Electric vehicle cluster demand response capability assessment method, system, device and medium

Abstract

The invention discloses an electric vehicle cluster demand response capability assessment method, system and device and a medium. The method comprises the following steps: acquiring charging demand data of an electric vehicle cluster; constructing an initial electric vehicle cluster regulation and control model based on the charging demand data, and constraining the initial electric vehicle cluster regulation and control model to obtain a target electric vehicle cluster regulation and control model; establishing two electric vehicle charging modes according to different use scenes; and regulating and controlling the electric vehicle cluster based on the real-time demand response, and quantitatively evaluating the demand response capability of the electric vehicle cluster in different charging modes according to the target electric vehicle cluster regulation and control model. According to the invention, based on the charging demand data of the electric vehicle cluster, the influence on the real-time demand response capability of the electric vehicle in different charging modes is considered, the real-time demand response capability of the electric vehicle cluster in different charging modes is evaluated, the operation and maintenance management of the power grid side is ensured, and the adjustment capability and the operation efficiency of the power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a method, system, device and medium for evaluating the demand response capability of an electric vehicle cluster. Background Art

[0002] In the context of the current energy structure transformation, the widespread use of renewable energy has brought new challenges to the balance of supply and demand in the power system. In particular, the intermittent and instability of renewable energy has exacerbated the volatility of the power grid, making it difficult for traditional power grid regulation methods to meet the needs of modern power systems. In order to meet these challenges, demand response, as a flexible regulation method, has been widely used in load management and energy optimization of power grids. As a distributed mobile energy storage unit, the rapid increase in the number of electric vehicles provides huge regulation potential for the power grid. Electric vehicles can not only provide peak shaving services by discharging during peak power demand periods, but also achieve valley filling by charging during low power demand periods.

[0003] Electric vehicles are constrained by many factors in actual operation, such as user travel needs, battery capacity limitations, and charging and discharging power limitations. Comprehensive consideration of these factors is crucial to accurately assessing the demand response capabilities of electric vehicles. However, this demand response capability of electric vehicles has not been fully explored and effectively evaluated. In existing technologies, the evaluation of electric vehicle demand response capabilities is mostly focused on the level of a single vehicle or a small fleet, and there is a lack of a systematic evaluation method for the demand response capabilities of large-scale electric vehicle clusters. Summary of the invention

[0004] The purpose of the present invention is to achieve a quantitative evaluation of the demand response capability of a large-scale electric vehicle cluster, thereby providing power grid operators with an effective aggregation and regulation strategy for electric vehicle resources and improving the regulation capability and operating efficiency of the power grid. In order to achieve the above purpose, the present invention provides a method, system, device and medium for evaluating the demand response capability of an electric vehicle cluster.

[0005] In a first aspect, an embodiment of the present invention provides a method for evaluating the demand response capability of an electric vehicle cluster, comprising:

[0006] Acquire charging demand data of the electric vehicle cluster, wherein the charging demand data includes battery capacity, maximum charging power, maximum discharging power, minimum battery state of charge when leaving, battery state of charge when arriving, and departure time, wherein the minimum battery state of charge when leaving is the minimum battery state of charge expected when the electric vehicle leaves the charging pile, and the battery state of charge when arriving is the battery state of charge when the electric vehicle arrives at the charging pile;

[0007] An initial electric vehicle cluster control model is constructed based on the charging demand data, and the initial electric vehicle cluster control model is constrained to obtain a target electric vehicle cluster control model, wherein the objective function of the initial electric vehicle cluster control model includes the maximum dispatchable capacity of the electric vehicle cluster and the charging and discharging energy loss of the electric vehicle cluster, and the constraints include the charging and discharging power constraints and the user demand power constraints;

[0008] Two electric vehicle charging modes are established according to different usage scenarios, wherein the electric vehicle charging modes include a fast charging mode that uses maximum power for charging and a smart charging mode that uses average power for charging;

[0009] The electric vehicle cluster is regulated based on real-time demand response, and the demand response capability of the electric vehicle cluster under different charging modes is quantitatively evaluated according to the target electric vehicle cluster regulation model.

[0010] Preferably, the constructing of an initial electric vehicle cluster control model based on the charging demand data includes:

[0011] The objective function of the initial electric vehicle cluster control model is characterized by the following formula:

[0012]

[0013] Among them, C need It represents the total demand power of the power grid demand response during the demand response period. The positive and negative values ​​represent the valley filling demand response service and the peak shaving demand response service respectively. i,t represents the charging and discharging power of the ith electric vehicle in the tth regulation time period, N represents the number of electric vehicles in the electric vehicle cluster, T represents the number of regulation time periods, λ represents the weighted coefficient of the responsiveness of the electric vehicle cluster, P loss,i It represents the sum of energy loss of the i-th electric vehicle during the entire control period.

[0014] Preferably, the following formula is used to calculate the sum of the energy loss of the electric vehicle during the entire control time period:

[0015]

[0016] η loss =1-η

[0017] Among them, P loss,i represents the sum of energy losses of the i-th electric vehicle during the entire control period, P i,t represents the charging and discharging power of the ith electric vehicle in the tth regulation time period, T represents the number of regulation time periods, η lossIt represents the power loss rate during the charging and discharging process of the electric vehicle under normal conditions, and η represents the charging and discharging efficiency of the electric vehicle under normal conditions.

[0018] Preferably, constraining the initial electric vehicle cluster control model to obtain a target electric vehicle cluster control model includes:

[0019] The following formula is used to characterize the charge and discharge power constraint:

[0020] n i,t ·P i,min ≤P i,t ≤n i,t ·P i,max t=1,2,3....T

[0021] Among them, P i,t represents the charging and discharging power of the i-th electric vehicle in the t-th control time period, n i,t represents the stay state of the i-th electric vehicle in the t-th control time period, P i,min represents the minimum charging and discharging power of the i-th electric vehicle, P i,max represents the maximum charging and discharging power of the i-th electric vehicle, and T represents the number of control time periods;

[0022] The following formula is used to characterize the user demand power constraint:

[0023]

[0024] Among them, η represents the charging and discharging efficiency of electric vehicles under normal conditions, Δt represents the time interval of each control period, and Cp i represents the battery capacity of the i-th electric vehicle, SOC i,a It represents the battery state of charge of the i-th electric vehicle when it arrives at the charging station, SOC i,b It represents the minimum battery state of charge expected when the i-th electric vehicle leaves the charging station.

[0025] Preferably, the two electric vehicle charging modes are established according to different usage scenarios, including:

[0026] Based on the usage scenario where the power needs to be replenished quickly to meet emergency travel needs, the electric vehicle is charged at maximum power to build a fast charging mode;

[0027] Based on the usage scenario that the electric vehicle stays for a long time and is not in a hurry to travel, the average charging power is calculated according to the expected stay time of the electric vehicle, and the electric vehicle is charged with the average charging power to construct a smart charging mode.

[0028] Preferably, regulating the electric vehicle cluster based on real-time demand response, and quantitatively evaluating the demand response capability of the electric vehicle cluster under different charging modes according to the target electric vehicle cluster regulation model, includes:

[0029] Performing limit regulation on the electric vehicle cluster in the fast charging mode according to the real-time demand response, and solving the target electric vehicle cluster regulation model to obtain the maximum dispatchable capacity of the electric vehicle cluster in the fast charging mode;

[0030] The maximum dispatchable capacity of the electric vehicle cluster is characterized as a quantitative evaluation result of the demand response capability of the electric vehicle cluster.

[0031] Preferably, the regulating the electric vehicle cluster based on real-time demand response and quantitatively evaluating the demand response capability of the electric vehicle cluster under different charging modes according to the target electric vehicle cluster regulation model further includes:

[0032] According to the real-time demand response, the electric vehicle cluster in the smart charging mode is subjected to extreme regulation, and the target electric vehicle cluster regulation model is solved to obtain the maximum dispatchable capacity of the electric vehicle cluster in the smart charging mode;

[0033] The maximum dispatchable capacity of the electric vehicle cluster is characterized as a quantitative evaluation result of the demand response capability of the electric vehicle cluster.

[0034] In a second aspect, an embodiment of the present invention provides an electric vehicle cluster demand response capability evaluation system, comprising:

[0035] A data acquisition module is used to acquire charging demand data of the electric vehicle cluster, wherein the charging demand data includes battery capacity, maximum charging power, maximum discharging power, minimum battery state of charge when leaving, battery state of charge when arriving, and departure time, wherein the minimum battery state of charge when leaving is the minimum battery state of charge expected when the electric vehicle leaves the charging pile, and the battery state of charge when arriving is the battery state of charge when the electric vehicle arrives at the charging pile;

[0036] A model building module is used to build an initial electric vehicle cluster control model based on the charging demand data, and constrain the initial electric vehicle cluster control model to obtain a target electric vehicle cluster control model, wherein the objective function of the initial electric vehicle cluster control model includes the maximum dispatchable capacity of the electric vehicle cluster and the charging and discharging energy loss of the electric vehicle cluster, and the constraints include charging and discharging power constraints and user demand power constraints;

[0037] A charging mode building module, used to establish two electric vehicle charging modes according to different usage scenarios, wherein the electric vehicle charging modes include a fast charging mode that uses maximum power for charging and a smart charging mode that uses average power for charging;

[0038] The quantitative evaluation module is used to regulate the electric vehicle cluster based on real-time demand response, and quantitatively evaluate the demand response capability of the electric vehicle cluster under different charging modes according to the target electric vehicle cluster regulation model.

[0039] In a third aspect, an embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the electric vehicle cluster demand response capability evaluation method as described above when executing the computer program.

[0040] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the electric vehicle cluster demand response capability evaluation method as described above.

[0041] Compared with the prior art, the method, system, device and medium for evaluating the demand response capability of an electric vehicle cluster in the embodiment of the present invention have the following beneficial effects: based on the charging demand data of the electric vehicle cluster and taking into account the impact of different charging modes on the real-time demand response capability of the electric vehicle, the real-time demand response capability of the electric vehicle cluster under different charging modes is evaluated, which is beneficial to ensuring the operation and maintenance management on the power grid side, thereby improving the regulation capability and operation efficiency of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of a method for evaluating the demand response capability of an electric vehicle cluster according to an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of a process of constructing an electric vehicle charging mode according to an embodiment of the present invention;

[0044] Figure 3 It is a result schematic diagram of the electric vehicle cluster facing real-time grid demand response capability according to an embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram of the quantitative results of the response capability of an electric vehicle cluster using different charging modes to real-time demands at each moment according to an embodiment of the present invention;

[0046] Figure 5 It is a structural schematic diagram of an electric vehicle cluster demand response capability evaluation system according to an embodiment of the present invention;

[0047] Figure 6 It is a schematic diagram of the structure of a terminal device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0049] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the demand response capability of an electric vehicle cluster, comprising the steps of:

[0050] S1. Obtain charging demand data of electric vehicle clusters;

[0051] The charging demand data includes battery capacity, maximum charging power, maximum discharging power, minimum battery state of charge when leaving, battery state of charge when arriving, and departure time. Among them, the minimum battery state of charge when leaving is the minimum battery state of charge expected when the electric vehicle leaves the charging pile, and the battery state of charge when arriving is the battery state of charge when the electric vehicle arrives at the charging pile. It can be understood that the maximum charging power is the maximum charging power that the electric vehicle and its charging pile can withstand, and the maximum discharging power is the maximum discharging power that the electric vehicle and its charging pile can withstand. Specifically, in this embodiment, each electric vehicle user uploads his own charging demand data to the cloud server to obtain the charging demand data of the electric vehicle cluster.

[0052] S2. construct an initial electric vehicle cluster control model based on charging demand data, and constrain the initial electric vehicle cluster control model to obtain a target electric vehicle cluster control model;

[0053] The objective function of the initial electric vehicle cluster control model includes the maximum dispatchable capacity of the electric vehicle cluster and the charging and discharging energy loss of the electric vehicle cluster. Among them, the constraints include the charging and discharging power constraints and the user demand power constraints.

[0054] Specifically, the objective function consists of two key parts. The first part focuses on quantifying the extreme response capability of the electric vehicle cluster during the demand response period, that is, the maximum dispatchable capacity of the electric vehicle cluster, which involves the battery energy storage potential of the electric vehicle cluster and its response speed to the grid regulation demand; the second part focuses on calculating the charging and discharging energy loss of the electric vehicle cluster during the same period, which includes the energy loss caused by factors such as conversion efficiency during the charging and discharging process. Further, the following formula is used to characterize the objective function of the initial electric vehicle cluster regulation model:

[0055]

[0056] Among them, C needIt represents the total demand power of the power grid demand response during the demand response period. The positive and negative values ​​represent the valley filling demand response service and the peak shaving demand response service respectively. i,t represents the charging and discharging power of the ith electric vehicle in the tth regulation time period, N represents the number of electric vehicles in the electric vehicle cluster, T represents the number of regulation time periods, λ represents the weighted coefficient of the responsiveness of the electric vehicle cluster, P loss,i It represents the sum of energy loss of the i-th electric vehicle during the entire control period.

[0057] It should be noted that parameter C need In actual applications, it is issued by the power grid control center. In order to explore the extreme response capability of electric vehicles in the face of real-time demand response, this embodiment sets it to a positive and negative infinite value. Taking into account the control capability of the electric vehicle aggregator and the solution capability of the algorithm in real situations, this embodiment divides the regulation period of the next 24 hours into 15-minute intervals. When a demand response event is triggered, the electric vehicle regulation power of the next 96 points is solved each time. In other words, this embodiment sets parameter T to 96. The parameter λ is usually set to a larger value. This embodiment sets it to 10000. Its purpose is to quantify the extreme response capability of the electric vehicle cluster while reducing the charging and discharging losses during the regulation process.

[0058] Electric vehicles will generate energy loss during the charging and discharging process, which leads to a waste of power resources. Specifically, the following formula is used to calculate the sum of the energy loss of electric vehicles during the entire regulation period:

[0059]

[0060] η loss =1-η

[0061] Among them, P loss,i represents the sum of energy losses of the i-th electric vehicle during the entire control period, P i,t represents the charging and discharging power of the ith electric vehicle in the tth regulation time period, T represents the number of regulation time periods, η loss It represents the power loss rate during the charging and discharging process of the electric vehicle under normal conditions, and η represents the charging and discharging efficiency of the electric vehicle under normal conditions.

[0062] In order to ensure the safety limits inherent in the normal operation of electric vehicle batteries and charging piles, it is necessary to set the charging and discharging power constraints of electric vehicles for the initial electric vehicle cluster control model. Specifically, the following formula is used to characterize the charging and discharging power constraints:

[0063] n i,t ·P i,min ≤P i,t ≤n i,t ·Pi,max t=1,2,3....T

[0064] Among them, P i,t represents the charging and discharging power of the i-th electric vehicle in the t-th control time period, n i,t represents the stay state of the i-th electric vehicle in the t-th control time period, P i,min represents the minimum charging and discharging power of the i-th electric vehicle, P i,max represents the maximum charging and discharging power of the i-th electric vehicle, and T represents the number of control time periods.

[0065] It should be noted that in order to ensure that the control strategy will not control the electric vehicle after it leaves, it is necessary to set the parameter n i,t Specifically, this embodiment uses the following formula to obtain the parameter n i,t :

[0066] n i,t =0t>t i,out

[0067] n i,t =1t≤t i,out

[0068] Among them, t i,out represents the departure time of the i-th electric car. Parameter n i,t is a 0 / 1 variable, indicating the stay state of the i-th electric vehicle in the t-th control time period. i,t When it is equal to 0, it means that the electric car has left; when n i,t When it is equal to 1, it means that the electric vehicle is still in a stationary state and can be controlled.

[0069] In order to ensure the electric vehicle user's own travel power demand, it is necessary to set the electric vehicle user's travel power constraint. Specifically, the following formula is used to represent the user's demand power constraint:

[0070]

[0071] Wherein, η represents the charging and discharging efficiency of the electric vehicle under normal conditions, which is set to 95% in this embodiment, Δt represents the time interval of each control time period, which is set to 15 minutes in this embodiment, and Cp i represents the battery capacity of the i-th electric vehicle, SOC i,a It represents the battery state of charge of the i-th electric vehicle when it arrives at the charging station, SOC i,b It represents the minimum battery state of charge expected when the i-th electric vehicle leaves the charging station.

[0072] S3. Establish two electric vehicle charging modes according to different usage scenarios;

[0073] This embodiment designs and simulates two different electric vehicle charging modes to adapt to different usage scenarios and grid requirements. Specifically, the electric vehicle charging mode includes a fast charging mode that uses maximum power for charging and a smart charging mode that uses average power for charging.

[0074] like Figure 2 As shown, step S3 includes:

[0075] S301, based on a usage scenario where rapid power replenishment is required to meet emergency travel needs, charging the electric vehicle at maximum power to establish a rapid charging mode;

[0076] The fast charging mode can significantly increase the power of an electric vehicle in a short period of time, ensuring that it can be put into use quickly.

[0077] S302: Based on a usage scenario where the electric vehicle stays for a long time and is not in a hurry to travel, an average charging power is calculated according to the expected stay time of the electric vehicle, and the electric vehicle is charged at the average charging power to construct a smart charging mode.

[0078] Specifically, the average charging power is calculated using the following formula:

[0079]

[0080] in, represents the average charging power of the ith electric vehicle, t i,tol It is necessary to explain that when the electric vehicle is connected to the power grid and after the limit regulation, its average charging power is updated in a rolling manner.

[0081] S4. Regulate the electric vehicle cluster based on real-time demand response, and quantitatively evaluate the demand response capability of the electric vehicle cluster under different charging modes according to the target electric vehicle cluster regulation model.

[0082] Different charging modes will result in different demand response capability evaluation results. When a demand response event is triggered, the electric vehicle will switch from the original charging mode to an orderly charging and discharging strategy, that is, accept the control of the target electric vehicle cluster control model to respond to the real-time demand of the power grid.

[0083] Specifically, step S4 includes:

[0084] According to the real-time demand response, the electric vehicle cluster in the fast charging mode is regulated to the limit, and the target electric vehicle cluster regulation model is solved to obtain the maximum dispatchable capacity of the electric vehicle cluster in the fast charging mode;

[0085] The maximum dispatchable capacity of the electric vehicle cluster is represented as a quantitative evaluation result of the demand response capability of the electric vehicle cluster.

[0086] Furthermore, step S4 further includes:

[0087] According to the real-time demand response, the electric vehicle cluster in the smart charging mode is regulated to the limit, and the target electric vehicle cluster regulation model is solved to obtain the maximum dispatchable capacity of the electric vehicle cluster in the smart charging mode;

[0088] The maximum dispatchable capacity of the electric vehicle cluster is represented as a quantitative evaluation result of the demand response capability of the electric vehicle cluster.

[0089] In order to verify the effectiveness of an electric vehicle cluster demand response capability evaluation method according to an embodiment of the present invention, in a specific embodiment, the electric vehicle cluster charging demand data of a certain community is used, including 72 AC electric vehicle power supply devices, which represent the charging scenario of a residential area, and the time span is from April 1, 2022 to April 30, 2022. By simulating the electric vehicle cluster charging demand data, the electric vehicle cluster demand response capability of the residential area can be well quantitatively evaluated, thereby providing an important reference for related research.

[0090] Figure 3 The distribution of the results of the real-time peak-shaving / valley-filling demand response capabilities of electric vehicle clusters in residential charging scenarios based on meeting their own charging needs is presented. In this scenario, since residents usually return to the residential area at night to start charging their electric vehicles and then leave in the early morning, the daily demand response capability is reflected in a parabolic shape.

[0091] Figure 4 The average quantitative results of the real-time demand response capabilities of electric vehicle clusters using different charging modes at each moment are presented. Since the fast charging mode causes the battery capacity of electric vehicles to be fully charged early, the smart charging mode, which charges at a lower power, can absorb more electric energy when facing the charging demand response, resulting in greater charging flexibility for the electric vehicle cluster in the smart charging mode at the charging response moment. In contrast, the discharge flexibility in the smart charging mode is reduced, because its battery capacity is not as good as that in the fast charging mode under the discharge demand response requirements, resulting in reduced discharge flexibility.

[0092] An embodiment of the present invention provides an electric vehicle cluster demand response capability evaluation method, which is based on the charging demand data of the electric vehicle cluster and takes into account the impact of different charging modes on the real-time demand response capability of the electric vehicles. The real-time demand response capability of the electric vehicle cluster under different charging modes is evaluated, which is beneficial to ensuring the operation and maintenance management on the power grid side, thereby improving the regulation capability and operation efficiency of the power grid.

[0093] Based on the above electric vehicle cluster demand response capability evaluation method, Figure 5 As shown, an embodiment of the present invention provides an electric vehicle cluster demand response capability evaluation system, comprising:

[0094] Data acquisition module 1 is used to acquire charging demand data of the electric vehicle cluster, the charging demand data including battery capacity, maximum charging power, maximum discharging power, minimum battery state of charge when leaving, battery state of charge when arriving and departure time, the minimum battery state of charge when leaving is the minimum battery state of charge expected when the electric vehicle leaves the charging pile, and the battery state of charge when arriving is the battery state of charge when the electric vehicle arrives at the charging pile;

[0095] Model building module 2 is used to build an initial electric vehicle cluster control model based on charging demand data, and constrain the initial electric vehicle cluster control model to obtain a target electric vehicle cluster control model. The objective function of the initial electric vehicle cluster control model includes the maximum dispatchable capacity of the electric vehicle cluster and the charging and discharging energy loss of the electric vehicle cluster. The constraints include charging and discharging power constraints and user demand power constraints.

[0096] A charging mode building module 3 is used to establish two electric vehicle charging modes according to different usage scenarios. The electric vehicle charging modes include a fast charging mode that uses maximum power for charging and a smart charging mode that uses average power for charging;

[0097] The quantitative evaluation module 4 is used to regulate the electric vehicle cluster based on real-time demand response, and quantitatively evaluate the demand response capability of the electric vehicle cluster under different charging modes according to the target electric vehicle cluster regulation model.

[0098] It should be noted that each module in the above-mentioned electric vehicle cluster demand response capability assessment system can be fully or partially implemented by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of an electric vehicle cluster demand response capability assessment system, please refer to the definition of an electric vehicle cluster demand response capability assessment method above. The two have the same functions and effects, which will not be repeated here.

[0099] An embodiment of the present invention further provides a terminal device, the terminal device comprising:

[0100] processor, memory, and bus;

[0101] The bus is used to connect the processor and the memory;

[0102] The memory is used to store operation instructions;

[0103] The processor is used to call the operation instruction, and the executable instruction enables the processor to perform the operation corresponding to the electric vehicle cluster demand response capability evaluation method as described above in the present invention.

[0104] In an optional embodiment, a terminal device is provided, such as Figure 6 As shown, Figure 6 The terminal device 5000 shown includes: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, such as through a bus 5002. Optionally, the terminal device 5000 may also include a transceiver 5004. It should be noted that in actual applications, the transceiver 5004 is not limited to one, and the structure of the terminal device 5000 does not constitute a limitation on the embodiments of the present invention.

[0105] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0106] The bus 5002 may include a path to transmit information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0107] The memory 5003 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, a CD-ROM or other optical disk storage, an optical disk storage (including a compressed optical disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0108] The memory 5003 is used to store application code for executing the solution of the present invention, and the execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the above method embodiments.

[0109] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method for evaluating the demand response capability of an electric vehicle cluster described above is implemented.

[0110] Yet another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed on a computer, enables the computer to execute the corresponding contents of the aforementioned method embodiments.

[0111] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

[0112] To sum up, compared with the prior art, the electric vehicle cluster demand response capability evaluation method, system, device and medium of the embodiment of the present invention have the following beneficial effects: based on the charging demand data of the electric vehicle cluster and taking into account the impact of different charging modes on the real-time demand response capability of the electric vehicle, the real-time demand response capability of the electric vehicle cluster under different charging modes is evaluated, which is conducive to ensuring the operation and maintenance management on the power grid side, thereby improving the regulation capability and operation efficiency of the power grid.

[0113] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A method for evaluating the demand response capability of electric vehicle clusters, characterized in that: include: Acquire charging demand data of the electric vehicle cluster, wherein the charging demand data includes battery capacity, maximum charging power, maximum discharging power, minimum battery state of charge when leaving, battery state of charge when arriving, and departure time, wherein the minimum battery state of charge when leaving is the minimum battery state of charge expected when the electric vehicle leaves the charging pile, and the battery state of charge when arriving is the battery state of charge when the electric vehicle arrives at the charging pile; An initial electric vehicle cluster control model is constructed based on the charging demand data, and the initial electric vehicle cluster control model is constrained to obtain a target electric vehicle cluster control model, wherein the objective function of the initial electric vehicle cluster control model includes the maximum dispatchable capacity of the electric vehicle cluster and the charging and discharging energy loss of the electric vehicle cluster, and the constraints include the charging and discharging power constraints and the user demand power constraints; Two electric vehicle charging modes are established according to different usage scenarios, wherein the electric vehicle charging modes include a fast charging mode that uses maximum power for charging and a smart charging mode that uses average power for charging; The electric vehicle cluster is regulated based on real-time demand response, and the demand response capability of the electric vehicle cluster under different charging modes is quantitatively evaluated according to the target electric vehicle cluster regulation model.

2. The demand response capability evaluation method according to claim 1, characterized in that: The constructing of an initial electric vehicle cluster control model based on the charging demand data includes: The objective function of the initial electric vehicle cluster control model is characterized by the following formula: Among them, C need It represents the total demand power of the power grid demand response during the demand response period. The positive and negative values ​​represent the valley filling demand response service and the peak shaving demand response service respectively. i,t represents the charging and discharging power of the ith electric vehicle in the tth regulation time period, N represents the number of electric vehicles in the electric vehicle cluster, T represents the number of regulation time periods, λ represents the weighted coefficient of the responsiveness of the electric vehicle cluster, P loss,i It represents the sum of energy loss of the i-th electric vehicle during the entire control period.

3. The demand response capability evaluation method according to claim 2, characterized in that: The following formula is used to calculate the sum of the energy loss of electric vehicles during the entire control period: or loss =1st Among them, P loss,i represents the sum of energy losses of the i-th electric vehicle during the entire control period, P i,t represents the charging and discharging power of the ith electric vehicle in the tth regulation time period, T represents the number of regulation time periods, η loss It represents the power loss rate during the charging and discharging process of the electric vehicle under normal conditions, and η represents the charging and discharging efficiency of the electric vehicle under normal conditions.

4. The demand response capability evaluation method according to claim 1, characterized in that: The constraining the initial electric vehicle cluster control model to obtain a target electric vehicle cluster control model includes: The following formula is used to characterize the charge and discharge power constraint: n i,t ·P i,min ≤P i,t ≤n i,t ·P i,max t=1,2,3....T Among them, P i,t represents the charging and discharging power of the i-th electric vehicle in the t-th control time period, n i,t represents the stay state of the i-th electric vehicle in the t-th control time period, P i,min represents the minimum charging and discharging power of the i-th electric vehicle, P i,max represents the maximum charging and discharging power of the i-th electric vehicle, and T represents the number of control time periods; The following formula is used to characterize the user demand power constraint: Among them, η represents the charging and discharging efficiency of electric vehicles under normal conditions, Δt represents the time interval of each control period, and Cp i represents the battery capacity of the i-th electric vehicle, SOC i,a It represents the battery state of charge of the i-th electric vehicle when it arrives at the charging station, SOC i,b It represents the minimum battery state of charge expected when the i-th electric vehicle leaves the charging station.

5. The demand response capability evaluation method according to claim 1, characterized in that: The two electric vehicle charging modes are established according to different usage scenarios, including: Based on the usage scenario where the power needs to be replenished quickly to meet emergency travel needs, the electric vehicle is charged at maximum power to build a fast charging mode; Based on the usage scenario that the electric vehicle stays for a long time and is not in a hurry to travel, the average charging power is calculated according to the expected stay time of the electric vehicle, and the electric vehicle is charged with the average charging power to construct a smart charging mode.

6. The demand response capability evaluation method according to claim 1, characterized in that: The regulating the electric vehicle cluster based on real-time demand response and quantitatively evaluating the demand response capability of the electric vehicle cluster under different charging modes according to the target electric vehicle cluster regulation model include: Performing limit regulation on the electric vehicle cluster in the fast charging mode according to the real-time demand response, and solving the target electric vehicle cluster regulation model to obtain the maximum dispatchable capacity of the electric vehicle cluster in the fast charging mode; The maximum dispatchable capacity of the electric vehicle cluster is characterized as a quantitative evaluation result of the demand response capability of the electric vehicle cluster.

7. The demand response capability evaluation method according to claim 1, characterized in that: The regulating the electric vehicle cluster based on real-time demand response and quantitatively evaluating the demand response capability of the electric vehicle cluster under different charging modes according to the target electric vehicle cluster regulation model also includes: According to the real-time demand response, the electric vehicle cluster in the smart charging mode is subjected to extreme regulation, and the target electric vehicle cluster regulation model is solved to obtain the maximum dispatchable capacity of the electric vehicle cluster in the smart charging mode; The maximum dispatchable capacity of the electric vehicle cluster is characterized as a quantitative evaluation result of the demand response capability of the electric vehicle cluster.

8. An electric vehicle cluster demand response capability evaluation system, characterized in that: include: A data acquisition module is used to acquire charging demand data of the electric vehicle cluster, wherein the charging demand data includes battery capacity, maximum charging power, maximum discharging power, minimum battery state of charge when leaving, battery state of charge when arriving, and departure time, wherein the minimum battery state of charge when leaving is the minimum battery state of charge expected when the electric vehicle leaves the charging pile, and the battery state of charge when arriving is the battery state of charge when the electric vehicle arrives at the charging pile; A model building module is used to build an initial electric vehicle cluster control model based on the charging demand data, and constrain the initial electric vehicle cluster control model to obtain a target electric vehicle cluster control model, wherein the objective function of the initial electric vehicle cluster control model includes the maximum dispatchable capacity of the electric vehicle cluster and the charging and discharging energy loss of the electric vehicle cluster, and the constraints include charging and discharging power constraints and user demand power constraints; A charging mode building module, used to establish two electric vehicle charging modes according to different usage scenarios, wherein the electric vehicle charging modes include a fast charging mode that uses maximum power for charging and a smart charging mode that uses average power for charging; The quantitative evaluation module is used to regulate the electric vehicle cluster based on real-time demand response, and quantitatively evaluate the demand response capability of the electric vehicle cluster under different charging modes according to the target electric vehicle cluster regulation model.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the electric vehicle cluster demand response capability evaluation method as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the electric vehicle cluster demand response capability evaluation method according to any one of claims 1 to 7.

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