CN114400653A - Electric vehicle charging optimization method based on day-ahead load predicted value - Google Patents

Electric vehicle charging optimization method based on day-ahead load predicted value Download PDF

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CN114400653A
CN114400653A CN202111543224.3A CN202111543224A CN114400653A CN 114400653 A CN114400653 A CN 114400653A CN 202111543224 A CN202111543224 A CN 202111543224A CN 114400653 A CN114400653 A CN 114400653A
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transformer
power
charging
expression
load
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周星月
王智东
张紫凡
王玕
吴�灿
郭琳
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Guangzhou City University of Technology
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Guangzhou City 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • 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
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • 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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • 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)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

An electric vehicle charging optimization method based on a day-ahead load predicted value comprises the following steps: acquiring parameters and reactive economic equivalent of a transformer of a charging station; calculating the optimal comprehensive operation efficiency of the charging station transformer; dividing one day into 24 time intervals, taking each hour as one time interval, respectively optimizing the charging power of the charging pile of each time interval charging station in real time, and defining the benchmark optimization coefficient of each time interval as kiObtaining the power reference optimization coefficient k of the electric automobile in each time periodi(ii) a Acquiring the number of electric vehicles being charged in a charging station and the battery residual capacity SOC of each electric vehicle in a certain period; acquiring the number of electric vehicles being charged in a charging station and the battery residual capacity SOC of each electric vehicle in a certain period; the invention can optimize the power of the electric automobile which is accessed to the charging station in each time period in one day in real time, and optimize the power by using the reference optimization coefficient of each time period, thereby greatly reducing the calculation amount and leading the electric automobile to be accessed to the charging station in each time periodThe transformer operates with optimum overall efficiency.

Description

Electric vehicle charging optimization method based on day-ahead load predicted value
Technical Field
The invention relates to the technical field of electric vehicle charging, in particular to an electric vehicle charging optimization method based on a day-ahead load predicted value.
Background
In recent years, the demand for energy and environment is increasing, the shortage of fossil fuel and global warming are attracting more and more attention, and the increase of environmental protection concept makes people strongly demand the reduction of petroleum consumption in the aspect of traffic. The electric automobile has good energy-saving and low-emission potential due to a special energy driving mode, and is widely developed. The electric automobile can improve the energy utilization efficiency and reduce the pollution to the environment, the popularization and promotion of the electric automobile become the future trend, and active policy measures are also adopted by various countries to encourage the development of the electric automobile.
The electrified revolution in transportation systems has gradually shifted the energy demand of vehicles from fossil fuels to electric power systems. However, with the large-scale development of electric vehicles, because the charging behavior of vehicle owners is often random, a large number of electric vehicles are connected to a power grid for charging, and will certainly cause huge pressure on the structure and operation of the power grid. The load peak-valley difference is an important safe and economic index for the operation of the power system, and the aggravation of the peak-valley difference can bring adverse consequences such as the reduction of the utilization efficiency of power grid equipment, the increase of the power purchasing risk of a power supply side and the like. A large number of electric vehicles are randomly accessed into a power grid to carry out disordered charging, so that the load peak-valley difference of the system is further aggravated, and negative effects are brought to the running state of a distribution network.
The charging and discharging of the electric automobile connected to the power distribution network are reasonably controlled, so that the influence of large-scale electric automobile charging on the power distribution network can be reduced, and the requirements of the stability and the economy of a power distribution system are met. At present, many research achievements for ordered charging and discharging of electric vehicles are available at home and abroad. The current charging control strategies for electric vehicles are mainly divided into two types: one method is that a reasonable time-of-use electricity price is formulated, a reasonable electricity price gradient is set by avoiding a dead zone threshold value of electricity price difference, the willingness of an electric vehicle user to actively transfer a charging time period is actively mobilized, so that loads are reasonably distributed in a time dimension, the load peak clipping and valley filling effects of a power system are achieved, and meanwhile, the charging cost of the electric vehicle user is reduced; the other is that the charging station operator signs an agreement with the user, and according to the requirements of peak clipping, valley filling and the like of the power grid, the charging load is directly controlled by changing the charging power of the electric vehicle or stopping the charging of the electric vehicle, and certain economic compensation is carried out on the user.
In order to solve the technical problem, a method for optimally scheduling charging and discharging of an electric vehicle based on a virtual electricity price is disclosed in a patent document with a chinese patent number of 201510458366.8 and a publication date of 2017.12.29, and comprises the following steps: the electric energy public service platform predicts and samples basic daily load information of a target area in an optimization time period; when a new EV is accessed to a charging pile in a target area, the network access information of the charging pile is read; a user inputs charging information of a vehicle; constructing an EV charge-discharge power model; calculating virtual electricity price information, and indirectly reflecting the load level of a target area; constructing a scheduling model with charge and discharge power as an optimization variable; determining dynamic time-of-use electricity price for user cost calculation by integrating wavelet analysis preprocessing and fuzzy clustering methods; an autonomous response decision of the user; and carrying out charging and discharging operations on the EV according to the user decision and uploading the plan.
According to the technical scheme disclosed in the patent document, the corresponding scheduling scheme of the user is stimulated by combining the power supply and demand and the dynamic time-of-use electricity price, so that the optimal scheduling of the discharging of the charging station is performed, and although the discharging pressure of a power grid system is relieved to a certain extent, the technical scheme has the following problems:
(1) the willingness of the user to transfer the charging period is called by the time-of-use price, so that the uncertainty is high, and the controllability is low; meanwhile, the willingness of the electric vehicle user to actively change the living habits or the travel demands to transfer the charging time period is low, and the electric vehicle needs to be scheduled by a large peak-to-valley electricity price difference; on the other hand, in order to improve the enthusiasm of the users for participating in scheduling, the charging station operator needs to make a large concession on the electricity price, and the profit demand of the charging station operator will be affected, so that the operator has a low willingness to adopt the strategy and needs to make a certain degree of subsidy on the charging station operator by a local government.
(2) The compensation excitation type control strategy needs to acquire real-time power change of a power system, control the electric vehicle connected to the charging station at each moment, and optimize the calculated amount to be large; meanwhile, the method directly schedules the charging load, and has great influence on the charging activity and the charging experience satisfaction of the user.
Thus, the load optimization calculation amount of the charging station is large, and the initiative of power regulation and control cannot be mastered.
Disclosure of Invention
The invention provides an electric vehicle charging optimization method based on a day-ahead load predicted value, by utilizing the scheme of the invention, under the condition of not making time-of-use electricity price, the initiative of power regulation and control is mastered, the real-time requirement of a power system is not required to be obtained, the calculation amount of load optimization is greatly reduced, and a transformer is enabled to run at the optimal comprehensive efficiency
In order to achieve the purpose, the technical scheme of the invention is as follows: a real-time electric vehicle charging power optimization method based on a day-ahead load predicted value is used for charging electric vehicles by a charging station, and comprises the following steps:
(S1) acquiring total loss power and reactive economic equivalent K of the transformer of the charging stationq；
When the transformer operates, the total loss power of the transformer includes no-load loss and load loss, and then the expression of active power loss Δ P is:
ΔP=P0+β2PK （1）。
in expression (1), Δ P is the active power loss of the transformer, P0Is the no-load loss of the transformer, beta is the load factor of the transformer, PkActive loss for short circuit of the transformer; delta P, P0And PkAll units are KW.
The expression of the transformer load factor beta is as follows:
Figure 473446DEST_PATH_IMAGE001
（2）。
in expression (2), S2For the second time of the transformerSide calculation load, SNRated capacity of the transformer, P2Is the secondary side power of the transformer, cos phi2Is the load power factor; s2And SNAll units of (A) are kVA, P2In KW.
The expression of the primary side power of the transformer is as follows: p1=P2+ΔP（3）。
The expression for the efficiency of the transformer as output power versus input power is:
Figure 185050DEST_PATH_IMAGE002
（4）。
in the expression (4), ηpFor the operating efficiency of the transformer, P1Is the transformer primary side power.
The first derivative is taken for β and made equal to 0 by expression (4):
Figure 253500DEST_PATH_IMAGE003
（5）。
the conditions that need to be satisfied by the load factor when the maximum efficiency of the transformer can be found are as follows:
P0=β2 jPPk（6）。
in the expression (6), βjPThe load factor is the corresponding load factor when the transformer operates at the maximum efficiency; expression (6) illustrates the active loss P when the transformer is short-circuitedKEqual to no-load loss P of transformer0Then the transformer reaches the optimum operation efficiency and the corresponding load factor betajPExpression (6) of (a) can be simplified as:
Figure 392358DEST_PATH_IMAGE004
（7）。
the reactive power loss Δ Q of the transformer comprises QkAnd Q0，QkFor transformer loaded with reactive losses, Q0For the no-load reactive loss of the transformer, the relational expression is as follows:
ΔQ=Q0+β2Qk （8）。
as can be seen from expression (8), the reactive power transmission efficiency expression of the transformer is:
Figure 241365DEST_PATH_IMAGE005
（9）。
Q1for reactive power loss at the primary side of the transformer, Q2Is the reactive power loss on the secondary side of the transformer.
When the transformer load coefficient obtained by obtaining the maximum value of the expression (9) satisfies the following expression, the transformer reactive loss is minimum, and the load coefficient beta of the transformer reactive power maximum transmission efficiency is the load coefficient betajQThe expression is as follows:
Figure 491081DEST_PATH_IMAGE006
（10）。
in the expression (10), I0For no-load current of the transformer, UkIs the short-circuit voltage of the transformer.
(S2) determining an optimal overall operating efficiency of the charging station transformer based on the total power loss of the charging station transformer.
When the electric energy saving is considered, the transformer can be operated at the optimal operation efficiency of betajPThe vicinity of the point; when the power factor is considered to be improved, the transformer can be operated at the maximum transmission efficiency beta of the reactive powerjQThe vicinity of the point; when the two are considered comprehensively, the comprehensive power operation efficiency of the transformer is as follows:
Figure 804250DEST_PATH_IMAGE007
，
PZ0= P0+KqQ0，PZk= Pk+KqQk（11）。
in expression (11), KqFor the economic equivalent of reactive power, is an intrinsic parameter of the transformer, which is defined as 1kvar per reduction in the transformation and distribution systemReactive power, enabling active power loss to be reduced by KqkW。
When the expression (11) is maximum, the load coefficient beta at the maximum of the integrated power of the transformer can be obtainedjZExpression (c):
Figure 114009DEST_PATH_IMAGE008
（12）。
at the moment, the comprehensive power transmission efficiency of the transformer reaches the maximum value.
According to the calculation formula of the transformer load factor, the optimal charging power P of the charging station can be calculated according to the transformer capacity and the power factore=P2（β=βjZ）（13）。
(S3) dividing one day into 24 time intervals, wherein each hour is one time interval, and the charging station defines the reference optimization coefficient of each time interval as k according to the charging power of the charging station charging pile of each time intervaliAnd obtaining the power reference optimization coefficient k of the electric automobile in each time periodi。
Figure 450312DEST_PATH_IMAGE009
（14）。
Wherein, Pi(t) is the charging load magnitude based on the i-th period predicted before the day, assuming Pi(t) known, PeThe optimal charging power of the charging station.
(S4) the charging station acquires the number of electric vehicles being charged in the charging station and the battery remaining capacity SOC of each electric vehicle during a certain period of time.
(S5) the charging station adopts charging power regulation and control according to the time interval and the battery residual capacity SOC of each electric automobile.
When the charging pile number of the charging station is sufficient, the charging pile in the charging station acquires the charge state of the accessed electric vehicle battery, namely the residual electric quantity SOC of the battery of the electric vehiclej，（j∈[0，SOCm]）；SOCmMaximum charge capacity, SOC, to ensure battery life of an electric vehiclem∈[0.9，0.98](ii) a Setting the rated charging power of all electric automobiles to be P1And the upper limit of the charging power is P1maxLimit P under charging power1min(ii) a Based on the predicted charging load size P of the periodi（t）。
(S5.1) when the residual battery capacity SOC of the electric automobile is detectedj<25% or
Figure 546761DEST_PATH_IMAGE012
(15) During, the regulation and control of the charging power of the electric automobile are laterally reduced, and the charging power of the electric automobile is improved: pC=P1。
In expression (15), i.e., when the battery remaining amount SOC of the electric vehicle is too low or when the battery of the electric vehicle can be fully charged in this period, PC=P1(ii) a S in expression (15)nIs the capacity of the battery of the electric vehicle, etacFor the charging efficiency of the present electric vehicle, etac∈[0.85，0.9]；TjThe remaining charging time of the electric vehicle; s2(kVA) is rated capacity of transformer, P1Primary side power of the transformer; therefore, on one hand, the charging requirement of a user with partial electric quantity emergency can be met, on the other hand, the user requirement that part of the user is about to be fully charged in the period can be guaranteed, the satisfaction degree of the user is improved, and meanwhile, the regulation and control pressure in the next period is reduced.
(S5.2) the current battery residual capacity of the electric automobile is 25%<SOCj<When 50 percent of the total weight is:
Figure 293001DEST_PATH_IMAGE013
Figure 585442DEST_PATH_IMAGE014
（16）。
then
Figure 521168DEST_PATH_IMAGE015
(17) (ii) a Namely, it is
Figure 215454DEST_PATH_IMAGE016
In order to adjust the total amount of power required,
Figure 132595DEST_PATH_IMAGE017
indicating that the total amount of regulated power needs to be shared equally among each electric vehicle that accesses the charging station during that time period.
(S5.3) when the residual battery capacity SOC of the electric automobile is detectedj>50%, then improve the regulation and control to this electric automobile's charging power, reduce charging power:
then:
Figure 177911DEST_PATH_IMAGE018
（18）；
Figure 776383DEST_PATH_IMAGE019
（19）。
when the residual capacity of the electric automobile meets the SOCj>At 50%, the remaining electric quantity of the electric vehicle is more sufficient than the condition in (S5.2), so the regulated power of the electric vehicle can be properly increased to
Figure 325176DEST_PATH_IMAGE020
Wherein n is the number of electric vehicles arriving at the charging station in a certain period of time obtained based on the day-ahead data, and X is the residual electric quantity of the corresponding electric vehicles meeting the SOCj>And (4) a scheduling coefficient of charging power of the electric automobile at 50%, wherein X belongs to (1, 2.5).
Further, in the step (S5.1), SOCmIs 0.95.
Further, in the step (S3), when the predicted charging power is lower than the optimum charging power for a period of time, in order to ensure the operation efficiency of the transformer, the charging power of each vehicle is increased within the limit range of the charging power of the electric vehicle, at which time k is set to be lower than kiA negative number may be taken.
Further, in said step (S5.3), X is 2.
Drawings
FIG. 1 is a block flow diagram of the present invention.
FIG. 2 is a day-ahead load curve of the electric vehicle according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1-2, an electric vehicle charging optimization method based on a day-ahead load predicted value is used for charging an electric vehicle by a charging station, and the optimization method includes the following steps:
(S1) acquiring total loss power and reactive economic equivalent K of the transformer of the charging stationq。
When the transformer is in operation, the total loss power of the transformer comprises no-load loss P0And load loss PKThen, the expression of the active power loss Δ P is:
ΔP=P0+β2PK （1）。
in expression (1), Δ P is the active power loss of the transformer, P0Is the no-load loss of the transformer, beta is the load factor of the transformer, PkActive loss for short circuit of the transformer; delta P, P0And PkThe units are KW; i.e. the copper loss of the transformer at rated current.
The expression of the transformer load factor beta is as follows:
Figure 678797DEST_PATH_IMAGE021
（2）。
in expression (2), S2Calculating a load for a secondary side of the transformer, the secondary side of the transformer being an output side of the transformer; sNRated capacity of the transformer, P2Is the secondary side power of the transformer, cos phi2Is the load power factor; s2And SNAll units of (A) are kVA, P2In KW.
The expression of the primary side power of the transformer is as follows: p1=P2+ΔP（3）。
The expression for the efficiency of the transformer as output power versus input power is:
Figure 945830DEST_PATH_IMAGE022
（4）。
in the expression (4), ηpFor the operating efficiency of the transformer, P1Is the transformer primary side power.
The first derivative is taken for β and made equal to 0 by expression (4):
Figure 472626DEST_PATH_IMAGE023
（5）。
the conditions that need to be satisfied by the load factor when the maximum efficiency of the transformer can be found are as follows:
P0=β2 jPPk（6）。
in the expression (6), βjPThe load factor is the corresponding load factor when the transformer operates at the maximum efficiency; pKActive loss for short circuit of the transformer, i.e. copper loss; p0The no-load loss of the transformer, namely iron loss; expression (6) illustrates the active loss P when the transformer is short-circuitedKEqual to no-load loss P of transformer0Then the transformer reaches the optimum operation efficiency and the corresponding load factor betajPExpression (6) of (a) can be simplified as:
Figure 875926DEST_PATH_IMAGE024
（7）。
the reactive power loss Δ Q of the transformer comprises QkAnd Q0，QkFor transformer loaded with reactive losses, Q0For the no-load reactive loss of the transformer, the relational expression is as follows:
ΔQ=Q0+β2Qk （8）。
as can be seen from expression (8), the reactive power transmission efficiency expression of the transformer is:
Figure 400448DEST_PATH_IMAGE025
（9）。
Q1for reactive power loss at the primary side of the transformer, Q2Is the reactive power loss on the secondary side of the transformer.
When the transformer load coefficient obtained by obtaining the maximum value of the expression (9) satisfies the following expression, the transformer reactive loss is minimum, and the load coefficient beta of the transformer reactive power maximum transmission efficiency is the load coefficient betajQThe expression is as follows:
Figure 154777DEST_PATH_IMAGE026
10）。
in the expression (10), I0For no-load current of the transformer, UkIs the short-circuit voltage of the transformer.
(S2) determining an optimal overall operating efficiency of the charging station transformer based on the total power loss of the charging station transformer.
When the electric energy saving is considered, the transformer can be operated at the optimal operation efficiency of betajPThe vicinity of the point; when the power factor is considered to be improved, the transformer can be operated at the maximum transmission efficiency beta of the reactive powerjQThe vicinity of the point; when the two are considered comprehensively, the comprehensive power operation efficiency of the transformer is as follows:
Figure 95052DEST_PATH_IMAGE027
，
PZ0= P0+KqQ0，PZk= Pk+KqQk（11）。
in expression (11), KqIs an inherent parameter of the transformer for the reactive economic equivalent, and is defined as that the active power loss can be reduced by K for every 1kvar of reactive power reduction in the variable power distribution systemqkW。
When the expression (11) is maximum, the load coefficient beta at the maximum of the integrated power of the transformer can be obtainedjZExpression (c):
Figure 618437DEST_PATH_IMAGE028
（12）。
at the moment, the comprehensive power transmission efficiency of the transformer reaches the maximum value.
According to the calculation formula of the transformer load factor, the optimal charging power P of the charging station can be calculated according to the transformer capacity and the power factore=P2（β=βjZ）（13）。
(S3) dividing one day into 24 time periods, each hour being one time period, the charging station defining a reference optimization coefficient k for each time period according to the charging power of the charging station charging post for each time periodiAnd calculating the power reference optimization coefficient k of the electric automobile in each time periodi(ii) a In this embodiment, when the predicted charging power is lower than the optimal charging power in a certain period of time, in order to ensure the operation efficiency of the transformer, the charging power of each vehicle may be appropriately increased within the limit range of the charging power of the electric vehicle, at this time kiA negative number may be taken.
Figure 48281DEST_PATH_IMAGE029
（14）。
Wherein, Pi(t) is the charging load magnitude based on the i-th period predicted before the day, assuming Pi(t) known, PeThe optimal charging power of the charging station.
(S4) the charging station acquires the number of electric vehicles being charged in the charging station and the battery remaining capacity SOC of each electric vehicle during a certain period of time.
(S5) the charging station adopts charging power regulation and control according to the time interval and the battery residual capacity SOC of each electric automobile.
When the charging pile number of the charging station is sufficient, the charging pile in the charging station acquires the charge state of the accessed electric vehicle battery, namely the residual electric quantity SOC of the battery of the electric vehiclej，（j∈[0，SOCm]）；SOCmMaximum charge capacity, SOC, to ensure battery life of an electric vehiclem∈[0.9，0.98](ii) a Setting the rated charging power of all electric automobiles to be P1And the upper limit of the charging power is P1maxThe lower limit of charging power is P1min(ii) a Based on the predicted charging load size P of the periodi（t）。
(S5.1) when the residual battery capacity SOC of the electric automobile is detectedj<25% or
Figure 830609DEST_PATH_IMAGE012
(15) During, the regulation and control of the charging power of the electric automobile are laterally reduced, and the charging power of the electric automobile is improved: pC=P1。
In expression (15), i.e., when the battery remaining amount SOC of the electric vehicle is too low or when the battery of the electric vehicle can be fully charged in this period, PC=P1(ii) a S in expression (15)nIs the capacity of the battery of the electric vehicle, etacFor the charging efficiency of the present electric vehicle, etac∈[0.85，0.9]In the present embodiment, ηcIs 0.9, SOCmIs 0.95; t isjThe remaining charging time of the electric vehicle; s2(kVA) is rated capacity of transformer, P1Primary side power of the transformer; therefore, on one hand, the charging requirement of a user with partial electric quantity emergency can be met, on the other hand, the user requirement that part of the user is about to be fully charged in the period can be guaranteed, the satisfaction degree of the user is improved, and meanwhile, the regulation and control pressure in the next period is reduced.
(S5.2) the current battery residual capacity of the electric automobile is 25%<SOCj<When 50 percent of the total weight is:
Figure 51244DEST_PATH_IMAGE013
Figure 386410DEST_PATH_IMAGE014
（16）；
then
Figure 380911DEST_PATH_IMAGE030
（17）。
When the battery of the electric automobile remainsThe residual electricity quantity is 25 percent<SOCj<50%, and the charging power is set to the original rated charging power P1And the part is increased or reduced, and the charging power is still within the limit range after the change, otherwise, the corresponding upper limit power P is taken1maxOr lower limit power P1min(ii) a Power change amount Δ P1In relation to the difference between the predicted load and the optimum load for the period, the larger the difference, the more the portion needs to be adjusted. Thus, the coefficient k can be optimized with the referenceiTo perform a calculation, i.e.
Figure 459726DEST_PATH_IMAGE031
For the total amount of power that needs to be adjusted, and
Figure 160965DEST_PATH_IMAGE032
the total amount of the regulated power is required to be averagely distributed to each electric vehicle which is accessed to the charging station in the time period; however, since n represents the number of electric vehicles arriving at the charging station at a certain time based on the day-ahead data, and if n is known to be larger than the number of electric vehicles satisfying the partial conditions, the total power cannot be actually distributed to each electric vehicle, and in order to make the actual charging power of the charging station close to the optimal charging power as much as possible and to improve the overall operation efficiency of the transformer, further power optimization is performed on other electric vehicles.
(S5.3) when the residual battery capacity SOC of the electric automobile is detectedj>When 50%, then improve the regulation and control to this electric automobile's charging power, reduce charging power:
then:
Figure 667033DEST_PATH_IMAGE033
（18），
Figure 148830DEST_PATH_IMAGE034
（19）；
when the residual capacity of the electric automobile meets the SOCj>At 50%, the remaining electric power of the electric vehicle is higher than that in (S5.2)Sufficient, so that the power change amount Δ P of the electric vehicle in this portion2Can also be increased appropriately to
Figure 31335DEST_PATH_IMAGE020
Wherein n is the number of electric vehicles arriving at the charging station in a certain period of time obtained based on the day-ahead data, and X belongs to (1, 2.5) to compensate the problem that the number of electric vehicles in (S5.2) is insufficient; in this embodiment, X is 2.
According to the method, the optimal comprehensive operation efficiency and the optimal charging power of the transformer are calculated according to the parameters of the transformer of the charging station; then, dividing one day into 24 periods, and providing a reference optimization coefficient k of the electric vehicle charging power in each period based on the predicted charging load size in a certain period before the dayi(ii) a If the difference between the predicted charging power and the optimal charging power in a certain time period is far away, the regulation and control force should be larger, and therefore the ratio of the difference between the predicted charging power and the optimal charging power in the time period to the average value of the difference in each time period is taken as a reference optimization coefficient of the time period so as to ensure proper regulation and control quantity; optimal comprehensive operation efficiency and optimal charging power based on transformer and reference optimization coefficient kiIn the electric vehicles connected to the charging station at the certain period, charging power regulation and control of electric vehicles with different residual electric quantity SOC of the battery are carried out in different amplitudes according to the residual electric quantity SOC of different batteries in different electric vehicles; for the electric automobile with lower residual capacity SOC, the regulation and control of the charging power of the electric automobile are reduced, so that the charging power of the electric automobile with lower residual capacity SOC is closer to the rated charging power of the electric automobile; for the electric automobile with higher residual capacity SOC, the regulation and control of the charging power of the electric automobile are improved, and the charging power of the electric automobile with higher residual capacity SOC is reduced; therefore, the actual charging power of the charging station can be close to the optimal charging power as much as possible, and the comprehensive operation efficiency of the transformer is improved; in addition, when the charging power is predicted to be lower than the optimal charging power in a certain period of time, in order to ensure the operation efficiency of the transformer, the charging power of each vehicle can be properly increased within the limit range of the charging power of the electric vehicle.

Claims (4)

1. The utility model provides an electric automobile optimization method that charges based on load predicted value day-ahead for the charging station charges electric automobile, its characterized in that:
the optimization method comprises the following steps:
(S1) acquiring total loss power and reactive economic equivalent K of the transformer of the charging stationq；
When the transformer operates, the total loss power of the transformer includes no-load loss and load loss, and then the expression of active power loss Δ P is:
ΔP=P0+β2PK （1）；
in expression (1), Δ P is the active power loss of the transformer, P0Is the no-load loss of the transformer, beta is the load factor of the transformer, PkActive loss for short circuit of the transformer; delta P, P0And PkThe units are KW;
the expression of the transformer load factor beta is as follows:
Figure RE-680525DEST_PATH_IMAGE001
（2）；
in expression (2), S2Calculating the load, S, for the secondary side of the transformerNRated capacity of the transformer, P2Is the secondary side power of the transformer, cos phi2Is the load power factor; s2And SNAll units of (A) are kVA, P2In KW;
the expression of the primary side power of the transformer is as follows: p1=P2+ΔP（3）；
The expression for the efficiency of the transformer as output power versus input power is:
Figure RE-330949DEST_PATH_IMAGE002
Figure RE-848518DEST_PATH_IMAGE003
（4）；
in the expression (4), ηpFor the operating efficiency of the transformer, P1Primary side power of the transformer;
the first derivative is taken for β and made equal to 0 by expression (4):
Figure RE-13920DEST_PATH_IMAGE004
（5）；
the conditions that need to be satisfied by the load factor when the maximum efficiency of the transformer can be found are as follows:
P0=β2 jPPk（6）；
in the expression (6), βjPThe load factor is the corresponding load factor when the transformer operates at the maximum efficiency; expression (6) illustrates the active loss P when the transformer is short-circuitedKEqual to no-load loss P of transformer0Then the transformer reaches the optimum operation efficiency and the corresponding load factor betajPExpression (6) of (a) can be simplified as:
Figure RE-48872DEST_PATH_IMAGE005
（7）；
the reactive power loss Δ Q of the transformer comprises QkAnd Q0，QkFor transformer loaded with reactive losses, Q0For the no-load reactive loss of the transformer, the relational expression is as follows:
ΔQ=Q0+β2Qk （8）；
as can be seen from expression (8), the reactive power transmission efficiency expression of the transformer is:
Figure RE-930634DEST_PATH_IMAGE006
（9）；
Q1for reactive power loss at the primary side of the transformer, Q2Is the reactive power loss of the secondary side of the transformer;
the maximum value of expression (9) is determinedWhen the load factor of the transformer meets the following expression, the reactive loss of the transformer is minimum, and the load factor beta of the maximum transmission efficiency of the reactive power of the transformer isjQThe expression is as follows:
Figure RE-556788DEST_PATH_IMAGE007
（10）；
in the expression (10), I0For no-load current of the transformer, UkIs the short circuit voltage of the transformer;
(S2) determining an optimal overall operating efficiency of the charging station transformer based on the total power loss of the charging station transformer;
when the electric energy saving is considered, the transformer can be operated at the optimal operation efficiency of betajPThe vicinity of the point; when the power factor is considered to be improved, the transformer can be operated at the maximum transmission efficiency beta of the reactive powerjQThe vicinity of the point; when the two are considered comprehensively, the comprehensive power operation efficiency of the transformer is as follows:
Figure RE-147169DEST_PATH_IMAGE008
，
PZ0= P0+KqQ0，PZk= Pk+KqQk（11）；
in expression (11), KqIs an inherent parameter of the transformer for the reactive economic equivalent, and is defined as that the active power loss can be reduced by K for every 1kvar of reactive power reduction in the variable power distribution systemqkW；
When the expression (11) is maximum, the load coefficient beta at the maximum of the integrated power of the transformer can be obtainedjZExpression (c):
Figure RE-782550DEST_PATH_IMAGE009
（12）；
at the moment, the comprehensive power transmission efficiency of the transformer reaches the maximum value;
according to the calculation formula of the transformer load factor, the optimal charging power P of the charging station can be calculated according to the transformer capacity and the power factore=P2（β=βjZ）（13）；
(S3) dividing one day into 24 time periods, each hour being one time period, the charging station defining a reference optimization coefficient k for each time period according to the charging power of the charging station charging post for each time periodiAnd obtaining the power reference optimization coefficient k of the electric automobile in each time periodi；
Figure RE-266621DEST_PATH_IMAGE010
（14）；
Wherein, Pi(t) is the charging load magnitude based on the i-th period predicted before the day, assuming Pi(t) known, PeThe optimal charging power of the charging station is obtained;
(S4) the charging station acquiring the number of electric vehicles being charged in the charging station and the battery remaining capacity SOC of each electric vehicle during a certain period of time;
(S5) the charging station adopts charging power regulation and control according to the time interval and the battery residual capacity SOC of each electric automobile;
when the charging pile number of the charging station is sufficient, the charging pile in the charging station acquires the charge state of the accessed electric vehicle battery, namely the residual electric quantity SOC of the battery of the electric vehiclej，j∈[0，SOCm]；SOCmMaximum charge capacity, SOC, to ensure battery life of an electric vehiclem∈[0.9，0.98](ii) a Setting the rated charging power of all electric automobiles to be P1And the upper limit of the charging power is P1maxLimit P under charging power1min(ii) a Based on the predicted charging load size P of the periodi（t）；
(S5.1) when the residual battery capacity SOC of the electric automobile is detectedj<25% or
Figure RE-266938DEST_PATH_IMAGE011
(15) While laterally lowering the pairThe regulation and control of electric automobile's charging power improves this electric automobile's charging power: pC=P1，
In expression (15), i.e., when the battery remaining amount SOC of the electric vehicle is too low or when the battery of the electric vehicle can be fully charged in this period, PC=P1(ii) a S in expression (15)nIs the capacity of the battery of the electric vehicle, etacFor the charging efficiency of the present electric vehicle, etac∈[0.85，0.9]；TjThe remaining charging time of the electric vehicle; s2Rated capacity of the transformer, P1Primary side power of the transformer;
(S5.2) the current battery residual capacity of the electric automobile is 25%<SOCj<When 50 percent of the total weight is:
Figure RE-141353DEST_PATH_IMAGE012
（16）；
then
Figure RE-393474DEST_PATH_IMAGE013
(17) (ii) a Namely, it is
Figure RE-872997DEST_PATH_IMAGE014
In order to adjust the total amount of power required,
Figure RE-106532DEST_PATH_IMAGE015
the total amount of the regulated power is required to be averagely distributed to each electric vehicle which is accessed to the charging station in the time period; (S5.3) when the residual battery capacity SOC of the electric automobile is detectedj>50%, then improve the regulation and control to this electric automobile's charging power, reduce charging power:
then:
Figure RE-530560DEST_PATH_IMAGE016
（18），
Figure RE-507743DEST_PATH_IMAGE017
（19），
when the residual capacity of the electric automobile meets the SOCj>At 50%, the remaining electric quantity of the electric vehicle is more sufficient than the condition in (S5.2), so the regulated power of the electric vehicle can be properly increased to
Figure RE-841772DEST_PATH_IMAGE018
Wherein n is the number of electric vehicles arriving at the charging station in a certain period of time obtained based on the day-ahead data, and X is the residual electric quantity of the corresponding electric vehicles meeting the SOCj>And (4) a scheduling coefficient of charging power of the electric automobile at 50%, wherein X belongs to (1, 2.5).
2. The electric vehicle charging optimization method based on the day-ahead load predicted value according to claim 1, characterized in that: in said step (S5.1), SOCmIs 0.95.
3. The electric vehicle charging optimization method based on the day-ahead load predicted value according to claim 1, characterized in that: in the step (S3), when the predicted charging power is lower than the optimum charging power for a period of time, the charging power of each vehicle is increased within the limit range of the charging power of the electric vehicle in order to secure the operation efficiency of the transformer, when k is the time when the charging power is predicted to be lower than the optimum charging poweriA negative number may be taken.
4. The electric vehicle charging optimization method based on the day-ahead load predicted value according to claim 1, characterized in that: in said step (S5.3), X is 2.
CN202111543224.3A 2021-12-16 2021-12-16 Electric vehicle charging optimization method based on day-ahead load predicted value Pending CN114400653A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116278915A (en) * 2023-05-16 2023-06-23 国网信息通信产业集团有限公司 Electric automobile load online optimization method, system, equipment and medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116278915A (en) * 2023-05-16 2023-06-23 国网信息通信产业集团有限公司 Electric automobile load online optimization method, system, equipment and medium
CN116278915B (en) * 2023-05-16 2023-10-13 国网信息通信产业集团有限公司 Electric automobile load online optimization method, system, equipment and medium

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