Optimized scheduling method and system for high-power quick charging station of electric vehicle

Abstract

The invention discloses an optimized dispatching method and system for a high-power quick charging station of an electric vehicle. The method comprises the following steps: constructing the optimized dispatching system for the high-power quick charging station of the electric vehicle considering energy storage; a day-ahead and intra-day multi-time scale optimization scheduling model is adopted to optimize the prediction error of the charging load of the electric vehicle; the day-ahead optimization scheduling determines an energy reference value of each energy storage moment on a long-time scale according to short-term charging load prediction information; according to the intra-day rolling optimization scheduling, rolling optimization is carried out according to ultra-short-term prediction data of the charging load of the electric vehicle under the reference of the result of the day-ahead optimization scheduling model, and the charging and discharging power of energy storage and the interaction power with a superior power grid are determined on a short time scale. The peak-valley difference of the load of the charging station can be reduced, the peak regulation pressure of the power grid is relieved, and the operation cost of the charging station can be reduced; and a day-ahead and intra-day multi-time scale optimization framework is adopted, so that the prediction error of the charging load can be effectively dealt with.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging scheduling, and in particular to an optimization scheduling method and system for electric vehicle high-power fast charging stations. Background Art

[0002] Stimulated by both technology upgrades and national policies, the number of electric vehicles in my country has grown rapidly, and the corresponding charging demand has also increased dramatically. Among them, the disorderly charging of a large number of electric vehicles has brought a huge impact on the power grid load, increased the peak load regulation pressure of the power grid, and seriously affected the safety of the power grid. Therefore, it is necessary to optimize the management of the charging load of electric vehicles in charging stations to alleviate the contradiction between power supply and demand.

[0003] In response to the growing demand for electric vehicle charging, two main charging modes are currently available: slow charging and fast charging. The slow charging mode allows for a longer charging process due to its lower charging power, so the charging period can be adjusted according to the time-of-use electricity price strategy, effectively reducing operating costs. However, for users who are in urgent need of fast energy replenishment, the fast charging mode has become the first choice due to its high power and short-time high efficiency. Although fast charging can complete the charging task in a short time, due to its high power characteristics, if the charging load is not effectively managed, it will have a significant impact on the power grid, especially when multiple fast charging piles are operating at the same time.

[0004] At present, most high-power fast-charging charging stations do not implement an effective control mechanism for charging loads. Electric vehicle users often choose to charge as they arrive. Although this method is convenient for users, it ignores the potential impact on the power grid. Research shows that after the energy storage system is reasonably configured, the burden on the power grid can be reduced by shaving peak loads and filling valleys, and the operating costs of charging stations can also be reduced.

[0005] In addition, there is a large uncertainty in the charging load of charging stations. Traditional optimization scheduling methods often only consider a single time scale and cannot effectively deal with the errors in the daily charging load forecast. This limitation means that even if optimization is performed in the design stage, the scheduling effect in actual operation may be greatly reduced. Therefore, in order to better adapt to the changes in electric vehicle charging load and realize the effective use of power resources, it is particularly urgent to develop an optimization scheduling method for electric vehicle high-power fast charging stations that can dynamically respond to daily load fluctuations. Summary of the invention

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides an optimized scheduling method and system for high-power fast charging stations for electric vehicles, which uses energy storage to alleviate the load impact of high-power fast charging stations on the power grid, solving the problem that the prior art does not regulate the charging load and has great uncertainty.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides an optimization and scheduling method for electric vehicle high-power fast charging stations, including: constructing an optimization and scheduling system for electric vehicle high-power fast charging stations taking into account energy storage through electric vehicle charging load parameters; based on the optimization and scheduling system for electric vehicle high-power fast charging stations taking into account energy storage, a multi-time scale optimization and scheduling model of the day-ahead and intraday is used to optimize the prediction error of the electric vehicle charging load; the day-ahead optimization and scheduling model determines the energy reference value of the energy storage at each moment on a long time scale according to the short-term charging load prediction information; the intraday rolling optimization and scheduling model performs rolling optimization according to the ultra-short-term prediction data of the electric vehicle charging load under the reference of the result of the day-ahead optimization and scheduling model, and optimizes the prediction error of the electric vehicle charging load in the short term.

[0010] The charging and discharging power of energy storage and the interaction power with the upper-level power grid are determined on a time scale.

[0011] As a preferred embodiment of the method for optimizing and dispatching a high-power fast charging station for electric vehicles according to the present invention

[0012] The scheme includes:

[0013] Taking the minimum load peak-valley difference and the minimum operating cost as the objective function, the distribution line power balance constraint, the continuous energy constraint of energy storage, the maximum and minimum energy range constraint of energy storage, the energy balance constraint of the first and last cycle of energy storage scheduling, the charge and discharge power constraint of energy storage, and the charge and discharge mutual exclusion constraint of energy storage are used to control the above

[0014] The objective function is constrained and the day-ahead optimization scheduling model is constructed;

[0015] The day-ahead optimization scheduling model determines the long-term charging load forecast information based on the short-term charging load forecast information.

[0016] Energy reference value of energy storage at each moment.

[0017] As a preferred embodiment of the method for optimizing and dispatching a high-power fast charging station for electric vehicles according to the present invention

[0018] The objective function of minimizing the load peak-to-valley difference and the operation cost is as follows:

[0019] The objective function corresponding to the operating cost is:

[0020]

[0021] Among them, f 1 represents the operating cost, T represents the scheduling period, and t represents the time index variable within the scheduling period. P represents the price of electricity purchased and sold by the power grid, grid,t represents the power interacting with the grid, Δt represents the day-ahead optimization

[0022] The time interval for scheduling;

[0023] The objective function corresponding to the load peak-to-valley difference is:

[0024]

[0025] Among them, f 2 Indicates the load peak-to-valley difference, and They represent the maximum load value and the minimum load value of the charging station respectively, and the corresponding constraints are:

[0026]

[0027] As a preferred solution of the method for optimizing and scheduling electric vehicle high-power fast charging stations according to the present invention, the constraints of the day-ahead optimization scheduling model include:

[0028] The power balance constraint of the distribution line is:

[0029]

[0030] The continuous energy constraint of the energy storage is:

[0031]

[0032] The maximum and minimum energy range constraints of the energy storage are:

[0033]

[0034] The energy balance constraint of the first and last cycles of the energy storage scheduling is:

[0035]

[0036] The charge and discharge power constraints of the energy storage are:

[0037]

[0038] The charge and discharge mutual exclusion constraints of the energy storage are:

[0039]

[0040] Among them, Pdis,t is the discharge power of energy storage, P load,t is the total charging load demand of the fast charging pile, P ch,t is the charging power of energy storage, E e,t 、E e,0 、E e,t-1 are the energy stored in period t, t=0, and t-1 respectively, η ch,e and η dis,e are the charging and discharging efficiency of energy storage, λ e,min and λ e,max are the minimum and maximum states allowed for energy storage, E e,rate is the rated capacity of energy storage, P ch,max and P dis,max are the maximum charging and discharging power of energy storage, σ ch,t and σ dis,t They are binary variables for charging and discharging of energy storage.

[0041] As a preferred solution of the method for optimizing and dispatching a high-power fast charging station for electric vehicles according to the present invention, the processing of the objective function of minimizing the peak-to-valley difference of load and minimizing the operating cost includes:

[0042] The objective function dimension is standardized to process the objective function with the minimum load peak-to-valley difference and the minimum operating cost as the objective function, and the actual value of the objective function is divided by its value before optimization, which is expressed as:

[0043]

[0044] Among them, f 1,0 and f 2,0 is the value of each objective function before optimization;

[0045] By setting the weights of each sub-objective function, the multi-objective optimization problem is transformed into a single-objective optimization problem for solution, namely:

[0046]

[0047] Among them, ω 1 and ω 2 Dispatch personnel for the weights corresponding to each objective function.

[0048] As a preferred solution of the method for optimizing and scheduling electric vehicle high-power fast charging stations according to the present invention, the intraday rolling optimization scheduling model includes:

[0049] The intraday rolling optimization scheduling model performs rolling optimization based on the ultra-short-term forecast data of the electric vehicle charging load with reference to the results of the day-ahead optimization scheduling model, and determines the charging and discharging power of the energy storage and the interaction power with the upper-level power grid on a short time scale. The objective functions and constraints of the intraday rolling optimization scheduling model and the day-ahead optimization scheduling model are basically the same, and only the time variable needs to be replaced, that is, t in Time index optimized for intraday, T in is the scheduling period of the day-ahead optimization scheduling model, Δt in Time intervals optimized for intraday rolling;

[0050] The energy storage capacity is constrained by the day-ahead optimization result as follows:

[0051]

[0052] Among them, ΔE da is the allowable deviation of the day-ahead energy of energy storage, Energy optimized for intraday rolling of energy storage.

[0053] As a preferred solution of the method for optimizing and dispatching a high-power fast charging station for electric vehicles according to the present invention, the method for constructing an optimized and dispatching system for high-power fast charging stations for electric vehicles taking energy storage into consideration comprises:

[0054] The electric vehicle high-power fast charging station contains multiple high-power fast charging piles that are uniformly connected to the distribution line. At the same time, the energy storage is also connected to the distribution line. In addition, the charging station is connected to the power grid to obtain electricity. The control center optimizes the charging and discharging power of the energy storage based on the day-ahead and short-term forecasts of the electric vehicle charging load in the charging station, thereby constructing an optimized scheduling system for electric vehicle high-power fast charging stations that takes energy storage into consideration.

[0055] In a second aspect, the present invention provides an optimization and scheduling system for high-power fast charging stations for electric vehicles, comprising:

[0056] An optimization dispatching system construction unit, used to construct an optimization dispatching system for electric vehicle high-power fast charging stations taking energy storage into consideration through electric vehicle charging load parameters;

[0057] An overall optimization unit, for optimizing the prediction error of the electric vehicle charging load by using a multi-time scale optimization framework of day-ahead and intra-day based on the optimization scheduling system of the electric vehicle high-power fast charging station considering energy storage;

[0058] The day-ahead optimization dispatching unit is used for the day-ahead optimization dispatching model to determine the energy reference value of energy storage at each moment on a long-term scale based on the short-term charging load forecast information;

[0059] The intraday rolling optimization scheduling unit is used for intraday rolling optimization. With reference to the results of the day-ahead optimization scheduling, the unit performs rolling optimization according to the ultra-short-term forecast data of the electric vehicle charging load, and determines the charging and discharging power of the energy storage and the interaction power with the upper-level power grid on a short time scale.

[0060] In a third aspect, the present invention provides a computing device, comprising:

[0061] Memory and processor;

[0062] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method are implemented.

[0063] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which implement the steps of the method when executed by a processor.

[0064] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides an optimization scheduling method and system for electric vehicle high-power fast charging stations taking energy storage into consideration, which can reduce the peak-to-valley difference of the charging station load, alleviate the peak-shaving pressure of the power grid, and reduce the operating cost of the charging station; a multi-time scale optimization framework of day-ahead and intra-day is adopted, and the day-ahead optimization scheduling gives the energy reference value of energy storage from a long-term perspective, and the intra-day optimization scheduling is rolled over and updated in a short time, which can effectively deal with the prediction error of the charging load. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0066] Figure 1 This is an overall flow chart of an optimized scheduling method for a high-power fast charging station for electric vehicles according to an embodiment of the present invention;

[0067] Figure 2 A schematic diagram of an optimized scheduling system for electric vehicle high-power fast charging stations considering energy storage in an optimized scheduling method for electric vehicle high-power fast charging stations according to an embodiment of the present invention;

[0068] Figure 3 A schematic diagram of a day-ahead and intra-day multi-time scale optimization framework of an optimization scheduling method for electric vehicle high-power fast charging stations according to an embodiment of the present invention;

[0069] Figure 4The waveform diagram of the charging load power predicted a day ago and the load power predicted in the short term within a day of the method for optimizing the scheduling of a high-power fast charging station for electric vehicles according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0071] Example 1

[0072] Reference Figure 1 to Figure 3 As an embodiment of the present invention, a method for optimizing and scheduling high-power fast charging stations for electric vehicles is provided, which uses energy storage to alleviate the load impact of high-power fast charging stations on the power grid and reduce the operating costs of charging stations; then a multi-time scale optimization framework of the day-ahead and intraday is used to deal with the prediction error of the charging load; wherein the day-ahead optimization scheduling determines the energy reference value of the energy storage at each moment on a long time scale based on the short-term charging load prediction information; and the intraday rolling optimization determines the actual charging and discharging power of the energy storage and the power interacting with the power grid on a short time scale based on the ultra-short-term charging load prediction information under the energy constraint of the energy storage day-ahead. Specifically, the following steps are included:

[0073] S100: Build an optimized dispatching system for high-power fast charging stations for electric vehicles considering energy storage;

[0074] In the embodiments of the present application, Figure 2 The figure shows the schematic diagram of the optimized dispatching system of high-power fast charging stations for electric vehicles considering energy storage. Figure 2 It can be seen that the electric vehicle high-power fast charging station contains multiple high-power fast charging piles and is uniformly connected to the distribution line. At the same time, the energy storage is also connected to the distribution line. In addition, the charging station is connected to the power grid to obtain electricity. The control center optimizes the charging and discharging power of the energy storage based on the day-ahead and short-term forecasts of the electric vehicle charging load in the charging station, thereby constructing an optimized scheduling system for electric vehicle high-power fast charging stations considering energy storage.

[0075] It should be noted that the control center optimizes the charging and discharging power of energy storage based on the day-ahead and short-term forecasts of the electric vehicle charging load in the charging station to reduce the peak-to-valley difference in load and reduce the electricity cost of the charging station.

[0076] S200: An optimal dispatching system for high-power fast charging stations for electric vehicles considering energy storage, which uses a multi-time scale optimal dispatching model for day-ahead and intra-day to optimize the forecast error of electric vehicle charging load;

[0077] It should be noted that the charging load forecast data of the charging station has errors. Using only a single time scale day-ahead optimization scheduling cannot cope with the large prediction errors that occur during the day, which reduces the effectiveness of the optimization strategy. The present invention proposes a multi-time scale optimization scheduling strategy, including day-ahead optimization and intraday rolling optimization, as shown in the schematic diagram. Figure 3 As shown. The day-ahead optimization determines the energy reference value of energy storage at each moment in the long-term perspective based on the forecast information of the charging station load of the previous day (24h), thereby providing a global optimization reference. The intraday rolling optimization is based on the reference of the day-ahead optimization scheduling results, and performs rolling optimization based on the ultra-short-term forecast data of the charging station load to determine the charging and discharging power of the energy storage in the latest period and the interaction power with the upper-level power grid.

[0078] S300: The day-ahead optimization dispatch model determines the energy reference value of energy storage at each moment on a long-term scale based on the short-term charging load forecast information;

[0079] In the embodiment of the present application, the objective function is to minimize the load peak-to-valley difference and the operation cost, and the objective function is constrained by the power balance constraint of the distribution line, the continuous energy constraint of the energy storage, the maximum and minimum energy range constraints of the energy storage, the energy balance constraint of the first and last cycle of the energy storage scheduling, the charge and discharge power constraint of the energy storage, and the charge and discharge mutual exclusion constraint of the energy storage, and the day-ahead optimization scheduling model is constructed; wherein the day-ahead optimization scheduling model determines the energy reference value of the energy storage at each moment on a long time scale according to the short-term charging load forecast information;

[0080] In a feasible implementation, the objective function of minimizing the load peak-to-valley difference and the operation cost includes:

[0081] The objective function corresponding to the operating cost is:

[0082]

[0083] Among them, f 1 represents the operating cost, T represents the scheduling period, and t represents the time index variable within the scheduling period. P represents the price of electricity purchased and sold by the power grid, grid,t represents the power interacting with the grid, Δt represents the time interval of day-ahead optimization scheduling;

[0084] The objective function corresponding to the load peak-valley difference is:

[0085]

[0086] Among them, f 2 Indicates the load peak-to-valley difference, and They represent the maximum load value and the minimum load value of the charging station respectively, and the corresponding constraints are:

[0087]

[0088] In a feasible implementation, the constraints of the day-ahead optimization scheduling model include:

[0089] The power balance constraint of the distribution line is:

[0090]

[0091] The continuous energy constraint of energy storage is:

[0092]

[0093] The maximum and minimum energy range constraints of energy storage are:

[0094]

[0095] The energy balance constraint of the first and last cycles of energy storage scheduling is:

[0096]

[0097] The charging and discharging power constraints of energy storage are:

[0098]

[0099] The mutually exclusive constraints of energy storage charging and discharging are:

[0100]

[0101] Among them, P dis,t is the discharge power of energy storage, P load,t is the total charging load demand of the fast charging pile, P ch,t is the charging power of energy storage, E e,t 、E e,0 、E e,t-1 are the energy stored in period t, t=0, and t-1 respectively, η ch,e and η dis,e are the charging and discharging efficiency of energy storage, λ e,min and λ e,max are the minimum and maximum states allowed for energy storage, E e,rate is the rated capacity of energy storage, P ch,max and P dis,max are the maximum charging and discharging power of energy storage, σ ch,t and σ dis,tThey are binary variables for charging and discharging of energy storage.

[0102] Furthermore, the objective function of minimizing the load peak-to-valley difference and the operation cost is processed as follows:

[0103] The objective function dimension is standardized to process the objective function with the minimum load peak-to-valley difference and the minimum operating cost as the objective function. The actual value of the objective function is divided by its value before optimization, which is expressed as:

[0104]

[0105] Among them, f 1,0 and f 2,0 is the value of each objective function before optimization;

[0106] By setting the weights of each sub-objective function, the multi-objective optimization problem is transformed into a single-objective optimization problem for solution, namely:

[0107]

[0108] Among them, ω 1 and ω 2 The weight scheduler corresponding to each objective function can set it according to the importance of the objective function.

[0109] It should be noted that the two objective functions of the optimized scheduling mainly include indicators of two different dimensions: load peak-valley difference and operating cost. The values ​​of the two objective functions are not in the same order of magnitude and need to be standardized to make their values ​​closer. Therefore, this embodiment uses the method of standardizing the objective function dimensions to process the objective functions with the minimum load peak-valley difference and the minimum operating cost as the objective functions, and then sets the weights for each sub-objective function to transform the multi-objective optimization problem into a single-objective optimization problem for solution.

[0110] S400: The intraday rolling optimization dispatch model uses the results of the day-ahead optimization dispatch model as a reference and performs rolling optimization based on the ultra-short-term forecast data of the electric vehicle charging load to determine the charging and discharging power of the energy storage and the interaction power with the upper-level power grid on a short time scale;

[0111] In the embodiment of the present application, the intraday rolling optimization scheduling model is based on the results of the day-ahead optimization scheduling model, and performs rolling optimization according to the ultra-short-term forecast data of the electric vehicle charging load, and determines the charging and discharging power of the energy storage and the interaction power with the upper-level power grid on a short time scale. Among them, the objective functions and constraints of the intraday rolling optimization scheduling model and the day-ahead optimization scheduling model are basically the same, and only the time variable needs to be replaced, that is, t in Time index optimized for intraday, T in is the scheduling period of the day-ahead optimization scheduling model, Δtin Time intervals optimized for intraday rolling;

[0112] The energy storage capacity is constrained by the day-ahead optimization result as follows:

[0113]

[0114] Among them, ΔE da is the allowable deviation of the day-ahead energy of energy storage, Energy optimized for intraday rolling of energy storage.

[0115] Therefore, the energy storage energy optimized on the previous day can be used as a reference for the energy storage energy optimized during the day, and the results obtained from the day's rolling optimization can be directly used for the charging and discharging power control of the energy storage.

[0116] From the above, it can be seen that the present invention provides an optimization scheduling method and system for electric vehicle high-power fast charging stations taking energy storage into consideration, which can reduce the peak-to-valley difference of the charging station load, alleviate the peak-shaving pressure of the power grid, and reduce the operating cost of the charging station; it adopts a multi-time scale optimization framework of day-ahead and intra-day, and the day-ahead optimization scheduling gives the energy reference value of energy storage from a long-term perspective, and the intra-day optimization scheduling is rolled over in a short time, which can effectively deal with the prediction error of the charging load.

[0117] The above is a schematic scheme of an electric vehicle high-power fast charging station optimization scheduling method of this embodiment. It should be noted that the technical scheme of the electric vehicle high-power fast charging station optimization scheduling system and the technical scheme of the electric vehicle high-power fast charging station optimization scheduling method described above belong to the same concept. For details not described in detail in the technical scheme of the electric vehicle high-power fast charging station optimization scheduling system in this embodiment, please refer to the description of the technical scheme of the electric vehicle high-power fast charging station optimization scheduling method described above.

[0118] This embodiment also provides an electric vehicle high-power fast charging station optimization and scheduling system, including:

[0119] An optimization dispatching system construction unit, used to construct an optimization dispatching system for electric vehicle high-power fast charging stations taking energy storage into consideration through electric vehicle charging load parameters;

[0120] The overall optimization unit is used to optimize the scheduling system of high-power fast charging stations for electric vehicles based on energy storage, and optimizes the forecast error of electric vehicle charging load using a multi-time scale optimization framework of day-ahead and intra-day;

[0121] The day-ahead optimization dispatching unit is used for the day-ahead optimization dispatching model to determine the energy reference value of energy storage at each moment on a long-term scale based on the short-term charging load forecast information;

[0122] The intraday rolling optimization dispatch unit is used for intraday rolling optimization. With reference to the results of the day-ahead optimization dispatch, it performs rolling optimization based on the ultra-short-term forecast data of the electric vehicle charging load, and determines the charging and discharging power of the energy storage and the interaction power with the upper-level power grid on a short time scale.

[0123] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0124] This embodiment also provides a computing device, which includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for optimizing the scheduling of a high-power fast charging station for electric vehicles is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0125] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method proposed in the above embodiment is implemented.

[0126] The storage medium proposed in this embodiment and the method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0127] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0128] Example 2

[0129] Reference Figure 4 Based on the previous embodiment, this embodiment provides an application example of an optimization scheduling method and system for electric vehicle high-power fast charging stations, in order to verify and illustrate the technical effects adopted in this method.

[0130] In order to verify the effectiveness of the method proposed in this invention, the algorithm is verified in a high-power fast charging station with 50 charging piles. Assuming that the maximum power of a single charging pile in the charging station is 80kW, the charging load power predicted by the day and the load power predicted within the day are as follows: Figure 4 As shown. The electricity price is based on the industrial and commercial peak-valley electricity price in Jiangsu Province (peak period 8:00-11:00, 17:00-22:00, flat period 11:00-17:00, 22:00-24:00, off-peak period 0:00-8:00), the electricity price for charging during peak period is 1.1526 yuan / kWh, the electricity price for charging during flat period is 0.6703 yuan / kWh, and the electricity price for charging during off-peak period is 0.2805 yuan / kWh. Assume that the energy storage configuration power and capacity in the charging station considering energy storage are 1000kW and 2000kW.h respectively. Run the program to compare the electricity purchase cost and load peak-valley difference results of three different methods (not considering energy storage, considering energy storage, and considering energy storage and prediction error), as shown in Table 1.

[0131] Table 1: Results of different methods.

[0132] No consideration of energy storage Consider energy storage Considering energy storage and prediction errors Power purchase cost 34548￥ 33371￥ 33248￥ Load peak-to-valley difference 2631kW 2070kW 1928kW

[0133] As can be seen from Table 1, the peak-to-valley difference of the charging station without considering energy storage is as high as 2631kW, and the electricity purchase cost is 34548￥. After considering energy storage, both the electricity purchase cost and the load peak-to-valley difference are reduced. In addition, when considering energy storage and prediction error at the same time, the electricity purchase cost and load difference are the lowest, which are 33248￥ and 1928kW respectively. This proves that the method proposed in the present invention can effectively deal with load prediction errors, improve the ability of energy storage to shave peaks and fill valleys, and reduce costs.

[0134] From the above, it can be seen that the present invention provides an optimization scheduling method and system for electric vehicle high-power fast charging stations taking energy storage into consideration, which can reduce the peak-to-valley difference of the charging station load, alleviate the peak-shaving pressure of the power grid, and reduce the operating cost of the charging station; it adopts a multi-time scale optimization framework of day-ahead and intra-day, and the day-ahead optimization scheduling gives the energy reference value of energy storage from a long-term perspective, and the intra-day optimization scheduling is rolled over in a short time, which can effectively deal with the prediction error of the charging load.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0136] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0137] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0138] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0140] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0141] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for optimizing the scheduling of electric vehicle high-power fast charging stations, characterized in that: include: The optimization dispatching system of high-power fast charging stations for electric vehicles considering energy storage is constructed through the charging load parameters of electric vehicles; Based on the above-mentioned optimization dispatching system of electric vehicle high-power fast charging stations considering energy storage, a multi-time scale optimization dispatching model of day-ahead and intra-day is adopted to optimize the prediction error of electric vehicle charging load; The day-ahead optimization dispatch model determines the energy reference value of energy storage at each moment on a long-term scale based on the short-term charging load forecast information; The intraday rolling optimization scheduling model uses the results of the day-ahead optimization scheduling model as a reference and performs rolling optimization based on the ultra-short-term forecast data of the electric vehicle charging load to determine the charging and discharging power of the energy storage and the interaction power with the upper-level power grid on a short time scale.

2. The method for optimizing and dispatching a high-power fast charging station for electric vehicles according to claim 1, characterized in that: The day-ahead optimization scheduling model includes: Taking the minimum peak-to-valley difference of load and the minimum operating cost as the objective function, the objective function is constrained by the power balance constraint of the distribution line, the continuous energy constraint of the energy storage, the maximum and minimum energy range constraints of the energy storage, the energy balance constraint of the first and last cycles of the energy storage scheduling, the charge and discharge power constraint of the energy storage, and the charge and discharge mutual exclusion constraint of the energy storage, and the day-ahead optimization scheduling model is constructed; The day-ahead optimization scheduling model determines the energy reference value of energy storage at each moment on a long-term scale according to the short-term charging load forecast information.

3. The method for optimizing and dispatching a high-power fast charging station for electric vehicles as claimed in claim 2, characterized in that: The objective function of minimizing the load peak-to-valley difference and the operation cost includes: The objective function corresponding to the operating cost is: Among them, f1 represents the operating cost, T represents the scheduling period, and t represents the time index variable within the scheduling period. P represents the price of electricity purchased and sold by the power grid, grid,t represents the power interacting with the grid, Δt represents the time interval of day-ahead optimization scheduling; The objective function corresponding to the load peak-to-valley difference is: Among them, f2 represents the load peak-to-valley difference, and They represent the maximum load value and the minimum load value of the charging station respectively, and the corresponding constraints are:

4. The method for optimizing and dispatching a high-power fast charging station for electric vehicles as claimed in claim 3, characterized in that: The constraints of the day-ahead optimization scheduling model include: The power balance constraint of the distribution line is: The continuous energy constraint of the energy storage is: The maximum and minimum energy range constraints of the energy storage are: The energy balance constraint of the first and last cycles of the energy storage scheduling is: The charge and discharge power constraints of the energy storage are: The charge and discharge mutual exclusion constraints of the energy storage are: Among them, P dis,t is the discharge power of energy storage, P load,t is the total charging load demand of the fast charging pile, P ch,t is the charging power of energy storage, E e,t 、E e,0 、E e,t-1 The energy stored in the time period t, t=0, and t-1 respectively, η ch,e and η dis,e are the charging and discharging efficiency of energy storage, λ e,min and λ e,max are the minimum and maximum states allowed for energy storage, E e,rate is the rated capacity of energy storage, P ch,max and P dis,max are the maximum charging and discharging power of energy storage, σ ch,t and σ dis,t They are binary variables for charging and discharging of energy storage.

5. The method for optimizing and dispatching a high-power fast charging station for electric vehicles as claimed in claim 4, characterized in that: The processing of the objective function with the minimum load peak-to-valley difference and the minimum operating cost includes: The objective function dimension is standardized to process the objective function with the minimum load peak-to-valley difference and the minimum operating cost as the objective function, and the actual value of the objective function is divided by its value before optimization, which is expressed as: Among them, f 1,0 and f 2,0 is the value of each objective function before optimization; By setting the weights of each sub-objective function, the multi-objective optimization problem is transformed into a single-objective optimization problem for solution, namely: Among them, ω1 and ω2 are the weighted dispatchers corresponding to each objective function.

6. The method for optimizing and dispatching a high-power fast charging station for electric vehicles as claimed in claim 1, characterized in that: The intraday rolling optimization scheduling model includes: The intraday rolling optimization scheduling model performs rolling optimization based on the ultra-short-term forecast data of the electric vehicle charging load with reference to the results of the day-ahead optimization scheduling model, and determines the charging and discharging power of the energy storage and the interaction power with the upper-level power grid on a short time scale. The objective functions and constraints of the intraday rolling optimization scheduling model and the day-ahead optimization scheduling model are basically the same, and only the time variable needs to be replaced, that is, t in Time index optimized for intraday, T in is the scheduling period of the day-ahead optimization scheduling model, Δt in Time intervals optimized for intraday rolling; The energy storage capacity is constrained by the day-ahead optimization result as follows: Where, ΔE da is the allowable deviation of the day-ahead energy of energy storage, Energy optimized for intraday rolling of energy storage.

7. The method for optimizing and dispatching a high-power fast charging station for electric vehicles as claimed in claim 1, characterized in that: The construction of an optimized dispatching system for high-power fast charging stations for electric vehicles considering energy storage includes: The electric vehicle high-power fast charging station contains multiple high-power fast charging piles that are uniformly connected to the distribution line. At the same time, the energy storage is also connected to the distribution line. In addition, the charging station is connected to the power grid to obtain electricity. The control center optimizes the charging and discharging power of the energy storage based on the day-ahead and short-term forecasts of the electric vehicle charging load in the charging station, thereby constructing an optimized scheduling system for electric vehicle high-power fast charging stations that takes energy storage into consideration.

8. A system using the method for optimizing and scheduling electric vehicle high-power fast charging stations as claimed in any one of claims 1 to 7, characterized in that: include: An optimization dispatching system construction unit, used to construct an optimization dispatching system for electric vehicle high-power fast charging stations taking energy storage into consideration through electric vehicle charging load parameters; An overall optimization unit, for optimizing the prediction error of the electric vehicle charging load by adopting a multi-time scale optimization framework of day-ahead and intra-day based on the optimization scheduling system of the electric vehicle high-power fast charging station considering energy storage; The day-ahead optimization dispatching unit is used for the day-ahead optimization dispatching model to determine the energy reference value of energy storage at each moment on a long-term scale based on the short-term charging load forecast information; The intraday rolling optimization scheduling unit is used for intraday rolling optimization. With reference to the results of the above-mentioned day-ahead optimization scheduling, rolling optimization is performed according to the ultra-short-term forecast data of the electric vehicle charging load to determine the charging and discharging power of the energy storage and the interaction power with the upper-level power grid on a short time scale.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having computer program instructions stored thereon, wherein the computer-executable instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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