Multi-new energy station low voltage ride through control parameter coordinated optimization method, system, device and medium

Abstract

The invention discloses a multi-new energy station low voltage ride through control parameter coordinated optimization method, system, device and medium, and the method comprises the steps: evaluating the coupling influence degree of the reactive power change of each new energy station on the grid-connected point voltage of other new energy stations in a target region; obtaining the reactive voltage influence coupling degree index value of the grid-connected point of each new energy station; setting the value range of the reactive current proportionality coefficient in the low voltage ride through control of each new energy station based on the reactive voltage influence coupling degree index value of the grid-connected point of each new energy station; and within the value range of the reactive current proportionality coefficient in the low-voltage ride-through control of each new energy station, performing coordinated optimization on the reactive current proportionality coefficient in the low-voltage ride-through control of each new energy station. Compared with the prior art, the overall deviation of the grid-connected point voltage of the multiple new energy stations can be reduced, and the voltage stability of the power grid is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system safety and control, and particularly to a coordinated optimization method, system, device and medium for low voltage ride-through control parameters of multiple new energy power stations to improve voltage stability. Background Art

[0002] To achieve optimal energy allocation and clean substitution of fossil energy, renewable energy sources such as wind power and photovoltaic power have achieved rapid development and wide application. However, the high proportion of new energy grid connection makes the operating characteristics of the power system more complex, and the voltage stability problem becomes increasingly prominent. To ensure the voltage stability of the power grid, relevant technical standards stipulate that new energy units should have the ability of low voltage ride-through, that is, when the grid connection point voltage is lower than the set threshold, they should be able to inject reactive current into the grid to provide reactive power support, so as to promote the rapid recovery of the system voltage. Therefore, the voltage ride-through control performance of new energy units has an important impact on the voltage stability of the power grid.

[0003] Existing research methods based on engineering experience or optimization algorithms can improve the voltage stability of the power grid to a certain extent by optimizing and setting the low voltage ride-through control parameters of new energy. However, the differences between different power stations are not considered in the setting of control parameters for multiple new energy sources in the target area. Under the same control parameter settings, different power stations have different voltage support capabilities for key nodes, and the reactive power output by new energy power stations will affect the voltage characteristics of the grid connection points of neighboring power stations. Ignoring the interactive influence of reactive power and voltage when setting control parameter values, it is difficult to ensure that the overall voltage deviation of the grid connection points of each new energy power station in the area is minimized. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide a coordinated optimization method, system, device and medium for low voltage ride-through control parameters of multiple new energy power stations, so as to solve the problem that it is difficult to ensure a small overall voltage deviation of the grid connection points of each new energy power station in the area when setting control parameter values while ignoring the interactive influence of reactive power and voltage in the scenario of multiple new energy power stations.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions.

[0006] In the first aspect, a coordinated optimization method for low voltage ride-through control parameters of multiple new energy power stations is provided, including the following steps:

[0007] S1: Evaluate the degree of coupling influence of the reactive power change of each new energy power station on the grid connection point voltage of other new energy power stations in the target area, and obtain the index value of the coupling degree of reactive power-voltage influence of each new energy power station's grid connection point;

[0008] S2: Based on the reactive voltage influence coupling degree index values of each new energy power station connection point, set the value range of the reactive current proportionality coefficient in the low voltage ride through control of each new energy power station;

[0009] S3: Coordinate and optimize the reactive current proportionality coefficient in the low voltage ride through control of each new energy power station within the value range of the reactive current proportionality coefficient in the low voltage ride through control of each new energy power station.

[0010] Further, the step S1 specifically includes:

[0011] Construct the power flow equation of the AC system as shown in Equation (1):

[0012] (1)

[0013] In the formula, is the change in node active power, is the change in node reactive power, represents the change in node voltage phase angle, represents the change in voltage amplitude;

[0014] The submatrices K and J in Equation (1) are obtained according to the node voltage level and the node impedance matrix:

[0015] (2)

[0016] In the formula, V represents the node voltage, B is the imaginary part of the node impedance matrix, the subscripts i and j represent the AC system node numbers, the subscripts k and m represent the PQ node numbers of the AC system, and h and o respectively represent the total number of nodes and the number of PQ nodes in the AC system.

[0017] For reactive voltage, there is the relationship shown in Equation (3), which characterizes the influence of the change in node reactive power on the voltages of other nodes:

[0018] (3)

[0019] Based on this, the reactive voltage influence coupling degree index of the connection point can be defined, as shown in Equation (4):

[0020] (4)

[0021] In the formula, represents the reactive voltage influence coupling degree index of the connection point of the p-th new energy power station; n represents the number of new energy power stations in the target area; the subscript p refers to the connection point of the p-th new energy power station, and q refers to the connection points of other new energy power stations.

[0022] Further, the step S2 specifically includes:

[0023] Sort the reactive voltage coupling degree indexes of the connection points of each new energy power station from large to small, and regard the new energy power stations ranked in the top M% as the power stations with a relatively large reactive voltage coupling degree, where M ranges from 40 to 60;

[0024] Assume that the reactive current proportion coefficient K in the low voltage ride-through control of the new energy power station q has a value range of [a, b];

[0025] If the voltage at the connection point of the new energy power station is less than the set value, the reactive current proportion coefficient K in its low voltage ride-through control q shall be limited to [(a + b) / 2, b];

[0026] If the voltage at the connection point of the new energy power station is greater than or equal to the set value, and this new energy power station is a power station with a relatively large reactive voltage coupling degree, the reactive current proportion coefficient K in its low voltage ride-through control q shall be limited to [a, (a + b) / 2];

[0027] If the voltage at the connection point of the new energy power station is greater than or equal to the set value, and this new energy power station does not belong to the power stations with a relatively large reactive voltage coupling degree, the reactive current proportion coefficient K in its low voltage ride-through control q shall be limited to [a, b].

[0028] Furthermore, in step S3, with the goal of minimizing the overall deviation of the voltages at the connection points of multiple new energy power stations, a reactive current proportion coefficient optimization method is constructed based on deep reinforcement learning, and the optimal reactive current proportion coefficients in the low voltage ride-through control of each new energy power station are obtained by solving.

[0029] Furthermore, step S3 specifically includes:

[0030] During the optimization process of constructing a reactive current proportion coefficient optimization method based on deep reinforcement learning, the reactive current proportion coefficients in the low voltage ride-through control of each new energy power station are used as target parameters, and the correction amount of the target parameters is used as the action value; the simulation curves of the voltages at the connection points of each new energy power station and the target parameters are used as state variables; a reward function is constructed based on the overall voltage deviation at the connection points of the new energy power stations and the out-of-bounds amount of the target parameters; and a constraint on the value range of the target parameters is constructed based on the value range of the reactive current proportion coefficients in the low voltage ride-through control of each new energy power station.

[0031] The intelligent agent makes action decisions based on the state quantities collected from the environment and applies the corresponding action values ​​to the environment. The environment changes under the influence of the action values ​​and evaluates the quality of the actions through the reward function and feeds back the corresponding rewards to the intelligent agent. The intelligent agent adjusts its own strategy based on the continuous interaction with the environment, iterates the process, optimizes the target parameters, and obtains the optimal reactive current proportional coefficient in the low voltage ride-through control of each renewable energy station.

[0032] Furthermore, the correction amount of the target parameter is used as the action value as shown below:

[0033]

[0034] In the formula, and They represent the reactive current proportional coefficients in the low voltage ride-through control of each renewable energy station in the kth iteration and the k+1th iteration, It represents the correction amount of the reactive current proportional coefficient in the low voltage ride-through control of each renewable energy station in the kth iteration, that is, the action value selected by the intelligent agent in the kth iteration;

[0035] The state of the kth iteration It is expressed as follows:

[0036]

[0037] In the formula, It represents the voltage simulation verification curve of each new energy station and grid connection point. It represents the grid connection point voltage of the i-th renewable energy station at time 1-T, T represents the end time of the fault ride-through of the renewable energy station, and n is the number of renewable energy stations in the target area.

[0038] Furthermore, the reward function is constructed based on the overall voltage deviation of the new energy station grid connection point and the target parameter crossing amount, which is expressed as follows:

[0039]

[0040] In the formula, R k represents the reward function value, F represents the overall voltage deviation of the new energy station grid connection point, R A Indicates that the parameter is out of bounds. and F and R respectively A The weight coefficient, U ti represents the grid connection point voltage of the i-th renewable energy station, U t0 represents the steady-state voltage of the i-th renewable energy station, n represents the number of renewable energy stations in the target area, and T represents the end time of the fault ride-through of the renewable energy station.

[0041] In a second aspect, a coordinated optimization system for low voltage ride-through control parameters of multiple new energy power stations is provided, including:

[0042] An influence coupling degree evaluation module, configured to evaluate the coupling influence degree of the reactive power change of each new energy power station on the grid connection point voltage of other new energy power stations in the target area, and obtain the reactive voltage influence coupling degree index value of each new energy power station's grid connection point;

[0043] A parameter value range setting module, configured to set the value range of the reactive current proportionality coefficient in the low voltage ride-through control of each new energy power station based on the reactive voltage influence coupling degree index value of each new energy power station's grid connection point;

[0044] A parameter optimization module, configured to coordinately optimize the reactive current proportionality coefficient in the low voltage ride-through control of each new energy power station within the value range of the reactive current proportionality coefficient in the low voltage ride-through control of each new energy power station.

[0045] In a third aspect, an electronic device is provided, including:

[0046] A memory, on which a computer program or instruction is stored;

[0047] A processor, configured to execute the computer program or instruction to implement the coordinated optimization method for low voltage ride-through control parameters of multiple new energy power stations as described above.

[0048] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program or instruction is stored, and when the computer program or instruction is executed by a processor, the coordinated optimization method for low voltage ride-through control parameters of multiple new energy power stations as described above is implemented.

[0049] The present invention proposes a coordinated optimization method, system, device and medium for low voltage ride-through control parameters of multiple new energy power stations, which considers and evaluates the differences and coupling degrees of the reactive power output of different new energy power stations on the voltage support capabilities of other nodes, delimits the value ranges of the low voltage ride-through control parameters of different new energy power stations by taking into account the reactive voltage interaction effects, and finally, to overcome the problem of low calculation efficiency when the optimization algorithm performs optimization on multiple objects, based on the deep reinforcement learning algorithm, the coordinated optimization of the control parameters of multiple new energy power stations is realized within the determined value ranges, which can reduce the overall deviation of the grid connection point voltage of multiple new energy power stations to ensure that the overall deviation of the grid connection point voltage of each new energy power station in the area is small enough, and improve the voltage stability of the power grid. Description of the Drawings

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0051] Figure 1 is the flowchart of the coordinated optimization method for low voltage ride-through control parameters of multiple new energy power stations provided by the embodiment of the present invention;

[0052] Figure 2 is an example of a power system including multiple new energy power stations provided by the embodiment of the present invention;

[0053] Figure 3 is the grid connection point voltage curve of new energy power station 1 provided by the embodiment of the present invention;

[0054] Figure 4 is the grid connection point voltage curve of new energy power station 2 provided by the embodiment of the present invention. Detailed implementation manners

[0055] To make the purpose, technical solutions and advantages of the present invention clearer, the following will describe the technical solutions of the present invention in detail. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other implementation manners obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by the present invention.

[0056] As Figure 1 shown, the embodiment of the present invention provides a coordinated optimization method for low voltage ride-through control parameters of multiple new energy power stations, including the following steps:

[0057] S1: Evaluate the coupling influence degree of the reactive power change of each new energy power station on the grid connection point voltage of other new energy power stations in the target area, and obtain the coupling degree index value of the reactive power-voltage influence of each new energy power station's grid connection point.

[0058] Specifically, step S1 includes:

[0059] S1.1: Construct the power flow equation of the AC system as shown in formula (1):

[0060] (1)

[0061] In the formula, is the change amount of the active power of the node, is the change amount of the reactive power of the node, represents the change amount of the node voltage phase angle, Indicates the change in voltage amplitude;

[0062] In Equation (1), the sub - matrices K and J are obtained based on the node voltage levels and the node impedance matrix:

[0063] (2)

[0064] Wherein, V represents the node voltage, B is the imaginary part of the node impedance matrix, the subscripts i and j represent the AC system node numbers, the subscripts k and m represent the AC system PQ node numbers, h and o represent the total number of AC system nodes and the number of PQ nodes respectively; the AC system nodes include PQ nodes, PV nodes and one balancing node.

[0065] S1.2: For reactive power and voltage, there is the relationship shown in Equation (3), which characterizes the influence of the change in node reactive power on the voltages of other nodes:

[0066] (3)

[0067] Based on this, the coupling degree index of the reactive power and voltage at the point of common connection of the new - energy power station can be defined, as shown in Equation (4):

[0068] (4)

[0069] Wherein, represents the coupling degree index of the reactive power and voltage at the point of common connection of the p - th new - energy power station; n represents the number of new - energy power stations in the target area; the subscript p refers to the point of common connection of the p - th new - energy power station, and q refers to the points of common connection of other new - energy power stations.

[0070] Equation (4) reflects the average influence degree of the reactive power output at the point of common connection of the new - energy power station on the points of common connection of other new - energy power stations. The larger its value, the greater the contribution degree of the reactive power output of this power station to maintaining the voltage of the key nodes of the system.

[0071] S2: Based on the coupling degree index values of the reactive power and voltage at the points of common connection of each new - energy power station, the value range of the reactive current proportion coefficient in the low - voltage ride - through control of each new - energy power station is set.

[0072] Specifically, step S2 includes:

[0073] Sort the coupling degree indexes of the reactive power and voltage at the points of common connection of each new - energy power station from large to small. Consider the new - energy power stations ranked in the top M% as the power stations with a larger coupling degree of reactive power and voltage. Their reactive power output has a greater contribution to maintaining the voltage of the key nodes of the system. Keeping these new - energy power stations continuously outputting reactive power during the post - fault transient process has a positive effect on improving the overall reactive power and voltage support ability; the value range of M is 40 - 60. In this embodiment, M takes 50;

[0074] Assume the reactive current proportionality coefficient K in the low-voltage ride-through control of a new energy power station q has a value range of [a, b], and usually the value of K q is in the range of [1.5, 3.0]. Therefore, this embodiment takes this value range as an example for illustration;

[0075] (1) If the grid connection point voltage of the new energy power station is less than the set value (such as 0.45 pu), it indicates that the grid connection point voltage drops deeply. The reactive current proportionality coefficient K q in the low-voltage ride-through control should be set at a higher level to quickly restore the grid connection point voltage of itself. Therefore, in this case, the value of K q is limited to [2.25, 3.0];

[0076] (2) If the grid connection point voltage of the new energy power station is greater than or equal to the set value, it indicates that the grid connection point voltage drops slightly, and this new energy power station is a power station with a large degree of reactive voltage coupling, indicating that its reactive power output contributes greatly to maintaining the voltage of other nodes. The reactive current proportionality coefficient K q in the low-voltage ride-through control should be set at a lower level so that the new energy power station can continuously output reactive power for a long time. Therefore, in this case, the value of K q is limited to [1.5, 2.25];

[0077] (3) If the grid connection point voltage of the new energy power station is greater than or equal to the set value, it indicates that the grid connection point voltage drops slightly, and this new energy power station does not belong to a power station with a large degree of reactive voltage coupling, indicating that its reactive power output contributes little to maintaining the voltage of other nodes. The value of the reactive current proportionality coefficient K q in its low-voltage ride-through control has little impact on the overall reactive voltage support ability. Therefore, in this case, the value range of K q remains [1.5, 3.0].

[0078] S3: Coordinate and optimize the reactive current proportionality coefficients in the low-voltage ride-through control of each new energy power station within their value ranges.

[0079] Specifically, aiming at minimizing the overall deviation of the grid connection point voltages of multiple new energy power stations, an optimization method for the reactive current proportion coefficient is constructed based on Deep Reinforcement Learning (DRL) to solve the optimal reactive current proportion coefficient in the low voltage ride-through control of each new energy power station. DRL mainly consists of elements such as Action, State, Reward, Agent, and Environment. The Agent makes decisions based on the states collected from the environment and applies the corresponding actions to the environment. The environment changes under the influence of the actions and evaluates the quality of the actions through the reward mechanism and feedbacks the corresponding rewards to the Agent. The Agent adjusts its own strategy based on the continuous interaction with the environment, thus achieving autonomous decision-making in complex scenarios. During offline training, a transient simulation model of the target area is constructed. The Agent inputs correction parameters into the transient simulation model according to the current parameters and transient simulation results, then calculates the reward after transient simulation and feedbacks it to the Agent for parameter correction. This process is looped multiple times until the reward function converges, and the final parameter setting value is output.

[0080] In this embodiment, during the optimization process of constructing the optimization method for the reactive current proportion coefficient based on deep reinforcement learning, the reactive current proportion coefficient in the low voltage ride-through control of each new energy power station is used as the target parameter, and the correction amount of the target parameter is used as the action value; the voltage simulation curve of the grid connection point of each new energy power station and the target parameter are used as the state variables; a reward function is constructed based on the overall voltage deviation of the grid connection point of the new energy power station and the out-of-bounds amount of the target parameter; and a constraint on the value range of the target parameter is constructed based on the value range of the reactive current proportion coefficient in the low voltage ride-through control of each new energy power station.

[0081] There is a non-linear mapping relationship between the system transient voltage response and the target parameter, and the optimal target parameter needs to be obtained through multiple rounds of iterative correction. Therefore, in this embodiment, the correction amount of the target parameter is used as the action value of the Agent:

[0082] (5)

[0083] In the formula, and respectively represent the reactive current proportion coefficients in the low voltage ride-through control of each new energy power station in the k-th iteration and the (k + 1)-th iteration, represents the correction amount of the reactive current proportion coefficient in the low voltage ride-through control of each new energy power station in the k-th iteration, that is, the action value selected by the Agent in the k-th iteration, , .

[0084] The setting of the state should be able to reflect the transient voltage characteristics of the system. In this embodiment, the voltage simulation curves at the grid connection points of each new energy power station are used as part of the state:

[0085] (6)

[0086] In the formula, represents the voltage simulation verification curves at the grid connection points of each new energy power station, represents the voltage at the grid connection point of the i-th new energy power station from 1 to T, T represents the end time of the fault ride-through of the new energy power station, and n is the number of new energy power stations in the target area.

[0087] Since the target parameters are corrected step by step, the target parameter values before each correction should also be used as part of the state to guide the next action of the Agent. Therefore, the interface function of the state is expressed as:

[0088] (7)

[0089] In the formula, S k represents the state quantity of the k-th iteration, and S k+1 represents the state quantity of the (k + 1)-th iteration; 、 represent the voltage simulation verification curves at the grid connection points of each new energy power station in the k-th iteration and the (k + 1)-th iteration respectively.

[0090] The purpose of optimizing the target parameters in this embodiment is to find the target parameters that minimize the overall voltage deviation at the grid connection points of the new energy power stations. Therefore, the main body of the reward function is composed of this voltage deviation. In addition, to reduce the occurrence of parameter out-of-bounds phenomena, a certain penalty should be given to out-of-bounds behaviors to reduce the correction amount when approaching the parameter boundary. Therefore, the reward function in this embodiment is set as:

[0091] (8)

[0092] In the formula, R k represents the reward function value, F represents the overall voltage deviation at the grid connection points of the new energy power stations, and R A represents the parameter out-of-bounds amount, and are the weight coefficients of F and R A respectively, U ti represents the voltage at the grid connection point of the i-th new energy power station, U t0 represents the steady-state voltage of the i-th new energy power station, n represents the number of new energy power stations in the target area, and T represents the end time of the fault ride-through of the new energy power station.

[0093] In addition, the target parameters (i.e., the reactive current proportion coefficient K in the low voltage ride-through control of each new energy power station qIt is also necessary to satisfy the value range constraint determined in step S2.

[0094] The present invention proposes a method for coordinating and optimizing the low-voltage ride-through control parameters of multiple new energy power stations. It considers and evaluates the differences and coupling degrees of the reactive power output of different new energy power stations on the voltage support capabilities of other nodes, delimits the value ranges of the low-voltage ride-through control parameters of different new energy power stations by taking into account the interactive effects of reactive power and voltage, and finally, to overcome the problem of low calculation efficiency when the optimization algorithm performs optimization on multiple objects, based on the deep reinforcement learning algorithm, the coordinated optimization of the control parameters of multiple new energy power stations is realized within the determined value ranges to ensure that the overall voltage deviation of the grid connection points of each new energy power station in the region is small enough.

[0095] The embodiment of the present invention also provides a system for coordinating and optimizing the low-voltage ride-through control parameters of multiple new energy power stations, including:

[0096] An influence coupling degree evaluation module, configured to evaluate the coupling influence degree of the reactive power change of each new energy power station on the grid connection point voltage of other new energy power stations in the target area, and obtain the reactive voltage influence coupling degree index value of each new energy power station's grid connection point;

[0097] A parameter value range setting module, configured to set the value range of the reactive current proportionality coefficient in the low-voltage ride-through control of each new energy power station based on the reactive voltage influence coupling degree index value of each new energy power station's grid connection point;

[0098] A parameter optimization module, configured to coordinately optimize the reactive current proportionality coefficient in the low-voltage ride-through control of each new energy power station within the value range of the reactive current proportionality coefficient in the low-voltage ride-through control of each new energy power station.

[0099] It should be understood that the functional unit modules in each embodiment of the present invention can be concentrated in one processing unit, or each unit module can exist physically alone, or two or more unit modules can be integrated in one unit module, and can be implemented in the form of hardware or software.

[0100] The embodiment of the present invention also provides an electronic device, including:

[0101] A memory, on which a computer program or instruction is stored;

[0102] A processor, configured to execute the computer program or instruction to implement the method for coordinating and optimizing the low-voltage ride-through control parameters of multiple new energy power stations as described above.

[0103] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program or instruction is stored, and when the computer program or instruction is executed by a processor, the method for coordinating and optimizing the low-voltage ride-through control parameters of multiple new energy power stations as described above is implemented.

[0104] It is understandable that the same or similar parts in the above embodiments can be referred to each other, and the content not described in detail in some embodiments can be referred to the same or similar content in other embodiments.

[0105] The effects of the technical solution of the present invention will be further described below in combination with specific cases. A power system containing multiple new energy power stations is built in the power system simulation software PSD-BPA, as Figure 2 shown. After the reactive current proportion coefficient Kq in the low voltage ride-through control of the new energy power station 1 and the new energy power station 2 is coordinated and optimized according to the method of the present invention, the voltage response curves at the connection points of the two power stations after the fault are respectively as Figure 3 and 4 shown, and it can be seen that the voltage stability is effectively improved.

[0106] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0108] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps of the functions specified in one block or a plurality of blocks.

[0110] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for coordinated optimization of low voltage ride-through control parameters of multiple renewable energy stations, characterized in that: The steps include: S1: Evaluate the coupling influence of reactive power changes of each new energy station on the voltage of other new energy stations in the target area, and obtain the coupling influence index value of reactive voltage of each new energy station; S2: Based on the reactive voltage impact coupling degree index value of each renewable energy station grid connection point, the value range of the reactive current proportional coefficient in the low voltage ride through control of each renewable energy station is adjusted; S3: within the value range of the reactive current proportional coefficient in the low voltage ride through control of each renewable energy station, coordinate and optimize the reactive current proportional coefficient in the low voltage ride through control of each renewable energy station.

2. The method for coordinated optimization of low voltage ride-through control parameters of multiple renewable energy stations according to claim 1 is characterized in that: The step S1 specifically includes: The power flow equation for the AC system is shown in formula (1): (1) In the formula, is the node active power change, is the node reactive power change, represents the phase angle change of the node voltage, Indicates the change in voltage amplitude; The sub-matrices K and J in formula (1) are obtained according to the node voltage level and the node impedance matrix: (2) Where V represents the node voltage, B is the imaginary part of the node impedance matrix, subscripts i and j represent the node numbers of the AC system, subscripts k and m represent the PQ node numbers of the AC system, and h and o represent the total number of nodes and the number of PQ nodes in the AC system, respectively. For reactive voltage, there is a relationship as shown in formula (3), which represents the impact of node reactive power change on other node voltages: (3) Based on this, the coupling degree index of the reactive voltage influence at the grid connection point can be defined, as shown in formula (4): (4) In the formula, It represents the reactive voltage coupling degree index of the p-th renewable energy station grid connection point; n represents the number of renewable energy stations in the target area; the subscript p refers to the p-th renewable energy station grid connection point, and q refers to other renewable energy station grid connection points.

3. The method for coordinated optimization of low voltage ride-through control parameters of multiple renewable energy stations according to claim 1 is characterized in that: Step S2 specifically includes: The reactive voltage coupling index of each new energy station grid connection point is ranked from large to small, and the new energy stations ranked in the top M% are regarded as stations with a larger reactive voltage coupling degree, and the value range of M is 40 to 60; Assume that the reactive current proportional coefficient K in the low voltage ride-through control of the new energy station q The value interval of is [a, b]; If the voltage at the grid connection point of the new energy station is lower than the set value, the reactive current proportional coefficient K in the low voltage ride-through control should be set. q The value is limited to [(a+b) / 2, b]; If the grid connection point voltage of the new energy station is greater than or equal to the set value, and the new energy station has a large reactive voltage coupling degree, the reactive current proportional coefficient K in the low voltage ride-through control should be set to q The value is limited to [a, (a+b) / 2]; If the grid connection point voltage of the new energy station is greater than or equal to the set value, and the new energy station does not belong to the station with a large degree of reactive voltage coupling, the reactive current proportional coefficient K in the low voltage ride-through control q The value is limited to [a, b].

4. The method for coordinated optimization of low voltage ride-through control parameters of multiple renewable energy stations according to claim 1 is characterized in that: In step S3, with the goal of minimizing the overall voltage deviation of the grid connection points of multiple renewable energy stations, a method for optimizing the reactive current proportional coefficient is constructed based on deep reinforcement learning to obtain the optimal reactive current proportional coefficient in the low voltage ride-through control of each renewable energy station.

5. The method for coordinated optimization of low voltage ride-through control parameters of multiple renewable energy stations according to claim 4 is characterized in that: Step S3 specifically includes: In the optimization process of constructing a reactive current proportional coefficient optimization method based on deep reinforcement learning, the reactive current proportional coefficient in the low voltage ride-through control of each new energy station is used as the target parameter, and the correction amount of the target parameter is used as the action value; the voltage simulation curve and target parameter of the grid connection point of each new energy station are used as the state quantity; the reward function is constructed based on the overall voltage deviation of the grid connection point of the new energy station and the target parameter crossing amount; and the target parameter value range constraint is constructed based on the value range of the reactive current proportional coefficient in the low voltage ride-through control of each new energy station; The intelligent agent makes action decisions based on the state quantities collected from the environment and applies the corresponding action values ​​to the environment. The environment changes under the influence of the action values ​​and evaluates the quality of the actions through the reward function and feeds back the corresponding rewards to the intelligent agent. The intelligent agent adjusts its own strategy based on the continuous interaction with the environment, iterates the process in a cycle, optimizes the target parameters, and obtains the optimal reactive current proportional coefficient in the low voltage ride-through control of each renewable energy station.

6. The method for coordinated optimization of low voltage ride-through control parameters of multiple renewable energy stations according to claim 5 is characterized in that: The correction amount of the target parameter is used as the action value as shown below: ； In the formula, and They represent the reactive current proportional coefficients in the low voltage ride-through control of each renewable energy station in the kth iteration and the k+1th iteration, It represents the correction amount of the reactive current proportional coefficient in the low voltage ride-through control of each renewable energy station in the kth iteration, that is, the action value selected by the intelligent agent in the kth iteration; The state of the kth iteration It is expressed as follows: ； In the formula, It represents the voltage simulation verification curve of each new energy station and grid connection point. It represents the grid connection point voltage of the i-th renewable energy station at time 1-T, T represents the end time of the fault ride-through of the renewable energy station, and n is the number of renewable energy stations in the target area.

7. The method for coordinated optimization of low voltage ride-through control parameters of multiple renewable energy stations according to claim 5 or 6, characterized in that: The reward function is constructed based on the overall voltage deviation of the new energy station grid connection point and the target parameter crossing amount, which is expressed as follows: ； In the formula, R k represents the reward function value, F represents the overall voltage deviation of the new energy station grid connection point, R A Indicates that the parameter is out of bounds. and F and R respectively A The weight coefficient, U ti represents the grid connection point voltage of the i-th renewable energy station, U t0 represents the steady-state voltage of the i-th renewable energy station, n represents the number of renewable energy stations in the target area, and T represents the end time of the fault ride-through of the renewable energy station.

8. A coordinated optimization system for low voltage ride-through control parameters of multiple renewable energy stations, characterized in that: include: The module for evaluating the degree of coupling impact is used to evaluate the degree of coupling impact of reactive power changes of each new energy station on the voltage of other new energy stations in the target area, and obtain the index value of the degree of coupling impact of reactive voltage at each new energy station. The parameter value range setting module is used to set the value range of the reactive current proportional coefficient in the low voltage ride-through control of each renewable energy station based on the reactive voltage influence coupling degree index value of each renewable energy station grid connection point; The parameter optimization module is used to coordinate and optimize the reactive current proportional coefficient in the low voltage ride through control of each renewable energy station within the value range of the reactive current proportional coefficient in the low voltage ride through control of each renewable energy station.

9. An electronic device, characterized in that: include: Memory on which computer programs or instructions are stored; A processor is used to execute the computer program or instruction to implement the method for coordinated optimization of low voltage ride-through control parameters of multiple renewable energy stations as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by the processor, the method for coordinated optimization of low voltage ride-through control parameters of multiple renewable energy stations as described in any one of claims 1 to 7 is implemented.

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