CN114169587A - Transformer substation extension planning method based on high-resolution second-order image data analysis - Google Patents

Transformer substation extension planning method based on high-resolution second-order image data analysis Download PDF

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CN114169587A
CN114169587A CN202111381884.6A CN202111381884A CN114169587A CN 114169587 A CN114169587 A CN 114169587A CN 202111381884 A CN202111381884 A CN 202111381884A CN 114169587 A CN114169587 A CN 114169587A
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substation
load
transformer substation
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王立
李雪凌
汪玉翔
罗晨
杨珂
张裕
彭军
杨世平
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Guizhou Power Grid Co Ltd
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Abstract

The invention discloses a transformer substation extension planning method based on high-score second-number image data analysis, which comprises the steps of acquiring geographic information by using a high-score second-number satellite and an interpretation system; inputting the geographic information into a geographic information generating system to generate a distance function between any two points with geographic economic information; inputting the distance function with the geographical economic information into a transformer substation planning system, and solving by the transformer substation planning system by using a dynamic bacterial foraging algorithm to obtain an optimal transformer substation extension scheme; the invention not only reduces the error caused by artificial division, but also reduces the cost of the extension of the transformer substation, and can be applied to the extension in the region of a perfect electric power system.

Description

Transformer substation extension planning method based on high-resolution second-order image data analysis
Technical Field
The invention relates to the technical field of optimization planning of a power supply area of a transformer substation, in particular to a transformer substation extension planning method based on high-grade No. two image data analysis.
Background
In recent years, with the continuous improvement of living standard, the increase of household power consumption and factory power consumption is caused, the power demand is continuously increased, a power system needs to be expanded to meet the power demand, the problem is complex, the simultaneous design and economic consideration of a plurality of different parameters and constraint conditions are involved, in the prior art, the geographic information of each area is manually marked according to experience standards, so that a substation expansion planning scheme with lower cost is obtained, the traditional scheme has high requirement on experience and extremely large error, the expansion cost is increased, the traditional scheme can only be applied to the site selection of the expansion of a new substation, and further expansion in the area of the well-established power system cannot be performed.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides a transformer substation extension planning method based on high-grade second image data analysis, which can solve the problems that the traditional scheme has large error and high cost and cannot be further extended in the region of a perfect electric power system.
In order to solve the technical problems, the invention provides the following technical scheme: acquiring geographic information by using a high-score second satellite and an interpretation system; inputting the geographic information into a geographic information generating system to generate a distance function between any two points with geographic economic information; and inputting the distance function with the geographical economic information into a transformer substation planning system, and solving by using a dynamic bacterial foraging algorithm by the transformer substation planning system to obtain an optimal transformer substation extension scheme.
As an optimal scheme of the substation extension planning method based on high-grade second image data analysis, the method comprises the following steps: the geographic information comprises remote sensing image information with high positioning precision and high spatial resolution obtained by a high-resolution second satellite, and the difference of different ground features is represented by the image interpretation system through the difference of brightness values or pixel values and spatial variation, so that the physical elements of the different ground features are distinguished.
As an optimal scheme of the substation extension planning method based on high-grade second image data analysis, the method comprises the following steps: the geographic information generation system comprises the steps of inputting a defined geographic condition function and two-dimensional geographic position information of a power transformation station and a load point to be planned into an economic distance functional to solve a functional extreme value; and taking the functional extreme value as a value of a distance function, and correspondingly taking the functional extreme value as a distance function between any two points with geographical economic information.
As an optimal scheme of the substation extension planning method based on high-grade second image data analysis, the method comprises the following steps: the transformer substation planning system comprises a load and pricing function, an objective function, constraint conditions and parameters.
As an optimal scheme of the substation extension planning method based on high-grade second image data analysis, the method comprises the following steps: the load and pricing functions include the number of features,
load function
Figure BDA0003365900660000021
Comprises the following steps:
Figure BDA0003365900660000022
wherein,
Figure BDA0003365900660000023
for the base power demand of load point j in year h, DLFtThe load power demand factor when the load level is t;
the pricing function comprises a sales energy price model and an outsourcing energy price model;
selling energy price model
Figure BDA0003365900660000024
Comprises the following steps:
Figure BDA0003365900660000025
wherein,
Figure BDA0003365900660000026
for a base price of selling 1kWh of energy to load point j, PLFtIs a price level factor, alpha, at a load level of thIs the rate of price growth at h years;
outsourcing energy price model
Figure BDA0003365900660000027
Comprises the following steps:
Figure BDA0003365900660000028
wherein,
Figure BDA0003365900660000029
base price for buying 1KWh energy, PLF, for substation itIs a price level factor, alpha, at a load level of thIs the rate of price increase at h years.
As an optimal scheme of the substation extension planning method based on high-grade second image data analysis, the method comprises the following steps: the objective function OF includes that,
OF＝RSE-CPE-CIS-CCF
the current value RSE of the sales energy income is as follows:
Figure BDA0003365900660000031
the current value CPE of the purchase energy cost is as follows:
Figure BDA0003365900660000032
the CIS of the current value of the investment cost of the installation or upgrade transformer substation is as follows:
Figure BDA0003365900660000033
the construction cost CCF of the medium-voltage feeder line is as follows:
Figure BDA0003365900660000034
wherein RSE is the current value of energy income for sale, CPE is the current value of energy cost for purchase, CIS is the current value of investment cost for installation or upgrade of the transformer substation, CCF is the construction cost of the medium-voltage feeder, infra is the inflation rate of the currency, intr is the interest rate, H is a constant, M is the load progression, H is the total year of planning, T is the total year of planningh,tFor the time at the load level t of the year h,
Figure BDA0003365900660000035
the power required for the ith substation, at the load level in the h year,
Figure BDA0003365900660000036
for the investment required per unit scale of extended capacity of substation i,
Figure BDA0003365900660000037
for the capacity of substation i in h years,
Figure BDA0003365900660000038
for the capacity of substation i in h-1 year,
Figure BDA0003365900660000039
for the set of existing and candidate substations,
Figure BDA00033659006600000310
in order to operate the capacity of the h-year transformer station i at the load level t, theta is a load point set, and KfIs the construction cost, beta, of a medium voltage feeder line per unit lengthi,jThe connection relation between the ith transformer substation and the jth load is obtained; di,jIs the distance between substation number i and load number j.
As an optimal scheme of the substation extension planning method based on high-grade second image data analysis, the method comprises the following steps: the constraint conditions comprise network radiability constraint, operation capability constraint of the transformer substation, capacity expansion capability constraint of the transformer substation and voltage drop under load point constraint;
the network radiometric constraints are:
Figure BDA0003365900660000041
the constraint of the operation capacity of the transformer substation is as follows:
Figure BDA0003365900660000042
the capacity expansion capacity of the transformer substation is restricted as follows:
Figure BDA0003365900660000043
Figure BDA0003365900660000044
the voltage drop under the load point constraint is:
Figure BDA0003365900660000045
wherein, betai,jIs the connection relation between a transformer substation i and a load j, wherein j is the j load, MaxopeFor the maximum operating capacity allowed for any substation,
Figure BDA0003365900660000046
for the capacity of substation i in h years,
Figure BDA0003365900660000047
for substation i to be built or upgraded in h years,
Figure BDA0003365900660000048
the maximum extended capacity of the substation i is,
Figure BDA0003365900660000049
operating capacity of the branch connected between the i substation and the j load at the h year t load level, DijIs the distance between No. i substation and No. j load, RijFor the branch resistance, V, between substation i and load jiThe voltage of the transformer substation i is delta V, and the maximum voltage drop allowed by a load end is delta V; j is the j load; h is the h year and t is the t load level.
As an optimal scheme of the substation extension planning method based on high-grade second image data analysis, the method comprises the following steps: the parameters comprise a medium-voltage feeder distance parameter, a medium-voltage feeder loss parameter, a medium-voltage feeder operation capacity parameter and a transformer substation operation capacity parameter;
the medium-voltage feeder line distance parameters are as follows:
Figure BDA00033659006600000410
the medium-voltage feeder loss parameters are as follows:
Figure BDA00033659006600000411
wherein, VjTerminal voltage of load j;
the medium-voltage feeder line operation capacity parameters are as follows:
Figure BDA0003365900660000051
the transformer substation operation capacity parameters are as follows:
Figure BDA0003365900660000052
where ξ is the distance correction factor,
Figure BDA0003365900660000053
the x-coordinate of the substation i is,
Figure BDA0003365900660000054
which is the x-coordinate of the load i,
Figure BDA0003365900660000055
for the y-coordinate of the substation i,
Figure BDA0003365900660000056
is the y coordinate of load i.
As an optimal scheme of the substation extension planning method based on high-grade second image data analysis, the method comprises the following steps: the dynamic bacterial foraging algorithm comprises the step of inputting binary variables for determining the connection of a load point and a transformer substation in a binary process into an integer process to determine the extension plan of the transformer substation.
The invention has the beneficial effects that: the method and the device not only reduce errors caused by artificial division, but also reduce the cost of the extension of the transformer substation, and can be applied to the extension in a perfect electric power system area.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a flowchart of a substation extension planning method based on high-score second image data analysis according to a first embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a substation extension planning method based on high-score second image data analysis, including:
s1: and acquiring geographic information by using a high-score second satellite and an interpretation system.
The remote sensing image information with high positioning precision and high spatial resolution is obtained by utilizing a high-resolution second satellite, and the difference of different ground objects is represented by utilizing an image interpretation system through the difference of brightness values or pixel values and spatial variation, so that the physical elements of the different ground objects are distinguished.
The differences of different ground objects are differences of the ground objects to be recognized in shape, size, shadow, tone, color, texture, pattern, position and layout; the physical elements of different ground objects are those of terrain, altitude and hydrology.
Preferably, the resolution of the embodiment is higher than that of the traditional remote sensing picture, and more elements are acquired by using the image interpretation system, so that more data are provided for subsequent planning.
S2: and inputting the geographic information into a geographic information generation system to generate a distance function between any two points with geographic economic information.
Inputting the defined geographical condition function and the two-dimensional geographical position information of the power transformation station and the load point to be planned into an economic distance functional to solve a functional extreme value; and taking the functional extreme value as a value of the distance function, and corresponding to the distance function between any two points with the geographical economic information.
Wherein, it should be noted that, the defined geographic condition function is to calculate the investment ratio of the investment of the point to the ideal geographic condition according to the geographic condition of the point by an empirical method;
the economic distance functional is a functional which takes the investment ratio of a line from the position of a transformer substation to be planned to a load point to be supplied as a starting point and a terminal point to an ideal state as output;
distance function: solving a minimum value of the economic distance functional, outputting the minimum value as a minimum investment ratio under an ideal geographic condition, and recording a curve path when the minimum investment ratio appears;
preferably, in the embodiment, the problem of functional extremum is solved directly in the weighted distance functional space by using the weighted distance, which has the advantage of avoiding the error caused by artificially dividing the grid, and the solving method of the functional extremum is a finite element method of a computer.
S3: and inputting the distance function with the geographical economic information into a transformer substation planning system, and solving by using a dynamic bacterial foraging algorithm by the transformer substation planning system to obtain an optimal transformer substation extension scheme.
(1) The transformer substation planning system comprises a load and pricing function, an objective function, constraint conditions and parameters.
a) Load function
Figure BDA0003365900660000071
Comprises the following steps:
Figure BDA0003365900660000072
wherein,
Figure BDA0003365900660000073
for the base power demand of load point j in year h, DLFtThe load power demand factor when the load level is t;
b) the pricing function includes a sales energy price model and an outsourcing energy price model;
selling energy price model
Figure BDA0003365900660000074
Comprises the following steps:
Figure BDA0003365900660000075
wherein,
Figure BDA0003365900660000076
for a base price of selling 1kWh of energy to load point j, PLFtIs a price level factor, alpha, at a load level of thIs the rate of price growth at h years;
outsourcing energy price model
Figure BDA0003365900660000077
Comprises the following steps:
Figure BDA0003365900660000081
wherein,
Figure BDA0003365900660000082
base price for buying 1KWh energy, PLF, for substation itIs a price level factor, alpha, at a load level of thIs the rate of price increase at h years.
c) The objective function OF includes,
OF＝RSE-CPE-CIS-CCF
wherein RSE is the current value of the income of selling energy, CPE is the current value of the cost of purchasing energy, CIS is the current value of the investment cost of installing or upgrading a transformer substation, and CCF is the construction cost of a medium-voltage feeder line;
the current value of sales energy revenue RSE is:
Figure BDA0003365900660000083
wherein infr is the inflation rate of the goods, intr is the interest rate, H is a constant, M is the load grade number, H is the total planning age, Th,tFor the time at the load level t of the year h,
Figure BDA0003365900660000084
the power required for the ith substation at the load level of the th year;
the current value of the energy cost of purchase CPE is:
Figure BDA0003365900660000085
wherein,
Figure BDA0003365900660000086
for the set of existing and candidate substations,
Figure BDA0003365900660000087
the capacity is operated when the load level of a transformer station i is t in h years;
the CIS of the present value of the investment cost for installing or upgrading the transformer substation is as follows:
Figure BDA0003365900660000088
wherein,
Figure BDA0003365900660000089
for each extension unit mark of transformer substation iThe investment required for the capacity of the degree,
Figure BDA00033659006600000810
for the capacity of substation i in h years,
Figure BDA00033659006600000811
capacity of the i-type substation in h-1 year;
the construction cost CCF of the medium voltage feeder is:
Figure BDA0003365900660000091
where θ is the set of load points, KfIs the construction cost, beta, of a medium voltage feeder line per unit lengthi,jThe connection relation between the ith transformer substation and the jth load is 1, which represents that the transformer substation and the load are in line connection, and 0 represents that no direct line connection exists between the transformer substation and the load; di,jIs the distance between substation number i and load number j.
d) The constraint conditions comprise network radiometric constraints, constraints on the operational capacity of the substation, constraints on the capacity expansion capacity of the substation and voltage drop under load point constraints;
the network radiation constraints are:
Figure BDA0003365900660000092
wherein, betai,jThe j is the connection relation between a transformer substation i and a load j, and the j is the j load;
the constraints on the operational capacity of the substation are:
Figure BDA0003365900660000093
among them, MaxopeFor the maximum operating capacity allowed for any substation,
Figure BDA0003365900660000094
capacity of substation i in h years;
the constraint of the capacity expansion capability of the transformer substation is as follows:
Figure BDA0003365900660000095
Figure BDA0003365900660000096
wherein,
Figure BDA0003365900660000097
for substation i to be built or upgraded in h years,
Figure BDA0003365900660000098
the maximum expansion capacity of the substation i;
the voltage drop under the point-of-load constraint is:
Figure BDA0003365900660000099
wherein,
Figure BDA00033659006600000910
operating capacity of the branch connected between the i substation and the j load at the h year t load level, DijIs the distance between No. i substation and No. j load, RijFor the branch resistance, V, between substation i and load jiThe voltage of the transformer substation i is delta V, and the maximum voltage drop allowed by a load end is delta V; j is the j load; h is the h year and t is the t load level.
e) The parameters comprise a medium voltage feeder distance parameter, a medium voltage feeder loss parameter, a medium voltage feeder operation capacity parameter and a substation operation capacity parameter;
the medium-voltage feeder line distance parameters are as follows:
Figure BDA0003365900660000101
where ξ is the distance correction factor,
Figure BDA0003365900660000102
the x-coordinate of the substation i is,
Figure BDA0003365900660000103
which is the x-coordinate of the load i,
Figure BDA0003365900660000104
for the y-coordinate of the substation i,
Figure BDA0003365900660000105
is the y coordinate of load i;
the medium-voltage feeder loss parameters are as follows:
Figure BDA0003365900660000106
wherein, VjTerminal voltage of load j;
the medium-voltage feeder operation capacity parameters are as follows:
Figure BDA0003365900660000107
the operation capacity parameters of the transformer substation are as follows:
Figure BDA0003365900660000108
(2) and the transformer substation planning system utilizes a dynamic bacterial foraging algorithm to solve to obtain an optimal transformer substation extension scheme.
a) Determining a binary variable and an integer variable of the expansion capacity of the transformer substation in the process of determining the binary variable and the integer of the connection of the load point and the transformer substation;
calculating the voltage drop of the medium-voltage feeder between the transformer substation and the peak demand load point, and classifying the medium-voltage feeders of the transformer substations according to the voltage drop;
evaluating the initial capacity provided by the existing transformer substation i to the load point j during peak demand;
randomly allocating an existing substation i to be connected to each load point j;
consider voltage drop and operating capacity constraints. When the constraint is not violated, define betaij1 is ═ 1; otherwise, betaij＝0；
Randomly assigning another existing substation to connect to the remaining load point j;
if the residual load point j exists, the next step is carried out; otherwise, go to step IV;
expanding the capacity of the transformer substation i (the existing and/or candidate transformer substations) to fully meet the peak value requirements of all the residual load points j;
randomly connecting one substation i (existing or candidate) to the remaining load point j;
when the above constraints are not violated, β is defined, taking into account voltage drop and operating capacity constraintsij1 is ═ 1; otherwise, betaij＝0；
Randomizing another substation i, connecting to the remaining load point j, and then going to (f) until there is no remaining load point j;
and fourthly, ending.
b) Determining an integer variable of the expansion capacity of the substation;
firstly, acquiring a binary variable from a binary process;
② press type
Figure BDA0003365900660000111
Summing the capacities of the substations;
extending the capacity of the transformer substation, which is divided into two situations:
when the extension planning of the single-stage transmission transformer substation is considered, each transformer substation is expanded in the first year of planning so as to fully meet all load points at the time of planning a target annual demand peak; this process will add one stepInti,h varUsing the formula:
Figure BDA0003365900660000112
considering the constraint operation capacity of the transformer substation until the constraint is violated;
when the extension planning of the multi-stage transmission substations is considered, capacity of each substation can be expanded every year of planning so as to fully supply all load points with the demand peak in the year; this process will add one step Inti,h varThen, using the formula:
Figure BDA0003365900660000113
considering substation operation capacity constraints until the constraints are violated;
fourthly, repeating the third step for all the transformer substations;
and fifthly, ending.
Preferably, the present embodiment employs a new substation planning system and is directed to how to build a well-established power system within an area, so that the solution is optimal and the cost is reduced.
Example 2
The technical effects adopted in the method are verified and explained, the embodiment selects the traditional method and adopts the method to carry out comparison test, and the test results are compared by means of scientific demonstration to verify the real effect of the method.
The traditional method has high experience requirement and extremely large error, increases the extension cost and can only be applied to the site selection of the extension of a new substation.
Compared with the traditional method, the method has the advantages of reducing errors, reducing the cost of the extension of the transformer substation and being applicable to the extension in the region of a perfect power system.
In this embodiment, a conventional method and a method are respectively used to perform real-time measurement and comparison on a small power transmission system in a certain area of China under the same condition as shown in tables 1 to 3 to obtain the total investment comparison of the two strategies in table 4.
Table 1: and (4) substation information.
Figure BDA0003365900660000121
Table 2: technical information and economic parameters.
Parameter(s) Parameter value
Interest Rate (%) 5
Inflation ratio (%) 9
Basic electricity sale amount (Yuan/KWh) 65
Price fluctuation (%) 5
Maximum operating capacity limit of substation (%) 80
Distance correction factor 1.12
Line resistance (ohm/km) 0.236
Investment for medium voltage line construction (Yuan/km) 18900
Secondary side voltage of transformer substation 20
Table 3: and setting the load level.
Load rating classification Description of load classes Annual running time (hours)
T1 Low load 5100
T2 Middle load 2550
T3 High load 1300
Table 4: and comparing the total investment of the two strategies.
Policy Total investment (Yi Yuan)
SSDSEP 1.4489
MSDSEP 1.3986
It can be seen that, in this embodiment, the total investment effect is significantly improved by using the model proposed in the present invention, the total investment is reduced by 503 ten thousand yuan, which is reduced by 3.47%, and a significant effect is obtained.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.

Claims (9)

1. A transformer substation extension planning method based on high-resolution second-order image data analysis is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
acquiring geographic information by using a high-score second satellite and an interpretation system;
inputting the geographic information into a geographic information generating system to generate a distance function between any two points with geographic economic information;
and inputting the distance function with the geographical economic information into a transformer substation planning system, and solving by using a dynamic bacterial foraging algorithm by the transformer substation planning system to obtain an optimal transformer substation extension scheme.
2. The substation extension planning method based on high-grade second image data analysis according to claim 1, characterized in that: the geographical information may include, for example,
the remote sensing image information with high positioning precision and high spatial resolution is obtained by utilizing a high-resolution second satellite, and the difference of different ground objects is represented by utilizing an image interpretation system through the difference of brightness values or pixel values and spatial variation, so that the physical elements of the different ground objects are distinguished.
3. The substation extension planning method based on high-grade second image data analysis according to claim 1 or 2, characterized in that: the geographic information generation system includes a geographic information generation system including,
inputting the defined geographical condition function and the two-dimensional geographical position information of the power transformation station and the load point to be planned into an economic distance functional to solve a functional extreme value;
and taking the functional extreme value as a value of a distance function, and correspondingly taking the functional extreme value as a distance function between any two points with geographical economic information.
4. The substation extension planning method based on high-grade second image data analysis according to claim 3, characterized in that: the transformer substation planning system comprises a load and pricing function, an objective function, constraint conditions and parameters.
5. The substation extension planning method based on high-grade second image data analysis according to claim 4, characterized in that: the load and pricing functions include the number of features,
load function
Figure FDA0003365900650000011
Comprises the following steps:
Figure FDA0003365900650000012
the pricing function comprises a sales energy price model and an outsourcing energy price model;
selling energy price model
Figure FDA0003365900650000013
Comprises the following steps:
Figure FDA0003365900650000014
outsourcing energy price model
Figure FDA0003365900650000021
Comprises the following steps:
Figure FDA0003365900650000022
wherein,
Figure FDA0003365900650000023
for the base power demand of load point j in year h, DLFtThe load power demand factor at a load level t,
Figure FDA0003365900650000024
for a base price of selling 1kWh of energy to load point j, PLFtIs a price level factor, alpha, at a load level of thFor the rate of price increase at h years,
Figure FDA0003365900650000025
a base price of 1KWh of energy is purchased for substation i.
6. The substation extension planning method based on high-grade second image data analysis according to claim 4 or 5, characterized in that: the objective function OF includes that,
OF＝RSE-CPE-CIS-CCF
the current value RSE of the sales energy income is as follows:
Figure FDA0003365900650000026
the current value CPE of the purchase energy cost is as follows:
Figure FDA0003365900650000027
the CIS of the current value of the investment cost of the installation or upgrade transformer substation is as follows:
Figure FDA0003365900650000028
the construction cost CCF of the medium-voltage feeder line is as follows:
Figure FDA0003365900650000029
wherein RSE is the current value of energy income for sale, CPE is the current value of energy cost for purchase, CIS is the current value of investment cost for installation or upgrade of the transformer substation, CCF is the construction cost of the medium-voltage feeder, infra is the inflation rate of the currency, intr is the interest rate, H is a constant, M is the load progression, H is the total year of planning, T is the total year of planningh,tFor the time at the load level t of the year h,
Figure FDA0003365900650000031
the power required for the ith substation, at the load level in the h year,
Figure FDA0003365900650000032
for the investment required per unit scale of extended capacity of substation i,
Figure FDA0003365900650000033
for the capacity of substation i in h years,
Figure FDA0003365900650000034
for the capacity of substation i in h-1 year,
Figure FDA0003365900650000035
for the set of existing and candidate substations,
Figure FDA0003365900650000036
to operate the capacity at load level t in the h year substation i,
Figure FDA00033659006500000316
as a set of load points, KfIs the construction cost, beta, of a medium voltage feeder line per unit lengthi,jThe connection relation between the ith transformer substation and the jth load is obtained; di,jIs the distance between substation number i and load number j.
7. The substation extension planning method based on high-grade second image data analysis according to claim 6, characterized in that: the constraint conditions comprise network radiability constraint, operation capability constraint of the transformer substation, capacity expansion capability constraint of the transformer substation and voltage drop under load point constraint;
the network radiometric constraints are:
Figure FDA0003365900650000037
the constraint of the operation capacity of the transformer substation is as follows:
Figure FDA0003365900650000038
the capacity expansion capacity of the transformer substation is restricted as follows:
Figure FDA0003365900650000039
Figure FDA00033659006500000310
the voltage drop under the load point constraint is:
Figure FDA00033659006500000311
wherein, betai,jIs the connection relation between a transformer substation i and a load j, wherein j is the j load, MaxopeFor the maximum operating capacity allowed for any substation,
Figure FDA00033659006500000312
for the capacity of substation i in h years,
Figure FDA00033659006500000313
for substation i to be built or upgraded in h years,
Figure FDA00033659006500000314
the maximum extended capacity of the substation i is,
Figure FDA00033659006500000315
operating capacity of the branch connected between the i substation and the j load at the h year t load level, DijIs the distance between No. i substation and No. j load, RijFor the branch resistance, V, between substation i and load jiThe voltage of the transformer substation i is delta V, and the maximum voltage drop allowed by a load end is delta V; j is the j load; h is the h year and t is the t load level.
8. The substation extension planning method based on high-grade second image data analysis according to claim 7, characterized in that: the parameters comprise a medium-voltage feeder distance parameter, a medium-voltage feeder loss parameter, a medium-voltage feeder operation capacity parameter and a transformer substation operation capacity parameter;
the medium-voltage feeder line distance parameters are as follows:
Figure FDA0003365900650000041
the medium-voltage feeder loss parameters are as follows:
Figure FDA0003365900650000042
wherein, VjTerminal voltage of load j;
the medium-voltage feeder line operation capacity parameters are as follows:
Figure FDA0003365900650000043
the transformer substation operation capacity parameters are as follows:
Figure FDA0003365900650000044
where ξ is the distance correction factor,
Figure FDA0003365900650000045
the x-coordinate of the substation i is,
Figure FDA0003365900650000046
is the x-coordinate of the load i, Yi subFor the Y coordinate of substation i, Yi loadIs the y coordinate of load i.
9. The substation extension planning method based on high-score second image data analysis according to any one of claims 1, 2, 7 and 8, characterized in that: the dynamic bacterial foraging algorithm includes,
and inputting the binary variable of the connection of the binary process determined load point and the transformer substation into an integer process to determine the extension plan of the transformer substation.
CN202111381884.6A 2021-11-22 2021-11-22 Transformer substation extension planning method based on high-resolution second-order image data analysis Pending CN114169587A (en)

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