Minkowski addition thought-based electric vehicle controlled capability calculation method

Abstract

The invention relates to the field of participation of electric vehicles in power grid interaction, in particular to a Minkowski addition idea-based electric vehicle controlled capability calculation method. Comprising the steps of establishing a friendly interaction framework between an electric vehicle user and a power grid; a calculation method for the controlled capability of the electric vehicle cluster is provided, firstly, state vectors of the electric vehicles after network access are defined, secondly, a classification method for the state vectors of the electric vehicles with different characteristics is given, and finally, a calculation method for the controlled capability of the electric vehicle cluster of the same category is given based on the Minkowski addition idea. According to the calculation method for the electric vehicle cluster participating in the power grid interaction controlled capability, important theoretical basis and reference can be provided for optimally giving a system optimization scheduling scheme containing the electric vehicle cluster or a market optimization bidding decision, and high-quality interaction between a power grid and a large number of electric vehicle users is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of the interaction between electric vehicles and the power grid, and relates to a calculation method for the controllable capacity of electric vehicles based on the idea of Minkowski addition. Background Art

[0002] Electric vehicles (EVs) are vigorously promoted for their advantages of energy conservation and zero emissions. The grid connection of a large number of EVs not only improves the utilization rate of power market resources in China, but also effectively alleviates the imbalance of power and electricity within the grid. Charging stations are mainly responsible for aggregating EVs in a certain area, which can not only provide unified charging services for EVs, but also provide an effective means for large-scale EVs to discharge power to the grid during peak load periods. At present, a large number of scholars have carried out research on the controllable capacity of EVs participating in the orderly charging and discharging of the power grid. The existing methods mainly include:

[0003] When studying the dispatchable potential of the interaction between electric vehicles and the power grid, the charging and discharging model of large-scale EVs is directly regarded as a generalized energy storage model, and the modeling is too idealized. The complex constraint relationships within various types of EV clusters and the influence of changes in the quantity and state parameters of various types of EVs on the system calculation results are not considered. Therefore, the system optimization dispatch plan obtained may be difficult to implement.

[0004] In summary, at the current stage, EVs are usually regarded as conventional loads that can be plugged in and charged, which wastes their considerable storage capacity. Therefore, the charging and discharging power of EVs can be managed through charging stations, enabling EVs to participate in the electricity retail market or large system optimization dispatch as flexible loads. Evaluating the controllable capacity of electric vehicle clusters directly affects the feasibility and flexibility of the bidding plan or dispatch plan. Therefore, it is of great significance to study the calculation method for the controllable capacity of EV clusters participating in the interaction with the power grid. Summary of the Invention

[0005] 1. Technical problems to be solved:

[0006] When studying the dispatchable potential of the interaction between electric vehicles and the power grid, the complex constraint relationships within various types of EV clusters and the influence of changes in the quantity and state parameters of various types of EVs on the system calculation results are not considered. Therefore, the system optimization dispatch plan obtained may be difficult to implement.

[0007] 2. Technical solutions:

[0008] To solve the above problems, the present invention provides a calculation method for the controllable capacity of electric vehicles based on the idea of Minkowski addition, which is characterized by including the following steps:

[0009] Step 1: Establish a friendly interaction framework between the EV users and the power grid.

[0010] Step 2: Propose a definition method for the state vector of the EV accessing the grid.

[0011] Step 3: Classify the states of individual EVs according to the state vector after the EV accesses the grid.

[0012] Step 4: Based on the idea of Minkowski addition, accurately give the calculation method for the controlled ability of various EV clusters.

[0013] In Step 3, classify the states of individual EVs according to the state vector after the EV accesses the grid, specifically as follows:

[0014] The first type of state: The EV in the first type of state j has sufficient time to discharge from the remaining grid-connected power to the minimum value and is charged to the off-grid required power before leaving the grid.

[0015] The second type of state: The EV in the second type of state j , does not have enough time to fully discharge to the lowest power, and the ISO preferentially arranges for the EV j to discharge as much as possible and switches the EV from discharging to the charging state at a certain moment j until the off-grid moment.

[0016] The third type of state: The EV in the third type of state j , has a grid-connected time so short that it cannot meet its own basic charging requirements, and the ISO cannot schedule the charging and discharging behavior of this type of EV. In this state, after the EV j accesses the grid, the ISO immediately charges it to maximize its charging requirements.

[0017] 3. Beneficial effects:

[0018] The calculation method for the controlled ability of electric vehicles based on the idea of Minkowski addition provided by the present invention first proposes a definition method for the state vector after the EV accesses the grid, secondly classifies the states of individual EVs based on the state vector after the EV accesses the grid, and finally accurately gives the calculation method for the controlled ability of various EV clusters according to the idea of Minkowski addition. Description of the drawings

[0019] Figure 1 It is a schematic diagram of the schedulable time period for the charging and discharging behavior of the EV in the first type of state in the present invention.

[0020] Figure 2 It is a schematic diagram of the schedulable time period for the charging and discharging behavior of the EV in the second type of state in the present invention.

[0021] Figure 3 It is a schematic diagram of the schedulable time period for the charging and discharging behavior of the EV in the third type of state in the present invention.

[0022] Figure 4Schematic diagram of the Minkowski addition principle in the present invention.

[0023] Figure 5 Friendly interaction framework between the electric vehicle and the power grid in the present invention.

[0024] Figure 6 Charge and discharge power boundaries of the EV cluster under different controlled proportionality coefficients in the present invention.

[0025] Figure 7 SOC boundaries of the EV cluster under different controlled coefficients in the present invention. Detailed implementation manners

[0026] The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0027] A calculation method for the controlled ability of an electric vehicle based on the idea of Minkowski addition specifically includes the following steps:

[0028] Step 1: Establish a friendly interaction framework between the EV users and the power grid

[0029] The framework of vehicle-grid friendly interaction in Step 1 includes: an EV user terminal framework, a charging station terminal framework, and a power grid terminal framework, as Figure 5 shown, where:

[0030] The power grid terminal framework is the system independent operator, responsible for collecting the controlled abilities of the EV user clusters represented by each charging station, and issuing an orderly charge and discharge scheduling plan for each charging station based on this;

[0031] The charging station terminal framework includes all EV users under the agency of the station. After a single EV user accesses the charging pile in the station, during the on-grid time to the off-grid time, it voluntarily accepts the unified scheduling of the charging station, provides the base state parameters to the charging station, and each charging station aggregates the on-grid base state parameters of all EVs in the station through the idea of Minkowski addition to form the controlled ability of the EV cluster it represents, and reports it to the ISO;

[0032] The EV user terminal framework includes multiple single EV users. After each EV user accesses the grid, through the intelligent terminal in the charging station, it voluntarily synchronizes the information to the charging station operator and fully participates in the friendly charge and discharge interaction with the power grid.

[0033] Step 2: Propose a definition method for the state vector of the on-grid EV.

[0034] The specific process is as follows:

[0035] For a single on-grid EV, the state can be represented by a column vector, that is, the state vector

[0036]

[0037] Wherein: respectively represent the grid connection time and off-grid time of the jth EV (hereinafter referred to as EV j ) in the EV cluster; respectively represent the state of charge (SOC) at the time of grid connection and the SOC at the time of off-grid of the EV j .

[0038] For example, O EV,j =(17, 6, 0.5, 0.8) T , indicating that the EV j is connected to the grid at 17:00 on the same day and disconnected from the grid at 6:00 the next day, and the grid connection time is 13:00. If the time for the EV to immediately discharge to the minimum power after grid connection is 3 hours and the charging time to the off-grid required power is 6 hours, since the grid connection time is greater than the time required for a complete charge and discharge, the charging station has sufficient time to flexibly schedule the charge and discharge behavior of the EV; if O EV,j =(20, 3, 0.5, 0.8) T , indicating that the grid connection time of the EV j is 7:00. If the 6-hour charging demand of the EV owner must be met, the time for the charging station to flexibly schedule the EV is 1 hour; O EV,j =(21, 2, 0.5, 0.8) T , the grid connection time represented by the EV j is only 5:00, that is, even if the EV j charges immediately after grid connection, it cannot meet the basic charging demand of 6:00, so the ISO cannot flexibly schedule such EVs.

[0039] Step 3: Classify the individual EVs according to the state vector after the EV is connected to the grid.

[0040] The following assumptions are given in advance: ① Considering the satisfaction of EV users, the ISO should meet the minimum power for EV travel needs, and the battery loss caused by the EV's discharge behavior is uniformly borne by the ISO, but the EV should fully accept the scheduling arrangement of the ISO after grid connection; ② After the EV enters the charging state, it cannot be switched to the discharge or idle state when its power is not fully charged to the maximum value or before the off-grid time; ③ The EVs in the second type of state should accept the ISO scheduling in the order of discharging first and then charging. Based on this, the EVs after grid connection can be classified into 3 types according to the state vector, and the specific process is as follows:

[0041] The first type of state

[0042] The EVs in the first type of state j have sufficient time to discharge from the remaining power after grid connection to the minimum value and charge to the off-grid required power before off-grid. The EVs in this state j, ISO has sufficient time to flexibly schedule its charging and discharging behavior.

[0043] Due to the differences in the grid connection times of individual EVs, let the domain of all EV grid connection times be as shown in the following formula:

[0044]

[0045] EV j The charging time required to meet the off-grid demand electricity is:

[0046]

[0047] In the formula: represents the minimum value of the EV j battery SOC; E EV,j represents the EV j battery capacity; P c represents the magnitude of the charging and discharging power of the EV j .

[0048] EV j The time for discharging from the remaining grid-connected electricity to the minimum electricity is:

[0049]

[0050] EV j The grid connection time of is:

[0051]

[0052] In summary, for the EV j in the first type of state, the grid connection time should satisfy the following formula:

[0053]

[0054] As Figure 1 shown, for the EV j in the first type of state, ISO can arbitrarily select a time period within to schedule the charging and discharging of the EV c at a constant power P j .

[0055] The second type of state

[0056] For the EV j in the second type of state, due to the limitations of the grid connection time and the off-grid demand electricity, there is not enough time to fully discharge to the minimum electricity. ISO can only give priority to arranging the EV j to discharge as much as possible and switch the EV j from discharging to the charging state at a certain moment until the off-grid moment.

[0057] In this state, the EVj The moment when the discharge state is forced to change to the charge state is:

[0058]

[0059] In the formula: represents the charge and discharge efficiency of the EV.

[0060] Let the EV j The shortest charging time to meet its own travel power demand is:

[0061]

[0062] In summary, for the EV in the second type of state j The grid connection time should all satisfy the following formula:

[0063]

[0064] As Figure 2 shown, for the EV in the second type of state j , the ISO can arbitrarily select a time period within to perform discharge scheduling on the EV c with a constant power P j and charge the EV c to off-grid with a constant power P j within .

[0065] The third type of state

[0066] For the EV in the third type of state j , the grid connection time is too short, even too short to meet its own basic charging demand, so that the ISO cannot schedule the charge and discharge behavior of this type of EV. In this state, after the EV j is connected to the grid, the ISO must immediately charge it to meet its charging demand to the greatest extent.

[0067] In summary, for the EV in the third type of state j The grid connection time should all satisfy the following formula:

[0068]

[0069] As Figure 3 shown, for the EV in the third type of state j , the ISO cannot freely perform charge and discharge scheduling on the EV within j , and the EV j is charged to off-grid with a constant power P c after being connected to the grid.

[0070] Step 4: Based on the idea of Minkowski addition, accurately give the calculation method for the controlled ability of various EV clusters.

[0071] The controlled ability of an EV cluster refers to the decision envelope space composed of all possible charge and discharge behaviors of all EV variables obtained through the idea of Minkowski summation. Based on the above idea, the cumbersome solution caused by separately modeling each EV is avoided. This idea not only considers the grid-connected state vector of a single EV and the constraint relationship between a single EV and the EV cluster, but also can accurately calculate the controlled ability of the EV cluster. The schematic diagram of the Minkowski addition principle is shown in Figure 4 the figure.

[0072] As Figure 6 shown in the figure, for the EV in the I-th state j , the value boundary of its charging power is:

[0073]

[0074] The value boundary of the discharge power is the same as that of the charging power.

[0075] For the EV in the II-th state j , the value boundary of its charging power is:

[0076]

[0077] The value boundary of the discharge power is:

[0078]

[0079] For the EV in the III-th state j , the value boundary of its charging power is the same as that in the fully controlled state, and the value boundary of its discharge power is:

[0080]

[0081] In any state, the value boundary of the electric quantity during the charge and discharge process of the EV j is expressed as:

[0082]

[0083] The mathematical modeling variables of the EV cluster are:

[0084]

[0085] Where: represents the charge and discharge power of the EV cluster at time t; represents the charge and discharge power of the EV j at time t; I EV is the EV cluster; SEV,t represents the state of charge of the EV cluster at time t; S EV,j,t represents an EV j state of charge at time t.

[0086] The controllable capacity of the EV cluster is:

[0087]

[0088] In summary, in the formula represents the ability of the EV cluster to be dispatched by the ISO. In this way, the controllable capacity of the EV cluster is introduced into the power system optimal dispatch model or the power market bidding model in the form of boundary constraints for subsequent extended applications.

[0089] The method described in the present invention is based on a calculation system for the controllable capacity of electric vehicles based on the idea of Minkowski addition, including:

[0090] An interaction framework establishment module for establishing an interaction framework between the EV user cluster and the power grid.

[0091] A definition module for the state vector of the EVs connected to the grid, which characterizes the connection time, disconnection time, remaining power at connection, and required power at disconnection of the EVs by defining a column vector.

[0092] A single EV state classification module that classifies single EVs into three types of states, namely type I, type II, and type III, based on the state vector of the EVs connected to the grid. Among them, the charging and discharging behaviors of the EVs in the first type of state are completely controllable, the charging and discharging behaviors of the EVs in the second type of state are partially controllable, and the charging behavior of the EVs in the third type of state is completely controllable, while there is no discharging behavior.

[0093] A calculation module for the controllable capacity of each type of EV cluster, which accurately gives the calculation methods for the controllable capacity of EV users in the first type of state, the second type of state, and the third type of state based on the idea of Minkowski addition.

[0094] Embodiment

[0095] The present invention generates the state vectors of 1,500 EVs connected to the grid through the Monte Carlo method. The sampling function obtained after clustering can be seen in Table 1 in the appendix. Let the vector represent the proportions of EVs in the first type, the second type, and the third type of states in the EV cluster, hereinafter referred to as the controllable proportion coefficients. Analyze the controllable capacity of the EV cluster for ξ = [0.6, 0.3, 0.1], [0.4, 0.5, 0.1], [0.2, 0.7, 0.1], [0.4, 0.3, 0.3], [0.2, 0.5, 0.3], [0.2, 0.3, 0.5]. The results can be seen in Figure 6 and Figure 7 .

[0096] Sampling Parameters of Electric Vehicles in Table 1

[0097]

[0098] The envelope space of the charging and discharging power of the EV cluster and the SOC boundary value represents the controllable ability of the EV cluster. This space contains all possible charging and discharging behaviors of the EVs, which determines the ability of the EV cluster to be scheduled by the ISO and will directly affect the feasibility of formulating the system scheduling plan.

[0099] In Figure 6 when remains unchanged, as decreases and increases, the difference between the boundary values of the charging and discharging power of the EV cluster in the same time period will be greater. This is because as the EVs in the first type of state are connected to the grid, the boundary values of the charging and discharging power of the EV cluster will show a synchronous growth trend. However, as the scheduling time progresses, the EVs in the second and third types of states gradually connect to the grid, and their grid connection time is relatively late and the off-grid time is early, making it impossible for the ISO to fully schedule their discharging behavior. It is also for this reason that the boundary value of the discharging power of the EV cluster decreases faster within the same time period, and the change of the boundary values of the charging and discharging power is affected by more than by . When remains unchanged, as increases and decreases, the peak value of the boundary of the charging and discharging power of the EV cluster within the same time period is larger and the peak time is closer. This is because the more EVs are in the first type of state, the more EVs can participate in the charging and discharging scheduling of the VPP, and the change trend of the boundary values of the charging and discharging power of the EV cluster is more similar. Within the same time period, the change of the difference between the boundary values of the charging and discharging power of the EV cluster is relatively unchanged, increases, decreases less. This is because the EVs in the second type of state can still provide part of the discharging power and accept the discharging scheduling of the ISO, but the EVs in the third type of state cannot accept the discharging scheduling of the ISO at all due to the too short grid connection time.

[0100] In Figure 7 , the change of the upper limit of the SOC of the EV cluster is similar to the change of the upper limit of the charging power. There is a slight "bump" in the lower limit of the SOC of the EV cluster. This is because the electricity of the EVs when leaving the grid should meet the travel demand, so this phenomenon will occur when the number of EVs leaving the grid in this time period is large. In addition, Figure 6 and Figure 7Among them, (g)-(i) respectively represent the controlled capacity curves of the EV cluster in three extreme cases where ζ = [1, 0, 0], [0, 1, 0], and [0, 0, 1]. When ζ = [1, 0, 0], the controlled capacity of the EV cluster is equivalent to the controlled capacity curve without classifying the states of EVs after they are connected to the grid, which is obviously not accurate enough.

[0101] Based on the above analysis, the controlled capacity of the EV cluster varies greatly under different controlled proportion coefficients. Therefore, when formulating the scheduling plan, ISO must fully consider the controlled capacity of the EV cluster to ensure the stable and economic operation of the system.

Claims

1. A method for calculating the controllability of an electric vehicle based on the Minkowski addition concept, characterized in that: The following steps are involved: Step 1: Establish a friendly interaction framework between the EV user and the power grid; Step 2: Propose a definition method for the state vector of the EV connected to the network; Step 3: Classify the state of individual EVs according to the state vector after EVs are connected to the network; Step 4: Based on the Minkowski addition idea, an accurate calculation method for the controllability of various EV clusters is given.

2. The method for calculating the controllability of an electric vehicle based on the Minkowski addition concept according to claim 1 is characterized in that: The framework for vehicle-user interaction in step 1 includes: an EV user terminal framework, a charging station subframe, and a power grid terminal framework, wherein: The grid terminal framework is an independent operator of the system, responsible for collecting the controllable capacity of the EV user cluster represented by each charging station, and based on this, issuing an orderly charging and discharging scheduling plan for each charging station; The charging station subframe includes all EV users under the agency of the station. After a single EV user accesses the charging pile in the station, from the time of network access to the time of network disconnection, they all voluntarily accept the unified dispatch of the charging station and provide the base state parameters to the charging station. Each charging station aggregates the network access base state parameters of all EVs in the station through the Minkowski addition idea to form the controllable capacity of the EV cluster it represents and report it to ISO. The EV user terminal framework includes multiple individual EV users. After each EV user joins the network, he or she voluntarily synchronizes information to the charging station operator through the smart terminal in the charging station, and fully participates in the friendly charging and discharging interaction with the power grid.

3. The method for calculating the controllability of an electric vehicle based on the Minkowski addition concept as claimed in claim 1, characterized in that: The basic state parameters include the individual's network access time, network off time, network remaining power and network off demand power.

4. The method for calculating the controllability of an electric vehicle based on the Minkowski addition concept according to claim 2 is characterized in that: In step 2, the definition method of the state vector of the EV connected to the grid is proposed, specifically: For a single EV after it is connected to the grid, the state is represented by a column vector, i.e., the state vector Where: They represent the grid-entry time and grid-leave time of the jth EV in the EV cluster respectively; Respectively represent EV j The charge state when entering the grid and the charge state when leaving the grid.

5. The method for calculating the controllability of an electric vehicle based on the Minkowski addition concept according to claim 3, characterized in that; In step 3, the state of the single EV is classified according to the state vector after the EV is connected to the network, specifically: Category I status: EV in Category I status j There is enough time to discharge the remaining power after entering the grid to the minimum value, and charge to the required power before leaving the grid; Category II status: EV in Category II status j , there is not enough time to fully discharge to the minimum power, ISO prioritizes EV j Discharge as much as possible and at some point turn the EV j Switch from discharge to charging state until the time of disconnection from the grid; Category III status: EV in Category III status j , the grid connection time is too short to meet its own basic charging needs, and ISO cannot dispatch the charging and discharging behavior of such EVs. In this state, EV j After joining the network, ISO will charge it immediately to meet its charging needs to the greatest extent possible.

6. The method for calculating the controllability of an electric vehicle based on the Minkowski addition concept as claimed in claim 5, characterized in that; In the first type of state, the time for individual EVs to access the network varies. Assume that the domain of the time for all EVs to access the network is as shown in formula (2): EV j The charging time required to meet off-grid demand is: Where: Indicates EV j The minimum value of battery SOC; E EV,j Indicates EV j Battery capacity; P c Indicates EV j The size of the charging and discharging power; EV j The time from the remaining power of the grid to the minimum power is: EV j The grid connection time is: In summary, EV in Class I state j The grid connection time should satisfy formula (3-6): EV in Category I status j , ISO in Any time period is selected with constant power P c Scheduling EV j Charge and discharge.

7. The method for calculating the controllability of an electric vehicle based on the Minkowski addition concept as claimed in claim 5, characterized in that ; Under Class II status, EV j The moment when the discharge state is forced to change to the charge state is: Where: Indicates the charging and discharging efficiency of EV, Set EV j The shortest charging time to meet the power demand for travel is: In summary, EV in Class II state j The grid connection time should satisfy formula (7): EV in Category II status j , ISO in Select any time period within the constant power P c EV j Discharge scheduling is performed. Constant power P c EV j Charge off-grid.

8. The method for calculating the controllability of an electric vehicle based on the Minkowski addition concept as claimed in claim 5, characterized in that; EV under the Class III status j The grid connection time of all satisfies formula (10): EV in Category III status j , ISO in Unable to freely EV j Carry out charging and discharging scheduling, EV j After entering the grid, the constant power P c Charge off-grid.

9. The method for calculating the controllability of an electric vehicle based on the Minkowski addition concept according to claims 5-8, characterized in that: In step 4, the calculation method of the controllable capacity of various EV clusters is accurately given based on the Minkowski addition idea, specifically: The charging and discharging power and power boundary values ​​of EV under three conditions are given as shown in the following formula (11-16): EV in Category I state j , the value boundary of its charging power is: The value boundary of the discharge power is the same as the value boundary of the charging power; EV in Category II status j , the value boundary of its charging power is: The discharge power value boundary is: EV in Category III status j , the value boundary of its charging power is the same as that of the charging power in the fully controlled state, and the value boundary of its discharging power is: In any state, EV j The value boundaries of the charge during the charge and discharge process are expressed as: The mathematical modeling variables of the EV cluster are: Where: represents the charging and discharging power of the EV cluster in period t; Indicates EV j The charging and discharging power in the period t; I EV is the EV cluster; S EV,t represents the charge state of the EV cluster during period t; S EV,j,t Indicates EV j The state of charge during the period t; The constraint boundary of the feasible region of EV cluster charging and discharging power is: In formula (18), Indicates the ability of the EV cluster to be dispatched by ISO.

10. The method for calculating the controllability of an electric vehicle based on the Minkowski addition concept as claimed in claim 1 is based on a calculation system for the controllability of an electric vehicle based on the Minkowski addition concept, characterized in that: include: An interactive framework establishment module, used to establish an interactive framework between the EV user cluster and the power grid; The definition module of the state vector of the grid-connected EV defines a column vector to represent the grid-connected time, grid-off time, grid-connected remaining power, and grid-off required power of the EV; The single EV state classification module divides single EVs into Class I, II, and III states based on the EV's grid-connected state vector. In Class I, the EV's charging and discharging behavior is fully controllable; in Class II, the EV's charging and discharging behavior is partially controlled; and in Class III, the EV's charging behavior is fully controllable, but there is no discharging behavior. The calculation module of the controllable capacity of various EV clusters accurately gives the calculation method of the controllable capacity of EV users in Class I, Class II and Class III states based on the Minkowski addition idea.

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