CN113708366A - Power distribution network optimal scheduling method and system considering new energy and electric automobile - Google Patents

Power distribution network optimal scheduling method and system considering new energy and electric automobile Download PDF

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CN113708366A
CN113708366A CN202110943764.4A CN202110943764A CN113708366A CN 113708366 A CN113708366 A CN 113708366A CN 202110943764 A CN202110943764 A CN 202110943764A CN 113708366 A CN113708366 A CN 113708366A
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power distribution
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李冬雪
王涛
刘岩
贾博
利相霖
陈国龙
高�勋
卢天琪
石进永
汪映辉
赵明宇
刘小燕
许紫晗
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STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
State Grid Corp of China SGCC
NARI Group Corp
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
State Grid Electric Power Research Institute
Beijing State Grid Purui UHV Transmission Technology Co Ltd
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STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
State Grid Corp of China SGCC
NARI Group Corp
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
State Grid Electric Power Research Institute
Beijing State Grid Purui UHV Transmission Technology Co Ltd
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The invention belongs to the technical field of power distribution network scheduling, and particularly relates to a power distribution network optimal scheduling method and system considering new energy and electric vehicles, wherein the method comprises the steps of establishing an electric vehicle charging load model based on an electric vehicle charging initial time probability density function and an electric vehicle daily driving distance probability density function, establishing a wind power generation power model based on a wind speed probability density function, and establishing a photovoltaic power generation power model based on an illumination probability density function; considering user load, electric vehicle charging load and new energy output load to the power grid, and establishing a power distribution network comprehensive optimization objective function by taking power distribution network load fluctuation and power distribution network operation cost minimum as targets; and solving the comprehensive optimization objective function of the power distribution network by adopting a genetic algorithm. The method can be used for orderly planning the charging of the electric automobile, reducing the influence of the charging of the electric automobile on the power grid and reducing the operation cost of the power distribution network system.

Description

Power distribution network optimal scheduling method and system considering new energy and electric automobile
Technical Field
The invention belongs to the technical field of power distribution network scheduling, and particularly relates to a power distribution network optimal scheduling method and system considering new energy and electric vehicles.
Background
With the continuous promotion of structural reform of an energy supply side in China, the energy development mode is promoted to be changed from a rough mode to quality and efficiency improvement, and new energy such as photovoltaic, natural gas, wind power, biomass energy, geothermal energy and the like become important contents for coping with climate change and guaranteeing energy safety in China. With the gradual depletion of petroleum resources and the gradual increase of air pollution, China follows the concept of green travel, develops a plurality of preferential policies of electric automobiles, and greatly promotes the development of the electric automobiles, the quantity of electric automobiles kept is increased continuously under the environment basis and the policy basis, and the quantity of electric automobiles kept in our country reaches 344 thousands of vehicles by 2019 according to statistics. Meanwhile, the rapid increase of the electric automobile capacity also brings certain problems to modern power systems, the electric quantity supplement of the electric automobiles comes from public power grids, and huge charging loads brought by the charging of a large number of electric automobiles bring huge challenges to the power systems.
Relevant researches show that the large-scale disordered charging of the electric automobile can further increase the peak-valley difference of a power grid, a large amount of charging loads can cause the voltage drop of nodes of the power grid to increase, and the voltage drop can cause the power loss to increase and further cause the operation efficiency of the power grid to decrease. In order to solve the problems of the electric vehicle charging on the power grid, a power distribution network optimal scheduling method is urgently needed to orderly plan the electric vehicle charging so as to reduce the influence on the power grid caused by the electric vehicle charging and promote the electric vehicle industry development.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a power distribution network optimal scheduling method and system considering new energy and electric vehicles, wherein the charging load of the electric vehicle and the load output from the new energy to a power distribution network are considered, a power distribution network comprehensive optimization objective function is established by taking the load fluctuation of the power distribution network and the running cost of the power distribution network as the minimum target, and the power distribution network optimization scheme is obtained by solving, so that the controllability of electric vehicle charging and new energy power generation can be improved.
The technical scheme provided by the invention is as follows:
the invention provides a power distribution network optimal scheduling method considering new energy and electric vehicles, which comprises the following steps,
establishing an electric vehicle charging load model based on the electric vehicle charging initial time probability density function and the electric vehicle daily driving distance probability density function; establishing a model for outputting new energy to a power grid;
establishing a comprehensive optimization objective function of the power distribution network by taking the minimum load fluctuation and the minimum running cost of the power distribution network as targets; the load of the power distribution network comprises a user load, a charging load of the electric automobile and a load output by the new energy to the power distribution network;
and solving the comprehensive optimization objective function of the power distribution network to obtain an optimized scheduling scheme of the power distribution network.
Preferably, the first and second liquid crystal materials are,
the comprehensive optimization objective function of the power distribution network is as follows:
Figure BDA0003215851860000011
Figure BDA0003215851860000021
Figure BDA0003215851860000022
wherein F represents a comprehensive optimization objective function of the power distribution network, F1Representing the objective function of the load fluctuation of the distribution network, f1maxRepresenting the maximum value of the load fluctuation of the distribution network, f2Representing an objective function of the operating cost of the distribution network, f2maxRepresenting the maximum value, lambda, of the operating cost of the distribution network1、λ2Represents a weight coefficient satisfying lambda1+λ2T denotes a time period, Pload,tRepresenting the user load power at time t, PEV,k,tRepresents the charging power of the kth electric vehicle at the time t, NEVIndicating the size of the electric vehicle access, PWT,tRepresenting the power delivered by the wind farm to the grid at time t, PPV,tPower delivered to the grid by the photovoltaic station at time t, CMFor the maintenance cost coefficient, omega, of the new energy power generation systemtFor the electricity price at the time of the grid, omegaWTFor grid-connected electricity prices, omega, of wind powerPVFor photovoltaic grid-connected electricity prices, Pgrid,tPower, P, delivered to the distribution network for the main network at time tloss,tThe network loss of the power distribution network at the moment t.
Preferably, the first and second liquid crystal materials are,
the comprehensive optimization objective function of the power distribution network needs to meet constraint conditions:
Figure BDA0003215851860000023
wherein, PiFor injecting active power into node i of the distribution network, QiFor injecting reactive power into node i of the distribution network, UiFor distribution network node i voltage, UjFor the voltage of node j of the distribution network, BijIs the susceptance value, G, of the branch ij between node i and node jijIs the conductance value, θ, of the branch ij between node i and node jijFor the power factor angle of branch ij between node i and node j,
Figure BDA0003215851860000024
for transferring power from main network to distribution transformerA minimum value;
Figure BDA0003215851860000025
the main network delivers the maximum amount of power to the distribution transformer,
Figure BDA0003215851860000026
the voltage is the minimum value of the j voltage of the power distribution network node;
Figure BDA0003215851860000027
is the maximum value of j voltage of the distribution network node, SqjRepresenting the branch flow between node q and node j of the distribution network,
Figure BDA0003215851860000028
and the maximum value of the branch power flow between the node q and the node j of the power distribution network is represented.
Preferably, the first and second liquid crystal materials are,
the method for establishing the electric automobile charging load model based on the electric automobile charging initial time probability density function and the electric automobile daily driving distance probability density function comprises the following steps,
randomly extracting a charging starting time based on a probability density function of the charging starting time of the electric automobile; randomly extracting the daily driving distance based on the probability density function of the daily driving distance of the electric automobile, and calculating the SOC of the battery after the driving of the electric automobile is finished;
calculating the charging load of a single electric vehicle based on the charging starting time and the SOC of the battery after the electric vehicle finishes running;
and repeating the extraction and the calculation until the maximum allowable charging load of the electric automobile is reached, and obtaining a charging load curve of the electric automobile.
Preferably, the first and second liquid crystal materials are,
the probability density function of the electric automobile charging starting moment is as follows:
Figure BDA0003215851860000031
wherein s is the initial charging time of the electric automobile, musNormally distributedMean value, σsIs the variance of a normal distribution;
the probability density function of the daily driving distance of the electric automobile is as follows:
Figure BDA0003215851860000032
wherein d is the daily driving distance of the electric automobile mudIs the mean value, σ, of a normal distributiondIs the variance of a normal distribution.
Preferably, the first and second liquid crystal materials are,
the new energy comprises a power generation system and a photovoltaic power generation system,
the establishment of the wind power generation output model comprises the following steps:
determining a wind speed probability density function:
Figure BDA0003215851860000033
wherein f (v) is a wind speed probability density function, l is a parameter distributed by a Weibull function, c is an area average wind speed, and v is an area actual wind speed;
according to the wind speed probability density model of the area, acquiring the actual wind speed condition of the area, and establishing a wind power generation output model:
Figure BDA0003215851860000034
wherein, PWT,tIs the output power of the wind generating set at the moment t, PeRated output power, V, of the wind generating setinFor wind-driven generator cut-in wind speed, VoutCut-out wind speed, V, for a wind turbineeThe rated wind speed of the wind driven generator;
the building of the photovoltaic power generation output model comprises the following steps:
determining an illumination probability density function:
Figure BDA0003215851860000041
wherein Gamma is the Gamma function symbol, f (r)t) As a function of the probability density of illumination, rtmaxMaximum light intensity at time t, rtThe light intensity at the moment t, alpha and beta are distribution parameters;
obtaining illumination intensity statistics according to an illumination probability density function, and establishing a photovoltaic power generation output model:
PPV,t＝rtηaS
wherein, PPV,tIs the output power, eta, of the photovoltaic power generation systemaS is the area of the photovoltaic panel, which is the photoelectric conversion efficiency of the photovoltaic power generation system.
Preferably, the first and second liquid crystal materials are,
solving the comprehensive optimization objective function of the power distribution network, including,
setting the number of individuals and individuals of a population in a genetic algorithm, and randomly generating an initial population, wherein the individuals represent an optimal scheduling scheme of the power distribution network, and the method comprises the following steps: the optimal solution of the charging power of the electric automobile, the power transmitted to the power grid by the wind power plant, the power transmitted to the power grid by the photovoltaic power station and the power transmitted to the power distribution network by the main network is obtained;
calculating the fitness of all individuals in the current population based on a comprehensive optimization objective function of the power distribution network; selecting, crossing and mutating the current population, updating the population, and increasing the iteration times by 1;
judging whether the iteration times reach the maximum iteration times, if so, selecting the individual with the maximum fitness in the current population as the optimal solution of the optimal scheduling scheme of the power distribution network; and if not, recalculating the fitness of all individuals in the current population, and performing selection, crossing and mutation operations to update the population until the maximum iteration number is reached.
In another aspect, the invention provides a power distribution network optimal scheduling system for new energy and electric vehicles, which comprises,
the model building module is used for building an electric vehicle charging load model based on the electric vehicle charging initial time probability density function and the electric vehicle daily driving distance probability density function; establishing a model for outputting new energy to a power grid;
the target construction module is used for establishing a comprehensive optimization target function of the power distribution network by taking the minimum load fluctuation and the minimum running cost of the power distribution network as targets; the load of the power distribution network comprises a user load, a charging load of the electric automobile and a load output by the new energy to the power distribution network;
and the scheduling module is used for solving the comprehensive optimization objective function of the power distribution network to obtain an optimized scheduling scheme of the power distribution network.
Preferably, the first and second liquid crystal materials are,
the object building block is particularly adapted to,
the method comprises the following steps of establishing a comprehensive optimization objective function of the power distribution network:
Figure BDA0003215851860000051
Figure BDA0003215851860000052
Figure BDA0003215851860000053
wherein F represents a comprehensive optimization objective function of the power distribution network, F1Representing the objective function of the load fluctuation of the distribution network, f1maxRepresenting the maximum value of the load fluctuation of the distribution network, f2Representing an objective function of the operating cost of the distribution network, f2maxRepresenting the maximum value, lambda, of the operating cost of the distribution network1、λ2Represents a weight coefficient satisfying lambda1+λ2T denotes a time period, Pload,tRepresenting the user load power at time t, PEV,k,tRepresents the charging power of the kth electric vehicle at the time t, NEVIndicating the size of the electric vehicle access, PWT,tRepresenting the power delivered by the wind farm to the grid at time t, PPV,tPower delivered to the grid by the photovoltaic station at time t, CMFor the maintenance cost coefficient, omega, of the new energy power generation systemtFor the electricity price at the time of the grid, omegaWTFor grid-connected electricity prices, omega, of wind powerPVFor photovoltaic grid-connected electricity prices, Pgrid,tPower, P, delivered to the distribution network for the main network at time tloss,tThe network loss of the power distribution network at the moment t.
Preferably, the first and second liquid crystal materials are,
the scheduling module is specifically configured to,
setting the number of individuals and individuals of a population in a genetic algorithm, and randomly generating an initial population, wherein the individuals represent an optimal scheduling scheme of the power distribution network, and the method comprises the following steps: the optimal solution of the charging power of the electric automobile, the power transmitted to the power grid by the wind power plant, the power transmitted to the power grid by the photovoltaic power station and the power transmitted to the power distribution network by the main network is obtained;
calculating the fitness of all individuals in the current population based on a comprehensive optimization objective function of the power distribution network; selecting, crossing and mutating the current population, updating the population, and increasing the iteration times by 1;
judging whether the iteration times reach the maximum iteration times, if so, selecting the individual with the maximum fitness in the current population as the optimal solution of the optimal scheduling scheme of the power distribution network; and if not, recalculating the fitness of all individuals in the current population, and performing selection, crossing and mutation operations to update the population until the maximum iteration number is reached.
The invention has the beneficial effects that:
according to the method, the charging load of the electric automobile and the load output from the new energy to the power grid are considered, the comprehensive optimization objective function of the power distribution network is established according to the minimum fluctuation of the load of the power distribution network and the minimum system operation cost, the objective function is solved through a genetic algorithm, and the optimal scheduling method of the power distribution network considering the power generation of the new energy and the charging of the electric automobile is established, so that the influence of the charging of the electric automobile on the power grid can be reduced, the operation cost of the power distribution network system is reduced, and the development of the electric automobile industry is promoted.
Drawings
Fig. 1 is a flowchart of a power distribution network optimal scheduling method considering new energy and electric vehicles according to the present invention.
Detailed Description
The present invention will be further described with reference to the following embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and the protection scope of the present invention is not limited thereby.
The invention provides a power distribution network optimal scheduling method considering new energy and electric vehicles, and the method is shown in figure 1 and comprises the following steps:
the method comprises the following steps: the method comprises the following steps of establishing an electric automobile charging load model based on an electric automobile charging initial time probability density function and an electric automobile daily driving distance probability density function, and specifically comprises the following steps:
11) establishing a probability density function of the charging starting time of the electric automobile according to the statistical data:
Figure BDA0003215851860000061
wherein the content of the first and second substances,
s is the initial charging time of the electric automobile;
μsthe mean value of normal distribution is 17.6;
σsthe variance of the normal distribution is 3.4.
12) Establishing a probability density function of the daily driving distance of the electric automobile according to the statistical data:
Figure BDA0003215851860000062
wherein d is the daily driving distance of the electric automobile;
μdthe mean value of normal distribution is 3.2.
σdThe variance of a normal distribution is 0.88.
13) Randomly extracting a charging starting time based on a probability density function of the charging starting time of the electric automobile;
14) randomly extracting the daily driving distance based on the probability density function of the daily driving distance of the electric automobile, and calculating the SOC of the battery after the driving of the electric automobile is finished;
15) calculating the charging load of a single electric vehicle based on the charging starting time and the SOC of the battery after the electric vehicle finishes running;
16) and repeating the steps 13) -15) until the maximum scale of the electric automobile is reached, namely the total charging load of the electric automobile in one day, obtaining a charging load curve of the electric automobile, and obtaining the charging load power value of the electric automobile at the time t.
Step two: determining a new energy form of a power distribution network, and establishing a new energy electric energy output model, wherein in the embodiment, the new energy comprises a wind power generation system and a photovoltaic power generation system, and the new energy comprises the following specific steps:
21) establishing a wind power generation power model based on a wind speed probability density function:
211) calculating a wind speed probability density function:
Figure BDA0003215851860000071
wherein f (v) is a wind speed probability density function, l is a parameter distributed by a Weibull function, c is a regional average wind speed, and v is a regional actual wind speed.
212) Obtaining the actual wind speed condition statistics of the region according to the wind speed probability density model of the region, and establishing a wind power generation power model:
Figure BDA0003215851860000072
wherein, PWT,tIs the output power of the wind generating set at the moment t, PeRated output power, V, of the wind generating setinFor wind-driven generator cut-in wind speed, VoutCut-out wind speed, V, for a wind turbineeThe rated wind speed of the wind driven generator.
22) Establishing a photovoltaic power generation power model based on the illumination probability density function:
221) calculating an illumination probability density function:
Figure BDA0003215851860000073
wherein Gamma is the Gamma function symbol, f (r)t) As a function of the probability density of illumination, rtmaxMaximum light intensity at time t, rtThe light intensity at the time t, and α and β are distribution parameters.
222) Obtaining the illumination intensity statistics of the area according to the illumination probability density function, and establishing a photovoltaic power generation power model:
PPV,t＝rtηaS
wherein, PPV,tIs the output power, eta, of the photovoltaic power generation systemaS is the area of the photovoltaic panel, which is the photoelectric conversion efficiency of the photovoltaic power generation system.
Step three: the method comprises the following steps of establishing a comprehensive optimization objective function of the power distribution network based on an electric vehicle charging load model, a wind power generation power model and a photovoltaic power generation power model by taking the minimum load fluctuation and the minimum running cost of the power distribution network as targets, and specifically comprising the following steps of:
31) the load fluctuation of the power distribution network is represented by load variance:
Figure BDA0003215851860000074
wherein f is1For the distribution network load fluctuation value, T represents a time period, and takes 24 hours of a day, Pload,tRepresenting the user load power at time t, PEV,k,tRepresents the charging power of the kth electric vehicle at the time t, NEVIndicating the size of the electric vehicle access, PWT,tRepresenting the power delivered by the wind farm to the grid at time t, PPV,tAnd the power transmitted to the power grid by the photovoltaic power station at the moment t.
32) Establishing a power distribution network operation cost objective function:
Figure BDA0003215851860000081
wherein f is2For the operating costs of the distribution network, CMFor the maintenance cost coefficient, omega, of the new energy power generation systemtFor the electricity price at the time of the grid, omegaWTFor grid-connected electricity prices, omega, of wind powerPVFor photovoltaic grid-connected electricity prices, Pgrid,tPower, P, delivered to the distribution network for the main network at time tloss,tThe network loss of the power distribution network at the moment t.
33) Establishing a comprehensive optimization objective function of the power distribution network:
Figure BDA0003215851860000082
wherein F represents a comprehensive optimization objective function of the power distribution network, F1maxRepresenting the maximum value of the load fluctuation of the distribution network, f2maxRepresenting the maximum value, lambda, of the operating cost of the distribution network1、λ2Represents a weight coefficient satisfying lambda1+λ2＝1。
Specifically, the comprehensive optimization objective function of the power distribution network needs to meet constraint conditions:
1) and (3) network distribution flow constraint:
Figure BDA0003215851860000083
wherein, PiFor injecting active power into node i of the distribution network, QiFor injecting reactive power into node i of the distribution network, UiFor distribution network node i voltage, UjFor the voltage of node j of the distribution network, BijIs the susceptance value, G, of the branch ij between node i and node jijIs the conductance value, θ, of the branch ij between node i and node jijIs the power factor angle of branch ij between node i and node j.
2) And (3) distribution network power balance constraint:
Figure BDA0003215851860000084
3) and (3) transmission power constraint of the distribution transformer:
Figure BDA0003215851860000085
wherein the content of the first and second substances,
Figure BDA0003215851860000086
the minimum value of power transmitted to the distribution transformer for the main network;
Figure BDA0003215851860000087
transmitting the maximum value of power to the distribution transformer for the main network;
4) and limiting the voltage of the distribution network node:
Figure BDA0003215851860000091
wherein the content of the first and second substances,
Figure BDA0003215851860000092
the minimum value of the node voltage of the power distribution network is obtained;
Figure BDA0003215851860000093
the maximum value of the node voltage of the power distribution network;
5) branch flow limitation:
Figure BDA0003215851860000094
wherein S isqjRepresenting branch power flow between nodes q and j of the power distribution network;
Figure BDA0003215851860000095
and the maximum value of the branch power flow between the node q and the node j of the power distribution network is represented.
Step four: solving the comprehensive optimization objective function of the power distribution network by adopting a genetic algorithm, which specifically comprises the following steps:
41) taking a comprehensive optimization objective function of the power distribution network as a fitness function of the genetic algorithm, and setting the maximum iteration times of the genetic algorithm;
42) setting the number and length of individuals in the population, randomly generating an initial population, and initializing iteration times; each individual represents a power distribution network electric vehicle and new energy optimization scheduling scheme, namely the optimal values of the charging power of the electric vehicle, the power transmitted to a power grid by a wind power plant, the power transmitted to the power grid by a photovoltaic power station and the power transmitted to the power distribution network by a main network;
43) calculating the fitness of all individuals in the current population based on a comprehensive optimization objective function of the power distribution network; selecting, crossing and mutating the current population, updating the population, and increasing the iteration times by 1;
44) judging whether the iteration times reach the maximum iteration times, if so, selecting the individual with the maximum fitness in the current population as the optimal solution of the optimal scheduling scheme of the power distribution network; and if not, recalculating the fitness of all individuals in the current population, and carrying out selection, crossing and mutation operations until the maximum iteration times is reached.
The embodiment of the invention also provides a power distribution network optimal scheduling system considering the new energy and the electric automobile, which comprises,
the model building module is used for building an electric vehicle charging load model based on the electric vehicle charging initial time probability density function and the electric vehicle daily driving distance probability density function; establishing a model for outputting new energy to a power grid;
the target construction module is used for establishing a comprehensive optimization target function of the power distribution network by taking the minimum load fluctuation and the minimum running cost of the power distribution network as targets; the load of the power distribution network comprises a user load, a charging load of the electric automobile and a load output by the new energy to the power distribution network;
and the scheduling module is used for solving the comprehensive optimization objective function of the power distribution network to obtain an optimized scheduling scheme of the power distribution network.
In an embodiment of the present invention, the object building block is specifically configured to,
the method comprises the following steps of establishing a comprehensive optimization objective function of the power distribution network:
Figure BDA0003215851860000096
Figure BDA0003215851860000097
Figure BDA0003215851860000098
wherein F represents a comprehensive optimization objective function of the power distribution network, F1Representing the objective function of the load fluctuation of the distribution network, f1maxRepresenting the maximum value of the load fluctuation of the distribution network, f2Representing an objective function of the operating cost of the distribution network, f2maxRepresenting the maximum value, lambda, of the operating cost of the distribution network1、λ2Represents a weight coefficient satisfying lambda1+λ2T denotes a time period, Pload,tRepresenting the user load power at time t, PEV,k,tRepresents the charging power of the kth electric vehicle at the time t, NEVIndicating the size of the electric vehicle access, PWT,tRepresenting the power delivered by the wind farm to the grid at time t, PPV,tPower delivered to the grid by the photovoltaic station at time t, CMFor the maintenance cost coefficient, omega, of the new energy power generation systemtFor the electricity price at the time of the grid, omegaWTFor grid-connected electricity prices, omega, of wind powerPVFor photovoltaic grid-connected electricity prices, Pgrid,tPower, P, delivered to the distribution network for the main network at time tloss,tThe network loss of the power distribution network at the moment t.
In the embodiment of the invention, the target construction module establishes the comprehensive optimization objective function of the power distribution network and further needs to meet the constraint conditions:
Figure BDA0003215851860000101
wherein, PiFor injecting active power into node i of the distribution network, QiFor injecting reactive power into node i of the distribution network, UiFor distribution network node i voltage, UjFor the voltage of node j of the distribution network, BijIs the susceptance value, G, of the branch ij between node i and node jijIs the conductance value, θ, of the branch ij between node i and node jijFor the power factor angle of branch ij between node i and node j,
Figure BDA0003215851860000102
the minimum value of power transmitted to the distribution transformer for the main network;
Figure BDA0003215851860000103
the main network delivers the maximum amount of power to the distribution transformer,
Figure BDA0003215851860000104
the voltage is the minimum value of the j voltage of the power distribution network node;
Figure BDA0003215851860000105
is the maximum value of j voltage of the distribution network node, SqjRepresenting the branch flow between node q and node j of the distribution network,
Figure BDA0003215851860000106
and the maximum value of the branch power flow between the node q and the node j of the power distribution network is represented.
In the embodiment of the present invention, the scheduling module is specifically configured to,
setting the number of individuals and individuals of a population in a genetic algorithm, and randomly generating an initial population, wherein the individuals represent an optimal scheduling scheme of the power distribution network, and the method comprises the following steps: the optimal solution of the charging power of the electric automobile, the power transmitted to the power grid by the wind power plant, the power transmitted to the power grid by the photovoltaic power station and the power transmitted to the power distribution network by the main network is obtained;
calculating the fitness of all individuals in the current population based on a comprehensive optimization objective function of the power distribution network; selecting, crossing and mutating the current population, updating the population, and increasing the iteration times by 1;
judging whether the iteration times reach the maximum iteration times, if so, selecting the individual with the maximum fitness in the current population as the optimal solution of the optimal scheduling scheme of the power distribution network; and if not, recalculating the fitness of all individuals in the current population, and performing selection, crossing and mutation operations to update the population until the maximum iteration number is reached.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. A power distribution network optimal scheduling method considering new energy and electric vehicles is characterized by comprising the following steps of,
establishing an electric vehicle charging load model based on the electric vehicle charging initial time probability density function and the electric vehicle daily driving distance probability density function; establishing a model for outputting new energy to a power grid;
establishing a comprehensive optimization objective function of the power distribution network by taking the minimum load fluctuation and the minimum running cost of the power distribution network as targets; the load of the power distribution network comprises a user load, a charging load of the electric automobile and a load output by the new energy to the power distribution network;
and solving the comprehensive optimization objective function of the power distribution network to obtain an optimized scheduling scheme of the power distribution network.
2. The optimal scheduling method for the power distribution network considering the new energy and the electric automobile according to claim 1,
the comprehensive optimization objective function of the power distribution network is as follows:
Figure FDA0003215851850000011
Figure FDA0003215851850000012
Figure FDA0003215851850000013
wherein F represents a comprehensive optimization objective function of the power distribution network, F1Representing the objective function of the load fluctuation of the distribution network, f1maxRepresenting the maximum value of the load fluctuation of the distribution network, f2Representing an objective function of the operating cost of the distribution network, f2maxRepresenting the maximum value, lambda, of the operating cost of the distribution network1、λ2Represents a weight coefficient satisfying lambda1+λ2T denotes a time period, Pload,tRepresenting the user load power at time t, PEV,k,tRepresents the charging power of the kth electric vehicle at the time t, NEVIndicating the size of the electric vehicle access, PWT,tRepresenting the power delivered by the wind farm to the grid at time t, PPV,tPower delivered to the grid by the photovoltaic station at time t, CMFor the maintenance cost coefficient, omega, of the new energy power generation systemtFor the electricity price at the time of the grid, omegaWTFor grid-connected electricity prices, omega, of wind powerPVFor photovoltaic grid-connected electricity prices, Pgrid,tPower, P, delivered to the distribution network for the main network at time tloss,tThe network loss of the power distribution network at the moment t.
3. The optimal scheduling method for the power distribution network considering the new energy and the electric automobile as claimed in claim 2,
the comprehensive optimization objective function of the power distribution network needs to meet constraint conditions:
Figure FDA0003215851850000021
wherein, PiFor injecting active power into node i of the distribution network, QiFor injecting reactive power into node i of the distribution network, UiFor distribution network node i voltage, UjFor the voltage of node j of the distribution network, BijIs the susceptance value, G, of the branch ij between node i and node jijIs the conductance value, θ, of the branch ij between node i and node jijFor the power factor angle of branch ij between node i and node j,
Figure FDA0003215851850000022
the minimum value of power transmitted to the distribution transformer for the main network;
Figure FDA0003215851850000023
the main network delivers the maximum amount of power to the distribution transformer,
Figure FDA0003215851850000024
the voltage is the minimum value of the j voltage of the power distribution network node;
Figure FDA0003215851850000025
is the maximum value of j voltage of the distribution network node, SqjRepresenting the branch flow between node q and node j of the distribution network,
Figure FDA0003215851850000026
and the maximum value of the branch power flow between the node q and the node j of the power distribution network is represented.
4. The optimal scheduling method for the power distribution network considering the new energy and the electric vehicles according to claim 1, wherein the electric vehicle charging load model is established based on the probability density function of the charging start time of the electric vehicle and the probability density function of the daily driving distance of the electric vehicle, and comprises,
randomly extracting a charging starting time based on a probability density function of the charging starting time of the electric automobile; randomly extracting the daily driving distance based on the probability density function of the daily driving distance of the electric automobile, and calculating the SOC of the battery after the driving of the electric automobile is finished;
calculating the charging load of a single electric vehicle based on the charging starting time and the SOC of the battery after the electric vehicle finishes running;
and repeating the extraction and the calculation until the maximum allowable charging load of the electric automobile is reached, and obtaining a charging load curve of the electric automobile.
5. The optimal scheduling method for the power distribution network considering the new energy and the electric automobile as claimed in claim 4,
the probability density function of the electric automobile charging starting moment is as follows:
Figure FDA0003215851850000027
wherein s is the initial charging time of the electric automobile, musIs the mean value, σ, of a normal distributionsIs the variance of a normal distribution;
the probability density function of the daily driving distance of the electric automobile is as follows:
Figure FDA0003215851850000031
wherein d is the daily driving distance of the electric automobile mudIs the mean value, σ, of a normal distributiondIs the variance of a normal distribution.
6. The optimal scheduling method for the power distribution network considering the new energy and the electric vehicles as claimed in claim 1, wherein the new energy comprises a power generation system and a photovoltaic power generation system,
the establishment of the wind power generation output model comprises the following steps:
determining a wind speed probability density function:
Figure FDA0003215851850000032
wherein f (v) is a wind speed probability density function, l is a parameter distributed by a Weibull function, c is an area average wind speed, and v is an area actual wind speed;
according to the wind speed probability density model of the area, acquiring the actual wind speed condition of the area, and establishing a wind power generation output model:
Figure FDA0003215851850000033
wherein, PWT,tIs the output power of the wind generating set at the moment t, PeRated output power, V, of the wind generating setinFor wind-driven generator cut-in wind speed, VoutCut-out wind speed, V, for a wind turbineeThe rated wind speed of the wind driven generator;
the building of the photovoltaic power generation output model comprises the following steps:
determining an illumination probability density function:
Figure FDA0003215851850000034
wherein Gamma is the Gamma function symbol, f (r)t) As a function of the probability density of illumination, rtmaxMaximum light intensity at time t, rtThe light intensity at the moment t, alpha and beta are distribution parameters;
obtaining illumination intensity statistics according to an illumination probability density function, and establishing a photovoltaic power generation output model:
PPV,t＝rtηaS；
wherein, PPV,tIs the output power, eta, of the photovoltaic power generation systemaS is the area of the photovoltaic panel, which is the photoelectric conversion efficiency of the photovoltaic power generation system.
7. The optimal scheduling method for the power distribution network considering the new energy and the electric vehicles according to claim 1, wherein the comprehensive optimization objective function of the power distribution network is solved, including,
setting the number of individuals and individuals of a population in a genetic algorithm, and randomly generating an initial population, wherein the individuals represent an optimal scheduling scheme of the power distribution network, and the method comprises the following steps: the optimal solution of the charging power of the electric automobile, the power transmitted to the power grid by the wind power plant, the power transmitted to the power grid by the photovoltaic power station and the power transmitted to the power distribution network by the main network is obtained;
calculating the fitness of all individuals in the current population based on a comprehensive optimization objective function of the power distribution network; selecting, crossing and mutating the current population, updating the population, and increasing the iteration times by 1;
judging whether the iteration times reach the maximum iteration times, if so, selecting the individual with the maximum fitness in the current population as the optimal solution of the optimal scheduling scheme of the power distribution network; and if not, recalculating the fitness of all individuals in the current population, and performing selection, crossing and mutation operations to update the population until the maximum iteration number is reached.
8. An optimal dispatching system for a power distribution network considering new energy and electric vehicles is characterized by comprising,
the model building module is used for building an electric vehicle charging load model based on the electric vehicle charging initial time probability density function and the electric vehicle daily driving distance probability density function; establishing a model for outputting new energy to a power grid;
the target construction module is used for establishing a comprehensive optimization target function of the power distribution network by taking the minimum load fluctuation and the minimum running cost of the power distribution network as targets; the load of the power distribution network comprises a user load, a charging load of the electric automobile and a load output by the new energy to the power distribution network;
and the scheduling module is used for solving the comprehensive optimization objective function of the power distribution network to obtain an optimized scheduling scheme of the power distribution network.
9. The optimal dispatching system of distribution network considering new energy and electric vehicles as claimed in claim 8, wherein the object construction module is specifically used for,
the method comprises the following steps of establishing a comprehensive optimization objective function of the power distribution network:
Figure FDA0003215851850000041
Figure FDA0003215851850000042
Figure FDA0003215851850000043
wherein F represents a comprehensive optimization objective function of the power distribution network, F1Representing the objective function of the load fluctuation of the distribution network, f1maxRepresenting the maximum value of the load fluctuation of the distribution network, f2Representing an objective function of the operating cost of the distribution network, f2maxRepresenting the maximum value, lambda, of the operating cost of the distribution network1、λ2Represents a weight coefficient satisfying lambda1+λ2T denotes a time period, Pload,tRepresenting the user load power at time t, PEV,k,tRepresents the charging power of the kth electric vehicle at the time t, NEVIndicating the size of the electric vehicle access, PWT,tRepresenting the power delivered by the wind farm to the grid at time t, PPV,tPower delivered to the grid by the photovoltaic station at time t, CMFor the maintenance cost coefficient, omega, of the new energy power generation systemtFor the electricity price at the time of the grid, omegaWTFor grid-connected electricity prices, omega, of wind powerPVFor photovoltaic grid-connected electricity prices, Pgrid,tPower, P, delivered to the distribution network for the main network at time tloss,tThe network loss of the power distribution network at the moment t.
10. The optimal scheduling system for distribution network considering new energy and electric vehicles as claimed in claim 9, wherein the scheduling module is specifically configured to,
setting the number of individuals and individuals of a population in a genetic algorithm, and randomly generating an initial population, wherein the individuals represent an optimal scheduling scheme of the power distribution network, and the method comprises the following steps: the optimal solution of the charging power of the electric automobile, the power transmitted to the power grid by the wind power plant, the power transmitted to the power grid by the photovoltaic power station and the power transmitted to the power distribution network by the main network is obtained;
calculating the fitness of all individuals in the current population based on a comprehensive optimization objective function of the power distribution network; selecting, crossing and mutating the current population, updating the population, and increasing the iteration times by 1;
judging whether the iteration times reach the maximum iteration times, if so, selecting the individual with the maximum fitness in the current population as the optimal solution of the optimal scheduling scheme of the power distribution network; and if not, recalculating the fitness of all individuals in the current population, and performing selection, crossing and mutation operations to update the population until the maximum iteration number is reached.
CN202110943764.4A 2021-08-17 2021-08-17 Power distribution network optimal scheduling method and system considering new energy and electric automobile Pending CN113708366A (en)

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CN114626206A (en) * 2022-02-22 2022-06-14 南京理工大学 Alternating current-direct current power distribution network-oriented electric vehicle space-time scheduling modeling method
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CN115879651A (en) * 2023-02-21 2023-03-31 国网天津市电力公司城西供电分公司 Low-carbon optimization method and device of comprehensive energy system considering electric automobile participation
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114626206A (en) * 2022-02-22 2022-06-14 南京理工大学 Alternating current-direct current power distribution network-oriented electric vehicle space-time scheduling modeling method
CN115733169A (en) * 2022-12-01 2023-03-03 华北电力大学 Charging control method and device for new energy charging station
CN115733169B (en) * 2022-12-01 2023-09-22 华北电力大学 Charging control method and device of new energy charging station
CN115879651A (en) * 2023-02-21 2023-03-31 国网天津市电力公司城西供电分公司 Low-carbon optimization method and device of comprehensive energy system considering electric automobile participation
CN116073452A (en) * 2023-03-31 2023-05-05 天津电力工程监理有限公司 Active power distribution network double-order optimization method, system and equipment considering electric automobile
CN116632837A (en) * 2023-07-19 2023-08-22 国网江西省电力有限公司电力科学研究院 Voltage regulation method and system for active power distribution network
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