An evaluation method and system for the influence of power prediction on cross-section transmission power

Abstract

An evaluation method for power prediction influence on section transmission power, comprising: calculating transmission power on a line based on acquired voltage and current parameters of the wind power and photovoltaic power and a pre-constructed deterministic unit combination model; calculating section evaluation indexes based on the transmission power on the line and a pre-constructed power transmission evaluation index system; obtaining comprehensive evaluation values of the wind power and photovoltaic unit on the section transmission power influence by using a Latin hypercube sampling method based on the section evaluation indexes; the deterministic unit combination model comprises taking the minimum cost of the wind power and photovoltaic unit as the target. The technical scheme provided by the application can quickly determine the wind power plant or photovoltaic plant which mainly influences the section sending-out power, and accordingly help to take effective measures to reduce the prediction error and significantly improve the influence on the sending-out power.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system generation plan evaluation, in particular to a method and system for evaluating the influence of power prediction on section transmission power. BACKGROUND

[0002] Currently, there are studies on the influence of wind power and photovoltaic power generation on system transmission power, mainly including analysis of wind and light power generation convergence sending characteristics, research on dispatching strategy, and evaluation of system adequacy and reliability.

[0003] Analysis of wind and light power generation convergence sending. Currently, existing research mainly focuses on the analysis of the transmission capacity characteristics of wind farm and photovoltaic power plant cluster convergence sending, and the research on energy storage capacity allocation method based on energy storage system configuration function to balance photovoltaic prediction error.

[0004] Research on dispatching strategy. Currently, existing research mainly focuses on the research on rescheduling model caused by over-dispatching and under-dispatching problems caused by short-term, medium-term and long-term prediction errors of wind power.

[0005] Adequacy and reliability evaluation. Currently, existing research mainly includes: 1) evaluation research on the economy, environmental protection and safety of the dispatching scheme, but there is no method considering the influence of wind power prediction error; 2) research on the evaluation method of the influence of system operation flexibility based on wind power and load prediction uncertainty, but the model ignores the safety constraints of node voltage and line flow; 3) from the angles of system reserve capacity, power outage index, system load, voltage and transmission power, an evaluation method of system operation safety and reliability is constructed, but it does not consider the influence of wind power and photovoltaic power prediction uncertainty, and it does not involve the evaluation of the influence of system sending power.

[0006] Due to the influence of weather and other reasons, the prediction accuracy of wind power and photovoltaic power generation is low, and wind and light prediction error has a great influence on large-scale power transmission. Therefore, how to effectively evaluate the influence of wind power and photovoltaic power prediction error on large-scale power transmission, and then improve the effectiveness of long-term large-scale resource optimization configuration in large power grid, is of great significance to improve and enhance the safety and economy of power grid operation.

[0007] Existing power grid operation and planning evaluation methods related to wind power and photovoltaic grid connection mainly focus on the influence evaluation and analysis of grid connection on system operation safety and reliability, as well as the research on grid connection capacity, and so far there is no method to evaluate the influence of wind and light prediction error on large-scale power transmission section transmission power. SUMMARY

[0008] Based on the lack of effective evaluation of the influence of wind power and photovoltaic power prediction error on large-scale power transmission in the prior art, the present application provides an evaluation method for the influence of power prediction on transmission power of a section, comprising:

[0009] Based on the obtained voltage and current parameters of the wind power and photovoltaic power and the pre-constructed deterministic unit combination model, transmission power on a line is calculated.

[0010] Based on the transmission power on the line and the pre-constructed power transmission evaluation index system, section evaluation indexes are calculated.

[0011] Based on the section evaluation indexes, a Latin hypercube sampling method is used to obtain a comprehensive evaluation value of the influence of wind power and photovoltaic units on transmission power of a section.

[0012] The deterministic unit combination model comprises minimizing the cost of wind power and photovoltaic units as an objective.

[0013] Preferably, the calculation of transmission power on a line based on the obtained voltage and current parameters of the wind power and photovoltaic power and the pre-constructed deterministic unit combination model comprises:

[0014] Based on the obtained voltage and current parameters of the wind power and photovoltaic power and the pre-constructed deterministic unit combination model, conventional unit output value variables, actual dispatch output variables of wind power or photovoltaic units, load shedding variables and load quantities are obtained.

[0015] Based on the conventional unit output value variables, actual dispatch output variables of wind power or photovoltaic units, load shedding variables and load quantities and power calculation formulas, transmission power on a line is obtained.

[0016] Preferably, the deterministic unit combination model comprises:

[0017] A target function is constructed with the objective of minimizing the cost of wind power and photovoltaic units.

[0018] The target function is set with conventional unit start-up, shut-down, output value variable constraints, and unit climbing and descending speed constraints, and branch transmission power as a constraint condition.

[0019] A deterministic unit combination model is constructed based on the target function and the constraint condition.

[0020] Preferably, the target function is as shown in the following formula:

[0021]

[0022] In the formula, x g,t is a conventional unit running state variable at time t; y g,t : is a conventional unit start-up variable at time t; pg,t Output value of conventional generating units at time t; This is the unit start-up cost coefficient; The dispatchable output of wind or solar power units at time t; p w,t C represents the actual dispatch output variable of wind or solar power units at time t; d : Penalty cost for load shedding; d j,t For load shedding variables; C r Curtailment penalty fees; C g This is the unit operating cost coefficient.

[0023] Preferably, the transmission power on the line is calculated using the following formula:

[0024]

[0025] In the formula, F l,t p represents the transmission power of the l-th line during time period t. g,t : Output value of a conventional unit at time t; p w,t d represents the actual dispatch output variable of wind or solar power units at time t; j,t For load shedding variables; D j,t : This refers to the load.

[0026] Preferably, the construction of the power transmission evaluation index system includes:

[0027] It is constructed from the total electrical energy transmitted across the cross section, the average margin of power transmitted across the cross section, the maximum non-uniformity of power transmitted across the cross section, and the average fluctuation of power across the cross section.

[0028] Preferably, the total electrical energy transmitted across the cross-section, the average power margin of the cross-section, the maximum non-uniformity of the power transmitted across the cross-section, and the average power fluctuation of the cross-section are calculated using the following formulas:

[0029]

[0030] In the formula, W total To transmit total electrical energy to the cross section; M avr E represents the average power margin of the cross-section; max V represents the maximum non-uniformity of power transmission across the cross-section. avr The average fluctuation of cross-sectional power; F l,t Let be the transmission power of the l-th line during time period t.

[0031] Preferably, the comprehensive evaluation value of the impact of wind power and photovoltaic units on the cross-sectional transmission power obtained based on the cross-sectional evaluation index using the Latin hypercube sampling method includes:

[0032] Based on the preset hypercube sampling method, dimension and number of sampling points of the cross-section evaluation index vector sampling values are obtained;

[0033] Based on the cross-section evaluation index vector sampling values, expectation and standard deviation of each cross-section evaluation index are calculated;

[0034] Based on the expectation and standard deviation of each cross-section evaluation index, a sensitivity calculation formula is used to calculate the expectation value and standard deviation sensitivity of the prediction output error of each cross-section evaluation index relative to wind power and photovoltaic units;

[0035] Based on the expectation value and standard deviation sensitivity, the comprehensive influence factor of the power prediction error of each wind power and photovoltaic unit on each index is calculated;

[0036] The comprehensive influence factor of each wind power and photovoltaic unit is weighted and averaged to obtain the comprehensive evaluation value of the influence of wind power and photovoltaic units on cross-section transmission power.

[0037] Preferably, the expectation value and standard deviation sensitivity calculation formula is as follows:

[0038]

[0039]

[0040] v k =(0,0,...,0,...,1,1,...,1,...,0,0,...,0)

[0041] In the formula, The sensitivity of the expectation value of the prediction output error of the kth wind power and photovoltaic unit to index j; f j (W), f j (W+Δw·v k ): Latin square sampling is performed on the sampling points of the wind power and photovoltaic adjustable power random vector; is the expectation of index j; SD[U j ] is the standard deviation of index j; j is the index number, which is a positive integer; k is the number of wind power and photovoltaic units; W is the wind power and photovoltaic adjustable power random vector, Δw is 1e-4, and v k is a vector with dimension d; SEN k (SD[U j ]): The sensitivity of the standard deviation of the prediction output error of the kth wind power and photovoltaic unit to index j.

[0042] Preferably, the comprehensive influence factor is calculated according to the following formula:

[0043]

[0044] In the formula, γ j,k The comprehensive impact factor of power prediction errors of each wind power and photovoltaic unit on various indicators.

[0045] Preferably, it also includes: sorting the comprehensive evaluation values ​​according to their numerical values.

[0046] Based on the same inventive concept, this invention also provides an assessment system for the impact of wind power and photovoltaic power prediction on cross-sectional transmission power, comprising:

[0047] The power calculation module calculates the transmission power on the line based on the acquired voltage and current parameters of the wind power and photovoltaic power generation and the pre-constructed deterministic unit combination model.

[0048] The index calculation module calculates the cross-sectional evaluation index based on the transmission power on the line and the pre-built power transmission evaluation index system.

[0049] The comprehensive evaluation module uses the Latin hypercube sampling method based on the cross-sectional evaluation indicators to obtain a comprehensive evaluation value of the impact of wind power and photovoltaic units on the cross-sectional transmission power.

[0050] Preferably, the power calculation module includes:

[0051] The variable calculation unit obtains the conventional unit output value variable, the actual dispatch output variable of wind power or photovoltaic power unit, the load shedding variable and the load amount based on the obtained voltage and current parameters of wind power and photovoltaic power generation and the pre-constructed deterministic unit combination model.

[0052] The calculation unit obtains the transmission power on the line based on the output value variables of the conventional units, the actual dispatch output variables of the wind power or photovoltaic units, the load shedding variables and the load amount, as well as the power calculation formula.

[0053] The beneficial effects of this invention are as follows:

[0054] This invention discloses a method and system for assessing the impact of power prediction on cross-sectional transmission power. The method includes calculating the transmission power on the line based on the acquired voltage and current parameters of the wind and photovoltaic power generation and a pre-constructed deterministic unit combination model; calculating cross-sectional assessment indicators based on the transmission power on the line and a pre-constructed power transmission assessment index system; and obtaining a comprehensive assessment value of the impact of wind and photovoltaic units on cross-sectional transmission power using the Latin hypercube sampling method based on the cross-sectional assessment indicators. The deterministic unit combination model aims to minimize the cost of wind and photovoltaic units, and can quickly identify the wind farms or photovoltaic plants that have a major impact on the cross-sectional transmission power, thereby helping to take effective measures to reduce prediction errors and significantly improve their impact on transmission power. Attached Figure Description

[0055] Figure 1 Flow chart of the method for evaluating the influence of power prediction on cross-section transmission power according to the present application;

[0056] Figure 2 Flow chart of the method for evaluating the influence of power prediction on cross-section transmission power according to the present application;

[0057] Figure 3 Schematic diagram of the system for evaluating the influence of power prediction on cross-section transmission power according to the present application. DETAILED DESCRIPTION

[0058] The present application discloses a method and system for evaluating the influence of power prediction on cross-section transmission power, which considers the influence of wind power prediction error, and on this basis, constructs an evaluation of the economic, environmental and safety of the dispatching scheme from the angles of system reserve capacity, power outage index, system load, voltage and transmission power, and adds complex safety constraints such as node voltage and line power flow to the evaluation model.

[0059] Embodiment 1: an index evaluation method for the influence of key cross-section transmission power, as shown in Figure 1

[0060] Step 1: based on the obtained voltage and current parameters of the wind power and photovoltaic power generation and the pre-constructed deterministic unit combination model, the transmission power on the line is calculated;

[0061] Step 2: based on the transmission power on the line and the pre-constructed power transmission evaluation index system, the cross-section evaluation index is calculated;

[0062] Step 3: based on the cross-section evaluation index, the Latin hypercube sampling method is used to obtain the comprehensive evaluation value of the influence of wind power and photovoltaic units on cross-section transmission power;

[0063] The deterministic unit combination model includes taking the minimum cost of wind power and photovoltaic units as the target.

[0064] The specific implementation steps are as follows:

[0065] Step 1: based on the obtained voltage and current parameters of the wind power and photovoltaic power generation and the prediction error model, the adjustable power of the wind power and photovoltaic power generation is calculated

[0066] 1) a model of wind power and photovoltaic power generation prediction error is established;

[0067] 2) a power transmission evaluation index system and a probability calculation model are established;

[0068] 3) the Latin hypercube sampling method is used to calculate the expectation, standard deviation, sensitivity and comprehensive evaluation value of the cross-section transmission power evaluation index.

[0069] ​1) Establishing the wind power and photovoltaic power prediction error model includes:

[0070] The wind power and photovoltaic power prediction error model is:

[0071]

[0072] wherein, represents the schedulable power of wind power or photovoltaic power, represents the predicted power of wind power or photovoltaic power, and ε is subject to a normal distribution with mean μ and standard deviation σ, and the probability density function is as follows:

[0073]

[0074] Step 2: determining the power combination of wind power and photovoltaic units based on the schedulable power of the wind power and photovoltaic units and the pre-constructed deterministic unit combination model;

[0075] 2) Establishing the power transmission evaluation index system and the probability calculation model:

[0076] The power transmission evaluation index system includes the total power W transmitted at the section total , the average margin M of the power transmitted at the section avr , the maximum unevenness E of the power transmitted at the section max , and the average fluctuation V of the power at the section avr , and the specific expressions are as follows:

[0077]

[0078] wherein, F l,t is the transmission power of the lth line at the t period, F l max is the maximum transmission power of the lth line, and N l is the number of branches included in the specified section.

[0079] The probability calculation model is as follows:

[0080]

[0081] U=f(W) (5)

[0082] wherein, U=(W total ,M avr ,E max ,V avr ) is a vector composed of each power transmission index, and W=(W1,...,W d )=(ε 1,1 ,...,ε r,tW is a random vector composed of the prediction error of each wind power and photovoltaic power plant in each time period, and f(W) is a complex function relationship between the power transmission index and the wind and light output prediction error.

[0083] W of formula (5) in a given case, the variables on the right side of the corresponding formula (3) can be obtained by solving the following deterministic unit commitment problem, and the corresponding deterministic U value is obtained. The model of the deterministic unit commitment problem is:

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] The decision variables of the above optimization model are as follows: x g,t is a conventional unit operating state variable, and 0 value indicates that the time period is in shutdown state, and 1 value indicates that it is in startup state, y g,t is a conventional unit startup variable, and 0 value indicates that the unit startup and shutdown state has no change or shutdown occurs, and 1 value indicates that the unit startup occurs, v g,t is a conventional unit shutdown variable, and 0 value indicates that the unit startup and shutdown state has no change or startup occurs, and 1 value indicates that the unit shutdown occurs; p g,t is a conventional unit output value variable; p w,t is the actual dispatching output variable of wind power or photovoltaic unit; d j,t is a load shedding variable.

[0099] The constants and set symbols in the optimization model are explained below: For dispatchable output of wind power or photovoltaic units; D j,t For load; This is the unit start-up cost coefficient. C g This is the unit operating cost coefficient; The upper and lower limits of the unit's output; RU g RD g For the unit's climbing and descending speeds, SU g SD g For the unit's start-up and shutdown speed; SF l,n F is the branch-node transfer factor; l,t For branch transmission power, F l max S represents the upper limit of branch transmission power. G S R and S D G(n), R(n), and D(n) represent the set of conventional generating units, the set of wind or photovoltaic generating units, and the set of loads at node n, respectively.

[0100] Step 3: Based on the cross-sectional data obtained from the preset evaluation indicators and the power combination of the wind power and photovoltaic units, the comprehensive evaluation value of the impact of wind power and photovoltaic units on the cross-sectional transmission power is obtained using the Latin hypercube sampling method, including:

[0101] 3) The expected value, standard deviation, sensitivity, and comprehensive evaluation value of the cross-sectional transmission power assessment index are calculated using the Latin hypercube sampling method. The calculation methods for the expected value, standard deviation, and influence factors based on sensitivity analysis of the power transmission assessment index using the Latin hypercube sampling method are as follows:

[0102] (1) Define the sampling point dimension d and the number of points n for the Latin hypercube sampling method, and generate the original sampling points.

[0103] (2) Based on the output distribution of each wind power and photovoltaic power source, n corresponding random vectors (X1,...,X) are generated. d The original sampling points are transformed into corresponding random vectors (W1,...,W) of dispatchable wind and solar power. d sampling points The transformation formula is:

[0104]

[0105] (3) Calculate the corresponding sampling points according to equation (5) and unit combination models (6) to (19). n index vector sampling values U (1) ,...,U (n) .

[0106] (4) Calculate the expectation and standard deviation of each index U j ,j=1,...,4 by the following formula:

[0107]

[0108]

[0109] (5) Calculate the sensitivity of the expectation and standard deviation of each index relative to the expectation value of the prediction error of the kth wind power or photovoltaic unit:

[0110]

[0111]

[0112] v k =(0,0,...,0,...,1,1,...,1,...,0,0,...,0) (25)

[0113] Where W takes the expectation value, Dw takes 1e-4, and v k is a vector of dimension d, whose element corresponding to the position of the predicted output of all time periods of the kth wind power or photovoltaic unit is 1, and the rest of the elements are 0.

[0114] (6) Based on the sensitivity information of the index, calculate the comprehensive influence factor of the power prediction error of each wind power or photovoltaic unit on each index:

[0115]

[0116] (7) Weighted average of the four comprehensive influence factors of each wind power or photovoltaic unit to obtain the comprehensive evaluation value of its influence on the transmission power of the section, and then based on the size of the comprehensive evaluation value, obtain the ranking of the comprehensive influence of the power prediction error of each wind power or photovoltaic unit on the transmission power of the section.

[0117] Compared with the prior art, the present application has the following advantages:

[0118] 1) The evaluation method and sensitivity analysis method of the wind and light prediction error for the influence of the key section transmission power proposed in this paper can quickly determine the wind power plant or photovoltaic plant that mainly affects the key section transmission power, and accordingly help to take effective measures to reduce its prediction error and significantly improve its influence on the transmission power.

[0119] 2) Based on the evaluation results, the wind power plant or photovoltaic plant that mainly affects the key section transmission power can be quickly determined.

[0120] 3) Based on the identified wind farms or photovoltaic plants, measures can be taken to reduce their prediction errors and improve their impact on cross-sectional output power.

[0121] Example 2

[0122] Based on the same inventive concept, this invention also provides an assessment system for the impact of wind power and photovoltaic power prediction on cross-sectional transmission power, such as... Figure 3 As shown, it includes:

[0123] The power calculation module calculates the transmission power on the line based on the acquired voltage and current parameters of the wind power and photovoltaic power generation and the pre-constructed deterministic unit combination model.

[0124] The index calculation module calculates the cross-sectional evaluation index based on the transmission power on the line and the pre-built power transmission evaluation index system.

[0125] The comprehensive evaluation module uses the Latin hypercube sampling method based on the cross-sectional evaluation indicators to obtain the comprehensive evaluation value of the impact of wind power and photovoltaic units on the cross-sectional transmission power.

[0126] The power calculation module includes:

[0127] The variable calculation unit obtains the conventional unit output value variable, the actual dispatch output variable of wind power or photovoltaic power unit, the load shedding variable and the load amount based on the obtained voltage and current parameters of wind power and photovoltaic power generation and the pre-constructed deterministic unit combination model.

[0128] The calculation unit obtains the transmission power on the line based on the output value variables of the conventional units, the actual dispatch output variables of the wind power or photovoltaic units, the load shedding variables and the load amount, as well as the power calculation formula;

[0129] The comprehensive evaluation module includes:

[0130] The parameter acquisition submodule obtains the vector sampling values ​​of the cross-sectional evaluation index of wind power and photovoltaic units based on the pre-set sampling point dimension and number of points of the hypercube sampling method.

[0131] The calculation submodule calculates the expected value and standard deviation of the evaluation index for each section based on the sampled values ​​of the index vector, and uses the sensitivity calculation formula to calculate the sensitivity of the expected value and standard deviation of the evaluation index for each section relative to the predicted output error of wind power and photovoltaic units.

[0132] The evaluation value calculation sub-module calculates the comprehensive influence factor of the power prediction error of each wind power and photovoltaic unit on each index based on the expected value and the sensitivity of the standard deviation, and obtains the comprehensive evaluation value of the influence of the wind power and photovoltaic unit on the transmission power of the section by weighted average of the comprehensive influence factor of each wind power and photovoltaic unit.

[0133] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code.

[0134] The present application is described with reference to the flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the functions specified in the flowchart

[0135] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the functions specified in the flowchart

[0136] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 means for performing the functions specified in the flowchart

[0137] The above merely illustrates the embodiments of the present application, but should not be taken as limitations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall fall into the protection scope of the present application.

Claims

1. A method for evaluating the impact of power prediction on cross-sectional transmission power, characterized in that, include: The transmission power on the line is calculated based on the obtained voltage and current parameters of wind power and photovoltaic power generation and the pre-constructed deterministic unit combination model. The cross-sectional evaluation index is calculated based on the transmission power on the line and the pre-constructed power transmission evaluation index system. Based on the aforementioned cross-sectional evaluation indicators, the Latin hypercube sampling method was used to obtain a comprehensive evaluation value of the impact of wind power and photovoltaic units on the cross-sectional transmission power. The deterministic unit combination model includes one that aims to minimize the cost of wind power and photovoltaic units. The deterministic unit combination model includes: The dispatchable power of the wind power and photovoltaic power generation is calculated based on the obtained voltage and current parameters of the wind power and photovoltaic power generation combined with the prediction error model. Construct an objective function with the goal of minimizing the cost of wind power and photovoltaic units; The objective function is constrained by the following conditions: the start-up and shutdown of the conventional unit, the power value variables, the unit's ramp-up and ramp-down speeds, and the branch transmission power. A deterministic unit combination model is constructed based on the objective function and constraints. The prediction error model is as follows: (1) in, This indicates the dispatchable power of wind or solar power. This indicates the predicted power output of wind or solar power. Follow the mean The standard deviation is The normal distribution; The objective function is shown in the following equation; In the formula, For the set of unit start-up and shutdown variables, For the set of unit start-up variables, For the set of unit power variables, For wind power error, Let be the dimension of the sampling points. For regular unit numbering, A collection of conventional generating units. For a collection of wind or solar power units, t is time. For wind power or photovoltaic units, For load numbering, For load set, These are the operating state variables of a conventional generating unit at time t. : This represents the variable for the start-up of a conventional unit at time t; Power value change of conventional generating units at time t; This is the unit start-up cost coefficient; This represents the dispatchable power of wind or solar power units at time t. This represents the actual dispatch power variable of wind or solar power units at time t. : Penalty fee for load shedding; For load shedding variables; Curtailment penalty fees; , This is the unit operating cost coefficient; The calculation of transmission power on the line based on the acquired voltage and current parameters of the wind power and photovoltaic power generation and the pre-constructed deterministic unit combination model includes: Based on the obtained voltage and current parameters of the wind power and photovoltaic power generation and the pre-constructed deterministic unit combination model, the power value variables of conventional units, the actual dispatch power variables of wind power or photovoltaic units, the load shedding variables and the load amount are obtained. The transmission power on the line is obtained based on the power value variables of conventional units, the actual dispatch power variables of wind power or photovoltaic units, the load shedding variables and the load amount, as well as the power calculation formula. The construction of the power transmission evaluation index system includes: It is constructed from the total electrical energy transmitted across the cross section, the average power margin of the cross section, the maximum non-uniformity of the power transmitted across the cross section, and the average power fluctuation of the cross section. The total electrical energy transmitted at the cross-section, the average power margin of the cross-section, the maximum non-uniformity of the power transmitted at the cross-section, and the average power fluctuation at the cross-section are calculated according to the following formulas: In the formula, To deliver total electrical energy to the cross-section; This represents the average margin of power delivered across the cross-section. The maximum non-uniformity of power transmission across the cross section; The average fluctuation of cross-sectional power; For the first The line is in t Transmission power during a given time period; This is the upper limit of the branch transmission power. This represents the total number of lines. This is the line number.

2. The evaluation method as described in claim 1, characterized in that, The transmission power on the line is calculated using the following formula: In the formula, For the first l The line is in t Transmission power during a given time period; Power value change of conventional generating units at time t; This represents the actual dispatch power variable of wind or solar power units at time t. For load shedding variables; : refers to the load amount; Branch-node transfer factor; Let n be the set of regular units.

3. The evaluation method as described in claim 1, characterized in that, The comprehensive evaluation value of the impact of wind power and photovoltaic units on the cross-sectional transmission power is obtained by using the Latin hypercube sampling method based on the cross-sectional evaluation index, including: Based on the pre-defined sampling point dimension and number of points of the hypercube sampling method, the sampled values ​​of the cross-sectional evaluation index vector are obtained. The expected value and standard deviation of each cross-section evaluation index are calculated based on the sampled values ​​of the cross-section evaluation index vector. Based on the expected value and standard deviation of the evaluation indicators for each section, the sensitivity of the expected value and standard deviation of the evaluation indicators relative to the predicted power error of wind power and photovoltaic units is calculated using the sensitivity calculation formula. Based on the sensitivity of the expected value and standard deviation, the comprehensive impact factor of the power prediction error of each wind power and photovoltaic unit on each index is calculated. The comprehensive evaluation value of the impact of wind power and photovoltaic units on cross-sectional transmission power is obtained by weighted averaging of the comprehensive impact factors of each wind power and photovoltaic unit.

4. The evaluation method as described in claim 3, characterized in that, The sensitivity calculation formulas for the expected value and standard deviation are shown below: In the formula, : No. k The sensitivity of the expected value of the index j of the predicted power error of individual wind and photovoltaic units; , Latin square sampling is performed on the sampling points of the random vector of dispatchable power from wind and solar power. Let j be the expected value of the index. is the standard deviation of indicator j; j is the indicator number, which is a positive integer; k: is the number of wind power or photovoltaic unit; Let be a random vector of dispatchable power from wind and solar power. Take it as 1e-4, For dimension is d ; : No. k The sensitivity of the standard deviation of the predicted power error index j for individual wind and photovoltaic units.

5. The evaluation method as described in claim 4, characterized in that, The comprehensive impact factor is calculated using the following formula: In the formula, The comprehensive impact factor of power prediction errors of each wind power and photovoltaic unit on various indicators.

6. The evaluation method as described in claim 1, characterized in that, Also includes: The comprehensive evaluation values ​​are sorted according to their numerical values.

7. A system for implementing the assessment method for the impact of wind power and photovoltaic power prediction on cross-sectional transmission power as described in any one of claims 1-6, characterized in that, include: The power calculation module calculates the transmission power on the line based on the acquired voltage and current parameters of wind power and photovoltaic power generation, as well as the pre-built deterministic unit combination model. The index calculation module calculates the cross-sectional evaluation index based on the transmission power on the line and the pre-built power transmission evaluation index system. The comprehensive evaluation module uses the Latin hypercube sampling method based on the cross-sectional evaluation indicators to obtain the comprehensive evaluation value of the impact of wind power and photovoltaic units on the cross-sectional transmission power. The deterministic unit combination model includes: Construct an objective function with the goal of minimizing the cost of wind power and photovoltaic units; The objective function is constrained by the following conditions: the start-up and shutdown of the conventional unit, the power value variables, the unit's ramp-up and ramp-down speeds, and the branch transmission power. A deterministic unit combination model is constructed based on the objective function and constraints. The objective function is shown in the following equation; In the formula, For the set of unit start-up and shutdown variables, For the set of unit start-up variables, For the set of unit power variables, For wind power error, Let be the dimension of the sampling points. For regular unit numbering, A collection of conventional generating units. For a collection of wind or solar power units, t is time. For wind power or photovoltaic units, For load numbering, For load set, These are the operating state variables of a conventional generating unit at time t. : This represents the variable for the start-up of a conventional unit at time t; Power value change of conventional generating units at time t; This is the unit start-up cost coefficient; The dispatchable power of wind or solar power units at time t; This represents the actual dispatch power variable of wind or solar power units at time t. : Penalty fee for load shedding; For load shedding variables; Curtailment penalty fees; , This is the unit operating cost coefficient.

8. The evaluation system of claim 7, wherein the power calculation module comprises: The variable calculation unit obtains the conventional unit power value variable, the actual dispatch power variable of the wind power or photovoltaic power generation, the load shedding variable, and the load amount based on the obtained voltage and current parameters of the wind power and photovoltaic power generation and the pre-constructed deterministic unit combination model. The calculation unit obtains the transmission power on the line based on the conventional unit power value variables, the actual dispatch power variables of wind power or photovoltaic units, load shedding variables and load amount, and the power calculation formula.

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