A method and system for multi-dimensional evaluation of direct current carrying capacity of a receiving power grid

Abstract

The embodiment of the application provides a kind of receiving end power grid DC carrying capacity multidimensional evaluation method and system, it is related to new energy power grid technical field.The method is based on the transient short-circuit capacity function of new energy low voltage ride through control characteristic and synchronous machine short-circuit current transient process.Establish the improved short-circuit ratio index again, to pass through the curve characteristics corresponding to the improved short-circuit ratio index extraction, construct voltage intensity comprehensive evaluation function.Then establish the evaluation model of receiving end power grid DC carrying capacity, and according to basic particle swarm algorithm, the evaluation model is executed iteration solution, to obtain evaluation result information.The method can form improved short-circuit ratio index by introducing transient short-circuit capacity function and new energy short-circuit capacity reliability coefficient, for dynamic quantification transient voltage support intensity, and by establishing evaluation model, form efficient evaluation mechanism suitable for high proportion new energy receiving end power grid scene under DC feed-in carrying capacity, improve the accuracy of evaluation result.

Description

Technical Field

[0001] This application relates to the field of new energy power grid technology, and in particular to a multi-dimensional evaluation method and system for the DC carrying capacity of receiving-end power grids. Background Technology

[0002] With the large-scale integration of high-proportion renewable energy sources and high-voltage direct current (HVDC) transmission into the power system, the power system is gradually evolving into a grid structure with significant DC and AC characteristics. For the receiving-end grid, the increased penetration rate of renewable energy leads to a decrease in system inertia, a reduction in short-circuit capacity, and a weakening of voltage support capability, affecting the grid's strength and frequency stability. Therefore, during power system operation and maintenance, it is necessary to conduct power system analysis to assess the DC carrying capacity of the receiving-end grid in order to ensure the safe and stable operation of the grid and optimize grid planning and operation strategies.

[0003] To assess the receiving-end grid's capacity to handle DC feeds, static voltage strength assessment can be performed based on the short-circuit ratio. First, system modeling is used to establish component models of the DC feed lines, renewable energy plants, reactive power compensation devices, and key load nodes. Then, data such as the voltage at each node and the power transmitted by each DC line are acquired to determine the baseline operating point. Next, a quantified grid strength index, the generalized short-circuit ratio, is calculated. Finally, a preset critical value is used as a stability criterion for margin assessment and decision-making.

[0004] However, this method of measuring the carrying capacity of the receiving-end grid, which uses the ratio of system short-circuit capacity to DC rated power as a measure of grid voltage support capacity, is only applicable to traditional power systems dominated by synchronous machines. In receiving-end grids with a high proportion of renewable energy integration, the short-circuit current characteristics of renewable energy units are fundamentally different from those of synchronous machines. Their transient current output during low-voltage ride-through has time-varying and nonlinear characteristics, which means that the constructed measure cannot effectively characterize the dynamic impact of renewable energy transient response on system strength. It cannot accurately reflect the dynamic change process of grid voltage support capacity under renewable energy participation, resulting in low accuracy of the assessment results. Summary of the Invention

[0005] In view of this, embodiments of this application provide a multi-dimensional evaluation method and system for the DC carrying capacity of the receiving-end power grid, in order to solve the problem of low accuracy of the evaluation results of the DC carrying capacity of the receiving-end power grid with a high proportion of new energy access.

[0006] According to a first aspect of this application, a multi-dimensional evaluation method for the DC carrying capacity of a receiving-end power grid is provided, the method comprising: A transient short-circuit capacity function is constructed. The transient short-circuit capacity function is a time-varying function that reflects the short-circuit capacity contribution at different stages, based on the low-voltage ride-through control characteristics of new energy sources and the transient process of short-circuit current of synchronous machines. An improved short-circuit ratio index is defined based on the transient short-circuit capacity function. The improved short-circuit ratio index is formed by introducing the transient short-circuit capacity function and the new energy short-circuit capacity reliability coefficient on the basis of the short-circuit ratio. The improved short-circuit ratio index is used to dynamically quantify the transient voltage support strength. By extracting the curve features corresponding to the improved short-circuit ratio index, a comprehensive voltage intensity evaluation function is constructed. The curve features include the lowest point of the curve, the average value of the curve, and the steady-state value of the curve. An evaluation model for the DC carrying capacity of the receiving-end power grid is established. The constraints of the evaluation model include basic constraints and voltage intensity constraints set according to the voltage intensity comprehensive evaluation function. The evaluation model takes the DC feed-in capacity as the optimization objective, and the objective function of the evaluation model is the maximum power fed into the receiving-end power grid by DC. The evaluation model is iteratively solved using the basic particle swarm optimization algorithm to obtain the evaluation results.

[0007] In some embodiments, constructing a transient short-circuit capacity function includes: Traverse the synchronous generator sets and new energy grid-connected equipment in the receiving-end power grid; According to Thevenin's theorem, the synchronous generator set is equivalent to a voltage source series impedance, and the equivalent potential of the voltage source series impedance is the open-circuit voltage of the converter station node. The aforementioned new energy grid-connected equipment is equivalent to an ideal current source impedance; A simplified equivalent model is constructed for the simultaneous connection of new energy sources and DC power to the receiving-end grid. The simplified equivalent model includes the series impedance of the voltage source and the impedance of the ideal current source. The series impedance of the voltage source and the impedance of the ideal current source are the diagonal elements of the converter station node in the node impedance matrix. The short-circuit capacity is calculated based on the simplified equivalent model, and the short-circuit capacity is used to derive the initial short-circuit ratio considering the short-circuit capacity provided by new energy sources.

[0008] In some embodiments, calculating the short-circuit capacity based on the simplified equivalent model includes: A voltage-current relationship function is established based on the simplified equivalent model, wherein the voltage matrix is ​​the product of the impedance matrix and the current matrix. Based on the voltage-current relationship function, the voltage relationship of the DC feed node; An apparent power relationship is constructed by multiplying both sides of the voltage relationship by a conversion factor, where the conversion factor is the absolute value of the ratio of node voltage to equivalent impedance. Based on the power relationship, the short-circuit capacity provided by the receiving-end power grid is calculated.

[0009] In some embodiments, constructing the transient short-circuit capacity function further includes: Using simulation measurement data, the jump variables of the short-circuit current output of the new energy unit during the low voltage ride-through period are statistically analyzed to calculate the jump variables of the short-circuit current of the new energy unit. The jump variables of the short-circuit current of the new energy unit obtained from multiple simulation time points are collected to form a jump variable distribution set; Based on the set of jump variables, each time period is numbered into multiple intervals in chronological order; Based on the low-voltage ride-through control strategy for new energy sources, the theoretical value of the short-circuit current jump variable in each interval is calculated. The theoretical number of close approaches for each interval in the distribution set of jump variables is counted, and the fluctuation probability of the short-circuit current jump variable in each interval is calculated. The reliability coefficient of the short-circuit capacity of new energy sources is calculated based on the theoretical proximity count and fluctuation probability. The transient short-circuit capacity function of the receiving-end power grid is generated based on the confidence coefficient and the short-circuit capacity.

[0010] In some embodiments, an improved short-circuit ratio index is defined based on the transient short-circuit capacity function, including: Based on the short-circuit capacity, an initial short-circuit ratio considering the short-circuit capacity provided by new energy sources is derived; Obtain the standard specifications for the short-circuit current output during the low-voltage ride-through of new energy generating units; Combining the initial short-circuit ratio, the short-circuit current standard, and the reliability coefficient, an improved short-circuit ratio index considering the low-voltage ride-through characteristics of new energy sources is derived.

[0011] In some embodiments, the method further includes: Acquire the transient components of the synchronous machine and the reactive current output of the low-voltage ride-through control of new energy sources; An expression for the transient short-circuit current of the receiving-end power grid is established, wherein the transient short-circuit current of the receiving-end power grid is used to characterize the transient short-circuit current of the receiving-end power grid as the sum of the transient component of the synchronous machine and the reactive current output by the low-voltage ride-through control of the new energy source; Based on the transient short-circuit current expression of the receiving-end power grid, the response control time delay of the short-circuit transient process of the synchronous generator set and the low-voltage ride-through characteristic of the new energy converter is used to divide the short-circuit current transient process of the receiving-end power grid into an initial impact stage and a decay support stage. The derivatives of the transient short-circuit current expression of the receiving-end power grid are obtained in the initial impact stage and the attenuation support stage, respectively. Based on the derivative results, the transient short-circuit capacity function of the receiving-end power grid is fitted.

[0012] In some embodiments, a comprehensive voltage intensity evaluation function is constructed by extracting the curve features corresponding to the improved short-circuit ratio index, including: An index curve is established based on the improved short-circuit ratio index; Extract the curve features from the index curve; The entropy-weighted TOPSIS decision algorithm is used to construct a comprehensive evaluation value of voltage intensity based on the curve characteristics. By calculating the comprehensive evaluation value of the voltage intensity of each node under different DC power inputs, a comprehensive evaluation function of voltage intensity as DC power changes is fitted. Obtain the evaluation threshold corresponding to the comprehensive evaluation value of voltage intensity, and set voltage intensity constraint conditions according to the comprehensive evaluation function of voltage intensity and the evaluation threshold.

[0013] In some embodiments, an evaluation model for the DC carrying capacity of the receiving-end power grid is established, including: The evaluation model is constructed based on the voltage intensity comprehensive evaluation function; The evaluation model is set with basic constraints, which include at least one of power flow equation constraints, node voltage magnitude constraints, inertia constraints, and frequency stability constraints. With the renewable energy capacity fixed and the DC feed-in capacity as the optimization target, the objective function of the evaluation model is set according to the maximum power of the DC feed-in receiving-end grid.

[0014] In some embodiments, the evaluation model is iteratively solved according to the basic particle swarm optimization algorithm to obtain evaluation result information, including: A particle population is generated based on the evaluation model, the particle population comprising multiple particles; each particle is configured with a particle position and a particle velocity. Construct the particle matrix of the particle population, the particle matrix including the position matrix and velocity matrix of the particles; Based on the particle matrix, perform basic particle swarm iteration to obtain the optimal position, which includes the local optimal position and the global optimal position; Update the particle velocity according to the optimal position, and update the particle position based on the updated particle velocity; The evaluation results are output based on the updated particle positions.

[0015] According to a second aspect of this application, a multi-dimensional evaluation system for the DC carrying capacity of a receiving-end power grid is provided, characterized in that it is applied to the multi-dimensional evaluation method for the DC carrying capacity of a receiving-end power grid described in the first aspect; the system includes: The transient short-circuit capacity function construction module is used to construct a transient short-circuit capacity function. The transient short-circuit capacity function is a time-varying function that reflects the contribution of short-circuit capacity at different stages, based on the low-voltage ride-through control characteristics of new energy sources and the transient process of short-circuit current of synchronous machines. The index definition module is used to define an improved short-circuit ratio index based on the transient short-circuit capacity function. The improved short-circuit ratio index is formed by introducing the transient short-circuit capacity function and the new energy short-circuit capacity reliability coefficient on the basis of the short-circuit ratio. The improved short-circuit ratio index is used to dynamically quantify the transient voltage support strength. The feature extraction module is used to construct a comprehensive voltage intensity evaluation function by extracting the curve features corresponding to the improved short-circuit ratio index. The curve features include the lowest point of the curve, the average value of the curve, and the steady-state value of the curve. The model building module is used to build an evaluation model for the DC carrying capacity of the receiving-end power grid. The constraints of the evaluation model include basic constraints and voltage intensity constraints set according to the voltage intensity comprehensive evaluation function. The evaluation model takes the DC feed-in capacity as the optimization objective, and the objective function of the evaluation model is the maximum power fed into the receiving-end power grid by DC. The iterative solution module is used to perform iterative solutions on the evaluation model according to the basic particle swarm optimization algorithm to obtain evaluation result information.

[0016] According to a third aspect of this application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described multi-dimensional evaluation method for the DC carrying capacity of the receiving-end power grid.

[0017] According to a fourth aspect of this application, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the above-described multi-dimensional evaluation method for the DC carrying capacity of the receiving-end power grid.

[0018] By employing the above technical solutions, this application provides a multi-dimensional evaluation method and system for the DC carrying capacity of the receiving-end power grid. The method establishes a transient short-circuit capacity function that reflects the short-circuit capacity contribution at different stages based on the low-voltage ride-through control characteristics of new energy sources and the transient process of the short-circuit current in the synchronous machine. An improved short-circuit ratio index is defined based on the transient short-circuit capacity function. By extracting the curve features corresponding to the improved short-circuit ratio index, a comprehensive voltage strength evaluation function is constructed. Then, an evaluation model for the DC carrying capacity of the receiving-end power grid is established, and the evaluation model is iteratively solved using the basic particle swarm optimization algorithm to obtain the evaluation results. This method can introduce a transient short-circuit capacity function and a new energy short-circuit capacity reliability coefficient to form an improved short-circuit ratio index based on the short-circuit ratio, used to dynamically quantify transient voltage support strength. By establishing an evaluation model, an efficient evaluation mechanism for DC feed-in carrying capacity applicable to high-proportion new energy receiving-end power grid scenarios is formed, improving the accuracy of the evaluation results and effectively guiding subsequent UHVDC access planning.

[0019] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A schematic diagram of the multi-dimensional evaluation method for DC carrying capacity of the receiving-end power grid provided in this application embodiment; Figure 2 A schematic diagram of the new energy source and DC-fed receiving-end power grid provided in the embodiments of this application; Figure 3 A simplified equivalent model diagram of the receiving-end power grid provided in the embodiments of this application; Figure 4 This is a schematic diagram of the equivalent model of the receiving-end power grid provided in the embodiments of this application; Figure 5 A schematic diagram of the transient short-circuit capacity function curve provided in the embodiments of this application; Figure 6 This is a schematic diagram of low-voltage ride-through curves for different new energy generating units provided in the embodiments of this application; Figure 7 This application provides schematic diagrams of LVRT-ISCR curves for different types of renewable energy generating units at the receiving end of the power grid. Figure 8A schematic diagram of the structure of a multi-dimensional evaluation system for the DC carrying capacity of the receiving-end power grid provided in this application embodiment. Detailed Implementation

[0021] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0022] In this embodiment of the application, the multi-dimensional evaluation method and system for the DC carrying capacity of the receiving-end power grid can be applied to the receiving-end power grid. By evaluating the DC carrying capacity of the receiving-end power grid, adaptive adjustments can be made to the receiving-end power grid to adapt to a high proportion of new energy and high-voltage DC transmission.

[0023] The receiving-end power grid refers to a power grid area capable of receiving external power, with the core task of receiving and consuming external power. To adapt to the characteristics of receiving-end power supply, the receiving-end power grid may include power receiving equipment, power transformation equipment, power transmission and distribution equipment, safety control equipment, voltage support equipment, etc. The power receiving equipment is used to receive and process the input power into locally usable electrical energy. For example, the power receiving equipment may include converter stations or inverter stations. Inverter stations can use converter valves, converter transformers, and other equipment to convert direct current back to alternating current and integrate it into the receiving-end AC power grid.

[0024] Transformer equipment is used to step down high-voltage electricity from converter stations or the upstream power grid and distribute it to various power consumption networks. For example, transformer equipment may include power transformers, switchgear, busbars, etc. Power transformers, such as step-down transformers and on-load tap-changing transformers, perform targeted voltage regulation to adapt to rapid load changes. Switchgear includes circuit breakers, disconnectors, load switches, etc., which can safely disconnect fault currents for maintenance and isolation as needed. Busbars are responsible for collecting and distributing electrical energy.

[0025] Power transmission and distribution equipment can include transmission lines and distribution equipment. Transmission lines can transmit power using AC or DC methods, including overhead lines and power cables of different voltage levels, thereby delivering power to various power grids. Distribution equipment is located at the end of the power grid and can include distribution transformers, switchgear, poles, low-voltage lines, and other distribution components, used to convert the transmitted circuits into the form of electrical energy required by each power branch, ensuring that electrical energy is safely and reliably delivered to the power end.

[0026] Safety control equipment is used to ensure the safe and stable operation of the receiving-end power grid and to prevent faults. Safety control equipment may include a safety and stability control system, relay protection equipment, measurement and control system, and communication system. The safety and stability control system can monitor the power grid status in real time through control devices distributed throughout the network; the relay protection equipment can instantly issue commands to trip circuit breakers when a line or equipment fault occurs, isolating the fault point and preventing the accident from escalating; the measurement and control system and communication system are responsible for real-time measurement, monitoring, automatic control of the power grid, and communication with the dispatch center.

[0027] Voltage support equipment is used to achieve reactive power compensation, ensuring power quality and grid voltage stability. Voltage support equipment can include reactive power compensation devices, synchronous condensers, and local backup power sources. Reactive power compensation devices can suppress voltage fluctuations and flicker through components such as parallel capacitors or reactors, static var compensators (SVCs), and static synchronous compensators (SMCs). Synchronous condensers can supply or absorb reactive power to the system and have extremely strong short-term overload capacity. They can provide strong instantaneous reactive power support during system faults and are very effective in suppressing voltage drops after commutation failures. Local backup power sources can start quickly in emergencies, providing voltage support and rotational inertia to prevent grid collapse due to the loss of large amounts of external power.

[0028] For a receiving-end power grid, the included power receiving equipment, power transformation equipment, power transmission and distribution equipment, safety control equipment, voltage support equipment, and other equipment can be combined to form multiple functional nodes. These nodes refer to points in the receiving-end power grid used to connect different equipment, measure status, and perform mathematical calculations. As points of support for voltage support capacity and points of stability assessment, nodes in the receiving-end power grid can be used to measure key characteristics of whether the power grid can safely absorb external power.

[0029] With the large-scale integration of high-proportion renewable energy sources and high-voltage direct current (HVDC) transmission into the power system, the power system is gradually evolving into a grid structure with significant DC and AC characteristics. For the receiving-end grid, the increased penetration rate of renewable energy leads to a decrease in system inertia, a reduction in short-circuit capacity, and a weakening of voltage support capability, affecting the grid's strength and frequency stability. Therefore, during power system operation and maintenance, it is necessary to conduct power system analysis to assess the DC carrying capacity of the receiving-end grid in order to ensure the safe and stable operation of the grid and optimize grid planning and operation strategies.

[0030] In some embodiments, to assess the DC carrying capacity of the receiving-end power grid, static voltage strength assessment can be performed based on the short-circuit ratio index. The static voltage strength assessment method based on the short-circuit ratio index uses the ratio of system short-circuit capacity to rated DC power as a measure of the grid's voltage support capacity, and is suitable for traditional power systems dominated by synchronous machines.

[0031] DC carrying capacity assessment can also be performed based on frequency safety constraints. The DC carrying capacity assessment method based on frequency safety constraints mainly considers the limitations of system inertia reduction and frequency response capability on DC feed. By establishing a frequency response model, using indicators such as frequency deviation and frequency change rate as constraints, the maximum DC transmission capacity that meets frequency safety requirements is assessed.

[0032] DC carrying capacity can also be comprehensively evaluated based on multi-dimensional indicators. This involves establishing a system of indicators across multiple dimensions, such as voltage strength, static voltage stability, inertia support, and frequency safety, and then using weighted scoring or optimization models for comprehensive evaluation.

[0033] In receiving-end power grids with a high proportion of renewable energy integration, the short-circuit current characteristics of renewable energy units differ fundamentally from those of synchronous machines. Their transient current output during low-voltage ride-through exhibits time-varying and nonlinear characteristics. Furthermore, the DC carrying capacity assessment methods described in the above embodiments still largely employ steady-state or quasi-steady-state indices in their index construction, failing to effectively characterize the dynamic impact of renewable energy transient responses on system strength. Moreover, the steady-state short-circuit ratio cannot accurately reflect the dynamic changes in the grid's voltage support capacity under renewable energy participation, leading to assessment results that are either overly optimistic or conservative.

[0034] For example, in terms of voltage strength indicators, using the short-circuit ratio or its improved form as an evaluation indicator of voltage support capability has significant shortcomings in scenarios with high penetration of new energy sources. Specifically, it fails to take into account the dynamic short-circuit capacity provided by new energy units during low-voltage ride-through; furthermore, it does not consider the temporal and spatial differences in the output of short-circuit current from new energy sources; in addition, this evaluation method cannot characterize the dynamic changes in voltage support strength during transient processes, resulting in evaluation results that cannot truly reflect the voltage stability margin of the system during faults.

[0035] Regarding the evaluation model solution, the DC carrying capacity evaluation method described in the above embodiments uses analytical derivation, enumeration search or simple optimization algorithm for solution. When dealing with high-dimensional, nonlinear, and multi-constraint DC carrying capacity optimization problems, there are problems such as low solution efficiency, easy to get trapped in local optima, and difficulty in guaranteeing the global optimal solution, which limits its application in actual large systems.

[0036] To address the issue of low accuracy in assessing the DC carrying capacity of receiving-end power grids with high proportions of renewable energy integration, this application provides a multi-dimensional assessment method for the DC carrying capacity of receiving-end power grids in some embodiments. This method, based on the shortcomings of assessing voltage intensity in high-proportion renewable energy power grids by considering the short-circuit ratio, analyzes the low-voltage ride-through characteristics of renewable energy and the transient change process of the short-circuit current in the synchronous machine, constructing a transient short-circuit capacity function SSC(t), thereby proposing an improved short-circuit ratio index (LVRT-ISCR) that considers the low-voltage ride-through characteristics (LVRT) of renewable energy. Furthermore, based on the voltage intensity assessment using the LVRT-ISCR short-circuit ratio curve and using the improved short-circuit ratio index (LVRT-ISCR) of renewable energy as a constraint, a DC carrying capacity assessment model suitable for receiving-end power grids with high proportions of renewable energy is established. This forms an efficient assessment algorithm for DC feed-in carrying capacity in scenarios with high proportions of renewable energy receiving-end power grids, enabling scientific and accurate assessment of the receiving-end power grid's carrying capacity for DC feed-in, ensuring the safe and stable operation of the power grid, optimizing power grid planning and operation strategies, and effectively guiding subsequent UHVDC integration planning.

[0037] The method can be applied to electronic devices with data processing capabilities. These electronic devices include, but are not limited to, computers, servers, mobile terminals, smart wearable devices, and industrial control machines. For ease of description, this application embodiment uses an electronic device as the execution subject of the method. It should be understood that the method can also be applied to other types of execution subjects, which are not illustrated in this application embodiment. Figure 1 As shown, the method includes: S101. Construct the transient short-circuit capacity function.

[0038] When conducting a multi-dimensional assessment of the DC carrying capacity of the receiving-end power grid, the supporting role of reactive power reserves of different AC buses in the voltage recovery process after DC commutation failure can be considered. An improved effective short-circuit ratio index based on voltage sensitivity can be used to dynamically quantify the receiving-end power grid's ability to withstand commutation failure. In order to define the improved short-circuit ratio index, it is necessary to first construct a transient short-circuit capacity function. The transient short-circuit capacity function is a time-varying function that reflects the short-circuit capacity contribution at different stages, based on the low-voltage ride-through control characteristics of new energy sources and the transient process of synchronous machine short-circuit current.

[0039] In some embodiments, in order to construct the transient short-circuit capacity function, the synchronous generator sets and new energy grid-connected equipment in the receiving-end power grid can be traversed. According to Thevenin's theorem, the synchronous generator sets are equivalent to voltage source series impedances, and the new energy grid-connected equipment is equivalent to ideal current source impedances. The equivalent potential of the voltage source series impedance is the open-circuit voltage of the converter station node.

[0040] A simplified equivalent model is then constructed for the simultaneous connection of new energy sources and DC power to the receiving-end grid. This simplified equivalent model includes voltage source series impedance and ideal current source impedance; the voltage source series impedance and ideal current source impedance are the diagonal elements of the converter station node in the node impedance matrix. The short-circuit capacity is then calculated based on the simplified equivalent model.

[0041] In power systems dominated by synchronous generators, the system short-circuit capacity only considers the steady-state process of the short-circuit current of the synchronous generators. The short-circuit ratio can be used as one of the important indicators to describe the voltage strength of the receiving-end grid. However, with the gradual increase in the penetration rate of renewable energy, the factor affecting the short-circuit ratio has become the short-circuit capacity of renewable energy. In order to accurately characterize the voltage support strength of the receiving-end grid with a high proportion of renewable energy, it is necessary to take into account the short-circuit capacity of renewable energy. Figure 2 As shown in the figure, a schematic diagram of new energy sources and DC feed-in receiving-end power grid is presented.

[0042] In a power system, according to Thevenin's theorem, a synchronous generator is equivalent to a voltage source in series with an impedance, and its equivalent potential is the open-circuit voltage of the converter station node. Meanwhile, power electronic devices such as renewable energy grid-connected inverters can be equivalent to ideal current sources. The equivalent impedances in both equivalent scenarios are the diagonal elements of the converter station node in the node impedance matrix. When calculating short-circuit capacity, the short-circuit capacity provided by renewable energy units and synchronous generators needs to be discussed separately, and then the total short-circuit capacity of the receiving-end grid is obtained through the superposition theorem. A simplified equivalent model for renewable energy and DC power simultaneously connected to the receiving-end grid is shown below. Figure 3 As shown.

[0043] When calculating short-circuit capacity based on a simplified equivalent model, a voltage-current relationship function can be established first based on the simplified equivalent model. The voltage-current relationship function is used to characterize the voltage matrix as the product of the impedance matrix and the current matrix.

[0044] Next, based on the voltage-current relationship function, the voltage relationship of the DC-feed node is established. Then, by multiplying both sides of the voltage relationship by a conversion factor, an apparent power relationship is constructed, where the conversion factor is the absolute value of the ratio of node voltage to equivalent impedance. Finally, based on the power relationship, the short-circuit capacity provided by the receiving-end grid is calculated.

[0045] For example, according to Figure 2 The simplified equivalent model of the receiving-end power grid shown can be used to obtain the node voltage U. DCi U REj With DC current I i , New energy grid connection point current I j The relationship is:

[0046] In the formula, Z eqii Z is the self-impedance at the i-th DC feed bus; eqijU represents the mutual impedance between the i-th DC feed bus and the j-th renewable energy source connected to the grid; k This indicates the node voltage within the receiving-end power grid.

[0047] The voltage U of the DC-fed node can be obtained from the above equation. DCi for:

[0048] In the formula, I i For the first i The short-circuit current supplied by the DC feed bus. Then let the first... i The actual operating voltage of each DC feed bus node is U i Multiply both sides of the above equation by | U i / Z eqii |It can be obtained that:

[0049] In the formula, S DCi The actual apparent DC power injected into the i-th DC feed bus node; S REj Short-circuit capacity provided for new energy sources; S k Short-circuit capacity provided for the synchronous machine; Ω ij = Z eqij U i / Z eqii U j It is a complex power conversion factor between the DC access bus and the renewable energy grid connection bus, used to reflect the electrical connection between renewable energy and DC.

[0050] Based on the above formula, the short-circuit capacity provided by the receiving-end power grid can be obtained, that is:

[0051] The short-circuit capacity KEYI is used to derive the initial short-circuit ratio considering the short-circuit capacity provided by new energy sources. That is, based on the system short-circuit capacity obtained from the above formula, the improved short-circuit ratio considering the short-circuit capacity provided by new energy sources is derived as follows:

[0052] In the formula, Ω ik S kThe short-circuit capacity provided by the synchronous power supply is determined by the synchronous power supply and the network impedance; P dcN The rated transmission power (MW) of the DC system.

[0053] In new power systems composed of multiple power sources, including synchronous generators, DC feed-in generators, and renewable energy grid-connected systems, the increasing capacity of renewable energy grid connection makes the low inertia and low short-circuit capacity of the receiving-end grid more sensitive to fault disturbances. Furthermore, considering the very short timescale of the short-circuit current characteristics of renewable energy sources, it is necessary to further analyze the transient processes of faults in renewable energy sources and synchronous generators, more accurately characterize the changes in the short-circuit capacity of the receiving-end grid, and continue to improve the short-circuit ratio index.

[0054] In some embodiments, when constructing the transient short-circuit capacity function, simulation measurement data can be used to statistically analyze the jump variables of the short-circuit current output of the renewable energy unit during low-voltage ride-through, in order to calculate the jump variables of the renewable energy unit's short-circuit current. Then, the jump variables of the renewable energy unit's short-circuit current obtained from multiple simulation time points are collected to form a jump variable distribution set. Next, based on the jump variable distribution set, each time period is numbered into multiple intervals in chronological order, and the theoretical value of the short-circuit current jump variable within each interval is calculated according to the renewable energy low-voltage ride-through control strategy.

[0055] By statistically analyzing the theoretical number of close approaches in each interval of the jump variable distribution set and calculating the fluctuation probability of the short-circuit current jump variable in each interval, the reliability coefficient of the new energy short-circuit capacity is calculated based on the theoretical number of close approaches and the fluctuation probability. Then, the transient short-circuit capacity function of the receiving-end power grid is generated based on the reliability coefficient and the short-circuit capacity.

[0056] When considering the system transient short-circuit capacity function SSC(t) during the low-voltage ride-through of renewable energy sources, the short-circuit capacity provided by the renewable energy sources mainly occurs during the transient process of the renewable energy converter entering low-voltage ride-through. When the receiving-end grid is short-circuited, the short-circuit current output by the renewable energy source itself is mainly related to factors such as the voltage drop at the generator terminals, the renewable energy capacity, and the overcurrent multiple limited by the converter. To prevent grid cascading failures caused by renewable energy disconnection, industry standards stipulate the adoption of a low-voltage ride-through control strategy, prioritizing the injection of reactive current to support the grid voltage during the fault period, with the remaining capacity used for active power generation. However, this process is very short-lived, resulting in limited short-circuit capacity provided by the renewable energy sources.

[0057] According to industry standards, the short-circuit current output by new energy generating units during low-voltage ride-through must meet the following requirements:

[0058] In the formula, U REjK is the voltage of new energy node j; K1 is the converter overcurrent factor, ranging from 1.5 to 2.5. At this time, the new energy grid-connected converter only relies on single-loop current control. When the low voltage ride-through characteristics of each new energy power station provide different magnitudes of short-circuit current due to different electrical distances, the transient change process of the short-circuit current of different power sources can be obtained, as shown in the figure.

[0059] Based on the transient change process of the short-circuit current shown in the figure, it can be seen that the voltage drop of new energy sources varies depending on their distance from the fault, and the duration of the low-voltage ride-through phase also differs. The transient short-circuit capacity of the receiving-end grid containing a high proportion of new energy sources needs to comprehensively consider the low-voltage ride-through process of different new energy units and the short-circuit capacity provided by the synchronous machine. The influence of the low-voltage ride-through characteristics of new energy sources on the system voltage intensity differs significantly from that of traditional synchronous machine-dominated grids, making it impossible to directly calculate the short-circuit ratio index. Therefore, it is proposed that the short-circuit capacity is no longer a single numerical value, but a time-varying transient short-circuit capacity function SSC(t).

[0060] Due to the strong randomness, intermittency, and volatility of new energy sources, a large proportion of new energy in the receiving-end power grid significantly impacts the transient short-circuit capacity of the receiving-end grid. To address the temporal and spatial uncertainties in the short-circuit current output of new energy units during low-voltage ride-through, a new energy short-circuit capacity reliability coefficient λ(t) is introduced to reflect the differences in the stability, continuity, abrupt interruptions, and other characteristics of the short-circuit current output of different new energy sources. A measurement data reliability analysis method based on jump variable statistics is used to calculate the new energy short-circuit capacity reliability coefficient λ(t). Using simulation measurement data, jump variable statistical analysis is performed on the short-circuit current output of each new energy unit during low-voltage ride-through. For each moment, the jump variable of the new energy unit's short-circuit current is calculated:

[0061] N simulations of the receiving-end power grid yielded a collection of jump variable data for short-circuit current of new energy generating units, forming a jump variable distribution set D, namely:

[0062] Based on the jump variable distribution set D, each The intervals are numbered sequentially as L intervals: [ a 0, a 1) [ a 1, a 2), ..., [ a ( l-1 ), a l According to the low-voltage ride-through control strategy for new energy sources, the theoretical value of the short-circuit current jump in each interval is calculated. .

[0063] By statistically analyzing each interval in the jump variable distribution set D The number of times, that is, the theoretical approximation number, can be used to obtain:

[0064] Calculate the fluctuation probability of the short-circuit current jump in each interval:

[0065] The confidence coefficient λ(t) is obtained based on statistical data and the probability of fluctuation, that is:

[0066] In the formula, The greater the difference between the statistical jump variable and the calculated result, the more the reliability decreases exponentially. This indicates the probability of the jump variable appearing in the statistical data; the higher the probability, the higher the reliability.

[0067] Combining the short-circuit capacity provided by the receiving-end power grid with the reliability coefficient λ(t), the expression for the transient short-circuit capacity function of the receiving-end power grid is derived as follows:

[0068] In the formula, Indicates the transient short-circuit capacity of the receiving-end power grid; Let represent the j-th confidence coefficient.

[0069] S102. Improve the short-circuit ratio index based on the definition of transient short-circuit capacity function.

[0070] After constructing the transient short-circuit capacity function, an improved short-circuit ratio index can be defined based on the transient short-circuit capacity function. This improved short-circuit ratio index is formed by introducing the transient short-circuit capacity function and the reliability coefficient of the new energy short-circuit capacity, based on the short-circuit ratio. The improved short-circuit ratio index is used to dynamically quantify the transient voltage support strength.

[0071] To define the improved short-circuit ratio index, in some embodiments, the transient component of the synchronous machine and the reactive current output of the low-voltage ride-through control of the new energy source can also be obtained, and an expression for the transient short-circuit current of the receiving-end grid can be established. The expression for the transient short-circuit current of the receiving-end grid is used to characterize the transient short-circuit current of the receiving-end grid as the sum of the transient component of the synchronous machine and the reactive current output of the low-voltage ride-through control of the new energy source.

[0072] Based on the transient short-circuit current expression of the receiving-end power grid, and the response control time delay of the short-circuit transient process of the synchronous generator set and the low-voltage ride-through characteristics of the new energy converter, the transient process of the short-circuit current of the receiving-end power grid is divided into an initial impact stage and a decay support stage. Then, the derivative of the transient short-circuit current expression of the receiving-end power grid is calculated in both the initial impact stage and the decay support stage to obtain the derivative results. Based on the derivative results, the transient short-circuit capacity function of the receiving-end power grid is fitted.

[0073] Furthermore, an improved short-circuit ratio index is defined based on the transient short-circuit capacity function. When implementing this improved index, an initial short-circuit ratio considering the short-circuit capacity provided by renewable energy sources can be derived first from the short-circuit capacity. Then, the standard requirements for the short-circuit current output during the low-voltage ride-through of renewable energy units are obtained. Finally, by combining the initial short-circuit ratio, the standard requirements for short-circuit current, and the reliability coefficient, an improved short-circuit ratio index considering the low-voltage ride-through characteristics of renewable energy sources is derived.

[0074] For example, the transient short-circuit current of the receiving-end power grid is the sum of the transient component of the synchronous machine and the reactive current output from the low-voltage ride-through control of the new energy source, that is:

[0075] In the formula, , These are the subtransient time constant and the transient time constant, respectively. , These are subtransient reactance and transient reactance, respectively. The attenuation time constant for limiting and controlling the reactive current output of the new energy converter; This refers to the moment when the reactive current output of new energy sources decays.

[0076] Based on the transient short-circuit current of the receiving-end power grid, the short-circuit transient process of the synchronous generator and the LVRT response control time delay of the new energy converter can be synchronized. The transient process of the short-circuit current of the receiving-end power grid can be divided into an initial impact stage and a decay and support stage, such as... Figure 4 As shown.

[0077] For the instantaneous short circuit, the synchronous machine's electromagnetic transient response has no delay, instantly providing a huge inrush current, which then rapidly decays into a subtransient short-circuit current, determined by the subtransient reactance. The new energy converter experiences a low-voltage ride-through control delay; this stage has a very short timescale; the transient short-circuit current during this stage is the synchronous machine's subtransient current. Taking the derivative, we get:

[0078] Since this expression is always negative, it indicates that at the instant of a short circuit... To obtain the maximum value.

[0079] For short-circuit transients, the synchronous machine's short-circuit current decays from its subtransient value to its transient value, determined by the larger transient reactance. When the renewable energy unit begins to output short-circuit current, two scenarios occur: if the voltage drop is far from the grid's protection threshold, the short-circuit current output continues; if the voltage drop is too low for the nearby renewable energy unit, it disconnects from the grid or the duration reaches the specified support time, causing it to shut down. During this stage, the system's transient short-circuit current consists of the synchronous machine's transient current and the reactive current output from the renewable energy unit. Therefore, for... Differentiation yields:

[0080] when t = t d At that time, the short-circuit current of the synchronous generator is in a transient decay process, but when the penetration rate of new energy sources is very high, dI sys_k / dt >0, the transient short-circuit current of the receiving-end power grid is monotonically increasing at this time, and in t > t d At this point, there exists a maximum value, indicating that the transient short-circuit current provided by the low-voltage ride-through of new energy sources can influence and change the transient short-circuit current variation process of the system. Based on the above theoretical analysis, the transient short-circuit capacity function SSC(t) of the receiving-end power grid can be fitted, such as... Figure 5 As shown.

[0081] As can be seen from the transient short-circuit capacity function, the effectiveness of short-circuit capacity contributions from different sources varies at different stages. The transient short-circuit capacity exhibits two peaks: the first peak is determined by the subtransient short-circuit current of the synchronous machine, and the second peak is determined by the low-voltage ride-through characteristics of the renewable energy source. Between these two peaks, because the renewable energy source has not yet entered the low-voltage ride-through process while the synchronous machine's short-circuit current is decaying, there is a trough in the system's short-circuit capacity. For grids with a high proportion of renewable energy sources at the receiving end, the impact of the renewable energy source's transient short-circuit capacity on the grid voltage level cannot be ignored.

[0082] Finally, based on the initial short-circuit ratio, short-circuit current standard specifications, and reliability coefficient, the improved short-circuit ratio (LVRT-ISCR) considering the low-voltage ride-through characteristics of new energy sources can be derived, namely:

[0083] in, Indicates the first j The credibility coefficient of each energy node; Ω ij The complex power conversion factor between the DC access bus and the renewable energy grid-connected bus; U i Indicates the first i The actual operating voltage of each DC feed bus node; Represents the unit short-circuit current function at new energy node j; Ω ik S k Short-circuit capacity provided for the power supply of the synchronous machine; P dcN This indicates the rated transmission power of the DC system.

[0084] It is evident that the proposed improved short-circuit ratio index ( LVRT-ISCR The physical essence of this index lies in dynamically quantifying the evolution of transient voltage support strength in the receiving-end power grid during low-voltage ride-through of renewable energy sources, in conjunction with the synchronous machine. The theoretical boundaries of this index are primarily based on the standard LVRT control model and the classical synchronous machine transient model, making it particularly suitable for receiving-end power grids with high renewable energy penetration rates and where the explicit representation of voltage support capability relies on the active control of power electronic equipment.

[0085] S103. By extracting the curve features corresponding to the improved short-circuit ratio index, a comprehensive evaluation function for voltage intensity is constructed.

[0086] After defining the improved short-circuit ratio index, an evaluation model for the DC carrying capacity of the receiving-end power grid can be established based on the improved short-circuit ratio index. To this end, the voltage intensity comprehensive evaluation function can be constructed by extracting the curve features corresponding to the improved short-circuit ratio index. The curve features include the lowest point of the curve, the average value of the curve, and the steady-state value of the curve.

[0087] To construct a comprehensive voltage strength evaluation function, in some embodiments, an index curve can first be established based on the improved short-circuit ratio index, and curve features can be extracted from the index curve. Then, the entropy-weighted TOPSIS decision algorithm is used to construct a comprehensive voltage strength evaluation value based on the curve features. Next, by calculating the comprehensive voltage strength evaluation value of each node under different DC power inputs, a comprehensive voltage strength evaluation function that reflects the change in DC power is fitted. Finally, the evaluation threshold corresponding to the comprehensive voltage strength evaluation value is obtained, and voltage strength constraints are set based on the comprehensive voltage strength evaluation function and the evaluation threshold.

[0088] For example, different types of low-voltage ride-through (LVRT) control strategies for new energy sources have different impacts on LRVT-ISCR. Therefore, according to the photovoltaic low-voltage ride-through provisions in GB / T 37408-2019 "Technical Specification for Photovoltaic Grid-Connected Inverters", the impact begins from the moment of abnormal AC voltage on the inverter (U... T <0.9), the response time of the dynamic reactive current is no greater than 60ms, and the adjustment time is no greater than 150ms. During the low voltage ride-through period, the dynamic reactive current output by the inverter should track the voltage change at the grid connection point in real time, and the ratio of the inverter output dynamic reactive current to the voltage change is in the range of 1.5-2.5.

[0089] According to GB / T 36995-2018 "Test Procedure for Fault Voltage Ride-Through Capability of Wind Turbine Generators", when a three-phase short-circuit symmetrical voltage drop occurs at the grid connection point of a wind turbine generator, the wind turbine generator should respond quickly from the moment the voltage drop occurs, with a dynamic capacitive reactive current control response time not exceeding 75ms. The ratio of the inverter output dynamic reactive current to the voltage change is taken as 1.5. Fault low voltage ride-through curves for different new energy generator units are shown below. Figure 6 As shown.

[0090] The low-voltage ride-through curves of different renewable energy units show that when the AC side voltage of a photovoltaic inverter drops to 0, it can maintain uninterrupted grid-connected operation for 0.15 seconds. However, wind turbines will disconnect from the grid if the voltage drops below 0.2 pu. This indicates that photovoltaics have strong continuous output capability during low-voltage ride-through, while wind turbines are prone to interruption and have high uncertainty during low-voltage ride-through. For double-fed induction generator (DFIG) wind turbines, there is also a high risk of rotor overcurrent during low-voltage ride-through control, requiring the implementation of a crowbar protection strategy. When implemented, the DFIG switches to asynchronous motor mode, causing the reactive current support to be interrupted or reversed to absorb reactive power.

[0091] The differences in LVRT control strategies between wind power and photovoltaic inverters affect the transient short-circuit capacity they provide during faults, thus influencing the shape of the LVRT-ISCR index curve. The LVRT-ISCR curves of the receiving-end grid containing different types of renewable energy are shown below. Figure 7 As shown.

[0092] As can be seen from the LVRT-ISCR curve of the receiving-end grid, the low-voltage ride-through control strategies of different types of renewable energy units affect the second peak and valley of the LVRT-ISCR curve. For systems dominated by photovoltaic inverters, the second peak is significant, the valley is shallow, indicating strong voltage support capability and a smooth overall curve, demonstrating the continuity of short-circuit current output. For wind power (DFIG) dominated systems, the curve shows a steep drop or fluctuation, the second peak is low and indistinct, and the valley is deeper.

[0093] Therefore, the LVRT-ISCR index is suitable for weak receiving-end power grids dominated by renewable energy sources. In this case, system inertia and voltage support are scarce, and the transient voltage support from renewable energy sources may become dominant, providing a key constraint for determining DC carrying capacity. Thus, when constructing a receiving-end power grid DC carrying capacity assessment model, a two-stage receiving-end power grid DC carrying capacity assessment model can be established. The first stage assesses the voltage intensity of each node and sets a voltage intensity threshold as a constraint for subsequent assessments. The second stage uses the voltage intensity threshold from the first stage and the basic operating conditions of the power grid as constraints to assess the maximum DC power value to be connected.

[0094] Therefore, voltage intensity features can be extracted based on the improved short-circuit ratio (LVRT-ISCR) curve. To incorporate the time-varying short-circuit ratio index curve into the DC carrying capacity assessment model, curve features can be extracted and transformed into quantifiable evaluation indicators. Specifically, curve features can include the lowest point of the LVRT-ISCR curve at the node. This reflects the voltage support capability of the receiving-end power grid under the worst-case scenario; the average value of the LVRT-ISCR curve of the node. This reflects the average voltage intensity of the receiving-end power grid throughout the transient process; the steady-state value of the LVRT-ISCR curve at the node. This reflects the system voltage intensity after the new energy low-voltage ride-through phase ends.

[0095] Then, the improved entropy-weighted TOPSIS decision method is used to construct a comprehensive evaluation value of voltage intensity. S V The comprehensive evaluation function value of voltage intensity at each node under different DC power inputs is calculated, and the relationship reflecting the change of voltage intensity with DC power is obtained by fitting. S V = f ( P dc ).

[0096] S104. Establish an evaluation model for the DC carrying capacity of the receiving-end power grid.

[0097] After constructing the comprehensive voltage strength evaluation function, an assessment model for the DC carrying capacity of the receiving-end power grid can be established based on this function. This assessment model is a two-stage optimization model for evaluating the DC carrying capacity of the receiving-end power grid, taking into account dynamic voltage support capabilities. The first stage of the assessment model aims to determine the maximum construction capacity and optimal landing point of the DC line, evaluating and deciding on the landing point and capacity of the DC line from a macro-level investment planning perspective. The second stage of the assessment model is a safety verification model considering multiple anticipated faults, aiming to minimize system operating costs. It couples the safety requirements of the receiving-end power grid under multiple preset fault types to verify the adaptability of the decision scheme in the first stage.

[0098] The constraints of the evaluation model include basic constraints and voltage strength constraints set according to the voltage strength comprehensive evaluation function. That is, after constructing the voltage strength comprehensive evaluation function, the set threshold corresponding to the voltage strength comprehensive evaluation function can be used as the voltage strength constraint.

[0099] The evaluation model uses DC feed-in capacity as the optimization objective, and the objective function of the evaluation model is the maximum power fed into the receiving-end grid via DC. Therefore, in some embodiments, when establishing an evaluation model for the DC carrying capacity of the receiving-end grid, the evaluation model can first be constructed based on the voltage intensity comprehensive evaluation function, and basic constraints of the evaluation model can be set. These basic constraints include at least one of power flow equation constraints, node voltage magnitude constraints, inertia constraints, and frequency stability constraints. Then, with the renewable energy capacity fixed, and DC feed-in capacity as the optimization objective, the objective function of the evaluation model is set according to the maximum power fed into the receiving-end grid via DC.

[0100] For example, when setting the objective function of the evaluation model, the renewable energy capacity can be kept constant when evaluating the DC carrying capacity of the receiving-end grid, and the DC feed-in capacity can be used as the optimization objective. The objective function is the maximum power fed into the receiving-end grid by DC, i.e.:

[0101] In the formula: This represents the DC power connected at node i.

[0102] The constraints of the evaluation model may include power flow equation constraints, node voltage magnitude constraints, inertia constraints, and frequency stability constraints, among which the power flow equation constraints are expressed as:

[0103]

[0104] In the formula, This represents the injected active power at node i; This represents the injected reactive power at node i, i.e. and This includes synchronous machines, new energy sources, loads, and DC injected power; U i and U j Representing nodes respectively i With nodes j The voltage amplitude; θ ij This represents the voltage phase angle difference between node i and node j; and The real part (conductance) and imaginary part (susceptance) of the element in the i-th row and j-th column of the nodal admittance matrix Y are determined by parameters such as the line resistance, reactance, and capacitance to ground.

[0105] Node voltage magnitude constraints represent each node i voltage amplitude All must not exceed the upper limit specified for node voltage. and lower limit ,Right now:

[0106] In the formula, Represents a node i The voltage amplitude; Indicates the lower limit specified for node voltage; This indicates the upper limit specified for the node voltage.

[0107] Inertia constraints can be expressed as:

[0108] In the formula, It represents the equivalent inertia time constant of the system, used to reflect the overall inertia level of all synchronous units or inertia-providing equipment in the current operating state of the entire power system; This represents the minimum allowable inertia threshold value of the system. It is a threshold value specified by the dispatching agency or safety and stability guidelines to ensure that the rate of frequency change (RoCoF) does not exceed the safety limit when power disturbances such as generator tripping or load changes occur, so as to avoid triggering low-frequency load shedding or system collapse.

[0109] Frequency stability constraints can be expressed as:

[0110] In the formula, This represents the change in system frequency.

[0111] S105. Perform iterative solutions on the evaluation model according to the basic particle swarm optimization algorithm to obtain evaluation result information.

[0112] After establishing the evaluation model for the DC carrying capacity of the receiving-end power grid, the generalized Benders decomposition algorithm can be used to solve it, that is, the evaluation model is iteratively solved according to the basic particle swarm algorithm to obtain the evaluation results.

[0113] In some embodiments, during iterative solution, a particle swarm can first be generated based on the evaluation model, wherein the particle swarm includes multiple particles; each particle is assigned a position and a velocity. Then, a particle matrix is ​​constructed, comprising a position matrix and a velocity matrix. Next, basic particle swarm iteration is performed based on the particle matrix to obtain optimal positions, which include both local and global optimal positions. Finally, the particle velocities are updated according to the optimal positions, and the particle positions are updated based on the updated particle velocities, thereby outputting evaluation result information based on the updated particle positions.

[0114] For example, based on the basic particle swarm optimization (PSO) algorithm, the assessment of the DC carrying capacity of the receiving-end power grid involves finding the maximum value under a series of complex inequality constraints in a high-dimensional, nonlinear solution space. The PSO algorithm simulates the foraging behavior of a flock of birds and is an iterative optimization algorithm, where t represents the current iteration number. First, a population of M particles is formed, each particle having a specific position. and speed The position of a particle represents a feasible solution (m = 1, 2, ..., M). The objective function value at that position... F ( P i,m ( t This is called fitness. The particle's position and velocity matrix is ​​represented as:

[0115]

[0116] In the formula, P ij Indicates the first i In the scheme represented by the nth particle, injected into the nth... j The active power of each DC landing point; V ij Indicates the first i The particle in the first j The change in power at each DC landing point.

[0117] In the t-th iteration, all particles know their current position and the best position they have found, i.e., the local optimum. Simultaneously, based on each particle's fitness, they also know the maximum position found by all particles in the entire swarm, i.e., the global optimum. All particles follow these two optimum positions, updating their velocity and position according to the formula:

[0118]

[0119] In the formula: Inertia factor; , For learning factors; , It is a random number.

[0120] By applying the technical solutions of the above embodiments, the multi-dimensional evaluation method for the DC carrying capacity of the receiving-end power grid described in the above embodiments can evaluate the DC carrying capacity of the receiving-end power grid based on the voltage strength constraint of the LVRT-ISCR transient short-circuit ratio curve. The method takes into account the improved short-circuit ratio index (LVRT-ISCR) based on the low-voltage ride-through characteristics of new energy sources, achieving dynamic quantification of transient voltage support strength. By constructing the transient short-circuit capacity function SSC(t), taking into account the synergistic effect of the low-voltage ride-through (LVRT) control characteristics of new energy sources and the transient process of the short-circuit current of the synchronous machine, the LVRT-ISCR index is defined. This index can dynamically and accurately characterize the evolution of the voltage support strength of the system during fault transients, providing a more reliable voltage dimension constraint basis for DC carrying capacity evaluation.

[0121] The method further establishes a two-stage model for voltage intensity assessment and DC carrying capacity assessment to achieve systematic and hierarchical safety evaluation. In the first stage, based on LVRT-ISCR curve feature extraction, a comprehensive voltage intensity evaluation function is constructed and a threshold is set. In the second stage, based on this, a constraint set is formed by further considering basic operational constraints such as power flow, voltage, frequency, and inertia. This model has a clear structure and distinct levels, enabling systematic evaluation of the maximum DC feed-in capacity of the receiving-end power grid under different renewable energy penetration rates.

[0122] The proposed method fully considers the impact of changes in renewable energy penetration on the voltage intensity of the receiving-end power grid, and in particular, effectively characterizes the transient voltage characteristics of the power grid under renewable energy dominance through the LVRT-ISCR index. The model and algorithm have good generalization ability and scenario adaptability, and can be applied to the assessment of DC carrying capacity of receiving-end power grids with different renewable energy ratios and different power grid structures, providing quantitative and scientific decision support for power grid planning.

[0123] In some embodiments, as a specific implementation of the multi-dimensional evaluation method for the DC carrying capacity of the receiving-end power grid described in the above embodiments, some embodiments of this application also provide a multi-dimensional evaluation system for the DC carrying capacity of the receiving-end power grid, such as... Figure 8 As shown, the system includes: The transient short-circuit capacity function construction module is used to construct a transient short-circuit capacity function. The transient short-circuit capacity function is a time-varying function that reflects the contribution of short-circuit capacity at different stages, based on the low-voltage ride-through control characteristics of new energy sources and the transient process of short-circuit current of synchronous machines. The index definition module is used to define an improved short-circuit ratio index based on the transient short-circuit capacity function. The improved short-circuit ratio index is formed by introducing the transient short-circuit capacity function and the new energy short-circuit capacity reliability coefficient on the basis of the short-circuit ratio. The improved short-circuit ratio index is used to dynamically quantify the transient voltage support strength. The feature extraction module is used to construct a comprehensive voltage intensity evaluation function by extracting the curve features corresponding to the improved short-circuit ratio index. The curve features include the lowest point of the curve, the average value of the curve, and the steady-state value of the curve. The model building module is used to build an evaluation model for the DC carrying capacity of the receiving-end power grid. The constraints of the evaluation model include basic constraints and voltage intensity constraints set according to the voltage intensity comprehensive evaluation function. The evaluation model takes the DC feed-in capacity as the optimization objective, and the objective function of the evaluation model is the maximum power fed into the receiving-end power grid by DC. The iterative solution module is used to perform iterative solutions on the evaluation model according to the basic particle swarm optimization algorithm to obtain evaluation result information.

[0124] By applying the technical solutions of the above embodiments, the multi-dimensional evaluation system for the DC carrying capacity of the receiving-end power grid described in the above embodiments, after establishing a transient short-circuit capacity function that reflects the short-circuit capacity contribution at different stages based on the low-voltage ride-through control characteristics of new energy sources and the transient process of short-circuit current of synchronous machines, defines an improved short-circuit ratio index based on the transient short-circuit capacity function. By extracting the curve features corresponding to the improved short-circuit ratio index, a comprehensive voltage intensity evaluation function is constructed. Then, an evaluation model for the DC carrying capacity of the receiving-end power grid is established, and the evaluation model is iteratively solved according to the basic particle swarm optimization algorithm to obtain the evaluation result information. The system can introduce a transient short-circuit capacity function and a new energy short-circuit capacity reliability coefficient to form an improved short-circuit ratio index based on the short-circuit ratio, which is used to dynamically quantify the transient voltage support strength. By establishing an evaluation model, an efficient evaluation mechanism for DC feed-in carrying capacity applicable to high-proportion new energy receiving-end power grid scenarios is formed, improving the accuracy of the evaluation results and effectively guiding the subsequent UHVDC access planning.

[0125] It should be noted that other corresponding descriptions of the functional units involved in the multi-dimensional evaluation system for DC carrying capacity of receiving-end power grid provided in the embodiments of this application can be found in the corresponding descriptions in the multi-dimensional evaluation method for DC carrying capacity of receiving-end power grid provided in the above embodiments, and will not be repeated here.

[0126] This application also provides a computer device, specifically a personal computer, server, network device, etc. The computer device includes a bus, processor, memory, and communication interface, and may also include input / output interfaces and a display device. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores location information. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the various method embodiments.

[0127] Those skilled in the art will understand that the structure of the computer device described above is only a partial structure related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. A specific computer device may include more or fewer components, or combine certain components, or have different component arrangements.

[0128] In one embodiment, a computer-readable storage medium is also provided, which may be non-volatile or volatile, and a computer program is stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0129] In one embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0130] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0131] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0132] Any references to memory, database, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc.

[0133] Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can take many forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0134] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchain. The processors involved in the embodiments provided in this application may be, but are not limited to, general-purpose processors, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc.

[0135] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0136] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

Claims

1. A multi-dimensional evaluation method for the DC carrying capacity of a receiving-end power grid, characterized in that, The method includes: A transient short-circuit capacity function is constructed. The transient short-circuit capacity function is a time-varying function that reflects the short-circuit capacity contribution at different stages, based on the low-voltage ride-through control characteristics of new energy sources and the transient process of short-circuit current of synchronous machines. An improved short-circuit ratio index is defined based on the transient short-circuit capacity function. The improved short-circuit ratio index is formed by introducing the transient short-circuit capacity function and the new energy short-circuit capacity reliability coefficient on the basis of the short-circuit ratio. The improved short-circuit ratio index is used to dynamically quantify the transient voltage support strength. By extracting the curve features corresponding to the improved short-circuit ratio index, a comprehensive voltage intensity evaluation function is constructed. The curve features include the lowest point of the curve, the average value of the curve, and the steady-state value of the curve. An evaluation model for the DC carrying capacity of the receiving-end power grid is established. The constraints of the evaluation model include basic constraints and voltage intensity constraints set according to the voltage intensity comprehensive evaluation function. The evaluation model takes the DC feed-in capacity as the optimization objective, and the objective function of the evaluation model is the maximum power fed into the receiving-end power grid by DC. The evaluation model is iteratively solved using the basic particle swarm optimization algorithm to obtain the evaluation results.

2. The method according to claim 1, characterized in that, Constructing the transient short-circuit capacity function includes: Traverse the synchronous generator sets and new energy grid-connected equipment in the receiving-end power grid; According to Thevenin's theorem, the synchronous generator set is equivalent to a voltage source series impedance, and the equivalent potential of the voltage source series impedance is the open-circuit voltage of the converter station node. The aforementioned new energy grid-connected equipment is equivalent to an ideal current source impedance; A simplified equivalent model is constructed for the simultaneous connection of new energy sources and DC power to the receiving-end grid. The simplified equivalent model includes the series impedance of the voltage source and the impedance of the ideal current source. The series impedance of the voltage source and the impedance of the ideal current source are the diagonal elements of the converter station node in the node impedance matrix. The short-circuit capacity is calculated based on the simplified equivalent model, and the short-circuit capacity is used to derive the initial short-circuit ratio considering the short-circuit capacity provided by new energy sources.

3. The method according to claim 2, characterized in that, The short-circuit capacity is calculated based on the simplified equivalent model, including: A voltage-current relationship function is established based on the simplified equivalent model, wherein the voltage matrix is ​​the product of the impedance matrix and the current matrix. Based on the voltage-current relationship function, the voltage relationship of the DC feed node; An apparent power relationship is constructed by multiplying both sides of the voltage relationship by a conversion factor, where the conversion factor is the absolute value of the ratio of node voltage to equivalent impedance. Based on the power relationship, the short-circuit capacity provided by the receiving-end power grid is calculated.

4. The method according to claim 2, characterized in that, Constructing the transient short-circuit capacity function also includes: Using simulation measurement data, the jump variables of the short-circuit current output of the new energy unit during the low voltage ride-through period are statistically analyzed to calculate the jump variables of the short-circuit current of the new energy unit. The jump variables of the short-circuit current of the new energy unit obtained from multiple simulation time points are collected to form a jump variable distribution set; Based on the set of jump variables, each time period is numbered into multiple intervals in chronological order; Based on the low-voltage ride-through control strategy for new energy sources, the theoretical value of the short-circuit current jump variable in each interval is calculated. The theoretical number of close approaches for each interval in the distribution set of jump variables is counted, and the fluctuation probability of the short-circuit current jump variable in each interval is calculated. The reliability coefficient of the short-circuit capacity of new energy sources is calculated based on the theoretical proximity count and fluctuation probability. The transient short-circuit capacity function of the receiving-end power grid is generated based on the confidence coefficient and the short-circuit capacity.

5. The method according to claim 4, characterized in that, An improved short-circuit ratio index is defined based on the transient short-circuit capacity function, including: Based on the short-circuit capacity, an initial short-circuit ratio considering the short-circuit capacity provided by new energy sources is derived; Obtain the standard specifications for the short-circuit current output during the low-voltage ride-through of new energy generating units; Combining the initial short-circuit ratio, the short-circuit current standard, and the reliability coefficient, an improved short-circuit ratio index considering the low-voltage ride-through characteristics of new energy sources is derived.

6. The method according to claim 5, characterized in that, The method further includes: Acquire the transient components of the synchronous machine and the reactive current output of the low-voltage ride-through control of new energy sources; An expression for the transient short-circuit current of the receiving-end power grid is established, wherein the transient short-circuit current of the receiving-end power grid is used to characterize the transient short-circuit current of the receiving-end power grid as the sum of the transient component of the synchronous machine and the reactive current output by the low-voltage ride-through control of the new energy source; Based on the transient short-circuit current expression of the receiving-end power grid, the response control time delay of the short-circuit transient process of the synchronous generator set and the low-voltage ride-through characteristic of the new energy converter is used to divide the short-circuit current transient process of the receiving-end power grid into an initial impact stage and a decay support stage. The derivatives of the transient short-circuit current expression of the receiving-end power grid are obtained in the initial impact stage and the attenuation support stage, respectively. Based on the derivative results, the transient short-circuit capacity function of the receiving-end power grid is fitted.

7. The method according to claim 1, characterized in that, By extracting the curve features corresponding to the improved short-circuit ratio index, a comprehensive voltage strength evaluation function is constructed, including: An index curve is established based on the improved short-circuit ratio index; Extract the curve features from the index curve; The entropy-weighted TOPSIS decision algorithm is used to construct a comprehensive evaluation value of voltage intensity based on the curve characteristics. By calculating the comprehensive evaluation value of the voltage intensity of each node under different DC power inputs, a comprehensive evaluation function of voltage intensity as DC power changes is fitted. Obtain the evaluation threshold corresponding to the comprehensive evaluation value of voltage intensity, and set voltage intensity constraint conditions according to the comprehensive evaluation function of voltage intensity and the evaluation threshold.

8. The method according to claim 1, characterized in that, Establish an evaluation model for the DC carrying capacity of the receiving-end power grid, including: The evaluation model is constructed based on the voltage intensity comprehensive evaluation function; The evaluation model is set with basic constraints, which include at least one of power flow equation constraints, node voltage magnitude constraints, inertia constraints, and frequency stability constraints. With the renewable energy capacity fixed and the DC feed-in capacity as the optimization target, the objective function of the evaluation model is set according to the maximum power of the DC feed-in receiving-end grid.

9. The method according to claim 1, characterized in that, The evaluation model is iteratively solved using the basic particle swarm optimization algorithm to obtain evaluation result information, including: A particle population is generated based on the evaluation model, the particle population comprising multiple particles; each particle is configured with a particle position and a particle velocity. Construct the particle matrix of the particle population, the particle matrix including the position matrix and velocity matrix of the particles; Based on the particle matrix, perform basic particle swarm iteration to obtain the optimal position, which includes the local optimal position and the global optimal position; Update the particle velocity according to the optimal position, and update the particle position based on the updated particle velocity; The evaluation results are output based on the updated particle positions.

10. A multi-dimensional evaluation system for the DC carrying capacity of a receiving-end power grid, characterized in that, The system is applied to the method according to any one of claims 1-9; the system comprises: The transient short-circuit capacity function construction module is used to construct a transient short-circuit capacity function. The transient short-circuit capacity function is a time-varying function that reflects the contribution of short-circuit capacity at different stages, based on the low-voltage ride-through control characteristics of new energy sources and the transient process of short-circuit current of synchronous machines. The index definition module is used to define an improved short-circuit ratio index based on the transient short-circuit capacity function. The improved short-circuit ratio index is formed by introducing the transient short-circuit capacity function and the new energy short-circuit capacity reliability coefficient on the basis of the short-circuit ratio. The improved short-circuit ratio index is used to dynamically quantify the transient voltage support strength. The feature extraction module is used to construct a comprehensive voltage intensity evaluation function by extracting the curve features corresponding to the improved short-circuit ratio index. The curve features include the lowest point of the curve, the average value of the curve, and the steady-state value of the curve. The model building module is used to build an evaluation model for the DC carrying capacity of the receiving-end power grid. The constraints of the evaluation model include basic constraints and voltage intensity constraints set according to the voltage intensity comprehensive evaluation function. The evaluation model takes the DC feed-in capacity as the optimization objective, and the objective function of the evaluation model is the maximum power fed into the receiving-end power grid by DC. The iterative solution module is used to perform iterative solutions on the evaluation model according to the basic particle swarm optimization algorithm to obtain evaluation result information.

Images