A method and device for simulating the probabilistic time-series output of a large-scale wind farm

Abstract

The present invention provides a method and device for simulating the probabilistic time-series output of a large-scale wind farm. The method comprises the following steps: generating a time-series wind speed sequence according to a set time period based on historical wind speed; generating a time-series wind speed sequence with day and night characteristics according to the order of day and night alternation based on the time-series wind speed sequence of each time period; determining a wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm based on the time-series wind speed sequence with day and night characteristics; determining the probabilistic time-series output of the wind farm based on the time-series wind speed sequence with day and night characteristics or the wind speed sequence taking into account the wake effect, taking into account the wake effect and the day and night characteristics of the wind speed in the wind farm, and generating a time-series wind speed sequence with day and night characteristics or a wind speed sequence taking into account the wake effect according to the relationship between the output power of the wind farm and the wind speed, which is more in line with the actual wind speed characteristics, providing a basis for simulating the probabilistic time-series output of the wind farm, and further improving the accuracy of the prediction of the time-series output of the large-scale wind farm.

Description

Technical Field

[0001] The present invention relates to the technical field of power system simulation, and in particular to a method and device for simulating the probabilistic time-series output of a large-scale wind farm. Background Art

[0002] In recent years, the extensive use of oil, coal and fossil fuels has brought about a series of environmental problems. The increasing depletion of these conventional energy sources has also led to an energy crisis, posing severe challenges to countries around the world.

[0003] Clean energy represented by wind resources is inexhaustible. Wind power generation is a low-cost, large-scale, and fast-growing power generation method, and has developed rapidly around the world. In 2015, the global wind power installed capacity increased by 63.01 million kilowatts, achieving an annual market growth rate of 22%. The global cumulative installed capacity of wind power reached 432.42 million kilowatts, with a cumulative annual growth rate of 17%. China's wind power industry also maintained a strong growth momentum, with an additional installed capacity of 32.97 million kilowatts throughout the year, and a cumulative grid-connected installed capacity of 129 million kilowatts, accounting for 8.6% of the total installed capacity of power generation. In 2015, the grid-connected electricity of wind power reached 186.3 billion kilowatt-hours, accounting for 3.3% of the total power generation.

[0004] However, the intermittent and volatile nature of wind resources will bring many challenges to the large-scale grid connection of wind power. The random output of wind farms is the basis for studying the impact of large-scale wind farms on the power quality, stability, and line transmission power of the power system. Predicting and calculating the random output of wind farms is of guiding significance for the whole process test and verification of wind farm grid connection operation. Since the output of wind farms will change accordingly according to the change of wind speed, its randomness comes from the volatility of wind speed. Simulating and generating wind speeds that obey probability distribution functions is of great significance for establishing a random model of wind farm output. In the current large-scale wind farm time-series output simulation process, when simulating wind speed, the day and night differences in wind speed and the wind speed fluctuation characteristics are not fully considered. The simulated wind speed is quite different from the actual wind speed, which leads to low accuracy of the simulated wind farm time-series output. Summary of the invention

[0005] In order to overcome the shortcomings of low accuracy in the above-mentioned prior art, the present invention provides a large-scale wind farm probabilistic time-series output simulation method and device, which generate a time-series wind speed sequence according to a set time period based on historical wind speed; generate a time-series wind speed sequence with day and night characteristics according to the order of day and night alternation based on the time-series wind speed sequence of each time period; determine a wind speed sequence that takes into account the wake effect after considering other wind turbines in the wind farm based on the time-series wind speed sequence with day and night characteristics; determine the probabilistic time-series output of the wind farm based on the time-series wind speed sequence with day and night characteristics or the wind speed sequence that takes into account the wake effect after considering other wind turbines in the wind farm, and take into account the wake effect, while considering the wake effect and the day and night characteristics of the wind speed, so that the obtained probabilistic time-series output of the wind farm has high accuracy.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical scheme:

[0007] In one aspect, the present invention provides a large-scale wind farm probabilistic time-series output simulation method, comprising:

[0008] Generate a time series wind speed sequence based on historical wind speed according to the set time period;

[0009] Based on the time series wind speed sequence of each time period, a time series wind speed sequence with day and night characteristics is generated according to the order of day and night alternation;

[0010] Determine, based on the time-series wind speed sequence with day and night characteristics, a wind speed sequence that takes into account wake effects after taking into account other wind turbines in the wind farm;

[0011] The probabilistic time-series output of the wind farm is determined based on a time-series wind speed sequence with day and night characteristics or a wind speed sequence that takes into account wake effects after considering other wind turbines in the wind farm.

[0012] The generating of a time series wind speed sequence according to a set period based on historical wind speeds includes:

[0013] Generate random wind speed sequences based on historical wind speeds;

[0014] Determine a plurality of wind speed difference intervals based on the random wind speed sequence, and determine an interval frequency based on the wind speed difference intervals;

[0015] Generate multiple random numbers, and match the multiple random numbers with the interval frequency to obtain the fluctuation characteristics of the node;

[0016] Based on the fluctuation characteristics of the nodes and the random wind speed sequence, the time series wind speed sequences of different time periods are generated.

[0017] The wind speed sequence after taking into account the wake effect of other wind turbines in the wind farm is determined by the following formula:

[0018]

[0019] Where V2'' represents the wind speed sequence after considering the wake effect of other wind turbines in the wind farm, V1'' represents the time series wind speed sequence with day and night characteristics, D represents the diameter of the wind turbine blades, X represents the distance between two adjacent rows of wind turbines, and C t represents the thrust coefficient of the wind turbine, and K represents the wake attenuation coefficient.

[0020] The method of determining the probabilistic time-series output of the wind farm based on the time-series wind speed sequence with day and night characteristics or the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm comprises:

[0021] Determine the output power of each wind turbine in the wind farm based on a time-series wind speed sequence with day and night characteristics or a wind speed sequence including wake effects after considering other wind turbines in the wind farm;

[0022] The output power of all wind turbines is summed up to obtain the probabilistic time-series output of the wind farm.

[0023] The output power of each wind turbine in the wind farm is determined by the following formula:

[0024]

[0025] Where P is the output power of the wind turbine, P r is the rated power of the wind turbine, V in is the cut-in wind speed, V out is the cut-out wind speed, V r is the rated wind speed, V is an intermediate quantity, which represents the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm or the time-series wind speed sequence with day and night characteristics.

[0026] The generating of a random wind speed sequence based on historical wind speeds comprises:

[0027] Based on the diurnal characteristics of wind speed, the shape parameters and scale parameters of the probability distribution function of wind speed are determined in different time periods;

[0028] Determining a probability distribution function of wind speed based on the shape parameter and the scale parameter;

[0029] Based on the probability distribution function of wind speed, the historical wind speed is determined by the inverse function method;

[0030] A random wind speed is determined based on the historical wind speed, and a random wind speed sequence is obtained based on the random wind speed.

[0031] The random wind speed is determined by the following formula:

[0032] v'=[c k log(1-v)] 1 / k

[0033] Where v' is the random wind speed, v is the historical wind speed, c is the scale parameter, and k is the shape parameter.

[0034] On the other hand, the present invention also provides a large-scale wind farm probabilistic time-series output simulation device, comprising:

[0035] The first generation module is used to generate a time series wind speed sequence according to a set time period based on historical wind speed;

[0036] The second generating module is used to generate a time-series wind speed sequence with day and night characteristics based on the time-series wind speed sequence of each time period according to the order of day and night alternation;

[0037] A first determination module is used to determine a wind speed sequence that takes into account wake effects after taking into account other wind turbines in the wind farm based on the time-series wind speed sequence with day and night characteristics;

[0038] The second determination module is used to determine the probabilistic time-series output of the wind farm based on the time-series wind speed sequence with day and night characteristics or the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm.

[0039] The first generating module is specifically used for:

[0040] Generate random wind speed sequences based on historical wind speeds;

[0041] Determine a plurality of wind speed difference intervals based on the random wind speed sequence, and determine an interval frequency based on the wind speed difference intervals;

[0042] Generate multiple random numbers, and match the multiple random numbers with the interval frequency to obtain the fluctuation characteristics of the node;

[0043] Based on the fluctuation characteristics of the nodes and the random wind speed sequence, the time series wind speed sequences of different time periods are generated.

[0044] The first determination module determines the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm according to the following formula:

[0045]

[0046] Where V2'' represents the wind speed sequence after considering the wake effect of other wind turbines in the wind farm, V1'' represents the time series wind speed sequence with day and night characteristics, D represents the diameter of the wind turbine blades, X represents the distance between two adjacent rows of wind turbines, and C t represents the thrust coefficient of the wind turbine, and K represents the wake attenuation coefficient.

[0047] The first determining module is specifically used for:

[0048] Determine the output power of each wind turbine in the wind farm based on a time-series wind speed sequence with day and night characteristics or a wind speed sequence including wake effects after considering other wind turbines in the wind farm;

[0049] The output power of all wind turbines is summed up to obtain the probabilistic time-series output of the wind farm.

[0050] The first determination module determines the output power of each wind turbine in the wind farm according to the following formula:

[0051]

[0052] Where P is the output power of the wind turbine, P r is the rated power of the wind turbine, V in is the cut-in wind speed, V out is the cut-out wind speed, V r is the rated wind speed, V is an intermediate quantity, which represents the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm or the time-series wind speed sequence with day and night characteristics.

[0053] The first generating module is specifically used for:

[0054] Based on the diurnal characteristics of wind speed, the shape parameters and scale parameters of the probability distribution function of wind speed are determined in different time periods;

[0055] Determining a probability distribution function of wind speed based on the shape parameter and the scale parameter;

[0056] Based on the probability distribution function of wind speed, the historical wind speed is determined by the inverse function method;

[0057] A random wind speed is determined based on the historical wind speed, and a random wind speed sequence is obtained based on the random wind speed.

[0058] The generating module determines the random wind speed according to the following formula:

[0059] v'=[c k log(1-v)] 1k

[0060] Where v' is the random wind speed, v is the historical wind speed, c is the scale parameter, and k is the shape parameter.

[0061] Compared with the closest prior art, the technical solution provided by the present invention has the following beneficial effects:

[0062] In the large-scale wind farm probabilistic time-series output simulation method provided by the present invention, a time-series wind speed sequence is generated according to a set time period based on historical wind speed; a time-series wind speed sequence with day and night characteristics is generated according to the order of day and night alternation based on the time-series wind speed sequence of each time period; a wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm is determined based on the time-series wind speed sequence with day and night characteristics; the probabilistic time-series output of the wind farm is determined based on the time-series wind speed sequence with day and night characteristics or the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm, and the obtained probabilistic time-series output of the wind farm is highly accurate;

[0063] The present invention takes into account the wake effect in the wind farm and the day and night characteristics of the wind speed, and based on the relationship between the wind farm output power and the wind speed, the wind farm probabilistic time-series output reliability is high;

[0064] The technical solution provided by the present invention not only takes into account the influence of the wake effect in large-scale wind farms, but also pays attention to the day and night characteristics of wind speed. The generated time-series wind speed sequence with day and night characteristics or the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm is more in line with the actual wind speed characteristics, providing a basis for the simulation of the probabilistic time-series output of wind farms, and further improving the accuracy of the time-series output prediction of large-scale wind farms. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a flow chart of a method for simulating the probabilistic time-series output of a large-scale wind farm in an embodiment of the present invention;

[0066] Figure 2 is a schematic diagram of wind speed measured in a certain actual wind farm in an embodiment of the present invention;

[0067] Figure 3 Schematic diagram of probabilistic time-series output of a large-scale wind farm in an embodiment of the present invention;

[0068] Figure 4 It is a comparison diagram of the actual wind farm output and the generated wind farm probabilistic time-series output in the embodiment of the present invention. DETAILED DESCRIPTION

[0069] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0070] Example 1

[0071] Embodiment 1 of the present invention provides a large-scale wind farm probabilistic time-series output simulation method, the specific flow chart is as follows: Figure 1 As shown, the specific process is as follows:

[0072] S101: Generate a time series wind speed sequence according to a set time period based on historical wind speed;

[0073] S102: Generate a time series wind speed sequence with day and night characteristics based on the time series wind speed sequence of each time period according to the order of day and night alternation

[0074] S103: Determine a wind speed sequence that takes into account wake effects after taking into account other wind turbines in the wind farm based on a time-series wind speed sequence with day and night characteristics;

[0075] S104: Determine the probabilistic time-series output of the wind farm based on the time-series wind speed sequence with day and night characteristics or the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm.

[0076] Generate a time series of wind speeds based on historical wind speeds according to set time periods, including:

[0077] Generate random wind speed sequences based on historical wind speeds;

[0078] Determine a plurality of wind speed difference intervals based on the random wind speed sequence, and determine an interval frequency based on the wind speed difference intervals;

[0079] Generate multiple random numbers and match them with interval frequencies to obtain the fluctuation characteristics of the node;

[0080] Based on the fluctuation characteristics of the nodes and the random wind speed sequence, the time series wind speed sequences of different time periods are generated.

[0081] The wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm is determined by the following formula:

[0082]

[0083] Where V2'' represents the wind speed sequence after considering the wake effect of other wind turbines in the wind farm, that is, the wind speed sequence of the rear wind turbines in two adjacent rows of wind turbines, V1'' is the time-series wind speed sequence with day and night characteristics, that is, the wind speed sequence of the front wind turbines in two adjacent rows of wind turbines. The front and rear rows here are based on the wind direction as a reference. D represents the diameter of the wind turbine blade, X represents the distance between two adjacent rows of wind turbines, and C t represents the thrust coefficient of the wind turbine, and K represents the wake attenuation coefficient.

[0084] The probabilistic time-series output of a wind farm is determined based on a time-series wind speed sequence with day and night characteristics or a wind speed sequence that takes into account the wake effect after considering other wind turbines in the wind farm, including:

[0085] Determine the output power of each wind turbine in the wind farm based on a time-series wind speed sequence with day and night characteristics or a wind speed sequence that takes into account the wake effect after considering other wind turbines in the wind farm;

[0086] The output power of all wind turbines is summed up to obtain the probabilistic time-series output of the wind farm.

[0087] The output power of a wind farm will change due to changes in wind speed at different times, which is mainly determined by the wind speed value at the hub of the wind turbine. The output power of a wind farm can be determined by the distribution of wind speed and the relationship between the output power of the wind turbine and the wind speed. Under the premise of meeting the actual engineering requirements, the output power characteristic curve of the wind farm with linear characteristics can be simplified and fitted. Therefore, the output power of each wind turbine in the wind farm is determined by the following formula:

[0088]

[0089] Where P is the output power of the wind turbine, P r is the rated power of the wind turbine, V in is the cut-in wind speed, V out is the cut-out wind speed, V r is the rated wind speed, V is the intermediate quantity, which represents the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm or the time-series wind speed sequence with day and night characteristics.

[0090] Generates a random wind speed sequence based on historical wind speeds, including:

[0091] Based on the diurnal characteristics of wind speed, the shape parameters and scale parameters of the probability distribution function of wind speed are determined in different time periods;

[0092] Determine the probability distribution function of wind speed based on shape parameters and scale parameters;

[0093] Based on the probability distribution function of wind speed, the historical wind speed is determined by the inverse function method;

[0094] A random wind speed is determined based on the historical wind speed, and a random wind speed sequence is obtained based on the random wind speed.

[0095] In the embodiment of the present invention, the wind speed obeys the Weibull distribution, so the probability distribution function of the above wind speed is: Among them, F(v) is the probability distribution function of wind speed, c is the scale parameter of Weibull distribution, which reflects the size of the average wind speed, and Γ represents the Gamma function, σ is the standard deviation of historical wind speed, μ is the historical average wind speed; k is the shape parameter of the Weibull distribution, which reflects the characteristics of the wind speed distribution.

[0096] The shape parameter k and scale parameter c are determined based on the diurnal characteristics of wind speed and in different time periods, as follows:

[0097] The 24 hours of a day are divided into three time periods to consider the difference in the day and night characteristics of wind speed. The three time periods are: 00:03:30 to 05:00:00 in the morning, 00:05:30 to 17:00:00 in the evening, and 17:00:30 in the evening to 00:03:00 in the morning of the next day. The shape parameters k and scale parameters c of these three time periods are respectively counted, as shown in Table 1:

[0098] Table 1

[0099]

[0100] The random wind speed is determined by the following formula:

[0101] v'=[c k log(1-v)] 1 / k

[0102] Where v' is the random wind speed, v is the historical wind speed, c is the scale parameter, and k is the shape parameter.

[0103] Example 2

[0104] Based on the same inventive concept, Embodiment 2 of the present invention further provides a large-scale wind farm probabilistic time-series output simulation device, and the functions of each component are described in detail below:

[0105] The first generation module is used to generate a time series wind speed sequence according to a set time period based on historical wind speed;

[0106] The second generating module is used to generate a time-series wind speed sequence with day and night characteristics based on the time-series wind speed sequence of each time period according to the order of day and night alternation;

[0107] A first determination module is used to determine a wind speed sequence that takes into account wake effects after taking into account other wind turbines in the wind farm based on a time-series wind speed sequence with day and night characteristics;

[0108] The second determination module is used to determine the probabilistic time-series output of the wind farm based on the time-series wind speed sequence with day and night characteristics or the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm.

[0109] The first generation module is specifically used for:

[0110] Generate random wind speed sequences based on historical wind speeds;

[0111] Determine a plurality of wind speed difference intervals based on the random wind speed sequence, and determine an interval frequency based on the wind speed difference intervals;

[0112] Generate multiple random numbers and match them with interval frequencies to obtain the fluctuation characteristics of the node;

[0113] Based on the fluctuation characteristics of the nodes and the random wind speed sequence, the time series wind speed sequences of different time periods are generated.

[0114] The first determination module determines the wind speed sequence after taking into account the wake effect of other wind turbines in the wind farm according to the following formula:

[0115]

[0116] Where V2'' represents the wind speed sequence after considering the wake effect of other wind turbines in the wind farm, V1'' represents the time series wind speed sequence with day and night characteristics, D represents the diameter of the wind turbine blades, X represents the distance between two adjacent rows of wind turbines, and C t represents the thrust coefficient of the wind turbine, and K represents the wake attenuation coefficient.

[0117] The first determination module is specifically used for:

[0118] Determine the output power of each wind turbine in the wind farm based on a time-series wind speed sequence with day and night characteristics or a wind speed sequence that takes into account the wake effect after considering other wind turbines in the wind farm;

[0119] The output power of all wind turbines is summed up to obtain the probabilistic time-series output of the wind farm.

[0120] The first determination module determines the output power of each wind turbine in the wind farm according to the following formula:

[0121]

[0122] Where P is the output power of the wind turbine, P r is the rated power of the wind turbine, V in is the cut-in wind speed, V out is the cut-out wind speed, V r is the rated wind speed, V is the intermediate quantity, which represents the wind speed sequence taking into account the wake effect or the time-series wind speed sequence with day and night characteristics.

[0123] The first generation module is specifically used for:

[0124] Based on the diurnal characteristics of wind speed, the shape parameters and scale parameters of the probability distribution function of wind speed are determined in different time periods;

[0125] Determine the probability distribution function of wind speed based on shape parameters and scale parameters;

[0126] Based on the probability distribution function of wind speed, the historical wind speed is determined by the inverse function method;

[0127] A random wind speed is determined based on the historical wind speed, and a random wind speed sequence is obtained based on the random wind speed.

[0128] The generation module determines the random wind speed according to the following formula:

[0129] v'=[c k log(1-v)] 1 / k

[0130] Where v' is the random wind speed, v is the historical wind speed, c is the scale parameter, and k is the shape parameter.

[0131] Example 3

[0132] Example 3 of the present invention takes an actual wind farm located in Urumqi, Xinjiang as an example to introduce the specific process of the probabilistic time-series output simulation method for a large-scale wind farm. The terrain in the wind farm is flat, and the wind turbines are connected by on-site roads. There are 39 750kw wind turbines and 10 2MW wind turbines in the wind farm. Urumqi is located in the hinterland of the Eurasian continent and belongs to the temperate continental arid climate zone. There are strong winds in spring and autumn, hot summers, and long and cold winters. The mountainous areas have obvious vertical climate changes, and generally speaking, the wind energy resources are abundant. The average wind speed in the area reaches its minimum in June and its maximum in November. The wind speed measured at the actual wind farm is as follows: Figure 2 shown.

[0133] The Weibull distribution parameters of the simulated time-series wind speed data are very similar to the Weibull distribution parameters of the statistically obtained historical wind speed data, which fits the probability distribution characteristics of the wind speed data well. They are consistent with the fluctuations of historical data and the actual daily changes in wind speed in the area where "the wind speed is high at night and low during the day", and reflect the wind speed characteristics of the area where the wind farm is located more comprehensively and accurately.

[0134] After the time series wind speed is completed, the wind speed value at the next moment can be predicted based on the wind speed value at the current moment. Then, based on the probabilistic time series wind speed model, the time series output of the wind farm at the next moment can be predicted based on the relationship between the time series output of the wind farm and the wind speed. Figure 3 The comparison between the actual wind farm output and the generated wind farm output is shown in Figure 4 As shown, Figure 4 In the figure, the solid line is the actual wind farm output, and the dotted line is the simulated wind farm output. Figure 4 It can be seen that the actual wind farm output and the generated wind farm time-series output have similar changing trends, which verifies the rationality of the probabilistic time-series output, and the maximum power error does not exceed 5%, which is within the allowable error range, proving the correctness of the established wind farm output power model, and the simulation results are accurate and effective.

[0135] For the convenience of description, the various parts of the above-mentioned device are divided into various modules or units according to their functions and described separately. Of course, when implementing the present application, the functions of each module or unit can be implemented in the same or multiple software or hardware.

[0136] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0137] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0138] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Ordinary technicians in the relevant field can still modify or make equivalent substitutions to the specific implementation methods of the present invention with reference to the above embodiments. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention are within the scope of the present invention to be approved.

Claims

1. A large-scale wind farm probabilistic time-series output simulation method, characterized in that: include: Generate a time series wind speed sequence based on historical wind speed according to the set time period; Based on the time series wind speed sequence of each time period, a time series wind speed sequence with day and night characteristics is generated according to the order of day and night alternation; Determine, based on the time-series wind speed sequence with day and night characteristics, a wind speed sequence that takes into account wake effects after taking into account other wind turbines in the wind farm; Determine the probabilistic time-series output of the wind farm based on the time-series wind speed sequence with day and night characteristics or the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm; The generating of a time series wind speed sequence according to a set period based on historical wind speeds includes: Generate a random wind speed sequence based on historical wind speeds; Determine a plurality of wind speed difference intervals based on the random wind speed sequence, and determine an interval frequency based on the wind speed difference intervals; Generate multiple random numbers, and match the multiple random numbers with the interval frequency to obtain the fluctuation characteristics of the node; Based on the fluctuation characteristics of the node and the random wind speed sequence, a time series wind speed sequence of different time periods is generated; the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm is determined by the following formula: Where V2'' represents the wind speed sequence after considering the wake effect of other wind turbines in the wind farm, V1'' represents the time series wind speed sequence with day and night characteristics, D represents the diameter of the wind turbine blades, X represents the distance between two adjacent rows of wind turbines, and C t represents the thrust coefficient of the wind turbine, and K represents the wake attenuation coefficient; The method of determining the probabilistic time-series output of the wind farm based on the time-series wind speed sequence with day and night characteristics or the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm comprises: Determine the output power of each wind turbine in the wind farm based on a time-series wind speed sequence with day and night characteristics or a wind speed sequence including wake effects after considering other wind turbines in the wind farm; The output power of all wind turbines is summed up to obtain the probabilistic time-series output of the wind farm; The random wind speed is determined by the following formula: v'=[c k log(1-v)] 1 / k Where v' is the random wind speed, v is the historical wind speed, c is the scale parameter, and k is the shape parameter; Among them, the shape parameter k and scale parameter c are determined based on the diurnal characteristics of wind speed and in different time periods, as follows: The 24 hours of a day are divided into three time periods to consider the differences in the day and night characteristics of wind speed. These three time periods are: 00:03:30 to 05:00:00 in the morning, 00:05:30 to 17:00:00 in the evening, and 17:00:30 in the evening to 00:03:00 in the morning of the next day. The shape parameter k and scale parameter c of each of these three time periods are counted respectively.

2. The large-scale wind farm probabilistic time-series output simulation method according to claim 1 is characterized in that: The output power of each wind turbine in the wind farm is determined by the following formula: Where P is the output power of the wind turbine, P r is the rated power of the wind turbine, V in is the cut-in wind speed, V out is the cut-out wind speed, V r is the rated wind speed, V is an intermediate quantity, which represents the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm or the time-series wind speed sequence with day and night characteristics.

3. The large-scale wind farm probabilistic time-series output simulation method according to claim 1 is characterized in that: The generating of a random wind speed sequence based on historical wind speeds comprises: Based on the diurnal characteristics of wind speed, the shape parameters and scale parameters of the probability distribution function of wind speed are determined in different time periods; Determining a probability distribution function of wind speed based on the shape parameter and the scale parameter; Based on the probability distribution function of wind speed, the historical wind speed is determined by the inverse function method; A random wind speed is determined based on the historical wind speed, and a random wind speed sequence is obtained based on the random wind speed.

4. A large-scale wind farm probabilistic time-series output simulation device, characterized in that: include: The first generation module is used to generate a time series wind speed sequence according to a set time period based on historical wind speed; The second generating module is used to generate a time-series wind speed sequence with day and night characteristics based on the time-series wind speed sequence of each time period according to the order of day and night alternation; A first determination module is used to determine a wind speed sequence that takes into account wake effects after taking into account other wind turbines in the wind farm based on the time-series wind speed sequence with day and night characteristics; A second determination module is used to determine the probabilistic time-series output of the wind farm based on the time-series wind speed sequence with day and night characteristics or the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm; The first generating module is specifically used for: Generate a random wind speed sequence based on historical wind speeds; Determine a plurality of wind speed difference intervals based on the random wind speed sequence, and determine an interval frequency based on the wind speed difference intervals; Generate multiple random numbers, and match the multiple random numbers with the interval frequency to obtain the fluctuation characteristics of the node; Based on the fluctuation characteristics of the nodes and the random wind speed sequence, the time series wind speed sequences of different periods are generated; After considering other wind turbines in the wind farm, the first determination module determines the wind speed sequence taking into account the wake effect according to the following formula: Where V2'' represents the wind speed sequence after considering the wake effect of other wind turbines in the wind farm, V1'' represents the time series wind speed sequence with day and night characteristics, D represents the diameter of the wind turbine blades, X represents the distance between two adjacent rows of wind turbines, and C t represents the thrust coefficient of the wind turbine, and K represents the wake attenuation coefficient; The first determining module is specifically used for: Determine the output power of each wind turbine in the wind farm based on a time-series wind speed sequence with day and night characteristics or a wind speed sequence including wake effects after considering other wind turbines in the wind farm; The output power of all wind turbines is summed up to obtain the probabilistic time-series output of the wind farm; The generating module determines the random wind speed according to the following formula: v'=[c k log(1-v)] 1 / k Where v' is the random wind speed, v is the historical wind speed, c is the scale parameter, and k is the shape parameter; Among them, the shape parameter k and scale parameter c are determined based on the diurnal characteristics of wind speed and in different time periods, as follows: The 24 hours of a day are divided into three time periods to consider the differences in the day and night characteristics of wind speed. These three time periods are: 00:03:30 to 05:00:00 in the morning, 00:05:30 to 17:00:00 in the evening, and 17:00:30 in the evening to 00:03:00 in the morning of the next day. The shape parameter k and scale parameter c of each of these three time periods are counted respectively.

5. The large-scale wind farm probabilistic time-series output simulation device according to claim 4, characterized in that: The first determination module determines the output power of each wind turbine in the wind farm according to the following formula: Where P is the output power of the wind turbine, P r is the rated power of the wind turbine, V in is the cut-in wind speed, V out is the cut-out wind speed, V r is the rated wind speed, V is an intermediate quantity, which represents the wind speed sequence taking into account the wake effect after considering other wind turbines in the wind farm or the time-series wind speed sequence with day and night characteristics.

6. The large-scale wind farm probabilistic time-series output simulation device according to claim 4, characterized in that: The first generating module is specifically used for: Based on the diurnal characteristics of wind speed, the shape parameters and scale parameters of the probability distribution function of wind speed are determined in different time periods; Determining a probability distribution function of wind speed based on the shape parameter and the scale parameter; Based on the probability distribution function of wind speed, the historical wind speed is determined by the inverse function method; A random wind speed is determined based on the historical wind speed, and a random wind speed sequence is obtained based on the random wind speed.

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