Intelligent scheduling method and system for optical storage and charging micro-grid system

Abstract

The invention belongs to the technical field of photovoltaic energy storage charging scheduling, and particularly relates to an intelligent scheduling method and system for a photovoltaic energy storage charging micro-grid system, and the system comprises a photovoltaic power generation detection module, an energy storage equipment detection module, an electric equipment detection module, an upper computer, and an optimization scheduling module. The signal output ends of the photovoltaic power generation detection module, the energy storage equipment detection module and the electric equipment detection module are connected with the upper computer, and the output end of the upper computer is connected with the optimal scheduling module. According to the scheme, based on information collection of the photovoltaic power generation equipment, the energy storage equipment, the power utilization equipment and other equipment, comprehensive information conditions can be obtained, and the power utilization mode is comprehensively scheduled based on the comprehensive information, so that the method can be better suitable for the use environment. The advanced communication technology realizes rapid and reliable data transmission among devices, ensures that a scheduling instruction can be timely and accurately transmitted to each device, and ensures that the acquired data can be fed back to a scheduling center in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic energy storage charging scheduling, and particularly to an intelligent scheduling method and system for a photovoltaic energy storage charging microgrid system. Background Art

[0002] Photovoltaic, that is, a photovoltaic power generation system, is a power generation system that uses the photovoltaic effect of semiconductor materials to convert solar radiant energy into electrical energy. The energy source of the photovoltaic power generation system is solar energy that is inexhaustible and renewable, and it is a clean, safe and renewable energy. The photovoltaic power generation process does not pollute the environment or damage the ecology.

[0003] Photovoltaic power generation systems are divided into independent photovoltaic systems and grid-connected photovoltaic systems. A photovoltaic power generation system is composed of equipment such as a solar cell array, a battery pack, a charge and discharge controller, an inverter, an AC distribution cabinet, and a solar tracking control system.

[0004] The photovoltaic energy storage charging microgrid system combines photovoltaic power generation, energy storage devices and charging facilities to form a relatively independent small power system. The intelligent scheduling system is the "brain" of this microgrid system. By real-time monitoring and analyzing various data in the system, such as photovoltaic power generation, the power state of energy storage devices, charging and discharging requirements, etc., and applying advanced algorithms and control strategies, it realizes the coordinated control of each component to achieve goals such as the efficient operation of the system, energy balance and cost optimization.

[0005] Electric vehicle charging stations: The intelligent scheduling system of the photovoltaic energy storage charging microgrid system can effectively utilize photovoltaic power generation and energy storage devices to provide stable and efficient charging services for electric vehicles, reduce dependence on the large power grid, and at the same time achieve energy conservation, emission reduction and cost control.

[0006] Industrial parks: Provide reliable power supply for enterprises and facilities in the park, optimize energy utilization through intelligent scheduling, improve the energy self-sufficiency rate, reduce electricity costs, and at the same time reduce the impact on the environment.

[0007] Remote areas and islands: In remote areas and islands where it is difficult to cover the power grid, the intelligent scheduling system of the photovoltaic energy storage charging microgrid system can make full use of local solar energy resources and combine energy storage devices to meet the electricity demands of local residents and facilities, and improve the reliability and stability of power supply.

[0008] At present, the photovoltaic energy storage charging microgrid system usually outputs electric energy in a fixed manner. However, photovoltaic power generation does not always meet the power supply demand, which is related to many factors. For example, the external environment affects the photovoltaic power generation effect, and the structural composition of the photovoltaic power generation equipment itself makes the photovoltaic power supply unable to meet the demand, etc. Therefore, the fixed output of electric energy affects the normal charging of electrical equipment. Summary of the Invention

[0009] The object of the present invention is to provide an intelligent scheduling method and system for a photovoltaic energy storage charging microgrid system, so as to solve the problem that the current photovoltaic energy storage charging microgrid system usually outputs electric energy in a fixed manner, but photovoltaic power generation cannot always meet the power supply demand. This is related to many factors, such as the influence of external environmental reasons on the photovoltaic power generation effect, the structural composition of the photovoltaic power generation equipment itself, resulting in the inability of photovoltaic power supply to meet the demand, etc. Therefore, the fixed output of electric energy affects the normal charging of electrical equipment.

[0010] To achieve the above object, the present invention provides the following technical solution: An intelligent scheduling system for a photovoltaic energy storage charging microgrid system, including:

[0011] A photovoltaic power generation detection module, an energy storage device detection module, an electrical equipment detection module, a host computer, and an optimal scheduling module;

[0012] Among them, the signal output ends of the photovoltaic power generation detection module, the energy storage device detection module, and the electrical equipment detection module are connected to the host computer, and the output end of the host computer is connected to the optimal scheduling module.

[0013] Preferably, it further includes a communication module. The host computer is connected to the communication module, and the host computer is networked based on the communication module to obtain historical light intensity data, weather forecast information, and historical charging data of electrical equipment. The host computer predicts the future power generation of photovoltaic power generation, analyzes the historical charging data of electrical equipment, and predicts the charging load demand in different time periods.

[0014] Preferably, it further includes a display. The output end of the host computer is connected to the display, and the information and prediction conclusions obtained by the host computer are displayed through the display.

[0015] Preferably, the photovoltaic power generation detection module includes an output power detection circuit, a light intensity sensor, and a temperature sensor. The light intensity sensor and the temperature sensor are installed on the periphery of the photovoltaic power generation equipment, and the output power detection circuit is electrically connected to the photovoltaic power generation equipment for detecting the output power of the photovoltaic power generation equipment.

[0016] Preferably, the energy storage device detection module includes an energy storage voltage detection circuit, an energy storage current detection circuit, and a state of charge detection circuit. The energy storage voltage detection circuit, the energy storage current detection circuit, and the state of charge detection circuit are electrically connected to the energy storage device for detecting the voltage, current, and state of charge of the energy storage device.

[0017] Preferably, the electrical equipment detection module includes a charging power detection circuit and an electrical equipment information acquisition unit. The charging power detection circuit and the electrical equipment information acquisition unit can be electrically connected to the electrical equipment and can be separated for detecting the information and charging power of the electrical equipment.

[0018] An intelligent scheduling method for a photovoltaic-storage-charging microgrid system. This intelligent scheduling method for the photovoltaic-storage-charging microgrid system is based on an intelligent scheduling system of the photovoltaic-storage-charging microgrid system, and the specific steps are as follows:

[0019] S1: System initialization and connection setting

[0020] S11: In the initial stage of system construction, the photovoltaic power generation detection module establishes a two-way electrical connection with the photovoltaic power generation equipment through a high-temperature-resistant and anti-interference shielded cable. The voltage / current sensor built in the photovoltaic power generation detection module is directly connected to the output line of the photovoltaic panel to collect DC voltage and current data in real time; the data is transmitted to the upper computer through the RS485 communication protocol;

[0021] S12: The energy storage device detection module is electrically connected to the energy storage device through the CAN bus protocol. The energy storage device detection module measures and uploads the state of charge and health state data to the upper computer;

[0022] S13: The electrical equipment detection module is deployed on the electrical equipment. The electrical equipment detection module uses an intelligent electricity meter and an Internet of Things sensor to establish a connection with the upper computer through the ZigBee wireless communication technology;

[0023] S14: At the same time, in the upper computer, an industrial-grade server architecture is adopted to deploy a distributed recognition system and an intelligent analysis system. The recognition system is based on edge computing technology to perform noise reduction processing and feature extraction on the original data; the analysis system integrates a machine learning framework;

[0024] S2: Data collection, analysis and prediction

[0025] S21: During system operation, the photovoltaic power generation detection module collects the irradiance and conversion efficiency data of the photovoltaic power generation equipment at a high frequency interval of 100 ms and transmits it to the upper computer at a rate of 1 Gbps through the optical fiber network. The recognition system performs outlier detection on the data to eliminate invalid data caused by cloud cover and equipment failures; the analysis system combines the meteorological data API and uses the LSTM neural network model to predict the power generation for the next 24 hours;

[0026] S22: The energy storage device detection module monitors the charge and discharge current, voltage fluctuation, and cycle life attenuation parameters of the battery pack in real time, communicates with the upper computer through the OPCUA protocol. The analysis system uses a grey prediction model to evaluate the battery health state and predict the remaining service life of the battery pack; at the same time, according to the peak-valley electricity price period of the power grid, a charge and discharge strategy recommendation for the energy storage device is formulated;

[0027] S23: The electrical equipment detection module continuously collects the real-time power curve of the equipment and the historical charging period distribution data. The identification system classifies the electrical equipment through a clustering algorithm. The analysis system, based on the time series decomposition algorithm and combined with factors such as holidays and weather changes, predicts the charging load demand at different times in the next 7 days, providing data support for dispatching decisions;

[0028] Furthermore, in S2: Data Collection, Analysis, and Prediction, it also includes:

[0029] Prediction of the power generation of photovoltaic power generation equipment:

[0030] Suppose the factors affecting the power generation of photovoltaic power obtained through historical data and the detection module are light intensity I (unit: lux), temperature T (unit: degree Celsius), photovoltaic panel area S (unit: square meter), equipment efficiency η, etc. A simple linear regression prediction model can be established (the actual situation may be more complex, and this is an example here):

[0031] Epred = α + β1×I + β2×T + β3×S + β4×η

[0032] Among them, Epred is the predicted power generation of photovoltaic power (unit: kilowatt-hour), α is the constant term, and β1, β2, β3, β4 are the coefficients obtained through regression analysis of historical data.

[0033] Prediction of the charging load demand of electrical equipment:

[0034] Suppose the historical charging data of the electrical equipment contains the charging power Chist(t) (unit: kilowatt-hour) at different time periods t (in hours). The method of time series analysis, such as the autoregressive model (AR), can be used to predict the charging load demand at different future time periods.

[0035] Let Cpred(t) be the predicted charging load demand at time period t. For a p-order autoregressive model (AR(p)):

[0036] Cpred(t) = ∑i = 1p φi×Chist(t - i) + ∈(t)

[0037] Among them, φi is the autoregressive coefficient obtained by fitting the historical charging data; ∈(t) is the random error term, representing the part that cannot be explained by historical data.

[0038] Overall energy balance relationship of the system:

[0039] Let the power generation of photovoltaic power be Epv, the initial power of the energy storage device be Es0, the charge and discharge amount of the energy storage device be ΔEs (charging is positive, discharging is negative), and the power consumption of the electrical equipment be Eu. Then the energy balance relationship of the system can be expressed as:

[0040] Es0 + Epv + ΔEs - Eu = Es1

[0041] Among them, Es1 is the remaining power of the energy storage device at a certain moment.

[0042] S3: Intelligent scheduling and real-time control

[0043] Based on the conclusion output by the analysis system, the optimization scheduling module adopts a multi-objective genetic algorithm, comprehensively considering the charging and discharging limits of the energy storage device and the power limit constraints of the charging facilities, and generates an optimal scheduling plan.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] This solution is based on the information collection of devices such as photovoltaic power generation devices, energy storage devices, and electrical equipment, and can obtain a more comprehensive information situation. Based on this comprehensive information, the power consumption method is comprehensively scheduled, so that it can better adapt to the usage environment.

[0046] Advanced communication technology: realizes fast and reliable data transmission between devices, ensures that scheduling instructions can be conveyed to each device in a timely and accurate manner, and at the same time ensures that the collected data can be fed back to the scheduling center in real time.

[0047] Intelligent algorithms and models: various intelligent algorithms are used for power generation prediction, load prediction and optimization scheduling to improve the accuracy and effectiveness of the scheduling plan. At the same time, an accurate system model is established to better describe and analyze the operating characteristics of the photovoltaic energy storage charging microgrid system.

[0048] Energy Management System (EMS): As the core software platform of the intelligent scheduling system, EMS integrates various functions such as data collection, monitoring, analysis, prediction, scheduling and control, and realizes the comprehensive management and optimal operation of the photovoltaic energy storage charging microgrid system. Description of the drawings

[0049] Figure 1 It is the system logic block diagram of the present invention;

[0050] Figure 2 It is the schematic diagram of the system logic block structure of the photovoltaic power generation detection module of the present invention;

[0051] Figure 3 It is the system logic block diagram of the energy storage device detection module of the present invention;

[0052] Figure 4 It is the system logic block diagram of the electrical equipment detection module of the present invention;

[0053] Figure 5 It is the flow chart of the scheduling method of the present invention;

[0054] Figure 6 It is the specific step flowchart for the system initialization and connection setting of the present invention;

[0055] Figure 7 It is the specific step flowchart for the data collection, analysis and prediction of the present invention. Specific Embodiment

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0057] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0058] Embodiment 1:

[0059] Please refer to Figures 1-7 , the present invention provides a technical solution: an intelligent scheduling system for a photovoltaic-storage-charging microgrid system, including: a photovoltaic power generation detection module, a energy storage device detection module, an electrical equipment detection module, a host computer, and an optimization scheduling module;

[0060] Among them, the signal output ends of the photovoltaic power generation detection module, the energy storage device detection module, and the electrical equipment detection module are connected to the host computer, and the output end of the host computer is connected to the optimization scheduling module.

[0061] Analysis of the above content: This solution can monitor photovoltaic power generation, energy storage devices and charging facilities, forming a relatively independent small power system. The intelligent scheduling system is the "brain" of this microgrid system. By real-time monitoring and analyzing various data in the system, such as photovoltaic power generation, the power state of energy storage devices, charging and discharging requirements, etc., and using advanced algorithms and control strategies, it realizes the coordinated control of each component to achieve goals such as the efficient operation of the system, energy balance and cost optimization.

[0062] The technical solutions recorded in the present invention have the following functions:

[0063] Data collection and monitoring, power generation prediction, load prediction and optimization scheduling, specifically as follows:

[0064] Data Acquisition and Monitoring

[0065] Collect real-time data such as the output power, light intensity, temperature of the photovoltaic power generation system, as well as the voltage, current, state of charge (SOC) of the energy storage device, and the charging power of the charging facility, charging vehicle information, etc.

[0066] By monitoring these data, the system can comprehensively understand the operating status of the microgrid system and provide a basis for subsequent dispatching decisions.

[0067] Power Generation Prediction

[0068] Predict the future power generation of the photovoltaic power generation system based on historical light data, weather forecasts and other information.

[0069] Accurate power generation prediction helps to plan in advance the charging and discharging strategies of energy storage devices and the usage arrangements of charging facilities to cope with the intermittency and uncertainty of photovoltaic power generation.

[0070] Load Prediction

[0071] Analyze the historical charging data of the charging facility, vehicle arrival patterns, etc., and predict the charging load demand during different time periods.

[0072] Load prediction is crucial for reasonably allocating system resources, avoiding overloads and ensuring the stable operation of the system.

[0073] Optimal Dispatching

[0074] Based on the results of power generation prediction and load prediction, as well as the operating constraints of the system, such as the charging and discharging limits of energy storage devices, power limits of charging facilities, etc., use optimization algorithms (such as genetic algorithms, dynamic programming, etc.) to formulate the optimal dispatching plan.

[0075] The dispatching plan includes determining the charging and discharging time and power of energy storage devices, the power supply distribution from the photovoltaic power generation system to the charging facility, and the switching strategy between different operating modes (such as island operation, grid-connected operation), etc., to achieve the energy balance of the system and maximize economic benefits.

[0076] The island operation of the photovoltaic-storage-charging microgrid system refers to an operating mode in which the microgrid system operates independently and meets the internal load demand when it is disconnected from the external large power grid. The following is a detailed introduction to island operation:

[0077] Operating Characteristics

[0078] Self - sufficiency: In the island operation mode, the PV - energy storage - charging microgrid system relies on its own components such as photovoltaic power generation systems, energy storage devices, and charging facilities to achieve self - generation and self - consumption of electricity. The photovoltaic power generation system converts solar energy into electrical energy to supply power to charging facilities and other loads. The energy storage device stores electrical energy when the photovoltaic power generation is excessive and releases electrical energy when the photovoltaic power generation is insufficient or the load demand is large to maintain the energy balance of the system.

[0079] Stability challenges: Since the system loses connection with the large power grid during island operation and cannot rely on the powerful regulation ability of the large power grid to maintain the stability of voltage and frequency, the stability of the system faces great challenges. The intermittency and uncertainty of photovoltaic power generation, as well as the dynamic changes of the load, may cause fluctuations in the system voltage and frequency. To ensure the stable operation of the system, advanced control strategies and technologies are needed, such as power control of distributed power sources and fast - response control of energy storage devices.

[0080] High reliability requirements: The PV - energy storage - charging microgrid system in island operation usually provides power guarantee for specific areas or important loads, such as hospitals and communication base stations in remote areas. Therefore, high reliability requirements are imposed on the system. The system needs to have perfect fault detection and fault - tolerance capabilities, and be able to quickly take measures for isolation and repair when some components fail to ensure the continuous power supply of the system.

[0081] Control strategies

[0082] Voltage and frequency control: By adjusting the output voltage and frequency of distributed power sources (such as photovoltaic inverters) and energy storage converters to match the load demand and maintain the system voltage and frequency within the allowable range. Common control methods include droop control, virtual synchronous machine control, etc. Droop control adjusts the output power of the power source according to the voltage and frequency deviation of the system according to a certain droop characteristic; virtual synchronous machine control simulates the operating characteristics of traditional synchronous generators, enabling distributed power sources to have voltage and frequency regulation capabilities similar to synchronous generators and enhancing the stability of the system.

[0083] Power balance control: Real - time monitor the photovoltaic power generation, the state of charge of the energy storage device, and the load power. According to the energy balance of the system, reasonably distribute the output power of distributed power sources and energy storage devices. When the photovoltaic power generation is greater than the load demand, the excess electrical energy is stored in the energy storage device; when the photovoltaic power generation is insufficient, the energy storage device supplements the insufficient power to ensure the power balance of the system.

[0084] Island Detection and Protection: To ensure the safety and reliability of the system during island operation, effective island detection and protection mechanisms are required. Island detection methods include active detection methods and passive detection methods. The active detection method determines whether an island occurs by injecting specific signals into the system and detecting the system's response; the passive detection method determines the island state by monitoring changes in parameters such as the system's voltage, frequency, and phase. Once the island operation state is detected, the protection device will act quickly to isolate the island from the external power grid and adopt corresponding control strategies to ensure the stable operation of the system within the island.

[0085] Application Scenarios

[0086] Power Supply in Remote Areas: In remote areas, due to the high cost and difficulty of power grid construction, the island operation mode of the photovoltaic energy storage charging microgrid system can provide independent power supply for local residents and infrastructure. For example, in some mountainous areas, grasslands, or island areas, by building a photovoltaic energy storage charging microgrid system and utilizing the rich local solar energy resources, a self-sufficient power supply can be achieved to solve the power consumption problems in remote areas.

[0087] Emergency Power Supply: In emergency situations such as natural disasters and power grid failures, the island-operating photovoltaic energy storage charging microgrid system can serve as an emergency power source to provide reliable power support for important loads such as hospitals, fire departments, and communication centers. These important loads require continuous power supply during emergencies to ensure the safety of personnel and the normal operation of critical equipment. The photovoltaic energy storage charging microgrid system can quickly start and operate stably in island mode to meet the emergency power supply requirements.

[0088] Applications in Special Places: For some special places with high requirements for the independence and reliability of power supply, such as military bases and large data centers, the island operation mode of the photovoltaic energy storage charging microgrid system can provide additional power support. These places usually need to independently control the power supply to prevent the impact of external power grid failures or other factors on their operation. By adopting the island operation mode of the photovoltaic energy storage charging microgrid system, the power supply safety and reliability of these places can be improved.

[0089] The grid-connected operation of the photovoltaic energy storage charging microgrid system refers to a mode in which this microgrid system is connected to the external large power grid and conducts power exchange and coordinated operation. The following is a detailed introduction to grid-connected operation:

[0090] Operating Characteristics

[0091] Interaction with the Large Power Grid: In the grid-connected operation mode, the photovoltaic energy storage charging microgrid system can conduct two-way power transmission with the large power grid. When the photovoltaic power generation in the photovoltaic energy storage charging microgrid system is greater than the load demand, the excess electric energy can be transmitted to the power grid; when the photovoltaic power generation is insufficient or the energy storage device has a low power level and cannot meet the load demand, the system can obtain power from the large power grid to ensure stable power supply to the load.

[0092] With the regulation ability of the large power grid: The large power grid has a strong regulation ability, which can provide services such as voltage, frequency support and backup power supply for the PV-storage-charging microgrid system. This enables the voltage and frequency of the PV-storage-charging microgrid system to remain relatively stable during grid-connected operation, reducing the requirements for its own control equipment, and at the same time improving the reliability and stability of the system.

[0093] Optimizing energy utilization: Through coordinated operation with the large power grid, the PV-storage-charging microgrid system can better optimize energy utilization. It can reasonably arrange the charging and discharging time and power of energy storage equipment according to the grid electricity price policy and the system's own power generation and consumption conditions, realizing charging during low load periods and discharging during high load periods, thereby reducing the electricity cost and improving the energy utilization efficiency.

[0094] Control strategies

[0095] Grid-connected control: During grid-connected operation, it is necessary to ensure the synchronization and safe connection between the PV-storage-charging microgrid system and the large power grid. By controlling the output voltage, frequency and phase of the PV inverter and energy storage converter to make them consistent with the grid voltage, frequency and phase, smooth grid connection is achieved. At the same time, it also needs to have protection functions such as overcurrent, overvoltage and undervoltage to prevent system failures from affecting the power grid.

[0096] Power coordination control: According to the system's power generation and consumption conditions and the grid dispatching requirements, power coordination control is carried out on the distributed power sources and energy storage equipment in the PV-storage-charging microgrid system. For example, when the grid load is high, the power transmitted from the PV-storage-charging microgrid system to the grid can be increased; when the grid load is low, the output power can be appropriately reduced, or even a small amount of power can be absorbed from the grid to charge the energy storage equipment. In this way, power balance and optimal dispatching between the PV-storage-charging microgrid system and the large power grid are achieved.

[0097] Power quality control: In order to ensure the power quality delivered to the grid, it is necessary to control the quality of the output power of the PV-storage-charging microgrid system. This includes the monitoring and regulation of parameters such as voltage deviation, frequency deviation and harmonic content. By adopting advanced power electronic technologies and control algorithms, the harmonic components in the system's output power are reduced, improving the power quality and meeting the grid connection requirements.

[0098] Application scenarios

[0099] Urban Commercial Area: In the urban commercial area, the PV-storage-charging microgrid system can operate in parallel with the power grid to supply electricity to commercial buildings, parking lots, etc. in the commercial area. The electricity load in the commercial area is usually high, and there is an obvious peak-valley difference. Through the parallel operation of the PV-storage-charging microgrid system, part of the load demand can be met by PV power generation during the day, and the excess electricity can be sold to the power grid; at night or during peak electricity consumption, electricity can be obtained from the power grid to achieve optimal utilization of energy and cost reduction.

[0100] Industrial Park: There are a large number of industrial enterprises and production equipment in the industrial park, and high requirements are placed on the reliability and stability of power supply. The parallel operation of the PV-storage-charging microgrid system can provide various power supply guarantees for the industrial park. On the one hand, the rooftop PV power generation and energy storage equipment in the park are used to meet the electricity demand of some loads in the park and reduce the electricity cost of enterprises; on the other hand, through the parallel operation with the large power grid, when the PV power generation is insufficient or the load changes suddenly, electricity can be obtained from the power grid in a timely manner to ensure the normal operation of production equipment.

[0101] Residential Community: In the residential community, the parallel operation of the PV-storage-charging microgrid system can achieve local consumption of distributed energy and feed-in of surplus electricity. Residents can install PV power generation equipment on their own rooftops and be equipped with an energy storage system. After meeting their own electricity demand, the excess electricity can be sold to the power grid to obtain certain economic benefits. At the same time, through the parallel operation with the power grid, the power supply in the residential community is more reliable and normal electricity use will not be affected by the intermittency of PV power generation.

[0102] Control Execution

[0103] Convert the optimized scheduling plan into specific control instructions and send them to the controllers of each device, such as PV inverters, energy storage converters, charging modules, etc., to achieve real-time control of the devices.

[0104] Ensure that the devices operate according to the scheduling requirements to achieve the expected system performance and operation goals.

[0105] Safety Management

[0106] Real-time monitor the safety status of the system, including fault conditions such as overcurrent, overvoltage, undervoltage, and leakage.

[0107] When a safety problem is detected, quickly take corresponding protection measures, such as cutting off the faulty circuit, adjusting the operation state of the device, etc., to ensure the safety of personnel and equipment and at the same time ensure the stable operation of the system.

[0108] Example Two:

[0109] Please refer to Figures 1-7, the present invention provides a technical solution based on Embodiment 1: It further includes a communication module. The host computer establishes a connection with the communication module, and the host computer is connected to the network based on the communication module to obtain historical light data, weather forecast information, and historical charging data of electrical devices. The host computer predicts the future power generation of photovoltaic power generation, analyzes the historical charging data of electrical devices, and predicts the charging load requirements at different time periods.

[0110] Analysis of the above content: The communication module is connected to the network and can obtain network information, such as historical light data, weather forecast information, historical charging data of electrical devices, etc., which is convenient for the host computer to make predictions.

[0111] The communication module here adopts a WiFi module, and the models of the WiFi module are ESP8266, ESP32, or CC3200. These modules support WiFi wireless communication, can be used to connect to the Internet, and realize Internet of Things applications.

[0112] Embodiment 3:

[0113] Please refer to Figures 1-7 , the present invention provides a technical solution based on Embodiment 1: It further includes a display. The output end of the host computer is connected to the display, and the information and prediction conclusions obtained by the host computer are displayed through the display.

[0114] Analysis of the above content: The display is used for displaying data and information. When the host computer makes a decision, such as reducing the charging efficiency, the remaining power is not enough to fully charge the electrical device, etc., the above information is displayed through the display.

[0115] Embodiment 4:

[0116] Please refer to Figures 1-7 , the present invention provides a technical solution based on Embodiment 1: The photovoltaic power generation detection module includes an output power detection circuit, a light intensity sensor, and a temperature sensor. The light intensity sensor and the temperature sensor are installed on the periphery of the photovoltaic power generation device, and the output power detection circuit is electrically connected to the photovoltaic power generation device for detecting the output power of the photovoltaic power generation device.

[0117] The output power detection circuit adopts PZ80L-DP2: Ankerui intelligent programmable photovoltaic solar DC power meter, which is designed for application occasions such as DC panels and solar power supply. It can measure voltage, current, power, forward and reverse electric energy in the DC system, can be locally displayed, and can also be connected to industrial control devices and computers to form a measurement and control system. It has an RS-485 communication interface, adopts the Modbus-RTU protocol, and can also have analog output, relay alarm output, digital input / output and other functions.

[0118] Light intensity sensor: GY-3028: It uses the original ROHM BH1750FVI chip, with a power supply of 3 - 5V, a light intensity range of 0 - 65535 lx, an internal 16-bit AD converter in the sensor, direct digital output, adopts the standard NXP IIC communication protocol, and has a size of 13.9mm × 18.5mm;

[0119] Temperature sensor: PT1003: It is a common platinum resistance temperature sensor. The resistance value is 100 ohms at 0°C and changes with temperature. The measurable temperature range is generally -200°C to 850°C.

[0120] Example Five:

[0121] Please refer to Figures 1-7 , based on Example One, the present invention provides a technical solution: The energy storage device detection module includes an energy storage voltage detection circuit, an energy storage current detection circuit, and a state of charge detection circuit. The energy storage voltage detection circuit, the energy storage current detection circuit, and the state of charge detection circuit are electrically connected to the energy storage device and are used to detect the voltage, current, and state of charge of the energy storage device.

[0122] Energy storage voltage detection circuit ACTDS-DV.V5: A DC voltage sensor of Acrel Electric Co., Ltd., which uses Hall sensor technology and can accurately measure the energy storage voltage. It has the characteristics of fast response speed, strong overload capacity, high precision, and 3.5kV high insulation between the primary and secondary sides. It can be directly connected to the power supply or energy storage battery to monitor voltage changes in real time.

[0123] Energy storage current detection circuit CSNE1000N1: A current sensor of Honeywell, based on advanced magnetic sensing technology, powered by a single power supply, with CAN bus output. It can measure DC and AC currents of ±1000A, is suitable for energy storage scenarios of 1200V - 2500V, has an enhanced installation hole with a steel sleeve, supports 8K high-frequency sampling and square wave processing, and has an accuracy of 0.2%. It can be used for current monitoring in the battery management system of energy storage devices.

[0124] State of charge detection circuit BQ79616-Q12: A device launched by Texas Instruments (TI) for large lithium-ion battery pack applications. It is often used in conjunction with an evaluation board, such as the BQ79616EVM evaluation module. Each module can manage up to 16 batteries (up to 80V) at most, and up to 35 modules can be stacked to form a system with up to 560 series-connected batteries. It is equipped with precise measurement and synchronous communication functions and can support the controller to perform state of charge (SOC) and state of health (SOH) estimation.

[0125] Example Six:

[0126] Please refer to Figures 1-7, the present invention provides a technical solution based on Embodiment 1: The electrical device detection module includes a charging power detection circuit and an electrical device information acquisition unit. The charging power detection circuit and the electrical device information acquisition unit can establish an electrical connection with the electrical device and can be separated, and are used for detecting the information and charging power of the electrical device.

[0127] Charging power detection circuit: POWER-ZKM002C3: It is a tester under ChargerLAB, supporting voltage detection from 0 to 50V and current detection from 0 to 6A. It can detect the USB PD3.1 fast charging protocol, monitor the power of the charger for the device, and can also read the PD output gear of the charger, fast charging protocol, and detect the information of the cable, etc.

[0128] Electrical device information acquisition unit: Three-phase multi-protocol acquisition terminal: It includes an electrical information acquisition circuit, a 485 communication circuit, a main control circuit, a clock circuit, a lora communication circuit, a 4G communication circuit, and a power supply circuit. The acquisition chip in its electrical information acquisition circuit uses the RN8302B three-phase multi-functional anti-theft electricity metering chip. The sampling channels include seven ADCs and their sampling data processing circuits, which can obtain the sampling data of three-phase line currents, three-phase line voltages, and one neutral line current, and realize the acquisition and reporting of users' real-time electricity parameters.

[0129] Embodiment 7:

[0130] Please refer to Figures 1-7 , the present invention provides a technical solution: An intelligent scheduling method for a photovoltaic energy storage charging microgrid system. This intelligent scheduling method for the photovoltaic energy storage charging microgrid system is based on the intelligent scheduling system of the photovoltaic energy storage charging microgrid system. The specific steps are as follows:

[0131] S1: System initialization and connection setting

[0132] S11: In the initial stage of system construction, the photovoltaic power generation detection module establishes a two-way electrical connection with the photovoltaic power generation equipment through a high-temperature resistant and anti-interference shielded cable. The voltage / current sensors built in the photovoltaic power generation detection module are directly connected to the output line of the photovoltaic panel to collect DC voltage and current data in real time; the data is transmitted to the upper computer through the RS485 communication protocol;

[0133] S12: The energy storage device detection module realizes an electrical connection with the energy storage device through the CAN bus protocol, and the energy storage device detection module measures and uploads the state of charge and health state data to the upper computer;

[0134] S13: The electrical device detection module is deployed on the electrical device. The electrical device detection module establishes a connection with the upper computer through the intelligent meter and the Internet of Things sensor using the ZigBee wireless communication technology;

[0135] S14: Meanwhile, an industrial-grade server architecture is adopted in the upper computer to deploy a distributed recognition system and an intelligent analysis system. The recognition system, based on edge computing technology, performs noise reduction processing and feature extraction on the original data; the analysis system integrates a machine learning framework;

[0136] S2: Data collection, analysis and prediction

[0137] S21: When the system is running, the photovoltaic power generation detection module collects the irradiance and conversion efficiency data of the photovoltaic power generation equipment at a high frequency with an interval of 100 ms, and transmits the data to the upper computer at a rate of 1 Gbps through the fiber optic network. The recognition system performs outlier detection on the data and eliminates invalid data caused by cloud occlusion and equipment failures; the analysis system combines the meteorological data API and uses the LSTM neural network model to predict the power generation for the next 24 hours;

[0138] S22: The energy storage device detection module monitors the charge and discharge current, voltage fluctuation, and cycle life attenuation parameters of the battery pack in real time, communicates with the upper computer through the OPCUA protocol. The analysis system uses the grey prediction model to evaluate the battery health status and predict the remaining service life of the battery pack; meanwhile, according to the peak-valley electricity price periods of the power grid, it formulates suggestions for the charge and discharge strategies of the energy storage device;

[0139] S23: The electrical equipment detection module continuously collects the real-time power curve and historical charging period distribution data of the equipment. The recognition system classifies the electrical equipment through the clustering algorithm. The analysis system, based on the time series decomposition algorithm, combines factors such as holidays and weather changes to predict the charging load demand for different periods in the next 7 days, providing data support for scheduling decisions;

[0140] Furthermore, in S2: Data collection, analysis and prediction, it also includes:

[0141] Prediction of the power generation of photovoltaic power generation equipment:

[0142] Suppose the factors affecting the photovoltaic power generation obtained through historical data and the detection module are light intensity I (unit: lux), temperature T (unit: degree Celsius), photovoltaic panel area S (unit: square meter), equipment efficiency η, etc. A simple linear regression prediction model can be established (the actual situation may be more complex, this is an example here):

[0143] Epred = α + β1×I + β2×T + β3×S + β4×η

[0144] Among them, Epred is the predicted photovoltaic power generation (unit: kWh), α is the constant term, and β1, β2, β3, β4 are the coefficients obtained through regression analysis of historical data.

[0145] Prediction of the charging load demand of electrical equipment:

[0146] Suppose the historical charging data of the electrical equipment includes the charging power Chist(t) (unit: kWh) at different time periods t (in hours). The time series analysis method, such as the autoregressive model (AR), can be used to predict the charging load demand in different future time periods.

[0147] Let Cpred(t) be the predicted charging load demand in the time period t. For a p-order autoregressive model (AR(p)):

[0148] Cpred(t) = ∑i = 1p φi × Chist(t - i) + ∈(t)

[0149] where φi is the autoregressive coefficient obtained by fitting the historical charging data; ∈(t) is the random error term, representing the part that cannot be explained by the historical data.

[0150] Overall energy balance relationship of the system:

[0151] Let the photovoltaic power generation be Epv, the initial power of the energy storage device be Es0, the charge and discharge power of the energy storage device be ΔEs (charging is positive, discharging is negative), and the power consumption of the electrical equipment be Eu. Then the energy balance relationship of the system can be expressed as:

[0152] Es0 + Epv + ΔEs - Eu = Es1

[0153] where Es1 is the remaining power of the energy storage device at a certain moment.

[0154] S3: Intelligent scheduling and real-time control

[0155] Based on the conclusion output by the analysis system, the optimization scheduling module adopts the multi-objective genetic algorithm, comprehensively considering the charge and discharge limits of the energy storage device and the power limit constraints of the charging facilities, and generates the optimal scheduling plan.

[0156] The plan includes:

[0157] Energy storage device scheduling: Charge with a current of 0.3C during the valley electricity period of the power grid (such as 00:00 - 08:00), and discharge with a current of 0.2C during the peak electricity period (such as 18:00 - 22:00);

[0158] Photovoltaic power generation distribution: Give priority to meeting the local power consumption demand, and distribute the remaining power according to the priority of the charging piles (fast charging pile > slow charging pile);

[0159] Operation mode switching: When the photovoltaic power generation is less than 15% of the load demand, automatically switch to the mains supply supplementary mode; when the SOC of the energy storage device is less than 10%, start the protection mode to prohibit discharging.

[0160] The optimized scheduling scheme is converted into JSON format control instructions through the MQTT communication protocol and sent to each device controller. The controller uses a PLC programmable logic controller, which supports the ModbusTCP protocol, can quickly respond to instructions and adjust device parameters, and realizes the MPPT maximum power point tracking of photovoltaic power generation devices, the constant current and constant voltage charging control of energy storage devices, and the dynamic power regulation of charging facilities, ensuring that the system operates stably at an energy efficiency level of more than 98%.

[0161] Importantly, it should be noted that the construction and arrangement of the present application shown in multiple different exemplary embodiments are merely illustrative. Although only a few embodiments are described in detail in this disclosure, those who refer to this disclosure should easily understand that many modifications are possible without substantially departing from the novel teachings and advantages of the subject matter described in this application (for example, the dimensions, scales, structures, shapes and proportions of various components, and parameter values (such as temperature, pressure, etc.), installation arrangements, use of materials, color, orientation changes, etc.). For example, an element shown as integrally formed may be composed of multiple parts or elements, the position of the element may be inverted or otherwise changed, and the nature, number or position of discrete elements may be changed or altered. Therefore, all such modifications are intended to be included within the scope of the present invention. The order or sequence of any process or method steps may be changed or reordered according to alternative embodiments. In the claims, any "means plus function" clause is intended to cover the structure that performs the recited function herein, and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present invention. Therefore, the present invention is not limited to a particular embodiment, but extends to various modifications that still fall within the scope of the appended claims.

[0162] The above has shown and described the basic principles, main features and advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included within the present invention, and any reference signs in the claims should not be regarded as limiting the claimed rights involved.

[0163] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent scheduling system for a photovoltaic-storage-charging microgrid system, characterized in that, Including: Photovoltaic power generation detection module, energy storage device detection module, electrical equipment detection module, host computer, and optimization scheduling module; Among them, the signal output ends of the photovoltaic power generation detection module, energy storage device detection module, and electrical equipment detection module are connected to the host computer, and the output end of the host computer is connected to the optimization scheduling module.

2. The intelligent scheduling system of a photovoltaic-storage-charging microgrid system according to claim 1, wherein: It further includes a communication module. The host computer is connected to the communication module. The host computer is networked based on the communication module to obtain historical light intensity data, weather forecast information, and historical charging data of electrical equipment. The host computer predicts the future power generation of photovoltaic power generation, analyzes the historical charging data of electrical equipment, and predicts the charging load requirements at different time periods.

3. The intelligent scheduling system of a photovoltaic-storage-charging microgrid system according to claim 1, characterized in that: It further includes a display. The output end of the host computer is connected to the display. The information and prediction conclusions obtained by the host computer are displayed through the display.

4. The intelligent scheduling system of a photovoltaic-storage-charging microgrid system according to claim 1, characterized in that: The photovoltaic power generation detection module includes an output power detection circuit, a light intensity sensor, and a temperature sensor. The light intensity sensor and the temperature sensor are installed on the periphery of the photovoltaic power generation equipment. The output power detection circuit is electrically connected to the photovoltaic power generation equipment and is used to detect the output power of the photovoltaic power generation equipment.

5. The intelligent scheduling system of a photovoltaic-storage-charging microgrid system according to claim 1, characterized in that: The energy storage device detection module includes an energy storage voltage detection circuit, an energy storage current detection circuit, and a state of charge detection circuit. The energy storage voltage detection circuit, the energy storage current detection circuit, and the state of charge detection circuit are electrically connected to the energy storage device and are used to detect the voltage, current, and state of charge of the energy storage device.

6. The intelligent scheduling system of a photovoltaic-storage-charging microgrid system according to claim 1, characterized in that: The electrical equipment detection module includes a charging power detection circuit and an electrical equipment information acquisition unit. The charging power detection circuit and the electrical equipment information acquisition unit can be electrically connected to the electrical equipment and can be separated, and are used to detect the information and charging power of the electrical equipment.

7. An intelligent scheduling method using the intelligent scheduling system of a photovoltaic-storage-charging microgrid system according to claim 1, characterized in that: The intelligent scheduling method of this photovoltaic-storage-charging microgrid system is based on the intelligent scheduling system of the photovoltaic-storage-charging microgrid system, and the steps are as follows: S1: System initialization and connection setting; S2: Data collection, analysis, and prediction; S3: Intelligent scheduling and real-time control.

8. The intelligent scheduling method according to the intelligent scheduling system of a photovoltaic-storage-charging microgrid system as claimed in claim 7, characterized in that: S1: System initialization and connection setting S11: In the initial stage of system construction, the photovoltaic power generation detection module is bidirectionally electrically connected to the photovoltaic power generation equipment through a high-temperature-resistant and anti-interference shielded cable. The voltage / current sensor built in the photovoltaic power generation detection module is directly connected to the output line of the photovoltaic panel to collect DC voltage and current data in real time; the data is transmitted to the host computer through the RS485 communication protocol; S12: The energy storage device detection module is electrically connected to the energy storage device through the CAN bus protocol. The energy storage device detection module measures and uploads the state of charge and health state data to the host computer; S13: The electrical equipment detection module is deployed on the electrical equipment. The electrical equipment detection module is connected to the host computer through an intelligent electric meter and an Internet of Things sensor using ZigBee wireless communication technology; S14: Meanwhile, an industrial-grade server architecture is adopted in the host computer to deploy a distributed recognition system and an intelligent analysis system. The recognition system is based on edge computing technology to perform noise reduction processing and feature extraction on the original data; the analysis system integrates a machine learning framework.

9. The intelligent scheduling method for an intelligent scheduling system of a photovoltaic energy storage charging microgrid system according to claim 7, characterized in that: S2: Data collection, analysis and prediction S21: When the system is running, the photovoltaic power generation detection module high-frequency collects the irradiance and conversion efficiency data of the photovoltaic power generation equipment at intervals of 100 ms, and transmits them to the host computer through the fiber optic network at a rate of 1 Gbps. The recognition system performs outlier detection on the data to eliminate invalid data caused by cloud occlusion and equipment failures; the analysis system combines with the meteorological data API and uses the LSTM neural network model to predict the power generation for the next 24 hours. S22: The energy storage device detection module continuously monitors the charge and discharge current, voltage fluctuation, and cycle life attenuation parameters of the battery pack, communicates with the host computer through the OPCUA protocol. The analysis system uses the grey prediction model to evaluate the battery health status and predict the remaining service life of the battery pack; at the same time, according to the peak-valley electricity price period of the power grid, a charge and discharge strategy recommendation for the energy storage device is formulated. S23: The power consumption equipment detection module continuously collects the real-time power curve and historical charging period distribution data of the equipment. The recognition system classifies the power consumption equipment through a clustering algorithm. The analysis system is based on the time series decomposition algorithm, combines factors such as holidays and weather changes, and predicts the charging load demand for different periods in the next 7 days to provide data support for scheduling decisions.

10. The intelligent scheduling method for an intelligent scheduling system of a photovoltaic energy storage charging microgrid system according to claim 9, characterized in that: In S2: Data collection, analysis and prediction, it further includes: Power generation prediction of photovoltaic power generation equipment: Assume that the factors affecting the photovoltaic power generation obtained through historical data and the detection module are light intensity I, temperature T, photovoltaic panel area S, and equipment efficiency η; where the unit of light intensity I is lux, the unit of temperature T is degree Celsius, and the unit of photovoltaic panel area S is square meters. Establish a linear regression prediction model: Epred = α + β1×I + β2×T + β3×S + β4×η Where, Epred is the predicted photovoltaic power generation, the unit of photovoltaic power generation is kilowatt-hour, α is the constant term, and β1, β2, β3, β4 are the coefficients obtained through regression analysis of historical data. Prediction of charging load demand of power consumption equipment: Assume that the historical charging data of the power consumption equipment contains the charging power Chist(t) at different time periods t; use the method of time series analysis, the autoregressive model (AR) to predict the charging load demand for different future time periods; the time period t is in hours; the unit of the charging power Chist(t) is kilowatt-hour. Let Cpred(t) be the predicted charging load demand in the time period t. For a p-order autoregressive model (AR(p)): Cpred(t) = ∑i = 1p φi×Chist(t_i) + ∈(t) Among them, φi is the autoregressive coefficient obtained by fitting historical charging data; ∈(t) is the random error term, representing the part that cannot be explained by historical data; Overall system energy balance relationship: Let the photovoltaic power generation be Epv, the initial power of the energy storage device be Es0, the charge and discharge amount of the energy storage device be ΔEs (positive for charging and negative for discharging), and the power consumption of the electrical equipment be Eu. Then the energy balance relationship of the system can be expressed as: Es0 + Epv + ΔEs _ Eu = Es1 Among them, Es1 is the remaining power of the energy storage device at a certain moment.

11. The intelligent scheduling method for the intelligent scheduling system of a photovoltaic-storage-charging microgrid system according to claim 7, characterized in that: S3: Intelligent scheduling and real-time control Based on the conclusion output by the analysis system, the optimal scheduling module adopts a multi-objective genetic algorithm, comprehensively considering the charge and discharge limitations of the energy storage device and the power limitation constraints of the charging facilities, to generate an optimal scheduling plan.

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