A method for evaluating new energy carrying capacity considering main coordination

Abstract

This invention discloses a method for assessing the carrying capacity of new energy sources considering the coordination between main grid and distribution grid, comprising: inputting main grid load data and grid structure data, distribution grid load data and grid structure data, distributed photovoltaic location information, distributed wind power location information, historical photovoltaic output data, and historical wind power output data; considering the randomness of new energy output and load, constructing a stochastic model for new energy output and a stochastic model for load respectively; establishing a new energy carrying capacity assessment index system; constructing a main grid optimization model and a distribution grid optimization model for optimizing new energy carrying capacity; and combining the main grid optimization model and the distribution grid optimization model for optimizing new energy carrying capacity, constructing an overall framework for assessing the carrying capacity of main grid and distribution grid in coordination based on error analysis and iterative solution methods.

Description

Technical Field

[0001] This invention relates to the field of power grid operation technology, specifically a method for assessing the carrying capacity of new energy sources that takes into account the coordination between main and distribution systems. Background Technology

[0002] As the functionality of distribution networks in actively participating in the operation of large power grids and energy systems increases, it is crucial to develop a unified main-distribution coordination method to achieve comprehensive analysis of transmission and distribution networks. Furthermore, the extensive use of traditional fossil fuels has exacerbated global environmental and climate problems. To respond to the goals of "carbon peaking and carbon neutrality," achieve clean energy supply, and alleviate environmental pollution, the penetration rate of renewable energy in power systems is gradually increasing, necessitating research on the renewable energy carrying capacity of power grids under new conditions. Therefore, establishing a renewable energy carrying capacity assessment index system that considers source-load randomness and comprehensively taking into account the synergistic effects between the main grid and distribution network is essential for assessing renewable energy carrying capacity. To this end, a method for assessing renewable energy carrying capacity that considers main-distribution coordination is proposed. Summary of the Invention

[0003] To address the shortcomings mentioned in the background section, the present invention aims to provide a method for assessing the carrying capacity of new energy sources that takes into account the coordination between primary and secondary energy sources.

[0004] The objective of this invention can be achieved through the following technical solution: a method for assessing the carrying capacity of new energy sources considering the coordination between main and distribution systems, the method comprising the following steps:

[0005] Input the main grid load data and grid structure data, distribution grid load data and grid structure data, distributed photovoltaic location information, distributed wind power location information, historical photovoltaic output data, and historical wind power output data; among them, the input grid structure data, distributed photovoltaic location information, and distributed wind power location information are used to establish power flow constraints;

[0006] Taking into account the randomness of new energy output and load, random models of new energy output and load are constructed using historical data of photovoltaic output, historical data of wind power output, main grid load data and distribution network load data, respectively.

[0007] Establish a new energy carrying capacity assessment index system to evaluate the system's new energy absorption capacity after the optimization model has been implemented;

[0008] Construct a main grid optimization model and a distribution network optimization model for optimizing the carrying capacity of new energy sources;

[0009] By combining the main grid optimization model and the distribution network optimization model for optimizing the carrying capacity of new energy sources, and based on error analysis and iterative solution methods, an overall framework for assessing the coordinated carrying capacity of the main grid and distribution network is constructed.

[0010] Preferably, the process of constructing stochastic models for new energy output and loads respectively includes the following steps:

[0011] The new energy output includes photovoltaic (PV) power and wind power output, and the PV power output, wind power output, and load demand each follow the following mixed Gaussian distribution:

[0012] (1)

[0013] In the formula, , and These are stochastic models for wind power output, solar power output, and load demand, respectively. and This is the proportionality coefficient; , and These are random variables representing wind power output, solar power output, and load demand, respectively. , and These are the predicted values ​​for wind power output, solar power output, and load demand, respectively. , These represent the expected wind power output and the expected photovoltaic power output in the k-th Gaussian distribution, respectively. , These are the standard deviations of wind power output and photovoltaic power output in the k-th Gaussian distribution, respectively. and These represent the expected value and standard deviation of load demand, respectively.

[0014] Preferably, the probability density functions of wind power output, photovoltaic power output and load demand in equation (1) are subjected to Monte Carlo sampling to obtain m sets of wind power output, photovoltaic power output and load demand samples. Then, the m sets of wind power output, photovoltaic power output and load demand samples are reduced according to the scenario reduction method to obtain n sets of wind power output, photovoltaic power output and load demand samples.

[0015] Preferably, the process of establishing a new energy carrying capacity assessment index system includes the following steps:

[0016] The three key indicators for measuring the carrying capacity of new energy sources are: renewable energy absorption capacity, node voltage over-limit risk, and line overload risk. Their expressions are shown below:

[0017] (2)

[0018] In the formula, , and These are the indicators for renewable energy absorption capacity, grid node i voltage cross-limit risk, and line i overload risk; Let s be the probability of scenario s occurring. This represents the new energy output value in the s-th scenario; Maximum output for new energy sources; and Let be the voltage magnitude at node i and the active power flowing through line i; , and These are the lower limit of node voltage, the upper limit of node voltage, and the upper limit of active power flowing through the line, respectively. If and only if ,otherwise ; If and only if ,otherwise ; If and only if ,otherwise .

[0019] Preferably, the process of constructing the main grid optimization model for optimizing the carrying capacity of new energy sources includes the following steps:

[0020] For n sets of wind power output, photovoltaic power output, and load demand samples, the following main grid layer optimization objective function and constraints are established, where the main grid layer optimization objective function is:

[0021] (3)

[0022] In the formula, The objective function of the main network layer; These are the weighting coefficients; and These are the differences in electrical quantities at the main grid load reduction and the main distribution network connection point, respectively. , and These represent the active power reduction, reactive power reduction, and voltage of main grid node b, respectively. and These are the initial active and reactive power requirements of main network node b, respectively. , and The active power setting value, reactive power setting value, and voltage setting value of node b at the distribution network interface are respectively connected to these values. The set of main network nodes;

[0023] In the constraint section, firstly, power flow constraints and safety constraints are considered:

[0024] (4)

[0025] (5)

[0026] In the formula, and These represent the active and reactive power outputs of the generator at node b, respectively. and Let be the voltages at nodes b and j; Let be the admittance of line bj; and These are the voltage phase angles at nodes b and j, respectively. The voltage phase angle in the grid admittance matrix; , , and These are the lower and upper limits of the active power output of the generator, as well as the lower and upper limits of the reactive power output. and These are the upper limits for active and reactive power reduction in load, respectively;

[0027] Secondly, the operational risk constraints of the transmission network layer must be taken into account:

[0028] (6)

[0029] In the formula, and This refers to the upper limit of voltage overrun risk at node b and the upper limit of overload risk at line bj.

[0030] Preferably, the process of establishing the distribution network optimization model for optimizing the new energy carrying capacity includes the following steps:

[0031] The following objective functions and constraints for distribution network optimization are established, where the objective function for distribution network optimization is:

[0032] (7)

[0033] In the formula, The objective function for the distribution network layer is... These are the weighting coefficients; and These are the differences in electrical quantities at the distribution network load reduction and the main distribution network connection point, respectively. For distribution network nodes; For distribution network transformer nodes; , Active and reactive power are injected into node b on the d-th parallel distribution feeder respectively; Let be the voltage amplitude at node b on the d-th parallel distribution feeder; , and These are the active power setting value, reactive power setting value, and voltage setting value of node b on the d-th parallel distribution feeder, respectively.

[0034] In the constraint section, firstly, it includes both power flow constraints and safety constraints:

[0035] (8)

[0036] (9)

[0037] In the formula, , The active and reactive power generated by the distributed power source at node b on the d-th parallel distribution feeder. , Let be the active power and reactive power flowing through line bj on the dth parallel distribution feeder; This represents the connectivity status of the line between node b and node j. , , and Inject the lower and upper limits of active power and the lower and upper limits of reactive power into node b on the d-th parallel distribution feeder; and The maximum active power reduction and the maximum reactive power reduction at node b on the d-th parallel distribution feeder; The maximum active power output of the distributed power source at node b on the d-th parallel distribution feeder; The tangent of the power factor angle of the distributed power source.

[0038] Secondly, the operational risk constraints of the distribution network layer must be taken into account:

[0039] (10)

[0040] In the formula, and The upper limit for voltage overrun risk at distribution network node b and the upper limit for overload risk at line bj;

[0041] Finally, considering the network reconfiguration of the distribution network layer, the radial network constraint of the distribution network needs to be satisfied, as shown below:

[0042] (11)

[0043] In the formula, For set The number of elements in the middle.

[0044] Preferably, by solving the optimization scheduling models of the main grid and the distribution network, the boundary variables of node voltage, active power, and reactive power are obtained respectively. For the main grid and distribution network coordinated optimization calculation, the values ​​of the boundary variables should converge to the same value or a value that differs very little, as shown below:

[0045] (12)

[0046] In the formula, , and These are active power deviation, reactive power deviation, and voltage deviation, respectively. , and These are the upper limits for active power deviation, reactive power deviation, and voltage deviation, respectively.

[0047] Preferably, the overall framework for assessing the load-bearing capacity of the main and auxiliary structures includes:

[0048] a) Obtain boundary values ​​based on the main network optimization calculation model obtained from the solved new energy carrying capacity optimization calculation. , and ;

[0049] b) Determine whether the error meets the requirements of the following formula;

[0050] (13)

[0051] In the formula, , and These are the active power deviation, reactive power deviation, and voltage deviation at the it-th iteration, respectively. , and These represent the active power deviation, reactive power deviation, and voltage deviation at the (it-1)th iteration, respectively.

[0052] c) If equation (13) is satisfied, the optimization calculation of new energy carrying capacity ends;

[0053] d) If equation (13) is not satisfied, then the boundary values ​​are obtained based on the distribution network optimization calculation model obtained from the solution of the new energy carrying capacity optimization calculation. , and ;

[0054] e) Calculate the error between the corresponding quantities in steps a) and d), i.e., in step a). , and and d) , and The absolute value of the difference is used to determine whether the error satisfies equation (12).

[0055] f) If the error satisfies equation (12), the optimization calculation of new energy carrying capacity ends; if it does not satisfy equation (12), repeat the above steps.

[0056] Preferably, an apparatus includes:

[0057] One or more processors;

[0058] Memory, used to store one or more programs;

[0059] When one or more of the programs are executed by one or more of the processors, the one or more of the processors implement a method for assessing the carrying capacity of new energy sources that takes into account the coordination between primary and secondary components, as described above.

[0060] Preferably, a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a method for assessing the carrying capacity of new energy sources that takes into account the coordination between primary and secondary suppliers, as described above.

[0061] The beneficial effects of this invention are:

[0062] 1. This invention takes into account the randomness of power grid operation and constructs new energy carrying capacity assessment absorption capacity index, node voltage overrun index, and line overload index as main grid constraints and distribution network constraints for main grid and distribution network coordinated optimization calculation;

[0063] 2. This invention takes into account the interrelationship between the main grid and the distribution network. By designing an overall framework for assessing the collaborative carrying capacity of the main grid and distribution network, comparing the magnitude of the interaction error, and based on the iterative solution between the main grid and the distribution network, the assessment of the carrying capacity of new energy sources is realized. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a flowchart of the method of the present invention;

[0066] Figure 2 This is the overall framework diagram of the main and auxiliary co-supporting bearing capacity assessment constructed based on error analysis and iterative solution in this invention;

[0067] Figure 3 This is a diagram of the power distribution network structure in an embodiment of the present invention;

[0068] Figure 4 It is a new energy carrying capacity assessment index obtained by optimization solution according to the present invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] like Figure 1 As shown, this invention proposes a method for assessing the carrying capacity of new energy sources that takes into account the coordination between main and distribution systems. This method includes the following steps:

[0071] 1) Input the main grid load data and grid structure data, distribution grid load data and grid structure data, distributed photovoltaic (wind power) location information, photovoltaic power output historical data, and wind power output historical data;

[0072] 2) Taking into account the randomness of new energy output and load, a stochastic model for new energy output and a stochastic model for load demand are constructed respectively;

[0073] It is assumed that the photovoltaic power output, wind power output, and load demand over a period of time follow the following mixture Gaussian distributions:

[0074] (1)

[0075] In the formula, , and These are the probability density functions for wind power output, solar power output, and load demand, respectively. and This is the proportionality coefficient; , and These are random variables representing wind power output, solar power output, and load demand, respectively. , and These are the predicted values ​​for wind power output, solar power output, and load demand, respectively. , These represent the expected wind power output and the expected photovoltaic power output in the k-th Gaussian distribution, respectively. , These are the standard deviations of wind power output and photovoltaic power output in the k-th Gaussian distribution, respectively. and These represent the expected value and standard deviation of load demand, respectively.

[0076] Monte Carlo sampling is performed on the probability density function in equation (1) to obtain m sets of wind power output, photovoltaic power output and load demand samples. Then, the samples are reduced according to the scenario reduction method to obtain n sets of wind power output, photovoltaic power output and load demand samples.

[0077] 3) Establish a new energy carrying capacity assessment index system;

[0078] The three key indicators for measuring the carrying capacity of new energy sources are: renewable energy absorption capacity, node voltage over-limit risk, and line overload risk. Their expressions are shown below:

[0079] (2)

[0080] In the formula, , and These are the indicators for renewable energy absorption capacity, grid node i voltage cross-limit risk, and line i overload risk; Let s be the probability of scenario s occurring. This represents the new energy output value in the s-th scenario; Maximum output for new energy sources; and Let be the voltage magnitude at node i and the active power flowing through line i; , and These are the lower limit of node voltage, the upper limit of node voltage, and the upper limit of active power flowing through the line, respectively. If and only if ,otherwise ; If and only if ,otherwise ; If and only if ,otherwise .

[0081] 4) Construct a main grid optimization model for optimizing the calculation of new energy carrying capacity.

[0082] For all n sets of renewable energy output and load data, the following main grid layer optimization objective function and constraints are established, where the objective function is:

[0083] (3)

[0084] In the formula, The objective function of the main network layer; These are the weighting coefficients; and These are the differences in electrical quantities at the main grid load reduction and the main distribution network connection point, respectively. , and These represent the active power reduction, reactive power reduction, and voltage of main grid node b, respectively. and These are the initial active and reactive power requirements of main network node b, respectively. , and The active power setting value, reactive power setting value, and voltage setting value of node b at the distribution network interface are respectively connected to these values. The set of main network nodes.

[0085] In the constraint section, firstly, power flow constraints and safety constraints are considered:

[0086] (4)

[0087] (5)

[0088] In the formula, and These represent the active and reactive power outputs of the generator at node b, respectively. and Let be the voltages at nodes b and j; Let be the admittance of line bj; and These are the voltage phase angles at nodes b and j, respectively. The voltage phase angle in the grid admittance matrix; , , and These are the lower and upper limits of the active power output of the generator, as well as the lower and upper limits of the reactive power output. and These are the upper limits for active and reactive power reductions, respectively.

[0089] Secondly, operational risk constraints at the transmission network level must be considered:

[0090] (6)

[0091] In the formula, and This refers to the upper limit of voltage overrun risk at node b and the upper limit of overload risk at line bj.

[0092] 5) Construct a distribution network optimization model for optimizing the calculation of new energy carrying capacity.

[0093] The following objective functions and constraints for distribution network layer optimization are established, where the objective function is:

[0094] (7)

[0095] In the formula, The objective function for the distribution network layer is... These are the weighting coefficients; and These are the differences in electrical quantities at the distribution network load reduction and the main distribution network connection point, respectively. For distribution network nodes; For distribution network transformer nodes; , Active and reactive power are injected into node b on the d-th parallel distribution feeder respectively; Let be the voltage amplitude at node b on the d-th parallel distribution feeder; , and These are the active power setting value, reactive power setting value, and voltage setting value of node b on the d-th parallel distribution feeder, respectively.

[0096] In the constraint section, firstly, power flow constraints and safety constraints are considered:

[0097] (8)

[0098] (9)

[0099] In the formula, , The active and reactive power generated by the distributed power source at node b on the d-th parallel distribution feeder. , Let be the active power and reactive power flowing through line bj on the dth parallel distribution feeder; This represents the connectivity status of the line between node b and node j. , , and Inject the lower and upper limits of active power and the lower and upper limits of reactive power into node b on the d-th parallel distribution feeder; and The maximum active power reduction and the maximum reactive power reduction at node b on the d-th parallel distribution feeder; The maximum active power output of the distributed power source at node b on the d-th parallel distribution feeder; The tangent of the power factor angle of the distributed power source.

[0100] Secondly, the operational risk constraints of the distribution network layer must be taken into account:

[0101] (10)

[0102] In the formula, and The upper limit for voltage overrun risk at distribution network node b and the upper limit for overload risk at line bj.

[0103] Finally, considering the network reconfiguration of the distribution network layer, the radial network constraint of the distribution network needs to be satisfied, as shown below:

[0104] (11)

[0105] In the formula, For set The number of elements in the middle.

[0106] 6) Combining steps 4) and 5), construct an overall framework for assessing the coordinated bearing capacity of the main and auxiliary structures based on error analysis and iterative solutions:

[0107] By solving the optimization scheduling models of the main grid and distribution network, boundary variables such as node voltage, active power, and reactive power can be obtained. For the main grid and distribution network coordinated optimization calculation, the values ​​of these boundary variables should converge to the same value or a value that differs very little, as shown below:

[0108] (12)

[0109] In the formula, , and These are active power deviation, reactive power deviation, and voltage deviation, respectively. , and These are the upper limits for active power deviation, reactive power deviation, and voltage deviation, respectively.

[0110] Therefore, the overall framework for assessing the load-bearing capacity of the main and auxiliary buildings can be described as follows:

[0111] a) Based on step 4), solve the main network optimization calculation model for optimizing the new energy carrying capacity and obtain the boundary values. , and ;

[0112] b) Determine whether the error meets the requirements of the following formula;

[0113] (13)

[0114] In the formula, , and These are the active power deviation, reactive power deviation, and voltage deviation at the it-th iteration, respectively. , and These represent the active power deviation, reactive power deviation, and voltage deviation at the (it-1)th iteration, respectively.

[0115] c) If equation (13) is satisfied, the optimization calculation of new energy carrying capacity ends;

[0116] d) If equation (13) is not satisfied, then according to step 5), solve the distribution network optimization calculation model for optimizing the new energy carrying capacity and obtain the boundary values. , and ;

[0117] e) Calculate the error between the corresponding quantities in steps a) and d), and determine whether the error satisfies equation (12);

[0118] f) If the error satisfies equation (12), the optimization calculation of new energy carrying capacity ends. If it does not satisfy equation (12), then repeat steps a) to f).

[0119] It should be further explained that, in the embodiments of the present invention, the following methods are employed: Figure 3 The illustrated IEEE 33-node power distribution system has node 1 connected to the main network, which uses the standard IEEE 118-node network. In the power distribution system, nodes 11 and 28 are connected to stochastic wind turbines, and nodes 18 and 33 are connected to stochastic photovoltaic (PV) units. The simulation results of this embodiment are described below.

[0120] The distributed photovoltaic carrying capacity is solved under the following three scenarios: 1) The base capacity of the stochastic wind turbine is set to 100kW and the base capacity of the stochastic photovoltaic unit is set to 200kW; 2) The base capacity of the stochastic wind turbine is set to 500kW and the base capacity of the stochastic photovoltaic unit is set to 200kW. Figure 4 The invention presents two scenarios for assessing the carrying capacity of new energy sources. In Scenario 1, the absorption capacity index is 100%, indicating that the invention achieves complete absorption of new energy units. In Scenario 2, the absorption capacity index in the carrying capacity assessment system is 93%, the node voltage exceedance risk index is 0.0297, and the line overload risk index is 0.0093. Therefore, the new energy carrying capacity assessment method considering the coordination between the main and distribution networks proposed in this invention can take into account the coordination relationship between the main and distribution networks and the randomness of source and load, realizing the assessment of the carrying capacity after new energy is connected to the grid, and has practical application value.

[0121] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0122] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0123] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0124] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

Claims

1. A method for assessing the carrying capacity of new energy sources considering the coordination between primary and secondary energy sources, characterized in that, The method includes the following steps: Input the main grid load data and grid structure data, distribution grid load data and grid structure data, distributed photovoltaic location information, distributed wind power location information, historical photovoltaic output data, and historical wind power output data; among them, the input grid structure data, distributed photovoltaic location information, and distributed wind power location information are used to establish power flow constraints; Taking into account the randomness of new energy output and load, random models of new energy output and load are constructed using historical data of photovoltaic output, historical data of wind power output, main grid load data and distribution network load data, respectively. Establish a new energy carrying capacity assessment index system to evaluate the system's new energy absorption capacity after the optimization model has been implemented; Construct a main grid optimization model and a distribution network optimization model for optimizing the carrying capacity of new energy sources; The process of constructing the main network optimization model for optimizing the new energy carrying capacity includes the following steps: For n sets of wind power output, photovoltaic power output, and load demand samples, the following main grid layer optimization objective function and constraints are established, where the main grid layer optimization objective function is: （3） In the formula, The objective function of the main network layer; These are the weighting coefficients; and These are the differences in electrical quantities at the main grid load reduction and the main distribution network connection point, respectively. , and These represent the active power reduction, reactive power reduction, and voltage of main grid node b, respectively. and These are the initial active and reactive power requirements of main network node b, respectively. , and The active power setting value, reactive power setting value, and voltage setting value of node b at the distribution network interface are respectively connected to these values. The set of main network nodes; In the constraint section, firstly, power flow constraints and safety constraints are considered: （4） （5） In the formula, and These represent the active and reactive power outputs of the generator at node b, respectively. and Let be the voltages at nodes b and j; Let be the admittance of line bj; and These are the voltage phase angles at nodes b and j, respectively. The voltage phase angle in the grid admittance matrix; , , and These are the lower and upper limits of the active power output of the generator, as well as the lower and upper limits of the reactive power output. and These are the upper limits for active and reactive power reduction in load, respectively; Secondly, the operational risk constraints of the transmission network layer must be taken into account: （6） In the formula, and The upper limit for voltage overrun risk at node b and the upper limit for overload risk at line bj; By combining the main grid optimization model and the distribution network optimization model for optimizing the carrying capacity of new energy sources, and based on error analysis and iterative solution methods, an overall framework for assessing the coordinated carrying capacity of the main grid and distribution network is constructed.

2. The method for assessing the carrying capacity of new energy sources considering the coordination of primary and secondary distribution systems, as described in claim 1, is characterized in that... The process of constructing stochastic models for new energy output and loads respectively includes the following steps: The new energy output includes photovoltaic (PV) power and wind power output, and the PV power output, wind power output, and load demand each follow the following mixed Gaussian distribution: （1） In the formula, , and These are stochastic models for wind power output, solar power output, and load demand, respectively. and This is the proportionality coefficient; , and These are random variables representing wind power output, solar power output, and load demand, respectively. , and These are the predicted values ​​for wind power output, solar power output, and load demand, respectively. , These represent the expected wind power output and the expected photovoltaic power output in the k-th Gaussian distribution, respectively. , These are the standard deviations of wind power output and photovoltaic power output in the k-th Gaussian distribution, respectively. and These represent the expected value and standard deviation of load demand, respectively.

3. The method for assessing the carrying capacity of new energy sources considering the coordination of primary and secondary distribution systems, as described in claim 2, is characterized in that... Monte Carlo sampling is performed on the probability density functions of wind power output, photovoltaic power output and load demand in equation (1) to obtain m sets of wind power output, photovoltaic power output and load demand samples. Then, the m sets of wind power output, photovoltaic power output and load demand samples are reduced according to the scenario reduction method to obtain n sets of wind power output, photovoltaic power output and load demand samples.

4. The method for assessing the carrying capacity of new energy sources considering the coordination of primary and secondary distribution systems, as described in claim 1, is characterized in that... The process of establishing a new energy carrying capacity assessment index system includes the following steps: The three key indicators for measuring the carrying capacity of new energy sources are: renewable energy absorption capacity, node voltage over-limit risk, and line overload risk. Their expressions are shown below: （2） In the formula, , and These are the indicators of renewable energy absorption capacity and grid nodes. i Voltage overrun risk indicators and lines i Overload risk indicators; Let s be the probability of scenario s occurring. This represents the new energy output value in the s-th scenario; Maximum output for new energy sources; and Let be the voltage magnitude at node i and the active power flowing through line i; , and These are the lower limit of node voltage, the upper limit of node voltage, and the upper limit of active power flowing through the line, respectively. If and only if ,otherwise ; If and only if ,otherwise ; If and only if ,otherwise .

5. The method for assessing the carrying capacity of new energy sources considering the coordination of main and auxiliary power sources, as described in claim 1, is characterized in that... The process of establishing the distribution network optimization model for optimizing the new energy carrying capacity includes the following steps: The following objective functions and constraints for distribution network optimization are established, where the objective function for distribution network optimization is: （7） In the formula, The objective function for the distribution network layer is... These are the weighting coefficients; and These are the differences in electrical quantities at the distribution network load reduction and the main distribution network connection point, respectively. For distribution network nodes; For distribution network transformer nodes; , Active and reactive power are injected into node b on the d-th parallel distribution feeder respectively; Let be the voltage amplitude at node b on the d-th parallel distribution feeder; , and These are the active power setting value, reactive power setting value, and voltage setting value of node b on the d-th parallel distribution feeder, respectively. In the constraint section, firstly, it includes both power flow constraints and safety constraints: （8） （9） In the formula, , The active and reactive power generated by the distributed power source at node b on the d-th parallel distribution feeder. , Let be the active power and reactive power flowing through line bj on the dth parallel distribution feeder; This represents the connectivity status of the line between node b and node j. , , and Inject the lower and upper limits of active power and the lower and upper limits of reactive power into node b on the d-th parallel distribution feeder; and The maximum active power reduction and the maximum reactive power reduction at node b on the d-th parallel distribution feeder; The maximum active power output of the distributed power source at node b on the d-th parallel distribution feeder; The power factor tangent of the distributed power source; Secondly, the operational risk constraints of the distribution network layer must be taken into account: （10） In the formula, and The upper limit for voltage overrun risk at distribution network node b and the upper limit for overload risk at line bj; Finally, considering the network reconfiguration of the distribution network layer, the radial network constraint of the distribution network needs to be satisfied, as shown below: （11） In the formula, For set The number of elements in the middle.

6. The method for assessing the carrying capacity of new energy sources considering the coordination of main and auxiliary power sources, as described in claim 5, is characterized in that... By solving the optimization scheduling models of the main grid and distribution network, the boundary variables of node voltage, active power, and reactive power are obtained respectively. For the main grid and distribution network coordinated optimization calculation, the values ​​of the boundary variables should converge to the same value or a value that differs very little, as shown below: （12） In the formula, , and These are active power deviation, reactive power deviation, and voltage deviation, respectively. , and These are the upper limits for active power deviation, reactive power deviation, and voltage deviation, respectively.

7. The method for assessing the carrying capacity of new energy sources considering the coordination of main and auxiliary power sources, as described in claim 1, is characterized in that... The overall framework for assessing the co-supporting capacity of the main and auxiliary structures includes: a) Obtain boundary values ​​based on the main network optimization calculation model obtained from the solved new energy carrying capacity optimization calculation. , and ; b) Determine whether the error meets the requirements of the following formula; （13） In the formula, , and These are the active power deviation, reactive power deviation, and voltage deviation at the it-th iteration, respectively. , and These represent the active power deviation, reactive power deviation, and voltage deviation at the (it-1)th iteration, respectively. c) If equation (13) is satisfied, the optimization calculation of new energy carrying capacity ends; d) If equation (13) is not satisfied, then the boundary values ​​are obtained based on the distribution network optimization calculation model obtained from the solution of the new energy carrying capacity optimization calculation. , and ; e) Calculate the error between the corresponding quantities in steps a) and d), i.e., in step a). , and and d) , and The absolute value of the difference is used to determine whether the error satisfies equation (12). f) If the error satisfies equation (12), the optimization calculation of new energy carrying capacity ends; if it does not satisfy equation (12), repeat the above steps.

8. A device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more of the programs are executed by one or more of the processors, the one or more processors implement a method for assessing the carrying capacity of new energy sources that takes into account the coordination between primary and secondary components, as described in any one of claims 1-7.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform a method for assessing the carrying capacity of new energy sources that takes into account the coordination of primary and secondary components, as described in any one of claims 1-7.

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