CN117498362A - Power grid dispatching method and device and computer equipment - Google Patents

Power grid dispatching method and device and computer equipment Download PDF

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CN117498362A
CN117498362A CN202311463973.4A CN202311463973A CN117498362A CN 117498362 A CN117498362 A CN 117498362A CN 202311463973 A CN202311463973 A CN 202311463973A CN 117498362 A CN117498362 A CN 117498362A
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electric vehicle
parameters
load prediction
prediction model
factors
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李勋
黄鹏
葛静
毕德煌
黄智锋
邱熙
黎楚怡
邓华森
张君远
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Electric Vehicle Service of Southern Power Grid Co Ltd
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Electric Vehicle Service of Southern Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The application relates to a power grid dispatching method, a power grid dispatching device, computer equipment, a storage medium and a computer program product. The method comprises the steps of obtaining electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding capacity parameters, and establishing an electric vehicle charging load prediction model based on the electric vehicle influencing factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters. And obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter, and dispatching the power grid according to the electric vehicle load prediction quantity. According to the method and the device, the electric vehicle load is fully and comprehensively simulated and predicted according to the electric vehicle influence factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters, the reliable electric vehicle load prediction quantity is obtained, the power grid is further scheduled according to the electric vehicle load prediction quantity, the power grid can be adjusted on the basis of fully considering the electric vehicle charging load, and the stability of the power grid is guaranteed.

Description

Power grid dispatching method and device and computer equipment
Technical Field
The present application relates to the field of power system technologies, and in particular, to a power grid dispatching method, a power grid dispatching device, a computer readable storage medium and a computer program product.
Background
There are studies showing that about one third of greenhouse gases emitted from human society are generated by transportation. With the development of modern industrial technology and the combination of environmental protection requirements, electric vehicles gradually replace oil-driven vehicles, and become the main pushing direction of environment-friendly travel. The electric vehicle comprises a hybrid electric vehicle and a pure electric vehicle at present, wherein the pure electric vehicle has the characteristics of less emission and less pollution, and can become a trend of future development.
However, the electric vehicle industry, which is actively developing, relies on the electric grid for charging, and as the number of electric vehicles increases, the load on the electric grid is also increasing. With the increase of the charging load of the electric vehicle, when the charging load of the electric vehicle is suddenly changed, the electric power grid may have the problems of electric energy oscillation, unstable power supply and the like, and potential stability hazards exist.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a power grid dispatching method, apparatus, computer device, computer readable storage medium, and computer program product that can dispatch according to the charging load of an electric vehicle and maintain the stability of the power grid.
In a first aspect, the present application provides a power grid dispatching method, the method including:
Acquiring electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding quantity parameters;
establishing an electric vehicle charging load prediction model based on the electric vehicle influencing factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters;
obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter;
and dispatching the power grid according to the electric vehicle load pre-measurement.
In one embodiment, the establishing an electric vehicle charging load prediction model based on the electric vehicle influencing factors, the electric vehicle parameters, and the electric vehicle region holding capacity parameters includes:
inputting the electric vehicle influence factors into a factor screening model to obtain key influence factors and non-key influence factors;
and establishing an independent variable function according to the key influence factors, and establishing an electric vehicle charging load prediction model according to the non-key influence factors, the electric vehicle parameters and the electric vehicle regional holding quantity parameters and combining the independent variable function.
In one embodiment, the establishing an independent variable function according to the key influencing factors, and establishing an electric vehicle charging load prediction model according to the non-key influencing factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters in combination with the independent variable function includes:
Establishing an independent variable function according to the key influence factors;
obtaining an electric vehicle charging quantity model based on the independent variable function, the non-key influence factors and the electric vehicle parameters;
and establishing an electric vehicle charging load prediction model according to the electric vehicle regional holding quantity parameter and the electric vehicle charging electric quantity model.
In one embodiment, the building an electric vehicle charging load prediction model according to the electric vehicle region holding capacity parameter and the electric vehicle charging electric quantity model includes:
establishing an electric vehicle holding quantity prediction model according to the electric vehicle regional holding quantity parameters;
and establishing an electric vehicle charging load prediction model based on the electric vehicle storage quantity prediction model and the electric vehicle charging electric quantity model.
In one embodiment, the building an electric vehicle holding capacity prediction model according to the electric vehicle region holding capacity parameter includes:
obtaining a reserve potential function according to the regional reserve parameters of the electric vehicle and the historical use data of the electric vehicle;
and establishing an electric vehicle reserve prediction model based on the Bass diffusion model and the reserve potential function.
In one embodiment, the inputting the electric vehicle influencing factor into the factor screening model of the electric vehicle charging load prediction model to obtain the key influencing factor and the non-key influencing factor includes:
inputting the electric vehicle influence factors into a factor screening model to obtain influence factor main effect values corresponding to the electric vehicle influence factors;
and selecting key influence factors from the electric vehicle influence factors according to the influence factor main effect values, and marking the unselected electric vehicle influence factors as non-key influence factors.
In a second aspect, the present application further provides a power grid dispatching device, the device including:
the reference quantity acquisition module is used for acquiring electric vehicle influence factors, electric vehicle parameters and electric vehicle region holding quantity parameters;
the model building module is used for building an electric vehicle charging load prediction model based on the electric vehicle influence factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters;
the load prediction module is used for obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter;
And the power grid dispatching module is used for dispatching the power grid according to the electric vehicle load pre-measurement.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding quantity parameters;
establishing an electric vehicle charging load prediction model based on the electric vehicle influencing factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters;
obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter;
and dispatching the power grid according to the electric vehicle load pre-measurement.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding quantity parameters;
establishing an electric vehicle charging load prediction model based on the electric vehicle influencing factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters;
Obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter;
and dispatching the power grid according to the electric vehicle load pre-measurement.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
acquiring electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding quantity parameters;
establishing an electric vehicle charging load prediction model based on the electric vehicle influencing factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters;
obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter;
and dispatching the power grid according to the electric vehicle load pre-measurement.
The power grid dispatching method, the power grid dispatching device, the computer equipment, the computer readable storage medium and the computer program product comprise the steps of acquiring electric vehicle influence factors, electric vehicle parameters and electric vehicle regional holding capacity parameters, and establishing an electric vehicle charging load prediction model based on the electric vehicle influence factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters. And obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter, and dispatching the power grid according to the electric vehicle load prediction quantity. According to the electric vehicle load prediction method and the electric vehicle load prediction device, the electric vehicle load is fully and comprehensively simulated and predicted according to the electric vehicle influence factors, the electric vehicle parameters and the electric vehicle regional holding quantity parameters, so that the electric vehicle load prediction obtained according to the electric vehicle load prediction model and the time parameters is reliable, and further the electric network is scheduled according to the electric vehicle load prediction, the electric network can be adjusted on the basis of fully considering the electric vehicle load, and the stability of the electric network is guaranteed.
Drawings
FIG. 1 is an application environment diagram of a power grid scheduling method in one embodiment;
FIG. 2 is a flow chart of a power grid dispatching method in one embodiment;
FIG. 3 is a flow chart of steps for establishing an electric vehicle charge load prediction model based on electric vehicle influencing factors, electric vehicle parameters, and electric vehicle zone holding capacity parameters in one embodiment;
FIG. 4 is a flow chart of the steps of building an independent variable function based on key influencing factors, and building an electric vehicle charge load prediction model based on non-key influencing factors, electric vehicle parameters, and electric vehicle regional holding capacity parameters in combination with the independent variable function, according to one embodiment;
FIG. 5 is a flowchart illustrating steps for creating an electric vehicle charge load prediction model based on electric vehicle regional hold parameters and an electric vehicle charge model in one embodiment;
FIG. 6 is a flowchart illustrating steps for establishing an electric vehicle inventory prediction model based on electric vehicle zone inventory parameters, according to one embodiment;
FIG. 7 is a flow chart of the steps for inputting electric vehicle influencing factors into a factor screening model to obtain key influencing factors and non-key influencing factors in one embodiment;
FIG. 8 is a block diagram of a power grid dispatching apparatus in one embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another element. For example, a first resistance may be referred to as a second resistance, and similarly, a second resistance may be referred to as a first resistance, without departing from the scope of the present application. Both the first resistor and the second resistor are resistors, but they are not the same resistor.
It is to be understood that in the following embodiments, "connected" is understood to mean "electrically connected", "communicatively connected", etc., if the connected circuits, modules, units, etc., have electrical or data transfer between them.
As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," and/or the like, specify the presence of stated features, integers, steps, operations, elements, components, or groups thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or groups thereof.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
The power grid dispatching method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. The power grid 100 is connected with the dispatching system 102, and the dispatching system 102 is used for dispatching electric resources in the power grid 100 so as to ensure stable operation of the power grid 100. Wherein the electric vehicles are connected to the electric network 100 for charging, and as the number of electric vehicles is increased, the load of the electric network 100 is also increased, so the dispatching system 102 needs to dispatch the electric resource of the electric network 100 for the charging load of the electric vehicles. The number of electric vehicles charged at different moments is changed, and the dispatching system 102 can dispatch the power grid 100 according to the power grid dispatching method provided by the embodiment of the application, so that the influence of the electric vehicle charging load on the stability of the power grid 100 is reduced, and the stable operation of the power grid 100 is further ensured. The data to be used in the power grid dispatching method may be a storage device or a storage medium stored in the dispatching system 102, and is called when needed. Or stored in a cloud server or other storage devices communicatively connected to the dispatching system 102, and obtains required data through wireless or wired transmission, so as to facilitate execution of the power grid dispatching method. The carrier of the dispatch system 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, and tablet computers.
In one embodiment, as shown in fig. 2, a power grid dispatching method is provided, and the dispatching system 102 in fig. 1 is taken as an example for explaining the application of the method, and the method includes the following steps:
step 202, obtaining electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding capacity parameters.
The power grid dispatching method needs to predict the charging load of the electric vehicle based on the data information of the electric vehicle. The data of the electric vehicle comprises electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding quantity parameters. Electric vehicle influence factors refer to factors affecting electric vehicle charging obtained through social research and influence analysis, and can be generally divided into four categories, namely electric vehicle performance factors, charging factors, user use habit factors and use environment factors. The electric vehicle parameters refer to factory parameters of various electric vehicles, including, but not limited to, electric vehicle performance parameters corresponding to electric vehicle influencing factors, and in particular electric vehicle charging parameters. The electric vehicle region holding amount parameter refers to data related to the number of electric vehicles in a specified region, and relates to holding amount analysis of the electric vehicles. The designated area refers to an area corresponding to a power grid dispatching area, namely, when a dispatching system needs to dispatch a power grid in a certain regional range, the designated area is an area corresponding to the certain regional range.
By way of example, the electric vehicle performance factors may include electric power consumption per unit distance traveled by the electric vehicle, electric vehicle type, electric vehicle battery aging conditions, and electric vehicle battery capacity size. The charging factors may include an electric vehicle charging mode, an electric vehicle charging standard, a number of charging posts, a charging post distribution condition, and an electric vehicle charging efficiency. The user usage habit factors may include a daily driving distance of the electric vehicle, a driving road condition of the electric vehicle, a charging habit of the electric vehicle, and a driving habit of the user. The usage environment factors include the usage environment where the electric vehicle charge load is affected by the ambient temperature, weather conditions, etc.
Among the electric vehicle influencing factors, factors influencing the charging power of the electric vehicle include an electric vehicle charging mode and an electric vehicle charging standard. Factors that affect the charge time of an electric vehicle include the charge habit of the electric vehicle and the driving habit of the user. Factors affecting the charge capacity of an electric vehicle include the power consumption per unit travel distance of the electric vehicle, the type of electric vehicle, the aging condition of the electric vehicle battery, the capacity of the electric vehicle battery, the number of charging piles, the distribution condition of the charging piles, the charging efficiency of the electric vehicle, the daily travel distance of the electric vehicle, the traveling road condition of the electric vehicle, the ambient temperature and the weather condition.
Alternatively, the electric vehicle may include a four-wheel vehicle that uses an on-board power supply as power and drives wheels with a motor, and may also include other types of vehicles that use electric energy as power, including, but not limited to, electric bicycles, electric tricycles, electric buses, and the like. Among them, an electric vehicle may be equipped with other power systems based on electric energy, for example, a hybrid electric vehicle may include an internal combustion engine system based on petroleum fuel in addition to a power supply system based on electric energy.
Step 204, an electric vehicle charge load prediction model is established based on the electric vehicle influencing factors, the electric vehicle parameters and the electric vehicle zone holding capacity parameters.
Specifically, the dispatching system analyzes the electric vehicle charging rule based on electric vehicle influence factors, electric vehicle parameters and electric vehicle regional holding capacity parameters, and establishes an electric vehicle charging load prediction model by means of a software algorithm in the dispatching system. The dispatching system can predict the electric vehicle charging load at a specified moment through an electric vehicle charging load prediction model.
In one embodiment, as shown in FIG. 3, step 204 includes step 302 and step 304.
Step 302, inputting the electric vehicle influencing factors into a factor screening model to obtain key influencing factors and non-key influencing factors.
The factor screening model is used for classifying and screening the electric vehicle influence factors. Specifically, the electric vehicle influencing factors are input into a factor screening model, which may be an SB (sequential bifurcation, sequential branching) factor screening method, also known as sequential branching method. The sequential branching method can screen out key influence factors among a plurality of influence factors, and has high efficiency and high screening accuracy. The dispatching system inputs the electric vehicle influencing factors into a sequential branch method for operation, and the key influencing factors and the non-key influencing factors are obtained through screening.
And 304, establishing an independent variable function according to the key influence factors, and establishing an electric vehicle charging load prediction model according to the non-key influence factors, the electric vehicle parameters and the electric vehicle regional holding quantity parameters and combining the independent variable function.
The key influencing factors are important factors which mainly influence the charging behavior of the electric vehicle, and a user can change the charging time or the charging quantity of the electric vehicle due to the key influencing factors. The dispatching system needs to establish an independent variable function according to the key influence factors, so that a subsequently established electric vehicle charging load prediction model takes the key influence factors as a core, and electric vehicle charging load prediction can be accurately carried out. Specifically, the independent variable function is built according to key influence factors, wherein the key influence factors are used as independent variables to build the independent variable function. The corresponding non-critical influencing factors and electric vehicle parameters may also be selected as known quantities of the independent variable function, together establishing the independent variable function. And then, according to non-key influencing factors, electric vehicle parameters and electric vehicle regional holding capacity parameters, establishing an electric vehicle charging load prediction model by combining an independent variable function.
In this embodiment, the factor screening model is used to screen the electric vehicle influencing factors to obtain the key influencing factors and the non-key influencing factors, and then an independent variable function is built for the key influencing factors, so that the important factors influencing the electric vehicle charging are ensured to be considered. The accuracy of the obtained electric vehicle charging load prediction model can be improved, and the accuracy of power grid dispatching is further improved.
Further, in one embodiment, as shown in FIG. 4, step 304 includes step 402, step 404, and step 406.
Step 402, establishing an independent variable function according to the key influence factors.
Wherein, key influencing factors are taken as independent variables, and an independent variable function is established. The corresponding non-critical influencing factors and electric vehicle parameters may also be selected as known quantities of the independent variable function, together establishing the independent variable function.
Different key influence factors correspond to different independent variable functions, and the establishment process of the independent variable functions is described by taking the key influence factors as the driving road condition of the electric vehicle, the ambient temperature and the daily driving distance of the electric vehicle as examples.
The key influence factor of the running road condition of the electric vehicle can be extracted as the influence of the traffic condition on the electric vehicle charging. For daily traffic conditions, the change in traffic conditions may be considered to follow a normal distribution during peak hours, while traffic flow throughout the day may be considered to follow a normal distribution except for traffic increments during peak hours. Thus, the probability density of the traffic situation is obtained as follows:
（1）
In sigma q Sum mu q Are all normal distribution parameters, typically, sigma q Taking 15 mu q Taking 4; exp is an exponential function; in is a logarithmic function; t is a time parameter, and any appointed time within 24 hours a day can be taken.
And combining parameters related to the geographical relation in the parameters of the electric vehicle to obtain an independent variable function of the traffic condition, wherein the independent variable function is as follows:
（2）
wherein, the formula a or the formula b in the formula (2) is selected according to the city scale in the designated area. Cities are classified into large cities and small cities, for example, first-line cities are labeled as large cities, and cities other than first-line cities are labeled as small cities. When the city scale of the designated area is a big city, using an a formula as an independent variable function of the traffic condition; when the city size of the designated area is a small city, the b-equation is used as an independent variable function of the traffic condition.
For the key influencing factor of the environment temperature, the temperature condition can be considered to influence the running condition of an air conditioner when a user uses the electric vehicle, and further influence the electric quantity of the electric vehicle. Therefore, if the user selection of whether to switch on the air conditioner directly affects the electric quantity of the electric vehicle, the independent variable function of the established temperature condition is as follows:
（3）
wherein S is a (T) represents the probability of the user turning on the air conditioner in the electric vehicle, T represents the temperature at time T, T min1 Indicating the lowest temperature at which the user of the electric vehicle begins to turn on the air conditioner. T (T) max1 Indicating the lowest temperature at which the user of the electric vehicle fully turns on the air conditioner.
When the ambient temperature is greater than or equal to T min1 And less than or equal to T max1 The independent variable function of the temperature condition is shown in the formula (3). When the ambient temperature is less than T min1 At the time S a (t) equals 0, when S a And (t) when the value is 0, the air conditioner is not started by all electric vehicle users. When the ambient temperature is greater than T max1 At the time S a (t) is equal to 1, and when Sa (t) is 1, it means that all electric vehicle users turn on the air conditioner.
For the key situation factor of the daily driving distance of the electric vehicle, the independent variable function is established by calculating the driving distance of the electric vehicle in a single day, and the driving distance of the electric vehicle in the single day is generally assumed to be compliant with a normal distribution function, so that the probability density function of the daily driving distance of the electric vehicle is as follows:
（4）
in sigma D Sum mu D Are all normal distribution parameters, typically, sigma D Mu obtained from the historical daily travel distance of the designated area D Taking 4; d is the daily travel distance of the electric vehicle.
The probability density function formula (4) can obtain the value probability of the electric vehicle daily driving distance, which is equivalent to the independent variable function of the electric vehicle daily driving distance.
Step 404, obtaining an electric vehicle charge model based on the independent variable function, the non-key influencing factors and the electric vehicle parameters.
Specifically, the independent variable functions are integrated, and an electric vehicle charging quantity model is established by combining non-key influence factors and electric vehicle parameters:
（5）
wherein E is c Representing the current day of the electric vehicle, wherein P c (t) represents the electric energy consumption of the electric vehicle at time t, t1 and t2 represent the time when the electric vehicle starts traveling and the time when the electric vehicle ends traveling, η, respectively m Represents the charging efficiency, eta of the electric vehicle charger b Indicating the charging efficiency of the lithium battery of the electric vehicle.
Wherein P is c (t) is derived from the independent variable function, non-critical influencing factors and electric vehicle parameters:
（6）
wherein P is c (t) represents the power consumption of the electric vehicle at time t; e (E) bc Representing the power consumption of the electric vehicle in basic unit running; c (C) t A conversion factor representing traffic conditions; c (C) h A conversion factor representing driving habits; c (C) a A conversion factor of the electric vehicle air conditioner to the power consumption is represented; d represents the travel distance of the electric vehicle at the current trip, according to f d (d) Obtaining; t represents a conversion factor of the type of electric vehicle; t is t 1 Indicating the departure time t of the current trip of the electric vehicle 2 Indicating the end time of the current trip of the electric vehicle.
And step 406, establishing an electric vehicle charging load prediction model according to the electric vehicle region holding quantity parameter and the electric vehicle charging electric quantity model.
Specifically, the electric vehicle charging electric quantity model is a charging prediction model of a single electric vehicle, and the load accessed by the power grid is the charging load of all electric vehicles in a designated area. Therefore, the regional holding quantity parameter of the electric vehicle is combined with the electric vehicle charging electric quantity model to establish and obtain an electric vehicle charging load prediction model.
In one embodiment, as shown in FIG. 5, step 406 includes step 502 and step 504.
Step 502, an electric vehicle holding quantity prediction model is built according to electric vehicle region holding quantity parameters.
Specifically, after the charge prediction model of a single electric vehicle is obtained, the number of electric vehicles needs to be calculated again, and here, the electric vehicle holding quantity prediction model can be built through the electric vehicle regional holding quantity parameters.
In one embodiment, as shown in FIG. 6, step 502 includes step 602 and step 604.
Step 602, obtaining a reserve potential function according to the electric vehicle regional hold quantity parameter and the electric vehicle historical use data.
The historical use data of the electric vehicle comprises historical data such as historical purchase quantity of the electric vehicle, accumulated purchase number of the electric vehicle and the like, and the historical use data can be used for estimating the holding quantity of the electric vehicle in the designated area. Specifically, a reserve potential function is obtained according to the regional reserve parameters of the electric vehicle and the historical use data of the electric vehicle:
（7）
wherein P (t) is the change of the occupancy of the electric vehicle and the total amount of the traditional automobile in the historical use data of the electric vehicle; t is a time parameter; a is a constant parameter, and is obtained according to historical use data of the electric vehicle; m is m 0 Representing the maximum potential for development of the electric vehicle in the historical use data of the electric vehicle; and (c) a holding amount of the electric vehicle when N (t) is t.
Step 604, building an electric vehicle reserve quantity prediction model based on the Bass diffusion model and the reserve quantity potential function.
The Bass diffusion model is also called as a Bass diffusion model, and is a sales prediction mathematical model widely applied to new product diffusion.
Specifically, the mass diffusion model is improved by combining the mass conservation potential function, and the mass conservation prediction model of the electric vehicle is obtained after improvement. Wherein, the calculation formula of N (t) is as follows:
（8）
where p is an external influence factor (innovation coefficient) of the function, q is an internal influence coefficient (imitation coefficient), and p and q can be obtained from historical usage data of the electric vehicle or can be set manually.
In the formulas (7) and (8), different m (t) and N (t) can be calculated by taking different time parameters. The time parameter t is used as an independent variable, m (t) is used as an intermediate variable, and N (t) is used as an independent variable.
For ease of solution, the formula for calculating the rate of change of the number of people purchasing the electric vehicle is listed by the improved mass diffusion model:
（9）
wherein dN (t)/dt is the rate of change of the number of people buying the electric vehicle at time t; wherein the first term p [ m (t) -N (t) ] represents the number of people purchasing the electric vehicle due to external influence factors and the number of people purchasing the electric vehicle due to advertisement, propaganda and the like. The second, later term N (t) [ m (t) -N (t) ] represents the number of electric vehicles purchased as affected by the person who purchased the electric vehicle previously.
Step 504, an electric vehicle charging load prediction model is established based on the electric vehicle holding amount prediction model and the electric vehicle charging electric quantity model.
Specifically, an electric vehicle charge load prediction model is established based on an electric vehicle holding amount prediction model, electric vehicle influence factors, electric vehicle parameters and an electric vehicle charge amount model. Based on the electric vehicle holding quantity and the electric vehicle charging quantity, the electric vehicle charging quantity of the electric vehicle is distributed in time according to the driving habit of a user, and the electric vehicle charging load of the power grid can be predicted by combining the electric vehicle holding quantity superposition. The electric vehicle load prediction model is constructed as follows:
（10）
（11）
（12）
In the above, E c(n) Representing daily charge capacity of an nth electric vehicle, t cs(n) T represents a time when the nth electric vehicle starts charging ce(n) P indicating the time when the nth electric vehicle finishes charging (n) (t) represents the charging power of the nth electric vehicle at time t, P t (t) represents the sum of the charging powers of all the electric vehicles at time t, N represents the holding amount of the electric vehicles, and is calculated according to N (t).
In this embodiment, in the electric vehicle charging load prediction model, the effect of outputting the electric vehicle load prediction amount after inputting the time parameter can be achieved by the calculation of the formulas (10) to (12), and the accurate prediction of the electric vehicle charging load can be achieved.
And 206, obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter.
Specifically, the scheduling system predicts the load at a specified time according to the electric vehicle charging load prediction model. And inputting the appointed time as a time parameter into an electric vehicle charging load prediction model, and obtaining the electric vehicle load prediction quantity of the power grid at the appointed time after the calculation of the electric vehicle charging load prediction model.
Step 208, scheduling the power grid according to the electric vehicle load prediction.
After the electric vehicle load pre-measurement is obtained, the scheduling system executes corresponding scheduling operation on the power grid according to the electric vehicle load pre-measurement at the appointed moment, and changes the time or space distribution of electric resources in the power grid so as to ensure that the power grid can bear the electric vehicle charging load at the appointed moment and keep stable. For example, a preset electric vehicle load reference amount is stored in the dispatching system, when the obtained electric vehicle load reference amount is larger than the preset electric vehicle load reference amount, the dispatching system judges that the power grid bearing capacity is insufficient at the appointed moment, and the bearing capacity of the power grid is correspondingly expanded by dispatching power so as to cope with the electric vehicle charging load.
The electric network dispatching method comprises the steps of obtaining electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding capacity parameters, and establishing an electric vehicle charging load prediction model based on the electric vehicle influencing factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters. And obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter, and dispatching the power grid according to the electric vehicle load prediction quantity. According to the electric vehicle load prediction method and the electric vehicle load prediction device, the electric vehicle load is fully and comprehensively simulated and predicted according to the electric vehicle influence factors, the electric vehicle parameters and the electric vehicle regional holding quantity parameters, so that the electric vehicle load prediction obtained according to the electric vehicle load prediction model and the time parameters is reliable, and further the electric network is scheduled according to the electric vehicle load prediction, the electric network can be adjusted on the basis of fully considering the electric vehicle load, and the stability of the electric network is guaranteed.
In one embodiment, as shown in FIG. 7, step 302 includes step 702 and step 704.
Step 702, inputting the electric vehicle influence factors into a factor screening model to obtain influence factor main effect values corresponding to the electric vehicle influence factors.
Specifically, electric vehicle influence factors are input into a factor screening model, electric vehicle influence factors are screened by using a sequential branching method, the electric vehicle influence factors are preprocessed by using a dichotomy, and then the preprocessed electric vehicle influence factors are calculated to obtain the main effect value of the influence factors corresponding to each electric vehicle influence factor. The formula for specifically calculating the main effect value of the influence factor is as follows:
（13）
wherein y is j Observations representing simulated outputs resulting from factors 1 through j being at a high level and other factors being at a low level; y is i-1 Observations representing simulated outputs resulting from factors 1 through i-1 being at a high level and other factors being at a low level; beta i-j Then the sum of the principal effect values of factors i through j is represented. Wherein the high level refers to the maximum value in the range of the electric vehicle influencing factor, and the low level refers to the minimum value in the range of the electric vehicle influencing factor.
And step 704, selecting key influence factors from the electric vehicle influence factors according to the main effect values of the influence factors, and marking the unselected electric vehicle influence factors as non-key influence factors.
The main effect values of the obtained influence factors are selected, namely, a preset number of the influence factor main effect values are selected to be marked as key influence factors, and unselected electric vehicle influence factors are marked as non-key influence factors. Specifically, the larger the influence factor main effect value is, the larger the influence of the electric vehicle influence factor corresponding to the influence factor main effect value is on the charging behavior of the electric vehicle. The scheduling system marks the main effect values of the influence factors with a preset number from big to small as key influence factors. The preset number may be three, for example.
In the embodiment, the electric vehicle influence factors are input into the factor screening model to obtain the influence factor main effect values corresponding to the electric vehicle influence factors, and the key influence factors are selected according to the influence factor main effect values, so that the electric vehicle influence factors with larger influence on the charging behavior of the electric vehicle can be accurately distinguished, and the accuracy of the subsequently established electric vehicle charging load prediction model can be ensured.
In order to better understand the above solution, the following detailed explanation is made in connection with a specific embodiment in connection with the application scenario shown in fig. 1.
In one embodiment, a dispatch system obtains electric vehicle influencing factors, electric vehicle parameters, and electric vehicle zone holding capacity parameters. The dispatching system inputs the electric vehicle influence factors into a factor screening model, obtains influence factor main effect values corresponding to the electric vehicle influence factors through a formula (13), selects key influence factors from the electric vehicle influence factors according to the influence factor main effect values, and marks the unselected electric vehicle influence factors as non-key influence factors.
The scheduling system establishes an independent variable function according to a key influence factor, for example, independent variable functions established by the formulas (1) to (4). And obtaining the electric vehicle charging electric quantity model type (5) and the formula (6) based on the independent variable function, the non-key influence factors and the electric vehicle parameters.
And obtaining a conservation potential function formula (7) according to the regional conservation quantity parameter of the electric vehicle and the historical use data of the electric vehicle. An electric vehicle reserve capacity prediction model type (8) is established based on the Bass diffusion model and a reserve capacity potential function formula (7), wherein a formula (9) is used for calculating the formula (8). And (3) establishing electric vehicle charging load prediction model types (10) to (12) based on the electric vehicle holding quantity prediction model and the electric vehicle charging electric quantity model.
The dispatching system obtains electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter, and dispatches the power grid according to the electric vehicle load prediction quantity.
In the embodiment, the electric vehicle charge load prediction model can establish an electric vehicle holding quantity prediction model by analyzing historical use data of the electric vehicle under the condition that the historical data of the electric vehicle is less; and determining the influence factor main effect value of the electric vehicle influence factors through a factor screening model, and determining the key influence factors and the non-key influence factors. And establishing an independent variable function according to the key influence factors, analyzing based on the electric vehicle conservation quantity prediction model and the driving habit of the user, and establishing an electric vehicle charging load prediction model by combining the electric vehicle charging electric quantity model. And then, the electric vehicle load prediction model is used for calculating the electric vehicle load prediction quantity of the power grid, and scheduling is carried out according to the electric vehicle load prediction quantity, so that the stable and reliable operation of the power grid can be ensured, and the influence of the electric vehicle charge load on the stability of the power grid is reduced.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a power grid dispatching device for realizing the power grid dispatching method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the power grid dispatching device or devices provided below may be referred to the limitation of the power grid dispatching method hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 8, there is provided a power grid dispatching apparatus, including: a reference acquisition module 802, a model building module 804, a load prediction module 806, and a grid dispatching module 808, wherein:
the reference amount obtaining module 802 is configured to obtain an electric vehicle influencing factor, an electric vehicle parameter, and an electric vehicle region holding amount parameter.
The model building module 804 is configured to build an electric vehicle charging load prediction model based on electric vehicle influencing factors, electric vehicle parameters, and electric vehicle region holding capacity parameters.
The load prediction module 806 is configured to obtain an electric vehicle load prediction value according to the electric vehicle charging load prediction model and the time parameter.
The power grid dispatching module 808 is configured to dispatch a power grid according to the electric vehicle load prediction.
In one embodiment, the model building module 804 is further configured to input the electric vehicle influencing factors into the factor screening model, resulting in key influencing factors and non-key influencing factors. And establishing an independent variable function according to the key influence factors, and establishing an electric vehicle charging load prediction model according to the non-key influence factors, the electric vehicle parameters and the electric vehicle regional holding quantity parameters and combining the independent variable function.
In one embodiment, the model building module 804 is further configured to build an independent variable function based on the key influencing factors. And obtaining an electric vehicle charging electric quantity model based on the independent variable function, the non-key influence factors and the electric vehicle parameters, and establishing an electric vehicle charging load prediction model according to the electric vehicle regional holding quantity parameters and the electric vehicle charging electric quantity model.
In one embodiment, the model building module 804 is further configured to build an electric vehicle hold prediction model based on electric vehicle zone hold parameters. And establishing an electric vehicle charging load prediction model based on the electric vehicle holding quantity prediction model and the electric vehicle charging electric quantity model.
In one embodiment, the modeling module 804 is further configured to derive a hold-volume potential function based on the electric vehicle region hold-volume parameter and the electric vehicle historical usage data. And establishing an electric vehicle reserve quantity prediction model based on the Bass diffusion model and the reserve quantity potential function.
In one embodiment, the model building module 804 is further configured to input the electric vehicle influencing factors into the factor screening model to obtain the influence factor main effect values corresponding to the electric vehicle influencing factors. And selecting key influence factors from the electric vehicle influence factors according to the main effect values of the influence factors, and marking the unselected electric vehicle influence factors as non-key influence factors.
The various modules in the power grid dispatching device can be fully or partially implemented by software, hardware and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 9. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a power grid dispatching method. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 9 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding quantity parameters;
establishing an electric vehicle charging load prediction model based on electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding capacity parameters;
obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter;
and dispatching the power grid according to the electric vehicle load pre-measurement.
In one embodiment, the processor when executing the computer program further performs the steps of:
and inputting the electric vehicle influence factors into a factor screening model to obtain key influence factors and non-key influence factors. And establishing an independent variable function according to the key influence factors, and establishing an electric vehicle charging load prediction model according to the non-key influence factors, the electric vehicle parameters and the electric vehicle regional holding quantity parameters and combining the independent variable function.
In one embodiment, the processor when executing the computer program further performs the steps of:
and establishing an independent variable function according to the key influence factors. And obtaining an electric vehicle charging electric quantity model based on the independent variable function, the non-key influence factors and the electric vehicle parameters, and establishing an electric vehicle charging load prediction model according to the electric vehicle regional holding quantity parameters and the electric vehicle charging electric quantity model.
In one embodiment, the processor when executing the computer program further performs the steps of:
and establishing an electric vehicle holding quantity prediction model according to the electric vehicle regional holding quantity parameters. And establishing an electric vehicle charging load prediction model based on the electric vehicle holding quantity prediction model and the electric vehicle charging electric quantity model.
In one embodiment, the processor when executing the computer program further performs the steps of:
and obtaining a reserve potential function according to the regional reserve parameters of the electric vehicle and the historical use data of the electric vehicle. And establishing an electric vehicle reserve quantity prediction model based on the Bass diffusion model and the reserve quantity potential function. In one embodiment, the processor when executing the computer program further performs the steps of:
and inputting the electric vehicle influence factors into a factor screening model to obtain influence factor main effect values corresponding to the electric vehicle influence factors. And selecting key influence factors from the electric vehicle influence factors according to the main effect values of the influence factors, and marking the unselected electric vehicle influence factors as non-key influence factors.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding quantity parameters;
establishing an electric vehicle charging load prediction model based on electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding capacity parameters;
obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter;
and dispatching the power grid according to the electric vehicle load pre-measurement.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and inputting the electric vehicle influence factors into a factor screening model to obtain key influence factors and non-key influence factors. And establishing an independent variable function according to the key influence factors, and establishing an electric vehicle charging load prediction model according to the non-key influence factors, the electric vehicle parameters and the electric vehicle regional holding quantity parameters and combining the independent variable function.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and establishing an independent variable function according to the key influence factors. And obtaining an electric vehicle charging electric quantity model based on the independent variable function, the non-key influence factors and the electric vehicle parameters, and establishing an electric vehicle charging load prediction model according to the electric vehicle regional holding quantity parameters and the electric vehicle charging electric quantity model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and establishing an electric vehicle holding quantity prediction model according to the electric vehicle regional holding quantity parameters. And establishing an electric vehicle charging load prediction model based on the electric vehicle holding quantity prediction model and the electric vehicle charging electric quantity model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and obtaining a reserve potential function according to the regional reserve parameters of the electric vehicle and the historical use data of the electric vehicle. And establishing an electric vehicle reserve quantity prediction model based on the Bass diffusion model and the reserve quantity potential function. In one embodiment, the computer program when executed by the processor further performs the steps of:
and inputting the electric vehicle influence factors into a factor screening model to obtain influence factor main effect values corresponding to the electric vehicle influence factors. And selecting key influence factors from the electric vehicle influence factors according to the main effect values of the influence factors, and marking the unselected electric vehicle influence factors as non-key influence factors.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
Acquiring electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding quantity parameters;
establishing an electric vehicle charging load prediction model based on electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding capacity parameters;
obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter;
and dispatching the power grid according to the electric vehicle load pre-measurement.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and inputting the electric vehicle influence factors into a factor screening model to obtain key influence factors and non-key influence factors. And establishing an independent variable function according to the key influence factors, and establishing an electric vehicle charging load prediction model according to the non-key influence factors, the electric vehicle parameters and the electric vehicle regional holding quantity parameters and combining the independent variable function.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and establishing an independent variable function according to the key influence factors. And obtaining an electric vehicle charging electric quantity model based on the independent variable function, the non-key influence factors and the electric vehicle parameters, and establishing an electric vehicle charging load prediction model according to the electric vehicle regional holding quantity parameters and the electric vehicle charging electric quantity model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and establishing an electric vehicle holding quantity prediction model according to the electric vehicle regional holding quantity parameters. And establishing an electric vehicle charging load prediction model based on the electric vehicle holding quantity prediction model and the electric vehicle charging electric quantity model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and obtaining a reserve potential function according to the regional reserve parameters of the electric vehicle and the historical use data of the electric vehicle. And establishing an electric vehicle reserve quantity prediction model based on the Bass diffusion model and the reserve quantity potential function. In one embodiment, the computer program when executed by the processor further performs the steps of:
and inputting the electric vehicle influence factors into a factor screening model to obtain influence factor main effect values corresponding to the electric vehicle influence factors. And selecting key influence factors from the electric vehicle influence factors according to the main effect values of the influence factors, and marking the unselected electric vehicle influence factors as non-key influence factors.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.

Claims (10)

1. A method for grid dispatching, the method comprising:
acquiring electric vehicle influencing factors, electric vehicle parameters and electric vehicle regional holding quantity parameters;
establishing an electric vehicle charging load prediction model based on the electric vehicle influencing factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters;
obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter;
And dispatching the power grid according to the electric vehicle load pre-measurement.
2. The method of claim 1, wherein the establishing an electric vehicle charge load prediction model based on the electric vehicle influencing factors, the electric vehicle parameters, and the electric vehicle zone holding quantity parameters comprises:
inputting the electric vehicle influence factors into a factor screening model to obtain key influence factors and non-key influence factors;
and establishing an independent variable function according to the key influence factors, and establishing an electric vehicle charging load prediction model according to the non-key influence factors, the electric vehicle parameters and the electric vehicle regional holding quantity parameters and combining the independent variable function.
3. The method of claim 2, wherein the establishing an independent variable function based on the key influencing factors, and the electric vehicle charge load prediction model based on the non-key influencing factors, the electric vehicle parameters, and the electric vehicle zone holding amount parameters in combination with the independent variable function comprises:
establishing an independent variable function according to the key influence factors;
obtaining an electric vehicle charging quantity model based on the independent variable function, the non-key influence factors and the electric vehicle parameters;
And establishing an electric vehicle charging load prediction model according to the electric vehicle regional holding quantity parameter and the electric vehicle charging electric quantity model.
4. The method of claim 3, wherein said building an electric vehicle charge load prediction model from said electric vehicle regional hold parameter and said electric vehicle charge capacity model comprises:
establishing an electric vehicle holding quantity prediction model according to the electric vehicle regional holding quantity parameters;
and establishing an electric vehicle charging load prediction model based on the electric vehicle storage quantity prediction model and the electric vehicle charging electric quantity model.
5. The method of claim 4, wherein the establishing an electric vehicle hold quantity prediction model based on the electric vehicle zone hold quantity parameter comprises:
obtaining a reserve potential function according to the regional reserve parameters of the electric vehicle and the historical use data of the electric vehicle;
and establishing an electric vehicle reserve prediction model based on the Bass diffusion model and the reserve potential function.
6. The method of claim 2, wherein the inputting the electric vehicle influencing factors into the factor screening model of the electric vehicle charging load prediction model results in key influencing factors and non-key influencing factors, comprising:
Inputting the electric vehicle influence factors into a factor screening model to obtain influence factor main effect values corresponding to the electric vehicle influence factors;
and selecting key influence factors from the electric vehicle influence factors according to the influence factor main effect values, and marking the unselected electric vehicle influence factors as non-key influence factors.
7. A power grid dispatching apparatus, the apparatus comprising:
the reference quantity acquisition module is used for acquiring electric vehicle influence factors, electric vehicle parameters and electric vehicle region holding quantity parameters;
the model building module is used for building an electric vehicle charging load prediction model based on the electric vehicle influence factors, the electric vehicle parameters and the electric vehicle regional holding capacity parameters;
the load prediction module is used for obtaining electric vehicle load prediction quantity according to the electric vehicle charging load prediction model and the time parameter;
and the power grid dispatching module is used for dispatching the power grid according to the electric vehicle load pre-measurement.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
CN202311463973.4A 2023-11-06 2023-11-06 Power grid dispatching method and device and computer equipment Pending CN117498362A (en)

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