A method and system for intelligent load balancing in power distribution network

Abstract

The present invention relates to the technical field of distribution network load, and discloses an intelligent load balancing method and system for distribution network, including a distribution network historical load data collection module, a regional load data collection module, a dispatching analysis module and a load early warning module. The distribution network historical load data collection module includes a historical distribution network weather load data collection unit and a historical holiday distribution network load data collection unit. The historical distribution network weather load data collection unit collects the load data of the distribution network under historical extreme weather. The system obtains the power load value through all the load data of the distribution network under historical extreme weather, all the load data of the distribution network under historical holidays and the historical load data of each sub-region, and collects the total amount of electric energy stored in each energy storage device and calculates the power generation data of the forecast day, and considers the urban business district as a variable to consider the power load, so as to ensure that the power load can be predicted more accurately during holidays.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network loads, and in particular to an intelligent load balancing method and system for a distribution network. Background Art

[0002] Currently, when adjusting the load balance of the distribution network, the increased energy consumption of various business districts in the city during holidays or special days, as well as the increased energy demand under extreme conditions, have not been taken into account. Summary of the invention

[0003] Technical issues solved

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent load balancing method and system for a distribution network, which has the advantages of accurately predicting the load of the distribution network and solves the above-mentioned technical problems.

[0005] Technical Solution

[0006] To achieve the above object, the present invention provides the following technical solutions: an intelligent load balancing system for a distribution network, comprising a distribution network historical load data acquisition module, a regional load data acquisition module, a dispatch analysis module and a load early warning module;

[0007] The distribution network historical load data collection module includes a historical distribution network weather load data collection unit and a historical holiday distribution network load data collection unit. The historical distribution network weather load data collection unit is used to collect all load data of the distribution network under historical extreme weather conditions. The historical holiday distribution network load data collection unit is used to collect all load data of the distribution network under historical holidays and constitute a holiday index library.

[0008] The regional load data collection module includes a regional division unit and a regional load data collection unit, wherein the regional division unit divides the urban area into different sub-areas and collects historical load data of each sub-area through the regional load data collection unit;

[0009] The dispatch analysis module obtains the power load value based on all load data of the distribution network under historical extreme weather conditions, all load data of the distribution network under historical holidays, and historical load data of each sub-region;

[0010] The load warning module includes a power reserve data acquisition unit and a load warning unit. The power reserve data is used to collect the total stored electric energy of each energy storage device and calculate the power generation data of the predicted day. The load warning unit performs warning scheduling based on the power generation data and power load value of the predicted day.

[0011] As a preferred technical solution of the present invention, the specific expression of the historical distribution network weather load data collection unit collecting all load data of the distribution network under historical extreme weather conditions is as follows:

[0012] ,

[0013] in, represents the weather load dataset, They represent the distribution network load data under the first extreme weather, , No. Distribution network load data under extreme weather conditions, , No. Distribution network load data under extreme weather conditions.

[0014] As a preferred technical solution of the present invention, the specific expression of the historical holiday distribution network load data collection unit collecting all load data of the distribution network during historical holidays is as follows:

[0015] ,

[0016] in, represents the historical holiday load dataset, They represent the distribution network load data set under the first historical holiday, , No. Distribution network load datasets under historical holidays, , No. The distribution network load dataset under historical holidays is Distribution network load datasets under historical holidays The specific expression is as follows:

[0017]

[0018] in, Respectively represent The first distribution network load data stored in the distribution network load data set under historical holidays, , No. Distribution network load data, , No. Distribution network load data.

[0019] As a preferred technical solution of the present invention, the specific steps of the area division unit dividing the urban area into different sub-areas are as follows:

[0020] Step A1: Read the city map, establish the XOY coordinate system, and determine the current city Coordinates of commercial centers ;

[0021] Step A2: Create the The radiation equation of a commercial center;

[0022] Step A3: Determine whether the radiation equations of different commercial centers have overlapping areas. If so, merge them to generate new sub-areas.

[0023] Step A4: Repeat step A3 until there is no overlapping area, and output all sub-areas.

[0024] As a preferred technical solution of the present invention, the The specific expression of the radiation equation of a commercial center is as follows:

[0025]

[0026] in, Indicates the current city The coordinates of the commercial center. and Represents a variable, Indicates The radiation radius of a commercial center.

[0027] As a preferred technical solution of the present invention, in step A3, it is determined whether the radiation equations of different commercial centers have overlapping areas. If so, the specific steps of merging and generating new sub-areas are as follows:

[0028] Step A3.1: Determine the Commercial centers and Whether there are overlapping areas among the commercial centers is expressed as follows:

[0029]

[0030] in, Indicates Coordinates of commercial centers and Coordinates of commercial centers The distance between and Respectively represent The radiation radius of the commercial center and the The radiation radius of a commercial center, represents the absolute value, if If satisfied, proceed to step A3.2. If not satisfied, terminate step A3.1;

[0031] Step A3.2: Commercial centers and The commercial centers are merged to obtain sub-regions, and the specific expression is as follows:

[0032]

[0033]

[0034]

[0035]

[0036] in, , represents the coordinates of the center of the merged sub-region, represents the sub-region radius, and Respectively represent The coordinates of the commercial center and The coordinates of the commercial center. and Represents a variable.

[0037] As a preferred technical solution of the present invention, the dispatch analysis module obtains the specific expression of the power load value based on all load data of the distribution network under historical extreme weather conditions, all load data of the distribution network under historical holidays, and historical load data of each sub-region as follows:

[0038]

[0039] in, Indicates the power load value, Indicates The power demand value of each sub-region is Indicates The historical maximum electricity demand of each sub-region is Express The sum of the sub-regions is represents the extreme weather judgment function. If the same extreme weather as in the weather load dataset will occur on the forecast day, then Output the mean value of the distribution network load data under extreme weather conditions. If no extreme weather occurs on the forecast day, Output the average value of the distribution network throughout the year, Represents the holiday judgment function. If the predicted day corresponds to Historical holidays, Output Distribution network load datasets under historical holidays If the forecast day is not a holiday, then Output the annual distribution network average.

[0040] As a preferred technical solution of the present invention, the specific expression of the power reserve data collection of the total power stored in each energy storage device and the power generation data of the predicted day is as follows:

[0041]

[0042]

[0043] in, Represents the total amount of electrical energy stored in each energy storage device. Indicates The electrical energy stored in the energy storage device, Express The stored energy of each energy storage device is summed up. Indicates The power generation of each power generation device, express The sum of the power generation of each power generation device is Indicates the power generation data for the forecast day.

[0044] As a preferred technical solution of the present invention, the specific expression for the load warning unit to perform warning scheduling based on the power generation data and power load value of the forecast day is as follows:

[0045]

[0046] in, Indicates the warning value. Indicates the power load value, Represents the total amount of electrical energy stored in each energy storage device. Represents the power generation data for the forecast day. If No warning will be given.

[0047] The present invention also provides an intelligent load balancing method for a power distribution network, comprising the following steps:

[0048] Step 1: All load data of the distribution network under historical extreme weather conditions and historical holidays;

[0049] Step 2: Divide the urban area into different sub-areas and collect historical load data for each sub-area;

[0050] Step 3: Based on all load data of the distribution network under historical extreme weather conditions, all load data of the distribution network under historical holidays, and historical load data of each sub-region, the power load value is obtained;

[0051] Step 4: Collect the total amount of electric energy stored in each energy storage device and calculate the power generation data for the forecast day;

[0052] Step 5: Perform early warning dispatch based on the power generation data and power load value on the forecast day.

[0053] Compared with the prior art, the present invention provides an intelligent load balancing method and system for a distribution network, which has the following beneficial effects:

[0054] The present invention obtains the power load value through all load data of the distribution network under historical extreme weather conditions, all load data of the distribution network under historical holidays and the historical load data of each sub-region, collects the total electric energy stored in each energy storage device and calculates the power generation data of the predicted day, and considers the urban business district as a variable to ensure that the power load can be predicted more accurately when holidays occur. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the system framework of the present invention;

[0056] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] See also Figure 1-Figure 2 , an intelligent load balancing system for a distribution network, comprising a distribution network historical load data acquisition module, a regional load data acquisition module, a dispatching analysis module and a load early warning module;

[0059] The distribution network historical load data collection module includes a historical distribution network weather load data collection unit and a historical holiday distribution network load data collection unit. The historical distribution network weather load data collection unit is used to collect all load data of the distribution network under historical extreme weather conditions. The historical holiday distribution network load data collection unit is used to collect all load data of the distribution network under historical holidays and form a holiday index library. Extreme weather includes extreme rain and snow weather, extreme high temperature and power grid failure. Among them, extreme rain and snow weather and extreme high temperature are released by the meteorological department, and power grid failure is released by the power supply department. The specific expression of the historical distribution network weather load data collection unit collecting all load data of the distribution network under historical extreme weather conditions is as follows:

[0060]

[0061] in, represents the weather load dataset, They represent the distribution network load data under the first extreme weather, , No. Distribution network load data under extreme weather conditions, , No. The distribution network load data under extreme weather conditions, the specific expression of the distribution network load data collection unit for historical holidays to collect all the load data of the distribution network under historical holidays is as follows:

[0062]

[0063] in, represents the historical holiday load dataset, They represent the distribution network load data set under the first historical holiday, , No. Distribution network load datasets under historical holidays, , No. The distribution network load dataset under historical holidays is Distribution network load datasets under historical holidays The specific expression is as follows:

[0064]

[0065] in, Respectively represent The first distribution network load data stored in the distribution network load data set under historical holidays, , No. Distribution network load data, , No. Distribution network load data;

[0066] The regional load data collection module includes a regional division unit and a regional load data collection unit. The regional division unit divides the urban area into different sub-areas, and collects the historical load data of each sub-area through the regional load data collection unit. The specific steps of the regional division unit dividing the urban area into different sub-areas are as follows:

[0067] Step A1: Read the city map, establish the XOY coordinate system, and determine the current city Coordinates of commercial centers ;

[0068] Step A2: Create the The radiation equation of a commercial center, The specific expression of the radiation equation of a commercial center is as follows:

[0069]

[0070] in, Indicates the current city The coordinates of the commercial center. and Represents a variable, Indicates The radiation radius of a commercial center, The value of is usually 3km~5km;

[0071] Step A3: Determine whether the radiation equations of different commercial centers have overlapping areas. If yes, merge them to generate new sub-areas. The specific steps of determining whether the radiation equations of different commercial centers have overlapping areas in step A3 and, if yes, merge them to generate new sub-areas are as follows:

[0072] Step A3.1: Determine the Commercial centers and Whether there are overlapping areas among the commercial centers is expressed as follows:

[0073]

[0074] in, Indicates Coordinates of commercial centers and Coordinates of commercial centers The distance between and Respectively represent The radiation radius of the commercial center and the The radiation radius of a commercial center, represents the absolute value, if If satisfied, proceed to step A3.2. If not satisfied, terminate step A3.1;

[0075] Step A3.2: Commercial centers and The commercial centers are merged to obtain sub-regions, and the specific expression is as follows:

[0076]

[0077]

[0078]

[0079]

[0080] in, , represents the coordinates of the center of the merged sub-region, represents the sub-region radius, and Respectively represent The coordinates of the commercial center and The coordinates of the commercial center. and Represents a variable;

[0081] Step A4: Repeat step A3 until there is no overlapping area, and output all sub-areas;

[0082] The dispatching and analysis module obtains the power load value based on all the load data of the distribution network under historical extreme weather conditions, all the load data of the distribution network under historical holidays, and the historical load data of each sub-region. The dispatching and analysis module obtains the specific expression of the power load value based on all the load data of the distribution network under historical extreme weather conditions, all the load data of the distribution network under historical holidays, and the historical load data of each sub-region as follows:

[0083]

[0084] in, Indicates the power load value, Indicates The power demand value of each sub-region is Indicates The historical maximum electricity demand of each sub-region is Express The sum of the sub-regions is represents the extreme weather judgment function. If the same extreme weather as in the weather load dataset will occur on the forecast day, then Output the mean value of the distribution network load data under extreme weather conditions. If no extreme weather occurs on the forecast day, Output the average value of the distribution network throughout the year, Represents the holiday judgment function. If the predicted day corresponds to Historical holidays, Output Distribution network load datasets under historical holidays If the forecast day is not a holiday, then Output the average value of the distribution network throughout the year. By taking the urban business district into account as a variable to predict the power load, it can ensure that the power load can be predicted more accurately during holidays.

[0085] The load warning module includes a power reserve data acquisition unit and a load warning unit. The power reserve data is used to collect the total amount of electric energy stored in each energy storage device and calculate the power generation data of the forecast day. The load warning unit performs warning scheduling based on the power generation data and power load value of the forecast day. The specific expression of the power reserve data acquisition of the total amount of electric energy stored in each energy storage device and the power generation data of the forecast day is as follows:

[0086]

[0087]

[0088] in, Represents the total amount of electrical energy stored in each energy storage device. Indicates The electrical energy stored in the energy storage device, Express The stored energy of each energy storage device is summed up. Indicates The power generation of each power generation device, express The sum of the power generation of each power generation device is The specific expression for the load warning unit to perform warning scheduling based on the power generation data and power load value on the forecast day is as follows:

[0089]

[0090] in, Indicates the warning value. Indicates the power load value, Represents the total amount of electrical energy stored in each energy storage device. Represents the power generation data for the forecast day. If No warning will be issued. The specific steps of warning scheduling are: When the power output of all power generation equipment is increased by 2%, the warning value is recalculated. If still , then continue to increase the output of all power generation equipment by 1%. No warning will be given.

[0091] Example:

[0092] In this embodiment, the unit is 10,000 kWh, there is no extreme weather, and there are holidays. =2455, =493, with 2 sub-areas , , , , , , , 2,387.10875, at this time , no warning will be issued;

[0093] The present invention also provides an intelligent load balancing method for a power distribution network, comprising the following steps:

[0094] Step 1: All load data of the distribution network under historical extreme weather conditions and historical holidays;

[0095] Step 2: Divide the urban area into different sub-areas and collect historical load data for each sub-area;

[0096] Step 3: Based on all load data of the distribution network under historical extreme weather conditions, all load data of the distribution network under historical holidays, and historical load data of each sub-region, the power load value is obtained;

[0097] Step 4: Collect the total amount of electric energy stored in each energy storage device and calculate the power generation data for the forecast day;

[0098] Step 5: Perform early warning dispatch based on the power generation data and power load value on the forecast day.

[0099] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent load balancing system for a distribution network, characterized in that: It includes distribution network historical load data collection module, regional load data collection module, dispatch analysis module and load early warning module; The distribution network historical load data collection module includes a historical distribution network weather load data collection unit and a historical holiday distribution network load data collection unit. The historical distribution network weather load data collection unit is used to collect all load data of the distribution network under historical extreme weather conditions. The historical holiday distribution network load data collection unit is used to collect all load data of the distribution network under historical holidays and constitute a holiday index library. The regional load data collection module includes a regional division unit and a regional load data collection unit, wherein the regional division unit divides the urban area into different sub-areas and collects historical load data of each sub-area through the regional load data collection unit; The dispatch analysis module obtains the power load value based on all load data of the distribution network under historical extreme weather conditions, all load data of the distribution network under historical holidays, and historical load data of each sub-region; The load warning module includes a power reserve data acquisition unit and a load warning unit. The power reserve data is used to collect the total amount of electric energy stored in each energy storage device and calculate the power generation data of the forecast day. The load warning unit performs warning scheduling based on the power generation data and the power load value of the forecast day. The specific steps of the area division unit dividing the urban area into different sub-areas are as follows: Step A1: Read the city map, establish the XOY coordinate system, and determine the current city Coordinates of commercial centers ; Step A2: Create the The radiation equation of a commercial center; Step A3: Determine whether the radiation equations of different commercial centers have overlapping areas. If so, merge them to generate new sub-areas. Step A4: Repeat step A3 until there is no overlapping area, and output all sub-areas; The said The specific expression of the radiation equation of a commercial center is as follows: ， in, Indicates the current city The coordinates of the commercial center. and Represents a variable, Indicates The radiation radius of a commercial center; In step A3, it is determined whether there are overlapping areas in the radiation equations of different commercial centers. If yes, the specific steps of merging and generating new sub-areas are as follows: Step A3.1: Determine the Commercial centers and Whether there are overlapping areas among the commercial centers is expressed as follows: ， in, Indicates Coordinates of commercial centers and Coordinates of commercial centers The distance between and Respectively represent The radiation radius of the commercial center and the The radiation radius of a commercial center, express The absolute value of If the judgment is satisfied, then execute step A3.

2. If not satisfied, terminate step A3.1; Step A3.2: Commercial centers and The commercial centers are merged to obtain sub-regions, and the specific expression is as follows: ， ， ， ， in, , represents the coordinates of the center of the merged sub-region, represents the sub-region radius, and Respectively represent The coordinates of the commercial center and The coordinates of the commercial center. and Represents a variable.

2. The intelligent load balancing system for a distribution network according to claim 1, characterized in that: The specific expression of the historical distribution network weather load data collection unit collecting all load data of the distribution network under historical extreme weather conditions is as follows: ， in, represents the weather load dataset, They represent the distribution network load data under the first extreme weather, , No. Distribution network load data under extreme weather conditions, , No. Distribution network load data under extreme weather conditions.

3. The intelligent load balancing system for a power distribution network according to claim 2, characterized in that: The specific expression for the historical holiday distribution network load data collection unit to collect all load data of the distribution network during historical holidays is as follows: ， in, represents the historical holiday load dataset, They represent the distribution network load data set under the first historical holiday, , No. Distribution network load datasets under historical holidays, , No. The distribution network load dataset under historical holidays is Distribution network load datasets under historical holidays The specific expression is as follows: ， in, Respectively represent The first distribution network load data stored in the distribution network load data set under historical holidays, , No. Distribution network load data, , No. Distribution network load data.

4. The intelligent load balancing system for a distribution network according to claim 3, characterized in that: The dispatch analysis module obtains the specific expression of the power load value based on all load data of the distribution network under historical extreme weather conditions, all load data of the distribution network under historical holidays, and historical load data of each sub-region as follows: ， in, Indicates the power load value, Indicates The power demand value of each sub-region is Indicates The historical maximum electricity demand of each sub-region is Express The sum of the sub-regions is represents the extreme weather judgment function. If the same extreme weather as in the weather load dataset will occur on the forecast day, then Output the mean value of the distribution network load data under extreme weather conditions. If no extreme weather occurs on the forecast day, Output the average value of the distribution network throughout the year, Represents the holiday judgment function. If the predicted day corresponds to Historical holidays, Output Distribution network load datasets under historical holidays If the forecast day is not a holiday, then Output the annual distribution network average.

5. The intelligent load balancing system for a power distribution network according to claim 4, characterized in that: The specific expression for the power reserve data to collect the total power stored in each energy storage device and calculate the power generation data of the forecast day is as follows: ， ， in, Represents the total amount of electrical energy stored in each energy storage device. Indicates The electrical energy stored in the energy storage device, Express The stored energy of each energy storage device is summed up. Indicates The power generation of each power generation device, express The sum of the power generation of each power generation device is Indicates the power generation data for the forecast day.

6. The intelligent load balancing system for a power distribution network according to claim 5, characterized in that: The specific expression of the load early warning unit for early warning scheduling based on the power generation data and power load value of the forecast day is as follows: ， in, Indicates the warning value. Indicates the power load value, Represents the total amount of electrical energy stored in each energy storage device. Represents the power generation data for the forecast day. If No warning will be given.

7. An intelligent load balancing method for a distribution network, based on an intelligent load balancing system for a distribution network according to any one of claims 1 to 6, characterized in that: The following steps are involved: Step 1: All load data of the distribution network under historical extreme weather conditions and historical holidays; Step 2: Divide the urban area into different sub-areas and collect historical load data for each sub-area; Step 3: Based on all load data of the distribution network under historical extreme weather conditions, all load data of the distribution network under historical holidays, and historical load data of each sub-region, the power load value is obtained; Step 4: Collect the total amount of electric energy stored in each energy storage device and calculate the power generation data for the forecast day; Step 5: Perform early warning dispatch based on the power generation data and power load value on the forecast day.

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