CN117613923B

CN117613923B - Power distribution network scheduling method and system based on dynamic partition

Info

Publication number: CN117613923B
Application number: CN202410085572.8A
Authority: CN
Inventors: 牛原; 左磊; 鄂志君; 李振斌; 卞海波; 王超; 崇志强; 刘伟; 郑卫洪; 杨帮宇; 姚维平; 卢向东; 陈培育; 李刚; 于光耀; 郑骁麟; 李梦一; 孔祥玉; 姚昊阳
Original assignee: Ninghe Power Supply Branch Of State Grid Tianjin Electric Power Co; State Grid Corp of China SGCC; State Grid Tianjin Electric Power Co Ltd
Current assignee: Ninghe Power Supply Branch Of State Grid Tianjin Electric Power Co; State Grid Corp of China SGCC; State Grid Tianjin Electric Power Co Ltd
Priority date: 2024-01-22
Filing date: 2024-01-22
Publication date: 2024-04-05
Anticipated expiration: 2044-01-22
Also published as: CN117613923A

Abstract

The invention discloses a power distribution network scheduling method and system based on dynamic partitioning, wherein the method comprises the following steps: acquiring day-ahead power generation data, day-ahead load data and day-ahead scheduling data; dividing the area to obtain a plurality of first partitions based on the day-ahead power generation data, the day-ahead load data and the day-ahead scheduling data; periodically acquiring daily power generation data and daily load data and daily scheduling data of an area; updating the plurality of first partitions based on the intra-day power generation data, the intra-day load data and the intra-day scheduling data to obtain a plurality of second partitions; receiving edge data corresponding to each second partition; performing robust optimization processing based on the side data corresponding to the plurality of second partitions to obtain partition scheduling distribution results; and selecting a plurality of edge physical proxy base stations corresponding to the second partitions from the plurality of side base stations, and respectively sending the generated scheduling information to the plurality of edge physical proxy base stations based on the partition scheduling distribution result so as to partition schedule the power distribution network to be scheduled, thereby accurately scheduling the power distribution network.

Description

Power distribution network scheduling method and system based on dynamic partition

Technical Field

The invention relates to the technical field of power, in particular to a power distribution network scheduling method and system based on dynamic partitioning.

Background

Currently, on one hand, distributed energy sources (such as solar energy, wind energy and the like) are rapidly developed in recent years, and the distributed energy sources accessed by a power distribution network often require a scheduling scheme to have high definition; however, with rapid development of urban and industrialized technologies, the power distribution network is continuously enlarged in scale, the number of nodes is increased, the structure becomes more and more complex, and the existing power distribution network automatic scheduling system generally uses county as a basic unit for scheduling, so that the scheduling scheme is not fine enough.

Disclosure of Invention

In order to solve the technical problems, the embodiment of the invention provides a power distribution network scheduling method and system based on dynamic partitioning, which can accurately schedule a power distribution network to be scheduled.

In order to achieve the above object, an embodiment of the present invention provides a power distribution network scheduling method based on dynamic partitioning, applied to a cloud, the method including:

acquiring day-ahead power generation data and day-ahead load data of an area where a power distribution network to be scheduled is located, and day-ahead scheduling data of a higher power grid of the power distribution network to be scheduled; wherein a plurality of border base stations are deployed in the area, the daily load data from the plurality of border base stations;

Dividing the area based on the day-ahead power generation data, the day-ahead load data and the day-ahead scheduling data to obtain a plurality of first partitions; each first partition comprises at least one side base station;

periodically acquiring an intra-day data set according to a preset sampling step length; the daily data set comprises daily power generation data and daily load data of the area and daily scheduling data of the upper power grid; the daily load data come from the plurality of side base stations;

in response to acquiring the intra-day data set, updating the plurality of first partitions based on the intra-day power generation data, the intra-day load data and the intra-day scheduling data to obtain a plurality of second partitions;

receiving edge data corresponding to each second partition; the side data are data which are respectively counted and processed by all side base stations contained in the corresponding second partition and then transmitted;

performing robust optimization processing based on the edge data corresponding to the plurality of second partitions to obtain partition scheduling distribution results;

and selecting a plurality of edge physical proxy base stations corresponding to the second partitions one by one from the plurality of edge base stations, and respectively sending generated scheduling information to the plurality of edge physical proxy base stations based on the partition scheduling distribution result so as to perform partition scheduling on the power distribution network to be scheduled.

Further, the dividing the area based on the day-ahead power generation data, the day-ahead load data and the day-ahead scheduling data to obtain a plurality of first partitions includes:

taking the day-ahead dispatching data as a limiting condition, taking the minimum coordinated bus power exchange among the partitions as an objective function, and carrying out clustering processing on the areas based on the day-ahead power generation data and the day-ahead load data to obtain a plurality of clustering centers;

and dividing the region into a plurality of first partitions corresponding to the plurality of clustering centers.

Further, the daily load data comprise user load data which are obtained by statistics of corresponding user-side intelligent ammeter received by each side base station in a preset daily time period, and the daily power generation data comprise distributed power generation data of a plurality of distributed power generation devices deployed in the area in the preset daily time period, wherein each distributed power generation device corresponds to one of the side base stations;

and updating the plurality of first partitions based on the intra-day power generation data, the intra-day load data and the intra-day scheduling data to obtain a plurality of second partitions, including:

Calculating a net load value corresponding to each side base station based on the user load data and the distributed power generation data corresponding to each side base station;

taking the intra-day scheduling data and the preset clustering iteration number as limiting conditions, and carrying out clustering processing on the area based on the net load values corresponding to the side base stations so as to update the clustering centers;

and updating the plurality of first partitions by using the updated plurality of clustering centers to obtain a plurality of second partitions.

Further, the robust optimization processing is performed based on the edge data corresponding to the plurality of second partitions, so as to obtain a partition scheduling and distributing result, which specifically includes:

inputting the edge data corresponding to each of the plurality of second partitions into a two-stage robust optimization model to obtain partition scheduling distribution results corresponding to the plurality of second partitions, wherein the partition scheduling distribution results are output by the two-stage robust optimization model when termination conditions are met;

wherein the termination condition means that the cost constraint is the lowest.

Further, before the generated scheduling information is sent to the plurality of edge physical proxy base stations based on the partition scheduling allocation result, the method further includes:

Determining partition output corresponding to each of the plurality of second partitions based on the partition scheduling and distributing results;

calculating partition operation cost corresponding to each second partition based on the intra-day scheduling data and partition output corresponding to each second partition;

calculating the regional total income of the region based on the regional output corresponding to each of the plurality of second partitions and the preset regional transaction electricity price;

inputting the partition scheduling and distributing result, the partition operation cost corresponding to each of the plurality of second partitions and the total regional benefit into a pre-configured electric power market game model for processing to obtain an optimization strategy; wherein the electric power market gaming model is determined based at least on a master-slave gaming model;

and adjusting the partition scheduling and distributing result by utilizing the optimizing strategy to obtain a new partition scheduling and distributing result.

Further, the electric power market game model is a master-slave game model nested with a cooperative game model, and the cooperative game model is used for indicating cooperative games among the second partitions.

Further, the electric power market game modelIs determined by the following formula:

wherein,for the main game party, < > >To take part in game>To be the institutePartition scheduling allocation result,>for the second partitions, the electricity purchase price set at time t is +.>For the online electricity price in the t period area, < ->，For the whole game model, < >>For the ith upper objective function limit, +.>For the ith objective function lower limit, +.>Upper limit of the optimum value for the ith objective function,/->Lower limit of the optimum value for the ith objective function,/->For the trading electricity prices between the second partitions,the electric quantity purchased for one second partition to other second partitions, n is the number of objective functions, and i represents the ith objective function.

Further, the partition output corresponding to each second partition is used for representing the generated energy of the distributed power supply, the discharged quantity of the storage battery and the residual power of the distributed power supply corresponding to each second partition;

and calculating the partition running cost corresponding to each second partition based on the intra-day scheduling data and the partition output corresponding to each second partition, including:

calculating the running cost of the distributed power supply corresponding to each second partition based on the generated energy of the distributed power supply corresponding to each second partition;

calculating the environmental cost corresponding to each second partition based on the partition output corresponding to each second partition;

Calculating the cost of the storage battery corresponding to each second partition based on the discharge capacity of the storage battery corresponding to each second partition;

calculating the inter-partition electricity purchasing cost corresponding to each second partition based on the intra-day scheduling data and the partition output corresponding to each second partition;

calculating the residual power patch cost corresponding to each second partition based on the distributed power residual power corresponding to each second partition;

and calculating the partition operation cost corresponding to each second partition by utilizing the lowest cost objective function in the partition based on the distributed power supply operation cost, the environment cost, the storage battery cost, the inter-partition electricity purchasing cost and the residual electricity subsidy cost corresponding to each second partition.

Further, the step of sending the generated scheduling information to the plurality of edge physical proxy base stations based on the partition scheduling allocation result so as to perform partition scheduling on the power distribution network to be scheduled includes:

generating a plurality of scheduling information corresponding to the plurality of edge physical agent base stations one by one based on the partition scheduling allocation result;

and transmitting the plurality of scheduling information to the plurality of edge physical proxy base stations in a one-to-one correspondence manner, so that the plurality of edge physical proxy base stations control related equipment of the power distribution network to be scheduled in the second partition corresponding to each of the plurality of edge physical proxy base stations.

The embodiment of the invention also provides a power distribution network scheduling system based on dynamic partition, wherein the power distribution network scheduling system is arranged at the cloud end, and the power distribution network scheduling system comprises:

the system comprises a day-ahead data acquisition module, a day-ahead data processing module and a day-ahead data processing module, wherein the day-ahead data acquisition module is used for acquiring day-ahead power generation data and day-ahead load data of an area where a power distribution network to be scheduled is located and day-ahead scheduling data of a higher power grid of the power distribution network to be scheduled; wherein a plurality of border base stations are deployed in the area, the daily load data from the plurality of border base stations;

the first partition module is used for dividing the area based on the day-ahead power generation data, the day-ahead load data and the day-ahead scheduling data to obtain a plurality of first partitions; each first partition comprises at least one side base station;

the intra-day data acquisition module is used for periodically acquiring an intra-day data set according to a preset sampling step length; the daily data set comprises daily power generation data and daily load data of the area and daily scheduling data of the upper power grid; the daily load data come from the plurality of side base stations;

the second partition module is used for updating the plurality of first partitions based on the daily power generation data, the daily load data and the daily scheduling data to obtain a plurality of second partitions in response to the acquired daily data set;

The side data receiving module is used for receiving side data corresponding to each second partition; the side data are data which are respectively counted and processed by all side base stations contained in the corresponding second partition and then transmitted;

the robust optimization module is used for carrying out robust optimization processing based on the edge data corresponding to the plurality of second partitions to obtain partition scheduling distribution results;

and the partition scheduling module is used for selecting a plurality of edge physical proxy base stations corresponding to the second partitions one by one from the side base stations, and respectively sending the generated scheduling information to the edge physical proxy base stations based on the partition scheduling distribution result so as to perform partition scheduling on the power distribution network to be scheduled.

In summary, the invention has the following beneficial effects:

by adopting the embodiment of the invention, the day-ahead power generation data and the day-ahead load data of the area where the power distribution network to be scheduled is located and the day-ahead scheduling data of the upper power grid of the power distribution network to be scheduled are obtained; wherein a plurality of border base stations are deployed in the area, the daily load data from the plurality of border base stations; dividing the area based on the day-ahead power generation data, the day-ahead load data and the day-ahead scheduling data to obtain a plurality of first partitions; each first partition comprises at least one side base station; periodically acquiring an intra-day data set according to a preset sampling step length; the daily data set comprises daily power generation data and daily load data of the area and daily scheduling data of the upper power grid; the daily load data come from the plurality of side base stations; in response to acquiring the intra-day data set, updating the plurality of first partitions based on the intra-day power generation data, the intra-day load data and the intra-day scheduling data to obtain a plurality of second partitions; receiving edge data corresponding to each second partition; the side data are data which are respectively counted and processed by all side base stations contained in the corresponding second partition and then transmitted; performing robust optimization processing based on the edge data corresponding to the plurality of second partitions to obtain partition scheduling distribution results; and selecting a plurality of edge physical proxy base stations corresponding to the second partitions one by one from the plurality of edge base stations, and respectively sending generated scheduling information to the plurality of edge physical proxy base stations based on the partition scheduling distribution result so as to perform partition scheduling on the power distribution network to be scheduled, so that accurate partition can be performed on the area where the power distribution network to be scheduled is located so as to realize finer scheduling.

Drawings

FIG. 1 is a flow chart of one embodiment of a dynamic partition-based power distribution network scheduling method provided by the present invention;

fig. 2 is a schematic structural diagram of an embodiment of a power distribution network scheduling system based on dynamic partitioning according to the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In the description of this application, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", "a third", etc. may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.

In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context.

In the description of the present application, it should be noted that all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art unless defined otherwise. The terminology used in the description of the present invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention, as the particular meaning of the terms described above in this application will be understood to those of ordinary skill in the art in the specific context.

Referring to fig. 1, a flow chart of an embodiment of a dynamic partition-based power distribution network scheduling method provided by the present invention is shown, and the method is applied to a cloud, and includes steps S1 to S7, specifically as follows:

S1, acquiring day-ahead power generation data and day-ahead load data of an area where a power distribution network to be scheduled is located, and day-ahead scheduling data of a higher power grid of the power distribution network to be scheduled; wherein a plurality of border base stations are deployed in the area, the daily load data from the plurality of border base stations;

s2, dividing the area based on the day-ahead power generation data, the day-ahead load data and the day-ahead scheduling data to obtain a plurality of first partitions; each first partition comprises at least one side base station;

s3, periodically acquiring a daily data set according to a preset sampling step length; the daily data set comprises daily power generation data and daily load data of the area and daily scheduling data of the upper power grid; the daily load data come from the plurality of side base stations;

the preset sampling step may be 1 hour (1 h), for example.

S4, updating the plurality of first partitions based on the intra-day power generation data, the intra-day load data and the intra-day scheduling data to obtain a plurality of second partitions in response to the acquired intra-day data set;

s5, receiving edge data corresponding to each second partition; the side data are data which are respectively counted and processed by all side base stations contained in the corresponding second partition and then transmitted;

S6, carrying out robust optimization processing based on the edge data corresponding to the plurality of second partitions to obtain partition scheduling distribution results;

s7, selecting a plurality of edge physical proxy base stations corresponding to the second partitions one by one from the plurality of edge base stations, and respectively sending generated scheduling information to the plurality of edge physical proxy base stations based on the partition scheduling distribution result so as to perform partition scheduling on the power distribution network to be scheduled.

It should be noted that, the edge data refers to: and processing the respective statistical data (for example, information of the distribution network equipment correspondingly controlled by each side end base station, user load data counted by the user side intelligent ammeter correspondingly received by each side end base station, and the like) by all the side end base stations contained in the corresponding second partition, and then sending the processed data to the cloud in real time. Therefore, the data can be processed, such as desensitization processing, at the side base station to avoid revealing user privacy.

In an optional embodiment, the dividing the area based on the day-ahead power generation data, the day-ahead load data and the day-ahead schedule data to obtain a plurality of first partitions includes:

It should be noted that, in this embodiment, the limitation condition can be defined by the day-ahead scheduling scheme indicated by the day-ahead scheduling data issued by the upper-level power grid, and the region is divided on the basis that the division scheme can meet the limitation condition and the coordinated bus power exchange between the partitions is minimized, and the specific division scheme is obtained by the cloud based on the acquired day-ahead power generation data and day-ahead load data.

In an optional implementation manner, the daily load data includes user load data obtained by statistics of corresponding user-side smart meters received by each side base station in a preset daily time period, and the daily power generation data includes distributed power generation data of a plurality of distributed power generation devices deployed in the area in the preset daily time period, wherein each distributed power generation device corresponds to one of the plurality of side base stations;

The clustering processing is performed on the area based on the payload values corresponding to the plurality of edge base stations, specifically: and clustering the area by taking the minimum payload value as a target based on the payload values corresponding to the side base stations, so as to obtain a plurality of updated clustering centers.

The clustering processing is performed on the area based on the payload values corresponding to the side base stations by taking the intra-day scheduling data and the preset clustering iteration number as limiting conditions, so as to update the clustering centers, and specifically includes:

Calculating a payload value corresponding to each first partition based on the payload values corresponding to each of the plurality of edge base stations;

and clustering the region by taking the daily scheduling data and the preset clustering iteration number as limiting conditions and taking unified types and data characteristic approaching as targets so as to update the plurality of clustering centers.

Specifically, the payload value corresponding to each first partition is calculated by the following formula:

wherein,for the payload value, T is the statistical duration, +.>For the distributed photovoltaic output at time t, < >>For the output of energy storage at time t +.>For the total load of the first partition, +.>For average load +.>For the average distributed photovoltaic output +.>Is the average stored energy output.

Specifically, taking the intra-day scheduling data as a limiting condition includes:

upper grid scheduling capacity constraints:

wherein,is the total load actually expected to be needed in the region of time t,/>For the total usage capacity at time t in the area, < >>The total power uploaded or received by the upper power grid response in the t moment area is calculated;

power balance constraint:

wherein,is the total load actually expected to be needed in the region of time t,/>Predicting an actual total power generation value for a photovoltaic generator set at t moment +. >Predicting an actual total power generation value for the t-moment wind generating set,/->The total power uploaded or received by the upper power grid response in the t moment area is calculated;

generating capacity constraint:

wherein,lower limit of generated power for ith distributed power supply,/->Generating power for the ith distributed power supply, < >>An upper limit for the i-th distributed power source generated power;

battery charge level constraints:

wherein,is the lower limit of the charge level of the ith accumulator,/->For the charge level of the ith battery, < >>Is the upper limit of the charge level of the ith storage battery;

transmission power constraint:

wherein,lower limit of transmission power between the networks specified for the power transmission protocol, < >>For the transmission of power between the electrical grids,an upper limit of inter-grid transmission power specified for the power transmission protocol;

supply and demand balance constraint:

wherein,for the total output of the required photovoltaic generator set, < > for>The actual total power generation value is predicted for the photovoltaic power generation unit,for the total output of the required wind generating set, < > for>Predicting an actual total power generation value for a wind turbine generator system, < >>For the total output value of the desired energy storage device, < >>The existing reserve power value for the energy storage battery;

and (3) unit climbing constraint:

for the output of the g-th generator set of the ith partition at time t, + >The climbing upper limit value of the unit at the moment t;

minimum start-stop time constraint of generator:

wherein,for the minimum start-up time of the ith generator set,/->Minimum down time for the ith genset,/-for the ith genset>For the i-th set start-stop rated current, < >>The actual current value at the moment t of the ith unit is the actual current value;

the adjustable resource elastic safety boundary constraint is adopted, wherein the utilization rate of the adjustable resource is measured by using the load rate, the smaller the value of the load rate is, the larger the load curve fluctuation is, the larger the peak-valley difference is, and the safe and stable operation of the power grid is not facilitated; the load rate is more 1, the equipment utilization rate is higher, the peak-valley difference is reduced, and the safe transmission elastic space is limited;

transmission capacity constraint with the upper grid line:

wherein,for admittance between the region and the upper grid transmission node ij +.>For maximum transmission capacity between the region and the upper grid transmission node ij +.>For the voltage phase angle of node i at time t, +.>The voltage phase angle of the node j at the moment t;

the self-elasticity coefficient of the load to the electricity price is set as follows:

wherein,is self-demanded modulus of elasticity, < >>For executing the electricity consumption change value of the time-sharing electricity price t period before and after +.>For the power consumption of the t period, < > for>For executing the electricity price change value of t time period before and after time sharing electricity price, < > >Electricity price for period t;

setting the crossed elastic coefficient of load to electricity price:

wherein,is the crossed elastic coefficient>Presetting a value change quantity for the electricity price of the section j, < >>Presetting a value for electricity prices in the section j;

information entropy setting of each index of the load:

wherein,weight of the ith load indicator, < +.>The information entropy of the ith load index, k is the total number of indexes of the load;

translational elastic capability constraint of load:

wherein,representing power demand side resource load translation capability, < ->Reduced power consumption for the user during peak power consumption periods,/->And the electricity consumption amount is increased for the user in the electricity consumption valley period.

In an optional implementation manner, the robust optimization processing is performed based on the edge data corresponding to the plurality of second partitions to obtain a partition scheduling allocation result, which specifically includes:

wherein the termination condition means that the cost constraint is the lowest.

Specifically, the two-stage robust optimization model is determined by the following formula:

Wherein,for the time series set within the day,/->For all optimization policy sets ++>For the optimization set corresponding robust optimization coefficients +.>Optimizing influence weights in the partition at time t, < +.>Optimizing influence weights for t-moment inter-partition>And optimizing the influence weight for the partition and the region at the time t.

In this embodiment, the characteristics of the two-stage robust optimization model are utilized, so that the output partition scheduling distribution result is beneficial to uniform scheduling in the area where the power distribution network to be scheduled is located, and smooth transition between partitions can be realized.

In an alternative embodiment, before the generating scheduling information is sent to the plurality of edge physical proxy base stations respectively based on the partition scheduling allocation result, the method further includes:

In an alternative embodiment, the electric market gaming model is a master-slave gaming model nested with a cooperative gaming model for indicating cooperative gaming between respective secondary partitions.

In the embodiment, the cooperative game model is embedded into the master-slave game model, so that the situation that the individual second partitions are independent due to the fact that the optimization strategy falls into local optimum can be effectively prevented.

In an alternative embodiment, the electric market gaming modelIs determined by the following formula:

wherein,for the main game party, < >>To take part in game>Scheduling allocation results for said partitions, +.>For the second partitions, the electricity purchase price set at time t is +.>For the online electricity price in the t period area, < ->，For the whole game model, < >>For the ith upper objective function limit, +.>For the ith objective function lower limit, +.>Upper limit of the optimum value for the ith objective function,/->Lower limit of the optimum value for the ith objective function,/->For the trading electricity prices between the second partitions,the electric quantity purchased for one second partition to other second partitions, n is the number of objective functions, and i represents the ith objective function.

Illustratively, the set of policies in the power market gaming model is set to satisfy the following formula:

wherein,managing a set of electricity purchase and sales plans for an intra-regional division,/->For the electricity price collection of electricity purchase of base stations at different side ends in the second partition of the t period, the user is added with ∈>For t time periods different second inter-partition power rate sets,/->The allocation result is scheduled for the partition,to remove->Other partition scheduling allocation policies than +.>Assigning a result predictor set for partition scheduling, +.>And managing the electricity purchasing plan set for the area.

In an alternative embodiment, the partition output corresponding to each second partition is used to characterize the distributed power generation capacity, the storage battery discharge capacity and the distributed power residual power corresponding to each second partition;

specifically, the running cost of the distributed power supply corresponding to each second partition is calculated by the following formula:

wherein,for distributed power supply operation cost,/- >Cost required for unit power generation (1 kwh) of the ith generating set, +.>The operation and maintenance cost required for each 1kwh electric quantity of the ith generating set is +.>Distribution costs for photovoltaic power generation units for the lowest operating costs in the region, < >>The distribution cost of the wind generating set is the lowest running cost in the area.

specifically, the environmental cost corresponding to each second partition is calculated by the following formula:

wherein,for environmental cost->For the ith distributed unit the emission factor corresponding to the mth pollutant +.>For the mth emission factor corresponding to electricity purchase, +.>For the treatment of the costs of the mth emission, +.>Generating capacity of ith distributed unit at t moment,/->Is the electricity purchasing quantity at the time t.

specifically, the cost of the storage battery corresponding to each second partition is calculated by the following formula:

wherein,for the cost of the accumulator->For the amount of electricity released by the accumulator at time t +.>For a loss factor of 1kwh of battery per discharge, exemplary +. >。

specifically, the inter-partition electricity purchase cost corresponding to each second partition is calculated by the following formula:

wherein,cost of purchasing power to other second partition at time t +.>The amount of electricity purchased for one second partition to the other second partition, +.>For the electricity consumption in the partition at the moment t, < >>For the amount of electricity purchased already exchanged with the other second partition,/->And (5) collecting electricity purchase prices of the second partitions at the time t.

specifically, the residual electricity patch cost corresponding to each second partition is calculated by the following formula:

wherein,cost of supplementing surplus electricity>For the total power generation amount of the distributed wind power generation equipment and the distributed photovoltaic power generation equipment at the moment t, < >>For the total quantity of electricity consumption at the user side at the time t, < + >>For the online electricity price in the t period area, < ->And (5) subsidy price of the unit power generated by the distributed generator set.

Specifically, the lowest cost objective function within a partition is determined by the following formula:

wherein,for the integrated operating costs of the ith partition in the t hour period,/for the time period of t hours>、/>、/>、/>、/>Sequentially is a first cost weightCoefficients, a second cost weight coefficient, a third cost weight coefficient, a fourth cost weight coefficient, a fifth cost weight coefficient, and (2)>Running cost for distributed power supply,/->Is the environmental cost->For battery cost->Cost for inter-partition purchase of electricity,/->And the cost is compensated for residual electricity.

In an optional implementation manner, the sending the generated scheduling information to the plurality of edge physical proxy base stations based on the partition scheduling allocation result to perform partition scheduling on the power distribution network to be scheduled includes:

Correspondingly, the embodiment of the invention also provides a power distribution network scheduling system based on the dynamic partition, which can realize all the flows of the power distribution network scheduling method based on the dynamic partition provided by the embodiment.

Referring to fig. 2, a schematic structural diagram of an embodiment of a power distribution network scheduling system based on dynamic partition provided by the present invention, where the power distribution network scheduling system is located in a cloud, and the power distribution network scheduling system includes:

the day-ahead data acquisition module 101 is configured to acquire day-ahead power generation data and day-ahead load data of an area where a power distribution network to be scheduled is located, and day-ahead scheduling data of a higher power grid of the power distribution network to be scheduled; wherein a plurality of border base stations are deployed in the area, the daily load data from the plurality of border base stations;

the first partition module 102 is configured to divide the area based on the day-ahead power generation data, the day-ahead load data, and the day-ahead scheduling data, to obtain a plurality of first partitions; each first partition comprises at least one side base station;

an intra-day data acquisition module 103, configured to periodically acquire an intra-day data set according to a preset sampling step length; the daily data set comprises daily power generation data and daily load data of the area and daily scheduling data of the upper power grid; the daily load data come from the plurality of side base stations;

A second partition module 104, configured to update, in response to obtaining the intra-day data set, the plurality of first partitions based on the intra-day power generation data, the intra-day load data, and the intra-day scheduling data, to obtain a plurality of second partitions;

an edge data receiving module 105, configured to receive edge data corresponding to each second partition; the side data are data which are respectively counted and processed by all side base stations contained in the corresponding second partition and then transmitted;

the robust optimization module 106 is configured to perform robust optimization processing based on the edge data corresponding to the plurality of second partitions, so as to obtain partition scheduling allocation results;

and the partition scheduling module 107 is configured to select a plurality of edge physical proxy base stations corresponding to the second partitions from the plurality of edge base stations, and send the generated scheduling information to the plurality of edge physical proxy base stations respectively based on the partition scheduling allocation result, so as to perform partition scheduling on the power distribution network to be scheduled.

wherein the termination condition means that the cost constraint is the lowest.

wherein,for the main game party, < >>To take part in game>Scheduling allocation results for said partitions, +. >For the second partitions, the electricity purchase price set at time t is +.>For the online electricity price in the t period area, < ->，For the whole game model, < >>For the ith upper objective function limit, +.>For the ith objective function lower limit, +.>Upper limit of the optimum value for the ith objective function,/->Lower limit of the optimum value for the ith objective function,/->For the trading electricity prices between the second partitions,the electric quantity purchased for one second partition to other second partitions, n is the number of objective functions, and i represents the ith objective function.

In summary, the invention has the following beneficial effects:

From the above description of the embodiments, it will be clear to those skilled in the art that the present invention may be implemented by means of software plus necessary hardware platforms, but may of course also be implemented entirely in hardware. With such understanding, all or part of the technical solution of the present invention contributing to the background art may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the method described in the embodiments or some parts of the embodiments of the present invention.

While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.

Claims

1. A power distribution network scheduling method based on dynamic partitioning, which is characterized by being applied to a cloud, the method comprising:

Based on the day-ahead power generation data, the day-ahead load data and the day-ahead scheduling data, clustering the area to obtain a plurality of clustering centers so as to divide the area to obtain a plurality of first partitions; each first partition comprises at least one side base station;

in response to acquiring the intra-day data set, updating the plurality of first partitions based on the intra-day power generation data, the intra-day load data and the intra-day scheduling data to obtain a plurality of second partitions; the updating of the plurality of first partitions is performed by clustering the areas based on the payload values corresponding to the plurality of border base stations, wherein the payload values are calculated based on user load data included in the daily load data and distributed power generation data included in the daily power generation data;

2. The method for scheduling a power distribution network based on dynamic partitioning according to claim 1, wherein the clustering the area based on the pre-day power generation data, the pre-day load data and the pre-day scheduling data to obtain a plurality of cluster centers, so as to partition the area to obtain a plurality of first partitions, includes:

3. The power distribution network scheduling method based on dynamic partitioning according to claim 2, wherein the user load data is user load data obtained by statistics of corresponding user side smart meters received by each side base station in a preset time period, and the distributed power generation data is distributed power generation data of a plurality of distributed power generation devices deployed in the area in the preset time period, wherein each distributed power generation device corresponds to one of the plurality of side base stations;

4. The power distribution network scheduling method based on dynamic partitioning according to any one of claims 1-3, wherein the robust optimization processing is performed based on the edge data corresponding to the plurality of second partitions, so as to obtain a partition scheduling allocation result, specifically:

wherein the termination condition means that the cost constraint is the lowest.

5. The dynamic partition-based power distribution network scheduling method according to claim 1, wherein before the generating scheduling information is respectively transmitted to the plurality of edge physical proxy base stations based on the partition scheduling allocation result, the method further comprises:

6. The power distribution network scheduling method based on dynamic partitioning according to claim 5, wherein the electric power market gaming model is a master-slave gaming model nested with a cooperative gaming model for indicating cooperative gaming between respective second partitions.

7. The dynamic partition-based power distribution network scheduling method of claim 6, wherein the power market gaming modelIs determined by the following formula:

wherein,for the main game party, < >>To take part in game>Scheduling allocation results for said partitions, +.>For the second partitions, the electricity purchase price set at time t is +. >For the online electricity price in the t period area, < ->，/>For the whole game model, < >>For the ith upper objective function limit, +.>For the ith objective function lower limit, +.>Upper limit of the optimum value for the ith objective function,/->Lower limit of the optimum value for the ith objective function,/->For the trading electricity prices between the second partitions,the electric quantity purchased for one second partition to other second partitions, n is the number of objective functions, and i represents the ith objective function.

8. The method for scheduling a power distribution network based on dynamic partitioning as set forth in claim 5, wherein the partition output corresponding to each second partition is used for characterizing the power generation capacity of the distributed power supply, the discharge capacity of the storage battery and the residual power of the distributed power supply corresponding to each second partition;

9. The method for scheduling a power distribution network based on dynamic partitioning according to claim 1, wherein the sending the generated scheduling information to the plurality of edge physical proxy base stations respectively based on the partitioning scheduling allocation result, so as to perform partitioning scheduling on the power distribution network to be scheduled, includes:

10. Distribution network dispatch system based on dynamic subregion, its characterized in that, distribution network dispatch system locates the high in the clouds, distribution network dispatch system includes:

the first partition module is used for carrying out clustering processing on the area based on the day-ahead power generation data, the day-ahead load data and the day-ahead scheduling data to obtain a plurality of clustering centers so as to divide the area to obtain a plurality of first partitions; each first partition comprises at least one side base station;

The second partition module is used for updating the plurality of first partitions based on the daily power generation data, the daily load data and the daily scheduling data to obtain a plurality of second partitions in response to the acquired daily data set; the updating of the plurality of first partitions is performed by clustering the areas based on the payload values corresponding to the plurality of border base stations, wherein the payload values are calculated based on user load data included in the daily load data and distributed power generation data included in the daily power generation data;