An electric vehicle aggregator dispatching demand multi-stage decomposition method

Abstract

This invention discloses a multi-stage decomposition method for electric vehicle aggregator scheduling demand, relating to the field of electric vehicle scheduling analysis technology. The method includes the following steps: Step S1: Setting electric vehicle aggregator scheduling demand based on grid regulation demand, establishing a multi-stage decomposition architecture for electric vehicle aggregator scheduling demand, which includes an electric vehicle aggregator platform layer, an electric vehicle cluster layer, and an electric vehicle layer; Step S2: Determining the scheduling demand of each level of the electric vehicle aggregator in each time period based on the functions of each layer; Step S3: In the electric vehicle cluster layer, calculating the adjustable capacity of different electric vehicle clusters, obtaining the contribution value of the regulation demand of the electric vehicle aggregator platform layer based on the adjustable capacity, and reporting it to the electric vehicle aggregator platform layer; Step S4: In the electric vehicle layer, allocating the order of response to regulation demand by each electric vehicle within the cluster based on regulation capacity, and allocating the capacity of each electric vehicle to respond to regulation demand in sequence based on regulation willingness.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle scheduling analysis technology, and in particular to a method for multi-stage decomposition of scheduling demand of electric vehicle aggregators. Background Technology

[0002] With the widespread adoption of electric vehicles (EVs), their large-scale grid connection has become a significant trend. However, without effective guidance, the disorderly charging behavior of EVs will exacerbate the peak-valley difference in grid load, posing a serious challenge to system stability and power supply reliability. Therefore, promoting coordinated interaction between EVs and the grid is particularly crucial. By guiding users to charge during off-peak hours and discharge to the grid during peak hours, and optimizing charging behavior based on the timing characteristics of renewable energy generation, it is possible not only to alleviate the pressure of grid expansion but also to improve the absorption rate of renewable energy.

[0003] Currently, a large body of research focuses on the mechanism design for electric vehicle (EV) charging loads to participate in electricity market transactions. In recent years, my country has launched vehicle-to-grid (V2G) pilot projects in several regions, aiming to explore the participation of EVs (via charging facilities) as independent entities or through aggregators in various electricity markets, including demand response, ancillary services, and green electricity trading. Existing research largely concentrates on charging load forecasting, adjustable resource aggregation methods, and demand decomposition strategies for EVs participating in the ancillary services market. However, these studies mostly focus on the perspective of load aggregators and have not systematically considered how the dispatching level can optimize allocation based on the overall situation of regional adjustable charging load resources, nor have they explored in depth how the grid dispatching agency should rationally decompose macro-control demands to various charging load aggregators in the context of market-based transactions.

[0004] Against this backdrop, there is an urgent need to research instruction decomposition methods for electric vehicle (EV) response to distribution network regulation demands. This method must be based on the assessment and characteristic analysis of large-scale adjustable charging load resources, and fully consider the overall response capability of load aggregators after they participate in the market. The research results will contribute to the flexible regulation and optimized scheduling of EV charging loads, thereby improving grid stability and power supply security. Summary of the Invention

[0005] In view of the above-mentioned prior art, the present invention provides a method for multi-stage decomposition of scheduling demand of electric vehicle aggregators, which mainly solves the technical problems existing in the background art.

[0006] To achieve the above objectives, the technical solution of this invention is implemented as follows: This invention provides a method for multi-stage decomposition of scheduling demand for electric vehicle aggregators, the method comprising the following steps: Step S1: Set the dispatch requirements of electric vehicle aggregators according to the power grid regulation requirements, and establish a multi-stage decomposition architecture for the dispatch requirements of electric vehicle aggregators. The multi-stage decomposition architecture for the dispatch requirements of electric vehicle aggregators includes an electric vehicle aggregator platform layer, an electric vehicle cluster layer and an electric vehicle layer. Step S2: Based on the functions of each layer of the electric vehicle aggregator, determine the scheduling requirements of each layer of the electric vehicle aggregator in each time period; Step S3: In the electric vehicle cluster layer, calculate the adjustable capacity of different electric vehicle clusters based on the SOC difference method, obtain the contribution value of the adjustment demand of the electric vehicle aggregator platform layer based on the adjustable capacity, and report it to the electric vehicle aggregator platform layer. Step S4: In the electric vehicle layer, taking into account the differences in the adjustment capabilities and adjustment intentions of different electric vehicles, the order in which each electric vehicle in the cluster responds to the adjustment demand is allocated by the adjustment capability, and the capacity of each electric vehicle responding to the adjustment demand is allocated in sequence by the adjustment intention.

[0007] As a preferred embodiment of the present invention, the electric vehicle aggregator platform layer in step S1 interacts with the power distribution network to receive and feedback power grid regulation requirements. The electric vehicle cluster layer receives the scheduling requirements from the electric vehicle aggregator platform layer and sends them to the electric vehicle layer. The electric vehicle layer, as the actual executor of the scheduling requirements, responds to the scheduling requirements of the electric vehicle cluster layer through its adjustment willingness and adjustment capability.

[0008] As a preferred embodiment of the present invention, step S2, determining the scheduling needs of the electric vehicle aggregator in each time period, includes: Step S21: Determine the scheduling requirements of the electric vehicle aggregator platform layer. Specifically, at any scheduling time, the scheduling requirements received by the electric vehicle aggregator platform layer are equal to the adjustment requirements issued by the regional distribution network at that time. Step S22: Determine the scheduling requirements of the electric vehicle cluster layer. Specifically, for an aggregator that includes multiple substations, at any scheduling time, the sum of the adjustment requirements allocated to each electric vehicle cluster is equal to the adjustment requirements of the electric vehicle cluster layer. Step S23: Determine the scheduling requirements of the electric vehicle layer. Specifically, for each electric vehicle cluster, which contains multiple electric vehicles, the sum of the adjustment requirements allocated to each electric vehicle in the electric vehicle cluster is equal to the adjustment requirements of that electric vehicle layer.

[0009] As a preferred embodiment of the present invention, step S3, which calculates the adjustable capacity of different electric vehicle clusters based on the SOC difference method, includes: Step S31: Collect the current state of charge value of the electric vehicle, and calculate the potential adjustment space of the electric vehicle by combining the maximum allowable state of charge value of the electric vehicle and its rated battery capacity. Step S32: Based on the potential adjustment space of each electric vehicle in the cluster, sum the potential adjustment spaces of all electric vehicles in the k-th electric vehicle cluster to obtain the overall adjustable capacity of the cluster at the current time.

[0010] As a preferred embodiment of the present invention, step S3 further includes the electric vehicle aggregator platform layer correcting the adjustable capacity reported by each electric vehicle cluster through power flow constraints, and on the basis of correcting the adjustable capacity reported by each electric vehicle cluster, allocating the adjustment demand of the electric vehicle aggregator platform layer to the electric vehicle cluster layer using the adjustment capacity weight value of each electric vehicle cluster.

[0011] As a preferred embodiment of the present invention, step S4 specifically includes: Step S41: Calculate the adjustment capability of different electric vehicles in the cluster using adjustment duration, adjustment speed and adjustment accuracy, and sort the electric vehicles in the cluster in descending order of adjustment capability. Step S42: Based on the ranking results of adjustment capabilities and taking into account the differences in response willingness among different electric vehicles in the electric vehicle cluster, construct an electric vehicle response willingness model through the Weber-Fechner law, and allocate the adjustment demand capacity issued by the cluster layer in sequence.

[0012] As a preferred embodiment of the present invention, step S41 specifically includes: The adjustment time of an electric vehicle is quantified by the ratio of the process time of the electric vehicle through the battery energy storage regulation dead zone to the process time of responding to regulation demand. The adjustment speed of an electric vehicle is quantified by calculating the ratio of the change in output to the change in time during the response process. The adjustment accuracy of electric vehicles is quantified by the ratio of the specified output to the actual output of the electric vehicle to the maximum and average deviation values. The adjustment capability of the electric vehicle is calculated by assigning weight coefficients to the adjustment time, adjustment speed, and adjustment accuracy of the electric vehicle. Based on the calculation results of the regulation capacity of all electric vehicles in the electric vehicle cluster, they are arranged in descending order, and the regulation requirements issued by the electric vehicle cluster layer will also be allocated in this order.

[0013] As a preferred embodiment of the present invention, step S42, which involves constructing an electric vehicle response intention model using the Weber-Fechner law, includes: With the first The first electric vehicle cluster The incentive response intention of an electric vehicle is the psychological perception quantity, while the benefit of response adjustment needs and its own state of charge (SOC) are the stimulus quantities. An incentive response intention model for electric vehicles is constructed using the Weber-Fechner law.

[0014] As a preferred embodiment of the present invention, the step S42 of sequentially allocating the adjustment demand capacity issued by the cluster layer includes: The specific response capacity is calculated based on the maximum adjustable capacity of the electric vehicle; According to the regulation capacity of electric vehicles, the response capacity of each electric vehicle is compared and allocated with the remaining regulation demand in turn. When the cumulative allocated capacity reaches the total regulation demand issued by the cluster layer, the allocation process terminates.

[0015] The beneficial effects of this invention are as follows: This invention provides a multi-stage decomposition method for electric vehicle aggregator dispatching demand. This method effectively solves the coordination and control challenges of distributed electric vehicle grid access by constructing a three-layer decomposition architecture including an electric vehicle aggregator platform layer, an electric vehicle cluster layer, and an electric vehicle layer. In the demand allocation from the platform layer to the cluster layer, the SOC difference method is used to quantify the adjustable capacity of each cluster, and power flow constraint correction ensures the safe operation of the power grid. In the allocation process from the cluster layer to the electric vehicle layer, electric vehicles are ranked through a multi-index evaluation system of adjustment capability, and a response willingness model is constructed based on the Weber-Fechner law, achieving optimized allocation of adjustment capacity. This method considers both the objective adjustment capability of electric vehicles and the subjective response willingness of users. It improves the overall response efficiency of electric vehicle clusters, provides technical support for the reliable execution of distribution network adjustment demands, effectively alleviates the operational pressure on the power grid dispatch center, and optimizes the overall operating efficiency of the power system. Attached Figure Description

[0016] Figure 1 This is a schematic diagram illustrating the steps of a multi-stage decomposition method for electric vehicle aggregator scheduling requirements provided by the present invention. Figure 2 This is a schematic diagram of the multi-stage decomposition architecture for electric vehicle aggregator scheduling requirements provided by the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. In the following description, the expression "some embodiments" refers to a subset of all possible embodiments; however, it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.

[0018] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0019] It should be understood that the present invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Furthermore, the terminology used herein is intended only to describe particular embodiments and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “compose” and / or “comprising,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0020] It should also be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0021] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.

[0022] Firstly, this invention provides a method for multi-stage decomposition of scheduling demand for electric vehicle aggregators. Please refer to the appendix for details. Figure 1 and attached Figure 2 The method includes the following steps: Step S1: Set the electric vehicle aggregator scheduling requirements according to the power grid regulation requirements, and establish a multi-stage decomposition architecture for the electric vehicle aggregator scheduling requirements. The multi-stage decomposition architecture for the electric vehicle aggregator scheduling requirements includes an electric vehicle aggregator platform layer, an electric vehicle cluster layer, and an electric vehicle layer.

[0023] As a preferred embodiment of the present invention, the electric vehicle aggregator platform layer in step S1 interacts with the power distribution network to receive and feedback power grid regulation requirements. The electric vehicle cluster layer receives the scheduling requirements from the electric vehicle aggregator platform layer and sends them to the electric vehicle layer. The electric vehicle layer, as the actual executor of the scheduling requirements, responds to the scheduling requirements of the electric vehicle cluster layer through its adjustment willingness and adjustment capability.

[0024] Step S2: Based on the functions of each layer of the electric vehicle aggregator, determine the scheduling requirements of each layer of the electric vehicle aggregator in each time period.

[0025] As a preferred embodiment of the present invention, step S2, determining the scheduling needs of the electric vehicle aggregator in each time period, includes: Step S21: Determine the scheduling requirements of the electric vehicle aggregator platform layer. Specifically, at any scheduling time, the scheduling requirements received by the electric vehicle aggregator platform layer are equal to the adjustment requirements issued by the regional distribution network at that time. In this embodiment, the scheduling requirements of the electric vehicle aggregator platform layer can be described as follows:

[0026] in, For electric vehicle aggregators at any time The received scheduling requests will be further broken down into multiple levels and allocated to each electric vehicle. For the regional distribution network at time The regulatory requirements were issued to electric vehicle aggregators.

[0027] Step S22: Determine the scheduling requirements of the electric vehicle cluster layer. Specifically, for an aggregator that includes multiple substations, at any scheduling time, the sum of the adjustment requirements allocated to each electric vehicle cluster is equal to the adjustment requirements of the electric vehicle cluster layer. In this embodiment, the scheduling requirements of the electric vehicle cluster layer can be described as follows:

[0028] in, This refers to the number of distribution channels involved in the electric vehicle aggregator. For electric vehicle aggregators according to the results of the district division A cluster of electric vehicles at time The allocated adjustment needs.

[0029] Step S23: Determine the scheduling requirements of the electric vehicle layer. Specifically, for each electric vehicle cluster, which contains multiple electric vehicles, the sum of the adjustment requirements allocated to each electric vehicle in the electric vehicle cluster is equal to the adjustment requirements of that electric vehicle layer.

[0030] In this embodiment, the scheduling requirements of the electric vehicle layer can be described as follows:

[0031] in, For the first The number of electric vehicles contained in each electric vehicle cluster varies between different electric vehicle clusters; For the first The first electric vehicle cluster A car at a time The allocated adjustment needs.

[0032] Step S3: In the electric vehicle cluster layer, calculate the adjustable capacity of different electric vehicle clusters based on the SOC difference method, obtain the contribution value of the adjustment demand of the electric vehicle aggregator platform layer based on the adjustable capacity, and report it to the electric vehicle aggregator platform layer.

[0033] As a preferred embodiment of the present invention, step S3, which calculates the adjustable capacity of different electric vehicle clusters based on the SOC difference method, includes: Step S31: Collect the current state of charge value of the electric vehicle, and calculate the potential adjustment space of the electric vehicle by combining the maximum allowable state of charge value of the electric vehicle and its rated battery capacity. Step S32: Based on the potential adjustment space of each electric vehicle in the cluster, sum the potential adjustment spaces of all electric vehicles in the k-th electric vehicle cluster to obtain the overall adjustable capacity of the cluster at the current time.

[0034] In this embodiment, since the value contribution of different electric vehicle clusters in responding to the adjustment needs of the electric vehicle aggregator is not the same, the SOC difference method is used to quantify the adjustment space of different electric vehicle clusters and form an adjustable capacity to be reported to the electric vehicle aggregator platform layer. The adjustable capacity reported by each electric vehicle cluster is described as follows:

[0035]

[0036] in, For the first A cluster of electric vehicles at time Adjustable capacity reported to the electric vehicle aggregator platform layer; For the first The first electric vehicle cluster The adjustment range of an electric vehicle determined by the SOC difference method; For the first The first electric vehicle cluster A car at a time SOC value; For the first The first electric vehicle cluster The maximum SOC of an electric vehicle at any given time; For the first The first electric vehicle cluster The rated capacity of an electric vehicle; For the first The first electric vehicle cluster The adjustment space limitation coefficient for an electric vehicle is mainly determined by economic considerations of the electric vehicle's response to adjustment demands, and is generally taken as... .

[0037] As a preferred embodiment of the present invention, step S3 further includes the electric vehicle aggregator platform layer correcting the adjustable capacity reported by each electric vehicle cluster through power flow constraints, and on the basis of correcting the adjustable capacity reported by each electric vehicle cluster, allocating the adjustment demand of the electric vehicle aggregator platform layer to the electric vehicle cluster layer using the adjustment capacity weight value of each electric vehicle cluster.

[0038] In this embodiment, power flow constraints limit the upper and lower limits of the adjustable capacity of the electric vehicle cluster. Then the... The power flow constraints of a cluster of electric vehicles can be described as follows:

[0039] in, and The first The upper and lower limits of the transmission capacity of the transformer area to which each electric vehicle cluster belongs. The value is usually 0.

[0040] Modify the first according to the power flow constraint. A cluster of electric vehicles at time The adjustable capacity reported to the electric vehicle aggregator platform layer is as follows:

[0041] in, For the first time after correction by power flow constraints A cluster of electric vehicles at time Adjustable capacity reported to the electric vehicle aggregator platform layer.

[0042] Based on the adjustable capacity reported by different electric vehicle clusters to the electric vehicle aggregator platform layer after power flow constraint correction, the first... The platform-level adjustment capacity allocation weight values ​​for each electric vehicle cluster are as follows:

[0043] in, For electric vehicle aggregator platform layer at any time The adjustment demand is allocated to the first The weight values ​​of each electric vehicle cluster.

[0044] Then the first The capacity of the electric vehicle aggregator platform layer allocated to each electric vehicle cluster can be described as follows:

[0045] in, For electric vehicle aggregator platform layer at any time The adjustment demand is allocated to the first The regulation capacity of an electric vehicle cluster.

[0046] Step S4: In the electric vehicle layer, taking into account the differences in the adjustment capabilities and adjustment intentions of different electric vehicles, the order in which each electric vehicle in the cluster responds to the adjustment demand is allocated by the adjustment capability, and the capacity of each electric vehicle responding to the adjustment demand is allocated in sequence by the adjustment intention.

[0047] In this embodiment, electric vehicles with greater adjustment capabilities are given priority in receiving the adjustment capacity requests issued by the cluster layer. Within the first electric vehicle cluster The adjustment capability of an electric vehicle is calculated as follows, with the calculation data for each indicator derived from the historical data of the previous seven responses to the adjustment demand.

[0048] As a preferred embodiment of the present invention, step S4 specifically includes: Step S41: Calculate the adjustment capability of different electric vehicles in the cluster using adjustment duration, adjustment speed and adjustment accuracy, and sort the electric vehicles in the cluster in descending order of adjustment capability. Step S42: Based on the ranking results of adjustment capabilities and taking into account the differences in response willingness among different electric vehicles in the electric vehicle cluster, construct an electric vehicle response willingness model through the Weber-Fechner law, and allocate the adjustment demand capacity issued by the cluster layer in sequence.

[0049] As a preferred embodiment of the present invention, step S41 specifically includes: The adjustment time of an electric vehicle is quantified by the ratio of the process time of the electric vehicle through the battery energy storage regulation dead zone to the process time of responding to regulation demand. In this embodiment, the adjustment time refers to the delay time it takes for an electric vehicle to align its output direction with the adjustment demand when it receives the adjustment request from the electric vehicle cluster layer. Considering the charging and discharging characteristics of electric vehicles, the ratio of the electric vehicle's time to traverse the battery storage adjustment dead zone to the time it takes to respond to the adjustment demand is used to quantify the first adjustment time. Adjustment time for an electric vehicle.

[0050]

[0051] in, For the first The first electric vehicle cluster Adjustment time for an electric vehicle For the first The first electric vehicle cluster The time it takes for an electric vehicle to cross the adjustment dead zone after it begins to respond to adjustment demands. and The first The first electric vehicle cluster The start and end times of the electric vehicle's response adjustment needs.

[0052] The adjustment speed of an electric vehicle is quantified by calculating the ratio of the change in output to the change in time during the response process. In this embodiment, the adjustment speed refers to the rate at which an electric vehicle reaches the output specified by the scheduling requirement when it receives the adjustment request issued by the electric vehicle cluster layer. It mainly depends on the standard adjustment speed set by the electric vehicle aggregator and the actual adjustment speed of the electric vehicle.

[0053] its first The first electric vehicle cluster The method for calculating the actual adjustment speed of an electric vehicle is described as follows:

[0054] in, For the first The first electric vehicle cluster The actual adjustment speed of an electric vehicle and The first The first electric vehicle cluster The starting and ending power outputs of an electric vehicle.

[0055] The adjustment accuracy of electric vehicles is quantified by the ratio of the specified output to the actual output of the electric vehicle to the maximum and average deviation values. In this embodiment, adjustment accuracy refers to the degree of deviation of an electric vehicle from the adjustment requirements issued by the electric vehicle cluster layer, which is closely related to the actual charging and discharging behavior of the electric vehicle. To simplify calculations, the adjustment accuracy of the electric vehicle is quantified by the ratio of the specified output of the adjustment requirement to the maximum deviation and the average deviation of the actual output of the electric vehicle.

[0056] its first The first electric vehicle cluster The method for calculating the actual adjustment accuracy of an electric vehicle is as follows:

[0057] in, For the first The first electric vehicle cluster The adjustment precision of an electric vehicle and The first The first electric vehicle cluster The actual power output of an electric vehicle and the power output required by the regulation demand.

[0058] The adjustment capability of the electric vehicle is calculated by assigning weight coefficients to the adjustment time, adjustment speed, and adjustment accuracy of the electric vehicle. In this embodiment, the first The first electric vehicle cluster The adaptability of an electric vehicle is composed of the three indicators mentioned above, and the relative importance of the different indicators is balanced using a weighting method. Specifically:

[0059] in, For the first The first electric vehicle cluster The calculation results of the adjustment capability of an electric vehicle , and The first The first electric vehicle cluster The weighting coefficients for three indicators of electric vehicle adjustment: adjustment time, adjustment speed, and adjustment accuracy. Generally, .

[0060] Based on the calculation results of the regulation capacity of all electric vehicles in the electric vehicle cluster, they are arranged in descending order, and the regulation requirements issued by the electric vehicle cluster layer will also be allocated in this order.

[0061] As a preferred embodiment of the present invention, step S42, which involves constructing an electric vehicle response intention model using the Weber-Fechner law, includes: With the first The first electric vehicle cluster The incentive response intention of an electric vehicle is the psychological perception quantity, while the benefit of response adjustment needs and its own state of charge (SOC) are the stimulus quantities. An incentive response intention model for electric vehicles is constructed using the Weber-Fechner law.

[0062] In this embodiment, the Weber-Fechner law can be described as follows:

[0063] in, For the measure of psychological feelings; The stimulus amount; It is the Weber constant; is the stimulus constant.

[0064] With the first The first electric vehicle cluster The incentive response intention of an electric vehicle is represented by its psychological perception, while the benefits of response adjustment and its own state of charge (SOC) are represented by their stimulus. A model of the incentive response intention of an electric vehicle is constructed using the Weber-Fechner law, specifically as follows:

[0065]

[0066]

[0067] in, For the first The first electric vehicle cluster A car at a time The capacity of incentive response willingness; For the first The first electric vehicle cluster A car at a time The maximum adjustable charging capacity; For the first The first electric vehicle cluster The capacity benefit preference coefficient of the electric vehicle response cluster layer adjustment demand represents the degree of influence of adjustment benefits on the response willingness of electric vehicle users. For the first The first electric vehicle cluster A car at a time The normalized value of the regulation benefit per unit of electricity represents the stimulus amount of the regulation benefit. For the first The first electric vehicle cluster A car at a time The SOC value represents the amount of SOC stimulation of its own state of charge. For the first A cluster of electric vehicles at time The need for regulation; For the first The compensation electricity price for electric vehicles in each electric vehicle cluster; To compensate for the electricity price at any time The maximum value; For the first The first electric vehicle cluster The energy loss per unit of electricity in response to the cluster layer's adjustment needs of individual electric vehicles; For the first The first electric vehicle cluster The number of times an electric vehicle responded to cluster-level adjustment requests in the previous seven days; For the first The first electric vehicle cluster The number of times an electric vehicle's state of charge (SOC) is more affected by adjustment gains in the first seven days compared to its own state of charge (SOC).

[0068] As a preferred embodiment of the present invention, the step S42 of sequentially allocating the adjustment demand capacity issued by the cluster layer includes: The specific response capacity is calculated based on the maximum adjustable capacity of the electric vehicle; According to the regulation capacity of electric vehicles, the response capacity of each electric vehicle is compared and allocated with the remaining regulation demand in turn. When the cumulative allocated capacity reaches the total regulation demand issued by the cluster layer, the allocation process terminates.

[0069] As can be seen, this method takes into account the dispersed and regional characteristics of electric vehicles (EVs), dividing the EVs included in the EV aggregator into multiple EV clusters according to their distribution areas. This constructs a three-layer regulation demand decomposition architecture: the EV aggregator platform layer, the EV cluster layer, and the EV layer. Simultaneously, based on the functions of each layer of the EV aggregator, the scheduling requirements of each layer in each time period are determined. During the allocation of regulation demand from the EV aggregator platform layer to the EV cluster layer, the adjustable capacity of different EV clusters is calculated based on the SOC difference method. Considering the power flow constraints of the distribution areas to which each EV cluster belongs, the platform layer corrects the adjustable capacity reported by each EV cluster through power flow constraints, and uses the adjustment capacity weight values ​​of each EV cluster to achieve the allocation of the EV aggregator platform layer's regulation demand to the cluster layer. During the allocation of cluster layer regulation demand to the EV layer, the regulation capacity of different EVs within the cluster is calculated using regulation duration, regulation speed, and regulation accuracy indicators. The EVs within the cluster are sorted according to their regulation capacity from largest to smallest, and an EV response willingness model is constructed using the Weber-Fechner law. The regulation demand capacity issued by the cluster layer is then allocated sequentially. A multi-stage decomposition method for electric vehicle aggregator dispatch demand is proposed, which provides theoretical guidance for the execution of electric vehicle charging load response to distribution network regulation demand results. This method is beneficial to significantly alleviate the pressure on the power grid dispatch center and optimize the operation of the power system.

[0070] Secondly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-described method for multi-stage decomposition of scheduling requirements of electric vehicle aggregators.

[0071] In this embodiment, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0072] Thirdly, embodiments of this application also provide an electronic device, including a processor and a memory, wherein the memory stores a plurality of instructions; the processor loads instructions from the memory to execute steps in any of the multi-stage decomposition methods for electric vehicle aggregator scheduling demands provided in embodiments of this application.

[0073] Fourthly, embodiments of this application also provide a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps in any of the multi-stage decomposition methods for electric vehicle aggregator scheduling requirements provided in embodiments of this application.

[0074] In this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be accomplished by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0075] Therefore, embodiments of this application provide a computer-readable storage medium storing a plurality of instructions that can be loaded by a processor to execute steps in any of the multi-stage decomposition methods for electric vehicle aggregator scheduling requirements provided in embodiments of this application.

[0076] It should be noted that, through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0077] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for multi-stage decomposition of scheduling demand for electric vehicle aggregators, characterized in that, The method includes the following steps: Step S1: Set the dispatch requirements of electric vehicle aggregators according to the power grid regulation requirements, and establish a multi-stage decomposition architecture for the dispatch requirements of electric vehicle aggregators. The multi-stage decomposition architecture for the dispatch requirements of electric vehicle aggregators includes an electric vehicle aggregator platform layer, an electric vehicle cluster layer and an electric vehicle layer. Step S2: Based on the functions of each layer of the electric vehicle aggregator, determine the scheduling requirements of each layer of the electric vehicle aggregator in each time period; Step S3: In the electric vehicle cluster layer, calculate the adjustable capacity of different electric vehicle clusters based on the SOC difference method, obtain the contribution value of the adjustment demand of the electric vehicle aggregator platform layer based on the adjustable capacity, and report it to the electric vehicle aggregator platform layer. Step S4: In the electric vehicle layer, taking into account the differences in the adjustment capabilities and adjustment intentions of different electric vehicles, the order in which each electric vehicle in the cluster responds to the adjustment demand is allocated by the adjustment capability, and the capacity of each electric vehicle responding to the adjustment demand is allocated in sequence by the adjustment intention.

2. The method for multi-stage decomposition of electric vehicle aggregator scheduling demand according to claim 1, characterized in that, In step S1, the electric vehicle aggregator platform layer interacts with the power distribution network to receive and feedback grid regulation demands. The electric vehicle cluster layer receives scheduling demands from the electric vehicle aggregator platform layer and sends them to the electric vehicle layer. The electric vehicle layer, as the actual executor of scheduling demands, responds to the scheduling demands of the electric vehicle cluster layer through its adjustment willingness and adjustment capability.

3. The method for multi-stage decomposition of electric vehicle aggregator scheduling demand according to claim 2, characterized in that, Step S2, which determines the scheduling needs of the electric vehicle aggregator in each time period, includes: Step S21: Determine the scheduling requirements of the electric vehicle aggregator platform layer. Specifically, at any scheduling time, the scheduling requirements received by the electric vehicle aggregator platform layer are equal to the adjustment requirements issued by the regional distribution network at that time. Step S22: Determine the scheduling requirements of the electric vehicle cluster layer. Specifically, for an aggregator that includes multiple substations, at any scheduling time, the sum of the adjustment requirements allocated to each electric vehicle cluster is equal to the adjustment requirements of the electric vehicle cluster layer. Step S23: Determine the scheduling requirements of the electric vehicle layer. Specifically, for each electric vehicle cluster, which contains multiple electric vehicles, the sum of the adjustment requirements allocated to each electric vehicle in the electric vehicle cluster is equal to the adjustment requirements of that electric vehicle layer.

4. The method for multi-stage decomposition of electric vehicle aggregator scheduling demand according to claim 3, characterized in that, Step S3, which calculates the adjustable capacity of different electric vehicle clusters based on the SOC difference method, includes: Step S31: Collect the current state of charge value of the electric vehicle, and calculate the potential adjustment space of the electric vehicle by combining the maximum allowable state of charge value of the electric vehicle and its rated battery capacity. Step S32: Based on the potential adjustment space of each electric vehicle in the cluster, sum the potential adjustment spaces of all electric vehicles in the k-th electric vehicle cluster to obtain the overall adjustable capacity of the cluster at the current time.

5. The method for multi-stage decomposition of electric vehicle aggregator scheduling demand according to claim 4, characterized in that, Step S3 further includes the electric vehicle aggregator platform layer correcting the adjustable capacity reported by each electric vehicle cluster through power flow constraints, and on the basis of correcting the adjustable capacity reported by each electric vehicle cluster, allocating the adjustment demand of the electric vehicle aggregator platform layer to the electric vehicle cluster layer using the adjustment capacity weight value of each electric vehicle cluster.

6. The method for multi-stage decomposition of electric vehicle aggregator scheduling demand according to claim 5, characterized in that, Step S4 specifically includes: Step S41: Calculate the adjustment capability of different electric vehicles in the cluster using adjustment duration, adjustment speed and adjustment accuracy, and sort the electric vehicles in the cluster in descending order of adjustment capability. Step S42: Based on the ranking results of adjustment capabilities and taking into account the differences in response willingness among different electric vehicles in the electric vehicle cluster, construct an electric vehicle response willingness model through the Weber-Fechner law, and allocate the adjustment demand capacity issued by the cluster layer in sequence.

7. The method for multi-stage decomposition of electric vehicle aggregator scheduling demand according to claim 6, characterized in that, Step S41 specifically includes: The adjustment time of an electric vehicle is quantified by the ratio of the process time of the electric vehicle through the battery energy storage regulation dead zone to the process time of responding to regulation demand. The adjustment speed of an electric vehicle is quantified by calculating the ratio of the change in output to the change in time during the response process. The adjustment accuracy of electric vehicles is quantified by the ratio of the specified output to the actual output of the electric vehicle to the maximum deviation and the average deviation. The adjustment capability of the electric vehicle is calculated by assigning weight coefficients to the adjustment time, adjustment speed, and adjustment accuracy of the electric vehicle. Based on the calculation results of the regulation capacity of all electric vehicles in the electric vehicle cluster, they are arranged in descending order, and the regulation requirements issued by the electric vehicle cluster layer will also be allocated in this order.

8. The method for multi-stage decomposition of electric vehicle aggregator scheduling demand according to claim 7, characterized in that, Step S42 involves constructing an electric vehicle response intention model using the Weber-Fechner law, including: With the first The first electric vehicle cluster The incentive response intention of an electric vehicle is the psychological perception quantity, while the benefit of response adjustment needs and its own state of charge (SOC) are the stimulus quantities. An incentive response intention model for electric vehicles is constructed using the Weber-Fechner law.

9. The method for multi-stage decomposition of electric vehicle aggregator scheduling demand according to claim 8, characterized in that, The step S42, which involves sequentially allocating the adjustment capacity requests issued by the cluster layer, includes: The specific response capacity is calculated based on the maximum adjustable capacity of the electric vehicle; According to the regulation capacity of electric vehicles, the response capacity of each electric vehicle is compared and allocated with the remaining regulation demand in turn. When the cumulative allocated capacity reaches the total regulation demand issued by the cluster layer, the allocation process terminates.

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