Peak regulation and frequency modulation method and device for aggregator to participate in electricity market

Abstract

The invention relates to the technical field of power grid dispatching, and discloses a peak regulation and frequency modulation method and device for an aggregator to participate in a power market, and the method comprises the steps: obtaining electric vehicle operation parameters of an electric vehicle aggregator management region; determining the frequency modulation capacity and the response deviation rate of an electric vehicle charging station in the management area of the electric vehicle aggregator according to the electric vehicle operation parameters; and according to the frequency modulation capacity and the response deviation rate of the electric vehicle charging station, performing peak regulation and frequency modulation scheduling on the electric vehicle charging station. According to the method, the response accuracy of the electric vehicle aggregator can be quantitatively evaluated, the theoretical scheduling capacity of the electric vehicle aggregator is accurately matched with the actual adjustable capacity, the auxiliary service requirement of a power grid is efficiently responded while the charging requirement of a user is met, and the user experience is improved. Efficient and reasonable scheduling of an electric vehicle aggregator in a peak regulation and frequency modulation auxiliary service market is realized, and safe and stable operation of a power grid is ensured.

Description

Technical Field

[0001] This application relates to the field of power grid dispatching technology, specifically to a method and apparatus for aggregators to participate in peak shaving and frequency regulation in the electricity market. Background Technology

[0002] With the continuous increase in the proportion of renewable energy generation, the intermittency and volatility of its output pose severe challenges to the frequency stability and load balance of the power system, highlighting the increasing value of flexible load resources in the ancillary services market. Electric vehicles (EVs), as typical adjustable loads, have become an important potential resource for participating in ancillary services such as peak shaving and frequency regulation due to their fast response speed and low control costs. However, the power capacity of a single EV is limited, and its grid connection behavior is random. Therefore, it is necessary to use electric vehicle aggregators (EVAs) as intermediaries to achieve large-scale integration in order to meet the capacity and response performance requirements of the electricity market for ancillary service resources.

[0003] In related technologies, EVA's participation in ancillary service regulation strategies has significant limitations: First, most solutions focus on a single ancillary service type, failing to achieve coordinated optimization of peak shaving and frequency regulation services, resulting in the EV dispatchable potential not being fully explored; Second, frequency regulation capacity assessment relies heavily on probability distribution models, lacking dynamic quantification methods that combine real-time status, and does not incorporate response accuracy into the capacity assessment system, making it difficult to support accurate day-ahead market reporting and intraday instruction decomposition; Third, the dynamic characteristics of user charging demand are not adequately considered, static scheduling strategies are prone to conflicts between user satisfaction and ancillary service response performance, and there is a lack of differentiated incentive mechanisms for users with high response quality; Fourth, the lack of a multi-aggregator collaboration mechanism leads to low efficiency in large-scale EV cluster scheduling, and the absence of fair allocation rules based on response performance makes it difficult to adapt to the regulation needs of regional power systems. Summary of the Invention

[0004] This application aims to at least address the technical problem that electric vehicle aggregators, when participating in the power ancillary services market, find it difficult to balance multi-service coordination, real-time dynamic response, and user needs, resulting in poor dispatching effectiveness and efficiency, and potentially affecting the stability of power grid operation.

[0005] To address the aforementioned technical problems, embodiments of this application provide a method for aggregators to participate in peak shaving and frequency regulation in the electricity market, comprising: Obtain the operating parameters of electric vehicles within the managed region of the electric vehicle aggregator; Based on the electric vehicle operating parameters, determine the frequency regulation capacity and response deviation rate of electric vehicle charging stations within the management area of ​​the electric vehicle aggregator; Based on the frequency regulation capacity and response deviation rate of the electric vehicle charging station, peak shaving and frequency regulation scheduling are performed on the electric vehicle charging station.

[0006] In some embodiments, calculating the frequency regulation capacity and response deviation rate of charging stations within the management area of ​​the electric vehicle aggregator based on the electric vehicle operating parameters includes: The total frequency regulation capacity of a single electric vehicle charging station is generated by aggregating the frequency regulation capacity of each individual electric vehicle. Calculate the single response deviation rate of the electric vehicle charging station based on its total frequency regulation capacity. The dispatchable capacity of the electric vehicle aggregator is determined based on the total frequency regulation capacity of the electric vehicle charging station. The overall response deviation rate of the electric vehicle charging station is determined based on the single response deviation rate of the electric vehicle charging station.

[0007] In some embodiments, the single-response deviation rate of the electric vehicle charging station is determined based on the deviation between the effective response capacity of the electric vehicle charging station and a preset available response capacity. The formula for calculating the single-response deviation rate is as follows: ; ; in, The deviation between the effective response capacity and the available response capacity; is the initial performance lower bound; n is the total number of times electric vehicle aggregators participate in market clearing.

[0008] In some embodiments, determining the dispatchable capacity of the electric vehicle aggregator based on the total frequency regulation capacity of the electric vehicle charging stations includes: Based on the total frequency regulation capacity of the electric vehicle charging stations, the daily frequency regulation capacity of the electric vehicle aggregator is predicted using the PatchTST model. Based on the historical adjustability margin of each electric vehicle charging station, the adjustability margin of the electric vehicle charging station in the next frequency regulation period is predicted by an LSTM model. The dispatchable capacity of the electric vehicle aggregator is determined based on the daily frequency regulation capacity of the electric vehicle aggregator and the adjustability margin of the electric vehicle charging station during the next frequency regulation period.

[0009] In some embodiments, determining the overall response deviation rate of the electric vehicle charging station based on the single response deviation rate of the electric vehicle charging station includes: The recent response accuracy and historical response accuracy are determined based on the single response deviation rate of the electric vehicle charging station. The recent response accuracy and the historical response accuracy are weighted and calculated to obtain the comprehensive response accuracy; The recent response accuracy is based on the single response accuracy within a sliding time window and is obtained through a time decay weighted algorithm; the historical response accuracy is obtained by calculating the arithmetic mean of all long-term response accuracies.

[0010] In some embodiments, peak shaving and frequency regulation scheduling of the electric vehicle charging station is performed based on the frequency regulation capacity and response deviation rate of the electric vehicle charging station, including: If the overall response accuracy is greater than the preset accuracy threshold, additional frequency modulation capacity will be allocated to the electric vehicle charging station corresponding to the overall response accuracy. If the overall response accuracy is less than or equal to a preset accuracy threshold, a basic frequency regulation capacity is allocated to the electric vehicle charging station corresponding to the overall response accuracy.

[0011] In some embodiments, the method further includes: Based on the comprehensive response accuracy and the charging status of the electric vehicle, the peak shaving and frequency regulation scheduling of the electric vehicle charging station are corrected.

[0012] In some embodiments, the method further includes: Peak shaving and frequency regulation scheduling of the electric vehicle charging station are performed according to frequency regulation performance indicators, wherein the frequency regulation performance indicators include at least one of regulation rate, response time, and regulation accuracy weighted value.

[0013] In some embodiments, the method further includes: If the electric vehicle is about to be fully charged, the charging power of the electric vehicle will be reduced to the minimum charging power threshold. If the charging time of the electric vehicle is about to reach the preset time, the charging power of the charging station will be increased.

[0014] This application embodiment also provides a peak shaving and frequency regulation device for aggregators participating in the electricity market, including: The acquisition module is configured to acquire the operating parameters of electric vehicles in the management area of ​​electric vehicle aggregators; The module is configured to determine the frequency regulation capacity and response deviation rate of electric vehicle charging stations within the management area of ​​the electric vehicle aggregator based on the electric vehicle operating parameters. The scheduling module is configured to perform peak shaving and frequency regulation scheduling on the electric vehicle charging station based on the frequency regulation capacity and response deviation rate of the electric vehicle charging station.

[0015] This application also provides an electronic device, which includes at least a memory and a processor. The memory stores a computer program, and the processor executes the computer program in the memory to implement the above-described method for aggregators to participate in the electricity market for peak shaving and frequency regulation.

[0016] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for aggregators to participate in the electricity market for peak shaving and frequency regulation.

[0017] The peak shaving and frequency regulation method and apparatus for aggregators participating in the electricity market provided in this application embodiment obtains the operating parameters of electric vehicles in the management area of ​​the electric vehicle aggregator; determines the frequency regulation capacity and response deviation rate of electric vehicle charging stations in the management area of ​​the electric vehicle aggregator based on the electric vehicle operating parameters; and performs peak shaving and frequency regulation scheduling on the electric vehicle charging stations based on the frequency regulation capacity and response deviation rate of the electric vehicle charging stations. This enables quantitative evaluation of the response accuracy of the electric vehicle aggregator, accurately matches the theoretical scheduling capacity of the electric vehicle aggregator with the actual adjustable capacity, and efficiently responds to the grid ancillary service needs while meeting user charging needs. This achieves efficient and reasonable scheduling of electric vehicle aggregators in the peak shaving and frequency regulation ancillary service market and ensures the safe and stable operation of the power grid. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a first flowchart of a method for aggregators to participate in peak shaving and frequency regulation in the electricity market, according to an embodiment of this application. Figure 2 This is a cloud-edge collaborative architecture diagram for electric vehicle aggregators participating in AGC (Automatic Generation Control) services, as shown in this application embodiment. Figure 3 This is a schematic diagram of the structure of a peak-shaving and frequency regulation device for aggregators participating in the electricity market, as described in this application embodiment. Detailed Implementation

[0020] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0021] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0022] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0023] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0024] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application, which have the features described in the claims and are therefore all within the scope of protection defined herein.

[0025] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0026] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0027] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0028] Example 1 Figure 1 A flowchart illustrating a method for aggregators to participate in peak shaving and frequency regulation in the electricity market, according to an embodiment of this application, is shown. Figure 2 This illustration shows a cloud-edge collaborative architecture diagram of electric vehicle aggregators participating in AGC (Automatic Generation Control) services, as shown in an embodiment of this application. Figure 1 and Figure 2 As shown in the embodiments of this application, a method for aggregators to participate in peak shaving and frequency regulation in the electricity market is provided, including: S101: Obtain the operating parameters of electric vehicles in the electric vehicle aggregator's managed area.

[0029] like Figure 2 As shown, this embodiment utilizes a cloud-edge collaborative architecture of "terminal device layer - edge device layer - cloud layer" to achieve peak shaving and frequency regulation in the electricity market. The terminal device layer collects the electric vehicle operating parameters in real time through smart charging piles.

[0030] Assume there are n smart charging stations within the aggregator's managed area, distributed in residential and work areas, managed and controlled by the aggregator. The charging stations can collect electric vehicle (EV) operating parameters at 15-minute intervals, including access and departure times. , The state of charge (SOC) upon entry and the expected SOC upon exit: , Maximum and minimum values ​​of the state of charge of electric vehicles Maximum charging power for users Electric vehicle battery capacity And the reporting response capacity during peak shaving / frequency modulation. Actual effective response capacity Data such as...

[0031] S102: Based on the electric vehicle operating parameters, determine the frequency regulation capacity and response deviation rate of the electric vehicle charging stations within the management area of ​​the electric vehicle aggregator.

[0032] The edge device layer (EV charging station, EVCS) calculates the frequency regulation capacity (including up-adjustment margin and down-adjustment margin) and response deviation rate of EV charging stations within the management area of ​​the EV aggregator based on the EV operating parameters sent by the terminal layer. It calculates the frequency regulation capacity of a single EV, aggregates them to form the total frequency regulation capacity of a single EVCS, determines whether the user's charging demand can be met, and generates a single response accuracy index (single response deviation rate) in real time.

[0033] S103: Based on the frequency regulation capacity and response deviation rate of the electric vehicle charging station, perform peak shaving and frequency regulation scheduling on the electric vehicle charging station.

[0034] The cloud layer (cloud computing center) receives edge data from the edge device layer, calculates the overall response accuracy (overall response deviation rate) based on the frequency modulation capacity and single response accuracy data uploaded by the edge device layer, and allocates the total peak-shaving frequency modulation command according to the schedulable capacity and overall response accuracy of each EVA.

[0035] The peak shaving and frequency regulation method for aggregators participating in the electricity market provided in this application embodiment obtains the operating parameters of electric vehicles in the management area of ​​the electric vehicle aggregator; determines the frequency regulation capacity and response deviation rate of electric vehicle charging stations in the management area of ​​the electric vehicle aggregator based on the electric vehicle operating parameters; and performs peak shaving and frequency regulation scheduling on the electric vehicle charging stations based on the frequency regulation capacity and response deviation rate of the electric vehicle charging stations. This method can quantitatively evaluate the response accuracy of electric vehicle aggregators, accurately match the theoretical scheduling capacity of electric vehicle aggregators with the actual adjustable capacity, and efficiently respond to the grid ancillary service needs while meeting user charging needs. This enables efficient and reasonable scheduling of electric vehicle aggregators in the peak shaving and frequency regulation ancillary service market and ensures the safe and stable operation of the power grid.

[0036] In addition, this application, through a cloud-edge collaborative architecture of "terminal device layer - edge device layer - cloud layer", can achieve closed-loop control from data collection, capacity assessment, bidding decision to instruction execution.

[0037] In some embodiments, step S102, calculating the frequency regulation capacity and response deviation rate of charging stations within the management area of ​​the electric vehicle aggregator based on the electric vehicle operating parameters, includes: S1021: Based on the frequency regulation capacity of a single electric vehicle, aggregate to generate the total frequency regulation capacity of a single electric vehicle charging station; S1022: Calculate the single response deviation rate of the electric vehicle charging station based on the total frequency regulation capacity of the electric vehicle charging station; S1023: Determine the dispatchable capacity of the electric vehicle aggregator based on the total frequency regulation capacity of the electric vehicle charging station; S1024: Determine the overall response deviation rate of the electric vehicle charging station based on the single response deviation rate of the electric vehicle charging station.

[0038] The edge device layer can be used for localized computation. Based on the parameter settings of EV users collected from the terminal device layer, the edge device layer determines whether the charging station can meet the charging needs of EV users.

[0039] The edge device layer determines user requirements using the following formula:

[0040] In the formula, =1, =0 represents the charging needs of EV users and the inability to meet the charging needs of EV users, respectively. The maximum allowed charging time set for the user. The threshold coefficient for meeting the target charging demand is generally taken as 15%-30%. If If the value is 0, the terminal device will prompt the user to adjust the parameters.

[0041] The edge device layer calculates the frequency modulation capacity of a single electric vehicle:

[0042] The frequency regulation capacity of a single electric vehicle can be used for EVA day-ahead market capacity declaration and EVA intraday AGC instruction decomposition. By aggregating and calculating the data of a single EV under the jurisdiction of this electric vehicle charging station (EVCS), we can obtain:

[0043]

[0044] In the formula: K is the number of EVs within EVCS; , , These are the EVCS's frequency modulation capacity up-adjustment margin, frequency modulation capacity down-adjustment margin, and frequency modulation capacity, respectively.

[0045] Edge device layer based on effective response capacity With preset available response capacity The deviation between the two is used to calculate the single response deviation rate. Effective response capacity is the sum of effective response capacities over a continuous time period during a single demand response. The calculation is as follows:

[0046]

[0047] In the formula: The deviation between the effective response capacity and the available response capacity; is the initial performance lower bound; n is the total number of times electric vehicle aggregators participate in market clearing.

[0048] Use the default value for newly registered EVs. It can avoid indicator failure caused by random bias; n specifically refers to the total number of times the load EVA participates in market clearing (market equilibrium).

[0049] After calculating the frequency modulation capacity and single response deviation rate, the edge device layer uploads the calculated data to the cloud layer. The cloud layer integrates the data and determines the dispatchable capacity of the electric vehicle aggregator based on the total frequency modulation capacity of the electric vehicle charging station, and determines the comprehensive response deviation rate of the electric vehicle charging station based on the single response deviation rate of the electric vehicle charging station.

[0050] In some embodiments, step S1023, determining the dispatchable capacity of the electric vehicle aggregator based on the total frequency regulation capacity of the electric vehicle charging station, includes: S201: Based on the total frequency regulation capacity of the electric vehicle charging station, predict the daily frequency regulation capacity of the electric vehicle aggregator using the PatchTST model; S202: Based on the historical adjustability margin of each electric vehicle charging station, predict the adjustability margin of the electric vehicle charging station in the next frequency regulation period using an LSTM model; S203: Determine the dispatchable capacity of the electric vehicle aggregator based on the daily frequency regulation capacity of the electric vehicle aggregator and the adjustability margin of the electric vehicle charging station during the next frequency regulation period.

[0051] The cloud layer receives real-time EVCS data, merges it with historical databases, and performs normalization processing. Then, using the PatchTST model, it inputs capacity data from 672 time periods over the past 7 days and outputs predicted EVA frequency regulation capacity values ​​for multiple time periods (e.g., 96 time periods) for the next day, predicting the daily frequency regulation capacity of electric vehicle aggregators. The LSTM model uses online learning to update the network state, inputting EVCS adjustability margins from multiple time periods (e.g., 96 time periods) within the past day, and outputs predicted adjustability margins for electric vehicle charging stations in the next time period. Simultaneously, based on the predicted daily frequency regulation capacity and the adjustability margins of the electric vehicle charging stations in the next frequency regulation period, it calculates the comprehensive response deviation rate, achieving a dynamic quantitative evaluation of EVCS response performance.

[0052] Specifically, the cloud layer uses the PatchTST model to predict the daily frequency regulation capacity of EVA. The PatchTST model is a time series forecasting model based on the Transformer architecture, which can effectively capture long-term dependencies in the sequence, thereby improving forecasting performance. The steps for short-term EVA frequency regulation capacity forecasting using the PatchTST model in the cloud layer include: (1) Access the intraday historical data of EVA in the cloud data storage center, including the frequency modulation capacity and adjustable margin of EVA for all frequency modulation periods. Then, preprocess the historical data, including data cleaning and normalization. (2) The PatchTST prediction model was trained using the preprocessed data. The input data of the model was the EVA frequency modulation capacity and its adjustable margin for 672 frequency modulation periods in the past week, and the output data was the EVA frequency modulation capacity and its adjustable margin for 96 frequency modulation periods in the next day. (3) Use partial data to validate the trained PatchTST prediction model to check whether the model is overfitted or underfitted, and make appropriate adjustments. (4) The adjusted PatchTST model is used for prediction. The output data obtained is the frequency modulation capacity and adjustable margin of EVA for the 96 frequency modulation periods of the day used for market reporting.

[0053] Furthermore, the cloud layer employs an LSTM model to predict the adjustability margin of each EVCS. Based on the cloud-edge collaborative scheduling architecture, the cloud layer can receive real-time evaluations of the adjustability margin of each EVCS from edge devices. Therefore, an online learning approach is used to train the LSTM model to suit the aforementioned ultra-short-term prediction scenarios that require real-time adaptation to data changes. The specific prediction steps are as follows: (1) The cloud layer calls the historical data of each EVCS from the previous week, which is stored in the cloud data storage center, including the adjustable margin of each EVCS during all frequency adjustment periods. Then, the historical data is preprocessed, including data cleaning and normalization.

[0054] (2) The cloud layer trains an LSTM prediction model for each EVCS based on the preprocessed data. The input data of the model is the adjustable margin of the EVCS in the past 96 frequency modulation periods, and the output data is the adjustable margin of the EVCS in the next frequency modulation period. The LSTM prediction model is initialized in this way. (3) The cloud computing center will upload the upscaling margin of each EVCS frequency modulation capacity in real time from the edge device layer. Lowering the margin Add new input data to the LSTM prediction model and update the LSTM network status in real time; (4) Based on the updated LSTM prediction model, predict the EVCS frequency regulation capacity in the ultra-short term, and output the data as the up-adjustment margin of the EVCS frequency regulation capacity in the next frequency regulation period. Lowering the margin The number of LSTM network predictions and the number of LSTM network updates within a day represent the total number of available scheduling periods for that EVA within that day.

[0055] In some embodiments, step S1024, determining the overall response deviation rate of the electric vehicle charging station based on the single response deviation rate of the electric vehicle charging station, includes: S301: Determine the recent response accuracy and historical response accuracy based on the single response deviation rate of the electric vehicle charging station; S302: The recent response accuracy and the historical response accuracy are weighted and calculated to obtain the comprehensive response accuracy; The recent response accuracy is based on the single response accuracy within a sliding time window and is obtained through a time decay weighted algorithm; the historical response accuracy is obtained by calculating the arithmetic mean of all long-term response accuracies.

[0056] The cloud layer calculates the overall response accuracy by weighting the accuracy of recent responses with the accuracy of historical responses.

[0057] The recent response accuracy is based on the single-response accuracy within a sliding time window (e.g., the last 72 hours), and is obtained using a time-decay weighted algorithm.

[0058] in, The time discount factor is used because, when EVA participates in market clearing (market equilibrium), response accuracy indicators that are further away from that point in time have a relatively smaller impact on the current clearing. Therefore, recent data has a higher weight. Single-response accuracy is calculated based on the single-response deviation rate mentioned above.

[0059] Historical response accuracy is obtained by calculating the arithmetic mean of all response accuracies over a long period (e.g., the last 30 days):

[0060] Here, "all response accuracy" refers to the accuracy of all individual responses.

[0061] Overall response accuracy The accuracy was calculated by weighted fusion of short-term (recent) and long-term (historical) response.

[0062] In the formula, As the weight for the accuracy of historical responses, 1- Weighting for recent response accuracy.

[0063] As shown above, the cloud layer uses the PatchTST model for short-term prediction of EVA frequency modulation capacity and the LSTM model for ultra-short-term prediction of EVCS frequency modulation capacity. At the same time, it integrates peak shaving market information and accuracy data uploaded from the edge layer to calculate the comprehensive response deviation rate, thereby forming a global strategy for generating peak shaving and frequency modulation scheduling.

[0064] In some embodiments, step S103, based on the frequency regulation capacity and response deviation rate of the electric vehicle charging station, includes peak shaving and frequency regulation scheduling of the electric vehicle charging station, including: S401: If the overall response accuracy is greater than the preset accuracy threshold, additional frequency modulation capacity shall be allocated to the electric vehicle charging station corresponding to the overall response accuracy. S402: If the overall response accuracy is less than or equal to a preset accuracy threshold, allocate basic frequency regulation capacity to the electric vehicle charging station corresponding to the overall response accuracy.

[0065] The cloud layer can perform peak shaving and frequency regulation collaborative allocation based on the calculated schedulable capacity and overall response accuracy of the electric vehicle aggregator. The accuracy constraint is as follows:

[0066] in, This is the accuracy threshold.

[0067] EVCSs must meet accuracy constraints; EVCSs below a threshold are allocated only basic capacity; EVCSs with high accuracy (such as...) (≥0.9) can obtain an additional 10%-20% capacity quota and have priority to participate in emergency control tasks.

[0068] When implementing specific control based on the generated global peak-shaving and frequency-modulation scheduling strategy, instruction decomposition and execution are carried out through cloud-edge collaboration at the cloud layer, edge device layer, and terminal layer: (1) Intraday instruction response timing is initiated. At any given time, the edge device will calculate the EVCS frequency modulation capacity (including upsampling margin) for this time period. Lowering the margin The single-response deviation rate is uploaded to the cloud layer; At that moment, the power grid AGC master station system issues a general instruction, and the cloud layer immediately initiates the instruction decomposition process.

[0069] (2) The cloud layer decomposes the instructions. The cloud layer completes the instruction decomposition within a preset time (e.g., within 30 minutes): based on frequency modulation performance indicators. For comprehensive response accuracy The core principle is to prioritize assigning instructions to the higher-performing EVCS; during the decomposition process, power constraints and the aforementioned accuracy constraints are strictly followed to ensure that instruction allocation is within the adjustable margin range and to avoid exceeding the actual adjustable potential of the EVCS.

[0070] The power constraint is as follows:

[0071] in, , Peak shaving / valley filling capacity for time period t (kW); , The maximum discharge / charge potential (kW) of EVCS during time period t is uploaded in real time by the edge device.

[0072] (3) Edge device layer executes instructions. After receiving the scheduling instructions sent by the EVCS level, the edge device layer further decomposes the instructions to individual EVs, charges each EV, and adjusts the charging power according to the real-time status of each EV.

[0073] Optionally, peak shaving and frequency regulation scheduling of the electric vehicle charging stations also includes: (1) Determine peak-shaving capacity (kW), the peak-shaving capacity is determined based on peak shaving and valley filling. Peak shaving capacity is the total EV charging power that can be reduced during peak grid load periods; valley filling capacity is the total charging power that can be increased during off-peak load periods. The peak-shaving capacity is determined based on the real-time EV charging status uploaded by edge devices, prioritizing capacity reserves for periods when charging is about to complete or the maximum charging time is about to end.

[0074] (2) Determine the backup for frequency modulation. , standby (kW), the upper reserve is the total charging power that can be increased to respond to the low grid frequency command, and the lower reserve is the total charging power that can be reduced to respond to the high grid frequency command.

[0075] In some embodiments, the method further includes: S501: Based on the comprehensive response accuracy and the charging status of the electric vehicle, the peak shaving and frequency regulation scheduling of the electric vehicle charging station is corrected.

[0076] When performing peak shaving and frequency regulation scheduling for the electric vehicle charging stations, it is necessary to periodically (e.g., hourly) make feedback corrections based on actual operating data to achieve closed-loop feedback optimization of peak shaving and frequency regulation scheduling. When making corrections based on comprehensive response accuracy, multiple comprehensive response accuracies are statistically analyzed. If EVCS three consecutive times If the value is less than 0.6, the subsequent capacity quota is reduced to achieve a reasonable allocation of frequency regulation capacity. When adjusting based on the charging status of electric vehicles, the target SOC achievement rate of the EV is checked. If it is less than 95%, the instruction decomposition strategy of the edge device layer is adjusted to prioritize the charging needs of users corresponding to charging piles. In this embodiment, the scheduling strategy can be dynamically adjusted according to the real-time status of users and accuracy indicators. In addition, in this embodiment, the LSTM prediction model can be updated according to actual operating data to improve the prediction accuracy of frequency regulation capacity in the next cycle, forming a closed loop of "instruction-execution-feedback-optimization".

[0077] In some embodiments, the method further includes: S601: Perform peak shaving and frequency regulation scheduling on the electric vehicle charging station according to the frequency regulation performance index, wherein the frequency regulation performance index includes at least one of regulation rate, response time, and regulation accuracy weighted value.

[0078] Peak shaving and frequency regulation scheduling of electric vehicle charging stations are subject to frequency regulation performance indicators The specific performance constraints are as follows:

[0079] in, , , represents the regulation rate (kW / s), response time (s), and regulation accuracy (deviation rate) of the EVCS.

[0080] When multiple aggregators are involved, the cloud layer aggregates the adjustable capacity data of each EVA and calculates the frequency modulation performance index of EVCS. The regional overall dispatch instructions are allocated proportionally based on the frequency modulation performance index: if the overall frequency modulation performance index of a certain EVA is high, its dispatch allocation weight can be appropriately increased to improve the overall response efficiency of charging stations in the region; the allocation results can be synchronized to the cloud of each EVA to ensure the coordination of instruction execution. EVCSs with frequency modulation performance indices below a preset threshold (e.g., 0.5) do not participate in the decomposition of dispatch instructions.

[0081] In some embodiments, the method further includes: S701: If the electric vehicle is about to be fully charged, the charging power of the electric vehicle is reduced to the minimum charging power threshold. S702: If the charging time of the electric vehicle is about to reach the preset time, the charging power of the charging station will be increased.

[0082] Peak shaving and frequency regulation scheduling are subject to user constraints, including: (1) Time constraint: The charging time of a single EV is less than or equal to the user's allowed time (hours), and EV instructions that are about to expire are given priority.

[0083] (2) Power constraint: The initial SOC needs to be raised to the target SOC=1. If the target is not met, the charging effect of the charging station is considered to be poor.

[0084] In this step, peak shaving and frequency regulation can be adjusted based on the electric vehicle's status. Specifically, when a peak shaving or frequency regulation is detected... <0 (EV is about to be fully charged): The edge device forcibly reduces the charging power of the EV to the minimum threshold, prioritizing the achievement of the user's target SOC and avoiding overcharging that could affect battery life. When detected... <0 (Charging time is almost up): Edge devices are given priority in receiving charging power increase commands to shorten charging time and ensure that charging is completed within the user's allowed time.

[0085] In summary, the peak shaving and frequency regulation method for aggregators participating in the electricity market provided in this application embodiment can set up a precision-based coordination and dispatch mechanism for multi-aggregator scenarios. It receives dispatchable capacity and comprehensive response precision data of each EVA in the region through the cloud layer, and allocates the total dispatch instructions according to the dual indicators of dispatchable capacity and precision weights through the optimization model, giving instruction preference to high-precision EVAs. At the same time, based on the cloud-edge collaborative time-series interaction mechanism, it realizes real-time data rolling updates and rapid instruction decomposition, taking into account the charging needs of EV users in terms of power and time. That is, when an EV is about to finish charging or the maximum charging time is about to end, the corresponding instructions are prioritized to ensure user needs. Finally, the peak shaving capacity, frequency regulation upper and lower reserve capacity, etc. are output through model solution, realizing efficient and reasonable scheduling of peak shaving and frequency regulation, and ensuring the safe and stable operation of the power grid.

[0086] Example 2 Figure 3 A schematic diagram illustrating the structure of a method for aggregators to participate in peak shaving and frequency regulation in the electricity market, according to an embodiment of this application, is shown. Figure 3 As shown in the embodiment of this application, a peak shaving and frequency regulation device for aggregators participating in the electricity market is also provided, comprising: Module 10 is configured to acquire the operating parameters of electric vehicles in the management area of ​​electric vehicle aggregators. The determination module 20 is configured to determine the frequency regulation capacity and response deviation rate of electric vehicle charging stations within the management area of ​​the electric vehicle aggregator based on the electric vehicle operating parameters. The scheduling module 30 is configured to perform peak shaving and frequency regulation scheduling on the electric vehicle charging station based on the frequency regulation capacity and response deviation rate of the electric vehicle charging station.

[0087] In some embodiments, the acquisition module 10 is further configured to: The total frequency regulation capacity of a single electric vehicle charging station is generated by aggregating the frequency regulation capacity of each individual electric vehicle. Calculate the single response deviation rate of the electric vehicle charging station based on its total frequency regulation capacity. The dispatchable capacity of the electric vehicle aggregator is determined based on the total frequency regulation capacity of the electric vehicle charging station. The overall response deviation rate of the electric vehicle charging station is determined based on the single response deviation rate of the electric vehicle charging station.

[0088] In some embodiments, the single-response deviation rate of the electric vehicle charging station is determined based on the deviation between the effective response capacity of the electric vehicle charging station and a preset available response capacity. The formula for calculating the single-response deviation rate is as follows: ; ; in, The deviation between the effective response capacity and the available response capacity; is the initial performance lower bound; n is the total number of times electric vehicle aggregators participate in market clearing.

[0089] In some embodiments, the determining module 20 is further configured to: Based on the total frequency regulation capacity of the electric vehicle charging stations, the daily frequency regulation capacity of the electric vehicle aggregator is predicted using the PatchTST model. Based on the historical adjustability margin of each electric vehicle charging station, the adjustability margin of the electric vehicle charging station in the next frequency regulation period is predicted by an LSTM model. The dispatchable capacity of the electric vehicle aggregator is determined based on the daily frequency regulation capacity of the electric vehicle aggregator and the adjustability margin of the electric vehicle charging station during the next frequency regulation period.

[0090] In some embodiments, the determining module 20 is further configured to: The recent response accuracy and historical response accuracy are determined based on the single response deviation rate of the electric vehicle charging station. The recent response accuracy and the historical response accuracy are weighted and calculated to obtain the comprehensive response accuracy; The recent response accuracy is based on the single response accuracy within a sliding time window and is obtained through a time decay weighted algorithm; the historical response accuracy is obtained by calculating the arithmetic mean of all long-term response accuracies.

[0091] In some embodiments, the scheduling module 30 is further configured to: If the overall response accuracy is greater than the preset accuracy threshold, additional frequency modulation capacity will be allocated to the electric vehicle charging station corresponding to the overall response accuracy. If the overall response accuracy is less than or equal to a preset accuracy threshold, a basic frequency regulation capacity is allocated to the electric vehicle charging station corresponding to the overall response accuracy.

[0092] In some embodiments, the scheduling module 30 is further configured to: Based on the comprehensive response accuracy and the charging status of the electric vehicle, the peak shaving and frequency regulation scheduling of the electric vehicle charging station are corrected.

[0093] In some embodiments, the scheduling module 30 is further configured to: Peak shaving and frequency regulation scheduling of the electric vehicle charging station are performed according to frequency regulation performance indicators, wherein the frequency regulation performance indicators include at least one of regulation rate, response time, and regulation accuracy weighted value.

[0094] In some embodiments, the scheduling module 30 is further configured to: If the electric vehicle is about to be fully charged, the charging power of the electric vehicle will be reduced to the minimum charging power threshold. If the charging time of the electric vehicle is about to reach the preset time, the charging power of the charging station will be increased.

[0095] The peak shaving and frequency regulation device for aggregators participating in the electricity market provided in this application corresponds to the peak shaving and frequency regulation method for aggregators participating in the electricity market in the above embodiments. Any option in the embodiments of the peak shaving and frequency regulation method for aggregators participating in the electricity market is also applicable to the embodiments of the peak shaving and frequency regulation device for aggregators participating in the electricity market, and will not be described again here.

[0096] Example 3 This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for aggregators participating in the electricity market for peak shaving and frequency regulation.

[0097] The computer-readable storage medium in this application embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. In this application embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device; for example, it can be the aforementioned memory.

[0098] The computer programs of embodiments of this application can be organized into one or more computer-executable components or modules. Various aspects of this application can be implemented with any number and combination of such components or modules. For example, aspects of this application are not limited to the specific computer-executable instructions or specific components or modules shown in the drawings and described herein. Other embodiments may include different computer-executable instructions or components having more or fewer functions than those shown and described herein.

[0099] Example 4 This application also provides an electronic device, which includes at least a memory and a processor. The memory stores a computer program, and the processor executes the computer program in the memory to implement the above-described method for aggregators to participate in the electricity market for peak shaving and frequency regulation.

[0100] In some embodiments, the processor executing a computer program may be a processing device that includes one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), etc. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor that runs other instruction sets, or a processor that runs a combination of instruction sets. The processor may also be one or more special-purpose processing devices, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), system-on-a-chip (SoCs), etc.

[0101] The memory may be a read-only memory (ROM), random access memory (RAM), phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), electrically erasable programmable read-only memory (EEPROM), other types of random access memory (RAM), flash drives or other forms of flash memory, cache, registers, static memory, optical disc read-only memory (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape cassette or other magnetic storage devices, or any other possible non-transitory medium used to store information or instructions that can be accessed by computer equipment.

[0102] The electronic devices in this application embodiment may include, but are not limited to, fixed terminal devices such as servers, desktop computers, and digital TVs, as well as mobile terminal devices such as in-vehicle devices (e.g., head-up displays), handheld devices (e.g., mobile phones, tablets, etc.), and wearable devices (e.g., smartwatches, smart bracelets, etc.).

[0103] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for aggregators to participate in peak shaving and frequency regulation in the electricity market, characterized in that, include: Obtain the operating parameters of electric vehicles within the managed region of the electric vehicle aggregator; Based on the electric vehicle operating parameters, determine the frequency regulation capacity and response deviation rate of electric vehicle charging stations within the management area of ​​the electric vehicle aggregator; Based on the frequency regulation capacity and response deviation rate of the electric vehicle charging station, peak shaving and frequency regulation scheduling are performed on the electric vehicle charging station.

2. The method according to claim 1, characterized in that, Based on the electric vehicle operating parameters, calculate the frequency regulation capacity and response deviation rate of charging stations within the management area of ​​the electric vehicle aggregator, including: The total frequency regulation capacity of a single electric vehicle charging station is generated by aggregating the frequency regulation capacity of each individual electric vehicle. Calculate the single response deviation rate of the electric vehicle charging station based on its total frequency regulation capacity. The dispatchable capacity of the electric vehicle aggregator is determined based on the total frequency regulation capacity of the electric vehicle charging station. The overall response deviation rate of the electric vehicle charging station is determined based on the single response deviation rate of the electric vehicle charging station.

3. The method according to claim 2, characterized in that, The single-response deviation rate of the electric vehicle charging station is determined based on the deviation between the effective response capacity of the electric vehicle charging station and the preset available response capacity. The formula for calculating the single-response deviation rate is as follows: ； ； in, The deviation between the effective response capacity and the available response capacity; is the initial performance lower bound; n is the total number of times electric vehicle aggregators participate in market clearing.

4. The method according to claim 2, characterized in that, Determining the dispatchable capacity of the electric vehicle aggregator based on the total frequency regulation capacity of the electric vehicle charging stations includes: Based on the total frequency regulation capacity of the electric vehicle charging stations, the daily frequency regulation capacity of the electric vehicle aggregator is predicted using the PatchTST model. Based on the historical adjustability margin of each electric vehicle charging station, the adjustability margin of the electric vehicle charging station in the next frequency regulation period is predicted by an LSTM model. The dispatchable capacity of the electric vehicle aggregator is determined based on the daily frequency regulation capacity of the electric vehicle aggregator and the adjustability margin of the electric vehicle charging station during the next frequency regulation period.

5. The method according to claim 2, characterized in that, The overall response deviation rate of the electric vehicle charging station is determined based on the single response deviation rate, including: The recent response accuracy and historical response accuracy are determined based on the single response deviation rate of the electric vehicle charging station. The recent response accuracy and the historical response accuracy are weighted and calculated to obtain the comprehensive response accuracy; The recent response accuracy is based on the single response accuracy within a sliding time window and is obtained through a time decay weighted algorithm; the historical response accuracy is obtained by calculating the arithmetic mean of all long-term response accuracies.

6. The method according to claim 5, characterized in that, Based on the frequency regulation capacity and response deviation rate of the electric vehicle charging station, peak shaving and frequency regulation scheduling are performed on the electric vehicle charging station, including: If the overall response accuracy is greater than the preset accuracy threshold, additional frequency modulation capacity will be allocated to the electric vehicle charging station corresponding to the overall response accuracy. If the overall response accuracy is less than or equal to a preset accuracy threshold, a basic frequency regulation capacity is allocated to the electric vehicle charging station corresponding to the overall response accuracy.

7. The method according to claim 5, characterized in that, The method further includes: Based on the comprehensive response accuracy and the charging status of the electric vehicle, the peak shaving and frequency regulation scheduling of the electric vehicle charging station are corrected.

8. The method according to claim 1, characterized in that, The method further includes: Peak shaving and frequency regulation scheduling of the electric vehicle charging station are performed according to frequency regulation performance indicators, wherein the frequency regulation performance indicators include at least one of regulation rate, response time, and regulation accuracy weighted value.

9. The method according to any one of claims 1 to 8, characterized in that, The method further includes: If the electric vehicle is about to be fully charged, the charging power of the electric vehicle will be reduced to the minimum charging power threshold. If the charging time of the electric vehicle is about to reach the preset time, the charging power of the charging station will be increased.

10. A peak-shaving and frequency regulation device for aggregators participating in the electricity market, characterized in that, include: The acquisition module is configured to acquire the operating parameters of electric vehicles in the management area of ​​electric vehicle aggregators; The module is configured to determine the frequency regulation capacity and response deviation rate of electric vehicle charging stations within the management area of ​​the electric vehicle aggregator based on the electric vehicle operating parameters. The scheduling module is configured to perform peak shaving and frequency regulation scheduling on the electric vehicle charging station based on the frequency regulation capacity and response deviation rate of the electric vehicle charging station.

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