Active power coordinated control method, device and medium of clustered new energy power station

Abstract

The present invention discloses a cluster-type new energy active power coordinated control method, device, medium and equipment, constructs a power control model for each new energy power station in the cluster; obtains the cluster active power target value; takes the cluster actual output as the goal of being closest to the cluster active power target value and the shortest response time, and constructs an optimization objective function; wherein the cluster actual output is calculated by the power control model constructed by each new energy power station according to the control parameters of the power control model of each new energy power station and the power allocation parameters of each new energy power station; by optimizing the objective function, the control parameters of the power control model of each new energy power station and the power allocation of the new energy power station are obtained. By allocating reasonable active power and control parameter settings to each new energy power station in the cluster through the method of the present invention, the response time and response accuracy of the active power dispatch of the power grid of the aggregated power source can be significantly improved, and the network-related test process can also be faster and more effective.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system power generation control, and specifically relates to a method, device, medium and equipment for coordinated control of active power of a cluster-type new energy power station. Background Art

[0002] With the gradual advancement of the country's dual carbon goals, new energy construction is in full swing, and new energy stations are also evolving from a single form to multiple forms. At present, the main evolutionary directions are mainly divided into three types: (1) Large-scale new energy stations are often built in phases due to huge investments, and power equipment from different manufacturers is used in each phase; (2) Single power type gradually evolves into a composite new energy station with photovoltaic storage and wind storage; (3) Large-scale new energy bases integrate photovoltaic, wind power, and energy storage from different owner units, and aggregate different power sources into a large base. The above three power sources can be collectively referred to as clustered new energy stations.

[0003] Different power types, different manufacturers, and different station energy management topologies in clustered stations make their responses to active power instructions, reactive power instructions, voltage instructions, etc. of power grid dispatching different, which further makes the grid-related test process more and more complicated. On the other hand, large-scale power aggregation grid-related tests will have a reverse impact on the power grid, and the disturbances generated will become larger and larger, which in turn further affect the performance of grid-related tests. Based on this, it will be very important to control the active power distribution of each new energy power station with different power types, different manufacturers, and different stations. The distribution of active power of each new energy will directly affect the differences brought about by grid dispatching. Summary of the invention

[0004] The first purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a cluster-type new energy active power coordinated control method, through which reasonable active power and control parameters are allocated to each new energy power station in the cluster, which can significantly improve the response time and response accuracy of the active power dispatching of the aggregated power source grid, and also make the grid-related test process faster and more effective.

[0005] The second object of the present invention is to provide a cluster-type new energy active power coordination control device.

[0006] A third object of the present invention is to provide a storage medium.

[0007] A fourth object of the present invention is to provide a computing device.

[0008] The first object of the present invention is achieved by the following technical solution: a cluster type new energy active power coordinated control method, the steps comprising:

[0009] Build a power control model for each renewable energy power station in the cluster;

[0010] Get the cluster active power target value;

[0011] The optimization objective function is constructed with the goal of making the actual output of the cluster closest to the cluster active power target value and the shortest response time; wherein the actual output of the cluster is obtained by the power control model of each new energy power station based on the control parameters and the allocated power parameters;

[0012] By optimizing the objective function, the control parameters of the power control model of each renewable energy power station and the allocated power of the renewable energy power station are obtained.

[0013] Preferably, the power control model constructed for each new energy power station includes a first link, a second link and a third link; the first link is a PID control system, the second link is a control object, that is, corresponding to the new energy power station, and the third link is a delay link;

[0014] The output of the first link serves as the input of the second link, the output of the second link serves as the input of the third link, and the difference between the active power output allocated by the new energy power station and the output of the third link serves as the input of the first link.

[0015] Furthermore, each of the renewable energy power stations is simplified to a model P(S):

[0016]

[0017] Among them, T 1 is the time lag of the new energy power station, T 2 is the communication system delay of the new energy power station, P(S) is the Laplace transform, corresponding to the multiplication result of the second link and the third link;

[0018] Among them, the second link, namely the control object, is:

[0019]

[0020] The third stage is:

[0021] Furthermore, the power control model formula constructed by each new energy power station is:

[0022]

[0023] where y g is the actual output active power of the g-th new energy power station, x g is the allocated power of the g-th renewable energy power station; k gp , k gi , k gd is the PID control parameter of the first link of the power control model of the g-th renewable energy power station, namely the PID control system, T g1is the time lag of the g-th renewable energy power station, T g2 The communication system delay of the g-th renewable energy power station.

[0024] Furthermore, the optimization objective function is constructed in the following way:

[0025] Taking the minimum absolute value of the target output and the actual output in the shortest time as the optimization goal, the optimization objective function is constructed:

[0026] F=a*t s +b*abs(P tar -P real );

[0027] Among them, P tar is the target output, i.e. the cluster active power target value, P real is the actual output, a and b are constants, a+b=1; t s For the shortest response time;

[0028] Among them, the actual output P real for:

[0029] P real =y 1 +y 2 +,...,+y G ;

[0030] Among them, y 1 To G It is the actual output of the 1st to Gth new energy power stations, that is, the actual output active power.

[0031] Furthermore, through the inner and outer loop simulation steps, based on the optimization objective function, the control parameters of the power control model of each renewable energy power station and the allocated power of the renewable energy power station are obtained, as follows:

[0032] S1. Setting the control parameters of the power control model of each new energy power station and the value range of the power distribution parameters of the new energy power station;

[0033] S2. Setting the total number of times M of randomly selecting control parameters and the total number of times N of randomly selecting allocated power parameters for the power control model of the new energy power station;

[0034] S3, enter the outer loop simulation steps:

[0035] S31, for each new energy power station power control model, randomly select a control parameter PID (p g ,i g ,d g ), g=1,2,...G;PID(p g ,ig ,d g ) is a control parameter randomly selected for the power control model of the g-th new energy power station;

[0036] S32, verifying whether the operation of each renewable energy power station is stable under the control parameters selected by the power control model of each renewable energy power station in step S31;

[0037] If yes, the control parameters currently selected constitute a selected set of control parameters, and the current outer loop simulation is completed; then step S4 is executed;

[0038] If not, the current outer loop simulation is finished; return to step S31 to execute the next outer loop simulation step;

[0039] S4, enter the inner loop simulation steps:

[0040] S41. According to the constraints of the objective function, a distribution power parameter x is randomly selected for each new energy power station. g , g=1,2,...,G, and obtain a set of power allocation parameters; x g is the randomly selected allocation power parameter for the g-th new energy power station;

[0041] S42, based on the set of control parameters selected in S32 and the set of allocated power parameters selected in step S42, obtaining the actual output y of the power control model of each new energy power station g , g = 1, 2, ... G, and then obtain the actual output of the cluster P real ;

[0042] S43: The actual output P of the cluster obtained in step S42 real and the cluster active power target value P tar , through the objective function, calculate the objective function value F;

[0043] S44, when the current inner loop simulation is finished, determine whether the total number of executions of the inner loop simulation is less than N;

[0044] If yes, return to step S41 to perform the next inner loop simulation;

[0045] If not, a set of power allocation parameters with the smallest calculated objective function value F is selected and combined with the set of control parameters selected in step S32 as the selection result; then step S5 is executed;

[0046] S5, judging whether the total number of executions of the outer loop simulation is less than M;

[0047] If not, return to step S3 to perform the next outer loop simulation;

[0048] If yes, then end.

[0049] Furthermore, when executing steps S1 to S5, if the final selection result has multiple sets of parameters, each set of parameters corresponds to a set of control parameters and a set of power allocation parameters, the power control model constructed by the new energy power station is selected so that the steady-state response time t s The smallest set of parameters is selected as the final result.

[0050] The second object of the present invention is achieved by the following technical solution: a cluster type new energy active power coordination control device, comprising:

[0051] A model building module is used to build a power control model for each renewable energy power station in the cluster;

[0052] An acquisition module is used to obtain the cluster active power target value;

[0053] The objective function construction module is used to construct an optimization objective function with the goal of making the actual output of the cluster closest to the cluster active power target value and the shortest response time; wherein the actual output of the cluster is obtained by constructing a power control model of each new energy power station according to the control parameters of the power control model of each new energy power station and the power allocation parameters of each new energy power station;

[0054] The parameter determination module is used to obtain the control parameters of the power control model of each renewable energy power station and the allocated power of the renewable energy power station by optimizing the objective function.

[0055] The third purpose of the present invention is achieved through the following technical solution: a storage medium stores a program, and when the program is executed by a processor, the cluster-type renewable energy active power coordinated control method described in the first purpose of the present invention is implemented.

[0056] The fourth purpose of the present invention is achieved through the following technical solution: a computing device, comprising a processor and a memory for storing processor executable programs, when the processor executes the program stored in the memory, the cluster-type new energy active power coordinated control method described in the first purpose of the present invention is implemented.

[0057] Compared with the prior art, the present invention has the following advantages and effects:

[0058] (1) The cluster-type new energy active power coordinated control method of the present invention first constructs a power control model for each new energy power station in the cluster; obtains the cluster active power target value; then constructs an optimization objective function with the goal of making the cluster actual output closest to the cluster active power target value and the shortest response time; wherein the cluster actual output is calculated by constructing the power control model of each new energy power station according to the control parameters of the power control model of each new energy power station and the power allocation parameters of each new energy power station; finally, by optimizing the objective function, the optimization parameters of each new energy power station are obtained, including the active power and control parameters allocated to each new energy power station; based on this, the method of the present invention can allocate reasonable active power and control parameters to each new energy power station in the cluster, can significantly improve the response time and response accuracy of the active power dispatching of the aggregated power source, and also make the network-related test process faster and more effective.

[0059] (2) The present invention discloses a method for coordinated control of active power of clustered renewable energy sources, which models renewable energy sources stations using a simplified first-order model that can characterize the dispatch of renewable energy sources stations. The model takes into account different types of stations, such as wind power, photovoltaic power, and energy storage, as well as the response characteristics of stations of equipment from different manufacturers. At the same time, it models the time lag of the communication system, providing a simulation control model for the optimized coordinated control of power of clustered renewable energy power stations.

[0060] (3) In the cluster-type new energy active power coordinated control method of the present invention, when constructing the optimization objective function, the optimization goal is to minimize the absolute value of the target output and the actual output in the shortest time. Based on this, the control parameters of the power control model of each new energy power station and the allocated power of the new energy power station finally obtained by the method of the present invention can enable each new energy power station to achieve coordinated allocation at the fastest speed.

[0061] (4) In the cluster type new energy active power coordinated control method of the present invention, the total number of times M for randomly selecting control parameters and the total number of times N for randomly selecting allocated power parameters of the new energy power station power control model are set, and the optimization objective function is solved through the inner and outer loop simulation steps, wherein the PID control parameters of the power control model of each new energy power station are obtained through the outer loop simulation, and the allocated power of each new energy power station is obtained through the inner loop simulation; it can be seen that the method of the present invention decomposes the PID control parameters and power, and determines them in layers according to the optimization coordination target value, and based on the set total number of outer loop simulations and total number of inner loop simulations, multiple groups of PID control parameters and power decompositions can be obtained for selection, which effectively improves the agility and accuracy of new energy active power coordination. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flow chart of the method of the present invention.

[0063] Figure 2aIt is a control block diagram of a single new energy power station power control model constructed in the method of the present invention.

[0064] Figure 2b It is a control block diagram composed of power control models of multiple new energy power stations in a cluster in the method of the present invention.

[0065] Figure 3 It is a specific flow chart for selecting control parameters and power allocation parameters of the method of the present invention.

[0066] Figure 4 It is a relationship diagram between target output and actual output in the method of the present invention. DETAILED DESCRIPTION

[0067] The present invention is further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0068] Example 1

[0069] Based on the energy management issues of different power types, different manufacturers, and different stations brought about by the evolution of new energy stations from a single form to multiple forms, this embodiment discloses a cluster-type new energy active power coordination control method. Based on a given cluster active power target value, the control method of this embodiment can allocate reasonable active power and control parameters to each new energy power station in the cluster, which can significantly improve the response time and response accuracy of the active power dispatch of the aggregated power grid, and also make the grid-related test process faster and more effective.

[0070] To facilitate understanding of this embodiment, a cluster-type renewable energy active power coordinated control method disclosed in an embodiment of the present application is first introduced in detail.

[0071] See also Figure 1 The flowchart of a cluster type renewable energy active power coordinated control method is shown, and the method is executed by a server such as a computer, and includes the following steps:

[0072] S101. Construct a power control model for each new energy power station in the cluster.

[0073] Each new energy power station refers to a new energy base of various power sources; or, each new energy power station refers to a new energy power station of each phase of the project; each new energy power station is a wind farm power station, a photovoltaic power station or an energy storage power station, etc.; in this embodiment, Figure 2aAs shown in , the power control model of each new energy power station includes the first link, the second link and the third link; the first link is a PID control system, the second link is a control object, that is, it corresponds to a new energy power station, such as a wind farm power station, a photovoltaic power station, etc., and the third link is a delay link; the output of the first link is used as the input of the second link, and the output of the second link is used as the input of the third link. The difference between the active power output allocated by the new energy power station and the output of the third link is used as the input of the first link.

[0074] When a cluster requires a new energy base with multiple power sources, or a wind farm or photovoltaic power station with multiple phases of projects to adjust power, the value is first decomposed and the decomposed value is directly sent to each new energy station. That is, when the cluster includes multiple new energy power stations, the target value of the cluster's active power needs to be decomposed, and the decomposed active powers are respectively input into the corresponding power control models of each new energy power station, such as Figure 2b Shown in the figure is a control block diagram of the combination of three new energy power stations in the cluster.

[0075] For new energy power stations such as wind power stations and photovoltaic power stations, there are multiple dynamic components inside them, and their control models are complex. When studying the power dispatch at the station level, their external characteristics are mainly considered. In this embodiment, each new energy power station is simplified into a model P(S):

[0076]

[0077] Among them, T 1 is the time lag of the new energy power station, T 2 is the communication system delay of the new energy power station, P(S) is the Laplace transform, corresponding to the product of the second link and the third link, and S is a complex parameter. For wind power generation systems, due to their different manufacturers, the control strategies they adopt are also different. In this embodiment, when the new energy power station is a wind farm power station, T 1 The value is 2 to 4 seconds. For the photovoltaic system, since the photovoltaic power station is basically composed of power electronic devices, its response speed is relatively fast. In this embodiment, when the new energy power station is a photovoltaic power station, T 1 The value is 1 to 2 seconds. The communication system delay is generally hundreds of milliseconds.

[0078] Among them, the second link, namely the control object, is:

[0079]

[0080] The third stage is:

[0081] Based on the above, the power control model formula of the new energy power station constructed in this embodiment is:

[0082]

[0083] where y g is the actual output active power of the g-th new energy power station, x g is the allocated power of the g-th renewable energy power station; k gp , k gi , k gd is the PID control parameter of the first link of the power control model of the g-th renewable energy power station, namely the PID control system, T g1 is the time lag of the g-th renewable energy power station, T g2 Delay for the communication system of the g-th renewable energy power station.

[0084] S102, obtaining a cluster active power target value; in this embodiment, the cluster active power target value is set according to the actual demand for power generation of the power grid.

[0085] S103. Construct an optimization objective function with the goal of making the actual output of the cluster closest to the target value of the active power of the cluster and the shortest response time; wherein, the actual output of the cluster is obtained by constructing a power control model of each new energy power station according to the control parameters of the power control model of each new energy power station and the allocated power parameters of each new energy power station; when the power control model of each new energy power station is simulated according to the control parameters and the allocated power parameters, the output of the power control model of each new energy power station can be obtained, and then the actual output of the cluster can be obtained.

[0086] In this embodiment, the optimization objective function is constructed in the following manner:

[0087] Taking the minimum absolute value of the target output and the actual output in the shortest time as the optimization goal, the optimization objective function is constructed:

[0088] F=a*t s +b*abs(P tar -P real );

[0089] Among them, P tar is the target output, i.e. the target value of cluster active power, P real is the actual output of the cluster, that is, the actual output value of the cluster active power, a and b are constants, a+b=1, these two values ​​can be selected based on experience, mainly depending on whether the optimization goal focuses on response speed or response accuracy; t s is the steady-state response time;

[0090] Among them, the actual output P real for:

[0091] P real =y 1 +y2 +,...,+y G ;

[0092] Among them, y 1 To G The actual output of the 1st to Gth new energy power stations, i.e., the actual output active power;

[0093] S104: deriving control parameters of power control models of various new energy power stations and allocated power of the new energy power stations by optimizing the objective function.

[0094] In this embodiment, through the inner and outer loop simulation steps, based on the optimization objective function, the control parameters of the power control model of each new energy power station and the power distribution of the new energy power station are obtained, such as Figure 3 As shown in, the details are as follows:

[0095] S1. Determine the parameters T of each new energy power station 1 and T 2 , setting the control parameters of the power control model of each renewable energy power station and the value range of the power distribution parameters of the renewable energy power station;

[0096] S2. Setting the total number of times M of randomly selecting control parameters and the total number of times N of randomly selecting allocated power parameters for the power control model of the new energy power station;

[0097] S3, enter the outer loop simulation steps:

[0098] S31, for each new energy power station power control model, randomly select a control parameter PID (p g ,i g ,d g ), g=1,2,...G;PID(p g ,i g ,d g ) is a control parameter randomly selected for the power control model of the g-th renewable energy power station.

[0099] Each time the random selection is performed, the control parameters selected by the power control model of each renewable energy power station constitute a set of PID control parameters; for example, when the number of random selections of control parameters is the mth time, a set of PID control parameters PID is obtained. m = {PID(p 1 ,i 1 ,d 1 ), PID(p 2 ,i 2 ,d 2 ),...,PID(p G ,i G ,d G)}, that is, the group of PID control parameters includes the PID control parameters selected for the power control model of each renewable energy power station.

[0100] S32, verifying whether the operation of each renewable energy power station is stable under the control parameters selected by the power control model of each renewable energy power station in step S31;

[0101] If so, the currently selected control parameters constitute a selected set of control parameters, namely PID m , when the current outer loop simulation is finished, then execute step S4;

[0102] If not, the current outer loop simulation is finished; return to step S31 to execute the next outer loop simulation step;

[0103] Among them, in this embodiment, whether the system is stable can be determined based on the actual output changes of the power control models of each new energy power station during the simulation time. When the output change is less than the preset value, it can be determined that the power control model of the new energy power station is stable.

[0104] S4, enter the inner loop simulation steps:

[0105] S41, according to the value range, randomly select a distribution power parameter x for each new energy power station g , g=1,2,...,G,x g is the randomly selected allocation power parameter for the g-th new energy power station; forming a set of allocation power parameters (x 1 ,x 2 ,...,x G ) n ;

[0106] S42, a set of control parameters PID selected based on S32 m = {PID(p 1 ,i 1 ,d 1 ), PID(p 2 ,i 2 ,d 2 ),...,PID(p G ,i G ,d G )} and a set of randomly selected allocation power parameters (x 1 ,x 2 ,...,x G ) n , obtain the actual output y of the power control model of each new energy power station g , g=1,2,...G.

[0107] In this step, based on a set of control parameters PIDm and a set of power allocation parameters (x 1 ,x 2 ,...,x G ) n , n=1,2,...N Through each renewable energy power station power control model, the actual output y of each renewable energy power station power control model is obtained respectively g , the actual output y g It changes over time and gradually becomes stable. Therefore, the actual output of the cluster P real The output changes as Figure 4 middle.

[0108] For example, for the first new energy power station power control model, PID (p 1 ,i 1 ,d 1 ) and x 1 , combined with the parameters T of the first new energy power station 1 and T 2 , through the model y of step S101 g The actual output y of the power control model of the first new energy power station can be calculated 1 Furthermore, the actual output of the cluster P is obtained based on the actual output of the power control model of each new energy power station. real :P real =y 1 +y 2 +,...,+y G .

[0109] S43: The actual output P of the cluster obtained in step S42 real and the cluster active power target value P tar , through the objective function, calculate the objective function value F;

[0110] Specifically, each group allocates power parameters, such as Figure 4 As shown in , the cluster active power target value P is obtained. tar The closest actual output P real , and determine the steady-state response time t of the power control model of the new energy power station s , and then substitute them into the objective function F to solve the objective function value F.

[0111] S44, when the current inner loop simulation is finished, determine whether the total number of executions of the inner loop simulation is less than N;

[0112] If yes, return to step S41 to perform the next inner loop simulation;

[0113] If not, a set of power allocation parameters with the smallest calculated objective function value F is selected and combined with the set of control parameters selected in step S32 as the selection result; then step S5 is executed;

[0114] In this step, when each inner loop simulation ends, the objective function value F calculated at the end of the current inner loop simulation can be compared with the minimum objective function value F obtained at the end of the previous inner loop simulation. If the former is smaller than the latter, the minimum objective function value F is updated to the objective function value F calculated at the end of the current inner loop simulation. In this way, with the execution of each inner loop simulation, the minimum objective function value F can be selected. When the total number of inner loop simulation executions reaches N, the minimum objective function value F, i.e., minF, can be finally obtained.

[0115] S5, judging whether the total number of executions of the outer loop simulation is less than M;

[0116] If not, return to step S3 to perform the next outer loop simulation;

[0117] If yes, then end.

[0118] Based on the above outer loop simulation steps and inner loop simulation steps, the method of this embodiment performs outer loop simulation to obtain a set of PID control parameters, and selects a set of power allocation parameters from the set of PID control parameters by performing N inner loop simulations. If the number of outer loop simulations does not reach M times, the outer loop simulation and the inner loop simulation can continue to be performed, based on which multiple sets of PID control parameters and power allocation parameters can be selected.

[0119] S105: According to the control parameters of the power control model of each new energy power station obtained in step S104 and the power distribution of the new energy power station, the control parameters of each new energy power station are set and the active power distribution is performed. For example, the control parameters of the power control model of each new energy power station finally obtained in step S104 are PID m = {PID(p 1 ,i 1 ,d 1 ), PID(p 2 ,i 2 ,d 2 ),...,PID(p G ,i G ,d G )}, allocate power parameters (x 1 ,x 2 ,...,x G ) n ; then according to PID(p 1 ,i 1 ,d 1 )、PID(p 2 ,i2 ,d 2 ), ..., PID(p G ,i G ,d G ) Set the control parameters of the 1st to G new energy power stations. The control parameters assigned to the 1st to G new energy power stations are (x 1 ,x 2 ,...,x G ) n x in 1 to x G .

[0120] In this embodiment, when executing steps S1 to S5 in step S104, the final selection result has multiple groups of parameters, each of which corresponds to a group of control parameters and a group of power allocation parameters; then the steady-state response time t of the power control model constructed by the new energy power station is selected. s The smallest set of parameters is selected as the final result.

[0121] Those skilled in the art will appreciate that all or part of the steps in the method of the present embodiment can be completed by instructing the relevant hardware through a program, and the corresponding program can be stored in a computer-readable storage medium. It should be noted that although the method operation of the present embodiment 1 is described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the steps depicted can change the order of execution, some steps can also be performed simultaneously, additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution.

[0122] Example 2

[0123] This embodiment discloses a cluster-type new energy active power coordinated control device, including a model building module, an acquisition module, an objective function building module and a parameter determination module. The functions of each module are as follows:

[0124] A model building module is used to build a power control model for each renewable energy power station in the cluster;

[0125] An acquisition module is used to obtain the cluster active power target value;

[0126] The objective function construction module is used to construct an optimization objective function with the goal of making the actual output of the cluster closest to the cluster active power target value and the shortest response time; wherein the actual output of the cluster is calculated by the power control model constructed by each new energy power station according to the control parameters of the power control model of each new energy power station and the power allocation parameters of each new energy power station;

[0127] The parameter determination module is used to obtain the control parameters of the power control model of each renewable energy power station and the allocated power of the renewable energy power station by optimizing the objective function.

[0128] The specific implementation of each module in this embodiment can refer to the above-mentioned embodiment 1, and will not be described one by one here. It should be noted that the device provided in this embodiment is only illustrated by the division of the above-mentioned functional modules. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0129] Example 3

[0130] This embodiment discloses a storage medium storing a program. When the program is executed by a processor, the cluster type renewable energy active power coordinated control method described in Embodiment 1 is implemented. The steps include:

[0131] Build a power control model for each renewable energy power station in the cluster;

[0132] Get the cluster active power target value;

[0133] The optimization objective function is constructed with the goal of making the actual output of the cluster closest to the cluster active power target value and the shortest response time; wherein, the actual output of the cluster is calculated by the power control model constructed by each new energy power station according to the control parameters of the power control model of each new energy power station and the power allocation parameters of each new energy power station;

[0134] By optimizing the objective function, the control parameters of the power control model of each renewable energy power station and the allocated power of the renewable energy power station are obtained.

[0135] In this embodiment, the specific implementation of each of the above processes can refer to the above embodiment 1, and will not be described in detail here.

[0136] In this embodiment, the storage medium may be a disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a USB flash drive, a mobile hard disk, or the like.

[0137] Example 4

[0138] This embodiment discloses a computing device, including a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, a cluster-type renewable energy active power coordinated control method described in Embodiment 1 is implemented, and the steps include:

[0139] Build a power control model for each renewable energy power station in the cluster;

[0140] Get the cluster active power target value;

[0141] The optimization objective function is constructed with the goal of making the actual output of the cluster closest to the cluster active power target value and the shortest response time; wherein, the actual output of the cluster is calculated by the power control model constructed by each new energy power station according to the control parameters of the power control model of each new energy power station and the power allocation parameters of each new energy power station;

[0142] By optimizing the objective function, the control parameters of the power control model of each renewable energy power station and the allocated power of the renewable energy power station are obtained.

[0143] In this embodiment, the specific implementation of each of the above processes can refer to the above embodiment 1, and will not be described in detail here.

[0144] In this embodiment, the computing device may be a terminal device such as a server or a desktop computer.

[0145] In this embodiment, the computing device includes: a processor, a memory, a bus and a communication interface, and the processor, the communication interface and the memory are connected via the bus; the processor is configured to execute an executable module stored in the memory, such as a computer program.

[0146] The memory may include high-speed random access memory (RAM) and may also include non-volatile memory (Non-volatile memory), such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface (which may be wired or wireless), and the Internet, wide area network, local area network and metropolitan area network, etc. may be used.

[0147] The bus can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0148] Among them, the storage is configured to store a program, and the processor executes the program after receiving an execution instruction. The method executed by the device for flow process definition disclosed in the above-mentioned embodiment of the present application can be applied to the processor or implemented by the processor.

[0149] The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU) and a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices and discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as a hardware decoding processor to execute, or a combination of hardware and software modules in the decoding processor to execute. The software module can be located in a mature storage medium in the art such as random access memory, flash memory and / or read-only memory, programmable read-only memory or electrically erasable programmable memory and / or register. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0150] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A cluster-type new energy active power coordinated control method, It is characterized in that the steps include: Build a power control model for each renewable energy power station in the cluster; Get the cluster active power target value; The optimization objective function is constructed with the goal of making the actual output of the cluster closest to the cluster active power target value and the shortest response time; wherein the actual output of the cluster is obtained by the power control model of each new energy power station based on the control parameters and the allocated power parameters; Through the inner and outer loop simulation steps, based on the optimization objective function, the control parameters of the power control model of each renewable energy power station and the allocated power of the renewable energy power station are obtained; the details are as follows: S1. Setting the control parameters of the power control model of each new energy power station and the value range of the power distribution parameters of the new energy power station; S2. Setting the total number of times M of randomly selecting control parameters and the total number of times N of randomly selecting allocated power parameters for the power control model of the new energy power station; S3, enter the outer loop simulation steps: S31, for each new energy power station power control model, randomly select a control parameter PID (p g ,i g ,d g ), g=1,2,...G;PID(p g ,i g ,d g ) is a control parameter randomly selected for the power control model of the g-th new energy power station; S32, verifying whether the operation of each renewable energy power station is stable under the control parameters selected by the power control model of each renewable energy power station in step S31; If yes, the control parameters currently selected constitute a selected set of control parameters, and the current outer loop simulation is completed; then step S4 is executed; If not, the current outer loop simulation is finished; return to step S31 to execute the next outer loop simulation step; S4, enter the inner loop simulation steps: S41. According to the constraints of the objective function, a distribution power parameter x is randomly selected for each new energy power station. g , g=1,2,...,G, and obtain a set of power allocation parameters; x g is the randomly selected allocation power parameter for the g-th new energy power station; S42, based on the set of control parameters selected in S32 and the set of allocated power parameters selected in step S42, obtaining the actual output y of the power control model of each new energy power station g , g = 1, 2, ... G, and then obtain the actual output of the cluster P real ; S43: The actual output P of the cluster obtained in step S42 real and the cluster active power target value P tar , through the objective function, calculate the objective function value F; S44, when the current inner loop simulation is finished, determine whether the total number of executions of the inner loop simulation is less than N; If yes, return to step S41 to perform the next inner loop simulation; If not, a set of power allocation parameters with the smallest calculated objective function value F is selected and combined with the set of control parameters selected in step S32 as the selection result; then step S5 is executed; S5, judging whether the total number of executions of the outer loop simulation is less than M; If not, return to step S3 to perform the next outer loop simulation; If yes, then end.

2. According to claim 1, the cluster type new energy active power coordinated control method, It is characterized in that The power control model constructed for each new energy power station includes the first link, the second link and the third link; the first link is the PID control system, the second link is the control object, that is, the corresponding new energy power station, and the third link is the delay link; The output of the first link is used as the input of the second link, the output of the second link is used as the input of the third link, and the difference between the active power output allocated by the new energy power station and the output of the third link is used as the input of the first link; Each of the new energy power stations is simplified to a model P(S): Among them, T 1 is the time lag of the new energy power station, T 2 is the communication system delay of the new energy power station, P(S) is the Laplace transform, corresponding to the multiplication result of the second link and the third link; Among them, the second link, namely the control object, is: The third stage is: The power control model formula constructed by each new energy power station is: where y g is the actual output active power of the g-th new energy power station, x g is the allocated power of the g-th renewable energy power station; k gp , k gi , k gd is the PID control parameter of the first link of the power control model of the g-th renewable energy power station, namely the PID control system, T g1 is the time lag of the g-th renewable energy power station, T g2 The communication system delay of the g-th renewable energy power station.

3. The cluster type renewable energy active power coordinated control method according to claim 2, It is characterized in that The optimization objective function is constructed in the following way: Taking the minimum absolute value of the target output and the actual output in the shortest time as the optimization goal, the optimization objective function is constructed: F=a*t s +b*abs(P tar -P real ); Among them, P tar is the target output, i.e. the cluster active power target value, P real is the actual output, a and b are constants, a+b=1; t s For the shortest response time; Among them, the actual output P real for: P real =and 1 +y 2 +,...,+and G ; Among them, y 1 To G It is the actual output of the 1st to Gth new energy power stations, that is, the actual output active power.

4. The cluster type renewable energy active power coordinated control method according to claim 1, It is characterized in that When executing steps S1 to S5, if there are multiple sets of parameters in the final selection result, where each set of parameters corresponds to a set of control parameters and a set of power allocation parameters, the steady-state response time t of the power control model constructed by the new energy power station is selected. s The smallest set of parameters is selected as the final result.

5. A cluster type new energy active power coordination control device, It is characterized in that include: A model building module is used to build a power control model for each renewable energy power station in the cluster; An acquisition module is used to obtain the cluster active power target value; The objective function construction module is used to construct an optimization objective function with the goal of making the actual output of the cluster closest to the cluster active power target value and the shortest response time; wherein the actual output of the cluster is calculated by the power control model constructed by each new energy power station according to the control parameters of the power control model of each new energy power station and the power allocation parameters of each new energy power station; The parameter determination module is used to obtain the control parameters of the power control model of each renewable energy power station and the allocated power of the renewable energy power station based on the optimization objective function through the inner and outer loop simulation steps, as follows: S1. Setting the control parameters of the power control model of each new energy power station and the value range of the power distribution parameters of the new energy power station; S2. Setting the total number of times M of randomly selecting control parameters and the total number of times N of randomly selecting allocated power parameters for the power control model of the new energy power station; S3, enter the outer loop simulation steps: S31, for each new energy power station power control model, randomly select a control parameter PID (p g ,i g ,d g ), g=1,2,...G;PID(p g ,i g ,d g ) is a control parameter randomly selected for the power control model of the g-th new energy power station; S32, verifying whether the operation of each renewable energy power station is stable under the control parameters selected by the power control model of each renewable energy power station in step S31; If yes, the control parameters currently selected constitute a selected set of control parameters, and the current outer loop simulation is completed; then step S4 is executed; If not, the current outer loop simulation is finished; return to step S31 to execute the next outer loop simulation step; S4, enter the inner loop simulation steps: S41. According to the constraints of the objective function, a distribution power parameter x is randomly selected for each new energy power station. g , g=1,2,...,G, and obtain a set of power allocation parameters; x g is the randomly selected allocation power parameter for the g-th new energy power station; S42, based on the set of control parameters selected in S32 and the set of allocated power parameters selected in step S42, obtaining the actual output y of the power control model of each new energy power station g , g = 1, 2, ... G, and then obtain the actual output of the cluster P real ; S43: The actual output P of the cluster obtained in step S42 real and the cluster active power target value P tar , through the objective function, calculate the objective function value F; S44, when the current inner loop simulation is finished, determine whether the total number of executions of the inner loop simulation is less than N; If yes, return to step S41 to perform the next inner loop simulation; If not, a set of power allocation parameters with the smallest calculated objective function value F is selected and combined with the set of control parameters selected in step S32 as the selection result; then step S5 is executed; S5, judging whether the total number of executions of the outer loop simulation is less than M; If not, return to step S3 to perform the next outer loop simulation; If yes, then end.

6. A storage medium storing a program, It is characterized in that When the program is executed by a processor, the cluster type renewable energy active power coordinated control method according to any one of claims 1 to 4 is implemented.

7. A computing device comprising a processor and a memory for storing a program executable by the processor, It is characterized in that When the processor executes the program stored in the memory, the cluster type renewable energy active power coordinated control method according to any one of claims 1 to 4 is implemented.

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