CN116187541A - Collaborative optimization construction method for electric vehicle charging facilities and power distribution network - Google Patents
Collaborative optimization construction method for electric vehicle charging facilities and power distribution network Download PDFInfo
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Abstract
The invention provides a collaborative optimization construction method for a power distribution network of a newly built residential area of a city and electric vehicle charging facilities, and construction data is obtained; predicting the holding quantity of the electric automobile; the Monte Carlo method is combined with the charging probability of the electric automobile to predict; carrying out one-time planning on the distribution capacity required by each load node according to the prediction result; selecting data adopted by planning according to the result; substituting basic load peak value data, charging load data, node information, line parameters to be selected and the like of a newly built residential area into a collaborative optimization construction model of the newly built residential area, setting construction planning data, solving by combining a Prim algorithm and a single parent genetic algorithm based on tree structure coding, and solving the most economical topology and line model selection scheme. The invention solves the problems that after the newly built residential area is built, the capacity of the residential area and the capacity of the line cannot meet the requirements, so that the residential area is maintained and built on a large scale, and the charging facilities are idle due to overlarge quantity of the built residential area and the charging facilities in the initial building stage.
Description
Technical Field
The invention relates to the technical field of urban power distribution network planning, in particular to a collaborative optimization construction method for electric vehicle charging facilities and a power distribution network.
Background
As a necessary supporting facility of the new energy electric automobile, the establishment of the community charging pile has not broken through the bottleneck. The new energy automobile is with promoting, and construction community facility of charging is basic, and community vehicle charging solution is applicable to situations such as residential area have slow charge demand, concentrate more parking stall, and residential area electric automobile charge demand's characteristics are concentrated the charging at night, especially appear for the residential area resident starts the charging after coming home off duty, the early morning before the work vehicle full charge can, direct current quick charge demand is lower.
The rapid increase of the electric automobile conservation quantity leads to the increase of night load of the distribution network of the residential area, and leads to the overload of distribution transformer of the area and the reduction of power supply quality, thereby influencing the normal electricity utilization of residents. In the aspects of power distribution network topology, power supply line type, distribution transformer capacity and the like, a newly built residential area can be planned before construction, so that the problems of large-scale maintenance and construction caused by that the capacity of a station area and the capacity of a line cannot meet the requirements after construction are avoided.
The current charging facilities are an important foundation for the development of electric vehicles, and have important promotion effects on promoting the commercialization and industrialization of the electric vehicles. Along with popularization of electric automobiles, the planning and construction of charging facilities are further large-scale, network make-up and intelligent. The existing charging quantity and the existing electric automobile quantity basically reach the standard proportion, but the distribution condition of the charging demands is not analyzed from the angle of the travel rule of the electric automobile in the layout process of the charging piles, so that the situation that a plurality of charging piles are idle is caused, and the charging demands cannot be met. In the aspect of charging facility construction, a new residential area needs to be planned in advance for a year-by-year construction scheme, and how to avoid idling caused by overlarge initial construction quantity is a technical problem to be solved currently.
Disclosure of Invention
The invention provides a collaborative optimization construction method for an electric vehicle charging facility and a power distribution network, which comprises the following steps:
s101, predicting the electric vehicle holding quantity of a new residential area based on the historical data of the holding quantity of thousands of private vehicles in the city of the new residential area and the planning and construction data of the new residential area;
s102, acquiring travel data of urban private cars and performance parameters of the electric cars, and predicting the charging probability of the electric cars in the residential area by combining a Monte Carlo method with the prediction result in S101;
s103, predicting the conventional residential electrical loads of each unit building of a newly built residential area year by year, and planning the distribution and transformation capacity required by each load node once according to the prediction result;
s104, calculating the working day and the double holiday load prediction result of the newly built residential area planning year by combining the electric vehicle holding quantity prediction result, the electric vehicle charging probability prediction result and the basic electric load calculation, and selecting data adopted by planning according to the result;
s105, carrying out power supply grid division on a newly built residential area, carrying out peak load density calculation, and selecting the position of a 10kV access node of a power distribution network of the residential area according to a load density calculation result and a 10kV line plan;
s106, substituting the saturated annual basic load peak value data, the charging load data, the node information and the line parameters to be selected of the newly built residential area into a collaborative optimization construction model of the newly built residential area, setting construction planning data such as line construction cost, line plan service life and the like, solving the collaborative optimization construction mathematical model by adopting a Prim algorithm and a single-parent genetic algorithm based on tree structure coding in combination with tide calculation, and solving the most economical topology and line model selection scheme;
s107, substituting the saturated annual basic load peak value data, the charging load data, the node information and the line parameters to be selected of the newly built residential area into a collaborative optimization construction model of the newly built residential area, setting construction planning data such as line construction cost, line plan service life and the like, and archiving and storing according to the received IP address and data type;
s108, constructing a data layout diagram in the step S107, substituting the saturated annual basic load peak value data, the charging load data, the node information and the line parameters to be selected of the newly built residential area in the data layout diagram into a newly built residential area collaborative optimization construction model, and setting construction planning data such as line construction cost, line plan service life and the like to update in real time;
s109, configuring data and an operation interface of a topology and line type selection scheme, so that a user adds data which is not stored or configured in the system; or the stored data and topology and line selection schemes are modified or deleted.
Preferably, the city private car growth rate is fitted by adopting a least square method to obtain city private car growth rate prediction model parameters, and the residential electric car retention is obtained by combining the newly built residential area occupancy change according to a Bass model.
Preferably, a nonlinear least square method is adopted to fit the maintenance quantity acceleration of thousands of private cars in the city, wherein the function of a fitted curve can be expressed as follows:
wherein: y (t) is the holding quantity of thousands of private cars at t time, and the unit is: a vehicle; a. b and c are constants;
then, in the case where the occupancy is 1, the predicted number of electric vehicles in the t-th year using the bas model may be expressed as:
wherein G (t) is the integral number of the electric vehicles accumulated to the t year; g (t) is the number of electric vehicles newly added in the t th year; m is the maximum capacity upper limit of the electric automobile; p is an external influence coefficient, and represents the influence degree of external media propaganda on the diffusion of the electric automobile, and the value of the external media propaganda is 0.01-0.03; q is an internal influence coefficient, and represents the influence degree of internal public praise propagation on the diffusion of the electric automobile, and the value is 0.3-0.7;
finally, the new residential area shows a rapid increasing trend of the occupancy rate, and the actual holding quantity of the electric automobile per year is as follows:
O(t)＝G(t)λ(t)
wherein: o (t) is the electric automobile holding quantity of the new residential area in the t year, and the unit is: a vehicle; lambda (t) is the occupancy rate of the newly built residential area in the t-th year.
Preferably, a Monte Carlo method is adopted to predict daily charging load and charging number of electric vehicles in a residential area:
probability distribution density function of electric automobile at end time of working day with working trip as purpose:
in sigma w Standard deviation sigma of working end time for working day w ＝1.747；μ w Mean value of working end time, mu for working day w ＝17.3；
The probability distribution density function of the electric automobile at the end time of social shopping behavior on the working day:
wherein alpha is 1 、β 1 Is a proportionality coefficient alpha 1 ＝0.3840，β 1 ＝0.59；σ 1 、σ 2 Standard deviation sigma of travel ending time of social shopping on workday 1 ＝2.32，σ 2 ＝2.575；μ 1 、μ 2 Mean value mu of end time of social shopping trip on workday 1 ＝12，μ 2 ＝18.2；
Probability distribution density function of electric car at the end time of double holidays for social shopping trip purpose:
wherein alpha is 2 、β 2 Is a proportionality coefficient alpha 2 ＝0.302，β 2 ＝0.6395；σ 3 、σ 4 Standard deviation sigma of travel ending time of social shopping on double holidays 3 ＝2，σ 4 ＝3.2；μ 3 、μ 4 Mean value mu of travel ending time of social shopping on double holidays 3 ＝11.6，μ 4 ＝17；
Mileage probability distribution density function for single trip of electric automobile:
wherein d is a single trip driving mileage, and the unit is km; mu (mu) D Is the desire for ln d; sigma (sigma) D Standard deviation of ln d; the average value of the single trip mileage in the workday is 11.4km, and the standard deviation is 4.88km; the average value of the single trip mileage on double holidays is 13.2km, and the standard deviation is 5.23km;
electric automobile charging start SOC:
in SOC i Charging an ith electric automobile to initiate a state of charge; d, d k The travel mileage of the kth trip; n is the total number of times of the electric automobile going out; d, d full The maximum driving mileage after the battery of the electric automobile is fully charged;
electric automobile charge duration:
wherein t is c The charging time of the electric automobile is h; p (P) c The charging power is kW; e is the battery capacity of the electric automobile, and the unit is kW.h;
charging probability of residential electric automobile at 24 times of day:
wherein D (t) is the charging probability at time t; t (T) kt The state of charge at time t for the kth random sample.
Preferably, a Monte Carlo method for predicting daily charging load and charging number of electric vehicles in a newly built residential area comprises the following steps:
step 11: determining the battery capacity, the maximum driving mileage and the charging power of the electric vehicle;
step 12: according to the travel type and travel rule, extracting N random samples at the charging start time, and putting the samples into an array T s ；
Step 13: according to single trip mileage distribution and resident charging habits, extracting N accumulated trip mileage samples, calculating the charging time corresponding to each sample, and putting the charging time corresponding to each sample into an array T c ；
Step 14: number of samplesGroup T s And T c Judging the random samples in the database to obtain the charging state of each group of random samples within 24 hours in 1 day, and putting the charging state corresponding to each group of samples into an array T;
step 15: calculating the charging probability at each moment;
step 16: cycling the steps 12-15 for N times to obtain N groups of charging probability data;
step 17: and averaging the values of the N groups of data at the same time to obtain the final charging probability of the electric automobile in the residential area.
Preferably, the residential building of the urban residential area can be regarded as a base load node, and the base load prediction can adopt a load density method;
the new residential area is built with the target annual base load of
Wherein: p (P) base (i) The method comprises the steps of setting a target annual base load for an ith base load node of a newly built residential area, wherein the unit is kW; a is that house (i) The resident housing area for the ith base load node is given in m 2 ；D H (i) The unit is W/m for the load density index of the ith base load node 2 ；K Area (i) The area demand coefficient for the ith base load node; lambda is the occupancy rate of the residential area in the target year; n (N) base The number of basic load nodes for the newly built residential area;
and selecting the load density and area demand coefficient of all the unit buildings according to the electricity utilization characteristics of the urban residential area, calculating the conventional load of the newly built residential area, calculating the basic load peak value of each node of the residential area by combining the number of the units and the building area, and planning the required capacity of each node at one time.
Preferably, in the power grid, the farther the load is from the outlet point, the greater the network loss is caused by the line impedance, so that the outlet point is located in a region with higher load density;
the load density of different power supply grids of newly built residential areas is
Wherein: d (k) is the load density of the power grid k, in kW/km 2 ；P base The power utilization load of the basic load node in the power supply grid k is in kW; p (P) ev The power utilization load of the charging node of the electric automobile in the power supply grid k is in kW; a (k) is the area of the power supply grid k, and the unit is km 2 。
Preferably, the annual comprehensive cost of the construction cost of the distribution network and the network loss cost is taken as an objective function, and the distribution network line capacity and the distribution network node voltage are taken as constraint conditions to establish a residential area distribution network collaborative optimization construction mathematical model;
(1) The annual comprehensive cost of the construction cost of the power distribution network and the network loss cost is taken as an objective function:
wherein: f is the annual comprehensive cost of the newly built residential area, and the unit is: ten thousand yuan/year; i is the construction cost of newly built residential area lines, and the unit is: ten thousand yuan; r is (r) 0 Is the discount rate; y is the economic service life of the circuit, and the unit is: years of life; l is the line loss cost of the newly built residential area, and the unit is: ten thousand yuan/year;
the line construction of the newly built residential area adopts different line types to reduce the cost, and the line construction cost of the newly built residential area is as follows:
wherein: l (L) k The unit is km for the length of the line k;
the cross-sectional area of the line k is D k The building cost is expressed as Yuan-km；N k A newly built line set;
the network loss cost of the newly built residential area line is as follows:
wherein: alpha is electricity price, and the unit is: meta/kWh; p (P) k The unit is the active power flowing on line k: kW; q (Q) k The unit is kVar for reactive power flowing on line k; u is voltage, and the unit is: a kV;
taking D for the cross-sectional area of line k k The unit length resistance is as follows: omega/km; τ is the number of annual maximum load utilization hours;
(2) The power distribution network line capacity and the power distribution network node voltage are taken as constraint conditions:
in the running process of the power distribution network, the power flowing through each planned line should be smaller than the capacity of the planned line, namely:
wherein:
the maximum capacity of the kth line of the power distribution network;
the radial topological structure of the power distribution network easily causes serious voltage drop of the terminal nodes, and cannot meet the normal electricity demand; the node voltage of the newly built residential area distribution network is constrained, namely:
U min ≤U n ≤U max
wherein: u (U) min The node voltage of the power distribution network is lower than the voltage limit; u (U) max The upper voltage limit of the node of the power distribution network is set; u (U) n The lower limit of the node voltage of the nth node of the power distribution network is a per unit value.
Preferably, (1) generating an initial power distribution network topology structure by adopting a Prim minimum spanning tree algorithm; the key steps of Prim algorithm are as follows:
step 21: inputting the coordinate position of each load node of the power distribution network and inputting a network topology starting node;
step 22: establishing a set S for storing connected load nodes, a set V for storing unconnected load nodes, a set dist for storing the distance between each node of the set S and each node of the set V, and a set line for storing selected lines;
step 23: updating the set S, V, setting aside the set dist, calculating the line distance between each node of the set S and each node of the set V, and storing the line distance to the set dist;
step 24: selecting the shortest line distance in the set dist as a next connection line of the topology, adding the selected line to the set line, and adding the selected load node from the set V to the set S;
step 25: judging whether the set V is an empty set, if so, ending and outputting a result; if not, returning to the step 23;
(2) After an initial grid structure is generated, optimizing by adopting a single parent genetic algorithm based on tree structure coding; the method for the single parent genetic algorithm based on the tree structure coding mainly comprises the following steps:
step 31: inputting initial population (N individuals), shift probability e, iteration number m and the like;
step 32: calculating fitness of each body (the fitness of the invention takes the objective function value as fitness), and storing the optimal individuals of the present generation and the previous generation;
step 33: judging whether the iteration condition is met, if so, outputting an optimal individual, and if not, executing the step 34;
step 34: judging whether all individuals finish execution, if yes, returning to the step 33, and if not, executing the step 35;
step 35: randomly selecting individual nodes, and generating a shift probability r according to the setting;
step 36: judging whether the shift probability r is smaller than or equal to e, if so, executing the shift operation, and if not, executing the reassignment operation;
step 37: fitness calculation and roulette individual update are performed, returning to step 34.
Preferably, the newly built residential area data is input, and the power grid topology and the line type selection of the newly built residential area with optimal economy can be obtained by combining the power flow calculation of the power system.
From the above technical scheme, the invention has the following advantages:
the collaborative optimization construction method for the electric vehicle charging facility and the power distribution network can accurately predict the charging load of a newly built residential area in a city; charging facilities of newly built residential areas of cities can be reasonably built; the construction, operation and maintenance costs of the newly built residential area distribution network and the charging facilities can be reduced.
The invention can collect the electric vehicle charging facility information and the power distribution network cooperative information, is convenient for users to review, and effectively improves the topology and line type selection efficiency. The method can also collect and store the electric vehicle charging facility and power distribution network collaborative optimization construction data efficiently and process the data, process configuration can be realized based on electric vehicle charging facility information and power distribution network collaborative information, and the whole topology and line model selection process is described by using a multidimensional space. The precision and the accuracy of the topology and the line model selection are improved, hidden dangers of the topology and the line model selection are timely found and early warning is carried out, so that the coordination level and the efficiency of the topology and the line model selection are improved, and timeliness and scientificity of the overall process supervision, management and control of the topology and the line model selection are realized.
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In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for collaborative optimization construction of an electric vehicle charging facility and a power distribution network;
FIG. 2 is a graph of the change in occupancy rate of a newly built residential area;
FIG. 3 is a graph of a fitted rate of increase in the thousand people's held by a private car in a city;
fig. 4 is a schematic diagram of the amount of private car and electric car in a newly built residential area;
fig. 5 is a schematic diagram of prediction of charging probability of an electric vehicle in a newly built residential area;
FIG. 6 is a graph showing the unit value of a typical daily conventional electricity load in a residential area;
fig. 7 is a schematic diagram of residential area 2030 electricity load prediction;
FIG. 8 is a schematic diagram of a newly built residential area power grid division;
FIG. 9 is a shift schematic;
FIG. 10 is a diagram of reassignment;
FIG. 11 is a layout diagram of a newly built residential area circuit topology and circuit selection;
fig. 12 is a schematic diagram of a convergence procedure.
Detailed Description
According to the collaborative optimization construction method for the electric vehicle charging facility and the power distribution network, the associated data can be acquired and processed based on the artificial intelligence technology. The method for constructing the collaborative optimization of the electric automobile charging facility and the power distribution network utilizes a digital computer or a machine controlled by the digital computer to simulate, extend and expand the intelligence of people, sense the environment, acquire knowledge and acquire the theory, method, technology and application device of the best result by using the knowledge.
The method also has a machine learning function, wherein the machine learning and the deep learning in the method generally comprise the technologies of artificial neural network, confidence network, reinforcement learning, transfer learning, induction learning, teaching learning and the like.
As shown in fig. 1, the method for collaborative optimization construction of an electric vehicle charging facility and a power distribution network according to the present invention may be applied to one or more terminals, which are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, and the like.
The terminal may be any electronic product that can interact with a user, such as a personal computer, a tablet, a smart phone, a personal digital assistant (Personal Digital Assistant, PDA), an interactive internet protocol television (Internet Protocol Television, IPTV), a smart wearable device, etc.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 to 12 are schematic diagrams of a collaborative optimization construction method for an electric vehicle charging facility and a power distribution network in an embodiment.
Step 201: the thousand-person holding capacity and the annual growth rate of private cars in the city of the newly built residential area are shown in table 1, the electric car in 2019 occupies 0.0205, the number of living rooms in the newly built residential area is 1298, 3 persons are set for each room, 13 residential buildings are planned to be built, and the building area is 177300m 2 The change condition of the occupancy rate is shown in figure 2 by adopting the configuration of the 1:1 parking spaces of the underground parking lot.
Table 1 2011-2019 thousands of private cars in a certain city
Step 202: fitting a private car thousand person holding quantity increase rate prediction model of the city based on the private car thousand person holding quantity historical data of the city where the newly built residential area is located and the city private car thousand person holding quantity increase rate prediction model of the city based on the city private car thousand person holding quantity historical data of the city where the newly built residential area is located and performing fitting according to a fitting result shown in a figure 3; based on the prediction result of the thousand person holding quantity increase rate of the urban private car, judging that the thousand person holding quantity of the urban private car is about to reach saturation in 2035, namely 423 private cars/thousand persons, calculating the saturation value of the private car of the newly built residential area under the situation that the occupancy rate is 1 according to the result, and obtaining 1647 private car saturation values of the residential area; taking 70% of the saturation value of private cars in the residential area as the saturation value of the electric car, namely taking the value of M in the formula (2), taking p as 0.03, q as 0.38, and the urban electric car accounts for 2.93% in 2021; predicting the remaining quantity of the electric automobile in the past year under the situation that the living rate of the newly built residential area is 1 through the method (2); finally, the prediction result of the electric automobile conservation quantity of the newly built residential area for years in the future can be obtained through the formula (3) and the figure 2; the prediction results of the storage quantity of the private car and the electric car in the newly built residential area are shown in fig. 4.
Step 203: the electric automobile with the battery capacity of 40.62 kW.h, the maximum endurance mileage of 305km and the electricity consumption of 0.13 kW.h per kilometer is used as a simulation object; the charging mode of the electric automobile in the residential area is a conventional charging mode, and the charging power of the charging facility is 7kW; according to the invention, the electric automobile in the residential area is subjected to Monte Carlo simulation according to the formulas (4) to (10), and the charging prediction result of the electric automobile in the residential area is shown in fig. 5 and table 2; the Monte Carlo simulation method specifically comprises the following steps:
1) Determining the battery capacity, the maximum driving mileage and the charging power of the electric vehicle;
2) According to the travel type and travel rule, extracting N random samples at the charging start time, and putting the samples into an array T s ；
3) According to single trip mileage distribution and resident charging habits, extracting N accumulated trip mileage samples, calculating the charging time corresponding to each sample, and putting the charging time corresponding to each sample into an array T c ；
4) Array T of samples s And T c Judging the random samples in the database to obtain the charging state of each group of random samples within 24 hours in 1 day, and putting the charging state corresponding to each group of samples into an array T;
5) Calculating the charging probability at each moment;
6) Cycling 2) to 5) for N times to obtain N groups of charging probability data;
7) And averaging the values of the N groups of data at the same time to obtain the final charging probability of the electric automobile in the residential area.
Table 2 average charge duration of electric car in residential area
Step 204: taking the load density of all the unit buildings as 40W/m 2 The area demand factor is 0.17, the annual growth rate of the base load is 1%, the base load characteristics are the same as those of fig. 6, the number of building units and the building area are shown in table 3, the base load peak value of each node of the residential area is calculated, and the required capacity of each node is planned once, and the result is shown in table 4.
TABLE 3 planning and construction conditions of residential buildings in newly built residential areas
Table 4 saturation year (2035) new residential base load peak and district capacity plan
Step 205: combining the prediction result of the electric vehicle holding quantity (step 201-step 202), the prediction result of the electric vehicle charging probability (step 203) and the calculation of the basic electric load (step 204), the prediction result of the working days and the double holiday loads of the newly built residential area 2030 can be obtained as shown in fig. 7; the charging load of the electric automobile in the newly built residential area is rapidly increased, and the charging load of the working day is higher than the peak value of the charging load of the double holidays, so that the working day data is used as the basis of the planning data of the charging nodes of the electric automobile.
Step 206: the saturation year (2035 year) peak load density calculation is carried out on the residential area by taking 300m times 300m as a power supply grid, the grid division is as shown in fig. 8, and load nodes on grid junctions are subjected to grid attribution on the principle of rightward and upward. The calculation result of the peak load density of the residential area grid is shown in table 5; and selecting a grid of the area 1 as a 10kV access node of the residential power distribution network according to the load density calculation result and the actual 10kV line planning, wherein the coordinates are (0, 0).
Table 5 peak load density of saturation year (2035 year) power grid for newly built residential area
Step 207: substituting the saturated annual basic load peak value data, charging load data, node information and the like of the residential area into a newly built residential area collaborative optimization construction model, wherein the line parameters to be selected are shown in table 6; setting information such as line construction cost, line plan service life and the like as table 7; setting the initial population N as 100, the iteration times as 100, the shift probability as 0.7, the shift method as shown in figure 9, the reassignment probability as 0.3 and the reassignment method as shown in figure 10; and solving a collaborative optimization construction mathematical model by adopting a Prim algorithm and a single parent genetic algorithm based on tree structure coding, wherein the scheme for solving the most economical topology and line selection is shown in fig. 11, the convergence process is shown in fig. 12, and specific construction data is shown in table 8.
TABLE 6 Cable parameters and construction costs
TABLE 7 construction cost and construction service life
Table 8 new residential economic optimum topology construction data
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The invention can collect the electric vehicle charging facility information and the power distribution network cooperative information, is convenient for users to review, and effectively improves the topology and line type selection efficiency. The method can also collect and store the electric vehicle charging facility and power distribution network collaborative optimization construction data efficiently and process the data, process configuration can be realized based on electric vehicle charging facility information and power distribution network collaborative information, and the whole topology and line model selection process is described by using a multidimensional space. The precision and the accuracy of the topology and the line model selection are improved, hidden dangers of the topology and the line model selection are timely found and early warning is carried out, so that the coordination level and the efficiency of the topology and the line model selection are improved, and timeliness and scientificity of the overall process supervision, management and control of the topology and the line model selection are realized.
The electric vehicle charging facility and distribution network collaborative optimization construction method of the present invention is the units and algorithm steps of each example described in connection with the embodiments disclosed herein, and can be implemented in electronic hardware, computer software, or a combination of both, and to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
As will be readily understood by those skilled in the art from the description of the above embodiments, the method for collaborative optimization construction of an electric vehicle charging facility and a power distribution network described herein may be implemented by software, or may be implemented by combining software with necessary hardware. Accordingly, the technical solution according to the disclosed embodiments of the method for collaborative optimization construction of an electric vehicle charging facility and a power distribution network may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the indexing method according to the disclosed embodiments.
Those skilled in the art will appreciate that aspects of the method of collaborative optimization construction of an electric vehicle charging facility and a power distribution network may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. 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 invention. Thus, the present invention 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 (10)
1. The method for collaborative optimization construction of the electric vehicle charging facility and the power distribution network is characterized by comprising the following steps:
s101, predicting the electric vehicle holding quantity of a new residential area based on the historical data of the holding quantity of thousands of private vehicles in the city of the new residential area and the planning and construction data of the new residential area;
s102, acquiring travel data of urban private cars and performance parameters of the electric cars, and predicting the charging probability of the electric cars in the residential area by combining a Monte Carlo method with the prediction result in S101;
s103, predicting the conventional residential electrical loads of each unit building of a newly built residential area year by year, and planning the distribution and transformation capacity required by each load node once according to the prediction result;
s104, calculating the working day and the double holiday load prediction result of the newly built residential area planning year by combining the electric vehicle holding quantity prediction result, the electric vehicle charging probability prediction result and the basic electric load calculation, and selecting data adopted by planning according to the result;
s105, carrying out power supply grid division on a newly built residential area, carrying out peak load density calculation, and selecting the position of a 10kV access node of a power distribution network of the residential area according to a load density calculation result and a 10kV line plan;
s106, substituting the saturated annual basic load peak value data, the charging load data, the node information and the line parameters to be selected of the newly built residential area into a collaborative optimization construction model of the newly built residential area, setting construction planning data such as line construction cost, line plan service life and the like, and solving the collaborative optimization construction mathematical model by adopting a Prim algorithm and a single parent genetic algorithm based on tree structure coding in combination with tide calculation to form a topology and line model selection scheme;
s107, substituting the saturated annual basic load peak value data, the charging load data, the node information and the line parameters to be selected of the newly built residential area into a collaborative optimization construction model of the newly built residential area, setting construction planning data such as line construction cost, line plan service life and the like, and archiving and storing according to the received IP address and data type;
s108, constructing a data layout diagram in the step S107, substituting the saturated annual basic load peak value data, the charging load data, the node information and the line parameters to be selected of the newly built residential area in the data layout diagram into a newly built residential area collaborative optimization construction model, and setting construction planning data such as line construction cost, line plan service life and the like to update in real time;
s109, configuring data and an operation interface of a topology and line type selection scheme, so that a user adds data which is not stored or configured in the system; or the stored data and topology and line selection schemes are modified or deleted.
2. The method for collaborative optimization construction of electric vehicle charging facilities and a power distribution network according to claim 1, characterized in that,
and fitting the urban private car growth rate by adopting a least square method to obtain urban private car growth rate prediction model parameters, and obtaining the residential electric car holding quantity according to a Bass model and the newly built residential occupancy change.
3. The method for collaborative optimization construction of electric vehicle charging facilities and a power distribution network according to claim 2, characterized in that,
fitting the thousand conservation quantity acceleration of the urban private car by adopting a nonlinear least square method, wherein the function of a fitting curve can be expressed as follows:
wherein: y (t) is the holding quantity of thousands of private cars at t time, and the unit is: a vehicle; a. b and c are constants;
then, in the case where the occupancy is 1, the predicted number of electric vehicles in the t-th year using the bas model may be expressed as:
wherein G (t) is the integral number of the electric vehicles accumulated to the t year; g (t) is the number of electric vehicles newly added in the t th year; m is the maximum capacity upper limit of the electric automobile; p is an external influence coefficient, and represents the influence degree of external media propaganda on the diffusion of the electric automobile, and the value of the external media propaganda is 0.01-0.03; q is an internal influence coefficient, and represents the influence degree of internal public praise propagation on the diffusion of the electric automobile, and the value is 0.3-0.7;
finally, the new residential area shows a rapid increasing trend of the occupancy rate, and the actual holding quantity of the electric automobile per year is as follows:
O(t)＝G(t)λ(t)
wherein: o (t) is the electric automobile holding quantity of the new residential area in the t year, and the unit is: a vehicle; lambda (t) is the occupancy rate of the newly built residential area in the t-th year.
4. The method for collaborative optimization construction of electric vehicle charging facilities and a power distribution network according to claim 1, characterized in that,
the method comprises the following steps of predicting daily charging load and charging train number of electric vehicles in a residential area by adopting a Monte Carlo method:
probability distribution density function of electric automobile at end time of working day with working trip as purpose:
in sigma w Standard deviation sigma of working end time for working day w ＝1.747；μ w Mean value of working end time, mu for working day w ＝17.3；
The probability distribution density function of the electric automobile at the end time of social shopping behavior on the working day:
wherein alpha is 1 、β 1 Is a proportionality coefficient alpha 1 ＝0.3840，β 1 ＝0.59；σ 1 、σ 2 Standard deviation sigma of travel ending time of social shopping on workday 1 ＝2.32，σ 2 ＝2.575；μ 1 、μ 2 Mean value mu of end time of social shopping trip on workday 1 ＝12，μ 2 ＝18.2；
Probability distribution density function of electric car at the end time of double holidays for social shopping trip purpose:
wherein alpha is 2 、β 2 Is a proportionality coefficient alpha 2 ＝0.302，β 2 ＝0.6395；σ 3 、σ 4 Travel for social shopping on double holidaysStandard deviation of end time sigma 3 ＝2，σ 4 ＝3.2；μ 3 、μ 4 Mean value mu of travel ending time of social shopping on double holidays 3 ＝11.6，μ 4 ＝17；
Mileage probability distribution density function for single trip of electric automobile:
wherein d is a single trip driving mileage, and the unit is km; mu (mu) D Is the desire for ln d; sigma (sigma) D Standard deviation of ln d; the average value of the single trip mileage in the workday is 11.4km, and the standard deviation is 4.88km; the average value of the single trip mileage on double holidays is 13.2km, and the standard deviation is 5.23km;
electric automobile charging start SOC:
in SOC i Charging an ith electric automobile to initiate a state of charge; d, d k The travel mileage of the kth trip; n is the total number of times of the electric automobile going out; d, d full The maximum driving mileage after the battery of the electric automobile is fully charged;
electric automobile charge duration:
wherein t is c The charging time of the electric automobile is h; p (P) c The charging power is kW; e is the battery capacity of the electric automobile, and the unit is kW.h;
charging probability of residential electric automobile at 24 times of day:
wherein D (t) is the charging probability at time t; t (T) kt The state of charge at time t for the kth random sample.
5. The method for collaborative optimization construction of electric vehicle charging facilities and a power distribution network according to claim 1, characterized in that,
the Monte Carlo method for predicting daily charging load and charging number of electric vehicles in newly built residential areas comprises the following steps:
step 11: determining the battery capacity, the maximum driving mileage and the charging power of the electric vehicle;
step 12: according to the travel type and travel rule, extracting N random samples at the charging start time, and putting the samples into an array T s ；
Step 13: according to single trip mileage distribution and resident charging habits, extracting N accumulated trip mileage samples, calculating the charging time corresponding to each sample, and putting the charging time corresponding to each sample into an array T c ；
Step 14: array T of samples s And T c Judging the random samples in the database to obtain the charging state of each group of random samples within 24 hours in 1 day, and putting the charging state corresponding to each group of samples into an array T;
step 15: calculating the charging probability at each moment;
step 16: cycling the steps 12-15 for N times to obtain N groups of charging probability data;
step 17: and averaging the values of the N groups of data at the same time to obtain the final charging probability of the electric automobile in the residential area.
6. The method for collaborative optimization construction of electric vehicle charging facilities and a power distribution network according to claim 1, wherein residential buildings in urban residential areas can be regarded as a base load node, and the base load prediction can be carried out by adopting a load density method;
the new residential area is built with the target annual base load of
Wherein: p (P) base (i) The method comprises the steps of setting a target annual base load for an ith base load node of a newly built residential area, wherein the unit is kW; a is that house (i) The resident housing area for the ith base load node is given in m 2 ；D H (i) The unit is W/m for the load density index of the ith base load node 2 ；K Area (i) The area demand coefficient for the ith base load node; lambda is the occupancy rate of the residential area in the target year; n (N) base The number of basic load nodes for the newly built residential area;
and selecting the load density and area demand coefficient of all the unit buildings according to the electricity utilization characteristics of the urban residential area, calculating the conventional load of the newly built residential area, calculating the basic load peak value of each node of the residential area by combining the number of the units and the building area, and planning the required capacity of each node at one time.
7. The method for collaborative optimization construction of an electric vehicle charging facility and a power distribution network according to claim 1, wherein in the power grid, the farther the load is from an outgoing line point, the greater the network loss is caused by line impedance, so that the outgoing line point is located in a region with higher load density;
the load density of different power supply grids of newly built residential areas is
Wherein: d (k) is the load density of the power grid k, in kW/km 2 ；P base The power utilization load of the basic load node in the power supply grid k is in kW; p (P) ev The power utilization load of the charging node of the electric automobile in the power supply grid k is in kW; a (k) is the area of the power supply grid k, and the unit is km 2 。
8. The electric vehicle charging facility and power distribution network collaborative optimization construction method according to claim 1, characterized in that annual comprehensive cost of power distribution network construction cost and network loss cost is taken as an objective function, and power distribution network line capacity and power distribution network node voltage are taken as constraint conditions to establish a residential power distribution network collaborative optimization construction mathematical model;
(1) The annual comprehensive cost of the construction cost of the power distribution network and the network loss cost is taken as an objective function:
wherein: f is the annual comprehensive cost of the newly built residential area, and the unit is: ten thousand yuan/year; i is the construction cost of newly built residential area lines, and the unit is: ten thousand yuan; r is (r) 0 Is the discount rate; y is the economic service life of the circuit, and the unit is: years of life; l is the line loss cost of the newly built residential area, and the unit is: ten thousand yuan/year;
the line construction of the newly built residential area adopts different line types to reduce the cost, and the line construction cost of the newly built residential area is as follows:
wherein: l (L) k The unit is km for the length of the line k;
the cross-sectional area of the line k is D k The unit of construction cost is Yuan/km; n (N) k A newly built line set;
the network loss cost of the newly built residential area line is as follows:
wherein: alpha is electricity price, and the unit is: meta/kWh; p (P) k The unit is the active power flowing on line k: kW; q (Q) k The unit is kVar for reactive power flowing on line k; u is voltage, and the unit is: a kV;
taking D for the cross-sectional area of line k k The unit length resistance is as follows: omega/km; τ is the number of annual maximum load utilization hours;
(2) The power distribution network line capacity and the power distribution network node voltage are taken as constraint conditions:
in the running process of the power distribution network, the power flowing through each planned line should be smaller than the capacity of the planned line, namely:
wherein:
the maximum capacity of the kth line of the power distribution network;
the radial topological structure of the power distribution network easily causes serious voltage drop of the terminal nodes, and cannot meet the normal electricity demand; the node voltage of the newly built residential area distribution network is constrained, namely:
U min ≤U n ≤U max
wherein: u (U) min The node voltage of the power distribution network is lower than the voltage limit; u (U) max The upper voltage limit of the node of the power distribution network is set; u (U) n The lower limit of the node voltage of the nth node of the power distribution network is a per unit value.
9. The method for collaborative optimization construction of electric vehicle charging facilities and a power distribution network according to claim 1, characterized in that,
(1) Generating an initial power distribution network topological structure by adopting a Prim minimum spanning tree algorithm; the key steps of Prim algorithm are as follows:
step 21: inputting the coordinate position of each load node of the power distribution network and inputting a network topology starting node;
step 22: establishing a set S for storing connected load nodes, a set V for storing unconnected load nodes, a set dist for storing the distance between each node of the set S and each node of the set V, and a set line for storing selected lines;
step 23: updating the set S, V, setting aside the set dist, calculating the line distance between each node of the set S and each node of the set V, and storing the line distance to the set dist;
step 24: selecting the shortest line distance in the set dist as a next connection line of the topology, adding the selected line to the set line, and adding the selected load node from the set V to the set S;
step 25: judging whether the set V is an empty set, if so, ending and outputting a result; if not, returning to the step 23;
(2) After an initial grid structure is generated, optimizing by adopting a single parent genetic algorithm based on tree structure coding; the method for the single parent genetic algorithm based on the tree structure coding mainly comprises the following steps:
step 31: inputting initial population (N individuals), shift probability e, iteration number m and the like;
step 32: calculating fitness of each body (the fitness of the invention takes the objective function value as fitness), and storing the optimal individuals of the present generation and the previous generation;
step 33: judging whether the iteration condition is met, if so, outputting an optimal individual, and if not, executing the step 34;
step 34: judging whether all individuals finish execution, if yes, returning to the step 33, and if not, executing the step 35;
step 35: randomly selecting individual nodes, and generating a shift probability r according to the setting;
step 36: judging whether the shift probability r is smaller than or equal to e, if so, executing the shift operation, and if not, executing the reassignment operation;
step 37: fitness calculation and roulette individual update are performed, returning to step 34.
10. The method for collaborative optimization construction of electric vehicle charging facilities and power distribution networks according to claim 1, wherein newly built residential area data is input, and power system tide calculation is combined to obtain the power distribution network frame topology and line type selection with the optimal newly built residential area economy.
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