A scheduling method, computing device and storage medium for a battery swap station cluster

Abstract

This application discloses a scheduling method, computing device, and storage medium for a battery swap station cluster. The scheduling method for the battery swap station cluster includes: determining the battery demand within the service area of ​​each battery swap station; determining a first objective function by calculating the total cost of the battery swap station cluster; determining a second objective function by calculating the similarity between the power curve of renewable energy output and the charging power curve of the battery swap station; determining constraints based on the battery demand and the number of charging and discharging batteries; constructing a scheduling model using the first objective function, the second objective function, and the constraints; and solving the scheduling model using a genetic algorithm to obtain the number of charging and discharging batteries at each battery swap station during each time period.

Description

Technical Field

[0001] The present application relates to the field of energy and power technology, and more specifically, to a scheduling method, computing device, and storage medium for a battery swap station cluster. Background Art

[0002] With rapid urbanization driven by economic growth, China's vehicle population is growing significantly. According to statistics, carbon dioxide emissions from the transportation sector account for 10% of the country's total carbon emissions. Therefore, promoting a green and low-carbon transportation system has become a key priority in my country. Electric vehicles (EVs), as a new user-side flexibility resource, have experienced rapid growth in recent years. This rapid increase in EVs will also increase grid load volatility, impacting its safe operation. Faced with the continued rise in EVs, effectively regulating EV charging has become a pressing issue. Therefore, battery swapping for EVs and the efficient charging and discharging of battery swap stations will become crucial approaches to reducing load fluctuations.

[0003] As a virtual energy storage device, battery swap stations can flexibly provide V2G services during operation. During normal distribution network operation, they can help shift peaks and fill valleys, storing energy at low prices and discharging it at high prices. This promotes the absorption of wind power and improves the economic efficiency of distribution network operations. Discharging electric vehicle batteries can alleviate power shortages, thereby better absorbing renewable energy.

[0004] In summary, properly configuring the scale and location of battery swap stations can play an important role in improving the reliability of the distribution network. A solution is needed that can properly schedule the charging and discharging states of batteries in battery swap stations to achieve economic efficiency while more effectively absorbing renewable energy. Summary of the Invention

[0005] To this end, the present application provides a scheduling method, computing device, and storage medium for a battery swap station cluster, in an effort to solve or at least alleviate at least one of the above problems.

[0006] According to one aspect of the present application, a scheduling method for a battery swap station cluster is provided, including: determining the battery demand within the service area of ​​each battery swap station; determining a first objective function by calculating the total cost of the battery swap station cluster; determining a second objective function by calculating the similarity between the power curve of renewable energy output and the charging power curve of the battery swap station; determining constraints based on the battery demand and the number of charging batteries and the number of discharging batteries; constructing a scheduling model using the first objective function, the second objective function and the constraints; and solving the scheduling model using a genetic algorithm to obtain the number of charging batteries and the number of discharging batteries of each battery swap station in each time period.

[0007] Optionally, in the method according to the present application, the battery demand within the service area of ​​each battery swap station is determined, including: determining the electric vehicle demand in each time period within the service area of ​​each battery swap station based on the vehicle data of each traffic node; and determining the battery demand corresponding to each time period based on the electric vehicle demand and the electric vehicle battery swap probability within the service area of ​​each battery swap station.

[0008] Optionally, in the method according to the present application, the first objective function is determined by calculating the total cost of the battery swap station cluster, including: calculating the product of the number of charging batteries, charging power and electricity sales price of each battery swap station in each time period as the charging cost of each battery swap station; calculating the product of the number of discharging batteries, discharging power and discharging electricity price of each battery swap station in each time period as the discharge revenue of each battery swap station; calculating the difference between the charging cost and the discharging revenue as the total cost; and generating the first objective function with the goal of minimizing the total cost.

[0009] Optionally, in the method according to the present application, the second objective function is determined by calculating the similarity between the power curve of the renewable energy output and the charging power curve of the battery swap station, including: calculating the cosine similarity between the power curve of the renewable energy output and the charging power curve of the battery swap station; subtracting the difference between the cosine similarity from a fixed value to represent the degree of curve waveform fitting; and generating the second objective function with the goal of minimizing the degree of curve waveform fitting.

[0010] Optionally, in the method according to the present application, before calculating the cosine similarity of the power curve of renewable energy output and the charging power curve of the battery swap station, it also includes: normalizing the power curve of renewable energy output and the charging power curve of the battery swap station respectively.

[0011] Optionally, in the method according to the present application, constraints are determined based on the battery demand and the number of charged batteries and discharged batteries, including: in each time period, the number of full batteries at each battery swap station is not less than the battery demand in the corresponding service area; the total number of batteries at each battery swap station is balanced with the number of charged batteries, discharged batteries, full batteries, empty batteries, and spare batteries; in each time period, the number of charged batteries and the number of discharged batteries satisfy their respective constraints.

[0012] Optionally, in the method according to the present application, the constraint relationship between the number of charged batteries and the number of discharged batteries includes:

[0013] J it -DD it ≤H i(t+1) ,

[0014] J it +CD it ≥H i(t+1) ,

[0015] J i(t+1)+DD i(t+1) ≤J it -H it +CD it ,

[0016] K i(t+1) +CD i(t+1) ≤K it +H it +DD it ,

[0017] Among them, J it 、J i(t+1) are the number of full batteries maintained at battery swap station i in period t and period t+1; DD it ,DD i(t+1) is the number of batteries being discharged at battery swap station i during period t and period t+1; CD it 、CD i(t+1) is the number of batteries being charged at battery swap station i during period t and period t+1; H it 、H i(t+1) K is the battery demand of battery swap station i in period t and period t+1; it , K i(t+1) is the number of empty batteries at battery swap station i in period t and period t+1.

[0018] Optionally, in the method according to the present application, the constraint condition also includes: the number of rechargeable batteries at each battery swap station in each time period is not greater than the maximum number of charging slots of the corresponding battery swap station.

[0019] Optionally, in the method according to the present application, the constraint condition also includes: the difference between the output power of the renewable energy source and the total power of the battery swap station cluster is not less than 0.

[0020] Optionally, in the method according to the present application, a genetic algorithm is used to solve the scheduling model to obtain the number of charging batteries and the number of discharging batteries of each battery swap station in each time period, including: taking the set of the number of charging batteries and the number of discharging batteries of each battery swap station in each time period as the target individual; using a multi-objective genetic algorithm to iteratively generate multiple populations containing target individuals, and calculating the first objective function value and the second objective function value of each target individual under each population until the iteration ends, and selecting the target individual with the smallest first objective function value and second objective function value; and taking the number of charging batteries and the number of discharging batteries of each battery swap station in each time period corresponding to the selected target individual as the scheduling result of the scheduling model.

[0021] Optionally, in the method according to the present application, the battery demand in each time period within the service area of ​​each battery swap station is calculated as follows:

[0022] H i(t+1) =δN i(t+1)

[0023] Among them, H i(t+1) is the battery demand of battery swap station i in period t+1; N i(t+1) is the demand for electric vehicles in the service area of ​​battery swap station i during period t+1; δ is the probability of battery swapping.

[0024] According to another aspect of the present application, a computing device is provided, comprising: one or more processors; and a memory; one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described above.

[0025] According to another aspect of the present application, a computer-readable storage medium storing one or more programs is provided. The one or more programs include instructions. When executed by a computing device, the instructions cause the computing device to perform any of the methods described above.

[0026] In summary, according to this application's solution, we first calculate the battery demand within the service area of ​​each battery swap station. Next, we construct a scheduling model that minimizes the total battery swap cost and maximizes the similarity between the battery swap station power and renewable energy output while meeting the battery swap demand for all electric vehicles. Constraints are also established to achieve these two goals. The scheduling model is then solved using a genetic algorithm to produce a scheduling solution.

[0027] According to the solution of this application, the scheduling of the battery swap station cluster depends on only a few parameters, which is very versatile. Moreover, it can more effectively absorb renewable energy while achieving economic efficiency, and achieve better long-term economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] To achieve the above and related purposes, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings. These aspects indicate various ways in which the principles disclosed herein may be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The above and other objects, features, and advantages of the present application will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout this application, the same reference numerals generally refer to the same parts or elements.

[0029] Figure 1 1 shows a schematic diagram of the structure of a computing device 100 according to some embodiments of the present application;

[0030] Figure 2 A flow chart of a method 200 for scheduling a battery swap station cluster according to some embodiments of the present application is shown;

[0031] Figure 3The figure shows the distribution of the number of charging and discharging batteries in each time period at each battery swap station within the second ring road according to one embodiment of the present application;

[0032] Figure 4 The figure shows the charge and discharge quantity of a battery swap station cluster in 24 time periods in a day according to an embodiment of the present application;

[0033] Figure 5 A schematic diagram showing the battery charging and discharging power of each battery swap station in each time period of each battery swap station cluster according to one embodiment of the present application is shown;

[0034] Figure 6 A comparison diagram of the charging and discharging power curve of a battery swap station and a wind power output curve according to an embodiment of the present application is shown;

[0035] Figure 7 A comparison chart of the total costs of orderly charging and disorderly charging at each battery swap station according to one embodiment of the present application is shown;

[0036] Figure 8 A comparison diagram of wind power load and power of battery swap station cluster under different states (disordered charging state and ordered charging state) according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0037] The following describes exemplary embodiments of the present application in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0038] As a primary source of energy for electric vehicles, battery swapping stations (BSSs) are attracting significant attention. On the one hand, in BSS mode, electric vehicles can swap empty batteries for full ones in minutes, leaving the empty batteries to charge in the BSS, thereby improving the efficiency of power interaction between electric vehicles and the power grid. On the other hand, BSSs can rationally schedule battery charging and discharging based on electricity prices and distribution network conditions, improving the economic efficiency and reliability of distribution network operations.

[0039] Current research focuses primarily on the recharging model for electric vehicle charging. For example, some proposals consider that taxis typically operate in multiple shifts, alternating day and night, and cannot charge overnight at home. Therefore, they have designed a charging strategy for electric taxis to charge during the day at public charging stations during business hours. Other proposals use large amounts of taxi operating data to study the potential impact of charging stations on the power grid, but these ignore the waiting time for vehicle charging, which is still too long for electric taxis compared to battery swapping. Some literature has also studied only a single battery swap station and proposed a comprehensive management strategy for the orderly scheduling of electric vehicles at battery swap stations. This strategy takes into account the increased consumption of wind and photovoltaic power generation and grid peak load regulation. However, there is little research on the benefits of BSS operation on distribution network reliability.

[0040] To address these issues, the present application proposes a scheduling method 200 for a battery swap station cluster based on traffic node data of electric vehicles (e.g., electric taxis) to determine the battery charging and discharging plans of each battery swap station, including: the number of charging batteries, the number of discharging batteries, the number of full batteries, the number of empty batteries, the number of spare batteries, etc. at each battery swap station in each time period.

[0041] The method 200 may be performed by one or more computing devices.

[0042] Figure 1 1 is a block diagram of an example computing device 100. In a basic configuration 102, computing device 100 typically includes a system memory 106 and one or more processors 104. A memory bus 108 may be used for communication between processor 104 and system memory 106.

[0043] Depending on the desired configuration, the processor 104 can be any type of processor, including, but not limited to, a microprocessor (μP), a microcontroller (μC), a digital signal processing unit (DSP), or any combination thereof. The processor 104 can include one or more levels of cache, such as a level 1 cache 110 and a level 2 cache 112, a processor core 114, and registers 116. An example processor core 114 can include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP core), or any combination thereof. An example memory controller 118 can be used with the processor 104, or in some implementations, the memory controller 118 can be an internal part of the processor 104.

[0044] Depending on the desired configuration, system memory 106 can be any type of memory, including, but not limited to, volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 106 can include an operating system 120, one or more applications 122, and program data 124. In some embodiments, application 122 can be arranged to operate on the operating system using program data 124. In some embodiments, computing device 100 is configured to execute method 200 of the present application, and program data 124 includes instructions for executing method 200.

[0045] Computing device 100 also includes a storage device 132 including removable storage 136 and non-removable storage 138 , both of which are connected to storage interface bus 134 .

[0046] The computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 102 via the bus / interface controller 130. Example output devices 142 include a graphics processing unit 148 and an audio processing unit 150. These can be configured to facilitate communication with various external devices such as a display or speakers via one or more A / V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which can be configured to facilitate communication with external devices such as input devices (e.g., a keyboard, mouse, pen, voice input device, image input device) or other peripherals (e.g., a printer, scanner, etc.) via one or more I / O ports 158. Example communication devices 146 may include a network controller 160, which can be arranged to facilitate communication with one or more other computing devices 162 via a network communication link via one or more communication ports 164.

[0047] A network communication link can be an example of a communication medium. Communication media can generally be embodied as computer-readable instructions, data structures, program modules in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium. A "modulated data signal" can be a signal in which one or more of its data sets or changes thereto can be performed in a manner that encodes information in the signal. As non-limiting examples, communication media can include wired media such as a wired network or a dedicated line network, and various wireless media such as sound, radio frequency (RF), microwave, infrared (IR) or other wireless media. The term computer-readable medium as used herein can include both storage media and communication media. In some embodiments, one or more programs are stored in a computer-readable medium, and these one or more programs include instructions for executing method 200 according to the present application.

[0048] The computing device 100 may be implemented as part of a small portable (or mobile) electronic device, such as a cellular phone, a digital camera, a personal digital assistant (PDA), a personal media player device, a wireless network browsing device, a personal head-mounted device, an application-specific device, or a hybrid device that may include any of the above functions. The computing device 100 may also be implemented as a personal computer including desktop and notebook computer configurations.

[0049] Figure 2 A flow chart of a scheduling method 200 for a battery swap station cluster according to some embodiments of the present application is shown.

[0050] According to this application, the temporal and spatial distribution of electric vehicles is taken into consideration under the dual carbon goals, and a scheduling model for battery swap station clusters is constructed. By solving the scheduling model, a scheduling plan for charging and discharging batteries in the battery swap station clusters is obtained.

[0051] To ensure the rationality of the scheduling model, we first make some assumptions. The assumptions include the following.

[0052] 1. Since the research period of this application is 1 day, the day is divided into 24 time periods, corresponding to 00:00-01:00, 01:00-02:00, ..., 23:00-00:00, and the power price of the power grid in the time period t (t = 1, 2, ..., 24) is λ t .

[0053] 2. Once the battery in the battery swap station is in a charging state, subsequent operations will only be considered when it is fully charged. That is, the situation where the battery stops charging after being partially charged or sells electricity to the grid is not considered; correspondingly, once the battery is in a discharging state, subsequent operations will only be considered when it is fully discharged.

[0054] 3. The charging and discharging time required for each battery is 1 hour.

[0055] 4. The research period for this application is one day. The residual value of the battery and the operation and maintenance costs of the battery swap station are not considered for the time being. The goal is to minimize the electricity cost under the battery charging and discharging conditions of the battery swap station.

[0056] 5. The number of fully charged batteries in stock at the beginning of any time period must meet the demand for battery swaps by electric vehicles during that time period, regardless of the impact of vehicle arrival time distribution on the charging and discharging strategy of the battery swap station. Furthermore, empty batteries replaced by electric vehicles must be recharged in the next time period.

[0057] The following is a description of the specific execution process of method 200.

[0058] Method 200 begins at 210 , where the battery demand within the service area of ​​each battery swap station is determined.

[0059] According to the implementation of this application, for most electric vehicles, there are currently no dedicated battery swap stations, so battery swap stations are deployed at the locations of some current gas stations. Taking electric taxis as an example, the specific location of the battery swap station is selected based on the demand and distribution of taxis.

[0060] The following uses an electric taxi as an example to illustrate the process of determining battery requirements.

[0061] In order to reasonably configure the capacity of battery swap stations, the taxi is generally in a non-passenger-carrying state during battery swapping. When the taxi reaches the charged state that requires battery swapping and is in an empty state, the driver will look for a charging station to swap batteries. After the battery swap is completed, it will enter the state of looking for passengers again. Based on this, according to the implementation method of the present application, when an electric taxi selects a battery swap station as its destination, it will not only consider the distance between its location and the battery swap station, but also consider the distance required to travel from the battery swap station to the location where it looks for passengers. Therefore, in some embodiments, the demand for electric vehicles in each time period within the service area of ​​each battery swap station is first determined based on the vehicle data of each traffic node (such as traffic volume, time and space distribution, etc.). Taking into account that not all electric taxis need to be charged, research shows that the probability of battery swapping for electric taxis is proportional to the passenger demand at the traffic node. Let the battery swap probability be δ. Then, based on the demand for electric vehicles in the service area of ​​each battery swap station and the probability of battery swapping for electric vehicles, the battery demand corresponding to each time period is determined.

[0062] In some embodiments, the battery demand for each time period within the service area of ​​each battery swap station is calculated using the following formula:

[0063] H i(t+1) =δN i(t+1) (1)

[0064] Among them, H i(t+1) is the battery demand of battery swap station i in period t+1; N i(t+1) is the demand for electric vehicles in the service area of ​​battery swap station i during period t+1; δ is the probability of battery swap, which can be optionally set to 0.15.

[0065] In 220 , a first objective function is determined by calculating the total cost of the battery swap station cluster.

[0066] The total cost of the battery swap station is obtained by subtracting the discharge benefit from the charging cost. According to some embodiments of the present application, the first objective function is determined in the following manner.

[0067] The first step is to calculate the product of the number of charging batteries, charging power and electricity selling price of each battery swap station in each time period as the charging cost of each battery swap station.

[0068] The second step is to calculate the product of the number of discharged batteries, discharge power and discharge electricity price of each battery swap station in each time period as the discharge income of each battery swap station.

[0069] The third step is to calculate the difference between the charging cost and the discharging benefit as the total cost.

[0070] The fourth step is to generate the first objective function with the goal of minimizing the total cost.

[0071] In some embodiments, the first objective function is expressed as:

[0072]

[0073] Where F is the total cost; CD it is the number of batteries being charged at battery swap station i during period t; t P is the electricity price; c is the charging power; DD it is the number of batteries being discharged at battery swap station i during period t; μ t is the discharge electricity price; P d is the discharge power; R is the total number of battery swap stations in the battery swap station cluster.

[0074] In 230 , the second objective function is determined by calculating the similarity between the renewable energy output curve and the battery swap station charging power curve.

[0075] Currently, most studies introduce electric vehicles to smooth out the volatility of renewable energy, and then schedule the smoothed renewable energy to be connected to the grid to reduce the impact on the power grid. Although this can reduce the impact on the grid, it cannot reduce the peak-to-valley difference in load after grid connection. According to the implementation method of the present application, the coordinated scheduling of renewable energy and battery swap stations for coordinated grid connection is optimized to maximize the waveform similarity after grid connection. This can both mitigate the impact of renewable energy grid connection on the grid and reduce load volatility.

[0076] According to some embodiments of the present application, the second objective function is determined in the following manner.

[0077] The first step is to calculate the cosine similarity between the renewable energy output curve and the battery swap station charging power curve.

[0078] In the second step, the difference between the cosine similarity and the fixed value is used to express the degree of curve waveform fitting.

[0079] The third step is to generate a second objective function with the goal of minimizing the curve waveform fitting degree (that is, maximizing the waveform similarity).

[0080] In some embodiments, the second objective function is expressed as:

[0081]

[0082] in, The power curve representing the output of renewable energy at time t; represents the charging power curve of the battery swap station at time t; S zt Indicates the degree of curve waveform fitting. In formula (3), the fixed value is 1, of course it is not limited to this.

[0083] In some embodiments, the renewable energy output is typically a wind power load.

[0084] According to some further embodiments, when comparing the charging power curve of the battery swap station with the wind power generation load curve for similarity, due to the different units between the two, the compared curves are prone to large deviations. Therefore, before solving the waveform similarity, it also includes: normalizing the power curve of the renewable energy output and the charging power curve of the battery swap station respectively.

[0085] A method of per-unit processing is shown below.

[0086] In the time period T, the total power of battery charging and discharging at the battery swap station is

[0087]

[0088] l b (t) = P b(t) (5)

[0089]

[0090] In the above formula, α b P represents the total power of battery charging and discharging at the battery swap station during time period T; b (t) is the charging power at time t (discharging is a negative value); The power curve is normalized based on α; Yes b (t) is normalized to function f(l b per unit value of (t).

[0091] The total power of the battery swap station cluster is the sum of the charging powers of each battery swap station, expressed as:

[0092]

[0093] Where, P b (t) represents the total power of the battery swap station cluster during period t; P b,i represents the charging power of battery swap station i during period t; n represents the number of battery swap stations.

[0094] The total power generated by wind power during the T period is:

[0095]

[0096] l w (t) = P w (t) (9)

[0097]

[0098] Where, α w represents the total power of wind power generation in the period T; P w (t) is the wind power generation at time t; l w (t) represents the power curve of the predicted output of renewable energy at time t; The power curve is normalized based on α; Yes w (t) is normalized to function f(l w per unit value of (t).

[0099] At 240 , constraints are determined based on the battery demand and the number of charged batteries and discharged batteries.

[0100] According to the assumption 2 above, charging and discharging cannot be performed simultaneously, so the charging and discharging state can be expressed as:

[0101]

[0102] CD it =M·U it (12)

[0103] DD it =M·(1-U it ) (13)

[0104] Where M is a sufficiently large number. If U it =1, it means that the battery swap station i is in the charging state during period t; if U it =0, then the battery swap station i is in the discharging state during period t.

[0105] Since batteries between battery swap stations will not be distributed to meet demand, during time period t, it is necessary to ensure that the number of full batteries at each battery swap station meets the battery swap demand of electric vehicles in its service area, and the number of batteries should also be balanced with other quantities. According to some embodiments of the present application, the constraints include at least the following aspects.

[0106] (1) At each time period, the number of full batteries at each battery swap station is not less than the battery demand in the corresponding service area, which is expressed by the following formula:

[0107] J it ≥H it (14)

[0108] (2) The total number of batteries at each battery swap station should be balanced with the number of charged batteries, discharged batteries, full batteries, empty batteries, and spare batteries. The details are as follows:

[0109] The number of full batteries at battery swap station i in period t+1 is expressed as:

[0110] J i(t+1) =J it +CD it -DD it -H ii(t+1) +X ii(t+1) ≥0 (15)

[0111] The number of empty batteries at battery swap station i in period t+1 is expressed as:

[0112] K i(t+1) =K it +H i(t+1) +CD it -DD it (16)

[0113] The total number of batteries in the battery swap station i is balanced:

[0114]

[0115] In the above formula, J it 、J i(t+1) are the number of full batteries maintained at battery swap station i in period t and period t+1; DD it is the number of batteries being discharged at battery swap station i during period t; CD it is the number of batteries being charged at battery swap station i during period t; H it 、H i(t+1) K is the battery demand of battery swap station i in period t and period t+1; it , K i(t+1) is the number of empty batteries at battery swap station i in period t and period t+1; X it 、X i(t+1) is the number of empty batteries at battery swap station i in period t and period t+1; Z i is the total number of batteries in battery swap station i.

[0116] (3) In each time period, the number of charged batteries and the number of discharged batteries satisfy their respective constraints.

[0117] Charging and discharging are not arbitrary decisions; they must satisfy certain constraints, including:

[0118] In discharge state: J it -DD it ≤H i(t+1) (18)

[0119] In charging state: J it +CD it ≥H i(t+1) (19)

[0120] J i(t+1) +DD i(t+1) ≤J it -H it +CD it (20)

[0121] K i(t+1) +CD i(t+1) ≤K it +H it +DD it (twenty one)

[0122] For the definition of parameters in the formula, please refer to the above description, for example, DD it is the number of batteries being discharged at battery swap station i during period t, DD i(t+1) That is, the number of batteries being discharged at battery swap station i in period t+1, which will not be repeated here.

[0123] According to some further embodiments, the constraint condition further includes: the number of rechargeable batteries at each battery swap station in each time period is not greater than the maximum number of charging slots at the corresponding battery swap station, expressed as:

[0124] CD it ≤M imax (twenty two)

[0125] Where M imax is the maximum number of charging slots at battery swap station i.

[0126] According to some further embodiments, the constraint condition further includes: the difference between the renewable energy output power (i.e., charging power) and the total power of the battery swap station cluster is not less than 0 (the part greater than zero is the abandoned wind power), expressed as:

[0127]

[0128] Where, P w (t) is the wind power generation power at time t; P b (t) represents the total power of the battery swap station cluster during period t.

[0129] In 250, a scheduling model is constructed using the first objective function (see formula (2) above), the second objective function (see formula (3) above), and the constraints (see formulas (14)-(23) above).

[0130] The scheduling model aims to minimize the charging and discharging costs of the battery swap station cluster and maximize the similarity between the power curve of renewable energy output and the charging power curve of the battery swap station, and takes into account the quantitative balance of various battery numbers in the battery swap station.

[0131] In 260 , the scheduling model is solved using a genetic algorithm to obtain the number of charging batteries and the number of discharging batteries at each battery swap station in each time period.

[0132] The concept of Pareto dominance was proposed by Pareto in 1986. It is defined as follows: Given two solutions I1 and I2, if I1 is superior to I2 for all objectives, then I1 is said to dominate I2. If I1 is not dominated by any other solution, then I1 is called a Pareto solution. The set of Pareto solutions is called the Pareto front. According to some embodiments of the present application, the concept of Pareto dominance is used to solve scheduling models.

[0133] The first issue to be addressed in a multi-objective genetic algorithm based on Pareto sorting is the design of a fitness function. This paper seeks a set of Pareto solutions, rather than a single Pareto solution, and therefore employs a non-dominated sorting genetic algorithm with an elitist strategy. Thanks to the proposed fast non-dominated sorting method, the algorithm's computational complexity is significantly reduced.

[0134] According to the implementation methods of the present application, a genetic algorithm (e.g., the NSGA-II genetic algorithm) is used to solve the scheduling model. The basic idea of ​​the NSGA-II genetic algorithm is as follows: first, based on the problem description and chromosome encoding rules, an initial population of size N is randomly generated; second, the algorithm's fitness function is designed based on the objective function, and the fitness value of the initial population is calculated to form the first-generation parent population; then, based on the selection, crossover, and mutation operations of the genetic algorithm, a new offspring population is generated, and the offspring population is merged with the parent population to obtain the second-generation offspring population; finally, the fitness value of the offspring population is calculated, and the individual with the highest fitness value is retained; and so on, until the conditions for the end of the iteration are met.

[0135] Based on the above basic idea, in some embodiments of the present application, the specific steps for solving the scheduling model are as follows.

[0136] The first step is to use the set of the number of charged and discharged batteries at each battery swap station in each time period as the target individuals. An initial population is constructed, which contains multiple target individuals (for example, N target individuals). Each target individual in the initial population also needs to be encoded.

[0137] The second step is to use a multi-objective genetic algorithm to iteratively generate multiple populations containing the target individual. The first and second objective function values ​​for each target individual in each population are calculated until the iterations are complete. The target individual with the smallest first and second objective function values ​​is selected. This specifically includes steps a) through g).

[0138] a) Calculate the fitness value of the individual. According to this scheduling model, the objective function is to minimize the total cost and maximize the curve waveform fit. The smaller the solution value, the closer the result is to the optimal solution.

[0139] b) Selection Operator. The selection operator in this embodiment uses a tournament selection method. This method involves randomly selecting n individuals from the population as a sample, comparing the fitness values ​​of all individuals in the sample, and passing the individuals with the best fitness values ​​to the next generation. This process is repeated until the new population size reaches the original population size. Generally, the number of iterations of the tournament selection operation is equal to the population size.

[0140] c) Crossover operator: A crossover operation similar to the PMX crossover method is used to perform a crossover operation on the population.

[0141] d) Mutation operator: The reverse mutation method is used to perform mutation operations on the population.

[0142] e) Elite selection. A new population is formed through basic operations. Then, an elite strategy is adopted to eliminate solutions that do not meet the constraints in the population. The fitness values ​​of the individuals are compared to select the optimal individuals of size N, forming the second-generation population.

[0143] f) Update crossover and mutation probabilities. Calculate the maximum, minimum, and average fitness values ​​in the population and, through comparison, further calculate the crossover and mutation probabilities of the next generation.

[0144] g) Termination condition: When the number of iterations reaches the maximum number of iterations Genmax, the iteration terminates.

[0145] In the third step, the number of charging batteries and discharging batteries of each battery swap station corresponding to the selected target individual in each time period is used as the scheduling result of the scheduling model.

[0146] According to method 200 of the present application, the battery demand for each battery swap station per hour in a day is first determined. On this basis, a cost estimation method is proposed to calculate the total charging and discharging cost of battery swap station clusters (BSSs), and a fitting between battery swap stations and wind power is established through cosine similarity; a scheduling model is generated based on the objective function and constraints. Finally, a genetic algorithm is used to solve the scheduling model. According to the number of charging and discharging batteries at each battery swap station in each time period determined by method 200, renewable energy can be absorbed more effectively while achieving economy, thereby achieving better long-term economic and social benefits.

[0147] To verify the rationality and effectiveness of method 200, the Second Ring Road of a certain city was taken as the research area, and the taxi demand at typical daily traffic nodes within the Second Ring Road was used as the research sample. Assuming that all taxis in the area will be electrified in the future, the battery charging and discharging quantity of each battery swap station was determined, and the charging and discharging power within one day of battery swapping was fitted with the wind power. The Pareto optimal solution was solved using the NSGA-II algorithm using Matlab software.

[0148] The locations of electric taxi battery swap stations within the Second Ring Road and the service range of each station were determined. The latitude range within the Second Ring Road of City C is: 30.621604-30.695777, and the longitude range is: 104.022089-104.115161. The number of battery swap stations selected is 20. The locations of each battery swap station are shown in Table 1. The actual data of the traffic nodes within the Second Ring Road of City C on August 10, 2016 were used as the research sample. Table 2 shows the number of taxi vehicles required at some traffic nodes.

[0149] Table 1 Location of battery swap stations

[0150]

[0151]

[0152] Table 2 Vehicle demand at some traffic nodes

[0153]

[0154] In this example, the number of electric taxi battery swap stations is 20, the scheduling cycle is 1 day, the study period is set to 1 hour, the number of NSGA-II algorithm iterations is 500, and the population size is 100. The population size is based on the "Notice on Further Improving the Time-of-Use Electricity Pricing Mechanism in Our Province" issued by the province, which clarifies the time-of-use electricity pricing method starting from January 1, 2022, as shown in Table 3.

[0155] Table 3 Time-of-use electricity price parameter values

[0156]

[0157] Based on the service areas of each battery swap station within the Second Ring Road, the vehicle demand points within the coverage area of ​​the battery swap station in each time period are determined.

[0158] According to the implementation method of this application, the distribution of traffic nodes within the service area of ​​the battery swap station cluster (i.e., the service area of ​​each battery swap station within the Second Ring Road) is obtained to preliminarily determine the battery demand within the service area of ​​each battery swap station. The goal of this embodiment is to use MATLAB software to randomly generate the number of batteries charged and discharged per hour, while only considering the charging cost and discharge revenue of the battery swap station cluster. After 500 iterations, the Pareto set with the minimum total cost and the optimal fit is obtained.

[0159] Discharging when electricity prices are high and charging when electricity prices are low can not only reduce the cost of battery swap stations, but also alleviate the impact of load fluctuations on the power grid. Figure 3 The figure shows the distribution of the number of batteries charged and discharged at each battery swap station within the Second Ring Road in each time period, where 1-20 correspond to the serial numbers of each battery swap station. The horizontal axis represents the charging and discharging period of the battery swap station (1-24h), and the vertical axis represents the number of batteries charged and discharged at each battery swap station.

[0160] During off-peak electricity prices, the vast majority of battery swap stations are charging, thereby promoting the consumption of wind power and reducing wind curtailment. During peak electricity price periods, when there is mostly high demand for vehicles, about 50% of the battery swap stations in the battery swap station cluster are discharging, thereby alleviating the power supply pressure on the power grid. Figure 4 The chart shows the charge and discharge data for the battery swap station cluster over 24 time periods throughout the day. During peak hours, the cluster doesn't fully discharge the batteries, but it does charge a small number of batteries during peak usage periods, reducing charging costs and ensuring battery discharge while meeting passenger demand. During off-peak hours, overall battery charging is planned to reduce charging costs.

[0161] Figure 5A schematic diagram showing the battery charge and discharge power of each battery swap station in a cluster according to one embodiment of the present application. The three axes of the diagram indicate the battery swap station number (1-20), the time period within a day (1-24 hours), and the charge and discharge power (-1000kW-1500kW).

[0162] While meeting the minimum cost, the charging and discharging power and wind power output curve waveform analysis is performed. Since the wind power output value is much larger than the output value of each battery swap station, it should be normalized during the calculation process. However, since this embodiment only compares the trends of the two curves in each time period, in one embodiment, a new concept of stacked area chart is introduced. The stacked area chart is used to display the trends of various parts of the whole over time, which is consistent with the waveform similarity concept described above. Figure 6 The charging and discharging power curve of the battery swap station according to one embodiment of the present application is shown (ie, Figure 6 The “ordered state” shown in Figure 6 The comparison chart of “wind power” shown in Figure 6 As shown in Figure 2, the waveform of the charging and discharging power curve is consistent with the waveform trend of the wind power output curve, that is, the waveform similarity tends to 0.

[0163] Furthermore, in order to verify the rationality and effectiveness of this scheduling model, electric taxis are charged and replaced in each period. When the demand for battery replacement is 0 for m consecutive periods (m>=2), all batteries in the first m-1 periods are discharged (except for the backup battery), and the number of batteries required for battery replacement in the m+1 period are charged in time in the mth period. Figure 7 A comparison chart of the total costs of ordered and unordered charging at each battery swap station according to one embodiment of the present application is shown. By adding up the total costs of ordered and unordered charging for each period of the battery swap station cluster, it is clear that unordered charging is profitable during periods 0-13, but overall, the total cost of ordered charging is more stable, and the total cost of unordered charging within a day is much higher than that of ordered charging.

[0164] Furthermore, a comparative analysis of battery swap station clusters with both disordered and ordered charging was conducted, with the power load curves for each station at each time period being analyzed. Ordered charging facilitates peak load shaving and promotes safe grid operation. Disordered charging is characterized by a more random charging and discharging pattern. Furthermore, most battery swap stations perform large-scale charging during peak demand periods, increasing grid power supply difficulties. Discharging occurs during off-peak demand periods, leading to greater wind curtailment and counteracting peak load shifting.

[0165] In addition, the disordered charging state and the ordered charging state are compared with the wind power load curve after grid connection and the original wind power power curve, and the results are obtained. Figure 8 .from Figure 8As can be seen from the figure, under orderly charging (e.g., the curve corresponding to the "ordered state" in Figure 8), the overall trend of charging load shifts from peak grid load periods to periods with abundant wind and low electricity consumption. This effectively absorbs fluctuations in wind power, achieving peak-to-valley shifting and valley-filling on the grid load curve, improving the economic efficiency and safety of grid operation. The peak-to-valley difference is significantly reduced, load fluctuations are reduced, and the peak-to-valley pressure on the grid is greatly reduced. In contrast, under disorderly charging (e.g., the curve corresponding to the "disordered state" in Figure 8), the load increase caused by the random charging behavior of battery swap stations is mainly concentrated in the evening peak period of residential electricity consumption. The combination of these two factors further increases the peak-to-valley difference in the distribution network. During the medium wind power output period from 4:00 PM to 7:00 PM, wind power meets the charging needs of some users, but users' charging behavior does not respond to electricity prices. As a result, during the nighttime high wind power output period (low charging prices), the charging load does not fully absorb wind energy in line with the changes in wind power, resulting in "wind curtailment" due to fully charged batteries.

[0166] After comparative analysis, the wind power generation after disorderly charging during peak hours is far less than the original wind power. During low hours, the energy storage battery discharges a lot. When the wind power load is low, the battery is charged at the battery swap station and discharged at the peak, which makes the peak-to-valley difference even greater. At the same time, it causes the energy storage system to fail to participate in regulating the wind power curve, resulting in higher wind curtailment, which is contrary to the direction of my country's energy structure transformation. On the contrary, after orderly charging is involved in regulation, the energy storage system using the power difference control strategy can effectively complete the task of peak-to-valley shifting and valley-filling of the power grid through reasonable charging and discharging planning. At the same time, it takes into account the capacity constraints of the energy storage system and can plan the charging and discharging strategy according to the changes in wind power load while maintaining the balance of charging and discharging power. In the current context of expensive charging costs and imperfect charging facilities, the solution of this application can directly promote the absorption of wind power, reduce the impact of the charging and discharging load of the battery swap station and the uncertainty of wind power access on the original distribution network, and thus effectively reduce the battery swap cost of electric vehicle owners (especially electric taxis), adapt to the widespread promotion of electric vehicles in the future, and achieve a win-win situation for the power grid and users.

[0167] In summary, to address the charging and discharging issues at battery swap stations, the method 200 of this application constructs a scheduling model for battery swap station clusters to reduce the total cost of battery swap station clusters and leverage peak shaving and valley filling and wind power absorption as a flexible resource at the user end. This is crucial to the safe operation of the power grid and enables the rational utilization of batteries at large-scale battery swap stations and the absorption of new energy. At the same time, this application was also verified through case analysis, and the following conclusions were drawn:

[0168] (1) The viewpoint of fitting the cluster load of taxi battery swap stations with the renewable energy curve is proposed;

[0169] (2) A dual-objective model is constructed. The first is to minimize the charging and discharging cost of the battery swap station cluster to ensure economic benefits. At the same time, the cosine similarity is used to obtain the similarity between the charging and discharging power of the battery swap station and the waveform curve of the renewable energy power generation power to ensure social benefits and thus ensure the safe operation of the power grid.

[0170] (3) The effectiveness of the model has been demonstrated through actual case analysis, and it has good application and promotion value.

[0171] The various techniques described herein may be implemented in conjunction with hardware or software, or a combination thereof. Thus, the methods and apparatus of the present application, or certain aspects or portions of the methods and apparatus of the present application, may be implemented in the form of program code (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, a USB flash drive, a floppy disk, a CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into a machine such as a computer and executed by the machine, the machine becomes an apparatus for practicing the present application.

[0172] When program code is executed on a programmable computer, the computing device generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The memory is configured to store the program code; and the processor is configured to execute the method of the present application according to the instructions in the program code stored in the memory.

[0173] This application also discloses:

[0174] A8. A method as described in A6 or 7, wherein the constraint condition also includes: the number of rechargeable batteries at each battery swap station in each time period is not greater than the maximum number of charging slots at the corresponding battery swap station.

[0175] A9. A method as described in any one of A6-8, wherein the constraint condition also includes: the difference between the output power of renewable energy and the total power of the battery swap station cluster is not less than 0.

[0176] A10. A method as described in any one of A1-9, wherein the scheduling model is solved by a genetic algorithm to obtain the number of charging batteries and the number of discharging batteries of each battery swap station in each time period, including: taking the set of the number of charging batteries and the number of discharging batteries of each battery swap station in each time period as the target individual; using a multi-objective genetic algorithm to iteratively generate multiple populations containing target individuals, and calculating the first objective function value and the second objective function value of each target individual under each population until the iteration is completed, and selecting the target individual with the smallest first objective function value and the smallest second objective function value; and taking the number of charging batteries and the number of discharging batteries of each battery swap station in each time period corresponding to the selected target individual as the scheduling result of the scheduling model.

[0177] By way of example and not limitation, readable media include readable storage media and communication media. Readable storage media store information such as computer-readable instructions, data structures, program modules, or other data. Communication media typically embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and include any information delivery medium. Combinations of any of the above are also included within the scope of readable media.

[0178] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used together with the examples of the present application. Based on the above description, it is obvious that the structure required for constructing such systems is suitable. In addition, the present application is not directed to any specific programming language. It should be understood that various programming languages ​​can be utilized to implement the content of the present application described herein, and the above description of specific languages ​​is intended to disclose the preferred embodiment of the present application.

[0179] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0180] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various disclosed aspects, in the above description of the exemplary embodiments of the present application, various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of application should not be interpreted as reflecting the intention that the claimed application requires more features than those explicitly recited in each claim. Rather, as reflected in the claims below, the disclosed aspects consist of fewer than all the features of the individual embodiments disclosed above. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the present application.

[0181] Those skilled in the art will appreciate that the modules, units, or components of the devices in the examples disclosed herein may be arranged in the device described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or further divided into multiple submodules.

[0182] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0183] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims below, any of the claimed embodiments may be used in any combination.

[0184] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions described. Thus, a processor having the necessary instructions for implementing the method or method element forms a device for implementing the method or method element. In addition, the elements described herein of the device embodiments are examples of devices for implementing the functions performed by the elements for the purpose of implementing the disclosed subject matter.

[0185] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved and are not intended to imply that the objects so described must have a given order in time, space, ranking, or in any other manner.

[0186] Although the present application has been described in terms of a limited number of embodiments, it will be apparent to those skilled in the art, having benefit of the foregoing description, that other embodiments are contemplated within the scope of the present application as described herein. Furthermore, it should be noted that the language used in this specification has been selected primarily for readability and didactic purposes, rather than for the purpose of explaining or limiting the subject matter of the present application. Consequently, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure made herein is illustrative and non-restrictive of the scope of the present application, and the scope of the present application is defined by the appended claims.

Claims

1. A scheduling method for a battery swap station cluster, comprising: Determine the battery demand within the service area of ​​each battery swap station; The first objective function is determined by calculating the total cost of the battery swap station cluster, including: calculating the product of the number of charged batteries, charging power, and electricity price at each battery swap station in each time period as the charging cost of each battery swap station; calculating the product of the number of discharged batteries, discharge power, and discharge electricity price at each battery swap station in each time period as the discharge income of each battery swap station; calculating the difference between the charging cost and the discharge income as the total cost; and generating the first objective function with the goal of minimizing the total cost; Determining a second objective function by calculating the similarity between the power curve of the renewable energy output and the charging power curve of the battery swap station includes: calculating the cosine similarity between the power curve of the renewable energy output and the charging power curve of the battery swap station; subtracting the difference between the cosine similarity and the fixed value to represent the degree of curve waveform fitting; and generating the second objective function with the goal of minimizing the degree of curve waveform fitting; Based on the battery demand and the number of charged batteries and discharged batteries, constraints are determined, including: in each time period, the number of full batteries at each battery swap station is not less than the battery demand in the corresponding service area; the total number of batteries at each battery swap station is balanced with the number of charged batteries, discharged batteries, full batteries, empty batteries, and spare batteries; in each time period, the number of charged batteries and the number of discharged batteries meet their respective constraints; Constructing a scheduling model using the first objective function, the second objective function, and constraints; A genetic algorithm is used to solve the scheduling model to obtain the number of charged batteries and discharged batteries at each battery swap station in each time period, including: taking the set of the number of charged batteries and the number of discharged batteries at each battery swap station in each time period as a target individual; using a multi-objective genetic algorithm to iteratively generate multiple populations containing the target individuals, and calculating the first objective function value and the second objective function value of each target individual under each population until the iteration ends, and selecting the target individual with the smallest first objective function value and the smallest second objective function value; and taking the number of charged batteries and the number of discharged batteries at each battery swap station in each time period corresponding to the selected target individual as the scheduling result of the scheduling model; The constraints on the number of charged batteries and discharged batteries include: J it -DD it ≤H i(t+1) , I it +CD it ≥H i(t+1) , J i(t+1) +DD i(t+1) ≤J it -H it +CD it , K i(t+1) +CD i(t+1) ≤K it +H it +DD it , Among them, J it 、J i(t+1) are the number of full batteries maintained at battery swap station i in period t and period t+1; DD it ,DD i(t+1) is the number of batteries being discharged at battery swap station i during period t and period t+1; CD it 、CD i(t+1) is the number of batteries being charged at battery swap station i during period t and period t+1; H it 、H i(t+1) K is the battery demand of battery swap station i in period t and period t+1; it , K i(t+1) is the number of empty batteries at battery swap station i in period t and period t+1.

2. The method according to claim 1, wherein Determining the battery demand within the service area of ​​each battery swap station includes: Determine the demand for electric vehicles in each period within the service area of ​​each battery swap station based on vehicle data at each traffic node; The battery demand for each time period is determined based on the electric vehicle demand and the electric vehicle battery replacement probability within the service area of ​​each battery swap station.

3. The method according to claim 1 or 2, wherein Before calculating the cosine similarity between the power curve of the renewable energy output and the charging power curve of the battery swap station, the method further includes: The power curve of the renewable energy output and the charging power curve of the battery swap station are respectively normalized.

4. The method according to claim 1 or 2, wherein the constraint condition further comprises: The number of charging batteries at each battery swap station in each time period shall not exceed the maximum number of charging slots of the corresponding battery swap station.

5. The method according to claim 1 or 2, wherein the constraint condition further comprises: The difference between the output power of renewable energy and the total power of the battery swap station cluster shall not be less than 0.

6. The method of claim 2, wherein: The battery demand at each time period within the service area of ​​each battery swap station is calculated as follows: H i(t+1) =δN i(t+1) Among them, H i(t+1) is the battery demand of battery swap station i in period t+1; N i(t+1) is the demand for electric vehicles in the service area of ​​battery swap station i during period t+1; δ is the probability of battery swapping.

7. A computing device comprising: one or more processors; and Memory; One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for executing the method according to any one of claims 1-6.

8. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which, when executed by a computing device, cause the computing device to perform the method according to any one of claims 1 to 6.

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