Power control method, system, device and medium for optical storage and charging station

Abstract

The application discloses a power control method, system, device and medium of a light storage and charging station, belongs to the field of light storage, determines a health degree factor based on collected equipment operation data, determines a prediction factor based on collected photovoltaic data, establishes a weight function to calculate the instruction weight of each equipment based on a power distribution weight and an operation factor, the operation factor includes a planned maintenance factor, a sudden failure factor, an energy storage correlation factor, a health degree factor and a prediction factor of parallel equipment, calculates the power instruction of each parallel equipment, and controls the parallel equipment to operate at the power instruction through a station controller. The application measures the equipment bearing degree by calculating the dynamic weight obtained based on the health factor, the prediction factor, the maintenance factor, the sudden failure factor and the energy storage correlation factor, and allocates the power instruction with inconsistent degrees in combination with the actual operation condition, thereby improving the operation stability.

Description

Technical Field

[0001] This application relates to the field of photovoltaic energy storage, and in particular to a power control method, system, equipment and medium for a photovoltaic energy storage charging station. Background Technology

[0002] Currently, photovoltaic (PV) energy storage and charging stations are intelligent, integrated clean energy charging stations that combine photovoltaic power generation, energy storage systems, and charging piles. To maximize resource utilization and improve system cost-effectiveness, a comprehensive energy management system controls multiple devices to operate in parallel. Generally, either average command distribution or master-slave control methods are used. However, with average command distribution, the total power demand is evenly allocated to each device. If any device is malfunctioning, it may not output the required value upon receiving the command, leading to uneven power output and affecting the operational stability of the PV energy storage and charging station system. Master-slave control often relies excessively on the master device. The master device's lifespan is reduced due to prolonged continuous operation, which can cause some master devices to malfunction, further affecting the operational stability of the PV energy storage and charging station system. Therefore, existing technologies suffer from low operational stability when managing the energy of PV energy storage and charging stations.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a power control method for photovoltaic energy storage and charging stations, which aims to solve the problem of low operational stability when performing energy management on photovoltaic energy storage and charging stations in the prior art.

[0005] To achieve the above objectives, this application provides a power control method for a photovoltaic-storage-charging power station. The method is applied to an integrated energy management system, which includes a power station controller and a data acquisition unit. The data acquisition unit collects operational data to obtain acquired data. The method includes:

[0006] If it is determined that the total power command is not 0 and there are online parallel devices, then the collected data is obtained from the data acquisition device, wherein the total power command is obtained according to the external power grid requirements, internal real-time forecast data, and preset economic optimization target planning;

[0007] Based on the equipment operation data in the collected data, a health factor for the parallel equipment is determined, and the health factor is related to the health level of the parallel equipment;

[0008] Based on the photovoltaic data in the collected data, a predictor of the power generation of the photovoltaic module is determined.

[0009] Based on the preset power allocation weight of the parallel devices and the operating factors of the parallel devices during operation, a weight function is established, and the instruction weight corresponding to each parallel device is calculated based on the weight function. The operating factors include the planned maintenance factor, sudden failure factor, energy storage correlation factor, health factor and prediction factor of the parallel devices. The energy storage correlation factor and health factor of the offline parallel devices are set to 0.

[0010] Calculate the product of the instruction weight corresponding to the parallel device and the total power instruction to obtain the power instruction corresponding to each parallel device;

[0011] The power command is distributed to the parallel devices through the station controller, and each station controller controls the parallel devices to operate according to the power command.

[0012] In one possible implementation of this application, the step of establishing a weight function based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices, and calculating the instruction weight corresponding to each of the parallel devices based on the weight function, includes:

[0013] The sum of the health factor, the prediction factor, the planned maintenance factor, the sudden failure factor, and the energy storage correlation factor is obtained by adding them together.

[0014] The weighting function is obtained by using the preset power allocation weight of the parallel devices as the numerator and the sum of all factors as the denominator.

[0015] The instruction weight corresponding to each of the parallel devices is calculated based on the weighting function.

[0016] In one possible implementation of this application, before the step of establishing the weighting function based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices, the method includes:

[0017] Obtain the list of online parallel devices;

[0018] Obtain the maintenance plan for the parallel equipment;

[0019] Based on the maintenance schedule, the planned maintenance time for the parallel equipment is determined;

[0020] When the scheduled maintenance time is earlier than a preset time, the parallel equipment to be maintained is removed from the list of online parallel equipment, and the updated list of online parallel equipment is obtained.

[0021] The maintenance factor is calculated by taking the number of currently connected parallel devices corresponding to the list of parallel devices as the numerator and the number of online parallel devices within the maintenance cycle corresponding to the maintenance plan as the denominator.

[0022] In one possible implementation of this application, the device operating data in the collected data includes voltage, current, temperature, humidity, capacitance, inductance, and impedance. The step of determining the health factor of the parallel devices based on the device operating data in the collected data includes:

[0023] The current voltage, current current, current temperature, current humidity, current capacitance, current inductance, and current impedance are multiplied by their respective characteristic weights and then summed to obtain the current health value of the device.

[0024] Using the device health value as the numerator and the preset device standard health value as the denominator, the health factor of the parallel device is obtained.

[0025] In one possible implementation of this application, the photovoltaic data in the collected data includes light intensity and the actual temperature of the photovoltaic module. The step of determining a predictor of the photovoltaic module's power generation capacity based on the photovoltaic data in the collected data includes:

[0026] The current light intensity and the actual temperature of the photovoltaic module are collected and input into a preset photovoltaic prediction model. Based on the output of the photovoltaic prediction model, the predicted power generation is obtained.

[0027] Using the predicted power generation as the numerator and the rated power generation as the denominator, we obtain the prediction factor for the power generation of the photovoltaic module.

[0028] In one possible implementation of this application, before the step of establishing the weighting function based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices, the method includes:

[0029] Obtain the continuous operating time of the parallel devices;

[0030] Obtain the preset average maximum failure probability of the parallel devices;

[0031] The sudden failure factor is calculated by multiplying the continuous running time by the average maximum failure probability.

[0032] In one possible implementation of this application, the energy storage correlation factor includes a photovoltaic energy storage correlation factor, a load energy storage correlation factor, and a grid energy storage correlation factor. The photovoltaic energy storage correlation factor is used to measure the responsiveness of the energy storage system to photovoltaic power generation, the load energy storage correlation factor is used to measure the responsiveness of the energy storage system to load demand, and the grid energy storage correlation factor is used to measure the responsiveness of the energy storage system to grid participation.

[0033] Furthermore, to achieve the above objectives, this application also provides a power control device for a photovoltaic-storage-charging station, and a power control method for the photovoltaic-storage-charging station applied to an integrated energy management system. The integrated energy management system includes a station controller and a data acquisition unit, which collects operational data through the data acquisition unit. The power control device for the photovoltaic-storage-charging station includes:

[0034] The acquisition module is used to acquire the collected data from the data acquisition unit if it is determined that the total power command is not 0 and there are online parallel devices. The total power command is obtained based on external power grid requirements, internal real-time forecast data, and preset economic optimization targets.

[0035] The first determining module is used to determine the health factor of the parallel device based on the device operation data in the collected data, wherein the health factor is related to the health level of the parallel device;

[0036] The second determining module is used to determine the predictive factor of the power generation of the photovoltaic module based on the photovoltaic data in the collected data;

[0037] The first calculation module is used to establish a weight function based on the preset power allocation weight of the parallel devices and the operating factors of the parallel devices during operation, and to calculate the instruction weight corresponding to each of the parallel devices based on the weight function. The operating factors include the planned maintenance factor, sudden failure factor, energy storage correlation factor, health factor and prediction factor of the parallel devices, and the energy storage correlation factor and health factor of the offline parallel devices are set to 0.

[0038] The second calculation module is used to calculate the product between the instruction weight corresponding to the parallel device and the total power instruction to obtain the power instruction corresponding to each parallel device;

[0039] A control module is used to distribute the power command to the parallel devices through the station controller, and to control the parallel devices to operate according to the power command through each of the station controllers.

[0040] In addition, to achieve the above objectives, this application also provides a power control device for an optical storage and charging station. The power control device for the optical storage and charging station is a physical node device. The power control device for the optical storage and charging station includes: a memory, a processor, and a power control program for the optical storage and charging station stored in the memory and executable on the processor. The processor executes the power control program for the optical storage and charging station to implement the steps of the power control method for the optical storage and charging station.

[0041] In addition, to achieve the above objectives, this application also provides a medium storing a program for implementing a power control method for an optical storage and charging station. When the power control program for the optical storage and charging station is executed by a processor, it implements the steps of the power control method for the optical storage and charging station described above.

[0042] This application provides a power control method, system, device, and medium for photovoltaic-storage-charging stations. Compared with the existing technology, which suffers from low operational stability when managing energy in photovoltaic-storage-charging stations, this application, if it is determined that the total power command is not 0 and there are online parallel devices, then the collected data is obtained from the data acquisition device. The total power command is obtained based on external grid requirements, internal real-time forecast data, and a preset economic optimization target. Based on the device operation data in the collected data, a health factor for the parallel devices is determined, and this health factor is related to the health level of the parallel devices. Based on the photovoltaic data in the collected data, a prediction factor for the power generation of the photovoltaic modules is determined. Based on the parallel devices... A weighting function is established based on preset power allocation weights and operating factors of the parallel devices. The command weight for each parallel device is calculated based on this weighting function. The operating factors include planned maintenance factors, sudden failure factors, energy storage correlation factors, health factors, and prediction factors for the parallel devices. The energy storage correlation factors and health factors of offline parallel devices are set to 0. The product of the command weight for each parallel device and the total power command is calculated to obtain the power command for each parallel device. The power command is allocated to the parallel devices through the site controller, and each site controller controls the parallel devices to operate according to the power command. In this application, the power control method for parallel equipment operation is neither an average power distribution to the parallel equipment nor a master-slave power distribution. Instead, it measures the tolerability of the parallel equipment by calculating a dynamic weight based on a combination of equipment health factors, predictive factors of power generation, planned maintenance factors, sudden failure factors, and energy storage-related factors. Based on the tolerability of dynamic changes in the equipment, it allocates power commands with inconsistent levels of variation. Therefore, the allocated power commands are combined with the actual operating conditions, which improves the operational stability when managing the energy of photovoltaic-storage-charging stations. Attached Figure Description

[0043] Figure 1 This is a first flowchart illustrating an embodiment of the power control method for the photovoltaic-storage-charging station of this application;

[0044] Figure 2 This is a schematic diagram of the power control device of the photovoltaic energy storage and charging station in an embodiment of the power control method of the photovoltaic energy storage and charging station of this application;

[0045] Figure 3 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the embodiment of the power control method for the photovoltaic energy storage and charging station of this application. Detailed Implementation

[0046] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Example 1

[0048] This application provides a power control method for a photovoltaic energy storage and charging station. In the first embodiment of the power control method for the photovoltaic energy storage and charging station, refer to... Figure 1 The method is applied to an integrated energy management system, which includes a station controller and a data acquisition unit. The data acquisition unit collects operational data to obtain collected data. The method includes:

[0049] Step S110: If it is determined that the total power command is not 0 and there are online parallel devices, then the collected data is obtained from the data acquisition device, wherein the total power command is obtained according to the external power grid requirements, internal real-time forecast data, and preset economic optimization target planning.

[0050] The integrated energy management system for photovoltaic-storage-charging power stations integrates various energy facilities such as photovoltaic power generation, energy storage batteries, and electric vehicle charging piles at power stations (e.g., parking lots, service areas, logistics parks). It is a system that coordinates, controls, and optimizes these facilities through an intelligent management platform. This system enables the self-circulation and efficient utilization of energy at the power station, minimizing dependence on the power grid and electricity costs, while simultaneously improving operational efficiency and stability.

[0051] The integrated energy management system for photovoltaic, energy storage, and charging stations consists of a physical hardware layer, a data acquisition and execution layer, a network communication layer, and a cloud management platform.

[0052] The physical hardware layer includes photovoltaic systems, energy storage systems, charging systems, and power distribution facilities.

[0053] As an example, a photovoltaic system consists of solar panels, or photovoltaic modules, installed in locations such as carports and rooftops, which are responsible for converting solar energy into electrical energy.

[0054] As an example, energy storage systems typically use lithium-ion battery packs, which act as the system's "batteries." They are responsible for storing surplus electricity generated by photovoltaics and releasing it when needed.

[0055] As an example, the charging system includes DC fast charging stations and AC slow charging stations of various power levels to provide charging services for electric vehicles.

[0056] As an example, power distribution facilities include transformers, switchgear, cables, etc., to ensure the safe power supply of the entire station.

[0057] The data acquisition layer can be various sensors that can collect voltage, current, temperature, humidity, capacitance, inductance, and impedance, as well as light intensity and the actual temperature of the photovoltaic module.

[0058] The cloud management platform in the integrated energy management system for photovoltaic, energy storage, and charging stations is the "brain" of the entire system. It analyzes, predicts, and optimizes the scheduling of collected data to achieve various advanced functions.

[0059] A key function of the integrated energy management system for photovoltaic-storage-charging stations is dynamic power allocation. Based on photovoltaic output, energy storage status, and grid limitations, it adjusts the output power of each charging pile in real time to meet the charging needs of multiple vehicles while keeping the total power within limits. However, to maximize resource utilization and improve system cost-effectiveness, the integrated energy management system controls multiple devices to operate in parallel. Common methods include average command allocation or master-slave control. However, with average command allocation, the total power demand is evenly distributed to each device. If any device is malfunctioning, it may not output the required value upon receiving the command, leading to uneven output and affecting the system's operational stability. Master-slave control often relies excessively on the master device. Prolonged continuous operation of the master device reduces its lifespan, potentially causing some master devices to malfunction, thus impacting the system's stability. Therefore, existing technologies suffer from low operational stability when managing energy in photovoltaic-storage-charging stations.

[0060] The strategy proposed in this application operates on the controller of the integrated energy management system for photovoltaic-storage-charging power stations, enabling parallel control of multiple devices within the station. By acquiring the operating information of the devices, the power commands for all devices are ultimately established. This control algorithm simultaneously considers factors such as the health status of the devices, the power generation potential of the photovoltaic system, the energy storage potential of the energy storage devices, planned maintenance, and unexpected failures, ensuring more stable, reliable, and precise operation of the power station system.

[0061] The control algorithm considers the health status of the equipment by establishing a health status prediction model and obtaining an equipment health status factor. The control algorithm also considers the power generation potential of the photovoltaic system by establishing a photovoltaic prediction model and combining it with weather forecasts to predict photovoltaic power generation over a future period and obtain a prediction factor.

[0062] The total power command is the overall power command of the integrated energy management system for photovoltaic, energy storage, and charging stations. It serves as the "brain" and "command stick" for its efficient, economical, and safe operation. This command does not originate from a single source but is the core decision result derived from complex calculations based on external grid requirements, internal real-time status and forecast data, and preset economic optimization objectives. It is used to coordinate the energy flow between photovoltaic, energy storage, charging loads, and the grid. When the total power command is detected to be non-zero, the system begins to monitor the online status of all parallel devices.

[0063] If the total power command is determined to be non-zero and there are online parallel devices, then the collected data is obtained from the data acquisition unit.

[0064] Step S120: Based on the device operation data in the collected data, determine the health factor of the parallel device, wherein the health factor is related to the health level of the parallel device;

[0065] As shown in formula (1), a health prediction model for the device is established to predict the health level of the device, and a health factor W1 is obtained. The health factor W1 varies from 0 to 1, where 0 indicates the worst equipment condition and 1 indicates the best equipment condition. Formula (1):

[0066] ;

[0067] Where fd(d) is the standard health value of the device, fd(x) is the current health value of the device, and W0 is the base value. i Features related to health status are device operating data collected, such as voltage, current, temperature, humidity, capacitance, inductance, and impedance. Wi represents the feature weight, which is related to X. iThe corresponding characteristic weights are, here, voltage weight, current weight, temperature weight, humidity weight, capacitance weight, inductance weight, and impedance weight. The current voltage, current current, current temperature, current humidity, current capacitance, current inductance, and current impedance are multiplied by their respective characteristic weights, summed, and then a base value is added to obtain the device's current health value fd(x). The device's standard health value is a pre-set fixed value. Using the device health value as the numerator and the pre-set device standard health value as the denominator, the health factor of the parallel devices is obtained.

[0068] The collected data includes equipment operating data such as voltage, current, temperature, humidity, capacitance, inductance, and impedance. Step S120, based on the collected equipment operating data, determines the health factor of the parallel equipment, including steps S1201-S1202:

[0069] Step S1201: Multiply the current voltage, current current, current temperature, current humidity, current capacitance value, current inductance value, and current impedance obtained by the current voltage, current current, current temperature, current humidity, current capacitance value, current inductance value, and current impedance by their respective characteristic weights and then add them together to obtain the current health value of the device.

[0070] The collected current voltage, current, temperature, humidity, capacitance, inductance, and impedance Xi are multiplied by their corresponding characteristic weights, Wi*X. i Then add them together to get the current device health value fd(x).

[0071] Step S1202: Using the device health value as the numerator and the preset device standard health value as the denominator, the health factor of the parallel device is obtained.

[0072] Using the equipment health value fd(x) as the numerator and the preset equipment standard health value fd(d) as the denominator, the health factor W1 of the parallel equipment is obtained.

[0073] Step S130: Based on the photovoltaic data in the collected data, determine the prediction factor for the power generation of the photovoltaic module;

[0074] A photovoltaic prediction model was established to predict the power generation and potential of photovoltaic power generation, and a prediction factor was derived, as shown in formula (2), where 0 indicates no power generation and 1 indicates rated power generation. Formula (2):

[0075] ;

[0076] Where Ppv(d) is the rated generating power, Ppv(x) is the predicted generating power, and Wpv is the predicted generating factor. STC Power value at standard temperature, G(t) Actual illuminance (W / m2), GSTC Standard light intensity, K T Power temperature coefficient, Tc(t), actual temperature of photovoltaic module (°C), T STC (t) represents the standard temperature (°C). For inverter efficiency, For system efficiency.

[0077] The photovoltaic data collected includes irradiance G(t) and actual photovoltaic module temperature Tc(t). Step S130, based on the photovoltaic data collected, is a step to determine the predictive factor for the power generation of the photovoltaic module, including steps S1301-S1302:

[0078] Step S1301: Input the collected current light intensity and the current actual temperature of the photovoltaic module into the preset photovoltaic prediction model, and obtain the predicted power generation based on the output of the photovoltaic prediction model;

[0079] The current light intensity G(t) and the current actual temperature Tc(t) of the photovoltaic module are collected and input into the preset photovoltaic prediction model to obtain the predicted power generation Ppv(x).

[0080] Step S1302: Using the predicted power generation as the numerator and the rated power generation as the denominator, a prediction factor for the power generation capacity of the photovoltaic module is obtained.

[0081] Using the predicted power generation Ppv(x) as the numerator and the rated power generation Ppv(d) as the denominator, the prediction factor Wpv of the photovoltaic module's power generation capacity is obtained.

[0082] Considering that the equipment under maintenance is about to be taken out of parallel operation, power commands should not be assigned to that equipment.

[0083] Step S140, prior to establishing the weighting function based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices during operation, includes steps A1-A5:

[0084] Step A1: Obtain the list of online parallel devices;

[0085] Retrieve the list of online parallel devices and determine whether any of these devices have maintenance plans based on the list. The program will exit the current loop when all devices are offline.

[0086] Step A2: Obtain the maintenance plan for the parallel equipment;

[0087] Based on the list of online parallel devices, obtain the maintenance plan for the parallel devices in the list.

[0088] Step A3: Based on the maintenance schedule, determine the planned maintenance time for the parallel equipment;

[0089] Based on the obtained maintenance schedule, determine the actual planned maintenance time for the online parallel equipment.

[0090] Step A4: When the scheduled maintenance time is earlier than the preset time, the parallel equipment to be maintained is removed from the list of online parallel equipment, and the updated list of online parallel equipment is obtained.

[0091] As an example, five minutes before the planned maintenance, the equipment is gradually decommissioned, and the parallel equipment scheduled for maintenance is removed from the online parallel equipment list, resulting in an updated online parallel equipment list. Five minutes before the planned maintenance, the equipment's weight is reset to zero, thus taking it out of operation.

[0092] Step A5: Calculate the maintenance factor by taking the number of currently connected parallel devices corresponding to the list of parallel devices as the numerator and the number of online parallel devices within the maintenance cycle corresponding to the maintenance plan as the denominator.

[0093] After the list of online parallel devices is updated, the number of current parallel devices corresponding to the list is used as the numerator, and the number of online parallel devices within the maintenance cycle corresponding to the maintenance plan is used as the denominator to obtain the maintenance factor.

[0094] Step S140, prior to establishing the weighting function based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices during operation, includes steps B1-B3:

[0095] Step B1: Obtain the continuous operating time of the parallel devices;

[0096] As parallel equipment operates for extended periods, the probability of failure increases. Therefore, it is important to obtain the continuous operating time of parallel equipment.

[0097] Step B2: Obtain the preset average maximum failure probability of the parallel devices;

[0098] Obtain the preset average maximum failure probability of the parallel devices.

[0099] Step B3: Multiply the continuous running time by the average maximum failure probability to calculate the sudden failure factor.

[0100] The sudden failure factor is calculated by multiplying the continuous running time by the average maximum failure probability.

[0101] Step S140: Based on the preset power allocation weight of the parallel devices and the operating factors of the parallel devices during operation, establish a weight function, calculate the instruction weight corresponding to each parallel device based on the weight function, wherein the operating factors include the planned maintenance factor, sudden failure factor, energy storage correlation factor, health factor and prediction factor of the parallel devices, and set the energy storage correlation factor and health factor of the offline parallel devices to 0;

[0102] In this embodiment, the operating factors of the parallel devices include the planned maintenance factor W of the parallel devices. m Sudden failure factor Wf, energy storage correlation factor W bat The health factor W1 and the predictive factor W pv The energy storage correlation factor and health factor of the offline parallel devices are set to 0;

[0103] Each parallel device has a different preset power allocation weight. A weighting function is established based on the preset power allocation weight of the parallel devices and the operating factors of the parallel devices during operation.

[0104] The energy storage correlation factors include photovoltaic energy storage correlation factors, load energy storage correlation factors, and grid energy storage correlation factors. The photovoltaic energy storage correlation factor is used to measure the responsiveness of the energy storage system to photovoltaic power generation. The load energy storage correlation factor is used to measure the responsiveness of the energy storage system to load demand. The grid energy storage correlation factor is used to measure the responsiveness of the energy storage system to grid participation.

[0105] Step S140, based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices during operation, establishes a weight function, and calculates the instruction weight corresponding to each of the parallel devices based on the weight function, including steps S1401-S1403:

[0106] Step S1401: Add the health factor, the prediction factor, the planned maintenance factor, the sudden failure factor, and the energy storage correlation factor together to obtain the sum of all factors;

[0107] Establish the weighting function, as shown in formula (3):

[0108] ;

[0109] Among them, W all W is the sum of all factors. w This is the instruction weight ratio. The health factor W1 and the prediction factor W are weighted. pv The planned maintenance factor W m The sudden failure factor Wf and the energy storage correlation factor W bat Add them together to get the sum of all factors W. all The ability to withstand the load of parallel equipment is measured by calculating a dynamic weight that is based on a combination of equipment health factors, predictive factors of power generation, planned maintenance factors, sudden failure factors, and energy storage-related factors.

[0110] Step S1402: The weighting function is obtained by using the preset power allocation weight of the parallel devices as the numerator and the sum of all factors as the denominator.

[0111] Using the preset power distribution weight W of the parallel devices as the numerator, the sum of all factors W all As the denominator, we obtain the weight function W. W .

[0112] Step S1403: Calculate the instruction weight corresponding to each of the parallel devices based on the weight function.

[0113] Based on weight function The instruction weight corresponding to each parallel device is calculated, and power instructions with inconsistent levels of variation are allocated based on the dynamic change tolerance of the devices. The allocated power instructions take into account the actual operating conditions.

[0114] Step S150: Calculate the product between the instruction weight corresponding to the parallel device and the total power instruction to obtain the power instruction corresponding to each parallel device;

[0115] Total power command is P t P cmd The power command for parallel devices, the corresponding power command P for parallel devices. cmd The calculation method for P is as follows: cmd= P t * W W。

[0116] Step S160: The power command is distributed to the parallel devices through the station controller, and each station controller controls the parallel devices to operate according to the power command.

[0117] This application provides a power control method, system, device, and medium for photovoltaic-storage-charging stations. Compared with the existing technology, which suffers from low operational stability when managing energy in photovoltaic-storage-charging stations, this application, if it is determined that the total power command is not 0 and there are online parallel devices, then the collected data is obtained from the data acquisition device. The total power command is obtained based on external grid requirements, internal real-time forecast data, and a preset economic optimization target. Based on the device operation data in the collected data, a health factor for the parallel devices is determined, and this health factor is related to the health level of the parallel devices. Based on the photovoltaic data in the collected data, a prediction factor for the power generation of the photovoltaic modules is determined. Based on the parallel devices... A weighting function is established based on preset power allocation weights and operating factors of the parallel devices. The command weight for each parallel device is calculated based on this weighting function. The operating factors include planned maintenance factors, sudden failure factors, energy storage correlation factors, health factors, and prediction factors for the parallel devices. The energy storage correlation factors and health factors of offline parallel devices are set to 0. The product of the command weight for each parallel device and the total power command is calculated to obtain the power command for each parallel device. The power command is allocated to the parallel devices through the site controller, and each site controller controls the parallel devices to operate according to the power command. In this application, the power control method for parallel equipment operation is neither an average power distribution to the parallel equipment nor a master-slave power distribution. Instead, it measures the tolerability of the parallel equipment by calculating a dynamic weight based on a combination of equipment health factors, predictive factors of power generation, planned maintenance factors, sudden failure factors, and energy storage-related factors. Based on the tolerability of dynamic changes in the equipment, it allocates power commands with inconsistent levels of variation. Therefore, the allocated power commands are combined with the actual operating conditions, which improves the operational stability when managing the energy of photovoltaic-storage-charging stations.

[0118] Example 2

[0119] Furthermore, based on all the above embodiments, another embodiment of this application is provided, in which, as... Figure 2 A site energy storage and dispatching device is provided, the device comprising:

[0120] The acquisition module is used to acquire the collected data from the data acquisition unit if it is determined that the total power command is not 0 and there are online parallel devices. The total power command is obtained based on external power grid requirements, internal real-time forecast data, and preset economic optimization targets.

[0121] The first determining module is used to determine the health factor of the parallel device based on the device operation data in the collected data, wherein the health factor is related to the health level of the parallel device;

[0122] The second determining module is used to determine the predictive factor of the power generation of the photovoltaic module based on the photovoltaic data in the collected data;

[0123] The first calculation module is used to establish a weight function based on the preset power allocation weight of the parallel devices and the operating factors of the parallel devices during operation, and to calculate the instruction weight corresponding to each of the parallel devices based on the weight function. The operating factors include the planned maintenance factor, sudden failure factor, energy storage correlation factor, health factor and prediction factor of the parallel devices, and the energy storage correlation factor and health factor of the offline parallel devices are set to 0.

[0124] The second calculation module is used to calculate the product between the instruction weight corresponding to the parallel device and the total power instruction to obtain the power instruction corresponding to each parallel device;

[0125] A control module is used to distribute the power command to the parallel devices through the station controller, and to control the parallel devices to operate according to the power command through each of the station controllers.

[0126] In one possible implementation of this application, the device for establishing a weight function based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices, and calculating the instruction weight corresponding to each of the parallel devices based on the weight function, includes:

[0127] The addition module is used to add the health factor, the prediction factor, the planned maintenance factor, the sudden failure factor and the energy storage correlation factor to obtain the sum of all factors;

[0128] A weight function module is established to use the preset power allocation weight of the parallel devices as the numerator and the sum of all factors as the denominator to establish the weight function.

[0129] The third calculation module is used to calculate the instruction weight corresponding to each of the parallel devices based on the weight function.

[0130] In one possible implementation of this application, before the step of establishing the weighting function based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices, the following steps are included:

[0131] The first acquisition module is used to acquire a list of online parallel devices;

[0132] The second acquisition module is used to acquire the maintenance plan table for the parallel equipment;

[0133] The third determining module is used to determine the planned maintenance time of the parallel equipment based on the maintenance plan table;

[0134] The removal module is used to remove the parallel equipment scheduled for maintenance from the list of online parallel equipment when the maintenance time is a preset time period earlier than the scheduled maintenance time, so as to obtain an updated list of online parallel equipment.

[0135] The fourth calculation module is used to calculate the maintenance factor by taking the number of currently connected parallel devices corresponding to the list of parallel devices as the numerator and the number of online parallel devices within the maintenance cycle corresponding to the maintenance plan as the denominator.

[0136] In one possible implementation of this application, the device operating data in the collected data includes voltage, current, temperature, humidity, capacitance value, inductance value, and impedance. The step of determining the health factor of the parallel devices based on the device operating data in the collected data includes the following apparatus:

[0137] The fifth calculation module is used to multiply the collected current voltage, current current, current temperature, current humidity, current capacitance value, current inductance value, and current impedance by their corresponding characteristic weights and then add them together to obtain the current health value of the device.

[0138] The sixth calculation module is used to take the health value of the equipment as the numerator and the preset standard health value of the equipment as the denominator to obtain the health factor of the parallel equipment.

[0139] In one possible implementation of this application, the photovoltaic data in the collected data includes light intensity and actual temperature of the photovoltaic module. The step of determining a predictor of the power generation capacity of the photovoltaic module based on the photovoltaic data in the collected data includes:

[0140] The prediction module is used to input the current light intensity and the current actual temperature of the photovoltaic module into a preset photovoltaic prediction model, and obtain the predicted power generation based on the output of the photovoltaic prediction model.

[0141] The seventh calculation module is used to take the predicted power generation as the numerator and the rated power generation as the denominator to obtain the prediction factor of the power generation of the photovoltaic module.

[0142] In one possible implementation of this application, before the step of establishing a weighting function based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices, the device includes:

[0143] The third acquisition module is used to acquire the continuous operating time of the parallel devices;

[0144] The fourth acquisition module is used to acquire the preset average maximum failure probability of the parallel devices;

[0145] The sudden failure factor is calculated by multiplying the continuous running time by the average maximum failure probability.

[0146] The specific implementation method of the power control system of the photovoltaic energy storage and charging station in this application is basically the same as the various embodiments of the power control method of the photovoltaic energy storage and charging station described above, and will not be repeated here.

[0147] Example 3

[0148] Furthermore, based on all the above embodiments, another embodiment of this application is provided. In this embodiment, a power control device for an optical storage and charging station is provided. The power control device for the optical storage and charging station is a physical node device. The power control device for the optical storage and charging station includes: a memory, a processor, and a program stored in the memory for implementing the power control method of the optical storage and charging station. The memory is used to store the program for implementing the power control method of the optical storage and charging station. The processor is used to execute the program for implementing the power control method of the optical storage and charging station to implement the steps of the power control method of the optical storage and charging station in the above embodiments.

[0149] Reference Figure 3 , Figure 3 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application.

[0150] like Figure 3 As shown, the power control device of this optical storage and charging station may include: a processor 1001, such as a CPU, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to establish communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM or a stable, non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0151] In one possible embodiment of this application, the power control device of the optical storage and charging station may further include a network interface, audio circuit, display, connecting cable, sensor, input module, etc. The network interface may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface or a Bluetooth interface). The input module may optionally include a keyboard, a system soft keyboard, voice input, wireless receiver input, etc.

[0152] Those skilled in the art will understand that the structure of the power control equipment of the photovoltaic energy storage and charging station does not constitute a limitation on the power control equipment of the photovoltaic energy storage and charging station, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0153] The memory, as a deterministic medium, may include an operating system, an information exchange module, and a power control program for the optical storage and charging station. The operating system is a program that manages and controls the hardware and software resources of the power control equipment in the optical storage and charging station, supporting the operation of the power control program and other software and / or programs. The information exchange module is used to enable communication between various components within the memory, as well as communication with other hardware and software in the management system.

[0154] In the power control device of the optical storage and charging station, the processor is used to execute the power control program of the optical storage and charging station stored in the memory to implement the above-mentioned power control steps of the optical storage and charging station.

[0155] The specific implementation method of the power control equipment for the photovoltaic energy storage and charging station in this application is basically the same as the various embodiments of the power control method for the photovoltaic energy storage and charging station described above, and will not be repeated here.

[0156] Example 4

[0157] This application provides a medium that stores one or more programs, which can be executed by one or more processors to implement the steps of the power control method for the photovoltaic energy storage and charging station described above.

[0158] The specific implementation method of the medium in this application is basically the same as the power control method of the above-mentioned photovoltaic energy storage and charging station, and will not be described again here.

[0159] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0160] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0161] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM or RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0162] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A power control method for a photovoltaic-storage-charging station, characterized in that, The method is applied to an integrated energy management system, which includes a station controller and a data acquisition unit. The data acquisition unit collects operational data to obtain collected data. The method includes: If it is determined that the total power command is not 0 and there are online parallel devices, then the collected data is obtained from the data acquisition device, wherein the total power command is obtained according to the external power grid requirements, internal real-time forecast data, and preset economic optimization target planning; Based on the equipment operation data in the collected data, a health factor for the parallel equipment is determined, and the health factor is related to the health level of the parallel equipment; Based on the photovoltaic data in the collected data, a predictor of the power generation of the photovoltaic module is determined. Based on the preset power allocation weight of the parallel devices and the operating factors of the parallel devices during operation, a weight function is established, and the instruction weight corresponding to each parallel device is calculated based on the weight function. The operating factors include the planned maintenance factor, sudden failure factor, energy storage correlation factor, health factor and prediction factor of the parallel devices. The energy storage correlation factor and health factor of the offline parallel devices are set to 0. Calculate the product of the instruction weight corresponding to the parallel device and the total power instruction to obtain the power instruction corresponding to each parallel device; The power command is distributed to the parallel devices through the station controller, and each station controller controls the parallel devices to operate according to the power command.

2. The power control method for a photovoltaic-storage-charging station according to claim 1, characterized in that, The step of establishing a weight function based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices, and calculating the instruction weight corresponding to each of the parallel devices based on the weight function, includes: The sum of the health factor, the prediction factor, the planned maintenance factor, the sudden failure factor, and the energy storage correlation factor is obtained by adding them together. The weighting function is obtained by using the preset power allocation weight of the parallel devices as the numerator and the sum of all factors as the denominator. The instruction weight corresponding to each of the parallel devices is calculated based on the weighting function.

3. The power control method for a photovoltaic-storage-charging station according to claim 1, characterized in that, Before the step of establishing the weighting function based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices, the following steps are included: Obtain the list of online parallel devices; Obtain the maintenance plan for the parallel equipment; Based on the maintenance schedule, the planned maintenance time for the parallel equipment is determined; When the scheduled maintenance time is earlier than a preset time, the parallel equipment to be maintained is removed from the list of online parallel equipment, and the updated list of online parallel equipment is obtained. The maintenance factor is calculated by taking the number of currently connected parallel devices corresponding to the list of parallel devices as the numerator and the number of online parallel devices within the maintenance cycle corresponding to the maintenance plan as the denominator.

4. The power control method for a photovoltaic-storage-charging station according to claim 1, characterized in that, The collected equipment operating data includes voltage, current, temperature, humidity, capacitance, inductance, and impedance. The step of determining the health factor of the parallel equipment based on the collected equipment operating data includes: The current voltage, current current, current temperature, current humidity, current capacitance, current inductance, and current impedance are multiplied by their respective characteristic weights and then summed to obtain the current health value of the device. Using the device health value as the numerator and the preset device standard health value as the denominator, the health factor of the parallel device is obtained.

5. The power control method for a photovoltaic-storage-charging station according to claim 1, characterized in that, The photovoltaic data collected includes irradiance and actual temperature of the photovoltaic modules. The step of determining a predictor of the power generation capacity of the photovoltaic modules based on the collected photovoltaic data includes: The current light intensity and the actual temperature of the photovoltaic module are collected and input into a preset photovoltaic prediction model. Based on the output of the photovoltaic prediction model, the predicted power generation is obtained. Using the predicted power generation as the numerator and the rated power generation as the denominator, we obtain the prediction factor for the power generation of the photovoltaic module.

6. The power control method for a photovoltaic-storage-charging station according to claim 1, characterized in that, Before the step of establishing the weighting function based on the preset power allocation weights of the parallel devices and the operating factors of the parallel devices, the following steps are included: Obtain the continuous operating time of the parallel devices; Obtain the preset average maximum failure probability of the parallel devices; The sudden failure factor is calculated by multiplying the continuous running time by the average maximum failure probability.

7. The power control method for a photovoltaic-storage-charging station according to claim 1, characterized in that, The energy storage correlation factors include photovoltaic energy storage correlation factors, load energy storage correlation factors, and grid energy storage correlation factors. The photovoltaic energy storage correlation factor is used to measure the responsiveness of the energy storage system to photovoltaic power generation. The load energy storage correlation factor is used to measure the responsiveness of the energy storage system to load demand. The grid energy storage correlation factor is used to measure the responsiveness of the energy storage system to grid participation.

8. A power control device for a photovoltaic energy storage and charging station, characterized in that, The power control method for a photovoltaic-storage-charging power station is applied to an integrated energy management system. The integrated energy management system includes a power station controller and a data acquisition unit. The data acquisition unit collects operational data to obtain acquired data. The power control device for the photovoltaic-storage-charging power station includes: The acquisition module is used to acquire the collected data from the data acquisition unit if it is determined that the total power command is not 0 and there are online parallel devices. The total power command is obtained based on external power grid requirements, internal real-time forecast data, and preset economic optimization targets. The first determining module is used to determine the health factor of the parallel device based on the device operation data in the collected data, wherein the health factor is related to the health level of the parallel device; The second determining module is used to determine the predictive factor of the power generation of the photovoltaic module based on the photovoltaic data in the collected data; The first calculation module is used to establish a weight function based on the preset power allocation weight of the parallel devices and the operating factors of the parallel devices during operation, and to calculate the instruction weight corresponding to each of the parallel devices based on the weight function. The operating factors include the planned maintenance factor, sudden failure factor, energy storage correlation factor, health factor and prediction factor of the parallel devices, and the energy storage correlation factor and health factor of the offline parallel devices are set to 0. The second calculation module is used to calculate the product between the instruction weight corresponding to the parallel device and the total power instruction to obtain the power instruction corresponding to each parallel device; A control module is used to distribute the power command to the parallel devices through the station controller, and to control the parallel devices to operate according to the power command through each of the station controllers.

9. A power control device for a photovoltaic-storage-charging station, characterized in that, The system includes a memory, a processor, and a power control program for an optical storage and charging station stored in the memory and executable on the processor. The processor executes the power control program to implement the steps of the power control method for the optical storage and charging station as described in any one of claims 1 to 7.

10. A medium, characterized in that, The medium stores a program for implementing a power control method for an optical storage and charging station, and the program for implementing the power control method for an optical storage and charging station is executed by a processor to implement the steps of the power control method for an optical storage and charging station as described in any one of claims 1 to 7.

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