schematic diagram of an electronic device according to an embodiment of the present application.

[0037] Reference signs:

[0038] 10-device for energy storage participating in grid frequency emergency control under wind power low voltage ride through: 100-acquisition module, 200-judgment module and 300-control module; 1101-memory, 1102-processor and 1103-communication interface. DETAILED DESCRIPTION

[0039] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0040] A wind power low-voltage ride-through under energy storage participating in power grid frequency emergency control method and device of an embodiment of the present application is described below with reference to the accompanying drawings. In view of the fact that the low-voltage ride-through solution in the related art mentioned in the background art is limited by the limited support capability of new energy units, and the support of new energy itself is emphasized, which leads to the inability to cope with the frequency stability problem caused by low-voltage ride-through, poor control flexibility and excessive control cost, when large-area wind power low-voltage ride-through causes a large short-time power impact, the system regulation capability is insufficient to maintain the stability of the power grid frequency, and a series of chain problems may be caused, aggravating the frequency safety and stability problem, the present application provides a wind power low-voltage ride-through under energy storage participating in power grid frequency emergency control method. In this method, based on the operation data of the power system, the real-time emergency control resource pool and the real-time estimated emergency control amount under the expected fault condition, an online emergency control optimization strategy is generated when the power system fails under low-voltage ride-through. Thus, the energy storage is added to the frequency emergency control method of the power system under low-voltage ride-through failure, and its dynamic active support performance is utilized. By coordinating the support capability of each energy storage, the system frequency response characteristics of the power system are significantly optimized, the low-frequency load shedding action is effectively avoided or the transient high-frequency problem is avoided, the low-voltage ride-through failure of the power system can be timely responded to, and the stable operation of the power system is ensured. Thus, the low-voltage ride-through solution in the related art is limited by the limited support capability of new energy units, and the support of new energy itself is emphasized, which leads to the inability to cope with the frequency stability problem caused by low-voltage ride-through, poor control flexibility and excessive control cost, when large-area wind power low-voltage ride-through causes a large short-time power impact, the system regulation capability is insufficient to maintain the stability of the power grid frequency, and a series of chain problems may be caused, aggravating the frequency safety and stability problem, and other problems are solved.

[0041] Before the wind power low-voltage ride-through under energy storage participating in power grid frequency emergency control method in the embodiment of the present application is explained, the energy storage involved in the embodiment of the present application is explained.

[0042] For a wind power high-penetration power grid, a short-circuit fault easily causes a large number of wind turbine generators to enter low-voltage ride-through, which will cause a serious drop in system frequency, and the power system lacks emergency control resources. Energy storage is an important technology and system for supporting large-scale access and utilization of new energy and high-quality emergency control resources within the system. When large-scale wind power low-voltage ride-through occurs, the energy storage station will also enter low-voltage ride-through, and the output characteristics of the energy storage are similar to those of the doubly-fed wind turbine. With the recovery of the grid connection point voltage, the energy storage can output power. The specific output characteristics are as follows: after a certain communication delay, the energy storage device receives the corresponding signal and starts to increase the power output. The power increases linearly over time, and after reaching full response, it can be regarded as a constant active source for a period of time.

[0043] Based on this, for the poor frequency characteristics of the high-penetration wind power grid, after the system suffers from serious disturbance, specific control measures and means are needed to ensure that the system does not collapse and recovers to the normal state as soon as possible, and to maintain the integrity and reliability of the system. The energy storage station can be included in the emergency control of the power grid frequency.

[0044] Specifically, Figure 1 The flowchart of the wind power low-voltage ride-through under energy storage participating in power grid frequency emergency control method provided by the embodiment of the application.

[0045] As Figure 1 The wind power low-voltage ride-through under energy storage participating in power grid frequency emergency control method includes the following steps:

[0046] In step S101, the online operation data of the power system, the real-time emergency control resource pool and the real-time estimated emergency control amount under the expected fault condition are obtained.

[0047] It can be understood that the real-time emergency control resource pool and the real-time estimated emergency control amount under the expected fault condition can be understood as periodically refreshing the available control amount of each energy storage in the power system for emergency control based on certain setting rules, that is, the real-time emergency control resource pool can be obtained; further, based on certain safety guidelines, periodically online electromechanical transient simulation calculation can be carried out, that is, the real-time estimated emergency control amount required after the large-area wind power low-voltage ride-through expected fault occurs can be calculated.

[0048] In some embodiments, the criterion for whether to start emergency control is mostly to judge whether the system frequency deviation reaches a certain specific value, and when the emergency control needs to be started, the corresponding control measures are implemented from the corresponding offline control strategy table. However, for the system transient low-frequency problem under large-scale wind power low-voltage ride-through, if the starting criterion of the specific value trigger is still used, problems such as the setting of the specific value being too high, the emergency control strategy misoperation, or the setting of the trigger value being too small, missing the best control time, and the control effect not meeting the safety and stability demand may occur.

[0049] Based on this, in order to determine and formulate the starting criterion of the subsequent control strategy, the embodiment of the application can quantitatively analyze the system frequency response under large-scale wind power low voltage ride through, and formulate the starting criterion of the emergency control strategy in combination with the low-penetration related information of the wind turbine in the online operation data. Since the short-time power impact requires very precise emergency control strategy of the energy storage, the traditional mode of formulating the emergency control strategy based on offline conservative mode is very easy to cause the overlarge energy storage control amount and cause the cascading high-frequency problem, and it is extremely difficult to determine the conservative mode of large-scale wind power low voltage ride through caused by short-circuit fault. Therefore, the embodiment of the application can obtain the online data of the power system to analyze the fault characteristics of wind power low voltage ride through for pre-decision, and preliminarily estimate the emergency control amount of the energy storage based on the information, and then iteratively correct on this basis, so as to optimize and improve the control effect of the emergency control strategy.

[0050] Further, considering that in the actual power grid, the node position of the energy storage station is determined in the early stage of power grid planning, and is affected by the upper limit of active power support capacity of the node power flow constraint and the operation characteristics of the station itself, the estimated emergency control amount needs the joint action of the distributed energy storage stations in the whole network to realize the emergency control of frequency, therefore, the embodiment of the application can also obtain the real-time emergency control resource pool to guarantee that the emergency control effect is best through the coordinated allocation of each emergency control resource under the condition of certain emergency control amount.

[0051] Step S102, in the case of power system failure, it is judged whether the power system meets the preset starting emergency control condition.

[0052] In some embodiments, in the case of power system failure, sometimes the fault is relatively minor and does not need to start emergency control, therefore, before the energy storage participates in the emergency control of the power grid frequency under the implementation of wind power low voltage ride through, it is necessary to first judge whether the power system meets a certain starting emergency control condition, only when the power system meets a certain starting emergency control condition, the emergency control will be started.

[0053] Next, the process of judging whether the power system meets the preset starting emergency control condition in the embodiment of the application is further explained.

[0054] Optionally, in one embodiment of the present application, in the case of a power system failure, it is determined whether the power system meets the preset starting emergency control condition, including: based on online operation data, obtaining active power characteristic curves of different wind farms under low voltage ride through; performing equivalent fitting processing on the active power characteristic curves to obtain a system equivalent short-time power impact curve, and inputting information of the system equivalent short-time power impact curve into a pre-established system frequency response model to obtain an estimated system frequency minimum point of the power system; in the case of triggering low-frequency load shedding device action at the estimated system frequency minimum point, it is determined that the power system meets the preset starting emergency control condition.

[0055] Based on the related description of other embodiments, it can be understood that before the energy storage under wind power low voltage ride through participates in the grid frequency emergency control, it is necessary to first determine whether the power system meets a certain starting emergency control condition, and only when the power system meets a certain starting emergency control condition, the emergency control will be started.

[0056] Specifically, the embodiment of the present application can first obtain active power characteristic curves of different wind farms under low voltage ride through based on online operation data, that is, obtain short-time impact power, low ride duration and low ride recovery time and other information of different wind farms, and for the difference of active power curves under different wind turbine low ride strategies, a segmented correction method is used for equivalent fitting to obtain a system equivalent short-time power impact curve.

[0057] For example, when there is unbalanced active power in the power system, the electromagnetic power output by the generator changes in response to the unbalanced active power, resulting in a change in the overall active power distribution of the power system. Among them, the power impact caused by wind turbine low voltage ride through can be approximately divided into two processes, one is the step response in the low voltage ride through area, and the other is the slope response in the recovery area. Due to the different voltages at the grid connection points of each wind turbine after the actual power system failure, different low voltage ride through control strategies are adopted, and the active power recovery process at the power system level is also a segmented linear process, and the final recovery time depends on the wind turbine with the deepest active power drop, which can be fitted by a segmented correction method to obtain a system equivalent short-time power impact curve.

[0058] Further, with the increase of wind power penetration, if the maximum reactive current injection is still targeted in the case of low voltage sag (in low voltage ride through control, when the voltage sag reaches a certain level, the entire converter capacity will be used to provide reactive current), a large amount of active power will be lost without adjusting the active current control strategy, and once the active deficiency (the imbalance between supply and demand of active power in the power system, that is, the actual consumed active power is greater than the active power that the system can provide, which can also be understood as the difference between the actual required active power and the available active power. When the power demand exceeds the power supply, active deficiency occurs) exceeds the maximum capacity of the system, the low frequency load shedding device will be triggered to cause the system frequency to collapse.

[0059] Based on this, the embodiment of the present application can input the information of the equivalent short-time power impact curve of the system to the pre-established system frequency response model, and calculate the estimated system frequency minimum point through the model. Whether the system frequency minimum point reaches the low frequency load shedding device action setting value without applying emergency control is determined to decide whether a certain emergency control condition is met, that is, whether the subsequent emergency control strategy needs to be started. If the system frequency minimum point will trigger the low frequency load shedding device to act, the energy storage emergency control is applied to the system frequency response model.

[0060] Among them, the active recovery rate of low penetration wind turbine is positively correlated with the system frequency minimum point, that is, the greater the active recovery rate, the higher the frequency minimum point. The frequency minimum point caused by low voltage ride through of wind turbine occurs after power system fault removal, that is, in the active recovery stage. In the recovery area after the grid connection point is restored, the low voltage ride through control strategy can increase the active recovery rate of wind turbine to speed up the recovery of system frequency.

[0061] The frequency response model (System Frequency Response, SFR) can analyze the frequency response characteristics of the power system under disturbance. Due to its simplicity and convenience in implementation, the SFR model has been widely used in the field of power system frequency stability research. It can effectively predict the frequency minimum point and change rate of the system after active disturbance, and provide guidance for the operation and control of the power system. The basic principle of the model is to equivalent the frequency change process of the power system, which is equivalent to the speed change process of a synchronous generator equipped with a speed regulator and a reheated steam turbine. In the embodiment of the present application, in order to simplify the input and output of the frequency response model, the frequency modulation capability of the wind turbine and the nonlinear link in the speed regulation system are ignored, that is, the system frequency response model.

[0062] If the power system meets the preset starting emergency control condition, the real-time emergency control resource pool and the real-time estimated emergency control amount are input into the pre-established energy storage output distribution optimization model based on online operation data, the online emergency control optimization strategy is generated by solving the energy storage output distribution optimization model, and the power system is controlled to execute the online emergency control optimization strategy.

[0063] It can be understood that the energy storage output distribution optimization model can be understood as a model for coordinating and distributing each emergency control resource under the condition of a certain emergency control amount, so that the effect of emergency control is optimal, and the extreme value problem of optimal distribution of emergency support active power is solved.

[0064] In actual execution, after it is judged that the power system meets a certain starting emergency control condition, the real-time emergency control resource pool and the real-time estimated emergency control amount can be input into the pre-established energy storage output distribution optimization model, and the online emergency control optimization strategy is generated by solving the energy storage output distribution optimization model, and the output adjustment instruction of each energy storage is obtained. Then the output adjustment instruction is issued to the stability control system of the power system, so that each energy storage executes the online emergency control optimization strategy according to the output adjustment instruction.

[0065] In solving the energy storage output distribution optimization model, there are many methods. In the embodiment of the application, a particle swarm optimization algorithm can be used for solving, but is not limited to this.

[0066] The particle swarm optimization algorithm (PSO) fully utilizes the position information of excellent particles to search in parallel in the feasible solution space. Meanwhile, the algorithm fully utilizes the probability transfer rule as the internal working mechanism of the population, and does not need to add derivative information. In the particle swarm optimization algorithm, all particles in the population search for optimal solutions in the search space. Each particle can serve as a potential problem solution of the target problem, and the information of the particle is affected by the historical optimal experience of itself and the global historical optimal experience. The good and bad degree of each particle is evaluated by calculating the cost function. In the iterative search process of the particle swarm, all particles learn from the optimal particle of the population, and the learning process is accompanied by a random disturbance parameter, and finally the position information of the next generation of particles is determined.

[0067] In simple terms, in the particle swarm algorithm, a particle represents the answer to a problem to be solved, and in each iteration, the particle updates itself by tracking two "extreme values". After finding the two optimal values, the particle continuously updates its speed and position for iterative calculation, and finally finds the optimal solution. The update formula can be expressed as follows:

[0068] Suppose there are m particles in an n-dimensional space, x i=x i1 ,x i2 ,......,x in represents the ith particle, p i =p i1 , p i2 ,......, p in represents the optimal solution of the ith particle, p g =p g1 , p g2 ,......, p gn represents the global optimal solution. The movement direction of each particle is the vector sum of the individual optimal solution and the global optimal solution direction. represents the flight speed of the ith particle at the kth iteration, and the speed and position of the particle at the k+1th iteration can be represented as:

[0069]

[0070] wherein 1≤i≤m, 1≤d≤n, c1, c2 are learning factors, the values of which are positive numbers, r1, r2 are random numbers with a value range of [0, 1], and ω is an inertia weight that can affect the search ability of the algorithm.

[0071] It should be noted that the accuracy of the solution obtained by the particle swarm algorithm is not necessarily proportional to the iteration step number and the particle swarm size, that is, the larger the iteration step number and the particle swarm size, the higher the accuracy of the solution obtained is not necessarily. This is directly related to the setting of the initialized particle as a random solution, which has a great influence on the accuracy and iteration speed of the solution.

[0072] Further, when solving the energy storage output optimization allocation model, the embodiments of the present application can first obtain initial high-quality particles by comparing the energy storage-frequency sensitivity function, and then iteratively solve the optimal particles to improve the solution effect of the algorithm.

[0073] Optionally, in an embodiment of the present application, before inputting the real-time emergency control resource pool and the real-time estimated emergency control amount into the pre-established energy storage output allocation optimization model, it further includes: based on the target demand of the system frequency minimum point, optimizing the emergency control power of the energy storage of the power system to obtain the optimized emergency control power; based on the optimized emergency control power, establishing an emergency control resource optimization allocation mathematical model to optimize the energy storage output allocation optimization model according to the emergency control resource optimization allocation mathematical model. The function of the target demand of the system frequency minimum point can be represented as:

[0074] max(F1(p1)+F2(p2)+......+F n (p n )-n*F(0))

[0075] wherein, p1, p2,..., pn represent the output of each energy storage, n represent the output of each energy storage, F i (p i ) represents the lifting effect of the system frequency minimum point, F(0) represents the minimum point when the energy storage does not output, and n represents the number of energy storages.

[0076] Based on the related description of other embodiments, it can be understood that in an actual power grid, the node position of the energy storage station is determined in the early stage of power grid planning, and the estimated emergency control amount needs the joint action of the distributed energy storage stations in the whole grid to realize the emergency control of the frequency. Therefore, under the condition of a certain emergency control amount, it is extremely important to coordinate and allocate the emergency control resources to make the emergency control effect optimal.

[0077] Based on this, the embodiments of the present application can, but are not limited to, taking the target demand of the system frequency minimum point, i.e., the optimal lifting effect or the minimum emergency control amount, as the target, taking the emergency control power of each energy storage station in the system as the optimization object, and obtaining the optimized emergency control power. And based on the optimized emergency control power, an emergency control resource optimization allocation mathematical model is established, and an energy storage output distribution optimization model is optimized according to the emergency control resource optimization allocation mathematical model, so as to perfect the online emergency control optimization strategy.

[0078] For example, without considering the over-regulation, the more the emergency control amount, i.e., the better the lifting effect on the frequency minimum point, i.e., there is a certain coupling relationship between the energy storage emergency output and the system frequency minimum point. Based on this, the embodiments of the present application can obtain a fitting function of energy storage-frequency minimum point through polynomial fitting, and define the energy storage-frequency minimum point characteristic curve under the system emergency control, wherein the fitting function of energy storage-frequency minimum point can be expressed as follows:

[0079] F = a n p n +a n-1 p n-1 +…+a0

[0080] wherein, F is the system frequency minimum point, a n is the corresponding coefficient of the n-th polynomial, and p is the energy storage output.

[0081] Specifically, the decision variable of the emergency control resource optimization allocation mathematical model, i.e., the output of each energy storage, can be expressed as follows:

[0082] x = (p1, p2,..., pn). n

[0083] ​If the frequency characteristic optimization is taken as the objective function, the objective function of the energy storage output distribution optimization model under the frequency emergency control can be expressed as:

[0084] max(F1(p1)+F2(p2)+......+F n (p n )-n*F(0))

[0085] In addition, the embodiment of the present application also considers whether the optimal emergency control resource coordination scheme can minimize the control cost, that is, the minimum emergency control amount is taken to realize the emergency control of the frequency, that is, the minimum control amount optimization setting problem. When the minimum emergency control amount is taken as the target, the objective function can be expressed as:

[0086] min(p1+p2+......+p n )。

[0087] Optionally, in an embodiment of the present application, the constraint conditions of the emergency control resource optimization distribution mathematical model include power balance constraints, emergency control total amount equality constraints, energy storage adjustment range constraints, and node bus frequency inequality constraints.

[0088] In actual execution process, the energy storage output distribution optimization model in the embodiment of the present application is subject to various actual conditions. Including but not limited to system power balance constraints, emergency control total amount equality constraints, energy storage adjustment range constraints, and node bus frequency inequality constraints.

[0089] (1) The power balance constraint can be expressed as follows:

[0090]

[0091] Where, P i , Q i are the active power and reactive power of node i, U i , U i and θ ij are the voltage amplitude of node i, j and the phase angle difference between the two nodes, G ij , B ij are the conductance and susceptance between node i and node j.

[0092] (2) Emergency control total amount constraint

[0093] If the optimization objective function is the system frequency characteristic optimization, that is, the emergency control total amount is constrained, the equality constraint can be expressed as follows:

[0094]

[0095] wherein constant is a given emergency control amount.

[0096] (3) Energy storage station adjustment range constraint

[0097] The active power of the balance node and the reactive power adjustment capability of the wind power plant are continuous variables with range limits, so the above two variables corresponding to the inequality constraint can be expressed as follows:

[0098] P cmin ≤P c ≤P cmax ,

[0099] wherein P cmin and P cmax are the minimum and maximum values of the active power of the energy storage station node respectively.

[0100] (4) Each node bus frequency constraint

[0101] The minimum point of the frequency of each node is limited in range by the low-frequency load shedding device, so the above two variables corresponding to the inequality constraint can be expressed as follows:

[0102] f jnad ≥f res

[0103] wherein f res is the allowed minimum frequency under safety constraint, which in the embodiments of the present application can but is not limited to be 49.0 Hz according to the frequency triggering the low-frequency load shedding action; and f jnad is the minimum point of the frequency of any node in the whole network.

[0104] (5) Energy storage rated power inequality constraint

[0105] The rated power of each energy storage is also considered when the optimization distribution scheme is made, and the distributed measure amount cannot exceed the upper limit, which can be expressed as follows:

[0106] p imax ≥p i ≥0

[0107] wherein p imax is the maximum active power currently adjustable by the energy storage on the node.

[0108] The present application is described and verified in detail in the following specific embodiments.

[0109] Figure 2 is a flow chart of the method for the energy storage participating in the emergency frequency control of the power grid under the wind power low-voltage ride-through of an embodiment of the present application. As shown in Figure 2 ,

[0110] Step S201, start.

[0111] Step S202, it is monitored that the expected failure occurs at the specific point of the power system.

[0112] Step S203, read the periodically refreshed simulation data and update the emergency control quantity to obtain the real-time estimated emergency control quantity.

[0113] Step S204, determine the constraint boundary condition according to the actual power grid data.

[0114] Step S205, read the energy storage-frequency characteristic curve and determine the station priority.

[0115] Step S206, input to the output distribution optimization model for solving.

[0116] Step S207, generate the online emergency control optimization strategy, distribute the energy storage, and perform the electromagnetic transient simulation.

[0117] Step S208, read the simulation data and calculate the fitness.

[0118] Step S209, perform natural selection and replace the end particles according to the priority.

[0119] Step S210, retain the high-quality particles to copy to the next generation and generate a new distribution scheme.

[0120] Step S211, perform the electromagnetic transient simulation on the updated scheme.

[0121] Step S212, calculate the updated fitness function and update the global best position of the particles.

[0122] Step S213, detect whether the minimum point of the frequency of each node is higher than 49.0 Hz, if the minimum point of the frequency of each node is not higher than 49.0 Hz, return to step S209.

[0123] Step S214, output the optimization decision variable in the case that the minimum point of the frequency of each node is higher than 49.0 Hz.

[0124] Step S215, issue the instruction value to each energy storage device according to the optimization decision variable.

[0125] Step S216, end.

[0126] Further, the embodiments of the present application can also verify the method.

[0127] For example, Figure 3 The structure diagram of the 39-node simulation system of one embodiment of the present application is as follows, Figure 3As shown, nodes 32, 35, and 38 are connected to the wind farm. During the low-voltage ride-through period, a specified power percentage control is adopted, with the low-voltage ride-through power being 30% of the normal active power. During the low-voltage ride-through recovery phase of nodes 32 and 38, the recovery rate is 30% of the rated power per second, and during the low-voltage ride-through recovery phase of node 35, the recovery rate is 50% of the rated power per second. All other parameters remain unchanged.

[0128] It is possible, but not limited to, that node (location) BUS2 can be connected to the energy storage station, and the emergency control response time of the energy storage station is set to 0.1s after the fault is cleared, and the emergency control is exited after 3s. Figure 4 This is a simulation diagram of the emergency power support output of energy storage according to one embodiment of this application. Figure 4 As shown, the frequency response of the system is simulated and calculated when the emergency boost power of the energy storage is 50MW, 100MW, 150MW and 200MW respectively.

[0129] Figure 5 This is a schematic diagram comparing the frequency deviation of a system under emergency control according to an embodiment of this application. Figure 5 As shown, by Figure 5 Simulation results show that when energy storage provides emergency power boost, the system frequency minimum point significantly increases, and the boost becomes more pronounced with increasing emergency power. Under 200MW emergency power support, the system frequency minimum point reaches 49.0Hz, the setpoint for the low-frequency load shedding device, ensuring system frequency stability. After the emergency control ends, the system frequency gradually recovers as the wind turbine's active power returns to normal, without causing high-frequency issues during the recovery process. These simulation results verify the effectiveness of energy storage in emergency frequency control.

[0130] Energy storage at five nodes (BUS6, BUS10, BUS19, BUS23, and BUS25) within the power system can be selected, with an emergency support power of 200MW. Simulations can be used to verify the differences in the effectiveness of energy storage at different nodes in emergency control. Figure 6 This is a schematic diagram comparing the improvement effect of energy storage under emergency control at different access locations according to an embodiment of this application. Figure 6 As shown, by Figure 6 Simulation results show that the frequency boosting effect of different nodes issuing the same emergency control quantity varies significantly. For example, BUS2, with a power output of 200MW, makes the lowest system frequency reach 49.0Hz, reaching the boundary value for the low-frequency load shedding device to operate. Emergency control of BUS10 makes the lowest system frequency close to 49.1Hz. After emergency control of random node 6 of BUS25, the lowest system frequency is still below 49.0Hz. The simulation results verify the necessity of selecting the optimal node for energy storage configuration.

[0131] The corresponding simulation data can be but not limited to input into the Matlab software for polynomial fitting, the degree can be but not limited to selected as 3 times, and the fitting effect of the BUS2 energy storage output-frequency minimum point characteristic curve is shown in Figure 7 The absolute value of the error is about 0.

[0132] To select the optimal node, the average energy storage consumption increment rate of each node can be but not limited to calculated in the 39-node system fault set, and the calculation results are shown in Table 1. According to the calculation results, BUS23 is the optimal node for connecting the energy storage, from the perspective of active power distribution, and the active power shortage of the system is caused by the low voltage ride-through of the wind turbine. BUS23 is close to the two wind farms in terms of electrical distance, and is in the center of active power fluctuation. BUS22 and BUS21 have good effects on the improvement of the system frequency minimum point.

[0133] Table 1 is a comparison of the energy storage consumption increment rates of different nodes, which can be represented as follows:

[0134] Table 1

[0135] Configuration node Average equal-energy incremental rate Configuration node Average equal-energy incremental rate 1 0.00141 20 0.00157 2 0.00157 21 0.00159 3 0.00149 22 0.00160 4 0.00142 23 0.00161 5 0.00139 24 0.00139 6 0.00156 25 0.00135 7 0.00143 26 0.00157 8 0.00155 27 0.00151 9 0.00145 28 0.00153 10 0.00159 29 0.00153 11 0.00156 30 0.00140 12 0.00157 31 0.00136 13 0.00152 32 0.00143 14 0.00137 33 0.00145 15 0.00138 34 0.00140 16 0.00140 35 0.00144 17 0.00147 36 0.00142 18 0.00150 37 0.00139 19 0.00151 38 0.00147 No No 39 0.00138

[0136] In order to verify the optimization effect and effectiveness of the emergency control strategy of the embodiment, the simulation results can also be used as original data, the energy storage access nodes in the system are set, the rated power of each energy storage station in the power grid is shown in Table 2, and no power is emitted during steady-state operation. The above simulation example is still analyzed.

[0137] Table 2 is the access node and rated power of the energy storage station in the power grid, which can be represented as follows:

[0138] Table 2

[0139] Access node Rated power / MW BUS2 25 BUS6 25 BUS10 25 BUS16 25 BUS19 25 BUS20 25 BUS22 25 BUS23 25 BUS25 25 BUS29 25

[0140] The node positions of each energy storage station are determined, the node energy storage-frequency minimum point curve is read, in order to verify the effectiveness of the optimized allocation strategy, according to the data in the expected fault set, the fixed control cost required to be set is obtained, and the frequency minimum point of each node under the energy storage output allocation scheme is simulated and calculated.

[0141] Scheme 1: average allocation, that is, the determined emergency control amount is uniformly distributed to the energy storage connected in the whole network.

[0142] Scheme 2: optimized allocation, that is, the determined emergency control amount is input into the optimization model, and the energy storage output allocation method is obtained by solving.

[0143] The total emergency control amount is estimated to be 190MW, which is input into the optimization allocation model and solved by using the improved particle swarm algorithm.

[0144] Table 3 is the system frequency characteristics under different energy storage output allocation schemes, which can be expressed as follows:

[0145] Table 3

[0146] Reference index Scheme 1 Scheme 2 Number of frequency-exceeded buses 4 0 Inertia center frequency minimum point 49.004 49.053

[0147] Figure 8 The minimum frequency of each node under two energy storage output allocation schemes of an embodiment of the present application is shown in the schematic diagram. From Table 3 and Figure 8 it can be seen that:

[0148] (1) In the case of average allocation of control quantity shown in scheme 1, the inertia center frequency is above the limit under emergency control, but there are some nodes that cannot meet the minimum frequency drop safety constraint value, and there are still frequency out-of-limit nodes.

[0149] (2) The optimization allocation scheme proposed in the embodiment of the present application, i.e. scheme 2, coordinates the support capacity of each energy storage, which maximizes the use of energy storage with high sensitivity and considers the frequency distribution characteristics of the system. After emergency control, it is ensured that the frequency of each bus meets the safety and stability constraints. The effect of improving the minimum point of system frequency under large-scale wind power low voltage ride through is the best, which shows that the scheme proposed in the present project can significantly optimize the system frequency response characteristics and effectively avoid triggering low-frequency load shedding action or appearing transient high-frequency problem.

[0150] Still taking the minimum control quantity as the objective function, the minimum emergency control quantity required to ensure that the frequency of each node of the system does not trigger the low-frequency load shedding device under the current working condition is obtained by continuously iterating the improved particle swarm algorithm, and compared with the minimum emergency control quantity required by the average allocation, Figure 9 the comparison result of the minimum emergency control quantity under two energy storage output allocation schemes of an embodiment of the present application is shown in the schematic diagram. As Figure 9 shown, the minimum emergency control quantity obtained by optimizing the output model will be lower than the minimum emergency control quantity of the average allocation, which reduces the emergency control cost.

[0151] The method for energy storage participation in grid frequency emergency control under low-voltage ride-through (LVRT) of wind power proposed in this application can generate an online emergency control optimization strategy based on power system operating data, a real-time emergency control resource pool, and real-time estimated emergency control quantities under anticipated fault conditions. This allows for the integration of energy storage into the frequency emergency control method under LVRT, leveraging its dynamic active power support performance. By coordinating and allocating the support capabilities of various energy storage units, the system frequency response characteristics of the power system are significantly optimized, effectively avoiding triggering low-frequency load shedding or transient high-frequency problems. This enables timely response to LVRT faults and ensures stable power system operation. This addresses the limitations of related technologies in LVRT solutions, which are constrained by the limited support capabilities of renewable energy units, overemphasis on renewable energy support leading to an inability to address frequency stability issues caused by LVRT, poor control flexibility, and excessive control costs. Furthermore, when large-scale wind power LVRT brings significant short-term power surges, the system's regulation capacity is insufficient to maintain grid frequency stability, potentially causing a series of cascading problems and exacerbating frequency security and stability issues.

[0152] Next, referring to the accompanying drawings, a device for energy storage to participate in emergency grid frequency control during wind power low-voltage ride-through, according to an embodiment of this application, is described.

[0153] Figure 10 This is a schematic diagram of the structure of the wind power low voltage ride-through energy storage participating in the grid frequency emergency control device according to an embodiment of this application.

[0154] like Figure 10 As shown, the wind power low voltage ride-through energy storage participating in grid frequency emergency control device 10 includes: acquisition module 100, judgment module 200 and control module 300.

[0155] The acquisition module 100 is used to acquire online operation data of the power system, real-time emergency control resource pool, and real-time estimated emergency control quantities under anticipated fault conditions.

[0156] The judgment module 200 is used to determine whether the power system meets the preset emergency control conditions in the event of a power system failure.

[0157] The control module 300 is used to input the real-time emergency control resource pool and the real-time estimated emergency control quantity into the pre-established energy storage output allocation optimization model based on online operation data when the power system meets the preset emergency control start conditions. The control module generates an online emergency control optimization strategy by solving the energy storage output allocation optimization model and controls the power system to execute the online emergency control optimization strategy.

[0158] Optionally, in an embodiment of the present application, the judging module 200 comprises an acquisition unit, a processing unit and a judging unit.

[0159] The acquisition unit is configured to acquire active power characteristic curves of different wind power plants under low voltage ride through based on online operation data.

[0160] The processing unit is configured to perform equivalent fitting processing on the active power characteristic curves to obtain a system equivalent short-time power impact curve, and input information of the system equivalent short-time power impact curve into a pre-established system frequency response model to obtain an estimated system frequency minimum point of the power system.

[0161] The judging unit is configured to determine that the power system needs to start an emergency control measure in a case where the estimated system frequency minimum point triggers action of a low frequency load shedding device.

[0162] Optionally, in an embodiment of the present application, the method further comprises a first optimization module and a second optimization module.

[0163] The first optimization module is configured to optimize emergency control power of the energy storage of the power system based on a target requirement of the system frequency minimum point before inputting the real-time emergency control resource pool and the real-time estimated emergency control amount into the pre-established energy storage output distribution optimization model to obtain optimized emergency control power.

[0164] The second optimization module is configured to establish an emergency control resource optimization distribution mathematical model based on the optimized emergency control power, so as to optimize the energy storage output distribution optimization model according to the emergency control resource optimization distribution mathematical model.

[0165] Optionally, in an embodiment of the present application, the constraint conditions of the emergency control resource optimization distribution mathematical model comprise power balance constraints, emergency control total amount equality constraints, energy storage adjustment range constraints, and node bus frequency inequality constraints.

[0166] Optionally, in an embodiment of the present application, the target requirement of the system frequency minimum point can be expressed as:

[0167] max(F1(p1)+F2(p2)+......+F n (p n )-n*F(0))

[0168] wherein p1, p2,..., p n represent the output of each energy storage, F i (p i ) represents the lifting effect of the system frequency minimum point, F(0) represents the minimum point when the energy storage does not output, and n represents the number of energy storages.

[0169] It should be noted that the foregoing explanation of the embodiment of the method for energy storage participating in emergency frequency control of a power grid under wind power low voltage ride through is also applicable to the device for energy storage participating in emergency frequency control of a power grid under wind power low voltage ride through, and details are not repeated here.

[0170] The device for energy storage participating in emergency frequency control of a power grid under wind power low voltage ride through according to the embodiment of the application can generate an online emergency control optimization strategy based on operation data of a power system, a real-time emergency control resource pool, and a real-time estimated emergency control amount under a predicted fault condition in the case of a low voltage ride through fault of the power system, thereby achieving the method of adding energy storage to frequency emergency control of a power system under a low voltage ride through fault, exerting the dynamic active power support performance of the energy storage, significantly optimizing system frequency response characteristics of the power system by coordinating the support capabilities of the energy storages, effectively avoiding triggering of a low frequency load shedding action or occurrence of a transient high frequency problem, and timely responding to a low voltage ride through fault of the power system to ensure stable operation of the power system. Thus, the problems in the related art that the low voltage ride through solution method is limited by limited support capabilities of new energy units, that the method mainly emphasizes support of the new energy units themselves and thus cannot respond to frequency stability problems caused by low voltage ride through, that control measures have poor flexibility and a large control cost, and that when large-area wind power low voltage ride through causes a large short-time power impact, system regulation capability is insufficient to maintain power grid frequency stability and may cause a series of cascading problems and aggravate frequency safety and stability problems are solved.

[0171] Figure 11 The structure schematic diagram of the electronic device provided by the embodiment of the application is shown. The electronic device can include:

[0172] The memory 1101, the processor 1102, and the computer program stored in the memory 1101 and executable on the processor 1102.

[0173] The processor 1102 implements the method for energy storage participating in emergency frequency control of a power grid under wind power low voltage ride through provided in the above embodiments when executing the program.

[0174] Further, the electronic device further includes:

[0175] The communication interface 1103 is used for communication between the memory 1101 and the processor 1102.

[0176] The memory 1101 is used for storing the computer program executable on the processor 1102.

[0177] The memory 1101 can include a high-speed RAM memory and can also include a non-volatile memory, for example, at least one disk memory.

[0178] If the memory 1101, the processor 1102 and the communication interface 1103 are implemented independently, the communication interface 1103, the memory 1101 and the processor 1102 can be connected with each other through a bus and complete the communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 11 Only one thick line is used to represent the bus in the figure, but it does not mean that there is only one bus or only one type of bus.

[0179] Optionally, in a specific implementation, if the memory 1101, the processor 1102 and the communication interface 1103 are integrated on a chip, the memory 1101, the processor 1102 and the communication interface 1103 can complete the communication between each other through an internal interface.

[0180] The processor 1102 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0181] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the wind power low-voltage ride-through under-frequency emergency control method.

[0182] The embodiments of the present application further provide a computer program product, which includes a computer program, and the computer program can run computer instructions, and the computer instructions are executed by a processor to implement the wind power low-voltage ride-through under-frequency emergency control method provided by the embodiments of the present application.

[0183] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "N" means at least two, for example, two, three or the like, unless explicitly stated otherwise.

[0184] Furthermore, the terms "first", "second", or the like, are used merely as a designation of certain elements or features, and do not imply or connote relative importance or a specific order of categorization thereof. Accordingly, features described as "first" or "second" can be explicitly or implicitly included in at least one of the features. In the description of the application, the meaning of "N" is at least two, for example, two, three, etc., unless explicitly specified otherwise.

[0185] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments which can be managed as one or more modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps, and alternate implementations are possible. In some embodiments, the processes and methods described can be executed by one or more apparatuses or devices, either directly or after conversion to another language. Alternate implementations are possible.

[0186] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions stored in a computer readable medium, which can be executed by an instruction execution system, apparatus or device, such as a computer-based system, a processor-based system, or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or a combination of the above. For the purposes of this specification, a "computer readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus or device. The computer readable medium can be a computer readable storage medium or a computer readable signal medium. The computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a propagation medium. The computer readable signal medium can include, but is not limited to, a computer readable medium that facilitates transfer of the program from one place to another. A specific example of a computer readable medium is a non-transitory computer-readable storage medium. A specific example of a computer readable signal medium is a source or destination of the computer readable medium. Another specific example of a computer readable signal medium is a computer readable signal travelling through space. Thus, a computer readable medium can take many forms of hardware to carry out the program for use by or in connection with the instruction execution system, apparatus or device.

[0187] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, the hardware can be implemented using any or a combination of the following technologies, which are each well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.

[0188] Those of skill in the art would understand that the steps of the methods carried out above can be carried out wholly or partly by a program instructing relevant hardware, and the program can be stored in a computer readable storage medium, and when executed, includes one or a combination of the steps of the method embodiments.

[0189] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0190] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for energy storage participating in emergency control of power grid frequency under wind power low voltage ride through, characterized in that, The method comprises the following steps: acquiring online operation data of a power system, a real-time emergency control resource pool and a real-time estimated emergency control amount under a contingency condition; in the case of a fault of the power system, judging whether the power system meets a preset starting emergency control condition; if the power system meets the preset starting emergency control condition, inputting the real-time emergency control resource pool and the real-time estimated emergency control amount into a pre-established energy storage output distribution optimization model based on the online operation data, generating an online emergency control optimization strategy by solving the energy storage output distribution optimization model, and controlling the power system to execute the online emergency control optimization strategy; wherein, in the case of a fault of the power system, judging whether the power system meets a preset starting emergency control condition comprises: based on the online operation data, acquiring active power characteristic curves of different wind power plants under low voltage ride through; performing equivalent fitting processing on the active power characteristic curves to obtain a system equivalent short-time power impact curve, and inputting information of the system equivalent short-time power impact curve into a pre-established system frequency response model to obtain an estimated system frequency minimum point of the power system; in the case of triggering a low-frequency load shedding device to act in the estimated system frequency minimum point, determining that the power system meets the preset starting emergency control condition; wherein, before inputting the real-time emergency control resource pool and the real-time estimated emergency control amount into the pre-established energy storage output distribution optimization model, further comprising: based on a target demand of the system frequency minimum point, optimizing emergency control power of energy storage of the power system to obtain optimized emergency control power; based on the optimized emergency control power, establishing an emergency control resource optimization distribution mathematical model to optimize the energy storage output distribution optimization model according to the emergency control resource optimization distribution mathematical model.

2. The method of claim 1, wherein, The constraint conditions of the emergency control resource optimization distribution mathematical model include power balance constraints, emergency control total amount equality constraints, energy storage regulation range constraints, and node bus frequency inequality constraints.

3. The method of claim 2, wherein, The function of the target demand of the system frequency minimum point is: wherein, represents the output of each energy storage, represents the lifting effect of the system frequency minimum point, represents the minimum point when the energy storage is not output, n represents the number of energy storages.

4. A device for participating in emergency control of power grid frequency by energy storage under wind power low voltage ride through, characterized in that, comprises: an acquisition module, configured to acquire online operation data of a power system, a real-time emergency control resource pool and a real-time estimated emergency control amount under a contingency condition; a judgment module, configured to, in the case of a fault of the power system, judge whether the power system meets a preset starting emergency control condition; a control module, configured to, in the case of the power system meeting the preset starting emergency control condition, input the real-time emergency control resource pool and the real-time estimated emergency control amount into a pre-established energy storage output distribution optimization model based on the online operation data, generate an online emergency control optimization strategy by solving the energy storage output distribution optimization model, and control the power system to execute the online emergency control optimization strategy; The judgment module comprises: an acquisition unit configured to acquire active power characteristic curves of different wind power plants under low voltage ride through based on the online operation data; a processing unit configured to perform equivalent fitting processing on the active power characteristic curves to obtain a system equivalent short-time power impact curve, and input information of the system equivalent short-time power impact curve into a pre-established system frequency response model to obtain an estimated system frequency minimum point of the power system; and a judgment unit configured to determine that the power system satisfies the preset starting emergency control condition in a case where the estimated system frequency minimum point triggers action of a low frequency load shedding device. The first optimization module is configured to optimize emergency control power of the energy storage of the power system based on a target requirement of the system frequency minimum point before inputting a real-time emergency control resource pool and a real-time estimated emergency control amount into a pre-established energy storage output distribution optimization model to obtain optimized emergency control power; and the second optimization module is configured to establish an emergency control resource optimization distribution mathematical model based on the optimized emergency control power to optimize the energy storage output distribution optimization model according to the emergency control resource optimization distribution mathematical model.

5. An electronic device, comprising: The computer program is executed by the processor to implement the method. The computer program is executed by the processor to implement the method.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method.

7. A computer program product comprising a computer program, characterized in that, ​

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