A new energy power supply capability evaluation method, device and medium

Abstract

The present application relates to a kind of new energy power supply capacity evaluation method, the method comprises: according to the historical data of new energy power generation of power grid system determines the wind power output probability distribution and photovoltaic output probability distribution of the power grid system;Using preset wind power output simulation model and photovoltaic output simulation model, respectively generate the wind power output time series curve and photovoltaic output time series curve of set group number;The wind power output time series curve and the photovoltaic output time series curve respectively comply with the wind power output probability distribution and the photovoltaic output probability distribution;To each group wind power output time series curve and photovoltaic output time series curve, combined with the predicted boundary condition of power grid system carries out simulation time series analysis, obtains the power surplus result of each group corresponding power balance analysis;According to the power surplus result of set group number, determine the predicted power gap of new energy power supply of the power grid system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power analysis, in particular to a new energy power supply capacity evaluation method, device and computer readable storage medium. BACKGROUND

[0002] With the development of clean and low-carbon energy, the proportion of new energy power generation (including wind power generation and photovoltaic power generation) in the power system is gradually rising. According to the estimation of relevant agencies, it is estimated that by 2030, the installed capacity of new energy will account for more than 36%, and by 2060, it will reach more than 65%. New energy power generation will become the largest power source in the power grid, and conventional power sources will become regulatory and protective power sources.

[0003] The present application relates to the technical field of power analysis, in particular to a new energy power supply capacity evaluation method, device and computer readable storage medium. SUMMARY

[0004] In view of the above problems, the present application provides a new energy power supply capacity evaluation method, device and computer readable storage medium, which fully considers the volatility and uncertainty of new energy power generation, and performs simulation time series analysis based on the reconstructed new energy output curve and the planning boundary conditions of the future power grid, thereby obtaining the power gap considering the new energy power supply capacity.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a new energy power supply capacity evaluation method, which comprises:

[0007] determining the wind power output probability distribution and the photovoltaic output probability distribution of the power grid system according to the historical data of new energy power generation of the power grid system;

[0008] generating a set number of wind power output time series curves and photovoltaic output time series curves respectively by using a preset wind power output simulation model and a photovoltaic output simulation model; the wind power output time series curves and the photovoltaic output time series curves respectively conform to the wind power output probability distribution and the photovoltaic output probability distribution;

[0009] for each group of wind power output time series curves and photovoltaic output time series curves, combining the predicted boundary conditions of the power grid system to perform simulation time series analysis, and obtaining the power surplus result of each group of corresponding power balance analysis;

[0010] According to the power surplus results of the set number of groups, a predicted power gap of new energy power supply of the power grid system is determined.

[0011] In an implementation manner of the present application, the preset wind power output simulation model adopts a random difference equation to simulate wind farm operation, generates a wind speed curve by generating a disturbance of standard Brown motion in the random difference equation, and generates a wind power output time sequence curve of the set number of groups according to the corrected wind speed curve and a wind turbine output curve.

[0012] In an implementation manner of the present application, the photovoltaic power output simulation model solves a photovoltaic power output time sequence curve according to a photovoltaic effect, based on performance parameters, a location and weather parameters of a solar cell panel.

[0013] In an implementation manner of the present application, the predicted boundary condition of the power grid system includes a predicted load, a reserve capacity, an installed capacity of each type of power generation source and power flow data of the power grid system at a predicted time.

[0014] In an implementation manner of the present application, in the step of obtaining the power surplus results of each group of corresponding power balance analysis, the power surplus results with a negative value are extracted, sorted, and a value meeting a set statistical rule is calculated as a power gap corresponding to each group of curves.

[0015] In an implementation manner of the present application, the determination of the predicted power gap of new energy power supply of the power grid system according to the set number of groups of power surplus results includes:

[0016] According to the power gap corresponding to the set number of groups of curves, an expected value is calculated, and the calculated expected value is taken as the predicted power gap of new energy power supply of the power grid system.

[0017] In an implementation manner of the present application, the set number of groups is not less than 50 groups.

[0018] In a second aspect, the present application provides a new energy power supply capacity evaluation device, the device includes:

[0019] A probability distribution calculation module is configured to determine wind power output probability distribution and photovoltaic output probability distribution of the power grid system according to historical data of new energy power generation of the power grid system.

[0020] The curve generation module is configured to generate a set number of wind power output time series curves and photovoltaic output time series curves respectively by using preset wind power output simulation models and photovoltaic output simulation models, wherein the wind power output time series curves and the photovoltaic output time series curves respectively conform to the wind power output probability distribution and the photovoltaic output probability distribution.

[0021] The time series analysis module is configured to perform simulation time series analysis on each set of the wind power output time series curves and the photovoltaic output time series curves in combination with the predicted boundary conditions of the power grid system, to obtain a corresponding power surplus result of power balance analysis of each set.

[0022] The prediction result output module is configured to determine a predicted power gap of new energy power supply of the power grid system according to the power surplus results of the set number of curves.

[0023] In an implementation manner of the present application, the curve generation module generates a set number of wind power output time series curves according to the preset wind power output simulation models, adopts a random difference equation to perform wind farm operation simulation, generates a wind speed curve by generating a disturbance of standard Brown motion in the random difference equation, and generates the wind power output time series curves according to the modified wind speed curve and a wind turbine output curve.

[0024] In an implementation manner of the present application, the curve generation module generates a set number of wind power output time series curves according to the preset wind power output simulation models, adopts a random difference equation to perform wind farm operation simulation, generates a wind speed curve by generating a disturbance of standard Brown motion in the random difference equation, and generates the wind power output time series curves according to the modified wind speed curve and a wind turbine output curve.

[0025] In an implementation manner of the present application, the time series analysis module, the predicted boundary conditions of the power grid system include: predicted time load, standby capacity, installed capacity of each type of power generation source and power flow data of the power grid system.

[0026] The time series analysis module is configured to extract results with negative power surplus results, sort the results, and calculate a value conforming to a set statistical rule as a power gap corresponding to each set of curves.

[0027] In an implementation manner of the present application, the prediction result output module is configured to calculate an expected value according to the power gap corresponding to the set number of curves, and take the calculated expected value as the predicted power gap of new energy power supply of the power grid system.

[0028] In a third aspect, the present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program runs to control the device where the computer readable storage medium executes the new energy power supply capacity evaluation method of the first aspect.

[0029] In a fourth aspect, the present application provides a computer device, comprising a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the new energy power supply capacity evaluation method of the first aspect.

[0030] The present application has the following advantages due to the above technical solutions: in the present application, the wind power output probability distribution and the photovoltaic output probability distribution of the power grid system are determined according to the historical data of new energy power generation of the power grid system, then a preset wind power output simulation model and a photovoltaic output simulation model are used to generate a set number of wind power output time series curves and photovoltaic output time series curves, and for each set of wind power output time series curves and photovoltaic output time series curves, a simulation time series analysis is performed in combination with the predicted boundary conditions of the power grid system to obtain the corresponding power surplus results of the power balance analysis of each set, and the predicted power gap of the new energy power supply of the power grid system is determined according to the set number of power surplus results, compared with the prior art, the fluctuation and uncertainty of new energy power generation are fully considered, and the simulation time series analysis is performed based on the reconstructed new energy output curve and the boundary conditions of the future planned power grid, so that a more accurate system power gap considering the new energy power supply capacity is obtained, thereby realizing the evaluation of the new energy power supply capacity and providing a reference for future power guarantee planning and design. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 is a flowchart of a new energy power supply capacity evaluation method provided by the present application;

[0032] Figure 2 is a wind power output probability distribution diagram in an application scenario of the present application;

[0033] Figure 3 is a photovoltaic output probability distribution diagram in an application scenario of the present application;

[0034] Figure 4 is a power surplus statistical result diagram of a power grid system in an application scenario of the present application;

[0035] Figure 5 is a power gap statistical diagram in an application scenario of the present application;

[0036] Figure 6 is a power gap diagram of a power grid system after guaranteeing supply in an application scenario of the present application;

[0037] Figure 7 is a new energy power supply capacity evaluation device in the present application;

[0038] Figure 8 is a structural diagram of a computer device related to the present application. DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions of the embodiments of the present application with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the described embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art belong to the scope of protection of the present application.

[0040] The future power guarantee planning in the prior art is usually based on experience to take 3% to 5% of the new energy installed capacity as the maximum load day new energy output level, and analyzes the future power gap of the system according to this, and the accuracy of the power gap obtained by analysis is relatively low. The present application provides a new energy power supply capacity evaluation method, device and medium. The method comprises: determining wind power output probability distribution and photovoltaic output probability distribution of a power grid system according to historical data of new energy power generation of the power grid system; generating a set number of wind power output time sequence curves and photovoltaic output time sequence curves respectively by using preset wind power output simulation models and photovoltaic output simulation models; the wind power output time sequence curves and the photovoltaic output time sequence curves respectively conform to the wind power output probability distribution and the photovoltaic output probability distribution; for each set of wind power output time sequence curves and photovoltaic output time sequence curves, a simulation time sequence analysis is performed in combination with a predicted boundary condition of the power grid system, to obtain a power surplus result corresponding to each set of power balance analysis; and determining a predicted power gap of new energy power supply of the power grid system according to the set number of power surplus results. The technical scheme of the present application fully considers the volatility and uncertainty of new energy power generation, and performs a simulation time sequence analysis based on the reconstructed new energy output curve and the boundary condition of the future planned power grid, so that a more accurate system power gap considering the new energy power supply capacity is obtained.

[0041] Reference Figure 1 In one aspect of the embodiments of the present application, a new energy power supply capacity evaluation method is provided.

[0042] The method comprises:

[0043] S11, determining wind power output probability distribution and photovoltaic output probability distribution of a power grid system according to historical data of new energy power generation of the power grid system;

[0044] S12, generating a set number of wind power output time sequence curves and photovoltaic output time sequence curves respectively by using preset wind power output simulation models and photovoltaic output simulation models; the wind power output time sequence curves and the photovoltaic output time sequence curves respectively conform to the wind power output probability distribution and the photovoltaic output probability distribution;

[0045] S13, for each group of the wind power output time series curve and the photovoltaic output time series curve, a simulation time analysis is performed in combination with the predicted boundary condition of the power grid system to obtain a corresponding power surplus result of the power balance analysis of each group;

[0046] S14, according to the power surplus results of the set number of groups, a predicted power gap of new energy power supply of the power grid system is determined.

[0047] The above method determines the wind power output probability distribution and the photovoltaic output probability distribution of the power grid system according to historical data of new energy power generation of the power grid system, then generates a set number of wind power output time series curves and photovoltaic output time series curves respectively by using a preset wind power output simulation model and a photovoltaic output simulation model, performs a simulation time analysis in combination with the predicted boundary condition of the power grid system for each group of wind power output time series curves and photovoltaic output time series curves to obtain a corresponding power surplus result of the power balance analysis of each group, and determines a predicted power gap of new energy power supply of the power grid system according to the power surplus results of the set number of groups. Compared with the prior art, the fluctuation and uncertainty of new energy power generation are fully considered, the simulation time analysis is performed based on the reconstructed new energy output curve and the boundary condition of the future planned power grid, so that a more accurate power gap of new energy power supply is obtained, and the evaluation of new energy power supply capacity is realized, thereby providing a reference for future power guarantee planning and design.

[0048] The flow of the above method is described below in a more detailed embodiment of the application.

[0049] In the embodiment of the application, the provided new energy power supply capacity evaluation method comprises:

[0050] S11, determining the wind power output probability distribution and the photovoltaic output probability distribution of the power grid system according to historical data of new energy power generation of the power grid system;

[0051] Specifically, for example, the actual wind power output of the research area in the past 5 years can be investigated and studied, and the wind power and photovoltaic standard values P η , P η = P 实际出力 / P 装机容量 are converted to obtain the wind power and photovoltaic output probability distribution with a statistical step of 5%.

[0052] S12, generating a set number of wind power output time series curves and photovoltaic output time series curves respectively by using a preset wind power output simulation model and a photovoltaic output simulation model; the wind power output time series curve and the photovoltaic output time series curve respectively conform to the wind power output probability distribution and the photovoltaic output probability distribution;

[0053] Specifically, for the wind power output simulation model, a stochastic difference equation is used to simulate the operation of the wind farm, a standard Brown motion disturbance is generated in the stochastic difference equation to generate a wind speed curve, and then the wind speed curve is corrected, and a set of wind power output time series curves are generated according to the corrected wind speed curve and the wind turbine output curve.

[0054] The wind farm operation simulation model based on the stochastic difference equation is used, the probability density function f(x) is non-negative, continuous and limited variance in its definition domain (l, u), the mathematical expectation E(x) = μ, and the stochastic differential equation

[0055]

[0056] where θ i : wind speed sequence autocorrelation coefficient of wind farm, where θ≥0, W t is a standard Brown motion. If a random process {W t :t≥0,} satisfies:

[0057] 1) W t is an independent increment process;

[0058] 2) the increment is stationary and subject to a normal distribution with an expectation of 0 and a variance of t, then it is called a standard Brown motion.

[0059] Let the wind speed conform to the Weibull distribution with scale parameter c and shape parameter k:

[0060]

[0061] v(X t ) is a wind speed function subject to the Weibull distribution.

[0062]

[0063] If multiple wind speed related wind farm wind speeds are generated, multiple dimensional correlated Brown motions W t , W t each dimension is a standard Brown motion, and the correlation coefficient matrix between each dimension is equal to the wind farm wind speed correlation coefficient matrix. Then, each wind farm wind speed sequence is generated using the W t dimension components.

[0064] To consider the seasonality and daily regularity of the wind farm wind speed, the randomly generated wind speed sequence is corrected as follows.

[0065]

[0066] where is the corrected wind speed of the wind farm at time t, kim is the wind speed seasonal factor of the wind farm, m = 1, 2,..., 12. k h is the hourly average wind speed curve of the wind farm, h = 1, 2,..., 24.

[0067] The wind farm time series output curve is generated by

[0068]

[0069] where C i (x) is the wind turbine output characteristic curve, which can be generally obtained by

[0070]

[0071] where v in , v rated and v out are the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine, respectively. η i is the wind farm wake effect coefficient, which represents the output loss of the wind farm due to the wake effect, and is usually taken as 5% to 10%.

[0072] By assigning different initial values W0∈[0, 1] to the multi-dimensional standard Brownian motion, n groups of wind speed curves are obtained, which follow the Weibull distribution with shape parameters c and k, respectively. Finally, n groups of wind power output curves are generated by using equation (1-6).

[0073] For the photovoltaic output simulation model, according to the photovoltaic effect, the performance parameters of the solar cell panel, the location and the weather parameters are used to solve the photovoltaic output time series curve.

[0074] The output of the solar cell panel at time t is P t which can be solved by the following formula:

[0075]

[0076] P stc : rated output of the solar cell panel.

[0077] I 0t : the plane solar radiation outside the atmosphere, which is only related to the relative position of the sun and the earth without considering the weakening effect of random factors such as atmospheric scattering of sunlight and cloud cover.

[0078] k t : clear sky index, defined as the ratio between the total radiation I t on the ground and the plane solar radiation intensity I 0t outside the atmosphere: k t = I t / I0t where I t is the irradiance on the ground surface (including direct and diffuse) for the tth time period. k t is mainly affected by cloud cover, weather changes, and altitude, etc.

[0079] R t : the ratio of the solar radiation intensity on the tilted surface to the total irradiance on the ground surface for the tth time period.

[0080] T: the atmospheric degree

[0081] α T : the power temperature coefficient of the solar panel.

[0082] I(R t , k t , I 0t ): the total irradiance on the photovoltaic panel considering the solar irradiance (direct, diffuse, and reflected), the clearness index, and the photovoltaic panel tracking type, etc.

[0083]

[0084] where I b represents the direct radiation intensity on the horizontal surface; R b represents the ratio of the solar radiation intensity on the tilted surface to the solar radiation intensity on the horizontal surface; I d represents the diffuse radiation intensity on the horizontal surface; β is the inclination angle of the photovoltaic panel to the ground; and p is the reflectivity of the ground. I t is the total radiation intensity on the horizontal surface (considering cloud cover), including the direct radiation intensity I b and the diffuse radiation intensity I d , i.e., I t = I b + I d . I β i.e., represents the total solar radiation intensity on the photovoltaic panel with an inclination angle of β to the ground.

[0085] The diffuse radiation I d on the horizontal surface has the following relationship with the total radiation I t on the horizontal surface:

[0086] I d = I t (p-qk t ) (9)

[0087] where p and q are parameters related to the atmospheric quality. k t is a random variable with a value range of [0, 1].

[0088] By analyzing the random variable k t0Assign different initial values, and finally simulate n groups of photovoltaic curves.

[0089] S13, for each group of wind power output time curve and photovoltaic output time curve, combined with the predicted boundary conditions of the power grid system, the simulation time analysis is carried out, and the corresponding power balance analysis of each group of power surplus result is obtained;

[0090] In the embodiment of the application, the predicted boundary conditions of the power grid system include: the predicted load of the power grid system, the standby capacity, the installed capacity of each type of power generation source and the power flow data.

[0091] In the embodiment of the application, the predicted boundary conditions of the power grid system include: the predicted load of the power grid system, the standby capacity, the installed capacity of each type of power generation source and the power flow data.

[0091] The embodiment of the application can take the provincial regional power grid as an example to determine the load level, load curve, standby rate, available installed capacity of thermal power, gas power, hydropower, nuclear power, pumped storage, energy storage, wind power and photovoltaic power of the regional (provincial) power grid planning level year, power flow arrangement, a group of wind and light output curves obtained by the foregoing steps, and carry out power balance analysis on the provincial power grid, and output 8760 hours of power surplus result, which specifically includes:

[0092] (1) Planning level year Y: according to the planning research target, the planning year is determined.

[0093] (2) Annual maximum load V load·max : The maximum load of the research area Y year is determined by using the power elasticity coefficient method.

[0094] (3) Load curve L load : The 8760-hour load curve of the research area Y year is determined by fitting the historical data of the past five years.

[0095] (4) Standby capacity V2: wherein the load standby capacity V 21 , the accident heat standby capacity V 22 , the accident cold standby capacity V 23 , V2=V 21 +V 22 +V 23 .

[0096] (5) System required capacity X: X=V load·max +V2+V3-V4-V5-V 23

[0097] (6) System installed capacity: the sum of installed capacity of all power sources of the system. Among them, the installed capacity of coal-fired units Y1, the installed capacity of gas units Y2, the installed capacity of nuclear units Y3, the installed capacity of hydropower units Y4, the installed capacity of pumped storage and energy storage units Y5, the installed capacity of wind power units Y6, and the installed capacity of photovoltaic units Y7.

[0098] (7) Received power flow V3: the total amount of power received from the external power grid in the study area Y according to the planning arrangement. The received is negative and the sent is positive.

[0099] (8) Actual wind power output V4: the actual output of wind power at the corresponding time. Wind power output coefficient η w is obtained by simulation in S1. V4 = η w · Y6

[0100] (9) Actual photovoltaic output V5: the actual output of photovoltaic at the corresponding time. Photovoltaic output coefficient η s is obtained by simulation in S1. V5 = η s · Y6

[0101] (10) System thermal power demand P xq : the working capacity that needs to be borne by thermal power to meet the system load and cold standby. P xq = P xq1 + P xq2

[0102] (11) Thermal power start-up demand P xq1 : the total start-up demand of the system minus the working capacity borne by non-thermal power units. P xq1 = X-(PPump+PHydro1+PNuc2)

[0103] (12) Cold standby demand P xq2 : the cold standby capacity borne by thermal power units.

[0104] (12) Pumping and energy storage contribution effective power PPump.

[0105] (13) Water power working capacity PHydro1: the actual output of water power units, determined according to the hydrological conditions in Y.

[0106] (14) Nuclear power working capacity PNuc2: the actual output of nuclear power units, generally considered as full output. PNuc2 = Y3

[0107] (15) Thermal power available installed capacity PStr1: the sum of the capacities of units in non-maintenance state. PStr1 = Y1 + Y2 - PStr5

[0108] (16) Thermal power start-up capacity PStr2: the sum of the rated capacities of thermal power units actually arranged in start-up state.

[0109] (17) Thermal power working capacity PStr3: the actual working capacity borne by thermal power units in start-up state at a certain time.

[0110] (18) Thermal power maintenance capacity PStr5, which is the sum of coal-fired and gas-fired unit maintenance capacity according to the maintenance arrangement.

[0111] (19) Thermal power blocked capacity PStr6: the capacity that cannot be fully generated due to technical reasons of the unit itself in the available installed capacity.

[0112] (20) Power surplus P at a certain time yy : an index for evaluating whether the power supply at the time is sufficient. yy = PStr1-PStr6-P xq

[0113] (21) Extract all results less than 0 in the 8760-hour power surplus result to form a set {P yy·t ∈P yy |P yy <0}. The sample size of the set is defined as T, representing that there are T hours of power gap in a year. The minimum value P yy·i min of the set represents the maximum power gap in a year.

[0114] (22) Find the power gap value for 95% of the time: that is, sort all P yy·t and find the 95th smallest value P yy·95％t .

[0115] S14, according to the power surplus result of the set number, determines the predicted power gap of new energy power supply of the power grid system.

[0116] Specifically, according to the power gap corresponding to the set number curve, the expected value is calculated, and the calculated expected value is taken as the predicted power gap of new energy power supply of the power grid system.

[0117] Each curve will output a unique 95% time power gap value i represents the number of wind and light curve groups i = 1 … n, forming a 95% time power gap set.

[0118]

[0119] Let F(x) = p, p ∈ P, represent the sample extracted from the set P, according to the central limit theorem, the probability distribution of the mathematical expectation of F(x) is subject to normal distribution N(μ,σ 2 ), and when the sample size is large enough, the mathematical expectation of F(x) is close to the mathematical expectation of P. When the generated wind and light curve groups are not less than 50 groups, the sample size is large enough, and at this time the mathematical expectation of F(x) is the reference value for the design of the power supply scheme.

[0120] The above method of the present application can be verified in an application scenario.

[0121] Taking the design of a power supply scheme of a provincial power grid in a certain region in 2030 as an example.

[0122] The annual utilization hours of wind power of the provincial power grid in the certain region are about 2200 hours, and the annual utilization hours of photovoltaic power are about 1100 hours. In the verification scenario, 50 sets of wind and light output curves are generated, for example, the utilization hours of wind power are between 2302 hours and 2113 hours, and the utilization hours of photovoltaic power are between 1150 hours and 1060 hours. The annual utilization hours of wind and light are less deviated from the measured results. The output probability distribution is as shown in Figure 2 and Figure 3 .

[0123] It is predicted that the maximum load of the provincial power grid in the certain region in 2030 is 170 million kilowatts, and the installed capacity of power supply and the arrangement of power flow are as shown in the following table.

[0124] Combined with the first set of wind and light curves, the maximum power gap of the provincial power grid in the certain region in 2030 is 16.9 million kilowatts, and the annual power shortage time is 571 hours, of which the power gap is within 11.69 million kilowatts 95% of the time. The statistical results are as shown in Figure 4 .

[0125] The remaining 49 sets of wind and light curves are substituted respectively, and finally 50 sets of 95% time power gaps of the certain region provincial power grid are obtained, and the calculation results are as shown in Figure 5 .

[0126] According to the central limit theorem, the average value of the 50 sets of 95% time power gaps is 11.37 million kilowatts, which is the reference value of the power supply scheme design.

[0127] If the provincial power grid in the certain region increases 11.37 million kilowatts of coal-fired power supply in 2030, the total installed capacity reaches 93.38 million kilowatts, and 50 times of production operation simulation are re-performed, and the new 95% time power gap is as shown in Figure 6 . It can be seen that among the 28 sets of curves, the 95% time power gap is 0 after considering the newly added 11.37 million kilowatts of power supply, and the 95% time power gap of the remaining 22 sets of curves is also within 1.6 million kilowatts. If there is still a power gap in the actual operation of the provincial power grid in the certain region after the newly added 11.37 million kilowatts of coal-fired power supply in 2030, the power gap is likely to be less than 1.6 million kilowatts, and demand side response and other short-term economic methods can be used to ensure reliable power supply.

[0128] In another aspect of the embodiments of the present application, a new energy power supply capacity evaluation device is also provided. The device can be realized in a computer device in the form of hardware or software.

[0129] As Figure 7 shown in the embodiments of the present application, a new energy power supply capacity evaluation device 700 is provided, which comprises:

[0130] a probability distribution calculation module 701 configured to determine wind power output probability distribution and photovoltaic output probability distribution of a power grid system according to historical data of new energy power generation of the power grid system;

[0131] a curve generation module 702 configured to generate a set number of wind power output time series curves and photovoltaic output time series curves respectively by using preset wind power output simulation model and photovoltaic output simulation model; the wind power output time series curves and the photovoltaic output time series curves respectively conform to the wind power output probability distribution and the photovoltaic output probability distribution;

[0132] a time series analysis module 703 configured to perform simulation time series analysis on each set of the wind power output time series curves and the photovoltaic output time series curves in combination with predicted boundary conditions of the power grid system to obtain power surplus results of power balance analysis corresponding to each set;

[0133] a prediction result output module 704 configured to determine predicted power gap of new energy power supply of the power grid system according to the set number of power surplus results.

[0134] The device provided by the above embodiments determines wind power output probability distribution and photovoltaic output probability distribution of a power grid system according to historical data of new energy power generation of the power grid system, generates a set number of wind power output time series curves and photovoltaic output time series curves respectively by using preset wind power output simulation model and photovoltaic output simulation model, performs simulation time series analysis on each set of the wind power output time series curves and the photovoltaic output time series curves in combination with predicted boundary conditions of the power grid system to obtain power surplus results of power balance analysis corresponding to each set, and determines predicted power gap of new energy power supply of the power grid system according to the set number of power surplus results. Compared with the prior art, the fluctuation and uncertainty of new energy power generation are fully considered, simulation time series analysis is performed based on reconstructed new energy output curve and future planned boundary conditions of the power grid, so that more accurate power gap of new energy power supply is obtained, thereby realizing evaluation of new energy power supply capacity and providing reference for future power guarantee planning and design.

[0135] The above device can be realized in a computer device in a hardware or software manner, so that the computer device realizes the new energy power supply capacity evaluation method in the embodiments of the present application. The specific method can refer to the description of the foregoing embodiments, which will not be repeated here.

[0136] In the embodiments of the present application, a computer readable storage medium is also provided, and the computer readable storage medium stores a computer program. When a computer device executes the computer program, the new energy power supply capacity evaluation method in the embodiments of the present application is implemented.

[0137] With reference to Figure 8 A computer device 800 is provided. The computer device 800 of the embodiment includes a processor 801, a memory 802, and a computer program 803 stored in the memory and executable on the processor 801. When the processor 801 executes the computer program 803, the new energy power supply capacity evaluation method in the embodiments is implemented. To avoid repetition, details are not described herein. Alternatively, when the computer program is executed by the processor 801, the functions of each model / unit in the new energy power supply capacity evaluation device in the embodiments are implemented. To avoid repetition, details are not described herein.

[0138] The computer device can include, but is not limited to, the processor 801 and the memory 802. Those skilled in the art can understand that the computer device can further include other components, for example, an input / output device, a network access device, a bus, and the like. Figure 8 The computer device 800 is only an example and does not constitute a limitation on the computer device 800. The computer device 800 can include more or fewer components than those shown, or combine some components, or include different components, for example, the computer device can also include an input / output device, a network access device, a bus, and the like.

[0139] The processor 801 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0140] The memory 802 can be an internal storage unit of the computer device 800, for example, a hard disk or a memory of the computer device 800. The memory 802 can also be an external storage device of the computer device 800, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card and the like equipped on the computer device 800. Further, the memory 802 can include both the internal storage unit and the external storage device of the computer device 800. The memory 802 is used to store computer programs and other programs and data required by the computer device. The memory 802 can also be used to temporarily store data that has been output or is to be output.

[0141] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0142] In several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented by other manners. For example, the above-described device embodiments are only schematic, for example, the division of the above-described units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0143] The integrated unit in the form of the software function unit described above can be stored in a computer readable storage medium. The software function unit described above is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute part of the steps of the method described in the embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk and various program code storage media.

[0144] The above is only a preferred embodiment of the present application, and is not used to limit the present application, and any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for assessing the power supply capacity of new energy sources, characterized in that, The method includes: The probability distributions of wind power output and photovoltaic power output of the power grid system are determined based on historical data of new energy power generation in the power grid system. Using preset wind power output simulation models and photovoltaic (PV) power output simulation models, a set number of wind power output time-series curves and PV power output time-series curves are generated respectively. These curves conform to the wind power output probability distribution and the PV power output probability distribution, respectively. The wind power output simulation model uses stochastic differential equations to simulate wind farm operation. Wind speed curves are generated by perturbations of standard Brownian motion within these equations. These wind speed curves are then corrected, and the set number of wind power output time-series curves are generated based on the corrected wind speed curves combined with the wind turbine output curves. The PV power output simulation model solves for the PV power output time-series curves based on the photovoltaic effect, the performance parameters and location of the solar panel, and weather parameters. For each set of wind power output time-series curves and photovoltaic power output time-series curves, a simulation time-series analysis is performed in conjunction with the predicted boundary conditions of the power grid system to obtain the power surplus results for each set of power balance analysis. Specifically, 8760 hours of power surplus results are output, and all results less than 0 in the 8760-hour power surplus results are extracted to form a set. The sample size of this set is defined as Characterized by the presence of throughout the year There is a power shortage at any given hour; the minimum value of this set. Characterizes the largest annual power shortage; and for all Sort the data and find the 95th smallest value. ; Based on the power surplus results of a set number of groups, the predicted power gap for renewable energy supply in the power grid system is determined. The expected value is calculated based on the power gap corresponding to the curves of the set number of groups, and this calculated expected value is used as the predicted power gap for renewable energy supply in the power grid system. Specifically, each set of curves will output a unique 95% time power gap value. , Indicates the number of the scenic curve group This forms a power shortage set that lasts 95% of the time. ,make , indicating that the samples drawn from set P, when the number of wind power output time series curves and photovoltaic power output time series curves is not less than 50, are considered as samples. The mathematical expectation is used as the predicted power gap for new energy power supply in the power grid system.

2. The method for assessing the power supply capacity of new energy sources according to claim 1, characterized in that, The predicted boundary conditions of the power grid system include: the load, reserve capacity, installed capacity of various types of power generation sources, and power flow data of the power grid system at the predicted time.

3. A new energy power supply capacity assessment device, characterized in that, The device includes: The probability distribution calculation module is used to determine the probability distribution of wind power output and photovoltaic power output of the power grid system based on historical data of new energy power generation in the power grid system. The curve generation module is used to generate a set number of wind power output time-series curves and photovoltaic output time-series curves using preset wind power output simulation models and photovoltaic output simulation models, respectively. The wind power output time-series curves and photovoltaic output time-series curves conform to the wind power output probability distribution and the photovoltaic output probability distribution, respectively. The wind power output simulation model uses stochastic differential equations to simulate wind farm operation, generates wind speed curves by generating standard Brownian motion perturbations in the stochastic differential equations, corrects the wind speed curves, and generates a set number of wind power output time-series curves based on the corrected wind speed curves and the wind turbine output curves. The photovoltaic output simulation model solves for the photovoltaic output time-series curves based on the photovoltaic effect, the performance parameters and location of the solar panel, and weather parameters. The time series analysis module is used to perform simulated time series analysis on each set of wind power output time series curves and photovoltaic power output time series curves, combined with the predicted boundary conditions of the power grid system, to obtain the power surplus results of the corresponding power balance analysis for each set. Specifically, it outputs 8760-hour power surplus results and extracts all results less than 0 from the 8760-hour power surplus results to form a set. The sample size of this set is defined as Characterized by the presence of throughout the year There is a power shortage at any given hour; the minimum value of this set. Characterizes the largest annual power shortage; and for all Sort the data and find the 95th smallest value. ; The prediction result output module is used to determine the predicted power gap of renewable energy supply in the power grid system based on the power surplus results of a set number of sets. It calculates the expected value of the power gap corresponding to the set number of curves and uses this expected value as the predicted power gap of renewable energy supply in the power grid system. Specifically, each set of curves will output a unique 95% time power gap value. , Indicates the number of the scenic curve group This forms a power shortage set that lasts 95% of the time. ,make , indicating that the samples drawn from set P, when the number of wind power output time series curves and photovoltaic power output time series curves is not less than 50, are considered as samples. The mathematical expectation is used as the predicted power gap for new energy power supply in the power grid system.

4. The new energy power supply capacity assessment device according to claim 3, characterized in that, In the time series analysis module, the predicted boundary conditions of the power grid system include: the load, reserve capacity, installed capacity of various types of power generation sources, and power flow data of the power grid system at the predicted time. The time-series analysis module is used to extract negative results of power surplus, sort them, and calculate the values ​​that conform to the set statistical rules as the power gap corresponding to each set of curves.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed, controls the device containing the computer-readable storage medium to perform the new energy power supply capacity assessment method according to any one of claims 1 to 2.

6. A computer device, characterized in that, include: A processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the new energy power supply capacity assessment method according to any one of claims 1 to 2.

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