Method, device and equipment for evaluating new energy bearing capacity of power system

Abstract

The invention provides a new energy bearing capacity assessment method, device and equipment of a power system, and relates to the technical field of new energy bearing capacity assessment. The method comprises the following steps: collecting system parameters, new energy unit data and substation data of a target power system; calculating a stability constraint value and a topology security constraint value according to the system parameters and the new energy unit data; calculating a voltage stability constraint value according to the system parameters, the new energy unit data and the substation data; and according to the stability constraint value, the topology security constraint value and the voltage stability constraint value, determining the new energy maximum bearing capacity of the target power system. Compared with a traditional method in which evaluation is carried out only from a single-dimensional index, the method provided by the embodiment of the invention is more comprehensive in consideration, so that the obtained evaluation result is more scientific and comprehensive to meet actual requirements.

Description

Technical Field

[0001] This invention relates to the field of new energy carrying capacity assessment technology, and in particular to a method, apparatus and equipment for assessing the new energy carrying capacity of a power system. Background Technology

[0002] In the global decarbonization process, rapid grid connection of renewable energy is a core pathway, which can significantly reduce the consumption of traditional fossil fuels and promote the green transformation of the energy structure. However, with the increase in its power generation penetration rate, grid stability faces severe challenges: the decline in system inertia exacerbates frequency fluctuations, which may cause the power supply frequency to deviate from the normal range; the degradation of grid strength at the grid connection point can easily lead to voltage instability, affecting the normal operation of electrical equipment; the increased pressure on transmission corridors will also bring static safety violation risks, threatening the overall security of the grid.

[0003] At this juncture, assessing the carrying capacity of new energy power generation is of great significance. On the one hand, it can accurately determine the scale of new energy grid integration that the power grid can withstand, avoiding damage to grid stability due to excessive integration, reducing the risk of accidents such as large-scale power outages, and ensuring the safe and stable operation of the power grid. On the other hand, scientific assessment can guide the rational layout of new energy sources, enabling the efficient utilization of clean energy, while also providing data support for the government to formulate energy policies and contributing to the healthy development of the industry.

[0004] However, the current assessment has obvious drawbacks. The assessment methods are limited, lacking comprehensive consideration and relying solely on single-dimensional indicators, resulting in assessment results that are not scientific or comprehensive enough to fully meet actual needs. Summary of the Invention

[0005] This invention provides a method, apparatus, and equipment for assessing the renewable energy carrying capacity of a power system, in order to solve the problem of inaccurate assessment of renewable energy power generation carrying capacity at present.

[0006] In a first aspect, embodiments of the present invention provide a method for assessing the renewable energy carrying capacity of a power system, comprising: Collect system parameters, new energy unit data, and substation data of the target power system; Based on system parameters and new energy unit data, calculate stability constraint values ​​and topology safety constraint values; Calculate voltage stability constraint values ​​based on system parameters, new energy unit data, and substation data; Based on stability constraints, topology security constraints, and voltage stability constraints, determine the maximum capacity of new energy sources in the target power system.

[0007] In one possible implementation, system parameters include the rated voltage of the power grid; renewable energy unit data includes the complex power of renewable energy sources injected into the renewable energy grid connection bus nodes; substation data includes the mutual impedance modulus, phase, active power, and voltage between substations; based on the system parameters, renewable energy unit data, and substation data, voltage stability constraints are calculated, including: Calculate the electrical distance weight based on the mutual impedance modulus and phase between substations; Calculate the transient power fluctuation sensitivity coefficient based on the active power and voltage of the substation. The voltage stability constraint value is calculated based on the grid's rated voltage, the complex power of the new energy source injected into the new energy grid-connected bus node, the electrical distance weight, and the transient power fluctuation sensitivity coefficient.

[0008] In one possible implementation, the voltage stability constraint value is calculated based on the grid's rated voltage, the complex power of the new energy source injected into the new energy grid-connected bus node, the electrical distance weight, and the transient power fluctuation sensitivity coefficient, including: The weighted composite short-circuit ratio is calculated based on the rated voltage of the power grid, the complex power of the new energy power injected into the new energy grid-connected bus node, the electrical distance weight, and the transient power fluctuation sensitivity coefficient. Calculate the voltage stability constraint value based on the weighted composite short-circuit ratio.

[0009] In one possible implementation, the voltage stability constraint value is calculated based on the weighted composite short-circuit ratio, including: Determine the installed capacity of new energy connected to each node of the power grid when the weighted composite short-circuit ratio is greater than or equal to the voltage stability threshold; The sum of the installed capacity of new energy sources connected to each node of the power grid is used as the voltage stability constraint value.

[0010] In one possible implementation, the system parameters also include the long-term allowable thermal stability current of the power grid lines, the instantaneous current before a short-circuit fault, the static thermal stability power transmission limit, and the load data connected to the end of each line; the renewable energy unit data includes the renewable energy generation power strongly correlated with each line in the power grid; based on the system parameters and the renewable energy unit data, the topology security constraint values ​​are calculated, including: The safety participation factor is calculated based on the long-term allowable thermal stability current of the power grid lines and the instantaneous current before a short-circuit fault. Based on the safety participation coefficient, the static thermal stability power transmission limit, the load data connected to the end of each line, and the strong correlation of new energy power generation in each line of the power grid, the topology safety constraint value is calculated.

[0011] In one possible implementation, the system parameters also include the grid's equivalent inertia, total grid capacity, rated frequency of the power system, and frequency deviation limits; the renewable energy unit data includes the installed capacity of renewable energy; and based on the system parameters and renewable energy unit data, stability constraint values ​​are calculated, including: The grid inertia attenuation coefficient is calculated based on the ratio of the installed capacity of new energy sources to the total grid capacity. The ratio of the product of the power grid's equivalent inertia, the power grid's inertia attenuation coefficient, and the power system's rated frequency to the frequency deviation limit is used as the stability constraint value.

[0012] In one possible implementation, the maximum capacity of renewable energy in the target power system is determined based on stability constraints, topology security constraints, and voltage stability constraints, including: The minimum carrying capacity obtained under stability constraints, topology security constraints, and voltage stability constraints will be taken as the maximum carrying capacity of new energy sources in the target power system.

[0013] In one possible implementation, after determining the maximum renewable energy carrying capacity of the target power system based on stability constraint values, topology security constraint values, and voltage stability constraint values, the method further includes: Determine whether the current new energy carrying capacity is less than the maximum new energy carrying capacity; If not, reduce the current new energy carrying capacity until it is less than the maximum new energy carrying capacity.

[0014] Secondly, embodiments of the present invention provide a device for assessing the renewable energy carrying capacity of a power system, comprising: The data acquisition module is used to collect system parameters, new energy unit data, and substation data of the target power system. The calculation module is used to calculate stability constraint values ​​and topology safety constraint values ​​based on system parameters and new energy unit data; The calculation module is also used to calculate voltage stability constraint values ​​based on system parameters, new energy unit data, and substation data; The evaluation module is used to determine the maximum capacity of new energy sources in the target power system based on stability constraints, topology security constraints, and voltage stability constraints.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0016] In this embodiment of the invention, based on the system parameters of the target power system, data from renewable energy units, and substation data, corresponding stability constraint values, topology security constraint values, and voltage stability constraint values ​​are calculated. Under the constraints of these values, the maximum renewable energy carrying capacity of the target power system is determined. Compared to traditional methods that evaluate only from a single-dimensional indicator, the method provided in this embodiment of the invention takes a more comprehensive approach, resulting in a more scientific and comprehensive evaluation result that meets practical needs. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the implementation of the new energy carrying capacity assessment method for power systems provided in this embodiment of the invention. Figure 2 This is a power grid frequency curve provided in an embodiment of the present invention; Figure 3 This is a comparison diagram of the voltage support strength of key nodes provided in the embodiments of the present invention; Figure 4 This is a comparison chart of new energy carrying capacity provided in the embodiments of the present invention; Figure 5 This is a voltage-frequency-carrying capacity relationship diagram provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of the power system new energy carrying capacity assessment device provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] Figure 1 A flowchart illustrating the implementation of the power system renewable energy carrying capacity assessment method provided in this embodiment of the invention. Figure 1 As shown, the method may include: Step 110: Collect system parameters, new energy unit data and substation data of the target power system.

[0020] In this embodiment, system parameters may include the rated voltage of the power grid itself, the current parameters of the lines in the power grid, the transmission power parameters, the load data connected to the end of each line, the equivalent inertia of the power grid, the total capacity of the power grid, the rated frequency of the power system, the frequency deviation limit, the regional power grid strength coefficient, and other data.

[0021] Data on new energy generating units can include the installed capacity of new energy, the combined power of new energy sources injected into the new energy grid-connected bus nodes, and the power generation of new energy sources strongly correlated with each line in the power grid.

[0022] Substation data can include the magnitude of mutual impedance between substations, phase, active power, and voltage.

[0023] In addition, relevant data on new energy converters can be obtained, such as the active power control gain coefficient of photovoltaic or wind power converters.

[0024] The above data can be collected by relevant data acquisition devices set up in the target power system. Parameters such as the rated voltage of the power grid itself, the rated frequency of the power system, the regional power grid strength coefficient, and the active power control gain coefficient can be directly obtained from the relevant reference tables built into the system.

[0025] Step 120: Calculate the stability constraint value and topology safety constraint value based on the system parameters and new energy unit data.

[0026] Step 130: Calculate the voltage stability constraint value based on system parameters, new energy unit data, and substation data.

[0027] This embodiment integrates system parameters, new energy unit data, and substation data, and comprehensively considers these data to obtain stability constraint values, voltage stability constraint values, and topology security constraint values, so as to comprehensively evaluate the carrying capacity of new energy power generation under multi-dimensional constraints.

[0028] Step 140: Determine the maximum capacity of new energy sources for the target power system based on the stability constraint value, topology security constraint value, and voltage stability constraint value.

[0029] In this embodiment, based on the system parameters of the target power system, data from renewable energy units, and substation data, corresponding stability constraint values, topology security constraint values, and voltage stability constraint values ​​are calculated. Under these constraints, the maximum renewable energy carrying capacity of the target power system is determined. Compared to traditional methods that evaluate based on only a single-dimensional indicator, the method provided in this embodiment of the invention takes a more comprehensive approach, resulting in more scientific and comprehensive evaluation results to meet practical needs.

[0030] In an optional embodiment, the system parameters further include the grid equivalent inertia, total grid capacity, rated frequency of the power system, and frequency deviation limit; the new energy unit data in step 120 includes the installed capacity of new energy; calculating the stability constraint value based on the system parameters and the new energy unit data may include: The grid inertia attenuation coefficient is calculated based on the ratio of the installed capacity of new energy sources to the total grid capacity.

[0031] The ratio of the product of the power grid's equivalent inertia, the power grid's inertia attenuation coefficient, and the power system's rated frequency to the frequency deviation limit is used as the stability constraint value.

[0032] Considering that the power grid will experience frequency deviation when there is a power deficit, and that the capacity of new energy connected to the power grid does not exceed the stability constraint value, it is necessary to ensure that the power grid containing new energy meets the frequency stability requirements when there is a power deficit, and that the frequency deviation of the power grid does not exceed the national standard limit. Based on this, this embodiment calculates the stability constraint value to determine the maximum capacity of new energy in the target power system under the stability constraint value.

[0033] Correspondingly, the inertia attenuation coefficient of the power grid can be calculated using the following formula:

[0034] In the formula, The inertial attenuation coefficient of the power grid; P ren To connect the installed capacity of new energy sources, P total This represents the total capacity of the power grid.

[0035] Accordingly, the stability constraint value can be calculated using the following formula:

[0036] In the formula, C f These are stability constraint values; The equivalent inertia of the power grid; This is the rated frequency of the power system, typically 50Hz; The frequency deviation limit for the power system can be ±0.2Hz; This is for the power deficit in the power system.

[0037] In an optional embodiment, the system parameters also include the long-term allowable thermal stability current of the lines in the power grid, the instantaneous current before a short-circuit fault, the static thermal stability power transmission limit, and the load data connected to the end of each line; the renewable energy unit data in step 120 includes the renewable energy power generation capacity strongly correlated with each line in the power grid; calculating the topology security constraint value based on the system parameters and the renewable energy unit data may include: The safety participation factor is calculated based on the long-term allowable thermal stability current of the power grid lines and the instantaneous current before a short-circuit fault.

[0038] Based on the safety participation coefficient, the static thermal stability power transmission limit, the load data connected to the end of each line, and the strong correlation of new energy power generation in each line of the power grid, the topology safety constraint value is calculated.

[0039] In this embodiment, the safety participation coefficient is calculated using the following formula:

[0040] In the formula, This refers to the long-term allowable thermal stability current of lines in the power grid. This refers to the instantaneous current before the short-circuit fault.

[0041] The topology safety constraint value is calculated using the following formula:

[0042] In the formula, branch road k The static thermal stability power transfer limit, This represents the sum of all conventional loads connected to the end of line k. m This is the number for the regular load. Indicates the connection with the line k The total power generated by electrically strongly correlated renewable energy sources, i.e., the total power of renewable energy sources connected to the grid. Indicates the connection with the line k A set of strongly correlated renewable energy power generation access nodes. This refers to the collection of all power grid lines.

[0043] In an optional embodiment, the system parameters include the rated voltage of the power grid; the renewable energy unit data includes the complex power of the renewable energy source injected into the renewable energy grid-connected bus node; the substation data includes the mutual impedance modulus, phase, active power, and voltage between substations; step 130, calculating the voltage stability constraint value based on the system parameters, renewable energy unit data, and substation data, may include: Calculate the electrical distance weight based on the mutual impedance modulus and phase between substations; Calculate the transient power fluctuation sensitivity coefficient based on the active power and voltage of the substation. The voltage stability constraint value is calculated based on the grid's rated voltage, the complex power of the new energy source injected into the new energy grid-connected bus node, the electrical distance weight, and the transient power fluctuation sensitivity coefficient.

[0044] The existing Multiple Renewable Energy Station Short Circuit Ratio (MRSCR) index has limitations in capturing the dynamic interactions between renewable energy power plants in high-penetration scenarios. Specifically, traditional methods oversimplify the handling of impedance coupling effects between renewable energy power plants, neglecting the dynamic processes of power electronic control during transient voltage events. To address this issue, this invention proposes a weighted load short circuit ratio index that considers electrical distance weighting and power fluctuation sensitivity correction. The electrical distance weighting characterizes the impedance coupling effect between renewable energy power plants, while the power fluctuation sensitivity characterizes the dynamic effects of power electronic equipment control in renewable energy power generation equipment.

[0045] In this embodiment, the electrical distance weight is calculated using the following formula:

[0046] In the formula, For electrical distance weighting, To determine the regional power grid strength coefficient calibrated through modal analysis, For substation nodes i With substation nodes j The mutual impedance magnitudes between them For substation nodes i With substation nodes j mutual impedance Z ij The impedance angle.

[0047] The transient power fluctuation sensitivity coefficient is calculated using the following formula:

[0048] In the formula, P j For substation j active power, U j For substation j voltage, This refers to the active power control gain coefficient of wind power or photovoltaic converters. The DC time constant is This represents the duration of the voltage sag.

[0049] Based on the electrical distance weight and transient power fluctuation sensitivity coefficient obtained from the above formula, as well as the rated voltage of the power grid, the complex power of the new energy source injected into the new energy grid-connected bus node, and the calculated voltage stability constraint value, this step may include: The weighted composite short-circuit ratio is calculated based on the grid's rated voltage, the complex power of the new energy source injected into the new energy grid-connected bus node, the electrical distance weight, and the transient power fluctuation sensitivity coefficient.

[0050] Calculate the voltage stability constraint value based on the weighted composite short-circuit ratio.

[0051] In this embodiment, the weighted composite short-circuit ratio is calculated using the following formula:

[0052] In the formula, The rated voltage of the power grid. For the first i The per-unit phasor of the actual operating voltage of each grid-connected bus node. and The first i The and the firstj The combined power of the new energy power injected into each new energy grid-connected bus node For nodes i and j Self-impedance between them For nodes i and j per-unit value of mutual impedance between them For nodes i and j The magnitude of the per-unit mutual impedance between them and They are nodes i and j The voltage phasor.

[0053] Accordingly, the calculation of voltage stability constraint values ​​based on the weighted composite short-circuit ratio can include: Determine the installed capacity of new energy sources connected to each node of the power grid when the weighted composite short-circuit ratio is greater than or equal to the voltage stability threshold.

[0054] The sum of the installed capacity of new energy sources connected to each node of the power grid is used as the voltage stability constraint value.

[0055] The installed capacity of new energy sources connected to each node of the power grid can be calculated using the following formula:

[0056] In the formula, ζ is the voltage stability threshold. Indicates satisfaction The first condition of the power grid i The installed capacity of new energy connected to each node.

[0057] Correspondingly, the voltage stability constraint value can be expressed as: .

[0058] In an optional embodiment, step 140, which determines the maximum renewable energy carrying capacity of the target power system based on stability constraint values, topology security constraint values, and voltage stability constraint values, may include: The minimum carrying capacity obtained under stability constraints, topology security constraints, and voltage stability constraints will be taken as the maximum carrying capacity of new energy sources in the target power system.

[0059] In this embodiment, the calculation formula can be expressed as:

[0060] This formula represents the minimum capacity for new energy to be determined under these three constraints, and the determined minimum capacity for new energy to be used as the maximum capacity for new energy to be carried by the target power system.

[0061] In an optional embodiment, after determining the maximum renewable energy carrying capacity of the target power system in step 140 based on stability constraint values, topology security constraint values, and voltage stability constraint values, the method further includes: Determine whether the current capacity of new energy sources is less than the maximum capacity of new energy sources.

[0062] If not, reduce the current new energy carrying capacity until it is less than the maximum new energy carrying capacity.

[0063] In this embodiment, if the current renewable energy carrying capacity of the power grid, i.e., the capacity of the renewable energy connected, is less than... If the amount of renewable energy connected is sufficient, it is considered to be able to withstand the influx of renewable energy; otherwise, the amount of renewable energy connected needs to be reduced until it is less than [a certain value]. until.

[0064] Therefore, compared to traditional methods that evaluate based on only a single dimension, the method provided in this embodiment of the invention takes a more comprehensive approach, resulting in more scientific and complete evaluation results to meet practical needs. Furthermore, this embodiment can also provide indicators for the capacity allocation of new energy sources. This process does not require grid expansion, can improve the short-circuit ratio at grid nodes, increase grid stability margin, improve grid damping characteristics, and increase the grid's new energy carrying capacity. Simultaneously, even under severe fault conditions, the grid can still support a significant amount of new energy generation power, significantly reducing low-frequency load shedding and minimizing power outage losses for users.

[0065] To verify the effectiveness of the method provided in this embodiment, in an optional embodiment, a verification method is also provided, specifically: This embodiment uses a power grid test system with 1975 nodes for simulation, where the peak capacity of the power grid is 3.2GW and the penetration rate of new energy power generation is 52.8%. The power grid is a 500kV backbone network, that is, 12 substations and a 220kV distribution network, that is, 87 substations interconnected, for a total of 1385 nodes. Among them, the 1385 nodes include 32 PCC nodes and 2927 transmission branches.

[0066] The critical line resistance-to-inductance ratio, or R / X ratio, ranges from 0.2 to 1.8, with a thermal stability limit of 1.5 to 4.2 kA. The grid includes 32 wind farms and 28 photovoltaic power plants. The total capacity of the wind farms is 1.8 GW, employing Type IV doubly-fed inverters with a virtual inertia constant of 2.0–4.5 s and a reactive power reserve of 25%. The total capacity of the photovoltaic power plants is 1.4 GW, using grid-following inverters with low-voltage ride-through capability and an active power ramp rate of 10–15% / s.

[0067] The grid's conventional power sources include 12 thermal power units (467MW each) and 4 hydropower units (300MW each). The total thermal power output is 5.6GW, with a response time (H) of 4.5~6.0s. The total hydropower output is 1.2GW, with an H of 3.8~4.2s. Grid flexibility is provided by 4 sets of 200MWh energy storage systems and 5 static synchronizing compensators (SRCs) with a rated capacity of 70Mvar. The total energy storage capacity is 800MWh, with a response time of <200ms. All SRCs are deployed at weak nodes.

[0068] The simulation was conducted using a power system analysis and simulation platform. The fault scenario included the tripping of Unit N-1, which translates to a loss of 1.2GW and a three-phase fault on a critical 500kV line at t=10s, with a fault clearing time of 150ms. The renewable energy generation power curves were derived from data collected by the grid monitoring and data acquisition system. System behavior was evaluated using quasi-steady-state time-series simulation at 8760 operating points.

[0069] In this embodiment, the reference evaluation method is the traditional Short Circuit Ratio Method (SCR). The convergence threshold is set as: carrying capacity. ε =1e-4, safety boundary δ =0.5%. The simulation comparison results are shown in Table 1: Table 1 Comparison of simulation results under severe faults

[0070] Table 1 illustrates the resilience advantages of the proposed method under a 1.2GW power generation loss fault. Compared with the traditional SCR method, the maximum RoCoF (rate of change of grid frequency) of the proposed method is 0.112Hz / s, which is lower than the protection threshold of 0.125Hz / s; this represents a 38.1% improvement compared to the traditional SCR method. The lowest frequency point rises from 49.15Hz to 49.52Hz, crossing the 49.5Hz safety boundary, reducing voltage over-limit by 82.9%, shortening recovery time by 41.6%, and improving the control damping characteristics of the renewable energy power generation converter. Compared with the traditional SCR method, the reduction in renewable energy power generation is reduced by 67.7%, which is equivalent to supplying electricity to an additional 193,000 households during the fault, indicating that the grid can still support a significant amount of renewable energy power generation even under severe faults.

[0071] Table 2 Impact on system strength indicators

[0072] As shown in Table 2, after optimizing the power grid using the method provided in this embodiment of the invention, the minimum short-circuit ratio increased from 1.8 to 2.35, a 30.6% improvement, exceeding the specified critical stability threshold of 2.0. This transformed the system from "extremely weak" to "sufficiently strong," resulting in a 13.9% increase in carrying capacity, a 78.4% reduction in the renewable energy reduction rate during faults, and a 27.1% increase in equivalent inertia. Eigenvalue analysis confirmed that the power grid stability margin increased by +127.8%, and the power grid damping characteristics were significantly improved.

[0073] Figure 2 This is a power grid frequency curve provided in the embodiments of the present invention, which includes frequency curves obtained by conventional, SCR, and methods provided in the embodiments of the present invention.

[0074] from Figure 2 It is evident that the frequency curves obtained using both the traditional SCR method and the method proposed in this invention are higher than the national standard safety threshold of 49.5Hz. However, the lowest frequency of the simulation curve obtained by the method proposed in this invention is higher than that obtained by the traditional SCR method. The simulation results verify the effectiveness of the method proposed in this invention in coordinating multi-source inertia in the power grid, improving the frequency response characteristics under severe power loss conditions, significantly reducing the dependence on low-frequency load shedding during severe faults, and reducing power outage losses for users.

[0075] Figure 3 This is a comparison diagram of the voltage support strength of key nodes provided by the embodiments of the present invention; it shows the comparison between the measured values ​​obtained by the conventional method, the measured values ​​obtained by the embodiments of the present invention, and the theoretical boundary and stability threshold.

[0076] from Figure 3 As can be seen, the traditional SCR method, with an SCR of 1.8 < 2.0, failed to detect node 2308 as a weak node in the power grid. However, the method proposed in this paper successfully identified this weak node through electrical distance weighting and power sensitivity correction.

[0077] Traditional methods reach their limit at a renewable energy penetration rate of 60%, or 3.6GW. Further increasing renewable energy capacity will lead to voltage collapse at vulnerable nodes, such as node 2308. This invention proposes a method that expands the maximum renewable energy capacity to 4.1GW, or 72% penetration, representing a 13.9% improvement over traditional methods.

[0078] Figure 4 This is a comparison chart of new energy carrying capacity provided by an embodiment of the present invention; it shows the maximum carrying capacity of the traditional method and the method provided in this embodiment under the growth of new energy penetration in Jiangsu and Zhejiang.

[0079] from Figure 4As shown, if the N-1 security constraint is considered, the actual carrying capacity of the power grid is 3.9GW (65% penetration rate), which meets the requirement of the 65% penetration rate target. However, traditional methods cannot meet this requirement.

[0080] Figure 5 This is a voltage-frequency-capacity relationship diagram provided in an embodiment of the present invention; as shown below. Figure 5 As shown, the traditional SCR method calculates a short-circuit ratio of 1.8 for the 2308 nodes, which is less than the threshold of 2.0 and thus falls within the instability risk zone. After adopting this method, the short-circuit ratio becomes 2.3, which is greater than the threshold of 2.0 and thus falls within the safe zone.

[0081] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0082] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0083] Figure 6 A schematic diagram of the structure of the power system renewable energy carrying capacity assessment device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 6 As shown, the power system's renewable energy carrying capacity assessment device 6 includes: Acquisition module 61 is used to acquire system parameters, new energy unit data and substation data of the target power system; Calculation module 62 is used to calculate stability constraint values ​​and topology safety constraint values ​​based on system parameters and new energy unit data; The calculation module 62 is also used to calculate voltage stability constraint values ​​based on system parameters, new energy unit data and substation data; Evaluation module 63 is used to determine the maximum capacity of new energy sources in the target power system based on stability constraint values, topology security constraint values, and voltage stability constraint values.

[0084] In one possible implementation, system parameters include the rated voltage of the power grid; new energy unit data includes the complex power of new energy sources injected into the new energy grid connection bus nodes; substation data includes the mutual impedance modulus, phase, active power, and voltage between substations; calculation module 62 is specifically used for: Calculate the electrical distance weight based on the mutual impedance modulus and phase between substations; Calculate the transient power fluctuation sensitivity coefficient based on the active power and voltage of the substation. The voltage stability constraint value is calculated based on the grid's rated voltage, the complex power of the new energy source injected into the new energy grid-connected bus node, the electrical distance weight, and the transient power fluctuation sensitivity coefficient.

[0085] In one possible implementation, the computation module 62 is specifically used for: The weighted composite short-circuit ratio is calculated based on the rated voltage of the power grid, the complex power of the new energy power injected into the new energy grid-connected bus node, the electrical distance weight, and the transient power fluctuation sensitivity coefficient. Calculate the voltage stability constraint value based on the weighted composite short-circuit ratio.

[0086] In one possible implementation, the computation module 62 is specifically used for: Determine the installed capacity of new energy connected to each node of the power grid when the weighted composite short-circuit ratio is greater than or equal to the voltage stability threshold; The sum of the installed capacity of new energy sources connected to each node of the power grid is used as the voltage stability constraint value.

[0087] In one possible implementation, the system parameters also include the long-term allowable thermal stability current of the power grid lines, the instantaneous current before a short-circuit fault, the static thermal stability power transmission limit, and the load data connected to the end of each line; the renewable energy unit data includes the renewable energy power generation capacity strongly correlated with each line in the power grid; the calculation module 62 is specifically used for: The safety participation factor is calculated based on the long-term allowable thermal stability current of the power grid lines and the instantaneous current before a short-circuit fault. Based on the safety participation coefficient, the static thermal stability power transmission limit, the load data connected to the end of each line, and the strong correlation of new energy power generation in each line of the power grid, the topology safety constraint value is calculated.

[0088] In one possible implementation, the system parameters also include the equivalent inertia of the power grid, the total capacity of the power grid, the rated frequency of the power system, and the frequency deviation limit; the calculation module 62 is specifically used for: The grid inertia attenuation coefficient is calculated based on the ratio of the installed capacity of new energy sources to the total grid capacity. The ratio of the product of the power grid's equivalent inertia, the power grid's inertia attenuation coefficient, and the power system's rated frequency to the frequency deviation limit is used as the stability constraint value.

[0089] In one possible implementation, evaluation module 63 is specifically used for: The minimum carrying capacity obtained under stability constraints, topology security constraints, and voltage stability constraints will be taken as the maximum carrying capacity of new energy sources in the target power system.

[0090] In one possible implementation, evaluation module 63 is also used for: Determine whether the current new energy carrying capacity is less than the maximum new energy carrying capacity; If not, reduce the current new energy carrying capacity until it is less than the maximum new energy carrying capacity.

[0091] Figure 7 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 7 As shown, the electronic device 7 of this embodiment includes a processor 70 and a memory 71. The memory 71 stores a computer program 72. When the processor 70 executes the computer program 72, it implements the steps in the various method embodiments described above. Alternatively, when the processor 70 executes the computer program 72, it implements the functions of each module / unit in the various device embodiments described above.

[0092] For example, computer program 72 may be divided into one or more modules / units, which are stored in memory 71 and executed by processor 70 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 72 in electronic device 7.

[0093] Electronic device 7 may include, but is not limited to, processor 70 and memory 71. Those skilled in the art will understand that... Figure 7 This is merely an example of electronic device 7 and does not constitute a limitation on electronic device 7. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 7 may also include input / output devices, network access devices, buses, etc.

[0094] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0095] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0096] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for assessing the renewable energy carrying capacity of a power system, characterized in that, include: Collect system parameters, new energy unit data, and substation data of the target power system; Based on the system parameters and new energy unit data, calculate the stability constraint value and the topology safety constraint value; Calculate the voltage stability constraint value based on the system parameters, the new energy unit data, and the substation data; The maximum capacity of new energy sources for the target power system is determined based on the stability constraint value, the topology security constraint value, and the voltage stability constraint value.

2. The method for assessing the renewable energy carrying capacity of a power system according to claim 1, characterized in that, The system parameters include the rated voltage of the power grid; the new energy unit data includes the complex power of the new energy power injected into the new energy grid connection bus node; the substation data includes the mutual impedance modulus, phase, active power, and voltage between substations; the calculation of voltage stability constraint values ​​based on the system parameters, the new energy unit data, and the substation data includes: The electrical distance weight is calculated based on the mutual impedance modulus and phase between the substations. Calculate the transient power fluctuation sensitivity coefficient based on the active power and voltage of the substation; The voltage stability constraint value is calculated based on the rated voltage of the power grid, the complex power of the new energy source injected into the new energy grid-connected bus node, the electrical distance weight, and the transient power fluctuation sensitivity coefficient.

3. The method for assessing the renewable energy carrying capacity of a power system according to claim 2, characterized in that, The calculation of voltage stability constraint values ​​based on the rated voltage of the power grid, the complex power of the new energy source injected into the new energy grid-connected bus node, the electrical distance weight, and the transient power fluctuation sensitivity coefficient includes: The weighted composite short-circuit ratio is calculated based on the rated voltage of the power grid, the complex power of the new energy power injected into the new energy grid-connected bus node, the electrical distance weight, and the transient power fluctuation sensitivity coefficient. Calculate the voltage stability constraint value based on the weighted composite short-circuit ratio.

4. The method for assessing the renewable energy carrying capacity of a power system according to claim 3, characterized in that, The step of calculating the voltage stability constraint value based on the weighted composite short-circuit ratio includes: Determine the installed capacity of new energy connected to each node of the power grid when the weighted composite short-circuit ratio is greater than or equal to the voltage stability threshold; The sum of the installed capacity of new energy sources connected to each node of the power grid is used as the voltage stability constraint value.

5. The method for assessing the renewable energy carrying capacity of a power system according to claim 1, characterized in that, The system parameters also include the long-term allowable thermal stability current of the power grid lines, the instantaneous current before a short-circuit fault, the static thermal stability power transmission limit, and the load data connected to the end of each line; the renewable energy unit data includes the renewable energy power generation capacity strongly correlated with each line in the power grid; the calculation of topology security constraint values ​​based on the system parameters and renewable energy unit data includes: The safety participation factor is calculated based on the long-term allowable thermal stability current of the lines in the power grid and the instantaneous current before the short-circuit fault. The topology security constraint value is calculated based on the security participation coefficient, the static thermal stability power transmission limit, the load data connected to the end of each line, and the strongly correlated new energy power generation power of each line in the power grid.

6. The method for assessing the renewable energy carrying capacity of a power system according to claim 1, characterized in that, The system parameters also include the equivalent inertia of the power grid, the total capacity of the power grid, the rated frequency of the power system, and the frequency deviation limit; the new energy unit data includes the installed capacity of new energy; the calculation of stability constraint values ​​based on the system parameters and the new energy unit data includes: The grid inertia attenuation coefficient is calculated based on the ratio of the installed capacity of the new energy source to the total grid capacity. The ratio of the product of the equivalent inertia of the power grid, the inertia attenuation coefficient of the power grid, and the rated frequency of the power system to the frequency deviation limit is used as the stability constraint value.

7. The method for assessing the renewable energy carrying capacity of a power system according to claim 1, characterized in that, The step of determining the maximum renewable energy carrying capacity of the target power system based on the stability constraint value, the topology security constraint value, and the voltage stability constraint value includes: The minimum carrying capacity obtained under the stability constraint value, the topology security constraint value, and the voltage stability constraint value shall be taken as the maximum carrying capacity of new energy in the target power system.

8. The method for assessing the renewable energy carrying capacity of a power system according to claim 1, characterized in that, After determining the maximum renewable energy carrying capacity of the target power system based on the stability constraint value, the topology security constraint value, and the voltage stability constraint value, the method further includes: Determine whether the current new energy carrying capacity is less than the maximum carrying capacity of the new energy source; If not, then reduce the current new energy carrying capacity until the current new energy carrying capacity is less than the maximum new energy carrying capacity.

9. A device for assessing the renewable energy carrying capacity of a power system, characterized in that, include: The data acquisition module is used to collect system parameters, new energy unit data, and substation data of the target power system. The calculation module is used to calculate stability constraint values ​​and topology safety constraint values ​​based on the system parameters and new energy unit data; The calculation module is also used to calculate voltage stability constraint values ​​based on the system parameters, the new energy unit data, and the substation data; The evaluation module is used to determine the maximum capacity of new energy sources in the target power system based on the stability constraint value, the topology security constraint value, and the voltage stability constraint value.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 8.

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