Electric vehicle participation system frequency modulation control method and system based on virtual synchronous machine

Abstract

The invention discloses an electric vehicle participation system frequency modulation control method and system based on a virtual synchronous machine. The method comprises the following steps: designing primary and secondary frequency modulation control strategies of an electric vehicle participation system; constructing an electric vehicle frequency modulation control mode based on the virtual synchronous machine, wherein the mode is composed of a charging energy monitoring system, an electric vehicle dispatching center, a power grid dispatching center and the like; when the electric vehicle arrives at the charging station, the charging energy monitoring system uploads vehicle electric quantity information, a user charging strategy and other related information to an electric vehicle dispatching center; and the electric vehicle dispatching center adopts a T-S type fuzzy controller, and regulates frequency modulation power by taking the global charging demand function SD and the power grid frequency deviation delta f as input quantities. According to the scheme provided by the invention, the response speed of the electric vehicle in system frequency modulation is effectively improved, the problems of slow response and lack of flexibility in a traditional frequency modulation method are solved, and the stability and frequency modulation efficiency of a power grid are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of frequency modulation control, and in particular relates to a method and system for frequency modulation control of an electric vehicle participating system based on a virtual synchronous machine. Background Art

[0002] In recent years, with the increasing penetration of energy resources in the power system, higher technical requirements have been put forward for the frequency control capabilities of renewable energy. The commercialization process of electric vehicles is accelerating, and the interaction technology between electric vehicles and power grids has also become a hot topic of research. The two-way interaction (V2G) technology of charging and discharging between electric vehicles and power grids uses smart grid technology to enable electric vehicles to be connected to the power grid when idle, playing a supporting role. Electric vehicles can not only serve as controllable loads, but also as mobile distributed power sources. Compared with traditional power generation units, their characteristics as mobile energy storage devices enable them to serve as the backup capacity of the system in the power grid, enhancing the controllability and stability of the power grid.

[0003] However, the widespread use of electric vehicles has also brought new challenges. On the basis of fully considering the operating characteristics of electric vehicles, exploring the control strategy of their participation in grid frequency regulation is crucial to improving the stable operation level of the grid. Virtual synchronous machine technology is a power electronic converter system or device that simulates the electromechanical transient characteristics of synchronous machines such as inertia and internal potential, making the power and voltage operating characteristics of the port similar to those of synchronous machines. As a grid-friendly power converter control technology, it can solve the problems of inertia and insufficient damping that may be caused by the access of a large number of electric vehicles, while preventing the impact on the grid caused by the excessive response of electric vehicles.

[0004] The application steps of virtual synchronous machine in electric vehicle charging and discharging control in the prior art are as follows: design the system structure of the interaction between electric vehicles and power grids; adopt the virtual synchronous machine control strategy to simulate the operating characteristics of traditional synchronous generators; set the charging and discharging mode to achieve constant current or constant power charging, and automatically adjust the charging and discharging power according to the changes in power grid frequency and voltage; consider the service life of the battery and design the charging and discharging control strategy to reduce damage to the battery.

[0005] Applying virtual synchronous machine technology to the field of electric vehicle charging and discharging control can effectively solve the problem of the charging and discharging machine responding too fast. When electric vehicles participate in frequency modulation, they can perform charging and discharging operations according to the fluctuation of the grid frequency. However, this process may cause a certain decline in the battery life of electric vehicles. The electric vehicle frequency modulation control strategy based on virtual synchronous machines in the existing technology has not fully coordinated the needs of electric vehicles, users and power grids, nor has it fully utilized the power output capacity of electric vehicles.

[0006] In summary, how to design a control strategy that can meet the grid frequency regulation needs while taking into account electric vehicles, users and grids, while making full use of the power output capacity of electric vehicles is a problem that needs to be solved. Summary of the invention

[0007] The purpose of the present invention is to provide a method and system for controlling the frequency regulation of an electric vehicle participating in a system based on a virtual synchronous machine, so as to solve the problem that the frequency regulation control strategy of an electric vehicle based on a virtual synchronous machine in the prior art has not fully coordinated the needs of electric vehicles, users and power grids.

[0008] In order to solve the above technical problems, the present invention provides a frequency modulation control method of an electric vehicle participating system based on a virtual synchronous machine, and the frequency modulation control method comprises the following steps:

[0009] The control system of the electric vehicle dispatch center clusters electric vehicles according to different charging and discharging rates and average daily mileage. The data management system of the electric vehicle dispatch center collects the real-time data of clustered electric vehicles sent by the charging energy monitoring system. The real-time data includes the remaining power of the electric vehicle, the charging and discharging rate, the user's expected power and the user's expected charging time. After the electric vehicle dispatch center summarizes the real-time data sent by each charging energy monitoring system, it uploads the real-time dispatchable capacity information to the power grid dispatch center. The power grid dispatch center updates the real-time dispatchable capacity of the electric vehicle. The power grid dispatch center issues control instructions when the power grid frequency fluctuates. The electric vehicle dispatch center adopts a TS-type fuzzy controller with a global charging demand function S D The grid frequency deviation Δf is used as input to adjust the frequency modulation power.

[0010] In a preferred embodiment, the grid dispatching center uses the dispatchable capacity of the clustered electric vehicle group as the reference power of the virtual synchronous machine, calculates the electric vehicle frequency response coefficient, i.e., the droop coefficient, and transmits the real-time electricity price and the droop coefficient to the electric vehicle dispatching center, which then sends the droop coefficient to each virtual synchronous machine; when the grid frequency deviation exceeds the set range, the electric vehicle is controlled to participate in the frequency modulation according to the primary and secondary frequency modulation strategies built into the virtual synchronous machine, and the charging / discharging power P of the electric vehicle is SET It is composed of the superposition of primary frequency modulation P1 and secondary frequency modulation P2 after fuzzy control. A TS type fuzzy controller is designed for the secondary frequency modulation. D And Δf are used as the input of the frequency modulation power to adjust the frequency modulation power, and after the electric vehicle is charged, the power information participating in the frequency modulation is uploaded to the power grid dispatching center.

[0011] In a preferred embodiment, the charging demand function S is calculated based on the remaining power of the electric vehicle. D , S DThe triangular membership function of the grid frequency deviation Δf is constructed by fuzzy theory; the grid dispatching center obtains the primary frequency modulation power reference value P1 according to the grid frequency deviation, and the electric vehicle adjusts the charging and discharging power according to the real-time change of the grid frequency to provide primary frequency modulation service; the secondary frequency modulation reference value P2 based on the charging demand function is obtained by fuzzy control theory, and the charging load is secondary modulated according to the primary frequency modulation result. SET Adjust to achieve frequency control.

[0012] In a preferred embodiment, the control system of the electric vehicle dispatching center clusters electric vehicles according to different charging and discharging rates and average daily mileage, including the following steps:

[0013] Step 1: Define a data structure containing the charge / discharge rate C / DR and the average daily mileage AVDT for each electric vehicle. Value is the specific value of the data structure of the charge / discharge rate C / DR or the average daily mileage AVDT. Class is to classify electric vehicles into different categories based on the charge / discharge rate C / DR or the average daily mileage AVDT. i =[(C / DR) i (value,class),(AVDT) i (value,class)], go to step 2;

[0014] Step 2: According to C / DR and AVDT, two data spaces are established respectively. In the C / DR data space X, the C / DR of all electric vehicles is included; in the AVDT data space Y, the AVDT of all electric vehicles is included, that is,

[0015] X=[(C / DR)1.value(1),......(C / DR) n .value(n)],

[0016] Y=[(AVDT)1.value(1),......(AVDT) n .value(n)], go to step 3;

[0017] Step 3: In the data space X and Y, add virtual standard points as initial cluster centers respectively; add 5 virtual points as initial cluster centers in the X space and 3 virtual points as initial cluster centers in the Y space, and execute step 4;

[0018] Step 4: Calculate the Euclidean distance from each electric vehicle data point to each cluster center in the data space and execute step 5;

[0019] Step 5: According to the calculated Euclidean distance, each electric vehicle is assigned to the cluster center closest to it to form an initial cluster. The average values ​​of C / DR and AVDT of all electric vehicles in each category are calculated as the new cluster center, and step 6 is executed;

[0020] Step 6: Evaluate whether the cluster center remains unchanged or changes less than a preset threshold, and determine whether the cluster center has changed. If it has changed, execute step 4; if it has not changed, execute step 7;

[0021] Step 7: Save the result EV i =[(C / DR) i .class=x,(AVDT) i .class=y], reorganize the electric vehicles into multiple subgroups according to the C / DR and AVDT classes, each subgroup contains electric vehicles with similar C / DR and AVDT, and execute step 8;

[0022] Step 8: After time T, execute step 1 again.

[0023] In a preferred embodiment, the charging demand function S is calculated based on the remaining power of the electric vehicle. D , charging demand function S D Considering the battery life and reflecting the user's charging needs, S D It indicates the ratio of the amount of charge after the electric vehicle is connected to the charging pile to the time consumed, including:

[0024] (1) When the time t2 for charging an electric vehicle at rated power to the user's expected power is less than the remaining time t1 for charging and discharging operations, it means that the electric vehicle can participate in grid frequency regulation under the premise of meeting the user's charging plan.

[0025]

[0026] Among them, S SOC is the current remaining power of the electric vehicle, S SOC_e The expected power set by the user, and are the upper and lower limits of battery power respectively; t1 is the remaining time for the electric vehicle to be charged and discharged; t2 is the time it takes for the electric vehicle to be charged to S at rated power. SOC_e the time required;

[0027] (2) When the time t2 for charging the electric vehicle at rated power to the user's expected power is greater than or equal to the remaining time t1 for charging and discharging operations, it means that the electric vehicle is only focused on meeting the user's charging needs.

[0028]

[0029] (3) When the user has no charging plan, that is, S SOC =S SOC_e ,

[0030] S D =0

[0031] In a preferred embodiment, the charging demand function S D The triangular membership function of the power grid frequency deviation Δf is constructed using fuzzy theory and divided into five levels of fuzzy subsets;

[0032] Given the charging demand function S D The value range of is [-1,1], For S D The fuzzy linguistic variables, whose fuzzy set F s1 It is divided into five language variables, which are represented as follows:

[0033]

[0034] In the formula, when S D A negative value indicates a negative deviation from 0, and a positive value indicates a positive deviation from 0; O indicates S D close to 0; PS and NS represent S D Positive deviation from 0 and S D The negative deviation from 0 is small; PB and NB represent S D Positive deviation from 0 and S D The degree of negative deviation from 0 is too large;

[0035] Frequency reference value f n The frequency deviation of the power grid is 50Hz, and the upper and lower limits of its fluctuation are ±0.2Hz. The value range of the power grid frequency deviation Δf is specified as:

[0036] [-0.2,-0.1)∪(-0.033,0.033)∪(0.1,0.2], is a fuzzy linguistic variable of Δf, and its fuzzy set F s2 It contains 5 language variables, which are represented as follows:

[0037]

[0038] In the formula, ZO means that the grid frequency is near the reference value, and the grid frequency deviation Δf is close to 0; NH and NL respectively mean that the grid frequency deviation Δf is in the range of (-0.2~-0.15) Hz and [-0.15~-0.1) Hz, indicating that the system power generation is insufficient; PL and PH respectively mean that the grid frequency deviation Δf is in the range of (0.1~0.15) Hz and [0.15~0.2) Hz, indicating that the system power generation is excessive.

[0039] In a preferred embodiment, the primary frequency regulation control strategy includes: when the electric vehicle participates in the primary frequency regulation, a primary frequency regulation dead zone is set. When the grid frequency deviation exceeds the dead zone, the electric vehicle exchanges power with the grid to participate in the grid frequency regulation. In addition, the charging and discharging power is limited by the power of the charger, and the user's travel needs and battery life are considered. max and P min They are the upper and lower limits of charging and discharging power, respectively, which are determined by the life of the power battery; specifically, they are as follows:

[0040]

[0041]

[0042] Among them, P ch-max is the maximum charging capacity allowed by the charging device; P disch-max is the maximum discharge capacity allowed by the charging equipment; η ch is the charging efficiency of the battery; η disch is the discharge efficiency of the battery; C bat is the battery capacity; Δ t It is to control time;

[0043] In the frequency modulation dead zone, electric vehicles use P e When the frequency is not in the dead zone, the power change ΔP1 is the droop coefficient K. pf The product of the grid frequency deviation Δf, the primary frequency modulation power P1 is calculated as follows:

[0044] P1=P e +ΔP1=P e +K pf Δf.

[0045] In a preferred embodiment, the secondary frequency modulation fuzzy control rule is as follows: The charging and discharging power is determined by the grid frequency deviation Δf and the user charging demand function S D Control, the specific design rules are characterized by the following formula:

[0046]

[0047] Among them, x1 and x2 are the two input quantities of the fuzzy controller; α i , β i is the fuzzy set corresponding to the input quantity; is the secondary frequency modulation power under the fuzzy control rule; g i (x1,x2) is the output function under the fuzzy control of x1 and x2; a i , b iis the input coefficient specified by each fuzzy rule; c is the constant fixed by the i-th fuzzy rule; i is the number of fuzzy rules;

[0048] Let x1 = Δf, x2 = S D , The specific calculation is as follows:

[0049]

[0050] The present invention also provides an electric vehicle participating system frequency modulation control system based on a virtual synchronous machine, comprising a synchronous inverter VSG module, a bidirectional DC / DC converter module, a charging demand function S D Calculation module, primary frequency modulation module, secondary frequency modulation module;

[0051] Synchronous inverter VSG module, the VSG module has the function of simulating the operating characteristics of traditional synchronous generators, ensuring that the system can respond to grid demand in a similar way to traditional generators;

[0052] Bidirectional DC / DC converter module, used to stabilize the DC bus voltage and ensure the energy conversion efficiency and reliability of the system;

[0053] Charging demand function S D The calculation module constructs a function reflecting the user's charging demand by combining the user's expected charging time, expected charging amount and current remaining power;

[0054] The primary frequency modulation module obtains the primary frequency modulation power reference value P1 according to the grid frequency deviation. The electric vehicle adjusts the charging and discharging power according to the real-time changes of the grid frequency to provide primary frequency modulation services.

[0055] The secondary frequency modulation module obtains the charging demand function S based on the fuzzy control theory. D The secondary frequency modulation reference value P2 is used to perform secondary frequency modulation on the charging load according to the primary frequency modulation result. SET Adjust for more precise frequency control.

[0056] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0057] The present invention realizes the rapid and accurate regulation of the grid frequency through the interaction between electric vehicles and the grid, while taking into account the stability of the grid and the frequency regulation efficiency. Compared with the traditional frequency regulation method, the present invention adopts a TS-type fuzzy controller, which can adjust the frequency of the grid according to the global charging demand function S DThe frequency modulation power can be flexibly adjusted according to the grid frequency deviation Δf to adapt to different grid requirements, effectively improving the response speed of electric vehicles in system frequency modulation. In addition, the present invention also takes into account the charging needs and battery life of electric vehicle users, avoids excessive charging and discharging, and optimizes the use efficiency of electric vehicles. At the same time, while meeting user needs, electric vehicles can also provide auxiliary services for the power grid, promote the interaction between electric vehicles and the power grid, and provide new ideas and methods for grid frequency modulation. The present invention greatly reduces the running time for determining the optimal frequency modulation strategy, and uses fuzzy control theory to determine the optimal frequency modulation power of the charging station, which solves the problems of slow response and lack of flexibility in traditional frequency modulation methods, and improves the overall operation efficiency and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more intuitively illustrate the embodiments of the present invention or the prior art solutions, the drawings involved in the description will be briefly introduced below. It should be noted that the drawings in the following description only show several embodiments of the present invention. For professionals in this technical field, other drawings can be obtained based on the existing drawings without performing innovative work.

[0059] Figure 1 A control mode for electric vehicles to participate in grid frequency regulation;

[0060] Figure 2 A clustering strategy for electric vehicles based on virtual synchronous machines;

[0061] Figure 3 It is an operation flow chart of a frequency modulation control method for an electric vehicle participating in a system based on a virtual synchronous machine;

[0062] Figure 4 This is the structure diagram of the frequency modulation control system of the electric vehicle participating system based on the virtual synchronous machine. DETAILED DESCRIPTION

[0063] The core of the present invention is to provide a method and system for controlling the frequency regulation of electric vehicles participating in the system based on a virtual synchronous machine, so that electric vehicles can adjust the charging and discharging power according to the frequency of the power grid and the power situation. While being able to effectively reduce the frequency deviation of the power grid, it can formulate user charging plans and avoid the risks of overcharging and over-discharging, thereby playing a role in assisting the frequency regulation of the power grid and improving the efficiency of the power grid.

[0064] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0065] Example 1

[0066] A method for controlling the frequency modulation of an electric vehicle participating in a system based on a virtual synchronous machine comprises: designing a primary and secondary frequency modulation control strategy for an electric vehicle participating in a system; constructing an electric vehicle frequency modulation control mode based on a virtual synchronous machine, the mode consisting of a charging energy monitoring system, an electric vehicle dispatching center, and a power grid dispatching center; when an electric vehicle arrives at a charging station, the charging energy monitoring system uploads relevant information such as vehicle power information and user charging strategy to the electric vehicle dispatching center; the electric vehicle dispatching center adopts a TS-type fuzzy controller to calculate the frequency modulation of an electric vehicle participating in a system based on a global charging demand function S D The grid frequency deviation Δf is used as input to adjust the frequency modulation power.

[0067] Please refer to Figure 1 The electric vehicle participating in the grid frequency regulation control mode based on the virtual synchronous machine is composed of a charging energy monitoring system, an electric vehicle dispatching center, and a grid dispatching center. The charging energy monitoring system is responsible for collecting electric vehicle information data. The control system of the regional electric vehicle dispatching center clusters electric vehicles according to different charging rates and average daily mileage. The data management system of the electric vehicle dispatching center collects real-time data of each type of electric vehicle group sent by the battery energy monitoring system, including the remaining power of the electric vehicle, the charging and discharging rate, the user's expected power, and the user's expected charging time. After obtaining the real-time data, the electric vehicle dispatching center reports the real-time dispatchable capacity information to the grid dispatching center. The grid dispatching center issues control instructions when the grid frequency fluctuates.

[0068] In this embodiment, electric vehicles are clustered according to different charging and discharging rates and average daily mileage; the specific operation steps are as follows:

[0069] Step (1): Classify and manage electric vehicles according to their characteristics, cluster electric vehicles according to the charge and discharge rate (C / DR) and the average daily driving distance (AVDT), and define the data structure of the charge and discharge rate (C / DR) and the average daily driving distance (AVDT) for each electric vehicle;

[0070] Step (2): Two data spaces are established based on C / DR and AVDT. The data space X of C / DR contains all electric vehicle C / DRs, and the data space Y of AVDT contains all electric vehicle AVDTs.

[0071] Step (3): Add virtual standard points in the data space X and Y, add 5 virtual points in the X space as initial cluster centers and add 3 virtual points in the Y space as initial cluster centers;

[0072] Step (4): Calculate the Euclidean distance from each electric vehicle data point to each cluster center in the data space;

[0073] Step (5): According to the calculated Euclidean distance, each electric vehicle is assigned to the cluster center closest to it to form an initial cluster;

[0074] Step (6): Calculate the average value of C / DR and AVDT of each category of electric vehicles as the new cluster center, evaluate whether the cluster center remains unchanged or changes less than a preset threshold, and determine whether the cluster center has changed. If it has changed, execute step (4); if it has not changed, save the result and execute step (7);

[0075] Step (7): Finally, the EVs are reorganized into multiple subgroups based on the C / DR and AVDT classes, and the EVs with the same C / DR and AVDT are merged into a single group.

[0076] Specifically, in this way, a large electric vehicle group is divided into several sub-electric vehicle groups, each of which contains electric vehicles with the same C / DR and AVDT categories. After the electric vehicles arrive at the charging station, the charging energy monitoring system monitors the C / DR and AVDT of the electric vehicles in real time and uploads them to the electric vehicle dispatch center. The electric vehicle dispatch center calculates the average C / DR and AVDT of each sub-group based on the clustering results.

[0077] Please refer to Figure 2 In this embodiment, the electric vehicle clustering control strategy based on the virtual synchronous machine, the electric vehicle charging and discharging structure based on the virtual synchronous machine consists of four parts: the upper auxiliary frequency modulation module, the AC / DC converter, the synchronous inverter VSG control module and the DC control module. The input quantity of the auxiliary frequency modulation module is the expected power S set by the user. SOC_e 、The current remaining power of the electric vehicle S SOC and grid frequency f, the output is the charge / discharge power signal P SET Compare the time t2 of charging the electric vehicle to the user's expected power at rated power with the remaining time t1 for charging and discharging operations, and calculate the charging demand function S D and the grid frequency deviation Δf are fuzzy processed, and S D The value range of S is set in the interval [-1,1]. D The fuzzy set F s1 It contains five linguistic variables, namely NB, NS, O, PS and PB. The value range of the power grid frequency deviation Δf is [-0.2, -0.1) ∪ (-0.033, 0.033) ∪ (0.1, 0.2], and the fuzzy set F of Δf S2 Contains 5 language variables, namely NH, NL, ZO, PL and PH.

[0078] In this embodiment, the electric vehicle participates in the primary frequency modulation by adopting droop control. When the grid frequency deviation changes, the charging and discharging power of the electric vehicle changes proportionally. A primary frequency modulation dead zone is set. Within the frequency modulation dead zone, the electric vehicle is charged with Pe power. When the grid frequency deviation exceeds the dead zone, the electric vehicle exchanges power with the grid to participate in the grid frequency modulation. The upper limit P of the charging and discharging power is set. max and the lower limit P min .

[0079] When the grid frequency fluctuates for a long time and with a large amplitude, it is difficult to restore the frequency to a normal value by relying solely on the primary frequency regulation strategy formed above. The specific embodiment of the present invention provides a secondary frequency regulation fuzzy control rule, in which the charging and discharging power is determined by the grid frequency deviation Δf and the user charging demand function S D Control, the specific design rules are characterized by the following formula:

[0080]

[0081] Among them, x1 and x2 are the two input quantities of the fuzzy controller; α i , β i is the fuzzy set corresponding to the input quantity; is the secondary frequency modulation power under the fuzzy control rule; g i (x1,x2) is the output function under the fuzzy control of x1 and x2; a i , b i is the input coefficient specified by each fuzzy rule; c is the constant fixed by the i-th fuzzy rule; i is the number of fuzzy rules;

[0082] Let x1 = Δf, x2 = S D , The specific calculation is as follows:

[0083]

[0084] The value of c varies according to the working conditions of electric vehicles. If the power grid is at peak load, it will cause Δf < 0. D The value of and the user's expected charging amount are used to determine whether the electric vehicle discharges to the grid. D Close to 1, indicating that the real frequency of the power grid is less than the rated frequency of the power grid, but because the expected power of the user is not reached, the electric vehicle only discharges but does not charge under this condition; if the power grid is in a low load, it will cause Δf>0, under this condition, the electric vehicle only charges but does not discharge, and the secondary frequency modulation power Determined by the constant c.

[0085] The grid dispatch center of this embodiment uses the dispatchable capacity of the clustered electric vehicle group as the reference power of the virtual synchronous machine, calculates the electric vehicle frequency response coefficient (i.e., droop coefficient), and transmits the real-time electricity price and droop coefficient to the electric vehicle dispatch center, which then sends the droop coefficient to each virtual synchronous machine. When the grid frequency deviation exceeds the set range, the electric vehicle is controlled to participate in the frequency modulation according to the primary and secondary frequency modulation strategies built into the virtual synchronous motor. The charge / discharge power P of the electric vehicle SET It is composed of the superposition of the primary frequency modulation P1 and the secondary frequency modulation P2 after fuzzy control, see Figure 4 . For the TS type fuzzy controller designed for secondary frequency modulation, S D And Δf are used as the input of the frequency modulation power to adjust the frequency modulation power, and after the electric vehicle is charged, the power information participating in the frequency modulation is uploaded to the power grid dispatching center.

[0086] See also Figure 3 The operation process of the frequency modulation control method of the electric vehicle participating system based on the virtual synchronous machine includes the following steps:

[0087] Step 1: Cluster electric vehicles according to different charging and discharging rates and average daily mileage;

[0088] Step 2: Obtain the vehicle information of each type of electric vehicle group collected in real time (remaining power of electric vehicles, charging and discharging rate, user expected power and user expected charging time, etc.);

[0089] Step 3: Construct the electric vehicle charging demand function S based on the remaining power of the electric vehicle and other information D , calculate the grid frequency deviation Δf according to the grid rated frequency;

[0090] Step 4: S D and Δf are fuzzy linguistically variableized to obtain its triangular membership function;

[0091] Step 5: Obtain the primary frequency regulation power reference value and the primary frequency regulation control strategy of the electric vehicle based on the droop coefficient;

[0092] Step 6: Obtain the secondary frequency modulation fuzzy control rules according to the TS fuzzy control system;

[0093] Step 7: After time t, execute step 1 again.

[0094] Example 2

[0095] Please refer to Figure 4 The electric vehicle frequency modulation control system based on virtual synchronous machine consists of synchronous inverter VSG module, bidirectional DC / DC converter module, charging demand function S DThe VSG module has the function of simulating the operating characteristics of traditional synchronous generators, ensuring that the system can respond to grid demand in a similar way to traditional generators; the bidirectional DC / DC converter module is responsible for stabilizing the DC bus voltage and ensuring the energy conversion efficiency and reliability of the system; the charging demand function S D The calculation module constructs a function that reflects the user's charging demand by combining the user's expected charging time, expected charging amount, and current remaining power, and then calculates the charging demand function based on S D The size of the power grid determines the working mode of the electric vehicle; the primary frequency modulation module: obtains the primary frequency modulation power reference value P1 according to the grid frequency deviation, and the electric vehicle adjusts the charging and discharging power according to the real-time change of the grid frequency to provide primary frequency modulation services; the secondary frequency modulation module: obtains the charging demand function S based on the fuzzy control theory D The secondary frequency modulation reference value P2 is used to perform secondary frequency modulation on the charging load according to the primary frequency modulation result. The primary frequency modulation power P1 and the secondary frequency modulation power P2 are finally added to obtain the synchronous machine reference power P SET , control the charging and discharging of electric vehicles. By controlling the charging / discharging power P of electric vehicles SET Adjust to achieve frequency control.

[0096] The present invention provides a method and system for controlling the frequency regulation of electric vehicles participating in the system based on a virtual synchronous machine, which can effectively reduce the frequency deviation of the power grid while completing the user's charging plan, play a role in assisting the frequency regulation of the power grid and improving the efficiency of the power grid, and provide a new idea for the participation of electric vehicles in the frequency regulation of the system.

[0097] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0098] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for controlling frequency modulation of an electric vehicle participating system based on a virtual synchronous machine, characterized in that: The frequency modulation control method comprises the following steps: The control system of the electric vehicle dispatch center clusters electric vehicles according to different charging and discharging rates and average daily mileage. The data management system of the electric vehicle dispatch center collects the real-time data of clustered electric vehicles sent by the charging energy monitoring system. The real-time data includes the remaining power of the electric vehicle, the charging and discharging rate, the user's expected power and the user's expected charging time. After the electric vehicle dispatch center summarizes the real-time data sent by each charging energy monitoring system, it uploads the real-time dispatchable capacity information to the power grid dispatch center. The power grid dispatch center updates the real-time dispatchable capacity of the electric vehicle. The power grid dispatch center issues control instructions when the power grid frequency fluctuates. The electric vehicle dispatch center adopts a TS-type fuzzy controller with a global charging demand function S D The grid frequency deviation Δf is used as input to adjust the frequency modulation power.

2. The method for controlling the frequency modulation of an electric vehicle participating system based on a virtual synchronous machine according to claim 1, characterized in that: The grid dispatch center uses the dispatchable capacity of the clustered electric vehicle group as the benchmark power of the virtual synchronous machine, calculates the electric vehicle frequency response coefficient, i.e., the droop coefficient, and transmits the real-time electricity price and droop coefficient to the electric vehicle dispatch center, which then sends the droop coefficient to each virtual synchronous machine. When the grid frequency deviation exceeds the set range, the electric vehicle is controlled to participate in the frequency modulation according to the primary and secondary frequency modulation strategies built into the virtual synchronous machine. The charging / discharging power P of the electric vehicle is SET It is composed of the superposition of primary frequency modulation P1 and secondary frequency modulation P2 after fuzzy control. A TS type fuzzy controller is designed for the secondary frequency modulation. D And Δf are used as the input of the frequency modulation power to adjust the frequency modulation power, and after the electric vehicle is charged, the power information participating in the frequency modulation is uploaded to the power grid dispatching center.

3. The method for controlling the frequency modulation of an electric vehicle participating system based on a virtual synchronous machine according to claim 2, characterized in that: Calculate the charging demand function S based on the remaining power of electric vehicles D , S D The triangular membership function of the grid frequency deviation Δf is constructed by fuzzy theory; the grid dispatching center obtains the primary frequency modulation power reference value P1 according to the grid frequency deviation, and the electric vehicle adjusts the charging and discharging power according to the real-time change of the grid frequency to provide primary frequency modulation service; the secondary frequency modulation reference value P2 based on the charging demand function is obtained by fuzzy control theory, and the charging load is secondary modulated according to the primary frequency modulation result. SET Adjust to achieve frequency control.

4. The method for controlling the frequency modulation of an electric vehicle participating system based on a virtual synchronous machine according to claim 3 is characterized in that: The control system of the electric vehicle dispatch center clusters electric vehicles according to different charging and discharging rates and average daily mileage, including the following steps: Step 1: Define a data structure containing the charge / discharge rate C / DR and the average daily mileage AVDT for each electric vehicle. Value is the specific value of the data structure of the charge / discharge rate C / DR or the average daily mileage AVDT. Class is to classify electric vehicles into different categories based on the charge / discharge rate C / DR or the average daily mileage AVDT. EV i =[(C / DR) i (value,class),(AVDT) i (value,class)], go to step 2; Step 2: According to C / DR and AVDT, two data spaces are established respectively. In the C / DR data space X, the C / DR of all electric vehicles is included; in the AVDT data space Y, the AVDT of all electric vehicles is included, that is, X=[(C / DR)1.value(1),.......(C / DR) n .value(n)], Y=[(AVDT)1.value(1),......(AVDT) n .value(n)], go to step 3; Step 3: In the data space X and Y, add virtual standard points as initial cluster centers respectively; add 5 virtual points as initial cluster centers in the X space and 3 virtual points as initial cluster centers in the Y space, and execute step 4; Step 4: Calculate the Euclidean distance from each electric vehicle data point to each cluster center in the data space and execute step 5; Step 5: According to the calculated Euclidean distance, each electric vehicle is assigned to the cluster center closest to it to form an initial cluster. The average values ​​of C / DR and AVDT of all electric vehicles in each category are calculated as the new cluster center, and step 6 is executed; Step 6: Evaluate whether the cluster center remains unchanged or changes less than a preset threshold, and determine whether the cluster center has changed. If it has changed, execute step 4; if it has not changed, execute step 7; Step 7: Save the result EV i =[(C / DR) i .class=x,(AVDT) i .class=y], reorganize the electric vehicles into multiple subgroups according to the C / DR and AVDT classes, each subgroup contains electric vehicles with similar C / DR and AVDT, and execute step 8; Step 8: After time T, execute step 1 again.

5. The method for controlling the frequency modulation of an electric vehicle participating system based on a virtual synchronous machine according to claim 4 is characterized in that: Calculate the charging demand function S based on the remaining power of electric vehicles D , charging demand function S D Considering the battery life and reflecting the user's charging needs, S D It indicates the ratio of the amount of charge after the electric vehicle is connected to the charging pile to the time consumed, including: (1) When the time t2 for charging an electric vehicle at rated power to the user's expected power is less than the remaining time t1 for charging and discharging operations, it means that the electric vehicle can participate in grid frequency regulation under the premise of meeting the user's charging plan. Among them, S SOC is the current remaining power of the electric vehicle, S SOC_e The expected power set by the user, SSOC max and SSOC min are the upper and lower limits of battery power respectively; t1 is the remaining time for the electric vehicle to be charged and discharged; t2 is the time it takes for the electric vehicle to be charged to S at rated power. SOC_e the time required; (2) When the time t2 for charging the electric vehicle at rated power to the user's expected power is greater than or equal to the remaining time t1 for charging and discharging operations, it means that the electric vehicle is only focused on meeting the user's charging needs. (3) When the user has no charging plan, that is, S SOC =S SOC_e , S D ＝0。 6. The method for controlling the frequency modulation of an electric vehicle participating system based on a virtual synchronous machine according to claim 5, characterized in that: The charging demand function S D The triangular membership function of the power grid frequency deviation Δf is constructed using fuzzy theory and divided into five levels of fuzzy subsets; Given the charging demand function S D The value range of is [-1,1], For S D The fuzzy linguistic variables, whose fuzzy set F s1 It is divided into five language variables, which are represented as follows: In the formula, when S D A negative value indicates a negative deviation from 0, and a positive value indicates a positive deviation from 0; O indicates S D close to 0; PS and NS represent S D Positive deviation from 0 and S D The negative deviation from 0 is small; PB and NB represent S D Positive deviation from 0 and S D The degree of negative deviation from 0 is too large; Frequency reference value f n The frequency deviation of the power grid is 50Hz, and the upper and lower limits of its fluctuation are ±0.2Hz; the value range of the power grid frequency deviation Δf is specified as: [-0.2,-0.1)∪(-0.033,0.033)∪(0.1,0.2], is a fuzzy linguistic variable of Δf, and its fuzzy set F s2 It contains 5 language variables, which are represented as follows: In the formula, ZO means that the grid frequency is near the reference value, and the grid frequency deviation Δf is close to 0; NH and NL respectively mean that the grid frequency deviation Δf is in the range of (-0.2~-0.15) Hz and [-0.15~-0.1) Hz, indicating that the system power generation is insufficient; PL and PH respectively mean that the grid frequency deviation Δf is in the range of (0.1~0.15) Hz and [0.15~0.2) Hz, indicating that the system power generation is excessive.

7. The method for controlling the frequency modulation of an electric vehicle participating system based on a virtual synchronous machine according to claim 6, characterized in that: The primary frequency regulation control strategy includes: when electric vehicles participate in primary frequency regulation, a primary frequency regulation dead zone is set. When the grid frequency deviation exceeds the dead zone, electric vehicles exchange power with the grid to participate in grid frequency regulation. In addition, the charging and discharging power is limited by the charger power, considering the user's travel needs and battery life, P max and P min They are the upper and lower limits of charging and discharging power, respectively, which are determined by the life of the power battery; specifically, they are as follows: Among them, P ch-max is the maximum charging capacity allowed by the charging device; P disch-max is the maximum discharge capacity allowed by the charging equipment; η ch is the charging efficiency of the battery; η disch is the discharge efficiency of the battery; C bat is the battery capacity; Δ t It is to control time; In the frequency modulation dead zone, electric vehicles use P e When the frequency is not in the dead zone, the power change ΔP1 is the droop coefficient K. pf The product of the grid frequency deviation Δf, the primary frequency modulation power P1 is calculated as follows: P1=P e +ΔP1=P e +K pf Δf。 8. The method for controlling the frequency modulation of an electric vehicle participating system based on a virtual synchronous machine according to claim 7, characterized in that: The secondary frequency modulation fuzzy control rules are as follows: The charging and discharging power is determined by the grid frequency deviation Δf and the user charging demand function S D Control, the specific design rules are characterized by the following formula: Among them, x1 and x2 are the two input quantities of the fuzzy controller; α i , β i is the fuzzy set corresponding to the input quantity; is the secondary frequency modulation power under the fuzzy control rule; g i (x1,x2) is the output function under the fuzzy control of x1 and x2; a i 、b i is the input coefficient specified by each fuzzy rule; c is the constant fixed by the i-th fuzzy rule; i is the number of fuzzy rules; Let x1 = Δf, x2 = S D , The specific calculation is as follows:

9. A frequency modulation control system for an electric vehicle participating system based on a virtual synchronous machine, used to implement the frequency modulation control method according to any one of claims 1 to 8, characterized in that: Including synchronous inverter VSG module, bidirectional DC / DC converter module, charging demand function S D Calculation module, primary frequency modulation module, secondary frequency modulation module; Synchronous inverter VSG module: The VSG module has the function of simulating the operating characteristics of traditional synchronous generators; Bidirectional DC / DC converter module: used to stabilize the DC bus voltage; Charging demand function S D The calculation module constructs a function reflecting the user's charging demand by combining the user's expected charging time, expected charging amount and current remaining power; Primary frequency regulation module: The primary frequency regulation power reference value P1 is obtained according to the grid frequency deviation. The electric vehicle adjusts the charging and discharging power according to the real-time changes of the grid frequency to provide primary frequency regulation services. Secondary frequency modulation module: According to fuzzy control theory, the charging demand function S is obtained. D The secondary frequency modulation reference value P2 is used to perform secondary frequency modulation on the charging load according to the primary frequency modulation result. SET Adjust to achieve frequency control.

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