Centralized charging station rolling optimization scheduling method and system considering distribution network bearing capacity

Abstract

The invention discloses a centralized charging station rolling optimization scheduling method and a centralized charging station rolling optimization scheduling system considering distribution network bearing capacity, and aims to improve the operation economy and the service capability of a charging station under the condition of meeting the safety constraint condition of a power distribution network. According to the method, based on a platform-station control two-stage cooperative control architecture, photovoltaic power generation, an energy storage device and an electric vehicle charging load are subjected to multi-energy cooperative optimization control, and power distribution network bearing capacity boundary constraint and differentiated charging demand modeling are introduced; and real-time optimization of charging power distribution, energy storage charging and discharging and power grid electricity purchasing and selling decision is realized through a rolling optimization algorithm. Through variable time scale scheduling combining fine granularity and coarse granularity, the short-term regulation and control precision is improved, the model complexity is reduced, the operation income and the charging service level of the charging station can be remarkably improved, and the power grid friendliness is improved. The method is suitable for various centralized electric vehicle charging stations containing distributed photovoltaic and energy storage, and particularly has outstanding advantages in the scene that the power supply capacity of a power distribution network is limited or dynamically changes.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging and energy management technology, specifically relating to a centralized charging station rolling optimization scheduling method and system that takes into account the carrying capacity of the distribution network. Background Technology

[0002] With the rapid growth of electric vehicle ownership, centralized charging stations are gradually becoming the main infrastructure to meet the large-scale charging needs of electric vehicles. Large charging stations are often connected to the power distribution network and equipped with distributed energy sources such as photovoltaic power generation and energy storage to utilize clean energy and reduce operating costs. However, the arrival time and required charging amount of electric vehicles are random, photovoltaic power generation is intermittent and uncertain, and the power distribution network has limited capacity to carry the charging load. These factors all pose challenges to the safe and efficient operation of charging stations.

[0003] Traditional charging control strategies typically employ a "charge on arrival" approach, where vehicles immediately charge at maximum power upon arrival. This approach lacks coordinated scheduling, potentially leading to excessive concentration of charging load during peak hours and causing distribution network overload risks. Simultaneously, during periods of ample sunlight but low vehicle traffic, surplus photovoltaic output may be wasted, resulting in the waste of clean energy. Regarding user service, a limited number of charging stations can lead to long queues, with some vehicles even failing to fully charge within the planned timeframe, negatively impacting user experience. While some existing scheduling methods consider time-of-use pricing or photovoltaic output forecasting to some extent, they often fail to fully account for distribution network capacity constraints, renewable energy uncertainties, and diverse user needs. They lack the utilization of electric vehicle-to-grid (V2G) potential and refined management of charging gun allocation, resulting in room for improvement in the overall operational efficiency and economic viability of charging stations.

[0004] Therefore, it is necessary to provide a new scheduling method to coordinate heterogeneous resources such as photovoltaics, energy storage, and electric vehicles within a charging station, and to fully consider grid constraints and uncertainties, so as to achieve intelligent and dynamic optimization control of the charging process and improve the energy utilization efficiency and service quality of the charging station. Summary of the Invention

[0005] The purpose of this invention is to provide a centralized charging station rolling optimization scheduling method and system that takes into account the carrying capacity of the distribution network, so as to overcome the shortcomings of the existing technology in charging station scheduling, such as lack of overall coordination, inability to take into account grid constraints and new energy uncertainties, and low user service level.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A rolling optimization scheduling method for centralized charging stations considering the carrying capacity of the distribution network includes the following steps: Step 1: Obtain the basic data required for charging station scheduling, including distribution network carrying capacity boundary parameters, dynamic electricity price information, photovoltaic power generation forecast output, energy storage configuration, and electric vehicle charging demand and behavior parameters; Step 2: Based on the basic data required for charging station scheduling, model electric vehicles and dual-gun charging piles at the platform level, establish vehicle "gun plugging / unplugging" status, single vehicle single gun occupancy and power convergence constraints of multiple guns on the same charging pile, and form an integrated vehicle-gun-pile scheduling model. Step 3: Based on the integrated vehicle-gun-pile scheduling model, the distribution network carrying capacity boundary constraint is introduced. Combining the power exchange relationship between photovoltaic power generation, energy storage devices and the power grid, with the goal of maximizing the daily net operating revenue of charging stations, and fully considering the differentiated charging needs of electric vehicle owners, a multi-source coupled optimization scheduling model of photovoltaic, energy storage and charging is established. The optimal operating strategy within each rolling window is obtained by solving the problem using a variable time scale rolling optimization method. Step 4: The optimal operating strategy obtained from the solution is sent from the platform level to the station control level. The station control level allocates charging guns and parking spaces to each electric vehicle according to the strategy, generates specific instruction sequences for the charging gun on / off status and charging / discharging power settings, and performs real-time adjustment and control of the power interaction between photovoltaic, energy storage, electric vehicles and the power grid in the charging station, thereby realizing multi-energy coordinated control and optimized operation in the charging station.

[0007] A further improvement of the present invention is that the charging requirements and behavioral parameters of electric vehicles include the estimated arrival time, estimated departure time, target charging amount, predetermined charging pile type, and acceptable waiting time for each electric vehicle.

[0008] A further improvement of this invention lies in that, based on the basic data required for charging station scheduling, a platform-level model is created for electric vehicles and dual-gun charging piles. This model establishes constraints on vehicle "plug-in / plug-out" status, single-vehicle single-gun occupancy, and multi-gun power convergence at the same charging pile, forming an integrated vehicle-gun-pile scheduling model, including: By establishing vehicle plug-in / plug-out status, single-vehicle single-plug occupancy, and multi-plug power convergence constraints of the same charging pile, the working state and power limit of the charging gun are finely characterized, thus forming a vehicle-gun-pile integrated scheduling model for subsequent scheduling solutions; among them, the charging gun related constraints are: Formula (2) indicates that vehicle plugging in and unplugging at the same time are mutually exclusive; Formula (3) indicates that each vehicle will have at most one "plugging in" and one "unplugging in" event during one trip to the station; under the assumption that the vehicle does not occupy the charging pile, the connection state between the vehicle and the charging gun can be recursively derived from Formula (4); Formula (5) indicates that under the assumption that all vehicles leave the charging station at the predetermined time, once the vehicle plugs in, it is not allowed to unplug until it leaves the charging station; Formula (6) indicates that if the vehicle is accepted by the charging station, in Insert the gun within a time period; Formula (7) indicates that each vehicle occupies at most one charging gun, and the gun remains unchanged during the charging process; Formula (8) indicates that when multiple charging guns of the same charging pile work in parallel, their combined power is limited by the rated capacity of the cabinet. (1) (2) (3) (4) (5) (6) (7) (8) in, H Optimize the window length for this scroll wheel; Optimize the first time index within the scrolling window for this round. Optimize the second time index within the window for this scrolling cycle, and so on; Optimize the time index set for this round of scrolling; For 0-1 variables, Indicates that the vehicle is armed with a gun; For 0-1 variables, This indicates that the vehicle has drawn its gun; For vehicles e The estimated arrival time; The estimated departure time for vehicle e; A 0-1 variable, representing a vehicle. e Is it at the moment t The device is connected to the charging gun; 1 indicates that it is connected. It is a 0-1 variable, representing the actual connection state at the previous moment before the start of this round of rolling optimization, and is used as the initial value of the window; Maximum waiting time for users; For vehicles e Lock the first j The first charging pile k The 0-1 variable of each gun, Meaning vehicle e Lock-on gun Conversely, it is 0; For the set of indices of the charging piles (0, 1, 2, 3...), The set of indices for the charging guns (0, 1, meaning there are only two guns on a single charging station). It is a variable of 0-1, indicating whether the charging gun is working properly. 1 indicates that it is working properly, and the default value for all charging guns is 1. For vehicles e exist t Time of the first j The first charging pile k The power of each gun; For the first j The first charging pile k A gun t Power at any given moment; For the first j The power limit of a charging station.

[0009] A further improvement of this invention is that, at the platform level, electric vehicles and dual-gun charging piles are modeled by establishing vehicle plug-in / plug-out status, single vehicle single gun occupancy, and multi-gun power convergence constraints of the same charging pile. The working state and power upper limit of the charging gun are finely characterized, thereby forming an integrated vehicle-gun-pile scheduling model for subsequent scheduling solutions. Among them, the relevant constraints of electric vehicles are: Formula (9) shows that the charging / discharging power of the vehicle is jointly constrained by the upper limit of the gun side and the vehicle side; Formula (10) shows that the SOC of the electric vehicle battery must meet the upper and lower limit constraints, and there is an upper limit to the load shedding; Formula (11) limits the cumulative maximum discharge amount in a single residence cycle, while balancing the grid service benefits, ensuring the basic travel needs of users and the value of battery assets. (9) (10) (11) in, For the first j The first charging pile k The minimum charging power allowed for each gun; For the first j The first charging pile k The maximum charging power allowed for each gun; , The first j The first charging pile k The minimum and maximum discharge power allowed for each gun; , , , vehicles e Minimum charging power, maximum charging power, minimum discharging power, maximum discharging power; M is a sufficiently large constant; It is a 0-1 variable used to control the mutual exclusion of vehicle charging and discharging; For vehicles e exist t Time period ; Improving the charging efficiency of electric vehicles; For the discharge efficiency of the tram; For vehicles e Battery capacity; For vehicles e of Lower limit; For vehicles e of Upper limit; For vehicles e Expectations ; For vehicles e Reduce load limit; For vehicles e The maximum permissible discharge amount.

[0010] A further improvement of this invention is that the integrated vehicle-charger-pile scheduling model adopts a charging gun and parking space allocation mechanism: when multiple electric vehicles request charging at the same time, each electric vehicle is allocated an available charging gun and a corresponding parking space according to the order of arrival time, ensuring that each charging gun serves only one electric vehicle at the same time and the charging power does not exceed the rated power of the charging gun; if the allocated charging gun is occupied, the newly arrived electric vehicle enters the waiting queue and starts charging when the charging gun is released; when there are too many vehicles in the queue and the charging station reaches its maximum capacity, new vehicles will be refused entry.

[0011] A further improvement of this invention is that, in step three, based on the vehicle-gun-pile integrated scheduling model, a distribution network carrying capacity boundary constraint is introduced. Combined with the power exchange relationship between photovoltaic power generation, energy storage devices and the power grid, with the goal of maximizing the daily net operating income of the charging station, the differentiated charging needs of electric vehicle owners are fully considered, and a multi-source coupling optimization scheduling model of photovoltaic, energy storage and charging is established. Among them, energy storage, photovoltaic and power grid all need to be constrained. Formula (13) is used to constrain the energy storage SOC to be within the given upper and lower limits at each time, and requires that the SOC at the end time be consistent with the SOC at the start time of operation. Formula (14) is used to constrain the energy storage to not be charged and discharged at any time. Formula (15) is used to constrain the charging station to not be purchased and sold at any time. Formula (16) is used to limit the upper limit of the amount of curtailed photovoltaic power. (12) (13) (14) (15) (16) (17) (18) in, for t ESS's SOC at all times; The self-discharge rate of the ESS; To improve energy storage charging efficiency; For energy storage discharge efficiency; and They are respectively t The charging and discharging power of the ESS at any given time; For ESS battery capacity; This is the upper limit of SOC for ESS; This is the lower limit of SOC for ESS; The initial SOC of the day; SOC at the end of the day; and To control the 0-1 variables of ESS charging and discharging, This indicates that ESS is charging. This indicates that the ESS is discharging; The upper limit of the power purchase capacity for charging stations; This is a non-negative time variable that adjusts the upper limit of the power purchase capacity of charging stations due to changes in the operating conditions or carrying capacity of the distribution network. A value of 0 indicates that the carrying capacity is at a normal level. This refers to the upper limit of the electricity sales capacity of charging stations; It is a variable between 0 and 1. When it is 1, it means that the charging station is in the state of purchasing electricity, and when it is 0, it means that the charging station is in the state of selling electricity. For a sufficiently large constant; for t Real-time photovoltaic output; for t Real-time photovoltaic curtailment power; for t Predict output in real time; This represents the maximum amount of light discarded. The total charging power of all electric vehicles; This represents the total discharge power of all electric vehicles. for t The power purchased from the power grid at any given time; This refers to the discharge power of the ESS. for t ESS charging power at all times; The power sold to the power grid.

[0012] A further improvement of the present invention is that, in step three, the objective function of the photovoltaic-storage-charging multi-source coupling optimization scheduling model is to maximize the net operating income of the charging station. The net operating income is defined as the sum of electric vehicle charging service revenue and electricity sales revenue to the grid minus the electricity purchase cost, and minus the penalty costs caused by photovoltaic power generation abandonment, electric vehicle queuing, electric vehicle V2G compensation and load reduction. (19) (20) in, for t The unit price of electricity sold to the grid at any given time; Revenue from selling electricity to the grid; The service revenue generated by charging stations for providing services to users. for Time of the first j The service price per unit of charging pile; for The unit price of electricity purchased from the grid at all times. Expenses for purchasing electricity from the grid; Punishment for abandoning light within the unit; Waiting in line for electric cars to receive a penalty The penalty coefficient for waiting in line; The cost of V2G compensation for charging stations to users. for t Time of the first j The V2G electricity unit price corresponding to each charging pile; To reduce vehicle load and compensate for costs, To reduce the unit price of load, For vehicles e Load shedding, i.e., vehicle load shedding e The difference between the expected energy and the actual battery energy when leaving the charging station.

[0013] A further improvement of this invention lies in the use of a variable time-scale rolling optimization method in step three to obtain the optimal operating strategy within each rolling window. This includes: within each rolling optimization window, the time axis is divided into continuous refined sub-time periods and coarse sub-time periods. The refined sub-time periods cover two consecutive hours from the current moment, using a 15-minute time granularity to finely model electric vehicles, explicitly depicting the charging status, charging and discharging power, and matching relationship with dual-gun charging piles for each vehicle within the window. The coarse sub-time periods cover the remaining time period within the rolling window, using a 1-hour time granularity, retaining the charging demand, maximum allowable discharge capacity, and distribution network carrying capacity constraints for each electric vehicle. The hourly aggregated energy demand replaces the refined time-series power variables, thereby reducing the number of decision variables and constraints and improving the rolling optimization solution speed.

[0014] A further improvement of this invention lies in that the optimal operating strategy obtained from the solution is distributed from the platform level to the station control level. The station control level allocates charging guns and parking spaces to each electric vehicle according to the strategy, and generates a specific instruction sequence for the charging gun on / off status and charging / discharging power setting values. This includes: allowing electric vehicles to feed energy back to the grid through their on-board batteries when the grid or charging station needs it, and limiting the cumulative maximum discharge of each electric vehicle during a single stay to ensure the basic driving needs of the vehicle and the battery life; at the same time, setting charging / discharging mode conversion constraints and state of charge limits for the energy storage system in the station to avoid the energy storage battery being in charging and discharging conditions at the same time, and maintaining the real-time state of charge of the energy storage battery within a preset range.

[0015] A rolling optimization scheduling method for centralized charging stations considering the carrying capacity of the distribution network includes: Basic data acquisition unit: Acquires basic data required for charging station scheduling, including distribution network carrying capacity boundary parameters, dynamic electricity price information, photovoltaic power generation forecast output, energy storage configuration, and electric vehicle charging demand and behavior parameters; Vehicle-gun-pile integrated scheduling model establishment unit: Based on the basic data required for charging station scheduling, electric vehicles and dual-gun charging piles are modeled at the platform level, and the vehicle "plug-in / plug-out" status, single vehicle single gun occupation and multi-gun power convergence constraints of the same charging pile are established to form a vehicle-gun-pile integrated scheduling model. Optimal Operation Strategy Solution Unit: Based on the integrated vehicle-gun-pile scheduling model, a distribution network carrying capacity boundary constraint is introduced. Combining the power exchange relationship between photovoltaic power generation, energy storage devices, and the power grid, with the goal of maximizing the daily net operating revenue of charging stations, a multi-source coupled optimization scheduling model of photovoltaic, energy storage, and charging is established, taking into full account the differentiated charging needs of electric vehicle owners. The optimal operation strategy within each rolling window is obtained by using a variable time scale rolling optimization method. Real-time adjustment and control unit: The optimal operating strategy obtained by solving is distributed from the platform level to the station control level. The station control level allocates charging guns and parking spaces to each electric vehicle according to the strategy, generates specific instruction sequences for the charging gun on / off status and charging / discharging power setpoints, and performs real-time adjustment and control of the power interaction between photovoltaic, energy storage, electric vehicles and the power grid in the charging station, thereby realizing multi-energy coordinated control and optimized operation in the charging station.

[0016] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention provides a centralized charging station rolling optimization scheduling method and system that considers distribution network carrying capacity. By introducing integrated vehicle-gun-pile modeling at the platform layer, along with constraints such as vehicle gun insertion / removal, single-gun occupancy by a single vehicle, and power convergence of multiple charging piles, it accurately depicts the physical connection relationship and power boundaries between electric vehicles and dual-gun charging piles, improving the safety and scheduling accuracy of the charging process. Furthermore, by unifying the upper limit of distribution network carrying capacity, grid power purchase and sale mutual exclusion constraints, and photovoltaic and energy storage operation constraints into the rolling optimization model, it coordinates photovoltaic output, energy storage charging and discharging, electric vehicle charging and discharging, and grid power exchange while meeting the distribution network safety boundaries. This achieves friendly interaction between charging stations and the distribution network and improves photovoltaic efficiency. On-site consumption level; by constructing a scheduling model aimed at maximizing net operating revenue, and comprehensively considering factors such as charging service revenue, grid power purchase and sale, electric vehicle queuing, V2G compensation, load reduction and curtailment penalties, it can significantly improve the economic efficiency and service capacity of charging stations while ensuring users' differentiated charging needs; by adopting a variable time scale modeling strategy that combines fine and coarse approaches within a rolling optimization framework, using fine-grained precise modeling for the current two hours and hourly aggregated energy description for subsequent periods, the model dimensionality and solution time are effectively reduced while ensuring short-term scheduling accuracy, making the proposed method feasible for online engineering applications and highly robust. Attached Figure Description

[0017] 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.

[0018] Figure 1 This is the overall flowchart of the present invention.

[0019] Figure 2 This is a schematic diagram of a two-level collaborative control architecture for intelligent scheduling of centralized charging stations.

[0020] Figure 3 A schematic diagram of the timeline framework optimized for scrolling.

[0021] Figure 4 Optimize the flowchart for scrolling.

[0022] Figure 5 A comparison chart of power purchase strategies for charging stations under different distribution network carrying capacity boundaries.

[0023] Figure 6 This is a system structure block diagram of the present invention. Detailed Implementation

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0030] Example 1 The centralized charging station rolling optimization scheduling method considering the distribution network carrying capacity provided by this invention includes the following steps: Step 1: Obtain the basic data required for charging station scheduling, including distribution network carrying capacity boundary parameters, dynamic electricity price information, photovoltaic power generation forecast output, energy storage configuration, and electric vehicle charging demand and behavior parameters; Step 2: Based on the basic data required for charging station scheduling, model electric vehicles and dual-gun charging piles at the platform level, establish vehicle "gun plugging / unplugging" status, single vehicle single gun occupancy and power convergence constraints of multiple guns on the same charging pile, and form an integrated vehicle-gun-pile scheduling model. Step 3: Based on the integrated vehicle-gun-pile scheduling model, the distribution network carrying capacity boundary constraint is introduced. Combining the power exchange relationship between photovoltaic power generation, energy storage devices and the power grid, with the goal of maximizing the daily net operating revenue of charging stations, and fully considering the differentiated charging needs of electric vehicle owners, a multi-source coupled optimization scheduling model of photovoltaic, energy storage and charging is established. The optimal operating strategy within each rolling window is obtained by solving the problem using a variable time scale rolling optimization method. Step 4: The optimal operating strategy obtained from the solution is sent from the platform level to the station control level. The station control level allocates charging guns and parking spaces to each electric vehicle according to the strategy, generates specific instruction sequences for the charging gun on / off status and charging / discharging power settings, and performs real-time adjustment and control of the power interaction between photovoltaic, energy storage, electric vehicles and the power grid in the charging station, thereby realizing multi-energy coordinated control and optimized operation in the charging station.

[0031] In this embodiment, the charging requirements and behavioral parameters of electric vehicles include the estimated arrival time, estimated departure time, target charging amount, predetermined charging pile type, and acceptable waiting time for each electric vehicle.

[0032] In this embodiment, based on the basic data required for charging station scheduling, electric vehicles and dual-gun charging piles are modeled at the platform level. This establishes vehicle "plug-in / plug-out" status, single-vehicle single-gun occupancy, and multi-gun power convergence constraints for the same charging pile, forming an integrated vehicle-gun-pile scheduling model, including: By establishing vehicle plug-in / plug-out status, single-vehicle single-plug occupancy, and multi-plug power convergence constraints of the same charging pile, the working state and power limit of the charging gun are finely characterized, thus forming a vehicle-gun-pile integrated scheduling model for subsequent scheduling solutions; among them, the charging gun related constraints are: Formula (2) indicates that vehicle plugging in and unplugging at the same time are mutually exclusive; Formula (3) indicates that each vehicle will have at most one "plugging in" and one "unplugging in" event during one trip to the station; under the assumption that the vehicle does not occupy the charging pile, the connection state between the vehicle and the charging gun can be recursively derived from Formula (4); Formula (5) indicates that under the assumption that all vehicles leave the charging station at the predetermined time, once the vehicle plugs in, it is not allowed to unplug until it leaves the charging station; Formula (6) indicates that if the vehicle is accepted by the charging station, in Insert the gun within a time period; Formula (7) indicates that each vehicle occupies at most one charging gun, and the gun remains unchanged during the charging process; Formula (8) indicates that when multiple charging guns of the same charging pile work in parallel, their combined power is limited by the rated capacity of the cabinet. (1) (2) (3) (4) (5) (6) (7) (8) in, H Optimize the window length for this scroll wheel; Optimize the first time index within the scrolling window for this round. Optimize the second time index within the window for this scrolling cycle, and so on; Optimize the time index set for this round of scrolling; For 0-1 variables, Indicates that the vehicle is armed with a gun; For 0-1 variables, This indicates that the vehicle has drawn its gun; For vehicles e The estimated arrival time; The estimated departure time for vehicle e; A 0-1 variable, representing a vehicle. e Is it at the moment t The device is connected to the charging gun; 1 indicates that it is connected. It is a 0-1 variable, representing the actual connection state at the previous moment before the start of this round of rolling optimization, and is used as the initial value of the window; Maximum waiting time for users; For vehicles e Lock the first j The first charging pile k The 0-1 variable of each gun, Meaning vehicle e Lock-on gun Conversely, it is 0; For the set of indices of the charging piles (0, 1, 2, 3...), The set of indices for the charging guns (0, 1, meaning there are only two guns on a single charging station). It is a variable of 0-1, indicating whether the charging gun is working properly. 1 indicates that it is working properly, and the default value for all charging guns is 1. For vehicles e exist t Time of the first j The first charging pile k The power of each gun; For the first j The first charging pile k A gun t Power at any given moment; For the first j The power limit of a charging station.

[0033] In this embodiment, the electric vehicle and the dual-gun charging pile are modeled at the platform level by establishing the vehicle plug-in / plug-out status, single vehicle single gun occupancy and power convergence constraints of multiple guns in the same charging pile. The working state and power limit of the charging gun are finely characterized, thus forming a vehicle-gun-pile integrated scheduling model for subsequent scheduling solutions. Among them, the relevant constraints of the electric vehicle are: Formula (9) shows that the charging / discharging power of the vehicle is jointly constrained by the upper limit of the gun side and the vehicle side; Formula (10) shows that the SOC of the electric vehicle battery must meet the upper and lower limit constraints, and there is an upper limit to the load shedding; Formula (11) limits the cumulative maximum discharge in a single residence cycle, while balancing the grid service benefits, ensuring the user's basic travel needs and the value of battery assets. (9) (10) (11) in, For the first j The first charging pile k The minimum charging power allowed for each gun; For the first j The first charging pile k The maximum charging power allowed for each gun; , The first j The first charging pile k The minimum and maximum discharge power allowed for each gun; , , , vehicles e Minimum charging power, maximum charging power, minimum discharging power, maximum discharging power; M is a sufficiently large constant; It is a 0-1 variable used to control the mutual exclusion of vehicle charging and discharging; For vehicles e exist t Time period ; Improving the charging efficiency of electric vehicles; For the discharge efficiency of the tram; For vehicles e Battery capacity; For vehicles e of Lower limit; For vehicles e of Upper limit; For vehicles e Expectations ; For vehicles eReduce load limit; For vehicles e The maximum permissible discharge amount.

[0034] In this embodiment, the integrated vehicle-charger-pile scheduling model adopts a charging gun and parking space allocation mechanism: when multiple electric vehicles request charging at the same time, each electric vehicle is allocated an available charging gun and corresponding parking space according to the order of arrival time, ensuring that each charging gun serves only one electric vehicle at the same time and the charging power does not exceed the rated power of the charging gun; if the allocated charging gun is occupied, the newly arrived electric vehicle enters the waiting queue and starts charging when the charging gun is released; when there are too many vehicles in the queue and the charging station reaches its maximum capacity, new vehicles will be refused entry.

[0035] In this embodiment, in step three, based on the vehicle-gun-pile integrated scheduling model, the distribution network carrying capacity boundary constraint is introduced. Combined with the power exchange relationship between photovoltaic power generation, energy storage devices and the power grid, with the goal of maximizing the daily net operating income of the charging station, the differentiated charging needs of electric vehicle owners are fully considered, and a multi-source coupling optimization scheduling model of photovoltaic, energy storage and charging is established. Among them, energy storage, photovoltaic and power grid all need to be constrained. Formula (13) is used to constrain the energy storage SOC to be within the given upper and lower limits at each time, and requires that the SOC at the end time be consistent with the SOC at the start time of operation. Formula (14) is used to constrain the energy storage to not be charged and discharged at any time. Formula (15) is used to constrain the charging station to not be purchased and sold at any time. Formula (16) is used to limit the upper limit of the amount of curtailed photovoltaic power. (12) (13) (14) (15) (16) (17) (18) in, for t ESS's SOC at all times; The self-discharge rate of the ESS; To improve energy storage charging efficiency; For energy storage discharge efficiency; and They are respectively t The charging and discharging power of the ESS at any given time; For ESS battery capacity; This is the upper limit of SOC for ESS; This is the lower limit of SOC for ESS; The initial SOC of the day; SOC at the end of the day; and To control the 0-1 variables of ESS charging and discharging, This indicates that ESS is charging. This indicates that the ESS is discharging; The upper limit of the power purchase capacity for charging stations; This is a non-negative time variable that adjusts the upper limit of the power purchase capacity of charging stations due to changes in the operating conditions or carrying capacity of the distribution network. A value of 0 indicates that the carrying capacity is at a normal level. This refers to the upper limit of the electricity sales capacity of charging stations; It is a variable between 0 and 1. When it is 1, it means that the charging station is in the state of purchasing electricity, and when it is 0, it means that the charging station is in the state of selling electricity. For a sufficiently large constant; for t Real-time photovoltaic output; for t Real-time photovoltaic curtailment power; for t Predict output in real time; This represents the maximum amount of light discarded. The total charging power of all electric vehicles; This represents the total discharge power of all electric vehicles. for t The power purchased from the power grid at any given time; This refers to the discharge power of the ESS. for t ESS charging power at all times; The power sold to the power grid.

[0036] In this embodiment, in step three, the objective function of the photovoltaic-storage-charging multi-source coupling optimization scheduling model is to maximize the net operating revenue of the charging station. The net operating revenue is defined as the sum of electric vehicle charging service revenue and electricity sales revenue to the grid minus the electricity purchase cost, and minus the penalty costs caused by photovoltaic power generation abandonment, electric vehicle queuing, electric vehicle V2G compensation and load reduction. (19) (20) in, for t The unit price of electricity sold to the grid at any given time; Revenue from selling electricity to the grid; The service revenue generated by charging stations for providing services to users. for Time of the first j The service price per unit of charging pile; for The unit price of electricity purchased from the grid at all times. Expenses for purchasing electricity from the grid; Punishment for abandoning light within the unit; Waiting in line for electric cars to receive a penalty The penalty coefficient for waiting in line; The cost of V2G compensation for charging stations to users. for t Time of the first j The V2G electricity unit price corresponding to each charging pile; To reduce vehicle load and compensate for costs, To reduce the unit price of load, For vehicles e Load shedding, i.e., vehicle load shedding e The difference between the expected energy and the actual battery energy when leaving the charging station.

[0037] In this embodiment, the photovoltaic-storage-charging multi-source coupled optimization scheduling model adopts a rolling correction strategy when considering the uncertainties of photovoltaic power generation and distribution network carrying capacity: during actual operation, the predicted value of photovoltaic power generation and the boundary condition parameters of distribution network carrying capacity are updated on a rolling basis, the photovoltaic-storage-charging multi-source coupled optimization scheduling model is solved again, and the scheduling plan is corrected in real time.

[0038] In this embodiment, step three employs a variable time-scale rolling optimization method within each rolling window to obtain the optimal operating strategy within that window. This includes: within each rolling optimization window, the time axis is divided into continuous refined sub-time periods and coarse sub-time periods. The refined sub-time periods cover two consecutive hours from the current moment, using a 15-minute time granularity to finely model electric vehicles, explicitly depicting each vehicle's charging status, charging and discharging power, and matching relationship with dual-gun charging piles within the window. The coarse sub-time periods cover the remaining time within the rolling window, using a 1-hour time granularity, retaining the charging demand, maximum allowable discharge capacity, and distribution network carrying capacity constraints for each electric vehicle. The hourly aggregated energy demand replaces the refined time-series power variables, thereby reducing the number of decision variables and constraints and improving the rolling optimization solution speed.

[0039] In this embodiment, the optimal operating strategy obtained from the solution is distributed from the platform level to the station control level. The station control level allocates charging guns and parking spaces to each electric vehicle according to the strategy, and generates a specific instruction sequence for the charging gun on / off status and charging / discharging power setting values. This includes: allowing electric vehicles to feed energy back to the grid through their on-board batteries when the grid or charging station needs it, and limiting the maximum cumulative discharge of each electric vehicle during a single stay to ensure the basic driving needs of the vehicle and the battery life; at the same time, setting charging / discharging mode conversion constraints and state of charge limits for the energy storage system in the station to avoid the energy storage battery being in charging and discharging conditions at the same time, and maintaining the real-time state of charge of the energy storage battery within a preset range.

[0040] Example 2 like Figure 1 As shown, the centralized charging station rolling optimization scheduling method considering distribution network carrying capacity provided by this invention generally includes four stages: data acquisition and prediction, model construction, variable time-scale rolling optimization solution, and scheduling strategy issuance and execution feedback. First, in the data acquisition and prediction stage, the charging station management system collects the carrying capacity boundary parameters and dynamic electricity price curves from the distribution network side, and obtains basic data such as the predicted output of photovoltaic power generation within the station, the operating parameters of the energy storage system, and electric vehicle reservation orders and real-time arrival information, forming the external inputs required for rolling optimization. Subsequently, in the model construction and optimization solution stage, the system establishes a multi-source coupled collaborative optimization model of photovoltaic, energy storage, and charging, as well as a vehicle-gun-pile integrated constraint model at the platform layer. It updates the model parameters and state variables using the current operating status of various devices and the SOC of each vehicle, and on this basis, uses a variable time-scale rolling optimization method to obtain the optimal scheduling scheme for a future period. This generates a specific instruction sequence and issues it to the station control level for execution, completing the collaborative control of various heterogeneous resources within the charging station.

[0041] like Figure 2 As shown, this invention adopts a two-level collaborative control architecture of platform and station control. The platform level, as the core of optimization decision-making, acquires dynamic electricity price signals and capacity boundary instructions from the distribution network, as well as information streams such as vehicle reservation charging demands from end users. It performs global optimization calculations by comprehensively considering electric vehicle charging behavior, distribution network power supply capacity, and the status of equipment within the station. The station control level, as the execution end, implements specific controls based on the scheduling results issued by the platform, including allocating charging station nozzles to each electric vehicle, controlling the start / stop of charging stations, and power output, thereby achieving the coordinated operation of various resources within the charging station.

[0042] In the platform-level optimization model, an integrated model of electric vehicles and charging guns / piles is established, considering constraints such as vehicle plug-in / plug-out status, single-vehicle single-gun occupancy, and power convergence of multiple guns on the same charging pile. This accurately depicts the physical connection relationship and power limitations between electric vehicles and dual-gun charging piles, improving the safety and accuracy of charging process scheduling. Simultaneously, the upper limit of distribution network carrying capacity, grid power purchase and sale mutual exclusion constraints, and operational constraints of photovoltaic power generation and energy storage devices are uniformly incorporated into the optimization model. Under the premise of meeting the distribution network safety boundaries, the model coordinates photovoltaic output, energy storage charging and discharging, electric vehicle charging / V2G discharging, and grid power exchange. Rolling optimization scheduling calculations are performed with the goal of maximizing the daily net operating revenue of charging stations, fully considering the differentiated charging needs and preferences of different car owners.

[0043] like Figure 3 As shown, this invention employs a variable-time-scale rolling optimization timeline framework. Each rolling optimization window covers a predetermined length of future scheduling periods (e.g., 4 hours) starting from the current moment. For the current shorter time period (e.g., the previous 2 hours), a fine-grained time scale (e.g., 15 minutes) is used for precise modeling, while for subsequent longer time periods (e.g., the following hours), a coarser time scale (e.g., 1 hour) is used for aggregate modeling. This combination of fine-grained and coarse-grained time scale design effectively reduces the dimensionality and solution time of the overall optimization model while ensuring recent scheduling accuracy, making the proposed scheduling method feasible for online real-time applications.

[0044] like Figure 4 As shown, the execution process of rolling optimization scheduling includes a closed-loop mechanism with three stages: prediction, optimization, and execution. At the beginning of each rolling cycle, the system acquires the latest photovoltaic power generation forecast, distribution network carrying capacity boundary, and vehicle status data, and updates the optimization scheduling model accordingly. Subsequently, the solver calculates the optimal scheduling strategy for each time period within the rolling window, including the charging pile allocation scheme, the charging and discharging power plan for each vehicle, and the charging and discharging arrangement of the energy storage device. Then, only the control actions of the obtained strategy in the first time step are executed: the platform level issues the charging control command for this time step to the station control level, which then allocates specific charging gun positions to each vehicle to be charged and controls the start, stop, and power output of the corresponding charging pile, completing the charging operation for the current time step. Afterward, the scheduling cycle rolls forward according to a preset step size (e.g., 15 minutes), enters the next cycle, and repeats the new prediction-optimization-execution process. Through the aforementioned real-time cyclical rolling optimization mechanism, the system can dynamically adjust the scheduling plan for subsequent time periods based on the latest information, and roll out the updated control instructions to the station control level for execution, forming a dynamic scheduling system of "prediction-optimization-execution" that is continuously corrected over time.

[0045] As a specific embodiment of the present invention, consider the operation scenario of a centralized charging station. This charging station is equipped with three types of dual-gun charging piles: including 3 supercharging piles, 5 ordinary fast charging piles, and 2 fast charging piles with V2G functionality (each charging pile contains two charging guns), for a total of 20 serviceable charging interfaces. Each dual-gun supercharging pile has a maximum single-gun charging power of 480kW and a maximum combined current power of 480kW; each ordinary fast charging pile has a maximum single-gun charging power of 120kW and a maximum combined current power of 120kW; each fast charging pile with V2G functionality has a maximum single-gun charging power of 60kW and a maximum combined current power of 60kW. The station also integrates an energy storage system (rated capacity 600kWh, maximum charging / discharging power 300kW) and a distributed photovoltaic power station (installed capacity 650kW), connected to the distribution network and subject to the upper limit of the distribution network's carrying capacity. Suppose that the charging station provides charging services for 350 electric vehicles on a certain day. A significant portion of these users submitted charging requests in advance through a reservation platform, while the remaining 50 vehicles arrived randomly without prior reservation. The energy needs of different vehicles vary: arrival times are distributed throughout the day, and initial battery state of charge (SOC), target charge amount, available charging time, and designated charging station type all differ. Some of these vehicles have bidirectional charging capabilities and, with the owner's permission, are willing to participate in V2G (vehicle-to-grid) discharge for financial compensation.

[0046] In the traditional "charge on demand" model, vehicles arrive at a station and begin charging immediately if an available charging gun is available. However, the scheduling process doesn't adequately consider the coordination of factors such as electricity price, photovoltaic output, and energy storage status, often resulting in high charging loads and electricity purchase costs during peak hours. In contrast, the intelligent scheduling model proposed in this invention requires vehicles to submit charging requests through a reservation platform. The system comprehensively analyzes information such as the urgency of the user's charging request, the grid's time-of-use electricity price, photovoltaic power generation forecasts, the current SOC of energy storage, and the availability of charging piles. It then uses a rolling optimization method to generate cost-effective charging solutions for each vehicle. For example, during periods of low electricity prices or when the distribution network has ample capacity, more vehicles are prioritized for charging; while during periods of high electricity prices or when the distribution network is limited, the charging start time for some vehicles is appropriately delayed, or V2G discharge is initiated for a few fully charged vehicles waiting to leave, to smooth out peak demand and ensure that the power supply capacity of the distribution network is not exceeded. Through such intelligent decision-making, charging stations achieve orderly guidance of charging loads and optimized utilization of resources such as photovoltaics and energy storage.

[0047] Table 1 Comparison of key performance indicators of charging stations under different scheduling modes

[0048] Simulation results demonstrate that the intelligent scheduling strategy of this invention can significantly improve the operational revenue of charging stations. As shown in Table 1, under the premise of serving the same number of vehicles, compared with the traditional on-demand charging mode, the intelligent charging station mode using the scheduling strategy of this invention improves both daily operational economic benefits and service quality. Specifically, charging service revenue increased from RMB 12,232.40 to RMB 13,075.74, and the number of vehicles that failed to complete their charging tasks decreased significantly from 52 to 10; at the same time, the load reduction penalty was also significantly reduced. This indicates that the method of this invention effectively improves the resource utilization efficiency and user satisfaction of charging stations, ensuring that all reserved vehicles are charged in a timely manner while minimizing service gaps caused by insufficient power or improper scheduling.

[0049] Furthermore, the adaptability of the proposed method to the power supply capacity of the distribution network was verified through further simulation.

[0050] Table 2 Comparison of key performance indicators of charging stations under different distribution network carrying capacity boundaries

[0051] As shown in Table 2, when the upper limit of the power distribution network's carrying capacity is tightened, charging stations need to adjust their charging strategies. Under conditions of sufficient power distribution network capacity, charging stations can flexibly arrange charging plans, achieving a peak-to-valley power purchase ratio of 416.478, a net operating income of 9198.64 yuan, and only 4 vehicles failing to complete their charging tasks. However, under conditions of insufficient power distribution network capacity, the peak-to-valley power purchase ratio of the charging station drops to 70.012, the net operating income is 8611.36 yuan, and the number of vehicles failing to complete their charging tasks increases to 10. Figure 5 As can be seen from the power purchase curve, even under conditions of limited distribution network capacity, the intelligent dispatching strategy of this invention can actively bring the power purchase curve of the charging station closer to the ideal curve shape when the capacity is sufficient. By optimizing the dispatching, the charging load is reasonably allocated to the time period allowed by the distribution network capacity, effectively avoiding a significant decline in service quality caused by grid constraints. This adaptive dispatching mechanism, while strictly adhering to the safety constraints of the distribution network, maximizes the operational efficiency of the charging station and the level of user service, significantly enhancing the friendly interaction between the charging station and the grid and the stability of system operation.

[0052] In summary, the analysis of the above embodiments shows that the centralized charging station variable time-scale rolling optimization scheduling method proposed in this invention, which considers the carrying capacity of the distribution network, can fully tap the potential of photovoltaic, energy storage and charging facilities in the charging station while meeting the safety power supply boundary of the distribution network. It can achieve efficient overall planning for the diversified charging needs of electric vehicles, significantly improve the economic benefits and user service level of the charging station, and has good practical application value.

[0053] Example 3 like Figure 6As shown, the centralized charging station rolling optimization scheduling method considering distribution network carrying capacity provided by the present invention includes: Basic data acquisition unit: Acquires basic data required for charging station scheduling, including distribution network carrying capacity boundary parameters, dynamic electricity price information, photovoltaic power generation forecast output, energy storage configuration, and electric vehicle charging demand and behavior parameters; Vehicle-gun-pile integrated scheduling model establishment unit: Based on the basic data required for charging station scheduling, electric vehicles and dual-gun charging piles are modeled at the platform level, and the vehicle "plug-in / plug-out" status, single vehicle single gun occupation and multi-gun power convergence constraints of the same charging pile are established to form a vehicle-gun-pile integrated scheduling model. Optimal Operation Strategy Solution Unit: Based on the integrated vehicle-gun-pile scheduling model, a distribution network carrying capacity boundary constraint is introduced. Combining the power exchange relationship between photovoltaic power generation, energy storage devices, and the power grid, with the goal of maximizing the daily net operating revenue of charging stations, a multi-source coupled optimization scheduling model of photovoltaic, energy storage, and charging is established, taking into full account the differentiated charging needs of electric vehicle owners. The optimal operation strategy within each rolling window is obtained by using a variable time scale rolling optimization method. Real-time adjustment and control unit: The optimal operating strategy obtained by solving is distributed from the platform level to the station control level. The station control level allocates charging guns and parking spaces to each electric vehicle according to the strategy, generates specific instruction sequences for the charging gun on / off status and charging / discharging power setpoints, and performs real-time adjustment and control of the power interaction between photovoltaic, energy storage, electric vehicles and the power grid in the charging station, thereby realizing multi-energy coordinated control and optimized operation in the charging station.

[0054] 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.

[0055] 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 rolling optimization scheduling method for centralized charging stations considering the carrying capacity of the distribution network, characterized in that, Includes the following steps: Step 1: Obtain the basic data required for charging station scheduling, including distribution network carrying capacity boundary parameters, dynamic electricity price information, photovoltaic power generation forecast output, energy storage configuration, and electric vehicle charging demand and behavior parameters; Step 2: Based on the basic data required for charging station scheduling, model electric vehicles and dual-gun charging piles at the platform level, establish vehicle "plug-in / plug-out" status, single vehicle single gun occupancy and power convergence constraints of multiple guns on the same charging pile, and form an integrated vehicle-gun-pile scheduling model. Step 3: Based on the integrated vehicle-gun-pile scheduling model, the distribution network carrying capacity boundary constraint is introduced. Combining the power exchange relationship between photovoltaic power generation, energy storage devices and the power grid, with the goal of maximizing the daily net operating revenue of charging stations, and fully considering the differentiated charging needs of electric vehicle owners, a multi-source coupled optimization scheduling model of photovoltaic, energy storage and charging is established. The optimal operating strategy within each rolling window is obtained by solving the problem using a variable time scale rolling optimization method. Step 4: The optimal operating strategy obtained from the solution is sent from the platform level to the station control level. The station control level allocates charging guns and parking spaces to each electric vehicle according to the strategy, generates specific instruction sequences for the charging gun on / off status and charging / discharging power settings, and performs real-time adjustment and control of the power interaction between photovoltaic, energy storage, electric vehicles and the power grid in the charging station, thereby realizing multi-energy coordinated control and optimized operation in the charging station.

2. The centralized charging station rolling optimization scheduling method considering distribution network carrying capacity according to claim 1, characterized in that, The charging demand and behavioral parameters of electric vehicles include the estimated arrival time, estimated departure time, target charging amount, reserved charging station type, and acceptable waiting time for each electric vehicle.

3. The centralized charging station rolling optimization scheduling method considering distribution network carrying capacity according to claim 1, characterized in that, Based on the basic data required for charging station scheduling, electric vehicles and dual-gun charging piles are modeled at the platform level. This establishes vehicle "plug-in / plug-out" status, single-gun occupancy for a single vehicle, and power convergence constraints for multiple guns on the same charging pile, forming an integrated vehicle-gun-pile scheduling model, including: By establishing vehicle plug-in / plug-out status, single-vehicle single-plug occupancy, and multi-plug power convergence constraints of the same charging pile, the working state and power limit of the charging gun are finely characterized, thus forming a vehicle-gun-pile integrated scheduling model for subsequent scheduling solutions; among them, the charging gun related constraints are: Formula (2) indicates that vehicle plugging in and unplugging at the same time are mutually exclusive; Formula (3) indicates that each vehicle will have at most one "plugging in" and one "unplugging in" event during one trip; under the assumption that the vehicle does not occupy the charging pile, the connection state between the vehicle and the charging gun can be recursively derived from Formula (4); Formula (5) indicates that under the assumption that all vehicles leave the charging station at the predetermined time, once the vehicle plugs in, it is not allowed to unplug until it leaves the charging station; Formula (6) indicates that if the vehicle is accepted by the charging station, in Insert the gun within a time period; Formula (7) indicates that each vehicle occupies at most one charging gun, and the gun remains unchanged during the charging process; Formula (8) indicates that when multiple charging guns of the same charging pile work in parallel, their combined power is limited by the rated capacity of the cabinet. (1) (2) (3) (4) (5) (6) (7) (8) in, H Optimize the window length for this scroll wheel; Optimize the first time index within the scrolling window for this round. Optimize the second time index within the window for this scrolling cycle, and so on; Optimize the time index set for this round of scrolling; For 0-1 variables, Indicates that the vehicle is armed with a gun; For 0-1 variables, This indicates that the vehicle has drawn its gun; For vehicles e The estimated arrival time; The estimated departure time for vehicle e; A 0-1 variable, representing a vehicle. e Is it at the moment? t The device is connected to the charging gun; 1 indicates that it is connected. It is a 0-1 variable, representing the actual connection state at the previous moment before the start of this round of rolling optimization, and is used as the initial value of the window; Maximum waiting time for users; For vehicles e Lock the first j The first charging pile k The 0-1 variable of each gun, Meaning vehicle e Lock-on gun Conversely, it is 0; For the set of indices of the charging piles (0, 1, 2, 3...), The set of indices for the charging guns (0, 1, meaning there are only two guns on a single charging station). It is a variable of 0-1, indicating whether the charging gun is working properly. 1 indicates that it is working properly, and the default value for all charging guns is 1. For vehicles e exist t Time of the first j The first charging pile k The power of each gun; For the first j The first charging pile k A gun t Power at any given moment; For the first j The power limit of a charging station.

4. The centralized charging station rolling optimization scheduling method considering distribution network carrying capacity according to claim 3, characterized in that, Modeling electric vehicles and dual-gun charging piles at the platform level involves establishing vehicle plug-in / plug-out status, single-vehicle single-gun occupancy, and multi-gun power convergence constraints for the same charging pile. This allows for a detailed characterization of the charging gun's working state and power limit, thus forming a vehicle-gun-pile integrated scheduling model for subsequent scheduling solutions. Among these constraints, electric vehicle-related constraints are: Formula (9) indicates that the vehicle's charging / discharging power is constrained by both the gun-side and vehicle-side upper limits; Formula (10) indicates that the electric vehicle battery SOC must meet upper and lower limit constraints, and there is an upper limit to load shedding; Formula (11) limits the maximum cumulative discharge within a single dwell period, balancing the grid service benefits while ensuring the user's basic travel needs and battery asset value. (9) (10) (11) in, For the first j The first charging pile k The minimum charging power allowed for each gun; For the first j The first charging pile k The maximum charging power allowed for each gun; , The first j The first charging pile k The minimum and maximum discharge power allowed for each gun; , , , vehicles e Minimum charging power, maximum charging power, minimum discharging power, maximum discharging power; M is a sufficiently large constant; It is a 0-1 variable used to control the mutual exclusion of vehicle charging and discharging; For vehicles e exist t Time period ; Improving the charging efficiency of electric vehicles; For the discharge efficiency of the tram; For vehicles e Battery capacity; For vehicles e of Lower limit; For vehicles e of Upper limit; For vehicles e Expectations ; For vehicles e Reduce load limit; For vehicles e The maximum permissible discharge amount.

5. The centralized charging station rolling optimization scheduling method considering distribution network carrying capacity according to claim 3, characterized in that, The integrated vehicle-charger-pile scheduling model adopts a charging gun and parking space allocation mechanism: when multiple electric vehicles request charging at the same time, each electric vehicle is allocated an available charging gun and corresponding parking space according to the order of arrival time, ensuring that each charging gun serves only one electric vehicle at the same time and the charging power does not exceed the rated power of the charging gun; if the allocated charging gun is occupied, the newly arrived electric vehicle enters the waiting queue and starts charging when the charging gun is released; when the number of vehicles in the queue is too large and the charging station reaches its maximum capacity, new vehicles will be refused entry.

6. The centralized charging station rolling optimization scheduling method considering distribution network carrying capacity according to claim 3, characterized in that, In step three, based on the vehicle-gun-pile integrated scheduling model, the distribution network carrying capacity boundary constraint is introduced. Combined with the power exchange relationship between photovoltaic power generation, energy storage devices and the power grid, with the goal of maximizing the daily net operating income of the charging station, the differentiated charging needs of electric vehicle owners are fully considered, and a multi-source coupling optimization scheduling model of photovoltaic, energy storage and charging is established. Among them, energy storage, photovoltaic and power grid all need to be constrained. Formula (13) is used to constrain the energy storage SOC to be within the given upper and lower limits at each time, and requires that the SOC at the end time be consistent with the SOC at the start time of operation. Formula (14) is used to constrain the energy storage to not be charged and discharged at any time. Formula (15) is used to constrain the charging station to not be purchased and sold at any time. Formula (16) is used to limit the upper limit of the amount of curtailed photovoltaic power. (12) (13) (14) (15) (16) (17) (18) in, for t ESS's SOC at all times; The self-discharge rate of the ESS; To improve energy storage charging efficiency; For energy storage discharge efficiency; and They are respectively t The charging and discharging power of the ESS at any given time; For ESS battery capacity; This is the upper limit of SOC for ESS; This is the lower limit of SOC for ESS; The initial SOC of the day; SOC at the end of the day; and To control the 0-1 variables of ESS charging and discharging, This indicates that ESS is charging. This indicates that the ESS is discharging; The upper limit of the power purchase capacity for charging stations; This is a non-negative time variable that adjusts the upper limit of the power purchase capacity of charging stations due to changes in the operating conditions or carrying capacity of the distribution network. A value of 0 indicates that the carrying capacity is at a normal level. This refers to the upper limit of the electricity sales capacity of charging stations; It is a variable between 0 and 1. When it is 1, it means that the charging station is in the state of purchasing electricity, and when it is 0, it means that the charging station is in the state of selling electricity. For a sufficiently large constant; for t Real-time photovoltaic output; for t Real-time photovoltaic curtailment power; for t Predict output in real time; This represents the maximum amount of light discarded. The total charging power of all electric vehicles; This represents the sum of the discharge power of all electric vehicles. for t The power purchased from the power grid at any given time; This refers to the discharge power of the ESS. for t ESS charging power at all times; The power sold to the power grid.

7. The centralized charging station rolling optimization scheduling method considering distribution network carrying capacity according to claim 6, characterized in that, In step three, the objective function of the photovoltaic-storage-charging multi-source coupling optimization scheduling model is to maximize the net operating revenue of the charging station. The net operating revenue is defined as the sum of electric vehicle charging service revenue and electricity sales revenue to the grid minus the electricity purchase cost, and minus the penalty costs caused by photovoltaic power generation abandonment, electric vehicle queuing, electric vehicle V2G compensation and load reduction. (19) (20) in, for t The unit price of electricity sold to the grid at any given time; Revenue from selling electricity to the grid; The service revenue generated by charging stations for providing services to users. for Time of the first j The service price per unit of charging pile; for The unit price of electricity purchased from the grid at all times. Expenses for purchasing electricity from the power grid; Punishment for abandoning light within the unit; Waiting in line for electric cars to receive a penalty The penalty coefficient for waiting in line; The cost of V2G compensation for charging stations to users. for t Time of the first j The V2G electricity unit price corresponding to each charging pile; To reduce vehicle load and compensate for costs, To reduce the unit price of load, For vehicles e Load shedding, i.e., vehicle load shedding e The difference between the expected energy and the actual battery energy when leaving the charging station.

8. The centralized charging station rolling optimization scheduling method considering distribution network carrying capacity according to claim 7, characterized in that, In step three, a variable time-scale rolling optimization method is used to solve the problem within each rolling window to obtain the optimal operating strategy within that window. This includes: within each rolling optimization window, the time axis is divided into continuous refined sub-time periods and coarse sub-time periods. The refined sub-time periods cover two consecutive hours from the current moment, using a 15-minute time granularity to finely model electric vehicles, explicitly depicting the charging status, charging and discharging power, and matching relationship with dual-gun charging piles for each vehicle within the window. The coarse sub-time periods cover the remaining time period within the rolling window, using a 1-hour time granularity to retain the charging demand, maximum allowable discharge capacity, and distribution network carrying capacity constraints for each electric vehicle. The hourly aggregated energy demand replaces the refined time series power variables, thereby reducing the number of decision variables and constraints and improving the speed of rolling optimization solution.

9. The centralized charging station rolling optimization scheduling method considering distribution network carrying capacity according to claim 1, characterized in that, The optimal operating strategy obtained from the solution is distributed from the platform level to the station control level. The station control level allocates charging guns and parking spaces to each electric vehicle according to the strategy, and generates specific instruction sequences for the charging gun on / off status and charging / discharging power settings. These include: allowing electric vehicles to feed energy back to the grid through their on-board batteries when needed by the grid or charging station, and limiting the maximum cumulative discharge of each electric vehicle during a single stay to ensure the basic driving needs of the vehicle and the battery life; at the same time, setting charging / discharging mode switching constraints and state of charge limits for the energy storage system in the station to avoid the energy storage battery being in charging and discharging conditions at the same time, and maintaining the real-time state of charge of the energy storage battery within a preset range.

10. A rolling optimization scheduling method for centralized charging stations considering the carrying capacity of the distribution network, characterized in that, include: Basic data acquisition unit: Acquires basic data required for charging station scheduling, including distribution network carrying capacity boundary parameters, dynamic electricity price information, photovoltaic power generation forecast output, energy storage configuration, and electric vehicle charging demand and behavior parameters; Vehicle-gun-pile integrated scheduling model establishment unit: Based on the basic data required for charging station scheduling, electric vehicles and dual-gun charging piles are modeled at the platform level, and the vehicle "plug-in / plug-out" status, single vehicle single gun occupation and multi-gun power convergence constraints of the same charging pile are established to form a vehicle-gun-pile integrated scheduling model. Optimal Operation Strategy Solution Unit: Based on the integrated vehicle-gun-pile scheduling model, a distribution network carrying capacity boundary constraint is introduced. Combining the power exchange relationship between photovoltaic power generation, energy storage devices, and the power grid, with the goal of maximizing the daily net operating revenue of charging stations, a multi-source coupled optimization scheduling model of photovoltaic, energy storage, and charging is established, taking into full account the differentiated charging needs of electric vehicle owners. The optimal operation strategy within each rolling window is obtained by using a variable time scale rolling optimization method. Real-time adjustment and control unit: The optimal operating strategy obtained by solving is distributed from the platform level to the station control level. The station control level allocates charging guns and parking spaces to each electric vehicle according to the strategy, generates specific instruction sequences for the charging gun on / off status and charging / discharging power setpoints, and performs real-time adjustment and control of the power interaction between photovoltaic, energy storage, electric vehicles and the power grid in the charging station, thereby realizing multi-energy coordinated control and optimized operation in the charging station.

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