A method and device for evaluating the new energy consumption capacity of a power grid

Abstract

The present application provides a method and device for evaluating the new energy consumption capacity of a power grid, which can be used in the field of electronic power technology. The method includes: establishing a short-circuit ratio calculation model for wind farm stations and photovoltaic power stations based on the mutual impedance between each wind farm station and photovoltaic power station, the self-impedance of each wind farm station, and the self-impedance of each photovoltaic power station; establishing a short-circuit ratio constraint model based on the short-circuit ratio calculation model and the critical short-circuit ratio; establishing a power grid operation constraint model based on the power system operation parameters; calculating the maximum value of the new energy consumption of the power grid according to the short-circuit ratio constraint model and the power grid operation constraint model. The device is used to execute the above method. The method and device for evaluating the new energy consumption capacity of the power grid provided by the present application achieve a reasonable evaluation of the new energy consumption capacity of the power grid, fully consider the influence of the short-circuit ratio on the new energy consumption capacity of the power grid, ensure the stable operation of the power grid, and improve the accuracy of the new energy consumption capacity of the power grid, providing a reliable basis for the development plan of new energy.

Description

Technical Field

[0001] The present application relates to the technical field of electric power, and in particular, to a method and device for evaluating the new energy accommodation capacity of a power grid. Background Art

[0002] In recent years, the proportion of new energy access in the power grid has been continuously increasing. However, due to the uncertainty of new energy output, it is difficult to meet the requirements of "source-load" power balance, resulting in serious phenomena of wind and light abandonment and difficulties in new energy accommodation. In addition, if the proportion of new energy access is too high, it will weaken the power grid strength, leading to prominent problems such as transient voltage instability and broadband oscillation. Therefore, accurately evaluating the new energy accommodation capacity of the power grid is of great significance for improving the new energy accommodation level and ensuring the safe and stable operation of the power grid.

[0003] At present, a large number of studies have been carried out at home and abroad on the method for evaluating the new energy accommodation capacity based on chronological production simulation. Some scholars have conducted research on the chronological production simulation model from aspects such as peak shaving capacity, frequency stability, and voltage stability; some scholars have carried out research on the fast solution algorithm for the chronological production simulation model; and some other scholars have carried out research on the impact of power market mechanism design on improving the new energy accommodation capacity. These studies all adopt the traditional chronological production simulation model.

[0004] In the prior art, the chronological production simulation model evaluates the output demand of new energy by the load under the existing grid structure through simulating the operation of the power grid hour by hour. However, due to the change of the power grid strength after the addition of new energy, the output of new energy often cannot be fully sent out, resulting in an overestimated evaluation result of the existing chronological production simulation model, which does not conform to the actual operation plan of the power grid. Summary of the Invention

[0005] In view of the problems in the prior art, embodiments of the present invention provide a method and device for evaluating the new energy accommodation capacity of a power grid, which can at least partially solve the problems existing in the prior art.

[0006] In a first aspect, the present invention provides a method for evaluating the new energy accommodation capacity of a power grid, including:

[0007] Establishing a short-circuit ratio calculation model for wind farms and photovoltaic power stations according to the mutual impedance between each wind farm and photovoltaic power station, the self-impedance of each wind farm, and the self-impedance of each photovoltaic power station;

[0008] Establishing a short-circuit ratio constraint model according to the short-circuit ratio calculation model and the critical short-circuit ratio;

[0009] Establishing a power grid operation constraint model according to the power system operation parameters;

[0010] Calculating the maximum value of the new energy accommodation amount of the power grid according to the short-circuit ratio constraint model and the power grid operation constraint model.

[0011] Among them, the power system operation parameters include the maximum theoretical output of the wind farm at each moment and the maximum theoretical output of the photovoltaic power station at each moment; establishing a power grid operation constraint model according to the power system operation parameters includes:

[0012] Establishing a maximum output constraint model for new energy power plants according to the maximum theoretical output of the wind farm at each moment and the maximum theoretical output of the photovoltaic power station at each moment.

[0013] Among them, the power system operation parameters include the system load value at each moment; establishing a power grid operation constraint model according to the power system operation parameters includes:

[0014] Establishing a power and electricity balance constraint model according to the system load value at each moment.

[0015] Among them, the power system operation parameters include the upper limit of the active power output of each conventional unit and the positive spinning reserve capacity of the power system; establishing a power grid operation constraint condition model according to the power system operation parameters includes:

[0016] Establishing a spinning reserve constraint model according to the upper limit of the active power output of each conventional unit and the positive spinning reserve capacity of the power system.

[0017] Among them, the power system operation parameters further include the upper limit and the lower limit of the active power output of each conventional unit; establishing a power grid operation constraint condition model according to the power system operation parameters further includes:

[0018] Establishing a conventional unit output constraint model according to the upper limit and the lower limit of the active power output of each conventional unit.

[0019] Among them, the power system operation parameters further include the up-ramping power and the down-ramping power of each conventional unit; establishing a power grid operation constraint condition model according to the power system operation parameters further includes:

[0020] Establishing a conventional unit ramping constraint model according to the up-ramping power and the down-ramping power of each conventional unit.

[0021] Among them, before establishing the power grid operation constraint conditions according to the power system operation parameters, it further includes:

[0022] Performing regression analysis on the measured wind speed data and wind power output data of each year before curtailment of electricity to obtain a wind speed-wind power output mapping relationship;

[0023] Correcting the wind power output data of the corresponding year according to the measured wind speed data of each year and the wind speed-wind power output mapping relationship to obtain the theoretical maximum wind power output curve of each year;

[0024] Calculate the mean value of the theoretical maximum wind power output curves for each year and perform per-unit processing to obtain a typical theoretical maximum wind power output curve;

[0025] Obtain the maximum theoretical output value of each wind farm at each moment according to the installed capacity of each wind farm and the typical theoretical maximum wind power output curve.

[0026] Among them, before establishing the grid operation constraint conditions according to the power system operation parameters, it further includes:

[0027] Perform regression analysis based on the light intensity data, temperature data, and wind power output data of each year before curtailment to obtain the light intensity-temperature - photovoltaic output mapping relationship;

[0028] Correct the photovoltaic output data of the corresponding year according to the light intensity data, temperature data of each year, and the light intensity-temperature - photovoltaic output mapping relationship to obtain the theoretical maximum photovoltaic output curves for each year;

[0029] Calculate the mean value of the theoretical maximum photovoltaic output curves for each year and perform per-unit processing to obtain a typical theoretical maximum photovoltaic output curve;

[0030] Obtain the maximum theoretical output value of each photovoltaic power station at each moment according to the installed capacity of each photovoltaic power station and the typical theoretical maximum photovoltaic output curve.

[0031] In a second aspect, the present application provides a device for evaluating the new energy consumption capacity of a power grid, including:

[0032] A short-circuit ratio calculation model establishment unit, configured to establish a short-circuit ratio calculation model for wind farms and photovoltaic power stations according to the mutual impedance between each wind farm and photovoltaic power station, the self-impedance of each wind farm, and the self-impedance of each photovoltaic power station;

[0033] A short-circuit ratio constraint model establishment unit, configured to establish a short-circuit ratio constraint model according to the short-circuit ratio calculation model and the critical short-circuit ratio;

[0034] A grid operation constraint model establishment unit, configured to establish a grid operation constraint model according to the power system operation parameters;

[0035] A maximum new energy consumption calculation unit, configured to calculate the maximum value of the new energy consumption of the power grid according to the short-circuit ratio constraint model and the grid operation constraint model.

[0036] Among them, the power system operation parameters include the maximum theoretical output value of each wind farm at each moment and the maximum theoretical output value of each photovoltaic power station at each moment; the grid operation constraint model establishment unit includes:

[0037] A maximum output constraint model establishment module for new energy power plants, which is used to establish a maximum output constraint model for new energy power plants according to the theoretical maximum output of the wind farm at each moment and the theoretical maximum output of the photovoltaic power station at each moment.

[0038] Among them, it also includes:

[0039] A wind speed-wind power output mapping relationship fitting unit, which is used to perform regression analysis on the wind speed data and wind power output data of each year before curtailment to obtain the wind speed-wind power output mapping relationship;

[0040] A wind power output data correction unit, which is used to correct the wind power output data of each year according to the measured wind speed data of each year and the wind speed-wind power output mapping relationship to obtain the theoretical maximum wind power output curve of each year;

[0041] A typical theoretical maximum wind power output curve establishment unit, which is used to calculate the mean value of the theoretical maximum wind power output curve of each year and perform per-unitization processing to obtain a typical theoretical maximum wind power output curve;

[0042] A first theoretical output maximum value acquisition unit, which is used to obtain the theoretical output maximum value of the wind farm at each moment according to the installed capacity of each wind farm and the typical theoretical maximum wind power output curve.

[0043] Among them, it also includes:

[0044] A light intensity-temperature-photovoltaic power output mapping relationship fitting unit, which is used to perform regression analysis on the light intensity data, temperature data and wind power output data of each year before curtailment to obtain the light intensity-temperature-photovoltaic power output mapping relationship;

[0045] A photovoltaic power output data correction unit, which is used to correct the photovoltaic power output data of each year according to the light intensity data, temperature data of each year and the light intensity-temperature-photovoltaic power output mapping relationship to obtain the theoretical maximum photovoltaic power output curve of each year;

[0046] A typical theoretical maximum photovoltaic power output curve establishment unit, which is used to calculate the mean value of the theoretical maximum photovoltaic power output curve of each year and perform per-unitization processing to obtain a typical theoretical maximum photovoltaic power output curve;

[0047] A second theoretical output maximum value acquisition unit, which is used to obtain the theoretical output maximum value of the photovoltaic power station at each moment according to the installed capacity of each photovoltaic power station and the typical theoretical maximum photovoltaic power output curve.

[0048] Thirdly, the present application provides a computer device, including a memory, a processor and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method described in any one of the above embodiments is implemented.

[0049] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the method described in any of the above embodiments.

[0050] In a fifth aspect, the present application provides a computer program product including a computer program, which when executed by a processor implements the method described in any of the above embodiments.

[0051] The method and device for evaluating the new energy consumption capacity of a power grid provided by the present application establish a short-circuit ratio calculation model for wind farm stations and photovoltaic farm stations by using the mutual impedance between each wind farm station and photovoltaic farm station, the self-impedance of each wind farm station, and the self-impedance of each photovoltaic farm station; establish a short-circuit ratio constraint model based on the short-circuit ratio calculation model and the critical short-circuit ratio; establish a power grid operation constraint model based on the power system operation parameters; calculate the maximum value of the new energy consumption of the power grid according to the short-circuit ratio constraint model and the power grid operation constraint model, fully considering the influence of the short-circuit ratio on the new energy consumption capacity of the power grid, ensuring the stable operation of the power grid while realizing a reasonable evaluation of the new energy consumption capacity of the power grid, improving the accuracy of the new energy consumption capacity of the power grid, and providing a reliable basis for the development plan of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0053] Figure 1 is a flowchart of the method for evaluating the new energy consumption capacity of a power grid provided by the first embodiment of the present invention;

[0054] Figure 2 is a flowchart of the method for evaluating the new energy consumption capacity of a power grid provided by the first embodiment of the present invention;

[0055] Figure 3 is a flowchart of the method for evaluating the new energy consumption capacity of a power grid provided by the first embodiment of the present invention;

[0056] Figure 4 is the load annual output curve of an evaluation year provided by an embodiment of the present application;

[0057] Figure 5 is the maximum annual hourly output curve of a photovoltaic unit of an evaluation year provided by an embodiment of the present application;

[0058] Figure 6 It is the maximum output curve of the annual time series unit of wind power in an evaluation year provided by an embodiment of the present application;

[0059] Figure 7 It is a schematic structural diagram of a power grid new energy consumption capacity evaluation device provided by an embodiment of the present application;

[0060] Figure 8 It is a schematic structural diagram of a power grid new energy consumption capacity evaluation device provided by an embodiment of the present application;

[0061] Figure 9 It is a schematic structural diagram of a power grid new energy consumption capacity evaluation device provided by an embodiment of the present application;

[0062] Figure 10 It is a schematic structural diagram of a power grid new energy consumption capacity evaluation device provided by an embodiment of the present application;

[0063] Figure 11 It is a schematic entity structure diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer and more understandable, the following further elaborates on the embodiments of the present invention with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but not to limit the present invention. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined arbitrarily with each other.

[0065] Taking the server as the execution entity as an example below, the specific implementation process of the power grid new energy consumption capacity evaluation method provided by the embodiments of the present invention is described.

[0066] Figure 1 It is a flowchart of the power grid new energy consumption capacity evaluation method provided by the first embodiment of the present invention. As Figure 1 shown, the power grid new energy consumption capacity evaluation method provided by the embodiments of the present invention includes:

[0067] S101: Establish a short-circuit ratio calculation model for wind farm stations and photovoltaic farm stations according to the mutual impedance between each wind farm station and photovoltaic farm station, the self-impedance of each wind farm station, and the self-impedance of each photovoltaic farm station;

[0068] Specifically, the short-circuit ratio calculation model for wind farm stations and photovoltaic farm stations can be expressed by the following formula:

[0069]

[0070] Among them, K SCRwd is the short-circuit ratio of the wind farm station, KSCRpvis the short - circuit ratio of the photovoltaic power station, X g (k, t) represents the operating state of unit k at time t, and its operating state is represented by a 0 - 1 variable, where 1 means unit k is in the operating state and 0 means unit k is in the shutdown state; Z g (k) is the impedance of conventional unit k; ntot is the total number of new - energy power stations, ntot = npv + nwd; Ωpv and Ωwd represent the sets of photovoltaic power stations and new - energy power stations respectively; S k (i, t), S k (j, t) represent the short - circuit capacities of photovoltaic power station i and wind farm j at time t respectively; Z i，i (t), Z j，j (t) represent the self - impedances of photovoltaic power station i and wind farm j at time t respectively; Z j，i (t), Z i，j (t) are the mutual impedances of new - energy power stations i and j at time t; the function f represents the functional relationship between the short - circuit capacity and the self - impedance, unit state, and unit impedance; P(i, t), P(j, t) represent the active power outputs of new - energy power stations i and j at time t; r j，i (t), r i，j (t) represent the voltage interaction influence factors of new - energy power stations j and i at time t respectively; ΔV(j, t), ΔV(i, t) represent the voltage change values of new - energy power stations j and i at time t respectively, and new - energy power stations include photovoltaic power stations and wind farms. Z g (k), Z i，i (t), Z j，j (t), Z j，i (t), Z i，j (t) can all be obtained from the BPA (Business Process Automation) model data provided by the power grid. For example, the grid framework data is obtained according to the BPA model data provided by the power grid, the system impedance matrix is obtained from the grid framework data, and each impedance value is extracted from the system impedance matrix. The method for obtaining each impedance value in this application is not limited.

[0071] S102: Establish a short - circuit ratio constraint model according to the short - circuit ratio calculation model and the critical short - circuit ratio;

[0072] Specifically, the server establishes the following short - circuit ratio constraint model according to the short - circuit ratio calculation model and the critical short - circuit ratio:

[0073]

[0074] Among them, K SCRis the critical short - circuit ratio of the power grid. Generally, when the short - circuit ratio is greater than 3, the power grid is a strong system; when the short - circuit ratio is between 2 and 3, the power grid is a weak system; when the short - circuit ratio is less than 2, the power grid is a severely weak system. The appropriate critical short - circuit ratio can be selected according to the actual requirements of the power grid, such as 2 or 1.5. The specific value of the critical short - circuit ratio in this application is not limited.

[0075] S103: Establish a power grid operation constraint model according to the power system operation parameters;

[0076] Specifically, the server considers each constraint condition during the actual operation of the power grid and establishes a power grid operation constraint model according to the power system operation parameters.

[0077] S104: Calculate the maximum value of the new - energy consumption of the power grid according to the short - circuit ratio constraint model and the power grid operation constraint model.

[0078] Specifically, the new - energy consumption includes wind - power consumption and photovoltaic - power consumption. The maximum value of the new - energy consumption of the power grid can be expressed by the following formula

[0079]

[0080] The server uses formula (3) as the objective function, solves it according to the short - circuit ratio constraint model and the power grid operation constraint model, and obtains the maximum value of the objective function, which is the maximum value of the new - energy consumption of the power grid.

[0081] The method for evaluating the new - energy consumption capacity of the power grid provided by this application includes: establishing a short - circuit ratio calculation model for wind farms and photovoltaic farms according to the mutual impedance between each wind farm and photovoltaic farm, the self - impedance of each wind farm, and the self - impedance of each photovoltaic farm; establishing a short - circuit ratio constraint model according to the short - circuit ratio calculation model and the critical short - circuit ratio; establishing a power grid operation constraint model according to the power system operation parameters; calculating the maximum value of the new - energy consumption of the power grid according to the short - circuit ratio constraint model and the power grid operation constraint model. It fully considers the influence of the short - circuit ratio on the new - energy consumption capacity of the power grid, ensures the stable operation of the power grid, realizes a reasonable evaluation of the new - energy consumption capacity of the power grid, improves the accuracy of the new - energy consumption capacity of the power grid, and provides a reliable basis for the new - energy development plan.

[0082] On the basis of the above embodiments, further, the power system operation parameters include the maximum theoretical output of wind farms at each moment and the maximum theoretical output of photovoltaic farms at each moment; establishing a power grid operation constraint model according to the power system operation parameters includes:

[0083] Establishing a maximum output constraint model of new - energy power plants according to the maximum theoretical output of wind farms at each moment and the maximum theoretical output of photovoltaic farms at each moment.

[0084] Specifically, the maximum output constraint model of the new energy power plant can be expressed by the following formula:

[0085]

[0086] Wherein, is the theoretical maximum value of the photovoltaic power station i at time t; is the theoretical maximum value of the wind farm j at time t. and can both be obtained from historical data.

[0087] In one embodiment, as Figure 2 shown, on the basis of the above embodiments, further, the power grid new energy consumption capacity evaluation method provided by the embodiments of the present invention, before S103, further includes:

[0088] S201: Perform regression analysis based on the measured wind speed data and wind power output data of each year before curtailment to obtain the wind speed-wind power output mapping relationship;

[0089] Specifically, select several years before curtailment of the power grid. The server performs regression analysis based on the measured wind speed data and wind power output data of each year before curtailment to obtain the wind speed-wind power output mapping relationship. Among them, both the measured wind speed data and the wind power output data include the detection data at multiple moments of each day in a year. An appropriate regression analysis method can be selected according to the actual situation for regression analysis, such as logical analysis method, ridge regression, etc. The present application does not limit the regression analysis method used.

[0090] S202: Correct the wind power output data of the corresponding year according to the measured wind speed data of each year and the wind speed-wind power output mapping relationship to obtain the theoretical maximum wind power output curve of each year;

[0091] Specifically, the server calculates the theoretical wind power output value corresponding to the corresponding moment according to the obtained wind speed-wind power output mapping relationship and the measured wind speed data at each moment, as the theoretical maximum wind power output at this moment. The theoretical maximum wind power outputs at each moment in a year constitute the theoretical maximum wind power output curve of this year.

[0092] S203: Calculate the mean value of the theoretical maximum wind power output curves of each year and perform per-unit normalization processing to obtain the typical theoretical maximum wind power output curve;

[0093] Specifically, the server calculates the mean value of the theoretical maximum wind power output at each moment according to the theoretical maximum wind power output curves of each year, and performs per-unit normalization processing on the mean value of the theoretical maximum wind power output at each moment to obtain the typical theoretical maximum wind power output curve. The values at each point on the typical theoretical maximum wind power output curve represent the maximum theoretical wind power output per unit installed capacity at the moment corresponding to this point.

[0094] S204: Obtain the maximum theoretical output of each wind farm at each moment according to the installed capacity of each wind farm and the typical theoretical maximum wind power output curve.

[0095] Specifically, the server obtains the maximum theoretical wind power output of each wind farm at each moment according to the installed capacity of each wind farm and the typical theoretical maximum wind power output curve, that is, according to the installed capacity of each wind farm and the maximum theoretical wind power output per unit installed capacity at each moment.

[0096] The grid new energy consumption capacity evaluation method provided by this application obtains the wind speed-wind power output mapping relationship through regression analysis based on the measured wind speed data and wind power output data of each year before curtailment; corrects the wind power output data of the corresponding year according to the wind speed data of each year and the wind speed-wind power output mapping relationship to obtain the typical theoretical maximum wind power output curve of each year; calculates the mean value of the typical theoretical maximum wind power output curve of each year and performs per-unit normalization processing to obtain the typical theoretical maximum wind power output curve, and obtains the maximum theoretical output of each wind farm at each moment according to the installed capacity of each wind farm, realizing a reasonable prediction of the maximum theoretical output of the wind farm in a year, and applying it to the maximum output constraint model of the new energy power plant, improving the accuracy of the grid new energy consumption capacity evaluation.

[0097] In one embodiment, as Figure 3 shown, on the basis of the above embodiments, further, the grid new energy consumption capacity evaluation method provided by the embodiment of the present invention further includes, before S103:

[0098] S301: Perform regression analysis according to the light intensity data, temperature data and wind power output data of each year before curtailment to obtain the light temperature-photovoltaic output mapping relationship;

[0099] Specifically, select several years before curtailment of this grid. The server performs regression analysis according to the measured light intensity data, temperature data and wind power output data of each year before curtailment to obtain the wind speed-wind power output mapping relationship. Among them, the measured light intensity data, temperature data and wind power output data all include the detection data of multiple moments every day in a year. Appropriate regression analysis methods can be selected according to the actual situation for regression analysis, such as logical analysis method, ridge regression, etc. This application does not limit the regression analysis method used.

[0100] S302: Correct the photovoltaic output data of the corresponding year according to the light intensity data, temperature data and light temperature-photovoltaic output mapping relationship of each year to obtain the typical theoretical maximum photovoltaic output curve of each year;

[0101] Specifically, the server calculates the theoretical value of the photovoltaic output at the corresponding moment based on the obtained mapping relationship between light intensity - photovoltaic output and the light intensity data and temperature data at each moment, and takes it as the theoretical maximum photovoltaic output at that moment. The theoretical maximum photovoltaic outputs at each moment in a year form the theoretical maximum photovoltaic output curve for that year.

[0102] S303: Calculate the mean value of the theoretical maximum photovoltaic output curves for each year and perform per-unit normalization to obtain a typical theoretical maximum photovoltaic output curve;

[0103] Specifically, the server calculates the mean value of the theoretical maximum photovoltaic output at each moment based on the theoretical maximum photovoltaic output curves for each year, and performs per-unit normalization on the mean value of the theoretical maximum photovoltaic output at each moment to obtain a typical theoretical maximum photovoltaic output curve. The values at each point on the typical theoretical maximum photovoltaic output curve represent the maximum theoretical photovoltaic output per unit installed capacity at the moment corresponding to that point.

[0104] S304: Obtain the maximum theoretical output of the photovoltaic power station at each moment based on the installed capacity of each photovoltaic power station and the typical theoretical maximum photovoltaic output curve.

[0105] Specifically, the server obtains the maximum theoretical photovoltaic output of the photovoltaic power station at each moment based on the installed capacity of each photovoltaic power station and the typical theoretical maximum photovoltaic output curve, that is, based on the installed capacity of each photovoltaic power station and the maximum theoretical photovoltaic output per unit installed capacity at each moment.

[0106] The method for evaluating the new energy consumption capacity of the power grid provided by this application performs regression analysis based on the light intensity data, temperature data, and wind power output data in each year before curtailment to obtain the mapping relationship between light intensity - photovoltaic output; corrects the photovoltaic output data for the corresponding year based on the light intensity data, temperature data, and the mapping relationship between light intensity - photovoltaic output in each year to obtain the theoretical maximum photovoltaic output curve for each year; calculates the mean value of the theoretical maximum photovoltaic output curves for each year and performs per-unit normalization to obtain a typical theoretical maximum photovoltaic output curve, and obtains the maximum theoretical output of the photovoltaic power station at each moment based on the installed capacity of each photovoltaic power station, realizing a reasonable prediction of the maximum theoretical output of the photovoltaic power station in a year and applying it in the maximum output constraint model of the new energy power plant, improving the accuracy of the evaluation of the new energy consumption capacity of the power grid.

[0107] Except Figure 2 、 Figure 3 for implementation exceptions, the historical maximum output value per unit installed capacity at each moment can also be used as the theoretical maximum output value. This application does not limit the method for obtaining the maximum theoretical output of the wind farm and photovoltaic power station at each moment.

[0108] Based on the above embodiments, further, the power system operation parameters include the system load values at each moment; establishing a power grid operation constraint model according to the power system operation parameters, including:

[0109] Establishing a power and electricity balance constraint model according to the system load values at each moment.

[0110] Specifically, the power and electricity balance constraint model can be expressed by the following formula:

[0111]

[0112] where, P g (k,t) is the active power output of the k-th conventional unit at time t; P load (t) is the system load value at time t; ng is the number of conventional units in the system. P g (k,t) is a constant, which varies according to the specific unit, and P load (t) can be obtained from the actual load data of the year for evaluation.

[0113] Based on the above embodiments, further, the power system operation parameters include the upper limit of the active power output of each conventional unit and the positive spinning reserve capacity of the power system; establishing a power grid operation constraint condition model according to the power system operation parameters, including:

[0114] Establishing a spinning reserve constraint model according to the upper limit of the active power output of each conventional unit and the positive spinning reserve capacity of the power system.

[0115] Specifically, the spinning reserve constraint model can be expressed by the following formula:

[0116]

[0117] where, P gmax (k) is the upper limit of the active power output of conventional unit k; P R is the positive spinning reserve capacity of the system. P gmax (k) is a constant, which varies according to the conventional unit, and P R can be obtained according to the actual situation of the power grid.

[0118] Based on the above embodiments, further, the power system operation parameters further include the upper limit and the lower limit of the active power output of each conventional unit; establishing a power grid operation constraint condition model according to the power system operation parameters further includes:

[0119] Establishing a conventional unit output constraint model according to the upper limit and the lower limit of the active power output of each conventional unit.

[0120] Specifically, the conventional unit output constraint model can be expressed by the following formula:

[0121] X g (k,t)·P gmin (k) ≤ P g (k,t) ≤ X g (k,t)·P gmax (k) (7)

[0122] Wherein, P gmin (k) is the lower limit of the active power output of the conventional unit k, and P gmin (k) is a constant, which varies according to different conventional units.

[0123] Based on the above embodiments, further, the power system operation parameters further include the up-ramp power and down-ramp power of each conventional unit; establishing a grid operation constraint condition model according to the power system operation parameters further includes:

[0124] Establishing a conventional unit ramp constraint model according to the up-ramp power and down-ramp power of each conventional unit.

[0125] Specifically, the conventional unit ramp constraint model can be expressed by the following formula:

[0126]

[0127] Wherein, P up (k) is the up-ramp rate of unit k, and P down (k) is the down-ramp power of unit k. P up (k) and P down (k) are both constants, which vary according to different conventional units.

[0128] In the grid new energy consumption capacity evaluation method provided by this application, the grid operation constraint model can include any combination of the above embodiments.

[0129] The grid new energy consumption capacity evaluation method provided by this application, by fully considering various constraint conditions in the actual operation of the grid and combining with the short-circuit ratio constraint model, improves the accuracy of the grid new energy consumption capacity and provides a reliable basis for the new energy development plan.

[0130] The following is a specific embodiment to illustrate in detail the grid new energy consumption capacity evaluation method provided by this application.

[0131] Figure 4 is the annual load output curve graph of an evaluation year provided by an embodiment of this application, as Figure 4As shown, the abscissa of each point in the figure corresponds to a certain moment in the 8,760 hours of the whole year, and the ordinate represents the load value at that moment, that is, through Figure 4 the system load values at each moment of that year can be obtained, that is, P load (t).

[0132] Figure 5 is the maximum output curve of the annual time series unit of photovoltaic power in an evaluation year provided by an embodiment of the present application. As Figure 5 shown, the horizontal coordinates of each point in the figure respectively represent the corresponding day of the point in the whole year and the corresponding moment in that day, and the vertical coordinate represents the theoretical maximum output value of the unit installed capacity of the photovoltaic power station at that moment. Therefore, from Figure 5 the data in and the installed capacity of each photovoltaic power station, the theoretical maximum values of each photovoltaic power station at each moment can be obtained, that is,

[0133] Figure 6 is the maximum output curve of the annual time series unit of wind power in an evaluation year provided by an embodiment of the present application. As Figure 6 shown, the horizontal coordinates of each point in the figure respectively represent the corresponding day of the point in the whole year and the corresponding moment in that day, and the vertical coordinate represents the theoretical maximum output value of the unit installed capacity of the wind power station at that moment. Therefore, from Figure 6 the data in and the installed capacity of each wind power station, the theoretical maximum values of each wind power station at each moment can be obtained, that is,

[0134] Table 1 provides other values required for the method for evaluating the new energy consumption capacity of the power grid provided by the present application.

[0135] Table 1

[0136] Parameter Value <![CDATA[P gmax (k) / P gmin (k)]]> 1 / 0.1 p.u. <![CDATA[P up (k) / P down (k)]]> 35 MW / h <![CDATA[P R > 0.05 <![CDATA[K SCR > 2

[0137] The power grid operation constraint model includes the models established in formulas (4) to (8). Taking formula (3) as the objective function and inputting various known data, the maximum value of the new energy consumption of the power grid can be calculated according to the short-circuit ratio constraint model and the power grid operation constraint model. The comparison of the results obtained by using the short-circuit ratio constraint model and not using the short-circuit ratio constraint model is shown in the table.

[0138] Table 2

[0139]

[0140] As can be seen from Table 2, using the short-circuit ratio constraint model can more accurately evaluate the new energy consumption capacity of the power grid and prevent the evaluation result from being too optimistic.

[0141] The power grid new energy consumption capacity evaluation method provided by this application establishes a short-circuit ratio calculation model for wind farms and photovoltaic power stations by using the mutual impedance between wind farms and photovoltaic power stations, the self-impedance of each wind farm, and the self-impedance of each photovoltaic power station; establishes a short-circuit ratio constraint model based on the short-circuit ratio calculation model and the critical short-circuit ratio; establishes a power grid operation constraint model based on the power system operation parameters; calculates the maximum value of the new energy consumption of the power grid according to the short-circuit ratio constraint model and the power grid operation constraint model. It fully considers the influence of the short-circuit ratio on the new energy consumption capacity of the power grid, realizes a reasonable evaluation of the new energy consumption capacity of the power grid while ensuring the stable operation of the power grid, improves the accuracy of the new energy consumption capacity of the power grid, and provides a reliable basis for the new energy development plan.

[0142] Based on the same inventive concept, the embodiment of this application also provides a power grid new energy consumption capacity evaluation device, which can be used to implement the method described in the above embodiment, as described in the following embodiment. Since the principle of the power grid new energy consumption capacity evaluation device for solving problems is similar to that of the power grid new energy consumption capacity evaluation method, the implementation of the power grid new energy consumption capacity evaluation device can refer to the implementation of the method for determining the software performance benchmark, and the repeated parts will not be described again. As used below, the term "unit" or "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0143] Figure 7 is a schematic structural diagram of a power grid new energy consumption capacity evaluation device provided by an embodiment of this application, as Figure 7 shown, the power grid new energy consumption capacity evaluation device provided by this application includes:

[0144] A short-circuit ratio calculation model establishment unit 710, configured to establish a short-circuit ratio calculation model for wind farms and photovoltaic power stations by using the mutual impedance between wind farms and photovoltaic power stations, the self-impedance of each wind farm, and the self-impedance of each photovoltaic power station;

[0145] Specifically, the short-circuit ratio calculation model established by the short-circuit ratio calculation model establishment unit 710 for wind farms and photovoltaic power stations can be represented by formula (1).

[0146] A short-circuit ratio constraint model establishment unit 720, configured to establish a short-circuit ratio constraint model based on the short-circuit ratio calculation model and the critical short-circuit ratio;

[0147] Specifically, the short-circuit ratio constraint model establishment unit 720 establishes a short-circuit ratio constraint model based on the short-circuit ratio calculation model and the critical short-circuit ratio, and the short-circuit ratio constraint model can be represented by formula (2).

[0148] The grid operation constraint model establishment unit 730 is configured to establish a grid operation constraint model according to the power system operation parameters;

[0149] Specifically, the grid operation constraint model establishment unit 730 considers various constraint conditions during the actual operation of the grid and establishes a grid operation constraint model according to the power system operation parameters.

[0150] The maximum new energy consumption calculation unit 740 is configured to calculate the maximum value of the new energy consumption of the grid according to the short-circuit ratio constraint model and the grid operation constraint model.

[0151] Specifically, the new energy consumption includes wind power consumption and photovoltaic power consumption, and the maximum value of the new energy consumption of the grid can be represented by formula (3). The maximum new energy consumption calculation unit 740 uses formula (3) as the objective function and solves it according to the short-circuit ratio constraint model and the grid operation constraint model to obtain the maximum value of the objective function, which is the maximum value of the new energy consumption of the grid.

[0152] The grid new energy consumption capacity evaluation device provided by this application realizes a reasonable evaluation of the grid new energy consumption capacity through the short-circuit ratio calculation model establishment unit 710, the short-circuit ratio constraint model establishment unit 720, the grid operation constraint model establishment unit 730, and the maximum new energy consumption calculation unit 740. It fully considers the influence of the short-circuit ratio on the grid new energy consumption capacity, ensures the stable operation of the grid, improves the accuracy of the grid new energy consumption capacity, and provides a reliable basis for the new energy development plan.

[0153] Figure 8 is a schematic structural diagram of the grid new energy consumption capacity evaluation device provided by an embodiment of this application. As Figure 8 shown, on the basis of the above embodiments, further, the power system operation parameters include the maximum theoretical output of the wind farm at each moment and the maximum theoretical output of the photovoltaic power station at each moment. The grid operation constraint model establishment unit 730 includes:

[0154] The new energy power plant output maximum constraint model establishment module 731 is configured to establish a new energy power plant output maximum constraint model according to the maximum theoretical output of the wind farm at each moment and the maximum theoretical output of the photovoltaic power station at each moment.

[0155] Figure 9 is a schematic structural diagram of the grid new energy consumption capacity evaluation device provided by an embodiment of this application. As Figure 9 shown, on the Figure 8 basis of the embodiment, further, the grid new energy consumption capacity evaluation device provided by this application further includes:

[0156] The wind speed - wind power output mapping relationship fitting unit 910 is used to perform regression analysis based on the measured wind speed data and wind power output data of each year before curtailment to obtain the wind speed - wind power output mapping relationship;

[0157] The wind power output data correction unit 920 is used to correct the wind power output data of the corresponding year according to the measured wind speed data of each year and the wind speed - wind power output mapping relationship to obtain the theoretical maximum wind power output curve of each year;

[0158] The typical theoretical maximum wind power output curve establishing unit 930 is used to calculate the mean value of the theoretical maximum wind power output curve of each year and perform per - unitization processing to obtain the typical theoretical maximum wind power output curve;

[0159] The first theoretical output maximum value obtaining unit 940 is used to obtain the theoretical output maximum value of the wind farm at each moment according to the installed capacity of each wind farm and the typical theoretical maximum wind power output curve.

[0160] The grid new - energy consumption capacity evaluation device provided by this application, through the wind speed - wind power output mapping relationship fitting unit 910, the wind power output data correction unit 920, the typical theoretical maximum wind power output curve establishing unit 930, and the first theoretical output maximum value obtaining unit 940, realizes the reasonable prediction of the theoretical output maximum value of the wind farm in a year and is applied in the new - energy power plant output maximum value constraint model, improving the accuracy of the grid new - energy consumption capacity evaluation.

[0161] Figure 10 is a schematic structural diagram of the grid new - energy consumption capacity evaluation device provided by an embodiment of this application. As Figure 10 shown, on the basis of the Figure 8 or Figure 9 embodiment, further, the grid new - energy consumption capacity evaluation device provided by this application further includes:

[0162] The light intensity - temperature - PV output mapping relationship fitting unit 1010 is used to perform regression analysis based on the light intensity data, temperature data, and wind power output data of each year before curtailment to obtain the light intensity - temperature - PV output mapping relationship;

[0163] The PV output data correction unit 1020 is used to correct the PV output data of the corresponding year according to the light intensity data, temperature data, and light intensity - temperature - PV output mapping relationship of each year to obtain the theoretical maximum PV output curve of each year;

[0164] The typical theoretical maximum PV output curve establishing unit 1030 is used to calculate the mean value of the theoretical maximum PV output curve of each year and perform per - unitization processing to obtain the typical theoretical maximum PV output curve;

[0165] The second theoretical output maximum value obtaining unit 1040 is configured to obtain the maximum theoretical output of the photovoltaic power station at each moment according to the installed capacity of each photovoltaic power station and the typical theoretical maximum photovoltaic output curve.

[0166] The grid new energy consumption capacity evaluation device provided by the present application realizes a reasonable prediction of the maximum theoretical output of the photovoltaic power station in a year through the illumination temperature-photovoltaic output mapping relationship fitting unit 1010, the photovoltaic output data correction unit 1020, the typical theoretical maximum photovoltaic output curve establishment unit 1030, and the second theoretical output maximum value obtaining unit 1040, and applies it to the maximum output value constraint model of the new energy power plant, improving the accuracy of the grid new energy consumption capacity evaluation.

[0167] Figure 11 It is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present application. As Figure 11 shown, the electronic device may include: a processor 1101, a communication interface 1102, a memory 1103, and a communication bus 1104. Among them, the processor 1101, the communication interface 1102, and the memory 1103 complete mutual communication through the communication bus 1104. The processor 1101 may call the logical instructions in the memory 1103 to execute the following method: establish a short-circuit ratio calculation model for the wind farm and the photovoltaic power station according to the mutual impedance between each wind farm and the photovoltaic power station, the self-impedance of each wind farm, and the self-impedance of each photovoltaic power station; establish a short-circuit ratio constraint model according to the short-circuit ratio calculation model and the critical short-circuit ratio; establish a grid operation constraint model according to the power system operation parameters; calculate the maximum value of the grid new energy consumption according to the short-circuit ratio constraint model and the grid operation constraint model.

[0168] In addition, when the logical instructions in the above-mentioned memory 1103 are implemented in the form of software function units and sold or used as an independent product, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0169] This embodiment discloses a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided in the above method embodiments, for example, including: establishing a short-circuit ratio calculation model for wind farm stations and photovoltaic power stations according to the mutual impedance between each wind farm station and photovoltaic power station, the self-impedance of each wind farm station, and the self-impedance of each photovoltaic power station; establishing a short-circuit ratio constraint model according to the short-circuit ratio calculation model and the critical short-circuit ratio; establishing a power grid operation constraint model according to the power system operation parameters; and calculating the maximum value of the new energy consumption of the power grid according to the short-circuit ratio constraint model and the power grid operation constraint model.

[0170] This embodiment provides a computer-readable storage medium, which stores a computer program. The computer program enables the computer to execute the methods provided in the above method embodiments, for example, including: establishing a short-circuit ratio calculation model for wind farm stations and photovoltaic power stations according to the mutual impedance between each wind farm station and photovoltaic power station, the self-impedance of each wind farm station, and the self-impedance of each photovoltaic power station; establishing a short-circuit ratio constraint model according to the short-circuit ratio calculation model and the critical short-circuit ratio; establishing a power grid operation constraint model according to the power system operation parameters; and calculating the maximum value of the new energy consumption of the power grid according to the short-circuit ratio constraint model and the power grid operation constraint model.

[0171] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention 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.

[0172] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. 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 realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0173] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one process Figure 1 or more processes and / or blocks Figure 1 or the functions specified in a block or more blocks.

[0174] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process Figure 1 or more processes and / or blocks Figure 1 or the functions specified in a block or more blocks.

[0175] In the description of this specification, the descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0176] The above-described specific embodiments further elaborate on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating the new energy consumption capacity of a power grid, characterized in that, Including: Establish a short-circuit ratio calculation model for wind farms and photovoltaic power stations based on the mutual impedance between each wind farm and photovoltaic power station, the self-impedance of each wind farm, and the self-impedance of each photovoltaic power station; Establish a short-circuit ratio constraint model according to the short-circuit ratio calculation model and the critical short-circuit ratio; Establish a grid operation constraint model based on the power system operation parameters; Calculate the maximum value of new energy consumption in the grid according to the short-circuit ratio constraint model and the grid operation constraint model; The power system operation parameters include the maximum theoretical output of wind farms at each moment and the maximum theoretical output of photovoltaic power stations at each moment; The establishing of the grid operation constraint model based on the power system operation parameters includes: Establish a maximum output constraint model for new energy power plants according to the maximum theoretical output of wind farms at each moment and the maximum theoretical output of photovoltaic power stations at each moment; Before establishing the grid operation constraint model based on the power system operation parameters, it also includes: Perform regression analysis on the measured wind speed data and wind power output data of each year before curtailment to obtain the wind speed-wind power output mapping relationship; Correct the wind power output data of the corresponding year according to the measured wind speed data of each year and the wind speed-wind power output mapping relationship to obtain the maximum theoretical wind power output curve of each year; Calculate the mean value of the maximum theoretical wind power output curves of each year and perform per-unit normalization to obtain a typical maximum theoretical wind power output curve; Obtain the maximum theoretical output of the wind farm at each moment according to the installed capacity of each wind farm and the typical maximum theoretical wind power output curve.

2. The power grid new energy consumption capacity evaluation method according to claim 1, characterized in that The power system operation parameters include the system load value at each moment; The establishing of the grid operation constraint model based on the power system operation parameters includes: Establish a power and energy balance constraint model according to the system load value at each moment.

3. The method for evaluating the new energy consumption capacity of the power grid according to claim 1, wherein, The power system operation parameters include the upper limit of the active power output of each conventional unit and the positive spinning reserve capacity of the power system; The establishing of the grid operation constraint condition model based on the power system operation parameters includes: Establish a spinning reserve constraint model according to the upper limit of the active power output of each conventional unit and the positive spinning reserve capacity of the power system.

4. The grid new energy consumption capacity evaluation method according to claim 2, characterized in that The power system operation parameters also include the upper limit and lower limit of the active power output of each conventional unit; The establishing of the grid operation constraint condition model based on the power system operation parameters also includes: Establish a conventional unit output constraint model according to the upper limit and lower limit of the active power output of each conventional unit.

5. The method for evaluating the new energy consumption capacity of the power grid according to claim 2, wherein The power system operation parameters also include the up-ramp power and down-ramp power of each conventional unit; the establishing of the grid operation constraint condition model based on the power system operation parameters also includes: Establish a conventional unit ramp constraint model according to the up-ramp power and down-ramp power of each conventional unit.

6. The method for evaluating the new energy consumption capacity of the power grid according to claim 1, characterized in that, Before establishing the grid operation constraint model based on the power system operation parameters, it also includes: Perform regression analysis on the light intensity data, temperature data, and wind power output data of each year before curtailment to obtain the light intensity-temperature-photovoltaic output mapping relationship; Revise the photovoltaic output data for the corresponding year according to the light intensity data, temperature data of each year and the light temperature - photovoltaic output mapping relationship to obtain the theoretical maximum photovoltaic output curve for each year; Calculate the mean value of the theoretical maximum photovoltaic output curve for each year and perform per-unit processing to obtain the typical theoretical maximum photovoltaic output curve; Obtain the maximum theoretical output value of the photovoltaic power station at each moment according to the installed capacity of each photovoltaic power station and the typical theoretical maximum photovoltaic output curve.

7. An evaluation device for the new energy consumption capacity of a power grid, characterized in that, It includes: A short-circuit ratio calculation model establishment unit, which is used to establish a short-circuit ratio calculation model for the wind farm and the photovoltaic power station according to the mutual impedance between each wind farm and the photovoltaic power station, the self-impedance of each wind farm and the self-impedance of each photovoltaic power station; A short-circuit ratio constraint model establishment unit, which is used to establish a short-circuit ratio constraint model according to the short-circuit ratio calculation model and the critical short-circuit ratio; A power grid operation constraint model establishment unit, which is used to establish a power grid operation constraint model according to the power system operation parameters; A maximum new energy consumption calculation unit, which is used to calculate the maximum value of the new energy consumption of the power grid according to the short-circuit ratio constraint model and the power grid operation constraint model; A wind speed - wind power output mapping relationship fitting unit, which is used to perform regression analysis according to the measured wind speed data and wind power output data of each year before curtailment to obtain the wind speed - wind power output mapping relationship; A wind power output data revision unit, which is used to revise the wind power output data of the corresponding year according to the measured wind speed data of each year and the wind speed - wind power output mapping relationship to obtain the theoretical maximum wind power output curve for each year; A typical theoretical maximum wind power output curve establishment unit, which is used to calculate the mean value of the theoretical maximum wind power output curve for each year and perform per-unit processing to obtain the typical theoretical maximum wind power output curve; A first theoretical output maximum value acquisition unit, which is used to obtain the maximum theoretical output value of the wind farm at each moment according to the installed capacity of each wind farm and the typical theoretical maximum wind power output curve; The power system operation parameters include the maximum theoretical output value of the wind farm at each moment and the maximum theoretical output value of the photovoltaic power station at each moment; the power grid operation constraint model establishment unit includes: A new energy power plant output maximum value constraint model establishment module, which is used to establish a new energy power plant output maximum value constraint model according to the maximum theoretical output value of the wind farm at each moment and the maximum theoretical output value of the photovoltaic power station at each moment.

8. The power grid new energy consumption capacity evaluation device according to claim 7, wherein It also includes: A light temperature - photovoltaic output mapping relationship fitting unit, which is used to perform regression analysis according to the light intensity data, temperature data and wind power output data of each year before curtailment to obtain the light temperature - photovoltaic output mapping relationship; A photovoltaic output data revision unit, which is used to revise the photovoltaic output data of the corresponding year according to the light intensity data, temperature data of each year and the light temperature - photovoltaic output mapping relationship to obtain the theoretical maximum photovoltaic output curve for each year; A typical theoretical maximum photovoltaic output curve establishment unit, which is used to calculate the mean value of the theoretical maximum photovoltaic output curve for each year and perform per-unit processing to obtain the typical theoretical maximum photovoltaic output curve; The second theoretical output maximum value acquisition unit is configured to obtain the theoretical output maximum value of each photovoltaic power station at each moment according to the installed capacity of each photovoltaic power station and the typical theoretical maximum photovoltaic output curve.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

11. A computer program product, characterized in that, The computer program product includes a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images