Method and device for evaluating scheduling of vehicle cluster participating in peak shaving, and electronic equipment

Abstract

This invention discloses a scheduling evaluation method, apparatus, and electronic device for vehicle clusters participating in peak shaving. The method includes: acquiring the peak shaving and valley filling periods for vehicle cluster participation in peak shaving, and the peak shaving index during these periods; generating a peak shaving command during these periods based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster at charging stations; responding to the peak shaving command, performing peak shaving scheduling on the power load of the vehicle cluster based on the total charging power of the vehicle cluster, obtaining the initial charging power and initial discharging power of the vehicle cluster; and evaluating the initial charging power and initial discharging power to obtain the scheduling evaluation result of the vehicle cluster, wherein the scheduling evaluation result is used to evaluate the stability of the power load of the vehicle cluster during the peak shaving and valley filling periods. This invention solves the technical problem of the inability to effectively evaluate the scheduling of vehicle clusters participating in peak shaving.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and more specifically, to a scheduling evaluation method, apparatus, and electronic equipment for vehicle clusters participating in peak shaving. Background Technology

[0002] Electric vehicles, as a flexible distributed resource, can play a significant role in the optimized scheduling of the power system if they can be aggregated and managed through demand-side mechanisms. This indicates that while the load of electric vehicles poses a significant challenge to the safe operation of the power system, it also has the potential to provide considerable peak-shaving capacity for the power grid.

[0003] Currently, multi-timescale scheduling optimization models of vehicle clusters are commonly used to achieve peak shaving of the power grid in order to obtain peak shaving results and ensure that the power system can maintain stable power supply during high or low loads. However, the aforementioned multi-timescale scheduling optimization models rely on a large amount of data to achieve peak shaving of the power grid. As a result, due to inaccurate data, the effect of peak shaving of the power grid may be insignificant, leading to the technical problem of not being able to effectively evaluate the scheduling of vehicle clusters participating in peak shaving.

[0004] There is currently no effective solution to the technical problem of being unable to effectively evaluate the participation of vehicle clusters in peak shaving scheduling. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for evaluating the scheduling of vehicle clusters participating in peak shaving, in order to at least solve the technical problem of being unable to effectively evaluate the scheduling of vehicle clusters participating in peak shaving.

[0006] According to one aspect of the invention, a scheduling evaluation method for vehicle clusters participating in peak shaving is provided. The method includes: obtaining the peak shaving and valley filling periods for vehicle cluster participation in peak shaving, and peak shaving indices during these periods, wherein the peak shaving and valley filling periods characterize the fluctuations in the power demand information corresponding to the vehicle cluster at different time periods, and the peak shaving indices characterize the proportion of reduced or increased load power required by the vehicle cluster; generating peak shaving commands during the peak shaving and valley filling periods based on the peak shaving indices, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster at charging stations, wherein the dispatchable potential value characterizes the changes in the power and electricity consumption of the vehicle cluster at charging stations; responding to the peak shaving commands, performing peak shaving scheduling on the power load of the vehicle cluster based on the total charging power of the vehicle cluster to obtain the initial charging power and initial discharging power of the vehicle cluster; evaluating the initial charging power and initial discharging power to obtain a scheduling evaluation result for the vehicle cluster, wherein the scheduling evaluation result is used to evaluate the stability of the power load of the vehicle cluster during the peak shaving and valley filling periods.

[0007] Optionally, obtaining the peak shaving and valley filling period for the vehicle cluster to participate in peak shaving includes: obtaining the distribution network load power of the vehicle cluster, and the first load power and the second load power corresponding to the distribution network load power, wherein the first load power is greater than the second load power; obtaining a first time period in response to the distribution network load power being greater than the first load power; obtaining a second time period in response to the distribution network load power being less than the second load power; and determining the first time period and the second time period as peak shaving and valley filling periods respectively.

[0008] Optionally, during the peak shaving and valley filling period, a peak shaving instruction is generated based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station. This includes: during the peak shaving and valley filling period, determining the network loss function based on the distribution network topology, wherein the network loss function is a function that targets the loss information of the distribution network corresponding to the vehicle cluster; and generating the peak shaving instruction based on the network loss function, the peak shaving index, and the dispatchable potential value.

[0009] Optionally, in response to a peak-shaving command, peak-shaving scheduling is performed on the power load of the vehicle cluster based on the total charging power of the vehicle cluster to obtain the initial charging power and initial discharging power of the vehicle cluster. This includes: in response to the peak-shaving command, establishing a first objective function and a first constraint condition for the charging station, wherein the first objective function is established with the revenue of the charging station as the objective, and the first constraint condition is used to constrain the service data of the charging station; based on the first objective function and the first constraint condition, establishing a second objective function and a second constraint condition for the vehicle cluster, wherein the second objective function is established with the charging cost of the vehicle cluster as the objective, and the second constraint condition is used to constrain the schedulable potential value; and based on the first objective function, the first constraint condition, the second objective function, and the second constraint condition, peak-shaving scheduling is performed on the power load of the vehicle cluster to obtain the initial charging power and initial discharging power.

[0010] Optionally, the initial charging power and initial discharging power are evaluated to obtain the scheduling evaluation results of the vehicle cluster, including: determining the scheduling revenue data of the charging station based on the initial charging power and initial discharging power; obtaining the scheduling correction amount of the charging station in response to the scheduling revenue data being less than or equal to the average scheduling revenue data; correcting the initial charging power and initial discharging power based on the peak shaving index and the scheduling correction amount to obtain the target charging power and target discharging power of the vehicle cluster; and evaluating the target charging power and target discharging power based on multiple evaluation indicators of the vehicle cluster's participation in peak shaving to obtain the scheduling evaluation results.

[0011] Optionally, based on multiple evaluation indicators for vehicle cluster participation in peak shaving, the target charging power and target discharging power are evaluated to obtain scheduling evaluation results, including: determining the target weights corresponding to the multiple evaluation indicators, and evaluating the target charging power and target discharging power based on the multiple evaluation indicators to obtain multiple evaluation values; establishing a target evaluation matrix based on the multiple evaluation indicators and multiple target weights; determining the evaluation level corresponding to the multiple evaluation indicators based on the multiple evaluation values; and determining the scheduling evaluation results based on the multiple target weights, the target evaluation matrix, and the multiple evaluation levels.

[0012] Optionally, the scheduling evaluation result is determined based on multiple target weights, a target evaluation matrix, and multiple evaluation levels, including: determining the degree of correlation of multiple evaluation indicators based on multiple target weights and a target evaluation matrix; and determining the scheduling evaluation result based on multiple degree of correlation and multiple evaluation levels.

[0013] According to one aspect of the present invention, a scheduling evaluation device for vehicle clusters participating in peak shaving is provided. The device further includes: a first acquisition unit, configured to acquire the peak shaving and valley filling periods of the vehicle clusters participating in peak shaving, and peak shaving indicators during the peak shaving and valley filling periods, wherein the peak shaving and valley filling periods characterize the fluctuation of power demand information corresponding to the vehicle clusters at different time periods, and the peak shaving indicators characterize the proportion of reduced or increased load power required by the vehicle clusters; and a generation unit, configured to, during the peak shaving and valley filling periods, based on the peak shaving indicators, the distribution network topology corresponding to the vehicle clusters, and the peak shaving indicators of the vehicle clusters participating in peak shaving, generate a scheduling evaluation device for the vehicle clusters participating in peak shaving, and generate a scheduling evaluation device for the vehicle clusters participating in peak shaving, based on the peak shaving indicators, the distribution network topology corresponding to the vehicle clusters, and the peak shaving indicators during the peak shaving and valley filling periods. The system generates peak-shaving commands based on the dispatchable potential value of the charging station. The dispatchable potential value characterizes the changes in power and energy consumption of the vehicle cluster within the charging station. A second acquisition unit, in response to the peak-shaving command, performs peak-shaving scheduling on the power load of the vehicle cluster based on the total charging power of the vehicle cluster, obtaining the initial charging power and initial discharging power of the vehicle cluster. An evaluation unit evaluates the initial charging power and initial discharging power to obtain the scheduling evaluation results of the vehicle cluster. The scheduling evaluation results are used to evaluate the stability of the power load of the vehicle cluster during peak-shaving and valley-filling periods.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program, when run by a processor, controls the device where the storage medium is located to execute the method of the present invention.

[0015] According to another aspect of the present invention, a processor is also provided for running a program, wherein the program executes the methods of the present invention during runtime.

[0016] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the method of the present invention.

[0017] According to another aspect of the present invention, an electronic device is also provided, comprising a processor and a memory for storing processor-executable instructions. The processor is configured to execute instructions to implement the methods of the embodiments of the present invention.

[0018] In this embodiment of the invention, the peak shaving and valley filling periods for vehicle clusters participating in peak shaving and valley filling are obtained, as well as the peak shaving and valley filling indices during these periods. The peak shaving and valley filling periods characterize the fluctuations in the power demand information corresponding to the vehicle cluster at different time intervals, and the peak shaving indices characterize the proportion of reduced or increased load power that the vehicle cluster needs to bear. During the peak shaving and valley filling periods, a peak shaving command is generated based on the peak shaving indices, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster at the charging station. The dispatchable potential value characterizes the changes in the power and electricity consumption of the vehicle cluster at the charging station. In response to the peak shaving command, peak shaving scheduling is performed on the power load of the vehicle cluster based on the total charging power of the vehicle cluster, obtaining the initial charging power and initial discharging power of the vehicle cluster. The initial charging power and initial discharging power are evaluated to obtain the scheduling evaluation results of the vehicle cluster, which are used to evaluate the stability of the power load of the vehicle cluster during the peak shaving and valley filling periods. In other words, the embodiments of the present invention can first obtain the peak shaving and valley filling periods during which the vehicle cluster participates in peak shaving and valley filling, as well as the peak shaving index during the peak shaving and valley filling periods. Then, during the peak shaving and valley filling periods, based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station, a peak shaving instruction is generated. Based on the obtained peak shaving instruction and the total charging power of the vehicle cluster, the power load of the vehicle cluster can be dispatched and peak shaving can be carried out to obtain the initial charging power and initial discharging power of the vehicle cluster. Finally, the initial charging power and initial discharging power can be evaluated to achieve the purpose of obtaining the dispatch evaluation results of the vehicle cluster. In other words, after determining the peak shaving and valley filling period, a peak shaving command is generated within that period based on the peak shaving index, distribution network topology, and dispatchable potential. In response to this command, the power load of the vehicle cluster is scheduled for peak shaving based on the total charging power of the vehicle cluster. This allows for the acquisition of the initial charging power and initial discharging power of the vehicle cluster. After evaluating the obtained initial charging power and initial discharging power, the scheduling evaluation results of the vehicle cluster can be obtained. This solves the technical problem of not being able to effectively evaluate the scheduling of vehicle clusters participating in peak shaving, and achieves the technical effect of effectively evaluating the scheduling of vehicle clusters participating in peak shaving. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0020] Figure 1 This is a flowchart of a scheduling evaluation method for vehicle clusters participating in peak shaving according to an embodiment of the present invention;

[0021] Figure 2 This is a flowchart of a multi-timescale optimization scheduling method for electric vehicles participating in peak shaving according to an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of a scheduling and evaluation device for vehicle clusters participating in peak shaving according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] According to an embodiment of the present invention, a scheduling evaluation method for vehicle clusters participating in peak shaving is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] The following describes the scheduling evaluation method for vehicle clusters participating in peak shaving according to an embodiment of the present invention.

[0027] Figure 1 This is a flowchart of a scheduling evaluation method for vehicle clusters participating in peak shaving according to an embodiment of the present invention, such as... Figure 1 As shown, the scheduling evaluation method for this vehicle cluster participating in peak shaving may include the following steps:

[0028] Step S101: Obtain the peak shaving and valley filling periods during which the vehicle cluster participates in peak shaving and valley filling, as well as the peak shaving and valley filling indicators during the peak shaving and valley filling periods.

[0029] In the technical solution provided by step S101 of the present invention, the peak shaving and valley filling periods during which the vehicle cluster participates in peak shaving and valley filling, as well as the peak shaving and valley filling indicators during these periods, can be obtained. The peak shaving and valley filling periods characterize the fluctuations in the electricity demand information corresponding to the vehicle cluster at different time periods, and can be obtained through t... * The time period is used to indicate the time.

[0030] Optionally, the peak-shaving index can be used to characterize the proportion of reduced or increased load power that a vehicle cluster needs to bear. For example, the peak-shaving index can be expressed as α(t * If we express α(t) in terms of 0 ≤ α ≤ 1, then α(t) * ) is used to indicate that at t * During the specified period, the percentage of reduced or increased load power that the aggregator's electric vehicle cluster needs to bear.

[0031] Step S102: During the peak shaving and valley filling period, a peak shaving instruction is generated based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station.

[0032] In the technical solution provided by step S102 of the present invention, during the peak shaving and valley filling period, peak shaving instructions can be generated based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station.

[0033] Optionally, the dispatchable potential value is used to characterize the changes in power and energy consumption of the vehicle cluster at the charging station, and the dispatchable potential value is within a preset dispatchable potential range. The distribution network topology can be simply referred to as the distribution network topology. Peak shaving commands can be transmitted via P... ref (t * ) is used to represent.

[0034] For example, based on the distribution network topology and the dispatchable potential of electric vehicles at each charging station in the aggregator, peak-shaving instructions P are allocated with the minimum network loss. ref (t * ), and proceed with the next step based on the peak-shaving instructions received at this time.

[0035] It should be noted that this is only a preferred implementation of generating peak shaving instructions, and the process and method of generating peak shaving instructions are not specifically limited. As long as it is within the peak shaving and valley filling period, the method and process of generating peak shaving instructions based on peak shaving indicators, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station are all within the protection scope of this invention, and will not be listed here.

[0036] In step S103, in response to the peak shaving command, the power load of the vehicle cluster is scheduled for peak shaving based on the total charging power of the vehicle cluster to obtain the initial charging power and initial discharging power of the vehicle cluster.

[0037] In the technical solution provided by step S103 of the present invention, in response to the obtained peak-shaving command, the power load of the vehicle cluster is scheduled for peak shaving according to the total charging power of the vehicle cluster, so as to obtain the initial charging power, initial discharging power and the next day's electricity price of the charging station of the vehicle cluster.

[0038] For example, in determining the optimal peak-shaving command for each charging station (a charging station can be represented by n), Then, under the premise of ensuring that the total charging power of all electric vehicles does not decrease, the charging station uses a master-slave game model to obtain the optimized daily electricity price of the charging station for the next day, as well as the charging and discharging power of electric vehicles, so as to guide electric vehicles to respond to peak shaving instructions to the greatest extent.

[0039] It should be noted that this is only a preferred embodiment for obtaining the initial charging power and initial discharging power of the vehicle cluster. The process and method for obtaining the initial charging power and initial discharging power of the vehicle cluster are not specifically limited. As long as the process and method of obtaining the initial charging power and initial discharging power of the vehicle cluster are based on the total charging power of the vehicle cluster in response to the peak shaving command and the power load of the vehicle cluster is peak shaving and scheduling, they are all within the protection scope of this invention and will not be listed here.

[0040] Step S104: The initial charging power and initial discharging power are evaluated to obtain the scheduling evaluation results of the vehicle cluster.

[0041] In the technical solution provided by step S104 of the present invention, after obtaining the initial charging power and the initial discharging power, it is necessary to evaluate the initial charging power and the initial discharging power in order to obtain the scheduling evaluation results of the vehicle cluster. The scheduling evaluation results are used to evaluate the stability of the power load of the vehicle cluster during the peak shaving and valley filling period.

[0042] For example, after completing peak-shaving scheduling of the power load of the vehicle cluster, the overall peak-shaving and valley-filling effect of the electric vehicle cluster is evaluated. This allows for the determination of the final peak-shaving unit price for charging stations and the scheduling evaluation results. Furthermore, the obtained scheduling evaluation results can be fed back to the power grid, enabling the grid to promptly and accurately grasp the actual response capabilities of electric vehicle aggregators and proactively adjust their peak-shaving instructions in subsequent peak-shaving scheduling.

[0043] It should be noted that this is only a preferred implementation method for obtaining the scheduling evaluation results of the vehicle cluster. The process and method for obtaining the scheduling evaluation results of the vehicle cluster are not specifically limited. As long as the initial charging power and initial discharging power are evaluated, the process and method for obtaining the scheduling evaluation results of the vehicle cluster are within the protection scope of this invention, and will not be listed here.

[0044] In steps S101 to S104 of the present invention, the peak shaving and valley filling periods during which the vehicle cluster participates in peak shaving and valley filling, as well as the peak shaving index during the peak shaving and valley filling periods, are first obtained. Then, during the peak shaving and valley filling periods, peak shaving instructions are generated based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station. Based on the obtained peak shaving instructions and the total charging power of the vehicle cluster, the power load of the vehicle cluster can be dispatched and peak shaving is performed to obtain the initial charging power and initial discharging power of the vehicle cluster. Finally, the initial charging power and initial discharging power can be evaluated to obtain the dispatch evaluation results of the vehicle cluster. In other words, after determining the peak shaving and valley filling period, a peak shaving command is generated within that period based on the peak shaving index, distribution network topology, and dispatchable potential. In response to this command, the power load of the vehicle cluster is scheduled for peak shaving based on the total charging power of the vehicle cluster. This allows for the acquisition of the initial charging power and initial discharging power of the vehicle cluster. After evaluating the obtained initial charging power and initial discharging power, the scheduling evaluation results of the vehicle cluster can be obtained. This solves the technical problem of not being able to effectively evaluate the scheduling of vehicle clusters participating in peak shaving, and achieves the technical effect of effectively evaluating the scheduling of vehicle clusters participating in peak shaving.

[0045] The method described in this embodiment will be further described below.

[0046] As an optional embodiment, obtaining the peak shaving and valley filling period for the vehicle cluster to participate in peak shaving includes: obtaining the power load of the distribution network of the vehicle cluster, and a first load power and a second load power corresponding to the power load of the distribution network, wherein the first load power is greater than the second load power; in response to the power load of the distribution network being greater than the first load power, obtaining a first time period; in response to the power load of the distribution network being less than the second load power, obtaining a second time period; and determining the first time period and the second time period as peak shaving and valley filling periods respectively.

[0047] In this embodiment, the power of the distribution network corresponding to the vehicle cluster, as well as the first load power and the second load power corresponding to the power of the distribution network, are obtained. The power of the distribution network load is compared with the first load power and the second load power respectively. If the power of the distribution network load is greater than the first load power, a first time period can be obtained; if the power of the distribution network load is less than the second load power, a second time period can be obtained. The first time period and the second time period obtained above are determined as peak shaving and valley filling periods.

[0048] Optionally, the distribution network load power can be the predicted distribution network load power for the next day, which can be simply referred to as the distribution network load at this time, and is determined through P. load The first load power can be represented by (t). The first load power can be the upper limit of the safe operation of the power grid, which can be expressed by P. max This is represented. The second load power can be the lower limit power for safe operation of the power grid, which can be expressed through P. min To express.

[0049] For example, the power grid can predict the distribution network load P the following day. load (t), and the load power exceeds the safe operating upper limit power P. max and power below the lower bound P min The time period is defined as the peak shaving and valley filling period t. * During peak shaving and valley filling periods, the power grid needs to reduce or fill the load power that exceeds the allowed range.

[0050] Optionally, peak-shaving instructions can be determined during peak-shaving and valley-filling periods. For example, based on this, the power grid can allocate an overall peak-shaving index α(t) to electric vehicle aggregators. * ), (0≤α≤1), α(t) * ) is used to indicate that at t * During the specified period, this represents the percentage of reduced or increased load power that the aggregator's electric vehicle cluster needs to bear. The aggregator's overall peak-shaving command P ref (t * This can be expressed by the following formula:

[0051]

[0052] As an optional implementation method, during the peak shaving and valley filling period, a peak shaving instruction is generated based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station. This includes: during the peak shaving and valley filling period, determining the network loss function based on the distribution network topology, wherein the network loss function is a function that targets the loss information of the distribution network corresponding to the vehicle cluster; and generating a peak shaving instruction based on the network loss function, the peak shaving index, and the dispatchable potential value.

[0053] In this embodiment, during the peak shaving and valley filling period, the network loss function can be determined based on the distribution network topology, and then the peak shaving command can be generated based on the network loss function, peak shaving index and dispatchable potential value obtained above.

[0054] Optionally, based on the distribution network topology, the optimal power flow of the distribution network is calculated using the second-order cone relaxation algorithm, and the objective function of the network is established with the goal of minimizing network losses. Then, the above network loss function can be expressed by the following formula:

[0055]

[0056] Where T represents the total number of operating time periods, E represents the set of lines, and (i, j) represents line l. ij I ij (t) is used to represent the line l during time period t. ij The square of the current amplitude, r ij Used to indicate line l ij In a distribution network, the resistance of a line between two adjacent nodes i and j can be expressed by the following formula: ij =r ij +jx ij y ij =1 / z ij =g ij -jb ij .

[0057] Optionally, after obtaining the network loss function, a peak-shaving command is generated based on the network loss function, peak-shaving index, dispatchable potential value, power balance constraints of the distribution network, operation constraints of the distribution network, and load constraints of the charging station.

[0058] The power balance constraint of the distribution network can be expressed by the following formula:

[0059]

[0060] S ij (t)=P ij (t)+jQ ij (t)

[0061] Wherein, branch ij represents the positive direction of the power flow from node i to node j, δ(j) represents the set of terminal nodes of the branch with node j as the starting node, and π(j) represents the set of starting nodes of the branch with node j as the ending node. M represents the set of nodes; in any (i, j) ∈ E, v j (t) is used to characterize the square of the voltage magnitude at node j during time period t. The active power used to characterize the conventional load of node j in a distribution network. The reactive power used to characterize the conventional load of node j in a distribution network. This is used to characterize the initial active power of electric vehicles at charging station n at node j in a distribution network. The peak-shaving index is used to characterize the charging station n at node j. If there is no charging station at node j, then... S ij (t) is used to characterize the complex power of the starting node of the line during time period t.

[0062] Furthermore, the operating constraints of the distribution network can be expressed by the following formula:

[0063]

[0064] U min ≤U i (t)≤U max

[0065] θ min ≤θ≤θ max

[0066] Among them, P i (t), Q i (t) represents the active power and reactive power of node i at time t, respectively. i (t), U j (t) are used to represent the voltage magnitudes at nodes i and j, respectively; θ ij The phase angle difference between nodes i and j is used to characterize the load constraint condition of the charging station, which can be expressed by the following formula:

[0067]

[0068] In the above formula, This is used to represent the maximum charging power of the electric vehicle cluster in charging station n as generalized energy storage during time period t. This is used to represent the maximum discharge power of the electric vehicle cluster in charging station n as generalized energy storage during time period t. ΔS n (t) represents the change in electricity consumption during time period t, where the electric vehicle cluster in charging station n is considered as generalized energy storage, due to the grid connection status of the electric vehicles. Used to indicate the lower limit of electrical charge. Used to indicate the upper limit of power consumption.

[0069] As an optional embodiment, in response to a peak-shaving command, the power load of the vehicle cluster is peak-shaving and scheduled based on the total charging power of the vehicle cluster to obtain the initial charging power and initial discharging power of the vehicle cluster. This includes: in response to the peak-shaving command, establishing a first objective function and a first constraint condition for the charging station, wherein the first objective function is established with the revenue of the charging station as the objective, and the first constraint condition is used to constrain the service data of the charging station; based on the first objective function and the first constraint condition, establishing a second objective function and a second constraint condition for the vehicle cluster, wherein the second objective function is established with the charging cost of the vehicle cluster as the objective, and the second constraint condition is used to constrain the schedulable potential value; and based on the first objective function, the first constraint condition, the second objective function, and the second constraint condition, peak-shaving and scheduling of the power load of the vehicle cluster is performed to obtain the initial charging power and initial discharging power.

[0070] In this embodiment, in response to the peak shaving command, a first objective function and a first constraint condition of the charging station can be established. Then, based on the first objective function and the first constraint condition obtained above, a second objective function and a second constraint condition of the vehicle cluster are established. Finally, based on the first objective function, the first constraint condition, the second objective function, and the second constraint condition, the power load of the vehicle cluster is scheduled for peak shaving to achieve the purpose of obtaining the initial charging power and the initial discharging power.

[0071] Optionally, the first objective function of the charging station can be called the objective function of the charging station itself. The first constraint condition of the charging station can be called the constraint condition of charging station n. The second objective function of the vehicle cluster can be called the objective function of the electric vehicle cluster. The second constraint condition of the vehicle cluster can be called the constraint condition of the electric vehicle cluster.

[0072] Optionally, in response to peak shaving commands, an objective function for charging station n is established to maximize revenue and minimize command response error, and this objective function can be expressed by the following formula:

[0073]

[0074] In the above formula, This is used to characterize the charging power of a cluster of electric vehicles at charging station n at time t. Used to characterize the discharge power of a cluster of electric vehicles at charging station n at time t. C g (t) is used to characterize the time-of-use electricity price of the power grid. and These are used to characterize the charging price and electricity sales price set by charging station n, respectively, and consist of the grid electricity price and the charging service fee or electricity sales service fee. and These are used to represent the additional charging service fee and electricity sales service fee of the charging station, respectively (charging stations do not have the authority to change the grid electricity price; they can only adjust the charging electricity price and electricity sales price of the charging station by optimizing the service fee portion of the electricity price). The penalty function term is used to characterize the charging and discharging power of charging station n, ensuring that it accurately responds to peak-shaving commands. Furthermore, the constraints on charging station n can be expressed by the following formula:

[0075]

[0076] The objective of the above constraints is to limit the upper and lower limits and the average value of the service fees set by charging station n.

[0077] Alternatively, to minimize charging costs, the objective function for the electric vehicle cluster with charging stations n can be established, as shown in the following formula:

[0078]

[0079] Alternatively, the constraints of the electric vehicle cluster can be expressed by the following formula:

[0080]

[0081] In the above formula, S n (t) represents the amount of electricity in the electric vehicle cluster at time t. According to the above formula, the cluster's power and electricity levels can be guaranteed to be within the dispatchable potential range, and the electric vehicle cluster can obtain the expected amount of electricity.

[0082] As an optional implementation method, the initial charging power and initial discharging power are evaluated to obtain the scheduling evaluation results of the vehicle cluster, including: determining the scheduling revenue data of the charging station based on the initial charging power and initial discharging power; obtaining the scheduling correction amount of the charging station in response to the scheduling revenue data being less than or equal to the average scheduling revenue data; correcting the initial charging power and initial discharging power based on the peak shaving index and the scheduling correction amount to obtain the target charging power and target discharging power of the vehicle cluster; and evaluating the target charging power and target discharging power based on multiple evaluation indicators of the vehicle cluster participating in peak shaving to obtain the scheduling evaluation results.

[0083] In this embodiment, the scheduling revenue data of the charging station can first be determined based on the initial charging power and initial discharging power. This scheduling revenue data is then compared with the average scheduling revenue data. If the scheduling revenue data is less than or equal to the average scheduling revenue data, the scheduling correction amount of the charging station can be obtained. Subsequently, based on the peak-shaving index and the scheduling correction amount, the initial charging power and initial discharging power are corrected to obtain the target charging power and target discharging power of the vehicle cluster. Finally, the target charging power and target discharging power are evaluated based on multiple evaluation indicators for the vehicle cluster's participation in peak shaving, thereby obtaining the scheduling evaluation results. The scheduling revenue data can be represented by the scheduling unit price. The average scheduling revenue data can be represented by the average peak-shaving revenue unit price, which can be obtained from historical transaction unit price data.

[0084] For example, considering that the dispatch price of a charging station increases with the total dispatched power, the charging station needs to weigh the profit and loss of participating in peak shaving. When the dispatch price does not exceed the average peak shaving revenue price in historical transactions, the charging station will correct the error. Each charging station can guide electric vehicles by optimizing electricity prices to maximize their response to the grid's dispatch instructions. However, due to various factors the following day, the response power will obviously have a certain degree of deviation. Therefore, charging station n needs to correct the deviation through intraday rolling optimization. This intraday rolling optimization needs to be carried out one hour in advance, adjusting the peak shaving and valley filling period t by directly dispatching the electric vehicle cluster. * The charging and discharging power is achieved within the system.

[0085] Optionally, based on the obtained scheduling correction, the objective function of the electric vehicle cluster at the charging station during the day can be determined, wherein the objective function of the electric vehicle cluster can be expressed by the following formula:

[0086]

[0087] In the above formula, ΔP is used to characterize the actual power of the electric vehicle cluster at charging station n within a day. n，in (t) is used to characterize the scheduling adjustment amount of charging station n at time t. This value needs to be within the range of the remaining scheduling potential of charging station n during the day, as shown in the following formula:

[0088]

[0089] in, A parameter used to characterize the intraday dispatchable potential of a cluster of electric vehicles at charging station n within a rolling window.

[0090] Furthermore, charging stations utilize the remaining dispatchable potential within the day to correct errors. This dispatching is not a spontaneous response from individual electric vehicles; therefore, charging stations need to invest additional dispatching costs as the revenue for electric vehicles accepting dispatch. The relationship between "remaining dispatchable charging or discharging power - dispatched power unit price" can be expressed by the following formula:

[0091]

[0092] In the above formula, C n，in The unit price of dispatched power used to characterize charging station n is determined by prospect theory. Electric vehicle clusters are more sensitive to dispatching remaining charging power; that is, when dispatching the same charging and discharging power, the unit price of discharging power is higher. Therefore, when... When m1 > m2. Considering that the dispatch price of a charging station will increase with the increase of the total dispatched power, the charging station needs to weigh the profit and loss of participating in peak shaving. When the dispatch price exceeds the average peak shaving revenue price in historical transactions, the charging station will give up continuing to correct the error, as shown in the following formula:

[0093]

[0094] in, Used to represent the average peak-shaving revenue per unit price.

[0095] As an optional implementation method, based on multiple evaluation indicators for vehicle cluster participation in peak shaving, the target charging power and target discharging power are evaluated to obtain scheduling evaluation results. This includes: determining the target weights corresponding to the multiple evaluation indicators, and evaluating the target charging power and target discharging power based on the multiple evaluation indicators to obtain multiple evaluation values; establishing a target evaluation matrix based on the multiple evaluation indicators and multiple target weights; determining the evaluation level corresponding to the multiple evaluation indicators based on the multiple evaluation values; and determining the scheduling evaluation results based on the multiple target weights, the target evaluation matrix, and the multiple evaluation levels.

[0096] In this embodiment, firstly, based on multiple evaluation indicators, the Analytic Hierarchy Process (AHP) is used to determine the target weights corresponding to each indicator. Then, based on these indicators, the target charging power and target discharging power are evaluated, yielding multiple evaluation values. Next, a target evaluation matrix is ​​established based on the multiple indicators and target weights. Then, based on the obtained evaluation values, the evaluation levels corresponding to each indicator are determined. Finally, based on the target weights, the target evaluation matrix, and the evaluation levels, the extension matter-element comprehensive evaluation method is used to determine the scheduling evaluation result. Here, the evaluation indicators can be referred to as assessment indicators.

[0097] Optionally, multiple evaluation indicators may include: the average response deviation at each time point, the difference between the maximum and minimum response deviations, the percentage of accurate responses, and the reduction in peak-to-valley difference rate. Among these, if the intraday scheduling effect is used as the final peak-shaving result (assuming that the intraday forecast data one hour in advance is relatively accurate and the error is small and negligible), the average response deviation at each time point can be expressed by the following formula:

[0098]

[0099] In the above formula, N represents the set of charging stations, and T * Used to characterize the set of peak-shaving and valley-filling periods, σ n (t) is used to characterize the response deviation of charging station n. The difference between the maximum and minimum response deviation can be expressed by the following formula:

[0100]

[0101] In the above formula, x2 represents the difference between the maximum and minimum response deviations. The proportion of accurate response can be expressed by the following formula:

[0102] x3=|T1 * | / |T * |

[0103]

[0104] In the above formula, σ is used to characterize the allowable error of the response, T1 * This is used to characterize the set of time periods within the allowable error range of the response. The reduction in peak-to-valley difference can be expressed by the following formula:

[0105] x4=β1-β2

[0106]

[0107] In the above formula, β1 and β2 are used to represent the peak-to-valley difference rate before and after peak shaving, respectively.

[0108] Optionally, the process of determining the target weights using the analytic hierarchy process is as follows: determine the judgment matrix A, and the elements a in the judgment matrix A are... ij The meaning is interpreted as a comparison between the indicators located in the i-th row and the j-th column, and the relative importance of the indicator is determined by quantitative representation. Table 1 is the judgment matrix construction table. As shown in Table 1, when a ij When x is 1, the index x i Relative to index x j The importance is equal; when a ij When the value is 3, the index x i Relative to index x jThe importance level is slightly important; as shown in Table 1, the index x can be obtained based on the different element values ​​in the matrix. i Relative to index x j The changes in the importance of [these factors].

[0109] Table 1. Judgment Matrix Construction Table

[0110] <![CDATA[Indicator x i Relative to Indicator x j Degree of importance]]> <![CDATA[a ij ]]> Equally important 1 Slightly important 3 Stronger and more important 5 Strongly important 7 Extremely important 9

[0111] From Table 1, the weights of the indicators can be determined, and can then be calculated using the following formula:

[0112]

[0113] From the above formula, we can see that the weight of the i-th indicator is: Therefore, the weight index vector can be obtained as: W = [w1, w2, w3, w4]. At this point, a matrix consistency check is needed, which can be expressed by the following formula:

[0114]

[0115] In the above formula, if CR < 0.1, the test is considered passed; otherwise, the test fails. Wherein, λ max n is used to characterize the largest eigenvalue of the judgment matrix A. x This is used to indicate the number of indicators. The RI value is related to the order of the judgment matrix. Table 2 shows the correspondence between the matrix order and the RI value. As shown in Table 2, when the matrix order is 1, the RI value is 0; when the matrix order is 2, the RI value is 0; when the matrix order is 3, the RI value is 0.58. Different RI values ​​can be determined according to different matrix orders. That is, the relationship between the RI value and the order of the judgment matrix is ​​shown in Table 2, which will not be elaborated here.

[0116] Table 2. Correspondence between matrix order and RI value

[0117]

[0118] Optionally, the process of determining the scheduling evaluation results using the extension matter-element evaluation method is as follows: constructing the matter-element matrix. That is, as shown in the following formula:

[0119]

[0120] In the above formula, N represents the object element to be evaluated. I1, I2, I3, and I4 represent different evaluation indicators. x1, x2, x3, and x4 represent the different specific calculated values ​​corresponding to different evaluation indicators. Determine the classical domain Q. j and the Q domain pThe matter-element matrix:

[0121]

[0122] In the above formula, N j This is used to represent the j-th level of the rating problem, and the rating is divided into four levels: "Excellent", "Good", "Pass", and "Poor". Therefore, j = 1, 2, 3, 4. j1 -Q j4 It is N j The range of values ​​for the evaluation metrics I1, I2, I3, and I4 is defined as the classical domain. Another matrix can be determined using the following formula:

[0123]

[0124] In the above formula, N p Used to represent the full set of data established for the problem to be rated. Q p1 -Q p4 It is N p The total range of values ​​for evaluation indicators I1, I2, I3, and I4 is called the section domain. The correlation function value of the measured indicator is calculated to represent the degree of correlation between the measured value and each level within the corresponding indicator, as shown in the following formula:

[0125]

[0126] Wherein, ρ(x) i Q ji This is used to characterize the distance between the measured value of the index and both the classical domain and the section domain. Its calculation formula is as follows:

[0127]

[0128] Therefore, the correlation function matrix can be constructed. K ij This indicates that the measured value is within the index x. i The degree of correlation between the upper level and the lower level j is:

[0129]

[0130] As can be seen from the above, the correlation function matrix can be determined based on multiple target weights and target evaluation matrices, and then the correlation degree data can be determined based on the correlation function matrix.

[0131] In this embodiment, the "average response deviation at each time point, the difference between the maximum and minimum response deviations, the proportion of accurate responses, and the reduction in peak-valley difference rate" are selected as evaluation indicators for the final response effect of the aggregator. The weights of different indicators are determined using the Analytic Hierarchy Process (AHP). Then, the peak-shaving effect of the electric vehicle aggregator is quantitatively evaluated and feedback is achieved through the extension matter-element comprehensive evaluation method.

[0132] As an optional implementation method, the scheduling evaluation result is determined based on multiple target weights, a target evaluation matrix, and multiple evaluation levels, including: determining the degree of correlation of multiple evaluation indicators based on multiple target weights and a target evaluation matrix; and determining the scheduling evaluation result based on multiple degree of correlation and multiple evaluation levels.

[0133] In this embodiment, the degree of correlation of multiple evaluation indicators is determined based on multiple target weights and a target evaluation matrix. Then, the scheduling evaluation result can be determined based on the multiple degree of correlation and multiple evaluation levels. Here, the degree of correlation can be simply referred to as the correlation degree.

[0134] Optionally, based on multiple target weights and target evaluation matrices, the rank association function matrix can be determined; based on the rank association function matrix, the rank association degree can be determined. The rank association function matrix can be simply referred to as the association function matrix.

[0135] For example, from the correlation function matrix By combining the weights of each indicator, the degree of correlation F between the various indicators can be calculated. j :

[0136]

[0137] From the above formula, we can see that the maximum correlation degree is taken as the evaluation level of the final peak-shaving and valley-filling effect: k(j) = max(F j The values ​​of j are: j = 1, 2, 3, 4. When j = 1, 2, 3, 4, the corresponding evaluation levels are "Excellent", "Good", "Pass", and "Poor", respectively, and the corresponding peak-shaving unit price C is... peak (j) will decrease sequentially. The final peak-shaving and valley-filling return that the power grid provides to electric vehicle aggregators is:

[0138]

[0139] This can incentivize aggregators to respond to grid dispatch instructions as accurately as possible.

[0140] Optionally, the general method for evaluating extension elements is to derive an evaluation level by integrating various indicators. However, in order to enable the power grid to more accurately analyze the actual dispatch effect, it is necessary to obtain the evaluation level of each individual indicator and provide feedback so that the power grid can proactively adjust the dispatch strategy for aggregators in the next dispatch cycle to address specific issues.

[0141] In addition, for the i-th index x i The evaluation level of scheduling effectiveness can be expressed by the following formula:

[0142] k i (j)=max(Fi，j ), i=1, 2, 3, 4, j=1, 2, 3, 4

[0143]

[0144] Therefore, evaluation levels are assigned to individual indicators x1, x2, x3, and x4, and the evaluation results of these four indicators are fed back to the power grid. Based on the evaluation results, the power grid will proactively adjust its dispatching strategy in the next cycle, making peak-shaving commands more aligned with the actual response capabilities of the electric vehicle clusters. Ultimately, this forms a multi-timescale closed-loop dispatching system that can continuously adapt to actual conditions. Detailed adjustment strategies are as follows:

[0145] If the evaluation of indicator x1 (average response deviation at each time point) is "poor", then the peak shaving and valley filling time periods T will be evaluated accordingly. * The peak-shaving and valley-filling index α(t) is uniformly lowered by Δα(t), i.e., α(t) = α(t) - Δα(t), (t∈T*); conversely, if the result is "excellent", then Δα(t) is uniformly increased, i.e., α(t) = α(t) + Δα(t), (t∈T*). * ).

[0146] If the rating of indicator x2 (the difference between the maximum and minimum response deviations) is "poor", then the 10% of moments with the largest response deviations are calculated. right The peak shaving and valley filling index α(t) for each time period is uniformly reduced by Δα(t), that is... Conversely, if the result is "excellent", then the top 10% of moments with the smallest response deviation are calculated. A uniform upward adjustment of Δα(t) is achieved, i.e.

[0147] If the rating of indicator x3 (percentage of accurate responses) is "poor", then all moments exceeding the allowable deviation of the response are calculated. right The peak shaving and valley filling indicators for each time period are uniformly reduced by Δα(t), that is... Conversely, if the result is "excellent", then all times within the allowable deviation of the response are calculated. A uniform upward adjustment of Δα(t) is achieved, i.e.

[0148] If the evaluation of indicator x4 (reduction in peak-valley difference rate) is "poor", then the distribution network load P is calculated. load (t) The first 10% of the highest and lowest times This is defined as the peak and valley time, and the peak-shaving and valley-filling index for these times is uniformly lowered by Δα(t), i.e. Conversely, if the result is "excellent", then the index for the peak and trough moments is uniformly increased by Δα(t), i.e.

[0149] In this embodiment, the peak shaving and valley filling periods during which the vehicle cluster participates in peak shaving and valley filling, as well as the peak shaving index during these periods, can be obtained first. Then, during these periods, peak shaving instructions are generated based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station. Based on the obtained peak shaving instructions and the total charging power of the vehicle cluster, the power load of the vehicle cluster can be dispatched and peak shaving can be performed to obtain the initial charging power and initial discharging power of the vehicle cluster. Finally, the initial charging power and initial discharging power can be evaluated to obtain the dispatch evaluation results of the vehicle cluster. In other words, after determining the peak shaving and valley filling period, a peak shaving command is generated within that period based on the peak shaving index, distribution network topology, and dispatchable potential. In response to this command, the power load of the vehicle cluster is scheduled for peak shaving based on the total charging power of the vehicle cluster. This allows for the acquisition of the initial charging power and initial discharging power of the vehicle cluster. After evaluating the obtained initial charging power and initial discharging power, the scheduling evaluation results of the vehicle cluster can be obtained. This solves the technical problem of not being able to effectively evaluate the scheduling of vehicle clusters participating in peak shaving, and achieves the technical effect of effectively evaluating the scheduling of vehicle clusters participating in peak shaving.

[0150] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0151] Electric vehicles, as a flexible distributed resource, can play a significant role in the optimized scheduling of the power system if they can be aggregated and managed through demand-side mechanisms. This indicates that while the load of electric vehicles poses a significant challenge to the safe operation of the power system, it also has the potential to provide considerable peak-shaving capacity for the power grid.

[0152] Currently, a multi-timescale scheduling optimization model for vehicle clusters can be used to achieve peak shaving of the power grid, enabling the power system to maintain stable power supply during high or low load periods. However, the aforementioned multi-timescale scheduling optimization model relies on a large amount of data to achieve peak shaving of the power grid. Consequently, due to inaccurate data, the effect of peak shaving on the power grid may be insignificant, leading to the technical problem of being unable to effectively evaluate the scheduling of vehicle clusters participating in peak shaving.

[0153] Therefore, to address the aforementioned issues, this invention proposes a multi-timescale optimized scheduling method for electric vehicles (EVs) participating in peak shaving. This method first determines the reward for charging stations participating in peak shaving by evaluating the accuracy of the actual peak shaving effect of EVs, thereby incentivizing charging stations to respond more accurately to peak shaving commands. Secondly, based on the feedback from the evaluation results, the power grid can proactively adjust the peak shaving commands for EVs in the next scheduling cycle to adapt to the actual dispatchable potential of EV clusters, achieving refined management of EV dispatch potential. This solves the technical problem of being unable to effectively evaluate the dispatching situation of vehicle clusters participating in peak shaving, achieving the technical effect of effectively evaluating the dispatching situation of vehicle clusters participating in peak shaving.

[0154] Figure 2 This is a flowchart of a multi-timescale optimization scheduling method for electric vehicles participating in peak shaving according to an embodiment of the present invention, such as... Figure 2 As shown, the method mainly includes the following steps:

[0155] Step S201 has been optimized recently.

[0156] In this embodiment, at the current stage, the power grid formulates dispatch instructions with the goal of peak shaving and valley filling, and allocates dispatch instructions to each charging station based on the dispatchable potential of each station, with the goal of minimizing network losses. Each charging station, while ensuring its own revenue does not decrease, aims to respond to dispatch instructions to the greatest extent possible, and sets electricity prices to guide electric vehicles. Day-ahead dispatch is 24-hour (h) dispatch with a time scale of 1 hour.

[0157] In step S2011, the grid objective is to minimize grid losses during peak shaving and valley filling; the aggregator objective is to maximize revenue and respond to commands to the greatest extent possible.

[0158] Step S2012: Based on the power grid topology, obtain the dispatchable potential of each charging station.

[0159] In step S2013, the power grid assigns peak shaving and valley filling instructions to each charging station.

[0160] In this embodiment, firstly, the power grid can predict the distribution network load P for the next day. load (t), and the load power exceeds the safe operating upper limit power P. max and power below the lower bound P min The time period is defined as the peak shaving and valley filling period t. * During peak shaving and valley filling periods, the power grid needs to reduce or fill loads exceeding the allowable range. Based on this, the power grid allocates an overall peak shaving target α(t) to electric vehicle aggregators. * ), (0≤α≤1), α(t) * ) is used to indicate that at t *During the specified period, this represents the percentage of reduced or increased load power that the aggregator's electric vehicle cluster needs to bear. The aggregator's overall peak-shaving command P ref (t * This can be expressed by the following formula:

[0161]

[0162] Then, based on the distribution network topology and the dispatchable potential of electric vehicles at each charging station in the aggregator, the peak-shaving command P is allocated with the minimum network loss. ref (t * The process of determining the dispatchable potential range of electric vehicles at each charging station is as follows: Based on the distribution network topology, the optimal power flow of the distribution network is calculated using the second-order cone relaxation algorithm, and the objective function of the power grid is established with the goal of minimizing network losses. The above network loss function can be expressed by the following formula:

[0163]

[0164] Where T represents the total number of operating time periods, E represents the set of lines, and (i, j) represents line l. ij I ij (t) is used to represent the line l during time period t. ij The square of the current amplitude, r ij Used to indicate line l ij In a distribution network, the resistance of a line between two adjacent nodes i and j can be expressed by the following formula: ij =r ij +jx ij y ij =1 / z ij =g ij -jb ij .

[0165] Finally, the optimal peak-shaving command for each charging station (a charging station can be represented by n) is obtained. Then, under the premise of ensuring that the total charging power of all electric vehicles does not decrease, the charging station uses a master-slave game model to obtain the optimized daily electricity price of the charging station for the next day, as well as the charging and discharging power of electric vehicles, so as to guide electric vehicles to respond to peak shaving instructions to the greatest extent.

[0166] Optionally, in response to peak shaving commands, an objective function for charging station n is established to maximize revenue and minimize command response error, and this objective function can be expressed by the following formula:

[0167]

[0168] In the above formula, This is used to characterize the charging power of a cluster of electric vehicles at charging station n at time t. Cg(t) is used to characterize the discharge power of the electric vehicle cluster at charging station n at time t. Cg(t) is used to characterize the time-of-use electricity price of the power grid. and These are used to characterize the charging price and electricity sales price set by charging station n, respectively, and consist of the grid electricity price and the charging service fee or electricity sales service fee. and These are used to represent the additional charging service fee and electricity sales service fee of the charging station, respectively. This is used to characterize the penalty function term, ensuring that the charging and discharging power of charging station n can accurately respond to peak shaving commands.

[0169] Step S202, intraday optimization.

[0170] In this embodiment, during the intraday phase, each charging station aims to minimize the deviation in the execution of grid dispatch instructions. By combining the relationship curve of "remaining dispatchable potential - dispatch cost" of the electric vehicle cluster, additional dispatch cost is added to correct the deviation, thereby optimizing the data of the electric vehicle cluster. The intraday optimization is 1-hour dispatch, with a time scale of 15 minutes.

[0171] Step S2021, the aggregator's goal is to minimize the deviation of the scheduling instructions.

[0172] Step S2022: Combine the curve between the remaining scheduling potential and scheduling cost of the electric vehicle cluster.

[0173] Step S2023: Obtain the aggregator's corrected scheduling curve.

[0174] In this embodiment, each charging station guides electric vehicles by optimizing electricity prices, maximizing their response to grid dispatch instructions. However, due to various factors the following day, the response power will obviously deviate to some extent. Therefore, charging station n needs to correct this deviation through intraday rolling optimization. This intraday rolling optimization needs to be performed one hour in advance, adjusting the electric vehicle cluster's performance during peak shaving and valley filling periods by directly dispatching the cluster. * The charging and discharging power is achieved within the system.

[0175] Optionally, based on the obtained scheduling correction, the objective function of the electric vehicle cluster at the charging station during the day can be determined, wherein the objective function of the electric vehicle cluster can be expressed by the following formula:

[0176]

[0177] In the above formula, ΔP is used to characterize the actual power of the electric vehicle cluster at charging station n within a day. n，in(t) is used to characterize the scheduling adjustment amount of charging station n at time t. This value needs to be within the range of the remaining scheduling potential of charging station n during the day, as shown in the following formula:

[0178]

[0179] in, A parameter used to characterize the intraday dispatchable potential of a cluster of electric vehicles at charging station n within a rolling window.

[0180] Furthermore, charging stations utilize the remaining dispatchable potential within the day to correct errors. This dispatching is not a spontaneous response from individual electric vehicles; therefore, charging stations need to invest additional dispatching costs as the revenue for electric vehicles accepting dispatch. The relationship between "remaining dispatchable charging or discharging power - dispatched power unit price" can be expressed by the following formula:

[0181]

[0182] In the above formula, C n，in The unit price of dispatched power used to characterize charging station n is determined by prospect theory. Electric vehicle clusters are more sensitive to dispatching remaining charging power; that is, when dispatching the same charging and discharging power, the unit price of discharging power is higher. Therefore, when... When m1 > m2. Considering that the dispatch price of a charging station will increase with the increase of the total dispatched power, the charging station needs to weigh the profit and loss of participating in peak shaving. When the dispatch price exceeds the average peak shaving revenue price in historical transactions, the charging station will give up continuing to correct the error, as shown in the following formula:

[0183]

[0184] In the above formula, Used to represent the average peak-shaving revenue per unit price.

[0185] Step S203, Evaluation and Feedback.

[0186] In this embodiment, during the evaluation and feedback phase, the final peak shaving and valley filling effect is assessed, the peak shaving cost is determined based on the completed effect, and the completed scheduling effect is fed back to the power grid as a reference for formulating the next peak shaving instruction.

[0187] In this embodiment, after peak-shaving scheduling of the vehicle cluster's power load is completed, the overall peak-shaving and valley-filling effect of the electric vehicle cluster is evaluated, thereby determining the final charging station peak-shaving unit price and scheduling evaluation results. Furthermore, the obtained scheduling evaluation results can be fed back to the power grid, which helps the grid to promptly and accurately grasp the actual response capabilities of electric vehicle aggregators and proactively adjust the peak-shaving instructions of electric vehicle aggregators in subsequent peak-shaving scheduling.

[0188] Optionally, the "average response deviation at each time point, the difference between the maximum and minimum response deviations, the proportion of accurate responses, and the reduction in peak-valley difference rate" can be selected as evaluation indicators for the final response effect of the aggregator. The weights of different indicators can be determined using the analytic hierarchy process, and then the peak-shaving effect of the electric vehicle aggregator can be quantitatively evaluated and feedback can be achieved through the extension matter-element comprehensive evaluation method.

[0189] Optionally, the process of determining the target weights using the analytic hierarchy process is as follows: determine the judgment matrix A, and the elements a in the judgment matrix A are... ij The meaning is interpreted as a comparison between the indicators located in the i-th row and the j-th column, and the relative importance of the indicator is determined by quantitative representation. As shown in Table 1, when a ij When x is 1, the index x i Relative to index x j The importance is equal; when a ij When the value is 3, the index x i Relative to index x j The importance level is slightly important; as shown in Table 1, the index x can be obtained based on the different element values ​​in the matrix. i Relative to index x j The changes in the importance of [these factors].

[0190] As can be seen from the above, the correlation function matrix can be determined based on multiple target weights and target evaluation matrices, and then the correlation degree data can be determined based on the correlation function matrix.

[0191] From the correlation function matrix By combining the weights of each indicator, the degree of correlation F between the various indicators can be calculated. j :

[0192]

[0193] From the above formula, we can see that the maximum correlation degree is taken as the evaluation level of the final peak-shaving and valley-filling effect: k(j) = max(F j The values ​​of j are: j = 1, 2, 3, 4. When j = 1, 2, 3, 4, the corresponding evaluation levels are "Excellent", "Good", "Pass", and "Poor", respectively, and the corresponding peak-shaving unit price C is... Peak (j) will decrease sequentially. The final peak-shaving and valley-filling return that the power grid provides to electric vehicle aggregators is:

[0194]

[0195] This can incentivize aggregators to respond to grid dispatch instructions as accurately as possible.

[0196] Optionally, the general method for evaluating extension elements is to derive an evaluation level by integrating various indicators. However, in order to enable the power grid to more accurately analyze the actual dispatch effect, it is necessary to obtain the evaluation level of each individual indicator and provide feedback so that the power grid can proactively adjust the dispatch strategy for aggregators in the next dispatch cycle to address specific issues.

[0197] In this embodiment, the peak shaving and valley filling periods during which the vehicle cluster participates in peak shaving and valley filling, as well as the peak shaving index during these periods, can be obtained first. Then, during these periods, peak shaving instructions are generated based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station. Based on the obtained peak shaving instructions and the total charging power of the vehicle cluster, the power load of the vehicle cluster can be dispatched and peak shaving can be performed to obtain the initial charging power and initial discharging power of the vehicle cluster. Finally, the initial charging power and initial discharging power can be evaluated to obtain the dispatch evaluation results of the vehicle cluster. In other words, after determining the peak shaving and valley filling period, a peak shaving command is generated within that period based on the peak shaving index, distribution network topology, and dispatchable potential. In response to this command, the power load of the vehicle cluster is scheduled for peak shaving based on the total charging power of the vehicle cluster. This allows for the acquisition of the initial charging power and initial discharging power of the vehicle cluster. After evaluating the obtained initial charging power and initial discharging power, the scheduling evaluation results of the vehicle cluster can be obtained. This solves the technical problem of not being able to effectively evaluate the scheduling of vehicle clusters participating in peak shaving, and achieves the technical effect of effectively evaluating the scheduling of vehicle clusters participating in peak shaving.

[0198] According to embodiments of the present invention, a scheduling evaluation device for vehicle clusters participating in peak shaving is provided. It should be noted that this scheduling evaluation device for vehicle clusters participating in peak shaving can be used to execute one of the scheduling evaluation methods for vehicle clusters participating in peak shaving described in the embodiments.

[0199] Figure 3 This is a schematic diagram of a scheduling evaluation device for vehicle clusters participating in peak shaving according to an embodiment of the present invention, as shown below. Figure 3 As shown, a scheduling evaluation device 300 for vehicle clusters participating in peak shaving may include: a first acquisition unit 301, a generation unit 302, a second acquisition unit 303, and an evaluation unit 304.

[0200] The first acquisition unit 301 is used to acquire the peak shaving and valley filling periods during which the vehicle cluster participates in peak shaving and valley filling, as well as the peak shaving index during the peak shaving and valley filling periods. The peak shaving and valley filling periods are used to characterize the fluctuation of the power demand information corresponding to the vehicle cluster in different time periods, and the peak shaving index is used to characterize the proportion of the reduced or increased load power that the vehicle cluster needs to undertake.

[0201] The generation unit 302 is used to generate peak shaving instructions during peak shaving and valley filling periods based on peak shaving indicators, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station. The dispatchable potential value is used to characterize the changes in power and electricity of the vehicle cluster in the charging station.

[0202] The second acquisition unit 303 is used to respond to the peak shaving command and perform peak shaving scheduling on the power load of the vehicle cluster based on the total charging power of the vehicle cluster, so as to obtain the initial charging power and initial discharging power of the vehicle cluster.

[0203] The evaluation unit 304 is used to evaluate the initial charging power and initial discharging power to obtain the scheduling evaluation results of the vehicle cluster. The scheduling evaluation results are used to evaluate the stability of the power load of the vehicle cluster during the peak shaving and valley filling period.

[0204] In this embodiment, the first acquisition unit acquires the peak shaving and valley filling periods during which the vehicle cluster participates in peak shaving and valley filling, as well as the peak shaving index within these periods. The peak shaving and valley filling periods characterize the fluctuations in the power demand information corresponding to the vehicle cluster at different time intervals, and the peak shaving index characterizes the proportion of reduced or increased load power that the vehicle cluster needs to bear. During the peak shaving and valley filling periods, the generation unit generates peak shaving instructions based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster at the charging station. The dispatchable potential value characterizes the power and energy consumption of the vehicle cluster at the charging station. The system analyzes the changes in power load and, in response to peak-shaving commands, uses the second acquisition unit to perform peak-shaving scheduling on the power load of the vehicle cluster based on the total charging power of the vehicle cluster, obtaining the initial charging power and initial discharging power of the vehicle cluster. The evaluation unit then evaluates the initial charging power and initial discharging power to obtain the scheduling evaluation results of the vehicle cluster. These results are used to evaluate the stability of the power load of the vehicle cluster during peak-shaving and valley-filling periods, thus solving the technical problem of not being able to effectively evaluate the scheduling of vehicle clusters participating in peak shaving and achieving the technical effect of effectively evaluating the scheduling of vehicle clusters participating in peak shaving.

[0205] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is run by a processor, it controls the device where the readable storage medium is located to execute the scheduling evaluation method for vehicle cluster participation in peak shaving in the embodiment.

[0206] According to an embodiment of the present invention, a processor is also provided for running a program, wherein the program executes the scheduling evaluation method for vehicle cluster participation in peak shaving in the embodiment.

[0207] According to an embodiment of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the scheduling evaluation method for vehicle cluster participation in peak shaving in the embodiments of the present invention.

[0208] According to embodiments of the present invention, an electronic device is also provided, comprising a processor and a memory for storing processor-executable instructions. The processor is configured to execute instructions to implement the scheduling evaluation method for vehicle cluster participation in peak shaving according to embodiments of the present invention.

[0209] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0210] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0211] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed can be through some interfaces; the indirect coupling or communication connection of units or modules can be electrical or other forms.

[0212] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0213] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0214] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0215] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A scheduling evaluation method for vehicle clusters participating in peak shaving, characterized in that, include: The peak shaving and valley filling periods in which the vehicle cluster participates in peak shaving are obtained, as well as the peak shaving and valley filling indicators within the peak shaving and valley filling periods. The peak shaving and valley filling periods are used to characterize the fluctuation of the power demand information corresponding to the vehicle cluster in different time periods, and the peak shaving indicators are used to characterize the proportion of the reduced or increased load power that the vehicle cluster needs to bear. During the peak shaving and valley filling period, a peak shaving instruction is generated based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station. The dispatchable potential value is used to characterize the changes in the power and electricity of the vehicle cluster in the charging station. In response to the peak shaving command, based on the total charging power of the vehicle cluster, the power load of the vehicle cluster is scheduled for peak shaving to obtain the initial charging power and initial discharging power of the vehicle cluster. The initial charging power and the initial discharging power are evaluated to obtain the scheduling evaluation results of the vehicle cluster. The scheduling evaluation results are used to evaluate the stability of the power load of the vehicle cluster during the peak shaving and valley filling period. In response to the peak-shaving command, based on the total charging power of the vehicle cluster, peak-shaving scheduling is performed on the power load of the vehicle cluster to obtain the initial charging power and initial discharging power of the vehicle cluster, including: In response to the peak shaving command, a first objective function and a first constraint condition are established for the charging station, wherein the first objective function is established with the revenue of the charging station as the objective, and the first constraint condition is used to constrain the service data of the charging station. Based on the first objective function and the first constraint, a second objective function and a second constraint are established for the vehicle cluster, wherein the second objective function is established with the charging cost of the vehicle cluster as the objective, and the second constraint is used to constrain the schedulable potential value. Based on the first objective function, the first constraint, the second objective function, and the second constraint, peak-shaving scheduling is performed on the power load of the vehicle cluster to obtain the initial charging power and the initial discharging power.

2. The method according to claim 1, characterized in that, The peak shaving and valley filling periods during which the vehicle cluster participates in peak shaving include: The power distribution network load of the vehicle cluster is obtained, as well as the first load power and the second load power corresponding to the power distribution network load power, wherein the first load power is greater than the second load power; In response to the distribution network load power being greater than the first load power, a first time period is obtained; In response to the distribution network load power being less than the second load power, a second time period is obtained; The first time period and the second time period are respectively defined as the peak shaving and valley filling time periods.

3. The method according to claim 1, characterized in that, During the peak shaving and valley filling period, based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station, a peak shaving instruction is generated, including: During the peak shaving and valley filling period, a network loss function is determined based on the distribution network topology, wherein the network loss function is a function that targets the loss information of the distribution network corresponding to the vehicle cluster; The peak shaving instruction is generated based on the network loss function, the peak shaving index, and the schedulable potential value.

4. The method according to claim 1, characterized in that, The initial charging power and the initial discharging power are evaluated to obtain the scheduling evaluation results of the vehicle cluster, including: Based on the initial charging power and the initial discharging power, determine the scheduling revenue data of the charging station; In response to the scheduling revenue data being less than or equal to the average scheduling revenue data, the scheduling correction amount of the charging station is obtained; Based on the peak shaving index and the scheduling correction amount, the initial charging power and the initial discharging power are corrected to obtain the target charging power and target discharging power of the vehicle cluster. Based on multiple evaluation indicators for the vehicle cluster's participation in peak shaving, the target charging power and the target discharging power are evaluated to obtain the scheduling evaluation results.

5. The method according to claim 4, characterized in that, Based on multiple evaluation indicators for the vehicle cluster's participation in peak shaving, the target charging power and the target discharging power are evaluated to obtain the scheduling evaluation results, including: Based on multiple evaluation indicators, target weights corresponding to the multiple evaluation indicators are determined respectively, and the target charging power and the target discharging power are evaluated based on the multiple evaluation indicators to obtain multiple evaluation values; Based on the multiple evaluation indicators and multiple target weights, a target evaluation matrix is ​​established; Based on the multiple evaluation values, the evaluation levels corresponding to the multiple evaluation indicators are determined respectively; The scheduling evaluation result is determined based on the multiple target weights, the target evaluation matrix, and the multiple evaluation levels.

6. The method according to claim 5, characterized in that, Based on the multiple target weights, the target evaluation matrix, and the multiple evaluation levels, the scheduling evaluation result is determined, including: Based on the multiple target weights and the target evaluation matrix, the degree of correlation of the multiple evaluation indicators is determined respectively; The scheduling evaluation result is determined based on multiple levels of correlation and multiple evaluation levels.

7. A scheduling and evaluation device for vehicle clusters participating in peak shaving, characterized in that, include: The first acquisition unit is used to acquire the peak shaving and valley filling period during which the vehicle cluster participates in peak shaving and valley filling, and the peak shaving index during the peak shaving and valley filling period. The peak shaving and valley filling period is used to characterize the fluctuation of the power demand information corresponding to the vehicle cluster in different time periods, and the peak shaving index is used to characterize the proportion of the reduced or increased load power that the vehicle cluster needs to bear. The generation unit is used to generate peak shaving instructions during the peak shaving and valley filling period based on the peak shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station. The dispatchable potential value is used to characterize the changes in the power and electricity of the vehicle cluster in the charging station. The second acquisition unit is used to respond to the peak shaving command and perform peak shaving scheduling on the power load of the vehicle cluster based on the total charging power of the vehicle cluster, so as to obtain the initial charging power and initial discharging power of the vehicle cluster. The evaluation unit is used to evaluate the initial charging power and the initial discharging power to obtain the scheduling evaluation result of the vehicle cluster, wherein the scheduling evaluation result is used to evaluate the stability of the power load of the vehicle cluster during the peak shaving and valley filling period. The second acquisition unit is specifically used to respond to the peak-shaving command by establishing a first objective function and a first constraint condition for the charging station, wherein the first objective function is established with the revenue of the charging station as the objective, and the first constraint condition is used to constrain the service data of the charging station; based on the first objective function and the first constraint condition, a second objective function and a second constraint condition for the vehicle cluster are established, wherein the second objective function is established with the charging cost of the vehicle cluster as the objective, and the second constraint condition is used to constrain the schedulable potential value; based on the first objective function, the first constraint condition, the second objective function, and the second constraint condition, peak-shaving scheduling is performed on the power load of the vehicle cluster to obtain the initial charging power and the initial discharging power.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program is run by a processor, it controls the device in which the storage medium is located to perform the method of any one of claims 1 to 6.

9. An electronic device, characterized in that, It includes one or more processors and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method of any one of claims 1 to 6.

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