CN117117843A - Power grid optimization control method and system based on power big data analysis - Google Patents
Power grid optimization control method and system based on power big data analysis Download PDFInfo
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- H—ELECTRICITY
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Abstract
The application relates to a power grid optimization control method and system based on power big data analysis, wherein the method comprises the following steps: predicting power grid load information of a target time period based on a preset power big database; determining power storage equipment demand information based on the grid load information; a target electrical storage device is determined based on the electrical storage device demand information so that a grid-connected operation can be performed by the target electrical storage device. The application has the effect of facilitating the input preparation of the power storage equipment according to the load demand of the power grid so as to reduce the occurrence of the condition of insufficient power supply.
Description
Technical Field
The application relates to the technical field of power grid optimization control, in particular to a power grid optimization control method and system based on power big data analysis.
Background
The electric power system refers to a system for transporting and utilizing electric energy, which is composed of power generation, power transmission, power distribution and the like. It generally includes power plants, transformers, transmission lines, and distribution networks. The power plant converts energy into electrical energy and delivers the electrical energy to substations in different areas via transmission lines. The transformer substation is responsible for converting, distributing and conveying the conveyed electric energy so as to meet the requirements of different users.
The power storage device system, which generally includes a storage battery, a super capacitor, an energy storage device, and the like, can store electric energy by chemically reacting the electric energy, capacitively charging and discharging or other means, and release the electric energy when necessary. The power storage device system may be used in a power system for balancing a grid load, coping with power outage, providing a backup power supply, and the like.
When the load of the power grid exceeds the limit, the power supply system can mobilize the power storage equipment to transmit electric energy to the power grid so as to meet the demand of the power consumption load. However, existing power storage device scheduling work determines that power storage devices need to be put into service by determining that the real-time load of the power grid is too high. If the power grid load suddenly increases, and the preparation work of the power storage equipment is not completed, the power storage equipment cannot be put into grid connection in a short time, the condition of insufficient power supply is caused, and inconvenience is caused.
Disclosure of Invention
In order to facilitate the input preparation of the power storage equipment according to the load demand of the power grid and reduce the occurrence of insufficient power supply, the application provides a power grid optimization control method and system based on power big data analysis.
In a first aspect, the application provides a power grid optimization control method based on power big data analysis, which adopts the following technical scheme:
the power grid optimization control method based on the power big data analysis comprises the following steps:
predicting power grid load information of a target time period based on a preset power big database;
determining power storage equipment demand information based on the grid load information;
a target electrical storage device is determined based on the electrical storage device demand information so that a grid-connected operation can be performed by the target electrical storage device.
By adopting the technical scheme, the power grid load information of the target area is predicted, so that the power consumption requirement of the target area can be known; the demand information of the power storage equipment is determined according to the power grid load information, so that whether the power storage equipment needs to be put into or not and the electric energy information needing to be put into can be known, and grid connection preparation of the power storage equipment is facilitated in advance; according to the demand information of the power storage equipment, the target power storage equipment can be determined, so that the proper power storage equipment can be conveniently selected for grid connection, and the occurrence of insufficient power supply is reduced.
Optionally, the method for constructing the power large database includes:
acquiring historical power grid load information of a target area and historical influence factors corresponding to the historical power grid load information;
after the historical power grid load information and the historical influence factors are subjected to data preprocessing, the historical influence factors in the same time period are associated with the historical power grid load information and stored in a preset database to obtain a large power database.
Optionally, the predicting the grid load information of the target area based on the preset power big database includes:
constructing a load prediction model based on a preset power big database;
acquiring a load influence factor of a target time period in a target area;
and predicting the power grid load information of the target area in the target time period based on the load influence factors and the load prediction model.
By adopting the technical scheme, the power grid load information in the target time period can be predicted more scientifically.
Optionally, the constructing the load prediction model based on the preset power big database includes:
constructing an initial regression model of power grid load prediction;
constructing a data set based on the historical influencing factors and the historical grid load information;
constructing a training set and a testing set based on the data set;
and training and testing the initial regression model based on the training set and the testing set to obtain a load prediction model.
By adopting the technical scheme, the power grid load information can be accurately predicted.
Optionally, the determining the power storage device demand information based on the grid load information includes:
judging whether the power grid load information exceeds a preset power grid load standard or not;
and if the electric energy requirement information exceeds the electric energy requirement information, generating electric storage equipment requirement information, wherein the electric storage equipment requirement information comprises electric energy requirement information required to be input and input standard information.
By adopting the technical scheme, the electric energy requirement can be known, and the power storage equipment system is convenient to carry out grid connection preparation.
Optionally, the determining the target power storage device based on the power storage device demand information includes:
screening out alternative electric storage equipment from the electric storage equipment based on the electric energy demand information;
the alternative power storage equipment is subjected to priority ranking based on a preset priority ranking rule, and an alternative power storage equipment sequence is obtained;
a target electrical storage device is determined based on the sequence of alternative electrical storage devices.
By adopting the technical scheme, the power storage equipment with a good state is convenient to select as the target power storage equipment, so that the grid connection quality is improved, and the possibility of accidents is reduced.
Optionally, the method further comprises:
acquiring real-time operation parameters of the target power storage equipment, and performing state analysis on the target power storage equipment based on the real-time operation parameters;
and if the operation state of the target power storage device is abnormal, switching the target power storage device based on the alternative power storage device sequence.
By adopting the technical scheme, the next power storage device can be conveniently switched in time to be connected with the grid when the power storage device is abnormal.
In a second aspect, the application provides a power grid optimization control system based on power big data analysis, which adopts the following technical scheme:
the power grid optimization control system based on the power big data analysis comprises a prediction module, an analysis module and a grid connection module;
the prediction module is used for predicting power grid load information of a target area based on a preset power big database;
the analysis module is used for determining the demand information of the power storage equipment based on the power grid load information;
the grid-connected module is used for determining a target power storage device based on the power storage device demand information so that grid-connected operation can be performed through the target power storage device.
By adopting the technical scheme, the power grid load information of the target area is predicted, so that the power consumption requirement of the target area can be known; the demand information of the power storage equipment is determined according to the power grid load information, so that whether the power storage equipment needs to be put into or not and the electric energy information needing to be put into can be known, and grid connection preparation of the power storage equipment is facilitated in advance; according to the demand information of the power storage equipment, the target power storage equipment can be determined, so that the proper power storage equipment can be conveniently selected for grid connection, and the occurrence of insufficient power supply is reduced.
In a third aspect, the present application provides a terminal device, which adopts the following technical scheme:
the terminal equipment comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the power grid optimization control method based on the power big data analysis is adopted when the processor loads and executes the computer program.
By adopting the technical scheme, the power grid optimization control method based on the power big data analysis generates a computer program, and the computer program is stored in the memory to be loaded and executed by the processor, so that terminal equipment is manufactured according to the memory and the processor, and the power grid optimization control method based on the power big data analysis is convenient to use.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer readable storage medium, in which a computer program is stored, which when loaded and executed by a processor, adopts the above-mentioned grid optimization control method based on power big data analysis.
By adopting the technical scheme, the power grid optimization control method based on the power big data analysis generates a computer program, and the computer program is stored in a computer readable storage medium to be loaded and executed by a processor, and the computer program is convenient to read and store through the computer readable storage medium.
Drawings
Fig. 1 is a schematic flow chart of a power grid optimization control method based on power big data analysis according to an embodiment of the application.
Fig. 2 is a schematic block diagram of a power grid optimization control system based on power big data analysis according to an embodiment of the application.
Reference numerals illustrate:
1. a prediction module; 2. an analysis module; 3. and the grid-connected module.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings.
The embodiment of the application discloses a power grid optimization control method based on power big data analysis, which comprises the following steps with reference to fig. 1:
s101, predicting power grid load information of a target time period based on a preset power big database;
s102, determining power storage equipment demand information based on grid load information;
s103, determining a target electric storage device based on the electric storage device demand information so that grid-connected operation can be performed by the target electric storage device.
In this embodiment, a power large database is preset, and the power grid load information of the target area in the target time period can be predicted by the data recorded in the power large database.
The construction of the power big database can be realized by the following ways: firstly, historical power grid load information and historical influence factors corresponding to the historical power grid load information are obtained, after data preprocessing is carried out on the historical power grid load information and the historical influence factors, the historical influence factors in the same time period are associated with the historical power grid load information, and the historical influence factors and the historical power grid load information are stored in a preset database to obtain a large power database.
The historical power grid load information refers to charge information required by a certain period of time in the history in the power grid, namely the total amount of the electric energy being used or the total amount of the electric energy required for a target area and a target time period, and the historical influence factors are information corresponding to the historical power grid load information in the same area and the same time period, and specifically comprise seasonal factors, weather factors, time factors, social factors and the like. Taking residential communities as an example for explanation, in general, the influence of seasonal factors on the power grid load can be analyzed from spring, autumn and winter, the air conditioner utilization rate in spring, autumn and winter is not high, and the air conditioner utilization rate in summer and winter is high, so that the power grid load in summer and winter is generally higher than the power grid load in spring, autumn and winter; the influence of weather factors on the power grid load can be analyzed from a overcast and rainy day and a sunny day, and the requirements of the overcast and rainy day and the sunny day on illumination are different, so that the power grid load in the overcast and rainy day is generally higher than the power grid load in the sunny day; the time factor can be considered from the daytime and the evening, and the electricity consumption requirement at night is higher than the electricity consumption requirement at daytime, so that the power grid load at night is generally higher than the power grid load at daytime; the social factors are that the power demand on the non-working day is generally higher than that on the working day in consideration of the difference between the working day and the non-working day, and therefore, the power grid load on the non-working day is higher than that on the working day.
In summary, the large power database stores historical influencing factors and historical grid load information corresponding to the historical influencing factors. It should be noted that, before the historical influencing factors and the historical grid load information are stored in the power large database, data preprocessing, such as data cleaning, feature scaling, abnormal value processing and the like, can be performed according to requirements, so that the quality and consistency of data are ensured.
Step S101, after the power large database is acquired, the power grid load information in the target time period of the target area can be predicted based on the power large database.
Firstly, a load prediction model is constructed according to a large electric power database, and the load prediction model learns the correlation rule between historical influencing factors and historical power grid load information through training, so that the power grid load information of a target area and a target time period predicted by the load prediction model can be obtained by acquiring the load influencing factors in the target area and the target time period and inputting the load influencing factors into the load prediction model as prediction input data after adjusting the load influencing factors.
Specifically, the construction of the load prediction model may be achieved by: firstly, an initial regression model of power grid load prediction is constructed, wherein the initial regression model can adopt a logic linear regression model algorithm, and of course, in other embodiments, algorithms such as decision tree regression, support vector regression and the like can also be adopted. After the initial regression model is built, a historical influence factor and historical power grid load information building data set are obtained from the power big database, so that the initial regression model is trained, and finally a load prediction model is obtained.
Specifically, in the constructed data set, the history influencing factor data is processed and then used as input data, the history grid load information is processed and then used as output data, the data set is divided into a training set and a testing set, the proportion of the training set and the testing set can be set to 7:3, or other proportions, a logistic regression algorithm is adopted, an initial regression model is trained by the training set, the trained initial regression model is tested by the testing set, and the cycle is performed until the testing precision of the initial regression model meets the requirement, for example, the testing precision requirement is set to 95%, and a load prediction model is obtained.
After the load prediction model is obtained, load influence factors of a target time period of the target area are obtained, for example, the load influence factors are summer, the time is 3-4 pm, the working day, the sunny day and the like, the load influence factors are dataized and then are input into the load prediction model as prediction input data, and then the power grid load information of the target time period of the target area is obtained.
Step S102, after the power grid load information is obtained through prediction, the power storage equipment requirement is determined according to the power grid load information. At this time, it is necessary to determine whether the power grid load information exceeds a preset power grid load standard. The power grid load standard is generally the limit of power grid load, and if the predicted power grid load information exceeds the power grid load standard, the standby power supply, namely the electric storage equipment, is determined to be needed to be put into; if the power grid load information does not exceed the power grid load standard, determining that the power storage equipment is not needed to be put into, and enabling the power grid to meet the power consumption requirement of the target area in the target time period. For example, the load prediction model predicts that the power grid load information of a certain cell at the next 10-12 am is 100KWh, and the power grid load limit of the region where the cell is located is 80kWh, and then the power storage equipment is determined to be connected to the grid so as to provide enough electric energy to meet the power consumption load requirement of the cell.
When the power grid load information exceeds the power grid load standard, the power supply system generates power storage equipment demand information and transmits the power storage equipment demand information to the power storage equipment system, and the power storage equipment system plans according to the power storage equipment demand information to prepare for grid connection. In this embodiment, the power storage device demand information includes power demand information and input standard information, where the power demand information refers to power information that needs to be provided by the power storage device, including power energy, input time period, and the like; the input standard information comprises grid-connected inverter specification, grid-connected specification, grid topology, grid parameters, access protocol, safety measure requirements and the like, so that the grid-connected safety of the power storage equipment is ensured.
Step S103, after obtaining the power storage device demand information, the power storage device system screens from all power storage devices according to the power demand information, and obtains the alternative power storage device. The screening is carried out on the electric energy reserves and the running states of the electric storage equipment, and because the electric energy can be lost in the transmission process, the electric energy reserves in the electric storage equipment which are input in a grid connection mode are required to be ensured to be larger than the electric energy information in the electric energy demand information after the electric energy reserves are lost, so that enough electric energy can be input. For example, the power consumption rate of grid connection of the power storage devices is 5%, and the power required to be provided by the power storage devices in the power grid is 50kWh, so that the power storage devices with the power storage capacity of more than 50/(100% -5%) = 52.63kWh need to be selected, and meanwhile, the power storage devices with idle running states need to be selected, so that all the alternative power storage devices are obtained. The alternative power storage device is a power storage device to be put into grid connection at any time.
After all the alternative power storage devices are determined, the alternative power storage devices are prioritized according to the basic information of the power storage devices, and an alternative power storage device sequence is obtained. The priority ranking rule in this embodiment is set for the following aspects, including the service life, the storage capacity, the charge-discharge efficiency, whether faults occur or not, and the like, performing data processing for each reference factor, configuring corresponding weights, comprehensively scoring all the alternative power storage devices according to the weights of the reference factors, and ranking the priorities of the alternative power storage devices according to the comprehensive scores to obtain an alternative power storage device sequence.
After the series of alternative power storage devices is determined, the target power storage device is determined in order of the composite score from high to low, and the first target power storage device is the first alternative power storage device in the series of alternative power storage devices.
After the target electric storage device is determined, grid-connected operation can be performed, and the required electric energy is provided for the grid through the target electric storage device.
In addition, in the grid connection process of the power grid, acquiring real-time operation parameters of the target power storage equipment, judging the operation state of the target power storage equipment according to the real-time operation parameters, and if the operation state is normal, continuing to use the current target power storage equipment; if the operation is abnormal, switching to the next alternative power storage device in the series of alternative power storage devices is immediately performed to ensure stable and effective power output.
In the present embodiment, the cases of the abnormal operation state that may occur include the following:
battery capacity fade: as the time of use increases, the capacity of the battery may decrease, resulting in the stored electrical energy not conforming to the recorded electrical energy;
charging failure: the electric storage device may suffer from problems in the charging process, such as overcharging, undercharging, low charging efficiency, and the like;
discharge failure: the power storage equipment may have faults in the discharging process, so that electric energy cannot be output or the output is unstable;
abnormal temperature: the power storage device may generate heat during operation, and an excessively high temperature may cause damage to the power storage device or accelerate capacity fade;
short circuit or fault current: the power storage equipment may have short circuit or abnormal fault current, so that grid-connected current is unstable or the equipment is damaged;
internal failure: faults of circuits, connectors or other components may occur inside the electric storage device, resulting in the device not functioning properly;
external environmental influence: the power storage device is affected by external environmental factors such as humidity, dust, vibration, etc., and may malfunction or have performance degraded;
power quality problem: power quality problems in the grid, such as voltage fluctuations, harmonics, voltage drastic changes, etc., may have an adverse effect on the electrical storage device.
The above are some common abnormal conditions of the operation state of the electrical storage device, and the specific conditions may also vary depending on the specific device type, design and use condition, which is not limited in this embodiment.
The implementation principle of the power grid optimization control method based on the power big data analysis in the embodiment of the application is as follows: the power grid load information of the target area is predicted, so that the power consumption requirement of the target area can be known; the demand information of the power storage equipment is determined according to the power grid load information, so that whether the power storage equipment needs to be put into or not and the electric energy information needing to be put into can be known, and grid connection preparation of the power storage equipment is facilitated in advance; according to the demand information of the power storage equipment, the target power storage equipment can be determined, so that the proper power storage equipment can be conveniently selected for grid connection, and the occurrence of insufficient power supply is reduced.
The application also discloses a power grid optimization control system based on the power big data analysis, referring to fig. 2, comprising a prediction module 1, an analysis module 2 and a grid connection module 3; the prediction module 1 is used for predicting power grid load information of a target area based on a preset power big database; the analysis module 2 is used for determining the demand information of the power storage equipment based on the power grid load information; the grid-connection module 3 is configured to determine a target electrical storage device based on the electrical storage device demand information so that a grid-connection operation can be performed by the target electrical storage device.
The specific implementation of the power grid optimization control system based on the power big data analysis in the embodiment of the application is the same as the specific implementation of the power grid optimization control method based on the power big data analysis, so that the description is omitted here.
The embodiment of the application also discloses a terminal device which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the power grid optimization control method based on the power big data analysis in the embodiment is adopted when the processor executes the computer program.
The terminal device may be a computer device such as a desktop computer, a notebook computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory, for example, the terminal device may further include an input/output device, a network access device, a bus, and the like.
The processor may be a Central Processing Unit (CPU), or of course, according to actual use, other general purpose processors, digital Signal Processors (DSP), application Specific Integrated Circuits (ASIC), ready-made programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., and the general purpose processor may be a microprocessor or any conventional processor, etc., which is not limited in this respect.
The memory may be an internal storage unit of the terminal device, for example, a hard disk or a memory of the terminal device, or an external storage device of the terminal device, for example, a plug-in hard disk, a Smart Memory Card (SMC), a secure digital card (SD), or a flash memory card (FC) provided on the terminal device, or the like, and may be a combination of the internal storage unit of the terminal device and the external storage device, where the memory is used to store a computer program and other programs and data required by the terminal device, and the memory may be used to temporarily store data that has been output or is to be output, which is not limited by the present application.
The power grid optimization control method based on the power big data analysis in the embodiment is stored in a memory of the terminal device through the terminal device, and is loaded and executed on a processor of the terminal device, so that the power grid optimization control method is convenient to use.
The embodiment of the application also discloses a computer readable storage medium, and the computer readable storage medium stores a computer program, wherein the power grid optimization control method based on the power big data analysis in the embodiment is adopted when the computer program is executed by a processor.
The computer program may be stored in a computer readable medium, where the computer program includes computer program code, where the computer program code may be in a source code form, an object code form, an executable file form, or some middleware form, etc., and the computer readable medium includes any entity or device capable of carrying the computer program code, a recording medium, a usb disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, etc., where the computer readable medium includes, but is not limited to, the above components.
The power grid optimization control method based on the power big data analysis in the embodiment is stored in the computer readable storage medium through the computer readable storage medium, and is loaded and executed on a processor, so that the storage and the application of the method are convenient.
The above embodiments are not intended to limit the scope of the present application, so: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.
Claims (10)
1. The power grid optimization control method based on the power big data analysis is characterized by comprising the following steps of:
predicting power grid load information of a target time period based on a preset power big database;
determining power storage equipment demand information based on the grid load information;
a target electrical storage device is determined based on the electrical storage device demand information so that a grid-connected operation can be performed by the target electrical storage device.
2. The power grid optimization control method based on power big data analysis according to claim 1, wherein the construction method of the power big database comprises the following steps:
acquiring historical power grid load information of a target area and historical influence factors corresponding to the historical power grid load information;
after the historical power grid load information and the historical influence factors are subjected to data preprocessing, the historical influence factors in the same time period are associated with the historical power grid load information and stored in a preset database to obtain a large power database.
3. The power grid optimization control method based on power big data analysis according to claim 2, wherein the predicting the power grid load information of the target area based on the preset power big database includes:
constructing a load prediction model based on a preset power big database;
acquiring a load influence factor of a target time period in a target area;
and predicting the power grid load information of the target area in the target time period based on the load influence factors and the load prediction model.
4. The power grid optimization control method based on power big data analysis according to claim 3, wherein the constructing a load prediction model based on a preset power big database comprises:
constructing an initial regression model of power grid load prediction;
constructing a data set based on the historical influencing factors and the historical grid load information;
constructing a training set and a testing set based on the data set;
and training and testing the initial regression model based on the training set and the testing set to obtain a load prediction model.
5. The electric power big data analysis-based electric power grid optimization control method according to claim 1, characterized in that the determining electric power storage device demand information based on the electric power grid load information includes:
judging whether the power grid load information exceeds a preset power grid load standard or not;
and if the electric energy requirement information exceeds the electric energy requirement information, generating electric storage equipment requirement information, wherein the electric storage equipment requirement information comprises electric energy requirement information required to be input and input standard information.
6. The electric power big data analysis-based grid optimization control method according to claim 5, characterized in that the determining a target electric storage device based on the electric storage device demand information includes:
screening out alternative electric storage equipment from the electric storage equipment based on the electric energy demand information;
the alternative power storage equipment is subjected to priority ranking based on a preset priority ranking rule, and an alternative power storage equipment sequence is obtained;
a target electrical storage device is determined based on the sequence of alternative electrical storage devices.
7. The power grid optimization control method based on power big data analysis according to claim 6, further comprising:
acquiring real-time operation parameters of the target power storage equipment, and performing state analysis on the target power storage equipment based on the real-time operation parameters;
and if the operation state of the target power storage device is abnormal, switching the target power storage device based on the alternative power storage device sequence.
8. The power grid optimization control system based on the power big data analysis is characterized by comprising a prediction module (1), an analysis module (2) and a grid connection module (3);
the prediction module (1) is used for predicting power grid load information of a target area based on a preset power big database;
the analysis module (2) is used for determining power storage equipment demand information based on the power grid load information;
the grid-connection module (3) is configured to determine a target electrical storage device based on the electrical storage device demand information, so that a grid-connection operation can be performed by the target electrical storage device.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, characterized in that the method according to any of claims 1-7 is used when the computer program is loaded and executed by the processor.
10. A computer readable storage medium having a computer program stored therein, characterized in that the method according to any of claims 1-7 is employed when the computer program is loaded and executed by a processor.
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