A method and system for evaluating power grid support capability of a network-constructed new energy station

Abstract

The application provides a power grid support capability evaluation method and system for a grid-constructed new energy station. The method comprises: obtaining basic parameters of a sending-end power grid of a new energy base under an evaluation configuration scheme, and establishing a sending-end power grid evaluation model containing grid-connected new energy stations and grid-constructed new energy stations based on the basic parameters; generating typical operating conditions and multi-source disturbance scenarios based on the basic parameters, and cross-loading the two to the sending-end power grid evaluation model to obtain dynamic response data; extracting multi-dimensional support characteristic quantities from the dynamic response data, and mapping the multi-dimensional support characteristic quantities to support capability evaluation indexes; performing dimensionless processing and combined weighting on the support capability evaluation indexes, and performing safety bottom line determination based on core safety indexes; and outputting a comprehensive support score, a support capability level, and weak links according to the safety bottom line determination result, the dimensionless processing result, and the combined weighting result. The application can realize multi-dimensional dynamic comprehensive evaluation of the support capability of the grid-constructed new energy station for the sending-end power grid.

Description

Technical Field

[0001] This application relates to the field of new energy grid connection safety assessment technology, and more specifically, to a method and system for assessing the grid support capability of grid-connected new energy power plants. Background Technology

[0002] As the proportion of power electronic power sources such as wind power, photovoltaics, and energy storage in the power system continues to increase, renewable energy bases typically transmit power to the receiving grid after amplification and voltage boosting through the sending-end grid. Because renewable energy bases are often located far from load centers, the sending-end grid has few conventional synchronous power sources, low short-circuit capacity, low inertia, and large transmission capacity, making the system prone to exhibiting weak grid characteristics. In such scenarios, relying solely on traditional synchronous machines to provide frequency, voltage, short-circuit capacity, and damping support is no longer sufficient to meet the demands of high-proportion renewable energy grid integration and large-scale power transmission.

[0003] Existing methods for assessing renewable energy grid connection typically employ indicators such as short-circuit ratio, effective short-circuit ratio, multi-infeed short-circuit ratio, steady-state voltage deviation, power flow over-limit, frequency deviation, and fault ride-through compliance for verification. Alternatively, they may conduct transient simulations based on typical fault scenarios to determine whether renewable energy power plants meet grid connection safety requirements after integration. While these methods can reflect the system strength, steady-state operation, and fault ride-through capability of the sending-end grid to some extent, they primarily focus on grid-connected renewable energy power plants or traditional grid-connected systems, often equating renewable energy power plants with controlled current sources or constant power sources. This makes it difficult to accurately reflect the controlled voltage source characteristics of grid-connected renewable energy power plants. Existing assessment methods tend to emphasize single indicators, making it difficult to characterize multi-dimensional dynamic support.

[0004] Therefore, this application provides a method and system for assessing the grid support capability of grid-connected new energy power plants to solve one of the aforementioned technical problems. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for assessing the grid support capability of grid-connected renewable energy power plants, which can solve at least one of the aforementioned technical problems. The specific solution is as follows:

[0006] According to a specific embodiment of this application, in a first aspect, this application provides a method for assessing the grid support capability of grid-connected renewable energy power plants, including: The basic parameters of the sending-end power grid of the new energy base under the configuration scheme to be evaluated are obtained, and an evaluation model of the sending-end power grid including grid-connected new energy power stations and grid-connected new energy power stations is established based on the basic parameters. The grid-connected new energy power stations are equivalent to controlled current source characteristics, and the grid-connected new energy power stations are equivalent to controlled voltage source characteristics. Typical operating conditions and multi-source disturbance scenarios are generated based on the basic parameters, and the typical operating conditions and multi-source disturbance scenarios are cross-loaded into the sending-end power grid evaluation model to obtain dynamic response data. Multi-dimensional support features are extracted from the dynamic response data, and the multi-dimensional support features are mapped to support capability evaluation indicators. The support capability evaluation indicators are dimensionless and weighted, and a safety baseline determination is made based on the core safety indicators in the support capability evaluation indicators. Based on the safety baseline determination results, dimensionless processing results, and weighting results, a comprehensive support score, support capability level, and weak links are output.

[0007] According to a specific embodiment of this application, in a second aspect, this application provides a grid support capability assessment system for grid-connected renewable energy power plants, comprising: The model building module is used to obtain the basic parameters of the sending-end power grid of the new energy base under the configuration scheme to be evaluated, and to build an evaluation model of the sending-end power grid including grid-connected new energy power stations and grid-connected new energy power stations based on the basic parameters; wherein, the grid-connected new energy power stations are equivalent to controlled current source characteristics, and the grid-connected new energy power stations are equivalent to controlled voltage source characteristics; the scenario loading module is used to generate typical operating conditions and multi-source disturbance scenarios based on the basic parameters, and cross-load the typical operating conditions and the multi-source disturbance scenarios into the sending-end power grid evaluation model to obtain dynamic response data; the feature mapping module is used to extract multi-dimensional support features from the dynamic response data, and map the multi-dimensional support features into support capability evaluation indicators; the indicator processing module is used to perform dimensionless processing and combined weighting on the support capability evaluation indicators, and to determine the safety bottom line based on the core safety indicators in the support capability evaluation indicators; the evaluation output module is used to output a comprehensive support score, support capability level and weak links based on the safety bottom line determination result, dimensionless processing result and combined weighting result.

[0008] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects: This application provides a method for assessing the grid support capability of grid-connected renewable energy power plants. By establishing a sending-end grid evaluation model that includes both grid-connected and grid-connected renewable energy power plants, and cross-loading typical operating conditions and multi-source disturbance scenarios into this model, dynamic response data under different configuration schemes can be obtained. Then, multi-dimensional support feature quantities are extracted from the dynamic response data and mapped into support capability evaluation indicators, enabling the evaluation of system strength, frequency, voltage, stability, fault recovery, and transmission capacity within a unified framework. At the same time, by combining dimensionless processing, combined weighting, and safety baseline determination, a comprehensive support score, support capability level, and weak links can be output, avoiding the problems of one-sided evaluation of single indicators and the average masking of safety indicators. Attached Figure Description

[0009] Figure 1 A flowchart is shown for a method to assess the grid support capability of a grid-connected renewable energy power plant; Figure 2 An exemplary flowchart of the comprehensive evaluation process for assessing the grid support capability of grid-connected renewable energy power plants is provided. Figure 3 A block diagram of a grid support capability assessment system for grid-connected renewable energy power plants according to an embodiment of this application is shown. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0011] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0012] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0013] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0014] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0015] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.

[0016] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0017] The optional embodiments of this application are described in detail below with reference to the accompanying drawings.

[0018] Existing methods for evaluating renewable energy grid connection typically employ indicators such as short-circuit ratio, effective short-circuit ratio, multi-infeed short-circuit ratio, steady-state voltage deviation, power flow over-limit, frequency deviation, and fault ride-through compliance for verification. Alternatively, they may conduct transient simulations based on typical fault scenarios to determine whether renewable energy power plants meet grid connection safety requirements after integration. While these methods can reflect the system strength, steady-state operation, and fault ride-through capability of the sending-end grid to a certain extent, they primarily focus on grid-connected renewable energy power plants or traditional grid-connected power plants, often equating renewable energy power plants with controlled current sources or constant power sources. This makes it difficult to accurately reflect the controlled voltage source characteristics of grid-connected renewable energy power plants.

[0019] However, the support capability of grid-connected renewable energy power plants for the sending-end grid cannot be described by a single indicator. Their supporting role is reflected in multiple aspects, including system strength, frequency response, voltage recovery, transient stability, small-disturbance stability, fault ride-through recovery, and transmission capacity. Existing methods tend to focus on single indicators or a few typical operating conditions, failing to cross-evaluate typical operating conditions and multi-source disturbance scenarios. They also lack unified mapping, dimensionless processing, combined weighting, and safety baseline determination mechanisms for multi-dimensional dynamic characteristics. This makes it difficult to make unified comparisons between different grid-connected configuration schemes and may lead to the problem of comprehensive scoring masking the risk of exceeding core safety limits.

[0020] In view of this, this application provides a method for evaluating the grid support capability of grid-connected renewable energy power plants. By establishing a sending-end grid evaluation model that includes both grid-connected and grid-connected renewable energy power plants, and cross-loading typical operating conditions and multi-source disturbance scenarios into this model, dynamic response data under different configuration schemes can be obtained. Then, multi-dimensional support feature quantities are extracted from the dynamic response data and mapped into support capability evaluation indicators, so that system strength, frequency, voltage, stability, fault recovery, and transmission capacity can be evaluated under a unified framework. At the same time, by combining dimensionless processing, combined weighting, and safety baseline determination, a comprehensive support score, support capability level, and weak links can be output, avoiding the problems of one-sided evaluation of single indicators and the average masking of safety indicators.

[0021] The embodiments provided in this application are embodiments of a method for assessing the grid support capability of grid-connected new energy power stations.

[0022] The following is combined Figure 1 The embodiments of this application will be described in detail.

[0023] In one embodiment, this application provides a method for assessing the grid support capability of grid-connected renewable energy power plants, referring to... Figure 1 Specifically, it includes the following steps: S101. Obtain the basic parameters of the power grid at the sending end of the new energy base under the configuration scheme to be evaluated, and establish an evaluation model of the power grid at the sending end that includes grid-connected new energy power stations and grid-connected new energy power stations based on the basic parameters.

[0024] Among them, grid-connected new energy power stations are equivalent to controlled current source characteristics, while grid-connected new energy power stations are equivalent to controlled voltage source characteristics.

[0025] S102. Generate typical operating conditions and multi-source disturbance scenarios based on basic parameters, and cross-load the typical operating conditions and multi-source disturbance scenarios into the sending-end power grid evaluation model to obtain dynamic response data.

[0026] S103. Extract multidimensional support features from dynamic response data and map the multidimensional support features into support capability evaluation indicators.

[0027] S104. Dimensionless processing and combined weighting are performed on the support capability evaluation indicators, and the safety baseline is determined based on the core safety indicators in the support capability evaluation indicators.

[0028] S105. Based on the safety baseline determination results, dimensionless processing results, and combined weighting results, output the comprehensive support score, support capability level, and weak links.

[0029] As a specific implementation, the evaluation model for the sending-end power grid is based on the actual structure of the sending-end power grid of the new energy base and the configuration scheme to be evaluated. The configuration scheme to be evaluated can be the current grid connection scheme of the new energy base, or it can be a scheme after changing the proportion of grid-connected capacity, the access location of grid-connected new energy power plants, energy storage configuration, reactive power compensation equipment configuration, or the control parameters of grid-connected power plants. The sending-end power grid of the new energy base includes wind farms, photovoltaic power plants, energy storage systems, grid-connected new energy power plants, grid-connected new energy power plants, conventional generating units, loads, collection lines, step-up transformers, points of common coupling, transmission lines, external transmission channels, reactive power compensation equipment, and equivalent power grid.

[0030] In the above embodiments, the devices and network objects are associated in the sending-end power grid evaluation model according to electrical topology, operating mode and control characteristics, so that subsequent operating condition switching, disturbance loading and dynamic response data acquisition are all completed in the same evaluation model.

[0031] In some embodiments, Figure 2 An exemplary flowchart illustrates the comprehensive assessment process for evaluating the grid support capability of grid-connected renewable energy power plants. (Refer to...) Figure 2 The evaluation process begins by defining the evaluation object and boundaries. By clarifying the range of power plants, the grid structure, transmission channels, and operating modes of the power grid at the sending end of the renewable energy base, subsequent model building, scenario loading, and evaluation output all revolve around the same configuration scheme to be evaluated. Subsequently, the data and the evaluation model of the sending end power grid are verified. The verification includes power flow data, short-circuit calculation data, dynamic model parameters, and control parameters of grid-connected power plants. This ensures that the evaluation model of the sending end power grid can reflect the controlled current source characteristics of grid-connected renewable energy power plants and the controlled voltage source characteristics of grid-connected renewable energy power plants.

[0032] For example, such as Figure 2As shown, the operating condition and disturbance scenario setting stage serves to support the basic parameters and evaluation model. Based on operating mode parameters such as the proportion of renewable energy output, the operating capacity of conventional units, the power transmitted to external locations, and the basic short-circuit ratio, typical operating conditions covering high output, low output, low short-circuit ratio, and high power transmission to external locations are formed. Simultaneously, multi-source disturbance scenarios such as short-circuit faults, line disconnection, DC commutation failure, or blocking are set up in conjunction with the point of common coupling, transmission lines, and transmission channels. The intersection of the typical operating conditions and multi-source disturbance scenarios serves as the data acquisition condition for the simulation and measurement data acquisition stages, ensuring that each configuration scheme to be evaluated generates dynamic response data under a unified evaluation scenario matrix.

[0033] For example, such as Figure 2 As shown, the simulation and measurement data acquisition stage obtains common coupling point voltage curves, key bus voltage curves, system frequency curves, active power curves of grid-type renewable energy power plants, reactive power curves of grid-type renewable energy power plants, power flow curves of outgoing lines, power oscillation curves, and post-fault recovery process data through power flow calculations, short-circuit calculations, transient simulations, small disturbance analysis, or on-site measurements. The aforementioned dynamic response data is then input into the multi-dimensional support feature extraction stage, from which features such as short-circuit ratio, multi-infeed short-circuit ratio, frequency change rate, minimum frequency point, voltage drop depth, damping ratio, and recovery time are extracted. These features serve as the basis for subsequent safety baseline constraint determination and comprehensive support scoring.

[0034] For example, such as Figure 2 As shown, the safety baseline constraint judgment step verifies each core safety indicator. Indicators such as dynamic short-circuit ratio, maximum frequency change rate, minimum frequency point, minimum voltage point, voltage recovery time, critical clearing time, damping ratio, and fault ride-through status are compared with their corresponding safety baselines, grade ranges, and engineering constraints. When all core safety indicators meet the safety baseline, the process enters the comprehensive support scoring branch, which normalizes, hierarchically weights, and aggregates scenarios for the support capability evaluation indicators to obtain a comprehensive support score and output the evaluation result. When any core safety indicator fails to meet the safety baseline, the process enters the weak link diagnosis branch, which identifies the weak indicators, corresponding operating conditions, key nodes, and influencing factors, and formulates improvement suggestions. Both branches ultimately enter the feedback subsequent optimization requirement stage, allowing the evaluation results to be used to identify optimizable links and feed back to network capacity configuration, access location selection, energy storage configuration, reactive power compensation configuration, or control parameter tuning.

[0035] As an exemplary implementation, to reflect the control differences between grid-connected and grid-connected renewable energy power plants, different equivalent methods are used for the two in the evaluation model. Grid-connected renewable energy power plants are equivalent to controlled current source characteristics, and their model reflects the grid-connected characteristics of these plants, which rely on external grid voltage and frequency references for synchronization and output controlled current. In contrast, grid-connected renewable energy power plants are equivalent to controlled voltage source characteristics, and their model reflects the operational characteristics of these plants, which actively form or support voltage and frequency references and participate in the sending-end grid support through virtual inertia, damping control, reactive power-voltage droop control, virtual impedance, and fault ride-through control. Through this differentiated modeling, the sending-end grid evaluation model includes both grid-connected and grid-connected renewable energy power plants, with grid-connected plants equivalent to controlled current source characteristics and grid-connected plants equivalent to controlled voltage source characteristics. This provides a model foundation for subsequent evaluations of system strength, frequency, voltage, transient stability, small disturbance stability, and fault recovery.

[0036] For example, after reading the wind power capacity, photovoltaic capacity, energy storage capacity, grid-connected capacity, grid-connected capacity, and total renewable energy capacity of the configuration scheme to be evaluated, the capacity parameters of the renewable energy power plants are obtained. These parameters are then used to determine the proportion of grid-connected capacity, the proportion of grid-connected capacity, and the total installed capacity of the renewable energy power plants. The proportions of grid-connected capacity and grid-connected capacity are used to distinguish between different configuration schemes to be evaluated. The total installed capacity of the renewable energy power plants is used in the calculation of dynamic short-circuit ratio and transmission capacity-related indicators, ensuring that the capacity parameters not only serve as model inputs but also continue to influence subsequent evaluation indicators.

[0037] For example, grid parameters are used to determine the electrical connections in the sending-end power grid evaluation model. After reading the parameters of the collecting lines, step-up transformers, point of common coupling voltage levels, transmission channel parameters, equivalent impedance of the sending-end power grid, and short-circuit capacity, the sending-end power grid grid parameters are obtained. These parameters are then used to determine the electrical connections of the point of common coupling, key collecting buses, sending lines, and transmission channels in the sending-end power grid evaluation model. The resulting electrical connections define both the location for collecting dynamic response data and the location where disturbance scenarios such as point of common coupling faults, sending line disconnections, DC commutation failures, and DC blocking are applied.

[0038] In some embodiments, operating mode parameters and grid-type power plant control parameters serve for operating condition generation and dynamic support characteristic modeling, respectively. After reading the proportion of renewable energy output, the operating capacity of conventional units, load level, transmitted power, reactive power compensation status, and base short-circuit ratio, operating mode parameters are obtained, and typical operating conditions are generated from these parameters. Simultaneously, virtual inertia, damping coefficient, active power-frequency droop parameter, reactive power-voltage droop parameter, virtual impedance parameter, current limiting parameter, and fault ride-through control parameter are read and written into the control model of the grid-type renewable energy power plant. This allows the evaluation model to set the voltage source equivalent characteristics, frequency support characteristics, voltage support characteristics, and fault recovery characteristics of the grid-type renewable energy power plant using the grid-type power plant control parameters. Specifically, virtual inertia and active power-frequency droop parameter affect the frequency change rate, frequency minimum point, and frequency recovery time; reactive power-voltage droop parameter and virtual impedance parameter affect voltage drop depth, voltage minimum point, voltage recovery time, and dynamic reactive power support; current limiting parameter and fault ride-through control parameter affect the fault ride-through success status, active power recovery time, and reactive power recovery time.

[0039] As a specific implementation, the sending-end power grid evaluation model can run on a server, workstation, dispatch simulation platform, or renewable energy base grid connection evaluation platform. After receiving the basic parameters, it generates node, branch, transformer, transmission channel, and substation models according to the grid electrical connection relationship, and writes the corresponding equivalent characteristics of grid-connected and grid-structured renewable energy substations into the model parameters. In the evaluation task during the planning phase, dynamic response data is obtained through electromagnetic transient simulation, electromechanical transient simulation, short-circuit calculation, and power flow calculation; in the verification task during the grid connection operation phase, dynamic response data is obtained by combining on-site measurement curves, fault recording data, and online monitoring data.

[0040] In some embodiments, typical operating conditions are generated from the operating mode parameters in the basic parameters to cover the support requirements of the sending-end power grid under different operating boundaries.

[0041] Specifically, based on the combined relationships between the proportion of renewable energy output, the operating capacity of conventional units, load levels, power transmission, and the short-circuit ratio, the following operating conditions are generated: high renewable energy output, low renewable energy output, high power transmission, low short-circuit ratio, low conventional unit operating capacity, and weak grid operation. The high renewable energy output condition reflects the operating state when wind and solar resources are abundant and the transmission channels are near heavy or full load; the low renewable energy output condition reflects the operating state when wind and solar output is low, the number of online equipment is reduced, or support resources are insufficient; the high power transmission condition reflects the state of the sending-end grid when the power of the transmission channels is close to the operating limit; the low short-circuit ratio and weak grid operation conditions reflect the operating state when the voltage stiffness of the sending-end grid is insufficient and impedance coupling is enhanced; and the low conventional unit operating capacity condition reflects the operating state where renewable energy power plants assume supporting functions after the synchronous machine inertia and short-circuit capacity decrease.

[0042] As an exemplary implementation, multi-source disturbance scenarios are generated from the point of common coupling (PCC), sending lines, transmission channels, and the grid-connected status of renewable energy plants. The sending-end grid evaluation model sets fault location, fault type, fault duration, line disconnection targets, DC channel disturbance events, renewable energy output changes, load changes, and plant disconnection status, thereby generating PCC three-phase short-circuit faults, single-phase ground faults, sending line disconnection, DC commutation failure, DC blocking, renewable energy output surges, load disturbances, and renewable energy plant disconnection scenarios. For example, by setting a PCC three-phase short-circuit fault, the system's fault ride-through and dynamic reactive power support characteristics during voltage dips can be tested; by simulating sending line disconnection, the system's transient stability support capability during power flow transfer can be evaluated; furthermore, after triggering a DC blocking disturbance, the frequency and voltage recovery and system stability level under large disturbances in the transmission channel can be verified.

[0043] For example, to provide a unified basis for comparison among different configuration schemes to be evaluated, typical operating conditions and multi-source disturbance scenarios are cross-combined. Each combination of a typical operating condition and each multi-source disturbance scenario forms an element in the evaluation scenario matrix. This element serves as a condition for calling the sending-end power grid evaluation model, and the corresponding disturbance is applied under the appropriate operating mode. Based on this, the cross-combination of each typical operating condition and each multi-source disturbance scenario forms the evaluation scenario matrix, which is then used as the data acquisition condition for simulation, calculation, or measurement. Thus, different grid configurations, different access locations, different control parameters, and different equipment configuration schemes all acquire dynamic response data under the same scenario matrix, avoiding biased evaluation results derived only under a single operating condition or a single fault.

[0044] In some embodiments, dynamic response data is time-series data and state results obtained from the sending-end power grid evaluation model after the evaluation scenario matrix is ​​loaded. For each combination of operating conditions and disturbance scenarios in the evaluation scenario matrix, electromagnetic transient simulation, electromechanical transient simulation, short-circuit calculation, power flow calculation, or field measurement are used to obtain data such as the point of common coupling voltage curve, key collecting bus voltage curve, system frequency curve, active power curve of grid-connected renewable energy power plants, reactive power curve of grid-connected renewable energy power plants, power flow curve of outgoing lines, power oscillation curve, fault ride-through status, and post-fault recovery process. The above dynamic response data is stored according to a unified time base, rated value base, scenario number, and configuration scheme number to be evaluated, so that the feature extraction results under different schemes, different operating conditions, and different disturbances are comparable.

[0045] As a specific implementation, system strength evaluation requires combining voltage response and short-circuit calculation results. Short-circuit capacity, voltage support changes, and weak grid strength changes are identified from the point of common coupling voltage curve, key bus voltage curve, and short-circuit calculation results. System strength characteristics are then extracted from these sources. These characteristics include short-circuit ratio, effective short-circuit ratio, multi-infeed short-circuit ratio, generalized short-circuit ratio, and dynamic short-circuit ratio. The dynamic short-circuit ratio further considers the impact of the voltage source equivalent characteristics, equivalent internal impedance, and virtual impedance of grid-connected renewable energy power plants on system strength during faults.

[0046] For example, the dynamic short-circuit ratio incorporates the ability of grid-connected renewable energy power plants to provide voltage source support during disturbances into system strength evaluation. The equivalent short-circuit capacity during a fault or disturbance is determined based on the equivalent internal impedance, virtual impedance, and equivalent impedance of the sending-end grid of the grid-connected renewable energy power plant, and the dynamic short-circuit ratio is calculated in conjunction with the total installed capacity of the renewable energy power plant. Accordingly, the dynamic short-circuit ratio is determined by the system short-circuit capacity taking into account the equivalent internal impedance and virtual impedance of the grid-connected renewable energy power plant, as well as the total installed capacity of the renewable energy power plant, and is used as a system strength support capability indicator and a core safety indicator in the support capability evaluation. The dynamic short-circuit ratio is determined according to the following formula: ; in, For dynamic short-circuit ratio, To account for the system short-circuit capacity after considering the equivalent internal impedance and virtual impedance of grid-type renewable energy power stations, This refers to the total installed capacity of new energy power plants. This indicator is used in the system strength support capacity scoring and is compared with the lower limit of the dynamic short-circuit ratio in the determination of the safety baseline.

[0047] In some embodiments, frequency support evaluation is reflected by the system frequency curve. To quantify the inertia support, active-frequency droop response, and frequency recovery capability of grid-connected renewable energy power stations after a disturbance, the processor takes the system frequency curve after the disturbance as input and extracts the frequency change rate, minimum frequency value, frequency deviation degree, and frequency recovery process during the disturbance. Specifically, the processor reads the system frequency function along the time axis. The frequency support features are extracted from the system frequency curve, including the maximum frequency change rate, the lowest frequency point, the maximum frequency deviation, and the frequency recovery time determined by the system frequency curve.

[0048] As a specific implementation, the maximum rate of frequency change is used to characterize the fastest degree of frequency change in the system after a disturbance. Its value is determined by the first-order rate of change of the frequency curve with respect to time, and can be calculated according to the following formula: ; in, The maximum rate of change of frequency. for The system frequency at any given time. This represents the time variable following the disturbance. A larger value indicates a faster frequency change after the disturbance, and a weaker equivalent inertia support; a smaller value indicates a stronger ability of the grid-type renewable energy power plants and the sending-end power grid to suppress frequency mutations.

[0049] As an example implementation, frequency recovery time characterizes the time required for the system frequency to return to and remain within the permissible frequency range after a disturbance. The processor is defined by the time the fault or disturbance occurs. Starting from this point, determine the system frequency. Relative to the rated frequency or the steady-state frequency before the disturbance Does the deviation fall within the allowable frequency deviation range? Within this timeframe, the earliest time after this condition is met is taken as the frequency recovery time, which can be determined according to the following formula: ; in, Frequency recovery time, The time when the fault or disturbance occurs. The rated frequency or the steady-state frequency before the disturbance. To allow for frequency deviation, The time variables are used to determine the frequency maintenance status after recovery. The maximum frequency change rate, the lowest frequency point, the maximum frequency deviation, and the frequency recovery time obtained through the above calculations can continue to be used as cost-type or benefit-type indicators in subsequent dimensionless processing and comprehensive support score calculations, thereby establishing a data connection between the frequency support evaluation and the comprehensive evaluation results.

[0050] As an exemplary implementation, voltage support evaluation combines voltage and reactive power curves. It identifies the voltage sag during a fault, the voltage minimum, and the recovery time after fault clearance from the point of common coupling (PCC) voltage curve and the critical bus voltage curve. Furthermore, it extracts rapid reactive power output changes during disturbances from the reactive power curves of grid-connected renewable energy power plants. Finally, it extracts voltage support-related features from the PCC, critical bus voltage, and grid-connected renewable energy power plant reactive power curves. Specifically, these features include the transient voltage sag depth, voltage minimum, and voltage recovery time determined by the voltage curves, and the dynamic reactive power support quantity determined by the reactive power curves of grid-connected renewable energy power plants. Therefore, voltage support evaluation not only focuses on whether the bus voltage exceeds limits but also considers the contribution of grid-connected renewable energy power plants to voltage sag suppression and post-fault voltage recovery based on dynamic reactive power output.

[0051] For example, transient stability and small-disturbance stability assessments are based on dynamic changes in power and power flow. The power swing amplitude, oscillation period, oscillation decay process, and stability recovery time after a disturbance are identified from the active power curves, power flow curves of transmission lines, and power oscillation curves of grid-connected renewable energy power plants. Transient stability and small-disturbance stability characteristics are then extracted from these curves. Specifically, these characteristics include the maximum amplitude of power oscillation, damping ratio, oscillation frequency, and oscillation convergence time determined by the power oscillation curve, and the critical clearing time determined by fault duration scanning. The damping ratio and oscillation frequency can be obtained through Proni mode identification of the power oscillation curve, power angle curve, or voltage curve; the critical clearing time is determined by changing the fault duration at the same fault location and judging whether the system returns to stability after the fault is cleared.

[0052] In some embodiments, fault ride-through and recovery evaluation is jointly determined by the grid connection status during the fault and the power recovery process after the fault is cleared. After reading the fault ride-through status and post-fault recovery process data, it is determined whether the renewable energy power station maintains grid connection during low voltage, high voltage, or other disturbances, and the time required for active and reactive power to recover to the specified range is calculated. Then, fault ride-through and recovery-related features are extracted from the fault ride-through status and post-fault recovery process data. Specifically, fault ride-through and recovery-related features include the successful fault ride-through status, active power recovery time, and reactive power recovery time. These features, along with voltage support features and frequency support features, reflect the recovery quality during and after the fault.

[0053] As a specific implementation, the transmission capacity evaluation is conducted under the premise of meeting the safety baseline. Power flow changes, channel utilization, and maximum transmission levels under different operating conditions and disturbance scenarios are obtained from the power flow curves of the transmission lines. These are then combined with the safety baseline determination results to screen out operating points that exceed frequency, voltage, or stability limits. Transmission capacity characteristics are then extracted from the power flow curves and the safety baseline determination results. These characteristics include maximum transmittable power, transmission channel utilization, renewable energy absorption level, stable transmission margin, and transmission capacity improvement rate. Because these transmission capacity characteristics are used in conjunction with the safety baseline determination results, the evaluation process will not misjudge schemes with higher transmission power but less than satisfactory safety conditions as having superior support capabilities.

[0054] In some embodiments, after the multidimensional support features are extracted, they are mapped to support capability evaluation indicators. To unify the evaluation direction of different physical quantities, features whose values ​​increase, representing enhanced support capability, are categorized into benefit-type indicators. Specifically, multidimensional support features whose support capability increases with increasing indicator values ​​are mapped to benefit-type indicators. Benefit-type indicators include dynamic short-circuit ratio, lowest frequency point, critical clearing time, damping ratio, fault ride-through success rate, maximum transmittable power, and transmission capacity improvement rate. When the values ​​of these indicators increase, it typically corresponds to improved system strength, increased frequency safety margin, enhanced transient stability margin, improved oscillation attenuation capability, or improved transmission capability.

[0055] As an example implementation, the characteristic quantity representing enhanced support capability with a decrease in value is categorized into cost-type indicators. Specifically, this is manifested by mapping the multi-dimensional support characteristic quantity with enhanced support capability when the indicator value decreases to cost-type indicators. Cost-type indicators include maximum frequency change rate, maximum frequency deviation, voltage drop depth, voltage recovery time, frequency recovery time, maximum power oscillation amplitude, and post-fault recovery time. When the values ​​of these indicators decrease, it typically corresponds to a reduction in disturbance impact, faster frequency and voltage recovery, a decrease in oscillation amplitude, and enhanced post-fault recovery capability.

[0056] For example, the proportion of grid-type capacity, virtual inertia, damping coefficient, reactive power-voltage droop parameter, virtual impedance parameter, and steady-state voltage deviation should not be evaluated simply by their larger or smaller values. Reasonable ranges should be set for these parameters, and the support effect should be evaluated according to the degree of deviation between the index value and the reasonable range. Specifically, this involves mapping multi-dimensional support characteristic quantities or basic parameters that meet the support effect requirements when the index value is within the preset reasonable range to range-based indices. When the proportion of grid-type capacity is lower than the reasonable range, the support capacity is insufficient; when the proportion of grid-type capacity is higher than the reasonable range, equipment investment and the complexity of multi-machine coordination increase. Similarly, virtual inertia, damping coefficient, reactive power-voltage droop parameter, and virtual impedance parameter all reflect their impact on dynamic support and control coordination through range-based evaluation.

[0057] In some embodiments, after the support capability evaluation indicators are formed, the processor performs a unified scale transformation on indicators with different dimensions, numerical ranges, and evaluation directions. To enable different configuration schemes to be evaluated to participate in the comprehensive scoring under the same evaluation scale, the processor constructs an original evaluation matrix under the evaluation scenario matrix. ,in, The number of configuration options to be evaluated. To support the number of capability evaluation indicators, For the first The configuration scheme to be evaluated is in the first The processor performs positive dimensionless processing on the benefit-type indicators, reverse dimensionless processing on the cost-type indicators, and interval scoring on the interval-type indicators according to their deviation from the preset reasonable interval, thereby obtaining standardized indicator scores.

[0058] As a specific implementation, the benefit-type indicator corresponds to an evaluation indicator whose larger value indicates stronger support capability. The processor targets the first... Individual benefit indicators, in Determine the maximum and minimum values ​​from the original index values ​​corresponding to each configuration scheme to be evaluated, and calculate the i-th value according to the following formula. Standardized metric scores for each configuration scheme to be evaluated: ; in, For the first The configuration scheme to be evaluated is in the first Standardized indicator scores under each supporting capability evaluation indicator For the first The configuration scheme to be evaluated is in the first The original index values ​​under each supporting capability evaluation indicator For the first Each supporting capability evaluation indicator is in The maximum value among the configuration schemes to be evaluated. For the first Each supporting capability evaluation indicator is in The minimum value among the configuration options to be evaluated.

[0059] As an example implementation, cost-related metrics are evaluation metrics where a smaller value indicates stronger support capability. The processor targets the first... A cost-type indicator, in Determine the maximum and minimum values ​​from the original index values ​​corresponding to each configuration scheme to be evaluated, and calculate the i-th value according to the following formula. Standardized metric scores for each configuration scheme to be evaluated: ; in, For the first The configuration scheme to be evaluated is in the first Standardized indicator scores under each supporting capability evaluation indicator For the first The configuration scheme to be evaluated is in the first The original index values ​​under each supporting capability evaluation indicator and The first Each supporting capability evaluation indicator is in The maximum and minimum values ​​among the configuration schemes to be evaluated.

[0060] For example, interval-type indicators are evaluation indicators where the support effect meets requirements when the indicator value is within a preset reasonable range, and the support effect decreases when it deviates from the reasonable range. For interval-type indicators such as network capacity ratio, virtual inertia, damping coefficient, reactive power-voltage droop parameter, virtual impedance parameter, and steady-state voltage deviation, the processor first reads the preset reasonable range corresponding to the indicator, and then determines whether the original indicator value falls within the preset reasonable range. When the original indicator value is within the preset reasonable range, the corresponding standardized indicator score remains at a high level; when the original indicator value deviates from the preset reasonable range, the corresponding standardized indicator score decreases with the degree of deviation. Therefore, interval-type indicators are not simply treated as "the larger the better" or "the smaller the better," but rather participate in subsequent combination weighting and comprehensive support score calculations according to the reasonable range of the project.

[0061] As a specific implementation, the combined weighting considers both engineering experience and data differences. A target layer, criterion layer, and indicator layer are constructed using the Analytic Hierarchy Process (AHP). The comprehensive evaluation of the grid-connected renewable energy power station's support capacity for the sending-end power grid is used as the target layer; system strength, frequency support, voltage support, transient stability, small-disturbance stability, fault recovery, and transmission capacity are used as the criterion layer; and the evaluation indicators for each support capacity are used as the indicator layer. Subjective weights are determined through pairwise comparisons and consistency checks by experts. Simultaneously, the entropy weight method is used to calculate the data differences of different configuration schemes under evaluation on the same indicator. The more significant the indicator difference, the stronger its ability to distinguish different configuration schemes, and the higher its objective weight. After the above processing, the AHP is used to determine the subjective weights reflecting the engineering safety and stability requirements, and the entropy weight method is used to determine the objective weights reflecting the data differences of different configuration schemes under evaluation. The subjective and objective weights are then merged to obtain the combined weights, which are used as inputs for calculating the comprehensive support score.

[0062] For example, the combined weights can be determined using a linear weighting method: ; in, For the first The combined weights of the evaluation indicators For the first The subjective weight of each evaluation indicator, For the first The objective weight of each evaluation indicator, and Here is the weighted fusion coefficient, and When the evaluation task is biased towards grid connection safety verification or operational safety evaluation, the subjective weight fusion coefficient is taken as a larger value, so that safety-related indicators such as system strength, frequency support, voltage support, transient stability and fault ride-through have higher weights; when the evaluation task is biased towards planning scheme comparison, under the premise of meeting the safety baseline constraints, the objective weight can enhance the evaluation results' reflection of the differences in data of different configuration schemes.

[0063] In some embodiments, the safety baseline determination precedes the final level output to avoid high scores on non-safety indicators masking grid safety risks. Core safety indicators are selected from the supporting capability evaluation indicators and compared with their corresponding engineering limits. Specifically, the lower limit of dynamic short-circuit ratio, the upper limit of maximum frequency change rate, the lower limit of minimum frequency point, the lower limit of minimum voltage point, the upper limit of voltage recovery time, the lower limit of critical clearing time, the lower limit of damping ratio, and the fault ride-through success condition are used as the determination conditions for core safety indicators. These determination conditions respectively cover the minimum requirements for system strength, frequency safety, voltage safety, transient stability, small disturbance stability, and fault ride-through capability.

[0064] As an exemplary implementation, the safety baseline determination result directly affects the qualification of the configuration scheme under evaluation. Each core safety indicator is compared with its corresponding judgment condition. When the dynamic short-circuit ratio is below the lower limit, the maximum frequency change rate is above the upper limit, the lowest frequency point is below the lower limit, the lowest voltage point is below the lower limit, the voltage recovery time is above the upper limit, the critical clearing time is below the lower limit, the damping ratio is below the lower limit, or the fault ride-through condition is not met, the corresponding configuration scheme under evaluation is judged as unqualified, and the core safety indicator that does not meet the corresponding judgment condition is output as an over-limit indicator. Therefore, the comprehensive evaluation will not ignore over-limit core safety indicators due to economic efficiency, transmission capacity, or high scores on local indicators.

[0065] For example, when all core security indicators meet the judgment criteria, a comprehensive support score is calculated based on the standardized indicator scores and combined weights, and the support capability level is output according to the interval to which the comprehensive support score belongs. Specifically, when all core security indicators meet the corresponding judgment criteria, a comprehensive support score is calculated based on the standardized indicator scores and combined weights, and the support capability level is output according to the comprehensive support score. The comprehensive support score is determined according to the following formula: ; in, For the first The overall support score of each configuration scheme to be evaluated. For the first Each safety baseline constraint corresponds to a safety constraint control multiplier, which is used when the corresponding safety baseline constraint meets the requirements. When the corresponding safety baseline constraint does not meet the requirements , To ensure a safety baseline, the quantity is limited. To evaluate the number of indicators, For the first The combined weights of the evaluation indicators For the first The configuration scheme to be evaluated is in the first The standardized index scores under each evaluation indicator are calculated in this way. When any safety baseline constraint is not met, the product of the safety constraint control multipliers is zero; when all safety baseline constraints are met, the comprehensive support score is obtained by weighting the scores of each standardized index and the combined weights.

[0066] In some embodiments, weak links are determined jointly by over-limit indicators and sub-item scores. After reading the safety baseline judgment result and the sub-item scores of each support capability evaluation indicator, weak links are determined based on the over-limit indicators or support capability evaluation indicators that are lower than the preset sub-item score thresholds. When the over-limit indicator is the dynamic short-circuit ratio, the weak link corresponds to insufficient system strength support; when the low sub-items are concentrated in the maximum frequency change rate, the lowest frequency point, or the frequency recovery time, the weak link corresponds to insufficient frequency support; when the low sub-items are concentrated in the voltage drop depth, the lowest voltage point, the voltage recovery time, or the dynamic reactive power support, the weak link corresponds to insufficient voltage support; when the low sub-items are concentrated in the critical clearing time, the maximum amplitude of power oscillation, the damping ratio, or the oscillation convergence time, the weak link corresponds to insufficient transient stability or small disturbance stability support; when the fault ride-through success status, active power recovery time, or reactive power recovery time does not meet the requirements, the weak link corresponds to insufficient fault ride-through and recovery capabilities.

[0067] As a specific implementation, the sending-end power grid of a large-scale new energy base is used as the evaluation object. This sending-end power grid includes multiple wind farms, photovoltaic power stations, energy storage systems, collection lines, step-up transformers, points of common coupling, AC transmission channels, equivalent receiving-end power grids, reactive power compensation equipment, and some conventional generating units. After reading the grid structure parameters, capacity parameters, operating mode parameters, and grid-type power station control parameters of the new energy base, the server establishes a sending-end power grid evaluation model that includes both a grid-following control model and a grid-type control model. The grid-following control model reflects the characteristics of controlled current sources, while the grid-type control model reflects the characteristics of controlled voltage sources, and parameters such as virtual inertia, droop control, virtual impedance, current limiting, and fault ride-through control are written into it.

[0068] As an exemplary implementation, the evaluation model for the power grid at the sending end of this new energy base selects typical operating conditions such as high output of new energy, low output of new energy, high power transmission, and low short-circuit ratio. It also sets up multi-source disturbance scenarios, including three-phase short-circuit faults at the point of common coupling, AC line disconnection, DC power disturbances, new energy output fluctuations, and load disturbances. The typical operating conditions and multi-source disturbance scenarios are combined to form an evaluation scenario matrix. For each scenario combination, electromechanical transient simulations or electromagnetic transient simulations are conducted to obtain dynamic response data such as voltage, frequency, active power, reactive power, power flow, and power oscillations at the point of common coupling, key collecting buses, near-area buses of the transmission channel, and grid-type new energy power plants.

[0069] For example, the aforementioned dynamic response data is further converted into support capability evaluation indicators. The dynamic short-circuit ratio is extracted from the short-circuit calculation results and voltage curve; the maximum frequency change rate, minimum frequency point, maximum frequency deviation, and frequency recovery time are extracted from the frequency curve; the minimum voltage point, voltage drop depth, voltage recovery time, and dynamic reactive power support are extracted from the voltage curve and reactive power curve; the critical clearing time, maximum power oscillation amplitude, damping ratio, and oscillation convergence time are extracted from the power oscillation curve and fault scan results; and the successful fault ride-through status, active power recovery time, and reactive power recovery time are extracted from the fault ride-through status and recovery process data. These indicators are categorized into benefit-type indicators, cost-type indicators, and interval-type indicators, respectively. After dimensionless processing and combined weighting, combined with the safety baseline determination, a comprehensive support score, support capability level, sub-item evaluation results, exceeding limits indicators, and weak links are output.

[0070] In some embodiments, multiple grid-type configuration schemes can be compared under the same sending-end grid evaluation model. In the original scheme, renewable energy power plants mainly adopt grid-following control. In the grid-type configuration scheme, some wind farms, photovoltaic power plants, or energy storage units are configured for grid-type control, and the proportion of grid-type capacity is gradually increased. All schemes maintain the same grid structure, load level, transmitted power, and disturbance scenarios, only changing the proportion of grid-type capacity, the grid-type configuration objects, the access location, or the control parameters. Dynamic response data for each scheme are obtained under the same evaluation scenario matrix, and system strength, frequency support, voltage support, transient stability, small disturbance stability, fault recovery, and transmission capacity-related indicators are calculated. Therefore, the differences between different configuration schemes are compared through multi-dimensional dynamic support capabilities under a unified scenario, rather than judging solely based on capacity size or a single steady-state indicator.

[0071] For example, in a three-phase short-circuit fault scenario at the same point of common coupling, a certain grid configuration scheme, compared to the original scheme, improves the dynamic short-circuit ratio and the voltage minimum point, shortens the voltage recovery time, and increases the dynamic reactive power support. Therefore, this scheme has a higher score in the system strength and voltage support dimensions. In scenarios involving load disturbances or sudden changes in renewable energy output, a certain grid configuration scheme reduces the maximum frequency change rate, raises the frequency minimum point, and shortens the frequency recovery time. Therefore, this scheme has a higher score in the frequency support dimension. In scenarios involving transmission line disconnection or DC blocking, a certain scheme improves the critical disconnection time, reduces the maximum amplitude of power oscillation, and improves the damping ratio. Therefore, this scheme has a higher score in the transient stability and small disturbance stability dimensions. The above sub-scores are weighted and combined to form a comprehensive support score. However, if any core safety indicator fails to meet the safety baseline judgment condition, the scheme is still judged as unqualified.

[0072] As a specific implementation, the evaluation results can also be used for engineering feedback. After outputting the comprehensive support score and support capability level, the system also outputs the scores for each evaluation dimension, the evaluation results for each operating condition and disturbance scenario, the reasons for exceeding the safety baseline indicators, the ranking of different configuration schemes to be evaluated, and the weak links.

[0073] When the system strength support capability score is low, the evaluation results indicate that the short-circuit capacity of the sending-end power grid is insufficient, the proportion of grid-type capacity is insufficient, or the grid-type new energy power station access location does not have an obvious support effect on the key bus. In the future, the layout location of the grid-type new energy power station can be adjusted or the grid-type capacity near the key node can be increased.

[0074] When the frequency support capability score is low, the evaluation results indicate that the frequency change rate after disturbance is too large, the minimum frequency point is too low, or the frequency recovery time is too long. In the future, virtual inertia, damping coefficient, active power-frequency droop parameter, or the active power rapid response capability of grid-type energy storage can be optimized.

[0075] When the voltage support capability score is low, the evaluation results indicate that the voltage drop is deep during the fault, the voltage recovery is slow after the fault, or the dynamic reactive power support is insufficient. In the future, the reactive power-voltage droop parameters and virtual impedance parameters can be optimized, and the configuration of static var generators, synchronous condensers or other dynamic reactive power support equipment can be coordinated.

[0076] When the transient stability or small disturbance stability support capability score is low, the evaluation results indicate insufficient synchronization maintenance capability after a fault, weak oscillation damping, or control coupling risk. Subsequently, the grid-type control parameters can be readjusted, and the control coordination relationship between the grid-type new energy power station, the grid-type new energy power station, the energy storage system, and the reactive power compensation equipment can be reviewed.

[0077] This application also provides system embodiments that follow the above embodiments, for implementing the method steps described in the above embodiments. The interpretation of the same names is the same as that in the above embodiments, and they have the same technical effects as those in the above embodiments, so they will not be repeated here.

[0078] Figure 3 A block diagram of a grid support capability assessment system 300 for grid-connected renewable energy power plants according to an embodiment of this application is shown.

[0079] The model building module 301 is used to obtain the basic parameters of the sending-end power grid of the new energy base under the configuration scheme to be evaluated, and to build an evaluation model of the sending-end power grid including grid-connected new energy power stations and grid-connected new energy power stations based on the basic parameters. Among them, grid-connected new energy power stations are equivalent to controlled current source characteristics, and grid-connected new energy power stations are equivalent to controlled voltage source characteristics.

[0080] The scenario loading module 302 is used to generate typical operating conditions and multi-source disturbance scenarios based on basic parameters, and cross-load the typical operating conditions and multi-source disturbance scenarios into the sending-end power grid evaluation model to obtain dynamic response data.

[0081] The feature mapping module 303 is used to extract multidimensional support features from dynamic response data and map the multidimensional support features into support capability evaluation indicators.

[0082] The indicator processing module 304 is used to perform dimensionless processing and combined weighting on the support capability evaluation indicators, and to determine the safety baseline based on the core safety indicators in the support capability evaluation indicators.

[0083] The evaluation output module 305 is used to output a comprehensive support score, support capability level, and weak links based on the safety baseline judgment result, dimensionless processing result, and combined weighting result.

[0084] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0085] Although the operations are described in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all of the operations shown to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0086] The methods and systems of this application can be implemented using standard programming techniques, utilizing rule-based logic or other logic to implement various method steps. It should also be noted that the terms "system" and "module" as used herein and in the claims are intended to include implementations using one or more lines of software code and / or hardware implementations and / or devices for receiving input.

[0087] Any step, operation, or procedure described herein may be performed or implemented using one or more hardware or software modules, either alone or in combination with other devices. In one embodiment, the software module is implemented using a computer program product comprising a computer-readable medium containing computer program code, which is executable by a computer to perform any or all of the described steps, operations, or procedures.

[0088] The foregoing description of implementations of this application has been provided for illustrative and descriptive purposes. The foregoing description is not exhaustive and is not intended to limit this application to the exact forms disclosed. Various modifications and variations may exist in accordance with the foregoing teachings, or may arise from practice of this application. These embodiments were chosen and described to illustrate the principles of this application and its practical application, enabling those skilled in the art to utilize this application in various implementations and modifications to suit the specific purpose of the concept.

[0089] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0090] It can be further understood that, unless otherwise specified, "connection" includes both direct connections where no other components exist between the two parties and indirect connections where other components exist between them.

[0091] It is further understood that although the operations are described in a specific order in the accompanying drawings in the embodiments of this application, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all the operations shown to be performed to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0092] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the field of this application that are not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0093] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

[0094] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for assessing the grid support capability of grid-connected renewable energy power plants, characterized in that, include: Obtain the basic parameters of the sending-end power grid of the new energy base under the configuration scheme to be evaluated, and establish an evaluation model of the sending-end power grid including grid-connected new energy power stations and grid-connected new energy power stations based on the basic parameters; wherein, the grid-connected new energy power stations are equivalent to controlled current source characteristics, and the grid-connected new energy power stations are equivalent to controlled voltage source characteristics; Based on the aforementioned basic parameters, typical operating conditions and multi-source disturbance scenarios are generated, and the typical operating conditions and multi-source disturbance scenarios are cross-loaded into the sending-end power grid evaluation model to obtain dynamic response data. Multidimensional support features are extracted from the dynamic response data, and the multidimensional support features are mapped to support capability evaluation indicators. The support capability evaluation indicators are processed into dimensionless values ​​and combined with weights, and the safety baseline is determined based on the core safety indicators in the support capability evaluation indicators. Based on the safety baseline determination results, dimensionless processing results, and combined weighting results, the comprehensive support score, support capability level, and weak links are output.

2. The method according to claim 1, characterized in that, The acquisition of basic parameters of the power grid at the sending end of the new energy base under the configuration scheme to be evaluated includes: Obtain the capacity parameters of new energy power stations, and determine the grid-connected capacity ratio, the grid-following capacity ratio, and the total installed capacity of new energy power stations based on the new energy power station capacity parameters; Obtain the grid parameters of the sending-end power grid, and determine the electrical connection relationships of the common connection point, key collection bus, transmission line and external transmission channel in the sending-end power grid evaluation model based on the grid parameters of the sending-end power grid; Obtain the operating mode parameters, and generate the typical operating conditions from the operating mode parameters; The control parameters of the grid-type power station are obtained, and the voltage source equivalent characteristics, frequency support characteristics, voltage support characteristics and fault recovery characteristics of the grid-type new energy power station are set by the control parameters of the grid-type power station.

3. The method according to claim 1, characterized in that, The process involves generating typical operating conditions and multi-source disturbance scenarios based on the fundamental parameters, and then cross-loading these typical operating conditions and multi-source disturbance scenarios into the sending-end power grid evaluation model to obtain dynamic response data, including: Based on the proportion of renewable energy output, the operating capacity of conventional units, load level, power transmission, and basic short-circuit ratio, the following operating conditions are generated: renewable energy high output, renewable energy low output, high power transmission, low short-circuit ratio, conventional unit low operating capacity, and weak grid operation. Based on the common connection point, transmission lines, external transmission channels, and grid connection status of new energy power plants, scenarios such as three-phase short-circuit fault at the common connection point, single-phase ground fault, transmission line disconnection, DC commutation failure, DC blocking, sudden change in new energy output, load disturbance, and grid disconnection of new energy power plants are generated. The typical operating conditions and the multi-source disturbance scenarios are cross-combined to form an evaluation scenario matrix, and the evaluation scenario matrix is ​​used as the data acquisition condition for simulation, calculation or measurement.

4. The method according to claim 1, characterized in that, The multidimensional support features include system strength features, frequency support features, voltage support features, transient stability and small disturbance stability features, fault ride-through and recovery features, and transmission capacity features. The extraction of multidimensional support features from the dynamic response data includes: System strength features are extracted from the voltage curves at the point of common coupling, the voltage curves at key busbars, and the short-circuit calculation results. Extract frequency-supporting features from the system frequency curve; Voltage support features are extracted from the voltage curves of the point of common coupling, the voltage curves of key busbars, and the reactive power curves of grid-type renewable energy power plants. Transient stability and small disturbance stability characteristics are extracted from the active power curve, power flow curve and power oscillation curve of grid-type renewable energy power plants; Extract fault-travel and recovery-related features from fault-travel status and post-fault recovery process data; Extract transmission capacity features from the power flow curves of the transmission lines and the results of the safety baseline determination.

5. The method according to claim 4, characterized in that, The system strength characteristic quantity includes the dynamic short-circuit ratio, which is determined by the system short-circuit capacity after taking into account the equivalent internal impedance and virtual impedance of the grid-type new energy power station and the total installed capacity of the new energy power station, and is used as the system strength support capability index and the core safety index in the support capability evaluation index; The dynamic short-circuit ratio is determined in the following manner: ； in, For dynamic short-circuit ratio, To account for the system short-circuit capacity after considering the equivalent internal impedance and virtual impedance of the grid-type renewable energy power stations, This refers to the total installed capacity of new energy power plants.

6. The method according to claim 4, characterized in that, The frequency support features include the maximum frequency change rate, the lowest frequency point, the maximum frequency deviation, and the frequency recovery time, which are determined by the system frequency curve. The voltage support characteristics include the transient voltage drop depth, voltage minimum point, and voltage recovery time determined by the voltage curve, as well as the dynamic reactive power support quantity determined by the reactive power curve of the grid-type new energy power station. The transient stability and small disturbance stability characteristics include the maximum amplitude of power oscillation, damping ratio, oscillation frequency and oscillation convergence time determined by the power oscillation curve, and the critical clearing time determined by the fault duration scan. The fault ride-through and recovery features include the fault ride-through success status, active power recovery time, and reactive power recovery time.

7. The method according to claim 1, characterized in that, The supporting capability evaluation indicators include benefit-type indicators, cost-type indicators, and interval-type indicators. Mapping the multidimensional supporting characteristics to supporting capability evaluation indicators includes: The multidimensional support characteristics that enhance support capability as the index value increases are mapped to benefit-type indicators, including dynamic short-circuit ratio, lowest frequency point, critical clearing time, damping ratio, fault ride-through success rate, maximum external power transmission capacity, and transmission capacity improvement rate. The multi-dimensional support characteristics that enhance support capability when the index value decreases are mapped to cost-type indicators, which include maximum frequency change rate, maximum frequency deviation, voltage drop depth, voltage recovery time, frequency recovery time, maximum power oscillation amplitude, and post-fault recovery time. The multidimensional support characteristics or basic parameters that meet the support effect requirements when the index value is within a preset reasonable range are mapped to interval-type indices. The interval-type indices include the proportion of grid-type capacity, virtual inertia, damping coefficient, reactive power-voltage droop parameter, virtual impedance parameter, and steady-state voltage deviation.

8. The method according to claim 7, characterized in that, The step of performing dimensionless processing and combined weighting on the support capability evaluation indicators includes: The benefit-type indicators are positively dimensionless, the cost-type indicators are reversely dimensionless, and the interval-type indicators are scored according to their deviation from the preset reasonable interval to obtain standardized indicator scores. The analytic hierarchy process (AHP) is used to determine subjective weights that reflect the safety and stability requirements of the project, and the entropy weight method is used to determine objective weights that reflect the differences in data between different configuration schemes to be evaluated. The subjective weights and the objective weights are then combined to obtain a combined weight, which is used as the input for calculating the comprehensive support score. The combined weights are determined in the following manner: ； in, For the first The combined weights of the evaluation indicators For the first The subjective weight of each evaluation indicator, For the first The objective weight of each evaluation indicator, and Here is the weighted fusion coefficient, and .

9. The method according to claim 8, characterized in that, Based on the safety baseline determination result, the dimensionless processing result, and the combined weighting result, the system outputs a comprehensive support score, support capability level, and weak links, including: The lower limit of dynamic short-circuit ratio, the upper limit of maximum frequency change rate, the lower limit of minimum frequency point, the lower limit of minimum voltage point, the upper limit of voltage recovery time, the lower limit of critical clearing time, the lower limit of damping ratio, and the fault ride-through success condition are used as the judgment conditions for the core safety indicators. When any of the core security indicators fails to meet the corresponding judgment conditions, the corresponding configuration scheme to be evaluated will be judged as unqualified, and the core security indicators that fail to meet the corresponding judgment conditions will be output as over-limit indicators. When all the core security indicators meet the corresponding judgment conditions, a comprehensive support score is calculated based on the standardized indicator scores and the combined weights, and the support capability level is output based on the comprehensive support score. The weak link is determined based on the over-limit index or the support capability evaluation index that is below the preset sub-item scoring threshold; The comprehensive support score is determined in the following manner: ； in, For the first The overall support score of each configuration scheme to be evaluated. For the first Each safety baseline constraint corresponds to a safety constraint control multiplier, which is used when the corresponding safety baseline constraint meets the requirements. When the corresponding safety baseline constraint does not meet the requirements , To ensure a safety baseline, the quantity is limited. To evaluate the number of indicators, For the first The combined weights of the evaluation indicators For the first The configuration scheme to be evaluated is in the first Standardized indicator scores under each evaluation indicator.

10. A grid-support capability assessment system for grid-connected new energy power stations, characterized in that, include: The model building module is used to obtain the basic parameters of the power grid at the sending end of the new energy base under the configuration scheme to be evaluated, and to build an evaluation model of the power grid at the sending end that includes grid-connected new energy power stations and grid-connected new energy power stations based on the basic parameters; wherein, the grid-connected new energy power stations are equivalent to controlled current source characteristics, and the grid-connected new energy power stations are equivalent to controlled voltage source characteristics; The scenario loading module is used to generate typical operating conditions and multi-source disturbance scenarios based on the basic parameters, and cross-load the typical operating conditions and the multi-source disturbance scenarios into the sending-end power grid evaluation model to obtain dynamic response data; The feature mapping module is used to extract multidimensional support features from the dynamic response data and map the multidimensional support features into support capability evaluation indicators. The indicator processing module is used to perform dimensionless processing and combined weighting on the support capability evaluation indicators, and to determine the safety baseline based on the core safety indicators in the support capability evaluation indicators. The evaluation output module is used to output a comprehensive support score, support capability level, and weak links based on the safety baseline judgment result, dimensionless processing result, and combined weighting result.

Images