CN117977715A - Wind farm scheduling method, system, device and medium - Google Patents
Wind farm scheduling method, system, device and medium Download PDFInfo
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Abstract
Wind farm scheduling method, system, device and medium, characterized in that the method comprises the following steps: collecting historical operation data of K wind power plants to be scheduled in an area, and acquiring distribution characteristics of operation states of the wind power plants; constructing associated distribution formulas of the K wind power plants to be scheduled, solving associated distribution functions, and randomly generating operation state samples of the K wind power plants to be scheduled; and inputting the operation state samples of the K wind power plants to be scheduled into a scheduling model to generate a wind power plant scheduling strategy. The method ensures random scheduling of the wind power plant, reduces the operation cost, increases wind power consumption and reduces the residual quantity of wind power.
Description
Technical Field
The invention relates to the field of power systems, in particular to a wind farm scheduling method, a system, a device and a medium.
Background
Along with the development and the scale growth of a power system, each layer of source load and supply and demand faces a great challenge, the number of distributed power supplies is increased, and grid connection of wind power, photovoltaic and the like can not avoid impact on a power grid due to fluctuation characteristics of the distributed power supplies. Research on the operating mechanism of the virtual power plant can provide technical support for the access of a demand-side distributed power supply to a power grid, wherein a wind farm is a common constituent unit of the virtual power plant. At present, most of the output of the wind power plant is analyzed through Weibull, rayleigh probability distribution of wind speed, and calculated by utilizing a wind speed-wind power function relation. However, this estimation is mostly used for a single wind farm, and the interaction between multiple wind farms is often ignored. In actual operation, the output of a plurality of wind power plants in the same area has correlation due to factors of climate inertia, and if the output correlation of the wind power plants is ignored, the rationality of a virtual power plant scheduling scheme is affected.
An important way of demand side resources is demand response, while interruptible load is one of the main forms based on incentive demand response. Currently, virtual power plant scheduling schemes that consider both wind farm output dependent structures and interruptible loads remain very lacking.
In view of the foregoing, there is a need for a new wind farm scheduling method, system, apparatus and medium.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a wind power plant dispatching method, a system, a device and a medium.
The invention adopts the following technical scheme.
The invention relates to a wind farm scheduling method, which comprises the following steps: collecting historical operation data of K wind power plants to be scheduled in an area, and acquiring distribution characteristics of operation states of the wind power plants; constructing associated distribution formulas of the K wind power plants to be scheduled, solving associated distribution functions, and randomly generating operation state samples of the K wind power plants to be scheduled; and inputting the operation state samples of the K wind power plants to be scheduled into a scheduling model to generate a wind power plant scheduling strategy.
Preferably, obtaining the distribution characteristics of the running state of the wind farm includes: distribution functions of wind power plant in different running statesThe method comprises the following steps:
，
in the method, in the process of the invention, For/>Operational status of individual wind farms,/>For wind farm/>In period/>Lower historical running state,/>The range of the value of (1) is from sampling time 1 to/>，/>For the first preset constant,/>Is a preset function.
Preferably, the preset function is:
，
in the method, in the process of the invention, Is a second predetermined constant.
Preferably, constructing an association distribution formula of the K wind farms to be scheduled includes: defining a related distribution formulaThe function is assumed to take the distribution quantity of the associated running states of K wind farms as independent variable/>Wherein the value range of each independent variable is between 0 and 1, the value range of the function value is between 0 and 1, and the distribution formula/>, for any independent variable, is associatedAs a monotonic non-decreasing function, there is/>。
Preferably, the functionThe method comprises the following steps:
，
in the method, in the process of the invention, For the associated distribution parameters.
Preferably, the association distribution parameters are increased when the association degree of the K wind power plants is set to be higherRelated distribution parameters/>The value of (2) is larger than 1.
Preferably, the historical operating data is substituted into the following formula,
，
Modification ofUntil the value of the formula is maximum, obtaining the optimal associated distribution parameters; Optimal said associated distribution parameters/>Substitution function/>To according to the function/>And randomly generating K running state samples of the wind power plants.
Preferably, the operation state samples of the K wind power plants to be scheduled are input into a scheduling model, and a wind power plant scheduling strategy is generated, including: constructing an objective function and constraint conditions based on a regional dispatching target, and solving an optimal sample from the running state samples of the wind power plant based on the objective function kernel constraint conditions; and taking the optimal sample as the wind power plant scheduling strategy.
The second aspect of the invention relates to a wind farm dispatching system utilizing the method in the first aspect of the invention, and the system comprises an acquisition module, a construction module and a dispatching module; the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical operation data of K wind power plants to be scheduled in an area and acquiring distribution characteristics of the operation states of the wind power plants; the construction module is used for constructing the associated distribution formulas of the K wind power plants to be scheduled, solving the associated distribution functions and randomly generating the running state samples of the K wind power plants to be scheduled; the scheduling module is used for inputting the K running state samples of the wind power plant to be scheduled into the scheduling model to generate a wind power plant scheduling strategy.
A third aspect of the present invention relates to a terminal, comprising a processor and a storage medium; the storage medium is used for storing instructions; the processor is operative to perform the steps of the method of the first aspect of the invention in accordance with the instructions.
The fourth aspect of the present invention relates to a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the method of the first aspect of the present invention.
Compared with the prior art, the method, the system, the device and the medium for dispatching the wind power plants have the advantages that the method is used for constructing the distribution characteristics of K wind power plants, constructing an objective function by using the operation targets of the regional power grid, taking the operation states of the K wind power plants which accord with the joint distribution as samples, inputting the samples into a dispatching model, solving the optimal samples and forming a dispatching strategy. The method ensures random scheduling of the wind power plant, reduces the operation cost, increases wind power consumption and reduces the residual quantity of wind power.
The beneficial effects of the invention also include:
1. According to the invention, in a power grid dispatching strategy, the factors of the virtual power plant including the wind power plant output dependent structure and the interruptible load are considered, the wind power dependent structure is described by introducing a joint probability density function, the wind power random characteristic is subjected to opportunistic constraint processing, a virtual power plant random dispatching model including the wind power plant and the interruptible load is established, and the precision of a dispatching result output by the model is ensured.
2. The method takes the minimum total running cost of the virtual power plant as a decision target, and comprehensively considers the running constraint of the conventional unit and the interruptible load equivalent unit to carry out model solving.
Drawings
FIG. 1 is a schematic diagram of steps of a method for scheduling a wind farm according to the present invention;
FIG. 2 is a schematic block diagram of a wind farm scheduling system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments of the invention are only some, but not all, embodiments of the invention. All other embodiments of the invention not described herein, which are obtained from the embodiments described herein, should be within the scope of the invention by those of ordinary skill in the art without undue effort based on the spirit of the present invention.
FIG. 1 is a schematic diagram of steps of a wind farm scheduling method according to the present invention. The invention relates to a wind farm scheduling method, which comprises the steps of 1 to 3.
Step 1, collecting historical operation data of K wind power plants to be scheduled in a region, and acquiring distribution characteristics of operation states of the wind power plants.
Preferably, obtaining the distribution characteristics of the running state of the wind farm includes:
Distribution functions of wind power plant in different running states The method comprises the following steps:
，
in the method, in the process of the invention, For/>The running state of the wind power plants,
For wind farm/>In period/>Lower historical running state,/>The range of the value of (1) is from sampling time 1 to/>，
For a first predetermined constant value, the first predetermined constant value,
Is a preset function.
In the invention, the output power historical data of K adjacent areas wind power plants to be analyzed can be firstly obtained as the historical operation data. And then, estimating and determining the distribution condition of the output power of the wind power plant according to the output power historical data of the wind power plant.
In one embodiment, the predetermined function is:
，
in the method, in the process of the invention, Is a second predetermined constant.
And 2, constructing associated distribution formulas of the K wind power plants to be scheduled, solving an associated distribution function, and randomly generating operation state samples of the K wind power plants to be scheduled.
Preferably, constructing an association distribution formula of the K wind farms to be scheduled includes: defining a related distribution formulaThe function is assumed to take the distribution quantity of the associated running states of K wind farms as independent variable/>Wherein the value range of each independent variable is between 0 and 1, the value range of the function value is between 0 and 1, and the distribution formula/>, for any independent variable, is associatedAs a monotonic non-decreasing function, there is/>。
In the invention, the distribution quantity of the relevant operation states of K wind power plantsIs the integral formula of the running state distribution characteristics in the step 1, namely/>. Wherein/>Is the real-time power of the wind power plant.
Preferably, the functionThe method comprises the following steps:
，
in the method, in the process of the invention, For the associated distribution parameters.
In the present invention, the monotonic non-decreasing function is constructed as described in the above formula. In the formula, parametersThe calculation can be performed according to actual conditions. For example, in an embodiment of the present invention, when the association degree of the K wind farms is set to be higher, the association distribution parameter/>, is increasedIs the value of the associated distribution parameter/>The value of (2) is larger than 1.
In other embodiments of the invention, the distribution parameters are correlatedThe value of (2) is calculated according to the historical operation data of the wind power plant.
Preferably, the historical operating data is substituted into the following formula,
，
Modification ofUntil the value of the formula is maximum, obtaining the optimal associated distribution parameters，
The optimal associated distribution parametersSubstitution function/>To according to the function/>And randomly generating K running state samples of the wind power plants.
After the optimal value of the associated distribution parameter is calculated accurately, the method is based on the functionAnd randomly generating the joint operation states of all possible K wind farms according to the distribution characteristics of the operation states of the wind farms calculated in the step.
The randomly generated running states can be used as a plurality of initial samples to be constructed into a sample group, and the sample group is input into a machine algorithm to realize generation of optimizing and wind power plant scheduling strategies. It is easy to think that in each input sample, the value of the wind farm power in K dimensions is included, and the power values in K dimensions can meet the requirement of the joint probability density function.
And step 3, inputting the operation state samples of the K wind power plants to be scheduled into a scheduling model to generate a wind power plant scheduling strategy.
Preferably, the operation state samples of the K wind power plants to be scheduled are input into a scheduling model, and a wind power plant scheduling strategy is generated, including: constructing an objective function and constraint conditions based on a regional dispatching target, and solving an optimal sample from the running state samples of the wind power plant based on the objective function kernel constraint conditions; and taking the optimal sample as the wind power plant scheduling strategy.
In one embodiment of the invention, the objective function is built with minimum total running cost in the area, for example, the scheduling model is built with minimum sum of system power generation cost, wind farm running maintenance cost and interruptible load compensation cost. The system power generation cost only considers the thermal power unit cost; the operation and maintenance cost of the wind power plant is positively correlated with the real-time power of K wind power plants; the interruptible load compensation cost is obtained based on the real-time interrupt load amount.
Among the constraints, the method may involve constraints such as a system power balance constraint, a unit output upper and lower limit constraint, a rotational reserve capacity constraint, a unit ramp rate constraint, a unit minimum start-stop time constraint, an interruptible load capacity constraint, an interruptible load interruption time constraint, and an interruptible load interruption number constraint.
For the system, the real-time interrupt load can be virtualized as a power generation source, and the power generation source, the thermal power generating unit and the wind generating unit can provide electric energy sources for the system load together, so that the balance of power input and output is ensured.
Thus, the system power balance constraint is:
，
in the method, in the process of the invention, Thermal power generating unit/>, for t periodReal-time power of/>，/>Is the number of thermal power units in the area,/>Wind farm/>, for period tReal-time power of/>，/>Interruptible load/>, for period tReal-time interrupt load of/>，/>Is the interruptible load number in the region,/>Is the system load of the t period.
Preferably, the method further comprises: real-time power samples of each wind power plant are respectively and randomly arranged based on an IWO algorithm; and calculating the associated distribution of the K wind power plants, constructing K-dimensional vectors based on the real-time power of the K wind power plants, inputting each K-dimensional vector as a sample into a scheduling model, and solving the scheduling model by utilizing a differential evolution strategy.
The method comprises the steps of randomly arranging a population according to an IWO algorithm for each wind power plant, obtaining real-time power samples of each wind power plant, obtaining association distribution, and then constructing a K-dimensional vector based on the association distribution. And (5) inputting the K-dimensional vector into a scheduling model for optimizing.
Through the process, the optimal sample is obtained by solving, and the K wind power plants are controlled by generating the scheduling strategy according to the predicted power of K dimensions in the optimal sample, so that the daily scheduling strategy of the virtual power plant is realized. In addition, according to the related constraint, the method can also provide advice for the power generation output of the thermal power generating unit.
In one embodiment of the invention, a virtual power plant consisting of 3 thermal power units, 2 wind power stations and 4 IL users is taken as an example for analysis. Thermal power plant parameter settings are listed in Table 1, and IL users and associated model parameters are listed in Table 2.
Table 1 set parameter table
TABLE 2 interruptible load and model parameter Table
FIG. 2 is a schematic block diagram of a wind farm scheduling system according to the present invention. As shown in fig. 2, a second aspect of the present invention relates to a wind farm dispatching system using the method in the first aspect of the present invention, the system includes an acquisition module, a construction module and a dispatching module; the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical operation data of K wind power plants to be scheduled in an area and acquiring distribution characteristics of the operation states of the wind power plants; the construction module is used for constructing the associated distribution formulas of the K wind power plants to be scheduled, solving the associated distribution functions and randomly generating the running state samples of the K wind power plants to be scheduled; the scheduling module is used for inputting the K running state samples of the wind power plant to be scheduled into the scheduling model to generate a wind power plant scheduling strategy.
A third aspect of the present invention relates to a terminal, comprising a processor and a storage medium; the storage medium is used for storing instructions; the processor is operative to perform the steps of the method described in the first aspect of the invention in accordance with the instructions.
The fourth aspect of the present invention relates to a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of the first aspect of the present invention.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (9)
1. A method of scheduling a wind farm, the method comprising the steps of:
Collecting historical operation data of K wind power plants to be scheduled in an area, and acquiring distribution characteristics of operation states of the wind power plants;
Constructing associated distribution formulas of the K wind power plants to be scheduled, solving associated distribution functions, and randomly generating operation state samples of the K wind power plants to be scheduled;
Wherein, an association distribution formula is defined Assume that the distribution formula/>The distribution quantity of the associated running states of K wind power plants is taken as independent variable/>，
For any one independent variable, the associated distribution formulaAs a monotonic non-decreasing function, there is/>；
，
In the method, in the process of the invention,For the associated distribution parameters, the value range of each independent variable is between 0 and 1, and the value range of the function value is between 0 and 1;
And inputting the operation state samples of the K wind power plants to be scheduled into a scheduling model to generate a wind power plant scheduling strategy.
2. A method of scheduling a wind farm according to claim 1, wherein:
the obtaining the distribution characteristics of the running state of the wind power plant comprises the following steps:
Distribution functions of wind power plant in different running states The method comprises the following steps:
，
in the method, in the process of the invention, For/>The running state of the wind power plants,
For wind farm/>In period/>Lower historical running state,/>The range of the value of (1) is from sampling time 1 to/>，
For a first predetermined constant value, the first predetermined constant value,
Is a preset function.
3. A method of scheduling a wind farm according to claim 2, wherein:
the preset function is as follows:
，
in the method, in the process of the invention, Is a second predetermined constant.
4. A method of scheduling a wind farm according to claim 3, wherein:
When the association degree of the K wind power plants is set to be higher, the association distribution parameters are increased Is used for the value of (a) and (b),
The associated distribution parametersThe value of (2) is larger than 1.
5.A method of scheduling a wind farm according to claim 4, wherein:
substituting the historical operating data into the following formula,
，
Modification ofUntil the value of the formula is maximum, obtaining the optimal associated distribution parameter/>，
The optimal associated distribution parametersSubstitution function/>To according to the function/>And randomly generating K running state samples of the wind power plants.
6. A method of scheduling a wind farm according to claim 5, wherein:
Inputting the operation state samples of the K wind power plants to be scheduled into a scheduling model to generate a wind power plant scheduling strategy, wherein the operation state samples comprise:
constructing an objective function and constraint conditions based on a regional dispatching target, and solving an optimal sample from the running state samples of the wind power plant based on the objective function and the constraint conditions;
And taking the optimal sample as the wind power plant scheduling strategy.
7. A wind farm dispatch system, characterized by:
The system is implemented with a wind farm scheduling method according to any of the claims 1-6;
the system comprises an acquisition module, a construction module and a scheduling module; wherein,
The acquisition module is used for acquiring historical operation data of K wind power plants to be scheduled in the area and acquiring distribution characteristics of the operation states of the wind power plants;
The construction module is used for constructing the associated distribution formulas of the K wind power plants to be scheduled, solving the associated distribution functions and randomly generating the running state samples of the K wind power plants to be scheduled;
The scheduling module is used for inputting the K running state samples of the wind power plant to be scheduled into a scheduling model to generate a wind power plant scheduling strategy.
8. A terminal device comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-6.
9. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-6.
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| CN116470587A (en) * | 2023-04-04 | 2023-07-21 | 国网电力科学研究院有限公司 | Power grid optimal scheduling method and device, electronic equipment and readable storage medium |
| CN117081170A (en) * | 2023-08-17 | 2023-11-17 | 广东电网有限责任公司 | Power system scheduling method, device and medium for offshore wind farm under typhoon |
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Patent Citations (5)
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| US20120185106A1 (en) * | 2011-01-14 | 2012-07-19 | International Business Machines Corporation | Integration of demand response and renewable resources for power generation management |
| CN110889603A (en) * | 2019-11-13 | 2020-03-17 | 国网北京市电力公司 | Power system economic dispatching method considering wind power correlation based on PCA-Copula theory |
| CN113098011A (en) * | 2021-05-20 | 2021-07-09 | 福州大学 | Wind power dispatching method and system based on improved NashQ algorithm |
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