CN109146202B - Power supply partition-based standard configuration method for indoor uniform distribution variable capacity - Google Patents
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Abstract
The application discloses a standard configuration method for the capacity of a power supply partition-based power distribution transformer for each user, which comprises the following steps: s1: classifying all residential areas to be configured according to the power supply subareas; s2: screening typical residential communities in a plurality of power supply subareas respectively; s3: calculating the user distribution variable capacity of a typical residential area of any one of the power supply subareas; s4: and (3) carrying out capacity configuration on the household distribution transformer in any power supply partition by taking the household distribution transformer capacity of a typical residential area in any power supply partition as a standard. By the method, the power supply requirements of the resident users can be met to the greatest extent under the condition of minimum comprehensive energy consumption cost, and the phenomena of insufficient power supply capacity or power resource waste and the like in the urban and rural distribution network construction are effectively avoided.
Description
Technical Field
The application relates to the technical field of power distribution network planning, in particular to a power supply partition-based standard configuration method for the capacity of a residential distribution transformer.
Background
The capacity of the household distribution transformer is a key index for embodying the infrastructure and power supply capacity of a regional power distribution network, the research on the capacity of the household distribution transformer is an important content for planning a regional power distribution system, and the capacity of the household distribution transformer is directly related to the reliability of power supply and the economic evaluation of transformation in the region. Therefore, it is an important issue in the regional power distribution technology to reasonably configure the per-user distribution capacity.
At present, a method for configuring a capacity of a per-user distribution transformer generally configures the capacity uniformly according to the design capacity of each power supply partition, that is: and at the early stage, each power supply partition is provided with an estimated design capacity, and then the distribution and transformation capacity of each power supply partition is distributed only by taking the estimated design capacity as a standard.
However, in the current method for configuring the capacity of the residential distribution transformer, because the configuration standard only refers to the design capacity, and the actual power utilization condition of the power supply partition is not analyzed, light load or heavy load of the power equipment is easily caused in actual use, so that the condition of power resource waste or insufficient power supply capacity occurs in the power distribution network, and the reasonable use of the power resource is not facilitated.
Disclosure of Invention
The application provides a standard configuration method for the power distribution capacity of a user based on power supply partitions, which aims to solve the problems of waste of power resources or insufficient power supply capacity in the prior art.
In order to solve the technical problem, the embodiment of the application discloses the following technical scheme:
a power supply partition-based standard configuration method for a user average distribution variable capacity (MDC), the method comprising:
s1: classifying all residential areas to be configured according to the power supply subareas, wherein the types of all residential areas comprise: class A +, class A, class B, class C, and class D;
s2: screening typical residential communities in a plurality of power supply subareas respectively;
s3: calculating the user distribution variable capacity of a typical residential area of any one of the power supply subareas;
s4: and carrying out capacity configuration on the household distribution transformer in any power supply partition by taking the household distribution transformer capacity of a typical residential area in any power supply partition as a standard.
Optionally, step S2 includes:
s21: acquiring one or more typical areas in any power supply subarea;
s22: acquiring typical daily load characteristic curves of all residential cells in the one or more typical areas;
s23: clustering typical daily load characteristic curves of all residential cells in the one or more typical regions by using a self-adaptive fuzzy C-means clustering method;
s24: and screening out typical residential communities in any power supply subarea by adopting a weighted gravity center method according to the clustering result.
Optionally, step S24 includes:
s241: according to the typical daily load characteristic curves of all residential areas in the one or more typical areas, acquiring the peak-valley characteristic F of the typical daily load characteristic curve of any power supply subareaiSum power level characteristic Qi；
S242: the peak-valley characteristic F of the typical daily load characteristic curve of any power supply subareaiSum power level characteristic QiAs the position and center of gravity of cell i, using the formula
Calculating the gravity center W of the j-type cell where the cell i is positionedjWherein F isi∈i，CjThe number of the j-type cells and the peak-valley characteristic F of the load curveiThe percentage of the electricity consumption in unit hour reflects the distribution of the load time interval of the cell i, and the distribution is divided into 24 time intervals in total, and Fi is recorded as { F0, F1 … and F23 }; power level characteristic QiThe daily electricity consumption of the user;
s243: using the formula MIN F | | | Fi-WjDetermining a typical daily load characteristic curve closest to the gravity Wj of the cell;
s244: and taking the residential area corresponding to the typical daily load characteristic curve closest to the center Wj of the residential area as a typical residential area in any power supply subarea.
Optionally, step S3 includes:
s31: calculating the total predicted load of the typical residential area in any power supply partition, and predicting the load of the typical residential area in any power supply partition;
s32: determining the distribution transformation load rate of a typical residential area in any power supply subarea by using a comprehensive energy consumption method;
s33: and determining the user average distribution variable capacity of the typical residential area in any power supply subarea according to the predicted load total amount and the distribution variable load rate of the typical residential area in any power supply subarea.
Optionally, step S31 includes:
s311: collecting basic data of a typical residential area in any power supply subarea, wherein the basic data comprises: the construction time, the number of households, the building area and the historical load of a typical residential area;
s312: determining the perspective saturation load of the typical residential area in any power supply subarea by using a demand coefficient method according to the number of the typical residential areas and the building area in any power supply subarea;
s313: determining a development stage of the typical residential area in any power supply partition according to the built-up time, the historical load, the load increase rate and the prospective saturation load of the typical residential area in any power supply partition, wherein the development stage comprises the following steps: early stage of development, period of high-speed development or period of development saturation;
s314: and when the development stage of the typical residential area in any power supply partition is in the initial development stage, simulating a load increase curve of the typical residential area in any power supply partition in the near and medium term by using a Logistic method according to basic data and a prospective saturation load of the typical residential area in any power supply partition.
Optionally, step S314 includes:
fitting a load increase curve of a typical residential area in any power supply subarea by using a Logistic method, wherein the curve equation of the Logistic method is
k>0，a>0，b<0 and are all constants, t is time, ytIs the power load value.
Optionally, step S32 includes:
s321: determining an optimal economic operation interval of various types of distribution and transformation of typical residential areas in any power supply subarea, wherein the optimal economic operation interval is as follows: under a certain load, the distribution transformer running interval when the distribution transformer loss rate is the lowest;
s322: in the optimal economic operation interval, utilizing a formula
Respectively calculating the comprehensive energy consumption cost of different types of distribution transformers in a typical residential area in any power supply partition, wherein TOC is the comprehensive energy consumption cost of the distribution transformers, CI is the initial cost of the distribution transformers, E is the average hourly electricity price of distribution transformer users, n is the economic service life of a transformer, i is the annual discount rate, Kpv is the current value coefficient of continuous n-year cost with the discount rate being i, Hpy is the annual electrification hours of the distribution transformers, tau is the annual maximum load loss hours, and eta is the initial load rate of the distribution transformers;
s323: comparing comprehensive energy consumption costs of distribution transformers of different types under the same distribution transformer load rate, and determining a distribution transformer configuration scheme with the minimum comprehensive energy consumption cost, wherein the distribution transformer configuration scheme comprises distribution transformers of various different types;
s324: and taking the distribution transformation load rate corresponding to the minimum comprehensive energy consumption cost as the distribution transformation load rate of the typical residential area in any power supply subarea.
Optionally, step S33 includes:
according to the predicted load total amount and distribution transformation load rate of typical residential areas in any power supply subarea, utilizing a formula
Calculating the total distribution and transformation capacity of a typical residential area in any power supply subarea, wherein S is the total distribution and transformation capacity, Ppre is the predicted total load, eta is the load rate of the transformer, and cos φ av is the average power factor after compensation;
according to the total distribution and transformation capacity of the typical residential area in any power supply subarea and the number of the typical residential areas in any power supply subarea, utilizing a formula
And calculating the capacity of the user average distribution transformer of a typical residential cell in any power supply partition, wherein Sav is the capacity of the user average distribution transformer, and Nc is the number of users involved in the distribution transformer.
Optionally, step S33 includes:
according to the predicted load total amount and distribution transformation load rate of typical residential areas in any power supply subarea, utilizing a formula
And calculating the user distribution variable capacity of a typical residential cell in any power supply subarea, wherein cos phi i is the power factor of the ith user.
Optionally, step S4 includes:
s41: determining an upper limit capacity margin and a lower limit capacity margin of the user average distribution transformer capacity in any power supply partition by using an expert evaluation method;
s42: calculating the maximum value of the user distribution variable capacity in any power supply partition according to the maximum value Savmax and the upper limit capacity margin of the user distribution variable capacity of the typical residential cell in any power supply partition by using a formula S1 (1+ A1) Savmax, wherein S1 is the maximum value of the user distribution variable capacity of any power supply partition, and Savmax is the maximum value of the user distribution variable capacity of the typical residential cell in any power supply partition;
s43: and calculating the minimum value S2 of the user average distribution variable capacity in any power supply partition according to the minimum value Savmin of the user average distribution variable capacity of the typical residential cell in any power supply partition and the lower limit capacity margin A2 by using a formula S2 ═ 1-A2 ×. Savmin, wherein S2 is the minimum value of the user average distribution variable capacity of any power supply partition, and Savmin is the minimum value of the user average distribution variable capacity of the typical residential cell in any power supply partition.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
the method comprises the steps of firstly classifying all residential cells to be configured according to power supply partitions; secondly, typical residential communities are screened in the plurality of power supply subareas respectively; then calculating the user distribution variable capacity of a typical residential area of any one of the power supply subareas; and finally, carrying out capacity distribution on the household distribution transformer in any power supply partition by taking the household distribution transformer capacity of a typical residential area in any power supply partition as a standard. The method comprises the steps of classifying residential cells, determining the total predicted load and distribution transformation load rate of a typical residential cell aiming at the residential cells of different power supply subareas, calculating the average distribution transformation capacity of the typical residential cell, and configuring the average distribution transformation capacity of all the residential cells in any power supply subarea by taking the average distribution transformation capacity of the typical residential cell as a standard. The minimum comprehensive energy consumption cost is considered in the calculation process of the variable capacity for the per-household distribution of the typical residential area, so that the minimum comprehensive energy consumption cost can be achieved by the variable capacity for the per-household distribution of all the residential areas in any power supply subarea taking the minimum comprehensive energy consumption cost as a standard, the power supply requirements of residential users can be met to the maximum extent under the condition of the minimum comprehensive energy consumption cost, and the phenomena of insufficient power supply capacity or power resource waste and the like in the construction of urban and rural distribution networks are effectively avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a standard configuration method for a power distribution capacity per user based on a power supply partition according to an embodiment of the present application;
FIG. 2 is a diagram illustrating a load development curve of a residential area in an embodiment of the present application;
FIG. 3 is a graph illustrating an annual load growth rate of a residential area according to an embodiment of the present application;
FIG. 4 is a TOC chart of various schemes under different distribution transformation load ratios in the embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For a better understanding of the present application, embodiments of the present application are explained in detail below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic flowchart of a power supply partition-based standard configuration method for a capacity of a user average distribution transformer according to an embodiment of the present application. As can be seen from fig. 1, the method for configuring the standard capacity of the power distribution unit based on the power supply partition in this embodiment mainly includes the following steps:
s1: and classifying all residential cells to be configured according to the power supply subareas.
In grid planning, the power supply areas are generally divided into five categories: class A +, class A, class B, class C, and class D. Wherein, A + and A, B, C are town districts, and D is rural residence. In this embodiment, all residential areas are classified according to the power supply partition, the residential areas are divided into five types, namely, a + type, a type B, a type C and a type D, and the power supply partition in which the residential area is located is the category of the power supply partition.
S2: typical residential cells are screened within a plurality of power supply zones, respectively.
And respectively screening out typical residential areas in each power supply subarea in the plurality of power supply subareas, and then carrying out the configuration of the per-unit distribution variable capacity on all residential areas in the current power supply subarea by taking the per-unit distribution variable capacity of the typical residential areas as a standard after the per-unit distribution variable capacity of the typical residential areas is calculated and obtained.
Specifically, step S2 includes the following processes:
s21: one or more representative zones within any powered partition are obtained.
One or more typical areas in any power supply subarea can be selected according to the characteristics and actual conditions of the power supply subarea.
S22: and acquiring typical daily load characteristic curves of all residential cells in one or more typical areas in any power supply subarea.
The typical daily load characteristic curve can reflect the load change trend and the load characteristic of the residential area, and the maximum load, the daily average load, the daily peak-valley difference daily load rate, the daily minimum load rate and the daily peak-valley difference rate of the current residential area can be obtained through the typical daily load characteristic curve.
S23: and clustering typical daily load characteristic curves of all residential cells in one or more typical areas in any power supply partition by using an adaptive fuzzy C-means clustering method.
Specifically, step S23 includes the following processes:
s231: numbering typical daily load characteristic curves of all residential cells in one or more typical areas in any power supply subarea;
s232: establishing an original power data set X ═ X of n residential areas in any power supply subarea1，…，xn}T，x1，x2，…，xnIs a vector consisting of s data;
s233: normalizing the original power data set to obtain a normalized sample X ═ X1’，x2’…，xn', and the power data for any residential cell in the normalized sample is:
s234: and defining fuzzy weighting indexes, iteration standards, clustering numbers and an initialized clustering matrix of the self-adaptive fuzzy C-means clustering.
A fuzzy weighting index of self-adaptive fuzzy C-means clustering is a smoothing factor m, and the value of m is 2 generally; iteration standard epsilon, and iteration termination condition epsilon is 0.00001; the validity function L (1) of the cluster number 1 is 0; the number of clusters c is 2; an initialization clustering matrix V (0) is defined.
S235: using formulas
Calculating a fuzzy matrix U and using a formula
The cluster center V is updated.
If j, r is present
Then order:
and when i ≠ r,
wherein: drj＝||vi-xj| | is the Euclidean distance between the ith cluster center and the jth vector, xjRepresenting the jth typical daily load characteristic curve vector.
S236: calculate | v (k +1) -v (k) |.
If | v (K +1) -v (K) | < epsilon, the iteration is stopped, otherwise K is set to K +1, returning to step S235.
S237: using formulas
The significance function l (C) of the cluster number C is calculated.
The larger the value of the validity function L (C) of the cluster number C is, the more reasonable the classification is, if L (C-1) > L (C-2) and L (C-1) > L (C), the clustering process is ended, otherwise, C ═ C + L is set, and the process returns to step S235. Wherein: l1 describes the distance between all load curves Xi in the same class and the clustering center Vi of the class, the smaller the value of the load curves Xi is, the more reasonable the classification is, and the index is an inverse index; l2 describes the distance sum of all the cluster centers, the larger the value of the distance sum, the more reasonable the classification is, the index is a positive index; l3 describes the average value of the maximum membership degree of each curve in the membership degree matrix generated by clustering, and the larger the value of the average value, the larger the membership degree of the curve to the curve is, the index is a positive index; umax, k represents the maximum degree of membership of the kth load curve in the degree of membership matrix.
S24: and screening out typical residential cells in any power supply subarea by adopting a weighted gravity center method according to the clustering result.
Specifically, step S24 includes the following processes:
s241: according to the clustering result of the typical daily load characteristic curves of all residential areas in one or more typical areas, the peak-valley characteristic F of the typical daily load characteristic curve of any power supply subarea is obtainediSum power level characteristic Qi。
S242: peak-to-valley characteristic F of typical daily load characteristic curve of any power supply partitioniSum power level characteristic QiAs the position and center of gravity of cell i, using the formula
Calculating the gravity center W of the j-type cell where the cell i is positionedjWherein F isi∈i，CjThe number of the j-type cells and the peak-valley characteristic F of the load curveiThe percentage of the electricity consumption in unit hour reflects the distribution of the load time interval of the cell i, and the distribution is divided into 24 time intervals in total, and Fi is recorded as { F0, F1 … and F23 }; power level characteristic QiThe daily electric quantity is used by the user.
S243: using the formula MIN F | | | Fi-Wj||，The typical daily load characteristic curve closest to the cell centroid Wj is determined.
S244: and taking the residential area corresponding to the typical daily load characteristic curve closest to the center Wj of the residential area as a typical residential area in any power supply subarea.
With continued reference to fig. 1, after the typical residential cell is acquired, step S3 is executed: and calculating the user distribution variable capacity of a typical residential cell of any one of the plurality of power supply subareas.
Specifically, step S3 includes the following processes:
s31: and calculating the total predicted load of the typical residential areas in any power supply subarea, and predicting the load of the typical residential areas in any power supply subarea.
Step S31 includes the following processes:
s311: collecting basic data of a typical residential area in any power supply subarea, wherein the basic data comprises: the construction time, number of households, building area, and historical load of a typical residential area.
S312: and determining the perspective saturation load of the typical residential area in any power supply subarea by using a demand coefficient method according to the number of the typical residential areas and the building area in any power supply subarea.
In this embodiment, the future saturated load is the total predicted load. In this embodiment, formula P is usedpre＝KxPtotAnd calculating the total predicted load, wherein Ppre is the total predicted load, Kx is a required coefficient, and Ptot is the total capacity of the electric equipment in the planning area. The required coefficient Kx represents the requirement of residences with different properties on electric appliance load and a coefficient used simultaneously, the required coefficient is related to the working properties, the using efficiency, the quantity and other factors of electric equipment, generally, when the quantity of equipment in an electric equipment group is large, the required coefficient should take a small value, otherwise, the required coefficient should take a large value, when the equipment utilization rate is high, the required coefficient should take a large value, and otherwise, the required coefficient should take a small value.
The calculation formula of the required coefficient Kx in this embodiment is:
in the formula: the P cell refers to the typical daily maximum load of a saturated cell similar to the residential cell to be configured, N is the number of residents in the saturated cell, and Pi is the maximum capacity of each electric device of the ith household in the saturated cell.
In this embodiment, the total capacity Ptot of the electrical equipment in the planned area is calculated by using a load density method, and the calculation formula is as follows: ptot＝NresSavPnIn the formula: nres is the number of users, Sav is the building area per household, and Pn is the index of the electricity consumption of residents.
S313: determining the development stage of the typical residential area in any power supply partition according to the built-up time, the historical load, the load increase rate and the distant view saturation load of the typical residential area in any power supply partition, wherein the development stage comprises the following steps: early development, high-rate development, or development saturation.
In this embodiment, a schematic view of a load development curve of a residential area in different development stages is shown in fig. 2, and a schematic view of an annual load growth rate curve of a residential area in different development stages is shown in fig. 3.
S314: and when the development stage of the typical residential area in any power supply partition is in the initial development stage, determining a load increase curve of the typical residential area in any power supply partition in the near-middle period by using a Logistic method according to basic data and a prospective saturated load of the typical residential area in any power supply partition.
Specifically, a Logistic method is used for fitting a load increase curve of a typical residential area in any power supply subarea, wherein a curve equation of the Logistic method is as follows:
k>0，a>0，b<0 and are all constants, t is time, ytIs the power load value. In this embodiment, K, a, and b are all unknown numbers, and the values of the constants K, a, b, and e can be obtained by using a curve equation with the historical load of the cell, the time of the historical load, the time of the saturation load, and the time of the saturation load known.
According to the load increase curve of the typical residential area in any power supply subarea in the near-middle period, the near-middle period load of the typical residential area can be acquired, and then the distribution transition scheme is implemented according to the near-middle period load. For example: if two distribution transformers need to be built, one distribution transformer can be put into operation firstly, and the other distribution transformer is put into operation after a certain period of time, so that the loss can be reduced according to a distribution transformer transition scheme realized by the load in the near-middle period, and the energy can be saved.
When the development stage of a typical residential area in any power supply subarea is not in the initial development stage, namely: when the development stage is the saturation stage of the rapid development futures, only the prospective saturation load is determined, and the step S314 is not required to be executed, that is: it is not necessary to calculate its load increase curve in the near-intermediate period.
S32: and determining the distribution transformation load rate of a typical residential area in any power supply subarea by using a comprehensive energy consumption method.
Step S32 includes the following processes:
s321: determining the optimal economic operation interval of various types of distribution and transformation of typical residential areas in any power supply subarea, wherein the optimal economic operation interval is as follows: and under a certain load, the distribution transformer running interval when the distribution transformer loss rate is the lowest.
The economic operation interval of the distribution transformer is researched, the optimal economic operation interval of distribution transformers of different types in any power supply subarea is mainly obtained, and generally, the distribution transformer load rate in the optimal economic operation interval is low.
In this embodiment, first, the formula Δ P ═ P is used0+η2PKCalculating the total loss of the distribution transformer, wherein, the Delta P is the total loss, P0For rated no-load loss, PKAnd eta is the rated load loss and the distribution transformation load rate.
Secondly, using the formula
Calculating the loss rate of the distribution transformer, wherein the delta P% is the loss rate of the distribution transformer, PLoad(s)Is the load carried by the transformer.
Then, using the formula
And calculating the optimal load rate of the distribution transformer.
And finally, determining the optimal economic operation interval of the distribution transformer.
S322: in the optimum economic operation interval, using formula
And respectively calculating the comprehensive energy consumption cost of different types of distribution transformers in typical residential areas in any power supply subarea.
Wherein, TOC is the comprehensive energy consumption cost of the distribution transformer, CI is the initial cost of the distribution transformer, E is the average hour electricity price of the distribution transformer user, n is the economic service life of the transformer, generally taking 20 years, i is the annual discount rate, the annual discount rate is usually not lower than the bank loan interest rate value in the same year, Kpv is the current value coefficient of the continuous n-year cost with the discount rate of i, Hpy is the live hours of the distribution transformer, τ is the maximum load loss hours of the year, and η is the initial load rate of the distribution transformer.
S323: and comparing the comprehensive energy consumption cost of different types of distribution transformers under the same distribution transformer load rate, and determining a distribution transformer configuration scheme with the minimum comprehensive energy consumption cost, wherein the distribution transformer configuration scheme comprises the types of the distribution transformers.
The comprehensive energy consumption method is that under the condition of certain load, a plurality of schemes are provided for configuration and distribution under the assumption that the load of a cell is certain. When the distribution transformer types are the same, the higher the distribution transformer load rate is, the smaller the comprehensive energy consumption is, and in order to determine the minimum comprehensive energy consumption cost, the distribution transformer type with the higher distribution transformer load rate is selected to determine a cell transformer configuration scheme; when the distribution transformer types are different, the configuration scheme of the cell transformer can be determined by taking the minimum comprehensive energy consumption cost as a target under the same load rate.
The distribution transformer solution that is usually finalized is a distribution type. After the scheme is determined, the distribution transformation load rate of the typical cell in each power supply partition can be recommended according to the determined distribution transformation model.
For example: the maximum load is set to be 1000kW, four distribution changes of S13-200, S13-400, SH15-200 and SH15-400 can be selected, and the TOC comparison results of all selection schemes under different load rates are shown in Table 1.
TABLE 1 TOC results table for each scheme at different load rates
Referring to fig. 4, the TOC diagrams of the schemes under different load rates show that although distribution transformers of various models have the lowest distribution loss rate in the economic operation zone, the distribution load rate in the economic operation zone is lower, so that the number of newly built distribution transformers is increased. If the calculation is carried out according to the economic operation years of the distribution transformer for 20 years, the distribution transformer load rate is between 55% and 70%, and the TOC of each scheme is the best, as can be seen from the graph of FIG. 4.
S324: and taking the distribution transformation load rate corresponding to the minimum comprehensive energy consumption cost as the distribution transformation load rate of a typical residential area in any power supply subarea.
The predicted total load of the typical residential cell in any power supply section is acquired through the step S31, and after the distribution load rate is acquired through the step S32, the step S33 is executed: and determining the user distribution transformation capacity of the typical residential area in any power supply subarea according to the predicted load total amount and the distribution transformation load rate of the typical residential area in any power supply subarea.
A distribution and transformation load rate interval can be obtained by utilizing a comprehensive energy consumption method, for example, (60%, 75%), and the lower limit of 60% is selected as a distribution and transformation load rate value.
Since the clustering result in this embodiment may be classified into multiple categories, for example: the clustering result is divided into 3 types, 3 typical residential areas can be screened out from the 3 types, the 3 typical residential areas correspond to 3 distribution load rates, and the minimum value of the distribution load rate, namely the lower limit, is selected out.
Specifically, there are two implementation methods for step S33, which are respectively:
the first realization method comprises the following steps:
s331: according to the predicted load total amount and distribution transformation load rate of typical residential areas in any power supply subarea, using formulas
Calculating the total distribution and transformation capacity of a typical residential area in any power supply subarea, wherein S is the total distribution and transformation capacity, Ppre is the predicted total load, eta is the load rate of the transformer, and cos phi av isCompensated average power factor.
S332: according to the total distribution and transformation capacity of typical residential areas in any power supply subarea and the number of the typical residential areas in any power supply subarea, utilizing a formula
And calculating the capacity of the user average distribution transformer of a typical residential cell in any power supply partition, wherein Sav is the capacity of the user average distribution transformer, and Nc is the number of users involved in the distribution transformer.
The second realization method comprises the following steps:
according to the predicted load total amount and distribution transformation load rate of typical residential areas in any power supply subarea, using formulas
And calculating the subscriber distribution variable capacity of a typical residential cell in any power supply subarea, wherein cos phi i is the power factor of the ith subscriber.
Wherein cos phi i of a 10kV side of the transformer substation is more than or equal to 0.9, cos phi i of a power user with the capacity of 100kVA or more is more than or equal to 0.9, and cos phi i of an agricultural user is more than or equal to 0.8.
In the above two methods for determining the subscriber distribution capacity of a typical residential cell in any power supply partition, the second implementation method is preferred because in actual operation, the power factor cos phi i of the ith subscriber is more convenient to calculate than the compensated average power factor cos phi av.
As can be seen from fig. 1, after the user distribution capacity of the typical residential cell of any power supply partition is obtained, step S4 is executed: and (3) carrying out capacity distribution on the household distribution transformer in any power supply partition by taking the household distribution transformer capacity of a typical residential area in any power supply partition as a standard.
Specifically, step S4 includes:
s41: and determining the upper limit capacity margin and the lower limit capacity margin of the user average distribution transformer capacity in any power supply partition by using an expert evaluation method.
In this embodiment, the capacity margin can be evaluated and determined according to the actual situation of the power supply area by using an expert evaluation method, so that the accuracy of typical cell selection can be further improved, the typical cell selection is more comprehensive and accurate, and the accuracy of user-allocated variable capacity calculation can be improved.
S42: and calculating the maximum value of the user distribution variable capacity in any power supply partition according to the maximum value Savmax and the upper limit capacity margin of the user distribution variable capacity of the typical residential cell in any power supply partition by using a formula S1 (1+ A1) Savmax, wherein S1 is the maximum value of the user distribution variable capacity of any power supply partition, and Savmax is the maximum value of the user distribution variable capacity of the typical residential cell in any power supply partition.
S43: and calculating the minimum value S2 of the user distribution variable capacity in any power supply partition according to the minimum value Savmin of the user distribution variable capacity of the typical residential cell in any power supply partition and the lower limit capacity margin A2 by using a formula S2 ═ 1-A2 ═ Savmin, wherein S2 is the minimum value of the user distribution variable capacity of any power supply partition, and Savmin is the minimum value of the user distribution variable capacity of the typical residential cell in any power supply partition.
In this embodiment, the upper limit capacity margin a1 and the lower limit capacity margin a2 obtained by the expert evaluation method are + 5% and-2%, respectively.
Comparing the average distribution variable capacity of the typical residential areas in each power supply subarea, wherein the maximum value is Savmax, and the minimum value is Savmin, so that when the upper limit capacity margin A1 is + 5%, and the lower limit capacity margin A2 is-2%, the upper limit of the average distribution variable capacity of the residential areas in the power supply subarea is 1.05Savmax, and the lower limit is 0.98 Savmin.
Since the clustering result in this embodiment may be classified into multiple categories, for example: the clustering result is divided into 3 classes, and 3 typical residential areas can be screened from the 3 classes. According to the method, the per-user distribution variable capacity of 3 typical cells is obtained, the per-user distribution variable capacity of the 3 typical cells is compared, the maximum value is Savmax, the minimum value is Savmin, 1.05Savmax is the upper limit per-user distribution variable capacity standard of the power supply partition, and 0.98Savmin is the lower limit per-user distribution variable capacity standard of the power supply partition
To sum up, the method determines the average distribution capacity of the households of the typical residential area from the angle of minimum comprehensive energy consumption cost, and then performs the standard configuration of the average distribution capacity of the households of the power supply subarea of the typical residential area by taking the average distribution capacity of the households of the typical residential area as a reference standard, thereby being beneficial to the reasonable use of power resources, meeting the power supply requirements of the residential users to the maximum extent under the condition of minimum comprehensive energy consumption cost, and further effectively avoiding the occurrence of phenomena such as insufficient power supply capacity or power resource waste in the construction of urban and rural distribution networks.
The foregoing are merely exemplary embodiments of the present application and are presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A power supply partition-based standard configuration method for the capacity of a user uniform distribution transformer is characterized by comprising the following steps:
s1: classifying all residential areas to be configured according to the power supply subareas, wherein the types of all residential areas comprise: class A +, class A, class B, class C, and class D;
s2: screening typical residential communities in a plurality of power supply subareas respectively;
s3: calculating the user distribution variable capacity of a typical residential area of any one of the power supply subareas;
s4: taking the capacity of the household distribution transformer of a typical residential area in any power supply subarea as a standard, and carrying out capacity configuration on the household distribution transformer in any power supply subarea;
wherein, step S3 includes:
s31: calculating the total predicted load of the typical residential area in any power supply partition, and predicting the load of the typical residential area in any power supply partition;
s32: determining the distribution transformation load rate of a typical residential area in any power supply subarea by using a comprehensive energy consumption method;
s33: determining the user average distribution variable capacity of the typical residential area in any power supply subarea according to the predicted load total amount and the distribution variable load rate of the typical residential area in any power supply subarea;
step S32 includes:
s321: determining an optimal economic operation interval of various types of distribution and transformation of typical residential areas in any power supply subarea, wherein the optimal economic operation interval is as follows: under a certain load, the distribution transformer running interval when the distribution transformer loss rate is the lowest;
s322: in the optimal economic operation interval, utilizing a formula
Respectively calculating the comprehensive energy consumption cost of different types of distribution transformers in a typical residential area in any power supply partition, wherein TOC is the comprehensive energy consumption cost of the distribution transformers, CI is the initial cost of the distribution transformers, E is the average hourly electricity price of distribution transformer users, n is the economic service life of a transformer, i is the annual discount rate, Kpv is the current value coefficient of continuous n-year cost with the discount rate being i, Hpy is the annual electrification hours of the distribution transformers, tau is the annual maximum load loss hours, and eta is the initial load rate of the distribution transformers;
s323: comparing comprehensive energy consumption costs of distribution transformers of different types under the same distribution transformer load rate, and determining a distribution transformer configuration scheme with the minimum comprehensive energy consumption cost, wherein the distribution transformer configuration scheme comprises distribution transformers of various different types;
s324: and taking the distribution transformation load rate corresponding to the minimum comprehensive energy consumption cost as the distribution transformation load rate of the typical residential area in any power supply subarea.
2. The method of claim 1, wherein the step S2 includes:
s21: acquiring one or more typical areas in any power supply subarea;
s22: acquiring typical daily load characteristic curves of all residential cells in the one or more typical areas;
s23: clustering typical daily load characteristic curves of all residential cells in the one or more typical regions by using a self-adaptive fuzzy C-means clustering method;
s24: and screening out typical residential communities in any power supply subarea by adopting a weighted gravity center method according to the clustering result.
3. The method of claim 2, wherein the step S24 includes:
s241: according to the typical daily load characteristic curves of all residential areas in the one or more typical areas, acquiring the peak-valley characteristic F of the typical daily load characteristic curve of any power supply subareaiSum power level characteristic Qi；
S242: the peak-valley characteristic F of the typical daily load characteristic curve of any power supply subareaiSum power level characteristic QiAs the position and center of gravity of cell i, using the formula
Calculating the gravity center W of the j-type cell where the cell i is positionedjWherein F isi∈i，CjThe number of the j-type cells and the peak-valley characteristic F of the load curveiThe percentage of the electricity consumption in unit hour reflects the distribution of the load time interval of the cell i, and the distribution is divided into 24 time intervals in total, and Fi is recorded as { F0, F1 … and F23 }; power level characteristic QiThe daily electricity consumption of the user;
s243: using the formula MIN F | | | Fi-WjDetermining a typical daily load characteristic curve closest to the gravity Wj of the cell;
s244: and taking the residential area corresponding to the typical daily load characteristic curve closest to the center Wj of the residential area as a typical residential area in any power supply subarea.
4. The method of claim 1, wherein the step S31 includes:
s311: collecting basic data of a typical residential area in any power supply subarea, wherein the basic data comprises: the construction time, the number of households, the building area and the historical load of a typical residential area;
s312: determining the perspective saturation load of the typical residential area in any power supply subarea by using a demand coefficient method according to the number of the typical residential areas and the building area in any power supply subarea;
s313: determining a development stage of the typical residential area in any power supply partition according to the built-up time, the historical load, the load increase rate and the prospective saturation load of the typical residential area in any power supply partition, wherein the development stage comprises the following steps: early stage of development, period of high-speed development or period of development saturation;
s314: and when the development stage of the typical residential area in any power supply partition is in the initial development stage, simulating a load increase curve of the typical residential area in any power supply partition in the near and medium term by using a Logistic method according to basic data and a prospective saturation load of the typical residential area in any power supply partition.
5. The method according to claim 4, wherein the step S314 includes:
fitting a load increase curve of a typical residential area in any power supply subarea by using a Logistic method, wherein the curve equation of the Logistic method is
k>0，a>0，b<0 and are all constants, t is time, ytIs the power load value.
6. The method of claim 1, wherein the step S33 includes:
according to the predicted load total amount and distribution transformation load rate of typical residential areas in any power supply subarea, utilizing a formula
Calculating the total distribution and transformation capacity of a typical residential area in any power supply subarea, wherein S is the total distribution and transformation capacity, Ppre is the predicted total load, eta is the load rate of the transformer, and cos φ av is the average power factor after compensation;
according to the total distribution and transformation capacity of the typical residential area in any power supply subarea and the number of the typical residential areas in any power supply subarea, utilizing a formula
And calculating the capacity of the user average distribution transformer of a typical residential cell in any power supply partition, wherein Sav is the capacity of the user average distribution transformer, and Nc is the number of users involved in the distribution transformer.
7. The method of claim 1, wherein the step S33 includes:
according to the predicted load total amount and distribution transformation load rate of typical residential areas in any power supply subarea, utilizing a formula
And calculating the user distribution variable capacity of a typical residential cell in any power supply subarea, wherein cos phi i is the power factor of the ith user.
8. The method for configuring the power supply partition-based user average distribution capacity standard according to claim 6 or 7, wherein the step S4 comprises:
s41: determining an upper limit capacity margin and a lower limit capacity margin of the user average distribution transformer capacity in any power supply partition by using an expert evaluation method;
s42: calculating the maximum value of the user distribution variable capacity in any power supply partition according to the maximum value Savmax and the upper limit capacity margin of the user distribution variable capacity of the typical residential cell in any power supply partition by using a formula S1 (1+ A1) Savmax, wherein S1 is the maximum value of the user distribution variable capacity of any power supply partition, and Savmax is the maximum value of the user distribution variable capacity of the typical residential cell in any power supply partition;
s43: and calculating the minimum value S2 of the user average distribution variable capacity in any power supply partition according to the minimum value Savmin of the user average distribution variable capacity of the typical residential cell in any power supply partition and the lower limit capacity margin A2 by using a formula S2 ═ 1-A2 ×. Savmin, wherein S2 is the minimum value of the user average distribution variable capacity of any power supply partition, and Savmin is the minimum value of the user average distribution variable capacity of the typical residential cell in any power supply partition.
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