CN117955137A - Electric vehicle auxiliary power grid frequency modulation control method considering battery SOH - Google Patents
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Abstract
The invention relates to an electric automobile auxiliary power grid frequency modulation control method considering battery SOH. The method of the invention discusses an auxiliary grid frequency adjustment control strategy of the electric vehicle (ELECTRIC VEHICLES, EVS), and considers the Health condition (SOH) of the battery. Electric vehicles have the potential to contribute to grid frequency regulation, but their participation should be carefully managed to ensure the life of their batteries. The method introduces the concept of the virtual synchronous generator (virtual synchronous generator, VSG), integrates the virtual synchronous generator into the electric automobile, provides inertial and damping support for the power grid, and further realizes the friendly behavior of the power grid. The control strategy involves three key aspects: primary frequency modulation, electric vehicle load scheduling, and battery capacity fade modeling. Simulation results show that the strategy is very effective in balancing the power grid frequency and maintaining the health state of the battery of the electric automobile. In addition, different scheduling strategies are compared, and the strategy is found to be capable of better maintaining the state of charge of the battery.
Description
Technical Field
The invention relates to an electric automobile auxiliary power grid frequency modulation control method considering battery SOH.
Background
Electric vehicles (ELECTRICVEHICLES, EVS) have become one of the key research directions in the energy system field [1] as a solution to the challenges of climate change and energy sustainability. The wide popularization of the electric automobile not only can reduce the emission of greenhouse gases, but also can provide powerful support for an electric power system, in particular to the aspect of auxiliary power grid frequency modulation. The present invention focuses on the potential contribution of an electric vehicle as an auxiliary power source for an electric power system while emphasizing the importance of considering battery health [2]. The battery of an electric vehicle is a core component, and the health condition of the battery is critical to the performance and service life of the electric vehicle [3]. Frequent rapid charging and discharging reduces battery life, and thus the manner in which the battery is utilized must be carefully selected when incorporating an electric vehicle into grid frequency modulation control.
Numerous scholars at home and abroad develop and intensely discuss the EV battery to participate in the frequency modulation mode of the power grid, li Jianlin et al conduct intensive research on the problem of stage power distribution, and simultaneously provide a control strategy model comprehensively considering multiple constraint conditions, so that a powerful theoretical basis is provided for the energy storage power station to participate in the frequency modulation output. Finally, a constructive proposal is provided for the future development of the energy storage system, and a prospective discussion [4] is carried out on the future research direction. Liao Shijiang et al have studied their mechanism of action in depth in order to optimize the performance of energy storage batteries in primary frequency modulation of the power grid. And constructing a high-permeability new energy regional power grid model, considering the energy storage battery, and finding that the energy storage battery obviously improves the frequency stability of the power grid through amplitude-frequency characteristic analysis. The research result shows that the strategy enhances the anti-interference performance of the power grid, has obvious effect especially under step load disturbance, and has the best maintenance effect on the Health condition (SOH) under long-time load disturbance. The research is hopeful to promote the energy storage battery to participate in the power grid frequency modulation more effectively and accelerate the realization of the double-carbon target [5]. A comprehensive control strategy is proposed by meeting groups and the like, and based on frequency modulation signal optimization, the battery energy storage system can assist the thermal power generating unit to participate in secondary frequency modulation. The distribution coefficients are optimized by a multi-objective evolutionary algorithm to reduce frequency offset and frequency modulation costs. The effectiveness of the strategy is verified through step and continuous disturbance simulation, and the result shows that the strategy reduces the system frequency deviation and the energy storage cost [6]. Liu Jian proposes a new capacity allocation method based on the multiplying power characteristic of the battery. The results indicate that the control method and battery type used for grid frequency modulation affects the optimal configuration [7]. Chang Kang et al provide an innovative method for optimizing secondary frequency modulation parameters of an energy storage battery to participate in the power grid in order to solve the problems of large frequency deviation and energy storage processing delay caused by the traditional method for optimizing the secondary frequency modulation parameters of the power grid. By analysis of battery performance characteristics. Experimental results show that the method effectively reduces frequency deviation, reduces energy storage processing delay and improves performance [8]. She Jianmin et al, through specific Shenzhen region battery energy storage frequency modulation project analysis, including construction conditions, technical schemes, project benefits and financial feasibility, show that battery energy storage participates in southern region electric power system frequency modulation service, has wide market demands and investment returns, and is expected to become an important development direction [9] in the future. Zhao Xilin et al propose a comprehensive control strategy that considers state of charge constraints to ensure the effectiveness of the energy storage battery in participating in primary frequency modulation of the power grid. The study verifies the feasibility and effectiveness [10] of the proposed method by comparison of various simulation methods.
According to the research situation of the students at home and abroad, the control strategy models are numerous, but the electric automobile auxiliary power grid frequency modulation control strategy considering the service life loss of the battery is few. Based on this, the present invention first introduces the concept of automatic power generation control (Automatic Generation Control, AGC) as a means of maintaining power balance and frequency stability. The study provides a basic model of the frequency modulation control of the electric automobile, and the model is helpful for analyzing the influence of the electric automobile on the system frequency adjustment. The core of the proposed strategy is multistage frequency modulation control, including scheduling instructions, load clustering and droop control, while taking into account the life of the electric vehicle battery. The strategy is used for carrying out intelligent clustering on the electric vehicle according to the frequency modulation capability of the electric vehicle, optimizing the charge state of the battery and reducing the frequent charge and discharge cycle times.
Disclosure of Invention
The invention aims to provide an electric vehicle auxiliary power grid frequency modulation control method considering battery SOH, which is very effective in balancing power grid frequency and maintaining the health state of batteries of electric vehicles.
In order to achieve the above purpose, the technical scheme of the invention is as follows: an electric automobile auxiliary power grid frequency modulation control method considering battery SOH comprises the following steps:
(1) The electric automobile participates in the establishment of a power grid frequency modulation model and a battery life model;
(2) The multi-stage frequency modulation control strategy of the battery life of the electric automobile is considered.
In an embodiment of the present invention, the step (1) is specifically implemented as follows:
The electric automobile is connected with the power network through the bidirectional converter, a virtual synchronous generator VSG technology is integrated into converter control, and the electric automobile can present inertia and damping characteristics similar to those of a synchronous generator when V2G participates in frequency modulation, so that the electric automobile has the characteristic of power network friendliness; the virtual synchronous generator VSG motion equation is shown in formula (1):
Wherein T e represents the electromagnetic torque of the motor, J represents the rotational inertia of the VSG, T m represents the mechanical torque of the motor, D p represents the damping coefficient of the rotor, ω n represents the reference angular frequency of the rotor, ω represents the actual angular frequency of the rotor; the values of D p and J are different in different systems, and when the parameters of the generator in the system are determined, the specific values of D p and J can be obtained by calculation according to the formula (1);
(1.1) establishment of electric automobile frequency modulation control basic model
The method comprises the steps of exploring the adjustment influence of an electric automobile on system frequency by adopting a single-region frequency adjustment model, equating each model comprising a generator-load model, a prime motor model, a speed regulator model, a load frequency model and a secondary frequency modulation model in the single-region frequency adjustment model into a low-order linearization model, and describing by adopting a transfer function; based on the VSG model, the frequency modulation characteristic of the EV needs to be comprehensively considered when the equivalent model is constructed, a second-order inertial response function of the EV is selected, the function is expressed in a form of a second-order inertial transfer function, and the specific transfer function is shown in a formula (2):
Wherein T EV represents an inherent time constant, J vr represents an equivalent virtual inertia of the VSG, and D vr represents an equivalent virtual damping of the VSG; the primary frequency modulation model and the secondary frequency modulation model are covered in the EV frequency modulation module, and the primary frequency modulation module and the secondary frequency modulation model are combined to form total frequency modulation power of the electric automobile, and in view of the fact that the comprehensive power is possibly higher than the maximum allowable charge and discharge power of the electric automobile, a power limiting module is introduced to avoid potential damage to a battery caused by excessive charge and discharge power, and finally, the electric automobile is processed through a power response characteristic function of the VSG to obtain the frequency modulation power of the electric automobile;
(1.2) establishment of EV Battery Capacity degradation model
In the construction process of the EV battery capacity degradation model, the temperature and the charge-discharge multiplying power are set to be fixed values, and only the influences of the cyclic charge-discharge depth delta SOC, the charge-discharge times and the average cyclic SOC of the battery on the battery capacity degradation are considered; under constant temperature conditions, the battery capacity fade rate Q site is as shown in formula (3):
wherein Q d represents the current capacity of the battery, and Q b represents the standard capacity of the battery; assuming that the EV batteries have equal discharge amounts in any interval of the same size, the expression of the equivalent cycle number is as shown in formula (4):
Wherein E e represents the total charge and discharge amount of the electric vehicle in the frequency modulation time, and E a represents the total charge and discharge amount of the electric vehicle in the complete cycle.
In an embodiment of the invention, the bidirectional converter is a bidirectional DC/DC converter of an electric vehicle charging and discharging machine, and adopts a bidirectional half-bridge topology structure.
In an embodiment of the present invention, the step (2) is specifically implemented as follows:
(2.1) scheduling instructions and load aggregation policy in one-time frequency adjustment
When a dispatching center sends a dispatching instruction to an electric automobile load aggregator, the dispatching instruction needs to cover the self-contained frequency modulation capacity; in this scenario, there are N electric vehicles available for frequency modulation scheduling, forming one electric vehicle set; in order to reduce battery loss caused by frequent charge and discharge state transition, a load combination participating in primary frequency modulation of a power grid may comprise a single member or a plurality of members in the same scheduling period; the frequency modulation group with higher frequency adjustment capacity is preferentially selected to participate in full-capacity frequency modulation so as to reduce the charge-discharge conversion frequency of the battery; based on the above, a primary frequency modulation control strategy based on electric automobile load clustering is provided, and when the system frequency fluctuates, the frequency modulation reserve capacity distribution is shown as a formula (5):
Wherein S 1,i represents the frequency adjustment capability of the ith electric vehicle in the electric vehicles capable of feeding power to the system, S 1 represents the standby frequency adjustment capability required by the current system, SOC 1,i represents the current state of charge of the load of the ith electric vehicle in the electric vehicles capable of being subjected to frequency modulation scheduling, SOC max represents the maximum state of charge of the electric vehicles capable of being subjected to frequency modulation scheduling, eta 1,i represents the frequency adjustment coefficient of the ith electric vehicle, L EV represents the EV load set, namely the electric vehicle set capable of being subjected to frequency modulation scheduling, The electric automobile set which represents that the EV can feed load to the system, namely can feed power to the system, is totally N 1; in the process of electric energy distribution, the electric automobile may have excessive frequency modulation capacity, so that power is supplied to a power grid to exceed the minimum capacity of the storage battery, or the electric automobile may be overcharged during charging, so that the charging state of the storage battery exceeds the upper limit; therefore, to cope with the overcharge or overdischarge problem, the power supply or charging of the electric vehicle is limited, specifically as shown in formula (6):
Wherein P 1,ikmax represents The upper power limit of the medium electric automobile feeding the power grid, P 1,icmax represents/>Upper limit of charging power of middle electric automobile, S 1,min represents/>In (3) the minimum frequency adjustment capability of the electric automobile, S 1,max represents/>In the electric vehicle maximum frequency adjustment capability, t represents time, and since power loss is caused in the process including charging and discharging, assuming that the power loss is borne by the vehicle battery, the calculation of the electric quantity state is as shown in formula (7):
Wherein SOC 1,i0 represents the initial state of charge of EV and P 1,i represents In the (i) th electric automobile, eta d represents the discharge efficiency, and t 1、t2 represents the discharge time period;
(2.2) droop control strategy in secondary frequency Regulation
The electric automobile must set basic charging power P c in the primary frequency modulation process to meet the charging requirement; in order to avoid frequent charge and discharge of the electric vehicle, the purpose of prolonging the service life of the battery of the electric vehicle is achieved; setting a primary frequency modulation dead zone to [ f n-0.03,fn +0.03], wherein f n represents a rated grid frequency, and in the dead zone, only P c is used for charging; when the frequency exceeds the dead zone, the power change is the product of the frequency droop coefficient and the frequency deviation; the primary frequency modulation power P 1 is shown in formula (8) and is limited in the range of [ P cmax,Pdmax ]:
Wherein K c represents a droop coefficient of the charging frequency, and K d represents a droop coefficient of the discharging frequency; to ensure that the user's charging schedule can be completed on time, P c and the droop coefficient K c、Kd should be dynamically adjusted with the charging process of the electric vehicle, f g represents the current grid frequency; therefore, a main frequency adjustment strategy is proposed that considers the user's charging plan; when S x is less than 0, P c is designed to be (1-alpha) P n, and can be reduced or increased along with the change of alpha until the rated power is P n, so that the electric automobile is ensured to have basic charging power to complete a charging plan, and parameters related to EV charging are taken between S x and alpha with values of [ -1,1 ]. If S x > 0, the electric vehicle has no charge demand, P c =0; therefore, the basic charging power P c of the electric vehicle is as shown in formula (9):
The droop coefficient is designed to be adjusted after the alpha value, and the smaller the alpha value is, the smaller the droop coefficient of the charging frequency is, and the larger the droop coefficient of the discharging frequency is; meanwhile, in order to avoid the situation that the electric automobile is overcharged and overdischarged in the frequency modulation process, the sagging coefficient of charging and discharging is limited by the real-time SOC, so that a fuzzy control method is adopted, the maximum sagging coefficient is set to be K max, and the sagging coefficient K c＝Kmax-Kd of the charging frequency and the sagging coefficient K d of the discharging frequency are obtained through a fuzzy controller.
Compared with the prior art, the invention has the following beneficial effects: the method provides a novel control strategy for the electric automobile cluster participating in the frequency modulation of the power grid, and simultaneously considers the health state of the battery. Grid frequency modulation is critical to maintaining power system stability, and electric vehicles can play an important role therein by providing auxiliary services. The invention first introduces the concept of automatic power generation control as a means of maintaining power balance and frequency stability. The invention adopts a basic model of electric automobile frequency modulation control, which is characterized in that the single-zone frequency adjustment model comprises a generator-load, a prime motor, a speed regulator, a load frequency and a frequency modulation model. The core of the strategy provided by the invention is multistage frequency modulation control, which comprises scheduling instructions, load aggregation and sagging control, and simultaneously considers the health state of the battery of the electric automobile. The strategy is used for carrying out intelligent clustering on the electric vehicle according to the frequency modulation capability of the electric vehicle, optimizing the charge state of the battery and reducing frequent charge and discharge cycles. The simulation result verifies the effectiveness of the frequency modulation strategy, and shows how the method provided by the invention can support the frequency modulation requirement of the power grid and simultaneously maintain the SOH of the battery. Different electric automobile load scheduling strategies are compared, and importance of strategy selection on balancing endurance mileage, battery life and system regulation performance is emphasized. In a word, the invention provides a comprehensive strategy for integrating the electric automobile into the power grid frequency modulation and keeping the battery healthy, optimizes the mode of the electric automobile for participating in the frequency adjustment of the electric power system, and paves the way for more intelligent and more sustainable energy management.
Drawings
Fig. 1 is an AGC control system architecture.
Fig. 2 is a schematic diagram of a bi-directional DC-DC converter control strategy.
Fig. 3 is a frequency modulation model of the single-area AGC unit of the electric automobile.
Fig. 4 is a model of an electric vehicle engaged in frequency modulation.
Fig. 5 is a sagging characteristic of the electromotive device involved in one-time frequency adjustment.
Fig. 6 is a diagram of the verification effect of virtual inertia and damping.
Fig. 7 is a comparison of electric vehicle states of charge.
Fig. 8 is a simulation result of EV participation in secondary frequency regulation.
Detailed Description
The technical scheme of the invention is specifically described below with reference to the accompanying drawings.
The invention provides an electric vehicle auxiliary power grid frequency modulation control method considering battery SOH, which comprises the following steps:
(1) The electric automobile participates in the establishment of a power grid frequency modulation model and a battery life model;
(2) The multi-stage frequency modulation control strategy of the battery life of the electric automobile is considered.
The following is a specific implementation procedure of the present invention.
1. Electric automobile participation power grid frequency modulation model and establishment of battery life model
1.1 Basic Structure of self-generating control and control mode introduction
Self-generating control (Automatic Generation Control, AGC) is seen as an important means [11] to maintain active power balance and system frequency stability of the power system. When the system is subjected to a large load disturbance, the system frequency is difficult to stabilize within a specified range by primary frequency modulation alone. In other words, one frequency adjustment is intended to bring the zone control bias (Area Control Error, ACE) to zero or remain in the normal range to bring the frequency back within the nominal or allowed range [12]. Typically, the secondary frequency modulation is implemented through an AGC system, and the dispatch center directly handles the increase or decrease in output of the generator set. The system control structure is shown in fig. 1.
The AGC dispatch center gathers grid operating parameters including system frequency deviation Δf and tie line plan deviation Δp tie. These parameters are used to calculate the regional control error value ACE and after filtering, a smooth ACE signal is obtained. After conversion by the PI controller, a zone control requirement, i.e., zone regulation requirement (Area regulatory requirements, ARR), is constructed. The ARR characterizes the power imbalance between the power generation and the load in the regional power system, and thus determines the amount of power that each AGC unit should adjust. The AGC system of the dispatch center is responsible for sending instructions to each fm power supply. The resources of each part are adjusted according to the received AGC command to realize the response to the AGC control requirement.
1.2 Charging and discharging topology and control mode of electric automobile
The electric automobile is connected with a power network through a bidirectional converter, and the VSG technology is integrated into converter control, so that inertia and damping characteristics similar to those of a synchronous generator can be displayed when V2G participates in frequency modulation, and further, the characteristic [13] of power grid friendliness is displayed. The electric automobile is connected with the electric power network through the bidirectional converter, and the virtual synchronous generator technology is integrated into a control strategy in converter control, so that the electric automobile is a controller integrating voltage and frequency regulation functions, and has the capabilities of real-time power control, frequency regulation and voltage stabilization. Currently, modeling problems with VSG have been studied in numerous documents, and modeling methods for electromechanical parts have tended to be substantially consistent. The synchronous machine model of the electromechanical and electromagnetic transient characteristics of the VSG further strengthens the connection between the virtual stator and the rotor, so that the virtual stator and the rotor are closer to the characteristics of the synchronous generator. Research in modeling has been relatively mature, and research in this area has provided a key determination of two important variables, namely virtual inertia and virtual damping. The VSG equation of motion is shown in formula (1).
Where T e represents the electromagnetic torque of the motor, J represents the rotational inertia of the VSG, T m represents the mechanical torque of the motor, D p represents the damping coefficient of the rotor, ω n represents the reference angular frequency of the rotor, ω represents the actual angular frequency of the rotor. The values of D p and J are different in different systems, and when the parameters of the generator in the system are determined, the specific values of D p and J can be calculated according to the formula. The bidirectional DC/DC converter of the charging and discharging motor of the electric automobile adopts a bidirectional half-bridge topological structure, and the layout has the characteristics of lower voltage stress and high efficiency [14]. The voltage and current double-loop control strategy is adopted, and the voltage stability of the direct current bus is kept. A schematic of the detailed control architecture is shown in fig. 2.
In the view of figure 3 of the drawings,A reference value representing the dc bus voltage, U dc represents the voltage across capacitor C 1, i.e. the dc bus voltage, I dc represents the current value through inductor L 1, the direction of which is considered to be forward from the battery to the grid.
1.3 Electric automobile frequency modulation control basic model
The invention utilizes the traditional single-area frequency adjustment model shown in fig. 3 to explore the adjustment effect of the electric automobile on the system frequency. The single region frequency tuning model includes a generator-load model, a prime mover model, a governor model, a load frequency model, and a secondary frequency tuning model [15]. As a complex nonlinear system, the power system only presents small load fluctuation in actual operation, so a linearization model is used in the present invention to characterize the dynamic characteristics of the near-operating point. On this premise, each element model in the system is equivalent to a low-order linearization model through reasonable assumption, and is described by adopting a transfer function. In the system, the thermal power generating unit is regarded as a main frequency modulation unit, and the prime motor is a non-reheat steam turbine.
In fig. 3, T g represents a governor time constant, T ch represents a prime mover time constant, Δp m and Δp L represent variations in mechanical power and load power of the prime mover, respectively, M represents an inertial time constant of a system in a region under consideration, D represents a load damping constant, R represents a slip ratio of a unit, K 1 represents a gain of self-power control, Δω represents a deviation of a system frequency, and Δp EV represents a power variation output when an electric vehicle participates in frequency modulation. Because the invention adopts the VSG model, the frequency modulation characteristic [16] of the Electric Vehicle (EV) needs to be comprehensively considered when the equivalent model is constructed. Under the background, the invention selects the second-order inertial response function of the electric automobile, the function can be expressed in the form of the second-order inertial transfer function, and the specific transfer function is shown in the formula (2).
Wherein T EV represents an inherent time constant, J vr represents an equivalent virtual inertia of the VSG, D vr represents an equivalent virtual damping of the VSG, and a model of the electric automobile integrated with the frequency modulation process is shown in FIG. 4. The EV frequency modulation module internally covers a primary frequency modulation branch and a secondary frequency modulation branch, and the primary frequency modulation branch and the secondary frequency modulation branch are combined to form the total frequency modulation power of the electric automobile. In view of the fact that the integrated power may be higher than the maximum allowable charge-discharge power of the electric vehicle, a power limiting module is particularly introduced to avoid potential damage to the battery caused by excessive charge-discharge power. And finally, processing through a power response characteristic function of the VSG to obtain the frequency modulation power of the electric automobile.
1.4 Building capacity fade model of EV battery
In the model construction process, the temperature and the charge-discharge multiplying power are set to fixed values, and only the influence [17] of the cyclic charge-discharge depth delta SOC, the charge-discharge times and the average cyclic SOC of the battery on the capacity degradation of the battery is considered. The rate of battery capacity degradation Q site under constant temperature conditions is shown in formula (3).
Where Q d represents the current capacity of the battery and Q b represents the standard capacity of the battery. Assuming that the power cells have equal discharge amounts in any interval of the same size, for example, the discharge amount in the interval of 10% to 20% SOC is equal to the discharge amount in the interval of 80% to 90%, based on which the equivalent cycle number can be expressed as shown in formula (4).
Wherein E e represents the total charge and discharge amount of the electric vehicle in the frequency modulation time, and E a represents the total charge and discharge amount of the electric vehicle in the complete cycle.
2. Multistage frequency modulation control strategy considering battery life of electric automobile
2.1 Scheduling instructions and load aggregation in Primary frequency Regulation
When the dispatching center sends dispatching instructions to the electric automobile load aggregator, the instructions need to cover the self-contained frequency modulation capacity. In this scenario, there are N electric cars available for fm scheduling, forming one electric car set. To reduce battery consumption due to frequent charge-discharge state transitions, the load combination participating in grid primary frequency modulation may include a single or multiple members during the same schedule period. The fm group with higher frequency tuning capacity is preferentially selected to participate in full capacity fm to reduce the battery charge-discharge switching frequency [18]. Based on the above, the invention provides a primary frequency modulation control strategy based on electric automobile load clustering. When the system frequency fluctuates, the frequency modulation reserve capacity allocation is shown as formula (5).
Wherein S 1,i represents the frequency adjustment capability of the ith electric vehicle in the electric vehicles capable of feeding power to the system, S 1 represents the standby frequency adjustment capability required by the current system, SOC 1,i represents the current state of charge of the load of the ith electric vehicle in the electric vehicles capable of being subjected to frequency modulation scheduling, SOC max represents the maximum state of charge of the electric vehicles capable of being subjected to frequency modulation scheduling, eta 1,i represents the frequency adjustment coefficient of the ith electric vehicle, L EV represents the EV load set, namely the electric vehicle set capable of being subjected to frequency modulation scheduling,The electric automobile set which represents that the EV can feed load to the system, namely can feed power to the system, is totally N 1; . During the distribution of electrical energy, the electric vehicle may have an excessive frequency modulation capacity, resulting in power being supplied to the grid beyond the minimum capacity of the battery, or may be overcharged during charging, resulting in a state of charge of the battery exceeding the upper limit [19]. Therefore, to cope with the overcharge or overdischarge problem, we will limit the power supply or charging of the electric vehicle, specifically as shown in formula (6).
Wherein P 1,ikmax representsThe upper power limit of the medium electric automobile feeding the power grid, P 1,icmax represents/>Upper limit of charging power of middle electric automobile, S 1,min represents/>In (3) the minimum frequency adjustment capability of the electric automobile, S 1,max represents/>The maximum frequency adjustment capability of the electric automobile in (a), and t represents time. Since power loss is caused during charging, discharging, and the like, the calculation of the state of charge is shown in equation (7) assuming that the power loss is borne by the vehicle-mounted battery.
Wherein SOC 1,i0 represents the initial state of charge of EV and P 1,i representsIn the (i) th electric automobile, power fed to a power grid by the electric automobile, eta d represents discharge efficiency, and t 1、t2 represents a discharge time period.
2.2 Droop control strategy in quadratic frequency Regulation
The electric automobile must set basic charging power P c to meet the charging requirement in the primary frequency modulation process. In order to avoid frequent charge and discharge of the electric vehicle, the aim [20] of prolonging the service life of the battery of the electric vehicle is fulfilled. The invention sets the dead zone of primary frequency modulation to [ f n-0.03,fn +0.03], where f n represents the rated grid frequency, i.e., f n =50 Hz. Within this dead zone, only P c is used for charging. When the frequency exceeds the dead zone, the power change is the product of the frequency droop coefficient and the frequency deviation. The sagging characteristics of the EV involved in primary frequency modulation are shown in fig. 5.
The primary frequency modulation power P 1 in fig. 5 is shown in the formula (8) and is limited to the range of [ P cmax,Pdmax ].
Where K c represents the droop coefficient of the charging frequency and K d represents the droop coefficient of the discharging frequency. To ensure that the user's charging schedule can be completed on time, P c and droop coefficient K c、Kd should be dynamically adjusted as the electric vehicle is charged. Therefore, the present invention proposes a main frequency adjustment strategy that considers the user's charging schedule. When S x is less than 0, P c is designed to be (1-alpha) P n, and can be reduced or increased along with the change of alpha until the rated power is P n, so that the electric automobile has basic charging power to complete a charging plan. If S x >0, the electric vehicle has no charge demand, P c =0. Therefore, the basic charging power P c of the electric vehicle is shown as formula (9).
The design of the sag factor is adjusted after the alpha value. The smaller the value of α, the smaller the charge sagging coefficient, and the larger the discharge sagging coefficient. Meanwhile, in order to avoid the situation that the electric automobile is overcharged and overdischarged in the frequency modulation process, the sagging coefficient of charging and discharging is also limited by the real-time SOC. There is a complex nonlinear relationship between the two, so a fuzzy control method is adopted. From the foregoing analysis, we set the maximum sag factor to K max and obtain the charge sag factors K c＝Kmax-Kd and K d by the fuzzy controller. The fuzzy control rule adopted by the primary frequency adjustment is as follows: if the inputs x 1 and x 2 of the fuzzy controller are located in the fuzzy sets W 1,i and W 2,j, the output of the ith fuzzy control rule is as shown in equation (10).
Kd,i＝aix1+bix2+c i＝1,2,…,n (10)
Where K d,i represents the discharge droop coefficient determined by the ith fuzzy rule, a i and b i represent input coefficients, c represents a constant coefficient, and n represents the number of fuzzy control rules.
3. Simulation analysis
The AGC technology can provide inertial and damping support for a power grid when the electric automobile participates in frequency modulation, and the effect of the AGC technology is verified through simulation. First, a model for simulation verification is established in a Matlab/Simulink environment. The power battery of the electric automobile adopts a lithium cobaltate battery, the rated voltage is 300V, the rated capacity is 100Ah, and the initial SOC is 65%. In terms of energy storage, we use a 800V dc voltage source instead and apply a droop control mode. The voltage of the alternating current bus is 380V, and the rated power of the load is 3kW. Simulation parameters of the VSG are shown in table 1.
TABLE 1 AGC simulation parameters
For long-term model simulation studies, the invention employs an average equivalent model of the inverter, which allows for larger simulation steps to be used. During simulation, when the power electronic converter is grid-connected, a large number of harmonics are generated, which can have an influence on the observation of the power response. Furthermore, the simulation speed can also become very slow due to the complexity of the control algorithm. In order to more accurately observe the power variation and shorten the simulation time, the present invention employs a simplified alternative method that eliminates power electronic inverter modules that are susceptible to harmonic effects and is replaced with a controlled three-phase voltage source. After the simplification, the invention sets the following simulation conditions: the electric vehicle is discharged at an initial time with a power of 3kW, and the discharge power is increased to 8kW at t=4 s. We observe the AGC response to power under different moment of inertia J and damping coefficient K conditions, respectively. As shown in particular in fig. 6.
As can be seen from the data analysis of fig. 6 (a), when the moment of inertia J is 0.1, the AGC exhibits the fastest speed in response to the power change, about 0.05 seconds, and the response process is free from overshoot; when J increases to 0.25, the power response speed of the AGC is slowed down, the response time is about 0.1 seconds, and the overshoot begins to appear; when J reaches 0.5, the AGC power response speed is the slowest, with a response time of about 0.3 seconds, but at the same time the overshoot is the greatest. Simulation results clearly show that the increase of the moment of inertia J can slow down the speed of power response, so that the potential problem of impact on a power network caused by too fast response of the electric automobile is effectively avoided. Furthermore, an increase in J results in an increase in dynamic response time, however accompanied by an increase in overshoot. From the data analysis of fig. 6 (b), it can be observed that the power response of the AGC exhibits different characteristics for different settings of the virtual damping parameter K d as the reference power of the system changes. At K d =6, the maximum oscillation amplitude occurs in the power response, while the dynamic response time is also the longest, mainly due to insufficient damping of the system. When the value of K d increases to 12, the damping of the system increases, the oscillation amplitude decreases and the response time shortens. When the damping of the system is further improved when the damping is further increased to K d =18, the AGC can be smoothly transited from a stable value before the change to a new stable value, no overshoot phenomenon exists, and the response time is also reduced. This stiffening of damping effectively dampens the oscillations of the power. Analysis based on simulation results shows that the increase of the virtual damping parameter K d can effectively inhibit the oscillation phenomenon of the power response, and the larger the value of K d is, the faster the attenuation rate of the oscillation amplitude in the dynamic response is. In order to further prove the effectiveness of the frequency modulation strategy, the invention simulates the following frequency modulation strategy. As shown in table 2.
Table 2 electric vehicle load scheduling policy comparison
The battery status of the car under the three strategies is shown in fig. 7.
As shown in fig. 7, the initial SOC values of all strategies at the initial time (t= 25190 seconds) are close to 0.688, 0.689, and 0.691, respectively. Over time, the SOC values under all strategies show a decreasing trend. This indicates that the electric vehicle is gradually discharging. The SOC of the scheduling strategy 1 (the electric automobile load does not participate in system frequency modulation) drops faster, and the SOC value drops fastest in the same time period. Scheduling strategy 2 and strategy 3 have relatively slow SOC falling rates, indicating that they more effectively maintain the battery state of charge. The SOC value of strategy 3 (using electric vehicle load clustering to participate in system frequency modulation) is relatively high throughout the time frame, indicating that this strategy better maintains the state of charge of the battery. The SOC of scheduling strategy 2 (electric vehicle load participating in system frequency modulation, but not aggregation) is slightly lower than strategy 3, but still better than strategy 1. The SOC value of the scheduling strategy 1 (the electric vehicle load does not participate in the system frequency modulation) drops fastest, and the battery state of charge loss is larger. In summary, according to the specific application scenario and the battery management target, different electric vehicle load scheduling strategies can be selected to balance the endurance mileage, the battery life and the system adjustment performance. According to the invention, three groups of six electric vehicles are designed, the initial SOC (recorded as S SOC_0) is 30%,50% and 95%, and each group of two electric vehicles with expected charging time is respectively set to be 4h and 10h, so that the control effects of the primary frequency modulation strategy on charging/discharging/electric power under different power grid frequencies, SOCs and expected charging time are compared. The primary frequency modulation simulation working condition setting is shown in table 3, the specific parameters of six electric vehicles are shown in table 4, and the simulation result is shown in fig. 8.
TABLE 3 working conditions for Main frequency control
Table 4 EV simulation parameters
As shown in fig. 8, operating condition 1: under this condition, the EV is not connected to the grid, the grid frequency is standard 50hz, and the EV has no charging or discharging power. This means that the electric vehicle does not participate in the grid frequency modulation, but remains stationary. Working condition 2: in this case, the EV is already connected to the grid, which is kept at 50Hz, so the electric vehicle does not need to provide frequency modulated power. Working condition 3: when grid frequency is higher than standard frequency, EVs need to charge or reduce discharge power to help stabilize grid frequency. EVs with smaller S X and a may provide more regulated power, so in this case, a smaller EV may reduce the charge power to support grid regulated demand. Working condition 4: when the grid frequency is below the standard frequency, the EV needs to provide frequency modulated power, or reduce charging power, to help increase the grid frequency. Smaller EVs may need to reduce charging power to support the grid's frequency modulation requirements. Working condition 5: under this condition, the grid frequency does not exceed the dead zone, and therefore no additional frequency modulated power is required. This is similar to the case of condition 2, with the EV continuing to charge to complete the charge schedule. In summary, the electric vehicle power grid frequency modulation strategy intelligently adjusts the charging and discharging power according to the power grid frequency and the charging state of the electric vehicle. The service life of the battery of the electric automobile is prolonged, the frequency modulation requirement of the power grid is supported when the electric automobile is needed, and intelligent interaction between the electric automobile and the power grid is realized.
4. Conclusion(s)
The invention provides a novel control strategy for the electric automobile cluster participating in power grid frequency modulation, and simultaneously considers the health state of the battery. Grid frequency modulation is critical to maintaining power system stability, and electric vehicles can play an important role therein by providing auxiliary services. The invention first introduces the concept of automatic power generation control as a means of maintaining power balance and frequency stability. The invention adopts a basic model of electric automobile frequency modulation control, which is characterized in that the single-zone frequency adjustment model comprises a generator-load, a prime motor, a speed regulator, a load frequency and a frequency modulation model. The core of the strategy provided by the invention is multistage frequency modulation control, which comprises scheduling instructions, load aggregation and sagging control, and simultaneously considers the health state of the battery of the electric automobile. The strategy is used for carrying out intelligent clustering on the electric vehicle according to the frequency modulation capability of the electric vehicle, optimizing the charge state of the battery and reducing frequent charge and discharge cycles. Simulation results verify the effectiveness of the frequency modulation strategy, demonstrating how the proposed method maintains SOH of the battery while supporting grid frequency modulation requirements. Different electric automobile load scheduling strategies are compared, and importance of strategy selection on balancing endurance mileage, battery life and system regulation performance is emphasized. In a word, the invention provides a comprehensive strategy for integrating the electric automobile into the power grid frequency modulation and keeping the battery healthy, optimizes the mode of the electric automobile for participating in the frequency adjustment of the electric power system, and paves the way for more intelligent and more sustainable energy management.
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The above is a preferred embodiment of the present invention, and all changes made according to the technical solution of the present invention belong to the protection scope of the present invention when the generated functional effects do not exceed the scope of the technical solution of the present invention.
Claims (4)
1. The electric automobile auxiliary power grid frequency modulation control method taking battery SOH into consideration is characterized by comprising the following steps of:
(1) The electric automobile participates in the establishment of a power grid frequency modulation model and a battery life model;
(2) The multi-stage frequency modulation control strategy of the battery life of the electric automobile is considered.
2. The electric vehicle auxiliary power grid frequency modulation control method considering the battery SOH according to claim 1, wherein the step (1) is specifically implemented as follows:
the electric automobile EV is connected with a power network through a bidirectional converter, a virtual synchronous generator VSG technology is integrated into converter control, and the electric automobile EV can present inertia and damping characteristics similar to those of a synchronous generator when V2G participates in frequency modulation, so that the electric automobile EV presents the characteristic of power network friendliness; the virtual synchronous generator VSG motion equation is shown in formula (1):
Wherein T e represents the electromagnetic torque of the motor, J represents the rotational inertia of the VSG, T m represents the mechanical torque of the motor, D p represents the damping coefficient of the rotor, ω n represents the reference angular frequency of the rotor, ω represents the actual angular frequency of the rotor; the values of D p and J are different in different systems, and when the parameters of the generator in the system are determined, the specific values of D p and J can be obtained by calculation according to the formula (1);
(1.1) building EV frequency modulation control basic model of electric automobile
The method comprises the steps of exploring the adjustment influence of an electric vehicle EV on system frequency by adopting a single-region frequency adjustment model, equating each model comprising a generator-load model, a prime motor model, a speed regulator model, a load frequency model and a secondary frequency modulation model in the single-region frequency adjustment model into a low-order linearization model, and describing by adopting a transfer function; based on the VSG model, the frequency modulation characteristic of the electric automobile EV needs to be comprehensively considered when the equivalent model is constructed, a second-order inertial response function of the electric automobile EV is selected, the function is expressed in a form of a second-order inertial transfer function, and the specific transfer function is shown in a formula (2):
Wherein T EV represents an inherent time constant, J vr represents an equivalent virtual inertia of the VSG, and D vr represents an equivalent virtual damping of the VSG; the primary frequency modulation model and the secondary frequency modulation model are covered in the EV frequency modulation module, and the primary frequency modulation module and the secondary frequency modulation model are combined to form total frequency modulation power of the electric automobile, and in view of the fact that the comprehensive power is possibly higher than the maximum allowable charge and discharge power of the electric automobile, a power limiting module is introduced to avoid potential damage to a battery caused by excessive charge and discharge power, and finally, the electric automobile is processed through a power response characteristic function of the VSG to obtain the frequency modulation power of the electric automobile;
(1.2) establishment of electric automobile EV battery capacity decay model
In the construction process of the EV battery capacity degradation model, the temperature and the charge-discharge multiplying power are set to be fixed values, and only the influences of the cyclic charge-discharge depth delta SOC, the charge-discharge times and the average cyclic SOC of the battery on the battery capacity degradation are considered; under constant temperature conditions, the battery capacity fade rate Q site is as shown in formula (3):
wherein Q d represents the current capacity of the battery, and Q b represents the standard capacity of the battery; assuming that the EV batteries have equal discharge amounts in any interval of the same size, the expression of the equivalent cycle number is as shown in formula (4):
Wherein E e represents the total charge and discharge amount of the electric vehicle in the frequency modulation time, and E a represents the total charge and discharge amount of the electric vehicle in the complete cycle.
3. The electric vehicle auxiliary power grid frequency modulation control method considering battery SOH according to claim 2, wherein the bidirectional converter is a bidirectional DC/DC converter of an electric vehicle charging and discharging machine, and adopts a bidirectional half-bridge topology structure.
4. The electric vehicle auxiliary power grid frequency modulation control method considering the battery SOH according to claim 1, wherein the step (2) is specifically implemented as follows:
(2.1) scheduling instructions and load aggregation policy in one-time frequency adjustment
When a dispatching center sends a dispatching instruction to an electric automobile load aggregator, the dispatching instruction needs to cover the self-contained frequency modulation capacity; in this scenario, there are N electric vehicles available for frequency modulation scheduling, forming one electric vehicle set; in order to reduce battery loss caused by frequent charge and discharge state transition, a load combination participating in primary frequency modulation of a power grid may comprise a single member or a plurality of members in the same scheduling period; the frequency modulation group with higher frequency adjustment capacity is preferentially selected to participate in full-capacity frequency modulation so as to reduce the charge-discharge conversion frequency of the battery; based on the above, a primary frequency modulation control strategy based on electric automobile load clustering is provided, and when the system frequency fluctuates, the frequency modulation reserve capacity distribution is shown as a formula (5):
Wherein S 1,i represents the frequency adjustment capability of the ith electric vehicle in the electric vehicles capable of feeding power to the system, S 1 represents the standby frequency adjustment capability required by the current system, SOC 1,i represents the current state of charge of the load of the ith electric vehicle in the electric vehicles capable of being subjected to frequency modulation scheduling, SOC max represents the maximum state of charge of the electric vehicles capable of being subjected to frequency modulation scheduling, eta 1,i represents the frequency adjustment coefficient of the ith electric vehicle, L EV represents the EV load set, namely the electric vehicle set capable of being subjected to frequency modulation scheduling, The electric automobile set which represents that the EV can feed load to the system, namely can feed power to the system, is totally N 1; in the process of electric energy distribution, the electric automobile may have excessive frequency modulation capacity, so that power is supplied to a power grid to exceed the minimum capacity of the storage battery, or the electric automobile may be overcharged during charging, so that the charging state of the storage battery exceeds the upper limit; therefore, to cope with the overcharge or overdischarge problem, the power supply or charging of the electric vehicle is limited, specifically as shown in formula (6):
Wherein P 1,ikmax represents The upper power limit of the medium electric automobile feeding the power grid, P 1,icmax represents/>Upper limit of charging power of middle electric automobile, S 1,min represents/>In (3) the minimum frequency adjustment capability of the electric automobile, S 1,max represents/>In the electric vehicle maximum frequency adjustment capability, t represents time, and since power loss is caused in the process including charging and discharging, assuming that the power loss is borne by the vehicle battery, the calculation of the electric quantity state is as shown in formula (7):
Wherein SOC 1,i0 represents the initial state of charge of EV and P 1,i represents In the (i) th electric automobile, the power fed to the power grid by the electric automobile, eta d represents the discharge efficiency, and t 1、t2 represents the discharge time period;
(2.2) droop control strategy in secondary frequency Regulation
The electric automobile must set basic charging power P c in the primary frequency modulation process to meet the charging requirement; in order to avoid frequent charge and discharge of the electric vehicle, the purpose of prolonging the service life of the battery of the electric vehicle is achieved; setting a primary frequency modulation dead zone to [ f n-0.03,fn +0.03], wherein f n represents a rated grid frequency, and in the dead zone, only P c is used for charging; when the frequency exceeds the dead zone, the power change is the product of the frequency droop coefficient and the frequency deviation; the primary frequency modulation power P 1 is shown in formula (8) and is limited in the range of [ P cmax,Pdmax ]:
Wherein K c represents a droop coefficient of the charging frequency, and K d represents a droop coefficient of the discharging frequency; to ensure that the user's charging schedule can be completed on time, P c and the droop coefficient K c、Kd should be dynamically adjusted with the charging process of the electric vehicle, f g represents the current grid frequency; therefore, a main frequency adjustment strategy is proposed that considers the user's charging plan; when S x is less than 0, P c is designed to be (1-alpha) P n, and can be reduced or increased along with the change of alpha until the rated power is P n, so that the electric automobile is ensured to have basic charging power to complete a charging plan, and parameters related to EV charging are taken between S x and alpha with values of [ -1,1 ]. If S x > 0, the electric vehicle has no charge demand, P c =0; therefore, the basic charging power P c of the electric vehicle is as shown in formula (9):
The droop coefficient is designed to be adjusted after the alpha value, and the smaller the alpha value is, the smaller the droop coefficient of the charging frequency is, and the larger the droop coefficient of the discharging frequency is; meanwhile, in order to avoid the situation that the electric automobile is overcharged and overdischarged in the frequency modulation process, the sagging coefficient of charging and discharging is limited by the real-time SOC, so that a fuzzy control method is adopted, the maximum sagging coefficient is set to be K max, and the sagging coefficient K c＝Kmax-Kd of the charging frequency and the sagging coefficient K d of the discharging frequency are obtained through a fuzzy controller.
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