A method, system and device for dispatching electric energy based on wind power uncertainty

Abstract

The invention discloses an electric energy dispatching method, system and equipment based on wind power uncertainty, and specifically relates to the technical field of electric energy dispatching. The technical key points are: calculating the error value of a mixed skewed distribution fitting curve and wind power output sample data, and then fitting the wind power output sample data and the error through a normal cloud model to obtain a fitting function for characterizing the distribution of wind power short-term output prediction errors; by sampling the fitting function and the load prediction error distribution curve, respectively obtaining a wind power prediction error value and a load prediction error value, and using the wind power prediction error value and the load prediction error value to establish uncertainty constraints; based on the uncertainty constraints combined with a pre-constructed first deterministic constraint, taking the minimum total operating cost of the main grid system within the dispatching period as the objective function, constructing an electric energy dispatching model; solving the electric energy dispatching model through a CPLEX solver to obtain an electric energy dispatching plan for the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy dispatching, and in particular to an electric energy dispatching method, system and equipment based on wind power uncertainty. Background Art

[0002] Compared with traditional thermal power and hydropower, which are highly controllable, wind power generation has a strong uncertainty. When the scale of wind power grid connection is small, the uncertainty it brings to the operation of the power grid is also correspondingly small, and the conventional units in the power grid are sufficient to meet the needs of system power balance. At this time, only the power demand fluctuations on the load side can be considered, and the power gap caused by load fluctuations can be fully met by the rotating reserve reserved by conventional units.

[0003] However, with the continuous increase in the wind power grid-connected capacity of the power system, if only the power demand fluctuation on the load side is considered and the traditional mode of configuring the reserve with a fixed ratio of the maximum load is adopted, it is completely unsuitable for the safe operation of large-scale wind power systems. In the existing technology, the market-based approach can determine the reserve according to the system operation requirements, and the real-time supply and demand scheduling matching of the two can be achieved by jointly optimizing the power market of electric energy and reserve; however, the uncertainty of wind power forecasting affects the scheduling planning, thereby affecting the safe operation of the power system.

[0004] Therefore, the present invention aims to provide a method, system and device for dispatching electric energy based on wind power uncertainty to solve the above-mentioned related problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is that the uncertainty of wind power prediction brings challenges to scheduling planning, thereby affecting the safe operation of the power system. The purpose is to provide an electric energy scheduling method, system and equipment based on wind power uncertainty. By considering the impact of wind power short-term output prediction error and load prediction error on power balance constraints and rotating reserve capacity constraints, uncertainty constraints are constructed. Then, under the constructed uncertainty constraints, an electric energy scheduling model is constructed with the minimum total operating cost of the main grid system within the scheduling period as the objective function, so that when large-scale wind turbines participate in the power market, the uncertainty factors of wind power can be minimized to achieve reasonable scheduling and matching of electric energy and ensure the safety of system operation; at the same time, considering the complexity of solving the constructed electric energy scheduling model, the present invention uses a deterministic conversion method of opportunity constraints to convert uncertainty constraints based on opportunity constraints into deterministic constraints, thereby improving the model solution speed and increasing the timeliness of model application.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for dispatching electric energy based on wind power uncertainty, the method comprising:

[0008] The mixed skewed distribution model is used to fit the wind power output sample data to obtain the mixed skewed distribution fitting curve, and the error value of the mixed skewed distribution fitting curve and the wind power output sample data is calculated. Then, the wind power output sample data and the error are fitted by the normal cloud model to obtain the fitting function used to characterize the distribution of wind power short-term output forecast error.

[0009] Obtain historical load forecast error data to construct a load forecast error distribution curve that conforms to the normal distribution, obtain wind power forecast error values ​​and load forecast error values ​​by sampling the fitting function and the load forecast error distribution curve, and use the wind power forecast error values ​​and the load forecast error values ​​to establish uncertainty constraints;

[0010] Based on the uncertainty constraint conditions combined with the pre-built first deterministic constraint conditions, the electric energy dispatch model is constructed with the minimum total operating cost of the main grid system within the dispatch period as the objective function;

[0011] The electric energy dispatch model is solved by CPLEX solver to obtain the electric energy dispatch plan of the power system.

[0012] Furthermore, the method further comprises:

[0013] The uncertain constraint condition is transformed by using the deterministic transformation method of the chance constraint condition to obtain the transformed second deterministic constraint condition;

[0014] The optimal power dispatch model is constructed by combining the objective function, the first deterministic constraint and the second deterministic constraint.

[0015] Furthermore, the uncertainty constraints include power balance constraints and spinning reserve constraints.

[0016] Furthermore, the power balance constraint is specifically: ,in, For wind turbines In the period The winning bid output; Representation Node In the period Load; For the crew Respectively The period Section output; Indicates the wind power prediction error value; Indicates the load forecast error value; is the relaxation constant; is the confidence level.

[0017] Furthermore, the spinning reserve constraints are specifically: , , where Indicates conventional unit exist Positive reserve capacity at the moment; Indicates conventional unit exist Negative reserve capacity at the moment; Indicates the wind power prediction error value; Indicates the load forecast error value; , Indicates the confidence level that each opportunity constraint condition is established; represents the minimum positive reserve capacity; Indicates the maximum negative reserve capacity; represents the minimum negative reserve capacity; is the relaxation constant; Indicates conventional unit In the period The start and stop status of the unit; Indicates the maximum technical output; Indicates conventional unit In the period The winning bid output; Indicates conventional unit The upward climbing rate; Indicates the minimum technical output; Indicates conventional unit Downward climbing rate.

[0018] Furthermore, the pre-constructed first deterministic constraints include upper and lower limit constraints of unit output, conventional unit ramp constraints, conventional unit minimum continuous start and stop constraints, conventional unit start and stop cost constraints and line flow constraints.

[0019] Furthermore, the Latin hypercube sampling method is used to sample the coupling probability distribution model fitting function and the load forecasting error distribution curve.

[0020] The present invention further provides an electric energy dispatching system based on wind power uncertainty, which is used in any one of the above-mentioned electric energy dispatching methods based on wind power uncertainty, and the system comprises:

[0021] A model coupling module is used to fit the wind power output sample data using a mixed skewed distribution model to obtain a mixed skewed distribution fitting curve, and calculate the error value between the mixed skewed distribution fitting curve and the wind power output sample data, and then fit the wind power output sample data and the error using a normal cloud model to obtain a fitting function for characterizing the distribution of wind power short-term output forecast errors;

[0022] The uncertainty constraint building module is used to obtain historical load forecast error data to build a load forecast error distribution curve that conforms to the normal distribution. By sampling the fitting function and the load forecast error distribution curve, the wind power forecast error value and the load forecast error value are obtained respectively, and the wind power forecast error value and the load forecast error value are used to establish uncertainty constraint conditions;

[0023] A model building module, used to build an electric energy dispatching model based on the uncertainty constraint condition combined with the pre-built first deterministic constraint condition, taking the minimum total operating cost of the main grid system within the dispatching period as the objective function;

[0024] The model solving module is used to solve the electric energy dispatching model through the CPLEX solver to obtain the electric energy dispatching plan of the power system.

[0025] The present invention also provides a computer device, comprising a system memory and a processor, wherein the system memory stores a computer program, and the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0026] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any one of the methods described above when executed by a processor.

[0027] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0028] In the present invention, by considering the influence of short-term wind power output forecast error and load forecast error on power balance constraint and rotating reserve capacity constraint, uncertainty constraint conditions are constructed, and then under the constructed uncertainty constraint conditions, an electric energy dispatching model is constructed with the minimum total operating cost of the main grid system within the dispatching period as the objective function, so that when large-scale wind turbines participate in the power market, the uncertainty factors of wind power can be minimized to achieve reasonable dispatching and matching of electric energy and ensure the safety of system operation; at the same time, considering the complexity of solving the constructed electric energy dispatching model, the present invention uses the deterministic conversion method of chance constraints to convert the uncertainty constraints based on chance constraints into deterministic constraints, so as to improve the model solving speed and increase the timeliness of model application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0030] Figure 1 is a method flow chart of a method for electric energy dispatching based on wind power uncertainty in this embodiment;

[0031] Figure 2 Schematic diagram of system modules of a spot market clearing optimization system based on uncertainty in this embodiment;

[0032] Figure 3 It is a structural schematic diagram of a computer device in this embodiment. DETAILED DESCRIPTION

[0033] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0034] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0035] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0036] In order to facilitate understanding of the embodiments of the present invention, first, some terms involved in the present invention are explained.

[0037] The main grid system refers to the main network for power transmission of power supply enterprises, which refers to the network from the distribution network to the power plant, generally referring to the transmission network of 110KV and above.

[0038] Example 1

[0039] It should be noted that, in this embodiment, before constructing the electric energy dispatching model, it is necessary to first obtain wind power output sample data and basic data of the electricity market, wherein the wind power output sample data includes actual wind power output data and wind power forecast processing data, and the basic data of the electricity market includes quotation curves, load curves, conventional unit information data and wind turbine unit information data. In other embodiments, other data for constructing the electric energy dispatching model can also be obtained; at the same time, the method of obtaining data is a conventional technical means in this field and will not be elaborated here.

[0040] See also Figure 1 As shown, this embodiment provides a method for dispatching electric energy based on wind power uncertainty, the method comprising:

[0041] S1: Fit the wind power output sample data using the mixed skewed distribution model to obtain the mixed skewed distribution fitting curve, and calculate the error value between the mixed skewed distribution fitting curve and the wind power output sample data. Then, fit the wind power output sample data and the error using the normal cloud model to obtain a fitting function for characterizing the distribution of wind power short-term output forecast errors.

[0042] It should be noted that, in this embodiment, the normal cloud model is a probability model based on triangular fuzzy numbers and normal distribution function, which consists of the expected value ,entropy and super entropy It consists of three parameters. Expected value represents the expected distribution of cloud droplets in the domain space, that is, the point that best represents the qualitative concept; entropy Represents the granularity of qualitative concepts. The larger the entropy, the more macroscopic the concept. Represents the uncertainty measure of entropy, i.e., the entropy of entropy.

[0043] The reverse cloud generation of the normal cloud model is as follows: , , , where Indicates expected value; represents entropy; represents super entropy; represents the number of samples; represents the sample data value; represents the mean of sample data;

[0044] The parameters calculated by the above formula can generate a normal cloud model, specifically: , , , where Represents the normal cloud model.

[0045] At the same time, it should be noted that in this embodiment, the mixed skewed distribution model combines multiple skewed normal distribution models to describe the data distribution. Compared with traditional normal distribution, t distribution, exponential distribution and other error distribution models, the mixed skewed distribution model can better describe the biased, heavy-tailed and multi-peak characteristics of data distribution. The normal cloud model can effectively handle the conversion between qualitative and quantitative, and establish the relationship between fuzziness and randomness.

[0046] Assuming that the random variable x follows a skewed normal distribution, its probability density function can be described as: , where: denote the location parameter, scale parameter, and skewness parameter respectively; The specific expression is: ;

[0047] The mixed skewed distribution is a linear combination of multiple skewed normal distributions, and its probability density function is: , where: represents the weight coefficient of each skew normal distribution, and .

[0048] Specifically, in this embodiment, the mixed skewed distribution model is used to fit the wind power output sample data to obtain the mixed skewed distribution fitting curve, and the error value of the mixed skewed distribution fitting curve and the wind power output sample data is calculated, and then the wind power output sample data and the error are fitted by the normal cloud model, and then the normal cloud model is coupled with the mixed skewed distribution model to improve the model fitting accuracy, and the fitting function for characterizing the distribution of the wind power short-term output forecast error is obtained, which is specifically:

[0049] (1) The mixed skewed distribution model is used to fit the wind power output sample data to obtain the mixed skewed distribution fitting curve, and the error value between the mixed skewed distribution fitting curve and the wind power output sample data is calculated: , It is the sample data of wind power output; is the sample data curve of wind power output; Fitting curve for mixed skewed distribution; is the error value;

[0050] (2) Error segmentation: According to the effective zero-crossing principle, Segmentation is performed; the segmentation principle is that the amount of data between two zero-crossing points does not exceed 5% of the total amount, and the excess is segmented based on 5%;

[0051] (3) Normal cloud model generation, specifically: , ,in, is the probability density function of the normal cloud model.

[0052] The coupling probability distribution fitting function is as follows: , where It is the fitting function of the coupled probability distribution model of the normal cloud model and the mixed skewed distribution model, and is used to characterize the fitting function of the distribution of the wind power short-term output forecast error; is the probability density function of the normal cloud model; Fit a curve to a mixed skewed distribution.

[0053] S2: Obtain historical load forecast error data to construct a load forecast error distribution curve that conforms to the normal distribution. By sampling the fitting function and the load forecast error distribution curve, wind power forecast error values ​​and load forecast error values ​​are obtained respectively. The wind power forecast error values ​​and load forecast error values ​​are used to establish uncertainty constraints.

[0054] It should be noted that, in this embodiment, historical data of load forecast errors are obtained to construct a load forecast error distribution curve that conforms to the normal distribution; at the same time, in this embodiment, in order to consider the uncertainty factors contained in the power balance constraints and the rotating reserve constraints in the electric energy dispatching model, the uncertain variables are described in the constraints containing uncertain variables in the form of deterministic prediction + uncertain prediction error value, and the constraints containing uncertain variables are expressed in the form of chance constraints.

[0055] Specifically, in this embodiment, the uncertainty constraint condition includes a power balance constraint and a spinning reserve constraint, which are:

[0056] (1) Power balance constraints: ,in, For wind turbines In the period The winning bid output; Representation Node In the period Load; For the crew Respectively The period Section output; Indicates the wind power prediction error value; Indicates the load forecast error value; is the relaxation constant; is the confidence level.

[0057] (2) Spinning reserve constraints include positive spinning reserve constraints and negative spinning reserve constraints, specifically: , , where Indicates conventional unit exist Positive reserve capacity at the moment; Indicates conventional unit exist Negative reserve capacity at the moment; Indicates the wind power prediction error value; Indicates the load forecast error value; , Indicates the confidence level that each opportunity constraint condition is established; represents the minimum positive reserve capacity; Indicates the maximum negative reserve capacity; represents the minimum negative reserve capacity; is the relaxation constant; Indicates conventional unit In the period The start and stop status of the unit; Indicates conventional unit The maximum technical output of Indicates conventional unit In the period The winning bid output; Indicates conventional unit The upward climbing rate; Indicates conventional unit Minimum technical output; Indicates conventional unit Downward climbing rate.

[0058] It should be noted that, in this embodiment, the power balance constraint is set according to the real-time balance requirement of power supply and demand; at the same time, since the demand on the load side is uncontrollable, the power generation side needs to reserve a certain output margin to balance the load changes in real time, so a rotating reserve constraint is set.

[0059] S3: Based on the uncertainty constraint condition combined with the pre-built first deterministic constraint condition, the electric energy dispatch model is constructed with the minimum total operation cost of the main grid system within the dispatch period as the objective function;

[0060] It should be noted that in this embodiment, the electric energy dispatch model optimizes the start and stop of conventional units, the output of each unit and positive and negative standby while satisfying the load and system safe operation constraints, so as to minimize the total operating cost of the main grid system within the dispatching period.

[0061] Specifically, in this embodiment, the objective function is: ,in, Indicates that the total operating cost of the main network system within the scheduling period is the minimum; Indicates that the main grid system supplies power to conventional units. The cost of purchasing electricity; Indicates conventional unit System backup costs; Indicates conventional unit The start-stop costs; Indicates that the main grid system supplies wind farms The cost of purchasing electricity.

[0062] Among them, the main grid system supplies conventional units The electricity purchase cost is quoted in the form of a non-decreasing quotation curve, specifically: , where Indicates that the main grid system supplies power to conventional units. The cost of purchasing electricity; Indicates conventional unit Respectively The period The winning bid output of the power segment quotation; Indicates conventional unit No. Segment power quotation;

[0063] System backup costs, specifically: , where Indicates conventional unit System backup costs; Indicates conventional unit exist Positive reserve capacity for the time period; Indicates conventional unit exist Negative reserve capacity during the period; Indicates conventional unit The positive reserve price; Indicates conventional unit Negative reserve price of

[0064] The start-up and shutdown costs of conventional units are as follows: , where Indicates conventional unit The start-stop costs; Indicates conventional unit exist Start-up costs for the time period; Indicates conventional unit exist The downtime cost for the period.

[0065] The power purchase cost of the main grid system from the wind farm is reported in the form of quotation, specifically: , where Indicates that the main grid system supplies wind farms The cost of purchasing electricity; For wind turbines In the period The winning bid output; Indicates wind turbine In the period quotation.

[0066] At the same time, in this embodiment, the pre-constructed first deterministic constraint conditions include upper and lower limit constraints of unit output, conventional unit ramp constraints, minimum continuous start and stop constraints of conventional units, conventional unit start and stop cost constraints and line flow constraints, specifically:

[0067] The upper and lower limits of the unit output are defined as the upper and lower limits of the unit output at any time. , the active output of conventional units in the startup state should be between their minimum technical output and maximum output; for wind turbines, their unit output should not be greater than their predicted output, specifically: , , where Indicates conventional unit In the period The start and stop status of the unit, "1" means start, "0" means stop; Indicates conventional unit In the period The winning bid output; For wind turbines In the period The predicted output of Indicates conventional unit Minimum technical output; Indicates conventional unit The maximum technical output of

[0068] Conventional unit climbing constraints are as follows: , where Indicates conventional unit The unit output in the t+1 period; Indicates conventional unit The unit output in period t; Indicates conventional unit The upward climbing rate; Indicates conventional unit Downward climbing rate;

[0069] The minimum continuous start-stop constraints for conventional units are as follows: , where Indicates conventional unit Minimum continuous power-on time; Indicates conventional unit Minimum continuous shutdown time; Indicates conventional unit In the period The start and stop status of the unit;

[0070] The constraints on the start-up and shutdown costs of conventional units are as follows: , where: Indicates conventional unit The single startup cost; Indicates conventional unit The cost of a single shutdown;

[0071] The line flow constraint includes the output of wind power and the uncertainty variables of wind power output. In order to reduce the difficulty of model calculation and take into account the impact of wind power generation uncertainty on the safety of power flow, the power flow over-limit problem caused by wind power generation uncertainty is solved by reserving transmission channel capacity. Specifically: , where It represents the ratio of the capacity reserved by each transmission channel to cope with the wind power output forecast error to the total transmission capacity of each transmission channel; For conventional units In the time period The unit output; For wind turbines In the period The winning bid output; Representation Node In the period Load; Indicates that the active power injected by node m causes Changes in the active power flow of the line.

[0072] The electric energy dispatch model is solved by CPLEX solver to obtain the electric energy dispatch plan of the power system.

[0073] Specifically, in this embodiment, uncertainty constraints are constructed by considering the impact of wind power short-term output forecast error and load forecast error on power balance constraints and rotating reserve capacity constraints. Then, under the constructed uncertainty constraints, an electric energy dispatching model is constructed with the minimum total operating cost of the main grid system within the dispatching period as the objective function. This allows the uncertainty factors of wind power to be minimized when large-scale wind turbines participate in the power market, thereby achieving reasonable dispatch and matching of electric energy and ensuring the safety of system operation.

[0074] Furthermore, the method further comprises:

[0075] S31: transforming the uncertainty constraint condition by using the deterministic transformation method of the chance constraint condition to obtain a transformed second deterministic constraint condition;

[0076] It should be noted that in this embodiment, the uncertainty constraints are expressed in the form of chance constraints, which increases the complexity of solving the electric energy scheduling model and reduces the timeliness of the model; therefore, the deterministic transformation method of chance constraints is used to transform the uncertainty constraints in the form of chance constraints into deterministic constraints; the deterministic transformation method of chance constraints generates a large number of random numbers that conform to the distribution law of uncertainty variables, and then sorts the random numbers according to the degree of influence on the degree of validity of the chance constraints, and selects the random number scenarios that just meet the required confidence level as the judgment conditions for the validity of the chance constraints, thereby transforming the uncertainty constraints into deterministic constraints to reduce the complexity of solving the electric energy scheduling model and increase the timeliness of the model; the deterministic transformation method of chance constraints is a conventional technical means in this field, and for details, please refer to the prior art "Li Zhiwei. Research on random optimization scheduling of multi-energy power systems with high proportion of wind and solar power [D]. Beijing: School of Electrical and Electronic Engineering, North China Electric Power University, 2019", and the specific content of the method will not be repeated here.

[0077] Specifically, in this embodiment, the Latin hypercube sampling method is used to sample the coupling probability distribution model fitting function and the load forecast error distribution curve S times. The sampling value of the sampling is recorded as , , and then use the sampling-based opportunity constraint deterministic transformation method to transform the uncertainty constraints in the power dispatch model to obtain the second deterministic constraint, which is: , where For wind turbines In the period The winning bid output; Representation Node In the period Load; For the crew Respectively The period Section output; Indicates the wind power prediction error value; Indicates the load forecast error value; is the relaxation constant; is the confidence level; represents real numbers greater than 10000, Represents a real number less than -10000; All represent 0-1 variables; represents the mth wind farm prediction error distribution law. A random number; It represents the first A random number;

[0078] The spinning reserve constraint in the uncertainty constraint is transformed into: , , where Indicates conventional unit exist Positive reserve capacity at the moment; Indicates conventional unit exist Negative reserve capacity at the moment; Indicates conventional unit n The maximum technical output of Indicates conventional unit Minimum technical output; Indicates the wind power prediction error value; Indicates the load forecast error value; , Indicates the confidence level that each opportunity constraint condition is established; represents the minimum positive reserve capacity; Indicates the maximum negative reserve capacity; is the relaxation constant; represents a real number less than -10000, Represents a real number less than -10000; All represent 0-1 variables;

[0079] Considering that the uncertainty variables in the chance constraints can be separated from the optimization variables, the above formula is further simplified as follows:

[0080] The transformed power balance constraint is simplified to: ;

[0081] The transformed spinning reserve constraint is simplified to: , ; In the formula, For wind turbines In the period The winning bid output; Representation Node In the period Load; For the crew Respectively The period Section output; Indicates the wind power prediction error value; Indicates the load forecast error value; is the relaxation constant; is the confidence level; Indicates conventional unit exist Positive reserve capacity at the moment; Indicates conventional unit exist Negative reserve capacity at the moment; Indicates the wind power prediction error value; Indicates the load forecast error value; , Indicates the confidence level that each opportunity constraint condition is established; represents the minimum positive reserve capacity; Indicates the maximum negative reserve capacity; represents the floor function; represents the ceiling function; Represents the ascending order function.

[0082] S32: Construct an optimal power dispatch model by combining the objective function, the first deterministic constraint and the second deterministic constraint.

[0083] Specifically, in this embodiment, in order to consider the complexity of solving the constructed electric energy scheduling model, this embodiment uses a deterministic transformation method of chance constraints to transform uncertainty constraints based on chance constraints into deterministic constraints, thereby improving the model solution speed and increasing the timeliness of model application.

[0084] Example 2

[0085] See also Figure 2 As shown, this embodiment further provides an electric energy dispatching system based on wind power uncertainty, which is used in any one of the above-mentioned electric energy dispatching methods based on wind power uncertainty, and the system includes:

[0086] The model coupling module 100 is used to fit the wind power output sample data using the mixed skew distribution model to obtain the mixed skew distribution fitting curve, and calculate the error value between the mixed skew distribution fitting curve and the wind power output sample data, and then fit the wind power output sample data and the error using the normal cloud model to obtain a fitting function for characterizing the distribution of the wind power short-term output forecast error;

[0087] The uncertainty constraint building module 200 is used to obtain historical load forecast error data to build a load forecast error distribution curve that conforms to the normal distribution, obtain wind power forecast error values ​​and load forecast error values ​​by sampling the fitting function and the load forecast error distribution curve, and establish uncertainty constraint conditions using the wind power forecast error values ​​and the load forecast error values;

[0088] A model building module 300 is used to build an electric energy dispatch model based on the uncertainty constraint condition combined with the pre-built first deterministic constraint condition, taking the minimum total operating cost of the main grid system within the dispatch period as the objective function;

[0089] The model solving module 400 is used to solve the electric energy dispatching model through the CPLEX solver to obtain the electric energy dispatching plan of the power system.

[0090] It should be noted that the modules in the system of Example 2 correspond to the steps in the method of Example 1. The steps in the method of Example 1 have been described in detail in Example 1. In this Example 2, the contents of the modules in the system will not be described in detail.

[0091] Example 3

[0092] See also Figure 3 As shown, this embodiment further provides a computer device, including a system memory 1005 and a processor 1001, wherein the system memory 1005 stores a computer program, and the processor 1001 implements the steps of any of the above methods when executing the computer program.

[0093] It should be noted that the processor 1001 is used to execute the steps in the above method embodiments according to the instructions in the program code. Alternatively, the processor 1001 implements the functions of each module / unit in the above system / device embodiments when executing the computer program.

[0094] Specifically, in this embodiment, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the system memory 1005, and are executed by the processor 1001 to complete the present application. One or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0095] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor 1001 and a system memory 1005. Those skilled in the art will appreciate that this does not constitute a limitation on the terminal device, and may include more or less components than shown in the figure, or combine certain components, or different components. For example, the terminal device may also include an input / output device 1003, a network access device 1002, a bus 1006, etc.

[0096] The processor 1001 may be a central processing unit (CPU), or 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, etc. The general-purpose processor 1001 may be a microprocessor or any conventional processor, etc.

[0097] The system memory 1005 may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. The system memory 1005 may also be a storage device 1004 of the terminal device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the terminal device. Further, the system memory 1005 may also include both the internal storage unit of the terminal device and the storage device 1004. The system memory 1005 is used to store computer programs and other programs and data required by the terminal device. The system memory 1005 may also be used to temporarily store data that has been output or is to be output.

[0098] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0099] Example 4

[0100] This embodiment 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 any one of the above methods are implemented.

[0101] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, system or device, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk. Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), registers, hard disks, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above, or any other form of computer-readable storage medium known in the art.

[0102] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). In an embodiment of the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system, or device.

[0103] Example 5

[0104] This embodiment also provides a computer program product including instructions. When the instructions are executed by a computer device cluster, the computer device cluster executes the method described in Embodiment 1.

[0105] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for dispatching electric energy based on wind power uncertainty, characterized in that: include: The mixed skewed distribution model is used to fit the wind power output sample data to obtain the mixed skewed distribution fitting curve, and the error value of the mixed skewed distribution fitting curve and the wind power output sample data is calculated. Then, the wind power output sample data and the error are fitted by the normal cloud model to obtain the fitting function used to characterize the distribution of wind power short-term output forecast error. The historical load forecast error data is obtained to construct a load forecast error distribution curve that conforms to the normal distribution. By sampling the fitting function and the load forecast error distribution curve, the wind power forecast error value and the load forecast error value are obtained respectively. The wind power forecast error value and the load forecast error value are used to establish uncertainty constraints. Among them, the uncertainty constraints include power balance constraints and spinning reserve constraints. The power balance constraints are specifically: ,in, For wind turbines In the period The winning bid output; Representation Node In the period Load; For the crew Respectively The period Section output; Indicates the wind power prediction error value; Indicates the load forecast error value; is the relaxation constant; is the confidence level; The spinning reserve constraints are as follows: 、 , where Indicates conventional unit exist Positive reserve capacity at the moment; Indicates conventional unit exist Negative reserve capacity at the moment; Indicates the wind power prediction error value; Indicates the load forecast error value; , Indicates the confidence level that each opportunity constraint condition is established; represents the minimum positive reserve capacity; Indicates the maximum negative reserve capacity; represents the minimum negative reserve capacity; is the relaxation constant; Indicates conventional unit In the period The start and stop status of the unit; Indicates the maximum technical output; Indicates conventional unit In the period The winning bid output; Indicates conventional unit The upward climbing rate; Indicates the minimum technical output; Indicates conventional unit Downward climbing rate; Based on the uncertainty constraint conditions combined with the pre-built first deterministic constraint conditions, the electric energy dispatch model is constructed with the minimum total operating cost of the main grid system within the dispatch period as the objective function; The electric energy dispatch model is solved by CPLEX solver to obtain the electric energy dispatch plan of the power system.

2. The electric energy dispatching method based on wind power uncertainty according to claim 1 is characterized in that: The method also includes: The uncertain constraint condition is transformed by using the deterministic transformation method of the chance constraint condition to obtain the transformed second deterministic constraint condition; The optimal power dispatch model is constructed by combining the objective function, the first deterministic constraint and the second deterministic constraint.

3. The electric energy dispatching method based on wind power uncertainty according to claim 1 is characterized in that: The pre-built first deterministic constraints include upper and lower limit constraints of unit output, conventional unit ramp constraints, minimum continuous start and stop constraints of conventional units, conventional unit start and stop cost constraints and line flow constraints.

4. The electric energy dispatching method based on wind power uncertainty according to claim 2 is characterized in that: The Latin hypercube sampling method is used to sample the fitting function of the coupled probability distribution model and the load forecasting error distribution curve.

5. An electric energy dispatching system based on wind power uncertainty, characterized in that: The system is used in a method for dispatching electric energy based on wind power uncertainty as described in any one of claims 1 to 4, and the system comprises: A model coupling module is used to fit the wind power output sample data using a mixed skewed distribution model to obtain a mixed skewed distribution fitting curve, and calculate the error value between the mixed skewed distribution fitting curve and the wind power output sample data, and then fit the wind power output sample data and the error using a normal cloud model to obtain a fitting function for characterizing the distribution of wind power short-term output forecast errors; The uncertainty constraint building module is used to obtain historical load forecast error data to build a load forecast error distribution curve that conforms to the normal distribution. By sampling the fitting function and the load forecast error distribution curve, the wind power forecast error value and the load forecast error value are obtained respectively, and the wind power forecast error value and the load forecast error value are used to establish uncertainty constraint conditions; A model building module, used to build an electric energy dispatching model based on the uncertainty constraint condition combined with the pre-built first deterministic constraint condition, taking the minimum total operating cost of the main grid system within the dispatching period as the objective function; The model solving module is used to solve the electric energy dispatching model through the CPLEX solver to obtain the electric energy dispatching plan of the power system.

6. A computer device comprising a system memory and a processor, wherein the system memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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