CN111564861A - Method, device and equipment for solving charge and discharge time period and storage medium - Google Patents

Method, device and equipment for solving charge and discharge time period and storage medium Download PDF

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CN111564861A
CN111564861A CN202010495858.5A CN202010495858A CN111564861A CN 111564861 A CN111564861 A CN 111564861A CN 202010495858 A CN202010495858 A CN 202010495858A CN 111564861 A CN111564861 A CN 111564861A
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charging
peak
discharging
power
time period
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张达敏
高少希
张强
曾汉超
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Xiamen University of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/64—Optimising energy costs, e.g. responding to electricity rates
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The embodiment of the invention provides a method, a device, equipment and a storage medium for solving a charging and discharging time period, and relates to the technical field of V2G. The method comprises the steps of firstly establishing a minimum peak-valley difference rate function and a minimum cost function, setting corresponding constraint conditions, solving the minimum peak-valley difference rate function, the minimum cost function, the constraint conditions and the established multi-objective optimization model by using a multi-objective genetic algorithm NSGA-II to obtain an optimal solution set, and obtaining an optimal charging and discharging time period according to the optimal solution set. The optimal charging and discharging time period is obtained by solving the calculation target of the minimum peak-valley difference rate and the minimum user cost, the obtained optimal charging and discharging time period fully considers the benefits of the user side while reducing the peak-valley difference rate, the participation degree of the user can be greatly improved, and the method has good practical significance.

Description

Method, device and equipment for solving charge and discharge time period and storage medium
Technical Field
The invention relates to the technical field of V2G, in particular to a method, a device, equipment and a storage medium for solving a charging and discharging time period.
Background
The power supply of the existing power grid is provided by a base load power plant and a peak shaving power plant, and when the demand of a power utilization end is larger than the power supply of the base load power plant, the peak shaving power plant is put into operation to meet the demand of the power utilization end. And when the demand of the power end is lower than the power supply amount of the base load power plant, the unused energy is wasted.
The V2G technology for the electric vehicle to supply power to the power grid is provided for the phenomenon, and the core idea is to regard the electric vehicle as a movable energy storage battery and use a large amount of energy storage sources of the electric vehicle as the buffer of the power grid. When the load of the power grid is too high, the energy storage of the electric automobile feeds power to the power grid; and when the load of the power grid is low, the power grid is used for storing the surplus generated energy of the power grid, so that waste is avoided, and meanwhile, the cost of the power grid is reduced.
The charging and discharging behaviors of the user on the electric automobile can be effectively guided by implementing the peak-valley electricity price, and the peak-valley difference rate of the power grid can be effectively reduced in a proper time period of the peak-valley electricity price. However, the inventor found that in the related art, only the economic benefit of the grid side is considered for the formulation of the peak-to-valley electricity price time period, the benefit of the user side is not considered, and the conventional peak-to-valley electricity price time period causes the peak-to-valley shift caused by the charging when the vehicle is concentrated on the valley, so that the charging and discharging time period of the electric vehicle is not optimal. Therefore, on one hand, the response of the electric vehicle user to the peak-valley electricity price is not positive, and on the other hand, the peak-valley difference rate cannot be effectively reduced.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for solving a charging and discharging time period, which are used for solving the problem that the user benefit is not considered in the determination process of a peak-valley electricity price pricing time period in the prior art.
In a first aspect, a preferred embodiment of the present invention provides a method for solving a charging/discharging time period, including:
establishing a minimum peak-valley difference rate function of the power grid according to the daily load peak value, the daily load valley value and the charge-discharge power of the electric automobile;
establishing a minimum cost function of a user according to the peak-valley electricity price, the charging and discharging power and the charging and discharging duration;
constructing a constraint condition of the minimum peak-to-valley difference rate function and the minimum cost function;
solving the multi-objective optimization model through a multi-objective genetic algorithm NSGA-II to obtain an optimal solution set; the multi-objective optimization model is established according to the minimum peak-to-valley difference rate function, the minimum cost function and the constraint condition;
and obtaining the optimal charging and discharging time period according to the optimal solution set.
Optionally, the expression of the minimum peak-to-valley difference rate function is:
Figure BDA0002522787120000021
is the daily load peak value, LminIs the daily load trough value, P0For the base load power, P, of period j in the gridijAnd the power value of the ith electric automobile at the charging and discharging power at the moment j represents charging when the power value is positive, represents discharging when the power value is negative, and N is the total amount of the electric automobile.
Optionally, the expression of the minimum cost function is:
Figure BDA0002522787120000022
wherein N is the total amount of the electric automobile, PijThe charging and discharging power of the ith electric automobile at the moment j is represented by charging when the power value is positive, and discharging and S when the power value is negativejAnd the electricity price at the moment j is the charge price, the positive value is the discharge price, the negative value is the charge price, and the delta t is the charge and discharge duration of the electric automobile.
Optionally, the constraint condition includes:
charge and discharge power constraint: pijmin≤Pij≤Pijmax(ii) a Wherein, PijminThe maximum discharge power of the ith electric vehicle at the moment j is negative and PijmaxThe maximum charging power of the ith electric vehicle at the moment j is positive and PijThe charging and discharging power of the ith electric automobile at the moment j is represented by charging when the power value is positive, and discharging when the power value is negative;
and (3) charge and discharge current restraint:
Figure BDA0002522787120000031
wherein, IcIs charging current, IdThe discharge current is C, and the current value is C required when the battery of the electric automobile is fully charged within one hour;
and (3) battery capacity constraint: SOCijmin≤SOCij≤SOCijmax(ii) a Therein, SOCijminIs 0.1, SOCijmaxIs 0.9, SOCijThe SOC and the SOC of the ith electric vehicle at the time j are the state of charge of the battery of the electric vehicle, which is the ratio of the remaining capacity to the maximum capacity of the battery.
Optionally, solving the multi-objective optimization model through a multi-objective genetic algorithm NSGA-ii to obtain an optimal solution set, including:
setting the minimum peak-to-valley difference rate value and the minimum user cost as the optimization target of the NSGA-II algorithm;
setting NSGA-II algorithm parameters including maximum iteration number GmaxCross rate Pc, variation rate Pm and individual number N of algorithm population;
coding the electric automobile according to the charging condition of the electric automobile so as to obtain individuals in the algorithm population;
according to the optimization target and the maximum iteration number GmaxThe cross rate Pc, the variation rate Pm and the individual number N of the algorithm population are solved by the NSGA-II algorithm to obtain an optimal solution set.
Optionally, the electric vehicle is encoded according to a charging and discharging condition of the electric vehicle to obtain individuals in the algorithm population, specifically:
the method comprises the steps that binary systems are adopted to code charging and discharging behaviors of the electric automobile in different time periods in one day so as to obtain individuals with the length of 96; wherein the expression of the individual is: [ X ]i.1Xi.2Xi.3……Xi.96]I represents the serial number of the individual, X is 0 or 1, 0 represents no charging, and 1 represents charging;
the algorithm population is a matrix with N rows and 96 columns, the row vector of the matrix represents a time period, and the column vector represents different individuals; wherein the expression of the matrix is:
Figure BDA0002522787120000041
optionally, the optimal solution set is input to the power grid peak-valley difference rate model and the user cost model respectively, so as to obtain a peak-valley difference rate and a user cost corresponding to the optimal solution set.
In a second aspect, an embodiment of the present invention further provides a device for solving a charging and discharging time period, where the device includes:
the first objective function establishing unit is used for establishing a minimum peak-valley difference rate function of the power grid according to the daily load peak value, the daily load valley value and the charge-discharge power of the electric automobile;
the second objective function establishing unit is used for establishing a minimum cost function of the user according to the peak-valley electricity price, the charging and discharging power and the charging and discharging duration;
a constraint condition construction unit, configured to construct constraint conditions of the minimum peak-to-valley difference rate function and the minimum cost function;
the solving unit is used for solving the multi-target optimization model through a multi-target genetic algorithm NSGA-II to obtain an optimal solution set; the multi-objective optimization model is established according to the minimum peak-to-valley difference rate function, the minimum cost function and the constraint condition;
and the charging and discharging time period acquisition unit is used for acquiring the optimal charging and discharging time period from the optimal solution set.
Optionally, the expression of the minimum peak-to-valley difference rate function is:
Figure BDA0002522787120000051
wherein L ismaxIs the daily load peak value, LminIs the daily load trough value, P0For the base load power, P, of period j in the gridijAnd the power value of the ith electric automobile at the charging and discharging power at the moment j represents charging when the power value is positive, represents discharging when the power value is negative, and N is the total amount of the electric automobile.
Optionally, the expression of the minimum cost function is:
Figure BDA0002522787120000052
wherein N is the total amount of the electric automobile, PijThe charging and discharging power of the ith electric automobile at the moment j is represented by charging when the power value is positive, and discharging and S when the power value is negativejAnd the electricity price at the moment j is the charge price, the positive value is the discharge price, the negative value is the charge price, and the delta t is the charge and discharge duration of the electric automobile.
Optionally, the constraint condition constructing unit specifically includes:
the first construction module is used for constructing charge and discharge power constraint; wherein the charge and discharge power constraint expression is: pijmin≤Pij≤Pijmax；PijminThe maximum discharge power of the ith electric vehicle at the moment j is negative and PijmaxThe maximum charging power of the ith electric vehicle at the moment j is positive and PijThe charging and discharging power of the ith electric automobile at the moment j is represented by charging when the power value is positive, and discharging when the power value is negative;
the second construction module is used for constructing charge and discharge current constraints; wherein, the expression of the charge-discharge current constraint is as follows:
Figure BDA0002522787120000053
wherein, IcIs charging current, IdFor discharging current, C is an electric vehicle batteryCurrent value required for filling within hours;
a third construction module for constructing a battery capacity constraint; wherein the expression of the battery capacity constraint is: SOCijmin≤SOCij≤SOCijmax(ii) a Therein, SOCijminIs 0.1, SOCijmaxIs 0.9, SOCijThe SOC and the SOC of the ith electric vehicle at the time j are the state of charge of the battery of the electric vehicle, which is the ratio of the remaining capacity to the maximum capacity of the battery.
Optionally, a solving unit, in particular for
The optimization target setting module is used for setting the minimum peak-valley difference rate value and the minimum user cost as the optimization target of the NSGA-II algorithm;
the algorithm parameter setting module is used for setting the algorithm parameters of NSGA-II; wherein the algorithm parameter comprises the maximum iteration number GmaxCross rate Pc, variation rate Pm and individual number N of algorithm population;
the individual acquisition module is used for coding the electric automobile according to the charging and discharging conditions of the electric automobile so as to obtain individuals in the algorithm population;
a solving module for solving the optimal target and the maximum iteration number GmaxThe cross rate Pc, the variation rate Pm and the individual number N of the algorithm population are solved by the NSGA-II algorithm to obtain an optimal solution set.
Optionally, the individual acquiring module is specifically configured to:
the method comprises the steps that binary systems are adopted to code charging and discharging behaviors of the electric automobile in different time periods in one day so as to obtain individuals with the length of 96; wherein the expression of the individual is: [ X ]i.1Xi.2Xi.3……Xi.96]I represents the serial number of the individual, X is 0 or 1, 0 represents no charging, and 1 represents charging;
optionally, the algorithm population is a matrix with N rows and 96 columns, a row vector of the matrix represents a time period, and a column vector represents different individuals; wherein the expression of the matrix is:
Figure BDA0002522787120000061
optionally, the solving means further comprises,
and the peak-valley difference rate and user cost acquisition unit is used for respectively inputting the optimal solution set into the power grid peak-valley difference rate model and the user cost model so as to obtain the peak-valley difference rate and the user cost corresponding to the optimal solution set.
In a third aspect, an embodiment of the present invention further provides a device for solving a charging and discharging time period, where the device includes: processor, memory and computer program stored in the memory, the computer program being executable by the processor to implement the method for solving the optimal charging and discharging time period
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to perform the method for solving the optimal charging and discharging time period as described above.
By adopting the technical scheme, the invention can obtain the following technical effects:
in the above embodiment, a minimum peak-to-valley difference rate function and a minimum cost function are first established, corresponding constraint conditions are set, the minimum peak-to-valley difference rate function, the minimum cost function, the constraint conditions and the established multi-objective optimization model are solved by using a multi-objective genetic algorithm NSGA-ii to obtain an optimal solution set, and an optimal charging and discharging time period is obtained according to the optimal solution set. The optimal charging and discharging time period is obtained by solving the calculation target of the minimum peak-valley difference rate and the minimum user cost, the obtained optimal charging and discharging time period fully considers the benefits of the user side while reducing the peak-valley difference rate, the participation degree of the user can be greatly improved, and the method has good practical significance.
The optimal charging and discharging time period obtained by the solving method provided by the invention can effectively reduce the peak-to-valley difference rate, so that a large number of peak-shaving power plants are not needed, the national energy is greatly saved, the operation of a power grid is ensured to be more stable, and the method has good practical significance.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a method for solving a charging and discharging time period according to a first embodiment of the present invention;
FIG. 2 is a block diagram of a multi-objective optimization model solved by a multi-objective genetic algorithm NSGA-II according to a first embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating a simulation of a charge-discharge load of disordered charge-discharge and ordered charge-discharge by using a monte carlo algorithm according to a first embodiment of the present invention;
fig. 4 is a daily load curve of the power grid under different conditions according to the first embodiment of the present invention;
fig. 5 is a schematic structural diagram of a device for solving charge and discharge time periods according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all 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 invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention 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.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
In the embodiments, the references to "first \ second" are merely to distinguish similar objects and do not represent a specific ordering for the objects, and it is to be understood that "first \ second" may be interchanged with a specific order or sequence, where permitted. It should be understood that "first \ second" distinct objects may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced in sequences other than those illustrated or described herein.
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the first embodiment is as follows:
referring to fig. 1, a first embodiment of the present invention provides a method for solving a charging/discharging time period, which can be executed by a device (hereinafter referred to as a solving device) of a charging/discharging time period, and in particular, executed by at least one processor in the solving device, so as to at least implement the following steps:
and S101, establishing a minimum peak-valley difference rate function of the power grid according to the daily load peak value, the daily load valley value and the charge-discharge power of the electric automobile.
In this embodiment, the solving device may be a server in a cloud, and may be connected to a power grid management system to manage charging or discharging of an electric vehicle connected to a power grid.
The peak-valley difference rate is reduced, the number of peak-shaving power plants can be effectively reduced, the national energy is greatly saved, the power grid is ensured to operate more stably, and the method has good practical significance. Therefore, how to reduce the peak-to-valley difference rate is the primary consideration in this embodiment. In order to reduce the peak-to-valley difference rate, the present embodiment aims at minimizing the grid-side peak-to-valley difference rate, and establishes a minimum peak-to-valley difference rate function as the first objective function. Specifically, the expression of the minimum peak-to-valley difference rate function is:
Figure BDA0002522787120000101
wherein L ismaxIs the peak daily load, LminIs the daily load trough, P0For the base load power, P, of the grid during period jijThe power value is positive and represents charging, the value is negative and represents discharging, and N is the total quantity of the electric automobile.
In the embodiment, a minimum peak-to-valley difference rate function of the power grid is established according to the daily load peak value, the daily load valley value and the charging and discharging power of the electric vehicle, so that the peak-to-valley difference rate can be accurately quantized, and the accuracy of the solving method of the embodiment is well ensured.
And S102, establishing a minimum cost function of the user according to the peak-valley electricity price, the charge-discharge power and the charge-discharge duration.
In this embodiment, the peak-valley difference rate is reduced, and meanwhile, the cost problem of the user side is taken into consideration, so that the user of the electric vehicle can actively respond to the optimization strategy of the peak-valley electricity price implemented by the V2G, the peak clipping and valley filling effects are better, and the peak-valley difference rate is lower. Therefore, the present embodiment also sets up the minimum cost function as the second objective function by targeting the lowest cost of the user side. Specifically, the expression of the minimum cost function is:
Figure BDA0002522787120000111
wherein N is the total amount of the electric automobile PijThe power value of the ith electric vehicle represents charging when the charging and discharging power is at the moment j, and represents discharging and S when the power value is negativejAnd the electricity price at the moment j is the charge price, the positive value is the discharge price, the negative value is the charge and discharge price, and delta t is the charge and discharge duration of the electric automobile.
And S103, constructing constraint conditions of a minimum peak-to-valley difference rate function and a minimum cost function.
In order to make the optimal solution set obtained by the solution conform to natural laws and satisfy objective conditions in daily life, some constraint conditions need to be established for the first objective function and the second objective function. Specifically, the constraints include:
(1) charge and discharge power constraint:
Pijmin≤Pij≤Pijmax；
wherein, PijminThe maximum discharge power of the ith electric vehicle at the moment j is negative and PijmaxThe maximum charging power of the ith electric vehicle at the moment j is positive and PijThe power value is positive and represents charging, and the value is negative and represents discharging.
(2) And (3) charge and discharge current restraint:
Figure BDA0002522787120000112
wherein, IcIs charging current, IdThe discharge current is C, and the current value is C required when the battery of the electric automobile is fully charged within one hour.
(3) And (3) battery capacity constraint:
SOCijmin≤SOCij≤SOCijmax；
therein, SOCijminIs 0.1, SOCijmaxIs 0.9, SOCijThe SOC and the SOC of the ith electric vehicle at the time j are the state of charge of the battery of the electric vehicle, which is the ratio of the remaining capacity to the maximum capacity of the battery.
The charging and discharging power, the charging and discharging current and the battery capacity of the electric automobile are constrained, so that the optimal solution set obtained by the solving method provided by the invention can be effectively ensured to accord with the natural law and meet the objective and practical conditions in the actual life.
And S104, solving the multi-objective optimization model through a multi-objective genetic algorithm NSGA-II to obtain an optimal solution set. The multi-objective optimization model is established according to the minimum peak-to-valley difference rate function, the minimum cost function and the constraint condition.
And S105, respectively inputting the optimal solution set into the power grid peak-valley difference rate model and the user cost model to obtain the peak-valley difference rate and the user cost corresponding to the optimal solution set and an optimal charging and discharging total load curve.
And S106, obtaining the optimal charging and discharging time period according to the optimal solution set.
The optimum charge/discharge time period obtained from the above-mentioned given constraint conditions is shown in table 1.
Table 1: optimum charge and discharge time period
Figure BDA0002522787120000121
It is noted that different constraints or initial conditions may lead to variations in the optimal charge and discharge time period, and are within the scope of the present invention.
According to the given conditions, the peak-to-valley difference rate corresponding to the solution result obtained by the solution method in the first embodiment of the present invention is 20.11%, and the user profit is 1639.05 yuan.
In this embodiment, S104 specifically includes the following steps:
s1041, setting the minimum peak-valley difference rate value and the minimum user cost as the optimization target of the NSGA-II algorithm.
S1042, setting NSGA-II algorithm parameters including maximum iteration number GmaxCross rate Pc, variation rate Pm, and number of individuals N of algorithm population.
And S1043, coding the electric vehicle according to the charging condition of the electric vehicle to obtain individuals in the algorithm population.
In this embodiment, S1043 specifically is:
and (3) encoding the charging and discharging behaviors of the electric automobile at different time periods in one day by adopting a binary system to obtain an individual with the length of 96. Wherein, each individual body represents a one-day charge-discharge strategy of the electric vehicle. 15 minutes was used as one time period and 96 time periods were used for one day. The expression for the individual is: [ X ]i.1Xi.2Xi.3……Xi.96](ii) a Wherein i represents the number of the individual, X is 0 or 1, 0 represents no charging, and 1 represents charging being performed. In other embodiments, 24 hours a day may be divided into more or fewer time periods, all of which are within the scope of the present invention.
The algorithm population is a matrix with N rows and 96 columns, the row vector of the matrix represents a time period, and the column vector represents different individuals; the expression of the matrix is:
Figure BDA0002522787120000131
it is understood that the row vectors in the matrix represent the charging and discharging behaviors of each electric vehicle in a day, and the column vectors in the matrix represent the charging and discharging behaviors of each electric vehicle at a certain time in a day.
S1044, according to the optimization target and the maximum iteration number GmaxThe cross rate Pc, the variation rate Pm and the individual number N of the algorithm population are solved by the NSGA-II algorithm to obtain an optimal solution set.
Referring to FIG. 2, for ease of understanding, the following illustrates the principle of solving a multi-objective optimization model using the NSGA-II algorithm:
s1, initializing the population, and randomly generating a parent population C with the number of the individuals being N0。
S2, non-dominated sorting and calculating crowdedness distance: calculating the population C of the individuals in the parent0And performing fast non-domination sorting on the individuals according to the fitness to obtain a non-domination set, calculating a crowding distance for each individual in the non-domination set, and recording the iteration number G as 1.
S3, population inheritance, selecting a plurality of father individuals through a tournament method based on the crowding distance, and crossing and mutating the plurality of father individuals to generate a child population D with the number of individuals being N0。
S4, population merging: merging the parent population C0And said progeny population D0To obtain a temporary population R with the number of individuals of 2N0。
S5, Elite preservation, temporary population R0Performing rapid non-dominant sorting, calculating crowding distance, and taking the first N individuals to form a new parent population CI。
S6, judging whether the iteration times G are larger than the maximum iteration times GmaxWhen G is less than or equal to GmaxThen, 1 is added to the number of iterations, and the process returns to step S3.
S7, when G is>GmaxAnd then ending the iteration and outputting the Pareto optimal solution set meeting the constraint condition.
In order to more intuitively see the beneficial effects obtained by the solution method according to the first embodiment of the present invention, a reference group is set as a comparison with the present embodiment.
Reference group one: not implementing a peak-to-valley electricity price strategy
The charging starting time refers to the actual situation of China, the household electric automobile mainly comes and goes to and from a residential place and a working place, the normal off-duty time of most areas is 17:00 in the afternoon, under the condition that peak-valley electricity price guidance is not implemented, an electric automobile owner generally charges the electric automobile after arriving at home, and the charging starting time is assumed to be the time when the electric automobile finally runs in one day. And the returning moment of the last trip of the electric automobile is subjected to sectional normal distribution.
The probability density model expression of the initial charging of the electric automobile is as follows:
Figure BDA0002522787120000151
wherein, tcIs a charging start time; mu.scThe expected value of the charging starting moment is; sigmacIs the standard deviation of the charging start time.
In a one-day cycle, assuming that a user of the electric vehicle arrives at a unit of 9:00 in the morning and leaves at a unit of 17:00 in the afternoon, the discharge time period is 9:00 to 17: 00. Assuming that the discharge start time of the electric vehicle parked in a unit period under the disordered charge-discharge mode without implementing the peak-valley electricity price satisfies the uniform distribution, the expression of the probability density model of the discharge start time is obtained as follows:
Figure BDA0002522787120000152
wherein, tdIs the discharge start time. The electric automobile has a discharge probability of 1/7 in the range of 9:00 to 17:00 and a discharge probability of 0 in the remaining time.
The daily driving mileage of the electric automobile approximately follows the lognormal distribution. The expression of the probability density model for the daily mileage is:
Figure BDA0002522787120000153
wherein D is the daily mileage of the electric automobile in km and mu unitsDExpected value, σ, of daily mileage DDIs the standard deviation of the daily mileage D.
Simplifying the electric automobile model, simulating the charge and discharge load by using a Monte Carlo algorithm, and superposing a basic daily load curve to obtain a total load curve under disordered charge and discharge, wherein the flow is shown in figure 3.
Reference group two: implementing a peak-to-valley tariff strategy that considers only the grid side
The power grid company divides the electricity price according to the local actual load condition, divides 24h a day into peak hour, flat time, three time quantum of low ebb hour, and every time quantum corresponds different electricity price respectively, encourages people to pool the power consumption time with the electricity price of different time quantum, and smooth electric wire netting load curve improves energy utilization.
The model expression of the peak-valley time-of-use electricity price model is as follows:
Figure BDA0002522787120000161
wherein, Prv、Prp、PrrRespectively, the valley electricity price, the peak electricity price and the average electricity price, [ t ]v1,tv2]Is the valley price interval, [ tp1,tp2]The other is a peak power value interval, and the other is a flat power value interval.
The charging time length required by the known electric private car i is Tc.iAfter the electricity price policy is implemented, in order to reduce the charging cost by charging in the valley electricity price period as much as possible, the expression of the probability density model of the charging start time of the electric private car owner is as follows:
Figure BDA0002522787120000162
wherein, Tc.i.orderIs the charging start time, rand is [0,1 ]]One random number, Δ t, of the intervalvDuration of valley price, Δ tv＝tv2-tv1。
The owner who responds to the peak-valley time-of-use electricity price policy selects to discharge in the time period with higher electricity price, and the discharging time length required by the known electric private car i is Td.iThe expression of the probability density model of the discharge starting moment of the electric private car owner is as follows:
Figure BDA0002522787120000163
wherein, Td.i.orderIs the discharge start time, and rand is [0,1 ]]One random number, Δ t, of the intervalpDuration of peak electricity price,. DELTA.tp＝tp2-tp1。
The electric private car owner has different responsivities to the peak-valley time-of-use electricity price, the car owner responding to the electricity price adjusts the charging and discharging time of the electric car according to the time-of-use electricity price and the time periods corresponding to the different electricity prices, the electric car is charged in the valley electricity price period, and the electric car is discharged in the peak electricity price period. And the owner who does not respond to the time-of-use electricity price does not consider the electricity price, and the electric automobile carries out unordered charging and discharging. The peak-to-valley electricity price responsivity is defined herein as η, which represents the proportion of the electric private cars that are charged and discharged in order to the total number of the electric private cars.
The responsivity of the electric automobile participating in ordered charge and discharge is expressed as follows:
Figure BDA0002522787120000171
wherein n is the number of the electric vehicles responding to the time-of-use electricity price; and N is the total number of electric automobiles in the research area.
The Monte Carlo algorithm is utilized to simulate the charging and discharging of the electric automobiles with different responsivities, and the peak shaving effects with different responsivities are obtained, as shown in Table 2. Among them, the peak-to-valley difference rate is at least 22.62% when the responsivity is 0.8, so the optimum responsivity is 0.8.
Table 2: peak regulation effect with different responsivity
Responsivity Peak to valley difference rate Responsivity Peak to valley difference rate
0.1 33.96％ 0.6 24.18％
0.2 30.25％ 0.7 23.99％
0.3 27.60％ 0.8 22.62％
0.4 25.49％ 0.9 24.56％
0.5 25.13％ 1.0 28.10％
And simulating an ordered charging and discharging total load curve under the optimal responsivity by using the Monte Carlo.
The basic daily load curve, the unordered charging and discharging total load curve, the optimal charging and discharging total load curve, and the ordered charging and discharging total load curve are drawn in the same coordinate system, as shown in fig. 4. It can be seen visually that the peak-to-valley difference rate of the curve 2 obtained by the solving method according to the invention is minimum, and the peak and the trough of the optimal charge-discharge total load curve are improved.
As can be seen from fig. 4, the optimal charging and discharging time period obtained by the solving method according to this embodiment is charged and discharged, the peak value of the load is 3480kW, the valley value of the load is 2780kW, the peak value is reduced from 3715kW to 3480kW, the valley value is increased from 2343kW to 2780kW, the peak-valley difference is reduced from 1372kW to 700kW, and the peak-valley difference rate is reduced from 36.93% to 20.11%.
In the disordered case, the peak load was 4106kW, the valley load was 2466kW, and the peak-valley difference rate was 39.94%.
Under the ordered charge and discharge with the responsivity of 0.8, the peak load is 3625kW, the valley load is 2805kW, and the peak-valley difference rate is 22.62%.
The optimal charging and discharging time period obtained by the solving method of the embodiment is used for charging and discharging, so that the purpose of peak clipping and valley filling can be well achieved, and the power grid is most stable. A large amount of peak-shaving electric fields are not needed any more, so that the national energy is greatly saved, and the method has good practical significance.
From the above data, those skilled in the art can calculate the peak-to-valley difference rate and the user cost for disordered charge and discharge, ordered charge and discharge with responsivity of 0.8, and optimal charge and discharge, as shown in table 3. A
Table 3: peak to valley difference rate and user revenue for each case
Peak to valley difference rate Gain of
Disorder of the state of affairs 39.94％ -2992.37
0.8 order of responsivity 22.62％ 1709.28
Optimal charge and discharge 20.11％ 1639.05
The optimal charging and discharging time period obtained by the solving method of the embodiment is used for charging and discharging, and the owner of the electric vehicle can still obtain benefits. Under the condition that the peak-valley difference rate is minimum, the vehicle owner can still be guaranteed to have certain income, the vehicle owner can be well guaranteed to actively respond to the peak-valley electricity price optimization strategy formulated for V2G, a good guiding effect is achieved for peak clipping and valley filling of the power grid, the stability of the operation of the power grid is guaranteed, and the vehicle owner has good practical significance.
Second embodiment of the invention:
referring to fig. 5, a second embodiment of the present invention provides a device for solving a charging/discharging time period, including:
the first objective function establishing unit 100 is configured to establish a minimum peak-to-valley rate function of the power grid according to a daily load peak value, a daily load valley value, and a charging/discharging power of the electric vehicle. Wherein, the expression of the minimum peak-to-valley difference rate function is:
Figure BDA0002522787120000191
wherein L ismaxIs the daily peak load, LminIs daily load trough, P0For the base load power, P, of period j in the gridijThe charging and discharging function of the ith electric automobile at the time of jThe positive power value represents charging, the negative power value represents discharging, and N is the total amount of the electric automobile.
And a second objective function establishing unit 200 for establishing a minimum cost function of the user according to the peak-to-valley electricity price, the charging and discharging power, and the charging and discharging duration. Wherein the expression of the minimum cost function is:
Figure BDA0002522787120000192
wherein N is the total amount of the electric automobile PijThe power value of the ith electric vehicle represents charging when the charging and discharging power is at the moment j, and represents discharging and S when the power value is negativejAnd the electricity price at the moment j is the charge price, the positive value is the discharge price, the negative value is the charge and discharge price, and delta t is the charge and discharge duration of the electric automobile.
And a constraint condition constructing unit 300, configured to construct constraint conditions of the minimum peak-to-valley difference rate function and the minimum cost function.
The solving unit 400 is used for solving the multi-objective optimization model through a multi-objective genetic algorithm NSGA-II to obtain an optimal solution set; the multi-objective optimization model is established according to the minimum peak-to-valley difference rate function, the minimum cost function and the constraint condition.
A charging and discharging time period obtaining unit 500, configured to obtain an optimal charging and discharging time period from the optimal solution set.
And a peak-to-valley difference rate and user cost obtaining unit 600, configured to input the optimal solution set to the power grid peak-to-valley difference rate model and the user cost model, respectively, so as to obtain a peak-to-valley difference rate and a user cost corresponding to the optimal solution set.
On the basis of the foregoing embodiment, in a preferred embodiment of the present invention, the constraint condition constructing unit 300 specifically includes:
the first construction module is used for constructing charge and discharge power constraint; wherein, the expression of the charge-discharge power constraint is as follows:
Pijmin≤Pij≤Pijmax；
wherein, PijminIs the ith vehicleMaximum discharge power of the motor vehicle at the moment j, the value is negative, PijmaxThe maximum charging power of the ith electric vehicle at the moment j is positive and PijThe power value of the charging and discharging power of the ith electric automobile at the moment j is positive, which represents charging, and the power value of the ith electric automobile is negative, which represents discharging;
the second construction module is used for constructing charge and discharge current constraints; wherein, the expression of the charge-discharge current constraint is as follows:
Figure BDA0002522787120000201
wherein, IcIs charging current, IdThe discharge current is C, and the current value is C required when the battery of the electric automobile is fully charged within one hour;
a third construction module for constructing a battery capacity constraint; wherein the expression of the battery capacity constraint is:
SOCijmin≤SOCij≤SOCijmax；
therein, SOCijminIs 0.1, SOCijmaxIs 0.9, SOCijThe SOC and the SOC of the ith electric vehicle at the time j are the state of charge of the battery of the electric vehicle, which is the ratio of the remaining capacity to the maximum capacity of the battery.
On the basis of the foregoing embodiments, in a preferred embodiment of the present invention, the solving unit 400 specifically includes:
and the optimization target setting module is used for setting the minimum peak-valley difference rate value and the minimum user cost as the optimization target of the NSGA-II algorithm.
The algorithm parameter setting module is used for setting the algorithm parameters of NSGA-II; wherein the algorithm parameters comprise the maximum iteration number GmaxCross rate Pc, variation rate Pm, and number of individuals N of algorithm population.
And the individual acquisition module is used for coding the electric automobile according to the charging and discharging conditions of the electric automobile so as to obtain individuals in the algorithm population.
A solving module for solving the optimal target and the maximum iteration number GmaxCross rate ofPc, the variation rate Pm and the individual number N of the algorithm population, and solving the multi-objective optimization model by using the NSGA-II algorithm to obtain an optimal solution set.
On the basis of the foregoing embodiment, in a preferred embodiment of the present invention, the individual acquiring module is specifically configured to:
and (3) encoding the charging and discharging behaviors of the electric automobile at different time periods in one day by adopting a binary system to obtain an individual with the length of 96. Wherein the expression of the individual is:
[Xi.1Xi.2Xi.3…… Xi.96]；
where i represents the serial number of the individual, X is 0 or 1, 0 represents no charging, and 1 represents charging being performed. The algorithm population is a matrix with N rows and 96 columns, the row vector of the matrix represents a time period, and the column vector represents different individuals; wherein, the expression of the matrix is:
Figure BDA0002522787120000211
third embodiment of the invention:
a third embodiment of the present invention provides a device for solving a charging/discharging time period, including: a processor, a memory, and a computer program stored in the memory, the computer program being executable by the processor to implement the method of solving for an optimal charge-discharge time period as described above.
The fourth embodiment of the present invention:
a fourth embodiment of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the method for solving the optimal charging and discharging time period as described above.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or a part thereof, which essentially contributes to the prior art, can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, an electronic device 100, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method for solving a charge-discharge time period is characterized by comprising the following steps:
establishing a minimum peak-valley difference rate function of the power grid according to the daily load peak value, the daily load valley value and the charge-discharge power of the electric automobile;
establishing a minimum cost function of a user according to the peak-valley electricity price, the charging and discharging power and the charging and discharging duration;
constructing a constraint condition of the minimum peak-to-valley difference rate function and the minimum cost function;
solving the multi-objective optimization model through a multi-objective genetic algorithm NSGA-II to obtain an optimal solution set; the multi-objective optimization model is established according to the minimum peak-to-valley difference rate function, the minimum cost function and the constraint condition;
and obtaining the optimal charging and discharging time period according to the optimal solution set.
2. The method of claim 1, wherein the minimum peak-to-valley difference function is expressed as:
Figure FDA0002522787110000011
wherein L ismaxIs the daily load peak value, LminIs the daily load trough value, P0For the base load power, P, of period j in the gridijAnd the power value of the ith electric automobile at the charging and discharging power at the moment j represents charging when the power value is positive, represents discharging when the power value is negative, and N is the total amount of the electric automobile.
3. The method according to claim 1, wherein the minimum cost function is expressed by:
Figure FDA0002522787110000012
wherein N is the total amount of the electric automobile, PijThe charging and discharging power of the ith electric automobile at the moment j is represented by charging when the power value is positive, and discharging and S when the power value is negativejAnd the electricity price at the moment j is the charge price, the positive value is the discharge price, the negative value is the charge price, and the delta t is the charge and discharge duration of the electric automobile.
4. The method according to claim 1, wherein the constraint condition includes:
charge and discharge power constraint: pijmin≤Pij≤Pijmax(ii) a Wherein, PijminThe maximum discharge power of the ith electric vehicle at the moment j is negative and PijmaxThe maximum charging power of the ith electric vehicle at the moment j is positive and PijThe charging and discharging power of the ith electric automobile at the moment j is represented by charging when the power value is positive, and discharging when the power value is negative;
and (3) charge and discharge current restraint:
Figure FDA0002522787110000021
wherein, IcIs charging current, IdThe discharge current is C, and the current value is C required when the battery of the electric automobile is fully charged within one hour;
and (3) battery capacity constraint: SOCijmin≤SOCij≤SOCijmax(ii) a Therein, SOCijminIs 0.1, SOCijmaxIs 0.9, SOCijThe SOC and the SOC of the ith electric vehicle at the time j are the state of charge of the battery of the electric vehicle, which is the ratio of the remaining capacity to the maximum capacity of the battery.
5. The method for solving the charge and discharge time period according to claim 1, wherein the solving of the multi-objective optimization model through a multi-objective genetic algorithm NSGA-II to obtain an optimal solution set comprises:
setting the minimum peak-to-valley difference rate value and the minimum user cost as the optimization target of the NSGA-II algorithm;
setting NSGA-II algorithm parameters including maximum iteration number GmaxCross rate Pc, variation rate Pm and individual number N of algorithm population;
coding the electric automobile according to the charging and discharging conditions of the electric automobile so as to obtain individuals in the algorithm population;
according to the optimization target and the maximum iteration number GmaxThe cross rate Pc, the variation rate Pm and the individual number N of the algorithm population are solved by the NSGA-II algorithm to obtain an optimal solution set.
6. The method according to claim 5, wherein the electric vehicle is encoded according to the charging and discharging conditions of the electric vehicle to obtain the individuals in the algorithm population, and specifically comprises:
the method comprises the steps that binary systems are adopted to code charging and discharging behaviors of the electric automobile in different time periods in one day so as to obtain individuals with the length of 96; wherein the expression of the individual is: [ X ]i.1Xi.2Xi.3……Xi.96]I represents the serial number of the individual, X is 0 or 1, 0 represents no charging, and 1 represents charging;
the algorithm population is a matrix with N rows and 96 columns, the row vector of the matrix represents a time period, and the column vector represents different individuals; wherein the expression of the matrix is:
Figure FDA0002522787110000031
7. the method according to claim 1, wherein the optimal solution set is input to the grid peak-to-valley difference rate model and the user cost model respectively to obtain a peak-to-valley difference rate and a user cost corresponding to the optimal solution set.
8. A device for solving a charge-discharge time period is characterized by comprising:
the first objective function establishing unit is used for establishing a minimum peak-valley difference rate function of the power grid according to the daily load peak value, the daily load valley value and the charge-discharge power of the electric automobile;
the second objective function establishing unit is used for establishing a minimum cost function of the user according to the peak-valley electricity price, the charging and discharging power and the charging and discharging duration;
a constraint condition construction unit, configured to construct constraint conditions of the minimum peak-to-valley difference rate function and the minimum cost function;
the solving unit is used for solving the multi-target optimization model through a multi-target genetic algorithm NSGA-II to obtain an optimal solution set; the multi-objective optimization model is established according to the minimum peak-to-valley difference rate function, the minimum cost function and the constraint condition;
and the charging and discharging time period acquisition unit is used for acquiring the optimal charging and discharging time period from the optimal solution set.
9. A device for solving a charging and discharging time period, comprising a processor, a memory and a computer program stored in the memory, the computer program being executable by the processor to implement the method for solving an optimal charging and discharging time period according to any one of claims 1 to 7.
10. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program runs, the computer-readable storage medium controls a device to execute the method for solving the optimal charging and discharging time period according to any one of claims 1 to 7.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112183942A (en) * 2020-09-03 2021-01-05 珠海格力电器股份有限公司 Equipment running time scheduling method, device, equipment and computer readable medium
CN112418605A (en) * 2020-10-19 2021-02-26 国网上海市电力公司 Optimal operation method for energy storage system of optical storage type charging station
CN112510689A (en) * 2020-11-23 2021-03-16 国网北京市电力公司 Power supply method and device
CN112803464A (en) * 2021-03-16 2021-05-14 中国电力科学研究院有限公司 Energy storage system charge-discharge control method, system, equipment and storage medium
CN113762669A (en) * 2020-08-31 2021-12-07 北京沃东天骏信息技术有限公司 Charging and feeding method and device for electric vehicle
CN112183942B (en) * 2020-09-03 2024-06-07 珠海格力电器股份有限公司 Method, device, equipment and computer readable medium for scheduling running time of equipment

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104123598A (en) * 2014-08-07 2014-10-29 山东大学 Charging mode selection method based on multi-objective optimization for electric private car
CN109214095A (en) * 2018-09-13 2019-01-15 云南民族大学 Electric car charge and discharge Multiobjective Optimal Operation method
CN109447376A (en) * 2018-12-11 2019-03-08 国网山东省电力公司滨州供电公司 Residential block electric car charge and discharge Electric optimization based on user's comprehensive satisfaction
CN109740825A (en) * 2019-01-28 2019-05-10 杭州电子科技大学 A kind of electric car charging/discharging thereof considered under traffic congestion factor
CN109886501A (en) * 2019-03-06 2019-06-14 昆明理工大学 A kind of electric car charge and discharge Multipurpose Optimal Method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104123598A (en) * 2014-08-07 2014-10-29 山东大学 Charging mode selection method based on multi-objective optimization for electric private car
CN109214095A (en) * 2018-09-13 2019-01-15 云南民族大学 Electric car charge and discharge Multiobjective Optimal Operation method
CN109447376A (en) * 2018-12-11 2019-03-08 国网山东省电力公司滨州供电公司 Residential block electric car charge and discharge Electric optimization based on user's comprehensive satisfaction
CN109740825A (en) * 2019-01-28 2019-05-10 杭州电子科技大学 A kind of electric car charging/discharging thereof considered under traffic congestion factor
CN109886501A (en) * 2019-03-06 2019-06-14 昆明理工大学 A kind of electric car charge and discharge Multipurpose Optimal Method

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113762669A (en) * 2020-08-31 2021-12-07 北京沃东天骏信息技术有限公司 Charging and feeding method and device for electric vehicle
CN112183942A (en) * 2020-09-03 2021-01-05 珠海格力电器股份有限公司 Equipment running time scheduling method, device, equipment and computer readable medium
CN112183942B (en) * 2020-09-03 2024-06-07 珠海格力电器股份有限公司 Method, device, equipment and computer readable medium for scheduling running time of equipment
CN112418605A (en) * 2020-10-19 2021-02-26 国网上海市电力公司 Optimal operation method for energy storage system of optical storage type charging station
CN112510689A (en) * 2020-11-23 2021-03-16 国网北京市电力公司 Power supply method and device
CN112803464A (en) * 2021-03-16 2021-05-14 中国电力科学研究院有限公司 Energy storage system charge-discharge control method, system, equipment and storage medium
CN112803464B (en) * 2021-03-16 2022-05-31 中国电力科学研究院有限公司 Energy storage system charge-discharge control method, system, equipment and storage medium

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