Electric bus station optimization scheduling method and system based on light storage and charging cooperation

Abstract

The invention discloses an electric bus station optimization scheduling method and system based on light storage and charging cooperation. The method comprises the following steps: acquiring parameters; according to the obtained parameters, an electric bus charging and discharging behavior model, a photovoltaic output model, an energy storage system operation model and a charging pile operation model considering a one-pile multi-gun sharing mode are established; by taking the minimization of the total operation cost of the station as a target and taking the established unit models as constraints, constructing an optical storage and charging integrated electric bus station optimization scheduling model, and solving to obtain a scheduling plan in the station; and issuing the solved scheduling plan to a station operation control system to generate a scheduling instruction and a charging and discharging instruction of the electric bus so as to realize cooperative scheduling of the station optical storage and charging resources. According to the method, the physical matching relation of the vehicle pile gun is depicted finely, the problem of symmetric solution space explosion caused by neglecting equipment homogenization in a traditional model is solved, and the solving efficiency and feasibility of a large-scale scheduling problem are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of power system operation, specifically relating to an optimized scheduling method and system for electric bus depots based on photovoltaic-storage-charging coordination. Background Technology

[0002] With the accelerated electrification of urban transportation, pure electric buses are being widely promoted and applied globally. Electric bus depots, as core hubs for centralized vehicle parking, dispatching, and energy replenishment, are evolving from a single electricity consumption scenario into a complex energy system integrating distributed photovoltaic power generation, energy storage systems, multi-charger facilities, and electric bus fleets with vehicle-to-grid (V2G) interaction capabilities. Achieving efficient coordination and optimized dispatching of multiple elements—solar energy, energy storage, charging, and discharging—is of great significance for improving the economic efficiency of depots, enhancing grid interaction flexibility, and ensuring the reliability of public transportation operations.

[0003] However, current research on the optimal scheduling of integrated photovoltaic-energy storage-charging electric bus depots still has significant shortcomings. On the one hand, most studies have failed to accurately characterize the "one pile, multiple guns" charging facility sharing mechanism used in actual depots, ignoring the physical matching constraints between vehicles, piles, and guns, resulting in weak feasibility of optimization results in actual deployment. On the other hand, when modeling the charging and discharging behavior of electric buses as mobile energy storage, the coordination with rigid scheduling plans, battery start-stop losses, and physical constraints of state switching are not fully considered. In addition, the large number of vehicles and charging equipment in actual depots, and their high degree of homogeneity, easily generate a large number of symmetric solutions in the optimization model, significantly increasing the solution complexity and computation time of the mixed integer programming problem. Currently, no photovoltaic-energy storage-charging collaborative optimization scheduling method has been proposed that can simultaneously and accurately characterize the operating characteristics of electric buses and the "one pile, multiple guns" charging structure, comprehensively coordinate photovoltaic output, energy storage operation, and distribution network capacity constraints, and achieve a good balance between solution efficiency and model accuracy. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized scheduling method and system for electric bus depots based on photovoltaic-storage-charging coordination, in order to solve the problems of insufficient characterization of "one pile with multiple guns" charging facilities, poor coordination between vehicle charging and discharging behavior and scheduling plans, and low efficiency of large-scale optimization solutions in existing scheduling methods.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: An optimized scheduling method for electric bus depots based on photovoltaic-storage-charging coordination includes the following steps: Step 1: Obtain parameters, including electric bus battery and charging / discharging parameters, photovoltaic output prediction curve, energy storage system parameters, station charging pile parameters, bus scheduling plan, time-of-use electricity price curve, and distribution network capacity limitation parameters; Step 2: Based on the acquired parameters, establish a charging and discharging behavior model for electric buses, a photovoltaic power output model, an energy storage system operation model, and a charging pile operation model considering the "one pile, multiple guns" sharing mode. When constructing the charging pile operation model, a four-dimensional binary decision variable of vehicle-pile-gun-time is introduced to characterize the physical matching relationship, and gun-switching viscosity constraints and minimum start-stop time constraints for the charging and discharging process are set. By summarizing this variable, the total charging and discharging power of each vehicle at each time is obtained, and the summed power is correlated with the charging and discharging power variable of the vehicle by equation, thereby establishing power consistency constraints at the vehicle-gun-pile level. Step 3: Based on the established model, with the goal of minimizing the total operating cost of the depot, and with constraints such as vehicle charging and discharging behavior restrictions, energy storage operation restrictions, distribution network capacity restrictions, system power balance, and vehicle-gun-pile matching relationship, construct an integrated photovoltaic-storage-charging electric bus depot optimization scheduling model, and solve for the scheduling plan within the depot. Step 4: The obtained scheduling plan is sent to the station operation control system to generate scheduling instructions and charging / discharging instructions for electric buses, so as to realize the coordinated scheduling of the station's photovoltaic, energy storage and charging resources.

[0006] A further improvement of this invention is that, in step two, when modeling the charging and discharging behavior of electric buses, an instruction matrix for vehicle shifts is constructed based on the bus scheduling plan. Charging and discharging are prohibited while the vehicle is on a shift. The energy consumption at each discrete time step during operation is deducted from the vehicle's state of charge to ensure that the vehicle's charging and discharging behavior is coordinated with the shift's execution status throughout the entire operation. The vehicle's charging power, discharging power, and battery state of charge satisfy the following constraints: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) in, They represent electric buses During the period The charging and discharging power; vehicles Minimum / maximum charge / discharge power; Charging guns Minimum / maximum charge / discharge power; Indicates train number During the period Is it running? For electric buses Shift schedule binary variables, therefore Indicates electric bus Is it in operation? Indicates electric bus During the period Battery power status; These are the charging and discharging efficiency and discharge compensation coefficient of electric buses, respectively. For the weight of electric buses; The rated driving speed for electric buses; These are the time-flow coefficient and the spatial traffic coefficient, respectively. For vehicles During the period The amount of electricity consumed during the execution process is determined by the vehicle's mass. Rated driving speed It is calculated using empirical models of time-flow coefficient and spatial traffic coefficient.

[0007] A further improvement of this invention is that, in step two, the charging pile operation model considering the "one pile, multiple guns" sharing mode introduces a four-dimensional binary decision variable. This represents the occupancy relationship between vehicles, charging piles, charging guns, and time. Among them, formula (11) constrains that each vehicle can occupy at most one charging gun at any time; formula (12) constrains that each charging gun can serve at most one vehicle at any time; formula (13) constrains that vehicles can only connect to the charging gun during non-operational periods. These constraints together characterize the physical characteristics of vehicle-charging pile-charging gun, as shown below: (11) (12) (13) in, Indicates the total number of electric buses at the station; Indicates the total number of charging piles at the station; This indicates the number of charging guns in a charging station. Indicates the total time period.

[0008] A further improvement of this invention lies in that, in step two, a sticky constraint for switching charging guns is set based on the "one charging pile, multiple charging guns" sharing mode. That is, in the first time step after the vehicle connects to the charging pile and begins charging or discharging... The charging gun occupancy status at this moment is selected as the representative state for one hour of this continuous charging and discharging process. For multiple consecutive discrete time steps of equivalent length one hour from this time step, the vehicle's charging gun occupancy status is limited to be consistent with the representative state. This restricts the vehicle from frequently changing charging guns within one hour of connecting to the charging station. Specifically, the constraint is as follows: (14) in, This is the set of all time steps contained in that hour.

[0009] A further improvement of this invention is that, in step two, a minimum start-stop duration constraint is set for the vehicle charging and discharging process. Start-up and stop variables are constructed based on changes in the charging and discharging state. Furthermore, when the charging or discharging state changes from off to on, this state is required to remain on for the remainder of the charging or discharging process. The internal state remains unchanged; when the charging or discharging state changes from on to off, this state must remain unchanged in subsequent states. The battery should remain closed to reduce battery damage caused by frequent switching between charge and discharge states in a short period of time. The specific constraints are as follows: (15) (16) (17) (18) in, For vehicles During the period A binary variable indicating whether the device is in a charging / discharging state; a value of 1 indicates that the device is in a charging / discharging state. This variable indicates the start-up state when the vehicle switches from being unconnected to being connected to the charging gun. This variable indicates the shutdown state when the vehicle switches from being connected to the charging gun to being disconnected from the charging gun. These are the minimum time to connect to the charging gun and the minimum time to disconnect from the charging gun, respectively.

[0010] A further improvement of this invention is that, in step two, the charging and discharging power of the vehicle at each charging gun is determined by a four-dimensional decision variable. By summing the data at the charging pile and charging gun dimensions, the total charging power and total discharging power of each vehicle at each discrete time step are obtained. These total charging power and total discharging power are then constrained with the charging and discharging power variables in the vehicle's state of charge recursive model, thereby achieving power consistency at the vehicle-gun-pile level. Specifically, the constraints are as follows: (19) (20) in, and Charging piles charging gun For vehicles During the period The charging power and discharging power.

[0011] A further improvement of this invention is that, in step three, when constructing the integrated electric bus depot scheduling model, the capacity limitation of the bus depot's connection to the power distribution network is considered. By applying bidirectional capacity constraints to the power exchange between the depot and the power distribution network, the difference between the purchased power and the sold power at any given time does not exceed the maximum allowable capacity of the power distribution network, thus ensuring the safe operation of the power distribution network. The constraint is expressed as follows: (twenty one) (twenty two) in, and These represent the electricity purchased and sold by the power station to the main grid, respectively. and Charging piles charging gun For vehicles During the period Charging power and discharging power; For the distribution network during the time period The capacity limit is a parameter determined by the maximum allowable current of the distribution line and the rated capacity of the transformer. Based on this, the power purchased and the power sold are each limited to the upper limit corresponding to the distribution network capacity: (twenty three) .

[0012] A further improvement of this invention lies in step three, where, based on minimizing the total operating cost of the depot, a desymmetric term for scheduling based on the initial battery charge of vehicles is added to the objective function. Vehicles with higher initial battery charge are assigned greater scheduling weights, thereby reducing the symmetric solution space in the mixed-integer programming problem and improving the solution efficiency and convergence stability of the large-scale bus depot scheduling problem. The objective function is expressed as follows: (twenty four) in, Indicates the cost of electricity purchased at the power station; This indicates the revenue from electricity sales at the power station; This indicates a punishment for abandoning light; This indicates the cost of energy storage operation and maintenance; The weighting coefficients are much less than 1. The weights are derived from the initial battery level of the vehicles; vehicles with higher initial battery levels have higher weights. It is very small and will not affect the objective function; and , , , The specific expression is as follows: (25) (26) (27) (28) in, These are time-of-use electricity pricing, penalties for unit curtailment of solar power, and unit energy storage operation and maintenance costs. and These represent the electricity purchased and sold by the power station to the main grid, respectively. This refers to the amount of light discarded. and These represent the charging and discharging power of the energy storage system.

[0013] A further improvement of this invention is that, in step four, the solved scheduling plan is sent to the depot operation control system to generate scheduling instructions and charging / discharging instructions for electric buses, thereby realizing the coordinated scheduling of the depot's photovoltaic, energy storage, and charging resources, including: Based on the principle of precise matching between scheduling instructions and physical equipment, the scheduling plan obtained in step three is converted into a specific sequence of control instructions for each charging pile, each charging gun, and each bus, and then sent to the station energy management system and charging pile controller for execution in a timely manner, thereby completing the refined collaborative scheduling of the station's photovoltaic, energy storage, and charging resources.

[0014] An optimized scheduling system for electric bus depots based on photovoltaic-storage-charging coordination includes: Parameter acquisition unit: Acquires parameters, including electric bus battery and charging / discharging parameters, photovoltaic output prediction curve, energy storage system parameters, station charging pile parameters, bus scheduling plan, time-of-use electricity price curve, and distribution network capacity limitation parameters; Model building unit: Based on the acquired parameters, a charging and discharging behavior model for electric buses, a photovoltaic power output model, an energy storage system operation model, and a charging pile operation model considering the "one pile, multiple guns" sharing mode are established. When constructing the charging pile operation model, a four-dimensional binary decision variable of vehicle-pile-gun-time is introduced to characterize the physical matching relationship, and a sticky constraint for gun switching and a minimum start-stop time constraint for the charging and discharging process are set. By summarizing this variable, the total charging and discharging power of each vehicle at each time is obtained, and the summed power is correlated with the charging and discharging power variable of the vehicle by an equation, thereby establishing a power consistency constraint at the vehicle-gun-pile level. Scheduling plan solving unit: Based on the established model, with the goal of minimizing the total operating cost of the depot, and with constraints such as vehicle charging and discharging behavior restrictions, energy storage operation restrictions, distribution network capacity restrictions, system power balance, and vehicle-gun-pile matching relationship, an integrated photovoltaic-storage-charging electric bus depot optimization scheduling model is constructed, and the scheduling plan within the depot is solved. Collaborative scheduling unit: The obtained scheduling plan is sent to the station operation control system to generate the scheduling instructions and charging and discharging instructions for electric buses, so as to realize the collaborative scheduling of the station's photovoltaic, energy storage and charging resources.

[0015] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention provides an optimized scheduling method and system for electric bus depots based on photovoltaic-storage-charging coordination. This invention fully considers the operational characteristics of "one charging pile with multiple charging guns" in actual bus depots, the coordination requirements of electric bus charging and discharging behavior and rigid scheduling plans. By constructing a vehicle-charging pile-charging gun physical matching model and introducing gun-switching viscosity constraints and minimum start-stop time constraints into the model, a hybrid integer programming optimization scheduling model integrating photovoltaic, storage, and charging is established. This invention reduces the symmetric solution space of the hybrid integer programming model by setting a scheduling desymmetric term in the objective function, thereby improving the solution efficiency and convergence stability of large-scale electric bus depot scheduling problems. This invention provides an overall economic scheduling plan for the depot from the perspective of system-wide collaborative optimization, and generates specific control commands that can be accurately matched to each vehicle and each charging gun at the execution level, thus providing a systematic solution for the efficient, economical, and reliable operation of actual electric bus depots. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1This is an overall flowchart of the method of the present invention.

[0018] Figure 2 For bus station schedules.

[0019] Figure 3 This is the time-of-use electricity price curve.

[0020] Figure 4 This is a schematic diagram of power limitation in a distribution network.

[0021] Figure 5 This is a comparison chart of the number of guns used in the traditional scheduling model and the proposed model.

[0022] Figure 6 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0023] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0024] In the description of this invention, it should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0025] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0026] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0028] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0029] Example 1 like Figure 1 As shown, the present invention provides an optimized scheduling method for electric bus depots based on photovoltaic-storage-charging coordination, comprising the following steps: Step 1: Obtain parameters, including electric bus battery and charging / discharging parameters, photovoltaic output prediction curve, energy storage system parameters, station charging pile parameters, bus scheduling plan, time-of-use electricity price curve, and distribution network capacity limitation parameters; Step 2: Based on the acquired parameters, establish a charging and discharging behavior model for electric buses, a photovoltaic power output model, an energy storage system operation model, and a charging pile operation model considering the "one pile, multiple guns" sharing mode. When constructing the charging pile operation model, a four-dimensional binary decision variable of vehicle-pile-gun-time is introduced to characterize the physical matching relationship, and gun-switching viscosity constraints and minimum start-stop time constraints for the charging and discharging process are set. By summarizing this variable, the total charging and discharging power of each vehicle at each time is obtained, and the summed power is correlated with the charging and discharging power variable of the vehicle by equation, thereby establishing power consistency constraints at the vehicle-gun-pile level. Step 3: Based on the established model, with the goal of minimizing the total operating cost of the depot, and with constraints such as vehicle charging and discharging behavior restrictions, energy storage operation restrictions, distribution network capacity restrictions, system power balance, and vehicle-gun-pile matching relationship, construct an integrated photovoltaic-storage-charging electric bus depot optimization scheduling model, and solve for the scheduling plan within the depot. Step 4: The obtained scheduling plan is sent to the station operation control system to generate scheduling instructions and charging / discharging instructions for electric buses, so as to realize the coordinated scheduling of the station's photovoltaic, energy storage and charging resources.

[0030] In step two of this embodiment, when modeling the charging and discharging behavior of electric buses, an instruction matrix for vehicle shifts is constructed based on the bus scheduling plan. Charging and discharging are prohibited while the vehicle is on a shift. The energy consumption at each discrete time step during operation is deducted from the vehicle's state of charge to ensure that the vehicle's charging and discharging behavior is coordinated with the shift's execution status throughout the entire operation. The vehicle's charging power, discharging power, and battery state of charge satisfy the following constraints: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) in, They represent electric buses During the period The charging and discharging power; vehicles Minimum / maximum charge / discharge power; Charging guns Minimum / maximum charge / discharge power; Indicates train number During the period Is it running? For electric buses Shift schedule binary variables, therefore Indicates electric bus Is it in operation? Indicates electric bus During the period Battery power status; These are the charging and discharging efficiency and discharge compensation coefficient of electric buses, respectively. For the weight of electric buses; The rated driving speed for electric buses; These are the time-flow coefficient and the spatial traffic coefficient, respectively. For vehicles During the period The amount of electricity consumed during the execution process is determined by the vehicle's mass. Rated driving speed It is calculated using empirical models of time-flow coefficient and spatial traffic coefficient.

[0031] In step two of this embodiment, the charging pile operation model considering the "one pile, multiple guns" sharing mode introduces four-dimensional binary decision variables. This represents the occupancy relationship between vehicles, charging piles, charging guns, and time. Among them, formula (11) constrains that each vehicle can occupy at most one charging gun at any time; formula (12) constrains that each charging gun can serve at most one vehicle at any time; formula (13) constrains that vehicles can only connect to the charging gun during non-operational periods. These constraints together characterize the physical characteristics of vehicle-charging pile-charging gun, as shown below: (11) (12) (13) in, Indicates the total number of electric buses at the station; Indicates the total number of charging piles at the station; This indicates the number of charging guns in a charging station. Indicates the total time period.

[0032] In step two of this embodiment, a sticky constraint for gun switching is set based on the "one charging pile, multiple guns" sharing mode. That is, in the first time step after the vehicle connects to the charging pile and starts charging or discharging, a sticky constraint for gun switching is set. The charging gun occupancy status at this moment is selected as the representative state for one hour of this continuous charging and discharging process. For multiple consecutive discrete time steps of equivalent length one hour from this time step, the vehicle's charging gun occupancy status is limited to be consistent with the representative state. This restricts the vehicle from frequently changing charging guns within one hour of connecting to the charging station. Specifically, the constraint is as follows: (14) in, This is the set of all time steps contained in that hour.

[0033] In step two of this embodiment, a minimum start-stop duration constraint is set for the vehicle charging and discharging process. Start-up and stop variables are constructed based on changes in the charging and discharging state. Furthermore, when the charging or discharging state changes from off to on, this state is required to remain on for the remainder of the charging or discharging process. The internal state remains unchanged; when the charging or discharging state changes from on to off, this state must remain unchanged in subsequent states. The battery should remain closed to reduce battery damage caused by frequent switching between charge and discharge states in a short period of time. The specific constraints are as follows: (15) (16) (17) (18) in, For vehicles During the period A binary variable indicating whether the device is in a charging / discharging state; a value of 1 indicates that the device is in a charging / discharging state. This variable indicates the start-up state when the vehicle switches from being unconnected to being connected to the charging gun. This variable indicates the shutdown state when the vehicle switches from being connected to the charging gun to being disconnected from the charging gun. These are the minimum time to connect to the charging gun and the minimum time to disconnect from the charging gun, respectively.

[0034] In step two of this embodiment, the charging and discharging power of the vehicle at each charging gun is determined by a four-dimensional decision variable. By summing the data at the charging pile and charging gun dimensions, the total charging power and total discharging power of each vehicle at each discrete time step are obtained. These total charging power and total discharging power are then constrained with the charging and discharging power variables in the vehicle's state of charge recursive model, thereby achieving power consistency at the vehicle-gun-pile level. Specifically, the constraints are as follows: (19) (20) in, and Charging piles charging gun For vehicles During the period The charging power and discharging power.

[0035] In step three of this embodiment, when constructing the integrated electric bus depot scheduling model, the capacity limitation of the bus depot's connection to the power distribution network is considered. By applying bidirectional capacity constraints to the power exchange between the depot and the power distribution network, the difference between the purchased power and the sold power at any given time does not exceed the maximum allowable capacity of the power distribution network, thus ensuring the safe operation of the power distribution network. The constraint is expressed as follows: (twenty one) (twenty two) in, and These represent the electricity purchased and sold by the power station to the main grid, respectively. and Charging piles charging gun For vehicles During the period Charging power and discharging power; For the distribution network during the time period The capacity limit is a parameter determined by the maximum allowable current of the distribution line and the rated capacity of the transformer. Based on this, the power purchased and the power sold are each limited to the upper limit corresponding to the distribution network capacity: (twenty three) .

[0036] In step three of this embodiment, based on minimizing the total operating cost of the depot, a desymmetric term for scheduling based on the initial battery charge of vehicles is added to the objective function. Vehicles with higher initial battery charge are given greater scheduling weights, thereby reducing the symmetric solution space in the mixed integer programming problem and improving the solution efficiency and convergence stability of the large-scale bus depot scheduling problem. The objective function is expressed as follows: (twenty four) in, Indicates the cost of electricity purchased at the power station; This indicates the revenue from electricity sales at the power station; This indicates a punishment for abandoning light; This indicates the cost of energy storage operation and maintenance; The weighting coefficients are much less than 1. The weights are derived from the initial battery level of the vehicles; vehicles with higher initial battery levels have higher weights. It is very small and will not affect the objective function; and , , , The specific expression is as follows: (25) (26) (27) (28) in, These are time-of-use electricity pricing, penalties for unit curtailment of solar power, and unit energy storage operation and maintenance costs. and These represent the electricity purchased and sold by the power station to the main grid, respectively. This refers to the amount of light discarded. and These represent the charging and discharging power of the energy storage system.

[0037] In step four of this embodiment, the solved scheduling plan is sent to the depot operation control system to generate scheduling instructions and charging / discharging instructions for electric buses, thereby realizing the coordinated scheduling of the depot's photovoltaic, energy storage, and charging resources. This includes: Based on the principle of precise matching between scheduling instructions and physical equipment, the scheduling plan obtained in step three is converted into a specific sequence of control instructions for each charging pile, each charging gun, and each bus, and then sent to the station energy management system and charging pile controller for execution in a timely manner, thereby completing the refined collaborative scheduling of the station's photovoltaic, energy storage, and charging resources.

[0038] Example 2 like Figure 1 As shown, the present invention provides an optimized scheduling method for electric bus depots based on photovoltaic-storage-charging coordination, comprising the following steps: Step 1: Obtain the electric bus battery and charging / discharging parameters, photovoltaic power output prediction curve, energy storage system parameters, station charging pile parameters, bus scheduling plan, time-of-use electricity price curve, and distribution network capacity limit parameters.

[0039] In this example, a 24-hour dispatch plan for an electric bus depot in a city is used as the basis. The dispatch time scale is 15 minutes, and the entire day is divided into 96 discrete time periods, starting from 6:00 AM. This depot serves a typical bus route, 40 km long, and its dispatch plan strictly follows the depot's needs, such as... Figure 2 As shown, the depot is equipped with 15 pure electric buses, each with a battery capacity of approximately 250 kWh and a rated charging and discharging power limit of 120 kW. The depot is also equipped with 4 DC charging piles, each with 2 DC charging guns and a maximum output power of 120 kW per pile, forming a mainstream dual-gun charging pile system.

[0040] A distributed photovoltaic (PV) power station with an installed capacity of approximately 220kW is installed on the roof of the power station. The PV module parameters and tilt angle are determined based on the roof conditions, resulting in a PV output prediction curve for 96 time periods within a dispatching day. The power station is also equipped with a battery energy storage system with a rated charging and discharging power of 500kW each, a capacity of 1000kWh, and a charging and discharging efficiency of around 95%. The distribution network side provides the electricity purchase price and sales price curves for the power station's access node, using a typical time-of-use electricity price from a provincial capital city's distribution network, such as... Figure 3 As shown; at the same time, the power limit of the distribution network is given according to the power distribution lines and transformer capacity, such as Figure 4 As shown.

[0041] Step Two: Based on the parameters obtained in Step One, establish a charging and discharging behavior model for electric buses, a photovoltaic power output model, an energy storage system operation model, and a charging pile operation model considering the "one pile, multiple guns" sharing mode. Specifically, when establishing the charging and discharging behavior model for electric buses, firstly, construct an instruction matrix for vehicle shifts based on the bus scheduling plan. Charging and discharging are prohibited during the time period when the vehicle is on a shift, and the energy consumption during vehicle operation is deducted from the battery's state of charge, ensuring that the vehicle's charging and discharging behavior and shift execution status remain coordinated throughout the scheduling cycle. The vehicle's charging power, discharging power, and battery state of charge (SOC) must meet constraints such as upper and lower power limits, safe SOC range, and SOC recursive relationship.

[0042] When considering the charging pile operation model of the "one pile, multiple guns" sharing mode, a four-dimensional binary decision variable of vehicle-charging pile-charging gun-time is introduced to represent which charging gun each vehicle occupies in each time period. By constraining that each vehicle can occupy at most one charging gun at any given time, each charging gun can serve at most one vehicle at any given time, and that vehicles can only connect to charging guns during non-operational periods, the physical matching relationship between vehicle-pile-gun is accurately characterized. Furthermore, based on this, a gun-switching stickiness constraint is set. That is, when a vehicle connects to a charging pile and starts charging or discharging, the first time period of that hour is used as the representative frame, and the charging gun occupancy status at that moment is selected as the representative status for the continuous charging and discharging process of that hour. During all time periods included in that hour, the charging gun occupied by the vehicle is forced to be consistent with the representative status, limiting the vehicle from frequently switching charging guns within one hour.

[0043] Simultaneously, minimum start-stop duration constraints are set for the vehicle charging and discharging process. By constructing start-up and stop variables based on changes in charging and discharging states, when the charging or discharging state changes from off to on, this state is required to remain unchanged for a certain number of subsequent time periods; when the charging or discharging state changes from on to off, this state is required to remain off for a certain number of subsequent time periods, thereby effectively reducing battery damage caused by frequent start-stop cycles in a short period of time. By summarizing the four-dimensional decision variables at the charging pile and charging gun dimensions, the total charging power and total discharging power of each vehicle in each time period are obtained, and these are constrained with the charging and discharging power variables in the vehicle SOC recursive model to achieve power consistency at the vehicle-gun-pile level.

[0044] The photovoltaic (PV) output model is constructed based on the PV output prediction curve obtained in step one. It decomposes PV power generation into grid-connected power and curtailed power, ensuring that the power generation does not exceed the maximum output of the modules during that period. By setting a curtailment penalty coefficient, the objective function encourages increased PV grid integration. The energy storage system operation model considers the capacity constraints, charge / discharge power range, efficiency, and SOC (State of Charge) limits of the energy storage devices. It describes the energy balance of energy storage in each time period through a recursive SOC relationship, requiring that the SOC of the energy storage at the beginning and end of the scheduling cycle not be lower than the initial level.

[0045] Step 3: Based on the above model, an integrated photovoltaic, energy storage, and charging electric bus depot optimization scheduling model is constructed with the goal of minimizing the total operating cost of the depot. The objective function comprehensively considers the depot's electricity purchase cost, electricity sales revenue, photovoltaic curtailment penalties, and energy storage operation and maintenance costs. Furthermore, a desymmetric term based on the initial SOC ranking of vehicles is added, assigning greater scheduling weight to vehicles with higher initial battery levels to reduce the symmetric solution space of the mixed-integer programming model and improve solution efficiency. Constraints include vehicle charging and discharging behavior constraints, energy storage system operation constraints, "one pile, multiple guns" sharing constraints for charging piles, gun replacement stickiness and minimum start-stop time constraints, bidirectional capacity constraints of the distribution network, and system power balance constraints, ensuring that the internal energy expenditure of the depot is balanced at any given time and that the power exchange between the depot and the distribution network does not exceed the allowable capacity.

[0046] Step 4: The station scheduling plan obtained in Step 3 is sent to the station operation control system. Based on the principle of precise matching between scheduling instructions and physical equipment, the optimization results are converted into a specific control instruction sequence for each charging pile, each charging gun and each bus. The sequence is then sent to the station energy management system and charging pile controller for execution in chronological order. Finally, the scheduling instructions and charging and discharging instructions for electric buses are generated, and the refined collaborative scheduling of the station's photovoltaic, energy storage and charging resources is completed.

[0047] Firstly, to verify the advantages of this invention in scheduling feasibility, based on the aforementioned station parameters, load, and photovoltaic curves, a traditional station-level scheduling model and the vehicle-pile-gun refined model proposed in this invention were constructed to simulate the same electric bus depot. The traditional station-level scheduling model does not explicitly distinguish between each charging pile and charging gun, only setting total charging power and total discharging power constraints at the station level, without considering the "one pile, multiple guns" shared structure or the situation where a vehicle occupies a specific charging gun. The method of this invention, however, introduces a four-dimensional binary decision variable—vehicle-charging pile-charging gun-time—to finely characterize the vehicle-pile-gun matching relationship and pile-level power constraints, while simultaneously considering gun replacement stickiness, minimum start-stop time, and distribution network capacity constraints. Figure 5 The comparison results of the number of charging guns used at various time points under the two models are presented. It is noted that the number of charging guns used by the traditional scheduling model may exceed the actual number of charging guns in the site at certain times, making the scheduling plan infeasible. The proposed model not only ensures that the scheduling plan conforms to the actual physical constraints, but also has small fluctuations in the number of charging guns used and high utilization rate of charging guns.

[0048] To further verify the superiority of this invention in suppressing high-frequency switching of charging and discharging states and arbitrary gun switching, a comparative model was constructed without setting minimum start-stop duration and gun switching viscosity constraints, while keeping other parameters unchanged. By statistically analyzing the number of gun switching times, load fluctuations, and battery SOC fluctuations for each vehicle within the scheduling cycle under the two models, the comparison results shown in Table 1 were obtained.

[0049] Table 1. Performance Comparison Analysis between the Model Without Weapon Switching Constraints and the Proposed Model

[0050] As shown in Table 1, compared to the model that does not consider the charging gun switching constraint, the method of this invention significantly reduces the number of times the vehicle's charging and discharging states switch between adjacent time periods, significantly reduces the number of times the vehicle needs to change charging guns within a day, reduces SOC fluctuations, and helps to delay the performance degradation of the power battery caused by frequent charging and discharging and frequent plugging and unplugging. At the same time, the reduced number of charging gun switching and the more stable SOC fluctuations help to reduce the load fluctuations of the distribution network and improve the operational stability of the power grid.

[0051] Finally, the solution performance of the method of this invention and its applicability at different scales are further discussed. Electric bus depot scheduling examples of different scales are constructed by increasing the number of vehicles. For each scale example, a comparison is made between the solution method without scheduling desymmetry constraints and heuristic initial solutions, and the improved solution method using the proposed scheduling desymmetry constraints and MIP initial solutions based on initial SOC sorting. The comparison results are shown in Table 2.

[0052] Table 2 Comparison of solution performance for scheduling examples of electric bus depots of different sizes

[0053] As shown in Table 2, compared to methods without desymmetry, the proposed method significantly reduces the solution time and the number of iterations for both 15 and 20 buses. This demonstrates that the proposed desymmetry method effectively addresses the combinatorial explosion problem in the symmetric solution space caused by homogenization in traditional scheduling models, offering a speed advantage and providing strong support for solving large-scale integrated photovoltaic-storage-charging bus station scheduling problems.

[0054] Example 3 like Figure 6 As shown, the present invention provides an optimized scheduling system for electric bus depots based on photovoltaic-storage-charging coordination, comprising: Parameter acquisition unit: Acquires parameters, including electric bus battery and charging / discharging parameters, photovoltaic output prediction curve, energy storage system parameters, station charging pile parameters, bus scheduling plan, time-of-use electricity price curve, and distribution network capacity limitation parameters; Model building unit: Based on the acquired parameters, a charging and discharging behavior model for electric buses, a photovoltaic power output model, an energy storage system operation model, and a charging pile operation model considering the "one pile, multiple guns" sharing mode are established. When constructing the charging pile operation model, a four-dimensional binary decision variable of vehicle-pile-gun-time is introduced to characterize the physical matching relationship, and a sticky constraint for gun switching and a minimum start-stop time constraint for the charging and discharging process are set. By summarizing this variable, the total charging and discharging power of each vehicle at each time is obtained, and the summed power is correlated with the charging and discharging power variable of the vehicle by an equation, thereby establishing a power consistency constraint at the vehicle-gun-pile level. Scheduling plan solving unit: Based on the established model, with the goal of minimizing the total operating cost of the depot, and with constraints such as vehicle charging and discharging behavior restrictions, energy storage operation restrictions, distribution network capacity restrictions, system power balance, and vehicle-gun-pile matching relationship, an integrated photovoltaic-storage-charging electric bus depot optimization scheduling model is constructed, and the scheduling plan within the depot is solved. Collaborative scheduling unit: The obtained scheduling plan is sent to the station operation control system to generate the scheduling instructions and charging and discharging instructions for electric buses, so as to realize the collaborative scheduling of the station's photovoltaic, energy storage and charging resources.

[0055] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0056] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for optimizing the scheduling of electric bus depots based on photovoltaic-storage-charging coordination, characterized in that, Includes the following steps: Step 1: Obtain parameters, including electric bus battery and charging / discharging parameters, photovoltaic output prediction curve, energy storage system parameters, station charging pile parameters, bus scheduling plan, time-of-use electricity price curve, and distribution network capacity limitation parameters; Step 2: Based on the acquired parameters, establish a charging and discharging behavior model for electric buses, a photovoltaic power output model, an energy storage system operation model, and a charging pile operation model considering the "one pile, multiple guns" sharing mode. When constructing the charging pile operation model, a four-dimensional binary decision variable of vehicle-pile-gun-time is introduced to characterize the physical matching relationship, and gun-switching viscosity constraints and minimum start-stop time constraints for the charging and discharging process are set. By summarizing this variable, the total charging and discharging power of each vehicle at each time is obtained, and the summed power is correlated with the charging and discharging power variable of the vehicle by equation, thereby establishing power consistency constraints at the vehicle-gun-pile level. Step 3: Based on the established model, with the goal of minimizing the total operating cost of the depot, and with constraints such as vehicle charging and discharging behavior restrictions, energy storage operation restrictions, distribution network capacity restrictions, system power balance, and vehicle-gun-pile matching relationship, construct an integrated photovoltaic-storage-charging electric bus depot optimization scheduling model, and solve for the scheduling plan within the depot. Step 4: The obtained scheduling plan is sent to the station operation control system to generate scheduling instructions and charging / discharging instructions for electric buses, so as to realize the coordinated scheduling of the station's photovoltaic, energy storage and charging resources.

2. The method for optimizing the scheduling of electric bus depots based on photovoltaic-storage-charging coordination as described in claim 1, characterized in that, In step two, when modeling the charging and discharging behavior of electric buses, an instruction matrix for vehicle shifts is constructed based on the bus scheduling plan. Charging and discharging are prohibited while the vehicle is on a shift. The energy consumption at each discrete time step during operation is deducted from the vehicle's state of charge to ensure that the vehicle's charging and discharging behavior is coordinated with the shift's execution status throughout the entire operation. The vehicle's charging power, discharging power, and battery state of charge satisfy the following constraints: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) in, They represent electric buses During the period The charging and discharging power; vehicles Minimum / maximum charge / discharge power; Charging guns Minimum / maximum charge / discharge power; Indicates train number During the period Is it running? For electric buses Shift schedule binary variables, therefore Indicates electric bus Is it in operation? Indicates electric bus During the period Battery power status; These are the charging and discharging efficiency and discharge compensation coefficient of electric buses, respectively. For the weight of electric buses; The rated driving speed for electric buses; These are the time-flow coefficient and the spatial traffic coefficient, respectively. For vehicles During the period The amount of electricity consumed during the execution process is determined by the vehicle's mass. Rated driving speed It is calculated using empirical models of time-flow coefficient and spatial traffic coefficient.

3. The method for optimizing the scheduling of electric bus depots based on photovoltaic-storage-charging coordination as described in claim 2, characterized in that, In step two, the charging pile operation model considering the "one pile, multiple guns" sharing mode introduces four-dimensional binary decision variables. This represents the occupancy relationship between vehicles, charging piles, charging guns, and time. Among them, formula (11) constrains that each vehicle can occupy at most one charging gun at any time; formula (12) constrains that each charging gun can serve at most one vehicle at any time; formula (13) constrains that vehicles can only connect to the charging gun during non-operational periods. These constraints together characterize the physical characteristics of vehicle-charging pile-charging gun, as shown below: (11) (12) (13) in, Indicates the total number of electric buses at the station; Indicates the total number of charging piles at the station; This indicates the number of charging guns in a charging station. Indicates the total time period.

4. The method for optimizing the scheduling of electric bus depots based on photovoltaic-storage-charging coordination according to claim 3, characterized in that, In step two, a sticky constraint for gun switching is set based on the "one charging station, multiple guns" sharing model. That is, in the first time step after the vehicle connects to the charging station and begins charging or discharging, the gun switching constraint is set. The charging gun occupancy status at this moment is selected as the representative state for one hour of this continuous charging and discharging process. For multiple consecutive discrete time steps of equivalent length one hour from this time step, the vehicle's charging gun occupancy status is limited to be consistent with the representative state. This restricts the vehicle from frequently changing charging guns within one hour of connecting to the charging station. Specifically, the constraint is as follows: (14) in, This is the set of all time steps contained in that hour.

5. The method for optimizing the scheduling of electric bus depots based on photovoltaic-storage-charging coordination according to claim 4, characterized in that, In step two, a minimum start-stop duration constraint is set for the vehicle charging and discharging process. Start-up and stop variables are constructed based on changes in the charging and discharging state. Furthermore, when the charging or discharging state transitions from off to on, the start-stop duration is required to remain unchanged in the subsequent charging or discharging process. The internal state remains unchanged; when the charging or discharging state changes from on to off, this state must remain unchanged in subsequent states. The battery should remain closed to reduce battery damage caused by frequent switching between charge and discharge states in a short period of time. The specific constraints are as follows: (15) (16) (17) (18) in, For vehicles During the period A binary variable indicating whether the device is in a charging / discharging state; a value of 1 indicates that the device is in a charging / discharging state. This variable indicates the start-up state when the vehicle switches from being unconnected to being connected to the charging gun. This variable indicates the shutdown state when the vehicle switches from being connected to the charging gun to being disconnected from the charging gun. These are the minimum time to connect to the charging gun and the minimum time to disconnect from the charging gun, respectively.

6. The method for optimizing the scheduling of electric bus depots based on photovoltaic-storage-charging coordination as described in claim 5, characterized in that, In step two, the charging and discharging power of the vehicle at each charging gun is calculated using four-dimensional decision variables. By summing the data at the charging pile and charging gun dimensions, the total charging power and total discharging power of each vehicle at each discrete time step are obtained. These total charging power and total discharging power are then constrained with the charging and discharging power variables in the vehicle's state of charge recursive model, thereby achieving power consistency at the vehicle-gun-pile level. Specifically, the constraints are as follows: (19) (20) in, and Charging piles charging gun For vehicles During the period The charging power and discharging power.

7. The method for optimizing the scheduling of electric bus depots based on photovoltaic-storage-charging coordination according to claim 1, characterized in that, In step three, when constructing the integrated electric bus depot scheduling model, the capacity limitations of the bus depot's connection to the power distribution network are considered. By applying bidirectional capacity constraints to the power exchange between the depot and the power distribution network, the difference between the purchased power and the sold power at any given time is ensured to not exceed the maximum allowable capacity of the power distribution network, thus ensuring the safe operation of the power distribution network. These constraints are expressed as follows: (21) (22) in, and These represent the electricity purchased and sold by the power station to the main grid, respectively. and Charging piles charging gun For vehicles During the period Charging power and discharging power; For the distribution network during the time period The capacity limit is a parameter determined by the maximum allowable current of the distribution line and the rated capacity of the transformer. Based on this, the power purchased and the power sold are each limited to the upper limit corresponding to the distribution network capacity: (23) 。 8. The method for optimizing the scheduling of electric bus depots based on photovoltaic-storage-charging coordination according to claim 7, characterized in that, In step three, based on minimizing the total operating cost of the depot, a desymmetric term for scheduling based on the initial battery charge of vehicles is added to the objective function. Vehicles with higher initial battery charge are assigned greater scheduling weights, thereby reducing the symmetric solution space in the mixed-integer programming problem and improving the solution efficiency and convergence stability of the large-scale bus depot scheduling problem. The objective function is expressed as follows: (24) in, Indicates the cost of electricity purchased at the power station; This indicates the revenue from electricity sales at the power station; This indicates a punishment for abandoning light; This indicates the cost of energy storage operation and maintenance; The weighting coefficients are much less than 1. The weights are derived from the initial battery level of the vehicles; vehicles with higher initial battery levels have higher weights. It is very small and will not affect the objective function; and , , , The specific expression is as follows: (25) (26) (27) (28) in, These are time-of-use electricity pricing, penalties for unit curtailment of solar power, and unit energy storage operation and maintenance costs. and These represent the electricity purchased and sold by the power station to the main grid, respectively. This refers to the amount of light discarded. and These represent the charging and discharging power of the energy storage system.

9. The method for optimizing the scheduling of electric bus depots based on photovoltaic-storage-charging coordination as described in claim 1, characterized in that, In step four, the solved scheduling plan is sent to the depot's operation control system to generate scheduling instructions and charging / discharging instructions for electric buses, realizing the coordinated scheduling of the depot's photovoltaic, energy storage, and charging resources, including: Based on the principle of precise matching between scheduling instructions and physical equipment, the scheduling plan obtained in step three is converted into a specific sequence of control instructions for each charging pile, each charging gun, and each bus, and then sent to the station energy management system and charging pile controller for execution in a timely manner, thereby completing the refined collaborative scheduling of the station's photovoltaic, energy storage, and charging resources.

10. An optimized scheduling system for electric bus depots based on photovoltaic-storage-charging coordination, characterized in that, include: Parameter acquisition unit: Acquires parameters, including electric bus battery and charging / discharging parameters, photovoltaic output prediction curve, energy storage system parameters, station charging pile parameters, bus scheduling plan, time-of-use electricity price curve, and distribution network capacity limitation parameters; Model building unit: Based on the acquired parameters, a charging and discharging behavior model for electric buses, a photovoltaic power output model, an energy storage system operation model, and a charging pile operation model considering the "one pile, multiple guns" sharing mode are established. When constructing the charging pile operation model, a four-dimensional binary decision variable of vehicle-pile-gun-time is introduced to characterize the physical matching relationship, and a gun-switching viscosity constraint and a minimum start-stop time constraint for the charging and discharging process are set. By summarizing this variable, the total charging and discharging power of each vehicle at each time is obtained, and the summed power is correlated with the charging and discharging power variable of the vehicle by an equation, thereby establishing a power consistency constraint at the vehicle-gun-pile level. Scheduling plan solving unit: Based on the established model, with the goal of minimizing the total operating cost of the depot, and with constraints such as vehicle charging and discharging behavior restrictions, energy storage operation restrictions, distribution network capacity restrictions, system power balance, and vehicle-gun-pile matching relationship, an integrated photovoltaic-storage-charging electric bus depot optimization scheduling model is constructed, and the scheduling plan within the depot is solved. Collaborative scheduling unit: The obtained scheduling plan is sent to the station operation control system to generate the scheduling instructions and charging and discharging instructions for electric buses, so as to realize the collaborative scheduling of the station's photovoltaic, energy storage and charging resources.

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