Virtual power plant adjustable resource intelligent regulation and control method and system based on vehicle pile cooperation

Abstract

The invention belongs to the field of virtual power plant resource regulation and control, and discloses a virtual power plant adjustable resource intelligent regulation and control method and system based on vehicle pile cooperation. Comprising the steps of collecting state information and user charging preferences of vehicle pile units in a regulation and control area, and generating an adjustable parameter range; constructing a vehicle pile regulation and control model, and outputting a power regulation strategy and a charging time window; on the basis of the remaining connection time and the power adjustment amplitude, verifying the feasibility of the adjustment strategy, and identifying a bicycle pile unit having a compensation demand; and the power adjusting strategy and the charging time window are corrected for the bicycle pile unit, and a final strategy meeting the minimum electric quantity requirement is formed. The system comprises a data acquisition module, a model construction module, a strategy evaluation module and a parameter correction module, which cooperatively realize intelligent generation and dynamic adjustment of a regulation and control strategy. According to the method, the response precision of the adjustable resources of the virtual power plant and the user charging demand matching degree can be improved, and good expandability and practicability are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of virtual power plant resource regulation, more particularly, the present application relates to a virtual power plant adjustable resource intelligent regulation method and system based on vehicle and pile cooperation. BACKGROUND

[0002] The virtual power plant is a kind of power plant that participates in power market and power grid operation through advanced information communication technology and software system, realizes the aggregation and coordination optimization of distributed power supply, energy storage system, controllable load, electric vehicle and other distributed energy resources, and is a kind of power supply coordination management system; The power regulation system of the existing virtual power plant technology is based on hierarchical control architecture: the power grid dispatching system is responsible for power prediction and resource allocation at the power grid level, the charging management platform interfaces with electric vehicles and charging pile terminals, realizes local load coordinated control, and the user terminal is mainly used for state display and limited charging reservation adjustment function; The virtual power plant regards the vehicle and pile and electric vehicle as a distributed unit, and accesses the power regulation system; In order to reduce the load pressure of power grid in the power consumption peak period, the virtual power plant uses V2G technology to supply power to the power grid by taking the electric vehicle as a power generation unit in the power consumption peak period, and regulates the power grid resources to charge the vehicle and pile and electric vehicle when the power consumption load is low.

[0003] However, the existing technology usually defaults that the users accessing the vehicle and pile system will accept V2G regulation, ignores the willingness of users to participate in regulation and the individualized demand of charging time preference, and is difficult to accurately calculate the time window for discharging and charging the vehicle of the user, so that the automobile power supply discharges to participate in the regulation of the power grid, and the automobile power is not fully charged when the user takes the car, which affects the charging experience of the user. In view of this, the present application provides a virtual power plant adjustable resource intelligent regulation method and system based on vehicle and pile cooperation to solve the above problems. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, in order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a virtual power plant adjustable resource intelligent regulation method based on vehicle and pile cooperation, comprising: Step S1: collecting the state information of each vehicle and pile unit in the regulation area and the charging preference information uploaded by the user terminal, preprocessing the collected data, and generating the adjustable parameter range of each vehicle and pile unit; Step S2: constructing a vehicle and pile regulation model, and introducing a pre-arrangement mechanism, taking the adjustable parameter range of each vehicle and pile unit and the regulation target of the virtual power plant as the input of the pre-trained model, and outputting the power regulation strategy and charging time window of each vehicle and pile unit; Step S3: verifying whether the output power adjustment strategy and charging time window meet the demand according to the remaining time of user connection and the current power adjustable range, if not, demand compensation is carried out, and the vehicle-pile unit with demand compensation is recorded; Step S4: correcting the power adjustment strategy and charging time window of the vehicle-pile unit with demand compensation.

[0005] Further, the method of generating the adjustable parameter range of each vehicle-pile unit comprises: The state information includes the current connection state, the remaining power and the vehicle-pile battery capacity of the vehicle-pile; the charging preference information includes the expected minimum power, the expected parking time and the V2G technology participation willingness; the preprocessing is based on the collected vehicle-pile unit state information and charging preference information, and the power adjustment range and the available regulation time window that each vehicle-pile unit can participate in are calculated as the adjustable parameter range of each vehicle-pile unit.

[0006] Further, the method of constructing the vehicle-pile regulation model and introducing the pre-arrangement mechanism comprises: The vehicle-pile regulation model is constructed based on the neural network structure, and the pre-arrangement mechanism is added after the input layer; According to the historical data, the training sample is set, in the sample, the adjustable parameter range of each vehicle-pile unit and the adjustment target of the virtual power plant are taken as the input of the input layer, and the power adjustment strategy and the charging time window of each vehicle-pile unit are taken as the output of the output layer, the vehicle-pile regulation model is pre-trained, and the trained vehicle-pile regulation model is obtained.

[0007] Further, the pre-arrangement mechanism comprises: According to the V2G response willingness of each vehicle-pile unit, the vehicle-pile units are divided into discharge participation category and only charging category, for the vehicle-pile units in the discharge participation category, it is judged whether the expected parking time is greater than the regulation demand time, if the expected parking time is sufficient, the vehicle-pile unit is included in the power adjustment task range, and the discharge and charging time period is allocated in the charging time window, and the power size corresponding to each period is determined according to the adjustable power range in each period and the overall load adjustment target of the virtual power plant; if the expected parking time is insufficient, the discharge task is not arranged, and only charging is carried out in the charging time window; the vehicle-pile units in the only charging category do not participate in the regulation task, and only the charging time window is allocated according to the original charging plan.

[0008] Further, the method of verifying whether the output power adjustment strategy and charging time window meet the demand, if not, demand compensation is carried out, comprises: Based on the predicted parking remaining time and the power adjustment range that can be participated in the regulation, it is verified whether the charging quantity can reach the user's expected minimum quantity by regulating the power in the predicted parking remaining time; if the expected minimum quantity can be reached, the normal regulation is participated in; if the expected minimum quantity cannot be reached, the vehicle and pile unit with compensation demand is recorded.

[0009] Further, the method for correcting the power regulation strategy and the charging time window of the vehicle and pile unit comprises: For the vehicle and pile unit with compensation demand, based on the difference between the user's expected minimum quantity and the current remaining quantity, the required additional charging time is calculated combined with the existing charging power; if the predicted parking remaining time is sufficient, the sum of the additional charging time and the original planned charging time is taken as the charging time window correction value; if the predicted parking remaining time is insufficient, the predicted parking remaining time is entirely allocated to the charging time window, and the required regulated charging power is calculated according to the difference between the quantity and the predicted parking remaining time, and the required regulated charging power is taken as the power compensation amount; the power regulation strategy and the charging time window parameters are corrected combined with the charging time window correction value and the power compensation amount, and the corrected strategy meeting the minimum quantity demand is generated.

[0010] A virtual power plant adjustable resource intelligent regulation system based on vehicle and pile cooperation comprises: A data acquisition unit acquires the state information of the vehicle and pile unit in the regulation area and the charging preference information uploaded by the user terminal, and pre-processes the acquired data to generate the adjustable parameter range of each vehicle and pile unit; A regulation model unit constructs a vehicle and pile regulation model of a neural network structure, introduces a pre-arrangement mechanism, receives the adjustable parameter range of the vehicle and pile unit and the regulation target of the virtual power plant as input, and outputs the power regulation strategy and the charging time window of each vehicle and pile unit; A demand verification unit verifies whether the power regulation strategy and the charging time window meet the user's minimum quantity demand based on the predicted parking remaining time and the power adjustment range; A strategy correction unit calculates the power compensation amount and the corresponding charging time window correction value for the vehicle and pile unit with compensation demand, and adjusts the output power or extends the charging time window based on the predicted parking remaining time to generate the corrected power regulation strategy and the charging time window parameters.

[0011] The technical effects and advantages of the virtual power plant adjustable resource intelligent regulation method and system based on vehicle and pile cooperation of the present application are as follows: 1. By acquiring the state information of the vehicle and pile unit and the user charging preference information, the adjustable parameter range is constructed, and the pre-arrangement mechanism is introduced to generate differentiated power regulation strategy and charging time window, thereby improving the response ability and regulation flexibility of the virtual power plant to multiple user demands; 2. On the basis of meeting the power regulation task of the power grid, identify the vehicle and pile unit caused by the discharge task due to insufficient power, combine the remaining connection time and power adjustment range to calculate the compensation power and time window correction value, and intelligently correct the strategy parameters, so as to guarantee the minimum power demand of the user; 3. By collecting the actual response data in the regulation process, the regulation model is dynamically updated and the parameters are optimized, so as to improve the execution accuracy, system stability and sustainable regulation ability of the overall regulation strategy of the virtual power plant. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 A virtual power plant intelligent regulation method based on vehicle and pile cooperation; Figure 2 A virtual power plant intelligent regulation system based on vehicle and pile cooperation; DETAILED DESCRIPTION

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. Embodiment 1

[0014] Please refer to Figure 1 The virtual power plant intelligent regulation method based on vehicle and pile cooperation described in this embodiment includes: Step S1: Collect the state information of each vehicle and pile unit in the regulation area and the charging preference information uploaded by the user terminal, preprocess the collected data, and generate the adjustable parameter range of each vehicle and pile unit; Step S2: Construct a vehicle and pile regulation model, and introduce a pre-arrangement mechanism, take the adjustable parameter range of each vehicle and pile unit and the regulation target of the virtual power plant as the input of the pre-trained model, and output the power regulation strategy and charging time window of each vehicle and pile unit; Step S3: According to the remaining time of user connection and the current power adjustable range, verify whether the output power regulation strategy and charging time window meet the demand, if not, carry out demand compensation, and record the vehicle and pile unit with demand compensation; Step S4: Correct the power regulation strategy and charging time window of the vehicle and pile unit with demand compensation.

[0015] In order to realize accurate scheduling of vehicle and pile resources in a virtual power plant, it is necessary to determine the adjustable range of each vehicle and pile unit in power and time before regulation to avoid exceeding the acceptable range of users and ensure the executability of overall regulation. In order to generate the adjustable parameter range of each vehicle and pile unit, an intelligent regulation method for adjustable resources of a virtual power plant based on vehicle and pile cooperation is provided, comprising: The state information includes the current connection state, the remaining power and the battery capacity of the vehicle and pile; the charging preference information includes the expected minimum power, the expected parking time and the V2G technology participation willingness; the preprocessing is based on the collected state information and charging preference information of the vehicle and pile unit, and the power adjustment range and the available regulation time window of each vehicle and pile unit that can participate in regulation are calculated as the adjustable parameter range of each vehicle and pile unit. For example: The vehicle and pile unit is currently connected and supports regulation, the current remaining power is 18 kWh, the user expects the minimum power to be 45 kWh, and the total battery capacity is 60 kWh; The user expects to park at the site for 120 minutes, and the maximum charging power of the vehicle and pile is 22 kW; the theoretical adjustable duration is 120 minutes, and the required supplementary power is 27 kWh; if V2G discharging is allowed during this period, the power adjustment range can be set to 22 kW; The adjustable parameter range of the vehicle and pile unit is: the power adjustment range is 22 kW, and the available regulation time window is 0-120 minutes.

[0016] V2G technology is a technology in which electric vehicles as distributed units of a virtual power plant supply power to the power grid.

[0017] The vehicle and pile unit refers to a basic control unit composed of an electric vehicle accessing a virtual power plant platform and its corresponding charging pile, which has charging and discharging capacity and information interaction capability, and is a basic execution unit for realizing V2G bidirectional energy regulation.

[0018] The power adjustment range is the controllable adjustment range of the vehicle and pile unit in charging power or discharging power, which is usually expressed in kilowatts.

[0019] The available regulation time window refers to the time interval from the start of regulation to the stop of regulation of the vehicle and pile unit under the current connection state, which is usually expressed in minutes.

[0020] It needs to be explained that through joint analysis of the state information and charging preference information of the vehicle and pile unit, the adjustable parameter range of each vehicle and pile unit is accurately calculated. Through the above operation, the physical adjustable range of the vehicle and pile unit and the user's individualized preference are effectively integrated, and the adjustable parameter range is provided for the accurate formulation of the subsequent regulation strategy.

[0021] In the overall regulation of the virtual power plant, the large number of vehicles and piles and the large difference in state lead to long response time and large calculation pressure; in order to quickly obtain a feasible power allocation scheme of the vehicle and pile before regulation, and reduce the calculation amount in actual execution, it is necessary to construct a vehicle and pile regulation model that can directly output the regulation strategy according to the input conditions, in order to construct a vehicle and pile regulation model with a pre-arrangement mechanism, an intelligent regulation method for adjustable resources of a virtual power plant based on vehicle and pile cooperation is provided, comprising: According to the V2G response willingness of each vehicle and pile unit, the vehicle and pile unit is divided into a discharge participation category and a charging only category, for the vehicle and pile unit in the discharge participation category, it is judged whether the expected parking time is greater than the regulation demand time, if the expected parking time is sufficient, the vehicle and pile unit is included in the power regulation task range, and the discharge and charging time periods are allocated within the charging time window, and the power size corresponding to each period is determined according to the adjustable power range in each period and the overall load regulation target of the virtual power plant; if the expected parking time is insufficient, no discharge task is arranged, and only charging is performed within the charging time window; the vehicle and pile unit in the charging only category does not participate in the regulation task, and only allocates the charging time window according to the original charging plan; A vehicle and pile regulation model is constructed based on a neural network structure, and a pre-arrangement mechanism is added after the input layer; According to historical data, training samples are set, in the samples, the adjustable parameter range of each vehicle and pile unit and the regulation target of the virtual power plant are input into the input layer, and the power regulation strategy and the charging time window of each vehicle and pile unit are output from the output layer, the vehicle and pile regulation model is pre-trained, and a trained vehicle and pile regulation model is obtained; For example: A and B units indicate willingness to participate in V2G discharge, and C unit only wants to charge.

[0022] Among them, the expected parking time of the A unit is 180 minutes, the current remaining power is 20kWh, the user expects the minimum power to be 45kWh, the total capacity of the battery is 60kWh, and the maximum charging and discharging power is 11kW; the expected parking time of the B unit is 30 minutes, the current remaining power is 35kWh, the expected minimum power is 40kWh, and the maximum power is 7kW; the C unit does not participate in V2G regulation, and only plans to complete the standard charging task during parking.

[0023] According to the power regulation target issued by the system (for example: provide +22kW of power support within the next 60 minutes), the units that meet the conditions are selected. The A unit has sufficient expected parking time and discharge regulation capacity, is included in the regulation range, and the first 0-30 minutes of the parking time is allocated as a discharge period (11kW) and the second period as a charging period (gradually increase the power to complete the remaining power supplement); The B unit is willing to participate in V2G discharge, but its expected parking time of 30 minutes is less than the demand time of regulation, so it is not arranged to discharge, but only charges during the parking time; the C unit does not participate in the regulation task, but only completes charging according to the original user plan.

[0024] The adjustable parameter range of the vehicle pile unit is: the power adjustment amplitude is 22kW, and the available regulation time window is 0-120 minutes; the adjustment target received by the virtual power plant in this period is 150kW, and the duration is 60 minutes; the system inputs the above vehicle pile unit and other vehicle pile units with regulation capacity into the neural network model as training samples, and finally forms the regulation result, that is, the specific power adjustment strategy of the vehicle pile unit (such as maintaining 9.5kW output during the 40th to 85th minute) and the adjusted charging time window (such as 35th to 90th minute) as the output layer of the supervision signal; The neural network is a calculation model composed of multiple nodes, which are usually organized in a hierarchical structure, including an input layer, a hidden layer and an output layer. The input layer is used to receive external input data, the hidden layer is used to perform nonlinear mapping and combination of input features, and the output layer is used to output prediction results. In this embodiment, the vehicle pile regulation model introduces a pre-arrangement mechanism between the input layer and the hidden layer of the traditional three-layer neural network, forming a four-layer structure composed of an input layer, a pre-arrangement layer, a hidden layer and an output layer. Among them, the input layer receives the adjustable parameter range of each vehicle pile unit and the adjustment target of the virtual power plant; the pre-arrangement layer preliminarily filters the input data, and eliminates the vehicle pile units that do not meet the current adjustment conditions; the hidden layer extracts and combines the filtered input data; the output layer generates a prediction result, including the power adjustment strategy of each vehicle pile unit and the charging time window.

[0025] The overall load adjustment target of the virtual power plant is generated by the grid dispatching system, which represents the target to be completed by the vehicle pile unit in cooperation, including the adjustment direction (charging or discharging) and the size of the power.

[0026] It needs to be explained that the pre-arrangement mechanism jointly judges the V2G participation willingness and parking time of the vehicle pile unit, filters out the units that do not have regulation capacity or have conflict restrictions, and ensures that the subsequent strategy generation process focuses on effective regulation resources, improving the overall regulation efficiency; among the vehicle pile units participating in the regulation, the regulation period is divided within the charging time window, and the discharge and power compensation tasks are allocated respectively, and combined with the regulation time demand and the power target, the power output value of each period is allocated, realizing dynamic adjustment in time and power dimensions; in the training process of the vehicle pile regulation model, the adjustable parameter range of each vehicle pile unit in the historical sample and the target power of the virtual power plant are input into the input layer, and after being processed by the pre-arrangement layer and the hidden layer, the optimal power adjustment strategy and charging time arrangement matched with the target are obtained in the output layer, so as to complete the supervision training of the vehicle pile regulation model.

[0027] In the centralized regulation process of the virtual power plant, there are differences in the parking time and adjustable power amplitude of different vehicle and pile units. If a unified regulation strategy is directly executed, it may lead to the fact that part of the vehicle and pile units cannot complete the minimum charging demand within the expected parking time. Therefore, after generating the power adjustment strategy and the charging time window, the feasibility needs to be verified, and the vehicle and pile units that cannot meet the minimum power requirement are identified and recorded. In order to verify whether the output power adjustment strategy and the charging time window meet the demand, an intelligent regulation method for adjustable resources of a virtual power plant based on vehicle and pile cooperation is provided, which comprises: Based on the predicted remaining parking time and the power adjustment amplitude that can be involved in the regulation, it is verified whether the charging amount can reach the user's expected minimum power through power regulation within the predicted remaining parking time. If the expected minimum power can be reached, the vehicle and pile unit normally participates in the regulation. If the expected minimum power cannot be reached, the vehicle and pile unit is recorded as a vehicle and pile unit with compensation demand; For example: The current remaining power of the vehicle and pile unit is 40 kWh, the user sets the expected minimum power to 45 kWh, the total capacity of the battery is 60 kWh, the maximum charging and discharging power is 11 kW, the current predicted remaining parking time is 60 minutes, and the user supports V2G response. Under the system regulation strategy, the vehicle and pile unit is first arranged to participate in the V2G discharging task, the discharging period lasts for 30 minutes, the power is 11 kW, and the discharged power is 5.5 kWh; The remaining 30 minutes are used for charging, the charging power is 11 kW, and the compensable power is 5.5 kWh. Finally, the remaining power of the vehicle and pile unit is 40 kWh, which does not reach the user's expected minimum power of 45 kWh, so it is determined that the regulation strategy does not meet the demand and needs to enter the subsequent compensation process; The power adjustment strategy refers to the power adjustment plan (charging or discharging and power size) executed by the vehicle and pile unit within a given time window.

[0028] The charging time window refers to the time interval arranged by the system for the vehicle and pile unit to actually execute the charging task, which is within the user's allowed parking time range.

[0029] The predicted remaining parking time refers to the remaining time from the current time to the time when the vehicle leaves the pile.

[0030] It needs to be explained that the charging strategy result is compared with the user's minimum power demand, and whether the charging power and the remaining time are sufficient to complete the target power charging task is considered comprehensively. Through verification, the problem of insufficient charging power of the current regulation strategy on some vehicle and pile units can be identified in time, which provides a basis for subsequent parameter correction and ensures that the overall regulation ability and executability of the virtual power plant are maximized without affecting the user's vehicle demand.

[0031] The recorded vehicle-pile unit cannot meet the minimum charging demand due to insufficient parking time or limited power adjustment range, and if no strategy correction is made, it may lead to insufficient energy use guarantee for users; therefore, for vehicle-pile units with compensation needs, the power adjustment strategy and the charging time window need to be corrected to ensure the achievement of the minimum power target; in order to correct the power adjustment strategy and the charging time window of the vehicle-pile unit, a virtual power plant adjustable resource intelligent regulation method based on vehicle-pile cooperation is provided, comprising: For vehicle-pile units with compensation needs, based on the difference between the user's expected minimum power and the current remaining power, the required additional charging time is calculated in combination with the existing charging power, and if the remaining parking time is sufficient, the sum of the additional charging time and the original planned charging time is taken as the charging time window correction value; if the remaining parking time is insufficient, the remaining parking time is first allocated to the charging time window, and the required regulated charging power is calculated according to the difference in power and the remaining parking time, and the required regulated charging power is taken as the power compensation amount; the power adjustment strategy and the charging time window parameters are corrected in combination with the charging time window correction value and the power compensation amount, and a corrected strategy that meets the minimum power demand is generated; For example: The initial power of the vehicle-pile unit is 40kWh, the user sets the minimum expected power to 45kWh, and the expected total parking time is 90 minutes; in the original regulation strategy, the vehicle-pile unit participates in V2G discharge, and the discharge period lasts for 30 minutes, with a total discharge of 5.5kWh; then charging starts from the 30th minute, with a charging time of 60 minutes and an average power of 5.5kW, a total charging of 5.5kWh, and the final power restored to 40kWh, which does not meet the user's minimum power requirement of 45kWh; there is a 5kWh power gap that needs to be compensated. Cancel the original discharge task and adjust the original discharge time window to the charging time window. If the power remains at 5.5kW during this period, 1.83kWh can be supplemented; at the same time, the charging power of the remaining time is appropriately increased to 7.5kW, which can supplement 3.75kWh, a total of 5.58kWh of compensation power, exceeding the user's minimum power requirement. Therefore, the system generates a corrected power adjustment strategy: cancel the discharge task and change it to charging, and increase the charging power from 5.5kW to 7.5kW from the 30th minute to the 90th minute; The charging time window correction value refers to the range adjustment of the original charging time window, which is embodied as the extension of the original window within the expected remaining parking time.

[0032] The power compensation amount refers to the additional power value required to achieve the target when the vehicle-pile unit cannot meet its expected minimum power demand within the remaining parking time under the current regulation strategy.

[0033] The modified power regulation strategy refers to a new control strategy generated on the basis of the original power regulation strategy in consideration of the power compensation amount and the charging time window correction value.

[0034] It needs to be explained that for the vehicle-pile unit with compensation demand, the method updates the regulation strategy by extending the charging time window or increasing the charging power to meet the minimum power demand; solves the situation of insufficient charging caused by discharge after the user agrees to V2G discharge regulation; the system can modify the regulation strategy according to the current time resource and power resource, thereby guaranteeing the achievement of the user's minimum power target.

[0035] In this embodiment, the state information of the vehicle-pile unit and the charging preference uploaded by the user terminal are collected to generate the adjustable parameter range; a vehicle-pile regulation model is constructed based on a neural network structure to output the power regulation strategy and the charging time window of each vehicle-pile unit; the vehicle-pile unit that cannot meet the minimum power demand is identified to construct a demand compensation mechanism, taking the minimum power demand, the predicted remaining parking time, and the current power adjustable range as parameters; based on the power gap of the vehicle-pile unit, the required power value and the charging time window correction range are calculated on the basis of the power target and time requirement issued by the power grid dispatching system; when the predicted parking time is sufficient, the discharge task in the original strategy is cancelled and the corresponding time period is adjusted to a charging time window to increase the charging time; when the predicted parking time is insufficient, the output power is increased within the limited time to supplement the gap power in combination with the remaining adjustable power capacity; the power compensation amount and the corrected charging time window are used as boundary constraint conditions to modify the original power regulation strategy and the time parameter to generate a feasible control strategy that meets the minimum power requirement; the modified strategy is associated with the real-time state information of the vehicle-pile to generate a final control instruction, which is issued to the vehicle-pile side controller through the charging management platform for driving execution; after the regulation period ends, the actual power data of the vehicle-pile is collected and compared with the expected execution value to identify the response unit whose compensation effect does not meet the expectation, and the compensation rules and regulation parameters in the regulation model are further optimized to provide dynamic feedback support for subsequent strategy iteration. Embodiment 2

[0036] Please refer to Figure 2 The embodiment not described in detail is described in embodiment 1, and provides a virtual power plant adjustable resource intelligent regulation system based on vehicle-pile cooperation, which comprises: A data acquisition unit acquires the state information of the vehicle-pile unit in the regulation area and the charging preference information uploaded by the user terminal, and pre-processes the collected data to generate the adjustable parameter range of each vehicle-pile unit; The regulation model unit constructs a vehicle pile regulation model of a neural network structure, and introduces a pre-arrangement mechanism, receives a range of adjustable parameters of the vehicle pile unit and a regulation target of the virtual power plant as inputs, and outputs a power regulation strategy and a charging time window of each vehicle pile unit; The demand verification unit verifies whether the power regulation strategy and the charging time window meet a user's minimum power demand based on a predicted remaining parking time and a power adjustable range. The strategy correction unit calculates a power compensation amount and a corresponding charging time window correction value for the vehicle pile unit with a compensation demand, adjusts an output power or expands the charging time window based on the predicted remaining parking time, and generates a corrected power regulation strategy and charging time window parameter.

[0037] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be included in the protection scope of the present application.

[0038] Finally: the above is only a preferred embodiment of the present application and is not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A virtual power plant adjustable resource intelligent regulation method based on vehicle-pile cooperation, characterized in that, The method comprises: Step S1: Collecting state information of each vehicle pile unit in the regulation area and charging preference information uploaded by the user terminal, preprocessing the collected data, and generating the adjustable parameter range of each vehicle pile unit; Step S2: Constructing a vehicle pile regulation model and introducing a pre-arrangement mechanism, taking the adjustable parameter range of each vehicle pile unit and the regulation target of the virtual power plant as the input of the pre-trained model, and outputting the power regulation strategy and charging time window of each vehicle pile unit; Step S3: According to the remaining time of user connection and the current power adjustable range, verify whether the output power regulation strategy and charging time window meet the demand, if not, carry out demand compensation, and record the vehicle pile unit with demand compensation; Step S4: Correcting the power regulation strategy and charging time window of the vehicle pile unit with demand compensation. 2.The virtual power plant adjustable resource intelligent regulation and control method based on vehicle-pile coordination according to claim 1, wherein The method for generating the adjustable parameter range of each vehicle pile unit comprises: The state information includes the current connection state, remaining power and vehicle pile battery capacity of the vehicle pile; the charging preference information includes the expected minimum power, estimated parking time and V2G technology participation willingness; the preprocessing is based on the collected vehicle pile unit state information and charging preference information, and the power adjustment range and available regulation time window of each vehicle pile unit that can participate in regulation are calculated as the adjustable parameter range of each vehicle pile unit. 3.The virtual power plant adjustable resource intelligent regulation and control method based on vehicle-pile coordination according to claim 2, characterized in that, The method for constructing a vehicle pile regulation model and introducing a pre-arrangement mechanism comprises: Constructing a vehicle pile regulation model based on a neural network structure, and adding a pre-arrangement mechanism after the input layer; According to historical data, set training samples, in the sample, take the adjustable parameter range of each vehicle pile unit and the regulation target of the virtual power plant as the input of the input layer, and take the power regulation strategy and charging time window of each vehicle pile unit as the output of the output layer, pre-train the vehicle pile regulation model, and obtain the trained vehicle pile regulation model. 4.The method of claim 3, wherein, The pre-arrangement mechanism comprises: According to the V2G response willingness of each vehicle pile unit, the vehicle pile units are divided into discharge participation category and only charging category; for the vehicle pile units in the discharge participation category, it is judged whether the estimated parking time is greater than the regulation demand time, if the estimated parking time is sufficient, the vehicle pile unit is included in the power regulation task range, and the discharge and charging time periods are allocated in the charging time window of the vehicle pile unit, and the power size corresponding to each period is determined according to the adjustable power range in each period and the overall load regulation target of the virtual power plant; if the estimated parking time is insufficient, no discharge task is arranged, and only charging is carried out in the charging time window; the vehicle pile units in the only charging category do not participate in the regulation task, and only the charging time window is allocated according to the original charging plan.

5. The virtual power plant adjustable resource intelligent regulation and control method based on vehicle-pile cooperation according to claim 4, characterized in that, The method for verifying whether the output power regulation strategy and charging time window meet the demand, if not, carrying out demand compensation, comprises: Based on the estimated remaining parking time and the power adjustment range that can participate in regulation, it is verified whether the charging amount can reach the expected minimum power of the user through regulating power in the estimated remaining parking time; if the expected minimum power can be reached, the regulation is normally participated in; if the expected minimum power cannot be reached, the vehicle pile unit with compensation demand is recorded. 6.The method of claim 5, wherein, The method for correcting the power regulation strategy of the vehicle pile unit and the charging time window parameter comprises: For the vehicle pile unit with compensation demand, based on the difference between the user's expected minimum power and the current remaining power, the required additional charging time is calculated combined with the existing charging power. If the estimated remaining parking time is sufficient, the sum of the additional charging time and the original planned charging time is taken as the charging time window correction value. If the estimated remaining parking time is insufficient, the estimated remaining parking time is all allocated to the charging time window, and the required regulated charging power is calculated according to the difference in power and the estimated remaining parking time. The required regulated charging power is taken as the power compensation amount. The power regulation strategy and the charging time window parameter are corrected combined with the charging time window correction value and the power compensation amount, and a corrected strategy meeting the minimum power demand is generated.

7. A virtual power plant adjustable resource intelligent regulation system based on vehicle and pile coordination, which is used to realize the virtual power plant adjustable resource intelligent regulation method based on vehicle and pile coordination according to any one of claims 1 to 6, characterized in that, Comprise: A data acquisition unit acquires state information of vehicle pile units in the regulation area and charging preference information uploaded by user terminals, and pre-processes the acquired data to generate adjustable parameter ranges of each vehicle pile unit; A regulation model unit constructs a vehicle pile regulation model with a neural network structure, introduces a pre-arrangement mechanism, receives the adjustable parameter ranges of the vehicle pile units and the regulation target of the virtual power plant as inputs, and outputs the power regulation strategy and the charging time window of each vehicle pile unit; A demand verification unit verifies whether the power regulation strategy and the charging time window meet the user's minimum power demand based on the estimated remaining parking time and the power adjustable range; A strategy correction unit calculates the power compensation amount and the corresponding charging time window correction value for the vehicle pile unit with compensation demand, and adjusts the output power or extends the charging time window based on the estimated remaining parking time to generate the corrected power regulation strategy and the charging time window parameter.

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