Charging and replacing integrated power station scheduling method, equipment and medium

Abstract

The invention discloses a charging and replacing integrated power station scheduling method and device and a medium, and relates to the field of charging and replacing integrated power station scheduling, and the method comprises the steps: determining the number of to-be-charged / replacing electric vehicles at a charging side and a replacing side of a charging and replacing integrated power station at the current moment; if the current moment is the charging / battery replacing peak period, initial queuing is carried out on the electric vehicles on the charging side and the battery replacing side according to the initial energy complementing willingness and the comprehensive integral of the arrival electric vehicles, and the initial queuing position of each arrival electric vehicle in the initial queue is determined; based on the initial queuing position of each arrival electric vehicle, determining the estimated waiting time before and after the transfer of the arrival electric vehicle and the cost before and after the transfer, and determining the expected transfer number of people and transfer feedback money; and determining the actual transfer number of people of the arrival electric vehicles by using the willingness remodeling model, and determining a final queue by combining the expected transfer number of people and the initial queue. The utilization rate of the charging side and the battery replacing side is balanced, and the operation efficiency of the charging and replacing integrated power station is improved.

Description

Technical Field

[0001] The present application relates to the field of integrated charging and battery swapping station scheduling, and particularly to a method, device, and medium for integrated charging and battery swapping station scheduling. Background Art

[0002] In recent years, due to the rapid growth in the number of EVs (Electric Vehicles) and their urgent need for efficient energy replenishment, a batch of integrated charging and battery swapping stations that combine charging and battery swapping functions have been booming. Integrated charging and battery swapping stations are developing rapidly across China due to their unique advantages of being fast, time-saving, and being able to flexibly meet the diverse energy replenishment needs of users. Taking 2024 as an example, the number of newly added integrated charging and battery swapping stations in China increased by as much as 59.8% year-on-year. In addition, driven by a series of policies, integrated charging and battery swapping stations are expected to continue to maintain a growth trend. However, the differences in charging and battery swapping habits and cognitions among EV users make it difficult to give full play to the advantages of integrated stations. This is manifested as: uneven distribution of charging and battery swapping resources during peak hours causes station congestion, while the idleness of charging and battery swapping equipment during off-peak hours reduces the station's efficiency. On the other hand, the management of batteries on the battery swapping side also faces huge challenges. If the battery management is improper, it is very easy to cause a shortage of inventory batteries, which in turn leads to battery swapping congestion and a decline in the operation efficiency and economy of the station.

[0003] To sum up, the differences in EV users' cognitions of charging and battery swapping, their energy replenishment habits, and battery management problems together cause an imbalance in the utilization rate of integrated station equipment, which in turn leads to station congestion. Therefore, in an integrated station with a certain proportion of EV access, how to effectively guide EVs to change their charging and battery swapping behavior habits and cognitions, and at the same time ensure the stable supply of battery swapping batteries, has become a key issue in alleviating station congestion and improving the operation revenue of the station. Summary of the Invention

[0004] The purpose of the present application is to provide a method, device, and medium for integrated charging and battery swapping station scheduling, which can balance the utilization rates of the charging side and the battery swapping side and improve the operation efficiency of the integrated charging and battery swapping station.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] In the first aspect, the present application provides a method for integrated charging and battery swapping station scheduling, including:

[0007] Using the Monte Carlo method to determine the number of electric vehicles to be charged on the charging side and the number of electric vehicles to be battery-swapped on the battery swapping side of the integrated charging and battery swapping station to be scheduled at the current moment;

[0008] Judging whether the current moment is a charging / battery swapping peak period to obtain a first judgment result;

[0009] If the first judgment result is no, performing off-station scheduling;

[0010] If the first judgment result is yes, then the electric vehicles arriving at the station at the charging side and the battery swapping side are initially queued according to the initial energy replenishment intention and comprehensive score of the electric vehicles arriving at the station at the current moment, to obtain the initial queue of the charging side and the initial queue of the battery swapping side, and the initial queue position of each electric vehicle arriving at the station in the initial queue of the charging side or the initial queue of the battery swapping side is determined; the initial energy replenishment intention is charging or battery swapping; the comprehensive score is determined based on wealth points and power consumption trust;

[0011] Determine the current charging congestion and battery swap congestion according to the number of electric vehicles to be charged on the charging side and the number of electric vehicles to be swapped on the battery swapping side;

[0012] Based on the current charging congestion and battery swapping congestion, as well as the initial queue position of each of the electric vehicles arriving at the station, an estimated waiting time before the electric vehicle changes its mind, an estimated waiting time after the change, a fee before the change, and a fee after the change, are determined, as well as an expected number of switchers and a switch reward. The expected number of switchers and the switch reward are obtained by optimizing the objective function and constraints using an improved Northern Goshawk optimization algorithm. Switching intent refers to switching from charging to battery swapping or from battery swapping to charging.

[0013] Based on the estimated waiting time before the electric vehicle at the station changes its mind, the estimated waiting time after the change, the cost before the change, the cost after the change, and the change reward, the intention reshaping model based on the ABC attitude change theory is used to determine the actual number of electric vehicles that change their minds at the station;

[0014] According to the actual number of people intending to transfer, the expected number of people intending to transfer, the initial queue on the charging side and the initial queue on the battery swapping side, the final queue on the charging side and the final queue on the battery swapping side are determined to complete the charging / battery swapping scheduling.

[0015] Optionally, according to the initial energy replenishment intention and comprehensive score of the electric vehicles arriving at the station at the current moment, the electric vehicles arriving at the charging side and the battery swapping side are initially queued, respectively, to obtain the initial queue of the charging side and the initial queue of the battery swapping side, specifically including:

[0016] Determine the comprehensive score of each of the electric vehicles arriving at the station at the current moment;

[0017] According to the initial energy replenishment intention, the electric vehicles arriving at the station at the current moment are sorted from high to low according to the comprehensive score to obtain the initial queue on the charging side and the initial queue on the battery replacement side.

[0018] Optionally, determining the comprehensive score of each of the electric vehicles arriving at the station at the current moment specifically includes:

[0019] Use the formula to determine the wealth integral when the i-th arriving electric vehicle is recharged for the j-th time; where is the wealth integral when the i-th arriving electric vehicle is recharged for the j-th time; is the initial integral of the i-th arriving electric vehicle; θ j is the additional reward coefficient for the j-th recharge; is the recharge amount for the j-th time of the i-th arriving electric vehicle;

[0020] Use the formula to determine the power consumption trust degree when the i-th arriving electric vehicle is recharged for the j-th time; where is the power consumption trust degree when the i-th arriving electric vehicle is recharged for the j-th time; is the initial trust degree of the i-th arriving electric vehicle; ω a is the overlimit penalty coefficient when arriving at the station; ω l is the overlimit penalty coefficient when leaving the station; is the degree of over-discharge of the battery of the i-th arriving electric vehicle when arriving at the station; is the degree of overcharge of the battery of the i-th arriving electric vehicle when leaving the station;

[0021] Use the formula to determine the comprehensive integral of the i-th arriving electric vehicle; where ω S represents the adjustment coefficient of the wealth integral; ω R represents the adjustment coefficient of the power consumption trust degree.

[0022] Optionally, according to the current charging congestion degree and the battery swapping congestion degree, and the initial queuing positions of the arriving electric vehicles, determine the estimated waiting time before the arriving electric vehicle changes its intention, the estimated waiting time after the change of intention, the cost before the change of intention, and the cost after the change of intention, specifically including:

[0023] According to the current charging congestion degree and the battery swapping congestion degree, judge whether the to-be-scheduled charging and swapping integrated power station is congested, and obtain a second judgment result;

[0024] If the second judgment result is yes, then according to the initial queuing positions of the arriving electric vehicles, determine the estimated waiting time before the arriving electric vehicle changes its intention, the estimated waiting time after the change of intention, the cost before the change of intention, and the cost after the change of intention;

[0025] If the second judgment result is no, then arrange the initial charging queue at the current moment behind the charging queue at the previous moment, and arrange the initial battery swapping queue at the current moment behind the battery swapping queue at the previous moment.

[0026] Optionally, based on the current charging congestion and the current swapping congestion, determine whether the to-be-scheduled integrated charging and swapping station is congested, specifically including:

[0027] When both the current charging congestion and the current swapping congestion are less than the congestion threshold, determine that the to-be-scheduled integrated charging and swapping station is not congested;

[0028] When the current charging congestion is less than the congestion threshold and the current swapping congestion is greater than or equal to the congestion threshold, determine that the to-be-scheduled integrated charging and swapping station is congested;

[0029] When the current charging congestion is greater than or equal to the congestion threshold and the current swapping congestion is less than the congestion threshold, determine that the to-be-scheduled integrated charging and swapping station is congested;

[0030] When both the current charging congestion and the current swapping congestion are greater than or equal to the congestion threshold, determine that the to-be-scheduled integrated charging and swapping station is congested.

[0031] Optionally, based on the estimated waiting time before the intention change of the arriving electric vehicle, the estimated waiting time after the intention change, the cost before the intention change, the cost after the intention change, and the intention feedback bonus, use the intention reshaping model based on the ABC attitude change theory to determine the actual number of arriving electric vehicles whose intention changes, specifically including:

[0032] Based on the estimated waiting time before the intention change of the arriving electric vehicle, the estimated waiting time after the intention change, the cost before the intention change, the cost after the intention change, and the intention feedback bonus, use the intention reshaping model based on the ABC attitude change theory to determine the emotional intensity of the arriving electric vehicle;

[0033] Judge whether the emotional intensity is less than the emotional intensity threshold to obtain a third judgment result;

[0034] If the third judgment result is yes, the arriving electric vehicle does not agree to change its intention;

[0035] If the third judgment result is no, the arriving electric vehicle agrees to change its intention;

[0036] Based on the third judgment result, determine the actual number of arriving electric vehicles whose intention changes.

[0037] Optionally, based on the actual number of intention changes, the expected number of intention changes, the initial queue on the charging side, and the initial queue on the swapping side, determine the final queue on the charging side and the final queue on the swapping side, specifically including:

[0038] The expected number of intention changes includes the expected number of charging-to-swapping and the expected number of swapping-to-charging; the actual number of intention changes includes the actual number of charging-to-swapping and the actual number of swapping-to-charging;

[0039] If the actual number of charging and conversion is greater than the expected number of charging and conversion, the arriving electric vehicle with the highest emotional intensity among the actual number of charging and conversion is used as the object of intention change for charging and conversion;

[0040] If the actual number of charging and conversion is less than the expected number of charging and conversion and the difference between the actual number of charging and conversion and the expected number of charging and conversion is less than the set difference, the arriving electric vehicle corresponding to the actual number of charging and conversion is used as the object of intention change for charging and conversion;

[0041] If the actual number of charging and conversion is less than the expected number of charging and conversion and the difference between the actual number of charging and conversion and the expected number of charging and conversion is greater than or equal to the set difference, off-station scheduling is performed;

[0042] If the actual number of conversion to charging is greater than the expected number of conversion to charging, the arriving electric vehicle with the highest emotional intensity among the actual number of conversion to charging is used as the object of intention change for conversion to charging;

[0043] If the actual number of conversion to charging is less than the expected number of conversion to charging and the difference between the actual number of conversion to charging and the expected number of conversion to charging is less than the set difference, the arriving electric vehicle corresponding to the actual number of conversion to charging is used as the object of intention change for conversion to charging;

[0044] If the actual number of conversion to charging is less than the expected number of conversion to charging and the difference between the actual number of conversion to charging and the expected number of conversion to charging is greater than or equal to the set difference, off-station scheduling is performed;

[0045] Based on the initial charging queue, the object of intention change for charging and conversion, and the object of intention change for conversion to charging, determine the final charging queue;

[0046] Based on the initial battery swapping queue, the object of intention change for charging and conversion, and the object of intention change for conversion to charging, determine the final battery swapping queue.

[0047] Optionally, it further includes:

[0048] Determine the number of batteries to be charged in the long-term available charging area according to the charging power of the long-term available charging area;

[0049] Determine the number of batteries to be charged in the short-term available charging area according to the charging power of the short-term available charging area; the charging power of the long-term available charging area and the charging power of the short-term available charging area are obtained by optimizing the objective function and constraint conditions using an improved Northern Goshawk optimization algorithm;

[0050] Sort the batteries to be charged in the long-term available charging area in descending order of state of charge, and charge the sorted batteries to be charged in sequence;

[0051] Sort the rechargeable batteries in the short-term sellable area in descending order of state of charge, and charge the sorted rechargeable batteries in sequence.

[0052] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the charging and swapping integrated power station scheduling method described in any one of the above.

[0053] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the charging and swapping integrated power station scheduling method described in any one of the above.

[0054] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0055] The present application provides a charging and swapping integrated power station scheduling method, device and medium. By using the Monte Carlo method, the number of electric vehicles to be charged on the charging side and the number of electric vehicles to be swapped on the swapping side of the charging and swapping integrated power station to be scheduled at the current moment are determined; it is judged whether the current moment is a peak charging / swapping period. If not, off-station scheduling is performed; if so, according to the initial energy replenishment willingness and comprehensive score of the electric vehicles arriving at the station at the current moment, the electric vehicles arriving at the station on the charging side and the swapping side are initially queued respectively to obtain an initial queue on the charging side and an initial queue on the swapping side, and the initial queuing positions of each arriving electric vehicle in the initial queue on the charging side or the initial queue on the swapping side are determined; according to the number of electric vehicles to be charged on the charging side and the number of electric vehicles to be swapped on the swapping side at the current moment, the charging congestion degree and the swapping congestion degree at the current moment are determined; according to the charging congestion degree and the swapping congestion degree at the current moment, and the initial queuing positions of each arriving electric vehicle, the estimated waiting time before the arriving electric vehicle changes its intention, the estimated waiting time after the change of intention, the cost before the change of intention and the cost after the change of intention are determined, and the expected number of people changing their intention and the change-of-intention feedback amount are determined; according to the estimated waiting time before the arriving electric vehicle changes its intention, the estimated waiting time after the change of intention, the cost before the change of intention and the cost after the change of intention, and the change-of-intention feedback amount, using the intention reshaping model based on the ABC attitude change theory, the actual number of people changing their intention of the arriving electric vehicle is determined; according to the actual number of people changing their intention, the expected number of people changing their intention, the initial queue on the charging side and the initial queue on the swapping side, the final queue on the charging side and the final queue on the swapping side are determined, and the charging / swapping scheduling is completed. In the present application, the number of people changing from charging to swapping or from swapping to charging is determined through the intention reshaping model based on the ABC attitude change theory, the utilization rates of the charging side and the swapping side are balanced, and the operation efficiency of the charging and swapping integrated power station is improved. Description of the Drawings

[0056] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0057] Figure 1 It is a schematic flow chart of a charging and swapping integrated power station scheduling method provided by an embodiment of the present application;

[0058] Figure 2 It is the overall flow chart of the charging and swapping integrated power station scheduling method of the present application;

[0059] Figure 3 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0061] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0062] The present application proposes a charging and swapping integrated power station scheduling method. On the user side, a willingness reshaping model for changing EV perception is proposed to balance the utilization rates of the equipment on both the charging and swapping sides, thereby alleviating the congestion of the power station. On the power station side, an off-station scheduling model for EV stations is constructed to improve the comprehensive revenue of the power station. In the battery bin part, the proposed inventory battery SOC (State of Charge) threshold adjustment strategy and the battery charging strategy based on charging duration partitioning are used to achieve the economic charging of the battery while ensuring the stable supply of the inventory battery. Finally, the improved Northern Goshawk algorithm is used to solve the proposed two-stage scheduling strategy inside and outside the station, realizing the alleviation of the congestion degree of the charging and swapping integrated power station and the improvement of economic benefits.

[0063] In an exemplary embodiment, as Figure 1 and Figure 2 shown, a charging and swapping integrated power station scheduling method is provided, including the following steps:

[0064] S1: Determine the number of electric vehicles to be charged on the charging side and the number of electric vehicles to be battery-swapped on the battery-swapping side of the integrated charging and swapping power station to be scheduled at the current moment using the Monte Carlo method.

[0065] In practical applications, since only the number of EVs is predicted in this embodiment, the prediction feature factors only select the charging start time (normal distribution).

[0066] 1. The charging start time of EVs follows the normal distribution of the probability density function of.

[0067]

[0068] Among them, represents the charging start time of EVs; σ T represents the standard deviation of the charging start time of EVs; μ T represents the expected value of the charging start time of EVs.

[0069] 2. By setting σ T and μ T , the battery-swapping peak (battery-swapping peak period) appears at 8:00 am and 8:00 pm; the charging peak (charging peak period) appears at 11:00 am and 11:00 pm.

[0070] S2: Judge whether the current moment is the charging / battery-swapping peak period to obtain the first judgment result.

[0071] S3: If the first judgment result is no, off-station scheduling is performed. During off-peak hours, the idle rate of charging and battery-swapping equipment is high, and off-station scheduling is carried out.

[0072] The off-station scheduling strategy is as follows:

[0073] To ensure that the scheduled EVs have sufficient SOC reserves to meet the charging and battery-swapping requirements, first, the current SOC of all EVs in the scheduling area is screened for vehicle conditions. Then, for each EV that has passed the vehicle condition screening, based on the distance d from its current position to the power station and combined with the real-time road congestion situation, the estimated arrival time If EV i 's is less than or equal to the latest arrival time T min , then EV i is determined to be a schedulable EV, otherwise EV i will be excluded. This process aims to ensure the scheduling timeliness.

[0074]

[0075] Among them, represents the SOC of EV i at time t; Indicates the lower limit of SOC for vehicles dispatched outside the station; Indicates the SOC upper limit of vehicles dispatched outside the station; Indicates the base speed; Indicates the average driving speed.

[0076] After the screening is completed, the power station sends demand response information to all dispatchable EVs, including: the number of people who need to charge at time t and number of people who need battery replacement After receiving the invitation, EVs interested in participating must promptly provide the power plant with their desired refueling method, while EVs not interested must submit a rejection response. During this process, the power plant will immediately update the demand information after each dispatchable EV makes a decision, until the demand quantity is reduced to zero or all dispatchable EVs have made a decision.

[0077]

[0078]

[0079] in, is the number of idle charging piles at time t-1; is the number of people leaving the charging station at time t-1; N swp Indicates the number of motors to be replaced; Indicates the number of batteries in stock at time t.

[0080] S4: If the first judgment result is yes, then according to the initial energy replenishment intention and comprehensive score of the electric vehicles arriving at the station at the current moment, the electric vehicles on the charging side and the battery swapping side are initially queued respectively to obtain the initial queue of the charging side and the initial queue of the battery swapping side, and the initial queue position of each electric vehicle arriving at the station in the initial queue of the charging side or the initial queue of the battery swapping side is determined; the initial energy replenishment intention is charging or battery swapping; the comprehensive score is determined based on wealth points and electricity consumption trust.

[0081] According to the initial recharging intention of EVs arriving at the station at time t, the EVs on the charging and battery swapping sides are initially queued and their initial queue positions are determined.

[0082] As an optional implementation method, based on the initial charging willingness and comprehensive scores of the electric vehicles arriving at the station at the current moment, the electric vehicles on the charging side and the battery swapping side are initially queued, respectively, to obtain the initial queues on the charging side and the initial queues on the battery swapping side, specifically including:

[0083] Determine the comprehensive score of each of the electric vehicles arriving at the station at the current moment.

[0084] According to the initial energy replenishment intention, the electric vehicles arriving at the station at the current moment are sorted from high to low according to the comprehensive score to obtain the initial queue on the charging side and the initial queue on the battery replacement side.

[0085] At time t, the charging / recharging EVs arriving at the station are sorted respectively. For this purpose, the wealth integral and the trust degree of power consumption are selected as evaluation indicators, and a fair and efficient queuing model is proposed to ensure that the charging / recharging EVs are orderly managed in two independent queues respectively.

[0086] In the wealth integral management mechanism, the EV i can obtain the initial integral when participating in consumption for the first time as a reward. After that, according to the different consumption amounts each time, the accumulated integral obtained shows a stepped increase. The EV i The wealth integral at the j-th consumption

[0087]

[0088]

[0089] Among them, is the wealth integral of the i-th arriving electric vehicle at the j-th energy replenishment; is the initial integral of the i-th arriving electric vehicle; θ j is the additional reward coefficient for the j-th energy replenishment; is the j-th energy replenishment amount of the i-th arriving electric vehicle. and represent the critical values of the consumption interval. a, b, and c respectively represent the percentage of integral growth.

[0090] In the trust degree management mechanism of power consumption, the EV i is first given an initial trust degree After that, the trust degree of the EV i will be dynamically updated according to the over-discharge degree of the battery when arriving at the station each time and the over-charge degree of the battery when leaving the station The trust degree of power consumption at the j-th energy replenishment of the EV i

[0091]

[0092] Among them, is the trust degree of power consumption of the i-th arriving electric vehicle at the j-th energy replenishment; is the initial trust degree of the i-th arriving electric vehicle; ω a is the over-limit penalty coefficient when arriving at the station; ω l is the over-limit penalty coefficient when leaving the station; is the over-discharge degree of the battery of the i-th arriving electric vehicle when arriving at the station; is the over-charge degree of the battery of the i-th arriving electric vehicle when leaving the station; SOC max 、SOCmin respectively represent the upper and lower limits of the battery SOC.

[0093] According to the above mathematical models of wealth points and power consumption trust, the user's EV i comprehensive points at the jth charging

[0094]

[0095] where ω S represents the adjustment coefficient of wealth points; ω R represents the adjustment coefficient of power consumption trust.

[0096] S5: Determine the charging congestion degree and the swapping congestion degree at the current moment according to the number of electric vehicles to be charged on the charging side and the number of electric vehicles to be swapped on the swapping side at the current moment.

[0097] In this embodiment, the congestion degrees on both the charging and swapping sides (charging congestion degree and swapping congestion degree) are calculated respectively according to the number of waiting people (the number of electric vehicles to be charged and the number of electric vehicles to be swapped) at time t.

[0098] Charging congestion degree at time t = Number of electric vehicles to be charged at time t / Number of idle charging piles at time t.

[0099] Swapping congestion degree at time t = Number of electric vehicles to be swapped at time t / Number of idle swapping machines at time t.

[0100] Set a congestion threshold A. If the congestion degree >= A, it is congested; if it is less than A, it is not congested.

[0101] S6: Determine the estimated waiting time before intention conversion, the estimated waiting time after intention conversion, the cost before intention conversion, and the cost after intention conversion of the arriving electric vehicle according to the charging congestion degree and the swapping congestion degree at the current moment, and the initial queuing positions of the arriving electric vehicles, and determine the expected number of intention conversions and the intention conversion feedback amount; the expected number of intention conversions and the intention conversion feedback amount are obtained by optimizing the objective function and constraint conditions using an improved Northern Goshawk optimization algorithm; the intention conversion means changing from charging to swapping or from swapping to charging.

[0102] As an optional implementation manner, determining the estimated waiting time before intention conversion, the estimated waiting time after intention conversion, the cost before intention conversion, and the cost after intention conversion of the arriving electric vehicle according to the charging congestion degree and the swapping congestion degree at the current moment, and the initial queuing positions of the arriving electric vehicles, specifically includes:

[0103] According to the charging congestion degree and the battery swapping congestion degree at the current moment, determine whether the to-be-scheduled integrated charging and battery swapping station is congested, and obtain a second judgment result. There are three types of congestion in the to-be-scheduled integrated charging and battery swapping station: charging congestion while battery swapping is not congested; battery swapping congestion while charging is not congested; both charging and battery swapping are congested. As long as there is one type of congestion, it is determined that the to-be-scheduled integrated charging and battery swapping station is congested.

[0104] If the second judgment result is yes, then according to the initial queuing positions of the arriving electric vehicles, determine the estimated waiting time before the intention transfer, the estimated waiting time after the intention transfer, the cost before the intention transfer, and the cost after the intention transfer of the arriving electric vehicles.

[0105] The underlying variables of the cost are power (i.e., the amount of charge) and the amount of battery swapping. What is actually obtained by multiplying by the electricity price is the cost, but the cost is expressed in terms of the user's energy replenishment satisfaction. The less money paid, the higher the satisfaction.

[0106] In practical applications, the present application proposes a willingness reshaping model based on the ABC attitude change theory. This model can guide the EVs arriving at time t to have willingness transfer behaviors during peak hours, thereby coordinating the charging and battery swapping resources and alleviating the congestion of the station. First, for each EV arriving at time t, information on the estimated waiting time and cost before and after the intention transfer is provided. Secondly, a series of stimulating factors including the above information are quantified into an emotional intensity index that is more easily perceived by users through the willingness reshaping model. Finally, by comparing the emotional intensity and the emotional threshold, the reshaping result of the EV is determined.

[0107] By simulating the intention transfer scenario, provide the information on the estimated waiting time and cost before and after the intention transfer for each EV arriving at time t. Select two extreme scenarios, i.e., all EVs on the charging side are transferred to the battery swapping side and all EVs on the battery swapping side are transferred to the charging side, for trial calculation. During this process, the EVs on the side to be transferred should be arranged in the order of the original queue and inserted into the end of the target queue in turn, and their positions should also be updated according to the new queue.

[0108] Scenario 1: All EVs on the charging side are transferred to the battery swapping side.

[0109] EV i Charging waiting time before intention transfer Is determined jointly by the EV i Initial position in the charging queue And the rotation number of the charging pile at each moment. And the EV i Battery swapping waiting time after intention transfer Depends on its new position after transferring from the charging queue to the battery swapping queue And the rotation number of the battery swapping machine at each moment.

[0110]

[0111]

[0112]

[0113] Among them, and respectively represent the rotation numbers of the charging pile at times t, t + 1, and t + 2 before the conversion; and respectively represent the rotation numbers of the motor replacement at times t, t + 1, and t + 2 after the conversion; represents the rotation number of the charging pile at time t + n; represents the number of idle charging piles at time t + n - 1 before the conversion; represents the number of people leaving the station on the charging side at time t + n - 1; represents the rotation number of the motor replacement at time t + n after the conversion; N swp represents the number of motor replacements; represents the number of inventory batteries at time t + n after BMS; represents the number of people waiting for battery replacement at time t before the conversion.

[0114] EV i The charging cost before the conversion is obtained by accurately calculating the charging start time after its queuing the charging duration and then accumulating the costs at each moment during the entire charging cycle. When EV i is converted, its battery replacement cost is then determined by the product of the electricity price at the moment of battery replacement and the actual electricity quantity of the replaced battery, based on accurately calculating the battery replacement start time after the conversion. is determined by the product of the electricity price at the moment of battery replacement and the actual electricity quantity of the replaced battery.

[0115]

[0116]

[0117]

[0118] Among them, represents the charging electricity price at time h; represents the rated charging power of EV; represents the charging efficiency of EV; represents EV i 's expected charging SOC; represents EV i 's SOC when arriving at the station; represents EV i 's SOH of the battery; Indicates the actual capacity of the battery of the EV i ; The SOC of the battery for battery swapping Indicates the SOH of the battery for battery swapping.

[0119] Scenario 2: All EVs on the battery swapping side are transferred to the charging side.

[0120] EV i The battery swapping waiting time before transfer By the EV i The initial position in the battery swapping queue And the number of rotations of the battery swapping machine at each moment jointly determine. And the charging waiting time after the EV is transferred Then depends on its new position after being transferred to the charging queue And the number of rotations of the charging pile at each moment.

[0121]

[0122] Among them, And Respectively represent the number of rotations of the battery swapping machine at times t, t + 1, and t + 2 before transfer; And Respectively represent the number of rotations of the charging pile at times t, t + 1, and t + 2 after transfer; Represents the battery swapping rotation rate at time t + n before transfer; Represents the number of inventory batteries of the BMS at time t + n before; Represents the number of rotations of the charging pile at time t + n after transfer; Represents the number of idle charging piles at time t + n - 1 after transfer; Represents the number of people leaving the station on the charging side at time t + n - 1 after transfer; Represents the number of people waiting for charging at time t before transfer.

[0123] EV i The battery swapping cost before transfer Is based on accurately calculating its battery swapping start time On this basis, it is determined by the product of the electricity price at the moment of battery swapping And the actual power of the battery being swapped. When the EV i After being transferred, its charging cost Then is based on accurately calculating its charging start time after queuing Charging duration On this basis, it is calculated by accumulating the charging costs at each moment during the entire charging cycle.

[0124]

[0125] In addition, the improved Northern Goshawk optimization algorithm is used to optimize the objective function and constraint conditions to obtain the expected number of converted people and the converted feedback amount.

[0126] The objective function is as follows:

[0127] 1. Power supply efficiency objective function of the power station: The change in power supply efficiency of the power station = the change in charging-side power supply efficiency + the change in swapping-side power supply efficiency, that is, the change in power supply efficiency at time t before and after conversion. It consists of the change in charging-side power supply efficiency and the change in swapping-side power supply efficiency These two parts.

[0128] The decision variables are: the number of people on the charging side after conversion, the number of people on the swapping side after conversion.

[0129]

[0130]

[0131] Among them, represents the electricity price at time t; represents the number of charging people at time t after conversion; represents the number of charging people at time t before conversion; the number of swapping people at time t after conversion; represents the number of swapping people at time t before conversion; represents the number of EVs that have been converted from 0 to t and are still charging at time t; represents the swapping electricity price at time t; represents the average swapping power of EVs.

[0132] 2. Battery depreciation rate objective function: The change in the battery depreciation rate of the power station = the change in battery depreciation on the charging side Change in quantity + Change in the depreciation of the battery on the battery swapping side, i.e., the change in the battery depreciation rate at time t It consists of the loss caused by the change in the number of charging vehicles on the charging side and the loss caused by the change in charging power after battery management on the swapping side These two parts. The decision variables are: the number of people on the charging side after conversion, the charging power of the two areas of the battery compartment after conversion.

[0133]

[0134]

[0135] Among them, represents the unit depreciation rate.

[0136] 3. Congestion objective function: The change in power station congestion = the change in charging-side congestion + the change in swapping-side congestion, that is, the change in charging-side congestion and the change in swapping-side congestion The weighted sum constitutes the comprehensive congestion change of the power station at time t The decision variables are: the number of people on the charging side after intention change, the number of people on the swapping side after intention change.

[0137]

[0138]

[0139] Among them, respectively represent the weights of congestion on the charging and swapping sides; represents the rotation speed of the swapping machine at time t before intention change, respectively represent the rotation speed of the swapping machine at time t after intention change; represents the rotation speed of the charging pile at time t after intention change; respectively represent the rotation speed of the charging pile at time t before intention change; represents the number of people waiting to charge at time t before intention change; represents the number of people waiting to charge at time t after intention change; represents the number of people waiting to swap at time t before intention change; represents the number of people waiting to swap at time t after intention change.

[0140] 4. Inventory anxiety objective function: Business owners or managers may feel uneasy due to concerns about insufficient inventory affecting production and sales, that is, inventory anxiety. The change in anxiety level is only related to the swapping side, that is, the change in inventory anxiety level at time t before and after battery management is related to the decrease in the inventory of batteries at time t. The decision variables are: the charging power of the two areas of the battery warehouse after intention change.

[0141]

[0142] Among them, β c represents the speed factor; respectively represent the inventory levels at time t before and after battery management; represents the total number of batteries.

[0143] The total objective function of the integrated charging and swapping power station is as follows:

[0144]

[0145] Among them, ω H is the weight corresponding to the comprehensive congestion change of the power station at time t; ω A is the weight corresponding to the change in inventory anxiety level at time t before and after battery management.

[0146] The constraints are as follows:

[0147] The first constraint is that the total number of people remains unchanged before and after the transfer of intention. The formula is as follows:

[0148]

[0149] Among them, respectively represent the number of newly added charging and battery - swapping people at time t before the transfer of intention; respectively represent the number of newly added charging and battery - swapping people at time t after the transfer of intention.

[0150] The second constraint is that the charging power of each area of the battery compartment ≤ the number of batteries * the rated charging power. The formula is as follows:

[0151]

[0152] Among them, represents the number of batteries in the long - term sellable area at time t; represents the number of batteries in the short - term sellable area, is the total charging amount of long - term sellable area A l ; is the total charging amount of short - term sellable area A s ; represents the rated charging power of the battery.

[0153] If the second judgment result is negative, then the initial charging - side queue at the current moment is arranged after the charging - side queue at the previous moment, and the initial battery - swapping - side queue at the current moment is arranged after the battery - swapping - side queue at the previous moment.

[0154] S7: According to the estimated waiting time before the transfer of intention, the estimated waiting time after the transfer of intention, the cost before the transfer of intention, the cost after the transfer of intention, and the transfer feedback amount of the arriving electric vehicle, use the intention reshaping model based on the ABC attitude change theory to determine the actual number of transferred - intention people of the arriving electric vehicle.

[0155] As an optional implementation manner, S7 specifically includes:

[0156] According to the estimated waiting time before the transfer of intention, the estimated waiting time after the transfer of intention, the cost before the transfer of intention, the cost after the transfer of intention, and the transfer feedback amount of the arriving electric vehicle, use the intention reshaping model based on the ABC attitude change theory to determine the emotional intensity of the arriving electric vehicle.

[0157] Judge whether the emotional intensity is less than the emotional intensity threshold to obtain the third judgment result.

[0158] If the third judgment result is yes, the arriving electric vehicle does not agree to change its intention.

[0159] If the third judgment result is no, the arriving electric vehicle agrees to change its intention.

[0160] According to the third judgment result, determine the actual number of electric vehicles with changed intentions arriving at the station.

[0161] In this application, the willingness reshaping model based on the ABC attitude change theory integrates the stimulation information into an index of emotional intensity, so as to more intuitively coordinate the EVs on both sides of charging and swapping, and achieve the goal of alleviating congestion.

[0162] The stimulation information includes: the estimated waiting time, cost, and intention-changing feedback amount before and after the intention change.

[0163] This application takes the stimulation information as the output and input elements in the process of intention change, introduces the value rate of things to describe the relationship between the two, and then transforms it into the user's emotion through the first law of emotional intensity. However, the nature of the stimulation information will continuously change between output and input with the difference in intention change types, and it needs to be divided in detail, as shown in Table 1.

[0164] Table 1 Table of changes in the nature of stimulation information with different intention change types

[0165]

[0166]

[0167]

[0168]

[0169] Charging and swapping conversion:

[0170]

[0171] Swapping to charging:

[0172]

[0173] Among them, represents the emotional intensity of the EV i ; K m is the emotional intensity coefficient; is the value rate difference; is the value rate of things; is the median value rate; represents the intention-changing feedback amount at time t; represents the waiting time cost of the EV.

[0174] EV iThe final reshaping result is jointly determined by and the emotional intensity threshold at time t, where is determined by the standard deviation of all EV emotional intensities at time t.

[0175]

[0176] where 0 indicates disagreement, 1 indicates agreement, and α t is the emotional intensity threshold at time t; x is the empirical multiple; S t is the standard deviation at time t; is the mean value of all on-site EVs at time t.

[0177] S8: Determine the final charging queue and the final swapping queue based on the actual number of people with changed intentions, the expected number of people with changed intentions, the initial charging queue, and the initial swapping queue, and complete the charging / swapping scheduling.

[0178] As an alternative implementation, S8 specifically includes:

[0179] The expected number of people with changed intentions includes the expected number of people changing from charging to swapping and the expected number of people changing from swapping to charging; the actual number of people with changed intentions includes the actual number of people changing from charging to swapping and the actual number of people changing from swapping to charging.

[0180] If the actual number of people changing from charging to swapping is greater than the expected number of people changing from charging to swapping, then use the on-site electric vehicle with the highest emotional intensity among the actual number of people changing from charging to swapping as the object of intention change from charging to swapping.

[0181] If the actual number of people changing from charging to swapping is less than the expected number of people changing from charging to swapping and the difference between the actual number of people changing from charging to swapping and the expected number of people changing from charging to swapping is less than the set difference, then use the on-site electric vehicle corresponding to the actual number of people changing from charging to swapping as the object of intention change from charging to swapping.

[0182] If the actual number of people changing from charging to swapping is less than the expected number of people changing from charging to swapping and the difference between the actual number of people changing from charging to swapping and the expected number of people changing from charging to swapping is greater than or equal to the set difference, then perform off-site scheduling.

[0183] If the actual number of people changing from swapping to charging is greater than the expected number of people changing from swapping to charging, then use the on-site electric vehicle with the highest emotional intensity among the actual number of people changing from swapping to charging as the object of intention change from swapping to charging.

[0184] If the actual number of people changing from swapping to charging is less than the expected number of people changing from swapping to charging and the difference between the actual number of people changing from swapping to charging and the expected number of people changing from swapping to charging is less than the set difference, then use the on-site electric vehicle corresponding to the actual number of people changing from swapping to charging as the object of intention change from swapping to charging.

[0185] ​If the actual number of conversion and charging people is less than the expected number of conversion and charging people and the difference between the actual number of conversion and charging people and the expected number of conversion and charging people is greater than or equal to the set difference, off-site scheduling is performed.

[0186] Based on the initial charging-side queue, the charging-to-conversion intention objects, and the conversion-to-charging intention objects, determine the final charging-side queue.

[0187] Based on the initial battery-exchanging side queue, the charging-to-conversion intention objects, and the conversion-to-charging intention objects, determine the final battery-exchanging side queue.

[0188] As an optional implementation manner, it further includes:

[0189] According to the charging power of the long-term sellable area, determine the number of batteries to be charged in the long-term sellable area.

[0190] According to the charging power of the short-term sellable area, determine the number of batteries to be charged in the short-term sellable area; the charging power of the long-term sellable area and the charging power of the short-term sellable area are obtained by optimizing the objective function and constraint conditions using an improved Northern Goshawk optimization algorithm.

[0191] Sort the batteries to be charged in the long-term sellable area in descending order of state of charge, and charge the sorted batteries to be charged one by one.

[0192] Sort the batteries to be charged in the short-term sellable area in descending order of state of charge, and charge the sorted batteries to be charged one by one.

[0193] This application proposes a flexible SOC threshold adjustment strategy, aiming to alleviate a series of problems such as battery shortage, station congestion, and economic decline caused by the inability of traditional fixed thresholds to adapt to the fluctuations of battery-exchanging demands. For this purpose, the number of people waiting for battery-exchanging at time t and the battery-exchanging price are selected as key indicators to construct an SOC threshold adjustment model. Through flexible adjustment, this model can include batteries with an SOC slightly lower than the original threshold into the replaceable category, thereby alleviating the problem of tight battery supply during peak battery-exchanging periods.

[0194]

[0195] Among them, represents the battery-exchanging threshold after battery management; ω N , ω K respectively represent the weights of threshold adjustment due to the number of battery-exchanging people and the battery-exchanging electricity price; respectively represent the threshold change amounts caused by the number of people exchanging batteries at the station and the battery-exchanging electricity price; α N , α Krespectively represent the adjustment efforts for the threshold change amounts caused by the number of battery swapping people and the battery swapping electricity price; b N , c K respectively represent the change rates of the function.

[0196] The charging strategy for the battery compartment is as follows:

[0197] This application proposes a battery charging strategy aimed at alleviating the problem of the continuous increase in the power station cost caused by blind charging. First, the battery with the SOC at time t not reaching is defined as the battery to be charged. Secondly, the batteries to be charged are divided according to the time when their SOC is charged to . Thirdly, through macro-level optimization, the total charging amount of each area is determined. Finally, the local distribution method is used to accurately locate the individual batteries that need to be charged, realizing the refined management of the batteries.

[0198] The specific steps are as follows:

[0199] 1. According to the relationship between the charging time and the battery SOC, select SOC = α as the critical value for dividing the battery charging time length.

[0200] 2. Classify the batteries to be charged B i into the long-term sellable area A l or the short-term sellable area A s in sequence.

[0201]

[0202] represents the SOC of the battery B i .

[0203] 3. Arrange the batteries to be charged in each area in descending order of SOC.

[0204] 4. Calculate the number of charged batteries in the two areas (long-term sellable area and short-term sellable area) respectively according to the total charging amount l of the long-term sellable area A and the total charging amount s of the short-term sellable area A

[0205]

[0206] 5. According to and the sorting result, preferentially select the battery with a large SOC for charging.

[0207] This application proposes a willingness reshaping model, which aims to coordinate the EV resources on both sides of the charging and swapping during peak hours, thereby effectively alleviating the congestion of the power station. After adopting this model, the peak number of congested vehicles on the charging and swapping side decreased by 55% and 34% respectively, while the congestion duration was shortened by approximately 1 hour and 2 hours respectively. In addition, an advanced battery management strategy that integrates SOC threshold adjustment and duration partition management is proposed. This strategy not only ensures a stable supply of batteries, but also further improves the economic efficiency of the power station by optimizing the charging process. Finally, in order to comprehensively improve the economic benefits of the power station, this application also introduces an off-site scheduling strategy. This strategy improves the economic efficiency of the power station by guiding EV charging and swapping during non-peak hours. After implementing the above strategy, the comprehensive income of the power station increased by 6,972.77 yuan, an increase of 23.8%.

[0208] The integrated charging and swapping power station scheduling method of this application alleviates the congestion of the integrated charging and swapping power station during peak hours, improves the economic benefits during off-peak hours, enhances the supply stability of inventory batteries, and optimizes the charging power of batteries. In addition, a comprehensive scheduling model is constructed to achieve optimal scheduling of the integrated charging and swapping power station. The main contributions of this application are as follows:

[0209] This paper introduces a two-stage dispatch strategy, on-site and off-site. This strategy uses an on-site dispatch model to coordinate EV charging and swapping requirements, balancing charging and swapping equipment utilization during peak hours and alleviating power station congestion. Alternatively, an off-site dispatch model can be used to efficiently manage off-site EVs, improve equipment utilization during off-peak hours, and ultimately increase overall power station revenue.

[0210] Differences in user charging and swapping behaviors lead to staggered peaks. This, coupled with the increasingly comprehensive charging and swapping compatibility of EVs, makes it possible to alleviate power station congestion by coordinating the charging and swapping of EVs on both sides. However, differences in user understanding of charging and swapping, coupled with their disordered charging and swapping behaviors, not only hinder the effective utilization of these advantages but also cause charging and swapping equipment to remain idle for extended periods during off-peak hours. This ultimately leads to a series of problems, including uncoordinated charging and swapping equipment utilization, power station congestion, and reduced economic efficiency.

[0211] The in-station scheduling part consists of a queuing model and a willingness reshaping model. The queuing model determines the priority of electric vehicles in the charging / battery swapping queue after they arrive at the station; the willingness reshaping model establishes a charging / battery swapping conversion mechanism to achieve scheduling between charging / battery swapping queues within the station.

[0212] The SOC threshold adjustment method for inventory batteries of this application can ensure a stable supply of inventory batteries by flexibly adjusting the SOC threshold. In addition, a new battery charging method is introduced to improve charging economy.

[0213] To maximize the power station's revenue, a comprehensive scheduling model is established. This model comprehensively considers economic and operation management indicators and uses an improved Northern Goshawk algorithm for solution, enabling the optimal economic scheduling of the power station.

[0214] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above-mentioned charging and swapping integrated power station scheduling method is implemented.

[0215] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above-mentioned charging and swapping integrated power station scheduling method is implemented.

[0216] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned charging and swapping integrated power station scheduling method is implemented.

[0217] In an exemplary embodiment, a computer device is provided. This computer device can be a server or a terminal, and its internal structure diagram can be as shown in Figure 3 The figure shows. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of this computer device is used for the processor to exchange information with external devices. The communication interface of this computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, a charging and swapping integrated power station scheduling method is implemented.

[0218] Those skilled in the art can understand that Figure 3 The structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0219] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0220] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0221] The databases involved in the various embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the various embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0222] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0223] In this application, specific examples are used to elaborate on the principles and implementation manners of this application. The descriptions of the above embodiments are only used to help understand the method and its core idea of this application. At the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A charging and swapping integrated power station scheduling method, characterized in that, include: The Monte Carlo method is used to determine the number of electric vehicles to be charged on the charging side and the number of electric vehicles to be swapped on the swapping side of the integrated charging and swapping power station to be scheduled at the current moment; Determine whether the current time is a charging / swapping peak period, and obtain a first determination result; If the first judgment result is no, performing off-station scheduling; If the first judgment result is yes, then the electric vehicles arriving at the station at the charging side and the battery swapping side are initially queued according to the initial energy replenishment intention and comprehensive score of the electric vehicles arriving at the station at the current moment, to obtain the initial queue of the charging side and the initial queue of the battery swapping side, and the initial queue position of each electric vehicle arriving at the station in the initial queue of the charging side or the initial queue of the battery swapping side is determined; the initial energy replenishment intention is charging or battery swapping; the comprehensive score is determined based on wealth points and power consumption trust; Determine the current charging congestion and battery swap congestion according to the number of electric vehicles to be charged on the charging side and the number of electric vehicles to be swapped on the battery swapping side; Based on the current charging congestion and battery swapping congestion, as well as the initial queue position of each of the electric vehicles arriving at the station, an estimated waiting time before the electric vehicle changes its mind, an estimated waiting time after the change, a fee before the change, and a fee after the change, are determined, as well as an expected number of switchers and a switch reward. The expected number of switchers and the switch reward are obtained by optimizing the objective function and constraints using an improved Northern Goshawk optimization algorithm. Switching intent refers to switching from charging to battery swapping or from battery swapping to charging. Based on the estimated waiting time before the electric vehicle at the station changes its mind, the estimated waiting time after the change, the cost before the change, the cost after the change, and the change reward, the intention reshaping model based on the ABC attitude change theory is used to determine the actual number of electric vehicles that change their minds at the station; According to the actual number of transfer intentions, the expected number of transfer intentions, the initial queue on the charging side and the initial queue on the battery swapping side, the final queue on the charging side and the final queue on the battery swapping side are determined to complete the charging / battery swapping scheduling.

2. The charging and swapping integrated power station scheduling method according to claim 1, wherein According to the initial charging willingness and comprehensive score of the electric vehicles arriving at the station at the current moment, the electric vehicles arriving at the charging side and the battery swapping side are initially queued, and the initial queues of the charging side and the battery swapping side are obtained. Specifically, they include: Determine the comprehensive score of each of the electric vehicles arriving at the station at the current moment; According to the initial energy replenishment intention, the electric vehicles arriving at the station at the current moment are sorted from high to low according to the comprehensive score to obtain the initial queue on the charging side and the initial queue on the battery replacement side.

3. The charging and swapping integrated power station scheduling method according to claim 2, wherein Determine the comprehensive score of each electric vehicle arriving at the station at the current moment, specifically including: Using the formula to determine the wealth score of the i-th arriving electric vehicle during its j-th charging; where is the wealth score of the i-th arriving electric vehicle during its j-th charging; is the initial score of the i-th arriving electric vehicle; θ j is the additional reward coefficient for the j-th charging; is the j-th charging amount of the i-th arriving electric vehicle; Using the formula to determine the power consumption trust level of the i-th arriving electric vehicle during its j-th charging; where is the power consumption trust level of the i-th arriving electric vehicle during its j-th charging; is the initial trust level of the i-th arriving electric vehicle; ω a is the overlimit penalty coefficient when arriving at the station; ω l is the overlimit penalty coefficient when leaving the station; is the degree of over-discharge of the battery of the i-th arriving electric vehicle when it arrives at the station; is the degree of over-charge of the battery of the i-th arriving electric vehicle when it leaves the station; Using the formula to determine the comprehensive score of the i-th arriving electric vehicle; where ω S represents the adjustment coefficient of the wealth score; ω R represents the adjustment coefficient of the power consumption trust degree.

4. The charging and swapping integrated power station scheduling method according to claim 1, characterized in that, According to the current charging congestion and the battery swapping congestion, and the initial queue position of each of the electric vehicles arriving at the station, the estimated waiting time before the electric vehicle at the station changes its mind, the estimated waiting time after the change of mind, the fee before the change of mind, and the fee after the change of mind are determined, specifically including: Determining whether the integrated charging and battery swapping power station to be scheduled is congested according to the current charging congestion and the battery swapping congestion, and obtaining a second judgment result; If the second judgment result is yes, then according to the initial queuing positions of the arriving electric vehicles, determine the estimated waiting time before the change of intention, the estimated waiting time after the change of intention, the cost before the change of intention, and the cost after the change of intention for each of the arriving electric vehicles; If the second judgment result is no, then arrange the initial charging-side queue at the current moment after the charging-side queue at the previous moment, and arrange the initial swapping-side queue at the current moment after the swapping-side queue at the previous moment.

5. The charging and swapping integrated power station scheduling method according to claim 4, wherein Judge whether the to-be-scheduled charging and swapping integrated power station is crowded according to the charging congestion degree and the swapping congestion degree at the current moment, specifically including: When both the charging congestion degree and the swapping congestion degree at the current moment are less than the congestion threshold, determine that the to-be-scheduled charging and swapping integrated power station is not crowded; When the charging congestion degree at the current moment is less than the congestion threshold and the swapping congestion degree is greater than or equal to the congestion threshold, determine that the to-be-scheduled charging and swapping integrated power station is crowded; When the charging congestion degree at the current moment is greater than or equal to the congestion threshold and the swapping congestion degree is less than the congestion threshold, determine that the to-be-scheduled charging and swapping integrated power station is crowded; When both the charging congestion degree and the swapping congestion degree at the current moment are greater than or equal to the congestion threshold, determine that the to-be-scheduled charging and swapping integrated power station is crowded.

6. The charging and swapping integrated power station scheduling method according to claim 1, wherein According to the estimated waiting time before the change of intention, the estimated waiting time after the change of intention, the cost before the change of intention, the cost after the change of intention of the arriving electric vehicles, and the intention feedback bonus, use the intention reshaping model based on the ABC attitude change theory to determine the actual number of arriving electric vehicles that change their intention, specifically including: According to the estimated waiting time before the change of intention, the estimated waiting time after the change of intention, the cost before the change of intention, the cost after the change of intention of the arriving electric vehicles, and the intention feedback bonus, use the intention reshaping model based on the ABC attitude change theory to determine the emotional intensity of the arriving electric vehicles; Judge whether the emotional intensity is less than the emotional intensity threshold to obtain a third judgment result; If the third judgment result is yes, then the arriving electric vehicle does not agree to change its intention; If the third judgment result is no, then the arriving electric vehicle agrees to change its intention; According to the third judgment result, determine the actual number of arriving electric vehicles that change their intention.

7. The charging and swapping integrated power station scheduling method according to claim 1, characterized in that According to the actual number of vehicles that change their intention, the expected number of vehicles that change their intention, the initial charging-side queue, and the initial swapping-side queue, determine the final charging-side queue and the final swapping-side queue, specifically including: The expected number of vehicles that change their intention includes the expected number of charging-to-swapping vehicles and the expected number of swapping-to-charging vehicles; the actual number of vehicles that change their intention includes the actual number of charging-to-swapping vehicles and the actual number of swapping-to-charging vehicles; If the actual number of charging-to-swapping vehicles is greater than the expected number of charging-to-swapping vehicles, then use the arriving electric vehicle with the highest emotional intensity among the actual number of charging-to-swapping vehicles as the charging-to-swapping intention-changing object; If the actual number of charging-to-swapping vehicles is less than the expected number of charging-to-swapping vehicles and the difference between the actual number of charging-to-swapping vehicles and the expected number of charging-to-swapping vehicles is less than the set difference, then use the arriving electric vehicles corresponding to the actual number of charging-to-swapping vehicles as the charging-to-swapping intention-changing objects; If the actual number of charging-to-conversion people is less than the expected number of charging-to-conversion people and the difference between the actual number of charging-to-conversion people and the expected number of charging-to-conversion people is greater than or equal to the set difference, off-site scheduling is performed; If the actual number of conversion-to-charging people is greater than the expected number of conversion-to-charging people, the electric vehicle arriving at the station with the highest emotional intensity among the actual number of conversion-to-charging people is used as the object of intention for conversion-to-charging; If the actual number of conversion-to-charging people is less than the expected number of conversion-to-charging people and the difference between the actual number of conversion-to-charging people and the expected number of conversion-to-charging people is less than the set difference, the electric vehicle arriving at the station corresponding to the actual number of conversion-to-charging people is used as the object of intention for conversion-to-charging; If the actual number of conversion-to-charging people is less than the expected number of conversion-to-charging people and the difference between the actual number of conversion-to-charging people and the expected number of conversion-to-charging people is greater than or equal to the set difference, off-site scheduling is performed; Based on the initial charging-side queue, the objects of intention for charging-to-conversion, and the objects of intention for conversion-to-charging, determine the final charging-side queue; Based on the initial battery-changing-side queue, the objects of intention for charging-to-conversion, and the objects of intention for conversion-to-charging, determine the final battery-changing-side queue.

8. The charging and swapping integrated power station scheduling method according to claim 1, wherein It further includes: Determine the number of batteries to be charged in the long-term available charging area according to the charging power of the long-term available charging area; Determine the number of batteries to be charged in the short-term available charging area according to the charging power of the short-term available charging area; The charging power of the long-term available charging area and the charging power of the short-term available charging area are obtained by optimizing the objective function and constraints using an improved Northern Goshawk optimization algorithm; Sort the batteries to be charged in the long-term available charging area in descending order of state of charge, and charge the sorted batteries to be charged in sequence; Sort the batteries to be charged in the short-term available charging area in descending order of state of charge, and charge the sorted batteries to be charged in sequence.

9. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the charging and battery-changing integrated power station scheduling method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the charging and battery-changing integrated power station scheduling method according to any one of claims 1-8.

Images