Control parameter optimization method, system and equipment for inhibiting repeated low-voltage penetration of new energy unit and medium

Abstract

The invention belongs to the technical field of power system automation, discloses a control parameter optimization method, system and equipment for inhibiting repeated low-voltage penetration of a new energy unit and a medium, and aims to solve the problem of repeated low-voltage penetration of the new energy unit. The method comprises the following steps: adjusting the terminal voltage of a new energy unit to a power grid operation voltage lower limit; identifying weak new energy units with repeated low-voltage crossing risks and corresponding key faults; for the weak new energy unit, generating a control parameter candidate adjustment scheme set by independently adjusting a low voltage ride through threshold value and an active current control value in a low voltage ride through period; and determining an optimal adjustment scheme from the candidate adjustment scheme set. According to the method, the new energy unit with the repeated low-voltage crossing risk in the power grid after the fault can be effectively identified, the problem of repeated low-voltage crossing of the new energy unit is solved by adjusting the control parameters of the new energy unit on the basis of not increasing additional equipment investment cost, and technical support is provided for safe and stable operation of the power grid and efficient consumption of new energy.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system automation, and particularly relates to a control parameter optimization method, system, device and medium for suppressing repeated low penetration of new energy units. BACKGROUND

[0002] The penetration rate of new energy such as photovoltaic and wind power in the power grid continues to increase, and the operating characteristics of the power system have changed profoundly. In addition to the common problem of wideband oscillation caused by the interaction between power electronic equipment and the grid or equipment, the phenomenon of active power oscillation of new energy units after line opening has also occurred in the power grid. The root cause of this phenomenon lies in the repeated entry and exit of new energy units into the low voltage penetration state, which can be referred to as the "repeated low penetration" problem. Specifically, line opening leads to a decrease in grid strength and insufficient reactive power support, which in turn causes the terminal voltage of the new energy unit to drop below the low voltage penetration threshold, triggering the low voltage penetration mechanism. Under the action of the low penetration control strategy, the active power output of the new energy unit decreases and the reactive power output increases, which promotes the terminal voltage to rise. Once the terminal voltage exceeds the threshold for exiting the low voltage, the new energy unit exits the low penetration state, which results in an increase in active power output and a decrease in reactive power output, which in turn causes the terminal voltage to drop again. This cycle continues, and the terminal voltage and active power of the new energy unit oscillate.

[0003] Currently, adjusting the control parameters of new energy units, such as reducing their low voltage penetration threshold or increasing the active current reference value during low penetration, is generally considered a potential effective way to suppress the problem from the source, with the advantages of low control cost and flexible approach. However, with the significant increase in the number of grid-connected new energy stations, implementing this strategy faces two key technical bottlenecks: first, the power grid structure is complex, and there are various fault modes. How to accurately and efficiently identify weak units that have a risk of repeated low penetration under a specific fault from a large number of new energy units lacks a systematic screening method. Second, after identifying weak units, how to quickly determine an optimal adjustment scheme that can effectively suppress oscillation and has the least impact on grid operation from numerous possible parameter adjustment combinations still lacks a mature solution.

[0004] In summary, the existing technology lacks a complete technical solution that can automatically identify weak units and optimize their control parameters to solve the repeated low penetration problem caused by line opening in a high-proportion new energy power grid. This defect restricts the safe and stable operation of the power grid and the efficient consumption of new energy. SUMMARY

[0005] Based on the above-mentioned shortcomings and deficiencies existing in the prior art, one of the purposes of the present application is to at least solve one or more of the above-mentioned problems existing in the prior art, in other words, one of the purposes of the present application is to provide a control parameter optimization method, system, device and medium for inhibiting repeated low penetration of new energy units to meet one or more of the aforementioned needs, so as to effectively solve the problem of repeated low penetration of new energy after failure by reasonably adjusting the control parameters of new energy units.

[0006] In order to achieve the above-mentioned purposes of the application, the following technical solutions are adopted in the present application: In a first aspect, the present application provides a control parameter optimization method for inhibiting repeated low penetration of new energy units, comprising the steps of: S1, constructing and solving an optimal power flow model with the deviation between the terminal voltage of the new energy unit and the lower limit of the grid operating voltage as the objective function, obtaining the adjustment value of each adjustable resource in the grid, and adjusting the terminal voltage of the new energy unit in the grid to the lower limit of the grid operating voltage based on the adjustment value; S2, based on the adjusted grid operating mode, calculating the short-circuit ratio of the new energy multi-station, and combining the line opening fault simulation of the near area of the new energy collection point, identifying the weak new energy unit and the corresponding key fault existing in the repeated low penetration risk from all new energy units in the grid; S3, for the weak new energy unit, generating a candidate adjustment scheme set of control parameters by independently adjusting the low voltage penetration threshold and the active current control value during low voltage penetration, respectively; S4, for each scheme in the candidate adjustment scheme set, checking in the simulation by applying the key fault, screening out the effective scheme that can avoid repeated low penetration, and determining the optimal adjustment scheme from the effective scheme according to the principle of minimizing the weighted comprehensive target value of the control parameter adjustment amount.

[0007] As a preferred scheme, step S1 comprises: S11, obtaining the data of conventional generators, new energy units, reactive power compensation devices, transformers, grid basic parameters and operating state data in the grid; S12, calculating the reactive power output limit value of each new energy unit according to its active power output and power factor to determine the corresponding reactive power adjustment range; S13, constructing an optimal power flow optimization model, taking the deviation between the terminal voltage of the new energy unit and the lower limit of the grid operating voltage as the objective function, taking the power flow equation, adjustable voltage transformer tap constraint, and number of switched capacitor bank constraint as the equality constraint, and taking the upper and lower limits of the reactive power of conventional units, the reactive power of switched capacitors, the reactive power of new energy units, the node voltage of non-new energy units and the transformer tap as the inequality constraint; S14, solve the optimal power flow optimization model to obtain the adjustment value of each adjustable resource in the power grid when the new energy unit terminal voltage is adjusted to the lower limit of the power grid operation voltage, and accordingly adjust the new energy unit terminal voltage to the lower limit of the power grid operation voltage.

[0008] As a preferred scheme, step S2 comprises: S21, calculate the new energy multi-station short-circuit ratio under the adjusted power grid operation mode; S22, define the new energy unit whose new energy multi-station short-circuit ratio is lower than the preset threshold value as a candidate weak new energy unit; S23, perform line N-1 or N-2 line outage fault simulation on the lines in the point near area of the candidate weak new energy unit; S24, if there is a new energy unit terminal voltage lower than its low voltage ride through threshold value after the line outage fault simulation, the new energy unit is determined as a weak new energy unit with repeated low penetration risk, and the corresponding fault is defined as a key fault.

[0009] As a preferred scheme, the preset threshold value is 3.0.

[0010] As a preferred scheme, step S3 comprises: S31, obtain the current value, upper limit and lower limit of the low voltage ride through threshold value and the active current control value of the weak new energy unit; S32, keep the active current control value unchanged, take the minimum value of the low voltage ride through threshold value as the starting point, the current value as the end point, and increase by a preset first step length, to generate a first number of low voltage ride through threshold value adjustment schemes; S33, keep the low voltage ride through threshold value unchanged, take the current value of the active current control value as the starting point, the maximum value as the end point, and increase by a preset second step length, to generate a second number of active current control value adjustment schemes; S34, the first number of adjustment schemes and the second number of adjustment schemes jointly constitute the control parameter candidate adjustment scheme set.

[0011] As a preferred scheme, the preset first step length and / or the preset second step length is 0.1.

[0012] As a preferred scheme, step S4 comprises: S41, under the adjusted power grid operation mode, each scheme in the candidate adjustment scheme set is subjected to simulation analysis under the key fault; S42, define the scheme that can make the new energy unit not repeatedly enter and exit the low voltage ride through state after simulation analysis as an effective control parameter adjustment scheme; S43, according to the low voltage ride through threshold value of the effective control parameter adjustment scheme, the adjustment value of the active current control value and the corresponding weight value, the comprehensive target value of each effective control parameter adjustment scheme is calculated; S44, the effective control parameter adjustment scheme with the minimum comprehensive target value is determined as the optimal adjustment scheme.

[0013] In a second aspect, the present application provides a control parameter optimization system for inhibiting repeated low penetration of new energy units, which is used to realize the control parameter optimization method as described in the first aspect, and comprises: A voltage adjustment module is configured to adjust the terminal voltage of the new energy unit in the power grid to the lower limit of the operating voltage of the power grid. A weak unit identification module is configured to identify weak new energy units with a risk of repeated low penetration based on the adjusted operating mode of the power grid. A candidate scheme generation module is configured to generate a set of candidate adjustment schemes for the control parameters of the weak new energy units. An optimal scheme determination module is configured to determine an optimal adjustment scheme for the control parameters of the new energy units from the set of candidate adjustment schemes.

[0014] In a third aspect, the present application provides an electronic device, which comprises a memory, a processor and a computer program, and the computer program is executed by the processor to realize the control parameter optimization method as described in the first aspect.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the control parameter optimization method as described in the first aspect.

[0016] Compared with the prior art, the present application has the following beneficial effects: 1. The present application constructs an optimal power flow model for precise voltage regulation, which lays a solid foundation for subsequent analysis. The short-circuit ratio is calculated and combined with fault simulation to accurately locate weak units and key faults. The control parameters are adjusted to generate candidate schemes and select the optimal one. This systematic scheme can solve the problem of repeated low penetration from the root cause and ensure stable operation of the power grid.

[0017] 2. The present application optimizes the control parameters to enable the units to better adapt to the power grid during faults, maintain power output during low voltage, provide support for the power grid, improve the anti-interference ability of the power grid, and enhance the operation reliability and stability.

[0018] 3. The present application selects the optimal scheme based on the principle of minimizing the weighted comprehensive target value of the control parameter adjustment amount, which reduces the adjustment amount and the interference to the units and the power grid while solving the problem of repeated low penetration, and improves the safety and stability of the power system.

[0019] Further or more detailed advantageous effects will be described in the specific embodiments in the specific implementation. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative effort based on these drawings.

[0021] Figure 1 is a flowchart of the control parameter optimization method provided by the embodiments of the present application.

[0022] Figure 2 is a structural diagram of the control parameter optimization system provided by the embodiments of the present application.

[0023] Figure 3 is a structural diagram of the electronic device provided by the embodiments of the present application.

[0024] LIST OF ELEMENTS IN THE DRAWINGS 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0026] In the following description, a plurality of embodiments of the present application are provided, and different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, the present application should also be considered to include embodiments including one or more of all other possible combinations of A, B, C, and D, even if the embodiment is not explicitly described in the following content.

[0027] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes can be made in the function and arrangement of elements described without departing from the scope of the present content. Various examples can appropriately omit, replace, or add various processes or components. For example, the described methods can be executed in different order from the described order, and various steps can be added, omitted, or combined. In addition, features described with respect to some examples can be combined into other examples.

[0028] In order to facilitate better understanding of the embodiments of the present application, before the specific embodiments of the present application are explained in detail, the application scenarios thereof are described.

[0029] The control parameter optimization method described in the embodiments of the present specification is applied to the operation process of new energy units accessing the power grid. In these scenarios, the application of the control parameter optimization method aims to solve the repeated low penetration problem of new energy units, ensure the stable operation of new energy units, and thus improve the reliability and stability of the entire power grid operation.

[0030] The new energy unit, the lower limit of the grid operating voltage, the short-circuit ratio of the new energy multi-station, the low voltage ride-through threshold, and the active current control value during low voltage ride-through involved in the multiple embodiments of the present specification are briefly explained as follows: New energy units generally refer to a combination of devices that generate electricity using renewable energy sources such as wind and solar energy. For example, a wind turbine generates electricity by rotating blades driven by wind, and a solar photovoltaic generator converts light energy into electricity directly through the photovoltaic effect of the semiconductor interface. These new energy units have characteristics such as intermittency and randomness, and their output power fluctuates with changes in natural conditions.

[0031] The lower limit of the grid operating voltage is the lowest value of the voltage allowed during normal operation of the power grid. The grid voltage needs to be maintained within a certain range to ensure the normal operation of various types of electrical equipment. When the voltage is lower than the lower limit of the operating voltage, it may cause electrical equipment to malfunction or even be damaged. Setting the lower limit of the grid operating voltage is to ensure the stable operation of the power grid and the quality of power supply.

[0032] The short-circuit ratio of new energy multi-station is an important indicator to measure the voltage support strength of new energy grid-connected system. It reflects the strength of the power grid after the new energy station is connected to the grid and the ability to withstand power fluctuations of the new energy station. The larger the short-circuit ratio of the new energy multi-station, the stronger the power grid's ability to accommodate power fluctuations of the new energy station, and the relatively smaller the impact of the new energy station on the power grid; otherwise, it indicates that the power grid is relatively weak, and the connection of the new energy station may have a greater impact on the stable operation of the power grid.

[0033] The low voltage ride-through threshold is the threshold value at which the new energy unit enters the low voltage ride-through state when the grid voltage drops. When the grid voltage drops below this threshold, the new energy unit enters the low voltage ride-through state and increases the reactive power to improve the voltage support capability of the grid. Setting a reasonable low voltage ride-through threshold can ensure that the new energy unit can continue to operate stably when the grid voltage fluctuates, providing certain power support for the grid.

[0034] The active current control value during low-voltage ride-through refers to a value for controlling the active current output by the new energy unit when the new energy unit is in a low-voltage ride-through state (i.e., the grid voltage drops below the low-voltage ride-through threshold and the unit remains connected to the grid). By reasonably adjusting the active current control value during low-voltage ride-through, the power output characteristics of the new energy unit during low-voltage ride-through can be optimized, and the repeated low-voltage ride-through of the new energy unit can be inhibited.

[0035] Embodiment one: As shown in Figure 1 The embodiment provides a control parameter optimization method for inhibiting repeated low-voltage ride-through of a new energy unit, comprising the steps of: S1, constructing and solving an optimal power flow model with the objective function of minimizing the deviation of the terminal voltage of the new energy unit from the lower limit of the grid operating voltage, obtaining the adjustment value of each adjustable resource in the grid, and adjusting the terminal voltage of the new energy unit in the grid to the lower limit of the grid operating voltage based on the adjustment value; S2, based on the adjusted grid operating mode, calculating the short-circuit ratio of new energy stations, and combining the line opening fault simulation of the near area of the new energy collection point, identifying the weak new energy unit and the corresponding key fault that exist repeated low-voltage ride-through risk from all new energy units in the grid; S3, for the weak new energy unit, by respectively and independently adjusting its low-voltage ride-through threshold and active current control value during low-voltage ride-through, a candidate adjustment scheme set of control parameters is generated; S4, for each scheme in the candidate adjustment scheme set, the key fault is applied in the simulation for verification, the effective scheme that can avoid repeated low-voltage ride-through is screened out, and the optimal adjustment scheme is determined from the effective scheme according to the principle of minimum weighted comprehensive target value of control parameter adjustment amount.

[0036] Specifically, step S1 comprises: S11, obtaining conventional generator data, new energy unit data, reactive power compensation device data, transformer data, grid basic parameter and operating state data in the grid.

[0037] More specifically, the conventional generator data includes the upper and lower limits of reactive power output of conventional generating units; the new energy generating unit data includes the current active power output, power factor or rated apparent power, and the upper and lower limits of reactive power output calculated based on its current operating status; the reactive power compensation device data includes the upper and lower limits of reactive power output of the reactive power compensation device (such as a static var generator SVG) located at the grid connection point of the new energy generating unit, as well as the single-unit reactive power capacity, current number of switching groups and maximum number of switching groups of the capacitive reactors or reactors installed in substations at all levels in the power grid; the transformer data includes the current tap position and total number of tap positions of each on-load tap-changing transformer, and the turns ratio or turns ratio adjustment range corresponding to each tap position; the power grid basic parameters and operating status data include network topology and line impedance parameters, active and reactive loads of each node, upper and lower limits of voltage operation of each node (bus), and the specific lower limit of voltage operation at the generator terminals of the new energy generating units.

[0038] S12. Calculate the reactive power output limit of each new energy unit based on its active power output and power factor, so as to determine the corresponding reactive power adjustment range.

[0039] More specifically, the formula for calculating the reactive power output limit is as follows: (1) In equation (1), For the first k Taiwan's renewable energy units' reactive power output limits For the first k The power output of Taiwan's new energy units For the first k The power factor of the new energy unit is preferably 0.95 in this embodiment.

[0040] Therefore, based on the reactive power output limit calculated using formula (1), the reactive power adjustment range of the new energy unit is [- Q k , Q k ].

[0041] S13. Construct an optimal power flow optimization model with the objective function of minimizing the deviation between the terminal voltage of new energy generating units and the lower limit of the grid operating voltage. The model uses the power flow equation, adjustable transformer tap constraints, and the number of capacitor switching groups as equality constraints, and the upper and lower limits of reactive power of conventional generating units, reactive power of capacitors, reactive power of new energy generating units, node voltage of non-new energy generating units, and transformer tap positions as inequality constraints.

[0042] More specifically, the expression for the objective function is as follows: (2) In equation (2), For the first k Taiwan's new energy unit terminal voltage, For the first k Lower limit of operating voltage at the generator terminals of new energy power units in Taiwan S e It is a collection of new energy generating units in the power grid.

[0043] The expression for the power flow equation is as follows: (3) In equation (3), and The first The active and reactive power of a conventional generating unit and For separate nodes The active and reactive power of the load, For nodes and The electrical conductance between them For nodes and The susceptance between them.

[0044] The expression for the tap constraint of the adjustable transformer is as follows: (4) In equation (4), For transformer k ij The corresponding values ​​for each tap position. m Its total gears, S T This is a collection of adjustable voltage transformers.

[0045] The expression for the constraint on the number of capacitive reactor switching groups is as follows: (5) In equation (5), For capacitor Q Ci The corresponding reactive power output values ​​for each group m Its total number of groups, S C It is a set of switchable capacitive reactors.

[0046] The inequality constraints are as follows: (6) (7) (8) (9) (10) In formula (6)-(10), , and are reactive power and upper and lower limits of the reactive power of the first unit of conventional unit, , and are reactive power and upper and lower limits of the reactive power of the first i node capacitor, , and are reactive power and upper and lower limits of the reactive power of the first i new energy unit, , and are the voltage and upper and lower limits of the remaining nodes in the grid except the new energy unit terminal node, , and are the first ij transformer gear and upper and lower limits.

[0047] S14, solving the optimal power flow optimization model, obtaining the adjustment value of each adjustable resource in the grid when the new energy unit terminal voltage is adjusted to the lower limit of the grid operation voltage, and adjusting the new energy unit terminal voltage to the lower limit of the grid operation voltage according to the adjustment value.

[0048] Specifically, step S2 comprises: S21, calculating the short-circuit ratio of the new energy multi-station under the adjusted grid operation mode; S22, defining the new energy unit with a short-circuit ratio lower than a preset threshold value as a candidate weak new energy unit; S23, performing line N-1 or N-2 line outage fault simulation on the lines in the point near area of the candidate weak new energy unit; S24, if there is a new energy unit terminal voltage lower than its low voltage ride-through threshold after the line outage fault simulation, the new energy unit is determined as a weak new energy unit with repeated low penetration risk, and the corresponding fault is defined as a key fault.

[0049] Specifically, the preset threshold value is 3.0.

[0050] Specifically, step S3 comprises: S31, obtaining the current value, upper limit and lower limit of the low voltage ride-through threshold and active current control value of the weak new energy unit; S32, keeping the active current control value unchanged, taking the minimum value of the low voltage ride-through threshold as the starting point and the current value as the end point, and increasing by a preset first step length to generate a first number of low voltage ride-through threshold adjustment schemes. S33. Keep the low voltage ride-through threshold unchanged, starting from the current value of the active current control value and ending at the maximum value, and increasing by a preset second step size to generate a second number of active current control value adjustment schemes. S34. The first number of adjustment schemes and the second number of adjustment schemes together constitute the set of candidate adjustment schemes for the control parameters.

[0051] Specifically, the preset first step length and / or the preset second step length is 0.1.

[0052] Specifically, step S4 includes: S41. Under the adjusted power grid operation mode, apply the key fault to each scheme in the candidate adjustment scheme set and perform simulation analysis; S42. The scheme that, after simulation analysis, prevents the new energy unit from repeatedly entering and exiting the low voltage ride-through state is defined as an effective control parameter adjustment scheme. S43. Based on the adjustment values ​​of the low-voltage ride-through threshold and active current control value of the effective control parameter adjustment scheme and their corresponding weight values, calculate the comprehensive target value of each effective control parameter adjustment scheme. The calculation formula is as follows: (11) In equation (11), For the first i The comprehensive target value of an effective control parameter adjustment scheme. For the first i The adjustment amount of the active current control value in each effective control parameter adjustment scheme. For the first i An effective control parameter adjustment scheme includes the adjustment amount of the low voltage ride-through threshold. , These are the weight values ​​for the active current control value adjustment and the low voltage ride-through threshold adjustment, respectively. In this embodiment, a comprehensive weighting method combining subjective and objective weighting methods is used to determine these weight values. S44. The effective control parameter adjustment scheme that minimizes the comprehensive target value is determined as the optimal adjustment scheme.

[0053] Example 2: like Figure 2 As shown, this embodiment provides a control parameter optimization system for suppressing repeated low-temperature runs by new energy units, used to implement the control parameter optimization method described in Embodiment 1, including: The voltage regulation module is used to adjust the terminal voltage of new energy generating units in the power grid to the lower limit of the grid operating voltage. The vulnerable generator identification module is used to identify vulnerable new energy generators that are at risk of repeated low-voltage power transmission, based on the adjusted grid operation mode. The candidate scheme generation module is used to generate a set of candidate adjustment schemes for the control parameters of the weak new energy unit; The optimal solution determination module is used to determine the optimal adjustment scheme for the control parameters of the new energy unit from the set of candidate adjustment schemes.

[0054] Example 3: like Figure 3 As shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0055] The communication bus can be used to enable communication between the various components mentioned above.

[0056] The user interface may include buttons, and optional user interfaces may also include standard wired interfaces and wireless interfaces.

[0057] The network interface may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0058] The processor may include one or more processing cores. It connects various parts of the electronic device via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in memory, and accessing data stored in memory to perform various functions and process data. Optionally, the processor can be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor.

[0059] The memory can include a RAM and can also include a ROM. Optionally, the memory includes a non-transitory computer-readable medium. The memory can be used to store instructions, programs, codes, code sets, or instruction sets. The memory can include a program storage area and a data storage area, wherein the program storage area can store the instructions for implementing the operating system, the instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), the instructions for implementing the various method embodiments described above, etc.; the data storage area can store the data involved in the various method embodiments described above, etc. The memory can also optionally be at least one storage device located away from the aforementioned processor. As a computer storage medium, the memory can include an operating system, a network communication module, a user interface module, and an optimization application. The processor can be used to invoke the optimization application stored in the memory and execute the steps of the control parameter optimization method mentioned in the foregoing embodiments.

[0060] Embodiment Four The embodiment provides a computer readable storage medium, which stores instructions, when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of one or more of the embodiments shown above. The various component modules of the electronic device described above can be stored in the computer readable storage medium if they are implemented in the form of software function units and sold or used as independent products. Figure 1

[0061] ​In the above embodiments, all or part of the methods can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the methods can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted by the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that includes one or more available media sets. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a digital versatile disc (DVD)), or a semiconductor medium (for example, a solid state disk (SSD)) and the like.

[0062] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned method embodiments can be implemented by a computer program to instruct the relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. The storage medium mentioned above includes ROM, RAM, magnetic or optical discs, and various media that can store program codes. In the case of no conflict, the technical features in the embodiments and the implementation schemes can be combined arbitrarily.

[0063] It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, some steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0064] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0065] The above merely illustrates the embodiments of the present application, and cannot be used to limit the scope of the present application. Any equivalent changes and modifications made according to the teachings of the present application shall still fall within the scope of the present application. Any of the embodiments of the present application will be readily apparent to those skilled in the art upon considering the specification and practicing the disclosure herein. The present application is intended to cover any variations, uses, or adaptive changes of the present application, which follow the general principles of the present application and include common knowledge or conventional technical means in the art not described in the present application. The specification and examples are merely regarded as exemplary, and the scope and spirit of the present application are defined by the claims.

Claims

1. A method for optimizing control parameters to suppress repeated low-temperature runs in new energy generating units, characterized in that, Including the following steps: S1. Construct and solve the optimal power flow model with the objective function of minimizing the deviation between the terminal voltage of the new energy generating units and the lower limit of the grid operating voltage. Obtain the adjustment values ​​of each adjustable resource in the grid and adjust the terminal voltage of the new energy generating units in the grid to the lower limit of the grid operating voltage based on this. S2. Based on the adjusted power grid operation mode, calculate the short-circuit ratio of multiple new energy power stations, and combine it with the simulation of line interruption faults in the vicinity of the new energy collection point to identify the weak new energy units with repeated low-voltage transmission risk and the corresponding key faults from all new energy units in the power grid. S3. For the weak new energy units, a set of candidate adjustment schemes for control parameters is generated by independently adjusting their low voltage ride-through threshold and active current control value during low voltage ride-through. S4. For each scheme in the candidate adjustment scheme set, apply the key fault in the simulation to verify it, screen out the effective schemes that can avoid repeated low-altitude penetration, and determine the optimal adjustment scheme from the effective schemes based on the principle of minimizing the weighted comprehensive target value of the control parameter adjustment amount.

2. The method for optimizing control parameters to suppress repeated low-temperature runs of new energy generating units according to claim 1, characterized in that, Step S1 includes: S11. Obtain data on conventional generators, new energy units, reactive power compensation devices, transformers, and basic grid parameters and operating status within the power grid. S12. Calculate the reactive power output limit of each new energy unit based on its active power output and power factor, so as to determine the corresponding reactive power adjustment range. S13. Construct an optimal power flow optimization model with the objective function of minimizing the deviation between the terminal voltage of new energy generating units and the lower limit of the grid operating voltage. The equation constraints are the power flow equation, the tap constraints of adjustable transformers, and the constraints of the number of switching groups of capacitive reactors. The inequality constraints are the reactive power of conventional generating units, the reactive power of capacitive reactors, the reactive power of new energy generating units, the node voltage of non-new energy generating units, and the upper and lower limits of transformer taps. S14. Solve the optimal power flow optimization model to obtain the adjustment values ​​of each adjustable resource in the power grid when the terminal voltage of the new energy generating unit is adjusted to the lower limit of the power grid operating voltage, and adjust the terminal voltage of the new energy generating unit to the lower limit of the power grid operating voltage accordingly.

3. The method for optimizing control parameters to suppress repeated low-temperature runs of new energy generating units according to claim 2, characterized in that, Step S2 includes: S21. Calculate the short-circuit ratio of multiple new energy power plants under the adjusted power grid operation mode; S22. New energy units with a short-circuit ratio lower than a preset threshold value at multiple new energy power stations are defined as candidate weak new energy units. S23. Simulate line N-1 or N-2 disconnection faults on the lines near the convergence point of the candidate weak new energy units. S24. If, after simulating a line break fault, the terminal voltage of a new energy unit is found to be lower than its low voltage ride-through threshold, then the new energy unit is identified as a weak new energy unit with a risk of repeated low voltage ride-through, and the corresponding fault is defined as a critical fault.

4. The method for optimizing control parameters to suppress repeated low-temperature runs of new energy generating units according to claim 3, characterized in that: The preset threshold value is 3.

0.

5. The method for optimizing control parameters to suppress repeated low-voltage runs of new energy generating units according to claim 4, characterized in that, Step S3 includes: S31. Obtain the current value, upper limit, and lower limit of the low voltage ride-through threshold and active current control value of the weak new energy unit; S32. Keep the active current control value unchanged, starting from the minimum value of the low voltage ride-through threshold and ending at the current value, and increasing by a preset first step length to generate a first number of low voltage ride-through threshold adjustment schemes. S33. Keep the low voltage ride-through threshold unchanged, starting from the current value of the active current control value and ending at the maximum value, and increasing by a preset second step size to generate a second number of active current control value adjustment schemes. S34. The first number of adjustment schemes and the second number of adjustment schemes together constitute the set of candidate adjustment schemes for the control parameters.

6. The method for optimizing control parameters to suppress repeated low-temperature runs of new energy generating units according to claim 5, characterized in that: The preset first step length and / or the preset second step length is 0.

1.

7. The method for optimizing control parameters to suppress repeated low-temperature runs of new energy generating units according to claim 6, characterized in that, Step S4 includes: S41. Under the adjusted power grid operation mode, apply the key fault to each scheme in the candidate adjustment scheme set and perform simulation analysis; S42. The scheme that, after simulation analysis, prevents the new energy unit from repeatedly entering and exiting the low voltage ride-through state is defined as an effective control parameter adjustment scheme. S43. Based on the adjustment values ​​of the low voltage ride-through threshold and active current control value of the effective control parameter adjustment scheme and the corresponding weight values, calculate the comprehensive target value of each effective control parameter adjustment scheme. S44. The effective control parameter adjustment scheme that minimizes the comprehensive target value is determined as the optimal adjustment scheme.

8. A control parameter optimization system for suppressing repeated low-temperature runs by new energy generating units, characterized in that, A method for implementing the control parameter optimization method as described in any one of claims 1 to 7 includes: The voltage regulation module is used to adjust the terminal voltage of new energy generating units in the power grid to the lower limit of the grid operating voltage. The vulnerable generator identification module is used to identify vulnerable new energy generators that are at risk of repeated low-voltage power transmission, based on the adjusted grid operation mode. The candidate scheme generation module is used to generate a set of candidate adjustment schemes for the control parameters of the weak new energy unit; The optimal solution determination module is used to determine the optimal adjustment scheme for the control parameters of the new energy unit from the set of candidate adjustment schemes.

9. A computer device, the computer device comprising a memory, a processor, and a computer program, characterized in that, When the computer program is executed by the processor, it implements the control parameter optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the control parameter optimization method as described in any one of claims 1 to 7.

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