CN111422094B - Charging and discharging coordination optimization control method for distributed charging pile - Google Patents
Charging and discharging coordination optimization control method for distributed charging pile Download PDFInfo
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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
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
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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
- B60L53/63—Monitoring or controlling charging stations in response to network capacity
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- 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
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- 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
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- 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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Abstract
The invention discloses a charge-discharge coordination optimization control method for a distributed charge pile. According to the method, the charging load obtained through daily load prediction is utilized, a function between the lowest charging cost and daily charging load profit margin is generated by a decision generator, so that a probability distribution table of the charging margin and the discharging margin of the electric automobile in each time period in the next day is obtained, and then the probability distribution table is issued to each charging pile. At the initial control moment of each day, the charging pile autonomously generates a charging and discharging plan according to the nuclear power condition, the driver demand and the charging probability distribution table of the electric vehicle connected with the charging pile, and then the plan is displayed to the electric vehicle user, and the electric vehicle user spontaneously responds to the control flow. According to the invention, real-time monitoring is not needed, and the automatic decision of the charging pile on the charging and discharging behaviors is utilized, so that the electric automobile user spontaneously responds to the control flow, and the purpose of considering the benefits of both the power grid and the electric automobile user is realized.
Description
Technical Field
The invention relates to the technical field of ordered charge and discharge and distributed management of electric vehicles, in particular to a charge and discharge coordination optimization control method of a distributed charge pile of an electric vehicle.
Background
With the generation of increasingly serious global energy crisis, the world governments and various automobile industries are mostly aware of the major trend of energy conservation and pollution emission reduction in future development of the automobile industry, along with the increasingly consumed petroleum resources and the serious and global rise in air pollution. Electric vehicles have created significant opportunities for their own development. A series of policy measures are carried out in various countries, so that the research of the electric automobile field with great benefits on energy conservation and emission reduction is supported, and the development of new energy automobiles is promoted.
As the number of electric vehicles will continue to grow in the future, the disordered charging behavior of electric vehicles connected to the power grid on a large scale will have a significant impact on the power system, including harmonics, voltage control, and supply-demand balance of electric energy. This will have a significant impact on the planning and operation of the power system.
On the other hand, the electric automobile can use the energy storage characteristic of the battery as a distributed and movable energy storage device, so that bidirectional electric energy transmission with a power grid is realized. Therefore, the charge and discharge of the electric automobile are coordinated and optimized, so that the frequency modulation, peak shaving and standby capacity improvement of the power distribution network are facilitated, the function of stabilizing load fluctuation can be realized, the running risk of the system is reduced, and the running reliability and benefit of the power system are improved. However, in the existing research on the charge and discharge control of the electric automobile, a mode of optimizing the charge and discharge time of the electric automobile is generally adopted, and centralized layered control is mainly applied to realize coordination of charge and discharge.
The centralized control is to process the information of the charging requirement and the charging state submitted by all electric vehicles accessing the system through the control center, and the control command of the electric vehicles is issued in a centralized way through the control center. The centralized control mode or the layered control mode utilizes direct control of the load to achieve the aim of charge and discharge management of the electric automobile, and a driver does not have the right of active regulation, namely the subjective intention of the driver is not considered. Even if the control mode can reduce the calculated amount for the control center and improve the efficiency to a certain extent, the control method which excessively depends on communication control still has great defects, for example, as long as communication errors occur, communication signals cannot be interacted accurately and timely, the control requirement required by the system cannot be met during real-time control, namely the control method cannot solve the problem of orderly charging and discharging when the communication system fails. Therefore, it is necessary to invent an ordered charge-discharge coordination optimization control method of the electric vehicle, which does not need centralized monitoring of a communication system and is directly and automatically decided by a charging pile. According to the control method, daily load prediction is firstly carried out, a multi-objective function for improving daily load utilization rate, reducing total load variance and charging cost is generated in a decision generator according to a prediction result, an optimal distribution result of charging load of the next day with respect to time is obtained through optimizing according to the obtained function, and therefore a charging probability table and a discharging probability table of each period of the next day are obtained through calculation and are sent to each charging pile. At the initial control moment of each day, the charging pile autonomously generates a charging and discharging plan according to the nuclear power condition, the driver demand and the charging probability distribution table of the electric vehicle connected with the charging pile, and then the plan is displayed to the electric vehicle user, and the electric vehicle user spontaneously responds to the control flow.
Disclosure of Invention
Aiming at the problems, the invention provides a control method for coordinating and optimizing the charging and discharging behaviors of distributed charging piles; the invention can realize the automatic decision of charging and discharging behaviors by the charging piles without real-time monitoring for large-scale and distributed management of the electric automobile; the electric automobile battery of the power exchange station can be charged through the power grid, and can feed back electric energy for the power distribution network when the whole power load is in a peak period, so that the purposes of peak clipping and valley filling are achieved, the network loss of the power distribution network is reduced, the upgrading pressure of the power grid is relieved, and the power exchange station can also carry out arbitrage by using the electric price difference in the peak-valley period.
In order to achieve the above purpose, the invention provides a charge-discharge coordination optimization control method of a distributed charge pile, which comprises the following specific steps:
s1: setting a time interval of 1h, namely dividing one day into 24 control time periods;
s2: the decision generator obtains conventional load prediction, charging load prediction and peak-valley time-of-use electricity price information of one day according to a residential electricity collection system;
s3: constructing a current load optimization objective function of the electric vehicle based on the obtained load prediction and time-of-use electricity price information, and taking the user charging cost, the daily load rate and the total load variance of the power grid as the objective function; the optimization objective 1 is to maximize the daily load rate, i.e
The optimization objective 2 is to minimize the total variance of the grid load, i.e
The optimization objective 3 is that the total charge for the driver is the lowest, i.e
Normalizing the sub-objective functions to generate dimensionless objective functions with equal magnitude, and integrating the dimensionless objective functions into a linear weighting method to change the multi-objective function into a single objective function, namely +.>
Wherein, P is used LD (i) The method comprises the steps that the predicted load of a cell is represented in the period i when no electric automobile is connected to a charging pile; p (i) represents an optimization object, namely, an optimal charging load of the electric vehicle in the i period; p (P) av Representing average load, w, of residential area after electric automobile is connected with charging pile 1 、w 2 、w 3 Respectively, weighting factors, deltatc grid.i The charging time of the electric automobile in the period i is set;
s4: obtaining optimal time distribution of charging load of the electric automobile by optimizing the single objective function, and calculating charging and discharging margins of the power distribution network at each period by integrating obtained load prediction information, wherein the charging margin at the period i is the optimized charging load P (i), and the discharging margin P dem (i) For the difference between the optimized charge load and the predicted charge load;
s5: quantifying the charge margin or the discharge margin of each period, and when the charge or discharge duration is known, calculating the total charge margin from each initial period to the end period to form probability distribution, so as to obtain a charge probability distribution table C and a discharge probability distribution table D of each period as the initial charge and discharge period;
s6: the time required for charging and discharging the electric automobile is 8 hours at most, and the probability set of each period as the initial charging period under the condition that the charging time is k hours is recorded as c k ，k∈[1，8]The method comprises the steps of carrying out a first treatment on the surface of the A charging probability distribution table C, c= (C) in which probability sets corresponding to charging durations of 1h to 8h, respectively, are arranged in a 24×8 matrix 1 c 2 c 3 c 4 c 5 c 6 c 7 c 8 )；
S7: when the discharging margin of the electric automobile in a certain period is not equal to 0, the electric automobile discharges to the power grid to relieve the pressure of the excessive load demand of the power grid, namely the period is a discharging period; calculating the probability of taking the period with the discharge margin as the initial discharge period, and recording the set thereof as d k ，k∈[1，8]The discharge probability at other times is 0; it should be noted that if the battery is discharged for period i, but i to i+k-1 are not continuous discharge periods, then d k The probability d from the i period from the start of discharge to the i+k-1 period from the end of discharge is not satisfied by the requirement of sustaining discharge for k hours i～(i+k-1) =0, except for this case, d k Is the same as c k The method comprises the steps of carrying out a first treatment on the surface of the A 24×8 discharge probability distribution table D, d= (D) is formed with probability sets corresponding to the respective time periods 1 d 2 ... d k ... d 8 )；
S8: the decision generator transmits the obtained charging probability distribution table C and discharging probability distribution table D to all charging piles of the residential area before the control time period of one day begins by using an online or offline method;
s9: after the control period of one day is started, when the electric vehicle EV k After the charging pile is connected, a driver can set the expected driving-off time T by himself via a man-machine interaction interface on the charging pile le.k Expected state of charge S OCex.k The method comprises the steps of carrying out a first treatment on the surface of the Charging control system of charging pile obtains EV via electric vehicle battery management system k Is a battery capacity W of (2) k And an initial state of charge S OCk The method comprises the steps of carrying out a first treatment on the surface of the The electric automobile is charged and discharged in a mode that the power is p constant power after being connected to the charging pile; so the charging pile can be according to the known charging information S OCex.k ，T le.k ，S OCK ，W k ，p]Predicted normal load P LD And probability distribution tables [ C, D]EV for individual access k Making a charge-discharge plan, and guiding a driver to add load regulation;
s10: after the control time of one day is finished, the decision generator counts the power consumption load in one day in the user power consumption acquisition system, updates the conventional prediction curve and the charging load prediction curve of the next day, and further starts the charging load guiding and optimizing of the next day.
In a preferred mode, the charging and discharging plan after the electric automobile enters the charging pile is as follows:
s1: when the charging pile detects that the EV exists k Connection, EV is automatically calculated based on the known charging information k Parking time T stay.k Time T required for charging need.k And determines the connected EV k Whether or not the discharge requirement is satisfied;
s2: regulating and optimizing an electric automobile application charging mode which does not meet the discharging condition; if EV is at this time k The value of the charging time will be updated by the charging post, i.e. T need.k ＝T stay.k The method comprises the steps of carrying out a first treatment on the surface of the The charging pile searches the charging probability distribution table C for the corresponding n=T need.k Column vector c of (2) n At c n In which the charging initial period is greater than or equal to T back.k And expiration timeLess than T le.k Selecting a period with the maximum corresponding probability value as a charging initial period; if the probability values are equal, comparing the conventional load values of the corresponding initial periods, selecting the period with the lowest conventional load as the initial charging period, and then setting the period after the initial charging period as T need.k The individual periods are as charge termination periods;
s3: EV if new access to charging pile k If the discharge requirement is satisfied, further judging EV k Whether the first-put and then-charge conditions are satisfied; if EV is provided with k Meets the condition of charging before and after, updates the charging plan, namely calculates and updates the parking time T stay.k Time T required for charging need.k The method comprises the steps of carrying out a first treatment on the surface of the If EV is provided with k If the vehicle does not meet the pre-charge condition, the vehicle is charged to EV k Updating the charging schedule, i.e. calculating and updating the parking time T, using a charge-before-discharge form stay.k Time T required for charging need.k The method comprises the steps of carrying out a first treatment on the surface of the The charging pile searches the probability distribution table [ C, D ]]The estimated charging and discharging initial period of the electric automobile is displayed for the driver, the driver decides whether to respond or not, and if the driver does not want to respond, the charging pile immediately executes a charging mode and performs a charging function; if the driver is willing to respond, according to said updated parking time T stay.k Time T required for charging need.k And the charge and discharge functions are implemented.
In a preferred mode, the decision generator is used as a data processing center for supporting automatic decision of the charging pile, load and electricity price information of the region are obtained before the day, and a day-ahead load optimization objective function of the electric automobile is constructed by using the obtained load prediction result, so that the time of charging load of the electric automobile in the next day is optimally distributed; calculating the charging margin and the discharging margin of each time period of the next day of the power distribution network; a charge-discharge probability distribution table regarding each period as an initial charge and discharge period is generated and transmitted to each charge stake.
In the preferred mode, the charging pile is intelligent equipment with a calculation function and a data storage function, and after receiving the charging probability distribution table transmitted by the decision generator, the charging pile can obtain battery information according to an electric vehicle battery management system connected with the charging pile, so that charging and discharging time for the electric vehicle to travel is calculated, and a charging and discharging plan is displayed to a user through the corresponding charging and discharging probability distribution table.
The beneficial effects of the invention are as follows: the problems of high control cost, complex control process and high dependence on a communication system in the control process of the charge and discharge centralized control of the electric automobile are solved; according to the invention, real-time monitoring is not needed, automatic decision of the charging pile on charging and discharging behaviors is utilized, and an effective and feasible guiding scheme can be created through a load prediction mechanism, so that the electric automobile responds to the discharging mechanism in the daytime electricity utilization peak period, and the charging load is uniformly transferred in the evening electricity utilization valley period, thereby achieving the purpose of considering both benefits of a power grid and electric automobile users.
Drawings
Fig. 1 is a schematic diagram of a charging pile charging and discharging strategy provided by the invention;
fig. 2 is a schematic diagram of a charge and discharge guiding method for an electric vehicle according to the present invention;
fig. 3 is a schematic diagram of automatic decision making of a charging pile according to the present invention.
Detailed Description
The distributed system of the distributed charging pile of the electric automobile comprises a daily load prediction, a charging and discharging management frame, a decision generator and automatic decision of the charging pile.
Load prediction before day: and constructing a daily electric vehicle charging load optimization model, namely an electric vehicle daily load optimization objective function, and obtaining optimal distribution of charging loads of each control time period in one day relative to time.
Charge-discharge management frame: and a correct guiding strategy is obtained by utilizing daily load prediction, so that the charging load of the electric automobile is uniformly transferred.
Decision generator: and the data processing center is used for supporting the charging pile to make automatic decisions.
Automatic decision of charging pile: and obtaining battery information of the electric automobile according to the specifically connected electric automobile battery management system, obtaining charging time and discharging time which meet the traveling requirements of the electric automobile, and displaying a corresponding charging and discharging plan to a driver by utilizing a corresponding charging probability distribution table and a corresponding discharging probability distribution table.
Current charge load optimization model: and carrying out coordination optimization on the charging load of the electric automobile in the next day according to the prediction result of the daily load prediction to obtain the optimal distribution of the charging load of each control time period in one day to time.
Charging margin: the maximum charging load value of the electric automobile within a certain time which can be born by the system; discharge margin: maximum load dissipation value of the electric automobile battery discharging to the power grid in a certain period of time; the optimized charging load is a standard for controlling the load balance of the power grid, so the optimized charging load is taken as a charging margin, and a driver is guided to discharge in a charging peak period so as to offset the absolute value of the power difference of the optimized load lower than the predicted demand as the discharging margin.
The invention provides a charge-discharge coordination optimization method according to daily load prediction on the premise that the battery of an electric automobile can store energy, and can be distinguished from other conventional loads based on the characteristic, and the charge-discharge coordination optimization method can create an effective and feasible guiding scheme through a load prediction mechanism, so that the electric automobile participates in discharging in the daytime power consumption peak time period and uniformly transfers the charge load in the evening power consumption valley time period.
As shown in fig. 1, before the start of daily control time, conventional load, predicted charging load and total daily load of an electric vehicle are taken as known data to be input, a daily charging load optimization model is constructed, charging cost, daily load rate and grid total load variance of a user are taken as targets, the charging load of the electric vehicle on the next day is coordinated and optimized according to the prediction result of daily load prediction, a decision generator is utilized for carrying out load prediction on the charging load and the conventional load, and the optimal distribution of the charging load of the electric vehicle with respect to time is obtained, so that a charging margin probability table and a discharging margin probability table of each period are obtained, and then the charging margin probability table is transmitted to a charging pile.
After the control is started every day, judging whether a certain electric automobile has the adjustability or not by the charging pile, displaying the result to a driver through a human-computer interaction interface, and determining whether to respond or not by the driver according to the personal condition; for the unresponsive driver, the charging stake immediately performs a charging function; more specifically, the charging pile obtains battery information of the electric vehicle according to a specifically connected electric vehicle battery management system, obtains charging time and discharging time which meet the traveling needs of the electric vehicle, and displays a corresponding charging and discharging plan for a driver by utilizing a corresponding charging probability distribution table and a corresponding discharging probability distribution table, so that a reasonable data basis can be provided for automatic decision-making behaviors of the charging pile, and the electric vehicle driver can be guided to charge and discharge orderly.
And after the control time of each day is finished, the decision generator counts the power consumption load in one day in the power consumption acquisition system of the user, updates the conventional prediction curve and the charging load prediction curve of the next day, and further starts the charging load guiding and optimizing of the next day.
The electric automobile driver leaves after changing the battery, and the replaced power battery to be charged is uniformly charged by the battery changing station.
The energy storage system of the electric battery of the power exchange station can charge through the power grid, and can feed back electric energy for the power distribution network when the whole power load is in a peak period, so that the purposes of peak clipping and valley filling are achieved, network loss of the power distribution network is reduced, upgrading pressure of the power grid is relieved, and the power exchange station can also carry out arbitrage by using the peak-valley period electricity price difference.
As shown in fig. 2, the charge-discharge coordination optimization control method of the distributed charging pile comprises the following specific steps:
s1: the study time of the invention is from 24:00 a day to 24:00 a next day, for convenience of study, the time interval is set to be 1h, and 1:00 to 2:00 are the first time period; i.e. dividing a day into 24 control time periods;
s2: the decision generator obtains conventional load prediction, charging load prediction and peak-valley time-of-use electricity price information of one day according to a residential electricity collection system;
s3: electric automobile charge-discharge coordination optimization generally involves problems such as load balance, power grid loss and the likeMeanwhile, the charging cost of the electric automobile and the travel requirement of a driver are also considered; therefore, the invention respectively aims at the daily load rate of the power grid, the total load variance of the power grid and the charging cost of the electric automobile, and optimizes the charging load of the electric automobile on the next day based on the known load prediction result; constructing a current load optimization objective function of the electric vehicle based on the obtained load prediction and time-of-use electricity price information, and taking the user charging cost, the daily load rate and the total load variance of the power grid as the objective function; the optimization objective 1 is to maximize the daily load rate, i.e
The optimization objective 2 is to minimize the total variance of the grid load, i.e
The optimization objective 3 is that the total charge for the driver is the lowest, i.e
Normalizing the sub-objective functions to generate dimensionless objective functions with equal magnitude, and integrating the dimensionless objective functions into a linear weighting method to change the multi-objective function into a single objective function, namely +.>
Wherein, P is used LD (i) The method comprises the steps that the predicted load of a cell is represented in the period i when no electric automobile is connected to a charging pile; p (i) represents an optimization object, namely, an optimal charging load of the electric vehicle in the i period; p (P) av Representing average load, w, of residential area after electric automobile is connected with charging pile 1 、w 2 、w 3 Respectively, weighting factors, deltatc grid.i The charging time of the electric automobile in the period i is set; constraint conditions: w (w) 1 +w 2 +w 3 Because the degree of response of the driver is a precondition for performing the coordinated optimization, the weight coefficient w of the cost of the driver is taken in the invention 3 Is 0.5, the weight coefficient w of the load characteristic index is taken 1 And w 2 0.25;
s4: obtaining the highest charging load of the electric automobile by optimizing the single objective functionThe time distribution is good, the load prediction information obtained through integration is used for calculating the charge and discharge margin of the distribution network at each period, the charge margin at the period i is the optimized charge load P (i), and the discharge margin P dem (i) For the difference between the optimized charge load and the predicted charge load;
s5: quantifying the charge margin or the discharge margin of each period, and when the charge or discharge duration is known, calculating the total charge margin from each initial period to the end period to form probability distribution, so as to obtain a charge probability distribution table C and a discharge probability distribution table D of each period as the initial charge and discharge period;
s6: the time required for charging and discharging the electric automobile is 8 hours at most, and the probability set of each period as the initial charging period under the condition that the charging time is k hours is recorded as c k ，k∈[1，8]The method comprises the steps of carrying out a first treatment on the surface of the Taking k=2 as an example, probabilities for the initial charging periods of 1 to 24 periods are calculated respectively and are denoted as a set c 2 I.e. c 2 ＝(c 1～2 c 2～3 ... c 22～23 c 23 ～ 24 c 24～1 ) T A charging probability distribution table C, c= (C) in which probability sets corresponding to charging durations of 1h to 8h, respectively, are arranged in a 24×8 matrix 1 c 2 c 3 c 4 c 5 c 6 c 7 c 8 )；
S7: the discharge margin of each period determines whether discharge demands exist in each period, and when the discharge margin of the electric automobile in a certain period is not equal to 0, the electric automobile discharges to the power grid to relieve the pressure of the excessive load demands of the power grid, namely the period is a discharge period; calculating the probability of taking the period with the discharge margin as the initial discharge period, and recording the set thereof as d k ，k∈[1，8]The discharge probability at other times is 0; it should be noted that if the battery is discharged for period i, but i to i+k-1 are not continuous discharge periods, then d k The probability d from the i period from the start of discharge to the i+k-1 period from the end of discharge is not satisfied by the requirement of sustaining discharge for k hours i～(i+k-1) =0, except for this case, d k Is the same as c k The method comprises the steps of carrying out a first treatment on the surface of the By respective time periodsThe probability sets of (c) constitute a 24×8 discharge probability distribution table D, d= (D) 1 d 2 ... d k ... d 8 )；
S8: the decision generator transmits the obtained charging probability distribution table C and discharging probability distribution table D to all charging piles of the residential area before the control time period of one day begins by using an online or offline method;
s9: after the control period of one day is started, when the electric vehicle EV k After the charging pile is connected, a driver can set the expected driving-off time T by himself via a man-machine interaction interface on the charging pile le.k Expected state of charge S OCex.k The method comprises the steps of carrying out a first treatment on the surface of the Charging control system of charging pile obtains EV via electric vehicle battery management system k Is a battery capacity W of (2) k And an initial state of charge S OCk The method comprises the steps of carrying out a first treatment on the surface of the The electric automobile is charged and discharged in a mode that the power is p constant power after being connected to the charging pile; so the charging pile can be according to the known charging information S OCex.k ，T le.k ，S OCk ，W k ，p]Predicted normal load P LD And probability distribution tables [ C, D]EV for individual access k Making a charge-discharge plan, and guiding a driver to add load regulation;
s10: after the control time of one day is finished, the decision generator counts the power consumption load in one day in the user power consumption acquisition system, updates the conventional prediction curve and the charging load prediction curve of the next day, and further starts the charging load guiding and optimizing of the next day.
As shown in fig. 3, the charge-discharge plan after the electric vehicle enters the charge pile is as follows:
s1: when the charging pile detects that the EV exists k Connection, EV is automatically calculated based on the known charging information k Parking time T stay.k Time T required for charging need.k And determines the connected EV k Whether or not the discharge requirement is satisfied;
s2: regulating and optimizing an electric automobile application charging mode which does not meet the discharging condition; if EV is at this time k The value of the charging time will be updated by the charging post, i.e. T need.k ＝T stay.k The method comprises the steps of carrying out a first treatment on the surface of the The charging pile searches the charging probability distribution table C for the corresponding n=T need.k Column vector c of (2) n At c n In which the charging initial period is greater than or equal to T back.k And the termination time is less than T le.k Selecting a period with the maximum corresponding probability value as a charging initial period; if the probability values are equal, comparing the conventional load values of the corresponding initial periods, selecting the period with the lowest conventional load as the initial charging period, and then setting the period after the initial charging period as T need.k The individual periods are as charge termination periods;
s3: EV if new access to charging pile k If the discharge requirement is satisfied, further judging EV k Whether the first-put and then-charge conditions are satisfied; if EV is provided with k Meets the condition of charging before and after, updates the charging plan, namely calculates and updates the parking time T stay.k Time T required for charging need.k The method comprises the steps of carrying out a first treatment on the surface of the If EV is provided with k If the vehicle does not meet the pre-charge condition, the vehicle is charged to EV k Updating the charging schedule, i.e. calculating and updating the parking time T, using a charge-before-discharge form stay.k Time T required for charging need.k The method comprises the steps of carrying out a first treatment on the surface of the The charging pile searches the probability distribution table [ C, D ]]The estimated charging and discharging initial period of the electric automobile is displayed for the driver, the driver decides whether to respond or not, and if the driver does not want to respond, the charging pile immediately executes a charging mode and performs a charging function; if the driver is willing to respond, according to said updated parking time T stay.k Time T required for charging need.k And the charge and discharge functions are implemented.
The decision generator is used as a data processing center for supporting automatic decision of the charging pile, load and electricity price information of the region are obtained before the day, and a day-ahead load optimization objective function of the electric automobile is constructed by using the obtained load prediction result, so that the time of charging load of the electric automobile in the next day is optimally distributed; calculating the charging margin and the discharging margin of each time period of the next day of the power distribution network; a charge-discharge probability distribution table regarding each period as an initial charge and discharge period is generated and transmitted to each charge stake.
The charging pile is intelligent equipment with a calculation function and a data storage function, and after receiving the charging and discharging probability distribution table transmitted by the decision generator, the charging pile can obtain battery information according to an electric vehicle battery management system connected with the charging pile, so that charging and discharging time for the electric vehicle to travel is calculated, and a charging and discharging plan is displayed to a user through the corresponding charging and discharging probability distribution table.
Specific calculations were made for a residential area with peak electricity consumption of approximately 11 a.m. and 18 a.m. with peak load of 3080kW. The peak electricity price of the residential area (06:00 to 22:00) is 0.617 yuan/kWh, the valley electricity price (22:00 to the next day 06:00) is 0.307 yuan/kWh, the charging price and the discharging price are the same, and the charging pile and the electric automobile in the residential area are equipped in a ratio of 1:1. Charging simulation was performed according to the running characteristics of the private car, and in addition, assuming that the rated charging and discharging power was 4kW, the charging and discharging efficiency was 0.95, and the driver intended S OCk 1. When the retention amount of the electric automobile in the residential area is 200 and the response coefficient h of the driver is 0, 0.3, 0.5, 0.7 and 1 respectively, the electric automobile load is superimposed on the basis of the conventional predicted load of the residential area, and an EVs charge-discharge load and daily load curve when the response degrees of the drivers are different is obtained. After the response coefficient of the driver is increased from 0 to 1, the peak-valley difference of the total load of the residential area is obviously reduced, the original 1.130MW is reduced to 0.770MW, and the influence of electric automobile charging on the peak-to-peak problem of the power distribution network is well reduced; and the load variance is obviously reduced, so that the load fluctuation of the power distribution network tends to be gentle, and the running efficiency of the power distribution equipment is obviously improved. When the response coefficient is increased to 0.5, the discharge amount of EVs between 16 and 21 points is higher than the charge amount of the electric automobile, so that the charging requirement of the electric automobile in the period is counteracted, and part of conventional electricity load is counteracted; when the response coefficient is increased to 0.7, the total discharge capacity of the electric automobile is more, the charge expense can be completely offset by the charge expense, and under the response coefficient, the total electricity price is lower than the total electricity price of a conventional load, and is reduced by 1003 yuan compared with a disordered charge mode; when the response coefficient increases to 1, the total discharge amount of the electric automobile reaches the highest, the discharge cost is removed by 1873 yuan,compared with the unordered charging mode, the resident area charging manager saves the power supply cost by 1382 yuan at most.
Those skilled in the art will appreciate that the methods may be implemented by those skilled in the art in combination with the prior art and the above schemes, and are not described in detail herein.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should be covered by the protection scope of the present invention by making equivalents and modifications to the technical solution and the inventive concept thereof.
Claims (4)
1. The charge-discharge coordination optimization control method of the distributed charging pile is characterized by comprising the following specific steps of:
s1: setting a time interval of 1h, namely dividing one day into 24 control time periods;
s2: the decision generator obtains conventional load prediction, charging load prediction and peak-valley time-of-use electricity price information of one day according to a residential electricity collection system;
s3: constructing a daily load optimization objective function of the electric automobile based on the obtained load prediction and time-of-use electricity price information, and taking the user charging cost, the daily load rate and the total load variance of the power grid as the objective function; the optimization objective 1 is to maximize the daily load rate, i.e
The optimization objective 2 is to minimize the total variance of the grid load, i.e
The optimization objective 3 is that the total charge for the driver is the lowest, i.e
Normalizing the objective functions to generate dimensionless objective functions with equal magnitude, and integrating the dimensionless objective functions into a linear weighting method to change the multi-objective function into a single objective function, namely +.>
Wherein, P is used LD (i) The method comprises the steps that the predicted load of a cell is represented in the period i when no electric automobile is connected to a charging pile; p (i) represents an optimization object, namely, an optimal charging load of the electric vehicle in the i period; p (P) av Representing average load, w, of residential area after electric automobile is connected with charging pile 1 、w 2 、w 3 Respectively, weighting factors, deltatc grid.i The charging time of the electric automobile in the period i is set;
s4: obtaining optimal time distribution of charging load of the electric automobile by optimizing the single objective function, and calculating charging and discharging margins of the power distribution network at each period by integrating obtained load prediction information, wherein the charging margin at the period i is the optimized charging load P (i), and the discharging margin P dem (i) For the difference between the optimized charge load and the predicted charge load;
s5: quantifying the charge margin or the discharge margin of each period, and when the charge or discharge duration is known, calculating the total charge margin from each initial period to the end period to form probability distribution, so as to obtain a charge probability distribution table C and a discharge probability distribution table D of each period as the initial charge and discharge period;
s6: the time required for charging and discharging the electric automobile is 8 hours at most, and the probability set of each period as the initial charging period under the condition that the charging time is k hours is recorded as c k ，k∈[1，8]The method comprises the steps of carrying out a first treatment on the surface of the A charging probability distribution table C, c= (C) in which probability sets corresponding to charging durations of 1h to 8h, respectively, are arranged in a 24×8 matrix 1 c 2 c 3 c 4 c 5 c 6 c 7 c 8 )；
S7: when the discharging margin of the electric automobile in a certain period is not equal to 0, the electric automobile discharges to the power grid to relieve the pressure of the excessive load demand of the power grid, namely the period is a discharging period; calculating the probability of taking the period with the discharge margin as the initial discharge period, and recording the set thereof as d k ，k∈[1，8]The discharge probability at other times is 0; it should be noted that if the battery is discharged for period i, but i to i+k-1 are not continuous discharge periods, then d k The probability d from the i period from the start of discharge to the i+k-1 period from the end of discharge is not satisfied by the requirement of sustaining discharge for k hours i～(i+k-1) =0, except for this case, d k Is the same as c k The method comprises the steps of carrying out a first treatment on the surface of the A 24×8 discharge probability distribution table D, d= (D) is formed with probability sets corresponding to the respective time periods 1 d 2 ...d k ...d 8 )；
S8: the decision generator transmits the obtained charging probability distribution table C and discharging probability distribution table D to all charging piles of the residential area before the control time period of one day begins by using an online or offline method;
s9: after the control period of one day is started, when the electric vehicle EV k After the charging pile is connected, a driver can set the expected driving-off time T by himself via a man-machine interaction interface on the charging pile le.k Expected state of charge S OCex.k The method comprises the steps of carrying out a first treatment on the surface of the Charging control system of charging pile obtains EV via electric vehicle battery management system k Is a battery capacity W of (2) k And an initial state of charge S OCk The method comprises the steps of carrying out a first treatment on the surface of the The electric automobile is charged and discharged in a mode that the power is p constant power after being connected to the charging pile; so the charging pile can be according to the known charging information S OCex.k ，T le.k ，S OCk ，W k ，p]Predicted normal load P LD And probability distribution tables [ C, D]EV for individual access k Making a charge-discharge plan, and guiding a driver to add load regulation;
s10: after the control time of one day is finished, the decision generator counts the power consumption load in one day in the user power consumption acquisition system, updates the conventional prediction curve and the charging load prediction curve of the next day, and further starts the charging load guiding and optimizing of the next day.
2. The charge-discharge coordination optimization control method of the distributed charge pile according to claim 1, wherein the charge-discharge plan of the electric vehicle after driving into the charge pile is:
SA1: when the charging pile detects that the EV exists k Connection, EV is automatically calculated based on the known charging information k Parking time T stay.k Time T required for charging need.k And determines the connected EV k Whether or not the discharge requirement is satisfied;
SA2: regulating and optimizing an electric automobile application charging mode which does not meet the discharging condition; if EV is at this time k The value of the charging time will be updated by the charging post, i.e. T need.k ＝T stay.k The method comprises the steps of carrying out a first treatment on the surface of the The charging pile searches the charging probability distribution table C for the corresponding n=T need.k Column vector c of (2) n At c n In which the charging initial period is greater than or equal to T back.k And the termination time is less than T le.k Selecting a period with the maximum corresponding probability value as a charging initial period; if the probability values are equal, comparing the conventional load values of the corresponding initial periods, selecting the period with the lowest conventional load as the initial charging period, and then setting the period after the initial charging period as T need.k The individual periods are as charge termination periods;
SA3: EV if new access to charging pile k If the discharge requirement is satisfied, further judging EV k Whether the first-put and then-charge conditions are satisfied; if EV is provided with k Meets the condition of charging before and after, updates the charging plan, namely calculates and updates the parking time T stay.k Time T required for charging need.k The method comprises the steps of carrying out a first treatment on the surface of the If EV is provided with k If the vehicle does not meet the pre-charge condition, the vehicle is charged to EV k Updating the charging schedule, i.e. calculating and updating the parking time T, using a charge-before-discharge form stay.k Time T required for charging need.k The method comprises the steps of carrying out a first treatment on the surface of the The charging pile searches the probability distribution table [ C, D ]]The estimated charging and discharging initial period of the electric automobile is displayed for the driver, the driver decides whether to respond or not, and if the driver does not want to respond, the charging pile immediately executes a charging mode and performs a charging function; if the driver is willing to respond, according to said updated parking time T stay.k Time T required for charging need.k And the charge and discharge functions are implemented.
3. The charge-discharge coordination optimization control method of the distributed charge pile according to claim 1, wherein the decision generator is used as a data processing center for supporting automatic decision of the charge pile, load and electricity price information of the region are obtained before the day, and a day-ahead load optimization objective function of the electric automobile is constructed by using the obtained load prediction result, so that the time of the charge load of the electric automobile in the next day is optimally distributed; calculating the charging margin and the discharging margin of each time period of the next day of the power distribution network; a charge-discharge probability distribution table regarding each period as an initial charge and discharge period is generated and transmitted to each charge stake.
4. The charge-discharge coordination optimization control method of the distributed charge pile according to claim 1, wherein the charge pile is an intelligent device with a calculation function and a data storage function, and after the charge pile receives the charge-discharge probability distribution table transmitted by the decision generator, the charge pile can obtain battery information according to an electric vehicle battery management system connected with the charge pile, so that charge-discharge time enough for traveling of an electric vehicle is calculated, and a charge-discharge plan is displayed to a user through the corresponding charge-discharge probability distribution table.
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| CN113506120A (en) * | 2021-02-04 | 2021-10-15 | 南昌工学院 | Method for improving charging cost of electric automobile |
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| CN113479096B (en) * | 2021-06-29 | 2023-03-10 | 欣旺达电动汽车电池有限公司 | Distributed discharging method, distributed charging method, electronic device, and storage medium |
| CN113657456B (en) * | 2021-07-26 | 2023-08-25 | 华南理工大学 | Ordered charging method for allocating user charging based on Markov chain |
| CN113978303B (en) * | 2021-11-16 | 2023-10-27 | 国网天津市电力公司 | Charging method and system for electric automobile |
| CN116691419B (en) * | 2023-08-03 | 2023-11-14 | 浙江大学 | Electric automobile autonomous charging control method for deep reinforcement learning under weak link communication |
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