CN115049266A - Power distribution network layout method based on multi-load characteristics - Google Patents
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Abstract
The invention discloses a power distribution network layout method based on multi-element load characteristics, which comprises the following steps: acquiring the existing power grid data, constructing a multi-load characteristic load flow calculation optimization model based on the existing power grid data, wherein the multi-load characteristic load flow calculation optimization model is used for forming a branch-industry load characteristic curve, and is simultaneously associated with a power distribution network equipment model to realize load flow calculation optimization so as to form a visual line load check curve; constructing a space analysis object model, wherein the space analysis object model can automatically analyze data related to a plot of the power distribution network to be installed; and confirming a wiring diagram of the power distribution network based on the multivariate load characteristic load flow calculation optimization model and the space analysis object model, and finally, automatically wiring cables or overhead lines according to different wiring modes. Through deep excavation of massive user load characteristic data, powerful support is provided for power distribution network planning, and accurate planning of the power distribution network is effectively guided.
Description
Technical Field
The application relates to the field of power grids, in particular to a power distribution network layout method based on a multi-load characteristic.
Background
The power distribution network is used as an important component of a power grid, is directly oriented to power consumers, and is closely related to the production life of the masses. The power distribution network planning work is a complex system engineering and has the characteristics of wide professional field, large data information amount, multiple uncertain factors, quick updating and changing and the like, and the core for improving the power distribution network planning is to solve the problem of accurate load prediction.
(1) The load prediction informatization is lack, and the adaptability of the power distribution network planning is not strong. At present, a space load density method is mainly adopted for target year load prediction, the load prediction in the stage year is more in components of 'experience and brain bag shooting', an information-based means is not adopted for auxiliary decision making, the precision of the load prediction is not high, and the adaptability of power distribution network planning is not strong directly. The concrete expression is as follows: one is the inability to accommodate regional economic development needs. The load prediction is not fully obtained and regional economic and social development information is deeply excavated, so that the power distribution network planning cannot meet the regional economic and social development. And secondly, the method cannot adapt to the transformation development requirement of clean low-carbon energy. The access requirements of regional roof resources, electric vehicles and other multi-load are not fully considered, the intermittent nature of distributed photovoltaic and other clean energy sources and the fluctuation prediction of electric charging pile loads are inaccurate, and the planning of a power distribution network cannot meet the requirements of full consumption of clean energy sources and full access of multi-load such as charging piles. And thirdly, the requirement of acquiring power by enterprise users cannot be met. The problem of load jump caused by changes of the recruitment investment projects is not fully considered, so that the power distribution network planning cannot meet the requirement that enterprise users can reliably obtain power nearby. And fourthly, the requirement of diversified services of the user cannot be met. The diversification of user energy consumption behaviors such as the response degree of a user to an incentive policy, the energy storage development at the user side and the like causes that the diversification requirements of a power distribution network planning user cannot be met. With the continuous improvement of the informatization level of the power system, mass power utilization information can be accurately acquired through various measurement systems, and the foundation for improving load prediction by adopting an informatization means and deep mass data mining is provided.
(2) The load prediction granularity is lost, and the economical efficiency of power distribution network planning is not strong. At present, a qualitative forecasting method of a plot development strength superposition S-shaped curve is mainly adopted for load forecasting in a phase year, granularity of load forecasting is lacked, the trend of user load development cannot be effectively simulated, the precision of load forecasting is not high, and the economical efficiency of power distribution network planning is not high directly. The concrete expression is as follows: firstly, the power grid equipment redundancy is high, and the economy is not strong. Traditional power distribution network planning relies on the electric wire netting capacity that constantly enlarges, utilizes great electric wire netting redundancy to deal with the uncertain of load, and the increase problem of load is solved through the power supply side alone promptly, has caused a large amount of equipment to be in long-term standby state, and equipment load rate and utilization ratio are low, and electric wire netting company economic nature is not strong. Secondly, the power grid equipment is repeatedly transformed and has low economy. Partial transformer substations and lines with high development speed are in a heavy overload state for a long time, the power supply capacity and the openable capacity of a power grid are insufficient, user access is guaranteed through repeated transformation and construction of the power grid, the power supply reliability and experience of users are reduced, and the economy of a power grid company is not strong. Thirdly, the cost of the user is high and the economy is not strong. When a user accesses a power grid, the access capacity of a line is generally determined in an extensive manner through a maximum load rate (heavy load limit-maximum load is the access capacity), load characteristic analysis of an access user is lacked, accurate matching between the line access capacity and the user access capacity cannot be achieved, partial users are forced to be shortened and required far, long-distance power supply is achieved, the matching cost of the users is high, and the economical efficiency of enterprise users is low.
Disclosure of Invention
The invention aims to provide a power distribution network layout method based on multi-load characteristics, which provides powerful support for power distribution network planning through deep mining of massive user load characteristic data and effectively guides accurate planning of a power distribution network; the method has the advantages of reducing the operation cost of the power grid, improving the safety and the economy of the power grid, practically improving the lean management level of the development and planning of a power grid company, promoting the cost reduction and the efficiency improvement of enterprises and improving the business handling feeling of users accessing the power grid.
The invention provides a power distribution network layout method based on multi-element load characteristics, which comprises the following steps of
Acquiring existing power grid data, and constructing a multi-load characteristic load flow calculation optimization model based on the existing power grid data, wherein the multi-load characteristic load flow calculation optimization model is used for forming a branch-industry load characteristic curve and is simultaneously associated with a power distribution network equipment model to realize load flow calculation optimization so as to form a visual line load check curve;
constructing a space analysis object model, wherein the space analysis object model can automatically analyze data related to a plot of the power distribution network to be installed;
and confirming a wiring diagram of the power distribution network based on the multivariate load characteristic load flow calculation optimization model and the spatial analysis object model, and finally, automatically wiring cables or overhead lines according to different wiring modes so as to meet the power supply load requirements of all the plots in the power supply unit and take the road and corridor resource information as constraints and solve the objective function with the minimum combined investment.
In a preferred embodiment of the present invention, the existing grid data includes, but is not limited to, customer load data and control information data, which are derived from multivariate load data and grid topology data.
In a preferred embodiment of the present invention, the multivariate load source data mainly originates from user measurement data of a user acquisition system, and the data items mainly include: the number of 22 columns such as date, office number, instantaneous active power, reactive power, house number, house name and the like, and the associated index information of the industry and the number of 4 columns of house number, house name and commissioning year.
In a preferred embodiment of the present invention, the constructing of the multivariate load characteristic load flow calculation optimization model specifically includes:
step 1: data calculation screening index
Discretizing the data, and eliminating data which does not meet the following conditions, wherein the proportion of the peak value or the valley value of the daily measuring point to the total peak value or the valley value of the user is more than or equal to 85 percent
In the formula:
is the mean value of the user load values, p i Judging that more than or equal to 85% of data is unreliable data through the index parameters for the power value at each moment;
the standard deviation of the peak value or the valley value of the daily measuring point and the standard deviation of the total peak value or the valley value of the user are more than 4.5
In the formula:
average of daily measured load values, p, for a user i Sigma is an index parameter after discrete processing for the power value of each moment;
step 2: scheme model algorithmic analysis
(1) Cluster analysis of normalized data
Carrying out group classification on multi-dimensional data such as daily measuring point load, time, user mounting capacity and the like of user load, carrying out weighted average on data of the same time of the same year of all users in the industry through aggregation, updating the maximum and minimum values of sample measurement data, carrying out normalization processing on generated typical curve data, and using a model with classified multistage index belt time sequence data to complete the analysis of an industry load characteristic curve; selection of optimal K value determined by combining elbow method and contour coefficient method with reference form
The formula:
ci is the ith cluster, p is the sample point in Ci, mi is the centroid of Ci, and SSE is the clustering error of all samples, which represents the good or bad clustering effect.
In a preferred embodiment of the present invention, the power distribution network device model specifically includes:
all equipment in the power system can be abstracted into parallel equipment and series equipment, and research level models are reflected to be single-ended models and double-ended models;
for grounded systems, the node voltage is typically taken as the phase voltage V a 、V b And V c For reference voltages, there are:
S＝S a +S b +S c
for ungrounded systems, the line voltage V is generally taken ab And V bc Is a reference voltage, at this time, V ca ＝-(V ab +V bc )。
S＝S ab +S bc
The distribution network is designed in a closed loop mode and operates in an open loop mode, so that the node voltage and the node current can be calculated through the next node voltage and the next node current, and the order is as follows:
the power distribution network load flow calculation process can be described as:
w i-1 ＝g i (w i )
wherein: w is a i Including the real and imaginary 12 x 1 vectors, g, of the voltages and currents of each phase i Determined by circuit structure parameters;
using V i The injection current at node i can be calculated, for the feeder seen in the above figure, there are:
in the formula, A i Is the set of branches connected to node i.
In a preferred embodiment of the present invention, the power distribution network equipment model implements power flow calculation, including:
initializing all node voltages in a first step, wherein the selected initialization voltage can enable the calculation to be rapidly converged;
second step, back substitution calculation; the back substitution calculation is to add the currents of all the branches;
thirdly, calculating the previous generation: the previous generation calculation is to calculate the voltage drop of the branch circuit according to the current obtained by the previous step of the back generation calculation and refresh the node voltage;
the above calculation steps are repeated until the required accuracy is met.
Compared with the prior art, the invention has the beneficial effects that:
(1) the regional economy and high-quality development is guaranteed. Through big accurate planning practice application of data, develop the prediction of universe saturation year load, accomplish the accurate overall arrangement of differentiation of regional transformer substation and circuit, realize with local government planning homeland department "the integration of multidimension". Meanwhile, by means of deep excavation of power supply capacity, distribution points and line galleries of the transformer substation are effectively reduced, and the maximum utilization of regional soil resources is realized. The regional economic high-quality prediction method has the advantages that the regional economic high-quality development is guaranteed, three achievements, namely the load scene index of each functional block, the regional open capacity division distribution table and the regional power grid power supply capacity thermodynamic diagram, are formed, achievement sharing and information intercommunication are achieved with local government and business recruitment departments, reasonable layout of regional industries and location of business recruitment quotation projects are guided.
(2) The user power acquisition inductance is improved. Through big accurate planning practice and application of data, the matching nature of accurate analysis electric wire netting and access user provides accurate power supply guarantee for regional user falls to the ground, and regional user all can realize near whole inserts 300 to 500 meters scope, greatly reduced the supporting cost that the user connects the electricity. Meanwhile, according to the industry-divided evaluation table of the open capacity of the power grid, the time difference examination and approval of the business expansion scheme 0 is realized, the response time of the business expansion scheme is shortened from 13 working days to 1 working day originally, the working efficiency is improved by 13 times, the business expansion scheme is accepted and responded in the same day, and a user does not run once. In recent years, no new district can deliver power on time due to the power grid, and the power acquisition feeling of users is greatly improved.
(3) The cost reduction and the efficiency improvement of the power grid development are realized. Through big accurate planning practice and application of data, the optimal intelligent planning of the power distribution network of the reclamation area of the navigation airport in the new district is completed.
(4) And (5) boosting, cleaning and low-carbon transformation development. Through big accurate planning practice application of data, the many first load demands such as new forms of energy such as distributing type photovoltaic, electric automobile fill electric pile and energy storage are considered overall to the planning, with the load characteristic curve iteration photovoltaic output characteristic curve of distribution network feeder, each feeder new forms of energy consumption capacity is evaluateed accurately, establishes many first load socket formula "map", has realized regional new forms of energy and has completely consumed, many first load inserts entirely. The upgrading and speeding-up of the approval of the photovoltaic project are guided and accelerated, meanwhile, an optimal grid-connected path is generated for a user, and distributed photovoltaic grid-connected one-stop service is achieved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a multivariate load characteristic load flow calculation optimization model provided in an embodiment of the present invention;
FIG. 2 is a logic flow diagram of a load characteristic curve generation algorithm provided by an embodiment of the present invention;
FIG. 3 is a flow chart of generating a characteristic curve according to an embodiment of the present invention;
fig. 4 is a flow chart of implementing load flow calculation by the power distribution network equipment model according to the embodiment of the present invention.
Fig. 5 is a diagram of a result of a multivariate load characteristic curve in the automotive industry according to an embodiment of the present invention.
FIG. 6 is a diagram of the result of a multi-load characteristic curve produced by a metal processing machine according to an embodiment of the present invention.
Fig. 7 is a diagram of a result of a multi-load characteristic curve in the universal device manufacturing industry according to an embodiment of the present invention.
Fig. 8 is a result diagram of a multi-element load characteristic curve of wool weaving and finishing provided by the embodiment of the invention.
Fig. 9 is a diagram of an outcome of a multi-component load characteristic curve of the automobile parts according to the embodiment of the invention.
Fig. 10 is a diagram of a result of a photovoltaic industry multi-load characteristic curve according to an embodiment of the present invention.
Fig. 11 is a planning layout of distribution networks of mini towns created by the Ningbo Hangzhou bay automobile intelligence provided in the embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention.
The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention.
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 invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they 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.
The power distribution network layout method based on the multi-load characteristics comprises the following steps: acquiring existing power grid data, and constructing a multi-load characteristic load flow calculation optimization model based on the existing power grid data, wherein the multi-load characteristic load flow calculation optimization model is used for forming a branch-industry load characteristic curve and is simultaneously associated with a power distribution network equipment model to realize load flow calculation optimization so as to form a visual line load check curve;
constructing a space analysis object model, wherein the space analysis object model can automatically analyze data related to a plot of the power distribution network to be installed;
and confirming a wiring diagram of the power distribution network based on the multivariate load characteristic load flow calculation optimization model and the spatial analysis object model, and finally, automatically wiring cables or overhead lines according to different wiring modes so as to meet the power supply load requirements of all the plots in the power supply unit and take the road and corridor resource information as constraints and solve the objective function with the minimum combined investment.
The invention focuses on the characteristics of multiple loads, so that the model adopting the classified multistage index belt time sequence data can adapt to the acquisition frequency and time span of various measuring points, can generate load characteristic curves of different industries more efficiently and concisely, and can meet the requirement of transition year load prediction required by planning through classifying the commissioning years of different industries.
And (3) carrying out industrial subdivision on the multi-element loads such as regional users, distributed photovoltaic, charging piles and the like to form an industrial load characteristic curve result, associating the industrial load characteristic curve result with a power distribution network equipment model to realize load flow calculation optimization, forming a visual line load check curve, and realizing accurate power grid calculation. On the other hand, through government regulation information mining, the road and land are subjected to objectification processing, an optimal wiring model from a power supply to land users is established, and accurate power grid layout is achieved.
In the invention, the existing power grid data includes, but is not limited to, user load data and control and regulation information data, and the data source multivariate load data and power grid topology data.
Specifically, the multivariate load source data mainly comes from user measurement data of a user acquisition system, and the data items mainly include: the number of 22 columns such as date, office number, instant active power, idle power, house number and house name, and the associated index information of the industry and the number of 4 columns of the house number, the house name and the commissioning year.
Example 1:
described below, by way of example, in accordance with some embodiments of the present application, are described with reference to fig. 1-4:
the power distribution network layout method based on the multi-load characteristics comprises the following steps: acquiring existing power grid data, and constructing a multi-load characteristic load flow calculation optimization model based on the existing power grid data, wherein the multi-load characteristic load flow calculation optimization model is used for forming a branch-industry load characteristic curve and is simultaneously associated with a power distribution network equipment model to realize load flow calculation optimization so as to form a visual line load check curve;
constructing a space analysis object model, wherein the space analysis object model can automatically analyze data related to a plot of the power distribution network to be installed;
and confirming a wiring diagram of the power distribution network based on the multivariate load characteristic load flow calculation optimization model and the spatial analysis object model, and finally carrying out automatic cable wiring or automatic overhead line wiring according to different wiring modes so as to meet the power supply load requirements of all plots in the power supply unit and take road and corridor resource information as constraints, and solve the objective function with minimum combined investment.
1. Existing data: the project data source mainly comprises two aspects, on one hand, multivariate load data and power grid topology and other data collected by a Zhe power cloud platform in a power grid company are provided; another aspect is the economic development information, regulatory and recruitment quotation data of government regions.
The multivariate load source data is mainly derived from user measurement data of a user acquisition system for thunderstorm cloud platform marketing and is provided in a CSV file format separated by commas. The data items mainly include: the number of 22 columns such as date, office number, instantaneous active power, reactive power, house number, house name and the like, and the associated index information of the industry and the number of 4 columns of house number, house name and commissioning year.
The multivariate load data of 473 electricity users, 21 photovoltaic power generation users and 6 charging pile users in 20 industries, which are representative in the Ningbo Hangzhou gulf new area, are mainly obtained. Wherein, the electricity consumption users total 5020268 row measuring point data, and the total file size is 1.2 GB; the total measuring point data of 165989 rows of photovoltaic power generation users is 35.8MB in total file size; the charging pile user totals 159297 row measurement point data, and the total file size is 33.8 MB.
The power distribution network equipment model data mainly come from CIM and SVG data derived from a PMS system. The method comprises the data of a topological structure of a power grid, equipment models, line parameters and the like. The transformation model comprises 893 transformation models, 8637 line segments and 3255 switches.
The external data mainly comprises new district control rule (2016 edition), economic development data and 2016 + 2018 recruitment data of Ningbo Hangzhou gulf. The data is from a plurality of departments such as a new district post office, a planning office, a business recruitment office and the like.
2. Data pre-processing
And acquiring data of the user within 7 years from the production year from the Zhe power cloud platform, and carrying out data transformation such as normalization, data discretization and the like on the data. Filling missing value cases, for example, filling by using a method of observing data continuity, a mean interpolation method, a manual interpolation, a model analysis, and the like; and (3) detecting data deviation, analyzing and calculating a value which is far away from the given attribute mean value and exceeds two standard deviations, marking the value as a possible outlier, processing the outlier data, ensuring the completeness and the availability of the data, and finally performing characteristic curve fitting on the data of each industry in nearly 7 years.
The data is further processed first by:
and (3) data filtering calculation: and (4) verifying data relevance, removing unnecessary fields, and processing unreasonable format characters and data (time, load values and the like) in the data. And generating user data corresponding to the binding industry according to the industry data, and performing duplicate removal calculation on the load data of the same user at the same date and time point in the industry users to obtain the load value at the current time.
And (3) null value filling processing: the small-scale data is manually filled in according to a data continuity rule or based on data information. And secondly, for large-scale data sets with missing null values, performing mean interpolation.
And (3) abnormal data elimination: and analyzing and identifying outliers and data by a clustering method. For example, clustering analysis calculation is carried out on the daily load measurement value at a certain moment, and the data of the daily measurement point is judged to be unreliable data by judging that the proportion deviation of the peak value or the valley value of the daily measurement point and the total peak value or the valley value of the user is more than 0.85 or the standard deviation of the peak value or the valley value of the daily measurement point and the total peak value or the valley value of the user is more than 4.5, and the data of the daily measurement point is eliminated.
As shown in fig. 2, for the flow chart of the multivariate load characteristic load flow calculation optimization algorithm, the constructing of the multivariate load characteristic load flow calculation optimization model specifically includes:
step 1: data calculation screening index
In order to facilitate the analysis of the availability and credibility of the data, the data is discretized, and the data which does not meet the following conditions is removed
The ratio of the peak value or the valley value of the daily measuring point to the total peak value or the valley value of the user is more than or equal to 85 percent
In the formula:
as the mean value of the user load values, p i And judging that more than or equal to 85% of the data is unreliable data through the index parameters for the power value at each moment.
The standard deviation of the peak value or the valley value of the daily measuring point and the standard deviation of the total peak value or the valley value of the user are more than 4.5
In the formula:
average of daily measured load values, p, for a user i For the power value at each moment, sigma is the index parameter after discrete processing
Step 2: solution model algorithmic analysis
(1) Cluster analysis of normalized data
In the clustering, data objects in the samples are divided into different clusters by taking a certain metric as a standard, the cluster classification is carried out according to multi-dimensional data such as daily measuring point load of a user, time, user assembling capacity and the like, the weighted average is carried out on the data of the same time of the same year of all users in the industry through aggregation, the maximum and minimum values of the measured data of the samples are updated, the generated typical curve data are subjected to normalization processing, and the model of the classified multistage index band time sequence data is used for completing the analysis of the load characteristic curve of the industry and realizing the simulation of the maximum load condition which can be connected by a new user.
The number of clusters determined from industry experience is excessive and not necessarily the true number of clusters we obtained the data, in order to determine the best number of clusters for the data. In this case, the selection of the optimum K value is determined by the elbow method in combination with the contour factor method in a reference format.
The formula:
ci is the ith cluster, p is the sample point in Ci, mi is the centroid of Ci (the mean of all samples in Ci), and SSE is the clustering error of all samples, which represents the good or bad clustering effect.
(2) ARIMA algorithm model prediction
Common time series prediction methods comprise a regression model, an ARIMA model, a Holt Winters method, a moving average method and an index smoothing method, and aiming at the existing data trend analysis, the data is found to have certain characteristics such as regularity and periodicity; for this purpose the ARIMA algorithm was chosen for predictive analysis.
The basic idea of the ARIMA model is to convert a non-stationary time sequence into a stationary time sequence, combine the data of the algorithm, and adopt the data in the ARMIA model: the moving average model-MA (q), the autoregressive model-AR (p), and the differential autoregressive moving average model ARIMA (p, d, q) are combined to perform data prediction analysis.
AR is "autoregressive", p is the number of autoregressive terms; i is the difference, d is the number of differences (order) made to make it a plateau sequence; MA is 'moving average', q is number of moving average terms
Further, the planning red line and the type information of the land parcel in the electronic map, and the road information and the information of the pipe gallery in the electronic map are utilized to carry out object vectorization modeling. And (3) searching prediction parameters such as load density, volume rate, simultaneous rate and the like of different block types by calculating the area of the land, and performing space load prediction calculation to obtain the saturated load demand of the planning region. And obtaining the adjacency of each land and the connectivity between roads by using a geographic space analysis method, and establishing a space analysis object model.
Firstly, a planar median method is used for site selection and volume measurement analysis of the transformer substation in a planning area, the planar median method is similar to a storage center point arrangement algorithm, and optimal construction and operation cost of the transformer substation is used as a target function for solving.
C1: construction cost and operation cost of the transformer substation; f1 (Si): the cost of future substation investment; r 0: annual interest rate; u (Si): the operating costs of the substation;
according to the target annual load requirement of a planning region, the positions of the transformer substations, the capacity of the main transformers and the number of the main transformers can be planned automatically, and meanwhile, the most economical power supply range of the transformer substations is provided.
And then according to the wiring mode (overhead single-contact, overhead same-pole parallel, single-ring and double-ring cable) types of the planning region, different types correspond to different wiring groups with load capacity access capabilities, and the power supply units are automatically divided by applying a Gaussian clustering algorithm with the nearest power supply distance from each land to two substations and the capacity and load rate of the power supply units as constraints.
The principle of the artificial intelligence-based Gaussian clustering algorithm is that a similarity matrix is calculated according to a data matrix, and points (plots) with approximate characteristics are automatically gathered into a power supply unit with given capacity by using the similarity matrix-based clustering method.
And finally, automatically wiring the cable or the overhead line according to different wiring modes. The method can meet the power supply load requirements of all the plots in the power supply unit, takes the resource information of roads and galleries as constraint, and solves the objective function with minimum combined investment. When automatic planning is carried out, the line can be strictly wired according to the road direction according to the road object information. When a plurality of groups of lines pass through the same road, the lines can be arranged in parallel, so that the attractiveness of wiring is ensured.
Further, as shown in fig. 4, the power distribution network equipment model implements power flow calculation, including:
initializing all node voltages in a first step, wherein the selected initialization voltage can enable the calculation to be rapidly converged;
second step, back substitution calculation; the back substitution calculation is to add the currents of all the branches;
thirdly, calculating the previous generation: the previous generation calculation is to calculate the voltage drop of the branch circuit according to the current obtained by the previous step of back generation calculation and refresh the node voltage;
the above calculation steps are repeated until the required accuracy is met.
Basic iteration method for power flow calculation of power distribution network
All devices in the power system can be abstracted into parallel devices and series devices, and research-level models are reflected in single-end and double-end models:
for grounded systems, the node voltage is typically taken as the phase voltage V a 、V b And V c Is a reference voltage. Comprises the following steps:
S＝S a +S b +S c
for ungrounded systems, the line voltage V is typically taken ab And V bc Is a reference voltage, at this time V ca ＝-(V ab +V bc )。
S＝S ab +S bc
The distribution network is designed in a closed loop mode and operates in an open loop mode, so that the node voltage and the node current can be calculated through the next node voltage and the next node current, and the order is as follows:
the power distribution network load flow calculation process can be described as:
w i-1 ＝g i (w i )
wherein: w is a i Including the real and imaginary 12 x 1 vectors, g, of the voltages and currents of each phase i Determined by the circuit configuration parameters.
By means of V i The injection current at node i can be calculated, for the feeder seen in the above figure, there are:
in the formula, A i Is the set of branches connected to node i.
Example 2:
according to typical load characteristic curves of 20 industries in a certain area, each industry load characteristic curve is classified by years according to the year of delivery. In the aspect of multi-load, a photovoltaic and charging pile multi-load characteristic curve is formed through key analysis. Meanwhile, accurate planning of the power grid is achieved through the optimal wiring model of the power grid.
(1) The characteristic curve achievement refers to fig. 5-10, which are graphs of the multi-load characteristic curve achievement in the branch industry;
(2) a typical regional power grid planning example; the method is explained by taking the planning of the power distribution network of the Ningbo Hangzhou bay automobile intelligent creation small town as an example. The load prediction is manually verified from the traditional load index value, the load in the transition year is converted into prediction of any granularity through big data matching of a characteristic curve by means of experience prediction.
Referring to fig. 11, by taking the actual load measurement in 2019 as an example, the actual load of the intelligent small town of the automobile is 5.35 ten thousand kilowatts, the load predicted manually by planning is 4.85 ten thousand kilowatts, the load is 5.15 ten thousand kilowatts obtained by matching the load characteristic results, the deviation degree of the load prediction is increased from 90.65% to 96.26%, and the prediction accuracy is greatly improved. On the basis of load prediction, the power grid planning layout can be automatically generated by automatically planning the optimal wiring model.
The town centers on the function positioning of 'the largest nationwide automobile research and development center and the most complete nationwide automobile intelligent industrial chain', and the access requirements of regional attracting and quotation projects are vigorous. One group of 10 kV double-ring connection wires (the more porcelain to the Xinyu and the Xinyi wires and the starfish to the Huwan and the Haichuan) are selected for division checking of openable capacity. And (4) from the current load curve analysis of the line, according to the operation capacity control of the line, iterating the characteristic curves of different industries, and finally evaluating the power supply capacity of the line to different regional industries.
Taking a 10 kilovolt new line as an example, the maximum power supply capacity of the line is controlled according to 7500kW of heavy load, the current load of the line is 5780kW, the capacity can be checked according to the conventional open capacity, the residual capacity is 1720kW, and the line can only meet the user access of the capacity. However, according to the line load condition, the residual power supply capacity of the line can be analyzed and calculated as shown in the figure, the line is automatically matched and checked one by applying the industry-divided load characteristic results, and the power supply capacity evaluation of the line aiming at different industries can be obtained, and the table shows that the line has obvious difference on the power supply capacity of different industries, and the access capacity of the line can reach 5560KVA for the relatively off-peak public lighting industry; for the auto parts industry, capacity 2020KVA may be accessed. The business expansion scheme is checked clearly for different types of peripheral users accessing the power grid.
TABLE 1 line division industry Power supply capability assessment table (Unit kVA)
Meanwhile, according to the project, tide check is carried out according to technical parameters such as photovoltaic access installed capacity, short-circuit current and voltage quality, the condition that the power grid is controlled to be sent back is taken as a boundary condition, a ring network station is planned to be used as a photovoltaic new energy access socket in a centralized mode, and full access and full consumption of distributed photovoltaic in a power supply unit are met.
Taking a 10 kilovolt new line as an example, the load curve is automatically matched with the photovoltaic output curve, and the line can meet 6000KVA photovoltaic access, so that quantitative evaluation of distributed photovoltaic access capacity is realized.
Meter 210 kv line photovoltaic access capacity quantitative evaluation
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. The power distribution network layout method based on the multi-load characteristics is characterized by comprising the following steps:
acquiring existing power grid data, constructing a multi-element load characteristic load flow calculation optimization model based on the existing power grid data, wherein the multi-element load characteristic load flow calculation optimization model is used for forming a branch industry load characteristic curve, and is simultaneously associated with a power distribution network equipment model to realize load flow calculation optimization so as to form a visual line load check curve;
constructing a space analysis object model, wherein the space analysis object model can automatically analyze data related to a plot of the power distribution network to be installed;
and confirming a wiring diagram of the power distribution network based on the multivariate load characteristic load flow calculation optimization model and the space analysis object model, and finally, automatically wiring cables or overhead lines according to different wiring modes.
2. The method according to claim 1, wherein the existing grid data includes, but is not limited to, customer load data and control information data, the data being derived from multivariate load data and grid topology data.
3. The method according to claim 2, wherein the multivariate load source data mainly originate from user measurement data of a user collection system, and the data items mainly include: the number of 22 columns such as date, office number, instantaneous active power, reactive power, house number, house name and the like, and the associated index information of the industry and the number of 4 columns of house number, house name and commissioning year.
4. The method for the distribution network layout based on the multivariate load characteristics as claimed in claim 2, wherein the step of constructing the multivariate load characteristic load flow calculation optimization model specifically comprises the steps of:
step 1: data calculation screening index
Discretizing the data, and eliminating data which does not meet the following conditions, wherein the ratio of the peak value or the valley value of the daily measuring point to the total peak value or the valley value of the user is more than or equal to 85 percent
In the formula:
is the mean value of the user load values, p i Judging that more than or equal to 85% of data is unreliable data through the index parameters for the power value at each moment;
the standard deviation of the peak value or the valley value of the daily measuring point and the standard deviation of the total peak value or the valley value of the user are more than 4.5
In the formula:
average of daily measured load values, p, for a user i Sigma is an index parameter after discrete processing for the power value of each moment;
and 2, step: solution model algorithmic analysis
(1) Cluster analysis of normalized data
Carrying out family group classification aiming at multi-dimensional data such as daily measuring point load, time, user mounting capacity and the like of a user load, carrying out weighted average on data of all users at the same time in the same year in the industry through aggregation, updating the maximum and minimum values of sample measurement data, carrying out normalization processing on generated typical curve data, and using a classified multi-stage index model with time sequence data to finish the analysis of an industry load characteristic curve; selection of optimal K value determined by elbow method and contour coefficient method in combination with reference form
The formula:
ci is the ith cluster, p is the sample point in Ci, mi is the centroid of Ci, and SSE is the clustering error of all samples, which represents the good or bad clustering effect.
5. The method according to claim 1, wherein the distribution network equipment model specifically comprises:
all equipment in the power system can be abstracted into parallel equipment and series equipment, and research level models are reflected to be single-ended models and double-ended models;
for grounded systems, the node voltage is typically taken as the phase voltage V a 、V b And V c For reference voltages, there are:
S＝S a +S b +S c
for ungrounded systems, the line voltage V is generally taken ab And V bc Is a reference voltage, at this time V ca ＝-(V ab +V bc )
S＝S ab +S bc
The power distribution network is designed in a closed loop mode and operates in an open loop mode, so that the voltage and the current of a node can be obtained through the voltage and the current of the next node, and the order is as follows:
the power distribution network load flow calculation process can be described as:
w i-1 ＝g i (w i )
wherein: w is a i Including the real and imaginary 12 x 1 vectors, g, of the voltages and currents of each phase i Determined by circuit structure parameters;
using V i The injection current at node i can be calculated, for the feeder seen in the above figure, there are:
in the formula, A i Is the set of branches connected to node i.
6. The method for distributing the power distribution network based on the multivariate load characteristics as claimed in claim 2, wherein the step of implementing the power flow calculation by the power distribution network equipment model comprises the following steps:
initializing all node voltages in a first step, wherein the selected initialization voltage can enable the calculation to be rapidly converged;
second step, back substitution calculation; the back substitution calculation is to add the currents of all the branches;
thirdly, calculating the previous generation: the previous generation calculation is to calculate the voltage drop of the branch circuit according to the current obtained by the previous step of back generation calculation and refresh the node voltage;
the above calculation steps are repeated until the required accuracy is met.
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| CN109657959A (en) * | 2018-12-12 | 2019-04-19 | 国家电网有限公司 | A kind of distribution network planning calculation and analysis methods containing multivariate data |
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| WO2022105944A1 (en) * | 2020-11-18 | 2022-05-27 | 国网青海省电力公司经济技术研究院 | A method for calculating optimal load capacity of 10 kv feeder taking into account impact of different load structures and reliabilities |
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| CN109657959A (en) * | 2018-12-12 | 2019-04-19 | 国家电网有限公司 | A kind of distribution network planning calculation and analysis methods containing multivariate data |
| WO2022105944A1 (en) * | 2020-11-18 | 2022-05-27 | 国网青海省电力公司经济技术研究院 | A method for calculating optimal load capacity of 10 kv feeder taking into account impact of different load structures and reliabilities |
| CN114400657A (en) * | 2021-12-28 | 2022-04-26 | 广东电网有限责任公司广州供电局 | Power distribution network cooperative regulation and control method and system considering electric vehicle access |
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