A battery dispatching method and computing device for urban bus exchange stations

Abstract

The present invention discloses a battery scheduling method and computing device for a city bus exchange station cluster, comprising: establishing an electric bus exchange demand model according to the electric bus exchange characteristics; determining H it , where H it represents the number of battery swapping demands at battery swap station i in time period t; a day-ahead scheduling model for battery charging and discharging of battery swap station clusters is established, whose decision variables include the number of batteries C being charged at battery swap station i in time period t it and the number of batteries being discharged, D it ; According to the constraints, the day-ahead scheduling model of battery charging and discharging of the battery swap station cluster is solved, and it is found that when the scheduling cost of the battery swap station cluster is the lowest and the fitting degree between the net load of the battery swap station cluster and the green power output is the best, C it and D it The optimal solution is obtained and used as the battery scheduling solution for the battery swap station cluster.

Description

Technical Field

[0001] The present invention relates to the field of power dispatching, and in particular to a battery dispatching method and computing equipment for a city bus exchange station. Background Art

[0002] Electric vehicles have achieved significant technological improvements in charging speed, battery cost, and charging efficiency. Among various electric vehicle types, the electrification of buses is considered one of the most prominent solutions. Electric buses are rapidly being promoted and adopted due to their large-scale operation and ease of centralized management. However, the long charging time of electric buses places high demands on the battery scheduling of battery swap stations. These stations must be able to provide efficient energy replenishment to electric buses in a short period of time, effectively alleviating the difficulty of charging.

[0003] If the batteries of electric buses are connected to the power grid for charging at will, the disorderly charging of the batteries of electric buses may increase the load on the power system, widen the peak-to-valley difference, and have an adverse impact on the operation of the power system.

[0004] Therefore, a battery scheduling method and computing equipment for urban bus exchange stations are needed. Summary of the Invention

[0005] To this end, the present invention provides a method for dispatching batteries at urban bus exchange stations, in an effort to solve or at least alleviate the above problems.

[0006] According to a first aspect of the present invention, a battery scheduling method for a cluster of urban bus exchange stations is provided, wherein the exchange station cluster is connected to a power grid, which is connected to a renewable energy site, and each exchange station includes a photovoltaic panel for charging batteries. The exchange station purchases electricity from the power grid to meet charging needs, and also discharges the batteries under its jurisdiction to sell electricity to the power grid, including: establishing an electric bus exchange demand model according to the electric bus exchange characteristics; determining H according to the electric bus exchange demand model; it , where H it represents the number of battery swapping demands at battery swap station i in time period t, where i ranges from 1 to I, I is the total number of battery swap stations included in the battery swap station cluster, and t ranges from 0 to T. After a day is divided into multiple time periods according to predetermined time intervals, the total number of time periods included in a day is T+1. A day-ahead scheduling model for battery charging and discharging of battery swap station clusters is established, whose decision variables include the number of batteries C being charged at battery swap station i in time period t. it and the number of batteries being discharged, D it , whose objective function includes min f1=C g -C d and Where f1 represents the cluster scheduling cost of battery swap stations, C grepresents the electricity purchase cost of the battery swap station cluster, C d represents the electricity sales revenue of the battery swap station cluster, f2 represents the fitting degree between the net load of the battery swap station cluster and the green power output, represents the normalized net load of the battery swap station cluster at time period t, represents the green power output power of the renewable energy station normalized in time period t; and according to the constraint conditions, the battery charging and discharging scheduling model of the battery swap station cluster is solved to obtain the lowest scheduling cost of the battery swap station cluster and the optimal fitting degree between the net load of the battery swap station cluster and the green power output, C it and D it The optimal solution is obtained and used as the battery scheduling solution for the battery swap station cluster, wherein the constraints include F it ≥H it , F it represents the number of fully charged batteries at battery swap station i in time period t.

[0007] According to a second aspect of the present invention, a computing device is provided, comprising: one or more processors; a memory; and 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 the urban bus exchange station battery scheduling method according to the present invention.

[0008] In the battery scheduling method for urban bus swap stations of the present invention, an electric bus battery swap demand model is established based on the battery swap characteristics of electric buses, which can more accurately determine the number of battery swap demands at each swap station in each time period; so as to solve the day-ahead scheduling model for battery charge and discharge of the swap station cluster according to the number of battery swap demands. When the swap station cluster scheduling cost is the lowest and the net load of the swap station cluster is optimally matched with the green power output, the number of batteries C being charged at swap station i in time period t is obtained. it and the number of batteries being discharged, D it , as a battery scheduling solution, so that when the battery scheduling solution is applied, the power scheduling cost of the battery swap station cluster can be reduced, the friendliness and adaptability of the battery swap station cluster to renewable energy sites can be improved, and more renewable energy can be absorbed. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] To achieve the above and related purposes, the present invention describes certain illustrative aspects in conjunction with the following description and accompanying drawings, which indicate various ways in which the principles disclosed in the present invention can 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 disclosed in the present invention will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout this disclosure, the same reference numerals generally refer to the same parts or elements.

[0010] Figure 1A schematic diagram showing a battery swap station connected to a power grid according to an exemplary embodiment of the present invention is shown;

[0011] Figure 2 shows a schematic diagram of battery cycle in a battery replacement mode according to an exemplary embodiment of the present invention;

[0012] Figure 3 A schematic diagram of a city bus exchange station battery scheduling method 300 according to an exemplary embodiment of the present invention is shown;

[0013] Figure 4 A schematic diagram of calculating battery swapping demand according to an exemplary embodiment of the present invention is shown;

[0014] Figure 5 FIG. 1 shows a schematic diagram of calculating a battery management state according to an exemplary embodiment of the present invention;

[0015] Figure 6 shows a structural block diagram of a computing device according to an exemplary embodiment of the present invention;

[0016] Figure 7 A schematic diagram showing the number of battery replacement requirements in each time period according to an exemplary embodiment of the present invention is shown;

[0017] Figure 8 A schematic diagram showing a comparison between the total output of a battery swap station cluster and an externally connected wind power curve according to an exemplary embodiment of the present invention is shown;

[0018] Figure 9 A schematic diagram showing the charge and discharge quantities of battery packs in a battery swap station cluster according to an exemplary embodiment of the present invention is shown;

[0019] Figure 10 A schematic diagram of power dispatching of a battery swap station cluster on a typical day in summer according to an exemplary embodiment of the present invention is shown. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. The same reference numerals generally refer to the same components or elements.

[0021] Figure 1 A schematic diagram of a power swap station connected to a power grid according to an exemplary embodiment of the present invention is shown. According to an embodiment of the present invention, the power grid to which the power swap station is connected can be a regional power grid, and the present invention does not limit the specific type of the power grid.

[0022] like Figure 1 As shown, multiple battery swap stations form a battery swap station cluster. The battery swap station cluster includes the first, second, third, and fourth battery swap stations. Each battery swap station includes photovoltaic panels and bidirectional charging piles. The battery swap stations charge the batteries within the station via the bidirectional charging piles.

[0023] At a battery swap station, an electric bus removes a battery that needs to be charged and installs a fresh one. Bus companies dispatch electric buses based on passenger volume and traffic conditions, and establish a schedule. Electric buses operate on fixed routes according to the schedule, and each bus undergoes a battery swap at a designated battery swap station.

[0024] The energy supply for battery swap stations primarily comes from photovoltaic panels and the connected regional power grid. PV panels utilize photovoltaic modules to directly convert solar energy into electrical energy, which is then connected to bidirectional charging stations via appropriate converter modules. PV panels are intelligent, environmentally friendly, energy-efficient, located close to users, and cost-effective. Under sufficient sunlight, the power provided by the panels exceeds the charging needs of the battery swap station. During periods of insufficient sunlight, when photovoltaic power generation cannot meet charging needs, the battery swap station must purchase electricity from external power grids, such as the regional power grid.

[0025] The battery swap station cluster and the regional power grid are both connected to the intelligent dispatching center, and the intelligent dispatching center is connected to the renewable energy station. The renewable energy station can be specifically implemented as a photovoltaic power station or a wind power station. The intelligent dispatching center monitors the power demand of the battery swap station cluster, the power supply status of the regional power grid, and the green power information provided by the renewable energy station. The intelligent dispatching center summarizes and analyzes the collected information, optimizes the allocation of power resources, and enables the battery swap station cluster to respond to the dynamic changes in the power demand of each battery swap station more flexibly and efficiently. The intelligent dispatching center collects data such as the output power of external renewable energy, conducts green power fitting and absorption analysis of the battery swap station, and controls the battery swap station cluster to use the green power provided by the renewable energy station. The use of an advanced intelligent dispatching platform contributes to the stable and reliable operation of the entire battery swap station cluster. The number of charging batteries and the number of discharging batteries of each battery swap station in each time period determined by the present invention can provide decision-making information to the intelligent dispatching platform.

[0026] Bidirectional charging piles can not only connect to photovoltaic panels and / or regional power grids to charge batteries in one direction, but can also discharge batteries and transmit them to the regional power grid to earn fees.

[0027] Currently, the widespread deployment of battery swap stations for electric buses in cities faces two major challenges: high power dispatch costs for these stations, and the significant load on the regional power grid caused by the charging and discharging of batteries in clusters of bus battery swap stations. Furthermore, in actual operation, the output power of renewable energy generation exhibits significant fluctuations and intermittency due to weather factors. The direct integration of renewable energy into the power grid also poses a significant challenge to its safe and stable operation.

[0028] According to one embodiment of the present invention, an on-site charging and battery swapping mode is adopted to replace the battery of an electric bus. The vehicle only needs to drive into the battery swapping station and remove and install the battery through automated equipment. The battery swapping station dispatches batteries in various states in an orderly management manner to ensure that the battery demand in each period is met. Electric buses are very suitable for this battery swapping mode due to their high travel frequency and high battery energy consumption. On the one hand, electric buses have large battery capacity and high battery loss, and the battery swapping mode can meet the battery swapping needs of the bus in a timely manner. On the other hand, the integrated charging and swapping facility configuration is more conducive to the flexible scheduling and battery maintenance of battery packs. The battery management strategy of this battery swapping station cluster is centralized and unified, which reduces the load pressure on the power grid. It can also transmit electricity to the power grid through intelligent scheduling during the peak period of the power grid, making a positive contribution to the stable operation of the power grid and the improvement of economic benefits.

[0029] Batteries with energy storage characteristics play an important role in the consumption of new energy, effectively reducing energy waste. When an electric bus is driving, once it is found that the power of the electric bus is insufficient to support the operation of the next route, the system will send a battery replacement invitation to the bus driver. After confirmation, the system will also send a battery replacement appointment notification to the battery replacement station to which the vehicle belongs. This requires that the number of fully charged batteries in each battery replacement station at any time must meet the battery replacement demand at that time. Therefore, the classified management of the number of battery packs in different states is particularly important for the normal operation of the battery replacement station cluster. Batteries can be divided into four categories, namely fully charged batteries, batteries waiting to be charged, batteries being charged, and batteries being discharged.

[0030] Figure 2 FIG1 shows a schematic diagram of battery cycle in a battery replacement mode according to an exemplary embodiment of the present invention. Figure 2 As shown, when an electric bus needs a battery swap, the empty battery is removed and becomes the battery waiting to be charged at the battery swap station. A fully charged battery is then installed, allowing the bus to continue driving. When a battery waiting to be charged is connected to a bidirectional charging station, it is considered a charging battery. Once the charging battery is fully charged, it becomes a fully charged battery. During periods of low electricity demand, the fully charged battery can be discharged, with electricity being fed to the regional power grid via the bidirectional charging station. At this point, the fully charged battery becomes a discharging battery.

[0031] When developing battery scheduling methods for battery swap stations, it's crucial to ensure that the stations have sufficient fully charged batteries. Maintaining a sufficient supply of fully charged batteries at the stations ensures that electric buses can quickly swap fully charged batteries upon arrival, avoiding long wait times and improving vehicle operating efficiency and the economic benefits of the battery swap station cluster. When the number of available batteries is low, battery demand outstrips supply, making it difficult to meet the battery swap needs of the battery swap station cluster, impacting the normal operation of the stations. Conversely, when the available battery inventory is excessive, more than enough to meet the battery swap demand, some batteries remain idle, further increasing the operating costs of the stations. To address these challenges, battery swap stations need to implement inventory management strategies. Based on historical data and forecasting models, battery swap stations should accurately forecast battery demand. This helps them plan inventory in advance, ensuring sufficient battery supply during peak hours and reducing inventory appropriately during off-peak hours to avoid wasted resources. These stations also employ advanced inventory management systems, including real-time inventory tracking and intelligent scheduling features, to monitor battery inventory status in real time and dynamically adjust the allocation and scheduling of battery charging and discharging based on demand, thereby improving battery utilization and reducing inventory costs.

[0032] Figure 3 FIG. 3 is a schematic diagram showing a method 300 for dispatching batteries of a city bus exchange station according to an exemplary embodiment of the present invention. Figure 3 As shown, firstly, step 310 is executed to establish an electric bus battery replacement demand model according to the electric bus battery replacement characteristics.

[0033] According to one embodiment of the present invention, the battery swapping characteristics of electric buses are modeled based on a Monte Carlo simulation method. The operation and battery swapping characteristics of the electric buses in the present invention are set as follows: there is an approximately proportional relationship between the mileage of the electric buses and the power consumed; along the entire route, the power consumption per unit mileage of the electric buses exhibits a bounded normal distribution; the average speed of all electric buses remains consistent, and the same bus model and battery are used; and each electric bus is set to use a fixed battery swap station for battery swapping, and this battery swap station is uniformly set as the starting station.

[0034] The battery swapping characteristics of electric buses include the single-trip power consumption of the route, the rated battery capacity, route operating parameters, and the number of batteries at the battery swapping stations. The single-trip power consumption of the route is determined by the route parameters and motor parameters. Route parameters include route length and average route energy consumption. Motor parameters include the efficiency of the traditional bus internal combustion engine, the conversion factor between traditional energy and electricity, and the electric bus motor efficiency.

[0035] According to one embodiment of the present invention, the one-way power consumption of the running route can be expressed as E m The one-way power consumption of the route is calculated according to the following formula:

[0036]

[0037] Where m represents the mth route, the value range of m is 1 to M, M represents the total number of electric bus routes in the city, τ m is the average energy consumption of the mth route of electric buses (kg / km), η NR is the efficiency of traditional bus internal combustion engine, γ is the conversion coefficient between traditional energy and electric energy (kWh / kg), S m is the length of the mth route where electric buses operate (km), η DD is the motor efficiency of the electric bus,

[0038] According to one embodiment of the present invention, the operating environment of the electric bus is a key factor, and the operating environment includes multiple factors, such as weather conditions, road conditions, etc. Bad weather, such as rain, snow and strong winds, will have a significant impact on the driving of the electric bus. In this case, the driving of the electric bus will be subject to external resistance, and the driving time and power consumption of the electric bus will increase. In addition, if a traffic accident or road congestion occurs, the frequent starting and stopping of the electric bus will also lead to serious power loss. The above objective factors will affect the battery replacement demand of the bus. Therefore, the present invention sets a fluctuation coefficient, and uses ε to represent the fluctuation coefficient caused by different road conditions and loads, which is dimensionless.

[0039] The maximum rated capacity of the battery is available SOC max Indicates that the battery's minimum rated capacity is available SOC min express.

[0040] The line operation parameters include the number of electric buses running on the line, the number of times each vehicle travels, the departure time of the first and last buses on the line, and the departure interval.

[0041] The factors that affect the mileage of electric buses mainly include departure time, return time, departure interval, route length, driving speed, etc. A reasonable range of mileage directly determines whether the bus can complete a round trip of a route normally. If the bus has a longer mileage, that is, the single driving distance is longer, then the amount of charging required for each bus will be greater. This means that the bus needs to be charged or the battery replaced more frequently to ensure that the power is not exhausted during operation. In addition, in order to cope with emergencies, such as vehicle breakdowns or traffic congestion, the battery swap station should also have a certain number of spare batteries and charging piles to ensure that the bus can be replenished in time when needed. It is worth mentioning that with the advancement of technology and the continuous development of the electric bus industry, the battery life is also constantly improving. However, this means that the demand for charging and swapping of electric buses will be more vigorous, and the requirements for charging and swapping facilities will also be more stringent. Therefore, the present invention reasonably sets the number of two-way charging piles and the number of batteries in the battery swap station.

[0042] As crucial supporting facilities for electric buses, battery swap stations play a crucial role in improving bus operational efficiency, reducing operating costs, and promoting the widespread adoption of electric vehicles. The bidirectional charging piles within a battery swap station cluster are a core component of this system. The bidirectional power transfer capability of bidirectional charging piles enables flexible and adaptable scheduling strategies within the battery swap station cluster, enabling it to adapt to diverse operational scenarios and needs. Specifically, bidirectional charging piles schedule battery charging based on the real-time charging needs of the battery swap station to ensure continuous bus operation. Furthermore, when grid load is low or renewable energy generation is sufficient, bidirectional charging piles can feed energy from the battery packs back into the grid, optimizing energy utilization. This bidirectional energy flow makes energy scheduling and management within the battery swap station cluster more intelligent and efficient. Furthermore, the number of bidirectional charging piles and their charge / discharge power are also important factors influencing the scheduling strategy of the battery swap station cluster. The number of bidirectional charging piles determines the number of buses a battery swap station can simultaneously service, impacting the operational efficiency of electric buses. The charge / discharge power of the bidirectional charging piles determines the number of batteries a battery swap station can provide for direct replacement in a short period of time, impacting bus operation plans.

[0043] According to one embodiment of the present invention, the travel characteristics of electric buses are analyzed, and it is found that the travel routes and operating hours of electric buses are fixed, the battery replacement behavior is economical, and the vehicle model and battery model are unified.

[0044] Then, step 320 is executed to determine H according to the electric bus battery replacement demand model. it . H itIt represents the number of battery swapping demands at battery swap station i in time period t. The value range of i is 1 to I, I is the total number of battery swap stations included in the battery swap station cluster, and the value range of t is 0 to T. After dividing a day into multiple time periods according to predetermined time intervals, the total number of time periods included in a day is T+1.

[0045] According to one embodiment of the present invention, the predetermined time interval can be set to 1 hour, then the value of T+1 is 24, and the predetermined time interval can also be set to 15 minutes, then the value of T+1 is 96. The present invention does not limit the specific value of the predetermined time interval.

[0046] According to one embodiment of the present invention, the bus company will make unified plans and arrangements for the travel routes of each electric bus, as well as the departure, return, and interval times. Each electric bus driver will drive in a cycle on their respective routes. Without considering interference factors such as traffic and weather, the power consumption per unit mileage of each electric bus is consistent, so the time it takes for the electric bus to arrive at each station can be determined by calculation. Electric buses mainly operate between 6:00 am and 10:00 pm every day. The departure and return time periods in the morning and evening overlap with the peak hours of the power grid. During non-operating hours, they are mainly in the low power consumption period of the power grid. Therefore, it can be seen that there is a certain positive correlation between the operation distribution of electric buses and the peak and valley distribution of power consumption in the regional power grid.

[0047] After each trip, electric buses return to designated battery swap stations, regardless of whether their batteries need replacing. This approach ensures that battery swaps do not incur additional travel and time costs, reducing the cost of battery swaps to zero. Therefore, the number and frequency of battery swaps for electric buses do not have an additional impact on their economic operation.

[0048] Standardized vehicle and battery models mean more standardized maintenance and management across the entire bus fleet. Maintenance personnel can be trained on these same models and share parts and maintenance plans, reducing maintenance costs and risks. Standardized models also provide greater flexibility for expansion and replacement. If the fleet needs to be expanded or aging vehicles need to be replaced, new vehicles and batteries of the same model can be introduced more easily, maintaining fleet uniformity and consistency.

[0049] According to one embodiment of the present invention, the battery replacement period of the electric bus is sampled according to the above-mentioned setting characteristics. For statistical convenience, the time period step is 60 minutes, and the whole day is divided into 24 time periods. The parameter h(n,t) is used as a flag to indicate whether the electric bus numbered n needs to replace the battery in the t period. h(n,t)=0 indicates that the vehicle does not need to replace the battery in the y period; h(n,t)=1 indicates that the electric bus is scheduled to replace the battery in the t period. The specific details are shown in the following formula:

[0050]

[0051] The value range of n is 1 to N, where N means there are N electric buses running on the mth route.

[0052] Whether each electric bus needs to replace the battery in time period t is determined according to the following steps: if the electric bus departs from the battery replacement station for the first time, the battery needs to be replaced; if it is not the first departure, the battery needs to be replaced when the remaining power of the electric bus is less than the one-way power consumption of the route.

[0053] The calculation formula for the battery swap demand of electric buses at battery swap stations is as follows:

[0054]

[0055] Where N bv,n H is the number of battery packs required for the nth bus. it is the number of battery swapping demands at battery swap station i during period t.

[0056] To determine whether the remaining power of the electric bus can support this operation, and whether the remaining power is greater than the one-way power consumption of the operation route, the following formula (4) can be used for calculation:

[0057] SOC back =SOC depart -E m (4)

[0058] Among them, SOC depart It is the remaining power of the electric bus when it returns to the battery swap station, SOC back It is the difference between the remaining power of the electric bus and the one-way power consumption. If the electric bus is leaving the battery swap station for the first time that day, the remaining power of the electric bus at the first departure is the rated power of the installed battery.

[0059] When calculating, if SOC back If SOC is less than or equal to 0, it is determined that the remaining power of the electric bus cannot support this operation. back If it is greater than 0, it is determined that the remaining power of the electric bus can support this operation.

[0060] The orderly charging and discharging of electric bus batteries is crucial for ensuring efficient public transportation systems. When an electric bus's battery capacity is small, its range is limited, requiring more frequent battery swaps. Therefore, the demand for battery swaps at battery swap stations increases as the battery capacity decreases to meet the vehicle's continuous operational needs. When the battery capacity is large, a single charge can provide a longer driving range, so the demand for battery swaps at battery swap stations may be relatively small. Before an electric bus's first departure, it is pre-charged with a fully charged battery. Therefore, the remaining battery capacity of an electric bus upon arrival at a battery swap station directly determines whether the vehicle requires a battery swap, and the battery capacity also influences the battery swap demand at the station. To ensure that the electric bus does not run out of batteries for the next mission, the battery swap station must promptly dispatch battery packs for charging to reach the required number of fully charged batteries. This strategy ensures that the electric bus maintains sufficient battery charge throughout its operation, thus ensuring the stable and efficient operation of the public transportation system.

[0061] According to one embodiment of the present invention, the whole day is divided into 24 time periods with a preset time interval of 1 hour.

[0062] Figure 4 FIG. 1 shows a schematic diagram of calculating the battery swapping demand according to an exemplary embodiment of the present invention. Figure 4 As shown, the steps for calculating the battery swapping demand include:

[0063] Determine the basic data of all bus routes served by the battery swap station, including the number of electric buses running on the route, the number of times each vehicle travels, the length of the route, the number of batteries in the battery swap station, the first and last bus times of the route, the departure interval, and the rated capacity of the battery.

[0064] Then, the initialization operation is performed, and m, n, and k are set to represent the operation status of the nth vehicle on the mth route at the kth time. The value range of k is 1-K, and K means that the nth vehicle on the mth route will run a total of K times in one day.

[0065] Determine whether the bus is on its first trip. If so, replace the battery with a new one and record the relevant information, such as the replacement time and the number of batteries replaced.

[0066] For buses that are not on the first trip, it is necessary to check whether the battery power is sufficient to complete the entire route before its k+1th trip. If the battery power is sufficient, the bus can be dispatched without replacing the battery. If the battery power is insufficient, the battery must be replaced, and the replacement time and number of batteries replaced must be recorded.

[0067] All battery replacement needs of all vehicles on all bus routes are counted and recorded.

[0068] Determine whether there are other vehicles departing during the operating time period. If they continue to depart, continue to check whether the battery power is sufficient to complete the entire journey.

[0069] If a day's operation has been completed, the calculation stops. The battery replacement time set of each bus on the route is obtained by statistics and the number of battery replacement requirements for buses on each route is calculated, including the number of fully charged batteries that need to be replaced at each battery replacement station in each time period.

[0070] Then, step 330 is executed to establish a day-ahead scheduling model for battery charging and discharging of battery swap station clusters. Its decision variables include the number of batteries C being charged at battery swap station i in time period t. it and the number of batteries being discharged, D it , whose objective function includes min f1=C g -C d and Where f1 represents the cluster scheduling cost of battery swap stations, C g represents the electricity purchase cost of the battery swap station cluster, C d represents the electricity sales revenue of the battery swap station cluster, f2 represents the fitting degree between the net load of the battery swap station cluster and the green power output, represents the normalized net load of the battery swap station cluster at time period t, represents the normalized green electricity output power of the renewable energy station in time period t.

[0071] According to one embodiment of the present invention, the battery swap station includes photovoltaic panels and bidirectional charging piles. The battery swap station is connected to the power grid and obtains electricity from the power grid and / or photovoltaic panels through the bidirectional charging piles to charge the battery. The battery swap station also discharges the battery through the bidirectional charging piles to sell electricity to the power grid.

[0072] According to one embodiment of the present invention, the scheduling cost function of the battery swap station cluster min f1=C is calculated based on the electricity purchase cost and electricity sales revenue of the battery swap station cluster. g -C d The net load of the battery swap station cluster in each period is determined based on the power purchased from the grid and the discharge power sold to the grid by each battery swap station in each period; the green power output power of the renewable energy station in each period is determined; the fitting function of the net load of the battery swap station cluster and the green power output is determined based on the green power output power of the renewable energy station in each period and the net load of the battery swap station cluster in each period.

[0073] According to one embodiment of the present invention, regional power grids currently use a peak-valley time-of-use (TOU) pricing system for electricity pricing, which divides the day into peak, flat, and valley periods, each with a different electricity price. This pricing strategy uses price signals to guide users to autonomously adjust their electricity usage, shifting electricity demand from peak periods to off-peak periods, thereby balancing the load on the power system and improving the efficiency of power resource utilization. In contrast, disorderly charging refers to users connecting their electric vehicles to the grid via charging piles at any time, charging until fully charged, without any guidance. This is similar to a "charge as you go" approach for buses arriving at their stations. This disorderly charging is likely to result in large numbers of batteries being charged during the same peak electricity consumption period, increasing not only charging costs for the battery swap station cluster but also grid power pressure and load fluctuations. Therefore, the present invention, guided by TOU electricity pricing, implements orderly charging and discharging planning for batteries in the battery swap station cluster. The battery swap station cluster generally chooses to charge batteries when electricity prices are low, and during peak grid electricity consumption periods, excess power is transferred back to the regional grid via bidirectional charging piles. In the battery swap station cluster, the impact of TOU electricity pricing on battery scheduling is primarily reflected in the following two aspects:

[0074] 1) Reduce dispatch costs: On the one hand, by charging during periods of low electricity prices, the battery swap station cluster can reduce electricity purchase costs. On the other hand, it can earn additional electricity sales revenue by feeding electricity to the regional power grid during periods of high electricity prices, i.e., peak electricity consumption periods.

[0075] 2) Balancing the grid load: Time-of-use electricity pricing can guide battery swap station clusters to charge during off-peak hours while discharging power to the grid during peak hours. This scheduling strategy helps balance the grid load and reduce fluctuations and stress.

[0076] The 24-hour period is divided into three time periods: peak, valley, and flat. Each time period corresponds to its specific equal time period. The time-of-use electricity price is expressed as follows:

[0077]

[0078] Where P f 、P p 、P g are the electricity prices during peak, flat and valley periods (yuan / kWh), t f , t p , t g Corresponding to peak, flat and valley periods respectively.

[0079] According to one embodiment of the present invention, the time-of-use electricity price P S (t) Determine λ g,t , that is, the electricity price of battery charging and discharging in time period t.

[0080] In order to maximize the utilization rate of clean electricity and improve the economic benefits of the battery swap station cluster, the present invention divides the battery charging and discharging process of the battery swap station cluster into two categories for detailed analysis. Clean electricity, such as clean electricity from renewable energy sites, is used together with the regional power grid to provide energy for the battery swap station cluster. During the summer daytime with sufficient sunlight, photovoltaic panels can generate a lot of electricity, sometimes even exceeding the needs of the battery swap station. However, at night, the power output of the photovoltaic panels is zero. When the output power of the photovoltaic panels exceeds the needs of the battery swap station, the excess electricity can be transmitted to the regional power grid. When the output power of the photovoltaic panels is insufficient to meet the needs of the battery swap station, the required additional electricity is reasonably purchased from the power grid to ensure the stable operation of the system and the balance of energy supply and demand.

[0081] When the charging power of the battery swap station is greater than the power of the photovoltaic panels, the photovoltaic panels and the regional power grid jointly serve as power suppliers to meet the charging needs of the battery swap station, which is expressed as the following formula:

[0082]

[0083] Where, is the output power (kW) of the photovoltaic panels in the battery swap station i during time period t, is the purchased power of battery swap station i in time period t (kW), is the charging power (kW) of battery swap station i in time period t.

[0084] When the charging power of the power station is less than the output power of the photovoltaic panels, the excess photovoltaic power will be discarded because the battery swap station cluster does not have additional energy storage devices. This is expressed as the following formula:

[0085]

[0086] According to one embodiment of the present invention, the net load of the battery swap station cluster is determined based on the operating conditions of each battery swap station. As renewable energy is incorporated into the power grid, the uncertainty of the power system increases, because the output of renewable energy depends on factors such as wind speed and solar irradiance. The following problems will arise when renewable energy is connected to the grid: renewable energy generation often fails to access the grid during periods of low grid demand, resulting in the phenomenon of "wind and solar abandonment"; the output power of green electricity varies greatly with the weather, which increases the difficulty of system scheduling. In order to maintain the safe and stable operation of the power system, the high energy consumption characteristics of electric bus battery swap stations can be used to participate in the absorption of renewable energy. Therefore, the present invention considers fitting the net load of the battery swap station cluster with the external green electricity output to achieve the friendliness of the battery swap station cluster to the external green electricity absorption in the entire integrated energy system.

[0087] The net load of the battery swap station cluster is mainly composed of the difference between the actual purchased power and the discharged power of the battery swap station cluster excluding photovoltaic power generation. The net load expression formula is as follows:

[0088]

[0089] Where P λ,t is the net load of the battery swap station cluster in time period t (kW), is the power purchased by battery swap station i from the grid during period t (kW), is the power sold to the grid when battery swap station i discharges the battery in time period t (kW).

[0090] When comparing the total power of a battery swap station cluster with green electricity output, the significant difference in their dimensions can interfere with model training and comparison results. Therefore, the data should be dimensionless before similarity calculation. This paper uses Min-Max normalization, also known as deviation normalization, to linearly transform the raw data and map the results to the range [0, 1].

[0091] The power of the battery swap station cluster in each period is normalized, and formula (9) is expressed as the normalized power of the battery swap station.

[0092]

[0093] Where, P λ,t * It is the normalized value of the total power of the battery swap station cluster in time period t. λ,t is the total power of the battery swap station cluster during period t, P λ,tmin and P λ,tmax They are the minimum and maximum values ​​of the total power of the battery swap station cluster in all periods of the day.

[0094] In order to obtain a higher accuracy of waveform similarity, the green power output power of each period is normalized. Formula (10) is expressed as the normalized green power output power:

[0095]

[0096] Where P δ,t is the benchmark value of green power output in period t (MW), is the normalized value of the green power output power in period t, P δ,tmin and P δ,tmax They are the minimum and maximum values ​​of green power output power in all periods of the day.

[0097] This invention does not consider the residual value of the battery and the operation and maintenance costs of the battery swap station, but only considers the lowest scheduling cost under the battery charging and discharging conditions of the battery swap station. The total scheduling cost of the battery swap station cluster within a cycle is minimized, as shown in the following formula:

[0098] min f1=C g -C d (11)

[0099] Among them, C g represents the electricity purchase cost of the battery swap station cluster, C d Represents the electricity sales revenue of the battery swap station cluster.

[0100] Among them, C g Determined according to the following formula:

[0101]

[0102] During the operation of the battery swap station, no additional electricity purchase fee is required for photovoltaic power generation. The electricity purchase cost mainly comes from the electricity purchase fee of the battery swap station from the power grid. The electricity purchase cost of the electric bus battery swap station cluster is determined by multiple factors. The first is the number of power battery packs being charged, and the second is the time-of-use electricity price and the time period for charging the power battery.

[0103] C d Determined according to the following formula:

[0104]

[0105] Among them, λ g,t is the electricity price of battery charging and discharging during period t, is the power purchased by the battery swap station i in period t, is the discharge power of battery swap station i in time period t, and Δt represents the duration of charging or discharging in each time period.

[0106] The electricity sales revenue of the battery swap station cluster is determined by the battery discharge quantity of the battery swap station cluster in each time period and the electricity sales price of that period. The battery swap station cluster sells the surplus fully charged battery packs to the power grid through charging and discharging equipment as the electricity sales revenue of the battery swap station cluster.

[0107] Regarding the distribution of charging power of power batteries in battery swap stations, in order to avoid overly complicated calculations, the present invention assumes that once the battery packs in the battery swap station cluster are charged or discharged, their charging and discharging power is constant at the rated charging and discharging power of the charging pile. The charging and discharging power of the battery packs in the battery swap station can be seen in formulas (14) and (15).

[0108]

[0109] in, is the battery charging power of battery swap station i in time period t, P is the discharge power of battery swap station i in time period t. e,c is the rated charging power per unit battery, P e,d is the rated discharge power of the unit battery.

[0110] To ensure peak load shaving and valley filling within the battery swap station cluster, a waveform similarity comparison was performed between the net load curve of the orderly controlled battery swap station cluster and the external green power output curve. The higher the degree of similarity between the battery swap station cluster's charge and discharge output curve and the green power output curve, the better the proposed scheduling scheme is compatible with large-scale renewable energy. The method selected in this paper is the Euclidean distance method, specifically expressed in Equation (16).

[0111]

[0112] The electric bus battery swapping characteristic model includes the following assumptions:

[0113] (1) Battery charging and discharging, as well as bus shift changes, are all under ideal conditions.

[0114] (2) All buses in the study area are electric buses and use battery swapping mode. The electric buses in the battery swapping station cluster are of the same size and have the same battery model.

[0115] (3) Since the research cycle of the present invention is 1 day, a day is divided into 24 time periods, corresponding to 00:00-01:00, 01:00-02:00, ..., 23:00-00:00 every day.

[0116] (4) Once the batteries in the battery swap station cluster begin charging, the next step will be considered only when the battery capacity reaches the set capacity. Similarly, the next step will be considered only when the battery is discharged to the set capacity. This operation process ensures the efficiency and safety of battery use.

[0117] (5) The battery swap station cluster uses automated equipment to swap batteries for electric buses. The battery swap time and the time it takes to place the empty battery on the charger are generally 4 to 5 minutes. It is assumed that the battery swap operation time has little impact on the overall scheduling plan and can be ignored.

[0118] Finally, step 340 is executed to solve the battery charging and discharging scheduling model of the battery swap station cluster according to the constraint conditions, and the battery swap station cluster scheduling cost is lowest and the fitting degree between the net load of the battery swap station cluster and the green power output is optimal. it and D it The optimal solution is used as the battery scheduling solution for the battery swap station cluster, where the constraints include F it ≥H it , F it represents the number of fully charged batteries at battery swap station i in time period t.

[0119] First, the constraints for solving the objective function are determined. According to one embodiment of the present invention, the constraints include battery charge and discharge power constraints, battery charge and discharge power constraints, charging demand constraints, battery state of charge and full battery state constraints, battery quantity constraints in different states, sustained operation constraints, backup margin constraints, and battery capacity protection constraints.

[0120] 1) Battery charge and discharge power constraints

[0121] The same battery cannot be in the charging state and the discharging state at the same time; the sum of the number of batteries being charged and the number of batteries being discharged in each battery swap station is less than or equal to the preset number of charging piles in the battery swap station. The above rules are expressed by equations (17)-(20) as follows:

[0122]

[0123] A binary variable representing the battery charging flag of the battery swap station i in period t. When it is 0, it indicates that the battery of the battery swap station i is not in the charging state in period t; when it is 1, it indicates that the battery of the battery swap station i is in the charging state in period t. It is a binary variable for the battery discharge flag of the battery swap station i in time period t. A value of 0 indicates that the battery of the battery swap station i is not in a discharging state in time period t; a value of 1 indicates that the battery of the battery swap station i is in a discharging state in time period t.

[0124] represents the maximum number of charging piles at battery swap station i, C it represents the number of batteries being charged at battery swap station i during period t, D it It represents the number of batteries being discharged at battery swap station i during period t.

[0125] Formulas (17), (18), and (19) indicate that the same battery in a battery swap station cannot be charged and discharged at the same time; Formula (20) indicates that the total number of batteries in the charging or discharging state in any battery swap station does not exceed the number of charging piles in the station.

[0126] 2) Battery charge and discharge power constraints

[0127] The charging power of the battery swap station is less than or equal to the maximum charging power of the battery swap station; the discharging power of the battery swap station is less than or equal to the maximum discharging power of the battery swap station, which can be expressed by formula (21):

[0128]

[0129] Formula (21) represents the maximum charging power limit and the maximum square power limit of the battery swap station i in period t.

[0130] P c,maxThe maximum charging power allowed for stable operation of the battery swap station, P d,max The maximum discharge power allowed for stable operation of the battery swap station.

[0131] 3) Charging demand constraints

[0132] The purchased power of the battery swap station and the output power of the photovoltaic panels are greater than or equal to the charging power of the battery swap station, which can be expressed as follows:

[0133]

[0134] Formula (22) indicates that the charging demand of battery swap station i in period t is met by both photovoltaic panels and the power grid.

[0135] 4) Constraints on the relationship between battery charge state and fully charged battery state

[0136] The battery capacity is equal to the charge at the maximum state of charge of the battery, which is a fully charged battery, expressed by equations (23) and (24):

[0137]

[0138] It is a flag indicating whether the zth battery in the tth period of the i-th battery swap station is a fully charged battery. When the value is 1, it is a fully charged battery, and when the value is 0, it is not a fully charged battery. Formula (23) indicates that a battery can be defined as fully charged only when it meets the maximum state of charge. represents the power of the zth battery at the i-th battery swap station during period t; Formula (24) represents the set of the number of fully charged batteries at the i-th battery swap station during period t, F it is the number of fully charged batteries at the battery swap station i during period t. The value range of z is 1 to Z i , Z i Represents the total number of batteries in the i-th battery swap station.

[0139] 5) Battery quantity constraints under different states

[0140] The total number of fully charged batteries, batteries waiting to be charged, batteries being charged, and batteries being discharged in each time period is equal, which can be expressed by formula (25);

[0141]

[0142] The number of batteries waiting to be charged, fully charged batteries, batteries being charged, and batteries being discharged in each time period are all non-negative integers, which can be expressed by formula (26):

[0143] W it ∈N + ,F it ∈N + ,Cit ∈N + ,D it ∈N + (26)

[0144] The sum of the number of fully charged batteries in period t and the number of fully charged batteries in period t+1, minus the number of fully charged batteries that have begun to discharge in period t+1 and the number of batteries that have been replaced on the electric bus in period t+1, equals the number of fully charged batteries in period t+1, which can be expressed as formula (27):

[0145] F i(t+1) =F it +C i(t+1)x1 -D i(t+1)x2 -H i(t+1)x3 (27)

[0146] C i(t+1)x1 Indicates the number of fully charged batteries in the battery being charged at time t+1, D i(t+1)x2 Indicates the battery that starts discharging in the fully charged battery at time t+1, H i(t+1)x3 F represents the number of batteries replaced on the electric bus during the t+1 period. i(t+1) Indicates the number of fully charged batteries at time period t+1.

[0147] The sum of the number of batteries that need to be charged in the battery charging period t, the battery replaced from the electric bus in the battery replacement period t+1, and the battery being discharged in the battery replacement period t+1, minus the number of batteries that have started to be charged in the battery waiting to be charged in the battery replacement period t+1, equals the number of batteries waiting to be charged in the battery replacement period t+1, which can be expressed as formula (28):

[0148] W i(t+1) =W it +H i(t+1)y3 +D i(t+1)y2 -C i(t+1)y1 (28)

[0149] H i(t+1)y3 represents the number of batteries replaced from the electric bus during the t+1 period, D i(t+1)y2 Indicates the number of batteries that need to be charged among the batteries that are discharging during the t+1 period, C i(t+1)y1 Indicates the number of batteries that have started charging among the batteries waiting to be charged in the t+1 period. i(t+1) Indicates the number of batteries waiting to be charged in time period t+1.

[0150] The number of batteries replaced on the electric bus during the t+1 period is equal to the number of batteries replaced from the electric bus during the t+1 period, that is, H in Eq. (27) i(t+1)x3 The sum of H in formula (28) i(t+1)y3 same.

[0151] Equations (27) and (28) respectively represent the state transition relationship between the number of fully charged batteries and the number of batteries to be fully charged.

[0152] 6) Persistent operation constraints

[0153] The number of batteries waiting to be charged in the initial period of each day at the battery swap station is equal to the number of batteries waiting to be charged in the final period; the number of fully charged batteries in the initial period of each day at the battery swap station is equal to the number of fully charged batteries in the final period, which can be expressed by equations (29) and (30):

[0154] W i0 =W i23 (29)

[0155] F i0 =F i23 (30)

[0156] W i0 The number of batteries waiting to be charged in the initial period, W i23 The number of batteries waiting to be charged at the end of the period, F i0 is the number of fully charged batteries in the initial period, F i23 The number of fully charged batteries at the end of the period.

[0157] Formula (29) indicates that the number of batteries waiting to be charged is equal at the beginning and end of a day; Formula (30) indicates that the number of fully charged batteries is equal at the beginning and end of a day.

[0158] According to one embodiment of the present invention, the constraints include F it ≥H it , F it represents the number of fully charged batteries at battery swap station i in time period t. The backup margin can be further considered to obtain the backup margin constraint.

[0159] 7) Reserve Margin Constraints

[0160] The number of fully charged batteries at the battery swap station in each period is greater than the number of batteries that need to be replaced when considering the backup margin, which can be expressed as follows:

[0161] F it ≥H it (1+β) (31)

[0162] Formula (31) indicates that the number of fully charged batteries can be continuously supplied. Setting up backup batteries can avoid the situation where the number of batteries in the battery swap station is insufficient to meet the battery swap demand due to emergencies. it is the number of fully charged batteries required by battery swap station i in time period t, and β is the battery backup margin coefficient.

[0163] 8) Battery power protection constraints

[0164] Overcharging and over-discharging of batteries will have a great adverse effect on battery life. The maximum charge of the battery during charging is less than or equal to the maximum rated capacity of the battery; the minimum charge of the battery during discharging is greater than or equal to the minimum rated capacity of the battery, which can be expressed by formula (32):

[0165]

[0166] According to one embodiment of the present invention, a particle swarm optimization algorithm may be used to solve a day-ahead scheduling model for battery charging and discharging of a battery swap station cluster, which may be specifically implemented as an improved particle swarm optimization algorithm.

[0167] Particle Swarm Optimization (PSO) is an intelligent optimization algorithm that uses swarm intelligence technology. It was originally developed by Kennedy and Eberhart in 1995 to solve target optimization problems in various engineering fields. The algorithm is expressed as a simple mathematical model that describes the social behavior of a flock of birds or bees, solving dynamic optimization problems in a collaborative and self-organizing manner. In PSO, each solution to a specific problem is called a particle, and the total number of solutions is called a swarm. Each particle has a memory of its own flight experience and can "self-learn," that is, it knows its current position x. j In addition to its own best position (Pbest), each particle also knows the best position (Gbest) found by all particles in the population. This can be seen as particles engaging in "social learning." The above two learning methods jointly determine the next flight of the particle. Each particle represents a potential solution in the search space and has its own position, fitness, and flight speed. The following equations (33) and (34) are used to update the speed and position:

[0168]

[0169] In the formula is the d-th dimension position vector of the j-th particle in the k-th iteration; W is the inertia weight; c1 and c2 are the self-learning coefficient and social learning coefficient of the particle; r1 and r2 are random numbers; is the individual optimal particle position; is the global optimal particle position, is the particle position.

[0170] Traditional particle swarm optimization mainly solves single optimization objective problems. For multi-objective problems, the introduction of Pareto sorting and effective solution updating strategies can evolve into a multi-objective particle swarm optimization algorithm. However, in practical applications, this algorithm has several problems: First, the lack of clear guidance on the value of the inertia weight can easily cause the algorithm to fall into local optimality or low search efficiency. Then, the update and maintenance strategies of the non-inferior solution set are often insufficient. This may cause the Pareto solution set to perform poorly in terms of diversity and distribution, making the algorithm unable to effectively cover all potential optimal solutions; finally, there is also a lack of clear guidance for the selection of the global optimal solution of the population. This may affect the convergence speed and accuracy of the algorithm, making the algorithm inefficient in seeking the optimal solution. The following three problems are optimized separately:

[0171] The present invention introduces adaptive inertia weight. In the particle swarm optimization algorithm, the setting of inertia weight is crucial to its convergence performance. Traditional inertia weight value methods, such as linear or nonlinear decrement, do not fully consider the dynamic characteristics of particles in the iterative process, resulting in a lack of clear guidance for the setting of inertia weight. To address this problem, the present invention proposes a new inertia weight value strategy. This strategy is guided by the degree of gap between the particle and the optimal particle in the swarm, aiming to more effectively adjust the inertia weight, thereby improving the convergence performance of the particle swarm optimization algorithm. Specifically, the difference between the jth particle in the k time period and the global optimal solution of the swarm is It can be calculated using the following equations (35) and (36):

[0172]

[0173] Where D is the dimension of the solution space; w start 、w end is the initial value and final value of w, that is, the initial value and final value of the particle state; x max 、x min are the maximum and minimum values ​​of the particle position variables, respectively.

[0174] The present invention also updates the non-inferior solution set based on dynamic dense distance. In the multi-objective optimization process, the non-inferior solution set needs to be updated after each iteration. In order to maintain the scale of the Pareto solution set and the uniformity of the solution distribution, it is necessary to "select" the Pareto solutions. Dense distance is an indicator that measures the density between a particle and its neighboring particles, which can be used to describe the uniformity of the solution. The present invention discusses a dual-objective function, so the particle x j The dense distance I(x j ) is calculated using formula (37):

[0175]

[0176] In formula (37) is the distance xj The two nearest particles; f1(x j )、f2(x j ) are the values ​​of particle objective function one and objective function two.

[0177] After calculating the Pareto distance of the solutions, they are sorted from largest to smallest and filtered. A common approach is to select the first N solutions with the largest distances, which is fast but can result in poor solution diversity and uniformity. This method uses a "one-by-one elimination" approach to update non-inferior solutions. This involves sorting and removing the solution with the smallest distance, recalculating and sorting the remaining solutions until N solutions are left, improving diversity and uniformity.

[0178] The present invention also selects the global optimal solution of the population. During the update process of the particle swarm algorithm, the historical optimal position and the global optimal position of each particle must be tracked and recorded. For single-objective optimization algorithms, the determination of the global optimal solution is relatively simple and can be obtained by simply comparing the fitness values ​​of each particle. However, for multi-objective optimization algorithms, each iteration process will produce a set of Pareto solution sets that do not dominate each other, which makes the selection of the global optimal solution complicated. The present invention calculates the dense distance of each solution in the Pareto solution set and sorts them according to the size of the dense distance. A solution is randomly selected from the top 20% of the larger solutions as the global optimal solution. By adjusting the position and speed of the particles to guide the algorithm towards a better solution, the population update is guided to ensure that the algorithm moves in a better direction and balance the quality, diversity and coverage of the solution.

[0179] Figure 5 FIG. 4 shows a schematic diagram of calculating a battery management state according to an exemplary embodiment of the present invention, as shown in FIG. Figure 5 As shown, the steps include:

[0180] Initialize the population position and velocity variables.

[0181] Substitute the particle position into the fitness function, obtain the fitness value of each particle, and put it into the non-inferior solution set.

[0182] Determine the historical optimal solution of each particle and the global optimal solution of the population.

[0183] The difference between each particle and the optimal particle is calculated according to formula (35), and on this basis, the inertia weight ω of each particle is updated according to formula (36).

[0184] Update the new velocity and position of the particle in the next period according to formulas (33) and (34).

[0185] Calculate the new fitness of each particle, update the particle's historical optimal solution Pbest according to the dominance relationship, and form the latest non-inferior solution set.

[0186] The non-inferior solution set is updated using the method of formula (37).

[0187] Select the global optimal solution Gbest of the population.

[0188] If the termination condition is not met, the process will jump to step 4 and continue. If the condition is met, the optimal Pareto solution set will be output.

[0189] According to one embodiment of the present invention, based on the example data of the urban area of ​​City A, the present invention takes the battery swap station cluster as the research object, uses the Matlab 2018b version tool, and adopts the improved multi-objective particle swarm optimization algorithm to solve the model, while considering the maximum load curve fitting and the minimum battery charge and discharge scheduling cost. The initial parameter values ​​of the algorithm are set as follows: individual learning factor c1 = 1.5, social learning factor c2 = 1, inertia weight initial value w start =0.9, the end value of inertia weight w end =0.4; difference The threshold is 0.1; the number of particles is 400, and the number of iterations is set to 250.

[0190] The present invention uses T=1h as an optimization control period. Assuming that the number of charging slots, battery capacity, rated charge and discharge power, and bus-related parameters of each battery swap station are the same, the specific relevant parameter settings of the electric bus battery swap station cluster are shown in Table 1:

[0191] Table 1 Basic operating parameters of bus battery swap station cluster

[0192]

[0193] Taking Battery Swap Station No. 1 in the region's battery swap station cluster as an example, its three electric bus routes, including bus cluster No. 57, are operated according to the "first-in, first-out" battery swap rule. Based on this strategy, the bus departure times are set to follow a uniform distribution of points U[5:30, 6:00], the arrival times are set to follow a uniform distribution of points U[21:30, 22:00], the departure intervals are set to follow a uniform distribution of points U[10 minutes, 20 minutes], and the average route length is set to follow a normal distribution of points N[20, 35], in kilometers. Based on this strategy, the generated bus operation status is detailed in Table 2:

[0194] Table 2 Bus operation status

[0195]

[0196] The battery swap station adopts the industrial electricity price. The present invention refers to the time-of-use electricity price data of the urban area of ​​City A in August 2023. The time-of-use electricity price is formed on the basis of the agent purchase price according to the peak-valley price ratio and time period division regulations, as shown in Table 3.

[0197] Table 3 Reference values ​​for charging and discharging electricity prices of battery swap station clusters

[0198]

[0199] Considering the economic benefits of PV power generation compared to traditional energy sources, the installed capacity of PV panels should be moderate due to the limited scale of BSSs and their lack of energy storage. To ensure data accuracy and practicality, actual data from an 800kWp distributed PV power station in a town in City A is used, as shown in Table 4.

[0200] Table 4 Distributed photovoltaic power generation

[0201]

[0202]

[0203] In order to verify the effectiveness of the proposed model and to ensure that it is consistent with the working cycle and duration set by the model, the present invention uses the wind power output of a wind farm in City A during 24 hours on a certain day in summer as the actual renewable energy power output for experimental solution. The wind power output data are shown in Table 5.

[0204] Table 5 Wind power output curve

[0205]

[0206] It can be seen that electricity prices rise during peak periods and are relatively low during off-peak periods. Since the peak-valley and off-peak time-of-use electricity prices are set based on the regional grid, the peak and valley load times in that region roughly coincide with the peak and valley periods of the time-of-use electricity price. Therefore, time-of-use electricity prices can be used to guide the orderly scheduling of battery charging and discharging in the battery swap station cluster, effectively regulating the grid load.

[0207] The present invention assumes that there are four electric bus battery swap stations in a certain area of ​​the city, and the scheduling optimization belongs to the day-ahead scheduling plan, that is, the battery packs of the battery swap station cluster are charged the day before, which does not affect the scheduling of the next day, ensuring efficient connection and smooth operation. In order to illustrate the effectiveness of the model, the experimental parameters are set according to the actual data collected, and the Monte Carlo simulation with a time interval of 1 hour is used to sample the battery swap period and the state of charge SOC of each electric bus multiple times. The number of Monte Carlo simulations is 10, and the variance coefficient is required to be less than 1%. The present invention takes τ m =0.067;η NR =0.8;η DD =0.9; γ=11.85; ε follows the U(1,0.25) distribution. The number of battery swaps in 24 time periods throughout the day is calculated. The day-ahead forecast of battery swap demand is based on the method and data in Section 2.4. The number of battery swap demands in each time period is as follows: Figure 7shown.

[0208] Figure 7 A schematic diagram showing the number of battery replacement requirements in each time period according to an exemplary embodiment of the present invention is shown. Figure 7 It can be seen that the peak of the battery swapping load occurs between 8:00 AM and 11:00 AM and between 6:00 PM and 8:00 PM. These two time periods correspond to the rush hour and the rush hour, respectively, and can well reflect the actual travel needs of residents. This shows that the constructed day-ahead scheduling model for battery charging and discharging of battery swap station clusters has high practical significance. In addition, it is worth noting that there is a certain overlap between the peak of the battery swapping load and the peak of the grid's base load. If a disordered charging method is adopted, the peak-to-valley difference in load will be further exacerbated, posing a potential threat to the safe and stable operation of the power grid. Therefore, it is necessary to adopt a reasonable charging strategy to optimize load distribution and ensure the stable operation of the power grid.

[0209] After applying the multi-objective particle swarm algorithm to solve the problem, the number of excellent individuals was small in the initial iterations, and the crossover characteristics were not obvious, resulting in a small difference in the convergence rates of the two algorithms. However, as the iterations progressed, the number of non-inferior solutions increased, accelerating the convergence rate. The two objectives achieved solution convergence at 226 and 239 iterations, respectively, effectively avoiding local optima and obtaining a better feasible solution. Furthermore, improvements to the Pareto solution set update strategy resulted in better diversity and distribution characteristics. The total scheduling cost obtained using the improved particle swarm algorithm was 11,017.01 yuan, with a fitting value of 0.1837.

[0210] The comparison of the total output of the battery swap station cluster and the external wind power curve is shown in Figure 8 As shown in the figure, the comparison of the waveform similarity of two curves is shown, which are the net load curve of the battery swap station cluster after data processing and the external wind power output curve. Figure 8 It can be seen that the net load curve of the battery swap station cluster and the external wind power output curve have a high degree of fit and basically similar trends. In particular, in the two time periods of 1:00-7:00 and 14:00-16:00, the wind power output has obvious output peaks. It is worth noting that these two peak periods correspond to the off-peak periods of power grid consumption. This time overlap may lead to a large amount of "wind abandonment phenomenon", that is, wind power cannot be effectively connected to the grid, which in turn has an adverse impact on the peak-shaving capacity of the grid. However, by observing Figure 8 It is not difficult to see that the net load curve of the battery swap station cluster shifts from the peak power consumption period of the power grid to the low power consumption period and the period when the wind power output is large. This positive load transfer trend can prove that the scheduling scheme proposed in this invention shows good matching and absorption friendliness in dealing with large-scale wind power.

[0211] The number of battery packs charged and discharged at the battery swap station cluster is as follows: Figure 9As shown in the figure, the number of batteries charged (red) and discharged (blue) at the four battery swap stations is shown in the figure. The optimization strategy for battery swap station cluster scheduling on a typical summer day is as follows: Figure 10 As shown in the figure, three curves are displayed, representing the distributed photovoltaic output, purchased electricity power, and the total charging power of the battery swap station cluster (charging demand).

[0212] On a typical summer day, the battery swap station cluster adjusts the amount of electricity purchased from the regional power grid based on its own charging needs. Figure 9 It can be seen that under the battery swapping mode, the battery swap station cluster not only meets charging demand in a timely manner, but also rationally utilizes excess fully charged battery packs for discharge. Battery pack discharge is mainly concentrated between 6:00 PM and midnight, and the charging demand of the battery swap station cluster during this time period is the lowest in the day. This is because after meeting the charging demand, the battery swap station cluster sells electricity back to the grid through the large-capacity battery energy storage of electric buses, thereby reducing the overall dispatch cost. In addition, between 9:00 AM and 12:00 PM, a large number of batteries are also involved in discharge at battery swap stations 2, 3, and 4. These two time periods coincide with periods with higher electricity prices, which helps to further increase the battery swap station cluster's electricity sales revenue and achieve the goal of reducing the overall dispatch cost.

[0213] Combine Figure 10 It can be seen that the total charging curve of the battery swap station cluster has two peak periods, namely 1:00-2:00 at night and 7:00 in the morning, and between 13:00-14:00 in the afternoon. The two peak periods correspond to the low and flat periods of the power grid, respectively. This shows that the battery swap station cluster has significantly improved the problem of high overlap between the original battery swap demand and the peak period of power grid consumption through the orderly scheduling strategy model, and effectively avoided the phenomenon of "peak on peak" power grid consumption that is likely to be caused by the "instant charge and discharge" of the battery. Through analysis, it can be seen that when the distributed photovoltaic output is 0, the charging power (that is, the net load is greater than 0) and the power purchase power curve of the battery swap station cluster basically coincide. This is because the battery pack charging demand of the battery swap station cluster is large, but the photovoltaic output is relatively small, so additional electricity needs to be purchased from the power grid. When PV output is greater than zero, particularly between 10:00 AM and 4:00 PM, the required electricity purchase significantly decreases, reaching zero at 2:00 PM. This demonstrates that PV generation covers the majority of the charging needs of the battery swap station cluster during this time period, saving approximately 12,784.02 yuan in charging costs. This data demonstrates that this scheduling scheme demonstrates that distributed PV generation can effectively reduce scheduling costs and alleviate grid power demand.

[0214] In summary, the scheduling scheme of the present invention is reasonable, and the scheduling model can well describe the charging and discharging scheduling problem of the battery swap station cluster. The above results prove the rationality of the model, the scientific nature of the algorithm, and the effectiveness of the scheduling scheme.

[0215] The present invention is suitable for execution in a computing device. Figure 6 6 shows a block diagram of a computing device according to an exemplary embodiment of the present invention. In a basic configuration, computing device 600 includes at least one processing unit 620 and system memory 610. According to one aspect, system memory 610 includes, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such storage, depending on the configuration and type of computing device. According to one aspect, system memory 610 includes an operating system 611.

[0216] According to one aspect, operating system 611, for example, is suitable for controlling the operation of computing device 600. Furthermore, examples may be practiced in conjunction with graphics libraries, other operating systems, or any other application programs and are not limited to any particular application or system. Figure 6 This basic configuration is illustrated in FIG by those components within dashed line 615. According to one aspect, computing device 600 has additional features or functionality. For example, according to one aspect, computing device 600 includes additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tape.

[0217] As stated above, according to one aspect, a program module 612 is stored in the system memory 610. According to one aspect, the program module 612 can be implemented as one or more computer program products. The present application does not limit the type of computer program products. For example, the computer program products may include: email, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided applications, web browsers, etc. In some embodiments according to the present application, computer programs / instructions related to the city bus exchange station battery scheduling method 300 are packaged into a computer program product. When these computer programs / instructions are executed by a processor (i.e., the processing unit 620), the city bus exchange station battery scheduling method 600 according to the present application is implemented.

[0218] According to one aspect, examples may be practiced on a circuit comprising discrete electronic components, a packaged or integrated electronic chip containing logic gates, a circuit utilizing a microprocessor, or a single chip containing electronic components or a microprocessor. Figure 6In the embodiment of the present invention, each or many components shown in the embodiment of the present invention can be integrated into a system on a chip (SOC) on a single integrated circuit to practice the example. According to one aspect, such a SOC device may include one or more processing units, a graphics unit, a communication unit, a system virtualization unit, and various application functions, all of which are integrated (or "burned") onto a chip substrate as a single integrated circuit. When operated via the SOC, the functions described in the present invention can be operated via a dedicated logic integrated with other components of the computing device 400 on a single integrated circuit (chip). Other technologies capable of performing logical operations (such as AND, OR, and NOT) can also be used to practice embodiments of the present invention, and the other technologies include but are not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the present invention can be practiced in a general-purpose computer or in any other circuit or system.

[0219] According to one aspect, the computing device 600 may also have one or more input devices 631, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. It may also include an output device 632, such as a display, a speaker, a printer, etc. The aforementioned devices are examples, and other devices may also be used. The computing device 600 may include one or more communication connections 633 that allow communication with other computing devices 640. Examples of suitable communication connections 633 include, but are not limited to: RF transmitter, receiver, and / or transceiver circuitry; Universal Serial Bus (USB), parallel, and / or serial ports. The computing device 600 may be communicatively connected to other computing devices 640 via the communication connection 633.

[0220] The embodiment of the present invention also provides a non-transitory readable storage medium, which stores instructions for causing the computing device to execute a method according to an embodiment of the present invention. The readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be a computer-readable instruction, a data structure, a module of a program, or other data. Examples of readable storage media include, but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, tape disk storage or other magnetic storage device or any other non-transitory readable storage medium.

[0221] According to one aspect, communication media is implemented by computer-readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transport mechanism), and includes any information delivery media. According to one aspect, the term "modulated data signal" describes a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0222] It should be noted that although the computing device shown above only includes a processing unit 620, a system memory 610, an input device 631, an output device 632, and a communication connection 633, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will understand that the device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0223] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention 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.

[0224] Similarly, it should be understood that in order to streamline the disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0225] When the 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; the processor is configured to execute the urban bus interchange station battery dispatching method of the present invention according to the instructions in the program code stored in the memory.

[0226] By way of example and not limitation, computer-readable media include computer storage media and communication media. Computer-readable media include computer storage media and communication media. Computer storage media stores information such as computer-readable instructions, data structures, program modules, or other data. Communication media generally embodies 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 includes any information delivery media. Combinations of any of the above are also included within the scope of computer-readable media.

[0227] Although the present invention has been described with respect to a limited number of embodiments, those skilled in the art, having the benefit of the foregoing description, will appreciate that other embodiments are contemplated within the scope of the invention thus described. Furthermore, it should be noted that the language used in this specification has been selected primarily for readability and instructional purposes, rather than for the purpose of explaining or limiting the subject matter of the present invention. Consequently, numerous modifications and variations will be apparent to those skilled in the art. The disclosure of the present invention is intended to be illustrative, not restrictive, of the scope of the invention.

Claims

1. A method for dispatching batteries in a cluster of urban public bus swap stations, wherein the cluster is connected to a power grid, which is connected to a renewable energy station. Each swap station includes photovoltaic panels for charging batteries. The swap stations purchase electricity from the power grid to meet charging needs and discharge their batteries to sell electricity to the power grid, comprising: Establish an electric bus battery replacement demand model based on the battery replacement characteristics of electric buses; Determine H based on the electric bus battery replacement demand model it , where H it represents the number of battery swapping requests at battery swap station i in time period t. The value of i ranges from 1 to I, I is the total number of battery swap stations included in the battery swap station cluster, and the value of t ranges from 0 to T. After dividing a day into multiple time periods according to predetermined time intervals, the total number of time periods included in a day is T+1; A day-ahead scheduling model for battery charging and discharging of battery swap station clusters is established, where the decision variables include the number of batteries C being charged at the battery swap station i in time period t. it and the number of batteries being discharged, D it , whose objective function includes min f1=C g -C d and Where f1 represents the cluster scheduling cost of battery swap stations, C g represents the electricity purchase cost of the battery swap station cluster, C d represents the electricity sales revenue of the battery swap station cluster, f2 represents the fitting degree between the net load of the battery swap station cluster and the green power output, represents the normalized net load of the battery swap station cluster at time period t, represents the normalized green power output power of the renewable energy station in time period t; as well as According to the constraints, the battery charging and discharging scheduling model of the battery swap station cluster is solved, and it is found that when the battery swap station cluster scheduling cost is the lowest and the fitting degree between the net load of the battery swap station cluster and the green power output is the best, C it and D it The optimal solution is obtained and used as the battery scheduling solution for the battery swap station cluster, wherein the constraints include F it ≥H it , F it represents the number of fully charged batteries at battery swap station i in time period t; Among them, H it It is determined based on whether each electric bus needs to replace the battery in time period t and the number of batteries that need to be installed when the battery replacement is required; Whether each electric bus needs to be replaced in time period t is determined according to the following steps: If the electric bus departs from the battery swap station for the first time, the battery needs to be replaced; If it is not the first departure, the battery of the electric bus needs to be replaced when the remaining power of the electric bus is less than the one-way power consumption of the route.

2. The method according to claim 1, wherein C g Determined according to the following formula: C d Determined according to the following formula: Among them, λ g，t is the electricity price of battery charging and discharging during period t, is the power purchased by the battery swap station i in period t, is the discharge power of battery swap station i in time period t, and Δt represents the duration of charging or discharging in each time period.

3. The method according to claim 2, wherein: The sum of the purchased power and the output power of the photovoltaic panel is greater than or equal to the charging power of the battery swap station represents the charging power of battery swap station i in time period t, which is determined according to the following formula: P e，c is the rated charging power of the unit battery, η c is the rated charging efficiency of the unit battery, Determined according to the following formula: P e，d is the rated discharge power of the unit battery, η d is the rated discharge power of the unit battery.

4. The method according to claim 2, wherein: According to the net load P of the battery swap station cluster λ，t Normalized to get, P λ，t is the net load of the battery swap station cluster in time period t, P λ，t Determined according to the following formula:

5. The method according to claim 1, wherein One-way power consumption E of the running route m Determined according to the following formula: m represents the mth route, and the value range of m is 1 to M. M represents the total number of electric bus routes in the city. m is the average energy consumption of the mth route of electric buses, η NR is the efficiency of the internal combustion engine of traditional buses, γ is the conversion coefficient between traditional energy and electric energy, S m is the length of the mth route where electric buses operate, η DD is the motor efficiency of the electric bus, and ε is the fluctuation coefficient caused by different road conditions and loads.

6. The method of claim 1, wherein: The constraints also include: one or more of: battery charging and discharging power constraints, charging demand constraints, relationship constraints between battery charge state and fully charged battery state, battery quantity constraints in different states, long-term operation constraints, backup margin constraints and battery power protection constraints.

7. The method of claim 1, wherein: The algorithm for solving the day-ahead scheduling model for battery charging and discharging of the battery swap station cluster includes a particle swarm optimization algorithm.

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

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