CN112350331B - Medium-voltage feeder prearranged power failure optimization method and device - Google Patents
Medium-voltage feeder prearranged power failure optimization method and device Download PDFInfo
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- CN112350331B CN112350331B CN202011131478.XA CN202011131478A CN112350331B CN 112350331 B CN112350331 B CN 112350331B CN 202011131478 A CN202011131478 A CN 202011131478A CN 112350331 B CN112350331 B CN 112350331B
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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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/242—Home appliances
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Abstract
The application discloses a medium-voltage feeder line prearranged power failure optimization method and device, and the method comprises the following steps: acquiring historical power failure events of medium-voltage feeders in a target power distribution network, wherein the historical power failure events at least comprise power failure types and the number of households during power failure, and the power failure types comprise fault power failure and prearranged power failure; calculating a monthly influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on historical power failure events; calculating a comprehensive influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on historical power failure events and monthly influence coefficients; and determining a prearranged power failure sequence of each medium-voltage feeder line in the target power distribution network according to the magnitude of the comprehensive influence coefficient corresponding to each medium-voltage feeder line. The method solves the technical problems that the influence degree of prearranged power failure implementation on fault power failure is not considered in the conventional prearranged power failure method, and an optimization decision reference cannot be provided for prearranged power failure.
Description
Technical Field
The application relates to the technical field of power distribution networks, in particular to a medium-voltage feeder prearranged power failure optimization method and device.
Background
The power supply reliability refers to the capability of the power supply system for continuously supplying power to users, and is an important index for checking the power quality of the power supply system. Power outages can be classified as prearranged power outages and fault outages, depending on the nature of the outage. Because the power failure that reasons such as equipment planning maintenance, electric wire netting transformation caused all belongs to prearranged power failure, wherein, the trouble hidden danger of equipment can in time be eliminated in equipment planning maintenance, reduces the trouble probability of taking place of equipment, and the electric wire netting transformation can reduce the influence scope and the power off time that the trouble had a power failure through measures such as rack optimization, distribution automation transformation. Thus, prearrangement of a power outage to a medium voltage feeder can generally reduce the number of households that have had their power outage.
At present, the optimization of the prearranged power failure plan mostly aims at how to merge power failure range overlapping so as to reduce the prearranged power failure times, and the influence degree of the prearranged power failure implementation on the fault power failure is not considered.
Therefore, it is an urgent need to solve the problem to provide a medium-voltage feeder prearranged power failure optimization method based on the influence of prearranged power failure on fault power failure.
Disclosure of Invention
The application provides a medium-voltage feeder prearranged power failure optimization method and device, which are used for solving the technical problems that the influence degree of prearranged power failure implementation on fault power failure is not considered in the existing prearranged power failure method, and optimization decision reference cannot be provided for prearranged power failure.
In view of this, a first aspect of the present application provides a medium-voltage feeder prearrangement power outage optimization method, including:
acquiring historical power failure events of medium-voltage feeders in a target power distribution network, wherein the historical power failure events at least comprise power failure types and the number of households during power failure, and the power failure types comprise fault power failure and prearranged power failure;
calculating monthly influence coefficients of the prearranged power failure of each medium-voltage feeder line on the fault power failure based on the historical power failure events;
calculating a comprehensive influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on the historical power failure event and the monthly influence coefficient;
and determining a prearranged power failure sequence of each medium-voltage feeder line in the target power distribution network according to the magnitude of the comprehensive influence coefficient corresponding to each medium-voltage feeder line.
Optionally, the calculating a monthly influence coefficient of a prearranged blackout of each medium-voltage feeder on a fault blackout based on the historical blackout event includes:
calculating monthly failure power failure number change values and prearranged power failure number change values of the medium-voltage feeders based on the historical power failure events;
based on a first preset formula, the monthly influence coefficient of the prearranged power failure of each medium-voltage feeder on the fault power failure is calculated through the monthly change value of the number of households when the fault power failure occurs and the change value of the number of households when the power failure is prearranged.
Optionally, the first preset formula is as follows:
wherein r is ij Monthly influence coefficient, Δ t, on fault blackout for prearranged blackout of medium voltage feeder i in month j f,ij For the number of users of the medium-voltage feeder i in the event of a fault power failure in month j, the value, Δ t s,ij(j-1) The number of users changes for the medium voltage feeder i at the scheduled power failure of month j-1.
Optionally, calculating a comprehensive influence coefficient of a prearranged blackout of each medium-voltage feeder on a fault blackout based on the historical blackout event and the monthly influence coefficient, including:
determining a target month according with a forward action according to the household number change value and the monthly influence coefficient when the fault of each medium-voltage feeder fails, wherein the target month according with the forward action is a month in which the monthly influence coefficient and the household number change value when the fault fails are both smaller than 0;
and calculating the average value of the monthly influence coefficients of the prearranged power failure of each medium-voltage feeder line on the fault power failure based on the target month through a second preset formula to obtain the comprehensive influence coefficient of the prearranged power failure of each medium-voltage feeder line on the fault power failure.
Optionally, the second preset formula is as follows:
wherein r is i For a comprehensive influence coefficient, r, of prearranged power outage of the medium voltage feeder i on the fault power outage ij The monthly influence coefficient of the prearranged power failure of the medium-voltage feeder line i in the month j on the fault power failure, Z is a target month set conforming to the forward action, N Z Is the total number of medium voltage feeders in the target set of months Z.
Optionally, the calculating, based on the historical outage event and the monthly influence coefficient, a comprehensive influence coefficient of a prearranged outage of each medium voltage feeder on a fault outage further includes:
and after the month with the maximum number of households during the fault power failure of each medium-voltage feeder is determined according to the historical power failure event of each medium-voltage feeder, configuring a prearranged power failure plan in the last month of the month with the maximum number of households during the fault power failure of each medium-voltage feeder.
Optionally, the calculating, based on the historical blackout event and the monthly influence coefficient, a comprehensive influence coefficient of a prearranged blackout of each medium-voltage feeder on a fault blackout further includes:
and determining the month with the smallest monthly influence coefficient of each medium-voltage feeder according to the monthly influence coefficient corresponding to each medium-voltage feeder, and configuring a prearranged power failure plan in the previous month of the month with the smallest monthly influence coefficient of each medium-voltage feeder.
The application second aspect provides a middling pressure feeder prearranged power failure optimization device, includes:
the system comprises an acquisition unit, a power distribution unit and a power distribution unit, wherein the acquisition unit is used for acquiring historical power failure events of medium-voltage feeders in a target power distribution network, the historical power failure events at least comprise power failure types and power failure household numbers, and the power failure types comprise failure power failure and prearranged power failure;
the first calculation unit is used for calculating a monthly influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on the historical power failure event;
the second calculation unit is used for calculating a comprehensive influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on the historical power failure event and the monthly influence coefficient;
and the determining unit is used for determining the prearranged power failure sequence of each medium-voltage feeder in the target power distribution network according to the magnitude of the comprehensive influence coefficient corresponding to each medium-voltage feeder.
Optionally, the first calculating unit specifically includes:
the first calculating subunit is used for calculating the monthly fault power failure number change value and the prearranged power failure number change value of each medium-voltage feeder on the basis of the historical power failure event;
and the second calculating subunit is used for calculating the monthly influence coefficient of the prearranged power failure of each medium-voltage feeder line on the fault power failure through the monthly number of households with the fault power failure change value and the prearranged power failure change value of each medium-voltage feeder line on the basis of a first preset formula.
Optionally, the second calculating unit specifically includes:
the determining subunit is configured to determine, according to the number of users during the fault power outage and the monthly influence coefficient of each medium-voltage feeder, a target month meeting a forward action, where the target month meeting the forward action is a month in which both the monthly influence coefficient and the number of users during the fault power outage are smaller than 0;
and the calculating subunit is used for calculating the average value of the monthly influence coefficients of the prearranged power failure of each medium-voltage feeder line on the fault power failure based on the target month through a second preset formula to obtain the comprehensive influence coefficient of the prearranged power failure of each medium-voltage feeder line on the fault power failure.
According to the technical scheme, the method has the following advantages:
the application provides a medium-voltage feeder line prearranged power failure optimization method, which comprises the following steps: acquiring historical power failure events of medium-voltage feeders in a target power distribution network, wherein the historical power failure events at least comprise power failure types and the number of users in power failure, and the power failure types comprise fault power failure and prearranged power failure; calculating a monthly influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on historical power failure events; calculating a comprehensive influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on historical power failure events and monthly influence coefficients; and determining a prearranged power failure sequence of each medium-voltage feeder in the target power distribution network according to the magnitude of the comprehensive influence coefficient corresponding to each medium-voltage feeder.
According to the method, the monthly influence coefficient of the predicted arrangement power failure of each medium-voltage feeder to the fault power failure is calculated according to the historical power failure event of each medium-voltage feeder in a target power distribution network, the comprehensive influence coefficient of the prearranged power failure of each medium-voltage feeder to the fault power failure is further calculated, finally, the prearranged power failure sequence of each medium-voltage feeder is determined according to the size of the comprehensive influence coefficient corresponding to each medium-voltage feeder, the prearranged power failure plan is optimized by analyzing the influence degree of the prearranged power failure to the fault power failure, the optimization decision reference is provided for the prearranged power failure plan, and the technical problem that the influence degree of the prearranged power failure implementation to the fault power failure is not considered in the conventional prearranged power failure method, and the optimization decision reference cannot be provided for the prearranged power failure is solved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive labor.
Fig. 1 is a schematic flowchart of a method for optimizing a medium-voltage feeder prearranged power outage according to an embodiment of the present application;
fig. 2 is another schematic flow chart of a medium-voltage feeder prearranged power outage optimization method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a medium-voltage feeder prearrangement power outage optimization apparatus according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, a method for optimizing prearranged power outage of a medium-voltage feeder according to an embodiment of the present application includes:
The historical power failure events of the medium-voltage feeders in the target power distribution network are obtained, and can be the historical power failure events of the power distribution network in the last three years. The historical power failure events at least comprise power failure types and the number of households in power failure, and can also comprise power failure starting time, power failure ending time and the like, wherein the power failure types comprise failure power failure and scheduled power failure. Taking a certain distribution network a as an example, the historical outage events of the medium voltage feeders of the last three years are shown in table 1.
TABLE 1 historical outage events for distribution network A
And 102, calculating monthly influence coefficients of the prearranged power failure of each medium-voltage feeder line on the fault power failure based on historical power failure events.
According to historical power failure events, the monthly influence coefficient of the prearranged power failure of each medium-voltage feeder line on the fault power failure is calculated, and the method specifically comprises the following steps: calculating the monthly failure power failure number change value and the prearranged power failure number change value of each medium-voltage feeder based on historical power failure events; based on a first preset formula, the monthly influence coefficient of the prearranged power failure of each medium-voltage feeder on the fault power failure is calculated through the monthly change value of the number of the households when the fault power failure occurs and the change value of the number of the households when the power failure is prearranged.
It can be understood that the number of households in the pre-arrangement power failure and the number of households in the fault power failure in each month of each medium-voltage feeder can be obtained through statistics according to the historical power failure event, and the change value of the number of households in the pre-arrangement power failure in each month relative to the number of households in the pre-arrangement power failure in the last month is calculated, so that the change value of the number of households in the pre-arrangement power failure is obtained; and calculating the change value of the number of households per month in the power failure of the fault relative to the number of households in the last month in the power failure of the fault, and obtaining the change value of the number of households in the power failure of the fault. Taking a medium-voltage feeder 1 in the distribution network a as an example, the number of households in the scheduled power failure and the number of households in the fault power failure in the last three months of 2017-2019 and the variation values thereof are obtained through statistics, and are shown in table 2.
TABLE 2 prearranged number of households during power failure and number of households during fault power failure of medium voltage feeder 1 and variation values thereof
And after the monthly change values of the number of the households during the fault power failure and the number of the households during the prearranged power failure of each medium-voltage feeder are obtained through calculation, the monthly influence coefficient of the prearranged power failure of each medium-voltage feeder on the fault power failure is calculated through a first preset formula. Wherein, the first preset formula is as follows:
wherein r is ij Monthly influence coefficient, delta t, of blackout to fault blackout for prearranged blackout of a medium voltage feeder i in month j f,ij For the change value of the number of users, delta t, of the medium-voltage feeder line i in the failure power failure of the month j s,ij(j-1) The number of households changes value for medium voltage feeder i at the time of the pre-scheduled power outage for month j-1.
After the parameters shown in table 2 are obtained by using the above example, the monthly influence coefficient of the scheduled power outage of the medium-voltage feeder 1 in each month from 2017 to 2019 on the fault power outage can be calculated by using the above first preset formula, as shown in table 3.
TABLE 3 monthly influence factor of prearranged outage of medium voltage feeder 1 on fault blackout
And 103, calculating a comprehensive influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on the historical power failure event and the monthly influence coefficient.
And determining a target month in accordance with the forward action according to the number of users change value and the monthly influence coefficient of each medium-voltage feeder line during the fault power failure, wherein the target month in accordance with the forward action is a month in which the monthly influence coefficient and the number of users change value during the fault power failure are both less than 0. The target months which accord with the forward action show that the implementation of prearranged power failure plays a role in reducing the number of users in the fault power failure, and the non-target months which do not accord with the forward action do not come into the calculation range.
And calculating the average value of the monthly influence coefficients of the prearranged power failure of each medium-voltage feeder line on the fault power failure based on the target month through a second preset formula to obtain the comprehensive influence coefficient of the prearranged power failure of each medium-voltage feeder line on the fault power failure. Wherein the second preset formula is:
wherein r is i Comprehensive influence coefficient r of prearranged power failure on fault power failure of medium-voltage feeder line i ij The influence coefficient of the prearranged power failure of the medium-voltage feeder i in the month j on the monthly degree of the fault power failure, Z is a target month set conforming to the forward action, N Z Is the total number of medium voltage feeders in the target set of months Z.
Continuing with the foregoing example, target months meeting the positive effect in nearly three years can be obtained by screening according to the number of users changing value and the monthly influence coefficient during the fault power failure in each month from 2017 to 2019, as shown in table 3. The comprehensive influence coefficient of the prearranged power failure of the medium-voltage feeder 1 on the fault power failure can be calculated to be 0.71 through a second preset formula.
And 104, determining a prearranged power failure sequence of each medium-voltage feeder line in the target power distribution network according to the magnitude of the comprehensive influence coefficient corresponding to each medium-voltage feeder line.
According to the magnitude of the comprehensive influence coefficient corresponding to each medium-voltage feeder, the comprehensive influence coefficients corresponding to the medium-voltage feeders can be sorted from large to small, and power failure is prearranged for each medium-voltage feeder according to the order, namely, power failure is prearranged for the medium-voltage feeder with large comprehensive influence coefficient preferentially. The comprehensive influence coefficient of the predicted scheduled blackout of the medium-voltage feeders in the distribution network a on the fault blackout is assumed to be calculated and is shown in table 4.
Table 4 comprehensive influence coefficient of prearranged blackout of medium voltage feeders in distribution network a on fault blackout
After the medium-voltage feeders are sorted according to the magnitude of the comprehensive influence coefficient corresponding to the medium-voltage feeders in the table 4, it can be known that the medium-voltage feeders 4 should be prearranged for power failure preferentially, then the medium-voltage feeders 5 should be prearranged for power failure, and the like, so that the prearranged power failure optimization scheme of the medium-voltage feeders in the power distribution network A is obtained.
In the embodiment of the application, a monthly influence coefficient of predicted arrangement power failure of each medium-voltage feeder to fault power failure is calculated according to historical power failure events of each medium-voltage feeder in a target power distribution network, a comprehensive influence coefficient of the pre-arrangement power failure of each medium-voltage feeder to the fault power failure is further calculated, finally, a pre-arrangement power failure sequence of each medium-voltage feeder is determined according to the size of the comprehensive influence coefficient corresponding to each medium-voltage feeder, and a pre-arrangement power failure plan is optimized by analyzing the influence degree of the pre-arrangement power failure to the fault power failure, so that an optimization decision reference is provided for the pre-arrangement power failure plan arrangement, and the technical problem that the influence degree of the pre-arrangement power failure implementation to the fault power failure is not considered in the existing pre-arrangement power failure method, and the optimization decision reference cannot be provided for the pre-arrangement power failure is solved.
The foregoing is an embodiment of a method for optimizing prearranged blackout of a medium-voltage feeder line provided by the present application, and the following is another embodiment of a method for optimizing prearranged blackout of a medium-voltage feeder line provided by the present application.
Referring to fig. 2, another embodiment of a method for optimizing a medium-voltage feeder prearrangement power outage provided in the present application includes:
And step 203, calculating a comprehensive influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on the historical power failure event and the monthly influence coefficient.
And 204, determining a prearranged power failure sequence of each medium-voltage feeder line in the target power distribution network according to the magnitude of the comprehensive influence coefficient corresponding to each medium-voltage feeder line.
The specific contents of steps 201 to 204 are the same as those of steps 101 to 104, and are not described herein again.
And step 205, determining a prearranged power outage optimization strategy of each medium-voltage feeder according to the historical power outage event and the monthly influence coefficient of each medium-voltage feeder.
And analyzing the change rule of the number of households along with the month when the medium-voltage feeder fails and the change rule of the monthly influence coefficient of the prearranged power failure on the failure power failure along with the month for each medium-voltage feeder, and determining a prearranged power failure optimization strategy for the medium-voltage feeders.
Specifically, after the month with the largest number of households during the fault power failure of each medium-voltage feeder is determined according to the historical power failure event of each medium-voltage feeder, a prearranged power failure plan is configured in the month which is the month with the largest number of households during the fault power failure of each medium-voltage feeder.
And determining the month with the smallest monthly influence coefficient of each medium-voltage feeder according to the monthly influence coefficient corresponding to each medium-voltage feeder, and configuring a prearranged power failure plan in the previous month of the month with the smallest monthly influence coefficient of each medium-voltage feeder.
Taking the medium-voltage feeder 1 shown in tables 2 and 3 in the foregoing embodiments as an example, the corresponding pre-arrangement outage optimization strategy is:
1) The analysis of the number of households in the fault power failure of each month in the best three years shows that the number of households in the fault power failure of 7 months is higher, and the prearranged power failure plan of the medium-voltage feeder 1 can be inclined to 6 months.
2) According to the analysis of the monthly influence coefficient of the prearranged power cut of the medium-voltage feeder 1 in each month in the last three years on the fault power cut, the monthly influence coefficient of the prearranged power cut of 9 months on the fault power cut is smaller, and the prearranged power cut of the medium-voltage feeder 1 can be calculated to incline to 8 months.
In summary, the obtained pre-arrangement power outage optimization strategy for the medium-voltage feeder 1 is as follows: the pre-scheduled blackout plan is skewed toward months 6 and 8.
It should be noted that, after step 203, step 205 may be performed simultaneously with step 204, or may be performed sequentially.
In the embodiment of the application, a monthly influence coefficient of the predicted arrangement power failure of each medium-voltage feeder to the fault power failure is calculated according to the historical power failure event of each medium-voltage feeder in a target power distribution network, a comprehensive influence coefficient of the prearranged power failure of each medium-voltage feeder to the fault power failure is further calculated, finally, a prearranged power failure sequence of each medium-voltage feeder is determined according to the size of the comprehensive influence coefficient corresponding to each medium-voltage feeder, the prearranged power failure plan is optimized by analyzing the influence degree of the prearranged power failure to the fault power failure, an optimization decision reference is provided for the prearranged power failure plan, and the technical problems that the influence degree of the prearranged power failure implementation to the fault power failure is not considered in the existing prearranged power failure method, and the optimization decision reference cannot be provided for the prearranged power failure are solved;
furthermore, the embodiment of the application optimizes the pre-arranged power failure plan from two dimensions of time and space by analyzing the influence degree of the pre-arranged power failure on the fault power failure, and provides an optimization decision reference for the pre-arranged power failure plan; in addition, the medium-voltage feeder line prearranged power failure optimization method in the embodiment of the application has the advantages of small data quantity, high analysis speed, suitability for practical engineering application and contribution to improvement of the reliability of the power distribution network.
The method for optimizing prearranged power failure of the medium-voltage feeder line provided by the embodiment of the application is as follows.
Referring to fig. 3, an embodiment of the present application provides a medium-voltage feeder prearranged power outage optimization apparatus, including:
the acquisition unit 301 is configured to acquire historical blackout events of each medium-voltage feeder in a target power distribution network, where the historical blackout events at least include blackout types and blackout household numbers, and the blackout types include fault outage and prearranged blackout;
the first calculating unit 302 is used for calculating a monthly influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on historical power failure events;
a second calculating unit 303, configured to calculate a comprehensive influence coefficient of a prearranged power outage of each medium-voltage feeder on a fault power outage based on the historical power outage event and the monthly influence coefficient;
the determining unit 304 is configured to determine a prearranged power outage sequence of each medium voltage feeder in the target power distribution network according to the magnitude of the comprehensive influence coefficient corresponding to each medium voltage feeder.
As a further improvement, the first calculating unit 302 specifically includes:
the first calculating subunit 3021 is configured to calculate, based on the historical power outage event, a monthly fault power outage household number change value and a prearranged power outage household number change value of each medium-voltage feeder;
and the second calculating subunit 3022 is configured to calculate, based on the first preset formula, a monthly influence coefficient of the scheduled power outage of each medium-voltage feeder on the fault power outage through the monthly change value of the number of households when the fault power outage occurs and the change value of the number of households when the power outage is scheduled.
As a further improvement, the second calculating unit 303 specifically includes:
a determining subunit 3031, configured to determine, according to the household number variation value and the monthly influence coefficient during the fault power outage of each medium-voltage feeder, a target month meeting the forward action, where the target month meeting the forward action is a month in which both the monthly influence coefficient and the household number variation value during the fault power outage are smaller than 0;
and the calculating subunit 3032 is configured to calculate, by using a second preset formula, an average value of monthly influence coefficients of the pre-arranged power outage of each medium-voltage feeder to the fault power outage based on the target month, so as to obtain a comprehensive influence coefficient of the pre-arranged power outage of each medium-voltage feeder to the fault power outage.
As a further improvement, the apparatus further comprises:
the first configuration unit 305, after determining the month with the largest number of users at the time of the failure power outage of each medium-voltage feeder according to the historical power outage event of each medium-voltage feeder, configures the prearranged power outage plan in the last month of the month with the largest number of users at the time of the failure power outage of each medium-voltage feeder.
As a further improvement, the apparatus further comprises:
the second configuration unit 306 determines the month with the smallest monthly influence coefficient of each medium-voltage feeder according to the monthly influence coefficient corresponding to each medium-voltage feeder, and configures the prearranged power failure plan in the previous month of the month with the smallest monthly influence coefficient of each medium-voltage feeder.
In the embodiment of the application, a monthly influence coefficient of the predicted arrangement power failure of each medium-voltage feeder to the fault power failure is calculated according to the historical power failure event of each medium-voltage feeder in a target power distribution network, a comprehensive influence coefficient of the prearranged power failure of each medium-voltage feeder to the fault power failure is further calculated, finally, a prearranged power failure sequence of each medium-voltage feeder is determined according to the size of the comprehensive influence coefficient corresponding to each medium-voltage feeder, the prearranged power failure plan is optimized by analyzing the influence degree of the prearranged power failure to the fault power failure, an optimization decision reference is provided for the prearranged power failure plan, and the technical problems that the influence degree of the prearranged power failure implementation to the fault power failure is not considered in the existing prearranged power failure method, and the optimization decision reference cannot be provided for the prearranged power failure are solved;
furthermore, the embodiment of the application optimizes the pre-arranged power failure plan from two dimensions of time and space by analyzing the influence degree of the pre-arranged power failure on the fault power failure, thereby providing an optimization decision reference for the pre-arranged power failure plan; in addition, the medium-voltage feeder line prearranged power failure optimization method in the embodiment of the application has the advantages of small data quantity, high analysis speed, suitability for practical engineering application and contribution to improvement of the reliability of the power distribution network.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for executing all or part of the steps of the method described in the embodiments of the present application through a computer device (which may be a personal computer, a server, or a network device). And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A medium-voltage feeder prearranged power failure optimization method is characterized by comprising the following steps:
acquiring historical power failure events of medium-voltage feeders in a target power distribution network, wherein the historical power failure events at least comprise power failure types and the number of users in power failure, and the power failure types comprise fault power failure and prearranged power failure;
calculating a monthly influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on the historical power failure event;
calculating a comprehensive influence coefficient of the prearranged power failure of each medium-voltage feeder line on the fault power failure based on the historical power failure event and the monthly influence coefficient to reflect the negative correlation between the prearranged power failure and the fault power failure;
and determining a prearranged power failure sequence of each medium-voltage feeder line in the target power distribution network according to the magnitude of the comprehensive influence coefficient corresponding to each medium-voltage feeder line.
2. The medium voltage feeder prearranged power outage optimization method according to claim 1, wherein said calculating monthly impact coefficients of prearranged power outages of each of said medium voltage feeders on failed power outages based on said historical power outage events comprises:
calculating a number of users change value of each medium-voltage feeder during monthly fault power failure and a number of users change value of each medium-voltage feeder during prearranged power failure based on the historical power failure events;
based on a first preset formula, the monthly influence coefficient of the prearranged power failure of each medium-voltage feeder on the fault power failure is calculated through the monthly change value of the number of households when the fault power failure occurs and the change value of the number of households when the power failure is prearranged.
3. The medium voltage feeder prearranged power outage optimization method according to claim 2, wherein the first preset formula is:
wherein r is ij Monthly influence coefficient, Δ t, on fault blackout for prearranged blackout of medium voltage feeder i in month j f,ij For the number of users of the medium-voltage feeder i in the event of a fault power failure in month j, the value, Δ t s,i(j-1) The number of households changes value for medium voltage feeder i at the time of the pre-scheduled power outage for month j-1.
4. The medium voltage feeder prearranged power outage optimization method according to claim 2, wherein calculating a comprehensive impact coefficient of prearranged power outages on a failed power outage for each of said medium voltage feeders based on said historical power outage events and said monthly impact coefficients to represent that prearranged power outages are negatively correlated with failed power outages comprises:
determining a target month in accordance with a forward action according to the household number change value and the monthly influence coefficient of each medium-voltage feeder line during the fault power failure, wherein the target month in accordance with the forward action is a month in which the monthly influence coefficient and the household number change value during the fault power failure are both less than 0;
and calculating the average value of monthly influence coefficients of the prearranged power failure of each medium-voltage feeder line on the fault power failure based on the target month through a second preset formula to obtain the comprehensive influence coefficient of the prearranged power failure of each medium-voltage feeder line on the fault power failure.
5. The medium voltage feeder prearranged power outage optimization method according to claim 4, wherein said second preset formula is:
wherein r is i For a comprehensive influence coefficient, r, of prearranged power outage of the medium voltage feeder i on the fault power outage ij The influence coefficient of the prearranged power failure of the medium-voltage feeder i in the month j on the monthly degree of the fault power failure, Z is a target month set conforming to the forward action, N Z Is the total number of medium voltage feeders in the target set of months Z.
6. The medium voltage feeder prearranged power outage optimization method according to claim 1, wherein said calculating a comprehensive impact coefficient of prearranged power outages of each of said medium voltage feeders on a failed power outage based on said historical power outage events and said monthly impact coefficients, thereafter further comprises:
and after the month with the maximum number of households during the fault power failure of each medium-voltage feeder is determined according to the historical power failure event of each medium-voltage feeder, configuring a prearranged power failure plan in the last month of the month with the maximum number of households during the fault power failure of each medium-voltage feeder.
7. The medium voltage feeder prearranged power outage optimization method according to claim 1, wherein said calculating a composite impact coefficient of prearranged power outages for each of said medium voltage feeders on a failed power outage based on said historical power outage events and said monthly impact coefficients, thereafter further comprises:
and determining the month with the minimum monthly influence coefficient of each medium voltage feeder according to the monthly influence coefficient corresponding to each medium voltage feeder, and configuring a prearranged power failure plan in the last month of the month with the minimum monthly influence coefficient of each medium voltage feeder.
8. The utility model provides a medium voltage feeder prearranges power failure optimizing apparatus which characterized in that includes:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring historical power failure events of medium-voltage feeders in a target power distribution network, the historical power failure events at least comprise power failure types and the number of users in power failure, and the power failure types comprise failure power failure and prearranged power failure;
the first calculation unit is used for calculating a monthly influence coefficient of prearranged power failure of each medium-voltage feeder line on fault power failure based on the historical power failure event;
the second calculation unit is used for calculating a comprehensive influence coefficient of the prearranged power failure of each medium-voltage feeder line on the fault power failure based on the historical power failure event and the monthly influence coefficient so as to reflect the negative correlation between the prearranged power failure and the fault power failure;
and the determining unit is used for determining the prearranged power failure sequence of each medium-voltage feeder in the target power distribution network according to the magnitude of the comprehensive influence coefficient corresponding to each medium-voltage feeder.
9. The medium voltage feeder prearranged power outage optimization device according to claim 8, wherein said first computing unit specifically comprises:
the first calculating subunit is used for calculating a change value of the number of the household units during the monthly fault power failure of each medium-voltage feeder line and a change value of the number of the household units during the prearranged power failure based on the historical power failure event;
and the second calculation subunit is used for calculating a monthly influence coefficient of the scheduled power failure of each medium-voltage feeder line on the fault power failure through the monthly household number change value of each medium-voltage feeder line during the fault power failure and the household number change value during the scheduled power failure based on a first preset formula.
10. The medium voltage feeder prearranged power outage optimization device according to claim 9, wherein said second computing unit specifically comprises:
the determining subunit is configured to determine, according to the household number change value and the monthly influence coefficient of each medium-voltage feeder at the time of the fault power outage, a target month meeting a forward action, where the target month meeting the forward action is a month in which both the monthly influence coefficient and the household number change value at the time of the fault power outage are smaller than 0;
and the calculating subunit is used for calculating the average value of the monthly influence coefficients of the pre-arranged power failure of each medium-voltage feeder line on the fault power failure based on the target month through a second preset formula to obtain the comprehensive influence coefficient of the pre-arranged power failure of each medium-voltage feeder line on the fault power failure.
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| CN104835000A (en) * | 2015-04-28 | 2015-08-12 | 国网上海市电力公司 | Power distribution network reliability assessment method taking regard of pre-arrangement of power failure |
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