Power grid primary frequency modulation multi-element comprehensive control method and system based on V2G technology

Abstract

The invention discloses a power grid primary frequency modulation multi-element comprehensive control method and system based on a V2G technology, relates to the technical field of power grid frequency modulation, and solves the problem that an optimal control effect cannot be achieved due to the fact that an existing power grid primary frequency modulation control strategy after an electric vehicle is added does not comprehensively consider the battery state of the electric vehicle and the travel demand of a user. According to the scheme, the method comprises the steps of obtaining real-time parameters after the electric vehicle accesses a power grid; in response to the frequency modulation instruction, calculating the frequency change rate of the power grid according to the frequency deviation of the power grid, and selecting to execute an improved virtual inertia control strategy introducing a health factor or an adaptive droop control strategy with a variable priority coefficient as an electric vehicle battery control strategy according to the frequency change rate of the power grid and the frequency deviation of the power grid; factors such as the battery state of an electric vehicle and user travel requirements are considered, multi-factor collection is considered for adaptive control, and corresponding control strategies are adopted in different stages of frequency disturbance, so that effective frequency support is provided for a power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid frequency regulation, and more specifically, to a multi-element integrated control method and system for power grid primary frequency regulation based on V2G technology. Background Art

[0002] In recent years, the country has vigorously developed new energy electric vehicles. For the power grid, a large number of electric vehicles can not only be regarded as a new type of load, but also can be used as a controllable power source to participate in the auxiliary frequency regulation of the power grid. Combined with the current situation of new energy consumption and large-scale access of electric vehicles to the grid, in order to ensure the safety and stability of the power grid under different disturbances, vehicle-to-grid interaction technology (V2G) is usually used to enable electric vehicles to participate in the interaction of power grid frequency regulation, and use the advantages of battery energy storage with fast response speed and flexible regulation to overcome the problems of insufficient climbing ability and delayed response of traditional thermal power units.

[0003] In the existing technology, how to make full use of the huge battery energy storage of electric vehicles to participate in frequency regulation is a hot topic of current research. From the disordered charging of electric vehicles to the orderly charging of electric vehicles, and then to the development and application of V2G technology of electric vehicles, it reflects the continuous maturity of vehicle-grid interaction technology. At present, in the technology of electric vehicles participating in grid frequency regulation, the core lies in the game and optimization of grid stability, user needs, dispatch economy, etc., and adopts different control strategies. The research and application directions mainly represented by the following aspects: First, V2G control strategy based on grid stability considerations. This strategy aims to improve the stability of the power system and minimize frequency deviation as much as possible, but it does not take into account the economy, battery equipment safety, user needs, etc. Second, V2G control strategy based on cost-effectiveness. This strategy usually adopts intermediate operators to manage and operate distributed electric vehicles in different areas and segments, and uniformly respond to the grid frequency regulation needs. Its main goal is to reduce operating costs and improve economic benefits. There is a certain uncertainty and risk in the stability of grid operation and dispatch costs. Third, V2G control strategy based on user behavior. This strategy is to meet user travel needs and improve satisfaction. When participating in grid frequency regulation, there are problems such as insufficient output and difficulty in predicting the randomness of user travel.

[0004] In addition, when electric vehicles participate in the primary frequency regulation of the power grid, on the one hand, the simple use of virtual droop control can only reduce the steady-state frequency deviation, and cannot effectively curb the frequency deterioration rate. On the other hand, after the introduction of traditional virtual inertia control, the deterioration of the frequency disturbance in the early stage can be effectively suppressed to a certain extent. However, since the maintenance of virtual inertia control requires the electric vehicle energy storage unit to discharge at high power, multiple cycles will accelerate battery aging. For the frequent fluctuations in the power grid frequency, the battery life will be greatly affected. And under the traditional strategy, simple virtual droop control and virtual inertia control can no longer meet today's frequency regulation needs.

[0005] It can be seen that the existing control strategy research and application do not comprehensively consider the battery health status, battery life, battery state of charge, and user travel needs, and lack global consideration of the participation of electric vehicles in grid frequency regulation. As a result, the primary frequency regulation control strategy of the grid after the addition of electric vehicles is lacking in comprehensiveness and cannot achieve the optimal control effect. Summary of the invention

[0006] The purpose of this application is to provide a multi-integrated control method and system for primary frequency regulation of a power grid based on V2G technology, so as to solve the problem that the primary frequency regulation control strategy of the existing power grid after the addition of electric vehicles does not comprehensively consider the health status of electric vehicle batteries, battery life, battery charge status, and user travel needs, and cannot achieve the optimal control effect; this application fully considers the aggregation of multiple factors for adaptive control, and provides effective frequency support for the power grid by adopting corresponding control strategies at different stages of frequency disturbance.

[0007] The present application provides a multi-integrated control method for primary frequency regulation of a power grid based on V2G technology, comprising: obtaining real-time parameters of an electric vehicle after accessing the power grid, the real-time parameters comprising: a frequency deviation of the power grid, a state of charge of an electric vehicle battery, a health state, and a remaining charging time; in response to a frequency regulation instruction, calculating a frequency change rate of the power grid according to the frequency deviation of the power grid, and selecting to execute an improved virtual inertia control strategy with a health factor or an adaptive droop control strategy with a variable priority coefficient as an electric vehicle battery control strategy according to the frequency change rate of the power grid and the frequency deviation of the power grid; wherein the improved virtual inertia control strategy with a health factor refers to: adjusting the output depth of each electric vehicle battery according to the health state of the electric vehicle battery; the adaptive droop control strategy with a variable priority coefficient refers to: calculating the priority coefficient of each electric vehicle battery according to the state of charge, health state, and remaining charging time of the electric vehicle battery, selecting an electric vehicle battery with a high priority coefficient to participate in droop control, calculating the droop control coefficient of each electric vehicle battery according to the state of charge, health state, and remaining charging time of the electric vehicle battery, and adjusting the output power of each electric vehicle battery according to the droop control coefficient of each electric vehicle battery.

[0008] By adopting the above technical scheme, the improved virtual inertia control strategy with the introduction of health factor or the adaptive droop control strategy with variable priority coefficient is selected to be executed by calculating the frequency change rate of the power grid; the influence of the battery health state is considered by the improved virtual inertia control strategy with the introduction of health factor, so as to ensure the safety of the battery while adjusting the stability of the power grid; the adaptive droop control strategy with variable priority coefficient comprehensively considers the battery's state of charge, health state and remaining charging time (user travel needs) to select some electric vehicle batteries to participate in droop control; and the droop control coefficient is jointly calculated by the battery's state of charge, health state and remaining charging time (user travel needs) to achieve adaptive droop control.

[0009] In a possible implementation manner, the improved virtual inertia control strategy introducing the health factor is:

[0010]

[0011] In the formula, △P i (t) is the virtual inertia response value of the i-th group of electric vehicle batteries at time t, G t is the improved virtual inertia coefficient at time t, G t Determined by the health status of the electric vehicle battery at time t, df t / f t is the frequency change rate at time t, R s It is the frequency modulation dead zone of virtual inertia control.

[0012] In a possible implementation manner, the virtual inertia coefficient G is improved at time t. t for:

[0013] G t =1-(S OH (t)-1) 2

[0014]

[0015] In the formula, S OH (t) is the estimated health status of the electric vehicle battery at time t, C(t) is the actual capacity of the electric vehicle battery at time t, and C e is the rated capacity of the electric vehicle battery.

[0016] In a possible implementation manner, the priority coefficient is:

[0017] K(t)=αS OC (t)+βS OH (t)+γU(t)

[0018] Where K(t) is the priority coefficient of the electric vehicle battery at time t, α, β, and γ are weight coefficients, and SOC (t) is the estimated state of charge of the electric vehicle battery at time t, S OH (t) is the estimated health status of the electric vehicle battery at time t, U(t) is the estimated travel demand status of the user at time t, and U(t) is determined by the remaining charging time of the electric vehicle battery at time t.

[0019] In a possible implementation, the charge state estimation S of the electric vehicle battery at time t is OC (t) is: when the frequency deviation of the power grid shows that the electric vehicle battery is in a charging state, the estimated state of charge S of the electric vehicle battery in the forbidden charging zone, the discharge priority zone, the normal charging and discharging zone, the charging priority zone and the forbidden discharge zone OC (t) increases successively; when the frequency deviation of the power grid shows that the electric vehicle battery is in a discharging state, the charge state estimation S of the electric vehicle battery in the forbidden charging zone, the discharge priority zone, the normal charging and discharging zone, the charging priority zone and the forbidden discharge zone OC (t) decreases successively.

[0020] In a possible implementation, the health status estimate S of the battery of the electric vehicle at time t is OH (t) is:

[0021]

[0022] Where C(t) is the actual capacity of the electric vehicle battery at time t, C e is the rated capacity of the electric vehicle battery.

[0023] In a possible implementation, the user travel demand state estimation U(t) at time t is:

[0024]

[0025] Where, T r represents the remaining charging time of the electric vehicle battery at time t, S OC (max) represents the estimated state of charge when the electric vehicle battery is fully charged, S OC (t) is the estimated state of charge of the electric vehicle battery at time t.

[0026] In a possible implementation manner, the adaptive droop control strategy with variable priority coefficient is:

[0027] ΔP EV =R t Δf

[0028] In the formula, △P EV is the output power of the electric vehicle battery involved in droop control, R tis the droop control coefficient of the electric vehicle battery participating in droop control, and △f is the frequency deviation.

[0029] In a possible implementation manner, the droop control coefficient R of the electric vehicle battery involved in the droop control is t for:

[0030] R t =S OC (t)S OH (t)U(t)

[0031] In the formula, S OC (t) is the estimated state of charge of the electric vehicle battery at time t, S OH (t) is the estimated health status of the electric vehicle battery at time t, U(t) is the estimated travel demand status of the user at time t, and U(t) is determined by the remaining charging time of the electric vehicle battery at time t.

[0032] The present application also provides a multi-integrated control system for primary frequency regulation of a power grid based on V2G technology, comprising: an electric vehicle cluster, a V2G charging pile, an electric vehicle aggregator and a power grid dispatching center; the V2G charging pile is used to collect the charge state, health state and remaining charging time of the electric vehicle battery in real time; the power grid dispatching center is used to collect the frequency deviation of the power grid in real time and send frequency regulation instructions to the electric vehicle aggregator; the electric vehicle aggregator is used to execute the multi-integrated control method for primary frequency regulation of a power grid based on V2G technology as mentioned above to obtain an electric vehicle battery control strategy; the V2G charging pile is also used to control the electric vehicle cluster according to the electric vehicle battery control strategy of the electric vehicle aggregator; the electric vehicle cluster is used to participate in the primary frequency regulation of the power grid through the batteries of multiple electric vehicles.

[0033] Compared with the prior art, the present application has the following beneficial effects: first, for different stages of frequency disturbance, a control method combining a virtual inertia control strategy and a droop control strategy is adopted. When the grid frequency change rate is large at the initial stage of load disturbance, a virtual inertia control strategy introducing a health factor is adopted to quickly prevent the grid frequency from deteriorating and reduce the maximum frequency deviation, thereby slowing down the frequency deterioration speed and degree of deterioration; when the grid frequency change rate is small at the later stage of load disturbance, an adaptive droop control strategy with a variable priority coefficient is adopted to reduce the frequency steady-state error while taking into account the energy storage charge state and health state; second, a virtual inertia control strategy introducing a health factor is adopted to calculate the virtual inertia response value of the battery by considering the influence of the battery health state on the basis of traditional virtual inertia control, thereby ensuring the safety of the battery while regulating the grid stability; third, an adaptive droop control strategy with a variable priority coefficient is adopted to set the priority coefficient and adaptively calculate the droop control coefficient on the basis of the traditional droop control strategy, comprehensively consider the battery charge state, health state and remaining charging time (user travel needs) to perform adaptive droop control, thereby reducing the frequency steady-state error while taking into account the battery charge state, health state and user travel needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0035] Figure 1 It is a flow chart of a multi-integrated control method for primary frequency regulation of a power grid based on V2G technology;

[0036] Figure 2 A model for electric vehicles to participate in primary frequency regulation of the power grid;

[0037] Figure 3 The block diagram of the electric vehicle frequency regulation model considering virtual inertia control and virtual droop control;

[0038] Figure 4 It is the SOC state partition rule diagram of the electric vehicle battery;

[0039] Figure 5 Battery charging and discharging power diagram for different SOC partitions;

[0040] Figure 6 It is a structural diagram of a multi-integrated control system for primary frequency regulation of a power grid based on V2G technology;

[0041] Figure 7 A flow chart of a multi-element integrated control method for primary frequency regulation of a power grid based on a system;

[0042] Figure 8 It is the curve of power grid frequency deviation change under different control strategies;

[0043] Fig. 9 is the continuous load disturbance curve;

[0044] Fig.10 It is the frequency deviation curve of different control strategies under continuous load disturbance;

[0045] Fig.11 The battery SOC change curve of a single electric vehicle;

[0046] Fig.12 This is the average value change curve of the battery SOC of electric vehicles participating in primary frequency modulation. DETAILED DESCRIPTION

[0047] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with examples and drawings. The illustrative implementation scheme of the present application and its description are only used to explain the present application and are not intended to limit the present application.

[0048] See also Figure 1 As shown, Figure 1 The present invention is a flow chart of a multi-integrated control method for primary frequency modulation of a power grid based on V2G technology. The method comprises: obtaining real-time parameters of an electric vehicle after accessing the power grid, the real-time parameters comprising: a frequency deviation △f of the power grid, a state of charge SOC of an electric vehicle battery, a state of health SOH and a remaining time of charging; in response to a frequency modulation instruction, calculating a frequency change rate of the power grid according to a frequency deviation of the power grid, and selecting to execute an improved virtual inertia control strategy with a health factor or an adaptive droop control strategy with a variable priority coefficient as an electric vehicle battery control strategy according to the frequency change rate of the power grid and the frequency deviation of the power grid; wherein the improved virtual inertia control strategy with a health factor refers to: adjusting the output depth of each electric vehicle battery according to the health state of the electric vehicle battery; the adaptive droop control strategy with a variable priority coefficient refers to: calculating the priority coefficient of each electric vehicle battery according to the state of charge, health state and remaining time of charging of the electric vehicle battery, selecting an electric vehicle battery with a high priority coefficient to participate in droop control, calculating the droop control coefficient of each electric vehicle battery according to the state of charge, health state and remaining time of charging of the electric vehicle battery, and adjusting the output power of each electric vehicle battery according to the droop control coefficient of each electric vehicle battery.

[0049] Specifically, after electric vehicles are connected to the power grid, the batteries of electric vehicles can be regarded as controllable energy storage power sources, which is similar to conventional generators participating in the primary frequency modulation of the power grid. First, the frequency deviation of the power grid and the charge state, health state, and remaining charging time of each electric vehicle battery are collected; when the frequency modulation instruction is received, the frequency change rate of the power grid is calculated according to the frequency deviation of the power grid. When the frequency change rate of the power grid is large, the improved virtual inertia control strategy with the introduction of health factors is executed; when the frequency change rate of the power grid is small and the frequency deviation of the power grid is not in the frequency modulation dead zone, the adaptive droop control strategy with variable priority coefficient is executed. Among them, the improved virtual inertia control strategy with the introduction of health factors takes into account the influence of the battery health state on the basis of traditional virtual inertia control, and ensures the safety of the battery while regulating the stability of the power grid. The adaptive droop control strategy with a variable priority coefficient sets a priority coefficient on the basis of the traditional droop control strategy, comprehensively considers the battery's state of charge, health status and remaining charging time (user travel needs) to select some electric vehicle batteries to participate in droop control; and sets an adaptively calculated droop control coefficient on the basis of the traditional droop control strategy, and realizes adaptive droop control by jointly calculating the droop control coefficient through the battery's state of charge, health status and remaining charging time (user travel needs).

[0050] The improvement of this method is that, first, a control method combining virtual inertia control strategy and droop control strategy is adopted. When the frequency change rate of the power grid is large at the initial stage of load disturbance, a virtual inertia control strategy with health factor is adopted to quickly prevent the deterioration of the power grid frequency and reduce the maximum frequency deviation, slowing down the frequency deterioration speed and degree; the dead zone of the frequency change rate is used as the switching point of the control strategy, and the adaptive droop control strategy with variable priority coefficient is switched to reduce the frequency steady-state error while taking into account the state of charge and health state of the energy storage. Second, a virtual inertia control strategy with health factor is adopted, and the virtual inertia response value of the battery is calculated by considering the influence of the battery health state on the basis of traditional virtual inertia control, so as to ensure the safety of the battery while regulating the stability of the power grid. Third, an adaptive droop control strategy with variable priority coefficient is adopted, and the priority coefficient is set on the basis of the traditional droop control strategy and the droop control coefficient is adaptively calculated, and the battery state of charge, health state and remaining charging time (user travel demand) are comprehensively considered to perform adaptive droop control, so as to reduce the frequency steady-state error while taking into account the battery state of charge, health state and user travel demand.

[0051] When an electric vehicle is connected to the power grid, its battery can be equivalent to a controllable energy storage power source, which can be compared to a conventional generator set participating in the primary frequency modulation of the power grid. In order to obtain the same external characteristics as the generator set, it is necessary to perform an equivalent simulation of its electromagnetic equations. The mechanical equations of the virtual synchronous machine are as follows:

[0052]

[0053] T d =D p (ω-ω0)(2)

[0054] Where J is the moment of inertia, in kg / m 2 ; T m , T e and T d They are mechanical, electromagnetic and damping torque, in N·m; D p is the damping coefficient, in N·m·s / rad; ω and ω0 are the virtual angular frequency and rated angular frequency of the virtual synchronous machine, in rad / s.

[0055] From the above two equations, the complex frequency domain expression of the virtual synchronous machine response frequency change can be obtained as follows:

[0056]

[0057] Among them, △P EV is the total load change of electric vehicles participating in frequency regulation, and △f is the frequency deviation.

[0058] Taking into account the inherent response delay and droop characteristics of electric vehicle battery charging and discharging, the transfer function of electric vehicle battery response to grid frequency changes can be expressed as follows:

[0059]

[0060] Among them, T EV is the time constant of the battery, K EV is the droop coefficient of the battery participating in the power-frequency response.

[0061] Combining the traditional frequency regulation links of the generator set, we can establish Figure 2 The model of electric vehicles participating in the primary frequency regulation of the power grid is shown in Figure 1. L It is the sum of load disturbance and renewable energy fluctuation; △P EV is the output power of the electric vehicle battery participating in the primary frequency regulation of the power grid; △P G It is the output power of the traditional generator set.

[0062] Depend on Figure 2 From the model of electric vehicles participating in the primary frequency regulation of the power grid, it can be seen that the participation of electric vehicle batteries in the primary frequency regulation of the power grid can be divided into virtual inertia control and virtual droop control. Virtual inertia control simulates the role of traditional mechanical inertia by adding a virtual inertia module. This module controls the speed of change of the generator output power, thereby maintaining the stability of the power system.

[0063] Virtual inertia control usually takes effect at the beginning of load disturbance, when the frequency change rate is large. Droop control alone cannot quickly reduce the frequency change rate. Virtual inertia control can quickly prevent the grid frequency from deteriorating and reduce the maximum frequency deviation. However, considering the energy storage capacity, inertia control cannot work for a long time. After preventing the frequency from deteriorating, droop control is used to continue frequency regulation, reducing the frequency steady-state error while taking into account the energy storage charge state and health state. The control block diagram is shown in the figure below. Figure 3 shown.

[0064] Traditional virtual inertia control does not consider the impact of the health status of electric vehicle batteries on battery charging and discharging, and has a great impact on the battery life. Therefore, this method fully considers the characteristics of battery energy storage structure and key state parameters, and adopts virtual inertia control with the introduction of health factors to slow down the speed and degree of frequency deterioration when the frequency change rate is large at the initial stage of disturbance. At the same time, the dead zone of the frequency change rate is used as the switching point of the control strategy, which ensures the rapid response to the primary frequency modulation of the power grid while also ensuring the economy and safety of electric vehicle battery operation.

[0065] In order to maintain the stable operation of the power grid, China generally stipulates that the power grid frequency deviation is 0.2~0.5Hz or -0.5~-0.2Hz. Inertial control provides high power support for the power grid in a short time, which can effectively alleviate the steepness of frequency change. The improved virtual inertial control strategy with the introduction of health factors is:

[0066]

[0067] In the formula, △P i (t) is the virtual inertial response value of the i-th group of electric vehicle batteries at time t; G t is the improved virtual inertia coefficient at time t, G t Determined by the health status of the electric vehicle battery at time t, it is a constant; df t / f t is the frequency change rate at time t, R s This is the dead zone of virtual inertia control frequency modulation. Virtual inertia control works for a short time and has little effect on the battery's state of charge. Therefore, it does not give priority to battery output and does not consider the impact of charge state changes on its output power. The output power is determined by the frequency change rate and the improved virtual inertia coefficient.

[0068] Considering that virtual inertia control requires high power output from the battery, multiple cycles will accelerate battery aging. Therefore, this method determines the improved virtual inertia coefficient G according to the health status of the battery. t .

[0069] G t =1-(S OH (t)-1) 2 (6)

[0070]

[0071] In the formula, S OH (t) is the estimated health status of the electric vehicle battery at time t, C(t) is the actual capacity of the electric vehicle battery at time t, and C e is the rated capacity of the electric vehicle battery.

[0072] In summary, when a battery energy storage power station composed of multiple electric vehicle batteries performs virtual inertia control in the early stage of grid frequency fluctuation, by introducing a health factor, each battery unit adjusts its output depth according to its own health status, so that the battery energy storage power station can continuously and quickly inject active power into the grid and effectively slow down the battery capacity attenuation rate.

[0073] After the frequency deterioration period, an adaptive droop control with a variable priority coefficient is used. When selecting electric vehicle batteries for droop control, the battery charge state, health state, and user travel needs are comprehensively evaluated to determine the priority coefficient of each electric vehicle battery and prioritize them. Electric vehicle batteries with high priority coefficients are selected to participate in droop control first. The high here can be a priority coefficient higher than the set threshold, or it can be the top N priority coefficients sorted from large to small. The adaptive droop control with a variable priority coefficient ensures the safety of electric vehicle batteries and meets user vehicle needs while accurately responding to grid frequency fluctuations.

[0074] In terms of health status SOH assessment, the above formula (7) is used to evaluate the health status of the battery, which is expressed as the health status estimate S OH (t).

[0075] In terms of SOC assessment, in order to avoid high-power charging and discharging when the battery's SOC is too high or too low, it is necessary to effectively maintain the battery's SOC and reduce battery aging time. Therefore, it is necessary to partition the battery's SOC and set different priority coefficient selection strategies. Figure 4 shown.

[0076] Depend on Figure 4 It can be seen that according to the current state of charge SOC of the battery, it can be divided into five working intervals: no-charge zone, discharge priority zone, normal charge and discharge zone, charge priority zone and no-discharge zone. Each interval matches a different priority coefficient. The priority coefficient shown in Table 1 is used, which is expressed as the state of charge estimation S OC (t).

[0077] Table 1 Battery priority coefficients at different charge states

[0078]

[0079] When the frequency deviation of the power grid △f<0, indicating that the electric vehicle battery is in a charging state, the charge state estimation S of the electric vehicle battery in the forbidden charging zone, discharge priority zone, normal charge and discharge zone, charging priority zone and forbidden discharge zone OC (t) increases successively; when the frequency deviation of the power grid △f>0, indicating that the electric vehicle battery is in a discharging state, the charge state estimation S of the electric vehicle battery in the forbidden charging area, the discharge priority area, the normal charging and discharging area, the charging priority area and the forbidden discharge area OC (t) decreases successively.

[0080] In terms of user travel demand assessment, when users connect their electric vehicles to the grid for charging, the user's intended charging time is collected, and the priority of the battery's participation in frequency regulation is evaluated by measuring the user's remaining charging time and battery state of charge. When the user has less remaining charging time and a lower battery state of charge, the priority of participating in grid frequency regulation is lowered to ensure the user's normal vehicle needs; when the user has more remaining charging time and a higher battery state of charge, the priority of participating in grid frequency regulation is increased. The priority coefficient of specific user travel demand is estimated according to the following formula, expressed as the user travel demand state estimate U(t).

[0081]

[0082] Where, T r Indicates the remaining charging time of the user, S OC (max) represents the estimated state of charge when the battery is fully charged, S OC (t) represents the estimated state of charge of the electric vehicle battery at time t.

[0083] Calculate the key battery parameters S OC (t), S OH After determining the values ​​of U(t) and U(t), in order to better control the switching of battery units, this method introduces a comprehensive battery priority sorting strategy to maximize the potential of electric vehicle batteries to participate in grid frequency regulation. The priority sorting calculation formula is:

[0084] K(t)=αS OC (t)+βS OH (t)+γU(t)(9)

[0085] Where K(t) is the priority coefficient of the electric vehicle battery at time t; α, β, and γ are weight coefficients, α:β:γ=1:2:3. The larger the priority coefficient, the higher the priority level of the battery pack action, and vice versa. OC (t) is the estimated state of charge of the electric vehicle battery at time t, S OH(t) is the estimated health status of the electric vehicle battery at time t, U(t) is the estimated travel demand status of the user at time t, and U(t) is determined by the remaining charging time of the electric vehicle battery at time t.

[0086] The droop control of electric vehicle battery-assisted frequency regulation controls the active output of the energy storage system according to the frequency deviation based on the droop characteristics, thereby stabilizing the grid frequency. According to the above transfer function of the electric vehicle battery in response to the change of grid frequency, the adaptive droop control strategy with variable priority coefficient can be simplified to the following formula:

[0087] ΔP EV =R t Δf(10)

[0088] In the formula, △P EV is the output power of the electric vehicle battery involved in droop control, R t is the droop control coefficient of the electric vehicle battery participating in droop control, and △f is the frequency deviation.

[0089] Based on the above analysis of different influencing factors of electric vehicle batteries and the priority strategy of comprehensive battery evaluation, a control strategy for the optimal output depth of electric vehicle batteries participating in system frequency regulation can be formulated. This method defines the droop control coefficient R of electric vehicles participating in frequency regulation. t , can achieve the best power output according to the SOC state, SOH state of the electric vehicle battery and the urgency of the user's travel needs. OC (t), S OH (t) and U(t) are used to realize the variable droop coefficient, among which S OC (t) The SOC interval coefficient method in the priority strategy is no longer used. Considering the smoothness and accuracy of the next frequency modulation, S OC (t) Dynamically adjust according to the real-time state of charge. The improved droop coefficient R t for:

[0090] R t =S OC (t)S OH (t)U(t)(11)

[0091] Among them, the control strategies corresponding to the SOC of different partitions are as follows:

[0092] 1) When the battery SOC is greater than 90%, that is, the battery is in the forbidden charging zone, the battery can only be discharged, and its discharge power coefficient is 1, that is, rated power discharge;

[0093] 2) When the battery is 75%<SOC≤90%, it is in the discharge priority zone, which is suitable for discharge but not for charging; the battery-assisted generator set participates in system frequency modulation, and the battery discharge power should be appropriately increased to reduce the battery charging process;

[0094] 3) When the battery is 25%<SOC≤75%, it is in the normal charge and discharge zone and can be charged and discharged moderately;

[0095] 4) When the battery is 10%<SOC≤25%, it is in the charging priority zone, which is suitable for charging but not for discharging; when the battery system assists the generator set in participating in the frequency modulation of the power system, the energy storage system should be reduced in the process of discharging and give priority to the charging process;

[0096] 4) When the battery SOC is ≤10%, it is in the prohibited discharge zone. The energy storage system is prohibited from discharging and can only be charged. Its charging power coefficient is 1, that is, rated power charging.

[0097] According to the above different SOC partition control strategies, the battery charging and discharging power is as follows: Figure 5 As shown in the figure. m Indicates the rated charge and discharge power of the battery. When △f>0, P m is negative, the battery supplies power to the grid, and the expression is as follows:

[0098]

[0099] When △f<0, P m If positive, the battery receives charging from the grid, and the expression is as follows:

[0100]

[0101] In summary, comprehensive S OC (t), S OH The coefficients of the three variables U(t) and U(t) are used to obtain the real-time droop control coefficient of the final battery output depth, and online adjustments are made according to the grid frequency deviation and battery status to maintain the optimal output. The improved inertial control and adaptive droop control are combined with the battery priority sorting strategy to form a multi-integrated control method for electric vehicles to participate in the primary frequency regulation of the grid.

[0102] See also Figure 6 As shown, Figure 6The structure diagram of the multi-integrated control system for primary frequency modulation of power grid based on V2G technology is shown in FIG. The system includes: electric vehicle cluster, V2G charging pile, electric vehicle aggregator and power grid dispatching center; V2G charging pile is used to collect the state of charge, health status and remaining charging time of electric vehicle battery in real time; the power grid dispatching center is used to collect the frequency deviation of the power grid in real time and send frequency modulation instructions to the electric vehicle aggregator; the electric vehicle aggregator is used to execute the multi-integrated control method for primary frequency modulation of power grid based on V2G technology as mentioned above to obtain the electric vehicle battery control strategy; the V2G charging pile is also used to control the electric vehicle cluster according to the electric vehicle battery control strategy of the electric vehicle aggregator; the electric vehicle cluster is used to participate in the primary frequency modulation of power grid through the batteries of multiple electric vehicles.

[0103] See also Figure 7 As shown, when the system executes the above-mentioned V2G-based multi-integrated control method for primary frequency regulation of the power grid, multiple electric vehicles form an electric vehicle cluster, which is distributedly connected to the power system and participates in the primary frequency regulation control of the power grid. The electric vehicle cluster is uniformly managed and deployed by the electric vehicle aggregator, and the electric vehicle aggregator reports and responds according to the frequency regulation needs of the power grid dispatching center, playing the role of capacity calculation and local distribution of droop coefficients. The electric vehicles in the electric vehicle cluster can adjust the charging and discharging power in real time according to the frequency fluctuations of the power grid, so that the electric vehicle cluster can be regarded as a frequency regulation power plant and participate in the primary frequency regulation service of the power grid.

[0104] See also Figure 8-12 As shown, the present application also provides specific application embodiments to illustrate the effects of the control method and system provided by the present application.

[0105] This application verifies the control strategy of the above method embodiment in the MATLAB 2017b environment, where the rated capacity of the traditional generator set is 100MVA, all expressed in per-unit value, the rated frequency of the power grid is 50Hz, the frequency modulation dead zone fd=±0.2Hz, and the SOC of the electric vehicle battery is assumed to be normally distributed in [0.1,0.9], so as to randomly determine the SOC of 50 electric vehicles before participating in the frequency modulation. The specific simulation parameters are shown in Table 2.

[0106] Table 2 Experimental simulation parameters

[0107] parameter Numeric System frequency / Hz 50 Speed ​​governor time constant / s 0.08 Steam turbine time constant / s 0.3 Generator inertia time constant (M) 10 <![CDATA[Generator load damping factor (D p )]]> 1 Time constant of power battery / s 0.1

[0108] When the power grid is operating normally, a step disturbance with an amplitude of 0.01 per unit is added at the 10th second to compare the primary frequency regulation performance of the control strategy proposed in this application, the fixed droop coefficient control strategy, and the electric vehicle cluster not participating in the frequency regulation. The frequency deviation change curve is shown in Figure 8 shown.

[0109] Depend on Figure 8 It can be seen that when the load is disturbed, due to the inertial control effect, the control method and system proposed in this application have a significant effect on suppressing the frequency deterioration degree in the early stage of the disturbance, and effectively reduce the deterioration rate of the frequency of the power grid system in the early stage. The later adaptive droop control plays a role, and within the time scale of a frequency modulation, the frequency deviation of the power grid system is maintained within a smaller range, which greatly reduces the frequency fluctuation of the power grid system, and the system adjustment time is also shorter than the other two control strategies.

[0110] Secondly, a 100-second continuous random load disturbance signal is applied to the power grid system, and the primary frequency regulation characteristics of the three control strategies are compared again.

[0111] Depend on Figure 9-10 It can be seen that under continuous load disturbance, the control strategy provided by this application significantly reduces the frequency deviation compared with the case where the fixed droop coefficient control strategy is adopted and the electric vehicle cluster does not participate in the primary frequency regulation, thereby better ensuring the safe and stable operation of the power grid.

[0112] like Fig.11 The figure shows the battery SOC change curve of a single electric vehicle. It can be seen from the figure that the control strategy and the fixed droop coefficient control strategy proposed in this application can enable each electric vehicle to autonomously participate in the primary frequency regulation of the power grid. However, by comparison, it can be seen that the control strategy proposed in this application can make electric vehicles with higher SOCs produce more when the power grid needs power compensation, and charge less when power is abundant. Conversely, it can make electric vehicles with smaller SOCs produce less when the power grid needs power compensation, and charge more when power is abundant, so as to better respond to frequency regulation needs to a greater extent according to the SOC situation of each electric vehicle.

[0113] like Fig.12 Shown is the average value change curve of the SOC of the electric vehicle battery participating in the primary frequency modulation. It can be seen from the figure that compared with the control strategy of participating in the primary frequency modulation with a fixed droop coefficient, the primary frequency modulation control strategy proposed in this application can achieve good frequency regulation function while consuming less electric vehicle battery power, and can better maintain the SOC level of the electric vehicle battery and better meet the travel needs of users.

[0114] It can be seen that the present application proposes a multi-integrated control strategy for electric vehicles to participate in the primary frequency modulation of the power grid, taking into account the state of charge, health status and travel needs of electric vehicle batteries; first, the battery health factor is introduced in the inertial control, and then the control method of the adaptive droop control coefficient is adopted to participate in the frequency modulation under the premise of participating in the frequency modulation priority sorting, so as to ensure the frequency modulation effect of the power grid while keeping the battery in a safe working area as much as possible. According to the simulation results, when a step load disturbance occurs in the power grid, the control strategy proposed in the present application can effectively suppress the frequency deterioration in the early stage of the disturbance, and stabilize the frequency deviation of the power grid system to a lower state within the time scale of the primary frequency modulation. When a continuous load disturbance occurs, the control strategy proposed in the present application participates in the frequency modulation reasonably according to the state of each electric vehicle battery, and achieves a better frequency adjustment effect with less power. At the same time, it can enable the electric vehicle cluster to maintain a better SOC state, meet the travel needs of users, and have excellent control effects.

[0115] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-element integrated control method for primary frequency modulation of a power grid based on V2G technology, characterized in that: include: Acquire real-time parameters of the electric vehicle after it is connected to the power grid, the real-time parameters including: frequency deviation of the power grid, state of charge, health status and remaining charging time of the electric vehicle battery; In response to the frequency modulation instruction, the frequency change rate of the power grid is calculated according to the frequency deviation of the power grid, and according to the frequency change rate of the power grid and the frequency deviation of the power grid, an improved virtual inertia control strategy with a health factor or an adaptive droop control strategy with a variable priority coefficient is selected as the electric vehicle battery control strategy; The improved virtual inertia control strategy introducing the health factor refers to: adjusting the output depth of each electric vehicle battery according to the health status of the electric vehicle battery; The adaptive droop control strategy with variable priority coefficients refers to: calculating the priority coefficient of each electric vehicle battery according to the state of charge, health state and remaining charging time of the electric vehicle battery, selecting electric vehicle batteries with high priority coefficients to participate in droop control, calculating the droop control coefficient of each electric vehicle battery according to the state of charge, health state and remaining charging time of the electric vehicle battery, and adjusting the output power of each electric vehicle battery according to the droop control coefficient of each electric vehicle battery.

2. The multi-element integrated control method for primary frequency modulation of power grid based on V2G technology according to claim 1 is characterized in that: The improved virtual inertia control strategy introducing the health factor is: In the formula, △P i (t) is the virtual inertial response value of the i-th group of electric vehicle batteries at time t, G t is the improved virtual inertia coefficient at time t, G t Determined by the health status of the electric vehicle battery at time t, df t / f t is the frequency change rate at time t, R s It is the frequency modulation dead zone of virtual inertia control.

3. The multi-element integrated control method for primary frequency modulation of power grid based on V2G technology according to claim 2 is characterized in that: The virtual inertia coefficient G is improved at time t t for: G t =1-(S OH (t)-1) 2 In the formula, S OH (t) is the estimated health status of the electric vehicle battery at time t, C(t) is the actual capacity of the electric vehicle battery at time t, and C e is the rated capacity of the electric vehicle battery.

4. The multi-element integrated control method for primary frequency modulation of power grid based on V2G technology according to claim 1 is characterized in that: The priority coefficients are: K(t)=αS OC (t)+βS OH (t)+γU(t) Where K(t) is the priority coefficient of the electric vehicle battery at time t, α, β, and γ are weight coefficients, and S OC (t) is the estimated state of charge of the electric vehicle battery at time t, S OH (t) is the estimated health status of the electric vehicle battery at time t, U(t) is the estimated travel demand status of the user at time t, and U(t) is determined by the remaining charging time of the electric vehicle battery at time t.

5. The multi-element integrated control method for primary frequency modulation of power grid based on V2G technology according to claim 4 is characterized in that: The estimated state of charge S of the electric vehicle battery at time t OC (t) is: When the frequency deviation of the power grid shows that the electric vehicle battery is in a charging state, the charge state estimation S of the electric vehicle battery in the forbidden charging zone, the discharge priority zone, the normal charging and discharging zone, the charging priority zone and the forbidden discharge zone OC (t) increases successively; When the frequency deviation of the power grid shows that the electric vehicle battery is in a discharging state, the charge state estimation S of the electric vehicle battery in the forbidden charging zone, the discharge priority zone, the normal charging and discharging zone, the charging priority zone and the forbidden discharge zone OC (t) decreases successively.

6. The multi-element integrated control method for primary frequency modulation of power grid based on V2G technology according to claim 4 is characterized in that: The health status of the electric vehicle battery at time t is estimated S OH (t) is: Where C(t) is the actual capacity of the electric vehicle battery at time t, C e is the rated capacity of the electric vehicle battery.

7. The multi-element integrated control method for primary frequency modulation of power grid based on V2G technology according to claim 4 is characterized in that: The estimated user travel demand state U(t) at time t is: Where, T r represents the remaining charging time of the electric vehicle battery at time t, S OC (max) represents the estimated state of charge when the electric vehicle battery is fully charged, S OC (t) is the estimated state of charge of the electric vehicle battery at time t.

8. The multi-element integrated control method for primary frequency modulation of power grid based on V2G technology according to claim 1 is characterized in that: The adaptive droop control strategy with variable priority coefficient is: ΔP EV =R t Δf In the formula, △P EV is the output power of the electric vehicle battery involved in droop control, R t is the droop control coefficient of the electric vehicle battery participating in droop control, and △f is the frequency deviation.

9. The multi-element integrated control method for primary frequency modulation of power grid based on V2G technology according to claim 8 is characterized in that: The droop control coefficient R of the electric vehicle battery involved in the droop control t for: R t =S OC (t)S OH (t)U(t) In the formula, S OC (t) is the estimated state of charge of the electric vehicle battery at time t, S OH (t) is the estimated health status of the electric vehicle battery at time t, U(t) is the estimated travel demand status of the user at time t, and U(t) is determined by the remaining charging time of the electric vehicle battery at time t.

10. A multi-integrated control system for primary frequency regulation of power grid based on V2G technology, characterized in that: include: Electric vehicle clusters, V2G charging stations, electric vehicle aggregators and grid dispatch centers; The V2G charging pile is used to collect the state of charge, health status and remaining charging time of the electric vehicle battery in real time; The power grid dispatching center is used to collect the frequency deviation of the power grid in real time and send frequency modulation instructions to the electric vehicle aggregator; The electric vehicle aggregator is used to execute the multi-integrated control method for primary frequency modulation of the power grid based on V2G technology as described in any one of claims 1 to 9 to obtain an electric vehicle battery control strategy; The V2G charging pile is also used to control the electric vehicle cluster according to the electric vehicle battery control strategy of the electric vehicle aggregator; The electric vehicle cluster is used to participate in the primary frequency regulation of the power grid through the batteries of multiple electric vehicles.

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