Method, device and computer program product for energy storage dispatch management of charging stations

Abstract

Embodiments of the present disclosure provide a method, device and computer program product for energy storage scheduling management of a charging station. The method comprises: obtaining load-related data of the charging station; establishing a profit objective function for the charging station, the profit objective function being related to a price spread profit of the charging station and a vehicle scheduling cost, the vehicle scheduling cost including a cost of scheduling a vehicle to charge at the charging station; determining a control guidance parameter of the charging station according to the load-related data and the profit objective function, the control guidance parameter including a power guidance parameter of an energy storage battery for charging and a scheduling guidance parameter; and controlling power of the energy storage battery and vehicle scheduling of the charging station based on the control guidance parameter. The profit of the charging station is further maximized by considering the way of scheduling the charging vehicles of the charging station.

Description

Technical Field

[0001] The embodiments of this disclosure relate to the field of charging station control, and more specifically, to a method, apparatus, and computer program product for energy storage scheduling management of charging stations. Background Technology

[0002] Electric vehicle charging stations are sites for charging electric vehicles. With the increasing popularity of electric vehicles, charging stations have become a key focus for the automotive and energy industries. Electric vehicle charging stations effectively solve the problem of fast charging, while also saving energy and reducing emissions. Some charging stations, in order to reduce peak load demand, save electricity costs, and thus improve load characteristics and participate in system peak shaving, have adopted distributed energy storage systems.

[0003] Currently, charging stations using distributed energy storage systems employ various load management strategies to increase revenue. However, none of these strategies consider further increasing revenue through vehicle scheduling. Summary of the Invention

[0004] According to a first aspect of this disclosure, a method for energy storage dispatch management of a charging station is provided. The method includes: acquiring load-related data of the charging station; establishing a revenue objective function for the charging station, the revenue objective function being related to the price difference revenue of the charging station and vehicle dispatch costs, the vehicle dispatch costs including the cost of dispatching vehicles to the charging station for charging; determining control guidance parameters for the charging station based on the load-related data and the revenue objective function, the control guidance parameters including power guidance parameters for the energy storage battery used for charging and dispatch guidance parameters; and controlling the power of the energy storage battery and vehicle dispatch at the charging station based on the control guidance parameters.

[0005] In some embodiments, determining the power guidance parameters for a charging station includes: determining load forecast data for the charging station based on load-related data; and determining the power guidance parameters and scheduling guidance parameters based on the load forecast data and the revenue objective function.

[0006] In some embodiments, load-related data includes at least one of the following: historical load data related to the charging station, weather data, date and time data, event data, grid electricity price, and the installed capacity of the charging station's energy storage batteries.

[0007] In some embodiments, determining the power guidance parameters for a charging station further includes: determining the energy storage constraints of the charging station; and determining control guidance parameters based on the energy storage constraints and the revenue objective function.

[0008] In some embodiments, determining the control guidance parameters further includes: determining a set of candidate control guidance parameters based on energy storage constraints; determining the fitness value of each candidate control guidance parameter in the set based on a profit objective function; selecting a subset of candidate control guidance parameters in the set based on the fitness values; and performing crossover and mutation on the selected subset of candidate control guidance parameters to obtain a subset of new candidate control guidance parameters.

[0009] In some embodiments, energy storage constraints include at least one of the following: the charge / discharge power range of the energy storage battery, the charge / discharge capacity range, the discharge time constraint, and the start and end capacity constraint.

[0010] According to a second aspect of this disclosure, an electronic device is provided. The electronic device includes: at least one processing unit; and at least one memory coupled to the at least one processing unit and storing machine-executable instructions, which, when executed by the at least one processing unit, cause the device to perform actions, including: acquiring load-related data of a charging station; establishing a revenue objective function for the charging station, the revenue objective function being related to the price difference revenue of the charging station and vehicle dispatching costs, the vehicle dispatching costs including the cost of dispatching vehicles to the charging station for charging; determining control guidance parameters for the charging station based on the load-related data and the revenue objective function, the control guidance parameters including power guidance parameters for an energy storage battery used for charging and dispatching guidance parameters; and controlling the power of the energy storage battery and vehicle dispatching at the charging station based on the control guidance parameters.

[0011] According to a third aspect of this disclosure, a computer program product is provided, which is tangibly stored on a non-volatile computer-readable medium and includes machine-executable instructions that, when executed, cause a machine to perform the steps of the method in the first aspect of this disclosure. Attached Figure Description

[0012] The above and other objects, features and advantages of this disclosure will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0013] Figure 1 A schematic diagram of an example environment in which the methods for energy storage scheduling management of charging stations according to various embodiments of the present disclosure can be implemented is shown;

[0014] Figure 2 The curves showing the changes in load and electricity price at the charging station over time are shown.

[0015] Figure 3 A schematic block diagram of a method for energy storage scheduling management of a charging station according to an example embodiment of the present disclosure is shown;

[0016] Figure 4 A schematic flowchart illustrating the determination of control guidance parameters according to some embodiments of the present disclosure is shown;

[0017] Figure 5A and Figure 5B A schematic diagram of control guidance parameters determined according to embodiments of the present disclosure is shown;

[0018] Figure 6 A flowchart illustrating a method for energy storage scheduling management of a charging station according to embodiments of the present disclosure is shown; and

[0019] Figure 7 A schematic block diagram of an electronic device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0020] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0021] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0022] The principles of this disclosure will now be described with reference to several exemplary embodiments illustrated in the accompanying drawings. While preferred embodiments of this disclosure are shown in the drawings, it should be understood that these embodiments are described only to enable those skilled in the art to better understand and implement this disclosure, and are not intended to limit the scope of this disclosure in any way.

[0023] Furthermore, the term "in response to" as used herein refers to the state in which a corresponding event occurs or a condition is satisfied. It will be understood that the timing of subsequent actions performed in response to such an event or condition is not necessarily strongly correlated with the time when the event occurs or the condition is met. For example, in some cases, subsequent actions may be performed immediately upon the occurrence of the event or the fulfillment of the condition; while in others, they may be performed some time after the occurrence of the event or the fulfillment of the condition.

[0024] Because electricity consumption occurs during peak and off-peak periods, and the scarcity and supply cost of electricity vary at different times, time-of-use (TOU) pricing was developed. TOU pricing is designed to guide users towards rational electricity consumption and reduce peak-valley imbalances in the power system. TOU pricing divides a 24-hour day into several time periods based on system operating conditions, charging a different price for each period.

[0025] Energy storage charging stations typically have energy storage batteries for storing electrical energy. These batteries can be charged at appropriate times (e.g., during off-peak hours) and discharged at appropriate times (e.g., during peak hours), thus enabling them to generate revenue through peak-shaving and valley-filling strategies that take advantage of time-of-use electricity pricing differences.

[0026] To maximize charging station revenue, most charging stations employ charging and discharging control strategies configured within their local energy management systems. These strategies are based on local peak and off-peak electricity demand, combined with the charging station's actual electricity usage. This means, as mentioned earlier, maximizing revenue by charging during off-peak hours and discharging during peak hours. Other charging stations consider the variations in vehicle traffic at different times and employ cloud-based control strategies. These cloud-based strategies combine daily electricity consumption data from cloud-based systems with peak-valley price differences to dynamically configure charging and discharging.

[0027] However, the peak-hour and off-peak charging control strategies configured by local energy management systems lack timeliness. They only consider price mechanisms to meet off-peak electricity demand, without guaranteeing whether the stored off-peak electricity can be fully utilized, resulting in very poor returns. While cloud-based control strategies dynamically adjust energy storage charging and discharging strategies based on charging station electricity consumption, better achieving a balance between charging and discharging, they still do not address maximizing the utilization of stored off-peak electricity. Relying entirely on the charging station's existing capacity for absorbing off-peak electricity also yields very limited returns.

[0028] This disclosure proposes a method for energy storage dispatch management of charging stations. This method further maximizes the revenue of charging stations by considering the dispatching of charging vehicles. Vehicle dispatching refers to dispatching vehicles to charging stations using appropriate methods (direct discounts, coupons). It should be understood that revenue has a leverage effect; as long as the overall revenue exceeds the expenditure, the charging station can obtain greater revenue. The method implemented according to this disclosure, while utilizing the peak-valley difference and new energy consumption revenue, fully considers the demand response subsidy difference as a balancing cost for vehicle dispatching activities. With the objective of maximizing the difference between energy storage charging and discharging revenue and vehicle dispatching costs, it establishes an optimal energy storage charging and discharging control method within the charging station based on vehicle dispatching, considering the load situation after vehicle dispatching, and formulating optimal energy storage charging and discharging strategies and vehicle dispatching load strategies.

[0029] The charging station according to embodiments of this disclosure is a station for charging vehicles such as electric vehicles, etc. Figure 1 As shown, it includes one or more charging parking spaces equipped with charging piles 101 for charging vehicles. The charging station also includes an energy storage battery 102 for charging the vehicle's battery when appropriate and discharging it when appropriate. For example, Figure 2 The diagram shows the load and peak / valley electricity price distribution of a charging station. In this paper, load is also referred to as output power, usually expressed in kilowatt-hours (kWh), which refers to the electrical power output by the charging station at a given time. To increase revenue, the energy storage battery 102 can be charged when electricity prices are low and discharged to charge electric vehicles when prices are high. The energy storage battery 102 used in the charging station generally has a predetermined capacity (also called installed capacity), that is, the maximum amount of electrical energy it can store, also usually expressed in kilowatt-hours (kWh).

[0030] To increase revenue, charging stations can charge the energy storage battery 102 when electricity prices are low and discharge it to charge electric vehicles when prices are high. However, as mentioned earlier, relying solely on the charging station's existing capacity to absorb traffic has limited effectiveness. According to embodiments of this disclosure, the demand response subsidy difference is fully considered as a balancing cost for vehicle dispatching activity compensation (direct price reduction, coupons), taking into account the load situation after vehicle dispatching, and an optimal energy storage charging / discharging strategy and vehicle dispatching load strategy are formulated. As will be discussed later, the energy storage charging / discharging strategy and vehicle dispatching load strategy will be represented by the output power guidance parameters for the energy storage battery 102 and the dispatching guidance parameters for the charging station, respectively. The power guidance parameters and dispatching guidance parameters will also be collectively referred to as control guidance parameters. That is, control guidance parameters include power guidance parameters and dispatching guidance parameters.

[0031] Figure 3 A flowchart illustrating a method for energy storage management of a charging station according to an embodiment of this disclosure is shown. Figure 3 As shown, the energy storage management method for a charging station according to an embodiment of this disclosure first acquires load-related data of the charging station. The load-related data includes various data related to the charging station's load, including but not limited to at least one of the following: historical load data related to the charging station, weather data, date and time data, event data, grid electricity price, and the installed capacity of the charging energy storage battery 102.

[0032] Historical load data related to charging stations in load-related data can include at least one of the following: historical load of the charging station at each previous time unit, average load at each time unit over a predetermined time period (e.g., the past three days, week, or month), average load for each day over the predetermined time period (e.g., the past three days, week, or month), and the ratio of the load value at a certain time unit on the previous day to the load values ​​at the corresponding time units on the previous two days. Using this data allows for more accurate prediction of future loads, thus preparing for subsequent power guidance parameters.

[0033] The unit of time mentioned here refers to the minimum time period used for load analysis and calculation. To achieve a balance between computational load and accuracy, the unit of time can be set to a reasonable value. For example, in some embodiments, the unit of time can be 15 minutes, or 1 / 4 hour. Of course, to reduce computational load, the unit of time can be set longer (e.g., 1 hour), while to improve calculation accuracy, the unit of time can be set shorter (e.g., 5 minutes or other appropriate time periods).

[0034] The load-related data includes weather data, such as historical weather data related to the charging station, including but not limited to temperature, humidity, wind speed, and precipitation. Date and time data includes but is not limited to season, month, weekday, weekend, and public holiday. For example, the load on a charging station during different times of the weekday may be completely different from that on different times of the weekend. Therefore, this date and time data needs to be fully considered when making load forecasts. In addition, date and time data can also include time-of-day data, such as the time value and the hour it belongs to. Event data can include various events that may affect the charging station's load, including but not limited to major social events (such as epidemics), accidents, and natural disasters. Utilizing the historical load data, weather data, date and time data, and event data related to the charging station mentioned above can make subsequent load forecasts more accurate.

[0035] In some embodiments, after obtaining the load-related data mentioned above, this data can be input into a load forecasting model to predict the load situation for the next day. Of course, to make the forecast more accurate, future weather data, date and time data, event data, etc., should also be considered. Before performing load forecasting, the obtained data can be preprocessed, such as data cleaning, outlier removal, and missing value filling.

[0036] In some embodiments, the model used for load forecasting may employ the XGBoost algorithm. XGBoost is a popular and efficient open-source implementation of the gradient boosting tree algorithm. Gradient boosting is a guided learning algorithm that attempts to combine a set of estimates from a set of simpler and weaker models to accurately predict the target variable, i.e., the load forecast data.

[0037] Of course, it should be understood that the example of using the XGBoost algorithm as the algorithm for the load forecasting model described above is merely illustrative and is not intended to limit the scope of protection of this disclosure. The method according to the embodiments of this disclosure can employ any suitable forecasting algorithm to forecast future loads using load-related data.

[0038] Next, a revenue objective model for the charging station can be established. This model can be represented as a revenue objective function, hence it is also called the revenue objective function. Considering vehicle scheduling, let the charging power of the energy storage battery 102 be positive, and the discharging power be negative. Let b(x) (x = 1, ..., N) be the output power of the energy storage battery 102 at unit times 1 to N, and d(x) (x = 1, ..., N) be the amount of electricity replenished by the vehicle at unit times 1 to N. The replenishment amount is the amount of electricity charged by the vehicle through activity compensation, expressed in kWh. Since activity compensation incurs costs (referred to as vehicle scheduling costs), these costs need to be subtracted when determining the revenue of the charging station. Assume that, based on long-term evaluation, the vehicle scheduling cost per kWh is a predetermined constant. This yields the revenue objective function. The revenue objective function can be expressed as the following equation.

[0039]

[0040] Where b(x) is the output power of the energy storage battery 102 at a unit time x, d(x) is the vehicle dispatch replenishment power, that is, the charging amount of the dispatched vehicle, Δt is the time length of a unit time, such as 1 / 4 hour, and cost is the vehicle dispatch cost per kWh.

[0041] The objective function of revenue and the price difference revenue of the charging station can be determined by the above equation (1). This is related to vehicle scheduling costs. The revenue objective function indicates maximizing revenue at all times within a predetermined period, taking vehicle scheduling into account. The predetermined period can be any suitable time length. For example, a day (24 hours) is typically used to maximize revenue. Of course, it should be understood that other lengths can also be used to determine revenue maximization; for example, the predetermined period can be chosen as a week, half a month, or a month, etc. The following description will primarily use a one-day predetermined period as an example to illustrate the concept according to this disclosure. The case for other predetermined periods is similar and will not be elaborated upon separately below.

[0042] In some embodiments, to make the determined control guidance parameters more reliable, energy storage constraints of the charging station may also be considered. Specifically, in some embodiments, energy storage constraints may include at least one of the following: the charge / discharge power range of the energy storage battery 102, the charge / discharge capacity range, the discharge time constraint, and the start and end capacity constraint.

[0043] The charging and discharging power range of the energy storage battery 102 represents the upper and lower limits of the charging and discharging power of the energy storage battery 102. That is, the output power of the energy storage battery 102 at each unit time cannot exceed the power range, which can be expressed by the following equation.

[0044] P min ≤b(x)≤P max x=1,…,N (2)

[0045] Where P min P represents the lower limit of the output power of the energy storage battery 102 per unit time. max The upper limit of output power per unit time.

[0046] The charge / discharge capacity range indicates that the amount of electricity in the energy storage battery 102 at any given time does not exceed the upper or lower limit of the battery capacity, and can be expressed by the following equation.

[0047] 0≤s(x)≤S up x = 1, 2, ..., N

[0048] s(x+1)=s(x)+b(x)*Δt x=1,2,…,N-1 (3)

[0049] In equation (3) above, s(x) represents the capacity of the energy storage battery 102 at unit time x, S up Here, b(x) represents the upper limit of the battery capacity of the energy storage battery 102, b(x) represents the output power per unit time x, and Δt represents the time length per unit time, such as 1 / 4 hour. By setting a charging and discharging capacity range constraint, the energy storage battery 102 can be better protected, and its lifespan and reliability can be improved.

[0050] As can be seen from the above equation, the capacity of the energy storage battery 102 needs to meet the upper limit of capacity S. up Between 0 and 0. Furthermore, to better protect the energy storage battery 102, in some embodiments, the depth of discharge (DOD) of the energy storage battery 102 can also be considered. The depth of discharge (DOD) represents the percentage of the battery's discharge capacity to its rated capacity (i.e., installed capacity). For example, setting the depth of discharge to 90 means that 90% of the installed capacity of the energy storage battery 102 can be used for discharge. For example, when the battery capacity is between 5% and 95%, the energy storage battery 102 can be discharged for charging electric vehicles. Setting the depth of discharge is to better protect the battery, thereby improving battery life. Considering the depth of discharge, the upper limit of the battery capacity of the energy storage battery 102 can be set to S. up -S up *(1-DOD) / 2, and set the lower limit of capacity to S. low =S up *(1-DOD) / 2, where S up For example, the upper limit of the capacity of the energy storage battery 102 (e.g., 100%), S low This is the lower limit of the capacity of the energy storage battery 102 (e.g., 0). In this way, the battery can be better protected.

[0051] The discharge time constraint in the energy storage constraints is intended to ensure that the discharge capacity of the energy storage battery 102 per unit time is less than the load and the load dispatched from the vehicle corresponding to that unit time, and can be expressed by the following equation.

[0052] -b(x)*Δt≤r(x)+d(x) if b(x)<0 x=1,2,…,N (4)

[0053] Where r(x) is the load at unit time x, b(x) is the output power of energy storage battery 102 at unit time x, Δt is the time length of unit time, such as 1 / 4 hour, and d(x) is the vehicle dispatch replenishment power.

[0054] The start and end point capacity constraints in energy storage constraints aim to keep the energy levels consistent at the start and end of a predetermined cycle, thus facilitating cyclic energy storage control of charging stations. The start and end point capacity constraints can be expressed by the following equation.

[0055] S(1)=S(N)=S initial (5)

[0056] Where S(1) represents the charge value of the energy storage battery 102 at the initial moment of the predetermined period, and S(N) represents the charge value of the energy storage battery 102 at the final moment of the predetermined period. initial This indicates the initial capacity of the battery. For example, the initial capacity of the battery can be set to 95% or any other suitable value.

[0057] After determining the objective function and constraints mentioned above, appropriate algorithms can be used to determine the control guidance parameters of the charging station based on load-related data and the revenue objective function. Figure 4 A flowchart illustrating the process of determining control guidance parameters using a control parameter determination algorithm is shown. It should be understood that the process described below using the control parameter determination algorithm to determine control guidance parameters is merely illustrative and not intended to limit the scope of this disclosure. Any other suitable algorithm is also possible.

[0058] In the process of using algorithms to determine the control guidance parameters of charging stations based on load-related data and revenue objective functions, the population size P and the maximum number of iterations Gen are first set. max Crossover and mutation probabilities are then calculated. The population is then initialized, i.e., an initial population (also called a set) of size P is randomly generated. This initial population includes candidate control parameters. Fitness values ​​are calculated using these candidate control parameters, and crossover and mutation are performed iteratively to determine the candidate control parameters. These candidate control parameters included in the initial population can be candidate control parameters determined after considering the above constraints. These candidate control parameters will be referred to as individuals or solutions below.

[0059] After determining the initial population P, a fitness value is assigned to each individual in the initial population P. Fitness is an indicator used to measure the quality of individuals in the population. In genetic algorithms, fitness is the value of a criterion for feature combinations. The selection of this criterion is crucial to genetic algorithms. Genetic algorithms generally do not require other external information during the search and evolution process; they only use an evaluation function to assess the quality of individuals or solutions, which serves as the basis for subsequent genetic operations. Since the fitness function in a genetic algorithm compares and ranks individuals and calculates selection probabilities based on this comparison, the fitness function must have a positive value. Therefore, the objective function can be mapped to a fitness function that maximizes the objective value and has a non-negative function value. In this paper, the fitness value of an individual can be determined by maximizing the objective benefit as mentioned earlier. Then, a selection step is performed, where individuals in the population are selected based on their fitness values. Individuals with higher fitness values ​​have a higher probability of being selected, and individuals with lower fitness values ​​have a lower probability of being selected. Through the selection step, a subset of individuals in the population are selected as parents for the subsequent crossover step. These individuals will also be added to the offspring population.

[0060] Crossover refers to splitting and recombinizing multiple components (similar to gene segments) contained in an individual in the offspring population. For example, one individual may contain three components: a, b, and c, while another individual may contain components: d, e, and f. Two individuals are crossed using an appropriate crossover operator to obtain new offspring individuals. For example, the new offspring individual may contain components a, e, and f, or any other suitable components from the two example parent individuals mentioned above, and this new offspring individual is placed into the offspring population. Next, a mutation operation is performed on the offspring population. Mutation involves altering some components of a subset of individuals (based on mutation probabilities). For example, for the newly acquired offspring individual containing components a, e, and f, one component is mutated, changing its value e to g, generating a new offspring individual containing components a, g, and f, which is then placed into the offspring population. The crossover and mutation operations are repeated until the number of individuals in the offspring population reaches P, which is the same as the number of individuals in the initial population. This process indicates that the initial population has completed one iteration.

[0061] Then, the offspring population is iterated again, that is, the fitness value of each individual is determined, a portion of individuals are selected based on the fitness value and added to the new offspring population, and subsequent crossover and mutation operations are continued until the number of individuals in the new offspring population reaches P again. When the above iteration count reaches the preset maximum iteration count Gen... max After that, it indicates that the termination condition has been met, and the control guidance parameters will be determined based on the individuals in the final determined offspring population. Figure 5A Exemplary power guidance parameters for energy storage battery 102, determined according to the process described above, are shown. Figure 5B The exemplary scheduling guidance parameters determined are shown, wherein Figure 5A and Figure 5B The horizontal axis represents time, and the vertical axis represents the load of the energy storage battery 102, where positive values ​​represent charging power and negative values ​​represent discharging power. Next, the energy storage and discharging of the energy storage battery 102, as well as the vehicle scheduling of the charging station, can be controlled based on the determined control guidance parameters. In this way, optimal energy storage charging and discharging strategies and vehicle scheduling load strategies can be formulated based on vehicle scheduling, thereby maximizing the revenue of the charging station.

[0062] Figure 6 A flowchart illustrating an energy storage scheduling and management method for a charging station according to an embodiment of the present disclosure is shown. In some embodiments, the method may be implemented by a management system for the charging station or other suitable equipment. For ease of understanding, the specific examples, figures, or values ​​mentioned in the following description are merely exemplary and are not intended to limit the scope of protection of this disclosure.

[0063] like Figure 6As shown, in the method performed by the charging station management system, in block 610, load-related data of the charging station is acquired. In some embodiments, as mentioned above, the load-related data includes various data related to the charging station's load, including but not limited to at least one of the following: historical load data related to the charging station, weather data, date and time data, event data, grid electricity price, and the installed capacity and depth of discharge of the charging energy storage battery 102.

[0064] In block 620, a revenue objective function for the charging station is established. This function is related to the price difference revenue of the charging station and the vehicle dispatching cost, which includes the cost of dispatching vehicles to the charging station for charging. Next, in block 630, control guidance parameters for the charging station are determined based on load-related data and the revenue objective function. These parameters include power guidance parameters for the energy storage battery 102 used for charging and dispatching guidance parameters. Then, in block 640, the power of the energy storage battery 102 and the vehicle dispatching at the charging station are controlled based on the control guidance parameters. The method according to embodiments of this disclosure fully considers the demand response subsidy difference as a balancing cost of vehicle dispatching activity compensation (direct reduction, coupons), considers the load situation after vehicle dispatching, and formulates an optimal energy storage charging and discharging strategy and a vehicle dispatching load strategy.

[0065] In some embodiments, determining the power guidance parameters for a charging station includes: determining load forecast data for the charging station based on load-related data; and determining the power guidance parameters and scheduling guidance parameters based on the load forecast data and the revenue objective function.

[0066] In some embodiments, load-related data includes at least one of the following: historical load data related to the charging station, weather data, date and time data, event data, grid electricity price, and the installed capacity of the charging station's energy storage battery 102.

[0067] In some embodiments, determining the power guidance parameters for a charging station further includes: determining the energy storage constraints of the charging station; and determining control guidance parameters based on the energy storage constraints and the revenue objective function.

[0068] In some embodiments, determining the control guidance parameters further includes: determining a set of candidate control guidance parameters based on energy storage constraints; determining the fitness value of each candidate control guidance parameter in the set based on a profit objective function; selecting a subset of candidate control guidance parameters in the set based on the fitness values; and performing crossover and mutation on the selected subset of candidate control guidance parameters to obtain a subset of new candidate control guidance parameters.

[0069] In some embodiments, energy storage constraints include at least one of the following: the charging and discharging power range of the charging station, the charging and discharging capacity range, the discharge time constraint, and the start and end point capacity constraint.

[0070] Figure 7 A schematic block diagram of an electronic device 700 suitable for implementing embodiments of the present disclosure is shown. The electronic device 700 may be the control system for a charging station mentioned above or any other suitable electronic device. As shown, the device 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) 702 or loaded from storage units into random access memory (RAM) 703. Various programs and data required for the operation of the device 700 may also be stored in RAM 703. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0071] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as touch screen, button, etc.; output unit 707, such as various types of display, speaker, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0072] The various processes and handling described above, such as those mentioned earlier, can be executed by processing unit 701. For example, in some embodiments, processes 610, 620, 630, and 640 can be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by CPU 701, one or more actions of processes 610, 620, 630, and 640 described above can be performed.

[0073] Embodiments of this disclosure relate to methods, electronic devices, and / or computer program products. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of this disclosure.

[0074] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, (but not limited to) electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0075] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0076] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0077] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0078] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0079] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0080] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0081] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1.A method for energy storage scheduling management of a charging station, comprising: obtaining load-related data of the charging station; establishing a profit objective function for the charging station, the profit objective function being related to a spread profit of the charging station and a vehicle scheduling cost, the vehicle scheduling cost including a cost of scheduling a vehicle to charge at the charging station; determining control guidance parameters of the charging station according to the load-related data and the profit objective function, the control guidance parameters including a power guidance parameter and a scheduling guidance parameter of an energy storage battery used for the charging; and controlling power of the energy storage battery and vehicle scheduling of the charging station based on the control guidance parameters. 2.The method of claim 1, wherein determining the power guidance parameter of the charging station comprises: determining load prediction data of the charging station based on the load-related data; and determining the power guidance parameter and the scheduling guidance parameter according to the load prediction data and the profit objective function. 3.The method of claim 1 or 2, wherein the load-related data includes at least one of historical load data related to the charging station, weather data, date-time data, event data, and grid price, and installed capacity of the energy storage battery of the charging station. 4.The method of claim 1 or 2, wherein determining the power guidance parameter of the charging station further comprises: determining energy storage constraints of the charging station; and determining the control guidance parameters based on the energy storage constraints and the profit objective function. 5.The method of claim 4, wherein determining the control guidance parameters further comprises: determining a set of candidate control guidance parameters according to the energy storage constraints; determining fitness values of each of the candidate control guidance parameters in the set according to the profit objective function; selecting a part of the candidate control guidance parameters in the set according to the fitness values; and crossing and mutating the selected part of the candidate control guidance parameters to obtain a sub-set including new candidate control guidance parameters. 6.The method of claim 5, wherein the energy storage constraints include at least one of a charge-discharge power range, a charge-discharge capacity range, a discharge time constraint, and a start-end point capacity constraint of the energy storage battery. 7.An electronic device, comprising: at least one processing unit; and at least one memory coupled to the at least one processing unit and storing machine executable instructions that, when executed by the at least one processing unit, cause the device to perform acts comprising: obtaining load-related data of a charging station; establishing a profit objective function for the charging station, the profit objective function being related to a spread profit of the charging station and a vehicle scheduling cost, the vehicle scheduling cost including a cost of scheduling a vehicle to charge at the charging station; determining control guidance parameters of the charging station according to the load-related data and the profit objective function, the control guidance parameters including a power guidance parameter and a scheduling guidance parameter of an energy storage battery used for the charging; and ​ ​ ​ ​ controlling power of the energy storage battery and vehicle dispatch of the charging station based on the control guidance parameter. 8.The electronic device of claim 7, wherein determining the power guidance parameter of the charging station comprises: determining load prediction data of the charging station based on the load-related data; and determining the power guidance parameter and the dispatch guidance parameter according to the load prediction data and the revenue objective function. 9.The electronic device of claim 7 or 8, wherein the load-related data comprises at least one of historical load data related to the charging station, weather data, date-time data, event data, and grid electricity price, and installed capacity of an energy storage battery of the charging station. 10.The electronic device of claim 7 or 8, wherein determining the power guidance parameter of the charging station further comprises: determining energy storage constraints of the charging station; and determining the control guidance parameter based on the energy storage constraints and the revenue objective function. 11.The electronic device of claim 10, wherein determining the control guidance parameter further comprises: determining a set of candidate control guidance parameters according to the energy storage constraints; determining fitness values of each of the candidate control guidance parameters in the set according to the revenue objective function; selecting a part of the candidate control guidance parameters in the set according to the fitness values; and crossing and mutating the selected part of the candidate control guidance parameters to obtain a sub-set comprising new candidate control guidance parameters. 12.The electronic device of claim 11, wherein the energy storage constraints comprise at least one of charge-discharge power range, charge-discharge capacity range, discharge time constraint, and start-end point capacity constraint of the energy storage battery. 13.A computer program product tangibly stored in a non-transitory machine readable medium and including machine executable instructions that, when executed, cause a machine to perform steps of the method of any one of claims 1 to 6.

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