Response map construction method for supporting power supply capacity and new energy consumption capability

Abstract

The invention discloses a supporting power supply capacity and new energy consumption capability response graph construction method, which comprises the following steps: carrying out explicit regularization constraint on the installation position of each supporting power supply, and simplifying the complex supporting power supply overall configuration problem into a capacity configuration problem on the premise that a project can be implemented; after sample data of capacity ratio vectors are collected, simulation calculation is performed based on a set configuration rule and a power grid simulation model to obtain a plurality of response indexes, so that a capacity ratio response map is constructed and obtained, and the map represents a mapping relation between any capacity ratio vector and the plurality of response indexes. The supporting power supply capacity configuration scheme can be rapidly adjusted and evaluated in the power grid planning and design stage, the overall influence of the supporting power supply capacity change on the new energy consumption capacity and the transient overvoltage risk can be accurately described, and the planning and design requirements of rapid change of the new energy scale and multi-scene repeated evaluation are well met.

Description

Technical Field

[0001] This invention relates to the field of power system planning and design technology, and in particular to a method and system for constructing response maps of supporting power supply capacity and renewable energy absorption capacity, electronic equipment, and computer-readable storage medium. Background Technology

[0002] With the continuous and rapid growth of installed capacity of new energy sources, areas with concentrated new energy grid connection generally face problems such as insufficient short-circuit capacity, weak voltage support capability, and frequent transient overvoltages after faults. These issues directly restrict the output level of new energy sources, leading to wind and solar power curtailment. Engineering practice shows that relying solely on grid reinforcement or single supporting equipment is difficult to solve these problems economically and effectively. It is usually necessary to coordinate multiple types of supporting power sources, such as SC (Synchronous Condenser), SVG (Static Var Generator), and BESS (Battery Energy Storage System), to improve grid strength and voltage support capability during faults.

[0003] However, existing technologies primarily rely on experience to configure multiple supporting power supply capacities and then compare different options using simulation verification. The specific process involves first empirically determining the capacity of each supporting power supply, then verifying it through electromagnetic transient simulation or electromechanical transient simulation. If the requirements are not met, the capacity configuration scheme is repeatedly adjusted. Therefore, because existing technologies heavily depend on human experience, the adjustment efficiency of supporting power supply capacity configuration schemes is low. It is difficult to systematically characterize the overall impact of changes in supporting power supply capacity on renewable energy absorption capacity and transient overvoltage risk, and it cannot establish a direct mapping relationship between renewable energy absorption targets and supporting power supply capacity configuration. Consequently, it cannot meet the planning and design needs of rapidly changing renewable energy scale and repeated evaluations across multiple scenarios. Summary of the Invention

[0004] This invention provides a method and system for constructing a response spectrum of supporting power supply capacity and renewable energy absorption capacity, as well as an electronic device and a computer-readable storage medium. It can quickly adjust and evaluate the supporting power supply capacity configuration scheme during the power grid planning and design stage, accurately characterize the overall impact of changes in supporting power supply capacity on renewable energy absorption capacity and transient overvoltage risk, and form a direct mapping relationship between renewable energy absorption targets and supporting power supply capacity configuration. It well meets the planning and design needs of rapid changes in renewable energy scale and repeated evaluation in multiple scenarios.

[0005] According to one aspect of the present invention, a method for constructing a response spectrum of supporting power capacity and renewable energy absorption capacity is provided, comprising the following: Construct a power grid simulation model; Configure configuration rules for each supporting power source; wherein, the configuration rules include location configuration rules and capacity configuration rules; A capacity allocation vector is constructed based on the capacity of multiple supporting power sources. Multiple sample data of the capacity allocation vector are collected, and the installed capacity of each sample data is allocated according to the set configuration rules. Simulation calculations are performed based on the installed capacity allocation results of each sample data and the power grid simulation model to obtain multiple response indicators. Among them, the response indicators include the response of renewable energy absorption capacity, the transient overvoltage probability response, and the response of minimum renewable energy multi-station short-circuit ratio. Based on multiple sample data and corresponding multiple response indicators, a capacity ratio response map is constructed; wherein, the capacity ratio response map represents the mapping relationship between any capacity ratio vector and multiple response indicators.

[0006] Furthermore, the process of constructing a capacity matching response map based on multiple sample data and corresponding multiple response indicators includes the following: Construct a basic capacity allocation response model between the capacity allocation vector and each response index; Based on multiple sample data and corresponding response indicators, parameter identification is performed on each basic capacity ratio response model to obtain the corresponding capacity ratio response optimization model. Based on multiple capacity ratio response optimization models, predicted values ​​of multiple response indicators corresponding to any capacity ratio vector are obtained, and a capacity ratio response map is constructed.

[0007] Furthermore, the supporting power source includes a synchronous condenser, an energy storage system, and a dynamic reactive power compensation device, and the expression for the capacity ratio response optimization model is: ; in, Represents the capacity matching vector The corresponding number j The predicted value of each response indicator, Represents the capacity matching vector The first in a dimensional variables, Represents the capacity matching vector The first in b dimensional variables, , and All are the first j The regression coefficients corresponding to each response indicator.

[0008] Furthermore, it also includes the following: A fast forward query of renewable energy absorption capacity can be performed based on the capacity ratio response map, or a fast reverse query of the capacity ratio of multiple supporting power sources can be performed based on the capacity ratio response map.

[0009] Furthermore, the process of performing a fast reverse lookup of multiple supporting power supply capacity ratios based on the capacity ratio response map includes the following: Given constraints for multiple response metrics; A regular spatial grid structure is constructed within the capacity allocation space. Each grid point in the spatial grid structure corresponds to a capacity allocation vector. For each grid point, the predicted values ​​of multiple response indicators are obtained based on the capacity allocation response map, and it is determined whether the predicted values ​​of multiple response indicators satisfy the constraints. After completing the grid point traversal, output the multiple grid points that satisfy the constraints as a feasible point set.

[0010] Furthermore, the supporting power supply includes a synchronous condenser, an energy storage system, and a dynamic reactive power compensation device. The capacity configuration rule for the energy storage system and the dynamic reactive power compensation device is to allocate capacity according to the proportion of new energy installed capacity of each bus. The capacity configuration rule for the synchronous condenser is to allocate capacity based on maximizing the minimum short-circuit ratio.

[0011] In addition, the present invention also provides a response spectrum construction system for supporting power capacity and renewable energy absorption capacity, comprising: The simulation model building module is used to build power grid simulation models; The configuration rule setting module is used to set the configuration rules for each supporting power source; wherein, the configuration rules include location configuration rules and capacity configuration rules; The simulation calculation module is used to construct a capacity allocation vector based on the capacity of multiple supporting power sources, collect multiple sample data of the capacity allocation vector, allocate the installed capacity of each sample data according to the set configuration rules, and perform simulation calculations based on the installed capacity allocation results of each sample data and the power grid simulation model to obtain multiple response indicators; among which, the response indicators include the response of renewable energy absorption capacity, the transient overvoltage probability response, and the response of minimum renewable energy multi-station short-circuit ratio. The response map construction module is used to construct a capacity ratio response map based on multiple sample data and corresponding multiple response indicators; wherein, the capacity ratio response map represents the mapping relationship between any capacity ratio vector and multiple response indicators.

[0012] Furthermore, it also includes: The response graph query module is used for quick forward query of new energy absorption capacity based on the capacity ratio response graph, or quick reverse query of multiple supporting power capacity ratios based on the capacity ratio response graph.

[0013] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0014] In addition, the present invention also provides a computer-readable storage medium for storing a computer program for constructing a response spectrum of supporting power capacity and new energy absorption capacity, wherein the computer program executes the steps of the method described above when running on a computer.

[0015] The present invention has the following beneficial effects: The method for constructing a response map of supporting power source capacity and renewable energy absorption capacity in this invention explicitly and regularly constrains the installation location of each supporting power source by setting location configuration rules for each supporting power source. This simplifies the complex overall configuration problem of supporting power sources into a capacity configuration problem, provided it is feasible in engineering. Furthermore, by constructing a capacity ratio vector using the capacity of multiple supporting power sources as optimization variables, and after collecting multiple sample data of the capacity ratio vector, simulation calculations are performed based on the set configuration rules and the constructed power grid simulation model to obtain multiple response indicators. This provides a data foundation for constructing the response map. Based on the collected sample data and the corresponding response indicators obtained from the simulation calculations, a capacity ratio response map is constructed. This response map characterizes the mapping relationship between any capacity ratio vector and multiple response indicators, facilitating rapid adjustment and evaluation of supporting power source capacity configuration schemes during the power grid planning and design phase. It can accurately depict the overall impact of supporting power source capacity changes on renewable energy absorption capacity and transient overvoltage risk, and also establish a direct mapping relationship between renewable energy absorption targets and supporting power source capacity configuration, effectively meeting the planning and design needs of rapidly changing renewable energy scale and repeated evaluations across multiple scenarios.

[0016] In addition, the response spectrum construction system for the supporting power capacity and the renewable energy absorption capacity of the present invention also has the above-mentioned advantages.

[0017] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating the method for constructing a response spectrum of supporting power capacity and renewable energy absorption capacity according to a preferred embodiment of this application. Figure 2 yes Figure 1 A schematic diagram of the sub-process of step S4; Figure 3 This is another flowchart illustrating the method for constructing the response spectrum of supporting power capacity and new energy absorption capacity according to a preferred embodiment of this application; Figure 4 yes Figure 3 A schematic diagram of the sub-process of step S5; Figure 5 This is a schematic diagram of the module structure of a response spectrum construction system for supporting power capacity and renewable energy absorption capacity according to another embodiment of this application. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Reference Figure 1 A preferred embodiment of this application provides a method for constructing a response spectrum of supporting power capacity and renewable energy absorption capacity, including the following: Step S1: Construct a power grid simulation model; Step S2: Set the configuration rules for each supporting power source; wherein the configuration rules include location configuration rules and capacity configuration rules; Step S3: Construct a capacity allocation vector based on the capacity of multiple supporting power sources, collect multiple sample data of the capacity allocation vector, and allocate the installed capacity of each sample data according to the set configuration rules. Perform simulation calculations based on the installed capacity allocation results of each sample data and the power grid simulation model to obtain multiple response indicators; among which, the response indicators include the response of renewable energy absorption capacity, the transient overvoltage probability response, and the response of minimum renewable energy multi-station short-circuit ratio. Step S4: Construct a capacity ratio response map based on multiple sample data and corresponding multiple response indicators; wherein the capacity ratio response map represents the mapping relationship between any capacity ratio vector and multiple response indicators.

[0021] It is understood that the response spectrum construction method of supporting power capacity and new energy absorption capacity in this embodiment, by setting location configuration rules for each supporting power source, explicitly and regularly constrains the installation location of each supporting power source. Under the premise of engineering feasibility, it can simplify the complex overall configuration problem of supporting power sources into a capacity configuration problem. Furthermore, by constructing a capacity allocation vector using the capacity of multiple supporting power sources as optimization variables, and after collecting multiple sample data of the capacity allocation vector, simulation calculations are performed based on the set configuration rules and the constructed power grid simulation model to obtain multiple response indicators. This provides a data foundation for the construction of the response map. Based on the collected sample data and the corresponding response indicators obtained from the simulation calculations, a capacity allocation response map can be constructed. This response map characterizes the mapping relationship between any capacity allocation vector and multiple response indicators, facilitating rapid adjustment and evaluation of supporting power source capacity configuration schemes during the power grid planning and design phase. It can accurately depict the overall impact of supporting power source capacity changes on renewable energy absorption capacity and transient overvoltage risk, and can also form a direct mapping relationship between renewable energy absorption targets and supporting power source capacity configuration, thus well meeting the planning and design needs of rapid changes in renewable energy scale and repeated evaluations in multiple scenarios.

[0022] In step S1, a planning-level power grid simulation model is constructed based on the actual network topology of the new energy base, including the main grid, collection stations, and new energy power plants, and the model parameters such as the installed capacity of new energy, grid connection voltage level, and output simultaneity rate are specified. Furthermore, the specific simulation model construction process and principles are existing technologies and will not be elaborated upon here.

[0023] Furthermore, in step S2, new energy bases typically employ multiple types of supporting power sources, such as synchronous condensers (SCs), static var generators (SVGs), and battery energy storage systems (BESSs), for coordinated configuration to enhance grid strength and voltage support capabilities during faults. The selection, capacity setting, and allocation of multiple supporting power sources constitute a complex combination problem. To ensure project feasibility and avoid combination explosion, this invention establishes configuration rules for each supporting power source, including location configuration rules and capacity configuration rules. Additionally, this invention will use synchronous condensers, static var generators, and battery energy storage systems as examples for illustrative purposes, without making specific limitations.

[0024] Specifically, regarding the location configuration rules, the dynamic reactive power compensation device needs to be installed on the 37kV collection bus of the renewable energy power station, the energy storage system needs to be connected to the 220kV step-up substation bus of the renewable energy power station, and the synchronous condenser needs to be installed on the 37kV collection bus and the key 220kV bus for renewable energy collection or transmission. Regarding the capacity configuration rules, the capacity allocation for the energy storage system and the dynamic reactive power compensation device is based on the renewable energy installed capacity ratio of each bus. The capacity allocation for the synchronous condenser adopts a discrete incremental allocation method, maximizing the minimum short-circuit ratio while ensuring a balanced increase in the output simultaneity rate of the renewable energy power station. The capacity configuration rule for the dynamic reactive power compensation device can be expressed as follows: ,in, Indicates busbar i The allocated capacity of the dynamic reactive power compensation device, This indicates the total capacity of the dynamic reactive power compensation device. Indicates busbar i The installed capacity of new energy sources, N This indicates the number of monitored buses. The capacity configuration rules for an energy storage system can be expressed as: ,in, Indicates busbar i The allocated energy storage system capacity (i.e., power). This represents the total capacity of the energy storage system. Additionally, the capacity configuration rules for the synchronous condenser are as follows: ; in, Indicates the minimum short-circuit ratio. and They represent the busbars respectively. i In minimum short-circuit ratio The active and reactive power of power generation are as follows: and They represent the busbars respectively. i The active and reactive loads, and They represent the busbars respectively. and busbar voltage amplitude, N Indicates the number of busbars monitored. Indicates busbar and busbar phase angle, and These are the real and imaginary parts of the elements of the bus admittance matrix, respectively. and They represent the busbars respectively. i The lower and upper limits of active power generation. and They represent the busbars respectively. iThe lower and upper limits of reactive power generation. Indicates a branch In minimum short-circuit ratio The current of meritorious service Indicates a branch Thermal stability limit, Indicates busbar i In minimum short-circuit ratio The voltage amplitude under the condition, and busbars i The lower and upper voltage limits, Indicates the bus after the fault In minimum short-circuit ratio The peak voltage under these conditions needs to be obtained through stability calculations. This indicates the required peak transient voltage after a fault; for example, the national standard requires it to be no greater than 1.3 pu. Indicates busbar The minimum short-circuit ratio requirement for multiple new energy power stations is set at 1.5 according to national standards. Indicates busbar In minimum short-circuit ratio The short-circuit ratio of multiple new energy power stations. Furthermore, the formula for calculating the short-circuit ratio of multiple new energy power stations is: ,in, Indicates busbar i Short-circuit capacity, , Indicates busbar i The actual voltage, Indicates busbar i Rated voltage, Indicates busbar Thevenin equivalent self-impedance, and They represent the busbars respectively. and busbar The new energy sources inject active power, Indicates busbar and busbar Thevenin equivalent mutual impedance between them Indicates busbar j The actual voltage.

[0025] It is understood that this invention explicitly and regularly constrains the installation location of each supporting power source by setting location configuration rules for each supporting power source. Furthermore, the dynamic reactive power compensation device and energy storage system allocate capacity based on the proportion of new energy installed capacity of each bus, while the synchronous condenser allocates capacity based on maximizing the minimum short-circuit ratio. This ensures the fairness and reliability of capacity configuration for various supporting power sources and simplifies the complex overall configuration problem of supporting power sources into a capacity configuration problem, provided that the project is feasible.

[0026] Furthermore, in step S3, the capacity matching vector constructed based on the total capacity of the dynamic reactive power compensation device, energy storage system, and synchronous condenser can be expressed as: The capacity allocation vector is used as a free variable in the graph construction. Furthermore, to reflect the impact of the capacity allocation vector of supporting power sources on grid operation, this invention adopts the response of renewable energy absorption capacity, the transient overvoltage probability response, and the response of the minimum renewable energy multi-station short-circuit ratio as response indicators. Among them, the renewable energy absorption capacity response characterizes the actual utilization rate of renewable energy generation in the grid under a given capacity allocation vector, and can be expressed as: ,in, Represented in the capacity ratio vector The actual utilization rate of new energy power generation under the following conditions Represented in the capacity ratio vector Below is the amount of wind and solar power curtailed due to safety and stability constraints (unit: MWh). This represents the available renewable energy power (in MWh) within the assessment time window, determined by wind and solar resources and installed capacity, and is correlated with the capacity matching vector. Irrelevant. Furthermore, the transient overvoltage probabilistic response characterizes the probability of a transient overvoltage occurring on the monitored bus in the system for a given preset fault set, given a capacity ratio vector, and can be expressed as: ,in, This indicates the probability of a transient overvoltage occurring on the monitored bus. This indicates the number of faults in the preset fault set. Indicates the fault start time. Indicates the simulation end time. Indicates the transient overvoltage limit value. This indicates that for a given capacity allocation vector busbar i In the fault f The instantaneous voltage value under, As an indicator function, for a given capacity-to-weight ratio vector When the busbar i In the fault fWhen the peak voltage exceeds the transient overvoltage limit, the value is 1; otherwise, the value is 0. Furthermore, the minimum short-circuit ratio response of multiple new energy power plants characterizes the minimum short-circuit ratio of multiple new energy power plants at the monitoring bus in the system under a given capacity ratio vector, and can be expressed as: ,in, Indicates monitoring bus k The short-circuit ratio of new energy multi-stations.

[0027] In order to construct a continuous mapping: ,in, Indicates the first j A response index in a given capacity allocation vector The actual value below, Indicates the first j A response index in a given capacity allocation vector To obtain the predicted values, a training dataset is required. ,in, , representing the first of the capacity matching vectors m One sample data, , representing the three-response output corresponding to the m-th sample data. The number of sample points is determined by the experimental design. For each sample data point... The system automatically allocates installation capacity according to the location and capacity configuration rules set in step S2, obtaining the installation capacity allocation results of SC / SVG / BESS and the corresponding bus information, i.e., the supporting power capacity installed on each bus. Then, combined with the power grid simulation model, fault set / disturbance set, equipment control parameters, etc., simulation tools (such as PSD-BPA) are used to perform simulation calculations, including the calculation of short-circuit ratio of new energy multi-station and fault simulation, etc., to extract the result data such as the short-circuit ratio of new energy multi-station and the voltage curve after each fault of the monitoring bus, thereby obtaining the three-response output corresponding to each sample data.

[0028] It is understood that after collecting multiple sample data of the capacity ratio vector, this invention performs simulation calculations on each sample data based on the set configuration rules and the constructed power grid simulation model, and obtains three response indicators corresponding to each sample data. It can obtain the mapping data between each sample data and the three response outputs, providing an accurate data foundation for the subsequent construction of the response spectrum.

[0029] In addition, such as Figure 2 As shown, in step S4, the process of constructing a capacity matching response map based on multiple sample data and corresponding multiple response indicators includes the following: Step S41: Construct a basic capacity matching response model between the capacity matching vector and each response index; Step S42: Based on multiple sample data and corresponding response indicators, perform parameter identification on each basic capacity ratio response model to obtain the corresponding capacity ratio response optimization model; Step S43: Based on multiple capacity ratio response optimization models, obtain the predicted values ​​of multiple response indicators corresponding to any capacity ratio vector, and construct a capacity ratio response map.

[0030] Specifically, for each response metric First, a basic capacity allocation response model is constructed to represent the mapping relationship between the capacity allocation vector and each response index. Optionally, the expression of the basic capacity allocation response model constructed in this invention is as follows: ; in, Represents the capacity matching vector The corresponding number j The predicted value of each response indicator, Represents the capacity matching vector The first in a Dimensional variables, capacity matching vector Includes three variables: the total capacity of SC, SVG, and BESS. Represents the capacity matching vector The first in b dimensional variables, , and All are the first j The regression coefficients corresponding to each response index. It can be understood that the response model constructed in this invention simultaneously includes linear and quadratic cross terms, which can characterize the coupling effects and nonlinearities between different supporting power sources, such as the complementarity of SC and SVG, and can more accurately describe the mapping relationship between the capacity ratio vector and each response index.

[0031] Then, based on the collected sample data and corresponding response indicators, parameter identification was performed on the basic model for each capacity ratio response, that is, the regression coefficients in the above model expression were identified. , and Solving this model yields the corresponding capacity-to-response optimization model. The regression coefficients can be obtained using the least squares method, for example, by constructing the design matrix. , To indicate the number of terms, then Its closed-form solution is: ,in, Represents the vector of regression coefficients (including regression coefficients) , and The solution to ) , indicating the first j Each response metric in M The output vector of each sample data. Furthermore, the least squares method is an existing algorithm, and its specific solution process and principles will not be elaborated here. Additionally, compared to the basic capacity-matching response model, the capacity-matching response optimization model explicitly defines the specific values ​​of the three regression coefficients.

[0032] After obtaining the capacity allocation response optimization model corresponding to each of the three response indicators. Then, based on the three capacity ratio response optimization models, the predicted values ​​of the three response outputs corresponding to any capacity ratio vector can be obtained. Specifically, the arbitrary capacity ratio vector is input into... In this way, the predicted values ​​of the three response indicators can be obtained respectively, and the predicted values ​​of the continuous response indicators at any point within the capacity allocation space can be obtained, thereby constructing a capacity allocation response map. This capacity allocation response map can directly represent the mapping relationship between any capacity allocation vector and the three response outputs. Additionally, contour plots can be directly drawn on the capacity allocation response map, for example, by fixing... Take several values.

[0033] Optional, such as Figure 3 As shown, the method for constructing the response spectrum of supporting power capacity and new energy absorption capacity also includes the following: Step S5: Perform a rapid forward query of the renewable energy absorption capacity based on the capacity ratio response map, or perform a rapid reverse query of the capacity ratio of multiple supporting power sources based on the capacity ratio response map.

[0034] It is understood that after completing the construction of the capacity ratio response map, this invention can also perform a rapid forward query of the renewable energy absorption capacity based on the capacity ratio response map. Given a supporting power capacity ratio, it can quickly evaluate the renewable energy absorption capacity and safety indicators. Alternatively, it can perform a rapid reverse query of multiple supporting power capacity ratios based on the capacity ratio response map. Given a renewable energy absorption target and safety constraints, it can quickly deduce the supporting power capacity ratio range that meets the requirements. This can avoid the traditional trial-and-error process of "repeated simulation - manual adjustment" in the planning and design stage, and greatly improve the efficiency of engineering decision-making.

[0035] The fast forward query and fast reverse query are based on the following existing achievements: 1) Capacity ratio response optimization model: ; 2) Engineering constraint range for each capacity variable: ,in, and These represent the lower and upper limits of the total capacity of the camera, respectively. and These represent the lower and upper limits of the total capacity of the dynamic reactive power compensation device, respectively. and These represent the lower and upper limits of the total capacity of the energy storage system, respectively. 3) Target absorption and safety constraints: given by planning or design, for example: renewable energy absorption target: Voltage safety requirements: Power grid strength requirements: ,in, This indicates the target value for the capacity to absorb new energy sources. This indicates the maximum probability of a transient overvoltage occurring on the monitored bus. This indicates the target value for the short-circuit ratio of multiple new energy power plants.

[0036] When performing a fast forward query of renewable energy absorption capacity based on the capacity ratio response map, that is, given a capacity ratio vector, to estimate the renewable energy absorption capacity, for example, inputting a set of supporting power supply capacity ratios: The input capacity allocation vector Substitute them into the three capacity ratio response optimization models respectively The corresponding three-response output prediction value can then be calculated. This allows for the assessment of the achievable renewable energy absorption level, whether voltage safety indicators meet requirements, minimum short-circuit ratio of multiple renewable energy stations, and their margins under the given capacity configuration. This process can be completed in seconds without re-simulating the power grid.

[0037] In addition, such as Figure 4 As shown, the process of performing a fast reverse lookup of multiple supporting power supply capacity ratios based on the capacity ratio response map includes the following: Step S51: Given constraints for multiple response metrics; Step S52: Construct a regular spatial grid structure in the capacity matching space. Each grid point in the spatial grid structure corresponds to a capacity matching vector. For each grid point, obtain the predicted values ​​of multiple response indicators based on the capacity matching response map, and determine whether the predicted values ​​of multiple response indicators all meet the constraints. Step S53: After completing the grid point traversal, output the multiple grid points that satisfy the constraints as a feasible point set.

[0038] It can be understood that performing a fast reverse lookup involves querying the capacity allocation vector based on the capacity allocation response map, given a new energy consumption target. A reverse lookup can be defined as finding a solution set within the capacity allocation space that satisfies the following constraints: ,in, This represents the set of feasible capacity allocations that satisfy the target absorption and safety constraints. Specifically, first, multiple response indicator constraints are given, for example, given... , , Then, a regular spatial grid structure is constructed within the capacity allocation space. For example, a three-dimensional space is constructed consisting of the total capacity of SC, the total capacity of SVG, and the total capacity of BESS. Each grid point in the spatial grid structure corresponds to a capacity allocation vector. For example, for a given capacity allocation vector... The coordinates of its grid points in this three-dimensional space are respectively , , , can be represented as For each grid point, predicted values ​​of multiple response indicators can be obtained based on the capacity-matching response map. Then, it is determined whether the predicted values ​​of all response indicators satisfy the constraints. After traversing all grid points, the multiple grid points that satisfy the constraints are selected as the feasible point set. Output. Furthermore, the value ranges for each dimension (i.e., each supporting power capacity) can be calculated further: .

[0039] It is understood that the present invention adopts a feasible region search method based on grid scanning, which transforms the complex new energy consumption assessment problem into a geometric feasible region problem in a low-dimensional capacity space. It can accurately realize the direct mapping from the target consumption level to the supporting power capacity configuration, and accurately obtain multiple capacity configuration schemes that meet the consumption target and safety constraints. It provides planning and design personnel with a capacity decision tool that can be quickly called up and repeatedly reused, which can significantly reduce the reliance on a large number of repeated simulations in the planning and design stage.

[0040] In other embodiments of the present invention, a constraint optimization-based reverse query scheme can be used to transform the reverse query problem into a constraint optimization problem, which can be represented as follows: ; in, It represents a comprehensive capacity indicator (which can represent investment scale, equipment scale, etc.). , , The weights are used to solve this problem using existing heuristic search algorithms or numerical optimization algorithms to obtain the minimum capacity configuration scheme or several Pareto optimal capacity schemes.

[0041] Understandably, based on the two reverse query schemes mentioned above, the reverse query results can output the following: 1) Capacity range recommendations, including the recommended range of total capacity for SC, SVG, and BESS; 2) Typical capacity combination schemes, such as minimum investment schemes, high margin schemes, and balanced schemes; 3) Graphical display, highlighting feasible areas that meet the constraints in the capacity response contour map, intuitively showing the safety-absorption trade-off relationship of different capacity configurations.

[0042] In addition, the present invention has also been simulated and verified. Taking a certain renewable energy transmission area (i.e., renewable energy base) in the Shanxi power grid as an example, the renewable energy installed capacity is about 5200MW. This renewable energy base is operating without support. , , <1.2, the capacity configuration scheme obtained by reverse lookup based on the capacity ratio response map is back-substituted into the simulation. Significant decrease Get promoted The efficiency has been increased to 1.7 or higher to meet the requirements for energy consumption and safety.

[0043] In addition, such as Figure 5 As shown, another embodiment of the present invention also provides a response map construction system for supporting power capacity and renewable energy absorption capacity, preferably employing the response map construction method for supporting power capacity and renewable energy absorption capacity as described above, including: The simulation model building module is used to build power grid simulation models; The configuration rule setting module is used to set the configuration rules for each supporting power source; wherein, the configuration rules include location configuration rules and capacity configuration rules; The simulation calculation module is used to construct a capacity allocation vector based on the capacity of multiple supporting power sources, collect multiple sample data of the capacity allocation vector, allocate the installed capacity of each sample data according to the set configuration rules, and perform simulation calculations based on the installed capacity allocation results of each sample data and the power grid simulation model to obtain multiple response indicators; among which, the response indicators include the response of renewable energy absorption capacity, the transient overvoltage probability response, and the response of minimum renewable energy multi-station short-circuit ratio. The response map construction module is used to construct a capacity ratio response map based on multiple sample data and corresponding multiple response indicators; wherein, the capacity ratio response map represents the mapping relationship between any capacity ratio vector and multiple response indicators.

[0044] It is understood that the response map construction system for the supporting power capacity and the renewable energy absorption capacity in this embodiment, by setting location configuration rules for each supporting power source, explicitly and regularly constrains the installation location of each supporting power source. Under the premise that the project is feasible, it can simplify the complex overall configuration problem of supporting power sources into a capacity configuration problem. Furthermore, by constructing a capacity allocation vector using the capacity of multiple supporting power sources as optimization variables, and after collecting multiple sample data of the capacity allocation vector, simulation calculations are performed based on the set configuration rules and the constructed power grid simulation model to obtain multiple response indicators. This provides a data foundation for the construction of the response map. Based on the collected sample data and the corresponding response indicators obtained from the simulation calculations, a capacity allocation response map can be constructed. This response map characterizes the mapping relationship between any capacity allocation vector and multiple response indicators, facilitating rapid adjustment and evaluation of supporting power source capacity configuration schemes during the power grid planning and design phase. It can accurately depict the overall impact of supporting power source capacity changes on renewable energy absorption capacity and transient overvoltage risk, and can also form a direct mapping relationship between renewable energy absorption targets and supporting power source capacity configuration, thus well meeting the planning and design needs of rapid changes in renewable energy scale and repeated evaluations in multiple scenarios.

[0045] In addition, the response map construction system for the supporting power capacity and the renewable energy absorption capacity also includes: The response graph query module is used for quick forward query of new energy absorption capacity based on the capacity ratio response graph, or quick reverse query of multiple supporting power capacity ratios based on the capacity ratio response graph.

[0046] It is understood that each module of this system embodiment corresponds to each step of the above method embodiment. Therefore, the specific working principle of each module will not be repeated here, and you can refer to the steps of the above method embodiment.

[0047] In addition, another embodiment of the present invention provides an electronic device including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0048] In addition, another embodiment of the present invention provides a computer-readable storage medium for storing a computer program for constructing a response spectrum of supporting power capacity and new energy absorption capacity, wherein the computer program executes the steps of the method described above when running on a computer.

[0049] Common computer-readable storage media include: floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical media with perforated patterns, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash erasable programmable read-only memory (FLASH-EPROM), any other memory chips or cartridges, or any other media readable by a computer. Instructions may further be transmitted or received by a transmission medium. The term transmission medium can include any tangible or intangible medium used to store, encode, or carry instructions for execution by a machine, and includes digital or analog carrier communication signals or intangible media that facilitate communication of such instructions. Transmission media include coaxial cables, copper wires, and optical fibers, which contain conductors for transmitting a bus of computer data signals.

[0050] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application 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 product implemented 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. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0051] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and 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.

[0052] 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.

[0053] 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.

[0054] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0055] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing a response spectrum of supporting power supply capacity and renewable energy absorption capacity, characterized in that, Includes the following: Construct a power grid simulation model; Configure configuration rules for each supporting power source; wherein, the configuration rules include location configuration rules and capacity configuration rules; A capacity allocation vector is constructed based on the capacity of multiple supporting power sources. Multiple sample data of the capacity allocation vector are collected, and the installed capacity of each sample data is allocated according to the set configuration rules. Simulation calculations are performed based on the installed capacity allocation results of each sample data and the power grid simulation model to obtain multiple response indicators. Among them, the response indicators include the response of renewable energy absorption capacity, the transient overvoltage probability response, and the response of minimum renewable energy multi-station short-circuit ratio. Based on multiple sample data and corresponding multiple response indicators, a capacity ratio response map is constructed; wherein, the capacity ratio response map represents the mapping relationship between any capacity ratio vector and multiple response indicators.

2. The method for constructing the response spectrum of supporting power capacity and new energy absorption capacity as described in claim 1, characterized in that, The process of constructing a capacity matching response map based on multiple sample data and corresponding multiple response indicators includes the following: Construct a basic capacity allocation response model between the capacity allocation vector and each response index; Based on multiple sample data and corresponding response indicators, parameter identification is performed on each basic capacity ratio response model to obtain the corresponding capacity ratio response optimization model. Based on multiple capacity ratio response optimization models, predicted values ​​of multiple response indicators corresponding to any capacity ratio vector are obtained, and a capacity ratio response map is constructed.

3. The method for constructing the response spectrum of supporting power capacity and new energy absorption capacity as described in claim 2, characterized in that, The supporting power source includes a synchronous condenser, an energy storage system, and a dynamic reactive power compensation device. The expression for the capacity ratio response optimization model is: ； in, Represents the capacity matching vector The corresponding number j The predicted value of each response indicator, Represents the capacity matching vector The first in a dimensional variables, Represents the capacity matching vector The first in b dimensional variables, , and All are the first j The regression coefficients corresponding to each response indicator.

4. The method for constructing the response spectrum of supporting power capacity and new energy absorption capacity as described in claim 1, characterized in that, Also includes the following: A fast forward query of renewable energy absorption capacity can be performed based on the capacity ratio response map, or a fast reverse query of the capacity ratio of multiple supporting power sources can be performed based on the capacity ratio response map.

5. The method for constructing the response spectrum of supporting power capacity and new energy absorption capacity as described in claim 4, characterized in that, The process of performing a fast reverse lookup of multiple supporting power supply capacity ratios based on the capacity ratio response map includes the following: Given constraints for multiple response metrics; A regular spatial grid structure is constructed within the capacity allocation space. Each grid point in the spatial grid structure corresponds to a capacity allocation vector. For each grid point, the predicted values ​​of multiple response indicators are obtained based on the capacity allocation response map, and it is determined whether the predicted values ​​of multiple response indicators satisfy the constraints. After completing the grid point traversal, output the multiple grid points that satisfy the constraints as a feasible point set.

6. The method for constructing the response spectrum of supporting power capacity and new energy absorption capacity as described in claim 1, characterized in that, The supporting power source includes a synchronous condenser, an energy storage system, and a dynamic reactive power compensation device. The capacity configuration rule for the energy storage system and the dynamic reactive power compensation device is to allocate capacity according to the proportion of new energy installed capacity of each bus. The capacity configuration rule for the synchronous condenser is to allocate capacity based on maximizing the minimum short-circuit ratio.

7. A response graph construction system for supporting power supply capacity and renewable energy absorption capacity, characterized in that, include: The simulation model building module is used to build power grid simulation models; The configuration rule setting module is used to set the configuration rules for each supporting power source; wherein, the configuration rules include location configuration rules and capacity configuration rules; The simulation calculation module is used to construct a capacity allocation vector based on the capacity of multiple supporting power sources, collect multiple sample data of the capacity allocation vector, allocate the installed capacity of each sample data according to the set configuration rules, and perform simulation calculations based on the installed capacity allocation results of each sample data and the power grid simulation model to obtain multiple response indicators; among which, the response indicators include the response of renewable energy absorption capacity, the transient overvoltage probability response, and the response of minimum renewable energy multi-station short-circuit ratio. The response map construction module is used to construct a capacity ratio response map based on multiple sample data and corresponding multiple response indicators; wherein, the capacity ratio response map represents the mapping relationship between any capacity ratio vector and multiple response indicators.

8. The response spectrum construction system for supporting power capacity and new energy absorption capacity as described in claim 7, characterized in that, Also includes: The response graph query module is used for quick forward query of new energy absorption capacity based on the capacity ratio response graph, or quick reverse query of multiple supporting power capacity ratios based on the capacity ratio response graph.

9. An electronic device, characterized in that, The method includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

10. A computer-readable storage medium for storing a computer program for constructing a response spectrum of supporting power capacity and renewable energy absorption capacity, characterized in that, The computer program, when run on a computer, performs the steps of the method as described in any one of claims 1 to 6.

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