Secondary frequency modulation control method and system considering electric vehicle battery

Abstract

The invention discloses a secondary frequency modulation control method and system considering an electric vehicle battery. The method comprises the following steps: acquiring key parameters of an electric vehicle; inputting the key parameters of the electric vehicle into a neural network, and obtaining a capacity aging coefficient and a power aging coefficient of a battery of the electric vehicle; calculating the minimum charging time of the electric vehicles based on the capacity aging coefficient and the power aging coefficient, and grouping the electric vehicles according to the minimum charging time; and calculating the frequency modulation reserve capacity of the electric vehicle based on the grouping result, and generating a frequency modulation strategy according to the frequency modulation reserve capacity. According to the method, the unknown battery aging coefficient is fitted by using the neural network, and the method has good adaptability to common battery aging phenomena in practical application; by designing grouping criteria and priority indexes, possible damage caused by frequent charging and discharging of batteries of vehicles about to leave the power grid is avoided; the frequency modulation participation priority of the vehicle with the high aging degree is reduced, the battery state is prevented from further deterioration, and the frequency modulation standby capacity is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system frequency regulation, and in particular to a secondary frequency regulation control method and system considering electric vehicle batteries. Background Art

[0002] Faced with the dual challenges of ecological environmental protection and energy structure transformation, the country has put forward the "dual carbon" goal, and building a new power system with new energy as the main body has become an inevitable choice. New energy is regarded as the cornerstone of the future energy system due to its advantages of cleanness, pollution-free and high energy efficiency. However, due to the intermittent and uncertain nature of its power generation, it also brings huge challenges to the stable operation of the power system.

[0003] In this context, vehicle-grid interaction technology has developed rapidly. The core of vehicle-grid interaction technology lies in the two-way energy flow between the power grid and electric vehicles. Due to their flexibility, electric vehicles can be aggregated by aggregators and used as distributed energy storage systems to help balance the supply and demand of the power system and participate well in the frequency regulation of the power grid. When the load on the power grid is too high, electric vehicles use the energy stored in the battery to feed power to the power grid; when the load on the power grid is too low, electric vehicles can be charged to store excess power. The benefits of electric vehicles participating in frequency regulation are multifaceted. First, in terms of response speed, the electrochemical energy storage characteristics of electric vehicle batteries have great advantages. They respond faster than traditional generators, even at the millisecond level, and can quickly suppress frequency fluctuations in the power grid and improve power quality. Secondly, for frequency regulation flexibility, electric vehicles can be connected to the power grid at any location. They are a distributed energy storage system that brings greater flexibility and robustness to the power system. In addition, as a new type of frequency regulation service, the participation of electric vehicles in frequency regulation can reduce maintenance and operation costs, absorb excess renewable energy power, reduce wind and solar power abandonment, and improve energy utilization. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a secondary frequency regulation control method considering electric vehicle batteries to solve the problem of improving the stability and frequency regulation capability of the power system under the condition of uncertainty of the aging degree of electric vehicle batteries.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a secondary frequency modulation control method considering an electric vehicle battery, comprising:

[0008] Obtain key parameters of electric vehicles;

[0009] Inputting the key parameters of the electric vehicle into a neural network to obtain a capacity aging coefficient and a power aging coefficient of the battery of the electric vehicle;

[0010] Calculating the minimum charging time of the electric vehicles based on the capacity aging coefficient and the power aging coefficient, and grouping the electric vehicles according to the minimum charging time;

[0011] Based on the grouping results, the frequency regulation reserve capacity of the electric vehicle is calculated, and a frequency regulation strategy is generated according to the frequency regulation reserve capacity.

[0012] As a preferred solution of the secondary frequency modulation control method considering the electric vehicle battery of the present invention, wherein: calculating the minimum charging time of the electric vehicle includes:

[0013] The minimum charging time is corrected using the capacity aging coefficient and the power aging coefficient to obtain the minimum charging time of the electric vehicle. The formula for calculating the minimum charging time is:

[0014]

[0015] in, is the minimum charging time, SOC i (t) represents the current battery state of charge of the i-th electric vehicle, SOC i,aim Indicates the target power level set by the owner when leaving. E i is the battery capacity of the ith electric car, P i,c,max is the maximum charging power of the i-th electric vehicle, η c is the charging efficiency of the ith electric vehicle, ξ i,E ∈(0,1] and ξ i,P ∈(0,1] are the battery capacity aging coefficient and power aging coefficient of the i-th electric vehicle respectively.

[0016] As a preferred solution of the secondary frequency modulation control method considering the electric vehicle battery of the present invention, wherein: grouping the electric vehicles according to the minimum charging time includes:

[0017] The electric vehicles are divided into three groups: a group not participating in frequency modulation, a charging group, and a discharging group;

[0018] When the departure time of an electric vehicle is greater than or equal to the minimum time required to complete charging, the electric vehicle is classified as a group not participating in frequency regulation;

[0019] When the current power of the electric vehicle reaches the target power and the time away from the vehicle is greater than the minimum time required to complete charging, the electric vehicle is divided into a discharge group to participate in the frequency regulation task of the power system;

[0020] When the current power of the electric vehicle does not reach the target power and the away time is greater than the minimum time required to complete charging, the electric vehicle is divided into a charging group.

[0021] As a preferred solution of the secondary frequency modulation control method considering the electric vehicle battery of the present invention, it also includes:

[0022] The grouping of each electric vehicle is not fixed. The aggregator periodically updates the status of the electric vehicles and regroups them.

[0023] As a preferred solution of the secondary frequency regulation control method considering the battery of an electric vehicle described in the present invention, the frequency regulation reserve capacity of the electric vehicle is calculated based on the grouping result, and the frequency regulation strategy is generated, including:

[0024] According to the grouping results of electric vehicles, the upper frequency regulation reserve capacity and the lower frequency regulation reserve capacity are calculated;

[0025] When the frequency regulation reserve capacity of electric vehicles is sufficient to support the frequency regulation power of the power grid, charging and discharging are carried out according to the frequency regulation power of the power system;

[0026] When the frequency regulation reserve capacity of electric vehicles is insufficient to support the frequency regulation power of the power grid, charging and discharging are performed at the maximum reserve capacity;

[0027] When the frequency regulation task is that the frequency regulation power of electric vehicles is equal to the frequency regulation power of the power grid, the frequency regulation priority index is set to sort the priority of electric vehicles participating in the frequency regulation, and the frequency regulation task is allocated according to the frequency regulation priority sorting result.

[0028] As a preferred solution of the secondary frequency modulation control method considering the electric vehicle battery described in the present invention, the frequency modulation priority includes:

[0029] If the electric vehicle battery is less aged, the difference between the current power and the target power is large, or the remaining time on the grid is long, the priority of the electric vehicle will be increased.

[0030] As a preferred solution of the secondary frequency modulation control method considering the electric vehicle battery of the present invention, the frequency modulation priority index calculation formula is:

[0031]

[0032] Among them, ξ i,E ∈(0,1] and ξ i,P ∈(0,1] are the battery capacity aging coefficient and power aging coefficient of the i-th electric vehicle, r iis the frequency modulation priority index, SOC i (t) represents the current battery state of charge of the i-th electric vehicle, SOC i,aim Indicates the target power level set by the owner when leaving. E i is the battery capacity of the ith electric car, t i,out is the departure time set by the owner of the i-th electric car, and t is the charging time of the electric car.

[0033] In a second aspect, the present invention provides a system for secondary frequency regulation control considering an electric vehicle battery, comprising:

[0034] Data acquisition module, used to obtain key parameters of electric vehicles;

[0035] A data calculation module, used to input the key parameters of the electric vehicle into a neural network to obtain a capacity aging coefficient and a power aging coefficient of the electric vehicle battery;

[0036] A grouping module, used to calculate the minimum charging time of the electric vehicles based on the capacity aging coefficient and the power aging coefficient, and group the electric vehicles according to the minimum charging time;

[0037] The frequency modulation module is used to calculate the frequency modulation reserve capacity of the electric vehicle based on the grouping result, and generate a frequency modulation strategy according to the frequency modulation reserve capacity.

[0038] In a third aspect, the present invention provides a computing device, comprising:

[0039] Memory and processor;

[0040] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the secondary frequency regulation control method considering the electric vehicle battery are implemented.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the secondary frequency regulation control method considering the battery of an electric vehicle.

[0042] Compared with the prior art, the present invention has the following beneficial effects: the present invention uses a neural network to fit the unknown battery aging coefficient, and has good adaptability to common battery aging phenomena in practical applications; by designing grouping criteria and priority indexes, it can avoid possible damage to the batteries of vehicles that are about to leave the power grid caused by frequent charging and discharging; it reduces the priority of vehicles with a high degree of aging to participate in frequency modulation, avoids further deterioration of the battery status, and at the same time improves the frequency modulation backup capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0044] Figure 1 It is a schematic diagram of the overall process of a secondary frequency modulation control method considering an electric vehicle battery according to an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a secondary frequency regulation process of an electric vehicle according to a secondary frequency regulation control method taking into account an electric vehicle battery according to an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of electric vehicle grouping according to a secondary frequency modulation control method considering electric vehicle batteries according to an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of the frequency modulation effect of a secondary frequency modulation control method considering an electric vehicle battery according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0049] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0051] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0052] At the same time, in the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0053] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] Example 1

[0055] Reference Figure 1-2 , as an embodiment of the present invention, provides a secondary frequency modulation control method considering an electric vehicle battery, comprising:

[0056] S100: Obtaining key parameters of electric vehicles;

[0057] In the embodiment of the present application, the key parameters of the electric vehicle specifically include quantified electric vehicle battery model information, electric vehicle usage time, and electric vehicle charging times;

[0058] After obtaining the electric vehicle's excessive battery model information, usage time, charging times and other key parameters, these data are preprocessed; preprocessing includes data cleaning, data formatting, data normalization or standardization, and possible missing value processing; through data preprocessing, data quality is ensured, noise interference in the analysis process is reduced, and the efficiency of data analysis and the reliability of results are improved, providing a data basis for subsequent frequency modulation strategies.

[0059] S102: inputting the key parameters of the electric vehicle into a neural network to obtain a capacity aging coefficient and a power aging coefficient of the battery of the electric vehicle;

[0060] Neural networks include BP neural networks, convolutional neural networks, recurrent neural networks or their variants (such as LSTM, GRU) and other types of neural networks. The BP neural network is simple, intuitive and easy to implement, so this invention uses it as a case to illustrate;

[0061] In an embodiment of the present application, a BP neural network model including an input layer, a hidden layer and an output layer is designed. The input layer is provided with 3 nodes, which correspond to the three key parameters of the quantitative model information of the electric vehicle battery, the usage time of the electric vehicle and the number of charging times of the electric vehicle; the hidden layer is designed to include 3 nodes, each node uses the hyperbolic tangent function (Tanh) as the activation function to introduce nonlinear factors and enhance the expressive ability of the model; the output layer includes 2 nodes, which are used to output the predicted capacity aging coefficient and power aging coefficient respectively.

[0062] In the forward propagation process, the data of the input layer is first weighted and summed with the weight matrix between the nodes of the hidden layer, and the bias term is added, and then the nonlinear transformation is performed through the hyperbolic tangent activation function to obtain the output of the hidden layer. This process is repeated inside the hidden layer until the output layer. In the output layer, the weighted summation and bias processing are also performed. The activation function can be selected according to specific needs, and finally the predicted capacity aging coefficient and power aging coefficient are obtained.

[0063] The BP neural network model is trained using a training data set containing quantitative information about the battery model of electric vehicles, usage time, number of charges, and the actual capacity aging coefficient and power aging coefficient obtained through statistics or experiments. The model can automatically adjust the weight and bias parameters through the gradient back propagation algorithm to minimize the error between the predicted value and the actual value. The learning process is continuously iterated until the performance of the model on the validation set is no longer significantly improved, or the preset upper limit of training rounds is reached.

[0064] It should be noted that by learning the key parameters of electric vehicles through neural networks and accurately predicting the capacity and power aging coefficient of batteries, which are used for the subsequent intelligent correction of electric vehicle charging time, the flexibility and accuracy of electric vehicles in participating in grid frequency regulation are enhanced, and the service life and efficiency of electric vehicle batteries are also improved; by flexibly applying BP neural networks and other advanced neural network models, the uncertainty of battery aging is effectively dealt with, providing more reliable and efficient technical support for the widespread application of electric vehicles in power systems, thereby promoting the consumption of renewable energy and the stable operation of power grids.

[0065] S104: Calculating the minimum charging time of the electric vehicles based on the capacity aging coefficient and the power aging coefficient, and grouping the electric vehicles according to the minimum charging time;

[0066] Preferably, the capacity aging coefficient and the power aging coefficient are used to correct the minimum charging time to obtain the minimum charging time of the electric vehicle. The minimum charging time is calculated as follows:

[0067]

[0068] in, is the minimum charging time, SOC i (t) represents the current battery state of charge of the i-th electric vehicle, SOC i,aim Indicates the target power level set by the owner when leaving. E i is the battery capacity of the ith electric car, P i,c,max is the maximum charging power of the i-th electric vehicle, η c is the charging efficiency of the ith electric vehicle, ξ i,E ∈(0,1] and ξ i,P ∈(0,1] are the battery capacity aging coefficient and power aging coefficient of the i-th electric vehicle respectively;

[0069] It should be noted that The time required for the i-th electric vehicle to reach the target charge at the maximum power starting from the current moment is called the minimum charging time. It is defined in SOC i,aim >SOC i (t) case; when the capacity aging coefficient and power aging coefficient are closer to 0, the battery aging degree is higher.

[0070] Preferably, the electric vehicles are divided into three groups, namely a group not participating in frequency modulation, a charging group and a discharging group;

[0071] In the embodiment of the present application, the electric vehicles are divided into three groups: group G not participating in frequency modulation; n , Charging Group G c and discharge group G d , the grouping judgment is expressed by the formula:

[0072]

[0073] Among them, t represents the current time, SOC i (t) represents the current battery state of charge of the i-th electric vehicle, SOC i,aim Indicates the target power level set by the owner when leaving, t i,out The departure time set by the owner of the i-th electric car, k i ∈(0,1] is a designable positive parameter, is the minimum charging time, SOC i (t) represents the current battery state of charge of the i-th electric vehicle, SOC i,aimIndicates the target power level when leaving the car set by the owner;

[0074] In another possible embodiment, electric vehicles can be divided into four groups: an emergency charging group, an on-demand scheduling group, a balanced contribution group and a non-participating group; the emergency charging group includes electric vehicles with extremely low power and urgently need to complete charging in a short time to meet the upcoming vehicle demand; the on-demand scheduling group is flexibly adjusted according to the real-time needs of the power grid. When the power grid needs additional load reduction, the electric vehicles in this group will delay charging; and when the power grid needs power support, if conditions permit, these vehicles can participate as discharge resources; the balanced contribution group is composed of electric vehicles with moderate power and relatively ample charging time. They will adjust the charging plan appropriately according to the power grid demand on the premise of ensuring their own charging needs, and provide stable power balance support for the power grid; the non-participating group includes electric vehicles that do not participate in any frequency modulation activities due to sufficient power, imminent departure or the owner's explicit refusal to participate in power grid scheduling; the present invention does not specifically limit the specific form of the grouping strategy. The grouping scheme in the embodiment of the present application is only used as an example to show how to achieve efficient utilization of electric vehicle resources and flexible response to power grid demand through dynamic grouping.

[0075] Preferably, when the departure time of the electric vehicle is greater than or equal to the minimum time required to complete charging, the electric vehicle is divided into a group not participating in frequency regulation; when the current power of the electric vehicle reaches the target power and the departure time is greater than the minimum time required to complete charging, the electric vehicle is divided into a discharge group and participates in the frequency reduction task of the power system; when the current power of the electric vehicle does not reach the target power and the departure time is greater than the minimum time required to complete charging, the electric vehicle is divided into a charging group;

[0076] Preferably, the grouping of each electric vehicle is not fixed, and the aggregator periodically updates the status of the electric vehicles and regroups them;

[0077] It should be noted that, based on the accurate calculation of the capacity aging coefficient and the power aging coefficient, the present invention realizes the accurate evaluation and grouping management of the electric vehicle's ability to participate in the grid frequency regulation by correcting the minimum charging time of the electric vehicle, which not only improves the intelligence level of electric vehicle charging management, but also ensures the effective utilization of grid frequency regulation resources; through dynamic grouping, that is, the flexible division of non-frequency regulation groups, charging groups and discharging groups, the present invention can optimize resource allocation according to the real-time status of the electric vehicle and the grid demand, reduce unnecessary charging waiting time, and enhance the flexibility and stability of the grid; in addition, the dynamic adjustment mechanism of electric vehicle grouping further improves the response speed and adaptability of the system.

[0078] S106: Based on the grouping result, the frequency regulation reserve capacity of the electric vehicle is calculated, and a frequency regulation strategy is generated according to the frequency regulation reserve capacity;

[0079] Preferably, the upper frequency regulation reserve capacity and the lower frequency regulation reserve capacity are calculated according to the grouping result of the electric vehicles;

[0080] In the embodiment of the present application, the formula for calculating the upper frequency regulation reserve capacity and the lower frequency regulation reserve capacity is:

[0081]

[0082] Among them, R up Indicates the upper frequency regulation reserve capacity, N d represents the number of electric vehicles in the discharge group, P i,d,max represents the maximum discharge power of the i-th electric vehicle, P i,c,max represents the maximum charging power of the i-th electric vehicle, R down Indicates the reserve capacity for down regulation, N c Indicates the number of electric vehicles in the charging group;

[0083] Preferably, when the frequency regulation reserve capacity of the electric vehicle is sufficient to support the frequency regulation power of the power grid, charging and discharging are performed according to the frequency regulation power of the power system; when the frequency regulation reserve capacity of the electric vehicle is insufficient to support the frequency regulation power of the power grid, charging and discharging are performed with the maximum reserve capacity;

[0084] In the embodiment of the present application, when frequency modulation is required, the secondary frequency modulation task of the electric vehicle is set as follows:

[0085]

[0086] Among them, P task is the frequency modulation power of the electric vehicle, P e is the grid frequency modulation power, R up Indicates the upper frequency regulation reserve capacity, R down Indicates the reserve capacity for lower frequency regulation;

[0087] Preferably, when the frequency modulation task is that the frequency modulation power of the electric vehicle is equal to the frequency modulation power of the power grid, the frequency modulation priority index is set to sort the priority of the electric vehicles participating in the frequency modulation, and the frequency modulation task is allocated according to the frequency modulation priority sorting result; if the battery aging degree of the electric vehicle is low, the difference between the current power and the target power is large, or the remaining time on the grid is long, the priority of the electric vehicle is increased;

[0088] Preferably, the frequency modulation priority index calculation formula is:

[0089]

[0090] Among them, ξ i,E ∈(0,1] and ξ i,P ∈(0,1] are the battery capacity aging coefficient and power aging coefficient of the i-th electric vehicle, ri is the frequency modulation priority index, SOC i (t) represents the current battery state of charge of the i-th electric vehicle, SOC i,aim Indicates the target power level set by the owner when leaving. E i is the battery capacity of the ith electric car, t i,out is the departure time set by the owner of the i-th electric car, and t is the charging time of the electric car;

[0091] In the embodiment of the present application, the priority index r corresponding to each electric vehicle is calculated. i Then, the electric vehicles are sorted accordingly, and frequency modulation tasks are allocated according to priority during frequency modulation. The number of electric vehicles participating in frequency modulation can be calculated according to the following formula:

[0092]

[0093] Among them, P task is the frequency modulation power of the electric vehicle, P i,d,max represents the maximum discharge power of the i-th electric vehicle, P i,c,max represents the maximum charging power of the i-th electric vehicle, [x] represents the rounding function, and N is the total number of electric vehicles participating in frequency modulation;

[0094] The power calculation formula for each electric vehicle participating in frequency regulation is:

[0095]

[0096] Among them, P task is the frequency modulation power of the electric vehicle, is the power of each electric vehicle, N is the total number of electric vehicles participating in frequency regulation;

[0097] It should be noted that, through the above method, the present invention realizes the accurate calculation and efficient utilization of the frequency regulation reserve capacity of electric vehicles, and improves the flexibility and response speed of power grid frequency regulation; by separately calculating the upper frequency regulation reserve capacity and the lower frequency regulation reserve capacity, and flexibly adjusting the charging and discharging strategy of electric vehicles according to the actual needs of the power grid, the maximum utilization of frequency regulation resources is ensured; when allocating frequency regulation tasks, the frequency regulation priority index is introduced to sort the electric vehicles, and the battery aging degree, power status, remaining network time and other factors are comprehensively considered, which effectively improves the execution efficiency and fairness of frequency regulation tasks; by accurately calculating the number of electric vehicles participating in frequency regulation and their power distribution, the accurate output of frequency regulation power is ensured, and the stability and reliability of the power grid are further enhanced.

[0098] The above is a schematic scheme of a secondary frequency modulation control method considering electric vehicle batteries in this embodiment. It should be noted that the technical scheme of the system considering secondary frequency modulation control of electric vehicle batteries and the technical scheme of the secondary frequency modulation control method considering electric vehicle batteries belong to the same concept, and the details not described in detail in the technical scheme of the secondary frequency modulation control system considering electric vehicle batteries in this embodiment can be referred to the description of the technical scheme of the secondary frequency modulation control method considering electric vehicle batteries.

[0099] In this embodiment, a secondary frequency modulation control system of an electric vehicle battery is considered, including:

[0100] Data acquisition module, used to obtain key parameters of electric vehicles;

[0101] A data calculation module, used to input the key parameters of the electric vehicle into a neural network to obtain a capacity aging coefficient and a power aging coefficient of the electric vehicle battery;

[0102] A grouping module, used to calculate the minimum charging time of the electric vehicles based on the capacity aging coefficient and the power aging coefficient, and group the electric vehicles according to the minimum charging time;

[0103] The frequency modulation module is used to calculate the frequency modulation reserve capacity of the electric vehicle based on the grouping result, and generate a frequency modulation strategy according to the frequency modulation reserve capacity.

[0104] This embodiment further provides a computing device, which is applicable to the case of considering secondary frequency modulation control of an electric vehicle battery, and includes:

[0105] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the secondary frequency modulation control method considering the electric vehicle battery as proposed in the above embodiment.

[0106] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the secondary frequency modulation control method considering the battery of an electric vehicle as proposed in the above embodiment is implemented.

[0107] The storage medium proposed in this embodiment and the secondary frequency modulation control method considering the electric vehicle battery proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0108] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0109] Example 2

[0110] Reference Figure 3-4 , which is an embodiment of the present invention, provides a secondary frequency modulation control method considering the battery of an electric vehicle. In order to verify its beneficial effects, the experimental verification results of the present invention are provided.

[0111] Using the electric vehicle grouping strategy method of the present invention, the groups are as follows: Figure 3 As shown, Figure 3 The process of electric vehicle grouping is demonstrated in detail. By comprehensively considering key parameters such as the battery capacity aging coefficient, power aging coefficient, current battery state of charge (SOC), target power level set by the owner when leaving, and departure time, electric vehicles are successfully divided into a group not participating in frequency regulation, a charging group, and a discharging group. This refined grouping strategy ensures that the power grid can quickly and accurately mobilize the most appropriate resources when frequency regulation is required, thereby improving the efficiency and accuracy of the frequency regulation response.

[0112] According to the grouping results, the frequency regulation reserve capacity of electric vehicles was calculated, including the upper frequency regulation reserve capacity and the lower frequency regulation reserve capacity. When the frequency regulation reserve capacity of electric vehicles is sufficient to meet the frequency regulation needs of the power grid, the power grid can accurately control the charging and discharging process of electric vehicles to ensure the stability of the power grid frequency. When the frequency regulation reserve capacity is insufficient, the power grid can intelligently select the maximum reserve capacity for charging and discharging to maximize the frequency regulation needs and reduce frequency fluctuations.

[0113] By means of the method of the present invention, a significant frequency modulation effect is produced, such as Figure 4As shown in the figure, the stability of the grid frequency has been significantly improved, and the fluctuation range has been significantly reduced. At the same time, the charging and discharging process of electric vehicles has also been optimized, avoiding unnecessary energy waste and battery loss. In addition, the grouping and frequency modulation strategy of the present invention also stimulates the enthusiasm of electric vehicles to participate in grid frequency modulation, improves the overall efficiency, and provides strong support for the further development of smart grids.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A secondary frequency modulation control method considering electric vehicle batteries, characterized in that: include: Obtain key parameters of electric vehicles; Inputting the key parameters of the electric vehicle into a neural network to obtain a capacity aging coefficient and a power aging coefficient of the battery of the electric vehicle; Calculating the minimum charging time of the electric vehicles based on the capacity aging coefficient and the power aging coefficient, and grouping the electric vehicles according to the minimum charging time; Based on the grouping results, the frequency regulation reserve capacity of the electric vehicle is calculated, and a frequency regulation strategy is generated according to the frequency regulation reserve capacity.

2. The secondary frequency modulation control method considering the battery of an electric vehicle as claimed in claim 1, characterized in that: Calculating the minimum charging time for an electric vehicle includes, The minimum charging time is corrected using the capacity aging coefficient and the power aging coefficient to obtain the minimum charging time of the electric vehicle. The formula for calculating the minimum charging time is: in, is the minimum charging time, SOC i (t) represents the current battery state of charge of the i-th electric vehicle, SOC i，aim Indicates the target power level set by the owner when leaving. E i is the battery capacity of the ith electric car, P i，c，max is the maximum charging power of the ith electric vehicle, η c is the charging efficiency of the ith electric vehicle, ξ i，E ∈(0, 1] and ξ i，P ∈(0, 1] are the battery capacity aging coefficient and power aging coefficient of the i-th electric vehicle respectively.

3. The secondary frequency modulation control method considering the battery of an electric vehicle as claimed in claim 1 or 2, characterized in that: According to the minimum charging time, the electric vehicles are grouped into groups including: The electric vehicles are divided into three groups: a group not participating in frequency modulation, a charging group, and a discharging group; When the departure time of an electric vehicle is greater than or equal to the minimum time required to complete charging, the electric vehicle is classified as a group not participating in frequency regulation; When the current power of the electric vehicle reaches the target power and the time away from the vehicle is greater than the minimum time required to complete charging, the electric vehicle is divided into a discharge group to participate in the frequency regulation task of the power system; When the current power of the electric vehicle does not reach the target power and the away time is greater than the minimum time required to complete charging, the electric vehicle is divided into a charging group.

4. The secondary frequency modulation control method considering the battery of an electric vehicle as claimed in claim 3 is characterized in that: Also includes, The grouping of each electric vehicle is not fixed. The aggregator periodically updates the status of the electric vehicles and regroups them.

5. The secondary frequency modulation control method considering the battery of an electric vehicle as claimed in claim 4, characterized in that: Based on the grouping results, the frequency regulation reserve capacity of electric vehicles is calculated, and the frequency regulation strategy is generated, including: According to the grouping results of electric vehicles, the upper frequency regulation reserve capacity and the lower frequency regulation reserve capacity are calculated; When the frequency regulation reserve capacity of electric vehicles is sufficient to support the frequency regulation power of the power grid, charging and discharging are carried out according to the frequency regulation power of the power system; When the frequency regulation reserve capacity of electric vehicles is insufficient to support the frequency regulation power of the power grid, charging and discharging are performed at the maximum reserve capacity; When the frequency regulation task is that the frequency regulation power of electric vehicles is equal to the frequency regulation power of the power grid, the frequency regulation priority index is set to sort the priority of electric vehicles participating in the frequency regulation, and the frequency regulation task is allocated according to the frequency regulation priority sorting result.

6. The secondary frequency modulation control method considering the battery of an electric vehicle as claimed in claim 5, characterized in that: FM priorities include, If the electric vehicle battery is less aged, the difference between the current power and the target power is large, or the remaining time on the grid is long, the priority of the electric vehicle will be increased.

7. The secondary frequency modulation control method considering the battery of an electric vehicle as claimed in claim 4, characterized in that: The frequency modulation priority index calculation formula is: Among them, ξ i，E ∈(0, 1] and ξ i，P ∈(0, 1] are the battery capacity aging coefficient and power aging coefficient of the i-th electric vehicle, r i is the frequency modulation priority index, SOC i (t) represents the current battery state of charge of the i-th electric vehicle, SOC i，aim Indicates the target power level set by the owner when leaving. E i is the battery capacity of the ith electric car, t i，out is the departure time set by the owner of the i-th electric car, and t is the charging time of the electric car.

8. A system considering secondary frequency modulation control of electric vehicle batteries, characterized in that: include, Data acquisition module, used to obtain key parameters of electric vehicles; A data calculation module, used to input the key parameters of the electric vehicle into a neural network to obtain a capacity aging coefficient and a power aging coefficient of the electric vehicle battery; A grouping module, used to calculate the minimum charging time of the electric vehicles based on the capacity aging coefficient and the power aging coefficient, and group the electric vehicles according to the minimum charging time; The frequency modulation module is used to calculate the frequency modulation reserve capacity of the electric vehicle based on the grouping result, and generate a frequency modulation strategy according to the frequency modulation reserve capacity.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the secondary frequency regulation control method considering the electric vehicle battery as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the secondary frequency regulation control method considering the battery of an electric vehicle as described in any one of claims 1 to 7.

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