Receiving end power grid active support capability assessment method and system based on system total risk potential

Abstract

The invention belongs to the technical field of receiving-end power grid evaluation, and particularly relates to a receiving-end power grid active support capability evaluation method and system based on the total risk potential of a system, and the method comprises the steps: firstly constructing an active support capability evaluation index system of a receiving-end power grid; then calculating an individual risk potential function of each index and a coupling risk coefficient between each index and other indexes, and then calculating a system total risk potential based on the individual risk potential function and the coupling risk coefficient, the system total risk potential including a system individual risk potential and a system coupling risk potential; and finally, active support capability evaluation of the receiving-end power grid is realized based on the total risk potential of the system. According to the method, on one hand, a scientific and comprehensive evaluation system is constructed, the deep evaluation of the active support capability is realized, on the other hand, on the basis of quantifying the deviation degree by using the individual risk potential, the non-linear superposition risk additionally generated by the interaction or simultaneous degradation among multiple indexes is quantified through the coupling risk potential field, and the evaluation accuracy of the active support capability is improved. And the diagnosis precision is obviously improved.

Description

Technical Field

[0001] This invention belongs to the field of receiving-end power grid assessment technology, specifically relating to a method, system, equipment, and medium for assessing the active support capability of receiving-end power grids based on the total system risk potential. Background Technology

[0002] With the rapid growth of renewable energy installed capacity and the advancement of inter-regional large-capacity DC transmission channels, the energy source and transmission pattern of the power system have undergone fundamental changes, posing numerous challenges to the safety and stability of power system operation. Due to the strong volatility and randomness of renewable energy output, it directly impacts the receiving-end grid through DC transmission channels, posing a severe test to the safety and stability of the receiving-end grid. The receiving-end grid is not only a load center for absorbing external power but also a hub platform connecting regional grids. When the "dual high" characteristics of high proportion of renewable energy and high proportion of power electronic equipment are combined with the dual roles of receiving end and hub, the traditional evaluation system for the active support capacity of the receiving-end grid is insufficient to meet the engineering needs under the new circumstances in terms of theoretical foundation, indicator dimensions, and evaluation methods. This is mainly reflected in the fact that traditional support capacity evaluations mostly focus on single dimensions such as traditional transient or voltage / frequency stability, ignoring the characteristics of the receiving-end grid as an organic whole and energy hub, lacking a comprehensive evaluation of power receiving and transmission capacity, and failing to fully measure the receiving-end grid's adaptability to large-scale DC feed-in and its flexible transmission and efficient distribution capabilities of internal power. In addition, there is a lack of quantitative assessment of the individual performance of key supporting power sources such as synchronous machines and energy storage, and there is also a failure to conduct in-depth evaluation of the source-grid synergy effect between supporting power sources and the power grid at key nodes, making it impossible to quantify the contribution of joint control to the overall stability improvement of the system.

[0003] In terms of evaluation methods, the main focus is on stable support modes dominated by synchronous machines. By establishing detailed and complex electromagnetic or electromechanical transient simulation models, the dynamic response of the system under different disturbances is simulated to identify the weak areas of inertia in the system. This time-domain simulation method has obvious defects: First, it has high requirements for the completeness of system parameters, making it difficult to apply to multi-infeed DC scenarios with missing mechanical inertia in new energy sources such as wind power and photovoltaics, and it is difficult to achieve rapid evaluation of multiple schemes and selection of the most suitable scheme during the planning stage of the receiving-end power grid; Second, its conclusions are biased towards qualitative analysis and cannot directly and quantitatively identify weak indicators and their degree of weakness, making it difficult to guide precise reinforcement measures.

[0004] The evaluation framework and the weighting mechanism of evaluation indicators directly affect the engineering applicability of the evaluation results. Mainstream weighting methods include expert subjective experience-based weighting and objective weighting based on the characteristics of the data itself; each method has its advantages and disadvantages. Expert weighting can reflect engineering experience and key concerns, but it is highly subjective and difficult to completely eliminate human bias. Objective methods such as entropy weighting emphasize the information content of data distribution, but may underestimate the importance of indicators when key indicators have small fluctuations or insufficient samples. If the evaluation method does not consider both subjective and objective factors in the indicator weighting process, the results will be biased in engineering decision-making, affecting the scientific validity of optimization measures. Summary of the Invention

[0005] The purpose of this invention is to address the problems of existing technologies in scenarios with high proportions of new energy and multiple DC feed-in systems, which cannot fully characterize the system's support capabilities and have weak diagnostic capabilities. This invention provides an index system that can comprehensively reflect the essential characteristics of the new receiving-end power grid. Furthermore, by utilizing the total system risk potential that takes into account both individual risk potential fields and coupled risk potential fields, a precise mapping relationship is established between qualitative levels and quantitative indicators. This enables a highly diagnostic method, system, device, and medium for assessing the active support capabilities of the receiving-end power grid based on the total system risk potential.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for assessing the active support capability of a receiving-end power grid based on the total risk potential of the system, the method comprising:

[0008] S1. Considering the capacity of new energy sources outside the region to receive power, the capacity of power transmission within the region, the transient stability capability of multi-DC coordination, the support capability of supporting power sources, and the source-grid synergy capability, construct an evaluation index system for the active support capability of the receiving-end power grid.

[0009] S2. Calculate the individual risk potential function for each indicator and the coupling risk coefficient between each indicator and other indicators; then, calculate the total system risk potential of the receiving-end power grid based on the individual risk potential function and the coupling risk coefficient. The formula for calculating the total system risk potential is as follows:

[0010] ;

[0011] In the above formula, The total risk potential of the system; For the first The weight of each indicator; For the first The first indicator and the first Risk coefficient of coupling between indicators; , The first The first indicator, the first Individual risk potential function value for each indicator;

[0012] S3. Assess the active support capability of the receiving-end power grid based on the total system risk potential of the receiving-end power grid.

[0013] Preferably, the formula for calculating the total risk potential of the system includes:

[0014] ;

[0015] In the above formula, The total risk potential of the system; For the first The weight of each indicator; For the first The first indicator and the first Risk coefficient of coupling between indicators; , The first The individual risk potential function value of each indicator.

[0016] Preferably, the coupling risk coefficient between each indicator and other indicators is calculated according to the following steps: first, the coupling strength between each indicator and other indicators is classified into levels using mechanism analysis, and then a semi-quantitative mapping method is used to map each level of strength to a corresponding value, so as to convert the coupling strength between each indicator and other indicators into a coupling risk coefficient.

[0017] Preferably, the calculation steps of the individual risk potential function include:

[0018] The indicators in the proactive support capability assessment indicator system are divided into benefit-oriented indicators, cost-oriented indicators, and moderate indicators.

[0019] For benefit-oriented indicators, the following individual risk potential function is constructed:

[0020] ;

[0021] In the above formula, For the first Individual risk potential function for each indicator; For the first The actual value of each indicator; For the first The ideal value of each indicator; For the first The limit threshold of each indicator;

[0022] For cost-based indicators, the following individual risk potential function is constructed:

[0023] ;

[0024] For moderate indicators, the following individual risk potential function is constructed:

[0025] ;

[0026] In the above formula, , The first The lower and upper bounds of the ideal range for each indicator; , The first The lower and upper bounds of the extreme range of an indicator.

[0027] Preferably, the index weights are determined using the AHP-Asymmetric Entropy Weight Game Theory combined weighting method. The AHP-Asymmetric Entropy Weight Game Theory combined weighting method includes: determining the subjective weights and objective weights of each index based on the AHP method and the asymmetric entropy weight method respectively, and then using game theory to combine and assign weights based on the subjective weights and objective weights to obtain the weights of each index.

[0028] Preferably, the asymmetric entropy weighting method includes:

[0029] First calculate the actual value of each indicator. Relative to its ideal value Asymmetric deviation:

[0030] ;

[0031] In the above formula, For the first The first sample Asymmetric deviation of each indicator; It is an asymmetric risk factor; For the first The first sample The actual value of each indicator; For the first The actual values ​​of each indicator are fitted using the probability density function obtained by kernel density estimation. The probability density at that location; It is a very small positive number;

[0032] Then, the deviation ratio is calculated based on the asymmetric deviation:

[0033] ;

[0034] In the above formula, For the first The first sample The proportion of deviation of each indicator; The number of samples;

[0035] Then, calculate the deviation entropy based on the deviation ratio:

[0036] ;

[0037] In the above formula, For the first Deviation entropy of each indicator;

[0038] Finally, the objective weights are calculated based on the deviation entropy:

[0039] ;

[0040] In the above formula, For the first The objective weight of each indicator; This refers to the number of indicators.

[0041] Preferably, the weighting of each indicator is obtained by combining subjective and objective weights using game theory, including:

[0042] A combination coefficient optimization model is constructed with the objective of minimizing the deviation between the weights and the subjective and objective weights. Solving the combination coefficient optimization model yields the optimal combination coefficients. and The objective function of the combined coefficient optimization model is:

[0043] ;

[0044] In the above formula, For the first The combination coefficient of class weights; For the first The weight value of the class weight; Time represents subjective weight. Time indicates objective weight;

[0045] Based on optimal combination coefficients and The indicator weights are calculated using the following formula:

[0046] ;

[0047] In the above formula, For the first The weight of each indicator; , The first The subjective weight and objective weight of each indicator.

[0048] Preferably, the active support capability assessment index system of the receiving-end power grid includes:

[0049] Short-circuit ratio, multi-infeed short-circuit ratio, DC feed-in power ratio, and transmission line capacity utilization rate;

[0050] Inter-regional power transfer rate, proportion of flexible load power consumption, and power flow entropy;

[0051] System equivalent inertia constant, metastability margin, system frequency change rate, and effective short-circuit ratio;

[0052] Power supply inertia, equivalent impedance, grid connection point short-circuit ratio, voltage droop rate;

[0053] Node inertia, voltage-reactive power sensitivity, proportion of grid-type devices, and proportion of VSC.

[0054] Secondly, the present invention provides a receiving-end power grid active support capability assessment system based on the total system risk potential, the receiving-end power grid active support capability assessment system comprising:

[0055] The indicator system construction module is used to construct an indicator system for evaluating the active support capability of the receiving-end power grid, taking into account the power receiving capacity of new energy sources outside the region, the power transmission capacity of the region, the transient stability capability of multi-DC coordination, the support capability of supporting power sources, and the source-grid synergy capability.

[0056] The system total risk potential calculation module is used to calculate the individual risk potential function of each indicator and the coupling risk coefficient between each indicator and other indicators; then, based on the individual risk potential function and the coupling risk coefficient, the system total risk potential of the receiving-end power grid is calculated. The formula for calculating the system total risk potential is as follows:

[0057] ;

[0058] In the above formula, The total risk potential of the system; For the first The weight of each indicator; For the first The first indicator and the first Risk coefficient of coupling between indicators; , The first The first indicator, the first Individual risk potential function value for each indicator;

[0059] The assessment module is used to assess the active support capabilities of the receiving-end power grid based on the total system risk potential of the receiving-end power grid.

[0060] Preferably, the system total risk potential calculation module is used to calculate the individual risk potential function according to the following steps:

[0061] The indicators in the proactive support capability assessment indicator system are divided into benefit-oriented indicators, cost-oriented indicators, and moderate indicators.

[0062] For benefit-oriented indicators, the following individual risk potential function is constructed:

[0063] ;

[0064] In the above formula, For the first Individual risk potential function for each indicator; For the first The actual value of each indicator; For the first The ideal value of each indicator; For the first The limit threshold of each indicator;

[0065] For cost-based indicators, the following individual risk potential function is constructed:

[0066] ;

[0067] For moderate indicators, the following individual risk potential function is constructed:

[0068] ;

[0069] In the above formula, , The first The lower and upper bounds of the ideal range for each indicator; , The first The lower and upper bounds of the extreme range of an indicator.

[0070] Preferably, the system total risk potential calculation module is used to calculate the coupling risk coefficient between each indicator and other indicators according to the following steps: first, the coupling strength between each indicator and other indicators is classified into levels by mechanistic analysis, and then each level of strength is mapped to a corresponding value by a semi-quantitative mapping method, so as to convert the coupling strength between each indicator and other indicators into a coupling risk coefficient.

[0071] Preferably, the system total risk potential calculation module is used to determine the weight of each indicator using the AHP-asymmetric entropy weight game theory combined weighting method. The AHP-asymmetric entropy weight game theory combined weighting method includes: determining the subjective weight and objective weight of each indicator based on the AHP method and the asymmetric entropy weight method respectively, and then using game theory to combine and assign weights based on the subjective weight and objective weight to obtain the weight of each indicator.

[0072] Preferably, the system total risk potential calculation module is used to determine the objective weights of each indicator using the asymmetric entropy weight method according to the following steps:

[0073] First calculate the actual value of each indicator. Relative to its ideal value Asymmetric deviation:

[0074] ;

[0075] In the above formula, For the first The first sample Asymmetric deviation of each indicator; It is an asymmetric risk factor; For the first The first sample The actual value of each indicator; For the first The actual values ​​of each indicator are fitted using the probability density function obtained by kernel density estimation. The probability density at that location; It is a very small positive number;

[0076] Then, the deviation ratio is calculated based on the asymmetric deviation:

[0077] ;

[0078] In the above formula, For the first The first sample The proportion of deviation of each indicator; The number of samples;

[0079] Then, calculate the deviation entropy based on the deviation ratio:

[0080] ;

[0081] In the above formula, For the first Deviation entropy of each indicator;

[0082] Finally, the objective weights are calculated based on the deviation entropy:

[0083] ;

[0084] In the above formula, For the first The objective weight of each indicator; This refers to the number of indicators.

[0085] Preferably, the system total risk potential calculation module is used to calculate the weight of each indicator according to the following steps:

[0086] A combination coefficient optimization model is constructed with the objective of minimizing the deviation between the weights and the objective and subjective weights. Solving the combination coefficient optimization model yields the optimal combination coefficients. and The objective function of the combined coefficient optimization model is:

[0087] ;

[0088] In the above formula, For the first The combination coefficient of class weights; For the first The weight value of the class weight; Time represents subjective weight. Time indicates objective weight;

[0089] Based on optimal combination coefficients and The indicator weights are calculated using the following formula:

[0090] ;

[0091] In the above formula, For the first The weight of each indicator; , The first The subjective weight and objective weight of each indicator.

[0092] Preferably, the active support capability assessment index system of the receiving-end power grid includes:

[0093] Short-circuit ratio, multi-infeed short-circuit ratio, DC feed-in power ratio, and transmission line capacity utilization rate;

[0094] Inter-regional power transfer rate, proportion of flexible load power consumption, and power flow entropy;

[0095] System equivalent inertia constant, metastability margin, system frequency change rate, and effective short-circuit ratio;

[0096] Power supply inertia, equivalent impedance, grid connection point short-circuit ratio, voltage droop rate;

[0097] Node inertia, voltage-reactive power sensitivity, proportion of grid-type devices, and proportion of VSC.

[0098] Thirdly, the present invention provides a receiving-end power grid active support capability assessment device based on the total system risk potential. The receiving-end power grid active support capability assessment device includes a memory and a processor. The memory is used to store computer program code and transmit the computer program code to the processor. The processor is used to execute the aforementioned receiving-end power grid active support capability assessment method according to the instructions in the computer program code.

[0099] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned method for assessing the active support capability of the receiving-end power grid.

[0100] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0101] 1. The active support capability assessment method for receiving-end power grids based on the total system risk potential described in this invention, on the one hand, establishes a five-dimensional active support capability evaluation chain covering power reception, transmission, transient stability, supporting power sources, and source-grid coordination. It systematically combines the functional attributes related to receiving and transmitting power grids with the safety attributes related to stability support, covering all key characteristics of multi-infeed DC receiving-end power grids. This constructs a scientific and comprehensive active support capability assessment system for receiving-end power grids, avoiding conclusion biases caused by missing assessment dimensions and facilitating in-depth evaluation of the active support capability of receiving-end power grids. On the other hand, by adopting the total system risk potential that considers individual risk potentials and coupled risk potential fields, it not only eliminates the reliance on complex and time-consuming time-domain simulations, but also, based on quantifying the degree of deviation using individual risk potential fields, quantifies the additional nonlinear superposition risks generated by the interaction or simultaneous deterioration of multiple indicators through coupled risk potential fields, ultimately significantly improving diagnostic accuracy.

[0102] 2. The receiving-end power grid active support capability assessment method based on the total system risk potential described in this invention combines the analytic hierarchy process (AHP) with the asymmetric entropy weight method, and then seeks the optimal balance between the two through game theory. This method not only retains valuable empirical information in engineering judgment, but also fully incorporates the empirical characteristics of data distribution, thereby effectively reducing the bias that may be caused by a single weighting strategy, ensuring the fairness and rationality of weight allocation, and greatly improving the engineering credibility of the final assessment conclusion. The objective weights obtained by the asymmetric entropy weight method are no longer determined by the arbitrary fluctuation of data, but by the probability of rare, dangerous, and asymmetric fluctuations. This solves the problem in the traditional entropy weight method where key indicators that are stable at the optimal value for a long time are wrongly assigned extremely low weights due to the lack of fluctuations, while indicators that are in a continuous state of zero deviation receive high weights, reflecting the extremely important information characteristic of high data stability. Therefore, compared with the traditional entropy weight method, the asymmetric entropy weight method can more clearly, profoundly, and deeply fit the power grid risk assessment scenario. Attached Figure Description

[0103] Figure 1 This is a flowchart of the active support capability assessment method for the receiving-end power grid described in this invention.

[0104] Figure 2 This is a structural block diagram of the receiving-end power grid active support capability assessment system described in this invention.

[0105] Figure 3 This is a structural block diagram of the receiving-end power grid active support capability assessment device described in this invention. Detailed Implementation

[0106] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0107] Example 1:

[0108] See Figure 1 A method for assessing the active support capability of the receiving-end power grid based on the total risk potential of the system is carried out in the following steps:

[0109] S1. Considering the capacity of new energy sources outside the region to receive electricity, the capacity of power transmission within the region, the transient stability capability of multiple DC transmission lines, the support capability of supporting power sources, and the source-grid synergy capability, an evaluation index system for the active support capability of the receiving-end power grid is constructed.

[0110] Specifically, the active support capability assessment index system of the receiving-end power grid includes:

[0111] 1. Short-circuit ratio, multi-infeed short-circuit ratio, DC feed-in power ratio, and transmission line capacity utilization rate. The above indicators are used together to evaluate the power receiving capacity of new energy sources outside the region, that is, the ability of the DC converter station bus and its nearby AC system to support DC power injection, and to measure the degree of acceptance and adaptability of the receiving-end grid to large-scale new energy sources (especially through DC feed-in).

[0112] Short-circuit ratio ( The voltage support capability of a single DC landing point for the short-circuit capacity of a specific AC system is reflected by the following formula: , The system three-phase short-circuit capacity of the converter bus. The rated DC power fed into the DC converter station;

[0113] Multi-infeed short-circuit ratio ( This is used to evaluate the voltage support capability of the AC system for converter stations and the interaction characteristics between converter stations in multi-DC-feed scenarios. The calculation formula is as follows: , The equivalent node impedance matrix entering from each DC converter bus The element in the i-th row and j-th column; The rated DC power of the i-th DC circuit. Let be the rated DC power of the j-th DC circuit; The number of DC cycles;

[0114] DC-fed power input ratio ( This is used to quantify the energy input contribution of external DC transmission channels, and its calculation formula is as follows: , This represents the total amount of DC power fed into the grid area within one year. The total AC power input from outside the power grid area within one year. The total power generation within the power grid area within one year;

[0115] Transmission line capacity utilization rate ( The formula for calculating the average capacity utilization of critical transmission channels is as follows: , , Let be the active power and reactive power flowing from node i to node j at time t, respectively, and T be the total operating time period. This represents the equivalent transmission capacity of line ij.

[0116] 2. Inter-regional power transfer rate, proportion of flexible load power consumption, and power flow entropy. These indicators are used together to assess the regional power transmission capacity, that is, the ability of the power grid as an energy hub to flexibly transmit and efficiently distribute power within the region.

[0117] Inter-regional power transfer rate ( This is used to quantify the power grid's ability to function as an energy transfer hub, and its calculation formula is as follows: , This refers to the total amount of electricity imported into the power grid from outside the region on an annual timescale. This refers to the amount of electricity transmitted across regions on an annual timescale.

[0118] The proportion of electricity consumption by flexible loads ( This is used to assess the potential of the power grid to achieve source-load interaction through flexible load-side regulation. The calculation formula is as follows: , , These are the electricity consumption of the flexible load of the power grid within one year and the electricity consumption of the total load of the power grid within one year, respectively.

[0119] Current Entropy ( Entropy (or entropy value) is used to measure the uniformity of power flow distribution in a power grid. A higher entropy value indicates a more balanced power flow distribution. Its calculation formula is: , For the active power flow of the k-th line, This is the sum of the absolute values ​​of the active power flow across all lines. This represents the total number of power grid lines at the receiving end.

[0120] 3. System equivalent inertia constant, transient stability margin, system frequency change rate, and effective short-circuit ratio. These indicators are used together to evaluate the transient stability capability of multiple DC systems working together, that is, the system's ability to maintain transient stability when encountering severe disturbances.

[0121] System equivalent inertia constant ( This is used to quantify the frequency buffering capacity of a power grid in response to power surges, and its calculation formula is as follows: , , , These are the rated capacity of the synchronizing machine in the system, the inertia constant of the synchronizing machine, and the operating status of the unit, respectively. , These are the rated capacity of the energy storage in the system and the inertia coefficient of the energy storage, respectively.

[0122] Temporary stability margin ( This is used to evaluate the stability margin of AC transmission channels under disturbances, and its calculation formula is as follows: , , These are the transient power limit of the AC transmission channel and the rated transmission power of the AC transmission channel under observation, respectively. The calculation method for the transient power limit is as follows: Set up multi-terminal DC operation, select two DC feeder lines with landing points at the same load center of a certain AC channel (with the target AC channel as the center, select the two most relevant DC lines based on topological correlation and the degree of power transfer influence), set the power drop to 0 through the DC control module to simulate a DC blocking fault. When two DC faults occur, the transmitted power will be quickly transferred to the AC network, causing a sudden change in the power of the target AC channel. By increasing the generator output, the rated power of the AC transmission channel under observation is increased by 5% each time from the rated transmission power, and the simulation is repeated until the power angle difference exceeds 180°. Record the power value. This power is the maximum stable power before the loss of synchronism, which is the transient power limit.

[0123] System frequency change rate ( This is used to reflect the degree of frequency dynamic response when the system has power imbalance, and its calculation formula is: , , These are the system frequency and system angular velocity, respectively. The calculation method is as follows: specify the AC bus of a DC sending and receiving converter station, change the DC transmission power from the rated value to 0, set the fault time, and simulate a blocking fault where the DC power instantly returns to zero.

[0124] Effective short-circuit ratio ( This is used to reflect the ability of a multi-infeed DC system to maintain voltage stability during disturbances. The disturbance is a three-phase short circuit at the bus for approximately 0.2 ms, resulting in a 1% step drop in converter bus voltage. If the voltage drop is greater than 1%, the short-circuit impedance is increased to reduce the voltage drop; if it is less than 1%, the short-circuit impedance is decreased to increase the voltage drop. The monitoring time is 10 s, and the calculation formula is as follows: , , These represent the short-circuit capacity at converter station i and the reactive power provided by the filters and parallel capacitors within the converter station when the AC bus voltage at converter station i is at its rated value, respectively. , , These are the rated line voltage and three-phase short-circuit subtransient current of converter bus i, respectively. , Here, i and j are the rated transmission capacities of DC systems i and j, respectively, and k is the total number of DC lines fed into the multi-infeed system. Let i be the interaction factor between converter bus i at inverter station and converter bus j at other inverter stations. ; The following simulation method can be used to obtain the voltage drop: When the DC transmission system is operating at rated power, a parallel reactive load is connected to the converter bus i of its inverter station to cause a step drop of about 1% in the converter bus voltage. Calculate the percentage change in voltage at converter bus j of other inverter stations. .

[0125] 4. Power source inertia, equivalent impedance, grid connection point short-circuit ratio, and voltage droop rate. These indicators are used together to evaluate the supporting power source's capacity, that is, the individual capacity of various supporting power sources such as synchronous machines and energy storage to provide stable support for the power grid in terms of frequency and voltage.

[0126] Power supply inertia ( This reflects the power supply's inertial support capability against changes in system frequency; its calculation formula is: , , , These are the generator rotor moment of inertia, synchronous angular velocity, and generator rated capacity, respectively.

[0127] Equivalent impedance ( This is used to reflect the comprehensive impedance characteristics of the power supply and the electrical connection of the system. Its calculation formula is: , The imaginary unit, , , These are the subtransient reactance of the synchronous machine, the impedance of the line from the synchronous machine to the grid connection point, and the impedance of the transformer from the synchronous machine to the grid connection point;

[0128] Short-circuit ratio at grid connection point ( This is used to assess the strength of the connection between the grid connection point and the system, as well as the grid connection point's ability to withstand system failure events. The calculation formula is as follows: , , For the three-phase short-circuit capacity of the grid connection point, The rated capacity of the grid-connected power supply; Rated voltage level; The equivalent impedance of the system; for The per-unit value; , These are the rated line voltage at the grid connection point and the three-phase short-circuit subtransient current, respectively.

[0129] Voltage droop rate ( This is used to measure the coordination ability of generators and transformers in reactive power regulation, and is of great significance for stabilizing the voltage quality at the grid connection point. Its calculation formula is: , This refers to the inherent voltage droop rate of the generator. Percentage of short-circuit voltage of the main transformer; , These refer to the rated capacity of the generator and the rated capacity of the main transformer, respectively.

[0130] 5. Node inertia, voltage-reactive power sensitivity, proportion of grid-connected devices, and VSC proportion. These indicators are used together to examine the cooperation effect between the supporting power source and the grid at key nodes, and to assess whether joint control can improve the overall stability of the system.

[0131] Nodal inertia ( This is used to measure the local inertial support of a node, especially the DC landing point and its adjacent nodes, to frequency changes. Its calculation formula is: , , The power and frequency of node A are respectively. Node A is the DC landing point and the node connecting the DC landing point. DC blocking is set for each DC-feedback line. The fault duration is 0.1s. The power and frequency of node A are monitored. The calculated inertia of each DC-blocked node A is calculated. The minimum value is taken as the final node inertia of node A.

[0132] Voltage-reactive power sensitivity ( This is used to quantify the improvement effect on the DC converter bus voltage after dynamic reactive power is applied at the node. It can be used to evaluate the marginal benefit of compensation investment. Its calculation formula is as follows: , , It is the voltage increase of all DC converter buses after dynamic reactive power compensation is installed at node i; To install dynamic reactive power compensation capacity at node i When q is the voltage rise value of the converter bus on the DC j inverter side; For the number of DC feed cycles, This represents the total number of DC converter buses; It is the voltage-reactive sensitivity index of dynamic reactive power compensation installed at node i.

[0133] Percentage of network-type devices ( This is used to statistically determine the proportion of grid-type power electronic devices in the total capacity of regional power electronic devices, reflecting the device basis of the power supply and grid self-stabilization capability. Its calculation formula is: , The rated capacity of the network-type device, Set the total capacity for power electronic interface equipment within the region;

[0134] VSC percentage ( The VSC converter is used to quantify the proportion of the total converter capacity in a DC converter station. Its calculation formula is as follows: , This refers to the rated converter capacity of the VSC type DC converter station; This represents the rated converter capacity of all DC converter stations in the power grid.

[0135] S2. Calculate the individual risk potential function of each indicator and the coupling risk coefficient between each indicator and other indicators; then calculate the total system risk potential of the receiving-end power grid based on the individual risk potential function and the coupling risk coefficient, wherein the total system risk potential is the sum of the system individual risk potential and the system coupling risk potential.

[0136] Specifically, the calculation steps for the individual risk potential function include:

[0137] First, the indicators in the proactive support capability assessment indicator system are divided into benefit-type indicators, cost-type indicators, and moderate indicators;

[0138] Specifically, the following indicators are classified as benefit-oriented indicators, with higher values ​​being better: short-circuit ratio, multi-infeed short-circuit ratio, inter-regional power transfer rate, proportion of flexible load power consumption, power flow entropy, system equivalent inertia constant, quasi-stability margin, effective short-circuit ratio, power source inertia, grid connection point short-circuit ratio, node inertia, voltage-reactive power sensitivity, proportion of grid-connected devices, and VSC proportion; the following indicators are classified as cost-oriented indicators, with lower values ​​being better: system frequency change rate and equivalent impedance; the following indicators are classified as moderate indicators, with values ​​within the ideal range being optimal: proportion of DC-fed power, transmission line capacity utilization rate, and voltage droop rate.

[0139] Then, for benefit-type indicators, the following individual risk potential function is constructed:

[0140] ;

[0141] In the above formula, For the first Individual risk potential function for each indicator; For the first The actual value of each indicator; For the first The ideal value for each indicator is determined based on industry standards or physical constraints in engineering. For the first The limit threshold of each indicator; the individual risk potential function of the benefit-type indicator is constructed using a piecewise quadratic function;

[0142] For cost-based indicators, the following individual risk potential function is constructed:

[0143] ;

[0144] For moderate indicators, the following individual risk potential function is constructed:

[0145] ;

[0146] In the above formula, , The first The lower and upper bounds of the ideal range for each indicator; , The first The lower and upper bounds of the extreme range of each indicator; for moderate indicators, since their ideal value is located within an ideal range, both excessively high and excessively low ideal values ​​will increase system risk, so their risk potential function exhibits a concave characteristic.

[0147] Specifically, the formula for calculating the total risk potential of the system includes:

[0148] ;

[0149] In the above formula, For the total risk potential of the system, This refers to the object to be evaluated, which consists of the actual values ​​of all indicators under a specific receiving-end power grid. For the first The weight of each indicator; For the first The first indicator and the first Risk coefficient of coupling between indicators; , The first Individual risk potential function value for each indicator; For individual risk potential within the system; The system is coupled with risk potential;

[0150] Specifically, the coupling risk coefficient between each indicator and other indicators is calculated according to the following steps: First, the degree of coupling between each indicator and other indicators is classified by mechanism analysis, for example, into several levels such as "strong coupling", "medium coupling", "weak coupling" and "basically independent". The two indicators, the equivalent inertia of the system and the rate of change of the system frequency, have a direct physical relationship determined by the rotor motion equation and are judged as "strong coupling". Then, a semi-quantitative mapping method is used to map each level of strength to a corresponding value, so as to convert the degree of coupling between each indicator and other indicators into a coupling risk coefficient.

[0151] Specifically, a qualitative coupling risk coefficient matrix (19*19 in this embodiment) can be obtained through a semi-quantitative mapping method. The steps to establish the coupling risk coefficient matrix include: first, organizing experts to evaluate the pairwise relationships between indicators as "strong coupling", "medium coupling", "weak coupling" and "basically independent". Then, a numerical range is pre-set for each qualitative level. For example, "strong coupling" corresponds to the numerical range [0.8, 1.0]. The "strong coupling" level is strictly reserved for indicator pairs that have a direct and necessary causal relationship in the basic physical equations of the power system. For example, there is a strong causal relationship between the effective short-circuit ratio and voltage-reactive power sensitivity, which is determined by the network impedance matrix and power flow equations, and is therefore "strong coupling". In addition, the mechanism analysis also includes the judgment of the interaction of the control system. For example, there is a risk that the control system with multiple infeeds may interact or even induce oscillations under disturbances, which also falls under the category of "strong coupling". "Medium coupling" corresponds to the numerical range [0.4, 0.6], "weak coupling" corresponds to the numerical range [0.1, 0.3], and "basically independent" is 0. For these index pairs determined to be weakly coupled by mechanistic analysis, targeted sensitivity simulation is used for objective calibration. Specifically, under baseline operating conditions, a standard degradation perturbation is applied to index A (e.g., causing its value to deviate from the optimal value by 50%), and then the response amplitude or percentage change of the other coupled index B caused by this perturbation is quantitatively observed and measured. This measurable response amplitude objectively reflects the true degree of coupling between the two indices. A mapping rule between response amplitude and coefficient value is established within the two intervals of "moderate coupling" [0.4, 0.6] and "weak coupling" [0.1, 0.3]. For example, if the simulated response amplitude of index B is less than 2%, it is determined to be in the lower range of "weak coupling" and assigned a value of 0.1; if the response amplitude is between 5% and 10%, it is determined to be in the middle range of "moderate coupling" and assigned a value of 0.5. The coupling risk coefficient described in this invention is first qualitatively classified by physical mechanisms, and then objectively fine-tuned by simulation sensitivity (precisely set within the range), thereby ensuring that the entire process of generating the coupling risk coefficient matrix is ​​scientific, objective and reproducible.

[0152] Traditional matter-element analysis first calculates the correlation between individual indicator values ​​and standard grade ranges, then weights and sums the correlations of all indicators to calculate the comprehensive correlation. This method is based on the assumption that all indicators are independent, which has significant shortcomings when assessing highly coupled and complex systems like power systems. This invention replaces the calculation of comprehensive correlation in traditional matter-element analysis with the calculation of the system's individual risk potential when indicators deviate from their ideal state. The total system risk potential described in this invention consists of two parts: the system's individual risk potential and the system's coupled risk potential. The system's individual risk potential assesses the bottleneck risk caused by a single indicator deviating from its ideal state, while the system's coupled risk potential is the incremental risk calculated based on the coupling risk coefficient, which significantly improves diagnostic accuracy. In real power systems, the system risk caused by the simultaneous deterioration of two or more indicators is often more dangerous than the simple superposition of the effects of a single indicator's deterioration. Traditional matter-element analysis cannot quantify this nonlinear superposition risk; this invention quantifies the additional nonlinear superposition risk generated by the interaction or simultaneous deterioration of multiple indicators through the coupling risk coefficient, thereby significantly improving diagnostic accuracy.

[0153] Specifically, the weights of the indicators are determined using the AHP-Asymmetric Entropy Weight Game Theory Combination Weighting Method. The AHP-Asymmetric Entropy Weight Game Theory Combination Weighting Method includes: determining the subjective weights and objective weights of each indicator based on the AHP method and the asymmetric entropy weight method respectively, and then using game theory to combine and assign weights based on the subjective weights and objective weights to obtain the weights of each indicator.

[0154] The asymmetric entropy weighting method includes:

[0155] First calculate the actual value of each indicator. Relative to its ideal value Asymmetric deviation:

[0156] ;

[0157] In the above formula, For the first The first sample Asymmetric deviation of each indicator; Asymmetric risk factors are set by experts based on experience. Then it means that for the first In terms of individual indicators, upward deviation is more noteworthy than downward deviation; if Then it means that for the first In terms of individual indicators, downward deviation is more noteworthy than upward deviation; For the first The first sample The actual value of each indicator; For the first The actual values ​​of each indicator are fitted using the probability density function obtained by kernel density estimation. The probability density at point , based on the first The actual values ​​of each indicator are fitted using the probability density function obtained by kernel density estimation. , For kernel functions, the Gaussian kernel function is usually chosen. For bandwidth, , For the first The standard deviation of each indicator For the first Interquartile range of each indicator The number of samples; It is a very small positive number; Used to reflect the magnitude of deviation;

[0158] Then, the deviation ratio is calculated based on the asymmetric deviation:

[0159] ;

[0160] In the above formula, For the first The first sample The proportion of deviation of each indicator; The number of samples;

[0161] Then, calculate the deviation entropy based on the deviation ratio:

[0162] ;

[0163] In the above formula, For the first Deviation entropy of each indicator;

[0164] Finally, the objective weights are calculated based on the deviation entropy:

[0165] ;

[0166] In the above formula, For the first The objective weight of each indicator; This refers to the number of indicators.

[0167] Traditional entropy weighting methods have the following problems in engineering applications: First, their assumptions about data distribution are too simplistic, often relying on maximum and minimum values, making them susceptible to interference from extreme outliers. Second, they handle data fluctuations symmetrically, failing to distinguish the distinct physical risks represented by upward or downward deviations from the optimal value. This invention introduces kernel density estimation, a non-parametric statistical method, to replace simple normalization. This allows the evaluation to move beyond crude extreme values ​​and instead be based on the true probability distribution of each indicator's data, including complex characteristics such as multimodal and skewed distributions. Furthermore, it constructs an asymmetric deviation degree, no longer measuring the data's dispersion within its own range, but rather the degree to which it deviates from a preset engineering optimal value. By introducing an asymmetric risk factor, different risk considerations can be assigned to upward and downward deviations based on engineering experience. Simultaneously, dividing the deviation magnitude by the probability density significantly amplifies rare events with "low probability, high risk" in the calculation. In summary, the resulting objective weights are no longer determined by arbitrary fluctuations, but rather by the probability of "rare, dangerous, and asymmetric" fluctuations. This solves the problem in traditional entropy weighting methods where key indicators that remain stable at their optimal values ​​for a long period are incorrectly assigned extremely low weights due to their lack of fluctuations. Simultaneously, indicators in a state of continuous zero deviation receive high weights, accurately reflecting the crucial information of "high stability." Compared to traditional entropy weighting methods, asymmetric entropy weighting provides a clearer, more profound, and more suitable approach for power grid risk assessment scenarios.

[0168] The AHP method includes: inviting multiple experts to compare the relative importance of each indicator pairwise, using a 1-9 scale to score them, and constructing a judgment matrix; calculating the largest eigenvalue of the judgment matrix. Perform consistency checks to ensure the logical consistency of expert judgments (when the consistency ratio is high enough). (The process is as follows: the subjective weights reflecting expert opinions are calculated).

[0169] The weights of each indicator are obtained by combining subjective and objective weights using game theory, including:

[0170] A combination coefficient optimization model is constructed with the objective of minimizing the deviation between the indicator weights and the subjective and objective weights. Solving the combination coefficient optimization model yields the optimal combination coefficients. and The objective function of the combined coefficient optimization model is:

[0171] ;

[0172] In the above formula, For the first The combination coefficient of class weights; For the first The weight value of the class weight; Time represents subjective weight. Time indicates objective weight;

[0173] Based on optimal combination coefficients and The indicator weights are calculated using the following formula:

[0174] ;

[0175] In the above formula, For the first The weight of each indicator; , The first The subjective weight and objective weight of each indicator.

[0176] To overcome the subjectivity or one-sidedness of a single weighting method, this invention treats the AHP (Analytic Hierarchy Process), which reflects expert experience, and the asymmetric entropy weighting method, which reflects objective data information, as two parties in a game. It then seeks the optimal combination coefficients of these two weights through a game theory model. The final weights are the result of both parties seeking to maximize their own interests (i.e., minimizing the deviation from the final weights) to achieve Nash equilibrium.

[0177] S3. Assess the active support capability of the receiving-end power grid based on the total system risk potential of the receiving-end power grid.

[0178] The calculated total system risk potential The overall risk potential is compared with the preset range of different risk levels to determine the level of proactive support capability. The preset level is four, and the grading rules are as follows:

[0179] ;

[0180] In the above formula, , , , They are categorized into four levels: "Excellent," "Good," "Average," and "Poor." , , All are segmentation thresholds.

[0181] S4. Output evaluation results and engineering recommendations.

[0182] Generate a diagnostic report. The diagnostic report must not only clearly state the final active support capability level of the solution to be evaluated (e.g., excellent, good, average, poor) and its corresponding total system risk potential evaluation value, but also include the following diagnostic information:

[0183] 1. By clearly displaying all indicators within each of the five primary indicator dimensions—"Power Receiving, Transmission, Stability, Power Supply Support, and Source-Grid Coordination"—a multi-dimensional capability radar chart is generated. The internal composition of the system's total risk potential is then deconstructed, intuitively identifying the system's weakest dimensions. This step represents a deepening from qualitative judgment to quantitative attribution, helping engineers accurately pinpoint the root causes of problems. By weighting and ranking the individual risk potentials of each indicator, the top weakest indicators contributing the most to the system's total risk can be quantitatively identified, revealing any single indicator deficiencies. By ranking the coupled risk potentials of each indicator, the most dangerous risk coupling pairs in the system can be precisely located, helping engineers pinpoint the root causes of problems from both the perspectives of single indicator deficiencies and systemic coupled risks.

[0184] 2. Provide precise engineering recommendations: Based on the specific weaknesses revealed in the diagnostic report, and in conjunction with sensitivity analysis, propose actionable engineering improvement measures directly linked to the diagnostic conclusions. Recommendations should be highly targeted, for example:

[0185] If the diagnostic results indicate that voltage support indicators (such as multi-infeed short-circuit ratio and voltage-reactive power sensitivity) are the main weaknesses, it is recommended to focus on planning and configuring dynamic reactive power compensation devices at key converter stations or grid nodes. If the diagnostic results indicate that frequency stability indicators (such as system equivalent inertia and nodal inertia) are the main weaknesses, it is recommended to focus on planning and configuring energy storage systems with configurable inertia and fast response capabilities at important nodes. If the diagnostic results indicate that transmission capacity indicators (such as power flow entropy and line capacity utilization) are the main weaknesses, it is recommended to focus on strengthening the transmission capacity of key transmission sections or optimizing grid operation modes.

[0186] Example 2:

[0187] See Figure 2A system for assessing the active support capability of a receiving-end power grid based on the total system risk potential includes an indicator system construction module, a total system risk potential calculation module, and an assessment module. The indicator system construction module considers the external renewable energy receiving capacity, the internal power transmission capacity, the multi-DC coordinated stability capability, the support capability of supporting power sources, and the source-grid synergy capability to construct a provisional indicator system for assessing the active support capability of the receiving-end power grid. Specifically, the active support capability assessment indicator system for the receiving-end power grid includes: short-circuit ratio, multi-infeed short-circuit ratio, DC feed-in power ratio, transmission line capacity utilization rate, inter-regional power transfer rate, and flexibility. The calculation formulas for the above indicators are the same as those in Example 1, and will not be repeated here. The total system risk potential calculation module is used to calculate the individual risk potential function of each indicator and the coupling risk coefficient between each indicator and other indicators. Then, based on the individual risk potential function and the coupling risk coefficient, the total system risk potential of the receiving-end grid is calculated. The calculation formula for the total system risk potential is as follows:

[0188] ;

[0189] In the above formula, The total risk potential of the system; For the first The weight of each indicator; For the first The first indicator and the first Risk coefficient of coupling between indicators; , The first Individual risk potential function value for each indicator;

[0190] Specifically, the system's total risk potential calculation module is used to calculate the individual risk potential function according to the following steps: The indicators in the proactive support capability assessment indicator system are divided into benefit-type indicators, cost-type indicators, and moderate-type indicators; for benefit-type indicators, the following individual risk potential function is constructed:

[0191] ;

[0192] In the above formula, For the first Individual risk potential function for each indicator; For the first The actual value of each indicator; For the first The ideal value of each indicator; For the first The limit threshold of each indicator;

[0193] For cost-based indicators, the following individual risk potential function is constructed:

[0194] ;

[0195] For moderate indicators, the following individual risk potential function is constructed:

[0196] ;

[0197] In the above formula, , The first The lower and upper bounds of the ideal range for each indicator; , The first The lower and upper bounds of the extreme range of each indicator;

[0198] Specifically, the system total risk potential calculation module is used to calculate the coupling risk coefficient between each indicator and other indicators according to the following steps: first, the mechanism analysis is used to classify the degree of coupling between each indicator and other indicators into levels, and then a semi-quantitative mapping method is used to map each level of strength to the corresponding value, so as to convert the degree of coupling between each indicator and other indicators into a coupling risk coefficient.

[0199] Specifically, the system total risk potential calculation module is used to determine the weights of each indicator using the AHP-asymmetric entropy weight game theory combined weighting method. The AHP-asymmetric entropy weight game theory combined weighting method includes: determining the subjective weights and objective weights of each indicator based on the AHP method and the asymmetric entropy weight method respectively, and then using game theory to combine and assign weights based on the subjective weights and objective weights to obtain the weights of each indicator.

[0200] Specifically, the system total risk potential calculation module is used to determine the objective weights of each indicator using the asymmetric entropy weight method according to the following steps:

[0201] First calculate the actual value of each indicator. Relative to its ideal value Asymmetric deviation:

[0202] ;

[0203] In the above formula, For the first The first sample Asymmetric deviation of each indicator; It is an asymmetric risk factor; For the first The first sample The actual value of each indicator; For the first The actual values ​​of each indicator are fitted using the probability density function obtained by kernel density estimation. The probability density at a given location, the probability density function , For kernel functions, the Gaussian kernel function is usually chosen. For bandwidth, , For the first The standard deviation of each indicator For the first Interquartile range of each indicator The number of samples; It is a very small positive number;

[0204] Then, the deviation ratio is calculated based on the asymmetric deviation:

[0205] ;

[0206] In the above formula, For the first The first sample The proportion of deviation of each indicator; The number of samples;

[0207] Then, calculate the deviation entropy based on the deviation ratio:

[0208] ;

[0209] In the above formula, For the first Deviation entropy of each indicator;

[0210] Finally, the objective weights are calculated based on the deviation entropy:

[0211] ;

[0212] In the above formula, For the first The objective weight of each indicator; For the number of indicators;

[0213] Specifically, the system's total risk potential calculation module is used to calculate the weights of each indicator according to the following steps:

[0214] A combination coefficient optimization model is constructed with the objective of minimizing the deviation between the indicator weights and the subjective and objective weights. Solving the combination coefficient optimization model yields the optimal combination coefficients. and The objective function of the combined coefficient optimization model is:

[0215] ;

[0216] In the above formula, For the first The combination coefficient of class weights; For the first The weight value of the class weight; Time represents subjective weight. Time indicates objective weight;

[0217] Based on optimal combination coefficients and The indicator weights are calculated using the following formula:

[0218] ;

[0219] In the above formula, For the first The weight of each indicator; , The first The subjective and objective weights of each indicator;

[0220] The assessment module is used to assess the active support capability of the receiving-end power grid based on the total system risk potential of the receiving-end power grid.

[0221] Example 3:

[0222] See Figure 3 A receiving-end power grid active support capability assessment device based on the total system risk potential includes a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the receiving-end power grid active support capability assessment method described in Embodiment 1 according to the instructions in the computer program code.

[0223] Example 4:

[0224] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the active support capability assessment method for the receiving-end power grid described in Embodiment 1.

[0225] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program goods. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0226] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0227] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0228] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0229] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the active support capability of a receiving power grid based on a total system risk potential, characterized in that: the method for evaluating the active support capability of the receiving power grid based on the total system risk potential comprises: S1, considering the new energy receiving load capacity outside the region, the power and electricity connection and transmission capacity within the region, the transient stability capacity of multi-direct current coordination, the support capacity of supporting power supply, and the source-grid collaboration capacity, an index system for evaluating the active support capability of the receiving power grid is constructed; S2, individual risk potential functions of each index and coupling risk coefficients between each index and other indexes are calculated; then, based on the individual risk potential functions and the coupling risk coefficients, a total system risk potential of the receiving power grid is calculated, and the calculation formula of the total system risk potential is: ； In the above formula, is the total risk potential of the system; is the weight of the first index; is the weight of the second index; is the coupling risk coefficient between the first index and the second index; is the individual risk potential function value of the first index; S3, based on the total system risk potential of the receiving power grid, the evaluation of the active support capability of the receiving power grid is realized.

2. The method for evaluating the active support capability of the receiving power grid based on the total system risk potential according to claim 1, characterized in that: the calculation step of the individual risk potential function comprises: the indexes in the index system for evaluating the active support capability are divided into benefit type indexes, cost type indexes, and moderate type indexes; for the benefit type indexes, the following individual risk potential function is constructed: ； In the above formula, the individual risk potential function for the first indicator; the actual value for the first indicator; the ideal value for the first indicator; the limit threshold value for the first indicator; for the cost type indexes, the following individual risk potential function is constructed: ； for the moderate type indexes, the following individual risk potential function is constructed: ； In the above formulae, , are lower and upper bounds of ideal intervals of the 1st index, respectively; , are lower and upper bounds of limit intervals of the 2nd index, respectively.

3. The method for evaluating the active support capability of the receiving power grid based on the total system risk potential according to claim 1 or 2, characterized in that: the coupling risk coefficients between each index and other indexes are calculated according to the following steps: first, the coupling strength between each index and other indexes is classified by mechanism analysis, and then semi-quantitative mapping is used to map each strength level to a corresponding numerical value, so as to convert the coupling strength between each index and other indexes into the coupling risk coefficients.

4. The method for evaluating the active support capability of the receiving power grid based on the total system risk potential according to claim 1 or 2, characterized in that: the index weight is determined by using an AHP-asymmetric entropy weight game theory combination weighting method, and the AHP-asymmetric entropy weight game theory combination weighting method comprises: the subjective weight and the objective weight of each index are determined based on the AHP method and the asymmetric entropy weight method respectively, and then the weights of the indexes are obtained by combination weighting based on the subjective weight and the objective weight by using the game theory.

5. The method for evaluating the active support capability of the receiving power grid based on the total system risk potential according to claim 4, characterized in that: the asymmetric entropy weight method comprises: The actual value of each indicator is first calculated relative to its ideal value asymmetry deviation: ； In the above formula, For the first The first sample Asymmetric deviation of each indicator; It is an asymmetric risk factor; For the first The first sample The actual value of each indicator; For the first The actual values ​​of each indicator are fitted using the probability density function obtained by kernel density estimation. The probability density at that location; It is a very small positive number; then, the deviation degree proportion is calculated based on the asymmetric deviation degree: ； In the above formula, is the deviation degree proportion of the i-th index of the j-th sample; is the deviation degree proportion of the i-th index of the j-th sample; is the deviation degree proportion of the i-th index of the j-th sample; is the sample number; then, the deviation degree entropy is calculated based on the deviation degree proportion: ； In the above formula, is the deviation entropy of the first index. finally, the objective weight is calculated based on the deviation degree entropy: ； In the above formula, is the objective weight of the th index; is the number of indices.

6. The method for evaluating the active support capability of the receiving power grid based on the total system risk potential according to claim 5, characterized in that: the combination weighting of the subjective weight and the objective weight by using the game theory to obtain the weights of the indexes comprises: An optimization model of combination coefficients is constructed with the objective of minimizing the deviation of the index weight from the subjective weight and the objective weight, and the optimal combination coefficients are obtained by solving the optimization model of combination coefficients and The objective function of the optimization model of combination coefficients is ； In the above formula, is a weight of the first class weight; is a weight value of the first class weight; subjective weight, objective weight; Based on the optimal combination coefficient And The index weight is calculated, and the calculation formula of the index weight is: ； In the above formula, is the weight of the first index; , is the subjective weight and the objective weight of the first index, respectively.

7. The method for evaluating the active support capability of the receiving power grid based on the total system risk potential according to claim 1 or 2, characterized in that: the index system for evaluating the active support capability of the receiving power grid comprises: Short-circuit ratio, multi-infeed short-circuit ratio, DC power injection ratio, transmission line capacity utilization rate; Power cross-zone transmission rate, flexible load power consumption ratio, power flow entropy; System equivalent inertia constant, transient stability margin, system frequency change rate, effective short-circuit ratio; Power source inertia, equivalent impedance, grid connection point short-circuit ratio, voltage regulation rate; Node inertia, voltage-reactive power sensitivity, network device ratio, VSC ratio.

8. A receiving-end power grid active support capability evaluation system based on system total risk potential, characterized in that: The receiving-end power grid active support capability evaluation system comprises: An index system construction module for constructing an active support capability evaluation index system of the receiving-end power grid by taking into account the out-of-area new energy power receiving carrying capacity, the in-area power and electricity connection and transmission capacity, the multi-DC coordinated transient stability capacity, the supporting power support capacity, and the source-grid collaboration capacity; A system total risk potential calculation module for calculating individual risk potential functions of each index and coupling risk coefficients between each index and other indexes; and then calculating the system total risk potential of the receiving-end power grid based on the individual risk potential functions and the coupling risk coefficients, wherein the calculation formula of the system total risk potential is: ； In the above formula, is the total risk potential of the system; is the weight of the first indicator; is the weight of the second indicator; is the coupling risk coefficient between the first indicator and the second indicator; , are the individual risk potential function values of the first indicator and the second indicator, respectively; An evaluation module for realizing active support capability evaluation of the receiving-end power grid based on the system total risk potential of the receiving-end power grid.

9. A receiving-end power grid active support capability evaluation device based on system total risk potential, characterized in that: The receiving-end power grid active support capability evaluation device comprises a memory and a processor; the memory is used to store computer program codes and transmit the computer program codes to the processor; The processor is used to execute the receiving-end power grid active support capability evaluation method according to the instructions in the computer program codes.

10. A computer readable storage medium having computer programs stored thereon, wherein the computer programs are executed by a processor to realize the receiving-end power grid active support capability evaluation method.

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