CN117134366B - Load control method, device, equipment and storage medium - Google Patents
Load control method, device, equipment and storage medium Download PDFInfo
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- CN117134366B CN117134366B CN202311402480.XA CN202311402480A CN117134366B CN 117134366 B CN117134366 B CN 117134366B CN 202311402480 A CN202311402480 A CN 202311402480A CN 117134366 B CN117134366 B CN 117134366B
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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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
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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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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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
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/58—The condition being electrical
- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
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Abstract
The application relates to a load control method, a device, equipment, a storage medium and a product, which are used for each target control terminal in a target power system, wherein the target power system also comprises a server, and the method comprises the following steps: for each target time period in the target time interval, rigid load prediction data and flexible load prediction data corresponding to the target time period are obtained; the method comprises the steps that rigid load prediction data and flexible load prediction data corresponding to each target time period are sent to a server, and the rigid load prediction data and the flexible load prediction data are used for generating a load control strategy by the server; and receiving a load control strategy sent by the server, and controlling the start and stop of the electrical equipment corresponding to the flexible load prediction data in a peak time period and a trough time period in a target time interval based on the load control strategy. By adopting the method, the accuracy of load control can be improved.
Description
Technical Field
The present disclosure relates to the field of power technologies, and in particular, to a load control method, apparatus, device, and storage medium.
Background
During peak hours of residential power consumption, the power system can have an overload condition. In order to reduce the load pressure of the power system in the peak period of electricity consumption, the load of residents in the peak period needs to be controlled, and part of the load in the peak period is transferred to the valley period of electricity consumption.
In the load control method of the traditional technology, the electricity consumption peak period and the electricity consumption valley period are determined according to the electricity consumption data of each household, and the flexible load in the electricity consumption peak period is controlled to be transferred to the electricity consumption valley period.
But the conventional load control method is low in accuracy.
Disclosure of Invention
Based on this, it is necessary to provide a load control method, apparatus, device, and storage medium with high accuracy in view of the above-described technical problems.
In a first aspect, the present application provides a load control method for each target control terminal in a target power system, the target power system further including a server, the method including:
for each target time period in the target time interval, rigid load prediction data and flexible load prediction data corresponding to the target time period are obtained, wherein the rigid load prediction data and the flexible load prediction data are determined by a target control terminal based on a preset load type judgment strategy, and the load type judgment strategy comprises a mapping relation between electrical equipment and load types;
The method comprises the steps that rigid load prediction data and flexible load prediction data corresponding to each target time period are sent to a server, and the rigid load prediction data and the flexible load prediction data are used for generating a load control strategy by the server;
and receiving a load control strategy sent by the server, and controlling the start and stop of the electrical equipment corresponding to the flexible load prediction data in a peak time period and a trough time period in a target time interval based on the load control strategy.
In one embodiment, obtaining rigid load prediction data and flexible load prediction data corresponding to a target time period includes:
acquiring rigid load historical data and flexible load historical data corresponding to at least one historical time period corresponding to a target time period, wherein the rigid load historical data and the flexible load historical data are determined for a target control terminal based on a preset load type judging strategy and ammeter reading data corresponding to the historical time period;
and determining rigid load prediction data and flexible load prediction data corresponding to the target time period according to the rigid load history data and the flexible load history data.
In one embodiment, the load control method further comprises:
obtaining ammeter reading data corresponding to each historical time period in the historical time interval,
Non-invasive identification processing is carried out on the ammeter reading data, and each electrical equipment corresponding to each historical time period and load data corresponding to each electrical equipment are obtained;
determining the load type of each electrical equipment according to the load type judging strategy;
and determining rigid load historical data and flexible load historical data corresponding to the historical time period according to the load data and the load type of each electrical equipment.
In one embodiment, the load type judgment policy further includes a mapping relationship among the electrical equipment, the load type and the environmental factor;
obtaining rigid load historical data and flexible load historical data corresponding to at least one historical time period corresponding to a target time period comprises:
determining each historical time period corresponding to the target time period from within the historical time period based on the target time period;
determining at least one historical time period as a reference time period according to a target environmental factor corresponding to the target time period and historical environmental factors corresponding to each historical time period;
rigid load historical data and flexible load historical data corresponding to the reference time period are obtained.
In a second aspect, the present application provides a load control method for a server in a target power system, the target power system further including a plurality of target control terminals, the method including:
For each target time period in the target time interval, rigid load prediction data and flexible load prediction data corresponding to the target time period sent by each target control terminal are obtained; the rigid load prediction data and the flexible load prediction data are determined by the target control terminal based on a preset load type judgment strategy, wherein the load type judgment strategy comprises a mapping relation between electrical equipment and load types;
determining a peak time period and a trough time period in a target time interval based on rigid load prediction data and flexible load prediction data corresponding to a plurality of target control terminals corresponding to each target time period;
according to the crest time period, the trough time period and the flexible load prediction data corresponding to the crest time period, a load control strategy is obtained and sent to each target control terminal, and the load control strategy is used for controlling the starting and stopping of electrical equipment corresponding to the flexible load prediction data in the crest time period and the trough time period in the target time interval by each target control terminal.
In a third aspect, the present application further provides a load control device applied to each target control terminal in a target power system, the target power system further including a server, the device including:
The data prediction module is used for acquiring rigid load prediction data and flexible load prediction data corresponding to target time periods according to each target time period in the target time period, wherein the rigid load prediction data and the flexible load prediction data are determined by a target control terminal based on a preset load type judgment strategy, and the load type judgment strategy comprises a mapping relation between electrical equipment and load types;
the signal output module is used for sending the rigid load prediction data and the flexible load prediction data corresponding to each target time period to the server, wherein the rigid load prediction data and the flexible load prediction data are used for the server to generate a load control strategy;
the control module is used for receiving the load control strategy sent by the server and controlling the start and stop of the electrical equipment corresponding to the flexible load prediction data in the peak time period and the trough time period in the target time interval based on the load control strategy.
In a fourth aspect, the present application further provides a load control device for a server in a target power system, the target power system further including a plurality of target control terminals, the device including:
the signal input module is used for acquiring rigid load prediction data and flexible load prediction data corresponding to target time periods sent by each target control terminal according to each target time period in the target time interval; the rigid load prediction data and the flexible load prediction number are determined by the target control terminal based on a preset load type judgment strategy;
The data analysis module is used for determining a peak time period and a trough time period in a target time interval based on a plurality of rigid load prediction data and a plurality of flexible load prediction data corresponding to each target time period;
the strategy generation module is used for obtaining a load control strategy according to the crest time period, the trough time period and the flexible load prediction data corresponding to the crest time period, and sending the load control strategy to each target control terminal, wherein the load control strategy is used for controlling the starting and stopping of the electrical equipment corresponding to the flexible load prediction data in the crest time period and the trough time period in the target time interval by each target control terminal.
In a fifth aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method according to the first and/or second aspect when the processor executes the computer program.
In a sixth 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 the method according to the first and/or second aspect.
The load control method, apparatus, device and storage medium described above, wherein the load control method of one aspect is used for each target control terminal in a target power system, the method comprising: for each target time period in the target time interval, rigid load prediction data and flexible load prediction data corresponding to the target time period are obtained, wherein the rigid load prediction data and the flexible load prediction data are determined by a target control terminal based on a preset load type judgment strategy, and the load type judgment strategy comprises a mapping relation between electrical equipment and load types; the method comprises the steps that rigid load prediction data and flexible load prediction data corresponding to each target time period are sent to a server, and the rigid load prediction data and the flexible load prediction data are used for generating a load control strategy by the server; receiving a load control strategy sent by a server, and controlling the start and stop of electrical equipment corresponding to flexible load prediction data in a peak time period and a trough time period in a target time interval based on the load control strategy; in this way, each target control terminal in the target power system divides the load of the predicted target time period into rigid load prediction data and flexible load prediction data based on a preset load type judgment strategy, different types of electrical equipment are controlled according to the load control strategy generated by the rigid load prediction data and the flexible load prediction data in a time-division mode, the electricity consumption time period and the electricity consumption type are further divided, the situation that each target control terminal in the prior art autonomously determines the mapping relation between the electrical equipment in each household and the load type through the corresponding household user is avoided, the obtained rigid load prediction data and the flexible load prediction data are determined based on the respective electricity consumption habits of each household user, the judgment strategies of the rigid load and the flexible load are inconsistent, and the situation that some household users possibly determine the electrical equipment which does not need to be started in the household as the rigid load exists, so that the accuracy of the load control strategy obtained by the rigid load prediction data and the flexible load prediction data obtained according to the traditional technology is lower is caused.
Drawings
FIG. 1 is a diagram of an application environment for a load control method in one embodiment;
FIG. 2 is a flow chart of a load control method according to one embodiment;
FIG. 3 is a flowchart illustrating steps for obtaining rigid load prediction data and flexible load prediction data in one embodiment;
FIG. 4 is a diagram of an application environment for a load control method in one embodiment;
FIG. 5 is a flow chart of steps for obtaining rigid load history data and flexible load history data in one embodiment;
FIG. 6 is a flow diagram of steps for obtaining rigid load history data and flexible load history data in one embodiment;
FIG. 7 is a flow chart of a load control method in one embodiment;
FIG. 8 is a schematic diagram of a regional rigid load prediction curve and a regional total load prediction curve in one embodiment;
FIG. 9 is a flow chart of a method of load control in one embodiment;
FIG. 10 is a block diagram showing the structure of a load control device in one embodiment;
FIG. 11 is a block diagram showing the structure of a load control device in one embodiment;
fig. 12 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.
The load control method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. The target control terminal 102 is disposed in each household, communicates with the server 104 via the internet, and the power generation source 106 transmits power to each household via the power distribution network. The target control terminal 102 is used for controlling the start and stop of electrical equipment in each family, and the electrical equipment can be, but is not limited to, various intelligent sound boxes, intelligent televisions, intelligent air conditioners and the like; there may be a plurality of target control terminals 102. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, a load control method is provided, and the method is applied to the target control terminal in fig. 1, for example, and includes the following steps:
step 202, for each target time period in the target time interval, obtaining rigid load prediction data and flexible load prediction data corresponding to the target time period.
The rigid load prediction data and the flexible load prediction data are determined by the target control terminal based on a preset load type judgment strategy.
The load type judging strategy comprises a mapping relation between the electrical equipment and the load type. The electric equipment mapped by the rigid load is not suitable to be used in a target time period, and the electric equipment mapped by the flexible load can be used in the target time period.
The electrical equipment used in the target time period comprises a refrigerator and an air conditioner according to the prediction of the target control terminal, the refrigerator and the air conditioner are determined to be rigid loads in the target time period based on a current preset load type judging strategy, and further the rigid load prediction data in the target time period are determined according to the predicted load data of the refrigerator and the load data of the air conditioner.
In another example, according to the prediction of the target control terminal, the electrical equipment used in the target time period includes a refrigerator and a television, the refrigerator is determined to be a rigid load in the target time period based on a current preset load type judgment policy, the television is determined to be a flexible load in the target time period, further, the rigid load prediction data in the target time period is determined according to the predicted load data of the refrigerator, and the flexible load prediction data in the target time period is determined according to the predicted load data of the television.
For example, the target time interval may be 24 hours of a future day, and each target time period within the target time interval may be each of the 24 hours of the future day.
And step 204, transmitting the rigid load prediction data and the flexible load prediction data corresponding to each target time period to a server.
Wherein the rigid load prediction data and the flexible load prediction data are used by the server to generate a load control strategy.
The target control terminal sends the rigid load prediction data and the flexible load prediction data corresponding to each target time period to the server; the server performs summarization analysis processing on the rigid load prediction data and the flexible load prediction data which are sent by each target control terminal and correspond to each target time period, so as to obtain regional flexible load prediction data and regional load prediction data which are corresponding to each target time period; and the server generates a load control strategy corresponding to each target time period according to the regional flexible load prediction data and the regional load prediction data corresponding to each target time period.
And 206, receiving a load control strategy sent by the server, and controlling the start and stop of the electrical equipment corresponding to the flexible load prediction data in the peak time period and the trough time period in the target time interval based on the load control strategy.
The peak time period refers to an electricity consumption peak time period determined by the server according to the rigid load prediction data and the flexible load prediction data corresponding to each target time period sent by each target control terminal; the trough time period refers to an electricity consumption trough time period determined by the server according to the rigid load prediction data and the flexible load prediction data corresponding to each target time period sent by each target control terminal.
In the load control method, for each target time period in the target time period, each target control terminal acquires rigid load prediction data and flexible load prediction data corresponding to the target time period, wherein the rigid load prediction data and the flexible load prediction data are determined by the target control terminal based on a preset load type judgment strategy, and the load type judgment strategy comprises a mapping relation between electrical equipment and load types; the method comprises the steps that rigid load prediction data and flexible load prediction data corresponding to each target time period are sent to a server, and the rigid load prediction data and the flexible load prediction data are used for generating a load control strategy by the server; receiving a load control strategy sent by a server, and controlling the start and stop of electrical equipment corresponding to flexible load prediction data in a peak time period and a trough time period in a target time interval based on the load control strategy; in this way, each target control terminal adopts a preset load type judgment strategy to judge and acquire the prediction data corresponding to each load type, so that the problem that the accuracy of the load prediction data is affected because each target control terminal obtains the subjective rigid load prediction data and flexible load prediction data through the mapping relation between the electrical equipment and the load type in each home, which is autonomously determined by the corresponding home user in the prior art; the server determines the use strategy of each type of load according to the load prediction data, and each target control terminal can more accurately control the use condition of the electric appliance corresponding to each type of load in the target time period, so that the accuracy of the load control method is improved.
In one embodiment, based on the embodiment shown in fig. 2, as shown in fig. 3, obtaining the rigid load prediction data and the flexible load prediction data corresponding to the target period of time includes:
step 302, obtaining rigid load historical data and flexible load historical data corresponding to at least one historical time period corresponding to a target time period.
The rigid load historical data and the flexible load historical data are determined by the target control terminal based on a preset load type judging strategy and ammeter reading data corresponding to a historical time period.
In the application environment of a home, as shown in fig. 4, the target control terminal 102 is connected to an electric meter and each electric device, and is connected to the internet, the target control terminal 102 obtains electric meter reading data collected in real time through the electric meter, is connected to each electric device so as to control each electric device, is connected to a server through the internet, and thus interacts with the server, and the electric meter is further connected to a power distribution network for receiving electric power transmitted in the power distribution network.
The target control terminal performs identification processing on ammeter reading data of each historical time period according to a load type judging strategy to obtain rigid load data and flexible load data of each historical time period; determining at least one historical time period corresponding to the target time period from the historical time periods as a reference time period, wherein if the reference time period is 1, the rigid load data of the reference time period is used as rigid load historical data, and the flexible load data is used as flexible load historical data; if the reference time periods are greater than 1, the average value of the rigid load data in each reference time period is the rigid load historical data, and the average value of the flexible load data is the flexible load historical data.
For example, each historical time period may be each hour in the past week, and the electric meter reading data of each historical time period is identified according to the load type judgment policy, so as to obtain the electrical equipment data used in each historical time period, and further obtain the rigid load data and the flexible load data used in each historical time period. If the target time period is 0-1 of a future day, at least one time period can be selected from 0-1 of each day of the past week, and further the rigid load data of the selected time period is determined to be used as the rigid load historical data, and the flexible load data is determined to be used as the flexible load historical data.
And step 304, determining rigid load prediction data and flexible load prediction data corresponding to the target time period according to the rigid load history data and the flexible load history data.
The rigid load prediction data corresponding to the target time period are rigid load historical data, and the flexible load prediction data corresponding to the target time period are flexible load historical data.
In this embodiment, a reference time period corresponding to a target time period is determined from each history time period, and rigidity load prediction data corresponding to the target time period is determined from rigidity load history data of the reference time period; according to the flexible load historical data of the reference time period, flexible load prediction data are determined, when the load of the target time period is predicted, the rigid load and the flexible load are respectively predicted, and the load data of the corresponding time period are used for predicting the load data of the target time period, so that the accuracy of load prediction can be effectively improved.
In one possible implementation manner, each target control terminal may send the rigid load historical data and the flexible load historical data to the server, and receive the rigid load prediction data and the flexible load prediction data corresponding to the target time period fed back by the server.
In one embodiment, based on the embodiment shown in fig. 3, as shown in fig. 5, the method further comprises:
step 502, obtaining ammeter reading data corresponding to each historical time period in the historical time interval.
For example, the historical time interval may be a past week and the respective historical time period may be a respective hour of the past week.
And 504, performing non-invasive identification processing on the ammeter reading data to obtain each electrical equipment corresponding to each historical time period and load data corresponding to each electrical equipment.
The non-invasive identification processing refers to identifying the electrical equipment used in each historical time period and calculating the corresponding load data of each electrical equipment through monitoring and analyzing the data of the readings of the ammeter under the condition that the electrical equipment and the ammeter do not need to be changed or intervened substantially.
By way of example, a non-invasive identification mode of dynamic load identification can be adopted, waveform characteristics and change conditions of current and voltage passing through the ammeter at each historical moment are monitored in real time, the on-off states of different electrical equipment and generated load data are judged, and each electrical equipment corresponding to ammeter reading data is obtained as shown in table 1.
Illustratively, time S j,n The time n of the j day is represented, wherein the value range of j is 1-7, namely the historical time interval is the past week; the difference between the time n and the time n-1 represents a preset time interval of the electricity meter for collecting the electricity meter reading data, which may be 1 second or 5 seconds, and is not specifically limited in this embodiment.
Table 1 Electrical equipment corresponding to each historic moment
And according to the preset time period length, counting the load data generated by different electrical equipment, and obtaining the load data corresponding to each historical time period.
Each historical time in table 1And counting the corresponding electrical equipment to obtain the electrical equipment corresponding to each history period, as shown in table 2. Exemplary, historical period T j,k A period k representing the j th day, wherein the value of k ranges from 1 to 24, namely T 1,1 Time period 1 of the first day corresponds to 0 time to 1 time of the first day.
Table 2 electrical equipment corresponding to each history period
And step 506, determining the load type of each electrical equipment according to the load type judging strategy.
And step 508, determining rigid load historical data and flexible load historical data corresponding to the historical time period according to the load data and the load type of each electrical equipment.
Illustratively, the electric appliance 1 is a refrigerator, the electric appliance 2 is an air conditioner, and the strategy is judged according to the load type, and the history time period T 1,1 The electrical appliance 1 is a rigid load, the electrical appliance 2 is a flexible load, and the historical period T 1,1 The corresponding rigid load historical data is the load data generated by the electric appliance 1, and the flexible load historical data is the load data generated by the electric appliance 2.
In the embodiment, through acquiring ammeter reading data corresponding to each historical time period in the historical time interval, non-invasive identification processing is performed on the ammeter reading data, and each electrical equipment corresponding to each historical time period and load data corresponding to each electrical equipment are acquired; determining the load type of each electrical equipment according to the load type judging strategy; according to the load data and the load type of each electrical equipment, determining rigid load historical data and flexible load historical data corresponding to a historical time period, so that the target control terminal can comprehensively acquire the service condition of the electrical equipment in the historical time period by identifying the ammeter data acquired by the ammeter in real time; by using the non-invasive identification method, each electrical equipment corresponding to each historical time period and load data corresponding to each electrical equipment can be simply, conveniently and accurately obtained.
In one embodiment, based on the embodiment shown in fig. 3, as shown in fig. 6, the load type determination policy further includes a mapping relationship among the electrical device, the load type, and the environmental factor;
the environmental factors may include sunlight, humidity, and temperature, among others. The change of environmental factors has great influence on the use condition of the electrical equipment, and meanwhile, under the influence of different environmental factors, the load type judgment strategy can be correspondingly adjusted.
By way of example, the load type determination policy may be represented as shown in table 3.
TABLE 3 load type determination strategy
Obtaining rigid load historical data and flexible load historical data corresponding to at least one historical time period corresponding to a target time period comprises:
step 602, determining each historical time period corresponding to the target time period from the historical time period based on the target time period.
Step 604, determining at least one historical time slot as a reference time slot according to the target environmental factor corresponding to the target time slot and the historical environmental factor corresponding to each historical time slot.
Exemplary, the target time period i represents the i-th target time period in the target time interval, the value range of i is 1-24, and the target environmental factors corresponding to each target time period can be represented as shown in table 4, and the history time period T 1,k The corresponding historical environmental factors may be represented as shown in table 5, and the target environmental factors and the historical environmental factors may be determined from weather forecast published by the local weather station.
TABLE 4 target environmental factors for each target time period
TABLE 5 historical time period T 1,k Corresponding historical environmental factors
Determining a historical time period T j,k In a history period T corresponding to a target period i j,i Further determining a historical time period T j,i At least one time period similar to the target environmental factor of the target time period i is a reference time period.
Step 606, rigid load historical data and flexible load historical data corresponding to the reference time period are obtained.
In this embodiment, the environmental factors are added to the load type judgment policy, and the judgment criteria of the load type are different along with the change of the environmental factors, so that the load type judgment policy is more fit to the actual application scenario, and the accuracy of load type judgment is improved, thereby improving the accuracy of the load control method.
In one embodiment, as shown in fig. 7, a load control method is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
step 702, for each target time period in the target time interval, obtaining rigid load prediction data and flexible load prediction data corresponding to the target time period sent by each target control terminal.
The rigid load prediction data and the flexible load prediction data are determined by the target control terminal based on a preset load type judgment strategy.
The load type judging strategy comprises a mapping relation between the electrical equipment and the load type.
Step 704, determining a peak time period and a trough time period in the target time period based on the rigid load prediction data and the flexible load prediction data corresponding to the plurality of target control terminals corresponding to each target time period.
The method comprises the steps that a server carries out superposition processing on rigid load prediction data and flexible load prediction data corresponding to a plurality of target control terminals according to target time periods to obtain regional rigid load prediction data, regional flexible load prediction data and regional total load prediction data corresponding to each target time period in the target time period.
And fitting the regional rigidity load prediction data corresponding to each target time period to obtain a regional rigidity load prediction curve, and fitting the regional total load prediction data to obtain a regional total load prediction curve, as shown in fig. 8.
Determining a peak time period Ty from a regional stiffness load prediction curve 1,u And trough period Ty 2,v Wherein u=1 to x, x is the number of peak time periods; v=1 to y, y being the number of trough time periods.
And step 706, obtaining a load control strategy according to the crest time period, the trough time period and the flexible load prediction data corresponding to the crest time period, and sending the load control strategy to each target control terminal.
Wherein, in the peak time period Ty 1,u Corresponding regional rigidity load prediction data Sg 1,u Regional flexible load prediction data Sr 1,u Regional total load prediction data Sd 1,u Trough period Ty 2,v Corresponding regional rigidity load prediction data Sg 2,v Regional flexible load prediction data Sr 2,v Regional total load prediction data Sd 2,v 。
The load control strategy is used for controlling the start and stop of the electrical equipment corresponding to the flexible load prediction data in the peak time period and the trough time period in the target time interval by each target control terminal.
In one embodiment, the load control method further comprises: and the server determines the regional power consumption quota according to the regional rigid load prediction data and the regional flexible load prediction data of the peak time period and the trough time period in the target time interval.
Illustratively, at the peak period Ty 1,u Prediction data Sg based on regional rigidity load 1,u Determining regional power consumption quota as regional rigid load prediction data Sg 1,u The load control strategy for obtaining the peak period is that each target control terminal stops using the peak period Ty 1,u Electrical equipment corresponding to flexible load prediction data。
In the trough period Ty 2,v Predicting data Sr from regional flexible load of x peak periods 1,u Obtaining power distribution value Sp for v trough periods 2,v ：
，
The load control strategy for obtaining the trough period is that each target control terminal is in normal use at the trough period Ty 2,v The peak period Ty can also be started to be used on the basis of the electrical equipment corresponding to the rigid load prediction data and the electrical equipment corresponding to the flexible load prediction data 1,u And the electrical equipment corresponds to the corresponding flexible load prediction data.
In this embodiment, a server in a target power system acquires, for each target time period in a target time period, rigid load prediction data and flexible load prediction data corresponding to the target time period sent by each target control terminal, and determines a peak time period and a trough time period in the target time period; according to the flexible load prediction data corresponding to the peak time period, the trough time period and the peak time period, a load control strategy is obtained and sent to each target control terminal, the load control strategy is used for controlling the start and stop of electrical equipment corresponding to the flexible load prediction data in the peak time period and the trough time period in the target time interval by each target control terminal, and the load control method of the embodiment performs total analysis on the rigid load prediction data and the flexible load prediction data of each target control terminal, so that the peak time period and the trough time period with higher dividing accuracy are obtained; different load control strategies are formed for the rigid load and the flexible load according to the peak time period and the trough time period, a fine load control strategy is formed for each target control terminal, and the accuracy of the load control method is further improved.
In a possible implementation manner, after determining the regional power consumption quota, the server sends an acquisition request for acquiring the power consumption quota to the power distribution network, and the power distribution network distributes the power consumption quota to the electric meter connected with the target control terminal according to the acquisition request, so that power is transmitted to each household according to the power consumption quota.
In one embodiment, a preset load type judgment policy is sent to each target control terminal by the server.
Optionally, the preset load type judgment policy is sent to each target control terminal at regular time by the server, and the load type judgment policy sent each time can be adjusted correspondingly according to the change of the application environment.
In a possible implementation manner, a preset load type judgment policy is pre-stored in each target control terminal, and each target control terminal communicates with each other through the internet, so that the load type judgment policy used in the process of obtaining the rigid load prediction data and the flexible load prediction data corresponding to the target time period is kept consistent.
In one embodiment, a server acquires rigid load historical data and flexible load historical data corresponding to at least one historical time period corresponding to a target time period sent by each target control terminal, wherein the rigid load historical data and the flexible load historical data are determined by the target control terminal based on a preset load type judging strategy and ammeter reading data corresponding to the historical time period; and determining rigid load prediction data and flexible load prediction data corresponding to each target control terminal in the target time period according to the rigid load historical data and the flexible load historical data.
In one embodiment, as shown in fig. 9, there is provided a load control method for the target control terminal in fig. 1, including the steps of:
and step 902, acquiring ammeter reading data corresponding to each historical time period in the historical time interval.
And 904, performing non-invasive identification processing on the ammeter reading data to obtain each electrical equipment corresponding to each historical time period and load data corresponding to each electrical equipment.
Step 906, determining the load type of each electrical equipment according to the load type judgment strategy.
Step 908, determining rigid load historical data and flexible load historical data corresponding to the historical time period according to the load data and the load type of each electrical device.
Step 910, for each target time period in the target time interval, determining each historical time period corresponding to the target time period from the historical time interval.
Step 912, determining at least one historical time slot as a reference time slot according to the target environmental factor corresponding to the target time slot and the historical environmental factor corresponding to each historical time slot.
Step 914, obtaining rigid load historical data and flexible load historical data corresponding to the reference time period.
And step 916, determining rigid load prediction data and flexible load prediction data corresponding to the target time period according to the rigid load history data and the flexible load history data.
And step 918, transmitting the rigid load prediction data and the flexible load prediction data corresponding to each target time period to a server.
And step 920, receiving a load control strategy sent by the server, and controlling the start and stop of the electrical equipment corresponding to the flexible load prediction data in the peak time period and the trough time period in the target time interval based on the load control strategy.
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 embodiments of the present application also provide a load control device for implementing the above-mentioned load control 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 one or more embodiments of the load control device provided below may refer to the limitation of the load control method hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 10, there is provided a load control device applied to each target control terminal in a target power system, including: a data prediction module 1002, a signal output module 1004, and a control module 1006, wherein,
the data prediction module 1002 is configured to obtain, for each target time period in the target time interval, rigid load prediction data and flexible load prediction data corresponding to the target time period, where the rigid load prediction data and the flexible load prediction data are determined by the target control terminal based on a preset load type determination policy, and the load type determination policy includes a mapping relationship between electrical equipment and a load type;
the signal output module 1004 is configured to send rigid load prediction data and flexible load prediction data corresponding to each target time period to a server, where the rigid load prediction data and the flexible load prediction data are used by the server to generate a load control policy;
The control module 1006 is configured to receive a load control policy sent by the server, and control start and stop of an electrical device corresponding to the flexible load prediction data in a peak time period and a trough time period within a target time interval based on the load control policy.
In one embodiment, the data prediction module 1002 of the load control device includes a data receiving unit, configured to obtain rigid load historical data and flexible load historical data corresponding to at least one historical time period corresponding to a target time period, where the rigid load historical data and the flexible load historical data are determined by the target control terminal based on a preset load type judgment policy and ammeter reading data corresponding to the historical time period; the data prediction module 1002 determines, from the rigid load history data and the flexible load history data, rigid load prediction data and flexible load prediction data corresponding to the target time period.
In one embodiment, the load control device further includes a load judging module, configured to obtain ammeter reading data corresponding to each historical time period in the historical time interval; non-invasive identification processing is carried out on the ammeter reading data, and each electrical equipment corresponding to each historical time period and load data corresponding to each electrical equipment are obtained; determining the load type of each electrical equipment according to the load type judging strategy; and determining rigid load historical data and flexible load historical data corresponding to the historical time period according to the load data and the load type of each electrical equipment.
In one embodiment, the load type determination policy further includes a mapping relationship between the electrical device, the load type, and the environmental factor; the data prediction module 1002 of the load control device includes a data receiving unit for determining each historical time period corresponding to a target time period from within the historical time period based on the target time period; determining at least one historical time period as a reference time period according to a target environmental factor corresponding to the target time period and historical environmental factors corresponding to each historical time period; rigid load historical data and flexible load historical data corresponding to the reference time period are obtained.
In one embodiment, as shown in fig. 11, there is provided a load control device for a server in a target power system, the target power system further including a plurality of target control terminals, the device including: a signal input module 1102, a data analysis module 1104, and a policy generation module 1106; wherein,
the signal input module 1102 is configured to obtain, for each target time period in the target time interval, rigid load prediction data and flexible load prediction data corresponding to the target time period sent by each target control terminal; the rigid load prediction data and the flexible load prediction number are determined by the target control terminal based on a preset load type judgment strategy;
A data analysis module 1104 for determining a peak time period and a trough time period within the target time period based on the plurality of rigid load prediction data and the plurality of flexible load prediction data corresponding to each target time period;
the policy generation module 1106 is configured to obtain a load control policy according to the crest period, the trough period, and the flexible load prediction data corresponding to the crest period, and send the load control policy to each target control terminal, where the load control policy is used for each target control terminal to control start and stop of an electrical device corresponding to the flexible load prediction data in the crest period and the trough period in the target time interval.
In one embodiment, the load control device further comprises a power interaction module, which is used for determining the power consumption quota of the target area according to the rigid load prediction data and the flexible load prediction data corresponding to the plurality of target control terminals and the peak time period and the trough time period; the target area is an area where a plurality of target control terminals are located; the electricity consumption quota is used as a basis for transmitting electricity to families where each target control terminal is located by the power distribution network.
The respective modules in the above-described load control device may be implemented in whole or in part 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. 12. 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 load control 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. 12 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain 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 implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
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 comply with the related laws and regulations and standards of the related countries and regions.
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 AccessMemory, 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 load control method for each target control terminal in a target power system, the target power system further comprising a server, the method comprising:
for each target time period in a target time interval, acquiring rigid load prediction data and flexible load prediction data corresponding to the target time period, wherein the rigid load prediction data and the flexible load prediction data are determined by the target control terminal based on a preset load type judgment strategy, and the load type judgment strategy comprises a mapping relation among electrical equipment, load types and environmental factors;
The method comprises the steps of sending rigid load prediction data and flexible load prediction data corresponding to target time periods to a server, wherein the rigid load prediction data are used for enabling the server to conduct superposition processing on the basis of the rigid load prediction data according to the target time periods to obtain regional rigid load prediction data corresponding to the target time periods in the target time periods, fitting the regional rigid load prediction data to obtain regional rigid load prediction curves, and determining peak time and trough time periods according to the regional rigid load prediction curves; the flexible load prediction data are used for enabling the server to carry out superposition processing according to each target time period based on the flexible load prediction data, so as to obtain regional flexible load prediction data corresponding to each target time period in the target time period, the regional rigid load prediction data and the regional flexible load prediction data are also used for enabling the server to respectively determine a load control strategy of the peak time period and a load control strategy of the trough time period according to the regional rigid load prediction data and the regional flexible load prediction data, wherein the load control strategy of the peak time period is to shut down electrical equipment corresponding to the flexible load prediction data in the peak time period, and the load control strategy of the trough time period comprises to start the electrical equipment corresponding to the flexible load prediction data in the peak time period;
Receiving a load control strategy of the peak time period and a load control strategy of the trough time period, which are sent by the server, and turning off electrical equipment corresponding to the flexible load prediction data in the peak time period based on the load control strategy of the peak time period; and controlling the start and stop of the electrical equipment corresponding to the flexible load prediction data in the trough time period based on the load control strategy of the trough time period.
2. The method of claim 1, wherein the obtaining the rigid load prediction data and the flexible load prediction data corresponding to the target time period comprises:
acquiring rigid load historical data and flexible load historical data corresponding to at least one historical time period corresponding to the target time period, wherein the rigid load historical data and the flexible load historical data are determined by the target control terminal based on the preset load type judging strategy and ammeter reading data corresponding to the historical time period;
and determining rigid load prediction data and flexible load prediction data corresponding to the target time period according to the rigid load history data and the flexible load history data.
3. The method according to claim 2, wherein the method further comprises:
acquiring ammeter reading data corresponding to each historical time period in the historical time interval;
non-invasive identification processing is carried out on the ammeter reading data, and each electrical equipment corresponding to each historical time period and load data corresponding to each electrical equipment are obtained;
determining the load type of each electrical equipment according to the load type judging strategy;
and determining rigid load historical data and flexible load historical data corresponding to the historical time period according to the load data and the load type of each electrical equipment.
4. The method of claim 2, wherein the obtaining rigid load history data and flexible load history data corresponding to at least one history period corresponding to the target period of time comprises:
determining each historical time period corresponding to the target time period from within a historical time interval based on the target time period;
determining at least one historical time period as a reference time period according to the target environmental factors corresponding to the target time period and the historical environmental factors corresponding to each historical time period;
And acquiring rigid load historical data and flexible load historical data corresponding to the reference time period.
5. A load control method for a server in a target power system, the target power system further comprising a plurality of target control terminals, the method comprising:
for each target time period in a target time interval, acquiring rigid load prediction data and flexible load prediction data corresponding to the target time period sent by each target control terminal; the rigid load prediction data and the flexible load prediction data are determined by the target control terminal based on a preset load type judgment strategy, wherein the load type judgment strategy comprises a mapping relation among electrical equipment, load types and environmental factors;
carrying out superposition processing on the rigid load prediction data according to the target time periods to obtain regional rigid load prediction data corresponding to the target time periods in the target time interval; superposing the flexible load prediction data according to the target time periods to obtain regional flexible load prediction data corresponding to the target time periods in the target time interval;
Fitting the regional rigid load prediction data to obtain a regional rigid load prediction curve;
determining peak time and trough time periods according to the regional rigid load prediction curve;
determining a load control strategy of the peak time period and a load control strategy of the trough time period according to the regional rigid load prediction data and the regional flexible load prediction data respectively, and sending the load control strategy to each target control terminal, wherein the load control strategy of the peak time period is to turn off electrical equipment corresponding to the flexible load prediction data in the peak time period, the load control strategy of the trough time period comprises to turn on electrical equipment corresponding to the flexible load prediction data in the peak time period, and the load control strategy of the peak time period is used for enabling each target control terminal to turn off the electrical equipment corresponding to the flexible load prediction data in the peak time period; the load control strategy of the trough time period is used for controlling the start and stop of the electrical equipment corresponding to the flexible load prediction data in the trough time period by each target control terminal.
6. The method of claim 5, wherein the method further comprises: determining electricity consumption quota of a target area according to the rigid load prediction data, the flexible load prediction data, the peak time period and the trough time period corresponding to the target control terminals; the target area is an area where the plurality of target control terminals are located; the electricity consumption quota is used as a basis for transmitting electricity to families where each target control terminal is located by the power distribution network.
7. A load control device, characterized by being applied to each target control terminal in a target power system, the target power system further comprising a server, the device comprising:
the data prediction module is used for acquiring rigid load prediction data and flexible load prediction data corresponding to target time periods according to each target time period in a target time period, wherein the rigid load prediction data and the flexible load prediction data are determined by the target control terminal based on a preset load type judgment strategy, and the load type judgment strategy comprises a mapping relation among electrical equipment, load types and environmental factors;
The signal output module is used for sending the rigid load prediction data and the flexible load prediction data corresponding to the target time periods to the server, wherein the rigid load prediction data are used for enabling the server to conduct superposition processing on the basis of the rigid load prediction data according to the target time periods to obtain regional rigid load prediction data corresponding to the target time periods in the target time periods, fitting the regional rigid load prediction data to obtain regional rigid load prediction curves, and determining peak time and trough time periods according to the regional rigid load prediction curves; the flexible load prediction data are used for enabling the server to carry out superposition processing according to each target time period based on the flexible load prediction data, so as to obtain regional flexible load prediction data corresponding to each target time period in the target time period, the regional rigid load prediction data and the regional flexible load prediction data are also used for enabling the server to respectively determine a load control strategy of the peak time period and a load control strategy of the trough time period according to the regional rigid load prediction data and the regional flexible load prediction data, wherein the load control strategy of the peak time period is to shut down electrical equipment corresponding to the flexible load prediction data in the peak time period, and the load control strategy of the trough time period comprises to start the electrical equipment corresponding to the flexible load prediction data in the peak time period;
The control module is used for receiving the load control strategy of the peak time period and the load control strategy of the trough time period, which are sent by the server, and shutting down electrical equipment corresponding to the flexible load prediction data in the peak time period based on the load control strategy of the peak time period; and controlling the start and stop of the electrical equipment corresponding to the flexible load prediction data in the trough time period based on the load control strategy of the trough time period.
8. A load control device for a server in a target power system, the target power system further comprising a plurality of target control terminals, the device comprising:
the signal input module is used for acquiring rigid load prediction data and flexible load prediction data corresponding to each target time period sent by each target control terminal according to each target time period in a target time period; the rigid load prediction data and the flexible load prediction number are determined by the target control terminal based on a preset load type judgment strategy, wherein the load type judgment strategy comprises a mapping relation among electrical equipment, load types and environmental factors;
The data analysis module is used for carrying out superposition processing on the rigid load prediction data according to the target time periods to obtain regional rigid load prediction data corresponding to the target time periods in the target time interval; superposing the flexible load prediction data according to the target time periods to obtain regional flexible load prediction data corresponding to the target time periods in the target time interval; fitting the regional rigid load prediction data to obtain a regional rigid load prediction curve; determining peak time and trough time periods according to the regional rigid load prediction curve;
the strategy generation module is used for respectively determining a load control strategy of the peak time period and a load control strategy of the trough time period according to the regional rigid load prediction data and the regional flexible load prediction data, and sending the load control strategy to each target control terminal, wherein the load control strategy of the peak time period is to stop electrical equipment corresponding to the flexible load prediction data in the peak time period, the load control strategy of the trough time period comprises to start the electrical equipment corresponding to the flexible load prediction data in the peak time period, and the load control strategy of the peak time period is used for each target control terminal to stop the electrical equipment corresponding to the flexible load prediction data in the peak time period; the load control strategy of the trough time period is used for controlling the start and stop of the electrical equipment corresponding to the flexible load prediction data in the trough time period by each target control terminal.
9. 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.
10. 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.
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