CN112949007A - Charging pile and distributed power supply location method and related device - Google Patents

Charging pile and distributed power supply location method and related device Download PDF

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CN112949007A
CN112949007A CN202110178787.0A CN202110178787A CN112949007A CN 112949007 A CN112949007 A CN 112949007A CN 202110178787 A CN202110178787 A CN 202110178787A CN 112949007 A CN112949007 A CN 112949007A
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CN112949007B (en
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高丽娟
杜宗伟
王正平
王虎
白洁
刘栋
张雷
韩建振
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Tianjin University
State Grid Corp of China SGCC
State Grid Hebei Electric Power Co Ltd
Hengshui Power Supply Co of State Grid Hebei Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Hebei Electric Power Co Ltd
Hengshui Power Supply Co of State Grid Hebei Electric Power Co Ltd
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Abstract

The application discloses a charging pile and distributed power supply site selection method and a related device, and relates to the technical field of power systems, wherein the site selection method comprises the following steps: constructing a distributed power output model based on preset illumination distribution data; constructing a traffic network model based on prestored traffic data; based on a distributed power output model and a traffic network model, constructing a multi-objective planning model by taking the lowest cost and the highest user satisfaction as targets, wherein the constraint conditions of the planning model comprise: charging pile installation constraints and distributed power supply installation constraints; solving the multi-target planning model based on a free search algorithm; and determining the addresses to be installed of the charging pile and the distributed power supply based on the solved result. Based on the technical scheme of this application, can reduce the cost of installation electric pile and distributed generator to improve user satisfaction.

Description

Charging pile and distributed power supply location method and related device
Technical Field
The application relates to the technical field of power systems, in particular to a charging pile and distributed power supply location method and a related device.
Background
With the development of the times, people have higher and higher requirements on charging piles and distributed power supplies, and how to select the sites of the charging piles and the distributed power supplies becomes the key point of research in the field.
In the prior art, a method for determining addresses to be installed of a charging pile and a distributed power supply does not exist, so that technicians can only plan the charging pile and the distributed power supply according to experience, and therefore high cost is generated and sufficient user satisfaction is difficult to obtain.
Disclosure of Invention
The application provides a charging pile and distributed power supply location method and a related device, which can effectively reduce the cost of installing the charging pile and the distributed power supply and improve the user satisfaction.
In order to achieve the above technical effect, a first aspect of the present application provides a method for selecting a site of a charging pile and a distributed power supply, including:
constructing a distributed power output model based on preset illumination distribution data;
constructing a traffic network model based on prestored traffic data;
based on the distributed power output model and the traffic network model, a multi-objective planning model is constructed with the goals of lowest cost and highest user satisfaction as targets, wherein the constraint conditions of the planning model comprise: charging pile installation constraints and distributed power supply installation constraints;
solving the multi-target planning model based on a free search algorithm;
and determining the addresses to be installed of the charging pile and the distributed power supply based on the solving result.
This application second aspect provides a fill electric pile and distributed generator's addressing device, includes:
the first construction unit is used for constructing a distributed power supply output model based on preset illumination distribution data;
the second construction unit is used for constructing a traffic network model based on pre-stored traffic data;
a third constructing unit, configured to construct a multi-objective planning model based on the distributed power supply output model and the traffic network model, with a target of lowest cost and highest user satisfaction, where constraint conditions of the planning model include: charging pile installation constraints and distributed power supply installation constraints;
the processing unit is used for solving the multi-target planning model based on a free search algorithm;
and the determining unit is used for determining the to-be-installed addresses of the charging pile and the distributed power supply based on the solving result.
A third aspect of the present application provides an address selection apparatus for a charging pile and a distributed power supply, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the address selection method mentioned in the first aspect or any one of the possible implementation manners of the first aspect when executing the computer program.
A fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the addressing method mentioned in the first aspect or any of the possible implementations of the first aspect.
From the above, according to the technical scheme, the distributed power supply output model is constructed based on the preset illumination distribution data; constructing a traffic network model based on prestored traffic data; based on a distributed power output model and a traffic network model, constructing a multi-objective planning model by taking the lowest cost and the highest user satisfaction as targets, wherein the constraint conditions of the planning model comprise: charging pile installation constraints and distributed power supply installation constraints; solving the multi-target planning model based on a free search algorithm; and determining the addresses to be installed of the charging pile and the distributed power supply based on the solved result. Based on the technical scheme, reasonable planning can be carried out on the to-be-installed addresses of the charging piles and the distributed generation, so that the cost of installing the charging piles and the distributed generation is reduced, and the user satisfaction is improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed for the embodiments or the prior art descriptions will be briefly described 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 without creative efforts.
Fig. 1 is a schematic flowchart illustrating an embodiment of a method for locating a charging pile and a distributed power supply according to the present application;
fig. 2 is a schematic structural diagram of an embodiment of an addressing device for a charging pile and a distributed power supply provided in the present application;
fig. 3 is a schematic structural diagram of another embodiment of an address selection device for a charging pile and a distributed power supply provided by the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The technical solutions in the embodiments of the present application are 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.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways than those described herein, and it will be apparent to those of ordinary skill in the art that the present application is not limited by the specific embodiments disclosed below.
Example one
The application provides a method for selecting a site of a charging pile and a distributed power supply, as shown in fig. 1, the method comprises the following steps:
step 101, constructing a distributed power output model based on preset illumination distribution data;
in the embodiment of the application, the illumination intensity at each moment can be determined based on preset illumination distribution data so as to determine the relevant information of photovoltaic power generation equipment in the distributed power supply, and further, a distributed power supply processing model is constructed.
Optionally, the illumination distribution data is illumination intensity data of Beta distribution;
the above-mentioned illumination distribution data based on predetermineeing, the construction distributed generator model of exerting oneself includes:
constructing a photovoltaic power output model based on the illumination intensity data of the Beta distribution;
constructing an energy storage battery output model based on preset parameters of the energy storage battery;
and constructing a distributed power supply output model based on the photovoltaic power supply output model and the energy storage battery output model.
Specifically, based on the illumination intensity data of the Beta distribution, a photovoltaic power output model is constructed as follows:
Figure BDA0002941540150000051
in the formula (10), eta is a derating factor of the photovoltaic array caused by non-meteorological factors, Ppv,NZeta is the power temperature coefficient, I is the actual illumination intensity, I is the rated capacity of the photovoltaic arrayNRated light intensity, T, for a photovoltaic arrayNIs the surface temperature of the photovoltaic array, and T (t) is the photovoltaic array at time tThe actual temperature of the column;
based on the preset parameters of the energy storage battery, an energy storage battery output model is constructed as follows:
Figure BDA0002941540150000061
in the formula (11), etadischarge(t) the output efficiency, η, of the energy storage cell at time tcharge(t) efficiency of charging of the energy storage cell at time t, Pbattery,NThe rated power of the energy storage battery;
it should be noted that, in the above embodiments, the distributed power source includes a photovoltaic power source and an energy storage power source, and the distributed power source may further include one or more of a wind power source, a hydroelectric power source, and other distributed power sources, which is not limited herein.
102, constructing a traffic network model based on pre-stored traffic data;
in the embodiment of the application, traffic data can be preset based on a traffic scene to be simulated, and then a required traffic network model is constructed based on the preset traffic data.
Optionally, the traffic data includes: the number of roads, the length of each road and the width of each road;
the above-mentioned traffic network model is constructed based on the traffic data that prestores includes:
and constructing the traffic network model based on the number of the roads, the length of each road, the width of each road and a Wardrop balance principle.
Specifically, the traffic data further includes: one or more of the actual traffic cost of each road under different traffic conditions, the traffic conditions of each road at different time, and other data related to traffic conditions, which are not limited herein.
103, constructing a multi-target planning model based on the distributed power supply output model and the traffic network model by taking the lowest cost and the highest user satisfaction as targets;
wherein, the constraint conditions of the planning model comprise: charging pile installation constraints and distributed power supply installation constraints;
in the embodiment of the application, after the distributed power output model and the traffic network model are constructed, a multi-objective planning model can be constructed according to parameters and corresponding targets related in the distributed power output model and the traffic network model.
Optionally, the constructing a multi-objective planning model based on the distributed power supply output model and the traffic network model with the objective of lowest cost and highest user satisfaction includes:
based on the distributed power output model, with the lowest cost as a target, constructing a first objective function of the multi-objective planning model as follows:
min(f1) (1)；
Figure BDA0002941540150000071
in the formulae (1) and (2), IijFor the current between node i and node j in the distributed power output model, RijIs the resistance between the node i and the node j in the distributed power output model, tau is the price of electricity sold corresponding to the distributed power output model, alphai,kAnd betai,kThe number of the kth distributed generation and the number of the charging piles, C, at the node i in the distributed generation output model are respectivelyDG,And CVG,The cost P of the kth distributed power supply and the cost P of the charging pile at the node i in the distributed power supply output model are respectivelybuyThe total active power of the electric quantity to be purchased to the upper-level power grid in the distributed power supply output model;
based on the traffic network model, with the highest user satisfaction as a target, constructing a second objective function of the multi-objective planning model as follows:
max(f2) (3)；
f2＝η∑m∈Mfmym+(1-η)∑m∈MdmXm (4)；
in the formulae (3) and (4), η is a weight coefficient, fmTraffic flow, y, for path m in the traffic network modelmFor decision variables in the traffic network model, dmFor the length, X, of the path m in the traffic network modelmThe number of the charging piles of the path m in the traffic network model is determined.
Further, the constraint conditions of the planning model further include: system power flow constraints, node voltage constraints and feeder current constraints;
the constructing of the multi-objective planning model based on the distributed power output model and the traffic network model with the objective of lowest cost and highest user satisfaction further comprises:
the flow constraint of the system is constructed as follows:
Figure BDA0002941540150000081
in the formula (5), PiFor the active power, Q, of node i in the distributed power supply output modeliFor the reactive power, V, of node i in the distributed power output modeliFor the voltage, θ, at node i in the distributed power contribution modelijIs the phase difference, G, between node i and node j in the distributed power output modelijAnd BijRespectively representing a real part and an imaginary part of an admittance matrix between a node i and a node j in the distributed power output model;
the node voltage constraints are constructed as follows:
Vi,min≤Vi≤Vi,max (6)；
in the formula (6), Vi,minAnd Vi,maxRespectively representing the minimum voltage and the maximum voltage of a node i in the distributed power supply output model;
the feeder current constraints are constructed as follows:
|Iij|≤Iijmax (7)；
in the formula (7), IijmaxFor the node in the distributed power supply output modelThe maximum current between i and node j;
the construction of the charging pile installation constraints are as follows:
PEV,min≤PEV,i≤PEV,max，i∈ΩEV (8)；
in the formula (8), PEV,iThe capacity P of the charging pile to be installed at the node i in the distributed power supply output modelEV,minAnd PEV,maxThe minimum capacity and the maximum capacity omega of the charging pile to be installed at the node i in the distributed power supply output model are calculatedEVA set of nodes allowing installation of charging piles in the distributed power supply output model is formed;
the distributed power installation constraints are constructed as follows:
PDG,min≤PDG,i≤PDG,max，i∈ΩDG (9)；
in the formula (9), PDG,iThe capacity P of the distributed power supply to be installed at the node i in the distributed power supply output modelDG,minAnd PDG,maxThe minimum capacity and the maximum capacity, omega, of the distributed power supply to be installed at the node i in the distributed power supply output modelDGA set of nodes that allow installation of the distributed power supply in the distributed power supply contribution model described above.
It should be noted that, based on the two objective functions and the constraint conditions, a multi-planning model may be constructed to make the user satisfaction (quantified as the average distance to be traveled by the vehicle to be charged) as high as possible and to make the cost (including the construction cost, the maintenance cost, and the like) as low as possible.
104, solving the multi-target planning model based on a free search algorithm;
in the embodiment of the application, the step of calculating the pheromone by the free search algorithm can be optimized so that the free search algorithm is more suitable for solving multiple targets, and then the multiple-target planning model is solved based on the free search algorithm, so that a result which is more in line with the target can be obtained.
Optionally, the solving the multi-target planning model based on the free search algorithm includes:
initializing parameters based on a free search algorithm, and setting a search process and a search step length, wherein the initialized parameters comprise parameters related in the multi-programming model;
searching the multi-target planning model based on a free search algorithm after initialization of parameters, the search process and the search step length;
calculating a fitness function of the multi-target planning model based on the search result;
calculating an pheromone function and a sensitivity function of the multi-target planning model based on the fitness function, and reserving a non-inferior solution;
judging whether a preset iteration number threshold value is reached currently;
if the current time does not reach the preset iteration time threshold value, determining the initial position of the population at the next iteration based on the latest obtained pheromone function and sensitivity function, and then returning to the step of calculating the fitness function of the multi-target planning model and the subsequent steps;
and if the current value reaches the preset iteration time threshold value, outputting the optimal solution of the non-inferior solutions of the multi-target planning model, and taking the optimal solution as a result of solving the multi-target planning model.
Specifically, the calculating of the fitness function of the multi-objective planning model includes:
Figure BDA0002941540150000101
Fk＝max(Ftk) (13)；
the above pheromone function for calculating the multi-objective planning model based on the fitness function is as follows:
Figure BDA0002941540150000111
in the formulae (12) to (14),
Figure BDA0002941540150000112
for the optimum of the objective function corresponding to the multi-objective planning model in all the new positions searched, FkTo a fitness degree, PkIs a pheromone.
It should be noted that, the conventional free search algorithm does not perform the step of squaring the optimal values of the objective functions corresponding to the multi-objective planning model in all the new searched positions, which results in that the subsequently calculated pheromone is not suitable for solving the multi-objective problem, so the step improves the calculation process of the pheromone, so that the solution result of the multi-objective planning model is faster and more accurate.
And 105, determining addresses to be installed of the charging pile and the distributed power supply based on the solving result.
In the embodiment of the application, the result of the solving comprises the positions and the corresponding quantity of the charging piles and the distributed power supplies, the cost of installing the charging piles and the distributed power supplies can be reduced by installing the charging piles and the distributed power supplies based on the result of the solving, and the user satisfaction is improved.
From the above, according to the technical scheme, the distributed power supply output model is constructed based on the preset illumination distribution data; constructing a traffic network model based on prestored traffic data; based on a distributed power output model and a traffic network model, constructing a multi-objective planning model by taking the lowest cost and the highest user satisfaction as targets, wherein the constraint conditions of the planning model comprise: charging pile installation constraints and distributed power supply installation constraints; solving the multi-target planning model based on a free search algorithm; and determining the addresses to be installed of the charging pile and the distributed power supply based on the solved result. Based on the technical scheme, reasonable planning can be carried out on the to-be-installed addresses of the charging piles and the distributed generation, so that the cost of installing the charging piles and the distributed generation is reduced, and the user satisfaction is improved.
Example two
The application provides a fill electric pile and distributed generator's addressing device, as shown in fig. 2, addressing device 20 includes:
a first construction unit 201, configured to construct a distributed power output model based on preset illumination distribution data;
a second construction unit 202, configured to construct a traffic network model based on pre-stored traffic data;
a third constructing unit 203, configured to construct a multi-objective planning model based on the distributed power generation output model and the traffic network model, with the goals of lowest cost and highest user satisfaction being set as targets, where constraint conditions of the planning model include: charging pile installation constraints and distributed power supply installation constraints;
the processing unit 204 is configured to solve the multi-target planning model based on a free search algorithm;
and the determining unit 205 is configured to determine, based on the result of the solution, addresses to be installed of the charging pile and the distributed power supply.
Optionally, the illumination distribution data is illumination intensity data of Beta distribution;
the first building unit 201 is specifically configured to:
constructing a photovoltaic power output model based on the illumination intensity data of the Beta distribution;
constructing an energy storage battery output model based on preset parameters of the energy storage battery;
and constructing a distributed power supply output model based on the photovoltaic power supply output model and the energy storage battery output model.
Optionally, the traffic data includes: the number of roads, the length of each road and the width of each road;
the second building unit 202 is specifically configured to:
and constructing the traffic network model based on the number of the roads, the length of each road, the width of each road and a Wardrop balance principle.
Optionally, the third constructing unit 203 is specifically configured to:
based on the distributed power output model, with the lowest cost as a target, constructing a first objective function of the multi-objective planning model as follows:
min(f1) (1)；
Figure BDA0002941540150000131
in the formulae (1) and (2), IijFor the current between node i and node j in the distributed power output model, RijIs the resistance between the node i and the node j in the distributed power output model, tau is the price of electricity sold corresponding to the distributed power output model, alphai,kAnd betai,kThe number of the kth distributed generation and the number of the charging piles, C, at the node i in the distributed generation output model are respectivelyDG,kAnd CVG,kThe cost P of the kth distributed power supply and the cost P of the charging pile at the node i in the distributed power supply output model are respectivelybuyThe total active power of the electric quantity to be purchased to the upper-level power grid in the distributed power supply output model;
based on the traffic network model, with the highest user satisfaction as a target, constructing a second objective function of the multi-objective planning model as follows:
max(f2) (3)；
f2＝η∑m∈Mfmym+(1-η)∑m∈MdmXm (4)；
in the formulae (3) and (4), η is a weight coefficient, fmTraffic flow, y, for path m in the traffic network modelmFor decision variables in the traffic network model, dmFor the length, X, of the path m in the traffic network modelmThe number of the charging piles of the path m in the traffic network model is determined.
Further, the constraint conditions of the planning model further include: system power flow constraints, node voltage constraints and feeder current constraints;
the third building unit 203 is specifically further configured to:
the flow constraint of the system is constructed as follows:
Figure BDA0002941540150000141
in the formula (5), PiFor the active power, Q, of node i in the distributed power supply output modeliFor the reactive power, V, of node i in the distributed power output modeliFor the voltage, θ, at node i in the distributed power contribution modelijIs the phase difference, G, between node i and node j in the distributed power output modelijAnd BijRespectively representing a real part and an imaginary part of an admittance matrix between a node i and a node j in the distributed power output model;
the node voltage constraints are constructed as follows:
Vi,min≤Vi≤Vi,max (6)；
in the formula (6), Vi,minAnd Vi,maxRespectively representing the minimum voltage and the maximum voltage of a node i in the distributed power supply output model;
the feeder current constraints are constructed as follows:
|Iij|≤Iijmax (7)；
in the formula (7), IijmaxThe maximum current between a node i and a node j in the distributed power output model is obtained;
the construction of the charging pile installation constraints are as follows:
PEV,min≤PEV,i≤PEV,max，i∈ΩEV (8)；
in the formula (8), PEV,iThe capacity P of the charging pile to be installed at the node i in the distributed power supply output modelEV,minAnd PEV,maxThe minimum capacity and the maximum capacity omega of the charging pile to be installed at the node i in the distributed power supply output model are calculatedEVA set of nodes allowing installation of charging piles in the distributed power supply output model is formed;
the distributed power installation constraints are constructed as follows:
PDG,min≤PDG,i≤PDG,max，i∈ΩDG (9)；
in the formula (9), PDG,iThe capacity P of the distributed power supply to be installed at the node i in the distributed power supply output modelDG,minAnd PDG,maxThe minimum capacity and the maximum capacity, omega, of the distributed power supply to be installed at the node i in the distributed power supply output modelDGA set of nodes that allow installation of the distributed power supply in the distributed power supply contribution model described above.
From the above, according to the technical scheme, the distributed power supply output model is constructed based on the preset illumination distribution data; constructing a traffic network model based on prestored traffic data; based on a distributed power output model and a traffic network model, constructing a multi-objective planning model by taking the lowest cost and the highest user satisfaction as targets, wherein the constraint conditions of the planning model comprise: charging pile installation constraints and distributed power supply installation constraints; solving the multi-target planning model based on a free search algorithm; and determining the addresses to be installed of the charging pile and the distributed power supply based on the solved result. Based on the technical scheme, reasonable planning can be carried out on the to-be-installed addresses of the charging piles and the distributed generation, so that the cost of installing the charging piles and the distributed generation is reduced, and the user satisfaction is improved.
EXAMPLE III
The application also provides another charging pile and distributed power supply location device, as shown in fig. 3, the location device in the embodiment of the application includes: a memory 301, a processor 302, and a computer program stored in the memory 301 and executable on the processor 302, wherein: the memory 301 is used to store software programs and modules, the processor 302 executes various functional applications and data processing by operating the software programs and modules stored in the memory 301, and the memory 301 and the processor 302 are connected by a bus 303.
Specifically, the processor 302 implements the following steps by running the above-mentioned computer program stored in the memory 301:
constructing a distributed power output model based on preset illumination distribution data;
constructing a traffic network model based on prestored traffic data;
based on the distributed power output model and the traffic network model, a multi-objective planning model is constructed with the goals of lowest cost and highest user satisfaction as targets, wherein the constraint conditions of the planning model comprise: charging pile installation constraints and distributed power supply installation constraints;
solving the multi-target planning model based on a free search algorithm;
and determining the addresses to be installed of the charging pile and the distributed power supply based on the solving result.
Assuming that the above is the first possible embodiment, in a second possible embodiment based on the first possible embodiment, the light distribution data is light intensity data of a Beta distribution;
the above-mentioned illumination distribution data based on predetermineeing, the construction distributed generator model of exerting oneself includes:
constructing a photovoltaic power output model based on the illumination intensity data of the Beta distribution;
constructing an energy storage battery output model based on preset parameters of the energy storage battery;
and constructing a distributed power supply output model based on the photovoltaic power supply output model and the energy storage battery output model.
In a second possible implementation manner based on the first possible implementation manner, the traffic data includes: the number of roads, the length of each road and the width of each road;
the above-mentioned traffic network model is constructed based on the traffic data that prestores includes:
and constructing the traffic network model based on the number of the roads, the length of each road, the width of each road and a Wardrop balance principle.
In a fourth possible implementation manner based on the first, second, or third possible implementation manner, the constructing a multi-objective planning model based on the distributed power generation output model and the traffic network model with the goals of lowest cost and highest user satisfaction includes:
based on the distributed power output model, with the lowest cost as a target, constructing a first objective function of the multi-objective planning model as follows:
min(f1) (1)；
Figure BDA0002941540150000171
in the formulae (1) and (2), IijFor the current between node i and node j in the distributed power output model, RijIs the resistance between the node i and the node j in the distributed power output model, tau is the price of electricity sold corresponding to the distributed power output model, alphai,kAnd betai,kThe number of the kth distributed generation and the number of the charging piles, C, at the node i in the distributed generation output model are respectivelyDG,kAnd CVG,kThe cost P of the kth distributed power supply and the cost P of the charging pile at the node i in the distributed power supply output model are respectivelybuyThe total active power of the electric quantity to be purchased to the upper-level power grid in the distributed power supply output model;
based on the traffic network model, with the highest user satisfaction as a target, constructing a second objective function of the multi-objective planning model as follows:
max(f2) (3)；
f2＝η∑m∈Mfmym+(1-η)∑m∈MdmXm (4)；
in the formulae (3) and (4), η is a weight coefficient, fmTraffic flow, y, for path m in the traffic network modelmFor decision variables in the traffic network model, dmFor the length, X, of the path m in the traffic network modelmThe number of the charging piles of the path m in the traffic network model is determined.
In a fifth possible implementation manner based on the fourth possible implementation manner, the constraint conditions of the planning model further include: system power flow constraints, node voltage constraints and feeder current constraints;
the constructing of the multi-objective planning model based on the distributed power output model and the traffic network model with the objective of lowest cost and highest user satisfaction further comprises:
the flow constraint of the system is constructed as follows:
Figure BDA0002941540150000181
in the formula (5), PiFor the active power, Q, of node i in the distributed power supply output modeliFor the reactive power, V, of node i in the distributed power output modeliFor the voltage, θ, at node i in the distributed power contribution modelijIs the phase difference, G, between node i and node j in the distributed power output modelijAnd BijRespectively representing a real part and an imaginary part of an admittance matrix between a node i and a node j in the distributed power output model;
the node voltage constraints are constructed as follows:
Vi,min≤Vi≤Vi,max (6)；
in the formula (6), Vi,minAnd Vi,maxRespectively representing the minimum voltage and the maximum voltage of a node i in the distributed power supply output model;
the feeder current constraints are constructed as follows:
|Iij|≤Iijmax (7)；
in the formula (7), IijmaxThe maximum current between a node i and a node j in the distributed power output model is obtained;
the construction of the charging pile installation constraints are as follows:
PEV,min≤PEV,i≤PEV,max，i∈ΩEV (8)；
in the formula (8), PEV,iThe capacity P of the charging pile to be installed at the node i in the distributed power supply output modelEV,minAnd PEV,maxThe minimum capacity and the maximum capacity omega of the charging pile to be installed at the node i in the distributed power supply output model are calculatedEVA set of nodes allowing installation of charging piles in the distributed power supply output model is formed;
the distributed power installation constraints are constructed as follows:
PDG,min≤PDG,i≤PDG,max，i∈ΩDG (9)；
in the formula (9), PDG,iThe capacity P of the distributed power supply to be installed at the node i in the distributed power supply output modelDG,minAnd PDG,maxThe minimum capacity and the maximum capacity, omega, of the distributed power supply to be installed at the node i in the distributed power supply output modelDGA set of nodes that allow installation of the distributed power supply in the distributed power supply contribution model described above.
From the above, according to the technical scheme, the distributed power supply output model is constructed based on the preset illumination distribution data; constructing a traffic network model based on prestored traffic data; based on a distributed power output model and a traffic network model, constructing a multi-objective planning model by taking the lowest cost and the highest user satisfaction as targets, wherein the constraint conditions of the planning model comprise: charging pile installation constraints and distributed power supply installation constraints; solving the multi-target planning model based on a free search algorithm; and determining the addresses to be installed of the charging pile and the distributed power supply based on the solved result. Based on the technical scheme, reasonable planning can be carried out on the to-be-installed addresses of the charging piles and the distributed generation, so that the cost of installing the charging piles and the distributed generation is reduced, and the user satisfaction is improved.
Example four
The present application also provides a computer readable storage medium having a computer program stored thereon, which when executed, can implement the steps provided by the above-described embodiments. Specifically, the computer program includes computer program code, which may be in one of a source code form, an object code form, an executable file or some intermediate form, and is not limited herein; the computer readable storage medium can be any entity or device capable of carrying the above computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium, and is not limited herein. It should be noted that the contents contained in the computer-readable storage medium can be increased or decreased as required by legislation and patent practice in the jurisdiction.
From the above, according to the technical scheme, the distributed power supply output model is constructed based on the preset illumination distribution data; constructing a traffic network model based on prestored traffic data; based on a distributed power output model and a traffic network model, constructing a multi-objective planning model by taking the lowest cost and the highest user satisfaction as targets, wherein the constraint conditions of the planning model comprise: charging pile installation constraints and distributed power supply installation constraints; solving the multi-target planning model based on a free search algorithm; and determining the addresses to be installed of the charging pile and the distributed power supply based on the solved result. Based on the technical scheme, reasonable planning can be carried out on the to-be-installed addresses of the charging piles and the distributed generation, so that the cost of installing the charging piles and the distributed generation is reduced, and the user satisfaction is improved.
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.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the apparatus may be divided into different functional units or modules to implement all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
It should be noted that, the methods and the details thereof provided by the foregoing embodiments may be combined with the apparatuses and devices provided by the embodiments, which are referred to each other and are not described again.
Those of ordinary skill in the art would appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. 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 application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described apparatus/device embodiments are merely illustrative, and for example, the division of the above-described modules or units is only one logical functional division, and the actual implementation may be implemented by another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit 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; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (10)

1. A method for selecting a site of a charging pile and a distributed power supply is characterized by comprising the following steps:
constructing a distributed power output model based on preset illumination distribution data;
constructing a traffic network model based on prestored traffic data;
based on the distributed power output model and the traffic network model, constructing a multi-objective planning model by taking the lowest cost and the highest user satisfaction as targets, wherein the constraint conditions of the planning model comprise: charging pile installation constraints and distributed power supply installation constraints;
solving the multi-target planning model based on a free search algorithm;
and determining the addresses to be installed of the charging pile and the distributed power supply based on the solving result.
2. The addressing method of claim 1, wherein said light distribution data is light intensity data for a Beta distribution;
the building of the distributed power output model based on the preset illumination distribution data comprises the following steps:
constructing a photovoltaic power output model based on the illumination intensity data of the Beta distribution;
constructing an energy storage battery output model based on preset parameters of the energy storage battery;
and constructing a distributed power supply output model based on the photovoltaic power supply output model and the energy storage battery output model.
3. The addressing method of claim 1, wherein the traffic data comprises: the number of roads, the length of each road and the width of each road;
the constructing a traffic network model based on the pre-stored traffic data comprises:
and constructing the traffic network model based on the number of the roads, the length of each road, the width of each road and a Wardrop balance principle.
4. The addressing method of any one of claims 1 to 3, wherein said constructing a multi-objective planning model based on said distributed power output model and said traffic network model with a goal of lowest cost and highest user satisfaction comprises:
based on the distributed power output model, aiming at the lowest cost, constructing a first objective function of the multi-objective planning model as follows:
min(f1) (1)；
Figure FDA0002941540140000021
in the formulae (1) and (2), IijFor the current, R, between node i and node j in the distributed power supply output modelijIs the resistance between the node i and the node j in the distributed power supply output model, tau is the electricity selling unit price corresponding to the distributed power supply output model, and alphai，kAnd betai，kRespectively representing the number of k-th type distributed power supplies and the number of charging piles, C, at the node i in the distributed power supply output modelDG，kAnd CVG，kRespectively representing the cost of the kth distributed power supply and the cost of a charging pile at a node i in the distributed power supply output model, PbuyThe total active power number of the electric quantity to be purchased to the upper-level power grid in the distributed power supply output model is calculated;
based on the traffic network model, with the highest user satisfaction as a target, constructing a second objective function of the multi-objective planning model as follows:
max(f2) (3)；
f2＝η∑m∈Mfmym+(1-η)∑m∈MdmXm (4)；
in the formulae (3) and (4), η is a weight coefficient, fmTraffic flow, y, for path m in the traffic network modelmFor decision variables in the traffic network model, dmIs the length, X, of the path m in the traffic network modelmAnd the number of the charging piles of the path m in the traffic network model.
5. The addressing method of claim 4, wherein the constraints of the planning model further comprise: system power flow constraints, node voltage constraints and feeder current constraints;
the constructing of the multi-objective planning model based on the distributed power output model and the traffic network model with the objective of lowest cost and highest user satisfaction further comprises:
the flow constraint of the system is constructed as follows:
Figure FDA0002941540140000031
in the formula (5), PiIs the active power, Q, of the node i in the distributed power supply output modeliIs the reactive power, V, of the node i in the distributed power supply output modeliIs the voltage of a node i in the distributed power supply output model, thetaijIs the phase difference, G, between node i and node j in the distributed power supply output modelijAnd BijRespectively representing a real part and an imaginary part of an admittance matrix between a node i and a node j in the distributed power supply output model;
the node voltage constraints are constructed as follows:
Vi，min≤Vi≤Vi，max (6)；
in the formula (6), Vi，minAnd Vi，maxRespectively obtaining the minimum voltage and the maximum voltage of a node i in the distributed power supply output model;
the feeder current constraints are constructed as follows:
|Iij|≤Iijmax (7)；
in the formula (7), IijmaxThe maximum current between a node i and a node j in the distributed power supply output model is obtained;
the construction of the charging pile installation constraints are as follows:
PEV，min≤PEV，i≤PEV，max，i∈ΩEV (8)；
in the formula (8), PEV，iThe capacity P of the charging pile to be installed at the node i in the distributed power supply output modelEV，minAnd PEV，maxThe minimum capacity and the maximum capacity, omega, of the charging pile to be installed at the node i in the distributed power supply output modelEVA set of nodes allowing installation of charging piles in the distributed power supply output model is obtained;
the distributed power installation constraints are constructed as follows:
PDG，min≤PDG，i≤PDG，max，i∈ΩDG (9)；
in the formula (9), PDG，iThe capacity P of the distributed power supply to be installed at the node i in the distributed power supply output modelDG，minAnd PDG，maxThe minimum capacity and the maximum capacity, omega, of the distributed power supply to be installed at the node i in the distributed power supply output modelDGA set of nodes in the distributed power output model that allow installation of a distributed power source.
6. The utility model provides a fill electric pile and distributed generator's addressing device which characterized in that includes:
the first construction unit is used for constructing a distributed power supply output model based on preset illumination distribution data;
the second construction unit is used for constructing a traffic network model based on pre-stored traffic data;
a third constructing unit, configured to construct a multi-objective planning model based on the distributed power output model and the traffic network model, with a target of lowest cost and highest user satisfaction, where constraint conditions of the planning model include: charging pile installation constraints and distributed power supply installation constraints;
the processing unit is used for solving the multi-target planning model based on a free search algorithm;
and the determining unit is used for determining the to-be-installed addresses of the charging pile and the distributed power supply based on the solving result.
7. The addressing device as recited in claim 6 wherein the light distribution data is light intensity data for a Beta distribution;
the first building unit is specifically configured to:
constructing a photovoltaic power output model based on the illumination intensity data of the Beta distribution;
constructing an energy storage battery output model based on preset parameters of the energy storage battery;
and constructing a distributed power supply output model based on the photovoltaic power supply output model and the energy storage battery output model.
8. The addressing device of claim 6, wherein the traffic data comprises: the number of roads, the length of each road and the width of each road;
the second building unit is specifically configured to:
and constructing the traffic network model based on the number of the roads, the length of each road, the width of each road and a Wardrop balance principle.
9. An addressing device for a charging pile and a distributed power supply, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method according to any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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