Automatic dispatching control system based on photovoltaic power generation

Abstract

The invention relates to the technical field of automatic dispatching and management of a power system, and discloses an automatic dispatching control system based on photovoltaic power generation, which comprises a photovoltaic power generation unit used for converting solar energy into electric energy and monitoring power generation power and power generation efficiency in real time; the energy storage unit is electrically connected with the photovoltaic power generation unit and is used for storing redundant electric energy and intelligently releasing the electric energy according to the load state of the power grid; the meteorological monitoring module is used for collecting and predicting meteorological data in real time, the meteorological data comprise sunlight intensity, temperature, wind speed and the like, and generating a short-term weather forecast; and the demand response management system is electrically connected with the power grid and the user side equipment, and the system intelligently identifies non-critical load equipment by monitoring the load of the power grid in real time. The intelligent scheduling module is combined with meteorological prediction and real-time data, so that the system can dynamically adjust the output power of the photovoltaic power generation unit, ensures that the power generation capacity is matched with the power grid demand, reduces the power waste, and maximizes the utilization of renewable energy sources.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic dispatching and management of electric power systems, and in particular to an automatic dispatching control system based on photovoltaic power generation. Background Art

[0002] With the continuous growth of global energy demand and the enhancement of environmental awareness, the use of renewable energy for power generation has become an important trend in the development of global electricity. As a clean and sustainable form of energy, photovoltaic power generation is widely used in industrial parks, commercial buildings and home systems due to its stability and low maintenance cost. By converting solar energy into electrical energy, photovoltaic power generation can effectively reduce dependence on traditional fossil fuels and reduce carbon emissions, thus helping to optimize the global energy structure.

[0003] Existing photovoltaic power generation systems usually convert solar energy into direct current, and then convert it into alternating current through an inverter for users to use. However, photovoltaic power generation is intermittent and volatile, and is limited by weather conditions and sunlight time, which makes it difficult for photovoltaic systems to ensure the continuity of power supply when the power generation capacity is unstable. In order to cope with these fluctuations, many systems have introduced energy storage units to store excess electricity when the sunlight is sufficient and release electricity when the power generation is insufficient to ensure the stable operation of the power grid. In addition, the existing technology also often controls the user's load through a demand response management system to reduce non-critical loads during peak load periods of the power grid and reduce the pressure on the power grid. However, the existing photovoltaic power generation scheduling system has shortcomings in overall optimization scheduling, especially when dealing with complex load demands and dynamic weather changes, the system responds slowly and the resource allocation efficiency is low.

[0004] The main problem with the existing technology is that the photovoltaic power generation system lacks an intelligent automatic scheduling mechanism and cannot achieve efficient and dynamic management of photovoltaic power generation, energy storage and load. This results in the system failing to fully utilize the energy storage system to shave peaks and fill valleys when the power generation capacity fluctuates, unreasonable scheduling of power resources, increased load pressure on the power grid, and affecting the overall efficiency and stability of the photovoltaic system. Therefore, the existing photovoltaic power generation system requires a solution that can automatically optimize and schedule power resources based on real-time power generation conditions, energy storage status, load demand and meteorological data, so as to achieve the optimal allocation of power resources, ensure the stable operation of the power grid and improve the overall energy utilization efficiency of the system. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an automated dispatching and control system based on photovoltaic power generation, which solves the problem that the photovoltaic power generation system lacks an intelligent automatic dispatching mechanism and cannot achieve efficient and dynamic management of photovoltaic power generation, energy storage and loads.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an automatic dispatching control system based on photovoltaic power generation, comprising:

[0007] Photovoltaic power generation unit, used to convert solar energy into electrical energy and monitor the power generation and efficiency in real time;

[0008] An energy storage unit, electrically connected to the photovoltaic power generation unit, is used to store excess electrical energy and intelligently release electrical energy according to the load status of the power grid;

[0009] The meteorological monitoring module is used to collect and predict meteorological data in real time, including sunshine intensity, temperature, wind speed, etc., and generate short-term weather forecasts;

[0010] A demand response management system is electrically connected to the power grid and user-side equipment. The system monitors the power grid load in real time and intelligently identifies non-critical load equipment, and dynamically adjusts the load consumption of the user-side based on the fluctuation of the power grid load.

[0011] An intelligent dispatching module dynamically optimizes the allocation of power resources by combining photovoltaic power generation, energy storage unit status, meteorological data, and load information fed back by the demand response management system. The intelligent dispatching module includes a prediction unit that can predict the power demand and power generation capacity in the future period based on meteorological data, historical load data, and energy storage status, and automatically adjust the working mode of the photovoltaic power generation unit and the energy storage unit based on the prediction results;

[0012] Among them, the intelligent scheduling module uses a multi-objective optimization algorithm to comprehensively consider the energy storage status, photovoltaic power generation efficiency, grid load conditions and user-side needs to maximize the utilization rate of photovoltaic power generation, peak shaving and valley filling effect and system stability.

[0013] Preferably, the meteorological monitoring module further includes an external meteorological data acquisition unit, which acquires regional meteorological data in real time by connecting to an external meteorological data source, and generates a high-precision short-term meteorological forecast in combination with the meteorological data within the system for dynamically adjusting the power generation prediction of the photovoltaic power generation unit.

[0014] Preferably, the meteorological monitoring module further comprises a data calibration unit, which is used to dynamically calibrate the externally and internally collected meteorological data according to historical meteorological data and photovoltaic power generation data.

[0015] Preferably, the prediction unit of the intelligent scheduling module predicts photovoltaic power generation and power demand through a long short-term memory network or a convolutional neural network model, and combines multi-period meteorological and load historical data for modeling to achieve accurate prediction of future photovoltaic power generation and energy storage demand.

[0016] Preferably, the prediction unit further combines historical load adjustment data of the demand response management system to optimize the load reduction strategy so as to give priority to reducing non-critical loads that have less impact on grid stability during load peaks.

[0017] Preferably, the battery management system of the energy storage unit includes a temperature monitoring unit, a battery power monitoring unit and an adaptive charge and discharge control unit, and the adaptive charge and discharge control unit dynamically adjusts the charging rate and discharge power according to the real-time grid load demand and the power state of the energy storage unit.

[0018] Preferably, the battery management system further includes an abnormality monitoring and early warning module. When the energy storage unit has excessive temperature, abnormal voltage, or abnormal charging and discharging, the early warning mechanism is immediately activated and switched to a safe mode through the intelligent scheduling module.

[0019] Preferably, the demand response management system can conduct two-way communication with the user-side smart devices through a wireless communication network, monitor the operating status of the user-side load equipment in real time, and send load adjustment instructions according to the system scheduling strategy. The user-side smart devices include air-conditioning systems, motors and lighting equipment.

[0020] Preferably, the demand response management system further includes a user-side feedback unit, which can return load reduction status data to the intelligent scheduling module in real time after the user-side load is adjusted, and the intelligent scheduling module optimizes subsequent load scheduling strategies based on the feedback data.

[0021] Preferably, the intelligent scheduling module comprehensively schedules the photovoltaic power generation unit, the energy storage unit and the demand response management system through a multi-objective optimization algorithm, and the optimization objectives of the multi-objective optimization algorithm include: minimizing power loss, maximizing the utilization rate of the energy storage unit, and improving the peak shaving and valley filling effect.

[0022] Working principle: This automatic dispatching and control system based on photovoltaic power generation realizes intelligent power dispatching and management through the collaborative work of multiple modules. First, the photovoltaic power generation unit converts solar energy into electrical energy, monitors the power generation in real time through sensors, and transmits the data to the core dispatching module of the system. In order to balance the fluctuations between power generation and demand, the system uses energy storage units to store excess electricity. When the grid load increases or photovoltaic power generation is insufficient, the energy storage unit releases electricity through the battery management system to balance the supply and demand of the grid, especially to effectively reduce the peak load and fill the valley when the load is peak. The meteorological monitoring module is responsible for collecting real-time meteorological data, including sunshine intensity, temperature and wind speed. Combined with historical meteorological data, it predicts future photovoltaic power generation and power demand through machine learning algorithms to provide accurate forecasting support. At the same time, the demand response management system monitors the grid load in real time. When the load approaches the set threshold, the system controls the non-critical load at the user end through the wireless communication network, and gives priority to reducing its power consumption to reduce the pressure on the grid. All data and prediction results are summarized in the intelligent scheduling module. This module uses a multi-objective optimization algorithm to comprehensively consider the energy storage status, power generation, weather forecast and grid load, and adjusts the operating status of the photovoltaic power generation unit and the energy storage unit in real time to ensure that the utilization rate of photovoltaic power generation is maximized, the efficiency of the energy storage system is improved, and the peak shaving and valley filling effect is optimized. The power scheduling strategy is continuously optimized through the feedback mechanism to ensure that the system maintains a stable and efficient power supply under load changes and fluctuating weather conditions.

[0023] The present invention provides an automatic dispatching and control system based on photovoltaic power generation. It has the following beneficial effects:

[0024] 1. The present invention combines meteorological forecasts and real-time data through an intelligent scheduling module. The system can dynamically adjust the output power of the photovoltaic power generation unit to ensure that the power generation capacity matches the grid demand, reduce power waste, and maximize the use of renewable energy.

[0025] 2. The energy storage unit of the present invention is closely integrated with the grid load, releasing the stored energy during peak load to reduce grid pressure, and storing excess energy during low load to ensure the stability and economy of power supply.

[0026] 3. The energy storage unit of the present invention performs adaptive charge and discharge control through a battery management system to ensure efficient operation of the energy storage device, extend battery life, and maintain system safety and stability during the charge and discharge process.

[0027] 4. The demand response management system monitors and controls the load equipment at the user end in real time through the wireless communication network, and gives priority to reducing non-critical loads when the power grid is overloaded, thereby avoiding insufficient power supply and improving the reliability of the power grid.

[0028] 5. The meteorological monitoring module of the present invention combines machine learning algorithms to predict future power generation and power demand, so that the system can plan the allocation of power resources in advance, effectively respond to meteorological fluctuations and demand changes, and improve the accuracy and response speed of overall scheduling.

[0029] 6. Through the multi-objective optimization algorithm, the system of the present invention can comprehensively consider the power generation efficiency, energy storage utilization rate and load demand, automatically realize the optimal allocation of power resources, and improve the overall stability and economy of the power grid.

[0030] 7. The present invention has a real-time feedback mechanism, which can dynamically adjust the operating status of each module according to changes in load and power generation conditions, ensure flexible and reliable power dispatching, and continuously optimize future dispatching strategies.

[0031] Through intelligent and automated power dispatching, the system improves the utilization rate of photovoltaic power generation, reduces the operating pressure of the power grid, ensures the stability and efficiency of power supply, reduces energy costs, and achieves sustainable energy management goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a system schematic diagram of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Embodiment 1:

[0035] Please see attached Figure 1 The embodiment of the present invention provides an automatic dispatching and control system based on photovoltaic power generation, comprising:

[0036] 1. Photovoltaic power generation unit

[0037] Photovoltaic power generation unit is the energy source of the system, and its main function is to convert solar energy into electrical energy.

[0038] The submodule structure of this module is as follows:

[0039] 1.1 Photovoltaic panel unit

[0040] Photovoltaic panels are the core component of the entire photovoltaic power generation unit. The panels are made of high-efficiency single-crystal silicon material and have high photoelectric conversion efficiency.

[0041] In this embodiment, the rated power of the photovoltaic panel is 300W, and the series or parallel configuration of the panels is determined according to the power demand of the household. Each photovoltaic panel has a current sensor and a voltage sensor to monitor the power generated by the panel in real time.

[0042] 1.2 Inverter Unit

[0043] Inverters are used to convert the direct current generated by the photovoltaic panels into the alternating current needed in the home.

[0044] The inverter has a built-in MPPT (maximum power point tracking) function to ensure that the system always operates at the highest efficiency point when the light changes. The inverter dynamically adjusts the output power according to the power demand by communicating with the intelligent scheduling module. The input of the inverter is DC power, the output is 220V AC power, and it supports bidirectional power transmission so that reverse charging can be performed through the inverter when the grid supplies power to the inverter at night.

[0045] 1.3 Sensor unit

[0046] The sensor unit is responsible for collecting the operating status of the photovoltaic panel, including data such as power generation current, voltage and temperature.

[0047] The sensor unit in this embodiment includes a current sensor (Hall effect sensor) and a voltage sensor with an accuracy of ±0.5%. The output data of the sensor is transmitted to the data acquisition unit via an RS-485 interface.

[0048] 1.4 Data Acquisition Unit

[0049] Responsible for preliminary processing of the data collected by the sensors and transmitting it to the intelligent scheduling module.

[0050] The data acquisition unit is connected to the intelligent scheduling module through a wireless transmission module (Zigbee protocol) to ensure real-time data transmission. The unit can simultaneously collect data from multiple sensors and convert them into a unified format to ensure the integrity and accuracy of the data.

[0051] 2. Energy storage unit

[0052] The energy storage unit is used to balance power generation and load demand, ensuring stable power supply when power supply fluctuates. Its detailed submodule structure is as follows:

[0053] 2.1 Lithium battery pack unit

[0054] The battery pack is the core component of the energy storage unit and is used to store excess electricity generated by photovoltaic power generation.

[0055] In this embodiment, the capacity of the lithium battery pack is 10 kWh and the rated voltage is 48 V. The battery pack adopts a modular design to facilitate the expansion of energy storage capacity. Each battery module is composed of multiple cells connected in series / parallel, and the operating voltage range of the cell is 3.0 V-4.2 V.

[0056] 2.2 Battery Management System (BMS)

[0057] BMS is used to monitor the working status of the battery pack, including battery voltage, current, temperature, etc., to ensure safe and efficient operation of the battery.

[0058] The BMS system integrates overcharge protection, over-discharge protection and temperature protection functions. The system adjusts the battery charge and discharge rate in real time through the built-in control algorithm to ensure that the battery pack always operates in the best working state. The BMS communicates with the intelligent scheduling module through the CAN bus to transmit battery status information in real time.

[0059] 2.3 Charge and discharge control unit

[0060] Used to control the charging and discharging behavior of the battery pack according to the instructions of the intelligent scheduling module.

[0061] The charge and discharge control unit regulates the battery charge and discharge process through power semiconductor switches (such as MOSFET). The charging control adopts the constant current-constant voltage (CC-CV) mode, and switches to the constant voltage mode when the battery power reaches 80% to ensure the battery life. The discharge process adjusts the output power according to the grid load and power generation forecast data.

[0062] 2.4 Temperature Management Unit

[0063] Responsible for monitoring and regulating the temperature of the battery pack to avoid overheating or overcooling.

[0064] In this embodiment, the temperature management unit includes multiple thermistors distributed in each battery module to detect the temperature in real time. The system sets a temperature threshold. When the temperature exceeds 50°C, the BMS will automatically reduce the charge and discharge rate and start the fan cooling device to keep the battery pack running within a safe temperature range.

[0065] 3. Weather monitoring module

[0066] The meteorological monitoring module is used to collect environmental meteorological data and predict future power generation capacity through machine learning algorithms. Its submodules include:

[0067] 3.1 Sunlight intensity sensor unit

[0068] It is used to detect the intensity of sunlight and provide an estimate of the power generation capacity under the current lighting conditions.

[0069] The sunlight intensity sensor uses a silicon photodiode with an operating wavelength range of 400-1100nm and a sensitivity of ±1%. The sensor transmits data via an I2C interface and updates the data once a minute.

[0070] 3.2 Temperature sensor unit

[0071] Used to monitor ambient temperature to assess its impact on photovoltaic panel efficiency.

[0072] Technical disclosure: The temperature sensor is a digital temperature sensor with a measurement range of -40°C to +85°C and an accuracy of ±0.5°C. The sensor is connected to the data processing unit via the SPI bus, and the data is updated every 30 seconds.

[0073] 3.3 Wind speed sensor unit

[0074] Used to detect wind speed and evaluate its impact on the heat dissipation of photovoltaic panels.

[0075] Technical disclosure: The wind speed sensor is an ultrasonic sensor with a detection range of 0.1-30m / s and an accuracy of ±0.1m / s. The sensor transmits data to the meteorological processing unit via the Modbus protocol.

[0076] 3.4 Meteorological processing unit

[0077] The collected meteorological data is processed and future photovoltaic power generation capacity is predicted through machine learning algorithms.

[0078] The weather processing unit has a built-in LSTM algorithm that can process weather data and power generation data from the past 24 hours and output power generation forecasts for the next 48 hours. The unit communicates with the intelligent scheduling module via Ethernet and updates the forecast every hour.

[0079] 4. Detailed submodules of demand response management system

[0080] The demand response management system is used to control the operation of non-critical load equipment to ensure the stability of the power grid and energy storage system. Its submodules include:

[0081] 4.1 Load monitoring unit

[0082] Used to monitor the power consumption of various devices in the home in real time.

[0083] The load monitoring unit measures the power consumption of each device in real time through a smart meter, and the data is transmitted to the demand response control unit through a wireless communication module. The meter supports the Zigbee protocol and has a measurement accuracy of ±1%.

[0084] 4.2 Load Control Unit

[0085] Used to control the opening or closing of equipment according to dispatch commands, giving priority to reducing non-critical loads.

[0086] The load control unit controls the operating status of household appliances through smart sockets or smart switches. The load control unit communicates with the intelligent scheduling module, receives scheduling instructions through the Zigbee network, and executes load reduction. Each load device has a power priority, and air conditioners, water heaters and other devices are given priority when the load is too high.

[0087] 5. Detailed submodules of the intelligent scheduling module

[0088] The intelligent scheduling module is the core controller of the system, responsible for the comprehensive management of all sub-modules. Its sub-modules include:

[0089] 5.1 Data acquisition and storage unit

[0090] Used to collect data from each sub-module and store it for scheduling decisions.

[0091] The data acquisition unit obtains real-time data from photovoltaic power generation, energy storage, meteorology and load modules through communication protocols such as Modbus and RS-485. The data storage unit has a built-in 4GB flash memory to save the data history of the past 7 days for subsequent analysis.

[0092] 5.2 Dispatching control unit

[0093] Based on real-time data and power generation forecasts, power dispatch decisions are made to control the working status of photovoltaic power generation units and energy storage units.

[0094] The dispatch control unit integrates a multi-objective optimization algorithm, combines power generation, energy storage, load demand and weather forecasts to generate a real-time power distribution plan. The dispatch is updated every 5 minutes to ensure the coordinated work of each module.

[0095] 5.3 Feedback Control Unit

[0096] Optimize scheduling strategies through real-time feedback data.

[0097] The feedback control unit performs adaptive adjustments based on the real-time data obtained from each module. The unit uses the PID control algorithm to ensure dynamic adjustment of load distribution and energy storage charging and discharging behavior when power supply and demand change, thereby optimizing the operational stability of the power grid.

[0098] The present invention provides a complete technical solution by disclosing in detail the submodule unit structure of the photovoltaic power generation unit, energy storage unit, meteorological monitoring module, demand response management system and intelligent scheduling module. Each submodule exchanges data through a communication protocol, and the intelligent scheduling module implements overall scheduling to ensure efficient operation of the system and optimal configuration of resources.

[0099] Embodiment 2:

[0100] In this embodiment, the industrial park performs comprehensive power management and optimized scheduling through a large-scale photovoltaic power generation system, an energy storage system, a demand response management system, and an intelligent scheduling module. The following describes in detail the submodule structure and function of this embodiment, and combines the interaction of each module to ensure the integrity of the system:

[0101] 1. Detailed submodules of photovoltaic power generation unit

[0102] In industrial park environments, photovoltaic power generation systems are large in scale, and the power generation is usually used to support the operation of production equipment in the park, and combined with energy storage systems and power grids to perform peak load shifting and valley filling operations. The detailed submodule structure of the photovoltaic power generation unit is as follows:

[0103] 1.1 Large-scale photovoltaic panel array units

[0104] Photovoltaic panel arrays are responsible for converting solar energy into direct current to support the park's electricity needs.

[0105] In this embodiment, the total capacity of the photovoltaic panel array is 5 MW, consisting of multiple high-efficiency monocrystalline silicon photovoltaic panels, each with a rated power of 500 W. The panels are connected in series / parallel to form a scalable power production system. The panels have high conversion efficiency and adapt to the high energy consumption requirements of industrial production environments.

[0106] 1.2 Centralized grid-connected inverter unit

[0107] The direct current generated by the photovoltaic panels is converted into alternating current of industrial electricity standards and connected to the power grid and energy storage system.

[0108] The inverter adopts a multi-channel design and supports multiple DC inputs. Each input port supports a maximum power conversion of 1 MW. The inverter is equipped with MPPT (maximum power point tracking) technology to ensure maximum conversion efficiency under different lighting conditions. The inverter communicates with the intelligent scheduling module through industrial Ethernet to transmit power generation data in real time.

[0109] 1.3 Sensors and Monitoring Units

[0110] Real-time monitoring of the operating status of each photovoltaic panel, including parameters such as power generation current, voltage and panel temperature.

[0111] The sensor unit includes current sensor, temperature sensor and voltage sensor, and the accuracy of each sensor is ±0.2%. All sensors are connected to the central control unit through Modbus RTU protocol, and transmit data to the intelligent scheduling module in real time. The system updates the power generation data every minute and automatically analyzes the efficiency changes.

[0112] 2. Detailed submodules of the energy storage system

[0113] The energy storage system plays an important role in balancing power supply and demand and shaving peak loads in industrial parks. The detailed submodule structure of the energy storage system is as follows:

[0114] 2.1 High-efficiency lithium battery pack unit

[0115] It is used to store excess electricity generated by photovoltaic power generation and release it when electricity demand peaks.

[0116] The total capacity of the lithium battery pack in this embodiment is 20 MWh and the operating voltage is 500 V. Each battery module is configured in series or parallel to support high load operation and meet the high power consumption requirements of industrial equipment. The battery pack adopts a liquid cooling system to ensure stable temperature control during high-power charging and discharging.

[0117] 2.2 Battery Management System (BMS)

[0118] Responsible for monitoring the battery pack's charge status, temperature, voltage, current, etc. to ensure safe and efficient operation of the system.

[0119] The BMS system communicates with the intelligent scheduling module via the CAN bus to monitor the status of the battery cells in real time. The BMS also integrates an automatic balancing function to ensure that the voltage and current of each battery cell remain consistent to avoid overcharging or over-discharging caused by imbalance.

[0120] 2.3 Charge and discharge control unit

[0121] According to the instructions of the intelligent scheduling module, the charging and discharging behavior of the battery pack is dynamically adjusted to cope with fluctuations in power demand.

[0122] The charge and discharge control unit uses IGBT (insulated gate bipolar transistor) for power control to ensure stable operation of the system during high-power discharge. The charging process is adjusted in real time according to the photovoltaic power generation and energy storage capacity. When the energy storage system is close to full load, the charging rate gradually decreases to ensure battery safety.

[0123] 2.4 Temperature Management Unit

[0124] Monitor and adjust the operating temperature of the battery pack in real time to avoid overheating or overcooling that affects battery performance.

[0125] The temperature management unit includes distributed temperature sensors, and each battery module is equipped with multiple thermistors with an accuracy of ±0.5°C. When the temperature exceeds the set value, the system will start the liquid cooling system for cooling and automatically adjust the charge and discharge rate.

[0126] 3. Detailed submodules of the meteorological monitoring module

[0127] Photovoltaic power generation in industrial parks depends on weather conditions, so the meteorological monitoring module is used to collect and predict future power generation capacity. Its detailed submodule structure is as follows:

[0128] 3.1 Sunlight intensity sensor unit

[0129] Monitor sunlight intensity and provide real-time feedback on current power generation efficiency and potential power generation capacity.

[0130] The sunlight intensity sensor is a high-precision silicon photodiode with a sensitivity range of 0-1500W / m 2 , the data is transmitted to the meteorological processing unit via the I2C bus. The installation position of the sensor can avoid shadow interference and ensure accurate measurement.

[0131] 3.2 Ambient temperature sensor unit

[0132] Monitor ambient temperature and analyze its impact on photovoltaic panel efficiency.

[0133] The temperature sensor uses a high temperature resistant digital sensor with an operating range of -50°C to +125°C and an accuracy of ±0.2°C. Data is transmitted to the intelligent scheduling module via the LoRa wireless protocol, and environmental data is updated every 5 minutes.

[0134] 3.3 Wind speed and direction sensor unit

[0135] Monitor wind speed and direction to assess the cooling effect of wind on photovoltaic panels.

[0136] The wind speed sensor is an ultrasonic sensor with a detection range of 0.1-50m / s and an accuracy of ±0.05m / s. The wind direction sensor is a magnetoelectric sensor with a detection angle of 360°, and data is transmitted via the Modbus TCP / IP protocol.

[0137] 3.4 Meteorological data processing unit

[0138] The collected meteorological data is processed and power generation is predicted through machine learning algorithms.

[0139] The meteorological data processing unit uses a convolutional neural network (CNN) to analyze the meteorological data of the past week and predict the power generation capacity in the next 72 hours. The system updates the forecast results every hour and transmits the data to the intelligent scheduling module via industrial Ethernet.

[0140] 4. Demand Response Management System

[0141] The demand response management system in the industrial park is used to dynamically adjust the operation of non-critical load equipment to ensure the power balance of the park. Its detailed sub-modules are as follows:

[0142] 4.1 Production equipment load monitoring unit

[0143] Monitor the power consumption of each industrial production equipment in real time to ensure real-time feedback of load status.

[0144] The load monitoring unit in this embodiment measures the real-time power of each device through a smart meter, which supports RS-485 communication protocol with an accuracy of ±0.5%. The data is updated every minute and transmitted to the demand response control unit.

[0145] 4.2 Load Control Unit

[0146] Used to receive commands from the intelligent scheduling module, dynamically control the operating status of production equipment, and give priority to reducing the power consumption of non-critical equipment.

[0147] The load control unit is connected to the control system of the production equipment through PLC. When receiving the load reduction instruction, the system automatically adjusts the equipment power or suspends the operation of secondary equipment. The control system presets the reduction priority to ensure that the core production equipment always gets the power supply first.

[0148] 5. Detailed submodules of the intelligent scheduling module

[0149] The intelligent dispatching module is responsible for the comprehensive management of photovoltaic power generation, energy storage system, meteorological data and load adjustment.

[0150] Its detailed submodule structure is as follows:

[0151] 5.1 Data acquisition and processing unit

[0152] Used to collect and process real-time data from each sub-module to ensure the accuracy of scheduling decisions.

[0153] The data acquisition unit obtains data from the photovoltaic power generation unit, energy storage unit and demand response system through industrial Ethernet. The data processing unit uses big data processing algorithms to clean and analyze historical data and real-time data, and updates the scheduling strategy every hour.

[0154] 5.2 Optimizing the Scheduling Unit

[0155] Photovoltaic power generation, energy storage and load are comprehensively dispatched through multi-objective optimization algorithms to ensure the optimal allocation of power resources.

[0156] The optimization scheduling unit in this embodiment uses a genetic algorithm to optimize the allocation of power resources in the park based on real-time data and power generation forecast results. The scheduling unit updates the scheduling decision every 5 minutes to ensure the coordination between the modules and optimize the future scheduling strategy through the feedback mechanism.

[0157] 5.3 Feedback and Adaptive Control Unit

[0158] Dynamically adjust the scheduling strategy based on real-time feedback data to ensure the system's adaptability.

[0159] The feedback unit receives the status data of each submodule in real time through the Modbus TCP / IP protocol, and adjusts the charging and discharging behavior of the energy storage unit and the operating status of the load equipment through the adaptive control algorithm. For example, when the power of the energy storage system approaches the critical value, the system will immediately reduce non-critical loads and give priority to ensuring the power supply needs of core equipment.

[0160] This embodiment discloses in detail the various submodules in the photovoltaic power generation and peak-shaving management system of the industrial park, including photovoltaic power generation units, energy storage systems, meteorological monitoring modules, demand response management systems and intelligent scheduling modules. Through the collaborative work of these submodules, the system can dynamically dispatch power resources, maximize the use of photovoltaic power generation capacity, optimize the use of energy storage systems, and ensure stable power supply for production equipment in the park.

[0161] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automatic dispatching and control system based on photovoltaic power generation, characterized in that: include: Photovoltaic power generation unit, used to convert solar energy into electrical energy and monitor the power generation and efficiency in real time; An energy storage unit, electrically connected to the photovoltaic power generation unit, is used to store excess electrical energy and intelligently release electrical energy according to the load status of the power grid; A meteorological monitoring module is used to collect and predict meteorological data in real time, including sunshine intensity, temperature, wind speed, and generate short-term weather forecasts; A demand response management system is electrically connected to the power grid and user-side equipment. The system monitors the power grid load in real time and intelligently identifies non-critical load equipment, and dynamically adjusts the load consumption of the user-side based on the fluctuation of the power grid load. An intelligent dispatching module dynamically optimizes the allocation of power resources by combining photovoltaic power generation, energy storage unit status, meteorological data, and load information fed back by the demand response management system. The intelligent dispatching module includes a prediction unit that can predict the power demand and power generation capacity in the future period based on meteorological data, historical load data, and energy storage status, and automatically adjust the working mode of the photovoltaic power generation unit and the energy storage unit based on the prediction results; Among them, the intelligent scheduling module comprehensively considers the energy storage status, photovoltaic power generation efficiency, grid load conditions and user-side needs through a multi-objective optimization algorithm.

2. The automatic dispatching and control system based on photovoltaic power generation according to claim 1 is characterized in that: The meteorological monitoring module further includes an external meteorological data acquisition unit, which acquires regional meteorological data in real time by connecting to an external meteorological data source, and generates a high-precision short-term meteorological forecast in combination with the meteorological data within the system for dynamically adjusting the power generation prediction of the photovoltaic power generation unit.

3. The automatic dispatching and control system based on photovoltaic power generation according to claim 1 is characterized in that: The meteorological monitoring module further includes a data calibration unit, which is used to dynamically calibrate the externally and internally collected meteorological data according to historical meteorological data and photovoltaic power generation data.

4. The automatic dispatching and control system based on photovoltaic power generation according to claim 1 is characterized in that: The prediction unit of the intelligent scheduling module predicts photovoltaic power generation and power demand through a long short-term memory network or a convolutional neural network model, and models it in combination with multi-period meteorological and load historical data to achieve accurate prediction of future photovoltaic power generation and energy storage demand.

5. The automatic dispatching and control system based on photovoltaic power generation according to claim 1 is characterized in that: The prediction unit further combines historical load adjustment data of the demand response management system to optimize the load reduction strategy so as to give priority to reducing non-critical loads that have less impact on grid stability during load peaks.

6. The automatic dispatching and control system based on photovoltaic power generation according to claim 1 is characterized in that: The battery management system of the energy storage unit includes a temperature monitoring unit, a battery power monitoring unit and an adaptive charge and discharge control unit. The adaptive charge and discharge control unit dynamically adjusts the charging rate and discharge power according to the real-time grid load demand and the power state of the energy storage unit.

7. The automatic dispatching and control system based on photovoltaic power generation according to claim 1 is characterized in that: The battery management system further includes an abnormality monitoring and early warning module. When the energy storage unit has excessive temperature, abnormal voltage, or abnormal charging and discharging, the early warning mechanism is immediately activated and switched to safe mode through the intelligent scheduling module.

8. The automatic dispatching and control system based on photovoltaic power generation according to claim 1 is characterized in that: The demand response management system can conduct two-way communication with the user-side smart devices through the wireless communication network, monitor the operating status of the user-side load equipment in real time, and send load adjustment instructions according to the system scheduling strategy. The user-side smart devices include air-conditioning systems, motors and lighting equipment.

9. The automatic dispatching and control system based on photovoltaic power generation according to claim 1, characterized in that: The demand response management system further includes a user-side feedback unit, which can return load reduction status data to the intelligent scheduling module in real time after the user-side load is adjusted, and the intelligent scheduling module optimizes subsequent load scheduling strategies based on the feedback data.

10. The automatic dispatching and control system based on photovoltaic power generation according to claim 1, characterized in that: The intelligent scheduling module comprehensively schedules the photovoltaic power generation unit, the energy storage unit and the demand response management system through a multi-objective optimization algorithm. The optimization objectives of the multi-objective optimization algorithm include: minimizing power loss, maximizing the utilization rate of the energy storage unit, and improving the peak shaving and valley filling effect.

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