Scheduling evaluation method and device for peak regulation participated by vehicle cluster, and electronic equipment

Abstract

The invention discloses a scheduling evaluation method and device for peak regulation participated by a vehicle cluster, and electronic equipment. The method comprises the steps of obtaining a peak clipping and valley filling time period when a vehicle cluster participates in peak shaving and a peak shaving index in the peak clipping and valley filling time period; in the peak load shifting period, generating a peak load regulation instruction based on the peak load regulation index, the power distribution network topological structure corresponding to the vehicle cluster and the schedulable potential value of the vehicle cluster in the charging station; in response to the peak regulation instruction, performing peak regulation 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 the initial discharging power of the vehicle cluster; the initial charging power and the initial discharging power are evaluated, a dispatching evaluation result of the vehicle cluster is obtained, and the dispatching evaluation result is used for evaluating the stability degree of the power load of the vehicle cluster in the peak clipping and valley filling period. According to the invention, the technical problem that the scheduling condition that the vehicle cluster participates in peak regulation cannot be effectively evaluated is solved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a scheduling evaluation method, device and electronic equipment for vehicle clusters participating in peak load regulation. Background Art

[0002] As a flexible distributed resource, electric vehicles will play a huge role in the optimal scheduling of power systems if they can be aggregated and managed through the demand side. This shows that while the load of electric vehicles poses huge challenges to the safe operation of the power system, it also has the ability to provide considerable peak-shaving potential for the grid.

[0003] At present, a multi-time scale scheduling optimization model of vehicle clusters is often used to achieve peak-shaving of the power grid in order to obtain peak-shaving results so that the power system can maintain stable power supply under high or low loads. However, the above-mentioned multi-time scale scheduling optimization model needs to 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 on the power grid is not significant, resulting in a technical problem that it is impossible to effectively evaluate the scheduling of vehicle clusters participating in peak-shaving.

[0004] With respect to the above-mentioned technical problem of being unable to effectively evaluate the dispatching situation of vehicle clusters participating in peak load regulation, no effective solution has been proposed so far. Summary of the invention

[0005] The embodiments of the present invention provide a method, device and electronic device for evaluating the scheduling of vehicle clusters participating in peak load regulation, so as to at least solve the technical problem that it is impossible to effectively evaluate the scheduling of vehicle clusters participating in peak load regulation.

[0006] According to one aspect of the embodiment of the invention, a scheduling and evaluation method for vehicle clusters participating in peak load regulation is provided. The method includes: obtaining the peak load shaving and valley filling period in which the vehicle cluster participates in peak load shaving, and the peak load shaving index in the peak load shaving and valley filling period, wherein the peak load 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 load shaving index is used to characterize the proportion of load power reduction or increase required by the vehicle cluster; in the peak load shaving and valley filling period, based on the peak load 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 load shaving instruction is generated, wherein the dispatchable potential value is used to characterize the change of power and electricity of the vehicle cluster in the charging station; in response to the peak load shaving instruction, based on the total charging power of the vehicle cluster, the power load of the vehicle cluster is peak-shaving and dispatched 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 result of the vehicle cluster, wherein the scheduling evaluation result is used to evaluate the stability of the power load of the vehicle cluster in the peak load shaving and valley filling period.

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

[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, including: during the peak shaving and valley filling period, based on the distribution network topology, determining the network loss function, wherein the network loss function is a function that takes the loss information of the distribution network corresponding to the vehicle cluster as the target; generating a 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 instruction, based on the total charging power of the vehicle cluster, the power load of the vehicle cluster is peak-shaving scheduled to obtain the initial charging power and initial discharging power of the vehicle cluster, including: in response to the peak-shaving instruction, establishing a first objective function and a first constraint condition of the charging station, wherein the first objective function is used to establish the charging station based on the revenue of the charging station, 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 of the vehicle cluster, wherein the second objective function is used to establish the charging cost of the vehicle cluster as the target, and the second constraint condition is used to constrain the dispatchable potential value; 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 peak-shaving scheduled to obtain the initial charging power and the initial discharging power.

[0010] Optionally, the initial charging power and the initial discharging power are evaluated to obtain a scheduling evaluation result of the vehicle cluster, including: determining the scheduling revenue data of the charging station based on the initial charging power and the 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 the 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; evaluating the target charging power and the target discharging power based on multiple evaluation indicators of the vehicle cluster participating in peak shaving to obtain a scheduling evaluation result.

[0011] Optionally, based on multiple evaluation indicators of vehicle clusters participating in peak shifting, the target charging power and the target discharging power are evaluated to obtain a scheduling evaluation result, including: based on multiple evaluation indicators, target weights corresponding to the multiple evaluation indicators are respectively determined, and based on the multiple evaluation indicators, the target charging power and the target discharging power are evaluated to obtain multiple evaluation values; based on the multiple evaluation indicators and the 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 respectively determined; based on the multiple target weights, the target evaluation matrix and the multiple evaluation levels, the scheduling evaluation result is determined.

[0012] Optionally, the scheduling evaluation results are determined based on multiple target weights, a target evaluation matrix and multiple evaluation levels, including: based on multiple target weights and a target evaluation matrix, respectively determining the level correlation of multiple evaluation indicators; based on multiple level correlations and multiple evaluation levels, determining the scheduling evaluation results.

[0013] According to one aspect of an embodiment of the present invention, a scheduling and evaluation device for vehicle clusters participating in peak load regulation is provided, and the device also includes: a first acquisition unit, used to obtain the peak-shaving and valley-filling period of the vehicle cluster participating in peak load regulation, and the peak-shaving index in the peak-shaving and valley-filling period, wherein 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 load power reduction or increase that the vehicle cluster needs to bear; a generation unit, used to generate a peak-shaving and valley-filling period based on the peak-shaving index, the distribution network topology corresponding to the vehicle cluster, and the peak-shaving index of the vehicle cluster in the peak-shaving and valley-filling period. The dispatchable potential value in the charging station generates a peak-shaving instruction, wherein the dispatchable potential value is used to characterize the changes in the power and power of the vehicle cluster in the charging station; the second acquisition unit is used to respond to the peak-shaving instruction, based on the total charging power of the vehicle cluster, to perform peak-shaving dispatch on the power load of the vehicle cluster, and 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 dispatching evaluation result of the vehicle cluster, wherein the dispatching evaluation result is used to evaluate the stability of the power load of the vehicle cluster during the peak shaving and valley filling period.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the device where the storage medium is located is controlled to execute the method in the embodiment of the present invention.

[0015] According to another aspect of an embodiment of the present invention, a processor is provided. The processor is used to run a program, wherein the method in the embodiment of the present invention is executed when the program is run.

[0016] According to another aspect of the embodiments of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in the embodiments of the present invention is implemented.

[0017] According to another aspect of an embodiment of the present invention, an electronic device is provided, the electronic device comprising a processor and a memory for storing instructions executable by the processor, wherein the processor is configured to execute the instructions to implement the method in the embodiment of the present invention.

[0018] In an embodiment of the present invention, a peak shaving and valley filling period in which a vehicle cluster participates in peak shaving, and a peak shaving index within the peak shaving and valley filling period are obtained, wherein 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 load power reduction or increase that the vehicle cluster needs to bear; within 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, wherein the dispatchable potential value is used to characterize the change in power and electricity of the vehicle cluster in the charging station; in response to the peak shaving instruction, the power load of the vehicle cluster is peak shaving-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; the initial charging power and the initial discharging power are evaluated to obtain a 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 within the peak shaving and valley filling period. That is to say, the embodiment of the present invention can first obtain the peak shaving and valley filling period in which the vehicle cluster participates in peak shaving, as well as the peak shaving index within the peak shaving and valley filling period, and then within the peak shaving and valley filling period, generate a peak shaving instruction according to 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. According to the peak shaving instruction obtained above and the total charging power of the vehicle cluster, the power load of the vehicle cluster can be scheduled for peak shaving, so as 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 scheduling evaluation results of the vehicle cluster. That is to say, after determining the peak shaving and valley filling period, within this period, a peak shaving instruction is generated according to the peak shaving indicators, the distribution network topology and the dispatchable potential value. In response to the peak shaving instruction, the power load of the vehicle cluster is peak-shaving dispatched according to the total charging power of the vehicle cluster, so as to obtain the initial charging power and the initial discharging power of the vehicle cluster. After evaluating the initial charging power and the initial discharging power obtained above, the dispatching evaluation result of the vehicle cluster can be obtained, thereby solving the technical problem that the dispatching situation of the vehicle cluster participating in peak shaving cannot be effectively evaluated, and achieving the technical effect that the dispatching situation of the vehicle cluster participating in peak shaving can be effectively evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] Figure 1 is a flow chart of a method for evaluating the dispatching of a vehicle cluster participating in peak load regulation according to an embodiment of the present invention;

[0021] Figure 2 is a flow chart of a multi-time scale optimization scheduling method for electric vehicles participating in peak load regulation according to an embodiment of the present invention;

[0022] Figure 3 It is a schematic diagram of a scheduling and evaluation device for vehicle clusters participating in peak load regulation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

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

[0026] The following is an introduction to the scheduling and evaluation method for vehicle clusters participating in peak load regulation according to an embodiment of the present invention.

[0027] Figure 1 is a flow chart of a method for evaluating the dispatching of a vehicle cluster participating in peak load regulation according to an embodiment of the present invention. Figure 1 As shown, the scheduling evaluation method for the vehicle cluster participating in peak load regulation may include the following steps:

[0028] Step S101, obtaining the peak shaving and valley filling period during which the vehicle cluster participates in peak shaving, and the peak shaving index within the peak shaving and valley filling period.

[0029] In the technical solution provided in step S101 of the present invention, the peak-shaving and valley-filling period in which the vehicle cluster participates in peak-shaving can be obtained, as well as the peak-shaving index in 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, which can be obtained by t * The time period is indicated.

[0030] Optionally, the peak load index can be used to characterize the proportion of load power reduction or increase that the vehicle cluster needs to bear. For example, the peak load index can be expressed by α(t * ) and 0≤α≤1, then α(t * ) is used to indicate that at t * The proportion of load power reduction or increase that the aggregator's electric vehicle fleet needs to bear as a whole during the period.

[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 in the above step S102 of the present invention, during the peak shaving and valley filling period, a peak shaving instruction can be generated according to 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 change in power and power of the vehicle cluster in the charging station, and the dispatchable potential value is within a preset dispatchable potential range. The distribution network topology can be referred to as the distribution network topology. The peak load instruction can be obtained through P ref (t * ) is used to indicate.

[0034] For example, according to the distribution network topology and the dispatchable potential value of electric vehicles at each charging station in the aggregator, the peak load instruction P is allocated with minimum network loss. ref (t * ), and perform the next step according to the peak-shaving instruction obtained at this time.

[0035] It should be noted that this is only a preferred implementation method for 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, based on the 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 method and process of generating peak-shaving instructions are within the protection scope of the present invention and are not listed here.

[0036] Step S103 , in response to the peak load regulation instruction, the power load of the vehicle cluster is peak-regulated based on the total charging power of the vehicle cluster to obtain the initial charging power and the initial discharging power of the vehicle cluster.

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

[0038] For example, after obtaining the optimal peak load instruction for each charging station (a charging station can be represented by n), Finally, the charging station uses the master-slave game model to obtain the optimized next-day electricity price of the charging station and the charging and discharging power of the electric vehicles, under the premise of ensuring that the total charging power of all electric vehicles is not reduced, so as to guide the electric vehicles to respond to the peak-shaving instructions to the greatest extent.

[0039] It should be noted that this is only a preferred implementation method for obtaining the initial charging power and initial discharging power of a vehicle cluster, and 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 peak-shaving instruction is responded to and the power load of the vehicle cluster is peak-shaving based on the total charging power of the vehicle cluster, the process and method for obtaining the initial charging power and initial discharging power of the vehicle cluster are all within the protection scope of the present invention and are not listed here.

[0040] Step S104, evaluating the initial charging power and the initial discharging power to obtain a scheduling evaluation result of the vehicle cluster.

[0041] In the technical solution provided in the above 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 to achieve the purpose of obtaining 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.

[0042] For example, after completing the peak load dispatching of the vehicle cluster, the overall peak load shaving and valley filling effect of the electric vehicle cluster is evaluated, thereby determining the final peak load shaving unit price of the charging station and the dispatch evaluation results. In addition, the dispatch evaluation results obtained above can be fed back to the power grid, which is conducive to the power grid to timely and accurately grasp the actual response capabilities of the electric vehicle aggregator, and actively adjust the peak load shaving instructions of the electric vehicle aggregator in the subsequent peak load dispatching.

[0043] It should be noted that this is only a preferred implementation method for obtaining the scheduling evaluation results of the vehicle cluster, and 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 the 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 the present invention and are not listed here.

[0044] In the above steps S101 to S104 of the present invention, the peak shaving and valley filling period in which the vehicle cluster participates in peak shaving and the peak shaving index in the peak shaving and valley filling period can be first obtained. Then, in the peak shaving and valley filling period, according to 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. According to the peak shaving instruction obtained above and the total charging power of the vehicle cluster, the power load of the vehicle cluster can be dispatched for peak shaving, so as to obtain the initial charging power and initial discharging power of the vehicle cluster. Finally, the initial charging power and the initial discharging power can be evaluated to achieve the purpose of obtaining the dispatching evaluation result of the vehicle cluster. That is to say, after determining the peak shaving and valley filling period, within this period, a peak shaving instruction is generated according to the peak shaving indicators, the distribution network topology and the dispatchable potential value. In response to the peak shaving instruction, the power load of the vehicle cluster is peak-shaving dispatched according to the total charging power of the vehicle cluster, so as to obtain the initial charging power and the initial discharging power of the vehicle cluster. After evaluating the initial charging power and the initial discharging power obtained above, the dispatching evaluation result of the vehicle cluster can be obtained, thereby solving the technical problem that the dispatching situation of the vehicle cluster participating in peak shaving cannot be effectively evaluated, and achieving the technical effect that the dispatching situation of the vehicle cluster participating in peak shaving can be effectively evaluated.

[0045] The above method of this embodiment is further introduced below.

[0046] As an optional implementation method, obtaining the peak shaving and valley filling time period when the vehicle cluster participates 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; in response to the distribution network load power being greater than the first load power, obtaining the first time period; in response to the distribution network load power being less than the second load power, obtaining the second time period; and respectively determining the first time period and the second time period as peak shaving and valley filling time periods.

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

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

[0049] For example, the power grid can predict the next day's distribution network load P load (t), and the load power exceeds the upper limit of safe operation power P max and below the lower bound power P min The time period is defined as the peak shaving and valley filling period t * During the peak load shaving and valley load filling period, the power grid needs to reduce or fill the load power that exceeds the range.

[0050] Optionally, during the peak-shaving and valley-filling period, a peak-shaving instruction can be determined. For example, based on this, the power grid allocates an overall peak-shaving index α(t * ), (0≤α≤1), α(t * ) is used to indicate that at t * The proportion of load power reduction or increase that the aggregator's electric vehicle cluster needs to bear during the period. The aggregator's overall peak load regulation instruction P ref (t * ) 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, including: during the peak shaving and valley filling period, based on the distribution network topology, determining the network loss function, wherein the network loss function is a function that takes the loss information of the distribution network corresponding to the vehicle cluster as the target; 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 according to the distribution network topology, and then the peak shaving instruction can be generated according to the network loss function, peak shaving index and dispatchable potential value obtained above.

[0054] Optionally, based on the topological structure of the distribution network, the optimal power flow of the distribution network is calculated using a second-order cone relaxation algorithm, and the power grid establishes an objective function with the goal of minimizing network loss. The network loss function can be expressed by the following formula:

[0055]

[0056] Where T is used to represent the total number of operation time periods, E is used to represent the line set, and (i, j) is used to represent the line l ij , I ij (t) is used to indicate that in time period t, line l ij The square of the current amplitude, r ij Used to indicate line l ij In the distribution network, the impedance and admittance of the 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 instruction is generated according to the network loss function, the peak-shaving index, the dispatchable potential value, the power balance constraint of the distribution network, the operation constraint of the distribution network and the load constraint of the charging station.

[0058] Among them, 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] Among them, branch ij is used to indicate that the positive direction of the flow is from node i to node j, δ(j) is used to represent the set of branch end nodes with j as the head node, and π(j) is used to represent the set of branch head nodes with j as the end node. M is used to represent the node set; in any (i, j)∈E, v j (t) is used to represent the square of the voltage amplitude at node j within time period t. Used to characterize the active power of conventional load at node j in the distribution network. Used to characterize the reactive power of conventional load at node j in the distribution network. Used to characterize the initial active power of the electric vehicle at charging station n at node j in the distribution network. It is used to characterize the peak load index assigned to the charging station n at node j. If there is no charging station at node j, then S ij (t) is used to represent the complex power of the line start node in the time period t.

[0062] Furthermore, the distribution network operation constraints 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) are used to represent the active power and reactive power of node i at time t. i (t), U j (t) are used to represent the voltage amplitude of node i and node j respectively; θ ij It is used to characterize the phase angle difference between nodes i and j. The load constraint of the charging station can be expressed by the following formula:

[0067]

[0068] In the above formula, It is used to represent the maximum charging power of the electric vehicle cluster in charging station n as generalized energy storage during period t. It is used to represent the maximum discharge power of the electric vehicle cluster in charging station n as generalized energy storage during the period t. ΔS n (t) is used to represent the change in power caused by the electric vehicle cluster in charging station n as generalized energy storage during the period t due to the grid-connected state of electric vehicles. Used to indicate the lower limit of power. Used to indicate the upper limit of power.

[0069] As an optional implementation method, in response to a peak-shaving instruction, based on the total charging power of the vehicle cluster, the power load of the vehicle cluster is peak-shaving scheduled to obtain the initial charging power and initial discharging power of the vehicle cluster, including: in response to the peak-shaving instruction, establishing a first objective function and a first constraint condition of the charging station, wherein the first objective function is used to establish it by taking the revenue of the charging station as the target, 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 of the vehicle cluster, wherein the second objective function is used to establish it by taking the charging cost of the vehicle cluster as the target, and the second constraint condition is used to constrain the dispatchable potential value; 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 peak-shaving scheduled to obtain the initial charging power and the initial discharging power.

[0070] In this embodiment, in response to the peak-shaving instruction, a first objective function and a first constraint condition of the charging station can be established, and 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 can be 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 peak-shaving scheduled 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 may be referred to as the objective function of the charging station. The first constraint of the charging station may be referred to as the constraint of the charging station n. The second objective function of the vehicle cluster may be referred to as the objective function of the electric vehicle cluster. The second constraint of the vehicle cluster may be referred to as the constraint of the electric vehicle cluster.

[0072] Optionally, in response to the peak load regulation instruction, an objective function of charging station n is established with maximum benefit and minimum instruction response error, and the objective function can be expressed by the following formula:

[0073]

[0074] In the above formula, Used to characterize the charging power of the electric vehicle cluster at charging station n at time t. It is used to characterize the discharge power of the electric vehicle cluster at charging station n at time t. g (t) is used to characterize the time-of-use electricity price of the power grid. and They are respectively used to characterize the charging electricity price and electricity selling price set by charging station n, and are composed of the grid electricity price and the charging service fee or electricity selling service fee. and They are respectively used to represent the additional charging service fees and electricity sales service fees of the charging station (the charging station has no authority to change the power grid electricity price and can only adjust the charging electricity price and electricity sales price of the charging station by optimizing the service fee part of the electricity price). It is used to characterize the penalty function term to ensure that the charging and discharging power of charging station n can accurately respond to the peak load instruction. Further, the constraint condition of charging station n can be expressed by the following formula:

[0075]

[0076] The objectives of the above constraints are used to limit the upper and lower limits and the average value of the service fee set by charging station n.

[0077] Optionally, the objective function of the electric vehicle cluster of charging station n can be established with the minimum charging cost, 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) is used to represent the power of the electric vehicle cluster at time t. According to the above formula, the cluster power and power can be guaranteed to be within the dispatchable potential range, and the electric vehicle cluster can also obtain the expected power.

[0082] As an optional implementation method, the initial charging power and the initial discharging power are evaluated to obtain a scheduling evaluation result of the vehicle cluster, including: determining the scheduling revenue data of the charging station based on the initial charging power and the 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 the 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; evaluating the target charging power and the target discharging power based on multiple evaluation indicators of the vehicle cluster participating in peak shaving to obtain a scheduling evaluation result.

[0083] In this embodiment, the dispatching revenue data of the charging station can be determined based on the initial charging power and the initial discharging power, and the dispatching revenue data can be compared with the average dispatching revenue data. If the dispatching revenue data is less than or equal to the average dispatching revenue data, the dispatching correction amount of the charging station can be obtained, and then the initial charging power and the initial discharging power are corrected according to the peak-shaving index and the dispatching correction amount to obtain the target charging power and the target discharging power of the vehicle cluster. Finally, the target charging power and the target discharging power are evaluated according to multiple evaluation indicators of the vehicle cluster participating in the peak-shaving, so as to obtain the dispatching evaluation results. Among them, the dispatching revenue data can be represented by the dispatching unit price. The average dispatching revenue data can be represented by the average peak-shaving revenue unit price, and the average peak-shaving revenue unit price can be obtained through the historical transaction unit price data.

[0084] For example, considering that the dispatch unit price of the charging station will increase with the increase of the total dispatch power, the charging station needs to weigh the profit and loss of participating in the peak load regulation. When the dispatch unit price does not exceed the average peak load regulation profit unit price of historical transactions, the charging station will correct the error. Each charging station can respond to the dispatch instructions of the power grid to the greatest extent by optimizing the electricity price to guide electric vehicles. However, due to the influence of various factors on the next 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. Among them, intraday rolling optimization needs to be carried out one hour in advance, by directly dispatching the electric vehicle cluster to adjust the peak load during the peak load and valley load period t * The charging and discharging power within is realized.

[0085] Optionally, according to the obtained scheduling correction amount, the objective function of the electric vehicle cluster of the charging station within 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, It is used to characterize the actual power of the electric vehicle cluster at charging station n within a day. ΔP n，in (t) is used to characterize the dispatch correction of charging station n at time t. This value needs to be within the range of the residual dispatchable 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 an electric vehicle cluster with charging station n within the rolling window.

[0090] Furthermore, the charging station uses the remaining dispatchable potential within the day to correct the error. This part of the dispatch is the spontaneous response of non-electric vehicle individuals. Therefore, the charging station needs to invest additional dispatch costs as the income of electric vehicles accepting dispatch, and the relationship between "remaining dispatchable charging or discharging power-dispatching power unit price" can be expressed by the following formula:

[0091]

[0092] In the above formula, C n，in The dispatch power unit price used to characterize charging station n. According to prospect theory, electric vehicle clusters are more sensitive to the dispatch of remaining charging power, that is, when dispatching the same charging power and discharging power, the dispatch power unit price is greater. When m1>m2. Considering that the dispatching unit price of the charging station will increase with the increase of the total dispatching power, the charging station needs to weigh the profit and loss of participating in the peak load regulation. When the dispatching unit price exceeds the average peak load regulation profit unit price of historical transactions, the charging station will give up correcting the error, as shown in the following formula:

[0093]

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

[0095] As an optional implementation method, based on multiple evaluation indicators of vehicle clusters participating in peak shifting, the target charging power and the target discharging power are evaluated to obtain a scheduling evaluation result, including: based on multiple evaluation indicators, target weights corresponding to the multiple evaluation indicators are respectively determined, and based on the multiple evaluation indicators, the target charging power and the target discharging power are evaluated to obtain multiple evaluation values; based on the multiple evaluation indicators and the 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 respectively determined; based on the multiple target weights, the target evaluation matrix and the multiple evaluation levels, the scheduling evaluation result is determined.

[0096] In this embodiment, the target weights corresponding to the multiple evaluation indicators can be determined respectively by using the Analytic Hierarchy Process (AHP) according to multiple evaluation indicators, and the target charging power and the target discharging power are evaluated according to the multiple evaluation indicators to obtain multiple evaluation values, and then a target evaluation matrix is ​​established based on the multiple evaluation indicators and the multiple target weights, and then the evaluation levels corresponding to the multiple evaluation indicators are determined respectively according to the multiple evaluation values ​​obtained above, and finally the scheduling evaluation results are determined by using the extension object-element comprehensive evaluation method based on the multiple target weights, the target evaluation matrix and the multiple evaluation levels. Among them, the evaluation indicator can be called an evaluation indicator.

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

[0098]

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

[0100]

[0101] In the above formula, x2 is used to represent the difference between the maximum and minimum response deviations. The percentage of accurate responses 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 * It is used to characterize the time period set within the allowable error range of the response. The reduction in the peak-to-valley difference rate 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 regulation, respectively.

[0108] Optionally, the process of determining the target weight using the hierarchical analysis method is as follows: determining a judgment matrix A, where the element a in the judgment matrix A is ij The meaning of is interpreted as the comparison between the indicators in the i-th row and the j-th column, and the relative importance of the indicator is determined by quantitative expression. Table 1 is a judgment matrix construction table. As shown in Table 1, when a ij When it is 1, the index x i Relative to index x j are equally important; when a ij When it is 3, the index x i Relative to index x jThe importance of is slightly important; from Table 1, we can see that according to the different element values ​​in the matrix, we can get the index x i Relative to index x j changes in the importance of .

[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 Strong and important 5 Strongly important 7 Extremely important 9

[0111] From Table 1, the weight of the indicator can be determined, which can be calculated by the following formula:

[0112]

[0113] From the above formula, we can see that the weight of the i-th indicator is: Thus, the vector of weight index can be obtained as: W = [w1, w2, w3, w4]. At this time, it is necessary to judge and check the consistency of the matrix, which can be expressed by the following formula:

[0114]

[0115] In the above formula, if CR<0.1, the test is considered to have passed, otherwise the test has failed. λ max It is used to characterize the maximum characteristic root of the judgment matrix A, n x It is used to indicate the number of indicators. The RI value is related to the judgment matrix order. Table 2 is the corresponding relationship table 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 judgment matrix order is shown in Table 2, which will not be repeated here.

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

[0117]

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

[0119]

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

[0121]

[0122] In the above formula, N j It is used to indicate the jth level of the rating problem, and the rating level is divided into four levels: "excellent, good, pass, poor", so j = 1, 2, 3, 4. Q j1 -Q j4 YesN j The range of values ​​of the evaluation indicators I1, I2, I3, and I4 is defined as the classical domain. Another matrix can be determined by the following formula:

[0123]

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

[0125]

[0126] Among them, ρ(x i , Q ji ) is used to characterize the distance between the actual measured value of the indicator and the classic domain and the section domain. The calculation formula is:

[0127]

[0128] From this, the correlation function matrix can be constructed K ij Indicates that the measured value is in the index x i The correlation degree between the level and level j is:

[0129]

[0130] From the above, it can be seen that according to multiple target weights and target evaluation matrices, the correlation function matrix can be determined, and then according to the correlation function matrix, the correlation data can be determined.

[0131] In this embodiment, "the average value of response deviation at each moment, the difference between the maximum and minimum response deviations, the proportion of accurate responses, and the reduction in peak-to-valley difference rate" are selected as evaluation indicators of the final response effect of the aggregator, and the AHP hierarchical analysis method is used to determine the weights of different indicators. Then, the extension material element comprehensive evaluation method is used to achieve quantitative comprehensive evaluation and feedback on the peak-shaving effect of the electric vehicle aggregator.

[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: based on multiple target weights and the target evaluation matrix, respectively determining the level correlation of multiple evaluation indicators; based on multiple level correlations and multiple evaluation levels, determining the scheduling evaluation result.

[0133] In this embodiment, the level correlation of multiple evaluation indicators is determined according to multiple target weights and target evaluation matrices, and then the scheduling evaluation result can be determined according to multiple level correlations and multiple evaluation levels. The level correlation can be referred to as correlation.

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

[0135] For example, from the correlation function matrix With the weight of each indicator, the level correlation F of each indicator can be comprehensively calculated j :

[0136]

[0137] From the above formula, we can know that the maximum value of correlation is taken as the evaluation level of the final peak shaving and valley filling effect: k(j)=max(F j ), j = 1, 2, 3, 4. When j = 1, 2, 3, 4, the corresponding evaluation levels are "excellent, good, pass, poor", and the corresponding peak load unit price C peak (j) will decrease in sequence. The peak-shaving and valley-filling rewards that the power grid ultimately gives to electric vehicle aggregators are:

[0138]

[0139] This incentivizes aggregators to respond to the grid’s dispatch instructions as accurately as possible.

[0140] Alternatively, the general extension material element evaluation method is to obtain the evaluation grade by integrating various indicators. However, in order to enable the power grid to analyze the actual dispatching effect more accurately, it is necessary to obtain the evaluation grade of a single indicator separately and provide feedback, so that the power grid can actively adjust the dispatching strategy for the aggregator according to specific problems in the next dispatching cycle.

[0141] In addition, for the i-th index x i The evaluation level in the scheduling effect 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] It can be obtained that the evaluation levels of single indicators x1, x2, x3, and x4 are respectively fed back to the power grid. According to the evaluation results, the power grid will actively adjust the dispatching strategy in the next cycle to make the peak load instruction more consistent with the response capability of the actual electric vehicle cluster, and finally form a multi-time scale closed-loop dispatching system that can continuously adapt to the actual situation. The detailed adjustment strategy is as follows:

[0145] If the indicator x1 (average value of response deviation at each moment) is evaluated as "poor", then the peak shaving and valley filling time periods T * The peak shaving and valley filling index α(t) is uniformly lowered by Δα(t), that is, α(t) = α(t) - Δα(t), (t∈T*); on the contrary, if the result is "excellent", Δα(t) is uniformly raised, that is, α(t) = α(t) + Δα(t), (t∈T * ).

[0146] If the indicator x2 (the difference between the maximum and minimum response deviations) is evaluated as "poor", the top 10% of the time with the largest response deviations are calculated. right The peak shaving and valley filling index α(t) of each period is uniformly reduced by Δα(t), that is, On the other hand, if the result is "excellent", the top 10% of the moments with the smallest response deviation are calculated. Uniformly increase Δα(t), that is

[0147] If the indicator x3 (accurate response ratio) is evaluated as "poor", all moments exceeding the response tolerance are calculated. right The peak shaving and valley filling indicators of each period in the equation are uniformly lowered by Δα(t), that is, On the contrary, if the result is "excellent", all the moments within the tolerance of the response are calculated. Uniformly increase Δα(t), that is

[0148] If the indicator x4 (peak-to-valley difference rate reduction) is evaluated as "poor", the distribution network load P is calculated. load (t) The highest and lowest 10% of the time It is defined as the peak-valley moment, and the peak-shaving and valley-filling index at the peak-valley moment is uniformly lowered by Δα(t), that is, On the contrary, if the result is "excellent", the indicators of peak and valley moments are uniformly adjusted upward by Δα(t), that is,

[0149] In this embodiment, the peak shaving and valley filling period in which the vehicle cluster participates in peak shaving and the peak shaving index within the peak shaving and valley filling period can be first obtained. Then, within the peak shaving and valley filling period, a peak shaving instruction is generated according to 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. According to the peak shaving instruction obtained above and the total charging power of the vehicle cluster, the power load of the vehicle cluster can be dispatched for peak shaving so as 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 scheduling evaluation results of the vehicle cluster. That is to say, after determining the peak shaving and valley filling period, within this period, a peak shaving instruction is generated according to the peak shaving indicators, the distribution network topology and the dispatchable potential value. In response to the peak shaving instruction, the power load of the vehicle cluster is peak-shaving dispatched according to the total charging power of the vehicle cluster, so as to obtain the initial charging power and the initial discharging power of the vehicle cluster. After evaluating the initial charging power and the initial discharging power obtained above, the dispatching evaluation result of the vehicle cluster can be obtained, thereby solving the technical problem that the dispatching situation of the vehicle cluster participating in peak shaving cannot be effectively evaluated, and achieving the technical effect that the dispatching situation of the vehicle cluster participating in peak shaving can be effectively evaluated.

[0150] The technical solution of the embodiment of the present invention is illustrated below in conjunction with preferred implementation modes.

[0151] As a flexible distributed resource, electric vehicles will play a huge role in the optimal scheduling of power systems if they can be aggregated and managed through the demand side. This shows that while the load of electric vehicles poses huge challenges to the safe operation of the power system, it also has the ability to provide considerable peak-shaving potential for the grid.

[0152] At present, a multi-time scale scheduling optimization model of vehicle clusters can be used to achieve peak-shaving of the power grid, so that the power system can maintain stable power supply under high or low loads. However, the above-mentioned multi-time scale scheduling optimization model needs to rely on a large amount of data to achieve peak-shaving of the power grid. Due to inaccurate data, the effect of peak-shaving on the power grid is not significant, resulting in the technical problem of being unable to effectively evaluate the scheduling of vehicle clusters participating in peak-shaving.

[0153] Therefore, in order to solve the above problems, the present invention proposes a multi-time scale optimization scheduling method for electric vehicles to participate in peak load regulation. The method first determines the remuneration of charging stations participating in peak load regulation by evaluating the accuracy of the actual peak load regulation effect of electric vehicles, thereby motivating charging stations to respond to peak load regulation instructions more accurately. Secondly, the power grid can actively adjust the peak load regulation instructions for electric vehicles in the next scheduling cycle based on the feedback of the evaluation results to adapt to the actual dispatchable potential of electric vehicle clusters and realize the refined management of the dispatching potential of electric vehicles, thereby solving the technical problem of being unable to effectively evaluate the dispatching situation of vehicle clusters participating in peak load regulation, and achieving the technical effect of being able to effectively evaluate the dispatching situation of vehicle clusters participating in peak load regulation.

[0154] Figure 2 is a flow chart of a multi-time scale optimization scheduling method for electric vehicles participating in peak load regulation according to an embodiment of the present invention. Figure 2 As shown, the method mainly includes the following steps:

[0155] Step S201, day-ahead optimization.

[0156] In this embodiment, at the current stage, the power grid formulates dispatching instructions with the goal of shaving peaks and filling valleys, and combines the dispatchable potential of each charging station to allocate dispatching instructions to each charging station with the goal of minimizing network losses. Each charging station, under the premise of ensuring that its own revenue does not decrease, responds to the dispatching instructions to the greatest extent, formulates electricity prices to guide electric vehicles, and the day-ahead dispatch is 24-hour (h) dispatch, with a time scale of 1h.

[0157] Step S2011, the goal of the power grid is to minimize network losses during the process of peak load shaving and valley load filling; the goal of the aggregator is to maximize profits and respond to instructions to the greatest extent.

[0158] Step S2012, obtaining the dispatchable potential of each charging station in combination with the grid topology.

[0159] Step S2013: the power grid distributes peak load shaving and valley load filling instructions to each charging station.

[0160] In this embodiment, first, the power grid can predict the next day's distribution network load P load (t), and the load power exceeds the upper limit of safe operation power P max and below the lower bound power P min The time period is defined as the peak shaving and valley filling period t * During the peak-shaving and valley-filling period, the power grid needs to reduce or fill the load power that exceeds the range. Based on this, the power grid allocates the overall peak-shaving index α(t * ), (0≤α≤1), α(t * ) is used to indicate that at t *The proportion of load power reduction or increase that the aggregator's electric vehicle cluster needs to bear during the period. The aggregator's overall peak load regulation instruction P ref (t * ) can be expressed by the following formula:

[0161]

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

[0163]

[0164] Where T is used to represent the total number of operation time periods, E is used to represent the line set, and (i, j) is used to represent the line l ij , I ij (t) is used to indicate that in time period t, line l ij The square of the current amplitude, r ij Used to indicate line l ij In the distribution network, the impedance and admittance of the 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 load instruction of each charging station (a charging station can be represented by n) is obtained. Finally, the charging station uses the master-slave game model to obtain the optimized next-day electricity price of the charging station and the charging and discharging power of the electric vehicles, under the premise of ensuring that the total charging power of all electric vehicles is not reduced, so as to guide the electric vehicles to respond to the peak-shaving instructions to the greatest extent.

[0166] Optionally, in response to the peak load regulation instruction, an objective function of charging station n is established with maximum benefit and minimum instruction response error, and the objective function can be expressed by the following formula:

[0167]

[0168] In the above formula, Used to characterize the charging power of the electric vehicle cluster at charging station n at time t. It 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 They are respectively used to characterize the charging electricity price and electricity selling price set by charging station n, and are composed of the grid electricity price, and the charging service fee or the electricity selling service fee. and They are used to represent the additional charging service fees and electricity sales service fees of charging stations respectively. It is used to characterize the penalty function term to ensure that the charging and discharging power of charging station n can accurately respond to the peak load instruction.

[0169] Step S202, intraday optimization.

[0170] In this embodiment, during the intraday stage, each charging station aims to minimize the execution deviation of the grid dispatching instructions, combines the "remaining dispatchable potential-dispatching cost" relationship curve of the electric vehicle cluster, adds the correction deviation of the additional dispatching cost dispatch, and optimizes the data of the electric vehicle cluster. The intraday optimization is 1h scheduling with a time scale of 15 minutes (min).

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

[0172] Step S2022, combining the curve between the remaining dispatching potential and the dispatching cost of the electric vehicle cluster.

[0173] Step S2023, obtaining the modified scheduling curve of the aggregator.

[0174] In this embodiment, each charging station can respond to the dispatching instructions of the power grid to the greatest extent by optimizing the electricity price to guide electric vehicles. However, due to the influence of various factors on the next 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. Among them, intraday rolling optimization needs to be carried out one hour in advance, and the electric vehicle cluster is adjusted to the peak-shaving period t by directly dispatching the electric vehicle cluster. * The charging and discharging power within is realized.

[0175] Optionally, according to the obtained scheduling correction amount, the objective function of the electric vehicle cluster of the charging station within 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, It is used to characterize the actual power of the electric vehicle cluster at charging station n within a day. ΔP n，in(t) is used to characterize the dispatch correction of charging station n at time t. This value needs to be within the range of the residual dispatchable 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 an electric vehicle cluster with charging station n within the rolling window.

[0180] Furthermore, the charging station uses the remaining dispatchable potential within the day to correct the error. This part of the dispatch is the spontaneous response of non-electric vehicle individuals. Therefore, the charging station needs to invest additional dispatch costs as the income of electric vehicles accepting dispatch, and the relationship between "remaining dispatchable charging or discharging power-dispatching power unit price" can be expressed by the following formula:

[0181]

[0182] In the above formula, C n，in The dispatch power unit price used to characterize charging station n. According to prospect theory, electric vehicle clusters are more sensitive to the dispatch of remaining charging power, that is, when dispatching the same charging power and discharging power, the dispatch power unit price is greater. When m1>m2. Considering that the dispatching unit price of the charging station will increase with the increase of the total dispatching power, the charging station needs to weigh the profit and loss of participating in the peak load regulation. When the dispatching unit price exceeds the average peak load regulation profit unit price of historical transactions, the charging station will give up correcting the error, as shown in the following formula:

[0183]

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

[0185] Step S203: evaluation and feedback.

[0186] In this embodiment, in the evaluation and feedback stage, the final peak shaving and valley filling effect is evaluated, the peak shaving fee is determined based on the completed effect, and the completed scheduling effect is fed back to the power grid as a reference for the next peak shaving instruction.

[0187] In this embodiment, after completing the peak load scheduling of the electric load of the vehicle cluster, the overall peak load shaving and valley filling effect of the electric vehicle cluster is evaluated, thereby determining the final peak load shaving unit price of the charging station and the scheduling evaluation results. In addition, the above-obtained scheduling evaluation results can be fed back to the power grid, which is conducive to the power grid timely and accurately grasping the actual response capability of the electric vehicle aggregator, and actively adjusting the peak load shaving instructions of the electric vehicle aggregator in the subsequent peak load scheduling.

[0188] Optionally, select "average response deviation at each moment, difference between maximum and minimum response deviations, accurate response ratio, and reduction in peak-to-valley difference rate" as evaluation indicators of the final response effect of the aggregator, use the analytic hierarchy process to determine the weights of different indicators, and then use the extension-physical-element comprehensive evaluation method to achieve quantitative comprehensive evaluation and feedback on the peak-shaving effect of the electric vehicle aggregator.

[0189] Optionally, the process of determining the target weight using the hierarchical analysis method is as follows: determining a judgment matrix A, where the element a in the judgment matrix A is ij The meaning of is interpreted as the comparison between the indicators in the i-th row and the j-th column, and the relative importance of the indicator is determined by quantitative expression. As shown in Table 1, when a ij When it is 1, the index x i Relative to index x j are equally important; when a ij When it is 3, the index x i Relative to index x j The importance of is slightly important; from Table 1, we can see that according to the different element values ​​in the matrix, we can get the index x i Relative to index x j changes in the importance of .

[0190] From the above, it can be seen that according to multiple target weights and target evaluation matrices, the correlation function matrix can be determined, and then according to the correlation function matrix, the correlation data can be determined.

[0191] By the correlation function matrix With the weight of each indicator, the level correlation F of each indicator can be comprehensively calculated j :

[0192]

[0193] From the above formula, we can know that the maximum value of correlation is taken as the evaluation level of the final peak shaving and valley filling effect: k(j)=max(F j ), j = 1, 2, 3, 4. When j = 1, 2, 3, 4, the corresponding evaluation levels are "excellent, good, pass, poor", and the corresponding peak load unit price C Peak (j) will decrease in sequence. The peak-shaving and valley-filling rewards that the power grid ultimately gives to electric vehicle aggregators are:

[0194]

[0195] This incentivizes aggregators to respond to the grid’s dispatch instructions as accurately as possible.

[0196] Alternatively, the general extension material element evaluation method is to obtain the evaluation grade by integrating various indicators. However, in order to enable the power grid to analyze the actual dispatching effect more accurately, it is necessary to obtain the evaluation grade of a single indicator separately and provide feedback, so that the power grid can actively adjust the dispatching strategy for the aggregator according to specific problems in the next dispatching cycle.

[0197] In this embodiment, the peak shaving and valley filling period in which the vehicle cluster participates in peak shaving and the peak shaving index within the peak shaving and valley filling period can be first obtained. Then, within the peak shaving and valley filling period, a peak shaving instruction is generated according to 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. According to the peak shaving instruction obtained above and the total charging power of the vehicle cluster, the power load of the vehicle cluster can be dispatched for peak shaving so as 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 scheduling evaluation results of the vehicle cluster. That is to say, after determining the peak shaving and valley filling period, within this period, a peak shaving instruction is generated according to the peak shaving indicators, the distribution network topology and the dispatchable potential value. In response to the peak shaving instruction, the power load of the vehicle cluster is peak-shaving dispatched according to the total charging power of the vehicle cluster, so as to obtain the initial charging power and the initial discharging power of the vehicle cluster. After evaluating the initial charging power and the initial discharging power obtained above, the dispatching evaluation result of the vehicle cluster can be obtained, thereby solving the technical problem that the dispatching situation of the vehicle cluster participating in peak shaving cannot be effectively evaluated, and achieving the technical effect that the dispatching situation of the vehicle cluster participating in peak shaving can be effectively evaluated.

[0198] According to an embodiment of the present invention, a scheduling evaluation device for vehicle clusters participating in peak load shifting is provided. It should be noted that the scheduling evaluation device for vehicle clusters participating in peak load shifting can be used to execute a scheduling evaluation method for vehicle clusters participating in peak load shifting in an embodiment.

[0199] Figure 3 is a schematic diagram of a scheduling evaluation device for vehicle clusters participating in peak load regulation according to an embodiment of the present invention. Figure 3 As shown, a scheduling evaluation device 300 for vehicle clusters participating in peak load regulation 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 obtain the peak shaving and valley filling period in which the vehicle cluster participates in peak shaving, and the peak shaving index within the peak shaving and valley filling period, wherein 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 load power reduction or increase that the vehicle cluster needs to bear.

[0201] The generation unit 302 is used to generate a peak-shaving instruction 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, wherein the dispatchable potential value is used to characterize the change in power and power of the vehicle cluster in the charging station.

[0202] The second acquisition unit 303 is used to respond to the peak load regulation instruction and perform peak load regulation 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.

[0203] The evaluation unit 304 is used to evaluate the initial charging power and the initial discharging power to obtain a 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.

[0204] In this embodiment, the peak shaving and valley filling period in which the vehicle cluster participates in peak shaving, and the peak shaving index within the peak shaving and valley filling period are obtained by a first acquisition unit, wherein 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 load power reduction or increase that the vehicle cluster needs to bear; the peak shaving instruction is generated by a generation unit within 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, wherein the dispatchable potential value is used to characterize the power and electricity of the vehicle cluster in the charging station. changes; responding to the peak-shaving instruction through the second acquisition unit, based on the total charging power of the vehicle cluster, the power load of the vehicle cluster is peak-shaving dispatched to obtain the initial charging power and initial discharging power of the vehicle cluster; evaluating the initial charging power and the initial discharging power through the evaluation unit 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, so as to solve the technical problem that the scheduling situation of the vehicle cluster participating in the peak-shaving cannot be effectively evaluated, and achieves the technical effect of effectively evaluating the scheduling situation of the vehicle cluster participating in the peak-shaving.

[0205] According to an embodiment of the present invention, a computer-readable storage medium is also provided, which includes a stored program, wherein when the program is executed by a processor, the device where the readable storage medium is located is controlled to execute the scheduling and evaluation method for vehicle clusters participating in peak load regulation in the embodiment.

[0206] According to an embodiment of the present invention, a processor is further provided, the processor being used to run a program, wherein when the program is run, the scheduling and evaluation method for vehicle clusters participating in peak load regulation in the embodiment is executed.

[0207] According to an embodiment of the present invention, a computer program product is also provided, including a computer program, which, when executed by a processor, implements the scheduling and evaluation method for vehicle clusters participating in peak load regulation in an embodiment of the present invention.

[0208] According to an embodiment of the present invention, an electronic device is also provided, the electronic device comprising a processor and a memory for storing instructions executable by the processor, wherein the processor is configured to execute instructions to implement the scheduling evaluation method for vehicle clusters participating in peak load regulation in an embodiment of the present invention.

[0209] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0210] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0211] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic, for example, the division of units can be a logical function division, and there can be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

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

[0213] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0214] If the integrated unit is implemented in the form of 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, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0215] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for evaluating the dispatching of vehicle clusters participating in peak load regulation, characterized in that: include: Obtain the peak-shaving and valley-filling time period in which the vehicle cluster participates in peak-shaving, and the peak-shaving index within the peak-shaving and valley-filling time period, wherein the peak-shaving and valley-filling time 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 load power reduction or increase 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, wherein the dispatchable potential value is used to characterize the change in power and electricity of the vehicle cluster in the charging station; In response to the peak load shaving instruction, based on the total charging power of the vehicle cluster, the power load of the vehicle cluster is peak-loaded and dispatched to obtain the initial charging power and the initial discharging power of the vehicle cluster; The initial charging power and the initial discharging power are evaluated to obtain a 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.

2. The method according to claim 1, characterized in that: Obtain the peak-shaving and valley-filling time periods when the vehicle cluster participates in peak-shaving, including: Acquire the load power of the distribution network of the vehicle cluster, and a first load power and a second load power corresponding to the load power of the distribution network, 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, acquiring a first time period; In response to the distribution network load power being less than the second load power, acquiring a second time period; The first time period and the second time period are respectively determined 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, 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, 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 takes loss information of the distribution network corresponding to the vehicle cluster as a target; The peak-shaving instruction is generated based on the network loss function, the peak-shaving index and the dispatchable potential value.

4. The method according to claim 1, characterized in that: In response to the peak load shaving instruction, based on the total charging power of the vehicle cluster, peak load shaving scheduling is performed on the power load of the vehicle cluster to obtain the initial charging power and the initial discharging power of the vehicle cluster, including: In response to the peak load instruction, establishing a first objective function and a first constraint condition of the charging station, wherein the first objective function is used to be established by taking the revenue of the charging station as a target, 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 of the vehicle cluster, wherein the second objective function is used to establish the vehicle cluster with the charging cost as the target, and the second constraint condition is used to constrain the dispatchable potential value; Based on the first objective function, the first constraint condition, the second objective function and the second constraint condition, peak load scheduling is performed on the power load of the vehicle cluster to obtain the initial charging power and the initial discharging power.

5. The method according to claim 1, characterized in that The initial charging power and the initial discharging power are evaluated to obtain a dispatch evaluation result of the vehicle cluster, including: Determining scheduling revenue data of the charging station based on the initial charging power and the initial discharging power; In response to the dispatching revenue data being less than or equal to the average dispatching revenue data, obtaining a dispatching correction amount of the charging station; Based on the peak load index and the scheduling correction amount, the initial charging power and the initial discharging power are corrected to obtain a target charging power and a target discharging power of the vehicle cluster; Based on a plurality of evaluation indicators of the vehicle cluster participating in peak load regulation, the target charging power and the target discharging power are evaluated to obtain the scheduling evaluation result.

6. The method according to claim 5, characterized in that Based on the multiple evaluation indicators of the vehicle cluster participating in the peak load shedding, the target charging power and the target discharging power are evaluated to obtain the scheduling evaluation result, including: Based on the multiple evaluation indicators, target weights corresponding to the multiple evaluation indicators are determined respectively, and based on the multiple evaluation indicators, the target charging power and the target discharging power are evaluated to obtain multiple evaluation values; Based on the plurality of evaluation indicators and the plurality of target weights, establishing a target evaluation matrix; Based on the multiple evaluation values, respectively determine the evaluation levels corresponding to the multiple evaluation indicators; The scheduling evaluation result is determined based on the multiple target weights, the target evaluation matrix and the multiple evaluation levels.

7. The method according to claim 6, characterized in that Determining the scheduling evaluation result based on the multiple target weights, the target evaluation matrix and the multiple evaluation levels includes: Based on the multiple target weights and the target evaluation matrix, respectively determine the level correlation of the multiple evaluation indicators; The scheduling evaluation result is determined based on the multiple level associations and the multiple evaluation levels.

8. A dispatching and evaluation device for vehicle clusters participating in peak load regulation, characterized in that: include: A first acquisition unit is used to acquire a peak-shaving and valley-filling period in which a vehicle cluster participates in peak-shaving, and a peak-shaving index in the peak-shaving and valley-filling period, wherein 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 load power reduction or increase that the vehicle cluster needs to bear; A generating unit, configured to generate a peak load shaving instruction during the peak load shaving and valley filling period based on the peak load shaving index, the distribution network topology corresponding to the vehicle cluster, and the dispatchable potential value of the vehicle cluster in the charging station, wherein the dispatchable potential value is used to characterize the change of power and electricity of the vehicle cluster in the charging station; A second acquisition unit is used to respond to the peak load regulation instruction and perform peak load regulation 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; An evaluation unit is used to evaluate the initial charging power and the initial discharging power to obtain a 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.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The invention comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any one of claims 1 to 7.

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