CN108099634B - Orderly charging method and system for electric automobile - Google Patents

Orderly charging method and system for electric automobile Download PDF

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CN108099634B
CN108099634B CN201710979816.7A CN201710979816A CN108099634B CN 108099634 B CN108099634 B CN 108099634B CN 201710979816 A CN201710979816 A CN 201710979816A CN 108099634 B CN108099634 B CN 108099634B
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charging
peak
valley
load
constraint
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CN108099634A (en
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范士雄
王智晖
卫泽晨
刘幸蔚
韩巍
王伟
蒲天骄
於益军
吴锟
刘宝柱
马维青
贾志义
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Yangquan Power Supply Co of State Grid Shanxi Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Yangquan Power Supply Co of State Grid Shanxi Electric Power Co Ltd
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    • 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
    • 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

Abstract

The invention provides an electric vehicle ordered charging method and system, which comprises the steps of establishing an electric vehicle peak-valley time period transfer proportion model in advance based on historical electric vehicle charging state data; defining a peak-to-valley electrovalence difference constraint for the peak-to-valley time period transfer ratio model; obtaining a user response proportion and a peak-valley power price difference which meet the optimal output of the distributed power supply at each moment by solving a charging load transfer equation containing peak-valley power price difference constraints; and guiding the electric vehicle to a corresponding charging station for charging based on the optimal user response proportion and the peak-valley electricity price difference. The ordered charging scheme is based on the statistical data of actual charging time, the maximum proportion of users capable of responding to the charging peak-valley electricity prices is searched within the power grid safety constraint range, and the maximum consumption of distributed energy is achieved by reasonably distributing charging loads in time and regions.

Description

Orderly charging method and system for electric automobile
Technical Field
The invention relates to the field of distribution network operation and distributed power supply control, in particular to an orderly charging method and system for an electric automobile.
Background
In order to solve the increasingly prominent energy crisis and environmental problems, the proportion of Distributed Generation (DG) in a power distribution network is increasing. However, due to the influence of natural environment, the output characteristics of the distributed power supply have intermittence and uncertainty, and a large amount of light and wind abandon phenomena can be caused in practical application. Therefore, research on improving the power output of the distributed power supply is receiving wide attention.
The control mode of the traditional power grid is 'power generation tracking load', the load is not regarded as an important means for regulating and absorbing new energy by the power grid, so that when a large number of DGs are connected into the power grid, the power output time sequence characteristics of the DGs are different from the load fluctuation time sequence characteristics, and the power output power in the system is limited. Therefore, the power utilization behavior of the user is guided, and the load peak-valley generation time is matched with the DG output to form the key for improving the permeability of the distributed power supply. At present, demand-side management (DMS) is an important method for changing the load timing rule, and a power market price signal or an incentive mechanism is set to guide a user to change an inherent power consumption mode, so that the power consumption behavior of the user is closer to the output rule of distributed energy. For example: some electric car public charging facilities charge electric cars by performing peak-valley time-of-use electricity rates, which are higher during peak charging periods and relatively lower during valley charging periods, in order to optimize the charging behavior of electric cars through the adjustment of electricity rates. However, the disordered charging load peak period of a general automobile is at night, the output peak period of a distributed power source such as a photovoltaic is in the daytime, and the output characteristics of the distributed power source and the photovoltaic are not matched, so that the electric automobile is in accordance with the requirement that the distributed new energy is not fully utilized, and the consumption of the distributed new energy is not facilitated. In the demand side management, flexible factors are considered when a load model is established, and controllable loads are selected as electricity price adjusting objects. In the existing controllable load which is put into use, the load fluctuation state cannot be matched with the power output, and the electric automobile cannot flexibly adjust the charge-discharge state in time and region according to the price of electricity, so that the electric automobile is not suitable for being used as a price-guided load to improve the DG power output.
Disclosure of Invention
In order to solve the problems, the invention uses a certain amount of electric vehicle charging time and charging nodes as control variables, and finds an ordered charging scheme meeting the maximum output of the distributed power supply in a demand side response mode. The ordered charging scheme is based on actual charging time statistical data, the maximum proportion of users capable of responding to charging peak-valley electricity prices is searched within a power grid safety constraint range, and the maximum consumption of distributed energy is achieved by reasonably distributing charging loads in time and regions.
The adopted solution for realizing the purpose is as follows:
an orderly charging method for an electric automobile, which is characterized by comprising the following steps:
establishing a peak-valley period transfer proportion model of the electric vehicle in advance based on historical charging state data of the electric vehicle;
defining a peak-to-valley electrovalence difference constraint for the peak-to-valley time period transfer ratio model; obtaining a user response proportion and a peak-valley power price difference which meet the optimal output of the distributed power supply at each moment by solving a charging load transfer equation containing peak-valley power price difference constraints; and guiding the electric automobile to a corresponding charging station for charging at a proper time and place based on the optimal user response proportion and the peak-valley electricity price difference.
Preferably, the establishing of the electric vehicle peak-valley period transfer proportion model based on the historical electric vehicle charging state data includes:
establishing a peak-valley period transfer proportion model of the electric automobile by the following formula:
L＝N·Pev·λ＝N·Pev·f(β,Δt) (2)
in the above formula, L represents the total load transferred during peak period of charge peak, λ is the user response ratio, β is the peak-to-valley electricity price difference, N is the number of cars charged during peak period, PevAnd charging power for the automobile.
Further, the peak to valley electricity price difference constraint is determined by:
βt.min≤βt≤βt.max (3)
in the above formula, betat.maxAnd betat.minRespectively an upper limit and a lower limit of the peak-to-valley valence difference at the time t, betatThe peak-to-valley electricity price difference at the time t; t denotes the current time.
Further, equation (3) is transformed into an equality constraint based on a trigonometric function, as follows:
Figure BDA0001439129170000021
in the above formula, δiThe variable constraint control angle is changed between 0 and 2 pi k.
Further, bringing equation (4) into equation (2), a charge load transfer equation including a peak-to-valley valence difference constraint is obtained as follows:
Figure BDA0001439129170000022
determining a DG output maximum optimization function and a constraint condition at each moment by the following formula:
minF＝f(Pt.DGmax-Pt.DG) t∈Nt (6)
Figure BDA0001439129170000031
in formula 7, N1For not containing the number of charging station nodes, N2For containing the number of charging station nodes, N is the number of nodes, thetaijIs the voltage angle difference between nodes i, j, NtIs the number of times, NsIs the number of branches, NDGFor the number of distributed power sources, NeFor the number of ESS, NSVCThe number of the reactive power sources; pDG.i，QG.iRespectively the active power and the reactive power output by a distributed power supply or a reactive power supply in the power grid, SlFor the actual power flowing through the branch, ESSiThe active power output by the energy storage device has a power output range related to the model and the state of charge of the battery.
Further, the peak-to-valley power price difference is obtained by averaging the peak-to-valley power price differences at each time of the valley period.
Further, the orderly charging method for the electric automobile further comprises the following steps: and calculating the maximum output power of the distributed power supply under the optimal output target of the distributed power supply by adopting a distributed power supply power output optimization algorithm.
Further, the calculating the maximum output power of the distributed power supply under the distributed power supply output optimal target includes:
calculating the charging power in each time interval;
defining a charging load distribution equation and a charging total load constraint;
constraining a control angle α by adjusting a variable in the total charge load constraintiThe size of the charging station is changed to change the load bearing capacity of each charging station;
and solving the distributed power supply output optimization equation considering the load optimal distribution scheme to obtain the load of each charging station under the distributed power supply output optimal target.
Further, the charging power for each period is determined by:
Figure BDA0001439129170000032
0≤Pt.evi≤Pt.evs (9)
in the formula, Pt.evsFor charging the total load, P, in the grid at time tt.evFor charging station load, n is the number of charging stations.
Further, the charge load distribution equation is determined by:
Figure BDA0001439129170000041
and a total charge load constraint as shown by:
Figure BDA0001439129170000042
the method is simplified to obtain:
Figure BDA0001439129170000043
in the above formula, n is the number of charging stations. Alpha is alphaiThe constraint control angle of the variable is expressed,
further, determining a distributed power output optimization equation considering the load optimal distribution scheme by the following formula:
minF＝f(Pt.DGmax-Pt.DG) t∈Nt (13)
Figure BDA0001439129170000044
an orderly electric vehicle charging system, the system comprising:
the building module is used for building a peak-valley period transfer proportion model of the electric automobile in advance based on historical charging state data of the electric automobile;
the defining module is used for defining peak-valley electricity price difference constraint for the peak-valley time period transfer proportional model;
the acquisition module is used for acquiring a user response proportion and a peak-to-valley electricity price difference which meet the optimal output of the distributed power supply at each moment by solving a charging load transfer equation containing peak-to-valley electricity price difference constraints;
the charging module is used for guiding the electric automobile to a corresponding charging station for charging based on the optimal user response proportion and the peak-valley electricity price difference;
and the calculation module is used for calculating the maximum output power of the distributed power supply under the optimal output target of the distributed power supply by adopting a distributed power supply power output optimization algorithm.
Preferably, the electric vehicle peak-valley period transfer proportion model is established by the following formula:
L＝N·Pev·λ＝N·Pev·f(β,Δt)
in the above formula, L represents the total load transferred during peak period of charge peak, λ is the user response ratio, β is the peak-to-valley electricity price difference, N is the number of cars charged during peak period, PevAnd charging power for the automobile.
Preferably, the peak to valley electricity price difference constraint is determined by:
βt.min≤βt≤βt.max
in the above formula, betat.maxAnd betat.minRespectively an upper limit and a lower limit of the peak-to-valley valence difference at the time t, betatThe peak-to-valley electricity price difference at the time t; t denotes the current time.
Preferably, the calculation module includes:
the conversion submodule is used for converting the peak-valley electricity price difference constraint into an equality constraint based on a trigonometric function;
the determining submodule is used for substituting the equation constraint into the electric vehicle peak-valley period transfer proportion model to obtain a charging load transfer equation containing peak-valley electricity price difference constraint;
the optimization submodule is used for determining a DG output maximum optimization function and a constraint condition at each moment by using the following formula;
minF＝f(Pt.DGmax-Pt.DG) t∈Nt
Figure BDA0001439129170000051
in the above formula, N1For not containing the number of charging station nodes, N2For containing the number of charging station nodes, N is the number of nodes, thetaijIs the voltage angle difference between nodes i, j, NtIs the number of times, NsIs the number of branches, NDGFor distributed power supply quantity，NeFor the number of ESS, NSVCThe number of the reactive power sources; pDG.i，QG.iRespectively the active power and the reactive power output by a distributed power supply or a reactive power supply in the power grid, SlFor the actual power flowing through the branch, ESSiThe active power output by the energy storage device is the power output range of the energy storage device, and the power output range of the energy storage device is related to the model and the state of charge of the battery;
and the obtaining submodule is used for obtaining the peak-valley electricity price difference by solving the average value of the peak-valley electricity price difference at each time of the valley period.
Preferably, the distributed computing module is further configured to compute the maximum output power of the distributed power supply under the distributed power supply output optimal target by using a distributed power supply power output optimization algorithm.
Preferably, the distributed computing module further comprises:
the charging power calculation submodule is used for calculating the charging power in each time period;
the charging power definition submodule is used for defining a charging load distribution equation and a charging total load constraint;
a charging power optimization submodule for constraining a control angle alpha by adjusting variables in the total charging load constraintiThe size of the charging station is changed to change the load bearing capacity of each charging station;
and the charging power solving submodule is used for solving the distributed power supply output optimization equation considering the load optimal distribution scheme to obtain the load of each charging station under the distributed power supply output optimal target.
Preferably, the charging power of each period is determined by the following formula:
Figure BDA0001439129170000061
0≤Pt.evi≤Pt.evs
in the formula, Pt.evsFor charging the total load, P, in the grid at time tt.evFor charging station load, n is the number of charging stations.
Preferably, the charge load distribution equation is determined by the following equation:
Figure BDA0001439129170000062
preferably, the charging power solving submodule includes:
a total charging load optimization subunit, configured to determine a distributed power supply output optimization equation considering the load optimal distribution scheme according to the following formula:
minF＝f(Pt.DGmax-Pt.DG) t∈Nt
Figure BDA0001439129170000071
compared with the closest prior art, the invention has the following beneficial effects:
in order to optimize the output value of a distributed power supply in a power distribution network, the invention provides an electric vehicle ordered charging method and system in which an electric vehicle is used as a controllable load and the charging load is optimally distributed on time and nodes in a demand side management mode, and a peak-valley period transfer proportion model of the electric vehicle is established in advance based on historical electric vehicle charging state data; defining peak-to-valley electrovalence difference constraints for the peak-to-valley time period transfer proportion model; obtaining a user response proportion and a peak-valley power price difference which meet the optimal output of the distributed power supply at each moment by solving a charging load transfer equation containing peak-valley power price difference constraints; and guiding the electric vehicle to a corresponding charging station for charging based on the optimal user response proportion and the peak-valley electricity price difference.
And finding the optimal charging load peak-valley electricity price difference and user response proportion meeting the constraint range through a charging load time sequence transfer equation, and improving the load fluctuation state to be matched with the power output. And calculating the maximum output power of the distributed power supply under the optimal output target of the distributed power supply by adopting a distributed power supply power output optimization algorithm. A charging load distribution method is provided, random charging of the electric vehicle at each charging station is changed into sequential charging, and output power of the distributed power supply is further improved. The charging load distribution equation is not influenced by the number of charging stations in the power grid, and has universality.
Drawings
FIG. 1 is a flow chart of an orderly charging method for an electric vehicle according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating an implementation of the ordered charging method for an electric vehicle with a maximum DG output according to the embodiment of the present invention.
8. Best mode for carrying out the invention
The method utilizes the electric automobile response peak-valley electricity price model, and optimizes the charging time and place of the electric automobile by dividing the charging load peak-valley time period of the electric automobile to improve the receiving capacity of the distributed power supply. According to the optimized result such as the optimal peak-valley electricity price difference, the optimal peak-valley electricity price difference can be used as a reference for formulating the charging peak-valley price of the electric automobile, and the peak-valley charging price and the time period of the electric automobile can be issued to an owner of the electric automobile in a charging APP or network mode. The peak-valley electricity price information issuing can lead to a certain proportion of user responses, the distributed power supply is maximally consumed according to the installation position of the charging pile, the proportion of the electric vehicle load distributed in the existing charging station is further optimized, and the charging station information with more charging load distribution in the optimization result is preferentially pushed to users. The peak-valley electricity price and the related information pushing of the charging stations can guide the electric automobile to be charged to the corresponding charging stations at proper time, so that the fluctuation trend of the charging load is close to the output rule of the distributed power supply, and the system acceptance capacity of the distributed power supply is improved.
Embodiments of the process of the present invention are described in detail below with reference to the accompanying drawings. As shown in fig. 1 and 2, an orderly charging method for an electric vehicle is provided, which includes:
and establishing a peak-valley period transfer proportion model of the electric vehicle in advance based on historical charging state data of the electric vehicle. Firstly, collecting the existing disordered charging state data of the electric automobile, including the charging time length and the parking starting time of the electric automobile in the disordered charging state, and providing basic data for the user response electricity price characteristic and the charging load peak-valley time period division of the electric automobile; and counting the charging loads of a certain number of automobiles at each moment in a single day based on the disordered charging state data of the electric automobiles.
And comparing the difference between the actual output of the distributed power supply and the upper limit of the capacity of the distributed power supply at each moment, and dividing the charging peak-valley time period.
And (3) exploring a peak-valley electricity price difference factor and an automobile use time factor which influence the user behavior, and searching functional relations between the user response proportion and the peak-valley electricity price difference and between automobile use states in different time periods to obtain an electric automobile peak-valley period transfer proportion model constructed by the charging load transfer equation (2).
And defining peak-to-valley electrovalence difference constraint for the peak-to-valley time period transfer proportion model. In order to derive a total charge load satisfying the user response constraint in the transfer scale model, the present invention determines the peak-to-valley current price difference constraint using equation (3).
Then, a charging load transfer equation containing peak-to-valley power price difference constraint is solved, so that a user response proportion and a peak-to-valley power price difference which meet the optimal output of the distributed power supply at each moment are obtained, and the method specifically comprises the following steps:
solving a user response proportion and a peak-valley power price difference which meet the optimal output of the distributed power supply at each moment in the period by using formulas (4) to (7);
and (4) counting the optimization results at all times, and taking the average value at all times as the user response proportion and the peak-valley electricity price difference which are unified in the valley period.
According to the optimized result such as the optimal peak-valley electricity price difference, the optimal peak-valley electricity price difference can be used as a reference for formulating the charging peak-valley price of the electric automobile, and the peak-valley charging price and the time period of the electric automobile can be issued to an owner of the electric automobile in a charging APP or network mode. Peak-to-valley electricity rate information distribution will result in a certain percentage of users shifting from peak-load periods to valley-load periods for charging.
The specific charging time of the transfer electric automobile responding to the peak-valley electricity price is determined by the charging time length of the transfer electric automobile, when the charging time length of a user is greater than the valley period length, the electric automobile can be charged only from the valley period starting time, when the charging time length of the user is less than the valley period length, the charging starting time can be selected within a certain range, the charging ending of the load valley period is ensured, and the specific transfer load at each time of the valley period is obtained.
In addition, the orderly charging method for the electric automobile provided by the invention further comprises the following steps: and calculating the maximum output power of the distributed power supply under the optimal output target of the distributed power supply by adopting a distributed power supply power output optimization algorithm.
Under the condition that the total amount of the transferred loads is known, the load distribution scheme of each charging station in the power grid is obtained according to the expressions (10) to (13), and the maximum output power of the distributed power supply is obtained. The peak-valley electricity price information issuing can lead to a certain proportion of user responses, the distributed power supply is maximally consumed according to the installation position of the charging pile, the proportion of the electric vehicle load distributed in the existing charging station is further optimized, and the charging station information with more charging load distribution in the optimization result is preferentially pushed to users. The peak-valley electricity price and the related information pushing of the charging stations can guide the electric automobile to be charged to the corresponding charging stations at proper time, so that the fluctuation trend of the charging load is close to the output rule of the distributed power supply, and the system acceptance capacity of the distributed power supply is improved.
The invention provides an electric automobile ordered charging mode with the maximum DG output as a target, and a user is guided to charge at a proper time and node. The method mainly comprises three key links of electric vehicle response electricity price ratio modeling, vehicle charging time optimization and load optimization in each charging station distribution scheme. Firstly, dividing charging load peak-valley time periods by comparing the output of a distributed power supply with the difference of the upper limit of the capacity of the distributed power supply by utilizing the statistical data of the existing unordered charging load of the electric automobile, and establishing a functional relation influencing the user response proportion of the electric automobile; secondly, obtaining the optimal peak-valley electricity price difference under the maximum DG output condition and the total amount of the charged automobiles responding to the electricity price through optimization calculation; and finally, transferring the charging automobiles responding to the electricity price to a valley period, and distributing the automobiles to be charged based on a load distribution scheme of each charging station so that the load in the power grid can track the output power of the distributed power supply to the maximum extent. The specific scheme is as follows:
(1) electric automobile peak-valley period transfer proportion model
The method is based on the fact that the charging load and the number of the electric vehicles in the disordered state of each time period in one day in the existing certain area are original data, and the functional relation between the user response ratio lambda and the peak-valley electricity price difference beta in different time periods is searched.
Due to the time desynchronization of the load demand peak and the DG output peak, there is a significant gap between the DG actual output power and the corresponding maximum capacity limit.
The charging peak-valley period should first be divided according to the difference between the actual output value of the distributed power supply and its maximum output power limit.
Secondly, in different time periods in a day, the automobile use states are different, and the user response peak-valley electricity price enthusiasm is also different. The user responds relatively negatively to peak-to-valley electricity prices when the vehicle is in a peak-to-peak period, whereas the user responds positively to peak-to-valley electricity prices when the user's demand for the vehicle is low. According to the peak-valley time period division result and by referring to the existing electricity price response statistical data, the invention fits the user response ratios lambda and beta in different periods into a specific function, which can be expressed as:
Figure BDA0001439129170000101
wherein p ispFor peak load electricity rates, pvFor the load of the valley time electricity price, preIs the original electricity price. By adjusting the peak-valley electricity price, part of the automobile load is transferred to the valley time period for charging, and the shortage of the charging load is made up. And according to the specific requirement of tracking the distributed power supply, calculating the peak-valley electricity price difference and the corresponding user response proportion of different charging loads by selecting the lambda and beta functional relations in different response periods.
(2) Optimal charge load transfer analysis
After the user response scale model is determined, the incremental load optimum that can be sustained during each period during the charging valley should be sought in order to maximize the magnitude of the increase in DG power output during that period. Therefore, the charging load L transferred in response to the electricity price during the charging peak period and the corresponding peak-to-valley electricity price difference constraint range can be expressed as:
L＝N·Pev·λ＝N·Pev·f(β,Δt)
βt.min≤βt≤βt.max
in the above formula, N is the number of cars charged in the peak period, PevAnd charging power for the automobile. In order to obtain the total charging load satisfying the user response constraint in the optimization equation, the present invention converts equation 3 into an equality constraint using a trigonometric function, as follows:
Figure BDA0001439129170000102
in the above formula, δiChanging the variable constraint control angle between 0 and 2 pi k, and combining the above formulas to obtain a charge load transfer equation containing peak-to-valley electrovalence difference constraint as follows:
Figure BDA0001439129170000103
the optimal function of the DG output at each time and the constraint conditions are as follows:
minF＝f(Pt.DGmax-Pt.DG) t∈Nt
Figure BDA0001439129170000111
in the formula, N1For not containing the number of charging station nodes, N2For containing the number of charging station nodes, N is the number of nodes, thetaijIs the voltage angle difference between nodes i, j, NtIs the number of times, NsIs the number of branches, NDGFor the number of distributed power sources, NeFor the number of ESS, NSVCThe number of reactive power sources. PDG.i，QG.iRespectively the active power and the reactive power output by a distributed power supply or a reactive power supply in the power grid, SlFor the actual power flowing through the branch, ESSiThe active power output by the energy storage device has a power output range related to the model of the battery and the state of charge (SOC). The maximum DG output at each moment is the maximum target, the upper limit value of the load can be transferred in the charging load valley period is solved by the formula 7, and the automobile charging time length and the timely response of the user are not consideredTherefore, the average value of the peak-to-valley power price differences at all times in the valley period is selected as the uniform peak-to-valley power price difference, and the corresponding user response proportion is calculated accordingly.
(3) Optimal distribution mode of charging load
When the power grid comprises a plurality of charging piles, in order to improve the power of the distributed power supply, the charging and discharging behaviors of a user need to be orderly adjusted in time dimension and also in space dimension. Setting the total charging load in the power grid at a certain moment t as Pt.evsAnd each charging station load Pt.eviThe achievable upper limit values are all the total charging load, and the equation can be written as:
Figure BDA0001439129170000112
0≤Pt.evi≤Pt.evs
in order to complete optimization calculation at each moment and find the optimal load of the charging station, the invention substitutes the above inequality constraint into an equality constraint into an equation calculation by means of a trigonometric function, and then a charging load distribution equation is as follows:
Figure BDA0001439129170000121
and the total charge load constraint can be written as:
Figure BDA0001439129170000122
or simply as:
Figure BDA0001439129170000123
in the above formula, n is the number of charging stations. Constraining the control angle α by adjusting the variables in equations 10,11iThe size of each charging station can be changed under the condition of ensuring the total load of the charging station to be certainThe power station carries the load. The equation constraint enables each charging station to possibly bear all charging loads, fully ensures the charging flexibility of users, and enables the power grid to have the capability of tracking the output of the distributed power supply to the maximum extent. The distributed power output optimization equation considering the load optimal distribution scheme in a certain time is as follows:
minF＝f(Pt.DGmax-Pt.DG) t∈Nt
Figure BDA0001439129170000124
according to the above formula, the load P of each charging station under the optimal output target of the distributed power supply can be obtainedt.eviAnd allocating the state, and making an automobile charging guide scheme according to the result.
Example (b):
the invention provides a method for orderly charging an electric automobile by responding to time-of-use electricity price, which meets the aim of optimal output of a distributed power supply in a power distribution network by adjusting the time and place for charging the automobile. The method is characterized in that a charging load peak-valley time period and a user response proportion equation are established based on historical data of a user using the automobile and the output state of the distributed power supply; a user transfer proportion optimization algorithm is proposed according to the peak-valley electricity price difference adjusting range so as to match the output characteristics of the distributed power supply; and providing an equation for distributing the charging load in each charging station, and meeting the output optimization target of the distributed power supply.
The peak-valley electricity price difference optimization equation under the power grid safety condition and under the user response range constraint is provided, so that a corresponding number of users are guided to be charged to a proper time period, the maximum output target of the distributed power supply is met, and the user response equation containing the peak-valley electricity price difference constraint is provided. The total load L transferred during peak-to-peak charging and the peak-to-valley difference of electricity price constraints can be expressed as:
L＝N·Pev·λ＝N·Pev·f(β,Δt)
βt.min≤βt≤βt.max
in the above formula, N is the number of cars charged in the peak period, PevIs steamVehicle charging power. In order to obtain the total charging load satisfying the user response constraint in the optimization equation, the present invention converts equation 3 into an equality constraint using a trigonometric function, as follows:
Figure BDA0001439129170000131
in the above formula, δiFor a variable constraint control angle, the variable constraint control angle is changed between 0 and 2 pi k, and for example, a charging load transfer equation containing peak-to-valley electrovalence difference constraint is as follows:
Figure BDA0001439129170000132
the maximum DG output optimization function and the constraint conditions at each time are as follows:
minF＝f(Pt.DGmax-Pt.DG) t∈Nt
Figure BDA0001439129170000133
in the formula, N1For not containing the number of charging station nodes, N2For containing the number of charging station nodes, N is the number of nodes, thetaijIs the voltage angle difference between nodes i, j, NtIs the number of times, NsIs the number of branches, NDGFor the number of distributed power sources, NeFor the number of ESS, NSVCThe number of reactive power sources. PDG.i，QG.iRespectively the active power and the reactive power output by a distributed power supply or a reactive power supply in the power grid, SlFor the actual power flowing through the branch, ESSiThe active power output by the energy storage device has a power output range related to the model of the battery and the state of charge (SOC). The maximum DG output at each moment is the target, the upper limit value of the load can be transferred in the low valley period of the charging load, the charging time of the automobile and the timeliness of user response are not considered, so the average value of the peak-valley power price differences at each moment in the valley period is selected as the uniform peak-valley power price difference, and the average value is used as the uniform peak-valley power price differenceAnd calculating the corresponding user response proportion.
When the position of the charging station is fixed, the optimal distribution scheme of the charging load at a plurality of charging stations is found to improve the output of the DG, and an automobile charging planning scheme is made in advance. Setting the total charging load in the power grid at a certain moment t as Pt.evsAnd each charging station load Pt.eviThe achievable upper limit values are all the total charging load, and the equation can be written as:
Figure BDA0001439129170000141
0≤Pt.evi≤Pt.evs
in order to complete optimization calculation at each moment and find the optimal load of the charging station, the invention substitutes the above inequality constraint into an equality constraint into an equation calculation by means of a trigonometric function, and then a charging load distribution equation is as follows:
Figure BDA0001439129170000142
and the total charge load constraint can be written as:
Figure BDA0001439129170000143
or simply as:
Figure BDA0001439129170000144
in the above formula, n is the number of charging stations. Constraining the control angle α by adjusting the variables in equations 10,11iThe size can change the load bearing capacity of each charging station under the condition of ensuring the total load capacity of the charging stations to be constant. The equation constraint makes each charging station possible to bear the whole charging load, and fully ensures the charging flexibility of users. The distributed power supply power optimization equation considering the load optimal distribution scheme in a certain time is as follows:
minF＝f(Pt.DGmax-Pt.DG) t∈Nt
Figure BDA0001439129170000151
according to the formula, the load P of each charging station under the maximum output target of the distributed power supply can be expected to be plannedt.eviAnd distributing the state to guide the user to charge at a proper charging station.
Based on the same inventive concept, the invention also provides an orderly charging system for electric vehicles, comprising:
the building module is used for building a peak-valley period transfer proportion model of the electric automobile in advance based on historical charging state data of the electric automobile;
the defining module is used for defining peak-valley electricity price difference constraint for the peak-valley time period transfer proportional model;
the acquisition module is used for acquiring a user response proportion and a peak-to-valley electricity price difference which meet the optimal output of the distributed power supply at each moment by solving a charging load transfer equation containing peak-to-valley electricity price difference constraints;
the charging module is used for guiding the electric automobile to a corresponding charging station for charging based on the optimal user response proportion and the peak-valley electricity price difference;
and the calculation module is used for calculating the maximum output power of the distributed power supply under the optimal output target of the distributed power supply by adopting a distributed power supply power output optimization algorithm.
Preferably, the electric vehicle peak-valley period transfer proportion model is established by the following formula:
L＝N·Pev·λ＝N·Pev·f(β,Δt)
in the above formula, L represents the total load transferred during peak period of charge peak, λ is the user response ratio, β is the peak-to-valley electricity price difference, N is the number of cars charged during peak period, PevAnd charging power for the automobile.
Preferably, the peak to valley electricity price difference constraint is determined by:
βt.min≤βt≤βt.max
in the above formula, betat.maxAnd betat.minRespectively an upper limit and a lower limit of the peak-to-valley valence difference at the time t, betatThe peak-to-valley electricity price difference at the time t; t denotes the current time.
Preferably, the calculation module includes:
the conversion submodule is used for converting the peak-valley electricity price difference constraint into an equality constraint based on a trigonometric function;
the determining submodule is used for substituting the equation constraint into the electric vehicle peak-valley period transfer proportion model to obtain a charging load transfer equation containing peak-valley electricity price difference constraint;
the optimization submodule is used for determining a DG output maximum optimization function and a constraint condition at each moment by using the following formula;
minF＝f(Pt.DGmax-Pt.DG) t∈Nt
Figure BDA0001439129170000161
in the above formula, N1For not containing the number of charging station nodes, N2For charging station, N is the number of nodes, thetaijIs the voltage angle difference between nodes i, j, NtIs the number of times NsIs the number of branches, NDGFor the number of distributed power sources, NeFor the number of ESS, NSVCThe number of the reactive power sources; pDG.i，QG.iRespectively the active power and the reactive power output by a distributed power supply or a reactive power supply in the power grid, SlFor the actual power flowing through the branch, ESSiThe active power output by the energy storage device is the power output range of the energy storage device, and the power output range of the energy storage device is related to the model and the state of charge of the battery;
and the obtaining submodule is used for obtaining the peak-valley electricity price difference by solving the average value of the peak-valley electricity price difference at each time of the valley period.
Preferably, the distributed computing module is further configured to compute the maximum output power of the distributed power supply under the distributed power supply output optimal target by using a distributed power supply power output optimization algorithm.
Preferably, the distributed computing module further comprises:
the charging power calculation submodule is used for calculating the charging power in each time period;
the charging power definition submodule is used for defining a charging load distribution equation and a charging total load constraint;
a charging power optimization submodule for constraining a control angle alpha by adjusting variables in the total charging load constraintiThe size of the charging station is changed to change the load bearing capacity of each charging station;
and the charging power solving submodule is used for solving the distributed power supply output optimization equation considering the load optimal distribution scheme to obtain the load of each charging station under the distributed power supply output optimal target.
Preferably, the charging power of each period is determined by the following formula:
Figure BDA0001439129170000171
0≤Pt.evi≤Pt.evs
in the formula, Pt.evsFor charging the total load, P, in the grid at time tt.evFor charging station load, n is the number of charging stations.
Preferably, the charge load distribution equation is determined by the following equation:
Figure BDA0001439129170000172
preferably, the charging power solving submodule includes:
a total charging load optimization subunit, configured to determine a distributed power supply output optimization equation considering the load optimal distribution scheme according to the following formula:
minF＝f(Pt.DGmax-Pt.DG) t∈Nt
Figure BDA0001439129170000173
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.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting the protection scope thereof, and although the present application is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: numerous variations, modifications, and equivalents will occur to those skilled in the art upon reading the present application and are within the scope of the claims appended hereto.

Claims (11)

1. An orderly charging method for an electric automobile, which is characterized by comprising the following steps:
establishing a peak-valley period transfer proportion model of the electric vehicle in advance based on historical charging state data of the electric vehicle;
defining a peak-to-valley electrovalence difference constraint for the peak-to-valley time period transfer ratio model;
obtaining a user response proportion and a peak-valley power price difference which meet the optimal output of the distributed power supply at each moment by solving a charging load transfer equation containing peak-valley power price difference constraints;
guiding the electric vehicle to a corresponding charging station for charging based on the optimal user response proportion and the peak-valley electricity price difference;
the establishing of the electric vehicle peak-valley period transfer proportion model based on the historical electric vehicle charging state data comprises the following steps:
establishing a peak-valley period transfer proportion model of the electric automobile by the following formula:
L＝N·Pev·λ＝N·Pev·f(β,Δt) (2)
in the above formula, L represents the total load transferred during peak period of charge peak, λ is the user response ratio, β is the peak-to-valley electricity price difference, N is the number of cars charged during peak period, PevCharging power for the vehicle;
determining a peak to valley electricity price difference constraint by:
βt.min≤βt≤βt.max (3)
in the above formula, betat.maxAnd betat.minRespectively an upper limit and a lower limit of the peak-to-valley valence difference at the time t, betatPeak to valley at time tThe electricity price difference; t represents the current time;
equation (3) is transformed into an equality constraint based on a trigonometric function, as follows:
Figure FDA0003420868220000011
in the above formula, δiThe variable constraint control angle is changed between 0 pi and 2 pi;
bringing equation (4) into equation (2), a charge load transfer equation including a peak-to-valley valence difference constraint is obtained as follows:
Figure FDA0003420868220000012
determining the maximum output optimization function and the constraint condition of the distributed power supply DG at each moment by the following formula:
minF＝f(Pt.DGmax-Pt.DG) t∈Nt (6)
Figure FDA0003420868220000021
in formula 7, N1For not containing the number of charging station nodes, N2For containing the number of charging station nodes, N is the number of nodes, thetaijIs the voltage angle difference between nodes i, j, NtIs the number of times, NsIs the number of branches, NDGFor the number of distributed power sources, NeThe number of ESS;
NSVCthe number of the reactive power sources; pDG.i，QG.iRespectively the active power and the reactive power output by a distributed power supply or a reactive power supply in the power grid, SlFor the actual power flowing through the branch, ESSiThe active power output by the energy storage device has a power output range related to the model and the state of charge of the battery.
2. The method of claim 1, wherein the peak-to-valley electrovalence differences are obtained by averaging the peak-to-valley electrovalence differences at each time during the valley period.
3. The method of any one of claims 1-2, wherein the method for orderly charging an electric vehicle further comprises: and calculating the maximum output power of the distributed power supply under the optimal output target of the distributed power supply by adopting a distributed power supply power output optimization algorithm.
4. The method of claim 3, wherein calculating the maximum distributed power output power at the distributed power output optimization target comprises:
calculating the charging power in each time interval;
defining a charging load distribution equation and a charging total load constraint;
constraining a control angle α by adjusting a variable in the total charge load constraintiThe size of the charging station is changed to change the load bearing capacity of each charging station;
and solving the maximum output optimization function of the distributed power supply DG considering the optimal load distribution scheme to obtain the load of each charging station under the optimal output target of the distributed power supply.
5. The method of claim 4, wherein the period charging power is determined by:
Figure FDA0003420868220000022
0≤Pt.evi≤Pt.evs (9)
in the formula, Pt.evsFor charging the total load, P, in the grid at time tt.eviFor charging station load, n is the number of charging stations.
6. The method of claim 3, wherein the charge load distribution equation is determined by:
Figure FDA0003420868220000031
and a total charge load constraint as shown by:
Figure FDA0003420868220000032
the method is simplified to obtain:
Figure FDA0003420868220000033
in the above formula, n is the number of charging stations, αiRepresenting the variable constraint control angle.
7. An orderly electric vehicle charging system, comprising:
the building module is used for building a peak-valley period transfer proportion model of the electric automobile in advance based on historical charging state data of the electric automobile;
the defining module is used for defining peak-valley electricity price difference constraint for the peak-valley time period transfer proportional model;
the acquisition module is used for acquiring a user response proportion and a peak-to-valley electricity price difference which meet the optimal output of the distributed power supply at each moment by solving a charging load transfer equation containing peak-to-valley electricity price difference constraints;
the charging module is used for guiding the electric vehicle to a corresponding charging station for charging based on the optimal user response proportion and the peak-valley electricity price difference;
the calculation module is used for calculating the maximum output power of the distributed power supply under the optimal output target of the distributed power supply by adopting a distributed power supply power output optimization algorithm;
the electric automobile peak-valley period transfer proportion model is established according to the following formula:
L＝N·Pev·λ＝N·Pev·f(β,Δt)
in the above formula, L represents the total load transferred during peak chargeλ is user response ratio, β is peak-to-valley electricity price difference, N is number of cars charged in peak period, P isevCharging power for the vehicle;
determining a peak to valley electricity price difference constraint by:
βt.min≤βt≤βt.max
in the above formula, betat.maxAnd betat.minRespectively an upper limit and a lower limit of the peak-to-valley valence difference at the time t, betatThe peak-to-valley electricity price difference at the time t; t represents the current time;
the calculation module comprises:
the conversion submodule is used for converting the peak-valley electricity price difference constraint into an equality constraint based on a trigonometric function;
the determining submodule is used for substituting the equation constraint into the electric vehicle peak-valley period transfer proportion model to obtain a charging load transfer equation containing peak-valley electricity price difference constraint;
the optimization submodule is used for determining the maximum output optimization function and the constraint conditions of the distributed power supply DG at each moment by using the following formula;
minF＝f(Pt.DGmax-Pt.DG)t∈Nt
Figure FDA0003420868220000041
in the above formula, N1For not containing the number of charging station nodes, N2For containing the number of charging station nodes, N is the number of nodes, thetaijIs the voltage angle difference between nodes i, j, NtIs the number of times, NsIs the number of branches, NDGFor the number of distributed power sources, NeFor the number of ESS, NSVCThe number of the reactive power sources; pDG.i，QG.iRespectively the active power and the reactive power output by a distributed power supply or a reactive power supply in the power grid, SlFor the actual power flowing through the branch, ESSiThe active power output by the energy storage device is the power output range of the energy storage device, and the power output range of the energy storage device is related to the model and the state of charge of the battery;
and the obtaining submodule is used for obtaining the peak-valley electricity price difference by solving the average value of the peak-valley electricity price difference at each time of the valley period.
8. The system of claim 7, further comprising a distributed computing module to compute a maximum distributed power output power at the distributed power output optimization target using a distributed power output optimization algorithm.
9. The system of claim 8, wherein the distributed computing module further comprises:
the charging power calculation submodule is used for calculating the charging power in each time period;
the charging power definition submodule is used for defining a charging load distribution equation and a charging total load constraint;
a charging power optimization submodule for constraining a control angle alpha by adjusting variables in the total charging load constraintiThe size of the charging station is changed to change the load bearing capacity of each charging station;
and the charging power solving submodule is used for solving the maximum output optimization function of the distributed power supply DG considering the load optimal distribution scheme to obtain the load of each charging station under the optimal output target of the distributed power supply.
10. The system of claim 9, wherein the charging power per period is determined by:
Figure FDA0003420868220000042
0≤Pt.evi≤Pt.evs
in the formula, Pt.evsFor charging the total load, P, in the grid at time tt.eviFor charging station load, n is the number of charging stations.
11. The system of claim 10, wherein the charge load distribution equation is determined by:
Figure FDA0003420868220000051
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CN109017399B (en) * 2018-08-13 2020-05-05 青岛特来电新能源科技有限公司 Intelligent charging system and method for electric automobile
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103683424A (en) * 2013-12-17 2014-03-26 清华大学 Electric vehicle charging station sequential charging control method based on dynamic time-of-use electricity price
KR20140038796A (en) * 2012-09-21 2014-03-31 한국전력공사 Method and apparatus for controlling charging station of electrical vehicle applying microgrid
CN104376385A (en) * 2014-11-27 2015-02-25 重庆大学 Microgrid power price optimizing method
WO2015123737A1 (en) * 2014-02-20 2015-08-27 Coordenação Dos Programas De Pós Graduação De Engenharia Da Universidade Federal Do Rio De Janeiro Smart energy management systems for electric and hybrid electric vehicles with bidirectional connection, smart energy management system for an energy generator, method for managing energy in a smart energy management system and method for controlling the operation of an energy generator
CN105515030A (en) * 2015-11-27 2016-04-20 中国电力科学研究院 Ordered charging method of electric cars connected to power grid
CN107248751A (en) * 2017-07-26 2017-10-13 武汉大学 A kind of energy storage station dispatch control method for realizing distribution network load power peak load shifting

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20140038796A (en) * 2012-09-21 2014-03-31 한국전력공사 Method and apparatus for controlling charging station of electrical vehicle applying microgrid
CN103683424A (en) * 2013-12-17 2014-03-26 清华大学 Electric vehicle charging station sequential charging control method based on dynamic time-of-use electricity price
WO2015123737A1 (en) * 2014-02-20 2015-08-27 Coordenação Dos Programas De Pós Graduação De Engenharia Da Universidade Federal Do Rio De Janeiro Smart energy management systems for electric and hybrid electric vehicles with bidirectional connection, smart energy management system for an energy generator, method for managing energy in a smart energy management system and method for controlling the operation of an energy generator
CN104376385A (en) * 2014-11-27 2015-02-25 重庆大学 Microgrid power price optimizing method
CN105515030A (en) * 2015-11-27 2016-04-20 中国电力科学研究院 Ordered charging method of electric cars connected to power grid
CN107248751A (en) * 2017-07-26 2017-10-13 武汉大学 A kind of energy storage station dispatch control method for realizing distribution network load power peak load shifting

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