CN113141020B - Electric vehicle virtual energy storage participation peak regulation auxiliary service control method and system - Google Patents
Electric vehicle virtual energy storage participation peak regulation auxiliary service control method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for controlling virtual energy storage participation peak shaving auxiliary service of an electric automobile, wherein the method comprises the following steps: acquiring relevant state information of each electric vehicle, information provided by a vehicle owner and a peak regulation demand provided by an energy storage demand party, and reporting the information to the EVDI; according to the collected relevant state information of each electric automobile and the information provided by an owner, the EVDI firstly classifies and aggregates the target electric automobile group according to the SOC value to obtain the aggregated electric automobile virtual energy storage; for the aggregated virtual energy storage of the electric automobile, establishing a scheduling arrangement model participating in peak shaving auxiliary service; based on the established scheduling model participating in the peak regulation auxiliary service, the virtual energy storage system of the electric automobile discharges energy at constant power. The invention can effectively utilize a large amount of idle mobile energy storage of the electrically charged automobile to participate in the peak regulation auxiliary service.
Description
Technical Field
The invention belongs to the technical field of electric automobiles, and particularly relates to a method and a system for controlling virtual energy storage participation peak shaving auxiliary service of an electric automobile.
Background
In recent years, the electric automobile industry enters a high-speed development stage, the nationwide electric automobile inventory has reached 400 million by 11 months and 11 months in 2020, and China has a mobile energy storage market of about 120 gigawatt-hours in 2020 by calculating with energy storage batteries which are averagely configured for 30 kilowatt-hours in each electric automobile. 8000 thousands of vehicles can be obtained by 2030, the equivalent energy storage capacity is nearly 5000 gigawatts, and the large-scale application of the electric automobile in the future is imperative.
The prior art discloses an electric vehicle ordered charging and discharging method based on cooperative game and dynamic time-of-use electricity price, wherein a maximum profit model under a non-cooperative game condition and a maximum profit model under a cooperative game condition are established, and an improved particle swarm algorithm is adopted to solve the models. And the equal distribution principle is utilized to fairly distribute the cooperative income to the agent and the electric vehicle user. And (4) carrying out example analysis by considering the actual condition, comparing the optimized time-of-use electricity price obtained by considering the simulation solution of the cooperative game under different situations, and verifying the effect. The technology mainly considers the economy, only considers the maximum profit under the cooperative game condition and the non-cooperative game condition, does not consider the possibility of poor stability of the power grid side caused by the instant and large-scale discharge of the electric automobile on the power grid side, and is lack of practicability.
The stop time of the electric automobile exceeds 95% every day, which means that a large amount of mobile energy storage is not effectively utilized every day, so that the waste of electric energy is caused, and how to effectively utilize the mobile energy storage is a technical problem in the field.
Disclosure of Invention
The invention aims to provide a method and a system for controlling virtual energy storage participation peak shaving auxiliary service of an electric automobile, so as to effectively utilize a large amount of idle mobile energy storage of the electric automobile. According to the method, the charging and discharging current constraint of the electric automobile, the charging and discharging power constraint of the electric automobile, the available capacity constraint of the battery and the power exchange of the network side are comprehensively considered, so that an energy storage demand side can adjust the load in a peak period through the control method, a resource allocation system is developed based on a corresponding model, and a technical support is provided for the virtual energy storage with the electric automobile as a carrier to participate in a peak regulation auxiliary service.
In order to achieve the purpose, the invention adopts the following technical scheme:
a virtual energy storage participation peak regulation auxiliary service control method for an electric automobile comprises the following steps:
acquiring relevant state information of each electric automobile, information provided by an automobile owner and peak shaving requirements provided by an energy storage demander; the information provided by the vehicle owner includes: the expected leaving time and the SOC value interval expected by the owner when leaving;
classifying and aggregating the target electric automobile group according to the SOC value according to the collected relevant state information of each electric automobile and the information provided by the owner of the electric automobile to obtain the aggregated virtual energy storage of the electric automobiles;
for the aggregated virtual energy storage of the electric automobile, establishing a scheduling arrangement model participating in peak shaving auxiliary service;
and determining the final discharge time period of the virtual energy storage of each type of electric automobile based on the established scheduling model participating in the peak regulation auxiliary service and the peak regulation requirement provided by the energy storage demander.
The invention further improves the following steps: the method comprises the following steps of obtaining relevant state information of each electric automobile, information provided by an automobile owner and peak shaving requirements provided by an energy storage demander:
the relevant state information of the electric automobile comprises: battery capacity, battery state of charge, battery power, charge and discharge depth, and battery life.
The invention further improves the following steps: the EVDI carries out classification and aggregation on a target electric automobile group according to the collected relevant state information of each electric automobile and information provided by an owner, and obtains the aggregated virtual energy storage of the electric automobiles, and the method specifically comprises the following steps:
establishing a discharge model of virtual energy storage of the electric automobile;
and (4) the electric automobile groups are classified into equivalent concentrated virtual energy storage according to the SOC values.
The invention further improves the following steps: the step of establishing the virtual energy storage discharge model of the electric automobile specifically comprises the following steps:
establishing a discharge model:
SOC at time ttThe value calculation formula is:
in the formula: SOC0The SOC value is the SOC value of the electric automobile at the initial moment; pdThe discharge power is between t and t-1; etadTo discharge efficiency; Δ t is a duration of the discharge; ebatThe rated capacity of the electric automobile;
establishing a constraint condition:
the constraint conditions of virtual energy storage of the electric automobile comprise SOC constraint and discharge constraint;
the SOC constraint conditions are as follows: SOCmin≤SOCi≤SOCmax(2)
In the formula: SOC (system on chip)minAnd SOCmaxRespectively is the lower limit and the upper limit of the charge state of the battery of the electric automobile;
the discharge constraint conditions are as follows: p is more than or equal to 0d≤Pdmax(3)
In the formula: pdmaxFor maximum discharge of electric automobileElectrical power.
The invention further improves the following steps: the step of classifying the electric automobile group into equivalent concentrated virtual energy storage according to the SOC value specifically comprises the following steps:
the electric automobile groups are classified into an equivalent centralized virtual energy storage for EVDI (electric vehicle discovery and integration) scheduling according to the SOC values; firstly, electric vehicles are classified into the following three types according to different SOC values: [ 75% -85%), [ 85% -95%), [ 95% -100%) ]; in each category, the expected SOC value after the service is ended according to the input of the vehicle owner is divided into the following three categories: [ 65% -75%), [ 75% -85%), [ 85% -95%) ]; obtaining electric vehicle group classification;
the discharge power of each electric automobile is determined jointly by adopting the rated power and the SOC value of each electric automobile virtual energy storage, and the specific determination mode is as follows:
in the formula, PrThe rated power of the electric automobile; pidThe discharge power of the electric automobile i; palldTotal discharge power required for scheduling; n is the total number of the electric automobiles; f. ofd(x) Is a discharge SOC function of the electric automobile;
according to the classification condition of the electric automobiles, the virtual energy storage of each electric automobile in each class is aggregated to obtain equivalent concentrated rated power, equivalent concentrated rated capacity and equivalent concentrated discharge efficiency, and the power of each virtual energy storage in the virtual energy storage of each electric automobile is distributed by adopting a formula (4).
The invention further improves the following steps: the step of establishing a scheduling model participating in the peak shaving auxiliary service for the aggregated virtual energy storage of the electric automobile specifically comprises the following steps:
establishing a peak regulation effect objective function based on the minimum power grid load variance:
in the formula: d1(i) For the load value of the ith time interval after the peak regulation of the virtual energy storage system of the electric automobile, i is 1,2, …, npWherein one day is changed to npAn equal time period;
the virtual energy storage system of the electric automobile is used for carrying out peak shaving auxiliary service on the power grid, and the minimum load variance of the power grid is met.
The invention further improves the following steps: the peak shaver effect objective function has the following constraints:
(1) load value constraint
In the formula: d0(i) The predicted load data of the ith time period is a known value; t isstartP (d) respectively represents the discharge starting time and the discharge power of the electric automobile;
(2) timing constraints
In the formula: t isstopThe end time of each discharge;
(3) electric vehicle discharge current restraint
In the formula: i isdFor discharging current of electric vehicle, and Id≥0；IinIs the maximum discharge current; u shapeEVIs the voltage of the cell during discharge; ebatThe rated capacity of the electric automobile;
(4) electric vehicle discharge power constraint
0≤Pd≤min(Pf,UEV×Id,Pdr)
Wherein, PfIs as follows; pdrDischarging electric vehiclesThe rated power of (d);
(5) available capacity constraint of battery
Available capacity E of each electric automobile participating in virtual energy storageaComprises the following steps:
wherein E isbThe residual electricity of the electric automobile is used; SOCLThe SOC value corresponds to the power consumption of the electric automobile during running; SOCrA minimum SOC value reserved for electric vehicle driving;
(6) historical credit rating score
The vehicle participating in the virtual energy storage service of the electric automobile obtains an evaluation score after the service is completed each time, and the calculation formula is as follows:
in the formula: t is tactIs the actual departure time, t, of the electric vehicleexpSpecifying a departure time, t, for an electric vehicleallThe total discharge time is specified for the electric vehicle.
The invention further improves the following steps: the step of determining the final discharge time period of the virtual energy storage of each type of electric automobile based on the established scheduling model participating in the peak regulation auxiliary service and the peak regulation demand provided by the energy storage demand party specifically comprises the following steps:
from equivalent rated capacity EallbatAnd equivalent rated power PallbatThe total discharge time was calculated as:
T＝Eallbat/Pallbat
according to a peak regulation demand provided by an energy storage demand party (the peak regulation demand is a next-day power grid load curve predicted according to historical data in each time period of a power grid), obtaining a next-day power grid load curve, finding out a load high peak value in 24h in the next-day power grid load curve, and making a horizontal line L at the highest position; starting from a high load peak value, setting a step length delta M to move upwards, intersecting a horizontal line L and a predicted power grid load curve at two points, and measuring the distance between the two points in real time; the measured distance between the two points is compared with the discharge time T: if the two are equal, the area is a reasonable discharging area of the virtual energy storage system of the electric automobile; if not, continuing to move the L upwards by the step length delta M until the distance between the L and the L is equal, and determining the final discharge time period of the virtual energy storage system of each type of electric automobile;
the virtual energy storage system of the electric automobile discharges energy at constant power in a determined discharge time period.
In a second aspect, the present invention further provides a virtual energy storage participating peak shaving auxiliary service control system for an electric vehicle, including:
the acquisition module is used for acquiring relevant state information of each electric automobile, information provided by an owner and peak shaving requirements provided by an energy storage demander; the information provided by the vehicle owner includes: the predicted leaving time and the SOC value interval expected by the owner when leaving;
the aggregation module is used for classifying and aggregating a target electric vehicle group according to the SOC value according to the collected relevant state information of each electric vehicle and the information provided by the vehicle owner to obtain the aggregated electric vehicle virtual energy storage;
the scheduling model establishing module is used for establishing a scheduling model participating in peak shaving auxiliary service for the virtual energy storage of the aggregated electric automobile;
and the determining module is used for determining the final discharging time period of the virtual energy storage of each type of electric automobile based on the established scheduling model participating in the peak shaving auxiliary service and the peak shaving demand provided by the energy storage demand party.
In a third aspect, the invention further provides an electric vehicle virtual energy storage participation peak shaving auxiliary service control system, which includes a processor and a memory, wherein the processor is used for executing a computer program stored in the memory to perform the electric vehicle virtual energy storage participation peak shaving auxiliary service control method.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the intention of an electric vehicle owner and the peak regulation requirement of an energy storage demand side are comprehensively considered, the reasonable power distribution method is adopted to aggregate the energy storage of the electric vehicles into an equivalent concentrated energy storage, and then a plurality of energy storage can be represented by only one equivalent concentrated energy storage.
The invention provides a constant power discharge control strategy at the level of EVDI, effectively enables the virtual energy storage system of the electric automobile to participate in peak shaving auxiliary service, and can simply and effectively regulate the load value at the time of the peak load of the power grid.
The invention provides historical credit evaluation, improves the constant power control discharge strategy provided at the EVDI level, effectively restricts the subjective service leaving behavior of the owner and provides a thought for solving the redundancy problem.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flowchart of a method for controlling an electric vehicle virtual energy storage participating in peak shaving auxiliary service according to the present invention;
FIG. 2 is a system control framework diagram of a peak shaving assistance service;
FIG. 3 is a next-day power grid load curve predicted according to historical load data of each time period of the power grid;
FIG. 4 is a block diagram of a virtual energy storage participating peak shaving auxiliary service control system of an electric vehicle according to the present invention;
fig. 5 is a block diagram of another electric vehicle virtual energy storage participating peak shaving auxiliary service control system according to the invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further explanation of the invention as claimed. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The noun explains:
1. V2G (Vehicle-to-grid, short for Vehicle to grid) has the core idea of using the energy storage source of a large number of electric vehicles as a buffer for the grid and renewable energy. When the load of the power grid is too high, the energy storage of the electric automobile feeds power to the power grid; and when the load of the power grid is low, the power grid is used for storing the surplus generated energy of the power grid, so that waste is avoided. By the mode, the electric automobile user can buy electricity from the power grid when the electricity price is low, and sell electricity to the power grid when the electricity price of the power grid is high, so that certain income is obtained.
2. Virtual energy storage of the electric automobile: the electric automobile is used as a miniature distributed energy storage device, available capacity is aggregated to form a large-scale virtual energy storage power station, and auxiliary service and support are provided for a power system.
3. SOC: state of Charge is used to indicate the State of Charge of the battery.
4. EVDI: electric Vehicle Demand Integrator (EVDI).
The virtual energy storage of the electric automobile utilizes an energy management device or a dispatching platform to interact with an electric power system so as to assist in improving the running reliability and the power supply quality of the system. Since electric vehicles are equipped with batteries of relatively large capacity and most private electric vehicles are parked for about 20-22 hours a day, they can provide energy buffer for the grid discharge when parked, and when the number of such electric vehicles is large enough, the total capacity of the batteries is quite large and can be used for auxiliary grid and renewable energy power supply. However, electric vehicles cannot be freely and unmanageably connected to the power grid, and the discharge requirements of a large number of vehicles inevitably affect the stable operation of the power grid. For the electric vehicle user, besides providing auxiliary services to the power grid, the electric vehicle user must also be able to meet daily driving requirements. Therefore, during the process of feeding power to the power grid, the energy storage state of the automobile must be considered so as to avoid influencing the normal use of the automobile. The virtual energy storage technology of the electric automobile is used for coordinating the charging and discharging between the automobile and the power grid, so that the operation of the power grid is not influenced, and the normal use of the automobile is not limited. When the load of the power grid is too high, the electric automobile can provide the stored energy to the power grid; when the grid load is too low, the electric vehicle may store excess power to avoid waste. The application value of the electric power system for virtual energy storage of the electric automobile comprises providing power generation capacity, providing power generation energy (including reducing renewable energy power abandonment), providing auxiliary service and avoiding (or delaying) power transmission and distribution investment value. In the electric power market, an electric vehicle user, a charging operator or a charging load integrator is a provider of virtual energy storage service of the electric vehicle, and the generated cost can be compensated by reducing capacity electric charge, electric quantity electric charge and the like.
If the available capacity can be aggregated during the idle time of the electric automobile, the power grid can fully utilize the idle available capacity. With the development of an energy storage technology and a charge and discharge control technology, the vehicle-mounted power battery system of the electric automobile can be used as a miniature distributed energy storage device only through communication and aggregation without secondary investment, and can provide service and support for a power system, namely virtual energy storage of the electric automobile. On the premise of ensuring normal use of the vehicle by the vehicle owner, the virtual energy storage of the electric vehicle brings certain benefits to the electric power system and the vehicle owner.
At present, the research on the technology is less, and the invention mainly focuses on three aspects of electric vehicles, demand integrators of the electric vehicles and energy storage demand parties and provides a method and a system for controlling virtual energy storage participation peak shaving auxiliary services of the electric vehicles. When the load of the power grid is in a peak period, the demand integrators of the electric automobiles aggregate the available capacity of the electric automobile groups which respond to the virtual energy storage service and apply the capacity to the peak shaving auxiliary service. The participation of the virtual energy storage of the electric automobile can effectively adjust the load of the power grid in the peak period, and plays a certain supporting role in the stability of the power grid.
In the prior art, historical credit evaluation of user discharging is not considered in the constraint of a discharging control model, which causes a redundancy problem in discharging planning, namely, a certain owner does not discharge in an originally planned discharging time period and leaves in advance, so that a vacancy occurs in the original discharging planning, and the key point of ordering cannot be realized.
Example 1
Referring to fig. 1, the present embodiment provides a method for controlling an electric vehicle to participate in peak shaving auxiliary service through virtual energy storage, including the following steps:
and setting a time period model of the virtual energy storage of the electric automobile according to the peak-valley time period of the load of the power grid so as to restrict the charging and discharging behaviors and the electric quantity control target of the electric automobile.
TABLE 1 electric vehicle virtual energy storage time period model
Where the "SOC target" is a control target of the SOC before the end of the current period. The "reserved time" is a target correction time, and the electric vehicle should ensure that the SOC measured value is operated in the vicinity of the "SOC target" within the "reserved time" before the end of the current period.
S1: in table 1, in "no charge only" and "chargeable and dischargeable" modes, the electric vehicle virtual energy storage participates in the peak shaving auxiliary service. A system control framework of the peak shaving auxiliary service is divided into an equipment layer, an equipment convergence layer and a Demand layer, wherein the Demand layer is an energy storage Demand party, the equipment convergence layer is an Electric Vehicle Demand Integrator distributed energy storage convergence agent (EVDI), and the equipment layer is provided with a bidirectional charging pile and an Electric Vehicle.
Referring to fig. 2, a bidirectional charging pile of an equipment layer collects relevant state information of an electric vehicle and information provided by a vehicle owner in real time and reports the information to an EVDI of a convergence layer; reporting the peak regulation requirement provided by the energy storage demander to the EVDI of the convergence layer; the relevant state information of the electric automobile comprises: battery capacity, battery state of charge, battery power, charge-discharge depth, battery life; the information provided by the vehicle owner includes: predicted leaving time and SOC value interval expected by the owner when leaving. The peak regulation requirement is the load value of each time period of the second day, and the final aim of the control method is to make the load curve more stable and reduce the peak-valley difference.
S2: the EVDI firstly classifies and aggregates the target electric vehicle group according to the SOC value according to the collected related state information of the electric vehicles and the information provided by the vehicle owners; the method specifically comprises the following steps:
s21, establishing discharge model of virtual energy storage of electric automobile
S211) establishing a discharge model
The electric automobile sends active power when the virtual energy storage is discharged, and the SOC is reduced. SOC at time ttThe value calculation formula is:
in the formula: SOC0The SOC value is the SOC value of the electric automobile at the initial moment; pdThe discharge power is between t and t-1; etadTo the discharge efficiency; Δ t is the duration of the discharge; ebatThe rated capacity of the electric automobile.
S212) constraint conditions
The constraint conditions of the virtual energy storage of the electric automobile comprise SOC constraint and discharge constraint.
The SOC constraint conditions are as follows: SOCmin≤SOCi≤SOCmax(2)
In the formula: SOCminAnd SOCmaxThe lower limit, the upper limit and the SOC of the battery of the electric automobile are respectivelyiThe state of charge of the battery of the ith electric vehicle; when the discharge depth breaks through the limitation of the state of charge, the energy storage battery enters a locking state, and the discharge operation is forbidden.
The discharge constraint conditions are as follows: p is more than or equal to 0d≤Pdmax(3)
In the formula: pdmaxThe maximum discharge power of the electric automobile.
And S22, classifying the electric automobile groups into equivalent concentrated virtual energy storage according to the SOC values.
S221) the electric automobile groups are grouped into an equivalent centralized virtual energy storage for EVDI (electric vehicle drive) to dispatch according to the SOC values.
Firstly, electric vehicles are classified into the following three types according to different SOC values: [ 75% -85%), [ 85% -95%), [ 95% -100% ]. In each category, the following three categories are further classified according to the expected SOC value after the service is ended and input by the owner: 65% -75%), 75% -85% and 85% -95%. Therefore, the electric vehicle group in the embodiment is classified as follows:
TABLE 2 electric vehicle group Classification
| Electric vehicle classification | A(75％—85％) | B(85％—95％) | C(95％—100％) |
| a(65％—75％) | √ | √ | √ |
| b(75％—85％) | √ | √ | √ |
| c(85％—95％) | √ | √ |
As can be seen from the table, the classification results are: aa. Ab, Ba, Bb, Bc, Ca, Cb and Cc.
Next, only one discharge variable for centralized virtual energy storage is needed in scheduling optimization calculation. In order to enable the power distribution of each electric vehicle virtual energy storage to be reasonable in the scheduling process, the discharge power of each electric vehicle virtual energy storage is determined by adopting the rated power and the SOC value of each electric vehicle virtual energy storage. The specific determination method is as follows:
in the formula, PrThe rated power of the electric automobile; p isidThe discharge power of the electric automobile i is obtained; pjdThe discharge power of the electric vehicle j; palldThe total discharge power required by the energy storage demander; n is the total number of the electric automobiles; f. ofd(x) The function is the discharge SOC function of the electric automobile.
The specific discharge SOC function expression is:
fd(x)＝1/(1+exp-20(x-0.5)) (5)
s222) according to the eight kinds of classification conditions of the electric automobiles, aggregating virtual energy storage of each kind of electric automobiles to obtain equivalent concentrated rated power, equivalent concentrated rated capacity, equivalent concentrated discharge efficiency and other effective parameters, and when each kind of virtual energy storage system of the electric automobiles has the same discharge duration under the rated power, defining the corresponding equivalent concentrated virtual energy storage parameters as follows:
a) the equivalent concentrated rated power is as follows:
in the formula: pallbatFor equivalent centralized virtualizationRated power for energy storage; pibatThe rated power of the ith electric automobile is N, and the total number of the electric automobiles is N.
b) The rated capacity of the equivalent concentrated virtual energy storage is as follows:
in the formula EallbatRated capacity for equivalent centralized virtual energy storage; eibatThe rated capacity of the ith electric automobile.
c) The equivalent concentrated discharge efficiency is:
in the formula: etaalldEquivalent concentrated discharge efficiency; etaidThe discharge efficiency of the ith electric vehicle.
After corresponding equivalent centralized parameters are defined, virtual energy storage of each type of electric automobile is represented by only one equivalent centralized energy storage during EVDI dispatching, and power of each virtual energy storage in the virtual energy storage of each type of electric automobile is distributed by adopting an equation (4).
S3: and for the virtual energy storage of the electric automobile after aggregation in the S2, establishing a scheduling model participating in the peak shaving auxiliary service. The virtual energy storage system of the electric automobile is utilized to carry out peak regulation auxiliary service on the power grid according to peak regulation requirements provided by an energy storage demand side, namely, the energy storage system discharges in the peak load period of the power grid, and the energy storage system charges in the valley period of the power grid so as to ensure that the load runs stably and reduce the starting and stopping times of the generator set. And the variance value of the load in the power grid is used for evaluating the peak clipping and valley filling effects of the power grid. The smaller the variance value of the power grid load is, the more stable the power grid load is, the smaller the difference between the power grid peak and the power grid valley is, and the better the effect of peak regulation by utilizing the virtual energy storage of the electric automobile is. Therefore, a peak regulation effect objective function is established based on the minimum power grid load variance:
in the formula: d1(i) For the load value of the ith time slot after the peak regulation of the virtual energy storage system of the electric automobile, i is 1,2, …, npWherein one day is changed to npFor an equal period of time.
The constraints are as follows:
(1) load value constraint
In the formula: d0(i) The predicted load data of the ith time period is a known value; t isstartAnd P (d) are the discharge starting time and the discharge power of the electric automobile respectively.
(2) Timing constraints
In the formula: t isstopIs the end time of each discharge.
(3) Electric vehicle discharge current restraint
For safety, the discharge current of the electric automobile cannot be too large, otherwise serious damage to the battery and even explosion of the battery can be caused.
The current of the electric automobile battery does not exceed the maximum discharge current I in the discharge processin。
In the formula: i isdFor discharging current of electric vehicle, and Id≥0；UEVIs the voltage of the cell during discharge; ebatRated capacity for electric vehicles, Ebat＝82kW·h。
(4) Electric vehicle discharge power constraint
The charge and discharge power of the virtual energy storage of the electric automobile is limited in many aspects, and mainly comprises a charging station facility line, the charge and discharge current, the voltage, the rated charge and discharge power of a battery and the like of the electric automobile. The actual charge/discharge power of the electric vehicle is the minimum power value obtained by the following constraint.
a) Charging station facility line to power limitation: the electric automobile is limited by facilities such as bidirectional charging and discharging piles during discharging, the power cannot be overlarge, otherwise equipment can be burnt out and even fire can happen, and the discharging power of the electric automobile does not exceed a discharging threshold value Pf(those skilled in the art will recognize that in order to ensure that the lithium battery of an electric vehicle does not over-charge, the discharge power is not allowed to exceed a threshold, which in the present invention is defined by a discharge threshold PfDenotes (different type of electric vehicle, PfDifferent)).
0≤Pd≤Pf
b) Discharge current and voltage versus power limit: the discharge power of the electric automobile has a direct relation with the current, and the discharge power of the electric automobile is limited to a certain extent due to the fact that the current is limited to a certain extent.
0≤Pd≤UEV×Id
c) Rated discharge power limit: the power of electric vehicle discharge can not be higher than the rated discharge power.
0≤Pd≤Pdr
In the formula: pdrRated power for discharging the electric automobile.
According to the constraint, the minimum value is selected for ensuring the safe power of the electric automobile in the discharging process of the electric automobile. Since the discharge is set to be positive, the discharge power P of the electric vehicledThe constraints are:
0≤Pd≤min(Pf,UEV×Id,Pdr)
(5) available capacity constraint of battery
The battery capacity of the electric automobile is limited, and the virtual energy storage available capacity of the electric automobile is the minimum SOC reserved by the driving mileage S of the electric automobile and the deep discharge of the batteryminAfter participating in virtual energy storage service and inputted by vehicle ownerPredicted driving mileage SrAnd the like.
a) The lithium ion battery is over-charged and over-discharged in the using process, so that the battery is lost, and the user cost is increased. In order to prevent the battery from being rapidly attenuated, the expected residual electric quantity E set by the owner of the electric automobile after the electric automobile is discharged is setexpWhen the residual capacity is larger than 10% of rated capacity, the residual capacity of the electric automobile is used for EbRepresents:
Eb≥max{Eexp,Ebat·10％}
b) electric automobile driving power consumption ELThe calculation formula of (2):
in the formula: l is the daily mileage subject to N (2.6957, 1.0192)2) Lognormal distribution of (a), in miles; eta is energy conversion efficiency, and eta is 95%; k is the power consumption of the electric automobile in hundred kilometers, and k is 20.5 kW.h.
c) The SOC value corresponding to the power consumption of the electric automobile is as follows:
when the electric automobile discharges to participate in the peak regulation auxiliary service, the automobile owner is required to input the driving mileage S required after the participation of the virtual energy storage is finishedrThe minimum SOC reserved for the driving of the electric vehicle is requiredrThe calculation formula of (2) is as follows:
according to the constraint, each electric automobile participates in the virtual energy storage available capacity EaThe calculation formula is as follows:
(6) historical credit rating score
When the virtual energy storage of the electric automobile participates in the peak shaving auxiliary service, the owner can decide to quit the service at any time, and the redundancy caused by the behavior of quitting the service in the specified service time can influence the control strategy. Therefore, the invention provides the historical credit evaluation score, and can inhibit the behavior to a certain extent. The system gives an evaluation score after each service of the vehicles participating in the virtual energy storage service of the electric automobile is completed, the influence factor of the score is whether the electric automobile participates in the service time period specified by the system, the score is correspondingly deducted when the electric automobile leaves in the service time period, and the specific score calculation formula is as follows:
in the formula: t is tactIs the actual departure time, t, of the electric vehicleexpSpecifying a departure time, t, for an electric vehicleallThe total discharge time is specified for the electric vehicle.
In the above mathematical model, the peak-shaving objective function and the constraint condition are nonlinear. Therefore, the model is solved by adopting a constant power method.
S4: determining the final discharge time period of virtual energy storage of each type of electric automobile based on the established scheduling model participating in the peak regulation auxiliary service and the peak regulation demand provided by the energy storage demand party, and performing constant-power discharge control;
referring to fig. 3, the constant power discharge control strategy refers to that no matter how the external power grid load changes, the virtual energy storage system of the electric vehicle makes a discharge rule at any time according to a historical load curve, and discharges energy with constant power.
The method mainly comprises the following steps:
s41, because the discharge is constant power, the discharge energy of the virtual energy storage system of the electric automobile in unit time is the same, and the rated capacity E of the equivalent concentrated virtual energy storage obtained in the step S4allbatAnd equivalent concentrationRated power P of virtual energy storageallbatThe total discharge time was calculated as:
T＝Eallbat/Pallbat
wherein, P of each type of electric automobile virtual energy storage systemallbatIs the constant power of such systems.
S42, firstly, obtaining a next-day power grid load curve according to a peak shaving demand provided by an energy storage demand party (the peak shaving demand is a next-day power grid load curve predicted according to historical data in each time period of a power grid), finding out a load peak value within 24h in the next-day power grid load curve, and making a horizontal line L at the highest position. Then, starting from the high load peak, a small step length Δ M is set to move downwards, at this time, the horizontal line L intersects with the predicted load curve of the power grid at two points, and the distance between the two points is measured in real time. Finally, the measured distance between the two points is compared with the discharge time T: if the two areas are equal, the area is a reasonable discharging area of the virtual energy storage system of the electric automobile; if not, continuing to move the L upwards by the step length delta M until the distance between the L and the L is equal, and determining the final discharge time period of the virtual energy storage system of each type of electric automobile;
the virtual energy storage system of the electric automobile discharges energy at constant power in a determined discharge time period.
Example 2
Referring to fig. 4, the present embodiment provides a virtual energy storage participating peak shaving auxiliary service control system for an electric vehicle, including:
the acquisition module is used for acquiring relevant state information of each electric automobile, information provided by an automobile owner and peak shaving requirements provided by an energy storage demander; the information provided by the vehicle owner includes: the expected leaving time and the SOC value interval expected by the owner when leaving;
the aggregation module is used for classifying and aggregating a target electric vehicle group according to the SOC values according to the collected relevant state information of each electric vehicle and information provided by a vehicle owner to obtain aggregated electric vehicle virtual energy storage;
the scheduling model establishing module is used for establishing a scheduling model participating in peak shaving auxiliary service for the virtual energy storage of the aggregated electric automobile;
and the determining module is used for determining the final discharging time period of the virtual energy storage of each type of electric automobile based on the established scheduling model participating in the peak shaving auxiliary service and the peak shaving demand provided by the energy storage demand party.
Example 3
Referring to fig. 5, the present embodiment further provides a control system 100 for the virtual energy storage participating peak shaving auxiliary service of the electric vehicle; comprising a memory 101, at least one processor 102, a computer program 103 stored in said memory 101 and executable on said at least one processor 102, and at least one communication bus 104.
The memory 101 may be configured to store the computer program 103, and the processor 102 implements the method steps of the method for controlling virtual energy storage participation in a peak shaving auxiliary service of an electric vehicle according to embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; data (such as audio data) created by the electric vehicle virtual energy storage participation peak shaving auxiliary service control system 100. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other non-volatile solid state storage device.
The at least one Processor 102 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The processor 102 may be a microprocessor, or the processor 102 may also be any conventional processor, and the processor 102 is a control center of the virtual energy storage and peak shaving auxiliary service control system 100 of the electric vehicle, and various interfaces and lines are used to connect various parts of the entire virtual energy storage and peak shaving auxiliary service control system 100 of the electric vehicle.
The memory 101 of the electric vehicle virtual energy storage participation peak shaving auxiliary service control system 100 stores a plurality of instructions to implement an electric vehicle virtual energy storage participation peak shaving auxiliary service control method, and the processor 102 can execute the plurality of instructions to implement:
acquiring relevant state information of each electric automobile, information provided by an automobile owner and peak shaving requirements provided by an energy storage demander;
according to the collected relevant state information of each electric automobile and the information provided by an owner, classifying and aggregating a target electric automobile group according to the SOC value to obtain aggregated electric automobile virtual energy storage;
for the aggregated virtual energy storage of the electric automobile, establishing a scheduling arrangement model participating in peak shaving auxiliary service;
and determining the final discharge time period of the virtual energy storage of each type of electric automobile based on the established scheduling model participating in the peak regulation auxiliary service and the peak regulation requirement provided by the energy storage demander.
Specifically, the processor 102 may refer to the description of the relevant steps in embodiment 1 for a specific implementation method of the instruction, which is not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (4)
1. A virtual energy storage participation peak shaving auxiliary service control method for an electric automobile is characterized by comprising the following steps:
acquiring relevant state information of each electric automobile, information provided by an automobile owner and peak shaving requirements provided by an energy storage demander; the information provided by the vehicle owner includes: the expected leaving time and the SOC value interval expected by the owner when leaving;
classifying and aggregating the target electric automobile group according to the SOC value according to the collected relevant state information of each electric automobile and the information provided by the owner of the electric automobile to obtain the aggregated virtual energy storage of the electric automobiles;
for the aggregated virtual energy storage of the electric automobile, establishing a scheduling arrangement model participating in peak shaving auxiliary service;
determining a final discharge time period of virtual energy storage of each type of electric automobile based on the established scheduling model participating in the peak regulation auxiliary service and the peak regulation demand provided by the energy storage demand party;
the method comprises the following steps of classifying and aggregating target electric automobile groups according to SOC values according to collected relevant state information of each electric automobile and information provided by an automobile owner to obtain aggregated electric automobile virtual energy storage, and specifically comprises the following steps:
establishing a discharge model of virtual energy storage of the electric automobile;
the electric automobile group is classified into equivalent concentrated virtual energy storage according to the SOC value;
the step of establishing the virtual energy storage discharge model of the electric automobile specifically comprises the following steps:
establishing a discharge model:
SOC at time ttThe value calculation formula is:
in the formula: SOC0The SOC value is the SOC value of the electric automobile at the initial moment; pdThe discharge power is between t and t-1; etadTo discharge efficiency; Δ t is a duration of the discharge; ebatIs the electric automobileFixing the volume;
establishing a constraint condition:
the constraint conditions of virtual energy storage of the electric automobile comprise SOC constraint and discharge constraint;
the SOC constraint conditions are as follows: SOCmin≤SOCi≤SOCmax(2)
In the formula: SOCminAnd SOCmaxRespectively is the lower limit and the upper limit of the charge state of the battery of the electric automobile;
the discharge constraint conditions are as follows: p is more than or equal to 0d≤Pdmax(3)
In the formula: pdmaxThe maximum discharge power of the electric automobile;
the step of classifying the electric automobile group into equivalent concentrated virtual energy storage according to the SOC value specifically comprises the following steps:
firstly, electric vehicles are classified into the following three types according to different SOC values: [ 75% -85%), [ 85% -95%), [ 95% -100%) ]; in each category, the expected SOC value after the service is ended according to the input of the vehicle owner is divided into the following three categories: [ 65% -75%), [ 75% -85%), [ 85% -95%) ]; obtaining electric vehicle group classification;
the discharge power of each electric automobile is determined jointly by adopting the rated power and the SOC value of each electric automobile virtual energy storage, and the specific determination mode is as follows:
in the formula, PrThe rated power of the electric automobile; pidThe discharge power of the electric automobile i; palldTotal discharge power required for scheduling; n is the total number of the electric automobiles; f. ofd(x) Is a discharge SOC function of the electric automobile;
according to the classification condition of the electric automobiles, aggregating each electric automobile virtual energy storage in each class to obtain equivalent concentrated rated power, equivalent concentrated rated capacity and equivalent concentrated discharge efficiency, wherein each virtual energy storage power in each electric automobile virtual energy storage is distributed by adopting a formula (4);
the step of establishing a scheduling model participating in the peak shaving auxiliary service for the aggregated virtual energy storage of the electric automobile specifically comprises the following steps:
establishing a peak regulation effect objective function based on the minimum power grid load variance:
in the formula: d1(i) The load value of the ith time slot after the peak regulation of the virtual energy storage system of the electric automobile is i ═ 1,2, ·, npWherein one day is changed to npAn equal time period;
the virtual energy storage system of the electric automobile is utilized to carry out peak shaving auxiliary service on the power grid, and the minimum load variance of the power grid is met;
the peak shaver effect objective function has the following constraint conditions:
(1) load value constraint
In the formula: d0(i) The predicted load data of the ith time period is a known value; t isstartP (d) is the discharge starting time and the discharge power of the electric vehicle respectively;
(2) timing constraints
In the formula: t isstopThe end time of each discharge;
(3) electric vehicle discharge current restraint
In the formula: i isdDischarging current for electric vehicle, andId≥0；Iinis the maximum discharge current; u shapeEVIs the voltage of the cell during discharge; ebatThe rated capacity of the electric automobile;
(4) electric vehicle discharge power constraint
0≤Pd≤min(Pf,UEV×Id,Pdr)
Wherein, PfIs a discharge threshold; pdrRated power for discharging the electric vehicle;
(5) available capacity constraint of battery
Available capacity E of each electric automobile participating in virtual energy storageaComprises the following steps:
wherein E isbThe residual electric quantity of the electric automobile; SOCLThe SOC value corresponds to the power consumption of the electric automobile during running; SOCrA minimum SOC value reserved for electric vehicle driving;
(6) historical credit rating score
The vehicle participating in the virtual energy storage service of the electric automobile obtains an evaluation score after each service is completed, and the calculation formula is as follows:
in the formula: t is tactIs the actual departure time, t, of the electric vehicleexpSpecifying a departure time, t, for an electric vehicleallSpecifying a total discharge time for the electric vehicle;
the step of determining the final discharge time period of the virtual energy storage of each type of electric automobile based on the established scheduling model participating in the peak regulation auxiliary service and the peak regulation demand provided by the energy storage demand party specifically comprises the following steps:
rated capacity E of virtual energy storage concentrated by equivalentallbatAnd the rated power P of equivalent centralized virtual energy storageallbatCalculate outThe total discharge time was:
T＝Eallbat/Pallbat
according to the peak regulation demand provided by the energy storage demand side, obtaining a next power grid load curve, finding out a load high peak value in 24h in the next power grid load curve, and making a horizontal line L at the highest position; starting from the load peak value, setting a step length delta M to move upwards, intersecting a horizontal line L and a predicted power grid load curve at two points, and measuring the distance between the two points in real time; the measured distance between the two points is compared with the discharge time T: if the two are equal, the regional time interval is a reasonable discharging region of the virtual energy storage system of the electric automobile; and if the distance between the L and the virtual energy storage system is not equal, continuing moving the L upwards by the step length delta M until the distance between the L and the virtual energy storage system is equal, and determining the final discharge time period of the virtual energy storage system of each type of electric automobile.
2. The method for controlling the virtual energy storage participation peak shaving auxiliary service of the electric vehicle as claimed in claim 1, wherein in the step of obtaining the relevant state information of each electric vehicle, the information provided by the vehicle owner and the peak shaving requirement provided by the energy storage demander:
the relevant state information of the electric automobile comprises the following steps: battery capacity, battery state of charge, battery power, charge and discharge depth, and battery life.
3. The utility model provides an electric automobile virtual energy storage participates in peak shaver and assists service control system which characterized in that includes:
the acquisition module is used for acquiring relevant state information of each electric automobile, information provided by an automobile owner and peak shaving requirements provided by an energy storage demander; the information provided by the vehicle owner includes: the expected leaving time and the SOC value interval expected by the owner when leaving;
the aggregation module is used for classifying and aggregating the target electric vehicle group according to the SOC values according to the collected relevant state information of each electric vehicle and the information provided by the vehicle owner to obtain the aggregated virtual energy storage of the electric vehicles;
the scheduling model establishing module is used for establishing a scheduling model participating in peak shaving auxiliary service for the virtual energy storage of the aggregated electric automobile;
the determining module is used for determining the final discharging time period of the virtual energy storage of each type of electric automobile based on the established scheduling and arranging model participating in the peak regulation auxiliary service and the peak regulation requirement provided by the energy storage demander;
the aggregation module classifies and aggregates the target electric vehicle group according to the SOC value according to the collected relevant state information of each electric vehicle and the information provided by the vehicle owner, and the step of obtaining the aggregated virtual energy storage of the electric vehicles specifically comprises the following steps:
establishing a discharge model of virtual energy storage of the electric automobile;
the electric automobile group is classified into equivalent concentrated virtual energy storage according to the SOC value;
the step of establishing the virtual energy storage discharge model of the electric automobile specifically comprises the following steps:
establishing a discharge model:
SOC at time ttThe value calculation formula is:
in the formula: SOC0The SOC value is the SOC value of the electric automobile at the initial moment; pdThe discharge power is between t and t-1; etadTo discharge efficiency; Δ t is a duration of the discharge; ebatThe rated capacity of the electric automobile;
establishing a constraint condition:
the constraint conditions of virtual energy storage of the electric automobile comprise SOC constraint and discharge constraint;
the SOC constraint conditions are as follows: SOCmin≤SOCi≤SOCmax (2)
In the formula: SOCminAnd SOCmaxRespectively is the lower limit and the upper limit of the charge state of the battery of the electric automobile;
the discharge constraint conditions are as follows: p is more than or equal to 0d≤Pdmax (3)
In the formula: p isdmaxThe maximum discharge power of the electric automobile;
the step of classifying the electric automobile group into equivalent concentrated virtual energy storage according to the SOC value specifically comprises the following steps:
firstly, electric vehicles are classified into the following three types according to different SOC values: [ 75% -85%), [ 85% -95%), [ 95% -100%) ]; in each category, the expected SOC value after the service is ended according to the input of the vehicle owner is divided into the following three categories:
[ 65% -75%), [ 75% -85%), [ 85% -95%) ]; obtaining electric vehicle group classification;
the discharge power of each electric automobile is determined jointly by adopting the rated power and the SOC value of each electric automobile virtual energy storage, and the specific determination mode is as follows:
in the formula, PrThe rated power of the electric automobile; pidThe discharge power of the electric automobile i; palldTotal discharge power required for scheduling; n is the total number of the electric automobiles; f. ofd(x) Is a discharge SOC function of the electric automobile;
according to the classification condition of the electric automobiles, aggregating each electric automobile virtual energy storage in each class to obtain equivalent concentrated rated power, equivalent concentrated rated capacity and equivalent concentrated discharge efficiency, wherein each virtual energy storage power in each electric automobile virtual energy storage is distributed by adopting a formula (4);
the scheduling model establishing module establishes a scheduling model participating in peak shaving auxiliary service for the aggregated virtual energy storage of the electric vehicle, and specifically comprises the following steps:
establishing a peak regulation effect objective function based on the minimum power grid load variance:
in the formula: d1(i) For passing through electric automobile virtualLoad value i of the ith time period after peak regulation of the energy storage system is 1,2, npWherein one day is changed to npAn equal time period;
the virtual energy storage system of the electric automobile is utilized to carry out peak shaving auxiliary service on the power grid, so that the minimum load variance of the power grid is met;
the peak shaver effect objective function has the following constraint conditions:
(1) load value constraint
In the formula: d0(i) The predicted load data of the ith time period is a known value; t isstartP (d) is the discharge starting time and the discharge power of the electric vehicle respectively;
(2) timing constraints
In the formula: t isstopThe end time of each discharge;
(3) electric vehicle discharge current restraint
In the formula: i isdFor discharging current of electric vehicle, and Id≥0；IinIs the maximum discharge current; u shapeEVIs the voltage of the cell during discharge; ebatThe rated capacity of the electric automobile;
(4) electric vehicle discharge power constraint
0≤Pd≤min(Pf,UEV×Id,Pdr)
Wherein, PfIs a discharge threshold; pdrRated power for discharging the electric vehicle;
(5) available capacity constraint of battery
Available capacity E of each electric automobile participating in virtual energy storageaComprises the following steps:
wherein E isbThe residual electric quantity of the electric automobile; SOC (system on chip)LThe SOC value corresponds to the power consumption of the electric automobile during running; SOCrA minimum SOC value reserved for driving of the electric vehicle;
(6) historical credit rating score
The vehicle participating in the virtual energy storage service of the electric automobile obtains an evaluation score after the service is completed each time, and the calculation formula is as follows:
in the formula: t is tactIs the actual departure time, t, of the electric vehicleexpSpecifying a departure time, t, for an electric vehicleallSpecifying a total discharge time for the electric vehicle;
the method comprises the following steps that a determining module determines a final discharge time period of virtual energy storage of each type of electric automobile based on an established scheduling model participating in peak regulation auxiliary service and a peak regulation demand provided by an energy storage demand party, and specifically comprises the following steps:
rated capacity E of virtual energy storage concentrated by equivalentallbatAnd rated power P of equivalent concentrated virtual energy storageallbatThe total discharge time was calculated as:
T＝Eallbat/Pallbat
according to the peak regulation demand provided by the energy storage demand side, obtaining a next power grid load curve, finding out a load high peak value in 24h in the next power grid load curve, and making a horizontal line L at the highest position; starting from a high load peak value, setting a step length delta M to move upwards, intersecting a horizontal line L and a predicted power grid load curve at two points, and measuring the distance between the two points in real time; the measured distance between the two points is compared with the discharge time T: if the two are equal, the regional time interval is a reasonable discharging region of the virtual energy storage system of the electric automobile; and if the distance between the L and the virtual energy storage system is not equal, continuing moving the L upwards by the step length delta M until the distance between the L and the virtual energy storage system is equal, and determining the final discharge time period of the virtual energy storage system of each type of electric automobile.
4. An electric vehicle virtual energy storage participation peak-shaving auxiliary service control system, which is characterized by comprising a processor and a memory, wherein the processor is used for executing a computer program stored in the memory to realize the electric vehicle virtual energy storage participation peak-shaving auxiliary service control method according to any one of claims 1 to 2.
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