Charging and discharging scheduling method and charging and discharging system

Abstract

The invention provides a charging and discharging scheduling method and a charging and discharging system, comprising: obtaining vehicle information about a plurality of electric vehicles, the vehicle information comprising departure time and battery capacity, each electric vehicle corresponding to a charging and discharging device; obtaining a plurality of pieces of system information including a grid-to-vehicle profit, a vehicle-to-grid profit, and a vehicle-to-vehicle profit; establishing an adaptability function according to the vehicle information and the system information; executing a biological heuristic algorithm according to the adaptive function so as to determine the charging and discharging operation of each electric vehicle in the corresponding charging and discharging device in each time interval; and applying corresponding charging and discharging operation to the corresponding electric vehicle through the charging and discharging device. Therefore, the optimal solution of charging and discharging can be found.

Description

Technical Field

[0001] The present disclosure relates to a charge and discharge scheduling method that can calculate the charge and discharge operations of an electric vehicle at each time interval based on a bio-inspired algorithm, and more particularly to a charge and discharge scheduling method and a charge and discharge system. Background Art

[0002] Electric vehicle charging stations (EV charging stations) are facilities that provide electricity to charge electric vehicles. Charging stations are typically composed of multiple charging piles, each of which can charge one electric vehicle. Charging stations can be deployed in different locations such as homes, public places, office areas, and highway service areas. Charging stations can provide one or more charging modes, such as fast charging, slow charging, or bidirectional charging. Bidirectional charging technology allows electric vehicles to not only obtain electricity from the power grid (charging) but also transfer the electricity in the battery back to the power grid (discharging). This technology is of great significance for power load balancing and renewable energy management. However, when performing bidirectional charging, it is necessary to consider information such as electricity price fluctuations, the battery level of all electric vehicles, and user demand. Therefore, the industry needs a charging and discharging scheduling method that considers multiple factors. Summary of the Invention

[0003] To solve the above problems, the present disclosure proposes a charge and discharge scheduling method and a charge and discharge system, which can use a bio-inspired algorithm to determine whether each electric vehicle should be charged, discharged, or remain unchanged within a period of time.

[0004] Embodiments of the present disclosure provide a charge-discharge scheduling method for a charge-discharge system electrically connected to a power grid, the charge-discharge system comprising a plurality of charge-discharge devices. The charge-discharge scheduling method comprises: obtaining vehicle information about a plurality of electric vehicles, the vehicle information comprising departure time and battery charge level, each electric vehicle corresponding to one of the charge-discharge devices; obtaining a plurality of system information about the charge-discharge system, the system information comprising a plurality of profits, the profits comprising a grid-to-vehicle profit, a vehicle-to-grid profit, and a vehicle-to-vehicle profit; establishing an adaptability function based on the vehicle information and the system information; executing a bio-inspired algorithm based on the adaptability function to determine the charge-discharge operation of each electric vehicle in the corresponding charge-discharge device at each of a plurality of time intervals; and applying the corresponding charge-discharge operation to the corresponding electric vehicle via the charge-discharge device.

[0005] In some embodiments, the system information further includes total power capacity, and the charge and discharge scheduling method further includes: in a first mode, when an electric vehicle is discharging, maintaining the total power capacity unchanged; and in a second mode, when an electric vehicle is discharging, increasing the total power capacity.

[0006] In some embodiments, the steps of executing a bio-inspired algorithm according to a fitness function include: initializing to generate multiple individuals, each individual including the charging and discharging operations of the electric vehicle in a corresponding charging and discharging device in all time intervals; calculating the fitness of each individual according to the fitness function; selecting multiple first individuals from all individuals according to the fitness; performing a mating program and a mutation program on the first individual to generate multiple offspring individuals; and adjusting the multiple offspring according to the system information and the vehicle information, deleting at least one adjusted offspring individual according to multiple conditions and performing the next iteration.

[0007] In some embodiments, the step of initializing to generate individuals includes: generating multiple permutations and combinations for electric vehicles; and for each permutation and combination, determining the charging and discharging operations of the electric vehicles in the corresponding charging and discharging devices in all time intervals according to the corresponding vehicle sequence, and each electric vehicle is in the first position in one of the permutations and combinations.

[0008] In some embodiments, the above-mentioned vehicle information also includes the time of entering the station, and the steps of establishing an adaptive function based on the vehicle information and system information include: calculating a charging amount based on the battery power of the electric vehicle at the corresponding time of entering the station and the charging and discharging operations in all time intervals; calculating a required power based on the battery power of the electric vehicle at the corresponding time of entering the station, an expected power when leaving the station, and the battery capacity; calculating a satisfaction level based on the charging amount and the required power; summing up multiple profits in all time intervals to obtain a total profit; and establishing an adaptive function based on the satisfaction level and the total profit.

[0009] In some embodiments, the step of establishing the adaptability function based on the vehicle information and the system information further includes: if the profit per unit of electricity in the grid-to-vehicle transmission mode increases over time and the total power capacity decreases over time, setting the adaptability function to a first adaptability function in at least one previous time interval and setting the adaptability function to a second adaptability function in the remaining time intervals. The first adaptability function further includes a bonus factor compared to the second adaptability function.

[0010] In some embodiments, the adaptability function includes a first penalty factor, and the charge-discharge scheduling method further includes: if the charge-discharge operation of an electric vehicle in the last time interval is discharge, setting the first penalty factor to reduce the adaptability calculated according to the adaptability function.

[0011] In some embodiments, the adaptability function includes a second penalty factor, and the charge-discharge scheduling method further includes: if the battery level of an electric vehicle at the time of leaving the station is lower than the battery level at the time of entering the station, setting the second penalty factor to reduce the adaptability calculated according to the adaptability function.

[0012] In some embodiments, the total profit further includes an importance, and the charge-discharge scheduling method further includes: setting the importance to decrease monotonically with the residence time of the vehicle in the charge-discharge system.

[0013] In some embodiments, the above-mentioned total profit also includes a weight, and the charging and discharging scheduling method further includes: when calculating the grid-to-vehicle profit, setting the weight to a constant; when calculating the vehicle-to-grid profit, setting the weight to be positively correlated with the difference between the unit electricity profit in the vehicle-to-grid transmission mode and the unit electricity profit in the grid-to-vehicle transmission mode; and when calculating the vehicle-to-vehicle profit, setting the weight to be positively correlated with the unit electricity profit in the vehicle-to-grid transmission mode.

[0014] From another perspective, an embodiment of the present disclosure proposes a charging and discharging system electrically connected to a power grid. This charging and discharging system includes multiple charging and discharging devices and a charging and discharging controller. The charging and discharging controller is electrically connected to the charging and discharging devices to perform multiple steps: obtaining vehicle information about multiple electric vehicles, the vehicle information including departure time and battery power, each electric vehicle corresponding to one of the charging and discharging devices; obtaining multiple system information about the charging and discharging system, the system information including multiple profits, the profits including grid-to-vehicle profit, vehicle-to-grid profit, and vehicle-to-vehicle profit; establishing an adaptive function based on the vehicle information and the system information; executing a bio-inspired algorithm based on the adaptive function to determine the charging and discharging operation of each electric vehicle in the corresponding charging and discharging device in each of multiple time intervals; and applying the corresponding charging and discharging operation to the corresponding electric vehicle through the charging and discharging device.

[0015] From another perspective, embodiments of the present disclosure provide a charge-discharge scheduling method for a charge-discharge system electrically connected to a power grid and comprising multiple charge-discharge devices. The charge-discharge scheduling method includes obtaining vehicle information for multiple electric vehicles; obtaining system information for the charge-discharge system; and generating a charge-discharge schedule based on the vehicle and system information. The charge-discharge schedule is then regenerated when the vehicle or system information changes.

[0016] In some embodiments, the vehicle information includes the type of electric vehicle, the required battery power value, or the expected stay time.

[0017] In some embodiments, the system information includes a grid-to-vehicle profit, a vehicle-to-grid profit, a vehicle-to-vehicle profit, and a total power capacity.

[0018] In order to make the above features and advantages of the present invention more clearly understood, embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic diagram illustrating a charging and discharging system according to an embodiment;

[0020] Figure 2 is a schematic diagram illustrating priorities of a V2V transmission mode and a V2G transmission mode according to an embodiment;

[0021] Figure 3 is a flow chart illustrating a charge and discharge scheduling method according to one embodiment;

[0022] Figure 4 is a schematic diagram showing a scheduling result according to an embodiment;

[0023] Figure 5 is a schematic diagram illustrating two modes according to an embodiment;

[0024] Figure 6 is a graph illustrating charge and discharge operations in various time periods in a first mode according to an embodiment;

[0025] Figure 7 is a graph illustrating charge and discharge operations in various time periods in the second mode according to an embodiment;

[0026] Figure 8 is a flow chart illustrating a biologically inspired algorithm according to an embodiment;

[0027] Figure 9 is a schematic diagram illustrating permutations and combinations according to an embodiment;

[0028] Figure 10 is a flow chart illustrating determining the charge and discharge operation in step 804 according to one embodiment;

[0029] Figure 11 is a schematic diagram illustrating the use of different adaptability functions at different time periods according to an embodiment;

[0030] Figure 12 is a schematic diagram illustrating a scenario of setting a first penalty factor according to an embodiment;

[0031] Figure 13 is an adjustment table showing the first penalty term and the second penalty term according to one embodiment;

[0032] Figure 14 is a table showing weights and importance when calculating various profits according to one embodiment;

[0033] Figure 15 FIG. 4 is a flow chart illustrating a charge and discharge scheduling method according to an embodiment. DETAILED DESCRIPTION

[0034] Reference will now be made in detail to exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Whenever possible, the same reference numerals are used in the drawings and the description to refer to the same or like parts.

[0035] The terms “first”, “second”, etc. used herein do not particularly refer to an order or sequence, but are only used to distinguish components or operations described with the same technical terms.

[0036] Figure 1 is a schematic diagram showing a charging and discharging system according to an embodiment. Figure 1 , the charging and discharging system 100 includes a charging and discharging controller 110 and a plurality of charging and discharging devices 121 to 123. The charging and discharging system 100 is also called a charging station, and the charging and discharging system 100 is electrically connected to the power grid 130. The charging and discharging controller 110 is electrically connected to the charging and discharging devices 121 to 123, and the charging and discharging devices 121 to 123 correspond to the electric vehicles EV1 to EV3 respectively. The charging and discharging controller 110 may include a processor, a controller, an AC-DC converter or a transformer, etc., but the present invention is not limited thereto. In this embodiment, the charging and discharging devices 121 to 123 are charging piles, and each charging pile can be a DC charging pile or an AC charging pile. In other embodiments, the charging and discharging devices 121 to 123 can be wirelessly charged, wherein an induction coil is provided. The implementation of the electric vehicles EV1 to EV3 may include cars, trucks, electric motorcycles, or other electric-powered vehicles.

[0037] Electric vehicles EV1-EV3 can be charged in a unidirectional manner or in a bidirectional manner. If charging is unidirectional, this means that the charging and discharging devices 121-123 provide power to the electric vehicles EV1-EV3. If charging is bidirectional, in addition to the charging and discharging devices 121-123 providing power to the electric vehicles EV1-EV3, the electric vehicles EV1-EV3 can also discharge power, with the electric vehicles EV1-EV3 providing power. In this embodiment, there are three power transmission modes: grid-to-vehicle (G2V), vehicle-to-grid (V2G), and vehicle-to-vehicle (V2V).

[0038] In the G2V transmission mode, power is supplied from the grid 130 to the charge-discharge controller 110. This power is then transmitted to one of the charge-discharge devices 121-123 and then to the corresponding electric vehicle. In the V2G transmission mode, electric vehicles EV1-EV3 discharge their energy, supplying power to the charge-discharge controller 110, which then transmits this energy to the grid 130. In the V2V transmission mode, one of the electric vehicles EV1-EV3 discharges its energy and supplies it to another electric vehicle.

[0039] Figure 2 This is a schematic diagram showing the priority of V2V transmission mode and V2G transmission mode according to one embodiment. If an electric vehicle is charging and another electric vehicle is discharging at the same time, the discharged power will be provided to the charging electric vehicle first. After the demand of the charging electric vehicle is met, any surplus power will be sold to the grid 130. For example, in Figure 2 In the example, electric vehicle EV1 is charging while electric vehicle EV2 is discharging. Therefore, the power generated by electric vehicle EV2 is preferentially provided to electric vehicle EV1, and any surplus power is sold to grid 130. In other words, V2V transmission mode 210 takes precedence over V2G transmission mode 220. If the power generated by electric vehicle EV2 is insufficient to meet the needs of electric vehicle EV1, grid 130 provides power to charge electric vehicle EV1.

[0040] Figure 3 is a flow chart showing a charge and discharge scheduling method according to an embodiment. Figure 3 This method is executed by the charge-discharge controller 110 and will not be repeated here. First, in step 301, vehicle information for multiple electric vehicles EV1-EV3 is obtained. This vehicle information includes each electric vehicle's entry and exit times at the charging station, the type of electric vehicle (unidirectional or bidirectional charging), the battery charge level at entry (also known as the state of charge (SOC)), the charging device at which the electric vehicle is docked, and the battery capacity of the electric vehicle. The estimated duration of the electric vehicle's stay at the charging station can be calculated based on the entry and exit times.

[0041] During simulations, this vehicle information can be obtained from historical data. In practical applications, this historical data can be used to predict vehicle information at future points in time. For example, at a charging station near a workplace, many electric vehicles arrive during work hours or lunchtime, and depart at the end of the day. Once sufficient historical data is collected, statistical or machine learning models can be used to predict vehicle information for a specific timeframe.

[0042] In step 302, system information about the charging and discharging system 100 is obtained. This system information includes the total power capacity of the charging and discharging system 100, the profits of various power transmission modes, the specifications of each charging and discharging device 121-123, the feedback profit of the consumer providing electricity, etc. The total power capacity represents the maximum power that the charging and discharging system 100 can provide. The specifications of the charging and discharging devices 121-123 include AC or DC power, power conversion efficiency, charging / discharging power, etc. For example, when the charging and discharging device provides AC power, the power is in the range of 1.4 to 7 kilowatts (kW); when the charging and discharging device provides DC power, the power is in the range of 5 to 50 kW. Each power transmission mode has its own profit, which includes grid-to-vehicle profit, vehicle-to-grid profit, and vehicle-to-vehicle profit.

[0043] The grid-to-vehicle profit is equal to the price at which charging / discharging system 100 sells electricity to users (providing electricity to electric vehicles) minus the price at which charging / discharging system 100 purchases electricity from grid 130. The grid-to-vehicle profit varies over time, with different periods of the day, but is generally positive.

[0044] The vehicle-to-grid profit is equal to the price at which the grid 130 purchases electricity from the charging / discharging system 100 minus the price at which the charging / discharging system 100 purchases electricity from users (electricity provided by the electric vehicle). The vehicle-to-grid profit also varies over time, with different times of day and periods of the day. This profit can be positive or negative.

[0045] The vehicle-to-vehicle profit is equal to the price at which the charging and discharging system 100 sells electricity to the user minus the price at which the charging and discharging system 100 buys electricity from another user. The price at which the charging and discharging system 100 buys electricity from the user must be greater than the price at which the charging and discharging system 100 sells electricity to the user. The difference between the two represents the aforementioned feedback profit for the consumer providing electricity. Assuming a 10% feedback profit and the price at which the charging and discharging system 100 sells electricity to the user is x yuan, the price at which the charging and discharging system 100 buys electricity from the user is 1.1x yuan. Therefore, the vehicle-to-vehicle profit remains constant throughout the 24 hours of a day and is always negative.

[0046] Figure 4 is a schematic diagram showing the scheduling results according to an embodiment. Figure 4, the horizontal axis is time, the vertical axis is power, and different long bars represent different electric vehicles. When the power is less than 0, it means that the electric vehicle is discharging, and when the power is greater than 0, it means that the electric vehicle is charging. Multiple time intervals are set here, such as 15 minutes. During each time interval, the electric vehicle will maintain the charging or discharging state, but the charging or discharging state can be changed in the next time interval. For example, in the first time interval, the electric vehicle EV1 is discharged. In the second time interval, the electric vehicle EV1 is charged, while the electric vehicle EV2 is discharged. In the third time interval, the electric vehicle EV1 is discharged, and the electric vehicle EV2 is charged. In the fourth time interval, the electric vehicle EV2 is charged.

[0047] The histogram portion 410 refers to the V2G transmission mode, the histogram portion 420 refers to the V2V transmission mode, and the histogram portion 430 refers to the G2V transmission mode. Specifically, the G2V profit is calculated as shown in the following mathematical formula 1.

[0048] [Mathematical formula 1]

[0049]

[0050] Where g2vρ is the accumulated grid-to-vehicle profit over a period of time. t represents time, n is the total length of time, and Figure 4 In the example, n = 4. ev represents the corresponding electric vehicle, and m represents the total number of electric vehicles. Represents the power to be converted by the electric vehicle ev at time t. Greater than 0 means charging the electric vehicle. Less than 0 means the electric vehicle is discharging. E is the conversion efficiency of electricity, for example 0.96. This is because there is energy loss when the electric vehicle provides electricity to another device, but the profit is calculated based on how much electricity the other device receives, so the conversion efficiency must be multiplied when discharging. is to calculate the power required for charging; and The amount of power to be discharged is calculated. Adding all the charged power to all the discharged power (a negative number) yields a value. If this value is greater than 0, it indicates that the power required from the grid is sufficient (as shown in histogram section 430). If this value is less than 0, no power is being drawn from the grid. Δt is the time interval, for example, 15 / 60. is the profit per unit of electricity in the grid-to-vehicle transmission mode. Multiply the electricity obtained from the grid by Δt and By summing up the time t, we can get the grid-to-vehicle profit g2vρ.

[0051] The calculation of vehicle-to-grid profit is shown in the following mathematical formula 2.

[0052] [Mathematical formula 2]

[0053]

[0054] v2gρ is the accumulated profit from the vehicle to the grid over a period of time. In Mathematical Formula 2, all discharged electricity is multiplied by -1 to get a positive number, and then all charged electricity is subtracted. If there is a surplus (greater than 0), it means that this electricity will be sold to the grid, just like Figure 4 The histogram portion 410 is shown. is the profit per unit of electricity in the vehicle-to-grid transmission mode. Multiply all the electricity sold to the grid by Δt and By summing up the time t, we can get the grid-to-vehicle profit v2gρ.

[0055] The calculation of V2V profit is shown in the following mathematical formula 3.

[0056] [Mathematical formula 3]

[0057]

[0058] In equation 3, the net power flowing from the grid (i.e., g2v_power) is subtracted from the total charging power to obtain a difference. If this difference is equal to 0, it means that the grid can meet all charging needs; if this difference is greater than 0, it means that the grid cannot meet all charging needs, which is equivalent to the vehicle-to-vehicle transmission mode (such as Figure 4 The histogram portion 420 corresponds to the upper half because only the charging portion is calculated. Similarly, The profit per unit of electricity in the vehicle-to-vehicle transmission mode is obtained by multiplying the electricity in the vehicle-to-vehicle transmission mode by Δt and By summing up the time t, we can get the vehicle-to-vehicle profit v2vρ.

[0059] Please refer to Figure 3 In step 303, a fitness function is established based on the vehicle and system information. The vehicle information can be used to determine whether the user's electric vehicle is receiving the required power, thereby calculating user satisfaction. Furthermore, the system information can be used to calculate various profits. By summing user satisfaction and profit, the fitness function is established.

[0060] For example, the charge capacity of an electric vehicle can be calculated based on the battery charge at the time of entering the station and the charge and discharge operations at all time intervals. Specifically, the total charge capacity (or total discharge capacity) can be obtained by adding up the charge and discharge operations at all time intervals. The total charge capacity (or total discharge capacity) is added to the battery charge at the time of entering the station to obtain the charge capacity at the time of leaving the station. Assuming that the battery charge at the time of leaving the station is SOC final, and the battery charge at the pit stop time is SOC start , then the charge capacity is (SOC final -SOC start )*C, where C is the battery capacity of the electric vehicle. On the other hand, the battery charge of the electric vehicle can be set to a default value (e.g. 80%, also known as the expected charge at departure) when leaving the station. The required charge can be calculated based on the battery charge of the electric vehicle at the time of entering the station, the expected charge at departure, and the battery capacity. This required charge is also known as the battery charge demand value. Assume that the expected charge at departure is expressed as SOC expect , subtract the expected power when leaving the station from the battery power when the electric vehicle enters the station to get a difference SOC expect -SOC start Multiplying this difference by the battery capacity C yields the power demand. In some embodiments, due to power transmission losses, this power demand can also be divided by the aforementioned conversion efficiency E. Next, the corresponding satisfaction level can be calculated based on the charge level and power demand. For example, user satisfaction can be calculated by dividing the charge level by the power demand, as shown in the following formula 4.

[0061] [Formula 4]

[0062] Satisfaction = charging capacity / required power

[0063] In other embodiments, satisfaction can also be calculated by calculating the difference between the charged amount and the required amount of electricity. Alternatively, if a nonlinear relationship is assumed between the charged amount and satisfaction, the charged amount and the required amount of electricity can be substituted into a nonlinear equation to calculate satisfaction. This nonlinear equation can be an exponential function, a logarithmic function, etc., but the present invention is not limited to this. In this embodiment, the units of the charged amount and the required amount of electricity are degrees (kilowatt-hours), but in other embodiments, percentages can also be used. In other embodiments, the expected amount of electricity left at the station can also be dynamically adjusted according to the electricity price, time, or location, or can be set by the user, but the present invention is not limited to this.

[0064] On the other hand, total profit can be obtained by summing all profits in all time intervals. For example, the total profit can be obtained by summing the above equations 1-3. Finally, a fitness function can be established based on the satisfaction and total profit calculated above. For example, a weight can be assigned to each of the satisfaction and total profit, and then the weighted sum of the satisfaction and total profit can be calculated as the fitness function. In some embodiments, nonlinear operations such as squaring or square rooting the satisfaction or total profit can be performed and then summed to obtain the fitness function. A person skilled in the art can design a suitable fitness function based on the above disclosure.

[0065] In step 304, a bio-inspired algorithm is executed based on the adaptability function to determine a charge / discharge operation of each electric vehicle in the corresponding charge / discharge device in each time interval. This charge / discharge operation can be expressed as In other words, the charge and discharge operation can be charging discharge Or remain unchanged In addition, the charging and discharging operations also include the power to be converted

[0066] The above-mentioned bio-inspired algorithms may include Genetic Algorithm (GA), Evolution Strategy (ES), Neuroevolution, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), etc. These algorithms are to generate multiple candidate solutions, select candidate solutions based on the fitness function, and then use different strategies based on different algorithms to generate candidate solutions for the next iteration. After multiple iterations, an optimal solution will be found. In other words, the above-mentioned algorithms are to find the optimal solution for all time and electric vehicles. The above-mentioned adaptability function is made to have an extreme value, which can be a maximum or a minimum value according to the design of the adaptability function. In this way, user satisfaction and profit can be maximized.

[0067] In some embodiments, the bio-inspired algorithm may be executed at regular intervals, such as every 15 minutes. In some embodiments, the bio-inspired algorithm may be re-executed when vehicle or system information is updated, such as when a new electric vehicle enters the charging and discharging system. However, the present invention does not limit the timing or frequency of executing the bio-inspired algorithm.

[0068] When performing the above-mentioned bio-inspired algorithm, each candidate solution must meet multiple conditions. These conditions are set based on vehicle information and system information, because the charging and discharging operations in each solution cannot violate the vehicle and system specifications. For example, if the electric vehicle is a one-way charging type, it cannot be discharged. Must be greater than 0. Converted power The specifications of the electric vehicle and the charging and discharging device must be met, and the specified maximum value or minimum value must not be exceeded. Furthermore, the battery charge level of the electric vehicle can be set to not fall below a default value (e.g., 20, 40, or 60%) when discharging. The power drawn from the grid 130 must also not exceed the total power capacity, which may change over time.

[0069] In step 305, the charge and discharge controller 110 applies corresponding charge and discharge operations to the corresponding electric vehicles EV1-EV3 through the charge and discharge devices 121-123. The electric car is charged. The electric vehicle is discharged. Remains unchanged.

[0070] Figure 5 is a schematic diagram showing two modes according to an embodiment. Figure 5 In this embodiment, there are two modes for charging and discharging. In the first mode 510, when an electric vehicle is discharging, the total power capacity remains unchanged. For example, in Figure 5 Middle curve 512 represents the total power capacity. During the first four time intervals, the total power capacity remains at 10 kW. After the fifth time interval, the total power capacity decreases to 5 kW. During the first time interval, an electric vehicle is discharging (histogram portion 511), and the total power capacity remains unchanged at 10 kW.

[0071] In the second mode 520, the total power capacity increases when an electric vehicle discharges. For example, during the first time interval, an electric vehicle discharges (histogram portion 521), and the discharge power is 5 kW. The total power capacity is then increased by 5 kW (histogram portion 523). The modified total power capacity is shown in curve 522.

[0072] The first mode 510 has a greater chance of selling the electricity released by the electric vehicle to the grid 130, thereby maximizing profits. In contrast, the second mode 520 allows other electric vehicles to have more charging resources, thereby maximizing user satisfaction. Figure 6 is a graph showing the charge and discharge operation in each period in the first mode according to an embodiment. Figure 6 The horizontal axis is time, the left vertical axis is power, and the right vertical axis is profit. Curve 610 represents total power capacity, curve 620 represents profit per unit of power in vehicle-to-grid transmission mode, and curve 630 represents profit per unit of power in grid-to-vehicle transmission mode. Note that in histogram portion 640, despite multiple electric vehicles discharging, curve 610, representing total power capacity, remains unchanged. Excess power is sold to the grid at this time, and since the profit from selling electricity to the grid is higher (curve 620 is higher), selling electricity at this time can generate more profit.

[0073] Figure 7 is a graph showing the charge and discharge operation in each period under the second mode according to an embodiment. Figure 7Again, the horizontal axis represents time, the left vertical axis represents power, and the right vertical axis represents profit. Curve 710 represents total power capacity, curve 720 represents profit per unit of electricity in vehicle-to-grid transmission mode, and curve 730 represents profit per unit of electricity in grid-to-vehicle transmission mode. Note that in histogram portion 740, multiple electric vehicles are discharging, causing curve 710, representing total power capacity, to move upward. This allows more electric vehicles to charge, improving user satisfaction, but there is no excess electricity to sell to the grid.

[0074] In some experiments, Figure 6 and Figure 7 Please refer to Table 1 below for satisfaction and profit.

[0075]

[0076] Table 1

[0077] As shown in Table 1, in the first mode, more electricity can be sold to the grid, which increases V2G profits, but reduces satisfaction. In contrast, in the second mode, satisfaction is higher, but V2G profits are lower.

[0078] Figure 8 is a flow chart illustrating a biologically inspired algorithm according to one embodiment. Figure 8 In step 801, for time t, it is determined whether there is an electric vehicle in the charging and discharging system. If so, step 802 is executed. In step 802, vehicle information is recorded and updated, including the battery power of each electric vehicle.

[0079] Next, initialization is performed to generate multiple individuals. Each individual contains the charge and discharge operations of all electric vehicles at the corresponding charging and discharging device during all time intervals. In other words, each individual represents a candidate solution. In this embodiment, initialization includes steps 803 and 804. In step 803, multiple permutations and combinations of the electric vehicles are generated. Figure 9 This is a schematic diagram showing an arrangement and combination according to an embodiment, please refer to Figure 8 and Figure 9 Here we assume that there are 5 electric vehicles EV1~EV5. Figure 9 Five permutations 901 to 905 are shown, each with its own vehicle sequence. For example, the vehicle sequence for permutation 901 is electric vehicle EV1, EV2, EV4, EV5, EV3; the vehicle sequence for permutation 902 is electric vehicle EV2, EV3, EV5, EV4, EV1; and so on.

[0080] In step 804, for each permutation and combination, the charge and discharge operations of each electric vehicle at its corresponding charging and discharging device during all time intervals are determined according to the corresponding vehicle sequence to generate multiple individuals. For permutation and combination 901, the charge and discharge operations of electric vehicle EV1 during all time intervals are first determined, followed by the charge and discharge operations of electric vehicle EV2 during all time intervals. Any probability distribution can be used to determine the charge and discharge operations. After the charge and discharge operations of electric vehicles EV1 through EV5 are determined according to the vehicle sequence, an individual is generated. This process is repeated, and samples are again taken according to the probability distribution to generate multiple individuals 911.

[0081] The charging and discharging operations of the electric vehicle that precedes the others may affect those of the electric vehicles that follow them. For example, if the total power capacity is reached after charging electric vehicle EV1, then charging of electric vehicle EV2 is impossible. To find the optimal solution, various possibilities should be tried during the initialization phase. Therefore, each electric vehicle EV1-EV5 can be set to be the first in a certain permutation. For example, electric vehicle EV1 is the first in permutation 901, electric vehicle EV2 is the first in permutation 902, and so on. In one embodiment, after the first position is determined, subsequent positions can be randomly generated.

[0082] In some embodiments, the probability distribution used in step 804 can also be adjusted based on system information, increasing the probability of charging when the profit of charging is higher, and increasing the probability of discharging when the profit of discharging is higher. For example, at a certain point in time, if the profit per unit of electricity in the grid-to-vehicle transmission mode is higher, or if the total power capacity is higher, both of these situations can increase the probability of setting the charging / discharging operation to charging. On the other hand, at a certain point in time, if the profit per unit of electricity in the vehicle-to-grid transmission mode is higher, the probability of setting the charging / discharging operation to discharging can be increased. Alternatively, during the stay time of an electric vehicle, the earlier the time point, the higher the probability of discharging, while the later the time point, the lower the probability of discharging.

[0083] Figure 10 is a flow chart showing the determination of the charge and discharge operation in step 804 according to one embodiment, Figure 10The process is used to determine whether the charge and discharge operation of an electric vehicle is charging, discharging, or maintaining the same during initialization. In step 1001, for an electric vehicle, it is determined whether the charge and discharge operation of the electric vehicle in the previous time interval is charging. If the result of the determination in step 1001 is yes, in step 1002, it is determined whether the required power of the electric vehicle is greater than or equal to the minimum power of the corresponding charging device. If the result of step 1002 is yes, in step 1003, the corresponding charge and discharge operation is set to charging. If the result of step 1003 is no, this means that the battery power of the electric vehicle is already very high, and further charging will exceed the required power. Therefore, in step 1004, the corresponding charge and discharge operation is randomly set to discharge or maintain the same.

[0084] If the result of step 1001 is negative, step 1005 determines whether the electric vehicle's charge / discharge operation during the previous time interval was discharging. If so, step 1006 is performed. Step 1006 determines whether the electric vehicle's current battery charge is greater than or equal to a first threshold. This first threshold, for example, is greater than or equal to 50% and less than or equal to 70%. In one embodiment, a value randomly selected from the range of 50% to 70% can be used as the first threshold. If the result of step 1006 is positive, step 1007 randomly sets the charge / discharge operation to discharging or maintains it unchanged. If the result of step 1006 is negative, step 1008 sets the charge / discharge operation to charging.

[0085] If the result of step 1005 is negative, step 1009 determines whether the current battery charge level of the electric vehicle is greater than or equal to a second threshold value. This second threshold value is less than or equal to the first threshold value, for example, 50%. If the result of step 1009 is positive, step 1010 randomly sets the charge / discharge state to charging or discharging. If the result of step 1009 is negative, step 1011 sets the charge / discharge state to charging. After determining whether the charge / discharge operation is charging, discharging, or maintaining the same, the power can be determined based on the probability distribution.

[0086] Each of the above permutations and combinations 901-905 will generate a plurality of individuals (e.g., 500), which are then collected for the next step. In step 805, the fitness of each individual is calculated based on a fitness function. The specific formula of this fitness function will be described in the following paragraphs.

[0087] In step 806, multiple first individuals are selected from all individuals based on their adaptability. In this embodiment, the greater the adaptability, the better, so multiple first individuals with the greatest adaptability can be selected, and the number of first individuals is not limited.

[0088] In step 807, the mating procedure is executed. Taking the evolutionary strategy as an example, each individual can be represented as a vector, and each element on the vector can be regarded as a gene. This gene corresponds to the time interval and the charging and discharging operation of the electric vehicle. From the first individual, any two individuals can be selected as the parent individual and the mother individual respectively. Then, intermediate mating (Intermediate Recombination) or discrete mating (Discrete Recombination) can be used. The present invention is not limited to this. If a genetic algorithm is used, a part of the elements can be selected from the mother individual, and the remaining elements can be selected from the father individual for mating. The present invention is not limited to this. After the mating procedure, multiple offspring individuals will be generated.

[0089] In step 808, a mutation process is performed. Taking the evolution strategy as an example, a probability distribution (such as a Gaussian distribution) can be selected first, and then a noise is added to each gene of each offspring individual according to the probability distribution. The noise can be positive or negative.

[0090] In step 809, the offspring individuals are adjusted. Here, the genes in the offspring individuals can be adjusted based on system and vehicle information. For example, during a time interval, when the total charge level of all electric vehicles exceeds the total power capacity, the total power capacity can be subtracted from the total charge level to obtain a difference. Next, for each electric vehicle, this difference is subtracted from the corresponding charge level to determine whether the charge level is less than the minimum power limit specified by the specification. If the charge levels of multiple electric vehicles do not fall below the minimum power limit specified by the specification after adjustment, the charge level of one of them is reduced.

[0091] In step 810, a determination is made as to whether the offspring individuals meet multiple conditions. As described above, these conditions require compliance with the specifications of the electric vehicle and the charging and discharging device. If, after the adjustments made in step 809, the offspring individuals are still determined not to meet the conditions in step 810, the adjusted offspring individuals are deleted in step 811. Offspring individuals that meet the conditions are retained in step 812. Next, in step 813, a determination is made as to whether the number of retained offspring individuals is greater than or equal to a predetermined number. If not, one or more offspring individuals are randomly selected in step 814 to continue the mutation process in step 808. This ensures that there are sufficient offspring individuals for the next iteration.

[0092] If the result of step 813 is yes, in step 815, determine whether the current number of iterations has reached the upper limit. If not, return to step 805 and treat the offspring individuals as individuals of the new generation to continue with the subsequent steps. If the current number of iterations has reached the upper limit, in step 816, retain a certain number of individuals for the next time interval, for example, take the 50 individuals with the highest adaptability. Although only one individual with the highest adaptability is needed, an additional 49 individuals can be used for the next time interval. When executing the bio-inspired algorithm in the next time interval, these individuals can be added to the initialized individuals, which helps to quickly find the optimal solution. In step 817, determine whether all time intervals have been processed. If so, end the process. If the result of step 817 is no, return to step 801 and process the next time interval.

[0093] Next, the adaptability function is described. In this embodiment, there are two adaptability functions, namely a first adaptability function and a second adaptability function. Figure 11 is a schematic diagram showing the use of different adaptability functions at different time periods according to an embodiment. Figure 11 , the horizontal axis is time, the left vertical axis is power, and the right vertical axis is profit. Curve 1110 represents the total power capacity, and curve 1120 represents the profit per unit of power in the grid-to-vehicle transmission mode. Figure 11 It can be seen that curve 1120 starts to rise after 9:00 am, which will cause the bio-inspired algorithm to tend to set the charge and discharge operation to charging after 9:00 am, in order to obtain higher profits. However, the total power capacity will gradually decrease after 9:00 am. If more charging is scheduled at this time, it may cause a charging bottleneck, so charging can be encouraged in the period before 9:00 am. In one embodiment, it can be determined whether the unit power profit corresponding to the grid-to-vehicle profit increases over time and the total power capacity decreases over time. For example, it can be determined whether the slope of curve 1120 is greater than 0 and whether the slope of curve 1110 is less than 0. Figure 11 The time interval that meets this judgment is 9:00 AM. Using this time interval as a reference, multiple time intervals (e.g., six) are taken as previous time intervals 1130. In this example, the previous time intervals are between 7:30 AM and 9:00 AM. Within the previous time intervals, the first adaptability function can be set, while the second adaptability function can be set for the remaining time intervals 1140. The first adaptability function is expressed as Equation 5 below, and the second adaptability function is expressed as Equation 6 below.

[0094] [Formula 5]

[0095] O1=max[T·Q·Q·R·P1·P2]

[0096] [Formula 6]

[0097] O2=max[T·Q·Q·P1·P2]

[0098] Where T represents total profit. Q represents user satisfaction. R represents the reward factor. P1 represents the first penalty factor. P2 represents the second penalty factor. In other words, the first fitness function O1 includes the additional reward factor R compared to the second fitness function O2. This reward factor R is greater than 1, so the first fitness function can be calculated to have a higher fitness, which encourages charging between 7:30 and 9:00.

[0099] The total profit T in Equations 5 and 6 is the sum of Equations 1 through 3. In one embodiment, the user satisfaction Q for each electric vehicle can be calculated once when the vehicle leaves the station, or at each time interval. The user satisfaction Q can be calculated by summing (or averaging) the user satisfaction scores for all electric vehicles. For example, user satisfaction Q can be calculated using Equation 7.

[0100] [Formula 7]

[0101]

[0102] where r t,ev Indicates the amount of electricity received by the electric vehicle ev in the time interval t. ev Indicates the power demand of the electric vehicle ev.

[0103] The first penalty factor is used to prevent the last few charge and discharge operations of an electric vehicle before leaving the station from being considered discharge. Therefore, if an electric vehicle has discharged during at least one of its last charge and discharge operations, the first penalty factor is set to reduce the fitness calculated by the fitness function. For example, the first penalty factor can be set to less than 1. In some embodiments, the first penalty factor can also be set to less than 0.

[0104] Figure 12 FIG is a schematic diagram showing a scenario of setting a first penalty factor according to an embodiment. Figure 12 In this case, it is determined whether the last two time intervals are for discharge. In scenario 1210, the electric vehicle is charged in the last two time intervals, so a penalty term is set. On the other hand, in scenario 1220, the electric vehicle is discharged in the last two time intervals, so the power discharged in these two time intervals can be added together as the penalty term. Specifically, the penalty The calculation of is shown in the following mathematical formula 8.

[0105] [Formula 8]

[0106]

[0107] in Indicates the power discharged during the last two time intervals. Since this power is less than 0, it must be multiplied by -1. Figure 12 In the example, the specific calculation is According to this setting, the penalty term There are only two possibilities: 0 or greater than 1. Next, according to the following mathematical formula 9, the penalty term The first penalty term P1 is calculated. When the first penalty term P1 is equal to 1, it indicates that there is no penalty. When the first penalty term P1 is greater than 1 (the value is N>0), it indicates that there is a penalty.

[0108] [Formula 9]

[0109]

[0110] On the other hand, the second penalty factor is intended to prevent the battery level of an electric vehicle from being lower than when it arrives at the station when it leaves the station. Therefore, if the battery level of an electric vehicle is lower than when it arrives at the station when it leaves the station, a second penalty factor can be set to reduce the adaptability calculated according to the adaptability function. For example, the second penalty factor can be set to less than 1. In some embodiments, the second penalty factor can also be set to less than 0.

[0111] In one embodiment, the battery charge of the electric vehicle at the time of leaving the station minus the battery charge of the electric vehicle at the time of entering the station can be calculated as a penalty term, which is expressed as For example, if the SOC start =70%, SOC final =68%, then If the battery level at the time of leaving the station is greater than the battery level at the time of entering the station, the penalty Under this setting, the penalty term There are only two cases: equal to 0 or less than 0. Next, the following mathematical formulas 10 and 11 are used to calculate the second penalty factor P2. When the second penalty factor P2 is equal to 1, it means there is no penalty. When the second penalty factor P2 is less than 0, it means there is a penalty (the absolute value of the second penalty factor P2 is M, where M>0).

[0112] [Formula 10]

[0113]

[0114] [Mathematical formula 11]

[0115]

[0116] Next, make adjustments based on the signs of the first penalty term P1 and the second penalty term P2. For specific adjustments, please refer to Figure 13 Table 1300. In the first scenario, the first penalty term P1 and the second penalty term P2 both indicate no penalty, and both have values ​​of 1, so the multiplication result is also 1.

[0117] In the second scenario, the first penalty term P1 indicates no penalty, while the second penalty term P2 indicates a penalty. Therefore, the value of the first penalty term P1 is 1, while the value of the second penalty term P2 is -M, where M > 0. The product of the first penalty term P1 and the second penalty term P2 is -M.

[0118] In the third scenario, the first penalty term P1 indicates a penalty, while the second penalty term P2 indicates no penalty. In this scenario, additional calculations must be performed to set the second penalty term P2 to -1. The value of the first penalty term P1 is N, where N > 0. The product of the first penalty term P1 and the second penalty term P2 is -N.

[0119] In the fourth scenario, the first penalty term P1 indicates a penalty, and the second penalty term P2 indicates a penalty. The value of the first penalty term P1 is N, and the value of the second penalty term P2 is -M. The product of the first penalty term P1 and the second penalty term P2 is -N×M.

[0120] In the second to fourth scenarios of Table 1300, the product of the first penalty term P1 and the second penalty term P2 is less than 0, which makes the fitness calculated according to the fitness function less than 0. It is less likely to select individuals with fitness less than 0 in the bio-inspired algorithm.

[0121] In some embodiments, importance and weights can be added to the calculation of total profit. Because importance is time-dependent, G2V profit, V2G profit, and V2V profit must be calculated for each time interval. Equations 1-3 above can be rewritten as Equations 12-14 below, respectively.

[0122] [Mathematical formula 12]

[0123]

[0124] [Mathematical formula 13]

[0125]

[0126] [Mathematical formula 14]

[0127]

[0128] In a certain time interval t, the profit adjusted by importance and weight is expressed as the following mathematical formula 15.

[0129] [Mathematical formula 15]

[0130]

[0131] where ρ t is the profit per unit of electricity under different transmission modes, and is the profit after weight and importance adjustment, Can be substituted into equations 12 to 14 as well as About variable δ, weight W, and importance I t Please refer to the settings of Figure 14 Form 1400.

[0132] When calculating the grid-to-vehicle profit g2vρ t When the variable δ is 1, the weight W is a constant (for example, 1), and the importance I t is a function I_g2v that decreases monotonically with the vehicle's dwell time in the charging and discharging system. For example, if the vehicle's dwell time in the charging and discharging system includes three time intervals, the importance of the first time interval can be set to 3, the importance of the second time interval can be set to 2, and the importance of the third time interval can be set to 1. In other embodiments, the importance of the three time intervals can be set to 3, 3, and 2, respectively, but the present invention is not limited to this. Importance is used to ensure that the vehicle is charged as soon as possible while maintaining the same profit.

[0133] When calculating the vehicle-to-grid profit v2gρ t When , the variable δ is 1. The weight W is positively correlated with the profit per unit of electricity in the vehicle-to-grid transmission mode. and the profit per unit of electricity in the grid-to-vehicle transmission mode This is because if the difference is larger, it is more suitable for vehicle-to-grid transmission mode. On the other hand, the importance of I t is a function I_v2g, which also decreases monotonically with the residence time of the vehicle in the charging and discharging system. In some embodiments, the function I_v2g is different from the function I_g2v.

[0134] When calculating the vehicle-to-vehicle profit v2vρ t When , the variable δ is -1. The weight W is positively correlated with the profit per unit of electricity in the vehicle-to-grid transmission mode. This is because if the profit of vehicle-to-grid transmission is high, the vehicle-to-vehicle transmission mode is not preferred (the relationship between the two is negative due to the variable δ being -1).t It is the function I_v2g.

[0135] After calculating the various profits using formulas 12 to 14, the time t can be added up to calculate the total profit.

[0136] Figure 15 is a flow chart showing a charge and discharge scheduling method according to an embodiment. Figure 15 In step 1501, vehicle information of multiple electric vehicles is obtained. In step 1502, system information of the charging and discharging system is obtained. The vehicle information and system information have been described in detail above and will not be repeated here.

[0137] In step 1503, a charging and discharging schedule is generated based on the vehicle information and system information. This charging and discharging schedule can be achieved using the aforementioned bio-inspired algorithm or other algorithms. For example, in some embodiments, a greedy algorithm can be used to prioritize charging the electric vehicle with the lowest battery level, and then schedule other electric vehicles based on their battery level.

[0138] In step 1504, the charging and discharging schedule is regenerated when vehicle information or system information changes. For example, the charging schedule may be regenerated when an electric vehicle enters or leaves a station. Alternatively, the charging schedule may be regenerated when the total power capacity changes or when any unit electricity profit changes.

[0139] The above system and method can balance profit and satisfaction and find the appropriate charging and discharging operation. In addition, the design of weights, importance, penalties, and early charging can improve profit or satisfaction.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A charge and discharge scheduling method for a charge and discharge system, characterized in that: The charging and discharging system is electrically connected to a power grid, and includes a plurality of charging and discharging devices. The charging and discharging scheduling method includes: Obtaining vehicle information about a plurality of electric vehicles, the vehicle information including departure time and battery power, each of the plurality of electric vehicles corresponding to one of the plurality of charging and discharging devices; Obtaining system information about the charging and discharging system, the system information including a plurality of profits, the plurality of profits including a grid-to-vehicle profit, a vehicle-to-grid profit, and a vehicle-to-vehicle profit; establishing an adaptability function according to the vehicle information and the system information; executing a bio-inspired algorithm according to the adaptability function to determine a charge / discharge operation of each of the plurality of electric vehicles at the corresponding charge / discharge device in each of a plurality of time intervals; as well as The corresponding charging and discharging operations are applied to the corresponding electric vehicles through the multiple charging and discharging devices.

2. The charge and discharge scheduling method according to claim 1, wherein: The system information further includes total power capacity, and the charge and discharge scheduling method further includes: In a first mode, when one of the plurality of electric vehicles is discharged, the total power capacity is maintained unchanged; and In a second mode, when one of the plurality of electric vehicles is discharged, the total power capacity is increased.

3. The charge and discharge scheduling method according to claim 1, wherein: The step of executing the bio-inspired algorithm according to the fitness function comprises: Initializing to generate a plurality of entities, each of the plurality of entities comprising the charge and discharge operations of the plurality of electric vehicles in the corresponding charge and discharge devices in the plurality of time intervals; calculating the fitness of each of the plurality of individuals according to the fitness function; selecting a plurality of first individuals from the plurality of individuals according to the adaptability; performing a mating procedure and a mutation procedure on the plurality of first individuals to produce a plurality of offspring individuals; and The plurality of offspring individuals are adjusted according to the system information and the vehicle information, and at least one of the adjusted offspring individuals is deleted according to a plurality of conditions, and the next iteration is performed.

4. The charge and discharge scheduling method according to claim 3, wherein: The step of performing the initialization to generate the plurality of individuals comprises: generating a plurality of permutations and combinations for the plurality of electric vehicles; and For each of the plurality of permutations, the charge and discharge operations of the plurality of electric vehicles in the corresponding charge and discharge devices in the plurality of time intervals are determined according to the corresponding vehicle sequence, wherein each of the plurality of electric vehicles is in the first order in one of the plurality of permutations.

5. The charge and discharge scheduling method according to claim 1, wherein: The vehicle information further includes a stop time. The step of establishing the adaptability function according to the vehicle information and the system information includes: Calculating the charging capacity according to the battery power levels of the multiple electric vehicles at the corresponding entry times and the charging and discharging operations in the multiple time intervals; Calculating the required power according to the battery power of the multiple electric vehicles at the corresponding arrival time, the expected departure power, and the battery capacity; Calculating satisfaction based on the charged amount and the required amount of electricity; summing the plurality of profits in the plurality of time intervals to obtain a total profit; and The adaptability function is established according to the satisfaction and the total profit.

6. The charge and discharge scheduling method according to claim 5, characterized in that: The step of establishing the adaptability function according to the vehicle information and the system information further includes: If the profit per unit of electricity in the grid-to-vehicle transmission mode increases over time and the total power capacity decreases over time, the adaptability function is set to a first adaptability function in at least one previous time interval of the plurality of time intervals, and the adaptability function is set to a second adaptability function in the remaining time intervals of the plurality of time intervals, wherein the first adaptability function further includes a reward factor than the second adaptability function.

7. The charge and discharge scheduling method according to claim 5, characterized in that: The adaptability function includes a first penalty factor, and the charge and discharge scheduling method further includes: If the charge and discharge operation of one of the electric vehicles in at least one of the last of the time intervals is discharging, the first penalty factor is set to reduce the adaptability calculated according to the adaptability function.

8. The charge and discharge scheduling method according to claim 7, wherein: The adaptability function includes a second penalty factor, and the charge and discharge scheduling method further includes: If the battery level of one of the electric vehicles at the departure time is lower than the battery level at the arrival time, the second penalty factor is set to reduce the adaptability calculated according to the adaptability function.

9. The charge and discharge scheduling method according to claim 5, wherein: The total profit also includes importance, and the charging and discharging scheduling method further includes: The importance is set to decrease monotonically as the plurality of electric vehicles stay in the charging and discharging system.

10. The charge and discharge scheduling method according to claim 5, wherein: The total profit also includes a weight, and the charging and discharging scheduling method further includes: When calculating the grid-to-vehicle profit, setting the weight to a constant; When calculating the vehicle-to-grid profit, setting the weight to be positively correlated to the difference between the unit electricity profit in the vehicle-to-grid transmission mode and the unit electricity profit in the grid-to-vehicle transmission mode; and When calculating the vehicle-to-vehicle profit, the weight is set to be positively correlated with the unit electricity profit in the vehicle-to-grid transmission mode.

11. A charging and discharging system electrically connected to a power grid, characterized in that: The charging and discharging system comprises: multiple charging and discharging devices; and A charge and discharge controller is electrically connected to the plurality of charge and discharge devices and is configured to perform a plurality of steps: Obtaining vehicle information about a plurality of electric vehicles, the vehicle information including departure time and battery power, each of the plurality of electric vehicles corresponding to one of the plurality of charging and discharging devices; Obtaining system information about the charging and discharging system, the system information including a plurality of profits, the plurality of profits including a grid-to-vehicle profit, a vehicle-to-grid profit, and a vehicle-to-vehicle profit; establishing an adaptability function according to the vehicle information and the system information; executing a bio-inspired algorithm according to the adaptability function to determine a charge and discharge operation of each of the plurality of electric vehicles at the corresponding charge and discharge device in each of a plurality of time intervals; and The corresponding charging and discharging operations are applied to the corresponding electric vehicles through the multiple charging and discharging devices.

12. The charge and discharge system according to claim 11, characterized in that: The system information further includes total power capacity, and the steps further include: In a first mode, when one of the plurality of electric vehicles is discharged, the total power capacity is maintained unchanged; and In a second mode, when one of the plurality of electric vehicles is discharged, the total power capacity is increased.

13. The charge and discharge system according to claim 11, characterized in that: The step of executing the bio-inspired algorithm according to the fitness function comprises: Initializing to generate a plurality of entities, each of the plurality of entities comprising the charge and discharge operations of the plurality of electric vehicles in the corresponding charge and discharge devices in the plurality of time intervals; calculating the fitness of each of the plurality of individuals according to the fitness function; selecting a plurality of first individuals from the plurality of individuals according to the adaptability; performing a mating procedure and a mutation procedure on the plurality of first individuals to produce a plurality of offspring individuals; and At least one of the plurality of offspring individuals is deleted according to a plurality of conditions and the next iteration is performed.

14. The charge and discharge system according to claim 13, wherein: The step of performing the initialization to generate the plurality of individuals comprises: generating a plurality of permutations and combinations for the plurality of electric vehicles; and For each of the plurality of permutations, the charge and discharge operations of the plurality of electric vehicles in the corresponding charge and discharge devices in the plurality of time intervals are determined according to the corresponding vehicle sequence, wherein each of the plurality of electric vehicles is in the first order in one of the plurality of permutations.

15. The charge and discharge system according to claim 11, wherein: The vehicle information further includes a stop time. The step of establishing the adaptability function according to the vehicle information and the system information includes: Calculating charging capacity based on the battery power levels of the multiple electric vehicles at the corresponding entry times and the multiple charging and discharging operations in the multiple time intervals; Calculating the required power according to the battery power of the multiple electric vehicles at the corresponding arrival time, the expected departure power, and the battery capacity; Calculating satisfaction based on the charged amount and the required amount of electricity; summing the plurality of profits in the plurality of time intervals to obtain a total profit; and The adaptability function is established according to the satisfaction and the total profit.

16. The charge and discharge system according to claim 15, characterized in that: The step of establishing the adaptability function according to the vehicle information and the system information further includes: If the profit per unit of electricity in the grid-to-vehicle transmission mode increases over time and the total power capacity decreases over time, the adaptability function is set to a first adaptability function in at least one previous time interval of the plurality of time intervals, and the adaptability function is set to a second adaptability function in the remaining time intervals of the plurality of time intervals, wherein the first adaptability function further includes a reward factor than the second adaptability function.

17. The charge and discharge system according to claim 15, characterized in that: The fitness function includes a first penalty factor, and the steps further include: If the charge and discharge operation of one of the electric vehicles in at least one of the last of the time intervals is discharging, the first penalty factor is set to reduce the adaptability calculated according to the adaptability function.

18. The charge and discharge system according to claim 17, characterized in that: The fitness function includes a second penalty factor, and the steps further include: If the battery level of one of the electric vehicles at the departure time is lower than the battery level at the arrival time, the second penalty factor is set to reduce the adaptability calculated according to the adaptability function.

19. The charge-discharge system according to claim 15, characterized in that: The total profit also includes importance, and the multiple steps further include: The importance is set to decrease monotonically as the plurality of electric vehicles stay in the charging and discharging system.

20. The charge and discharge system according to claim 15, characterized in that: The total profit also includes a weight, and the multiple steps further include: When calculating the grid-to-vehicle profit, setting the weight to a constant; When calculating the vehicle-to-grid profit, setting the weight to be positively correlated to the difference between the unit electricity profit in the vehicle-to-grid transmission mode and the unit electricity profit in the grid-to-vehicle transmission mode; and When calculating the vehicle-to-vehicle profit, the weight is set to be positively correlated with the unit electricity profit in the vehicle-to-grid transmission mode.

21. A charge and discharge scheduling method for a charge and discharge system, characterized in that: The charging and discharging system is electrically connected to a power grid, and includes a plurality of charging and discharging devices. The charging and discharging scheduling method includes: Obtain vehicle information of multiple electric vehicles; Obtaining system information of the charging and discharging system; and generating a charge and discharge schedule according to the vehicle information and the system information; When the vehicle information or the system information changes, the charge and discharge schedule is regenerated.

22. The charge and discharge scheduling method according to claim 21, wherein: The vehicle information includes types of the electric vehicles, required battery power values, or expected stay times.

23. The charge and discharge scheduling method according to claim 21, wherein: The system information includes grid-to-vehicle profit, vehicle-to-grid profit, vehicle-to-vehicle profit and total power capacity.

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