WO2022252426A1 - 一种电动汽车集群可调控能力确定方法、调度方法及系统 - Google Patents

一种电动汽车集群可调控能力确定方法、调度方法及系统 Download PDF

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WO2022252426A1
WO2022252426A1 PCT/CN2021/117054 CN2021117054W WO2022252426A1 WO 2022252426 A1 WO2022252426 A1 WO 2022252426A1 CN 2021117054 W CN2021117054 W CN 2021117054W WO 2022252426 A1 WO2022252426 A1 WO 2022252426A1
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electric vehicle
charging
time
power
cluster
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PCT/CN2021/117054
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English (en)
French (fr)
Inventor
杨烨
王明才
刘建坤
李培军
王文
彭晓峰
吴阚
李强
吴盛军
朱国才
仲宇璐
高琳
任亚钊
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国网江苏省电力有限公司电力科学研究院
国网电动汽车服务有限公司
国家电网有限公司
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Publication of WO2022252426A1 publication Critical patent/WO2022252426A1/zh

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]

Definitions

  • the invention relates to the field of electric vehicle charging, in particular to a method for determining the controllable capability of an electric vehicle cluster, a scheduling method and a system.
  • Existing technology 1 considers the constraints of electric vehicle (EV) traffic travel demand, through the statistical analysis of EV user travel characteristics and battery parameters, and using the Monte Carlo algorithm to evaluate the day-ahead response capability of the electric vehicle cluster, but does not combine The actual situation of EV travel within a day realizes the accurate modeling of the controllability of each time period.
  • Existing technology 2 estimates the adjustable capacity of electric vehicle clusters based on the Markov process, but the establishment of this model does not consider the influence of grid interactive scheduling.
  • Existing technology 3 evaluates the available capacity of large-scale EVs based on the aggregated queuing network model.
  • Prior Art 5 proposes a method for evaluating the ability of private electric vehicles to participate in power grid regulation based on the travel chain and willingness to participate. Although the model of the controllable ability of a single electric vehicle is disclosed, it considers the location of the trip and the evaluation of user satisfaction.
  • the high-degree EV aggregation modeling only has an overall estimate of the controllability of the cluster, without specific analysis of the charging process, and no refined modeling method based on real-time scheduling is proposed. Therefore, the current controllability of EV clusters The research needs to be further in-depth.
  • the present invention provides a method for determining the controllability of electric vehicle clusters, including:
  • the available charging/discharging capacity and available charging/discharging power at the current dispatching time of the electric vehicle are calculated using the pre-built single electric vehicle controllability evaluation model
  • the construction of the adjustable capacity assessment model of the single electric vehicle includes:
  • the current maximum operating area includes: the first boundary corresponding to the charging behavior with the highest charging priority for the purpose of the maximum charge state allowed by the electric vehicle; the priority discharge and charge priority for the purpose of the target state of charge of the electric vehicle
  • the scheduling period is a time period between two scheduling moments.
  • the determination of the maximum operating area includes:
  • the multiple state-of-charge time points include: the time when the electric vehicle is connected to the charging pile is Point A, based on the initial SOC, the time when the maximum charging operation reaches the maximum state of charge allowed by the electric vehicle is point B;
  • the driving time is point D, the time to reach the minimum state of charge allowed to be adjusted based on the initial SOC with the maximum discharge is point F, and the electric vehicle is operated at the minimum state of charge allowed to be adjusted with the maximum charge to reach the target load during the stop time.
  • the maximum time point of the electric state is point E;
  • the first boundary corresponding to the charging behavior with the highest charging priority and the highest charging priority is composed of the sides AB, BC and CD obtained by connecting points A, B, C and D in sequence;
  • edges AF, FE, and ED obtained by connecting points A, F, E, and D in turn constitute the second boundary corresponding to the charging behavior with the target state of charge as the priority discharge and the charging behavior with the lowest charging priority;
  • a maximum operating area is determined based on the first boundary and the second boundary.
  • the operating state X, the charging boundary point Y and the discharging boundary point Z of the maximum operating area respectively include: the state of charge and the time corresponding to point X, point Y and point Z.
  • the relationship between the charging state, charging and discharging power and time of the electric vehicle corresponding to each side in the maximum operating area during the charging and discharging process is as follows:
  • Q chgT is the maximum available charging capacity of the electric vehicle under the constraint of the scheduling period T;
  • Q dchgT is the maximum available discharge capacity of the electric vehicle under the constraint of the scheduling period T;
  • Q chgBC refers to the maximum charging capacity of the electric vehicle under the constraint of side BC ;
  • Q chgCD refers to the maximum charging capacity of electric vehicles under the constraint of edge CD;
  • Q dchgCD refers to the maximum discharge capacity of electric vehicles under the constraint of edge CD;
  • Q dchgED refers to the maximum discharge capacity of electric vehicles under the constraint of edge ED;
  • P max is the maximum charging power of the electric vehicle;
  • -P max is the maximum discharge power of the electric vehicle;
  • SOC 0 is the initial state of charge of the electric vehicle when it is connected to the grid;
  • SOC now is the current state of charge of the electric vehicle
  • the determination of the charging/discharging capacity boundary and charging/discharging power boundary of the electric vehicle at the current scheduling moment based on point X and point Y and between point X operating state X and point Z includes the following calculation formula:
  • Q chg is the boundary of available charging capacity of electric vehicles
  • Qdchg is the boundary of available discharge capacity of single electric vehicles
  • P chg is the boundary of available charging power of electric vehicles
  • Pdischg is the boundary of available discharge power of electric vehicles.
  • the present invention also provides a system for determining the controllability of electric vehicle clusters, including:
  • the single electric vehicle calculation module is used to calculate the available charging time of the electric vehicle at the current dispatching time based on the remaining charging time, current SOC, and target SOC of each electric vehicle based on the pre-built evaluation model of the adjustable ability of the single electric vehicle. /discharge capacity and available charge/discharge power;
  • the electric vehicle cluster calculation module is used to superimpose the available charging/discharging capacity and available charging/discharging power of each electric vehicle at the current dispatching time, determine the charging and discharging capacity boundary and power boundary of the electric vehicle cluster at the current dispatching time, and then determine the electric vehicle The current controllability of the cluster.
  • the present invention also provides a scheduling method for electric vehicle clusters, including:
  • the initial SOC, target SOC and stop time of each electric vehicle in the electric vehicle cluster use the method for determining the adjustable capacity to determine the adjustable capacity of the electric vehicle cluster in each scheduling period;
  • the power allocation of each electric vehicle in the electric vehicle cluster is carried out in each dispatching period with the goal of supply and demand balance.
  • determining the adjustable capacity of the electric vehicle cluster at each scheduling period at each moment includes:
  • At each scheduling time calculate the remaining charging duration and current SOC of the electric vehicle at the current scheduling time for the stop time of each electric vehicle in the electric vehicle cluster, the charging and discharging curve after connection, and the connection time;
  • the controllable capability of the electric vehicle cluster corresponding to the dispatching period at the current dispatching time is calculated by using the method for determining the adjustable capacity.
  • the determination of the state type of the electric vehicle according to the remaining charging time, current SOC and target SOC of each electric vehicle at the current dispatching time includes:
  • the state type of the electric vehicle is a rigid electric vehicle
  • the electric vehicle When the time required for charging the electric vehicle with the maximum charging power is less than the remaining charging time of the electric vehicle, the electric vehicle is a flexible electric vehicle.
  • power allocation is performed on each rigid electric vehicle in the electric vehicle cluster at each moment in the scheduling period, including:
  • power allocation is performed on each flexible electric vehicle in the electric vehicle cluster during each scheduling period, including:
  • the power distribution based on the charging and discharging priority of the electric vehicle includes:
  • the charging and discharging priority is determined according to the following formula:
  • PRI j (t sch ) is the charging and discharging priority index of the jth flexible electric vehicle at time t sch ; t sch is the scheduling time; The shortest charging time for the jth flexible electric vehicle to reach the target state of charge at time t sch ; is the stoppage time reached by the jth flexible electric vehicle.
  • the calculation formula of the charging and discharging power of the current electric vehicle in the current scheduling period is as follows:
  • the charging and discharging power of the jth flexible electric vehicle in the current scheduling period T is the battery capacity of the jth flexible electric vehicle; is the maximum state of charge allowed for the jth flexible electric vehicle; is the state of charge of the jth flexible electric vehicle at time t sch ; is the minimum state of charge allowed by the jth flexible electric vehicle; P flex is the total required power of the flexible electric vehicle allocated by dispatching.
  • the power allocation is performed on each electric vehicle in the electric vehicle cluster in each dispatching period with the goal of supply and demand balance, and then further includes:
  • the present invention also provides a scheduling system for electric vehicle clusters, including:
  • An adjustable capability determination module used to determine the adjustable capability of the electric vehicle cluster in each scheduling period by using the method for determining the adjustable capability of the electric vehicle cluster according to the initial SOC, target SOC and stop time of each electric vehicle in the electric vehicle cluster ;
  • the scheduling module is used to allocate power to each electric vehicle in the electric vehicle cluster in each scheduling period based on the grid demand time series and the controllability of the electric vehicle cluster in each scheduling period, with the goal of supply and demand balance.
  • a method and system for determining the controllability of electric vehicle clusters including: according to the remaining charging time, current SOC, and target SOC of each electric vehicle at the current scheduling time, using the pre-built single electric vehicle controllability
  • the evaluation model calculates the available charging/discharging capacity and available charging/discharging power of electric vehicles at the current dispatching time; superimposes the available charging/discharging capacity and available charging/discharging power of each electric vehicle at the current time, and determines the charging capacity of the electric vehicle cluster at the current dispatching time. Discharge capacity boundary and power boundary, and then determine the controllability of the electric vehicle cluster in the dispatching period corresponding to the current dispatching time. Real-time calculation at each time can accurately evaluate the controllability of the EV cluster.
  • a scheduling method and system for an electric vehicle cluster comprising: according to the initial SOC, target SOC and stop time of each electric vehicle in the electric vehicle cluster, using the method for determining the adjustable capacity to determine the electric vehicle The controllability of the cluster in each scheduling period; based on the power grid demand time series and the controllability of the electric vehicle cluster at each time and, with the goal of supply and demand balance, the power allocation of each electric vehicle in the electric vehicle cluster is carried out in each scheduling period; Based on the accurate evaluation of the controllability of the EV cluster, the reasonable power allocation of each EV can realize the real-time dynamic update of the EV cluster power and capacity boundary, which is of great significance for balancing the load power and formulating a real-time dispatch plan for the power grid.
  • Fig. 1 is a flow chart of the method for determining the controllability of electric vehicle clusters according to the present invention
  • Figure 2 is a schematic diagram of the control capability of a single electric vehicle
  • Fig. 3 is the flow chart of the dispatching method of electric vehicle cluster of the present invention.
  • Figure 4 shows the changes in the number of EVs in an office area in one day
  • Figure 5 shows the results of EV clusters participating in system scheduling
  • Figure 6 shows the EV cluster response capability
  • Figure 7 is a comparison chart of SOC before and after charging
  • Figure 8 shows the response of some monomer EVs
  • Fig. 9 is a structural block diagram of a system for determining the controllability of electric vehicle clusters
  • Fig. 10 is a structural block diagram of the dispatching system of the electric vehicle cluster.
  • the present invention provides a method for determining the controllability of electric vehicle clusters, as shown in Figure 1, including:
  • S1 Calculate the available charging/discharging capacity and available charging/discharging power of electric vehicles at the current dispatching time according to the remaining charging time, current SOC, and target SOC of each electric vehicle at the current dispatching time, using the pre-built single electric vehicle controllability evaluation model ;
  • S2 Superimpose the available charging/discharging capacity and available charging/discharging power of each electric vehicle at the current moment, determine the charging and discharging capacity boundary and power boundary of the electric vehicle cluster at the current dispatching time, and then determine the corresponding charging and discharging capacity boundary of the electric vehicle cluster at the current dispatching time. The ability to adjust the scheduling period.
  • step S1 the construction of the evaluation model for the controllability of the single EV is as follows:
  • the aggregator in the charging station can read parameters such as the initial state of charge SOC, target SOC, and stop time of the electric vehicle to evaluate the available charging and discharging power of the electric vehicle and capacity.
  • the adjustable capacity of a single electric vehicle is shown in Figure 2.
  • the area A ⁇ B ⁇ C ⁇ D ⁇ E ⁇ F is the maximum operating area of the single electric vehicle during this time period .
  • SOC t is the state of charge of the electric vehicle at time t; is the state of charge of the electric vehicle at time t 0 ; P t is the charging power of the electric vehicle at time t; Q n is the rated capacity of the electric vehicle.
  • the slope of the curve in Figure 2 can represent the charging power of the electric vehicle.
  • the area enclosed by AB ⁇ BC ⁇ CD ⁇ DE ⁇ EF ⁇ FA is the feasible region for EV operation, and its boundary can be expressed as:
  • SOC 0 ⁇ SOC min it needs to be charged first, and it is allowed to participate in regulation when SOC ⁇ SOC min is satisfied.
  • point A is the time when the electric vehicle connects to the charging pile
  • point B is the time when the electric vehicle reaches the maximum state of charge allowed by the maximum charging operation based on the initial SOC
  • point C is the time when the electric vehicle reaches the maximum available state of charge to stop driving
  • point D is the time when the electric vehicle reaches the stop time with the target state of charge
  • point F is the time when the electric vehicle reaches the minimum state of charge allowed to be regulated based on the initial SOC and the maximum discharge operation
  • point E is the minimum charge state of the electric vehicle that is allowed to be regulated.
  • the state of charge takes the point E as the maximum time point when the maximum charging operation reaches the target state of charge during the stoppage time;
  • edges AB, BC, and CD formed by connecting points A, B, C, and D in turn constitute the first boundary corresponding to the charging behavior with the highest available charging state for the purpose of priority charging and the highest charging priority;
  • edges AF, FE, and ED obtained by connecting points A, F, E, and D in turn constitute the second boundary corresponding to the charging behavior with the target state of charge as the priority discharge and the charging behavior with the lowest charging priority;
  • A-X curve as the charging and discharging operation curve of the electric vehicle since it is connected.
  • the electric vehicle goes through the charging and discharging process in the time period t 0 -t now , and reaches the operating state X at the time t now , and operates at the maximum charging and discharging power.
  • the curves are XY and XZ respectively, point Y is the charge boundary, point Z is the discharge boundary, and the expression is:
  • the adjustable capacity of a single electric vehicle is affected by the scheduling of the EV cluster control center and its own battery capacity.
  • T the scheduling cycle of the EV cluster as T, t refers to the time, which is equivalent to the independent variable (horizontal axis) in this formula (3)
  • the available charge and discharge capacity of the electric vehicle in the X running state is affected by T and the boundary BC in Figure 2 , CD, DE, and EF limits, respectively calculate the intersection points of XY, XZ and the boundary of the feasible region to obtain the charge and discharge capacity boundary of the electric vehicle at time t now , as shown in the formula.
  • Q chgT and Q dchgT refer to the maximum available charge and discharge capacity of a single EV under the constraint of scheduling time T;
  • Q chgBC refers to the maximum charge capacity of a single EV under the constraint of BC;
  • Q chgCD and Q dchgCD refer to the The maximum charge and discharge capacity of a bulk EV under the constraint of CD;
  • QdchgED refers to the maximum discharge capacity of a single EV under the constraint of ED;
  • QdchgFE refers to the maximum discharge capacity of a single EV under the constraint of FE.
  • the maximum available charge and discharge capacity of a single EV in the future scheduling period T is shown in the formula.
  • the maximum available charging and discharging power of a single EV is not only limited by the maximum charging and discharging power parameter P max of its own battery, but also limited by the maximum available charging and discharging capacity, and its calculation method is shown in the formula.
  • Q chg and Q dchg are the available charging and discharging capacity boundaries of a single EV;
  • P chg and P dischg are the available charging and discharging power boundaries of a single EV. Since the calculation of the available charge and discharge capacity boundary takes into account the maximum power limit within the scheduling time, the available charge and discharge power boundary will not exceed P max .
  • the available charging/discharging capacity and available charging/discharging power at the current dispatching time of the electric vehicle can be calculated.
  • S2 Superimpose the available charging/discharging capacity and available charging/discharging power of each electric vehicle at the current moment, determine the charging and discharging capacity boundary and power boundary of the electric vehicle cluster at the current dispatching time, and then determine the corresponding charging and discharging capacity boundary of the electric vehicle cluster at the current dispatching time.
  • the ability to adjust the scheduling period specifically including:
  • the controllability of the electric vehicle cluster is the superposition of the controllability of the single electric vehicle on the time axis.
  • the adjustable capacity of electric vehicle k can be obtained according to the energy storage capacity model of a single EV, then
  • the adjustable capacity of the EV cluster at time t is:
  • Q clu (t) is the available capacity boundary of the EV cluster at time t, including the charge and discharge capacity boundaries
  • P clu (t) is the available power boundary of the EV cluster at time t, including charge and discharge capacity boundaries
  • Discharge power boundary is the available capacity boundary of the EV cluster at time t, including the charge and discharge capacity boundaries
  • the present invention also provides a scheduling method for electric vehicle clusters, as shown in Figure 3, including:
  • Step 1 According to the initial SOC, target SOC and stop time of each electric vehicle in the electric vehicle cluster, use the adjustable capacity determination method to determine the adjustable capacity of the electric vehicle cluster in each scheduling period; the determination of the adjustable capacity of the electric vehicle cluster For the capability determination method, reference may be made to the specific example of the above-mentioned embodiment 1, which will not be repeated here;
  • Step 2 Based on the power grid demand time series and the controllability of electric vehicle clusters in each dispatching period, the power allocation of each electric vehicle in each dispatching period in the electric vehicle cluster is carried out with the goal of supply and demand balance.
  • step 1 specifically includes:
  • the controllable capability of the electric vehicle cluster in the dispatching period corresponding to the current dispatching time is calculated by using the method for determining the controllable capability described in Embodiment 1 above.
  • Each scheduling period is a time period between adjacent scheduling times.
  • the specific process of determining the state type of the electric vehicle is as follows:
  • the shortest charging time of the kth EV It can be calculated by the formula, the length of stay Can be calculated by formula. If the power grid issues an EV cluster output command at time t sch , it is necessary to judge the EV classification at this time. like Then the kth EV is a rigid electric vehicle; if The kth EV is a flexible electric vehicle.
  • Step 2 includes the following specific processes
  • the EV cluster control center evaluates the controllability of the cluster. According to the controllable capability boundary provided by the EV cluster control center, the grid optimizes the output curve of the EV cluster with the goal of system supply and demand balance. The EV cluster control center Through the internal scheduling strategy, the corresponding power optimal allocation is carried out for each EV, and the accurate tracking of the output curve of the EV cluster issued by the grid is realized.
  • the EV cluster control center classifies the electric vehicles that are connected to the charging pile at time t sch , and gives priority to charging the rigid EV with the maximum power. As shown in the formula.
  • m is the number of rigid EVs in the EV cluster at time t sch ; is the charging power of the i-th rigid EV; P rigid is the total charging power of the rigid EV cluster.
  • the total charging and discharging power of the flexible electric vehicle cluster in the scheduling period T can be expressed as:
  • P dem is the output command issued by the grid to the EV cluster
  • P flex is the total charging and discharging power of the flexible EV cluster.
  • the internal charging and discharging sequence of the flexible EV cluster is determined according to the time margin and state of charge margin of the single electric vehicle at t sch time. According to the priority index PRI(t sch ), the charging and discharging priority of the flexible EV cluster is sorted. The larger the PRI(t sch ), the greater the adjustable margin of the flexible EV.
  • PRI j (t sch ) is the charging and discharging priority index of the jth flexible EV at the time t sch ; is the shortest charging time for the jth flexible EV to reach the target SOC at time t sch , and its calculation method can be found in the formula; is the time for the jth flexible EV to leave the station after reaching the stop time, and t sch is the scheduling time.
  • the principle of meeting the charging needs of flexible EVs with higher charging priority is to first arrange EVs with small adjustment margins and lower PRI j (t sch ) values to be charged; when flexible EVs receive When the discharge command arrives, the EV with a large adjustment margin and a high value of PRI j (t sch ) is first arranged to discharge.
  • the charging and discharging power of the jth flexible EV in the time period [t sch , t sch + T] is shown in the formula.
  • the battery capacity of the jth flexible EV is the maximum state of charge allowed for the jth flexible EV; is the state of charge of the jth flexible EV at time t sch ; is the minimum state of charge allowed by the jth flexible EV. in, It should meet the maximum power limit of EV's own charge and discharge, that is,
  • the responsive power P al-flex of the flexible EV cluster is shown in the formula.
  • l represents the number of flexible EVs that have responded to the output command; is the charging and discharging power of the jth flexible EV in the time period [t sch , t sch +T].
  • the flexible electric vehicle when it responds according to the charge and discharge command, it is based on the principle of satisfying the charging demand of the flexible EV with a higher charging priority . For comparison, if the responding flexible electric vehicle power P al-flex does not meet the power demand, then allocate the next flexible electric vehicle, if it exceeds, then follow The power of the last electric vehicle is set so that the power of the corresponding dispatched flexible electric vehicle is consistent with the total demand of the flexible electric vehicle cluster.
  • simulation modeling is carried out based on the real time data of private cars entering and leaving an office area on a certain day, and the method for determining the controllable capacity of the electric vehicle cluster and the scheduling method mentioned in the present invention are described.
  • the simulation time is set to 0:00-24:00, and the scheduling cycle is set to 10 minutes.
  • a total of 169 electric vehicles participated in grid regulation that day, and the number of electric vehicles in different time periods is shown in Figure 4.
  • the initial state of charge of the electric vehicle cluster is SOC 0 ⁇ U(0.2,0.4)
  • the maximum state of charge SOC max is 0.9
  • the minimum state of charge SOC min is 0.15
  • the target state of charge SOC obj is 0.85
  • the battery capacity Q n is 70kW ⁇ h
  • the maximum charging power P max is 60kW
  • the maximum discharging power is -60kW.
  • the simulation process is as follows, and the cluster controllability evaluation results are analyzed according to the simulation process:
  • FIG. 5 shows the results of the EV cluster participating in the system scheduling.
  • the EV cluster responds to the grid charging scheduling command, the charging response capability of the EV cluster decreases and the discharge responsiveness increases at the next scheduling time, and the controllable boundary of the cluster moves down as a whole; on the contrary, the 65-
  • the EV cluster responds to the grid discharge dispatching command, the charging response capability of the EV cluster increases and the discharge response capability decreases at the next dispatching time, and the controllable boundary of the cluster moves up as a whole.
  • the EV cluster output command issued by the power grid within the controllable boundary of the cluster can be responded to.
  • the response of the EV cluster after receiving the scheduling instruction is shown in Figure 6- Figure 8. From the simulation results in Figure 6, it can be seen that the electric vehicle cluster can accurately track the given power demand within the response capability boundary, but there are minimal fluctuations due to some electric vehicles leaving the station in the dispatching interval.
  • Figure 7 shows the output response of a single EV under the dispatch command. It can be seen from the figure that the flexible electric vehicle can be charged to the interval [SOC obj , SOC max ] at the time of departure to meet the travel needs of the owner; there are 5 electric vehicles Due to the low initial charge and short residence time (belonging to rigid EV), even with maximum power charging, the target SOC cannot be reached.
  • Figure 8 shows the responses of the 2nd, 8th, 20th, and 156th EVs. It can be seen from the figure that the charging and discharging power and state of charge of a single EV during the station time closely follow the change of the output command of the EV cluster. Among them, the 20th EV is a rigid EV. No matter how the dispatching instructions change, it maintains the maximum charging power during the station, but it still does not reach the target SOC when it leaves the station.
  • the present invention can determine the operating state (rigidity or flexibility) of the electric vehicle according to the charging process of the electric vehicle, and realize real-time dynamic updating of the power and capacity boundaries of the EV cluster by carrying out reasonable power distribution to EVs in different states, and realize Carry out refined modeling on the controllability of EV clusters.
  • the present invention also provides a system for determining the controllable capability of the electric vehicle cluster, as shown in FIG. 9 , including:
  • the single electric vehicle calculation module is used to calculate the current state of the electric vehicle by using the pre-built single electric vehicle controllability evaluation model based on the remaining charging time, current SOC, and target SOC of each electric vehicle at each time within the scheduling period T. Available charge/discharge capacity and available charge/discharge power at all times;
  • the electric vehicle cluster calculation module is used to superimpose the available charging/discharging capacity and available charging/discharging power of each electric vehicle at the current moment, determine the charging and discharging capacity boundary and power boundary of the electric vehicle cluster at the current moment, and then determine the electric vehicle cluster at the current moment. Current scalability.
  • the functional modules of this embodiment are designed to realize the method for determining the controllable capability of the electric vehicle cluster. For details, refer to the above-mentioned embodiments, which will not be repeated here.
  • the system for determining the controllable capacity of electric vehicle clusters includes: a processor, wherein the processor is used to execute the following program modules stored in memory: a single electric vehicle calculation module, used to Time, according to the remaining charging time, current SOC, and target SOC of each electric vehicle, use the pre-built single electric vehicle controllability evaluation model to calculate the available charging/discharging capacity and available charging/discharging power of electric vehicles at the current moment; the electric vehicle cluster
  • the calculation module is used to superimpose the available charging/discharging capacity and available charging/discharging power of each electric vehicle at the current moment, determine the charging and discharging capacity boundary and power boundary of the electric vehicle cluster at the current moment, and then determine the current available charging/discharging power boundary of the electric vehicle cluster. Regulatory ability.
  • this embodiment provides a dispatching system for electric vehicle clusters, as shown in FIG. 10 , including:
  • the adjustable capability determination module is used to determine the adjustable capacity of the electric vehicle cluster by using the method described in claims 1 to n according to the initial SOC, target SOC and stop time of each electric vehicle in the electric vehicle cluster within the scheduling period T. ability;
  • the scheduling module is used to allocate power to each electric vehicle in the electric vehicle cluster at each time within the scheduling period T based on the grid demand time series and the adjustable capacity of the electric vehicle cluster at each time and with the goal of supply and demand balance.
  • the functional modules of this embodiment are designed to realize the scheduling method of electric vehicle clusters, and details are referred to the above embodiments, which will not be repeated here.
  • the dispatching system of the electric vehicle cluster includes: a processor, wherein the processor is used to execute the following program modules stored in the memory: an adjustable capacity determination module, used for, within the scheduling period T, according to the electric vehicle cluster
  • an adjustable capacity determination module used for, within the scheduling period T, according to the electric vehicle cluster
  • the initial SOC, target SOC and stop time of each electric vehicle in the electric vehicle utilize the method described in claim 1 to n to determine the controllability of the electric vehicle cluster
  • dispatching module for based on grid demand time series and electric vehicle cluster at each moment Adjustable capacity and, with the goal of supply and demand balance, power allocation is carried out to each electric vehicle in the electric vehicle cluster at each moment in the dispatching period T.
  • the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions
  • the device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

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Abstract

一种电动汽车集群可调控能力确定方法、调度方法及系统包括：在各调度时刻，根据各电动汽车(EV)的剩余充电时长、当前SOC、目标SOC，利用单体电动汽车可调控能力评估模型计算电动汽车各调度时刻可用充/放电容量和可用充/放电功率；对各电动汽车各调度时刻可用充/放电容量和可用充/放电功率进行叠加，确定电动汽车集群各调度时段可调控能力；基于电网需求和各时刻电动汽车集群可调控能力，以供需平衡为目标分别对电动汽车集群中的各电动汽车在调度时段内各时刻进行功率分配；采用本专利的方案能够精确评估EV集群可调控能力，并通过对不同状态的EV进行合理的功率分配实现对EV集群功率、容量边界的实时更新，对平衡负荷功率、电网制定实时调度计划有重要意义。

Description

一种电动汽车集群可调控能力确定方法、调度方法及系统 技术领域
本发明涉及电动汽车充电领域，具体涉及一种电动汽车集群可调控能力确定方法、调度方法及系统。
背景技术
现有技术1考虑电动汽车(EV)交通出行需求的约束，通过对EV用户出行特征以及电池参数进行统计分析，利用结合蒙特卡洛算法对电动汽车集群的日前响应能力进行评估，但并没有结合日内EV出行实际情况实现各时段可调控能力的精确建模。现有技术2基于马尔可夫过程估计电动汽车集群的可调控容量，但该模型的建立没有考虑电网互动调度的影响。现有技术3基于聚合排队网络模型对大规模EV的可用容量进行评估，虽然在电网互动调度的基础上提出了智能充电策略，但其假设EV集群可以充电或放电的时间呈指数分布，不具备典型性。现有技术4提出了一种基于实时智能充电调度的EV容量评估算法，分别计算每辆EV的可调控容量后通过聚合实现其可调控潜力评估。现有技术5提出了一种基于出行链和参与意愿的私家电动汽车参与电网调控能力评估方法，虽然公开了单体电动汽车可调控能力模型，但是，其考虑的是基于出行地点和评估用户满意度的EV聚合建模，只对集群的可调控能力有总体的估计，并没有对充电的过程进行具体的分析，没有基于实时调度提出精细化建模方法，因此目前对EV集群可调控能力的研究还有待进一步深入。
发明内容
为了解决现有技术中所存在的问题，本发明提供一种电动汽车集群可调控能力确定方法，包括：
根据各电动汽车当前调度时刻的剩余充电时长、当前SOC、目标SOC，利用预先构建的单体电动汽车可调控能力评估模型计算电动汽车当前调度时刻可用充/放电容量和可用充/放电功率；
对各电动汽车当前时刻可用充/放电容量和可用充/放电功率进行叠加，确定电动汽车集群在当前调度时刻的充放电容量边界和功率边界，进而确定电动汽车集群在当前调度时刻对应的调度时段的可调控能力。
优选的，所述单体电动汽车可调控能力评估模型的构建，包括：
根据电动汽车接入后的充放电曲线，以调度时段T为约束，确定所述电动汽车当前时刻应的运行状态点X；
根据当前时刻对应的运行状态点X，以当前时刻最大运行区域为约束，分别确定最快达到所述最大运行区域充电边界点Y和放电边界点Z；
基于点X和点Y以及点X和点Z分别确定电动汽车在当前时刻的可用充\放电容量边界和可用充\放电功率边界；
所述当前最大运行区域包括：以电动汽车允许的最大荷电状态为目的优先充电、充电优先级最高的充电行为对应的第一边界；以电动汽车目标荷电状态为目的优先放电、充电优先级最低的充电行为对应的第二边界；点Y位于所述第一边界，点Z位于所述第二边界；
所述调度时段为两个调度时刻之间的时间段。
优选的，所述最大运行区域的确定包括：
根据电动汽车接入充电桩时的初始SOC、目标SOC和停驶时间确定的多个荷电状态时间点；其中，所述多个荷电状态时间点包括：电动汽车接入充电桩的时间为点A、基于初始SOC以最大充电运行达到电动汽车允许的最大荷电状态的时间为点B、电动汽车以最大可用荷电状态达到停驶时间为点C、电动汽车以目标荷电状态达到停驶时间为点D、基于初始SOC以最大放电运行达到电动汽车允许调控的最小荷电状态的时间为点F、电动汽车在允许调控的最小荷电状态以最大充电运行在停驶时间达到目标荷电状态的最大时间点为点E；
由点A、B、C、D依次连接得到的边AB、BC和CD所构成以最大可用荷电状态为目的优先充电、充电优先级最 高的充电行为对应的第一边界；
由点A、F、E、D依次连接得到的边AF、FE和ED所构成以目标荷电状态为目的优先放电、充电优先级最低的充电行为对应的第二边界；
基于所述第一边界和第二边界确定最大运行区域。
优选的，所述运行状态X、所述最大运行区域充电边界点Y和所述放电边界点Z分别包括：点X、点Y和点Z对应的荷电状态和时刻。
优选的，所述最大运行区域中的各边对应的电动汽车充放电过程中的荷电状态、充放电功率与时间的关系如下式：
Figure PCTCN2021117054-appb-000001
式中，Q chgT为电动汽车在调度时段T约束下的最大可用充电容量；Q dchgT为电动汽车在调度时段T约束下的最大可用放电容量；Q chgBC指电动汽车边BC约束下的最大充电容量；Q chgCD指电动汽车在边CD约束下的最大充电容量、Q dchgCD指电动汽车在边CD约束下的最大放电容量；Q dchgED指电动汽车在边ED约束下的最大放电容量；Q dchgFE指电动汽车在边FE约束下的最大放电容量；P max为电动汽车最大充电功率；-P max为电动汽车最大放电功率；SOC 0为电动汽车接入电网时的初始荷电状态；SOC max为电动汽车电池允许的最大荷电状态；SOC now为电动汽车当前荷电状态；SOC min为电动汽车允许调控的最小荷电状态；SOC obj为电动汽车用户设置的目标荷电状态；Q n为电动汽车的额定容量；t 0为电动汽车接入时刻；t now为当前调度时刻；t leave为电动汽车离开时刻。
优选的，所述基于点X和点Y以及点X运行状态X和点Z之间的确定电动汽车在当前调度时刻的充\放电容量边界和充\放电功率边界，包括如下计算式：
Figure PCTCN2021117054-appb-000002
Figure PCTCN2021117054-appb-000003
式中，Q chg为电动汽车的可用充电容量边界；Q dchg为单体电动汽车的可用放电容量边界；P chg为电动汽车的可用充电功率边界；P dischg为电动汽车的可用放电功率边界。
基于同一种发明构思，本发明还提供一种电动汽车集群可调控能力确定系统，包括：
单体电动汽车计算模块，用于基于各调度时刻，根据各电动汽车的剩余充电时长、当前SOC、目标SOC，利用 预先构建的单体电动汽车可调控能力评估模型计算电动汽车当前调度时刻可用充/放电容量和可用充/放电功率；
电动汽车集群计算模块，用于对各电动汽车当前调度时刻可用充/放电容量和可用充/放电功率进行叠加，确定电动汽车集群在当前调度时刻的充放电容量边界和功率边界，进而确定电动汽车集群在当前的可调控能力。
基于同一种发明构思，本发明还提供一种电动汽车集群的调度方法，包括：
根据电动汽车集群中各电动汽车的初始SOC、目标SOC和停驶时间，利用所述的可调控能力确定方法确定电动汽车集群在各调度时段的可调控能力；
基于电网需求时间序列和各调度时段电动汽车集群可调控能力，以供需平衡为目标分别对电动汽车集群中的各电动汽车在各调度时段进行功率分配。
优选的，所述根据电动汽车集群中各电动汽车的初始SOC、目标SOC和停驶时间，确定各时刻电动汽车集群在各调度时段的可调控能力，包括：
在各调度时刻，依次针对电动汽车集群中各电动汽车的停驶时间、接入后的充放电曲线和接入时间，计算所述电动汽车的当前调度时刻剩余充电时长、当前SOC；
根据每个电动汽车的当前调度时刻剩余充电时长、当前SOC和目标SOC确定电动汽车状态类型；其中所述电动汽车状态类型包括刚性电动汽车和柔性电动汽车；
基于所有柔性电动汽车利用所述的可调控能力确定方法计算电动汽车集群在当前调度时刻对应调度时段的可调控能力。
优选的，所述根据每个电动汽车的当前调度时刻剩余充电时长、当前SOC和目标SOC确定电动汽车状态类型，包括：
当电动汽车在剩余充电时长内以最大充电功率进行充电仍不能达到目标SOC时，所述电动汽车状态类型为刚性电动汽车；
当电动汽车以最大充电功率进行充电所需时长小于所述电动汽车的剩余充电时长时，所述电动汽车为柔性电动汽车。
优选的，对电动汽车集群中的各刚性电动汽车在调度时段内各时刻进行功率分配，包括：
以最大功率进行充电。
优选的，对电动汽车集群中的各柔性电动汽车在各调度时段进行功率分配，包括：
基于各柔性电动汽车各调度时段对应的调度时刻达到目标荷电状态的最短充电时间确定充放电优先级；
基于电动汽车的充放电优先级进行功率分配。
优选的，所述基于电动汽车的充放电优先级进行功率分配，包括：
基于优先级顺序对各电动汽车依次执行：
计算已经响应调度分配的柔性电动汽车功率是否小于总需求功率；当小于时基于当前电动汽车在当前调度时段内的充放电功率对所述电动汽车进行充放电；否则，结束功率分配。
优选的，所述充放电优先级按下式确定：
Figure PCTCN2021117054-appb-000004
PRI j(t sch)为第j辆柔性电动汽车在t sch时刻的充放电优先级指标；t sch为调度时刻；
Figure PCTCN2021117054-appb-000005
为第j辆柔性电动汽车在t sch时刻达到目标荷电状态的最短充电时间；
Figure PCTCN2021117054-appb-000006
为第j辆柔性电动汽车达到的停驶时间。
优选的，所述当前电动汽车在当前调度时段内的充放电功率的计算式如下：
Figure PCTCN2021117054-appb-000007
式中，
Figure PCTCN2021117054-appb-000008
为第j辆柔性电动汽车在当前调度时段T内的充放电功率；
Figure PCTCN2021117054-appb-000009
为第j辆柔性电动汽车的电池容量；
Figure PCTCN2021117054-appb-000010
为第j辆柔性电动汽车允许的最大荷电状态；
Figure PCTCN2021117054-appb-000011
为第j辆柔性电动汽车在t sch时刻的荷电状态；
Figure PCTCN2021117054-appb-000012
为第j辆柔性电动汽车允许的最小荷电状态；P flex为调度分配的柔性电动汽车总需求功率。
优选的，所述基于电网需求时间序列和各时刻电动汽车集群可调控能力，以供需平衡为目标分别对电动汽车集群中的各电动汽车在各调度时段进行功率分配，之后还包括：
根据对各电动汽车在当前调度时段响应功率分配后的充放电曲线。
基于同一种发明构思，本发明还提供一种电动汽车集群的调度系统，包括：
可调控能力确定模块，用于根据电动汽车集群中各电动汽车的初始SOC、目标SOC和停驶时间，利用所述电动汽车集群可调控能力确定方法确定电动汽车集群在各调度时段的可调控能力；
调度模块，用于基于电网需求时间序列和各调度时段电动汽车集群可调控能力，以供需平衡为目标分别对电动汽车集群中的各电动汽车在各调度时段进行功率分配。
与现有技术相比，本发明的有益效果为：
1、本发明提供的一种电动汽车集群可调控能力确定方法及系统，包括：根据各电动汽车当前调度时刻的剩余充电时长、当前SOC、目标SOC，利用预先构建的单体电动汽车可调控能力评估模型计算电动汽车当前调度时刻可用充/放电容量和可用充/放电功率；对各电动汽车当前时刻可用充/放电容量和可用充/放电功率进行叠加，确定电动汽车集群在当前调度时刻的充放电容量边界和功率边界，进而确定电动汽车集群在当前调度时刻对应的调度时段的可调控能力，采用各时刻实时计算，能够精确评估EV集群可调控能力。
2、本发明提供的一种电动汽车集群的调度方法及系统，包括：根据电动汽车集群中各电动汽车的初始SOC、目标SOC和停驶时间，利用所述的可调控能力确定方法确定电动汽车集群在各调度时段的可调控能力；基于电网需求时间序列和各时刻电动汽车集群可调控能力和，以供需平衡为目标分别对电动汽车集群中的各电动汽车在各调度时段进行功率分配；在基于精确评估EV集群可调控能力的基础上，对各EV进行合理的功率分配实现对EV集群功率、容量边界的实时动态更新，对平衡负荷功率、电网制定实时调度计划有重要意义。
附图说明
图1为本发明的电动汽车集群可调控能力确定方法流程图；
图2为单体电动汽车调控能力示意图；
图3为本发明的电动汽车集群的调度方法流程图；
图4为某办公区一天内EV数量变化；
图5为EV集群参与系统调度结果；
图6为EV集群响应能力；
图7为充电前后SOC对比图；
图8为部分单体EV响应情况；
图9为电动汽车集群可调控能力确定系统结构框图；
图10为电动汽车集群的调度系统结构框图。
具体实施方式
针对现有技术中没有结合日内EV出行实际情况实现各时段可调控能力的精确建模、没有考虑与电网的互动调度的影响以及目前对EV集群可调控能力的研究还有待进一步深入的问题，本文对EV集群可调控能力进行精细化建模，以EV电池容量和车主的充电需求为约束，建立EV集群可调控功率、容量边界模型。对在站的电动汽车状态进行分类，提出了基于时间裕度和荷电状态优先级排序的EV集群优化调度方法。该模型可以分析电动汽车状态变化对EV集群可调控能力的影响，为电网根据其储能潜力制定调度计划提供依据。
为了更好地理解本发明，下面结合说明书附图和实例对本发明的内容做进一步的说明。
实施例1：
本发明提供一种电动汽车集群可调控能力确定方法，如图1所示，包括：
S1：根据各电动汽车当前调度时刻的剩余充电时长、当前SOC、目标SOC，利用预先构建的单体电动汽车可调控能力评估模型计算电动汽车当前调度时刻可用充/放电容量和可用充/放电功率；
S2：对各电动汽车当前时刻可用充/放电容量和可用充/放电功率进行叠加，确定电动汽车集群在当前调度时刻的充放电容量边界和功率边界，进而确定电动汽车集群在当前调度时刻对应的调度时段的可调控能力。
其中，步骤S1中单体EV可调控能力评估模型的构建如下：
当用户将处于停驶状态的电动汽车接入电网时，充电站内聚合商可以读取电动汽车的初始荷电状态SOC、目标SOC、停驶时间段等参数，从而评估电动汽车可用充放电功率及容量。单体电动汽车可调控能力如图2所示。
定义单体电动汽车于t 0时刻接入充电桩，并于t leave时刻断开连接，则区域A‐B‐C‐D‐E‐F为单体电动汽车在该时间段内的最大运行区域。
电动汽车充放电过程中的SOC与时间t之间的关系为
Figure PCTCN2021117054-appb-000013
式(1)中，SOC t为电动汽车在t时刻的荷电状态；
Figure PCTCN2021117054-appb-000014
为电动汽车在t 0时刻的荷电状态；P t为t时刻电动汽车的充电功率；Q n为电动汽车的额定容量。
由于单体电动汽车的额定容量保持不变，故图2中曲线的斜率可以表征电动汽车的充电功率。由AB‐BC‐CD‐DE‐EF‐FA围成的区域为EV运行可行域，其边界可以表示为：
Figure PCTCN2021117054-appb-000015
式(2)中，P max为电动汽车最大充电功率；-P max为电动汽车最大放电功率；SOC 0为电动汽车接入电网时的 初始荷电状态；SOC max为电动汽车电池允许的最大荷电状态；SOC min为电动汽车允许调控的最小荷电状态；SOC obj为电动汽车用户设置的目标荷电状态。特别地，当SOC 0＜SOC min时，需要先对其进行强制充电，当满足SOC≥SOC min时才允许参与调控。t now为当前调度时刻；t leave为电动汽车离开时刻。
上述点A为电动汽车接入充电桩的时间、点B为基于初始SOC以最大充电运行达到电动汽车允许的最大荷电状态的时间、点C为电动汽车以最大可用荷电状态达到停驶时间、点D为电动汽车以目标荷电状态达到停驶时间、点F为基于初始SOC以最大放电运行达到电动汽车允许调控的最小荷电状态的时间、点E为电动汽车在允许调控的最小荷电状态以最大充电运行在停驶时间达到目标荷电状态的最大时间点为点E；
由点A、B、C、D依次连接得到的边AB、BC和CD所构成以最大可用荷电状态为目的优先充电、充电优先级最高的充电行为对应的第一边界；
由点A、F、E、D依次连接得到的边AF、FE和ED所构成以目标荷电状态为目的优先放电、充电优先级最低的充电行为对应的第二边界；
定义A‐X曲线为电动汽车自接入后的充放电运行曲线，电动汽车在t 0-t now时间段经历充放电过程，并于t now时刻到达运行状态X，以最大充放电功率运行的曲线分别为XY、XZ，点Y为充电边界、点Z为放电边界，其表达式为：
Figure PCTCN2021117054-appb-000016
单体电动汽车的可调控能力大小受到EV集群控制中心调度和自身电池容量的影响。定义EV集群的调度周期为T，t指时间，在此公式(3)中相当于自变量(横轴)，则处于X运行状态的电动汽车的可用充放电容量受到T和图2中边界BC、CD、DE、EF限制，分别计算XY、XZ与可行域边界的交点进而得到电动汽车在t now时刻的充放电容量边界，如式所示。
Figure PCTCN2021117054-appb-000017
式(4)中，Q chgT、Q dchgT指单体EV在调度时间T约束下的最大可用充放电容量；Q chgBC指单体EV在BC约束下的最大充电容量；Q chgCD、Q dchgCD指单体EV在CD约束下的最大充放电容量；Q dchgED指单体EV在ED约束下的最大放电容量；Q dchgFE指单体EV在FE约束下的最大放电容量。
在式各边界约束下单体EV在未来调度周期T内最大可用充放电容量如式所示。单体EV最大可用充放电功率不 仅受自身电池充放电最大功率参数P max限制，而且受到最大可用充放电容量限制，其计算方式如式所示。
Figure PCTCN2021117054-appb-000018
Figure PCTCN2021117054-appb-000019
式(5)中，Q chg、Q dchg为单体EV的可用充电、放电容量边界；P chg、P dischg为单体EV的可用充电、放电功率边界。由于可用充放电容量边界的计算考虑了调度时间内的最大功率限制，故可用充放电功率边界不会超过P max。
单体电动汽车可调控能力评估模型构建好之后，可以计算电动汽车当前调度时刻可用充/放电容量和可用充/放电功率。
S2：对各电动汽车当前时刻可用充/放电容量和可用充/放电功率进行叠加，确定电动汽车集群在当前调度时刻的充放电容量边界和功率边界，进而确定电动汽车集群在当前调度时刻对应的调度时段的可调控能力，具体包括：
电动汽车集群可调控能力为单体电动汽车可调控能力在时间轴上的叠加。定义集合N(t)＝[1,…,k,k+1,…,n]为t时刻某集群EV的编号，电动汽车k的可调控能力可以根据单体EV储能能力模型得到，则t时刻EV集群可调控能力为：
Figure PCTCN2021117054-appb-000020
Figure PCTCN2021117054-appb-000021
式(7)和(8)中，Q clu(t)为t时刻EV集群的可用容量边界，包括充、放电容量边界；P clu(t)为t时刻EV集群的可用功率边界，包括充、放电功率边界。
实施例2：
基于同一种发明构思本发明还提供一种电动汽车集群的调度方法，如图3所示，包括：
步骤1：根据电动汽车集群中各电动汽车的初始SOC、目标SOC和停驶时间，利用可调控能力确定方法确定电动汽车集群在各调度时段的可调控能力；所述确定电动汽车集群的可调控能力确定方法可以参考上述实施例1的具体实例，这里不再累述；
步骤2：基于电网需求时间序列和各调度时段电动汽车集群可调控能力，以供需平衡为目标分别对电动汽车集群中的各电动汽车在各调度时段进行功率分配。
其中，步骤1具体包括：
1)在各调度时刻t sch，依次针对电动汽车集群中各电动汽车的停驶时间、接入后的充放电曲线和接入时间，计算所述电动汽车的当前调度时刻剩余充电时长、当前SOC；
2)根据每个电动汽车的当前调度时刻剩余充电时长、当前SOC和目标SOC确定电动汽车状态类型；其中所述电动汽车状态类型包括刚性电动汽车和柔性电动汽车；
3)基于所有柔性电动汽车利用上述实施例1所述的可调控能力确定方法计算电动汽车集群在当前调度时刻对应的调度时段的可调控能力。其中各调度时段为相邻调度时刻间的时间段。
根据每个电动汽车的当前时刻剩余充电时长、当前SOC和目标SOC确定电动汽车状态类型具体过程如下：
在处于停驶状态的电动汽车接入充电桩的时间段内，其状态可以分为为刚性电动汽车和柔性电动汽车两种。若EV停车时间较短、目标SOC较高，即使以最大充电功率进行充电，在用户离开时间仍不能达到用户设定的目标SOC，此类EV称为刚性EV。若EV停车时间以及目标SOC合理，EV充放电具有一定的调节裕度，则此类充电称为柔性EV。
第k辆EV的最短充电时间
Figure PCTCN2021117054-appb-000022
可通过式计算，停留时长
Figure PCTCN2021117054-appb-000023
可通过式计算。若电网在t sch时刻下发EV集群出力指令，此时需判断EV分类。若
Figure PCTCN2021117054-appb-000024
则第k辆EV为刚性电动汽车；若
Figure PCTCN2021117054-appb-000025
第k辆EV为柔性电动汽车。
Figure PCTCN2021117054-appb-000026
Figure PCTCN2021117054-appb-000027
式中，
Figure PCTCN2021117054-appb-000028
为第k辆EV在t sch时刻的荷电状态；
Figure PCTCN2021117054-appb-000029
为为第k辆EV的目标荷电状态；
Figure PCTCN2021117054-appb-000030
为第k辆EV的额定容量；
Figure PCTCN2021117054-appb-000031
为第k辆EV的最大充电功率。
步骤2包括如下具体过程
在EV集群调度时刻t sch，EV集群控制中心评估集群的可调控能力，电网根据EV集群控制中心提供的可调控能力边界，以系统供需平衡为目标优化得到EV集群的出力曲线，EV集群控制中心通过内部调度策略对每一辆EV进行相应的功率优化分配，实现对电网下发EV集群出力曲线的准确跟踪。
首先，EV集群控制中心对t sch时刻接入充电桩的电动汽车进行分类，优先对刚性EV以最大功率进行充电，[t sch,t sch+T]时间段内第i辆刚性EV的充电功率如式所示。
Figure PCTCN2021117054-appb-000032
则刚性EV集群的总充电功率为：
Figure PCTCN2021117054-appb-000033
式中，m为t sch时刻EV集群中刚性EV的数量；
Figure PCTCN2021117054-appb-000034
为第i辆刚性EV的充电功率；P rigid为刚性EV集群的总充电功率。
柔性电动汽车集群在调度周期T内的充放电总功率可以表示为：
P flex＝P dem-P rigid(13)
式中，P dem为电网对EV集群下发的出力指令，P flex为柔性EV集群的总充放电功率。
柔性EV集群内部充放电顺序根据单体电动汽车t sch时刻的时间裕度以及荷电状态裕度确定。按照式优先级指标PRI(t sch)大小对柔性EV集群内部进行充放电优先级排序。PRI(t sch)越大，代表柔性EV的可调裕度越大，若t sch时刻下发的柔性EV集群出力指令P flex<0，此部分EV优先放电；PRI(t sch)越小，代表柔性EV的可调裕度越小，若t sc h时刻下发的柔性EV集群出力指令P flex>0，此部分EV优先充电。
Figure PCTCN2021117054-appb-000035
式中，PRI j(t sch)为第j辆柔性EV在t sch时刻的充放电优先级指标；
Figure PCTCN2021117054-appb-000036
为第j辆柔性EV在t sch时刻达到目标SOC的最短充电时间，其计算方式可参见式；
Figure PCTCN2021117054-appb-000037
为第j辆柔性EV的达到停驶时间后离站的时间，t sch为调度时刻。
当柔性EV集群接收到充电指令时，以满足充电优先级较高的柔性EV充电需求为原则，率先安排调节裕度小、PRI j(t sch)值较低的EV进行充电；当柔性EV接收到放电指令时，率先安排调节裕度大、PRI j(t sch)值较高的EV进行放电。考虑到EV自身电池容量限制，[t sch,t sch+T]时间段内第j辆柔性EV的充放电功率如式所示。
Figure PCTCN2021117054-appb-000038
式中，
Figure PCTCN2021117054-appb-000039
为第j辆柔性EV的电池容量；
Figure PCTCN2021117054-appb-000040
为第j辆柔性EV允许的最大荷电状态；
Figure PCTCN2021117054-appb-000041
为第j辆柔性EV在t sch时刻的荷电状态；
Figure PCTCN2021117054-appb-000042
为第j辆柔性EV允许的最小荷电状态。其中，
Figure PCTCN2021117054-appb-000043
应满足EV自身充放电最大功率限制，即
Figure PCTCN2021117054-appb-000044
柔性EV集群已响应的功率P al-flex如式所示。
Figure PCTCN2021117054-appb-000045
式中，l表示已对出力指令作出响应柔性EV数量；
Figure PCTCN2021117054-appb-000046
为第j辆柔性EV的在[t sch,t sch+T]时间段内的充放电功率。P flex为调度分配的柔性电动汽车总需求功率，若P al-flex＜P flex，需按充放电优先级指标对柔性EV集群继续进行功率分配。若P al-flex＜P flex且
Figure PCTCN2021117054-appb-000047
则
Figure PCTCN2021117054-appb-000048
当P al-flex＝P flex时，代表对柔性EV集群的功率分配结束。
本发明中柔性电动汽车在按照充放电指令进行响应时，以满足充电优先级较高的柔性EV充电需求为原则，依次在每一辆柔性电动汽车进行功率响应完成后，都与功率需求P flex进行对比，若已响应的柔性电动汽车功率P al-flex未 达到功率需求，则分配下一辆柔性电动汽车，若超过，则按照
Figure PCTCN2021117054-appb-000049
设置最后一辆电动汽车的功率，以使得相应调度的柔性电动车的功率与柔性电动汽车集群的总需求一致。
实施例3：
下面利用某日某办公区私家车进出真实时间数据为基础进行仿真建模，对本发明提到的电动汽车集群可调控能力确定方法和调度方法进行说明。
本实施例设定仿真时间设置为0点‐24点，调度周期设置为10min。当天共有169辆电动汽车参与电网调控，不同时间段电动汽车的数量如图4所示。设电动汽车集群的初始荷电状态为SOC 0～U(0.2,0.4)，最大荷电状态SOC max为0.9，最小荷电状态SOC min为0.15，目标荷电状态SOC obj为0.85，电池容量Q n为70kW·h，最大充电功率P max为60kW，最大放电功率为‐60kW。
其仿真过程具体如下，并根据仿真过程对集群可调控能力评估结果分析：
EV集群可调控能力受电网下发出力指令的影响，图5为EV集群参与系统调度结果。如图5所示，60‐65调度周期内，EV集群响应电网充电调度指令，下一调度时刻EV集群的充电响应能力下降、放电响应能力上升，集群可调控边界整体下移；相反，65‐80调度周期内，EV集群响应电网放电调度指令，下一调度时刻EV集群的充电响应能力上升、放电响应能力下降，集群可调控边界整体上移。
电网在集群可调控边界内下发的EV集群出力指令可以得到响应。EV集群在收到调度指令后的响应情况如图6‐图8所示。由图6中仿真结果可以看出，电动汽车集群能够准确跟踪响应能力边界内给定的功率需求，但是由于调度区间内有部分电动汽车离站的情况，故存在极小程度上的波动。
图7为调度指令下单体EV的出力响应情况，从图中可以看出柔性电动汽车均可在离场时间充电至[SOC obj,SOC max]区间，满足车主出行需求；有5辆电动汽车由于初始电量过低、停留时间过短(属于刚性EV)，即使采用最大功率充电，仍不能达到目标SOC。
图8为第2、8、20、156辆EV的响应情况，从图中可以看出，单体EV在站时间内充放电功率和荷电状态紧随EV集群出力指令的变化。其中第20辆EV为刚性EV，不管调度指令如何变化，其在站时间一直保持最大充电功率，但离站时仍未达到目标SOC。
由此可见，本发明可以根据电动汽车充电过程确定电动汽车的运行状态(刚性或柔性)，并通过对不同状态的EV进行合理的功率分配实现对EV集群功率、容量边界的实时动态更新，实现对EV集群可调控能力进行精细化建模。
实施例4：
为了实现电动汽车集群可调控能力确定方法，本发明还提供一种电动汽车集群可调控能力确定系统，如图9所示，包括：
单体电动汽车计算模块，用于基于调度时段T内的各时刻，根据各电动汽车的剩余充电时长、当前SOC、目标SOC，利用预先构建的单体电动汽车可调控能力评估模型计算电动汽车当前时刻可用充/放电容量和可用充/放电功率；
电动汽车集群计算模块，用于对各电动汽车当前时刻可用充/放电容量和可用充/放电功率进行叠加，确定电动汽车集群在当前时刻的充放电容量边界和功率边界，进而确定电动汽车集群在当前的可调控能力。
本实施例各功能模块就是为了实现电动汽车集群可调控能力确定方法而设计，具体参考上述实施例，这里不再累述。
在另一个实施示例中，电动汽车集群可调控能力确定系统包括：处理器，其中所述处理器用于执行存在存储器的以下程序模块：单体电动汽车计算模块，用于基于调度时段T内的各时刻，根据各电动汽车的剩余充电时长、当前SOC、目标SOC，利用预先构建的单体电动汽车可调控能力评估模型计算电动汽车当前时刻可用充/放电容量和可用充/放电功率；电动汽车集群计算模块，用于对各电动汽车当前时刻可用充/放电容量和可用充/放电功率进行叠加，确定电动汽车集群在当前时刻的充放电容量边界和功率边界，进而确定电动汽车集群在当前的可调控能力。
实施例5：
为了实现本发明的电动汽车集群的调度方法，本实施例提供一种电动汽车集群的调度系统，如图10所示，包括：
可调控能力确定模块，用于在调度时段T内，根据电动汽车集群中各电动汽车的初始SOC、目标SOC和停驶时间，利用权利要求1至n所述的方法确定电动汽车集群的可调控能力；
调度模块，用于基于电网需求时间序列和各时刻电动汽车集群可调控能力和，以供需平衡为目标分别对电动汽车集群中的各电动汽车在调度时段T内各时刻进行功率分配。
本实施例各功能模块就是为了实现电动汽车集群的调度方法而设计，具体参考上述实施例，这里不再累述。
在另一个实施示例中，电动汽车集群的调度系统包括：处理器，其中所述处理器用于执行存在存储器的以下程序模块：可调控能力确定模块，用于在调度时段T内，根据电动汽车集群中各电动汽车的初始SOC、目标SOC和停驶时间，利用权利要求1至n所述的方法确定电动汽车集群的可调控能力；调度模块，用于基于电网需求时间序列和各时刻电动汽车集群可调控能力和，以供需平衡为目标分别对电动汽车集群中的各电动汽车在调度时段T内各时刻进行功率分配。
显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上仅为本发明的实施例而已，并不用于限制本发明，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均包含在申请待批的本发明的权利要求范围之内。

Claims (17)

  1. 一种电动汽车集群可调控能力确定方法，其特征在于，包括：
    根据各电动汽车当前调度时刻的剩余充电时长、当前SOC、目标SOC，利用预先构建的单体电动汽车可调控能力评估模型计算电动汽车当前调度时刻可用充/放电容量和可用充/放电功率；
    对各电动汽车当前时刻可用充/放电容量和可用充/放电功率进行叠加，确定电动汽车集群在当前调度时刻的充放电容量边界和功率边界，确定电动汽车集群在当前调度时刻对应的调度时段的可调控能力。
  2. 如权利要求1所述的方法，其特征在于，所述单体电动汽车可调控能力评估模型的构建，包括：
    根据电动汽车接入后的充放电曲线，以调度时段T为约束，确定所述电动汽车当前时刻应的运行状态点X；
    根据当前时刻对应的运行状态点X，以当前时刻最大运行区域为约束，分别确定最快达到所述最大运行区域充电边界点Y和放电边界点Z；
    基于点X和点Y以及点X和点Z分别确定电动汽车在当前时刻的可用充\放电容量边界和可用充\放电功率边界；
    所述当前最大运行区域包括：以电动汽车允许的最大荷电状态为目的优先充电、充电优先级最高的充电行为对应的第一边界；以电动汽车目标荷电状态为目的优先放电、充电优先级最低的充电行为对应的第二边界；点Y位于所述第一边界，点Z位于所述第二边界；
    所述调度时段为两个调度时刻之间的时间段。
  3. 如权利要求2所述的方法，其特征在于，所述最大运行区域的确定包括：
    根据电动汽车接入充电桩时的初始SOC、目标SOC和停驶时间确定的多个荷电状态时间点；其中，所述多个荷电状态时间点包括：电动汽车接入充电桩的时间为点A、基于初始SOC以最大充电运行达到电动汽车允许的最大荷电状态的时间为点B、电动汽车以最大可用荷电状态达到停驶时间为点C、电动汽车以目标荷电状态达到停驶时间为点D、基于初始SOC以最大放电运行达到电动汽车允许调控的最小荷电状态的时间为点F、电动汽车在允许调控的最小荷电状态以最大充电运行在停驶时间达到目标荷电状态的最大时间点为点E；
    由点A、B、C、D依次连接得到的边AB、BC和CD所构成以最大可用荷电状态为目的优先充电、充电优先级最高的充电行为对应的第一边界；
    由点A、F、E、D依次连接得到的边AF、FE和ED所构成以目标荷电状态为目的优先放电、充电优先级最低的充电行为对应的第二边界；
    基于所述第一边界和第二边界确定最大运行区域。
  4. 如权利要求2所述的方法，其特征在于，所述运行状态点X、所述最大运行区域充电边界点Y和所述放电边界点Z分别包括：点X、点Y和点Z对应的荷电状态和时刻。
  5. 如权利要求3所述的方法，其特征在于，所述最大运行区域中的各边对应的电动汽车充放电过程中的荷电状态、充放电功率与时间的关系如下式：
    Figure PCTCN2021117054-appb-100001
    式中，Q chgT为电动汽车在调度时段T约束下的最大可用充电容量；Q dchgT为电动汽车在调度时段T约束下的最 大可用放电容量；Q chgBC指电动汽车边BC约束下的最大充电容量；Q chgCD指电动汽车在边CD约束下的最大充电容量、Q dchgCD指电动汽车在边CD约束下的最大放电容量；Q dchgED指电动汽车在边ED约束下的最大放电容量；Q dchgFE指电动汽车在边FE约束下的最大放电容量；P max为电动汽车最大充电功率；-P max为电动汽车最大放电功率；SOC 0为电动汽车接入电网时的初始荷电状态；SOC max为电动汽车电池允许的最大荷电状态；SOC now为电动汽车当前荷电状态；SOC min为电动汽车允许调控的最小荷电状态；SOC obj为电动汽车用户设置的目标荷电状态；Q n为电动汽车的额定容量；t 0为电动汽车接入时刻；t now为当前调度时刻；t leave为电动汽车离开时刻。
  6. 如权利要求5所述的方法，其特征在于，所述基于点X和点Y以及点X运行状态X和点Z之间的确定电动汽车在当前调度时刻的充\放电容量边界和充\放电功率边界，包括如下计算式：
    Figure PCTCN2021117054-appb-100002
    Figure PCTCN2021117054-appb-100003
    式中，Q chg为电动汽车的可用充电容量边界；Q dchg为单体电动汽车的可用放电容量边界；P chg为电动汽车的可用充电功率边界；P dischg为电动汽车的可用放电功率边界。
  7. 一种电动汽车集群可调控能力确定系统，其特征在于，包括：
    单体电动汽车计算模块，用于基于各调度时刻，根据各电动汽车的剩余充电时长、当前SOC、目标SOC，利用预先构建的单体电动汽车可调控能力评估模型计算电动汽车当前调度时刻可用充/放电容量和可用充/放电功率；
    电动汽车集群计算模块，用于对各电动汽车当前调度时刻可用充/放电容量和可用充/放电功率进行叠加，确定电动汽车集群在当前调度时刻的充放电容量边界和功率边界，进而确定电动汽车集群在当前的可调控能力。
  8. 一种电动汽车集群的调度方法，其特征在于，包括：
    根据电动汽车集群中各电动汽车的初始SOC、目标SOC和停驶时间，利用权利要求1至6任一项所述的可调控能力确定方法确定电动汽车集群在各调度时段的可调控能力；
    基于电网需求时间序列和各调度时段电动汽车集群可调控能力，以供需平衡为目标分别对电动汽车集群中的各电动汽车在各调度时段进行功率分配。
  9. 如权利要求8所述的方法，其特征在于，所述根据电动汽车集群中各电动汽车的初始SOC、目标SOC和停驶时间，确定各时刻电动汽车集群在各调度时段的可调控能力，包括：
    在各调度时刻，依次针对电动汽车集群中各电动汽车的停驶时间、接入后的充放电曲线和接入时间，计算所述电动汽车的当前调度时刻剩余充电时长、当前SOC；
    根据每个电动汽车的当前调度时刻剩余充电时长、当前SOC和目标SOC确定电动汽车状态类型；其中所述电动汽车状态类型包括刚性电动汽车和柔性电动汽车；
    基于所有柔性电动汽车利用所述可调控能力确定方法计算电动汽车集群在当前调度时刻对应调度时段的可调控能力。
  10. 如权利要求9所述的方法，其特征在于，所述根据每个电动汽车的当前调度时刻剩余充电时长、当前SOC 和目标SOC确定电动汽车状态类型，包括；
    当电动汽车在剩余充电时长内以最大充电功率进行充电仍不能达到目标SOC时，所述电动汽车状态类型为刚性电动汽车；
    当电动汽车以最大充电功率进行充电所需时长小于所述电动汽车的剩余充电时长时，所述电动汽车为柔性电动汽车。
  11. 如权利要求9所述的方法，其特征在于，对电动汽车集群中的各刚性电动汽车在调度时段内各时刻进行功率分配，包括：
    以最大功率进行充电。
  12. 如权利要求9所述的方法，其特征在于，对电动汽车集群中的各柔性电动汽车在调度时段进行功率分配，包括：
    基于各柔性电动汽车各调度时段对应的调度时刻达到目标荷电状态的最短充电时间确定充放电优先级；
    基于电动汽车的充放电优先级进行功率分配。
  13. 如权利要求12所述的方法，其特征在于，所述基于电动汽车的充放电优先级进行功率分配，包括：
    基于优先级顺序对各电动汽车依次执行：
    计算已经响应调度分配的柔性电动汽车功率是否小于总需求功率；当小于时，基于当前电动汽车在当前调度时段内的充放电功率对所述电动汽车进行充放电；否则，结束功率分配。
  14. 如权利要求12所述的方法，其特征在于，所述充放电优先级按下式确定：
    Figure PCTCN2021117054-appb-100004
    PRI j(t sch)为第j辆柔性电动汽车在t sch时刻的充放电优先级指标；t sch为调度时刻；
    Figure PCTCN2021117054-appb-100005
    为第j辆柔性电动汽车在t sch时刻达到目标荷电状态的最短充电时间；
    Figure PCTCN2021117054-appb-100006
    为第j辆柔性电动汽车达到的停驶时间。
  15. 如权利要求13所述的方法，其特征在于，所述当前电动汽车在当前调度时段内的充放电功率的计算式如下：
    Figure PCTCN2021117054-appb-100007
    式中，
    Figure PCTCN2021117054-appb-100008
    为第j辆柔性电动汽车在当前调度时段T内的充放电功率；
    Figure PCTCN2021117054-appb-100009
    为第j辆柔性电动汽车的电池容量；
    Figure PCTCN2021117054-appb-100010
    为第j辆柔性电动汽车允许的最大荷电状态；
    Figure PCTCN2021117054-appb-100011
    为第j辆柔性电动汽车在t sch时刻的荷电状态；
    Figure PCTCN2021117054-appb-100012
    为第j辆柔性电动汽车允许的最小荷电状态；P flex为调度分配的柔性电动汽车总需求功率。
  16. 如权利要求8所述的方法，其特征在于，所述基于电网需求时间序列和各时刻电动汽车集群可调控能力，以供需平衡为目标分别对电动汽车集群中的各电动汽车在各调度时段进行功率分配，之后还包括：
    根据对各电动汽车在当前调度时段响应功率分配后的充放电曲线。
  17. 一种电动汽车集群的调度系统，其特征在于，包括：
    可调控能力确定模块，用于根据电动汽车集群中各电动汽车的初始SOC、目标SOC和停驶时间，利用权利要求1至6任一项所述的方法确定电动汽车集群在各调度时段的可调控能力；
    调度模块，用于基于电网需求时间序列和各调度时段电动汽车集群可调控能力，以供需平衡为目标分别对电动汽车集群中的各电动汽车在各调度时段进行功率分配。
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