CN116345465A - Power grid load regulation and control method and system based on big data technology - Google Patents
Power grid load regulation and control method and system based on big data technology Download PDFInfo
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- H—ELECTRICITY
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
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- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
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- 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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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract
The invention discloses a power grid load regulation and control method and system based on big data technology, comprising the following steps: acquiring an electricity utilization curve of a user side; establishing a cellular power supply diagram taking a power supply base station as a base point; acquiring a power supply capacity curve of each power supply base station; the redundant power supply load of the power supply base station is put into a dispatching library according to the power supply capacity curve of the power supply base station, and a dispatching center dispatches the load according to the power utilization curve and dispatching function of the user side after dispatching the data of the dispatching library; according to the invention, the electricity consumption curve of the user side is collected, the electricity consumption peak fluctuation identification is carried out based on the access of the type of the electric equipment, the electricity consumption judgment of all users in the area is carried out by one user, the user quantity judgment in the whole area is realized, the electricity consumption judgment of the user side is more accurate, meanwhile, the power supply quantity of the power grid is reasonably distributed, and the rapid and accurate load dispatching is carried out on the basis of preparing a power supply source and a power supply route, so that the power supply quality of the power grid is improved.
Description
Technical Field
The invention relates to the technical field of power grid load regulation and control, in particular to a power grid load regulation and control method and system based on a big data technology.
Background
Currently, the development of energy sources shows a trend of coexistence of centralized and distributed supplies, the supply relation is changed from unidirectional response demands to supply and demand multiple units and bidirectional interaction, and the energy utilization mode is developed from single energy sources to multi-energy complementation and comprehensive energy efficiency optimization.
And the access of large-scale new energy sources leads to insufficient in-situ digestion capacity of the power distribution network. With the large-scale access of the distributed new energy sources to the power distribution network, the phenomenon of power reversal occurs in part of areas, and the power distribution network dissipates the new energy sources on site and faces extremely high pressure.
And partial power supply areas are easy to cause insufficient power supply due to the fact that power consumption peaks appear simultaneously, power consumption of users is affected, bad experience is brought to the users, meanwhile, partial areas are easy to cause power shortage when uncontrollable faults occur to a power grid, and the areas cannot be timely supplied during maintenance, so that the power supply quality of the power grid is poor.
In order to maintain the supply and demand balance of the power system and safe and stable operation, the power supply company can meet these short-term peak demands by adding new installed capacity, but the cost is high and the utilization efficiency is low.
For example, chinese patent CN109687429B discloses a scheduling method, system and storage medium for flexible load device, comprising: constructing a user load using function and a load pricing function; constructing a load pricing scheme of the self-adaptive energy consumption level, and setting the average energy consumption level, the constant electricity price interval and the electricity price in the corresponding interval of the user flexible load equipment; acquiring an initial flexible load equipment scheduling scheme of a user; constructing an optimal scheduling model of dynamic flexible load and solving the optimal scheduling model by taking the minimum energy consumption cost as a target; if the expected electricity cost of the user is not greater than the actual electricity cost of the user, updating the flexible load equipment scheduling scheme, otherwise, iteratively solving the optimal scheduling model until the optimal scheduling scheme is obtained; according to the scheme, the energy consumption cost is used for optimal scheduling, but the variability of the power utilization state of a user and the redundant load adjustment of the new energy power supply are not considered, so that the power supply quantity is not well utilized, and the power supply quality of a power grid is not improved.
Disclosure of Invention
The method mainly solves the problems of poor load scheduling accuracy and timeliness caused by the fact that the load scheduling of the power grid in the prior art does not consider the change of the power consumption of a user; the utility model provides a power grid load regulation and control method and system based on big data technology, which improves the timeliness and accuracy of load scheduling.
The technical problems of the invention are mainly solved by the following technical proposal: a power grid load regulation and control method based on big data technology comprises the following steps:
acquiring an electricity utilization curve of a user side;
establishing a cellular power supply diagram taking a power supply base station as a base point;
acquiring a power supply capacity curve of each power supply base station;
and (3) placing redundant power supply load of the power supply base station into a dispatching library according to the power supply capacity curve of the power supply base station, and carrying out load dispatching according to the power utilization curve and dispatching function of the user side after the dispatching center fetches data of the dispatching library.
Preferably, the specific method for acquiring the electricity utilization curve of the user side comprises the following steps:
collecting electricity consumption data of each user in a preset area in a preset time period;
analyzing the power consumption ratio of different electric equipment of a user according to the power consumption data;
establishing a first neural network model, and training the first neural network model by taking the electricity consumption data, the electricity consumption time period and the electricity consumption equipment type as training set data to obtain an electricity consumption distribution function;
and obtaining electricity utilization curves of different time periods according to the electricity utilization distribution function.
Preferably, the cellular electrogram building method comprises the following steps:
traversing power supply base stations in the area;
acquiring coordinate information of each power supply base station;
converting the coordinate information of each power supply base station to a regional power grid map;
dividing the power supply base stations by adopting the honeycomb marking frames, scheduling the loads of the power supply base stations in the same honeycomb marking frame as the same-level power supply, and scheduling the loads of the power supply base stations in different honeycomb marking frames as the cross-region power supply.
Preferably, the method for establishing the power supply capacity curve of each power supply base station comprises the following steps:
acquiring the proportion of thermal power supply and new energy power supply of a power supply base station;
acquiring the power supply quantity of new energy power supply and the reference quantity affecting the power supply quantity within a preset time period;
establishing a second neural network model, and training the second neural network model by taking the reference quantity affecting the power supply quantity as a training set to obtain a power supply quantity change function of new energy power supply;
and acquiring a power supply capacity curve of the power supply base station based on the current reference quantity affecting the power supply quantity.
Preferably, the specific method for placing the redundant power supply load of the power supply base station into the allocation library comprises the following steps:
acquiring the power supply quantity of each power supply base station in each honeycomb marking frame, and if the power supply quantity of the power supply base station is larger than the power supply quantity requirement corresponding to the power supply capacity curve of the current time period, placing redundant power supply loads into a primary allocation library;
and counting whether the power supply quantity of all the power supply base stations in the honeycomb marking frame is larger than the total power supply quantity requirement corresponding to the power supply capacity curve of the current time period, if so, placing redundant power supply loads into a secondary allocation library, otherwise, sending out a honeycomb power supply request.
Preferably, the primary deployment library is used for peer power supply, and the secondary deployment library is used for cross-regional power supply.
Preferably, the specific method for load scheduling by the scheduling center is as follows:
monitoring the state of a power supply line of the regional power grid, and detecting whether the power grid has a power supply fault or not;
monitoring the electricity utilization state of the user according to the electricity utilization curve of the user side, and judging the electricity utilization information at the next moment according to the current electricity utilization amount;
acquiring load data in a deployment library;
establishing an allocation function according to the power grid power supply line state and the coordinate information of the power supply base station;
and carrying out load scheduling according to the allocation function, the electricity information of the user at the next moment and the load data in the allocation library.
The invention also provides a power grid load regulation and control system based on the big data technology, which comprises the following steps: the acquisition module is used for acquiring the power supply quantity of the power supply base station and the power consumption quantity of a user; the diagnosis module monitors the state of the power grid; and the dispatching center is used for carrying out load dispatching according to the state of the power grid, the power consumption of the user and the power supply quantity of the power supply base station.
Preferably, the scheduling center performs load scheduling including peer power supply scheduling and cross-regional power supply scheduling.
Preferably, the acquisition module comprises a power indication unit, an electric equipment identification unit and an electric energy meter, wherein the power indication unit obtains the power supply quantity of the power supply base station, the electric equipment identification unit identifies the type of the electric equipment according to the instantaneous current of the access equipment, and the electric energy meter obtains the total power consumption of a user.
Preferably, the device identification unit is a socket.
The invention also provides a computer storage medium storing a computer program which when run performs the above method.
The invention also provides an electronic device, which comprises a processor and a memory;
the memory stores computer-executable instructions;
the computer-executable instructions of the memory, when executed by the processor, cause the processor to perform the method described above.
The beneficial effects of the invention are as follows: the electricity consumption curves of the user side are collected, electricity consumption peak fluctuation identification is carried out based on the access of the type of the electric equipment, electricity consumption judgment of all users in the area is carried out through one user, user quantity judgment in the whole area is achieved, the electricity consumption judgment of the user side is more accurate, and arrival of the electricity consumption peak can be predicted more timely and accurately; meanwhile, the power supply quantity of the power grid is reasonably distributed, and on the basis of preparing a power supply source and a power supply route, rapid and accurate load dispatching is performed, so that the power supply quality of the power grid is improved.
Drawings
Fig. 1 is a flowchart of a power grid load regulation method according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for acquiring a power consumption curve at a user side according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, further detailed description of the technical solutions in the embodiments of the present invention will be given by the following examples with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Examples:
the power grid load regulation and control method based on the big data technology, as shown in fig. 1 and fig. 2, comprises the following steps:
s1: acquiring an electricity utilization curve of a user side; the specific method for acquiring the electricity utilization curve of the user side comprises the following steps: collecting electricity consumption data of each user in a preset area in a preset time period; analyzing the power consumption ratio of different electric equipment of a user according to the power consumption data; establishing a first neural network model, and training the first neural network model by taking the electricity consumption data, the electricity consumption time period and the electricity consumption equipment type as training set data to obtain an electricity consumption distribution function; and obtaining electricity utilization curves of different time periods according to the electricity utilization distribution function.
Setting a preset time period, setting holidays, workdays, double holidays and the like in the preset time period, and collecting electricity in the time period of 24 hours in different dates, for example, acquiring electricity data of a user every 15 minutes during the workdays, analyzing the type of electric equipment of the user at the current moment, judging electricity utilization of other users in the same district based on the type of electric equipment, manufacturing an electricity utilization curve, and improving electricity utilization redundancy for electricity utilization peaks.
S2: establishing a cellular power supply diagram taking a power supply base station as a base point; the method for establishing the cellular power supply diagram comprises the following steps: traversing power supply base stations in the area; acquiring coordinate information of each power supply base station; converting the coordinate information of each power supply base station to a regional power grid map; dividing the power supply base stations by adopting the honeycomb marking frames, scheduling the loads of the power supply base stations in the same honeycomb marking frame as the same-level power supply, and scheduling the loads of the power supply base stations in different honeycomb marking frames as the cross-region power supply.
S3: acquiring a power supply capacity curve of each power supply base station; the method for establishing the power supply capacity curve of each power supply base station comprises the following steps: acquiring the proportion of thermal power supply and new energy power supply of a power supply base station; acquiring the power supply quantity of new energy power supply and the reference quantity affecting the power supply quantity within a preset time period; establishing a second neural network model, and training the second neural network model by taking the reference quantity affecting the power supply quantity as a training set to obtain a power supply quantity change function of new energy power supply; and acquiring a power supply capacity curve of the power supply base station based on the current reference quantity affecting the power supply quantity.
S4: the redundant power supply load of the power supply base station is put into a dispatching library according to the power supply capacity curve of the power supply base station, and a dispatching center dispatches the load according to the power utilization curve and dispatching function of the user side after dispatching the data of the dispatching library; the specific method for placing the redundant power supply load of the power supply base station into the allocation library comprises the following steps: acquiring the power supply quantity of each power supply base station in each honeycomb marking frame, and if the power supply quantity of the power supply base station is larger than the power supply quantity requirement corresponding to the power supply capacity curve of the current time period, placing redundant power supply loads into a primary allocation library; and counting whether the power supply quantity of all the power supply base stations in the honeycomb marking frame is larger than the total power supply quantity requirement corresponding to the power supply capacity curve of the current time period, if so, placing redundant power supply loads into a secondary allocation library, otherwise, sending out a honeycomb power supply request.
Further, the primary allocation library is used for power supply at the same level, and the secondary allocation library is used for power supply across regions.
The specific method for the dispatching center to carry out load dispatching comprises the following steps: monitoring the state of a power supply line of the regional power grid, and detecting whether the power grid has a power supply fault or not; monitoring the electricity utilization state of the user according to the electricity utilization curve of the user side, and judging the electricity utilization information at the next moment according to the current electricity utilization amount; acquiring load data in a deployment library; establishing an allocation function according to the power grid power supply line state and the coordinate information of the power supply base station; and carrying out load scheduling according to the allocation function, the electricity information of the user at the next moment and the load data in the allocation library.
Firstly, a dispatching center marks a honeycomb marking frame from a secondary dispatching library, marks a honeycomb marking frame with redundant power supply load as red, marks a honeycomb power supply request with honeycomb power supply request as green, traverses all power supply base stations with redundant power supply load as power supply base stations, traverses all power supply base stations incapable of meeting power supply in a power supply area as power supply base stations, marks as power supply base stations required, generates transmission paths of each power supply base station and each power supply base station required, a plurality of transmission paths exist, detects whether line faults exist on each transmission path, adjusts weights if the line faults exist, and establishes proper load transmission of the power supply base stations and the power supply base stations required according to the distance between the power supply base stations and the power supply base stations required and the weights of the transmission paths.
A big data technology based power grid load regulation and control system, comprising: the acquisition module is used for acquiring the power supply quantity of the power supply base station and the power consumption quantity of a user; the diagnosis module monitors the state of the power grid; and the dispatching center is used for carrying out load dispatching according to the state of the power grid, the power consumption of the user and the power supply quantity of the power supply base station.
Further, the scheduling center performs load scheduling including peer power supply scheduling and cross-regional power supply scheduling.
Further, the acquisition module comprises a power indication unit, an electric equipment identification unit and an electric energy meter, wherein the power indication unit obtains the power supply quantity of the power supply base station, the equipment identification unit identifies the type of the electric equipment according to the instantaneous current of the access equipment, and the electric energy meter obtains the total power consumption of a user.
Further, the equipment identification unit is used as a socket, at least one socket is arranged, the socket is installed in a room of each user, the socket is installed on a wall or placed on the ground, when the electric equipment is electrified, instantaneous current is generated, and the type of the electric equipment is judged through the difference of the instantaneous current generated by each electric equipment.
A computer storage medium storing a computer program which, when run, performs the above method.
An electronic device includes a processor and a memory; the memory stores computer-executable instructions; the computer-executable instructions of the memory, when executed by the processor, cause the processor to perform the method described above.
Further, the device that detects the line condition of electric wire netting is fault indication device, a housing, current transformer, a spring, the slider, pressure sensor and MCU, on the transmission line of casing installation electric wire netting, current transformer's input is connected with the transmission line of electric wire netting, current transformer's output and spring coupling, the one end and the shells inner wall of first spring are connected, the other end and the slider of spring are connected, be equipped with the spout in the casing, pressure sensor and slider are all installed in the spout, pressure sensor and slider are directed against the one side butt of spring, pressure sensor is used for detecting the pressure information when the slider has certain trend, pressure sensor's output and MCU are connected, MCU is connected with dispatch center through wireless communication module. The current transformer detects the current of the power grid transmission line, the spring is precompressed firstly, the pressure sensor has a precompressed value to be detected, the precompressed amount is consistent with the compression degree of the spring due to the current transmitted by the current transformer under the normal current condition, the maximum threshold value detected by the pressure sensor is set, when the current is overlarge, the spring is further compressed, when the pressure sensor cannot detect the pressure value, the overlarge fault occurs, when the current is overlarge, the spring stretches, when the pressure value detected by the pressure sensor exceeds the maximum threshold value, the small fault occurs, the correct detection of the fault type of the line is realized, the detection device is simple, and the problem that the current meter exceeds the measuring range or the inaccurate detection is caused by the overlarge current of the common ammeter.
The above-described embodiment is only a preferred embodiment of the present invention, and is not limited in any way, and other variations and modifications may be made without departing from the technical aspects set forth in the claims.
Claims (13)
1. The power grid load regulation and control method based on the big data technology is characterized by comprising the following steps of:
acquiring an electricity utilization curve of a user side;
establishing a cellular power supply diagram taking a power supply base station as a base point;
acquiring a power supply capacity curve of each power supply base station;
and (3) placing redundant power supply load of the power supply base station into a dispatching library according to the power supply capacity curve of the power supply base station, and carrying out load dispatching according to the power utilization curve and dispatching function of the user side after the dispatching center fetches data of the dispatching library.
2. The method for regulating and controlling the load of a power grid based on the big data technology according to claim 1, wherein,
the specific method for acquiring the electricity utilization curve of the user side comprises the following steps:
collecting electricity consumption data of each user in a preset area in a preset time period;
analyzing the power consumption ratio of different electric equipment of a user according to the power consumption data;
establishing a first neural network model, and training the first neural network model by taking the electricity consumption data, the electricity consumption time period and the electricity consumption equipment type as training set data to obtain an electricity consumption distribution function;
and obtaining electricity utilization curves of different time periods according to the electricity utilization distribution function.
3. A power grid load control method based on big data technology according to claim 1 or 2, characterized in that,
the method for establishing the cellular power supply diagram comprises the following steps:
traversing power supply base stations in the area;
acquiring coordinate information of each power supply base station;
converting the coordinate information of each power supply base station to a regional power grid map;
dividing the power supply base stations by adopting the honeycomb marking frames, scheduling the loads of the power supply base stations in the same honeycomb marking frame as the same-level power supply, and scheduling the loads of the power supply base stations in different honeycomb marking frames as the cross-region power supply.
4. A power grid load control method based on big data technology according to claim 1 or 2, characterized in that,
the method for establishing the power supply capacity curve of each power supply base station comprises the following steps:
acquiring the proportion of thermal power supply and new energy power supply of a power supply base station;
acquiring the power supply quantity of new energy power supply and the reference quantity affecting the power supply quantity within a preset time period;
establishing a second neural network model, and training the second neural network model by taking the reference quantity affecting the power supply quantity as a training set to obtain a power supply quantity change function of new energy power supply;
and acquiring a power supply capacity curve of the power supply base station based on the current reference quantity affecting the power supply quantity.
5. A power grid load control method based on big data technology according to claim 3, wherein,
the specific method for placing the redundant power supply load of the power supply base station into the allocation library comprises the following steps:
acquiring the power supply quantity of each power supply base station in each honeycomb marking frame, and if the power supply quantity of the power supply base station is larger than the power supply quantity requirement corresponding to the power supply capacity curve of the current time period, placing redundant power supply loads into a primary allocation library;
and counting whether the power supply quantity of all the power supply base stations in the honeycomb marking frame is larger than the total power supply quantity requirement corresponding to the power supply capacity curve of the current time period, if so, placing redundant power supply loads into a secondary allocation library, otherwise, sending out a honeycomb power supply request.
6. The method for regulating and controlling the load of a power grid based on the big data technology according to claim 5, wherein,
the primary allocation library is used for power supply at the same level, and the secondary allocation library is used for power supply across regions.
7. A power grid load control method based on big data technology according to claim 1 or 2, characterized in that,
the specific method for the dispatching center to carry out load dispatching comprises the following steps:
monitoring the state of a power supply line of the regional power grid, and detecting whether the power grid has a power supply fault or not;
monitoring the electricity utilization state of the user according to the electricity utilization curve of the user side, and judging the electricity utilization information at the next moment according to the current electricity utilization amount;
acquiring load data in a deployment library;
establishing an allocation function according to the power grid power supply line state and the coordinate information of the power supply base station;
and carrying out load scheduling according to the allocation function, the electricity information of the user at the next moment and the load data in the allocation library.
8. A big data technology-based power grid load regulation system, adopting the big data technology-based power grid load regulation method as set forth in any one of claims 1 to 7, comprising:
the acquisition module is used for acquiring the power supply quantity of the power supply base station and the power consumption quantity of a user;
the diagnosis module monitors the state of the power grid;
and the dispatching center is used for carrying out load dispatching according to the state of the power grid, the power consumption of the user and the power supply quantity of the power supply base station.
9. A grid load regulation system based on big data technology as set forth in claim 8, wherein,
the scheduling center performs load scheduling including peer power supply scheduling and cross-regional power supply scheduling.
10. A grid load regulation system based on big data technology as set forth in claim 8, wherein,
the acquisition module comprises a power indication unit, an electric equipment identification unit and an electric energy meter, wherein the power indication unit obtains the power supply quantity of a power supply base station, the electric equipment identification unit identifies the type of the electric equipment according to the instantaneous current of the access equipment, and the electric energy meter obtains the total power consumption of a user.
11. A grid load regulation system based on big data technology as set forth in claim 10, wherein,
the equipment identification unit is a socket.
12. A computer storage medium, characterized in that it stores a computer program which, when run, performs a grid load regulation method based on big data technology as claimed in any of claims 1 to 7.
13. An electronic device comprising a processor and a memory;
the memory stores computer-executable instructions;
the processor executing the computer-executable instructions of the memory causes the processor to perform a method for regulating grid load based on big data technology as claimed in any of claims 1 to 7.
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