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CN113268815A - Power distribution network double-layer planning method, device, equipment and storage medium - Google Patents

Power distribution network double-layer planning method, device, equipment and storage medium Download PDF

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CN113268815A
CN113268815A CN202110707813.4A CN202110707813A CN113268815A CN 113268815 A CN113268815 A CN 113268815A CN 202110707813 A CN202110707813 A CN 202110707813A CN 113268815 A CN113268815 A CN 113268815A
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distribution network
charging
load curve
electric vehicle
planning
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林晓明
钱斌
肖勇
罗欣儿
田杰
杜进桥
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China South Power Grid International Co ltd
Shenzhen Power Supply Bureau Co Ltd
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Shenzhen Power Supply Bureau Co Ltd
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Abstract

本申请公开了一种配电网双层规划方法、装置、设备和存储介质，其中方法包括：获取待规划配电网对应的第一负荷曲线和电动汽车信息；根据所述电动汽车信息，模拟各电动汽车对应的充电负荷曲线；叠加所述第一负荷曲线和所述充电负荷曲线，得到第二负荷曲线；基于所述第二负荷曲线，根据机会约束方法构建用于配电网规划的双层优化模型；求解所述双层优化模型，得到所述待规划配电网的规划结果。解决了现有的配电网规划方法无法基于电动汽车负荷对配电网进行规划的技术问题。

Figure 202110707813

The present application discloses a method, device, equipment and storage medium for double-layer planning of a distribution network, wherein the method includes: acquiring a first load curve and electric vehicle information corresponding to the distribution network to be planned; The charging load curve corresponding to each electric vehicle; the second load curve is obtained by superimposing the first load curve and the charging load curve; based on the second load curve, a dual-control method for distribution network planning is constructed according to the opportunity constraint method. layer optimization model; solve the two-layer optimization model to obtain the planning result of the distribution network to be planned. The technical problem that the existing distribution network planning method cannot plan the distribution network based on the electric vehicle load is solved.

Figure 202110707813

Description

Power distribution network double-layer planning method, device, equipment and storage medium
Technical Field
The application relates to the technical field of power distribution network analysis, in particular to a power distribution network double-layer planning method, device, equipment and storage medium.
Background
The distribution network is a network which receives electric energy from a transmission network or a regional power plant, distributes the electric energy to various users on site through distribution facilities or distributes the electric energy to the electric power networks step by step according to voltage, and plays a role of distributing the electric energy in the electric power networks.
With the rapid increase of the electric automobile holding capacity in cities, the load bearing of the existing power distribution network is a serious challenge, and the planning of the power distribution network containing electric automobiles becomes a research hotspot in the background. Because the time and the space of the electric vehicle load access to the power distribution network are uncertain, the conventional power distribution network planning method cannot plan the power distribution network based on the electric vehicle load.
Disclosure of Invention
In view of this, the application provides a power distribution network double-layer planning method, device, equipment and storage medium, when planning a power distribution network, uncertainty of an electric vehicle is considered, the power distribution network is accurately planned, and the technical problem that the power distribution network cannot be planned based on electric vehicle loads in the existing power distribution network planning method is solved.
The application provides in a first aspect a power distribution network double-layer planning method, including:
acquiring a first load curve and electric vehicle information corresponding to a power distribution network to be planned;
simulating a charging load curve corresponding to each electric automobile according to the electric automobile information;
superposing the first load curve and the charging load curve to obtain a second load curve;
constructing a double-layer optimization model for planning the power distribution network according to an opportunity constraint method based on the second load curve;
and solving the double-layer optimization model to obtain a planning result of the power distribution network to be planned.
Optionally, according to the electric vehicle information, a charging load curve corresponding to each electric vehicle is simulated, and the method specifically includes:
acquiring a charging demand model corresponding to the electric automobile according to the electric automobile information;
calculating the corresponding charging required time according to the daily driving mileage corresponding to the electric automobile and the charging demand model;
and determining a charging load curve corresponding to the electric automobile according to the initial charging time, the charging power and the required charging time corresponding to the electric automobile.
Optionally, according to the electric vehicle information, a charging demand model corresponding to the electric vehicle is acquired, and the method specifically includes:
acquiring automobile types corresponding to the electric automobiles from the automobile information;
and acquiring a charging demand model corresponding to the automobile type based on the corresponding relation between the automobile type and the charging demand model according to the automobile type.
Optionally, the car types include: electric buses, electric taxis and electric private cars.
Optionally, an objective function of an upper-layer optimization planning model in the two-layer optimization model is:
Figure BDA0003132025880000021
in the formula, CTTo make a distribution transformation full lifecycle cost, CIFor initial investment costs of distribution transformers, CWFor distribution transformation operating loss cost, COFor distribution transformer maintenance cost, CFFor distribution transformation fault costs, CDFor distribution transformation decommissioning disposal costs, XTFor alternative distribution network transformer types, STNThe capacity of the transformer of the alternative distribution network.
Optionally, an objective function of a lower-layer optimized planning model in the two-layer optimized model is:
Figure BDA0003132025880000022
in the formula, f is the average active power equivalently injected at the low-voltage side of the distribution network transformer,
Figure BDA0003132025880000023
and equivalently injecting active power to the low-voltage side of the distribution network transformer in the ith time period of the ith year.
This application second aspect provides a distribution network double-deck planning device, includes:
the acquiring unit is used for acquiring a first load curve and electric vehicle information corresponding to the power distribution network to be planned;
the simulation unit is used for simulating a charging load curve corresponding to each electric automobile according to the electric automobile information;
the overlapping unit is used for overlapping the first load curve and the charging load curve to obtain a second load curve;
the construction unit is used for constructing a double-layer optimization model for planning the power distribution network according to an opportunity constraint method based on the second load curve;
and the solving unit is used for solving the double-layer optimization model to obtain a planning result of the power distribution network to be planned.
Optionally, the simulation unit specifically includes:
the obtaining subunit is used for obtaining a charging demand model corresponding to the electric automobile according to the electric automobile information;
the calculating subunit is used for calculating the corresponding required charging time according to the daily driving mileage corresponding to the electric automobile and the charging demand model;
and the determining subunit is used for determining a charging load curve corresponding to the electric automobile according to the initial charging time, the charging power and the required charging time corresponding to the electric automobile.
The third aspect of the application provides a power distribution network double-layer planning device, which comprises a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the power distribution network double-layer planning method according to the first aspect according to instructions in the program code.
A fourth aspect of the present application provides a storage medium, where the storage medium is used to store program codes, and the program codes are used to execute the power distribution network double-layer planning method according to the first aspect.
According to the technical scheme, the method has the following advantages:
the application provides a power distribution network double-layer planning method which includes the steps of firstly obtaining a first load curve and electric vehicle information corresponding to a power distribution network to be planned, then simulating a charging load curve corresponding to each electric vehicle according to the electric vehicle information, then superposing the first load curve and the charging load curve to obtain a second load curve, then building a double-layer optimization model for planning the power distribution network according to an opportunity constraint method based on the second load curve, and finally solving the double-layer optimization model to obtain a planning result of the power distribution network to be planned. When the power distribution network is planned, uncertainty of the electric automobile is considered when the power distribution network is planned, and the power distribution network is planned by combining electric automobile information of the electric automobile, so that the technical problem that the power distribution network cannot be planned based on electric automobile loads in the conventional power distribution network planning method is solved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a power distribution network double-layer planning method according to a first embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a power distribution network double-layer planning method according to a second embodiment of the present disclosure;
FIG. 3 is a daily charge load curve for an electric bus;
FIG. 4 is a daily charging load curve for an electric taxi;
FIG. 5 is a daily charge load curve for an electric private car;
fig. 6 is a schematic structural diagram of a power distribution network double-layer planning apparatus in an embodiment of the present application.
Detailed Description
The embodiment of the application provides a power distribution network double-layer planning method, a power distribution network double-layer planning device, power distribution network double-layer planning equipment and a storage medium, wherein when the power distribution network is planned, uncertainty of an electric automobile is considered, the power distribution network is accurately planned, and the technical problem that the power distribution network cannot be planned based on electric automobile loads in the conventional power distribution network planning method is solved.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The first aspect of the embodiment of the application provides an embodiment of a power distribution network double-layer planning method.
Referring to fig. 1, a schematic flow chart of a first embodiment of a power distribution network double-layer planning method in an embodiment of the present application includes:
step 101, obtaining a first load curve and electric vehicle information corresponding to a power distribution network to be planned.
It is understood that the first load curve is a conventional load curve in which the power distribution network to be planned does not include electric vehicle loads.
And 102, simulating a charging load curve corresponding to each electric automobile according to the electric automobile information.
After the electric vehicle information corresponding to the power distribution network to be planned is obtained, a charging load curve corresponding to the electric vehicle can be simulated based on the electric vehicle information. The charging load curve of the electric automobile in the region is reflected more truly in a load simulation mode.
And 103, overlapping the first load curve and the charging load curve to obtain a second load curve.
Because the charging load curve is obtained based on a simulation mode, the second load curve of the corresponding area of the power distribution network to be analyzed, which is obtained based on the charging load curve and the first load curve, is more real and closer to reality.
And 104, constructing a double-layer optimization model for planning the power distribution network according to an opportunity constraint method based on the second load curve.
It can be understood that after the second load curve corresponding to the power distribution network to be analyzed is obtained, a double-layer optimization model for power distribution network planning can be constructed according to an opportunity constraint method based on the second load curve. Because the charging load curve of the electric automobile is superposed in the second load curve, the electric automobile is also considered in the double-layer optimization model constructed according to the second load curve, and the uncertainty of the load of the electric automobile accessing the power distribution network is considered. Meanwhile, a double-layer optimization model for power distribution network planning is constructed based on an opportunity constraint method, and a more reasonable optimization scheme can be provided for power distribution network planning.
And 105, solving the double-layer optimization model to obtain a planning result of the power distribution network to be planned.
After a double-layer optimization model for planning the power distribution network is obtained, a planning result corresponding to the power distribution network to be planned can be obtained by solving the model.
In the embodiment, a first load curve and electric vehicle information corresponding to a power distribution network to be planned are obtained, a charging load curve corresponding to each electric vehicle is simulated according to the electric vehicle information, the first load curve and the charging load curve are superposed to obtain a second load curve, a double-layer optimization model for planning the power distribution network is constructed according to an opportunity constraint method based on the second load curve, and finally the double-layer optimization model is solved to obtain a planning result of the power distribution network to be planned. When the power distribution network is planned, uncertainty of the electric automobile is considered when the power distribution network is planned, and the power distribution network is planned by combining electric automobile information of the electric automobile, so that the technical problem that the power distribution network cannot be planned based on electric automobile loads in the conventional power distribution network planning method is solved.
The foregoing is a first embodiment of a power distribution network double-layer planning method provided in this application, and the following is a second embodiment of the power distribution network double-layer planning method provided in this application.
Referring to fig. 2, a schematic flow chart of a second embodiment of a power distribution network double-layer planning method in the embodiment of the present application includes:
step 201, obtaining a first load curve and electric vehicle information corresponding to a power distribution network to be planned.
It is understood that, in one embodiment, the electric vehicle information includes: the types of the electric vehicles and the number of the vehicles corresponding to each type of the electric vehicles are as follows, and specific electric vehicle information is as shown in the following table 1:
TABLE 1
Type of electric vehicle Number of cars
Electric bus 80
Electric taxi 200
Electric private car 450
It should be noted that, while the first load curve and the electric vehicle information are acquired, distribution transformer information corresponding to the distribution network to be planned also needs to be acquired. The capacity models of the distribution transformer are shown in the following table 2:
TABLE 2
Distribution network transformer model Rated capacity/kVA of transformer of power distribution network
S13 30,50,100,200,315,400,500,630
SCB10 80,100,125,160,200,250,315,400,500,630,800,1000
It can be understood that, because the model is subsequently constructed and solved, other parameters need to be obtained, and specifically, the other parameters are as shown in table 3 below:
TABLE 3
Figure BDA0003132025880000061
Figure BDA0003132025880000071
Step 202, acquiring a charging demand model corresponding to the electric automobile according to the electric automobile information.
It can be understood that, in an embodiment, obtaining, according to the information of the electric vehicle, a charging demand model corresponding to the electric vehicle specifically includes:
acquiring automobile types corresponding to the electric automobiles from the automobile information;
and acquiring a charging demand model corresponding to the automobile type based on the corresponding relation between the automobile type and the charging demand model according to the automobile type.
Further, the above-mentioned automobile types include: electric buses, electric taxis and electric private cars.
And 203, calculating the corresponding charging required time according to the daily driving mileage corresponding to the electric automobile and the charging demand model.
It can be understood that the daily mileage of the electric vehicles of different vehicle types is different, and specifically, the mathematical model of the daily mileage distribution corresponding to the electric bus is as follows:
Figure BDA0003132025880000072
wherein s is the daily mileage of the electric bus, mus1And σs1Respectively is the average value and the standard deviation of the daily driving mileage of the electric bus.
The mathematical model of daily driving mileage distribution corresponding to the electric taxi is as follows:
Figure BDA0003132025880000073
in the formula, mus2And σs2Respectively is the average value and the standard deviation of the daily driving mileage of the electric taxi.
The mathematical model of daily mileage distribution corresponding to the electric private car is as follows:
Figure BDA0003132025880000074
in the formula, mus3And σs3Respectively representing the logarithmic mean value and the logarithmic standard deviation of the daily driving mileage of the electric private car, and Ins is the logarithmic value of the daily driving mileage of the electric private car.
And 204, determining a charging load curve corresponding to the electric automobile according to the initial charging time, the charging power and the required charging time corresponding to the electric automobile.
And obtaining the charging end time of the electric automobile according to the initial charging time and the required charging time of the electric automobile, and obtaining a charging load curve in the charging process according to the charging power (the charging load is the charging power in the time period of starting charging and ending charging).
It is understood that the starting charging time of the electric vehicles of different vehicle types is different, and specifically, the probability model of the starting charging time of the electric bus is shown in the following table 4:
TABLE 4
Figure BDA0003132025880000081
The probability model of the initial charging time corresponding to the electric taxi is shown in the following table 5:
TABLE 5
Figure BDA0003132025880000082
The probability model of the initial charging time corresponding to the electric private car is shown in the following table 6:
TABLE 6
Figure BDA0003132025880000083
Figure BDA0003132025880000091
In the formula, anIs a mixing ratio coefficient, satisfies
Figure BDA0003132025880000092
μnMathematical expectation for daily mileage of electric private car, sigmanThe standard deviation of the daily driving mileage of the electric private car is shown.
It is understood that the steps 202 to 204 can be realized by a monte carlo simulation method, and the specific steps are as follows:
1) inputting system data, comprising: the charging power, the standard power consumption, the initial charging time and the probability model and the retention amount of the daily driving mileage of the electric automobile of each automobile type.
2) The number of times of initialization simulation M is 10000. Meanwhile, let m be 1, m is the number of times of simulation;
3) and initializing an electric vehicle holding quantity predicted value N. Meanwhile, let n be 1, n is the nth electric automobile to be sampled and simulated;
4) judging the automobile type of the nth electric automobile, and extracting the initial charging time and the daily driving mileage of the type of electric automobile by utilizing a Monte Carlo simulation method according to the charging demand models of the electric automobiles with different automobile types; combining the daily driving mileage obtained by random sampling with a charging demand model of the electric automobile, and calculating the time length required by charging according to the electric automobile; and obtaining a charging load curve of the electric automobile of the automobile through the sampled initial charging time, charging power and charging required time.
It can be understood that the charging load curve is calculated according to the daily driving mileage of the electric vehicle, that is, a daily charging load curve, in this embodiment, a daily charging load curve corresponding to the electric bus is shown in fig. 3, a daily charging load curve corresponding to the electric taxi is shown in fig. 4, and a daily charging load curve corresponding to the electric private car is shown in fig. 5.
And step 205, superposing the first load curve and the charging load curve to obtain a second load curve.
And during specific superposition, superposing (directly adding) the first load active curve and the charging load active curve at the same moment in the area to obtain a second load curve.
And step 206, constructing a double-layer optimization model for planning the power distribution network according to an opportunity constraint method based on the second load curve.
It should be noted that the decision variables of the upper-layer optimization planning model in the double-layer optimization model are the distribution network transformer model XT and the distribution network transformer rated capacity STN, the objective function is the minimum distribution network transformer LCC (full life cycle cost), and the objective function is:
Figure BDA0003132025880000101
in the formula, CTTo make a distribution transformation full lifecycle cost, CIFor initial investment costs of distribution transformers, CWFor distribution transformation operating loss cost, COFor distribution transformer maintenance cost, CFFor distribution transformation fault costs, CDFor distribution transformation decommissioning disposal costs, XTFor alternative distribution network transformer types, STNThe capacity of the transformer of the alternative distribution network.
The constraint conditions corresponding to the upper-layer optimization planning model comprise configuration constraints of the model number and the capacity of the power distribution network transformer:
Figure BDA0003132025880000102
in the formula, A is a model set of alternative distribution network transformers; and B is a capacity set of the alternative distribution network transformer.
The objective function of a lower-layer optimization planning model in the double-layer optimization model is that the active variance of equivalent load injection on the low-voltage side of the power distribution network transformer is minimum in the ith year, and the corresponding specific form is as follows:
Figure BDA0003132025880000103
in the formula, f is the average active power equivalently injected at the low-voltage side of the distribution network transformer,
Figure BDA0003132025880000104
and equivalently injecting active power to the low-voltage side of the distribution network transformer in the ith time period of the ith year.
The constraint conditions corresponding to the lower-layer optimization planning model are as follows:
(1) power distribution network transformer low-voltage side voltage opportunity constraint
Considering the influence of load and electric automobile access uncertainty, in order to avoid the voltage of the low-voltage side of the distribution network transformer from having a large voltage out-of-limit risk, the lower-layer optimized operation model sets the voltage opportunity constraint of the low-voltage side of the distribution network transformer, and the method specifically comprises the following steps:
Figure BDA0003132025880000111
in the formula (I), the compound is shown in the specification,
Figure BDA0003132025880000112
the voltage value of the low-voltage side of the distribution network transformer is the ith time period of the ith year; vT2.max、VT2.minThe upper limit value and the lower limit value of the voltage at the low-voltage side of the transformer of the power distribution network are respectively set; beta is aVAnd for the confidence coefficient of the voltage at the low-voltage side of the distribution network transformer, Pr {. cndot.) represents the probability of the event establishment. According to the national standard, the voltage tolerance is controlled to be +/-7% of the rated value. According to the voltage drop formulaCalculating the voltage of the low-voltage side of the transformer of the power distribution network, specifically as follows:
Figure BDA0003132025880000113
in the formula (I), the compound is shown in the specification,
Figure BDA0003132025880000114
the voltage value of the high-voltage side of the distribution network transformer in the ith time period of the ith year,
Figure BDA0003132025880000115
for the loaded active power of the ith year in the t-th period,
Figure BDA0003132025880000116
for the electric automobile load output value in the t time period of the jth type weather scene,
Figure BDA0003132025880000117
is the reactive power of the load in the ith time period of the ith year, kTTransformation ratio of a transformer of the power distribution network; r isT、xTRespectively the resistance and reactance value converted to the high voltage side.
(2) Distribution network transformer load rate opportunity constraints
Figure BDA0003132025880000118
In the formula, Pessi,j,tThe energy storage charging and discharging power is the energy storage charging and discharging power of the ith year and jth class weather scene at the t time period; sTNRated capacity of a transformer of the power distribution network; i ismaxThe load factor is the upper limit value of the transformer load factor of the power distribution network; beta is aIThe confidence coefficient of the load rate of the transformer of the power distribution network is obtained; eta is annual load growth rate; n is the upper limit value of the allowable load growth years; m is the total number of weather scenes; l isTThe service life of the transformer of the power distribution network is prolonged; superscript "-" represents a random variable; pr {. cndot } represents the probability that the event holds.
(3) Charge and discharge power constraint of cluster electric automobile
Figure BDA0003132025880000119
In the formula, PEVCS-C,iFor the AC charging power of the electric vehicles, PEVCS-D，iAnd D, the direct current charging power is supplied to the cluster electric automobile.
(4) Upper and lower limit constraints of energy storage SOC
SOCmin≤SOCi,j,t≤SOCmax
In the formula, SOCi,j,tThe State of Charge (SOC) of the energy storage in the ith year at the t-th time period, namely the ratio of the residual energy storage capacity to the rated capacity of the energy storage; SOCmax、SOCminRespectively an upper limit value and a lower limit value of the energy storage SOC.
(5) Equality constraint of residual capacity of stored energy
Figure BDA0003132025880000121
In the formula, Eessi，j，tThe residual capacity of stored energy in the t-th period of i year, Eessi，j，t-1Is the residual capacity of stored energy in the t-1 th time period of i year, Pessci，j，tCharging power for energy storage, Pessdi，j，tFor storing discharge power, ηc、ηdRespectively charging and discharging efficiency of energy storage; and delta t is a charge-discharge time interval.
(6) Energy storage charge-discharge balance constraint
Figure BDA0003132025880000122
And step 207, solving the double-layer optimization model to obtain a planning result of the power distribution network to be planned.
After the use scene of the transformer is determined, different load conditions need to be considered, and reasonable indoor distribution transformer capacity is selected to achieve the lowest LCC configuration target. The comprehensive simulation result analysis gives a recommended capacity table of the average distribution transformer of the user only considering the influence of the load fluctuation, which is shown in the following table 7:
TABLE 7
Type of load Oil change S13 Dry-change SCB10
Load of residents 2.8-5.0 2.8-3.5
Public load 2.8-5.0 2.8-3.5
Merchant load 3.1-6.7 3.1-4.2
In the embodiment, a first load curve and electric vehicle information corresponding to a power distribution network to be planned are obtained, a charging load curve corresponding to each electric vehicle is simulated according to the electric vehicle information, the first load curve and the charging load curve are superposed to obtain a second load curve, a double-layer optimization model for planning the power distribution network is constructed according to an opportunity constraint method based on the second load curve, and finally the double-layer optimization model is solved to obtain a planning result of the power distribution network to be planned. When the power distribution network is planned, uncertainty of the electric automobile is considered when the power distribution network is planned, and the power distribution network is planned by combining electric automobile information of the electric automobile, so that the technical problem that the power distribution network cannot be planned based on electric automobile loads in the conventional power distribution network planning method is solved.
The second aspect of the embodiment of the application provides an embodiment of a double-layer planning device for a power distribution network.
Referring to fig. 6, in an embodiment of the present application, a schematic structural diagram of a power distribution network double-layer planning apparatus includes:
the acquiring unit 601 is configured to acquire a first load curve and electric vehicle information corresponding to a power distribution network to be planned;
the simulation unit 602 is configured to simulate a charging load curve corresponding to each electric vehicle according to the electric vehicle information;
a superimposing unit 603, configured to superimpose the first load curve and the charging load curve to obtain a second load curve;
a constructing unit 604, configured to construct a double-layer optimization model for power distribution network planning according to an opportunity constraint method based on the second load curve;
and the solving unit 605 is configured to solve the double-layer optimization model to obtain a planning result of the power distribution network to be planned.
Further, the simulation unit 602 specifically includes:
the acquiring subunit is used for acquiring a charging demand model corresponding to the electric automobile according to the electric automobile information;
the calculating subunit is used for calculating the corresponding charging required time according to the daily driving mileage corresponding to the electric automobile and the charging demand model;
and the determining subunit is used for determining a charging load curve corresponding to the electric automobile according to the initial charging time, the charging power and the required charging time corresponding to the electric automobile.
Further, the acquiring subunit specifically includes:
the first obtaining subunit is used for obtaining the automobile type corresponding to each electric automobile from the automobile information;
and the second obtaining subunit is used for obtaining a charging demand model corresponding to the automobile type based on the corresponding relation between the automobile type and the charging demand model according to the automobile type.
Specifically, the automobile types include: electric buses, electric taxis and electric private cars.
Specifically, the objective function of the upper-layer optimization planning model in the two-layer optimization model is as follows:
Figure BDA0003132025880000141
in the formula, CTTo make a distribution transformation full lifecycle cost, CIFor initial investment costs of distribution transformers, CWFor distribution transformation operating loss cost, COFor distribution transformer maintenance cost, CFFor distribution transformation fault costs, CDFor distribution transformation decommissioning disposal costs, XTFor alternative distribution network transformer models, STNThe capacity of the transformer of the alternative power distribution network.
Optionally, the objective function of the lower-layer optimized planning model in the two-layer optimized model is:
Figure BDA0003132025880000142
in the formula, f is the average active power equivalently injected at the low-voltage side of the distribution network transformer,
Figure BDA0003132025880000143
and equivalently injecting active power to the low-voltage side of the distribution network transformer in the ith time period of the ith year.
In the embodiment, a first load curve and electric vehicle information corresponding to a power distribution network to be planned are obtained, a charging load curve corresponding to each electric vehicle is simulated according to the electric vehicle information, the first load curve and the charging load curve are superposed to obtain a second load curve, a double-layer optimization model for planning the power distribution network is constructed according to an opportunity constraint method based on the second load curve, and finally the double-layer optimization model is solved to obtain a planning result of the power distribution network to be planned. When the power distribution network is planned, uncertainty of the electric automobile is considered when the power distribution network is planned, and the power distribution network is planned by combining electric automobile information of the electric automobile, so that the technical problem that the power distribution network cannot be planned based on electric automobile loads in the conventional power distribution network planning method is solved.
The third aspect of the embodiments of the present application provides an embodiment of a power distribution network double-layer planning device.
A power distribution network double-layer planning device comprises a processor and a memory; the memory is used for storing the program codes and transmitting the program codes to the processor; the processor is configured to execute the power distribution network two-tier planning method of the first aspect according to instructions in the program code.
A fourth aspect of embodiments of the present application provides an embodiment of a storage medium.
A storage medium for storing program code for performing the power distribution network two-tier planning method of the first aspect.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of a unit is only one logical functional division, and there may be other divisions when the actual implementation is performed, for example, a plurality of units or components may be combined or may be integrated into another grid network to be installed, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (10)

1.一种配电网双层规划方法，其特征在于，包括：1. a double-layer planning method for distribution network, is characterized in that, comprises: 获取待规划配电网对应的第一负荷曲线和电动汽车信息；Obtain the first load curve and electric vehicle information corresponding to the distribution network to be planned; 根据所述电动汽车信息，模拟各电动汽车对应的充电负荷曲线；According to the electric vehicle information, simulate the charging load curve corresponding to each electric vehicle; 叠加所述第一负荷曲线和所述充电负荷曲线，得到第二负荷曲线；superimposing the first load curve and the charging load curve to obtain a second load curve; 基于所述第二负荷曲线，根据机会约束方法构建用于配电网规划的双层优化模型；Based on the second load curve, construct a two-layer optimization model for distribution network planning according to the chance constraint method; 求解所述双层优化模型，得到所述待规划配电网的规划结果。The two-layer optimization model is solved to obtain the planning result of the distribution network to be planned. 2.根据权利要求1所述的配电网双层规划方法，其特征在于，根据所述电动汽车信息，模拟各电动汽车对应的充电负荷曲线，具体包括：2. The method for double-layer planning of a distribution network according to claim 1, wherein, according to the electric vehicle information, simulating a charging load curve corresponding to each electric vehicle, specifically comprising: 根据所述电动汽车信息，获取电动汽车对应的充电需求模型；obtaining a charging demand model corresponding to the electric vehicle according to the electric vehicle information; 根据所述电动汽车对应的日行驶里程和所述充电需求模型，计算对应的充电所需时长；According to the daily driving mileage corresponding to the electric vehicle and the charging demand model, calculate the corresponding charging required time; 根据所述电动汽车对应的起始充电时间、充电功率和所述充电所需时长，确定所述电动汽车对应的充电负荷曲线。A charging load curve corresponding to the electric vehicle is determined according to the corresponding initial charging time, the charging power and the required charging time of the electric vehicle. 3.根据权利要求2所述的配电网双层规划方法，其特征在于，根据所述电动汽车信息，获取电动汽车对应的充电需求模型，具体包括：3. The method for double-layer planning of a distribution network according to claim 2, wherein, according to the electric vehicle information, acquiring a charging demand model corresponding to the electric vehicle, specifically comprising: 从所述汽车信息中，获取各电动汽车对应的汽车类型；From the vehicle information, obtain the vehicle type corresponding to each electric vehicle; 根据所述汽车类型，基于汽车类型和充电需求模型的对应关系，获取所述汽车类型对应的充电需求模型。According to the vehicle type, based on the corresponding relationship between the vehicle type and the charging demand model, a charging demand model corresponding to the vehicle type is acquired. 4.根据权利要求3所述的配电网双层规划方法，其特征在于，所述汽车类型包括：电动公交车、电动出租车和电动私家车。4 . The method for double-layer planning of a distribution network according to claim 3 , wherein the vehicle types include: electric buses, electric taxis and electric private cars. 5 . 5.根据权利要求1所述的配电网双层规划方法，其特征在于，所述双层优化模型中的上层优化规划模型的目标函数为：5. The double-layer planning method for distribution network according to claim 1, wherein the objective function of the upper-layer optimization planning model in the double-layer optimization model is:
Figure FDA0003132025870000011
Figure FDA0003132025870000011
 式中，CT为配变全生命周期成本，CI为配变初始投资成本，CW为配变运行损耗成本，CO为配变检修维护成本，CF为配变故障成本，CD为配变退役处置成本，XT为备选配电网变压器的型号，STN为备选配电网变压器的容量。In the formula, C T is the whole life cycle cost of the distribution transformer, C I is the initial investment cost of the distribution transformer, C W is the operation loss cost of the distribution transformer, CO is the maintenance cost of the distribution transformer, C F is the cost of the distribution transformer failure, and C D For the decommissioning and disposal costs of distribution transformers, X T is the model of the alternative distribution network transformer, and S TN is the capacity of the alternative distribution network transformer. 6.根据权利要求1所述的配电网双层规划方法，其特征在于，所述双层优化模型中的下层优化规划模型的目标函数为：6. The double-layer planning method for distribution network according to claim 1, wherein the objective function of the lower-layer optimization planning model in the double-layer optimization model is:
Figure FDA0003132025870000021
Figure FDA0003132025870000021
 式中，f为配电网变压器低压侧等效注入的平均有功功率，
Figure FDA0003132025870000022
为第i年第t时段的配电网变压器低压侧等效注入有功功率。In the formula, f is the average active power equivalently injected on the low-voltage side of the distribution network transformer,
Figure FDA0003132025870000022
The active power is equivalently injected into the low-voltage side of the distribution network transformer in the t-th period of the i-th year. 7.一种配电网双层规划装置，其特征在于，包括：7. A double-layer planning device for distribution network, characterized in that, comprising: 获取单元，用于获取待规划配电网对应的第一负荷曲线和电动汽车信息；an acquisition unit, used for acquiring the first load curve and electric vehicle information corresponding to the distribution network to be planned; 模拟单元，用于根据所述电动汽车信息，模拟各电动汽车对应的充电负荷曲线；a simulation unit, configured to simulate the charging load curve corresponding to each electric vehicle according to the electric vehicle information; 叠加单元，用于叠加所述第一负荷曲线和所述充电负荷曲线，得到第二负荷曲线；a superimposing unit, configured to superimpose the first load curve and the charging load curve to obtain a second load curve; 构建单元，用于基于所述第二负荷曲线，根据机会约束方法构建用于配电网规划的双层优化模型；a construction unit for constructing, based on the second load curve, a two-layer optimization model for distribution network planning according to a chance constraint method; 求解单元，用于求解所述双层优化模型，得到所述待规划配电网的规划结果。The solving unit is used for solving the two-layer optimization model to obtain the planning result of the distribution network to be planned. 8.根据权利要求7所述的配电网双层规划装置，其特征在于，所述模拟单元具体包括：8. The double-layer planning device for distribution network according to claim 7, wherein the simulation unit specifically comprises: 获取子单元，用于根据所述电动汽车信息，获取电动汽车对应的充电需求模型；an acquisition sub-unit for acquiring a charging demand model corresponding to the electric vehicle according to the electric vehicle information; 计算子单元，用于根据所述电动汽车对应的日行驶里程和所述充电需求模型，计算对应的充电所需时长；a calculation subunit, configured to calculate the corresponding charging required duration according to the daily mileage corresponding to the electric vehicle and the charging demand model; 确定子单元，用于根据所述电动汽车对应的起始充电时间、充电功率和所述充电所需时长，确定所述电动汽车对应的充电负荷曲线。A determination subunit, configured to determine a charging load curve corresponding to the electric vehicle according to the corresponding initial charging time, the charging power and the required charging time of the electric vehicle. 9.一种配电网双层规划设备，其特征在于，包括处理器以及存储器；9. A double-layer planning device for a distribution network, comprising a processor and a memory; 所述存储器用于存储程序代码，并将所述程序代码传输给所述处理器；the memory is used to store program code and transmit the program code to the processor; 所述处理器用于根据所述程序代码中的指令执行权利要求1至6中任一项所述的配电网双层规划方法。The processor is configured to execute the method for double-layer planning of a distribution network according to any one of claims 1 to 6 according to the instructions in the program code. 10.一种存储介质，其特征在于，所述存储介质用于存储程序代码，所述程序代码用于执行权利要求1至6中任一项所述的配电网双层规划方法。10 . A storage medium, wherein the storage medium is used to store program codes, and the program codes are used to execute the method for double-layer planning of a distribution network according to any one of claims 1 to 6 . 11 .
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