Multi-stage cooperation control method and device for optical storage and charging comprehensive supply station

Abstract

The invention discloses a control method and device for multi-level cooperation of an optical storage and charging comprehensive supply station, and relates to the technical field of optical storage and charging station control, and the method comprises the steps: obtaining optical storage and charging information, constructing a three-level control structure of an equipment level, a station level and a distribution network level according to the optical storage and charging information, and carrying out the control of the equipment level, the station level and the distribution network level based on the equipment level control architecture. And obtaining a charging strategy according to the charging vehicle data information and the charging pile data information. According to the invention, through constructing an equipment-level, station-level and distribution-network-level three-layer cooperative control architecture, fine management of photovoltaic power generation, an energy storage system, a charging pile and V2G equipment is realized, a constant-current, constant-voltage and trickle-current staged control charging strategy is introduced, and charging parameters are accurately adjusted in combination with a battery health degree, an SOC target value and an environment temperature correction coefficient, so that the charging efficiency is improved. And a group charging and group control mechanism based on the priority and the weight coefficient is designed, the charging power of all vehicles in the comprehensive supply station is adjusted, and the resource utilization rate is improved while the charging speed and the service life of the battery are considered.

Description

Technical Field

[0001] This invention relates to the field of control technology for photovoltaic-storage-charging-load stations, specifically to a control method and device for multi-level coordination of integrated photovoltaic-storage-charging-load stations. Background Technology

[0002] Integrated photovoltaic-storage-charging-load replenishment stations, by combining photovoltaic power generation and energy storage technologies, significantly increase the proportion of clean energy application in the transportation sector, reduce fossil fuel consumption and carbon emissions, and are an important path to solve the charging problem of new energy vehicles and optimize the efficiency of the energy system. They can alleviate the peak and valley pressure of the power grid, ensure charging stability, and realize local energy production and consumption. With the popularization of electric vehicles and the widespread application of renewable energy, integrated photovoltaic-storage-charging-load replenishment stations have become an important part of the energy system.

[0003] However, existing technologies have significant shortcomings in the following aspects: traditional charging stations usually adopt single or local control strategies, which make it difficult to coordinate the operation of photovoltaic power generation, energy storage systems and other equipment, resulting in energy waste or increased grid pressure; existing charging control is mostly based on fixed parameters (such as constant current / voltage) and does not consider dynamic factors such as battery state of health (SOH) and ambient temperature, which can easily lead to low charging efficiency; the priority allocation of charging vehicles lacks a scientific quantitative mechanism, which may lead to uneven resource allocation. Summary of the Invention

[0004] To address the aforementioned technical issues, this paper presents a multi-level collaborative control method for integrated photovoltaic-storage-charging-load replenishment stations. This technical solution solves the problems mentioned in the background section, such as the energy waste or increased grid pressure caused by the traditional replenishment station's single or local control strategy, the battery aging or low charging efficiency caused by existing charging control, and the lack of a scientific quantitative mechanism for prioritizing charging vehicles, which may lead to uneven resource allocation or a decline in user experience.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A control method for multi-level coordination of integrated photovoltaic-storage-charging-replenishment stations includes: Acquire photovoltaic and energy storage charging and load information, which includes photovoltaic data, energy storage system data, charging pile data, V2G equipment operation data, charging vehicle data, and feeder power flow information; Based on the photovoltaic and energy storage charging information, a three-tier control structure is constructed at the equipment level, station level, and distribution network level. Based on the device-level control architecture, a charging strategy is obtained according to the charging vehicle data information and the charging pile data information. The charging strategy is used to achieve precise control of each charging pile individually. Based on the station-level control architecture, the integrated photovoltaic-storage-charging-load replenishment station group control strategy is obtained according to photovoltaic data information, energy storage system data information, charging pile data information, V2G equipment operation data information and charging vehicle data information. Based on the feeder power flow information and group control and charging strategy of the stations included in the station level, a distribution voltage over-limit prediction and prevention mechanism is realized based on the distribution network level. The feeder power flow information includes the voltage, current and power distribution of each station, and the station is the charging pile of the integrated supply station. Based on the equipment level, station level, and distribution network level, and using the energy storage efficiency model, multi-level coordinated control of the photovoltaic-storage-charging-load integrated supply station is achieved. Preferably, the charging strategy obtained based on the device-level control architecture and the charging vehicle data information and charging pile data information specifically includes: Based on the charging vehicle data information, the maximum charging current, maximum charging voltage, battery internal resistance, battery SOH, initial voltage and SOC target value of the charging vehicle are obtained. The maximum charging current is the maximum charging current of the charging vehicle battery, the maximum charging voltage is the maximum charging voltage of the charging vehicle battery, and the initial voltage is the rated voltage corresponding to the minimum charging power of the charging vehicle. Obtain historical charging vehicle data and, based on this data, obtain the temperature correction coefficient. Based on the SOC target value and temperature correction factor, obtain the charging current and charging voltage; The temperature correction factor is specifically as follows: ; In the formula, This is a temperature correction factor. Ambient temperature; Specifically, the adjusted charging current is: ; ; In the formula, This is the charging current. The maximum charging current for the vehicle being charged. For battery health, To charge the vehicle's battery level. The target value for SOC is... It is a minimum value function. for Less than The charging current at that time; Specifically, the adjusted charging voltage is: ; In the formula, This is the charging voltage. The initial voltage, The maximum charging current for the vehicle being charged. The maximum charging voltage for the vehicle being charged. The internal resistance of the vehicle battery during charging. For battery health, It is a minimum value function. The battery level of the vehicle being charged.

[0006] Preferably, the station-level control architecture, based on photovoltaic data, energy storage system data, charging pile data, V2G equipment operation data, and charging vehicle data, obtains a comprehensive photovoltaic-energy storage-charging-load replenishment station group control strategy, specifically including: Based on the charging vehicle data information, a first priority is obtained, which includes high priority, medium priority and low priority; Based on the first priority and charging pile data information, obtain the group charging and group control strategy of the integrated photovoltaic-storage-charging-load replenishment station group; Based on photovoltaic data, V2G equipment operation data, and energy storage system data, optimize the group charging and control strategy.

[0007] Preferably, the step of obtaining the integrated photovoltaic-storage-charging-load replenishment station group control strategy based on the first priority and charging pile data information specifically includes: Based on the charging pile data and the total charging power of the integrated replenishment station, determine whether it is necessary to adjust the charging strategy of the integrated replenishment station. If the total charging power of the integrated supply station is less than the total power required by all charging vehicles, then the number of charging vehicles in each priority level in the first priority level is obtained based on the charging vehicle data information. Based on the charging pile data, obtain the total charging power allocated for high priority, medium priority, and low priority. The charging power of all vehicles charging at the integrated replenishment station is adjusted based on the charging power of high-priority, medium-priority, and low-priority vehicles. The total allocated charging power is specifically as follows: ; In the formula, Choose 1, 2, 3, Total charging power allocated to high-priority charging vehicles Total charging power allocated to medium-priority charging vehicles Total charging power allocated to low-priority charging vehicles. Choose 1, 2, 3, Let $\mathbf{j}$ represent the sum of the weights of vehicles charging at the $j$ priority level, where $\mathbf{j}$. The sum of the weights of all high-priority charging vehicles. The sum of the weights for vehicles charging with medium priority. The sum of the weights for charging low-priority vehicles. This refers to the total charging power of the integrated refueling station.

[0008] Preferably, adjusting the charging power of all charging vehicles in the integrated refueling station based on high-priority, medium-priority, and low-priority charging power specifically includes: The power supply sequence for the integrated supply station is set according to the time of day when electricity is used. Based on the charging vehicle data, obtain the battery SOC coefficient, battery SOH coefficient, battery temperature coefficient, and minimum charging power of the charging vehicle; The weighting coefficients of the charging vehicle are obtained based on the battery SOC coefficient, battery SOH coefficient, and battery temperature coefficient. Based on the weighting coefficients, obtain the power allocation weights for charging vehicles; The charging power of the charging vehicles is adjusted according to the total charging power allocated based on the corresponding priority, the power allocation weight, and the minimum charging power. Based on the adjusted charging power, adjust the charging power of all charging vehicles in the integrated supply station; The specific weighting coefficient for the charging vehicle is as follows: ; In the formula, Choose 1, 2, 3, The first in the high priority The weighting coefficient of each charging vehicle. The first in the medium priority The weighting coefficient of each charging vehicle. The first in the low priority The weighting coefficient of each charging vehicle. For the corresponding priority level The remaining battery capacity coefficient of a charging vehicle For the corresponding priority level The battery health coefficient of a charging vehicle. For the corresponding priority level Battery temperature coefficient of a charging vehicle; Specifically, the power allocation weight of the charging vehicle is as follows: ; In the formula, Choose 1, 2, 3, For the corresponding priority level Power allocation weights for each charging vehicle For the corresponding priority The weighting coefficient of each charging vehicle. The total number of vehicles charging according to the corresponding priority; Specifically, adjusting the charging power of the charging vehicle involves: ; In the formula, Choose 1, 2, 3, For the corresponding priority level The adjusted charging power for each charging vehicle For the corresponding priority level Minimum charging power for a single charging vehicle For the corresponding priority level Power allocation weights for each charging vehicle The total charging power allocated to the corresponding priority level. This represents the total number of charging vehicles in the corresponding priority category.

[0009] Preferably, the optimization of the group charging and control strategy based on photovoltaic data, V2G equipment operation data, and energy storage system data specifically includes: Based on the data information of the energy storage system, the charging and discharging efficiency loss coefficient of the energy storage system is obtained using a deep network model; The energy storage system's charge and discharge efficiency loss coefficient is used as a constraint to obtain the electricity purchased from the grid; Based on the electricity purchased from the grid, obtain the photovoltaic power generation, V2G equipment discharge power and energy storage system discharge power; Adjust the discharge power of the corresponding equipment in the integrated refueling station according to the photovoltaic power generation, V2G equipment discharge power and energy storage system discharge power. Specifically, the electricity purchased from the power grid includes: ; In the formula, Purchase electricity for the power grid It is a minimum value function. For the total load of the integrated supply station, Photovoltaic power generation capacity, For V2G equipment discharge power, This refers to the discharge power of the energy storage system. The charging and discharging efficiency loss coefficient of the energy storage system.

[0010] Furthermore, a control device for multi-level coordination of integrated photovoltaic-storage-charging-load replenishment stations is proposed to achieve the aforementioned multi-level coordination control for integrated photovoltaic-storage-charging-load replenishment stations, including: The main control module is used to acquire photovoltaic and energy storage charging and load information, construct a three-level control structure at the equipment level, station level, and distribution network level based on the photovoltaic and energy storage charging and load information, acquire the maximum charging current, maximum charging voltage, battery internal resistance, battery SOH, initial voltage, and SOC target value of the charging vehicle based on the charging vehicle data information, acquire the charging current and charging voltage based on the SOC target value, acquire the current charging temperature, adjust the charging current and charging voltage according to the temperature based on the deep learning network model, charge the charging vehicle based on the adjusted charging current and charging voltage, acquire the first priority based on the charging vehicle data information, acquire the group charging and group control strategy of the integrated photovoltaic and energy storage charging and load replenishment station based on the first priority and the charging pile data information, and optimize the group charging and group control strategy based on photovoltaic data information, V2G equipment operation data information, and energy storage system data information. The strategy adjustment module is used to determine whether the charging strategy of the integrated supply station needs to be adjusted based on the charging pile data information and the total charging power of the integrated supply station. If the total charging power of the integrated supply station is less than the total power required by all charging vehicles, the module obtains the number of charging vehicles of each priority in the first priority according to the charging vehicle data information, obtains the total charging power allocated to high priority, medium priority and low priority according to the charging pile data information, and adjusts the charging power of all charging vehicles in the integrated supply station according to the charging power of high priority, medium priority and low priority. The optimization module is used to obtain the charging and discharging efficiency loss coefficient of the energy storage system based on the data information of the energy storage system and a deep network model. The energy storage system charging and discharging efficiency loss coefficient is used as a constraint to obtain the electricity purchased from the grid. Based on the electricity purchased from the grid, the photovoltaic power generation, V2G equipment discharge power and energy storage system discharge power are obtained to optimize the group charging and group control strategy.

[0011] Optionally, the main control module specifically includes: The initial charging unit is used to acquire photovoltaic and energy storage charging and load information, construct a three-level control structure of equipment level, station level and distribution network level based on the photovoltaic and energy storage charging and load information, acquire the maximum charging current, maximum charging voltage, battery internal resistance, battery SOH, initial voltage and SOC target value of the charging vehicle based on the charging vehicle data information, acquire the charging current and charging voltage based on the SOC target value, acquire the current charging temperature, adjust the charging current and charging voltage according to the temperature based on the deep learning network model, and charge the charging vehicle according to the adjusted charging current and charging voltage. The group charging and control unit is used to obtain a first priority based on the charging vehicle data information, obtain a group charging and control strategy for the integrated photovoltaic-storage-charging-load replenishment station based on the first priority and the charging pile data information, and optimize the group charging and control strategy based on the photovoltaic data information, V2G equipment operation data information and energy storage system data information.

[0012] Optionally, the strategy adjustment module specifically includes: The judgment unit is used to determine whether the charging strategy of the integrated supply station needs to be adjusted based on the charging pile data information and the total charging power of the integrated supply station. An adjustment unit is configured to, if the total charging power of the integrated supply station is less than the total power required by all charging vehicles, obtain the number of charging vehicles of each priority in the first priority according to the charging vehicle data information, obtain the total charging power allocated to high priority, medium priority and low priority according to the charging pile data information, and adjust the charging power of all charging vehicles in the integrated supply station according to the charging power of high priority, medium priority and low priority.

[0013] Optionally, the optimization module specifically includes: A deep network unit is used to obtain the charge and discharge efficiency loss coefficient of the energy storage system based on the data information of the energy storage system and a deep network model. The optimization unit is used to obtain the grid-purchased electricity by taking the energy storage system's charging and discharging efficiency loss coefficient as a constraint, and then obtain the photovoltaic power generation, V2G equipment discharge power and energy storage system discharge power based on the grid-purchased electricity to optimize the group charging and control strategy.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes a multi-level collaborative control method and device for integrated photovoltaic-storage-charging-load replenishment stations. By constructing a three-layer control architecture at the equipment level, station level, and distribution network level, it achieves refined management of photovoltaic power generation, energy storage systems, charging piles, and V2G equipment. At the equipment level, the charging process is divided into three stages: constant current, constant voltage, and trickle charging, and the charging voltage and current for each stage are generated. Weighting coefficients are generated by combining battery SOH, battery SOC, and the battery temperature of the charging vehicle. At the station level, the group charging and control strategy is optimized by combining priority allocation and weighting coefficients to improve charging efficiency and protect the battery of the charging vehicle. At the same time, energy storage efficiency modeling and multi-energy collaborative optimization are introduced to further improve energy utilization and grid stability. Attached Figure Description

[0015] Figure 1 This is a flowchart of a multi-level coordinated control method for an integrated photovoltaic-storage-charging-replenishment station proposed in this invention. Figure 2This is a flowchart illustrating the adjustment of charging power for all charging vehicles at a comprehensive refueling station in this invention. Figure 3 This is a flowchart illustrating the optimized group charging and control strategy in this invention. Figure 4 This is a structural block diagram of a control device for multi-level coordination of a photovoltaic-storage-charging integrated replenishment station proposed in this invention. Detailed Implementation

[0016] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0017] Reference Figure 1 - Figure 3 As shown in the figure, a control method for multi-level coordination of integrated photovoltaic-storage-charging-replenishment stations in an embodiment of the present invention includes: Acquire photovoltaic and energy storage charging and load information, which includes photovoltaic data, energy storage system data, charging pile data, V2G equipment operation data, charging vehicle data, and feeder power flow information; Based on the photovoltaic and energy storage charging information, a three-tier control structure is constructed at the equipment level, station level, and distribution network level. Based on the device-level control architecture, a charging strategy is obtained according to the charging vehicle data information and the charging pile data information. The charging strategy is used to achieve precise control of each charging pile individually. Based on the station-level control architecture, the integrated photovoltaic-storage-charging-load replenishment station group control strategy is obtained according to photovoltaic data information, energy storage system data information, charging pile data information, V2G equipment operation data information and charging vehicle data information. Based on the feeder power flow information and group control and charging strategy of the stations included in the station level, a distribution voltage over-limit prediction and prevention mechanism is realized based on the distribution network level. The feeder power flow information includes the voltage, current and power distribution of each station, and the station is the charging pile of the integrated supply station. Based on the equipment level, station level, and distribution network level, and using the energy storage efficiency model, multi-level coordinated control of the photovoltaic-storage-charging-load integrated supply station is achieved. Specifically, based on photovoltaic, energy storage, charging and load information, a three-tiered control structure is constructed at the equipment level, station level, and distribution network level. The equipment level obtains charging strategies through charging vehicle data and charging pile data. The station level obtains group charging and group control strategies for integrated photovoltaic, energy storage, charging and load replenishment stations through photovoltaic data, energy storage system data, charging pile data, V2G equipment operation data, and charging vehicle data. The distribution network level implements a voltage over-limit prediction and prevention mechanism based on the feeder power flow information and group control and group charging strategies of the stations included in the station level. The three levels of equipment, station, and distribution network work together to achieve multi-level collaborative control of integrated photovoltaic, energy storage, charging and load replenishment stations. Specifically, based on a device-level control architecture, charging strategies are obtained according to charging vehicle data and charging pile data, including: Based on the charging vehicle data information, the maximum charging current, maximum charging voltage, battery internal resistance, battery SOH, initial voltage and SOC target value of the charging vehicle are obtained. The maximum charging current is the maximum charging current of the charging vehicle battery, the maximum charging voltage is the maximum charging voltage of the charging vehicle battery, and the initial voltage is the rated voltage corresponding to the minimum charging power of the charging vehicle. Obtain historical charging vehicle data and, based on this data, obtain the temperature correction coefficient. Based on the SOC target value and temperature correction factor, obtain the charging current and charging voltage; The temperature correction factor is specifically as follows: ; In the formula, This is a temperature correction factor. Ambient temperature; Specifically, the adjusted charging current is: ; ; In the formula, This is the charging current. The maximum charging current for the vehicle being charged. For battery health, To charge the vehicle's battery level. The target value for SOC is... It is a minimum value function. for Less than The charging current at that time; Specifically, the adjusted charging voltage is: ; In the formula, This is the charging voltage. The initial voltage, The maximum charging current for the vehicle being charged. The maximum charging voltage for the vehicle being charged. The internal resistance of the vehicle battery during charging. For battery health, It is a minimum value function. The battery level of the vehicle being charged; In this solution, the vehicle is charged in stages based on the target SOC value. Traditional solutions include two stages: constant current charging and constant voltage charging, balancing charging speed and safety. However, in practice, the charging current gradually decreases to a low level in the later stages of the constant voltage stage. If charging is stopped directly at this point, the battery may not be fully charged due to insufficient current (a phenomenon known as "phantom charge"). Continuing to charge results in extremely low efficiency and may still cause potential damage to the battery due to prolonged high voltage. This solution adds trickle charging to replenish the remaining battery capacity with a very small current, avoiding the "phantom charge" that may exist after the constant voltage stage ends. It also completely prevents overcharging, reducing the risk of battery overheating and leakage, while balancing efficiency and safety.

[0018] Specifically, based on the station-level control architecture, and according to photovoltaic data, energy storage system data, charging pile data, V2G equipment operation data, and charging vehicle data, a comprehensive photovoltaic-energy storage-charging-load replenishment station group control strategy is obtained, including: Based on the charging vehicle data information, a first priority is obtained, which includes high priority, medium priority and low priority; Based on the first priority and charging pile data information, obtain the group charging and group control strategy of the integrated photovoltaic-storage-charging-load replenishment station group; Based on photovoltaic data, V2G equipment operation data, and energy storage system data, optimize the group charging and control strategy; Specifically, based on the first priority and charging pile data, a group control strategy for the integrated photovoltaic-storage-charging-load replenishment station cluster is obtained, including: Based on the charging pile data and the total charging power of the integrated replenishment station, determine whether it is necessary to adjust the charging strategy of the integrated replenishment station. If the total charging power of the integrated supply station is less than the total power required by all charging vehicles, then the number of charging vehicles in each priority level in the first priority level is obtained based on the charging vehicle data information. Based on the charging pile data, obtain the total charging power allocated for high priority, medium priority, and low priority. The charging power of all vehicles charging at the integrated replenishment station is adjusted based on the charging power of high-priority, medium-priority, and low-priority vehicles. The total allocated charging power is specifically as follows: ; In the formula, Choose 1, 2, 3, Total charging power allocated to high-priority charging vehicles Total charging power allocated to medium-priority charging vehicles Total charging power allocated to low-priority charging vehicles. Choose 1, 2, 3, Let $\mathbf{j}$ represent the sum of the weights of vehicles charging at the $j$ priority level, where $\mathbf{j}$. The sum of the weights of all high-priority charging vehicles. The sum of the weights for vehicles charging with medium priority. The sum of the weights for charging low-priority vehicles. The total charging power of the integrated replenishment station; Specifically, the charging power of all vehicles charging at the integrated replenishment station is adjusted based on high-priority, medium-priority, and low-priority charging power, including: The power supply sequence for the integrated supply station is set according to the time of day when electricity is used. Based on the charging vehicle data, obtain the battery SOC coefficient, battery SOH coefficient, battery temperature coefficient, and minimum charging power of the charging vehicle; The weighting coefficients of the charging vehicle are obtained based on the battery SOC coefficient, battery SOH coefficient, and battery temperature coefficient. Based on the weighting coefficients, obtain the power allocation weights for charging vehicles; The charging power of the charging vehicles is adjusted according to the total charging power allocated based on the corresponding priority, the power allocation weight, and the minimum charging power. Based on the adjusted charging power, adjust the charging power of all charging vehicles in the integrated supply station; The specific weighting coefficient for the charging vehicle is as follows: ; In the formula, Choose 1, 2, 3, The first in the high priority The weighting coefficient of each charging vehicle. The first in the medium priority The weighting coefficient of each charging vehicle. The first in the low priority The weighting coefficient of each charging vehicle. For the corresponding priority level The remaining battery capacity coefficient of a charging vehicle For the corresponding priority level The battery health coefficient of a charging vehicle. For the corresponding priority level Battery temperature coefficient of a charging vehicle; Specifically, the power allocation weight of the charging vehicle is as follows: ; In the formula, Choose 1, 2, 3, For the corresponding priority level Power allocation weights for each charging vehicle For the corresponding priority The weighting coefficient of each charging vehicle. The total number of vehicles charging according to the corresponding priority; Specifically, adjusting the charging power of the charging vehicle involves: ; In the formula, Choose 1, 2, 3, For the corresponding priority level The adjusted charging power for each charging vehicle For the corresponding priority level Minimum charging power for a single charging vehicle For the corresponding priority level Power allocation weights for each charging vehicle The total charging power allocated to the corresponding priority level. This represents the total number of charging vehicles in the corresponding priority category. For example: The load power of the integrated photovoltaic-storage-charging-load replenishment station is 460kW. The charging vehicle information for the integrated replenishment station is as follows:

[0019] ; In the formula, Choose 1, 2, 3, The first in the high priority The SOC coefficient of a charging vehicle's battery. The first in the medium priority The SOC coefficient of a charging vehicle's battery. The first in the medium priority The SOC coefficient of a charging vehicle's battery. State of charge (SOC) of the battery in a charging vehicle; ; In the formula, Choose 1, 2, 3, For the corresponding priority level The SOH coefficient of the battery of a charging vehicle Battery health of the vehicle being charged; ; In the formula, Choose 1, 2, 3, For the corresponding priority level The battery temperature coefficient of a charging vehicle. For the temperature of the vehicle battery during charging, if Not here During this range, charging will be terminated; The sum of the weights for high priority, medium priority, and low priority is: 8*2 + 2*2 + 1*3 = 23; The total charging power allocated to high-priority charging vehicles is: 460*16 / 23=320kW; The weight coefficient of V1 in the high priority category is: 5 + 2 + 3 = 10, where, , , ; The weight coefficient for V2 in the high priority category is: 1 + 2 + 1 = 5, where, , , ; The charging power allocated to V1 in the high priority category is: 50 + (320 - 100) * 10 / 15 = 197 kW; The charging power allocated to V2 in the high priority category is: 50 + (320 - 100) * 5 / 15 = 123kW; In this embodiment, if the charging power allocated to V1 is 210kW and the charging power allocated to V2 is 110kW according to the weighting coefficient, since the maximum charging power of V1 is 200kW, the final charging power allocated to V1 is 200kW. The extra 10kW allocated to it will be redistributed according to the weighting coefficient of the remaining vehicles in the high priority category. The above operation is repeated for medium-priority and low-priority allocations; In this scheme, power is allocated to vehicles of the same priority based on weighting coefficients. Existing methods allocate the total load power of the integrated photovoltaic-storage-charging-load replenishment station according to high, medium, and low priorities, and then distribute the allocated power evenly within each priority level. However, in practice, this method ignores vehicle priorities and differentiated needs. For example, vehicles with lower SOC require rapid charging, while vehicles with higher SOC do not have a high urgency for rapid charging. Even distribution cannot dynamically adjust the power, resulting in low charging efficiency for some vehicles. By calculating weighting coefficients based on the battery SOC, SOH, and battery temperature data of the charging vehicles, and using the minimum charging power as a constraint, power is allocated to vehicles of the same priority, optimizing the power allocation for vehicles of different priorities and achieving efficient resource utilization. In addition, allocating charging power according to weighting coefficients can also improve the overall operating efficiency of the integrated replenishment station, reduce grid load fluctuations, and enhance the user experience.

[0020] Specifically, based on photovoltaic data, V2G equipment operation data, and energy storage system data, the group charging and control strategy is optimized, including: Based on the data information of the energy storage system, the charging and discharging efficiency loss coefficient of the energy storage system is obtained using a deep network model; The energy storage system's charge and discharge efficiency loss coefficient is used as a constraint to obtain the electricity purchased from the grid; Based on the electricity purchased from the grid, obtain the photovoltaic power generation, V2G equipment discharge power and energy storage system discharge power; Adjust the discharge power of the corresponding equipment in the integrated refueling station according to the photovoltaic power generation, V2G equipment discharge power and energy storage system discharge power. Specifically, the electricity purchased from the power grid includes: ; In the formula, Purchase electricity for the power grid It is a minimum value function. For the total load of the integrated supply station, Photovoltaic power generation capacity, For V2G equipment discharge power, This refers to the discharge power of the energy storage system. The charging and discharging efficiency loss coefficient of the energy storage system; In this scheme, the charging and discharging efficiency loss coefficient of the energy storage system is used as a constraint to optimize the acquisition of the minimum grid power purchase. This can more accurately reflect the energy loss in the actual operation of energy storage and avoid overly idealistic scheduling plans due to ignoring efficiency losses. By dynamically predicting this coefficient through a deep network model and incorporating it into the optimization process, the level of energy management can be improved. This ensures that, under the premise of meeting load demand, photovoltaic, V2G and energy storage resources are used efficiently and prioritized, maximizing the local consumption of clean energy, reducing unnecessary grid dependence, thereby reducing electricity costs and carbon emissions, and enhancing the economic efficiency and operational reliability of the integrated replenishment station.

[0021] Reference Figure 4 As shown, further, combining the above-mentioned control method for multi-level coordination of integrated photovoltaic-storage-charging-load replenishment stations, a control device for multi-level coordination of integrated photovoltaic-storage-charging-load replenishment stations is proposed, comprising: The main control module is used to acquire photovoltaic and energy storage charging and load information, construct a three-level control structure at the equipment level, station level, and distribution network level based on the photovoltaic and energy storage charging and load information, acquire the maximum charging current, maximum charging voltage, battery internal resistance, battery SOH, initial voltage, and SOC target value of the charging vehicle based on the charging vehicle data information, acquire the charging current and charging voltage based on the SOC target value, acquire the current charging temperature, adjust the charging current and charging voltage according to the temperature based on the deep learning network model, charge the charging vehicle based on the adjusted charging current and charging voltage, acquire the first priority based on the charging vehicle data information, acquire the group charging and group control strategy of the integrated photovoltaic and energy storage charging and load replenishment station based on the first priority and the charging pile data information, and optimize the group charging and group control strategy based on photovoltaic data information, V2G equipment operation data information, and energy storage system data information. The strategy adjustment module is used to determine whether the charging strategy of the integrated supply station needs to be adjusted based on the charging pile data information and the total charging power of the integrated supply station. If the total charging power of the integrated supply station is less than the total power required by all charging vehicles, the module obtains the number of charging vehicles of each priority in the first priority according to the charging vehicle data information, obtains the total charging power allocated to high priority, medium priority and low priority according to the charging pile data information, and adjusts the charging power of all charging vehicles in the integrated supply station according to the charging power of high priority, medium priority and low priority. The optimization module is used to obtain the charging and discharging efficiency loss coefficient of the energy storage system based on the data information of the energy storage system and a deep network model. The energy storage system charging and discharging efficiency loss coefficient is used as a constraint to obtain the electricity purchased from the grid. Based on the electricity purchased from the grid, the photovoltaic power generation, V2G equipment discharge power and energy storage system discharge power are obtained to optimize the group charging and group control strategy.

[0022] The main control module specifically includes: The initial charging unit is used to acquire photovoltaic and energy storage charging and load information, construct a three-level control structure of equipment level, station level and distribution network level based on the photovoltaic and energy storage charging and load information, acquire the maximum charging current, maximum charging voltage, battery internal resistance, battery SOH, initial voltage and SOC target value of the charging vehicle based on the charging vehicle data information, acquire the charging current and charging voltage based on the SOC target value, acquire the current charging temperature, adjust the charging current and charging voltage according to the temperature based on the deep learning network model, and charge the charging vehicle according to the adjusted charging current and charging voltage. The group charging and control unit is used to obtain a first priority based on charging vehicle data information, obtain a group charging and control strategy for the integrated photovoltaic-storage-charging-load replenishment station based on the first priority and charging pile data information, and optimize the group charging and control strategy based on photovoltaic data information, V2G equipment operation data information and energy storage system data information. The strategy adjustment module specifically includes: The judgment unit is used to determine whether the charging strategy of the integrated supply station needs to be adjusted based on the charging pile data information and the total charging power of the integrated supply station. An adjustment unit is configured to, if the total charging power of the integrated supply station is less than the total power required by all charging vehicles, obtain the number of charging vehicles of each priority in the first priority according to the charging vehicle data information, obtain the total charging power allocated to high priority, medium priority and low priority according to the charging pile data information, and adjust the charging power of all charging vehicles in the integrated supply station according to the charging power of high priority, medium priority and low priority. The optimization module specifically includes: A deep network unit is used to obtain the charge and discharge efficiency loss coefficient of the energy storage system based on the data information of the energy storage system and a deep network model. The optimization unit is used to obtain the grid-purchased electricity by taking the energy storage system's charging and discharging efficiency loss coefficient as a constraint, and then obtain the photovoltaic power generation, V2G equipment discharge power and energy storage system discharge power based on the grid-purchased electricity to optimize the group charging and control strategy.

[0023] In summary, the advantages of this invention are as follows: By constructing a three-layer control architecture at the equipment level, station level, and distribution network level, it achieves refined management of photovoltaic power generation, energy storage systems, charging piles, and V2G equipment. At the equipment level, the charging process is divided into three stages: constant current, constant voltage, and trickle charging, and the charging voltage and current for each stage are generated. Weighting coefficients are generated by combining the battery SOH, battery SOC, and the battery temperature of the charging vehicle. At the station level, the group charging and control strategy is optimized by combining priority allocation and weighting coefficients to improve charging efficiency and protect the battery of the charging vehicle. At the same time, the introduction of energy storage efficiency modeling and multi-energy collaborative optimization further improves energy utilization and grid stability.

[0024] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A control method for multi-stage coordination of a light storage charging and loading integrated supply station, characterized in that, The application comprises the following steps: acquiring light storage charging information, which includes photovoltaic data information, energy storage system data information, charging pile data information, V2G device operation data information, charging vehicle data information and feeder power flow information; constructing a three-level control architecture of device level, station level and distribution network level according to the light storage charging information; acquiring a charging strategy according to the charging vehicle data information and the charging pile data information based on the device level control architecture, which is used to realize individual and accurate control of each charging pile; acquiring a light storage charging comprehensive supply station group charging control strategy according to the photovoltaic data information, the energy storage system data information, the charging pile data information, the V2G device operation data information and the charging vehicle data information based on the station level control architecture; realizing distribution voltage line crossing prediction and prevention mechanism according to the feeder power flow information and the group charging control strategy, wherein the feeder power flow information includes voltage, current and power distribution of each station, and the station is a charging pile of the comprehensive supply station; realizing multi-level collaborative control of the light storage charging comprehensive supply station based on the energy storage efficiency model according to the three-level control architecture of device level, station level and distribution network level. 2.The multi-stage collaborative control method for a light-oriented storage and charging integrated supply station according to claim 1, wherein, The application further comprises the following steps: acquiring the maximum charging current, the maximum charging voltage, the battery internal resistance, the battery SOH, the initial voltage and the SOC target value of the charging vehicle according to the charging vehicle data information, wherein the maximum charging current is the maximum charging current of the battery of the charging vehicle, the maximum charging voltage is the maximum charging voltage of the battery of the charging vehicle, and the initial voltage is the rated voltage corresponding to the minimum charging power of the charging vehicle; acquiring the temperature correction coefficient according to the historical charging vehicle data information; acquiring the charging current and the charging voltage according to the SOC target value and the temperature correction coefficient; wherein the temperature correction coefficient is specifically: ； In the formula, is a temperature correction factor, is the ambient temperature; wherein the charging current is specifically: ； ； wherein is the charging current, is the maximum charging current of the charging vehicle, is the battery health, is the battery charge level of the charging vehicle, is the SOC target value, is the minimum function, is is less than the charging current when wherein the charging voltage is specifically: ； wherein, is the charging voltage, is the initial voltage, is the maximum charging current of the charging vehicle, is the maximum charging voltage of the charging vehicle, is the internal resistance of the battery of the charging vehicle, is the battery health, is the minimum function, is the battery level of the charging vehicle.

3. The multi-stage collaborative control method for the light-oriented storage and charging integrated supply station according to claim 1, characterized in that, The application further comprises the following steps: acquiring the first priority according to the charging vehicle data information, wherein the first priority includes high priority, medium priority and low priority; acquiring the light storage charging comprehensive supply station group charging control strategy according to the first priority and the charging pile data information; optimizing the group charging control strategy according to the photovoltaic data information, the V2G device operation data information and the energy storage system data information.

4. The multi-stage collaborative control method for the light-oriented storage and charging integrated supply station according to claim 3, characterized in that, The application further comprises the following steps: determining whether the charging strategy of the comprehensive supply station needs to be adjusted according to the charging pile data information and the total charging power of the comprehensive supply station; if the total charging power of the comprehensive supply station is less than the total power required by all charging vehicles, acquiring the number of charging vehicles in each priority in the first priority according to the charging vehicle data information; According to the charging pile data information, the total charging power allocated in high priority, medium priority and low priority is obtained; According to the charging power of high priority, medium priority and low priority, the charging power of all charging vehicles in the comprehensive supply station is adjusted; The total charging power allocated is specifically: ； wherein take 1, 2, 3, total charging power allocated to high-priority charging vehicles, total charging power allocated to medium-priority charging vehicles, total charging power allocated to low-priority charging vehicles, take 1, 2, 3, denotes the sum of the weights of the jth priority charging vehicles, wherein denotes the sum of the weights of all high-priority charging vehicles, denotes the sum of the weights of medium-priority charging vehicles, denotes the sum of the weights of low-priority charging vehicles, total charging power of the integrated refueling station.

5. The multi-stage collaborative control method for the light-oriented storage and charging integrated supply station according to claim 4, characterized in that, The adjustment of the charging power of all charging vehicles in the comprehensive supply station according to the charging power of high priority, medium priority and low priority specifically includes: According to the power consumption period, the power consumption supply sequence of the comprehensive supply station is set; According to the charging vehicle data information, the charging vehicle battery SOC coefficient, battery SOH coefficient, battery temperature coefficient and minimum charging power are obtained; According to the charging vehicle battery SOC coefficient, battery SOH coefficient and battery temperature coefficient, the weight coefficient of the charging vehicle is obtained; According to the weight coefficient, the power allocation weight of the charging vehicle is obtained; According to the total charging power allocated according to the corresponding priority, the power allocation weight and the minimum charging power, the charging power of the charging vehicle is adjusted; According to the adjusted charging power, the charging power of all charging vehicles in the comprehensive supply station is adjusted; The weight coefficient of the charging vehicle is specifically: ； In the formula, Take 1, 2, 3, is the weight coefficient of the first charging vehicle in high priority, is the weight coefficient of the first charging vehicle in medium priority, is the weight coefficient of the first charging vehicle in low priority, is the battery remaining power coefficient of the first charging vehicle in the corresponding priority, is the battery health coefficient of the first charging vehicle in the corresponding priority, is the battery temperature coefficient of the first charging vehicle in the corresponding priority; The power allocation weight of the charging vehicle is specifically: ； In the formula, Take 1, 2, 3, The power allocation weight of the corresponding priority in the charging vehicle, The weight coefficient of the corresponding priority in the charging vehicle, The total number of corresponding priority charging vehicles; The adjustment of the charging power of the charging vehicle is specifically: ； In the formula, Choose 1, 2, 3, For the corresponding priority level The adjusted charging power for each charging vehicle For the corresponding priority level Minimum charging power for a single charging vehicle For the corresponding priority level Power allocation weights for each charging vehicle The total charging power allocated to the corresponding priority level. This represents the total number of charging vehicles in the corresponding priority category.

6. The multi-stage collaborative control method for the light-oriented storage and charging integrated supply station according to claim 3, characterized in that, The optimization of the group charging and group control strategy according to the photovoltaic data information, V2G equipment operation data information and energy storage system data information specifically includes: According to the energy storage system data information, the energy storage system charging and discharging efficiency loss coefficient is obtained based on a deep network model; The energy storage system charging and discharging efficiency loss coefficient is taken as a constraint condition to obtain the power grid power purchase amount; According to the power grid power purchase amount, the photovoltaic power generation power, V2G equipment discharge power and energy storage system discharge power are obtained; According to the photovoltaic power generation power, V2G equipment discharge power and energy storage system discharge power, the discharge power of the corresponding equipment of the comprehensive supply station is adjusted; The power grid power purchase amount is specifically: ； wherein, is the grid electricity purchase amount, is the minimum value function, is the total load of the comprehensive supply station, is the photovoltaic power generation power, is the V2G device discharge power, is the energy storage system discharge power, is the energy storage system charge-discharge efficiency loss coefficient.

7. A control device for multi-stage coordination of a light storage charging and loading integrated supply station, for implementing the multi-stage coordination control method for a light storage charging and loading integrated supply station according to any one of claims 1-6, characterized in that, It includes: The main control module is used to obtain photovoltaic energy storage charging information, construct a three-level control structure of device level, station level and distribution network level according to the photovoltaic energy storage charging information, obtain the maximum charging current, maximum charging voltage, battery internal resistance, battery SOH, initial voltage and SOC target value of the charging vehicle according to the charging vehicle data information, obtain the charging current and charging voltage according to the SOC target value, obtain the current charging temperature, adjust the charging current and charging voltage based on the deep learning network model according to the temperature, charge the charging vehicle according to the adjusted charging current and charging voltage, obtain the first priority according to the charging vehicle data information, obtain the photovoltaic energy storage charging comprehensive supply station group charging and group control strategy according to the first priority and the charging pile data information, and optimize the group charging and group control strategy according to the photovoltaic data information, V2G equipment operation data information and energy storage system data information. The strategy adjustment module is configured to determine whether the charging strategy of the comprehensive supply station needs to be adjusted according to the charging pile data information and the total charging power of the comprehensive supply station; if the total charging power of the comprehensive supply station is less than the total power required by all the charging vehicles, the number of each priority charging vehicle in the first priority is obtained according to the charging vehicle data information, the total charging power allocated to the high priority, the medium priority and the low priority is obtained according to the charging pile data information, and the charging power of all the charging vehicles in the comprehensive supply station is adjusted according to the charging power of the high priority, the medium priority and the low priority. The optimization module is configured to obtain a storage system charging and discharging efficiency loss coefficient based on a deep network model according to storage system data information, take the storage system charging and discharging efficiency loss coefficient as a constraint condition to obtain a power grid power purchase amount, and obtain photovoltaic power generation power, V2G equipment discharging power and storage system discharging power according to the power grid power purchase amount to optimize the group charging and group control strategy.

8. The multi-stage collaborative control device for a light-oriented storage and charging integrated supply station according to claim 7, characterized in that, The main control module specifically comprises: An initial charging unit is configured to obtain photovoltaic storage charging information, construct a three-level control structure of a device level, a station level and a distribution network level according to the photovoltaic storage charging information, obtain a maximum charging current, a maximum charging voltage, a battery internal resistance, a battery SOH, an initial voltage and an SOC target value of a charging vehicle according to charging vehicle data information, obtain a charging current and a charging voltage according to the SOC target value, obtain a current charging temperature, adjust the charging current and the charging voltage based on a deep learning network model according to the temperature, and charge the charging vehicle according to the adjusted charging current and the charging voltage; A group charging and group control unit is configured to obtain a first priority according to charging vehicle data information, obtain a photovoltaic storage charging comprehensive supply station group charging and group control strategy according to the first priority and charging pile data information, and optimize the group charging and group control strategy according to photovoltaic data information, V2G equipment operation data information and storage system data information.

9. The multi-stage collaborative control device for a light-oriented storage and charging integrated supply station according to claim 7, characterized in that, The strategy adjustment module specifically comprises: A judgment unit is configured to determine whether the charging strategy of the comprehensive supply station needs to be adjusted according to the charging pile data information and the total charging power of the comprehensive supply station; An adjustment unit is configured to obtain the number of each priority charging vehicle in the first priority according to the charging vehicle data information if the total charging power of the comprehensive supply station is less than the total power required by all the charging vehicles, obtain the total charging power allocated to the high priority, the medium priority and the low priority according to the charging pile data information, and adjust the charging power of all the charging vehicles in the comprehensive supply station according to the charging power of the high priority, the medium priority and the low priority.

10. The multi-stage collaborative control device for a light-oriented storage and charging integrated supply station according to claim 7, characterized in that, The optimization module specifically comprises: A deep network unit is configured to obtain a storage system charging and discharging efficiency loss coefficient based on a deep network model according to storage system data information; An optimization unit is configured to take the storage system charging and discharging efficiency loss coefficient as a constraint condition to obtain a power grid power purchase amount, and obtain photovoltaic power generation power, V2G equipment discharging power and The energy storage system discharges power, and the group charging and group control strategy is optimized.

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