Wind power consumption energy optimization scheduling method and device

Abstract

The invention relates to the technical field of wind power consumption, in particular to a wind power consumption energy optimization scheduling method and device, and the method comprises the steps: obtaining daily new energy output and daily operation data, and obtaining an operation data set; constructing a normal distribution curve based on the operation data set to obtain a probability distribution function; performing correction processing on the operation data set based on the probability distribution function to obtain actual demand data; and introducing schedulable resources, and based on the actual demand data, analyzing the wind power resource utilization rate of the system under the participation of different scheduling resources to obtain a wind power consumption result. According to the method, the influence of participation of the flexible load on wind power consumption is comprehensively considered, the source load matching degree and the wind power energy dissipation capacity are improved, the uncertainty of output of the photovoltaic and wind turbine generators is described by adopting a scene generation and reduction method, a power curve of the output uncertainty is finally obtained, the source load matching degree is effectively improved, and the output uncertainty of the photovoltaic and wind turbine generators is improved. Interaction of the system with a large power grid and a large heat supply network is reduced, and system reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power consumption, and in particular to a method and device for optimizing the dispatching of wind power consumption energy. Background Art

[0002] As one of the important clean energy sources in the world today, wind power generation has developed rapidly throughout the country and even the world in recent years. However, wind power output is random and volatile, and the daily variation can reach 80% of the installed capacity. The complex energy coupling in the integrated energy system and the uncertainty of the output of renewable energy have increased the difficulty of scheduling the integrated energy system. In recent years, with the continuous enrichment of dispatchable resources on the load side, opportunities have been brought to improve such problems. Considering the coordinated operation of wind power, photovoltaics, energy storage, and dispatchable resources on the load side has become the main trend in the optimization and scheduling of integrated energy systems; most studies have paid more attention to demand response and uncertainty in new energy output, and rarely consider the impact of different dispatching orders of dispatchable resources on wind power consumption in combination with the above two points;

[0003] In the process of optimizing the dispatching of the integrated energy system, the existing technology only considers the impact of the dispatchable resources on the demand side on the consumption of wind power, instead of coordinating the dispatchable resources on the source side and the load side to promote the consumption of wind power, resulting in high economic and environmental costs of the system, and a large amount of interaction between the power grid and the system, and low source-load matching of the system. The system does not want to purchase too much electricity from the large power grid, resulting in excessive dependence on the large power grid, and the existing integrated energy system regulation mode is generally based on electricity to determine heat or heat to determine electricity, or based on the operation mode obtained by multi-objective optimization, without considering the impact of the different dispatching order of the dispatchable resources of the source and load on the system. That is, the existing literature only considers one optimization dispatching method, without considering the impact of different dispatching orders of the dispatching resources of the system, and the existing technology considers less energy storage equipment and energy conversion devices. Even if some projects consider multiple energy storage methods, there is no coordinated operation optimization between different energy storage methods. There is little consideration of the coordinated effect of the integrated source side, load side, and energy conversion device on the system optimization dispatching.

[0004] To sum up, how to design an efficient, stable and low-cost wind power consumption method is an urgent problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to provide a method for optimizing the dispatching of wind power consumption energy, so as to solve the problems of high cost, large interaction and low matching degree of existing wind power consumption.

[0006] In order to solve the above technical problems, the present invention provides a method for optimizing the dispatching of wind power consumption energy, comprising:

[0007] Obtain daily renewable energy output and daily operation data to obtain the operation data set;

[0008] Constructing a normal distribution curve based on the running data set to obtain a probability distribution function;

[0009] Based on the probability distribution function, the operating data set is corrected to obtain actual demand data;

[0010] Dispatched resources are introduced, and based on the actual demand data, the utilization rate of wind power resources in the system with the participation of different dispatchable resources is analyzed to obtain the wind power consumption result.

[0011] Preferably, constructing a normal distribution curve based on the running data set to obtain a probability distribution function comprises:

[0012] Assume that the running data set obeys the normal distribution N(μ, δ 2 ), construct a probability distribution function, and obtain the expected value and variance. The probability distribution function is:

[0013] F K =F K (X K )

[0014] Among them, X K is a random variable, K is the number of random variables, F K is the probability distribution function.

[0015] Preferably, the correcting the operating data set based on the probability distribution function to obtain the actual demand data includes:

[0016] A scenario generation method and a scenario reduction method are used to process the uncertainty of wind turbine output. The expected value of unit power is obtained based on the operating data set. The Latin hypercube sampling method is used to generate an original scenario set of unit output. The original scenario set is reduced through synchronous back-substitution reduction based on probability distance to obtain a simplified scenario. The actual demand data is obtained based on the simplified scenario and its corresponding power.

[0017] Preferably, the scene generation method comprises:

[0018] Assume that the sampling scale is M, and the F K The distribution curve of is divided into M non-overlapping sub-intervals, and the interval between each interval is 1 / M;

[0019] Select a value from each interval and calculate its cumulative probability:

[0020]

[0021] Among them, m iis a random number in the interval [0-1], P Ki is the cumulative probability, i is the interval number;

[0022] The P Ki Bring in the F K The sampling value of the unit output in interval i is obtained from the inverse function of , and the calculation formula is:

[0023]

[0024] Among them, X Mi is the sampling value of the unit output in interval i;

[0025] Get K sampling values ​​and obtain K scenes under a typical day.

[0026] Preferably, the scene reduction method comprises:

[0027] Preset the final target number of scenes to n, and the initial reduction scene to n * =C, based on the Latin hypercube sampling method, we get:

[0028]

[0029] Based on the P i Calculate any two scenes in the initialization reserved scene set and The distance between them is calculated as:

[0030] dis i，j =|P i LHS -P j LHS |, j = 1, 2, ..., C

[0031] Among them, dis i，j is the distance between the two scenes;

[0032] Based on the and stated Calculate and get the probability product, the calculation formula is:

[0033] Z i =P i ×P r LHS

[0034] Among them, P r LHS for The closest scene;

[0035] Based on obtaining the minimum probability product among C scenarios, the scenario is deleted after recording its scenario number, and the number of reduced scenarios is updated;

[0036] It is determined whether the preset value is reached in the scene number set. If it is reached, the reduction is stopped, otherwise, the reduction is continued, and finally the unit output data is obtained.

[0037] Preferably, the unit output data calculation formula is:

[0038]

[0039]

[0040] Among them, P WT (t), P PV (t) is the total output of wind power and photovoltaic units at time t, P WT，i (t), P PV，i (t) is the wind power and photovoltaic output in the i-th scenario at time t, is the probability corresponding to the scene obtained after reduction.

[0041] Preferably, the introducing of dispatchable resources, based on the actual demand data, analyzing the utilization rate of wind power resources of the system under the participation of different dispatchable resources, and obtaining the wind power consumption result includes:

[0042] Based on the goal of minimizing the economic and environmental costs of the system, an optimal scheduling model for the integrated energy system considering demand response loads and ground-source heat pump output is established. Different systems including ground-source heat pumps and demand response loads are analyzed. Based on the optimal system, three scenarios with different scheduling orders are proposed, and the scheduling results of different scheduling orders are compared. The impact of different scheduling orders of dispatchable resources on wind power consumption is analyzed, and the wind power consumption results are obtained.

[0043] The present invention also provides a wind power consumption energy optimization dispatching device, comprising:

[0044] The data acquisition module acquires daily renewable energy output and daily operation data to obtain the operation data set;

[0045] A normal curve construction module is used to construct a normal distribution curve based on the running data set to obtain a probability distribution function;

[0046] A data correction module, which corrects the operating data set based on the probability distribution function to obtain actual demand data;

[0047] The data analysis module introduces dispatchable resources, analyzes the utilization rate of wind power resources of the system under the participation of different dispatch resources based on the actual demand data, and obtains the wind power consumption result.

[0048] The present invention also provides a wind power consumption energy optimization dispatching device, comprising:

[0049] Memory for storing computer programs;

[0050] A processor is used to implement the steps of the above-mentioned method for optimizing the scheduling of wind power consumption energy when executing the computer program.

[0051] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for optimizing the scheduling of wind power energy consumption are implemented.

[0052] The present invention provides a method for optimizing the dispatch of wind power consumption energy. A wind turbine-ground source heat pump model is established on the source side to decouple the traditional "heat-based electricity" operation constraint. The ground source heat pump has a high conversion efficiency. While utilizing wind power, it is converted into the required load to improve the wind power consumption capacity. The influence of the participation of flexible loads on wind power consumption is comprehensively considered. By considering demand response on the load side, the effect of "peak shaving and valley filling" is achieved, the load curve is smoothed, the source-load matching and wind power consumption capacity are improved, and the scenario generation and reduction methods are used to describe the uncertainty of the output of photovoltaic and wind turbines, and finally the power curve of the output uncertainty is obtained, which effectively improves the source-load matching, reduces the interaction between the system and the large power grid and the large thermal network, and improves the system reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0054] Figure 1 A flowchart of a first specific embodiment of a method for optimizing the dispatching of wind power consumption energy provided by an embodiment of the present invention;

[0055] Figure 2 A flow chart of the optimal dispatching method for wind power consumption;

[0056] Figure 3 A structural block diagram of a wind power energy optimization and scheduling device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The core of the present invention is to provide a method and device for optimizing the dispatching of wind power consumption energy, and propose an optimization dispatching method that takes into account the participation of source-load dispatchable resources to improve the source-load matching degree of the park-level comprehensive energy system. A ground source heat pump system with higher conversion efficiency is coupled on the source side to promote wind power consumption, reduce the interaction between the system and the large power grid and large thermal network, and improve system reliability.

[0058] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0059] Please refer to Figure 1 , Figure 1 This is a flow chart of a first specific embodiment of a method for optimizing the dispatching of wind power energy consumption provided by the present invention; the specific operation steps are as follows:

[0060] Step S101: Acquire daily new energy output and daily operation data to obtain an operation data set;

[0061] Step S102: constructing a normal distribution curve based on the running data set to obtain a probability distribution function;

[0062] The constructing a normal distribution curve based on the running data set to obtain a probability distribution function comprises:

[0063] Assume that the running data set obeys the normal distribution N(μ, δ 2 ), construct a probability distribution function, and obtain the expected value and variance. The probability distribution function is:

[0064] F K =F K (X K )

[0065] Among them, X K is a random variable, K is the number of random variables, F K is the probability distribution function.

[0066] Step S103: modifying the operating data set based on the probability distribution function to obtain actual demand data;

[0067] The correcting the operating data set based on the probability distribution function to obtain actual demand data includes:

[0068] A scenario generation method and a scenario reduction method are used to process the uncertainty of wind turbine output. The expected value of unit power is obtained based on the operating data set. The Latin hypercube sampling method is used to generate an original scenario set of unit output. The original scenario set is reduced through synchronous back-substitution reduction based on probability distance to obtain a simplified scenario. The actual demand data is obtained based on the simplified scenario and its corresponding power.

[0069] The scene generation method comprises:

[0070] Assume that the sampling scale is M, and the F K The distribution curve of is divided into M non-overlapping sub-intervals, and the interval between each interval is 1 / M;

[0071] Select a value from each interval and calculate its cumulative probability:

[0072]

[0073] Among them, m i is a random number in the interval [0-1], P Ki is the cumulative probability, i is the number of intervals;

[0074] The P Ki Bring in the F K The sampling value of the unit output in interval i is obtained from the inverse function of , and the calculation formula is:

[0075]

[0076] Among them, X Mi is the sampling value of the unit output in interval i;

[0077] Get K sampling values ​​and obtain K scenes under a typical day.

[0078] The scene reduction method comprises:

[0079] Preset the final target number of scenes to n, and the initial reduction scene to n * =C, based on the Latin hypercube sampling method, we get:

[0080]

[0081] Based on the P i Calculate any two scenes in the initialization reserved scene set and The distance between them is calculated as:

[0082] dis i，j =|P i LHS -P j LHS |, j = 1, 2, ..., C

[0083] Among them, dis i，j is the distance between the two scenes;

[0084] Based on the and stated Calculate and get the probability product, the calculation formula is:

[0085] Z i =Pi ×P r LHS

[0086] Among them, P r LHS for The closest scene;

[0087] Based on obtaining the minimum probability product among C scenarios, the scenario is deleted after recording its scenario number, and the number of reduced scenarios is updated;

[0088] It is determined whether the preset value is reached in the scene number set. If it is reached, the reduction is stopped, otherwise, the reduction is continued, and finally the unit output data is obtained.

[0089] The calculation formula for the unit output data is:

[0090]

[0091]

[0092] Among them, P WT (t), P Pv (t) is the total output of wind power and photovoltaic units at time t, P wT，i (t), P PV，i (t) is the wind power and photovoltaic output in the i-th scenario at time t, is the probability corresponding to the scene obtained after reduction.

[0093] Step S104: Introduce dispatchable resources, analyze the utilization rate of wind power resources of the system under the participation of different dispatchable resources based on the actual demand data, and obtain the wind power consumption result.

[0094] The introduction of dispatchable resources, based on the actual demand data, analyzes the utilization rate of wind power resources of the system under the participation of different dispatchable resources, and obtains the wind power consumption results including:

[0095] Based on the goal of minimizing the economic and environmental costs of the system, an optimal scheduling model for the integrated energy system considering demand response loads and ground-source heat pump output is established. Different systems including ground-source heat pumps and demand response loads are analyzed. Based on the optimal system, three scenarios with different scheduling orders are proposed, and the scheduling results of different scheduling orders are compared. The impact of different scheduling orders of dispatchable resources on wind power consumption is analyzed, and the wind power consumption results are obtained.

[0096] This embodiment provides a method for optimizing the dispatching of wind power energy consumption, which couples a ground source heat pump as a dispatchable resource on the supply side to participate in the dispatching of the system, utilizes the characteristic that the ground source heat pump can efficiently convert into cooling load or heating load while consuming electricity, improves the matching degree between the supply side and the demand side, and adopts the scenario generation and reduction method to describe the uncertainty of the output of new energy machines; proposes to minimize the economic cost and environmental cost of the system as the goal, establishes an optimized dispatching model for the comprehensive energy system considering the demand response load and the output of the ground source heat pump, reduces the interaction between the system and the large power grid and the large heating network, improves the system reliability, and increases the proportion of renewable energy consumption.

[0097] Based on the above embodiment, this embodiment describes a method for optimizing the dispatching of wind power consumption energy. Figure 2 As shown, the details are as follows:

[0098] On the supply side, ground source heat pumps are coupled as dispatchable resources to participate in the system dispatch, and daily new energy output and daily operation data are obtained to obtain the operation data set;

[0099] Determine the optimal scheduling time interval for medium / short time scales, and divide the load-side scheduling resources into transferable load (TL) and reducible load (RL) as flexible loads to participate in system scheduling;

[0100] In the process of scheduling, the scenario generation and reduction method is used to describe the uncertainty of photovoltaic and wind turbine output, and finally the power curve of its output uncertainty is obtained. The case analysis and verification is carried out for a certain park to obtain its typical daily new energy output and daily operating load data. In the day-ahead scheduling model, the scenario generation and reduction method is used to deal with the uncertainty of wind turbine output. According to historical data, the power expectation value of the new energy unit can be obtained. Assuming that the power error of the wind turbine unit satisfies the normal distribution, the day-ahead forecast value of the wind turbine unit is the sum of the expected value and the forecast error. A large number of original scenario sets of new energy unit output that obey the probability distribution are generated through Latin hypercube sampling (LHS), and then the above scenario sets are reduced by the synchronous back-substitution reduction method based on probability distance, and the simplified scenarios with corresponding probabilities are derived to obtain 10 simplified scenario sets of new energy unit output and their corresponding probabilities Pi1. Finally, the probability of the above scenarios occurring is multiplied and summed by the corresponding power to obtain the power curve of the uncertainty of the new energy unit output.

[0101] With the goal of minimizing the economic and environmental costs of the system, an optimal scheduling model for an integrated energy system considering demand response loads and ground-source heat pump output was established, and seven different systems including ground-source heat pumps and demand response loads were analyzed. Based on the optimal system, three scenarios with different scheduling orders were proposed, and the scheduling results of different scheduling orders were compared. The impact of different scheduling orders of dispatchable resources on wind power consumption was analyzed, and the optimization results were output.

[0102] An optimized dispatching method for wind power consumption energy provided by an embodiment of the present invention comprehensively considers the influence of the participation of flexible loads on wind power consumption, achieves the effect of "peak shaving and valley filling" on the load side by considering demand response, smoothes the load curve, improves the source-load matching degree and wind power consumption capacity, adopts scenario generation and reduction methods to describe the instability of the output of new energy units, improves the consumption of wind power and the source-load matching degree, introduces dispatchable resources on the supply side and the demand side, analyzes the utilization of wind power resources by the system under the participation of different dispatching resources, and further studies the influence of different dispatching resources on wind power consumption under the influence of different dispatching sequences based on the system with the most economic efficiency, reduces the interaction between the system and the large power grid and the large thermal network, improves the system reliability, and increases the consumption ratio of renewable energy.

[0103] Please refer to Figure 3 , Figure 3 A structural block diagram of a wind power energy optimization and dispatching device provided in an embodiment of the present invention; the specific device may include:

[0104] The data acquisition module 100 acquires daily new energy output and daily operation data to obtain an operation data set;

[0105] A normal curve construction module 200 constructs a normal distribution curve based on the running data set to obtain a probability distribution function;

[0106] A data correction module 300 is configured to correct the operating data set based on the probability distribution function to obtain actual demand data;

[0107] The data analysis module 400 introduces dispatchable resources, and based on the actual demand data, analyzes the utilization rate of wind power resources of the system under the participation of different dispatchable resources to obtain the wind power consumption result.

[0108] A wind power energy consumption optimization scheduling device in this embodiment is used to implement the aforementioned wind power energy consumption optimization scheduling method. Therefore, the specific implementation method of a wind power energy consumption optimization scheduling device can be seen in the embodiment part of a wind power energy consumption optimization scheduling method in the previous text. For example, the data acquisition module 100, the normal curve construction module 200, the data correction module 300, and the data analysis module 400 are respectively used to implement steps S101, S102, S103, and S104 in the aforementioned wind power energy consumption optimization scheduling method. Therefore, its specific implementation method can refer to the description of the corresponding each part of the embodiment, which will not be repeated here.

[0109] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0110] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0111] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0112] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0113] The above is a detailed introduction to a method and device for optimizing the dispatching of wind power energy consumption provided by the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0114] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0115] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0116] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

Claims

1. A method for optimizing the dispatching of wind power consumption energy, characterized in that: include: Obtain daily renewable energy output and daily operation data to obtain the operation data set; Constructing a normal distribution curve based on the running data set to obtain a probability distribution function; Based on the probability distribution function, the operating data set is corrected to obtain actual demand data; Dispatched resources are introduced, and based on the actual demand data, the utilization rate of wind power resources in the system with the participation of different dispatchable resources is analyzed to obtain the wind power consumption result.

2. The method for optimizing the dispatching of wind power consumption energy according to claim 1, characterized in that: The constructing a normal distribution curve based on the running data set to obtain a probability distribution function comprises: Assume that the running data set obeys the normal distribution N(μ, δ 2 ), construct a probability distribution function, and obtain the expected value and variance. The probability distribution function is: F K = K (X K ) Among them, X K is a random variable, K is the number of random variables, F K is the probability distribution function.

3. The method for optimizing the dispatching of wind power consumption energy according to claim 1, characterized in that: The correcting the operating data set based on the probability distribution function to obtain actual demand data includes: A scenario generation method and a scenario reduction method are used to process the uncertainty of wind turbine output. The expected value of unit power is obtained based on the operating data set. The Latin hypercube sampling method is used to generate an original scenario set of unit output. The original scenario set is reduced through synchronous back-substitution reduction based on probability distance to obtain a simplified scenario. The actual demand data is obtained based on the simplified scenario and its corresponding power.

4. The method for optimizing the dispatching of wind power consumption energy according to claim 3, characterized in that: The scene generation method comprises: Assume that the sampling scale is M, and the F K The distribution curve of is divided into M non-overlapping sub-intervals, and the spacing between each interval is 1 / ; Select a value from each interval and calculate its cumulative probability: Among them, m i is a random number in the interval [0-1], p Ki is the cumulative probability, i is the interval number; The P Ki Bring in the F K The sampling value of the unit output in interval i is obtained from the inverse function of , and the calculation formula is: Among them, X Mi is the sampling value of the unit output in interval i; Get K sampling values ​​and obtain K scenes under a typical day.

5. The method for optimizing the dispatching of wind power consumption energy according to claim 3, characterized in that: The scene reduction method comprises: Preset the final target number of scenes to n, and the initial reduction scene to n * =, based on the Latin hypercube sampling method, we get: Based on the p i Calculate any two scenes in the initialization reserved scene set and The distance between them is calculated as: say i,j =P i LHS - j LHS |,j=1,2,..., Among them, dis i,j is the distance between the two scenes; Based on the and stated Calculate and get the probability product, the calculation formula is: WITH i = i ×P r LHS Among them, P r LHS for The closest scene; Based on obtaining the minimum probability product among C scenarios, the scenario is deleted after recording its scenario number, and the number of reduced scenarios is updated; It is determined whether the preset value is reached in the scene number set. If it is reached, the reduction is stopped, otherwise, the reduction is continued, and finally the unit output data is obtained.

6. The method for optimizing the dispatching of wind power consumption energy according to claim 5, characterized in that: The calculation formula for the unit output data is: Among them, P WT (), P PV () is the total output of wind power and photovoltaic units at time t, P WT, (), P PV, () is the wind power and photovoltaic output in the i-th scenario at time t, is the probability corresponding to the scene obtained after reduction.

7. The method for optimizing the dispatching of wind power consumption energy according to claim 1, characterized in that: The introduction of dispatchable resources, based on the actual demand data, analyzes the utilization rate of wind power resources of the system under the participation of different dispatchable resources, and obtains the wind power consumption results including: Based on the goal of minimizing the economic and environmental costs of the system, an optimal scheduling model for the integrated energy system considering demand response loads and ground-source heat pump output is established. Different systems including ground-source heat pumps and demand response loads are analyzed. Based on the optimal system, three scenarios with different scheduling orders are proposed, and the scheduling results of different scheduling orders are compared. The impact of different scheduling orders of dispatchable resources on wind power consumption is analyzed, and the wind power consumption results are obtained.

8. A wind power energy consumption optimization dispatching device, characterized in that: include: The data acquisition module acquires daily renewable energy output and daily operation data to obtain the operation data set; A normal curve construction module is used to construct a normal distribution curve based on the running data set to obtain a probability distribution function; A data correction module, which corrects the operating data set based on the probability distribution function to obtain actual demand data; The data analysis module introduces dispatchable resources, analyzes the utilization rate of wind power resources of the system under the participation of different dispatch resources based on the actual demand data, and obtains the wind power consumption result.

9. A wind power consumption energy optimization dispatching device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of a method for optimizing the dispatching of wind power energy consumption as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the scheduling of wind power energy consumption as claimed in any one of claims 1 to 7 are implemented.

Images