Rural power distribution network photovoltaic power generation and electric vehicle intelligent regulation and control system and method

Abstract

The invention belongs to the field of rural power distribution network hybrid power system intelligent regulation and control, and particularly relates to a rural power distribution network photovoltaic power generation and electric vehicle intelligent regulation and control system and method. The system comprises a rural power distribution network bus unit, a rural electric vehicle charging unit, a rural distributed photovoltaic unit, an intelligent energy storage unit and a rural household load unit. The country power distribution network bus unit is coupled with a 230V power distribution network and a country micro-grid through a power supply conversion module; the country electric vehicle charging unit reads the state of the electric vehicle and realizes a charging function; the rural distributed photovoltaic unit monitors and controls the photovoltaic output power in real time; the intelligent energy storage unit manages storage and release of electric energy, is connected with the rural power distribution network bus unit through a bidirectional DC / DC converter, and manages storage and release of the electric energy according to current control and mode switching. The method is suitable for energy management and optimization of the rural area, the energy utilization efficiency can be improved, and the rural power supply stability is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control of hybrid power systems in rural distribution networks, and in particular relates to a system and method for intelligent control of photovoltaic power generation and electric vehicles in rural distribution networks, specifically an intelligent control system with photovoltaic power generation and consideration of orderly charging of electric vehicles, and further relates to a PMA intelligent algorithm network-load-storage collaborative optimization control system and method. Background Art

[0002] As the importance of renewable energy becomes increasingly prominent, photovoltaic power generation systems have been widely used in rural areas, providing clean energy for the power grid, reducing dependence on traditional fossil energy, and reducing carbon emissions. The improvement of environmental awareness and the support of government policies have led to the rapid popularization of electric vehicles in rural areas. Compared with urban areas, rural distribution networks are smaller in scale and have larger load variations. It is more difficult to maintain stable and reliable operation of rural distribution networks. At the same time, the charging demand of electric vehicles has brought new challenges to the distribution network. Therefore, it is necessary to intelligently control the charging demand of electric vehicles to balance the relationship between the load and charging demand of rural distribution networks. According to the characteristics of rural distribution networks, an intelligent control system is designed to achieve effective management and optimization of photovoltaic power generation and electric vehicle charging.

[0003] At the same time, due to the great impact of photovoltaic power generation and electric vehicle charging on rural distribution networks, it is necessary to develop a set of intelligent control systems to realize dynamic management of the above-mentioned equipment, and intelligently adjust the working state of the energy storage system according to the grid load conditions and user needs, so as to achieve effective management of photovoltaic power generation and electric vehicle charging to ensure the balance and stable operation of the power system, promote the use of clean energy and the intelligent development of the power grid, which has become an important topic that technical personnel in this field urgently need to develop. Summary of the invention

[0004] In view of the shortcomings of the above-mentioned prior art, the present invention provides a rural distribution network photovoltaic power generation and electric vehicle intelligent control system and method. Its purpose is to achieve optimal coordination between photovoltaic power generation, energy storage system and rural loads including electric vehicles through real-time data collection and monitoring, optimize energy management in rural areas, improve energy utilization efficiency, support electric vehicles to enter rural areas, and ensure the stability of rural power supply.

[0005] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0006] A rural distribution network photovoltaic power generation and electric vehicle intelligent control system, comprising: a rural distribution network bus unit, a rural electric vehicle charging unit, a rural distributed photovoltaic unit, a smart energy storage unit and a rural household load unit; the rural distribution network bus unit comprises: a power conversion module; the rural distribution network bus unit realizes coupling with a 230V distribution network and a rural microgrid through the power conversion module; the rural electric vehicle charging unit comprises: a charging state monitoring module and a charging module, the rural distributed photovoltaic unit comprises: a photovoltaic intelligent monitoring module, a photovoltaic component module and a power control module, wherein the photovoltaic component module is connected to the photovoltaic intelligent monitoring module, the photovoltaic intelligent monitoring module is connected to the power control module, the photovoltaic component module realizes energy conversion from solar energy to electric energy, the power control module controls the photovoltaic output power, and adjusts the voltage level to the rural distribution network bus unit voltage; the smart energy storage unit comprises: a battery module, a battery monitoring module and a battery management module, the smart energy storage unit is connected to the rural distribution network bus unit through a bidirectional DC / DC converter, and manages the storage and release of electric energy according to current control and mode switching.

[0007] Furthermore, the power conversion module includes four IGBT drive circuits, and the power conversion unit controls the coupling of the 230V distribution network and the rural microgrid; the charging status monitoring module is used to realize the electric vehicle status reading, and the charging module is used to charge the electric vehicle; the charging status monitoring module uses the CAN communication protocol to obtain the electric vehicle charging information in real time through the electric vehicle charging pile, and the charging module realizes the discharge operation of the electric vehicle.

[0008] Furthermore, the photovoltaic intelligent monitoring module includes: a current sensor, a voltage sensor, a microcontroller, a temperature sensor, a radiation intensity meter, a data logger and a WIFI transceiver; wherein the microcontroller in the photovoltaic intelligent monitoring module, according to the configuration information, connects to a remote server, reads light and temperature values, and monitors the output current and voltage of each photovoltaic module to realize the monitoring and data recording of the photovoltaic module; the temperature sensor in the photovoltaic intelligent monitoring module is used to collect the temperature value of each photovoltaic module in real time; the radiation intensity meter in the photovoltaic intelligent monitoring module is used to measure the light intensity in real time to help the monitoring system collect the input value of each photovoltaic module; the data logger is used to collect solar radiation and temperature data, and record the output power of each photovoltaic module; the WIFI transceiver is used to record the information of the data logger and send it to a remote database at regular intervals, and save it to the cloud;

[0009] The current sensor is connected to the microcontroller and is used to accurately record the current value in the circuit in real time; the voltage sensor is connected to the microcontroller and maps the measured value to the required analog-to-digital converter input through a voltage divider to measure the voltage value; the temperature sensor, radiation intensity meter, data recorder, and WIFI transceiver are respectively connected to the microcontroller.

[0010] Furthermore, the smart energy storage unit is composed of an output end of a voltage input submodule connected to an input end of a voltage control submodule, an output end of a voltage control submodule connected to an input end of a PMA algorithm submodule, an output end of a power input submodule connected to an input end of a voltage control submodule and an input end of a PMA algorithm submodule, an output end of a battery state SOC input submodule connected to an input end of a PMA algorithm submodule, an output end of a PMA algorithm submodule connected to an input end of a PI control submodule, and an output end of a PI control submodule connected to an input end of a PWM switch submodule;

[0011] The battery module is responsible for power transfer, obtaining power from rural distributed photovoltaic units, or discharging power to rural electric vehicle charging units and rural household load units;

[0012] The battery monitoring module evaluates the battery capacity of the second-life battery and controls the charging and discharging of the battery;

[0013] The battery management module determines the operating state of the smart energy storage unit by considering the load power and production power available in the system; the battery management module includes: a voltage input submodule, a battery SOC state input submodule, a power input submodule, a PMA algorithm submodule, a PI control submodule, a PWM switch submodule, and a voltage control submodule; wherein the voltage input submodule is connected to the voltage control submodule, the power input submodule is connected to the voltage control submodule and the PMA algorithm submodule, the battery state SOC state input submodule is connected to the PMA algorithm submodule, the voltage control submodule is connected to the PMA algorithm submodule, the PMA algorithm submodule is connected to the PI control submodule, and the PI control submodule is connected to the PMA switch submodule;

[0014] The voltage input submodule uses the DC bus deviation voltage and DC bus voltage obtained by the voltage collector as the voltage input of the system;

[0015] The power input submodule, i.e., the power monitor obtains the relevant power parameters, including: average power component, transient power component, power monitored from the photovoltaic system and power monitored from the load, as the power input of the battery management unit;

[0016] The battery SOC state input submodule, that is, the battery capacity percentage monitored by the battery monitoring unit in the battery energy storage system, is input into the PMA algorithm submodule;

[0017] The voltage control submodule is responsible for generating the average value and transient value of the operating current flowing through the DC bus according to the voltage of the voltage input module;

[0018] The PMA algorithm submodule includes: a current management algorithm for creating a reference current and controlling different current converters;

[0019] The PI control submodule includes: a proportional link and an integral link;

[0020] The PWM switch submodule performs a current control link according to the reference current generated by the PMA, and controls the operation of other components of the rural microgrid by controlling the switching frequency of the PWM modulation of other circuits.

[0021] Furthermore, the PMA algorithm in the PMA algorithm submodule includes:

[0022] Step 1. Input the battery power P PV 、Load power P load And battery capacity SOC b ;

[0023] Step 2. Determine the battery power P PV Is it greater than or equal to the load power P? load , if it is greater than or equal to, go to step 3; if it is less than or equal to, go to step 4;

[0024] Step 3: Over power mode EPM or floating power mode FPM, including:

[0025] (1) If the battery capacity SOC b Less than the upper capacity limit MAX, then

[0026] I bref * =I b,ch

[0027]

[0028] Among them, I bref * is the reference current of the energy storage system, I gref * is the reference current of the grid, is the average current of the grid, I' t is the instantaneous current of the grid, I b,ch is the rated charging current of the battery;

[0029] (2) If the battery capacity SOC b Not less than the upper capacity limit MAX, then:

[0030] Ibref * =0

[0031]

[0032] Step 4. Under-power mode, including:

[0033] (1) If the battery capacity SOC b If it is greater than the capacity lower limit MIN, then:

[0034]

[0035] Where λ is the battery power sharing coefficient, which is determined by SOC b It is determined by the percentage of battery capacity represented;

[0036] (2) If the battery capacity SOC b Not greater than the capacity lower limit MIN, then:

[0037] I bref * =0

[0038]

[0039] Step 5. Generate reference current for the grid and current converter;

[0040] The modes of the PMA algorithm include:

[0041] Under-power mode: The rural distribution network bus unit, rural distributed photovoltaic unit and smart energy storage unit are responsible for meeting the average power gap demand. Before the SOC capacity of the battery reaches the predetermined value, the battery discharges to meet the total load demand; when the SOC capacity is lower than the lower limit MIN, the battery is idle; the rural distribution network bus unit is responsible for the transient power part;

[0042] Overpower mode: The remaining power is used to charge the smart energy storage unit until the battery reaches the maximum SOC capacity limit; when the battery reaches a higher SOC capacity limit, the excess power will be supplied to the rural distribution network bus unit through the VSC;

[0043] Floating power mode: The power of the rural distributed photovoltaic unit is equal to the power required by the load. If the rural distributed photovoltaic unit generates additional electricity, the electricity is used to charge the smart energy storage unit until a higher SOC capacity limit is reached; the excess electricity is transmitted to the rural distribution network bus unit.

[0044] Furthermore, the execution process of the photovoltaic intelligent monitoring module includes:

[0045] The photovoltaic intelligent monitoring module reads the configuration information, including the information of all connected photovoltaic modules;

[0046] The photovoltaic intelligent monitoring module attempts to connect to the remote server through the WiFi transceiver; if the connection is successful, the photovoltaic intelligent monitoring module synchronizes the existing data with the remote database and updates the existing configuration parameters;

[0047] The photovoltaic intelligent monitoring module reads the irradiance and temperature values ​​of the pyrometer and temperature sensor; the photovoltaic intelligent monitoring module reads the output of each photovoltaic module in the system according to the provisions in the configuration;

[0048] For each PV module, the PV intelligent monitoring module reads the output current and output voltage from the current and voltage sensors respectively; if the current reading of the PV module differs from the previous reading, the sensing data is recorded offline for local storage and transmitted to the remote database when the network connection is activated;

[0049] The photovoltaic intelligent monitoring module uses the characteristics of the photovoltaic cell module and the input of the photovoltaic cell module to determine whether the photovoltaic module is operating normally, and uses the artificial neural network reference module to predict the reference output power of the tested photovoltaic module;

[0050] For the PV modules under test, the actual sensed PV module power is compared with the predicted reference PV module power, the data is recorded and a maintenance notification is sent; when the difference between the PV module power and the reference PV module power is greater than or equal to 10%, a notification is sent to the remote communication device.

[0051] The rural distribution network photovoltaic power generation and electric vehicle intelligent control method is implemented by using the rural distribution network photovoltaic power generation and electric vehicle intelligent control system, wherein the PMA algorithm of the PMA algorithm submodule includes:

[0052] Step 1. Input the battery power P PV 、Load power P load And battery capacity SOC b ;

[0053] Step 2. Determine the battery power P PV Is it greater than or equal to the load power P? load , if it is greater than or equal to, go to step 3; if it is less than or equal to, go to step 4;

[0054] Step 3: Over power mode EPM or floating power mode FPM, including:

[0055] (1) If the battery capacity SOC b Less than the upper capacity limit MAX, then

[0056] I bref * =I b,ch

[0057]

[0058] Among them, I bref * is the reference current of the energy storage system, I gref * is the reference current of the grid, is the average current of the grid, I' t is the instantaneous current of the grid, I b,ch is the rated charging current of the battery;

[0059] (2) If the battery capacity SOC b Not less than the upper capacity limit MAX, then:

[0060] I bref * =0

[0061]

[0062] Step 4. Under-power mode, including:

[0063] (1) If the battery capacity SOC b If it is greater than the capacity lower limit MIN, then:

[0064]

[0065] Where λ is the battery power sharing coefficient, which is determined by SOC b It is determined by the percentage of battery capacity represented;

[0066] (2) If the battery capacity SOC b Not greater than the capacity lower limit MIN, then:

[0067] I bref * =0

[0068]

[0069] Step 5. Generate reference current for the grid and current converter;

[0070] The modes of the PMA algorithm include:

[0071] Under-power mode: The rural distribution network bus unit, rural distributed photovoltaic unit and smart energy storage unit are responsible for meeting the average power gap demand. Before the SOC capacity of the battery reaches the predetermined value, the battery discharges to meet the total load demand; when the SOC capacity is lower than the lower limit MIN, the battery is idle; the rural distribution network bus unit is responsible for the transient power part;

[0072] Overpower mode: The remaining power is used to charge the smart energy storage unit until the battery reaches the maximum SOC capacity limit; when the battery reaches a higher SOC capacity limit, the excess power will be supplied to the rural distribution network bus unit through the VSC;

[0073] Floating power mode: The power of the rural distributed photovoltaic unit is equal to the power required by the load. If the rural distributed photovoltaic unit generates additional electricity, the electricity is used to charge the smart energy storage unit until a higher SOC capacity limit is reached; the excess electricity is transmitted to the rural distribution network bus unit.

[0074] Furthermore, the execution process of the photovoltaic intelligent monitoring module includes:

[0075] The photovoltaic intelligent monitoring module reads the configuration information, including the information of all connected photovoltaic modules;

[0076] The photovoltaic intelligent monitoring module attempts to connect to the remote server through the WiFi transceiver; if the connection is successful, the photovoltaic intelligent monitoring module synchronizes the existing data with the remote database and updates the existing configuration parameters;

[0077] The photovoltaic intelligent monitoring module reads the irradiance and temperature values ​​of the pyrometer and temperature sensor; the photovoltaic intelligent monitoring module reads the output of each photovoltaic module in the system according to the provisions in the configuration;

[0078] For each PV module, the PV intelligent monitoring module reads the output current and output voltage from the current and voltage sensors respectively; if the current reading of the PV module differs from the previous reading, the sensing data is recorded offline for local storage and transmitted to the remote database when the network connection is activated;

[0079] The photovoltaic intelligent monitoring module uses the characteristics of the photovoltaic cell module and the input of the photovoltaic cell module to determine whether the photovoltaic module is operating normally, and uses the artificial neural network reference module to predict the reference output power of the tested photovoltaic module;

[0080] For the PV modules under test, the actual sensed PV module power is compared with the predicted reference PV module power, the data is recorded and a maintenance notification is sent; when the difference between the PV module power and the reference PV module power is greater than or equal to 10%, a notification is sent to the remote communication device.

[0081] A computer device comprises a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the steps of any one of the methods for intelligently controlling photovoltaic power generation and electric vehicles in a rural distribution network when executing the computer program.

[0082] A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods for intelligently controlling photovoltaic power generation and electric vehicles in a rural distribution network are implemented.

[0083] The present invention has the following beneficial effects and advantages:

[0084] The present invention provides an intelligent control system and method with photovoltaic power generation and orderly charging of electric vehicles. Through real-time data collection and monitoring, it realizes the optimal coordination between photovoltaic power generation, energy storage system and rural loads containing electric vehicles, and optimizes the energy management in rural areas. The present invention effectively ensures the stability of rural power supply through real-time data collection and monitoring, as well as the management of intelligent energy storage units. At the same time, through the application of the PMA algorithm, more sophisticated current control and mode switching are achieved in the management of intelligent energy storage units, which improves the stability and response speed of the system.

[0085] The present invention reduces dependence on traditional fossil energy and promotes the use of clean energy through the application of photovoltaic power generation systems. The rural distribution network photovoltaic power generation and electric vehicle intelligent control system of the present invention is suitable for energy management and optimization in rural areas, and significantly improves energy utilization efficiency. The intelligent control system of the present invention not only takes into account photovoltaic power generation and energy storage systems, but also takes into account the charging needs of electric vehicles, providing technical support for the popularization of electric vehicles in rural areas.

[0086] In addition, since the present invention adds the PMA algorithm in the battery management module of the smart energy storage unit, the algorithm realizes the optimal coordination between photovoltaic power generation, energy storage and load through real-time data collection and monitoring. The PMA algorithm can select the operating mode and generate the current reference value according to the photovoltaic power generation and load demand, so as to realize the efficient utilization of energy and the stable operation of the system. Compared with the existing algorithms, the PMA algorithm improves the response speed and stability of the system through more sophisticated current control and mode switching in the management of smart energy storage units. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0088] Figure 1 It is an overall block diagram of the rural distribution network photovoltaic power generation and electric vehicle intelligent control system of the present invention;

[0089] Figure 2 It is a structural block diagram of a rural distributed photovoltaic unit in the present invention;

[0090] Figure 3 It is the execution flow chart of the photovoltaic intelligent monitoring module in the present invention;

[0091] Figure 4 It is a structural block diagram of the smart energy storage unit in the present invention;

[0092] Figure 5 It is a connection diagram of the smart energy storage unit in the present invention;

[0093] Figure 6 It is a flow chart of the PMA algorithm of the present invention. DETAILED DESCRIPTION

[0094] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0095] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0096] Refer to the following Figure 1-Figure 6 The technical solutions of some embodiments of the present invention are described.

[0097] Example 1

[0098] The present invention provides an embodiment, which is a rural distribution network photovoltaic power generation and electric vehicle intelligent control system. Figure 1 As shown, Figure 1 It is an overall block diagram of the rural distribution network photovoltaic power generation and electric vehicle intelligent control system of the present invention.

[0099] The rural distribution network photovoltaic power generation and electric vehicle intelligent control system disclosed in the present invention includes: a rural distribution network bus unit, a rural electric vehicle charging unit, a rural distributed photovoltaic unit, a smart energy storage unit and a rural household load unit. The rural distribution network bus unit includes: a power conversion module; the power conversion module includes four IGBT drive circuits, the power conversion unit controls the coupling between the 230V distribution network and the rural microgrid, and the rural distribution network bus unit realizes the coupling with the 230V distribution network and the rural microgrid through the power conversion module.

[0100] The rural electric vehicle charging unit includes: a charging status monitoring module and a charging module, which are used to read the electric vehicle status and realize the charging function. Among them, the charging status monitoring module realizes the electric vehicle status reading, and the charging module is used to charge the electric vehicle. The charging status monitoring module uses the CAN communication protocol to obtain the electric vehicle charging information in real time through the electric vehicle charging pile; the charging module realizes the discharge operation of the electric vehicle.

[0101] The rural distributed photovoltaic unit includes: photovoltaic intelligent monitoring module, photovoltaic module and power control module, which can monitor and control photovoltaic output power in real time. Among them, the photovoltaic intelligent monitoring module includes: current sensor, voltage sensor, microcontroller, temperature sensor, radiation intensity meter, data logger and WIFI transceiver. The photovoltaic module is responsible for generating renewable energy, and the power control module controls the photovoltaic output power and increases the voltage level to the bus unit voltage of the rural distribution network.

[0102] The smart energy storage unit includes: battery module, battery monitoring module and battery management module, which are used to manage the storage and release of electric energy and are connected to the bus unit of the rural distribution network through a bidirectional DC / DC converter. Among them, the battery module is responsible for power transfer, obtaining power from rural distributed photovoltaic units, or discharging to rural electric vehicle charging units and rural household load units; the battery monitoring module evaluates the battery capacity of the cascade battery and controls the charging and discharging of the battery; the battery management module determines the operating status of the smart energy storage unit by considering the available load power and production power in the system.

[0103] The rural household load unit includes loads used in daily rural life, wherein the rural household load unit includes: rural life electricity in rural microgrids, rural production / irrigation / planting / breeding facility electricity, agricultural product processing electricity, rural general industrial and commercial electricity, and new energy vehicle charging piles.

[0104] Among them, the rural electric vehicle charging unit is connected to the rural distribution network bus unit, the rural distributed photovoltaic unit is connected to the rural distribution network bus unit through a boost converter, the smart energy storage unit is connected to the rural distribution network bus unit through a bidirectional DC / DC converter, and the rural household load unit is connected to the rural distribution network bus unit.

[0105] Example 2

[0106] The present invention also provides an example, which is a rural distribution network photovoltaic power generation and electric vehicle intelligent control system.

[0107] like Figure 2 As shown, Figure 2It is a structural block diagram of a rural distributed photovoltaic unit in the present invention, wherein the rural distributed photovoltaic unit comprises: a photovoltaic intelligent monitoring module, a photovoltaic component module and a power control module, wherein the photovoltaic component module is connected to the photovoltaic intelligent monitoring module, and the photovoltaic intelligent monitoring module is connected to the power control module.

[0108] The photovoltaic intelligent monitoring module includes: a current sensor, a voltage sensor, a microcontroller, a temperature sensor, a radiation intensity meter, a data logger and a WIFI transceiver. Among them, the microcontroller in the photovoltaic intelligent monitoring module connects to the remote server according to the configuration information, reads the light and temperature values, and monitors the output current and voltage of each photovoltaic module to realize the monitoring and data recording of the photovoltaic module. The role of the temperature sensor in the photovoltaic intelligent monitoring module includes real-time collection of the temperature value of each photovoltaic module. The role of the radiation intensity meter in the photovoltaic intelligent monitoring module is to measure the light intensity in real time to help the monitoring system collect the input value of each photovoltaic module. The role of the data logger includes collecting solar radiation and temperature data, and recording the output power of each photovoltaic module. The WIFI transceiver sends the information record of the data logger to the remote database at a regular time and saves it to the cloud. The photovoltaic module module is used to realize the energy conversion from solar energy to electrical energy, and the power control module controls the photovoltaic output power and raises the voltage level to the bus unit voltage of the rural distribution network.

[0109] The current sensor is connected to the microcontroller and is used to accurately record the current value in the circuit in real time.

[0110] The voltage sensor is connected to the microcontroller, and through a voltage divider, the measured value can be mapped to the required analog-to-digital converter input to measure the voltage value.

[0111] The temperature sensor, the radiation intensity meter, the data recorder and the WIFI transceiver are respectively connected to the microcontroller.

[0112] Example 3

[0113] The present invention also provides an example, which is a rural distribution network photovoltaic power generation and electric vehicle intelligent control system.

[0114] like Figure 3 As shown, Figure 3 The photovoltaic intelligent monitoring module execution flow chart of the present invention. The execution process of the photovoltaic intelligent monitoring module includes the following steps:

[0115] Step 1. The photovoltaic intelligent monitoring module first reads the configuration information.

[0116] The configuration information contains information about all connected PV modules, including the index of each PV module, manufacturing characteristics, WiFi connection information, reference module parameters, and other initialization parameters.

[0117] Step 2. The photovoltaic intelligent monitoring module attempts to connect to the remote server through the WiFi transceiver.

[0118] If the connection is successful, the PV intelligent monitoring module will synchronize the existing data with the remote database and update the existing configuration parameters as needed.

[0119] Step 3. After configuration and initialization, the photovoltaic intelligent monitoring module reads the radiation and temperature values ​​of the pyrometer and temperature sensor.

[0120] To check each PV module, the PV Smart Monitoring Module reads the output of each PV module in the system as specified in the configuration.

[0121] Step 4. For each PV module, the PV intelligent monitoring module reads the output current and output voltage from the current and voltage sensors respectively.

[0122] The calculation formula of the induced power of photovoltaic modules is P s =I s *V s If the current reading of the PV panel differs from previous readings, the sensed data is recorded offline for local storage. When the network connection is activated, these values ​​are transferred to the remote database.

[0123] Step 5. To check whether the photovoltaic cell module is operating normally, the photovoltaic intelligent monitoring module will use the characteristics of the photovoltaic cell module and the input of the photovoltaic cell module to judge, and use the artificial neural network reference module to predict the reference output power P of the tested photovoltaic cell module r Among them, the characteristics of the photovoltaic cell module refer to the open circuit voltage, and the input of the photovoltaic cell module refers to the radiation and temperature.

[0124] Step 6. For the PV module under test, the actual sensed PV module power P s Compared with the predicted reference PV module power P r When the photovoltaic module power P s and reference PV module power P r When the difference between the two is greater than or equal to 10%, a notification will be sent to the remote communication device.

[0125] Example 4

[0126] The present invention also provides an example, which is a rural distribution network photovoltaic power generation and electric vehicle intelligent control system.

[0127] like Figure 4 As shown, Figure 4 This is a block diagram of the smart energy storage unit in the present invention. The smart energy storage unit includes: a battery module, a battery monitoring module and a battery management module, wherein the output end of the battery module is connected to the input end of the battery monitoring module and the battery management module respectively. The battery management module includes: a voltage input submodule, a battery SOC state input submodule, a power input submodule, a PMA algorithm submodule, a PI control submodule, a PWM switch submodule, and a voltage control submodule.

[0128] Among them, the voltage input submodule is connected to the voltage control submodule, the power input submodule is connected to the voltage control submodule and the PMA algorithm submodule, the battery state SOC input submodule is connected to the input end of the PMA algorithm submodule, the output end of the voltage control submodule is connected to the input end of the PMA algorithm submodule, the output end of the PMA algorithm submodule is connected to the input end of the PI control submodule, and the output end of the PI control submodule is connected to the input end of the PMA switch submodule.

[0129] The voltage input submodule, namely the DC bus deviation voltage V obtained by the voltage collector DCref and DC bus voltage V DC , as the voltage input of the system.

[0130] The power input submodule, i.e. the power monitor, obtains relevant power parameters, including: average power component P t (t), transient power component P t '(t), the power P monitored from the photovoltaic system PV And the power P monitored from the load load , as the power input of the battery management unit.

[0131] The battery SOC state input submodule, that is, the battery capacity percentage monitored by the battery monitoring unit in the battery energy storage system, is input into the PMA algorithm submodule.

[0132] The voltage control submodule is responsible for generating an average value I of the operating current flowing through the DC bus according to the voltage of the voltage input module. t (t) and transient value I' t (t). The voltage control module can reduce the DC voltage deviation and restore the voltage at a faster speed.

[0133] The PMA algorithm submodule includes: creating reference currents and current management algorithms that control different current converters. The PMA selects the operating mode and generates current reference values ​​based on the photovoltaic power generation and load demand. Subsequently, the current control phase is performed based on these reference currents, and finally switching pulses are generated for all power converters.

[0134] The PI control submodule, including a proportional link and an integral link, can not only correct the deviation and make the process respond quickly, but also eliminate oscillation and improve the stability of the power management unit.

[0135] The PWM switch submodule executes the current control link according to the reference current generated by the PMA, and controls the operation of other components of the rural microgrid by controlling the switching frequency of the PWM modulation of other circuits.

[0136] Example 5

[0137] The present invention also provides an example, which is a rural distribution network photovoltaic power generation and electric vehicle intelligent control system.

[0138] like Figure 5 As shown, Figure 5 This is a connection diagram of the smart energy storage unit in the present invention. The specific connection method of the smart energy storage unit is as follows: the output end of the voltage input submodule is connected to the input end of the voltage control submodule, the output end of the voltage control submodule is connected to the input end of the PMA algorithm submodule, the output end of the power input submodule is connected to the input end of the voltage control submodule and the input end of the PMA algorithm submodule, the output end of the battery state SOC input submodule is connected to the input end of the PMA algorithm submodule, the output end of the PMA algorithm submodule is connected to the input end of the PI control submodule, and the output end of the PI control submodule is connected to the input end of the PWM switch submodule.

[0139] Example 6

[0140] The present invention also provides an example, which is a rural distribution network photovoltaic power generation and electric vehicle intelligent control system.

[0141] like Figure 6 As shown, Figure 6 It is a flow chart of the PMA algorithm in the present invention. The rural distribution network photovoltaic power generation and electric vehicle intelligent control system of the present invention uses an intelligent algorithm to comprehensively control photovoltaic power generation, electric vehicle charging and energy storage, thereby achieving efficient energy utilization and stable operation of the system. The method includes a photovoltaic intelligent monitoring module and a PMA algorithm of a smart energy storage unit, and realizes the optimal coordination between photovoltaic power generation, energy storage and load through real-time data collection and monitoring.

[0142] The PMA algorithm in the smart energy storage unit of the present invention specifically includes the following steps:

[0143] Step 1. Input the battery power P PV 、Load power P load And battery capacity SOC b ;

[0144] Step 2. Determine the battery power P PV Is it greater than or equal to the load power P? load If it is greater than or equal to, proceed to step 3; if it is less than or equal to, proceed to step 4.

[0145] Step 3. Over power mode EPM or floating power mode FPM, including the following situations:

[0146] Case 1. If the battery capacity SOC b Less than the upper capacity limit MAX, then

[0147] I bref * =I b,ch

[0148]

[0149] Among them, I bref * is the reference current of the energy storage system, I gref * is the reference current of the grid, is the average current of the grid, I' t is the instantaneous current of the grid, I b,ch is the rated charging current of the battery.

[0150] Case 2: If the battery capacity SOC b Not less than the upper capacity limit MAX, then

[0151] I bref * =0

[0152]

[0153] in, is the average current of the grid, I' t is the instantaneous current of the grid.

[0154] Step 4. Under-power mode, including the following two situations:

[0155] Case 1. If the battery capacity SOC b If it is greater than the capacity lower limit MIN, then:

[0156]

[0157] Where λ is the battery power sharing coefficient, which is determined by SOC b The percentage of battery capacity represented.

[0158] Case 2: If the battery capacity SOC b Not greater than the capacity lower limit MIN, then:

[0159] I bref * =0

[0160]

[0161] Step 5. Generate reference current for the grid and current converter.

[0162] The mode in the PMA algorithm of the smart energy storage unit is as follows:

[0163] Underpower mode: In this mode, the rural distribution network bus unit, rural distributed photovoltaic unit and smart energy storage unit are responsible for meeting the average power gap demand. Before the SOC capacity of the battery reaches the predetermined value, the battery will discharge to meet the total load demand. When it is lower than the SOC capacity lower limit MIN, the battery will be idle. The rural distribution network bus unit is responsible for the transient power part.

[0164] Overpower mode: In this mode, the surplus power is used to charge the smart energy storage unit until the battery reaches the maximum SOC capacity limit. When the battery reaches a higher SOC capacity limit, the excess power will be supplied to the rural distribution network bus unit through the VSC.

[0165] Floating power mode: In this mode, the power of the rural distributed photovoltaic unit is almost equal to the power required by the load. If the rural distributed photovoltaic unit generates additional electricity, this electricity will be used to charge the smart energy storage unit until it reaches a higher SOC b capacity limit. Then, the excess power is delivered to the rural distribution network bus unit. This operating condition is almost similar to the over power mode operation.

[0166] Example 7

[0167] The present invention further provides an example, which is a method for intelligently controlling photovoltaic power generation and electric vehicles in a rural distribution network, which is implemented by using a rural distribution network photovoltaic power generation and electric vehicle intelligent control system as described in any one of Embodiments 1 to 6, and specifically includes the following steps:

[0168] The rural distribution network bus unit is used to achieve coupling with the 230V distribution network and the rural microgrid through the power conversion module;

[0169] Use the rural electric vehicle charging unit to read the electric vehicle status and realize the charging function;

[0170] Real-time monitoring and control of photovoltaic output power using rural distributed photovoltaic units;

[0171] Use smart energy storage units to manage the storage and release of electric energy, and connect to the rural distribution network bus unit through a bidirectional DC / DC converter;

[0172] Rural household load units are used to meet the daily electricity needs of rural life and production in rural microgrids.

[0173] Example 8

[0174] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the steps of the method for intelligent control of photovoltaic power generation and electric vehicles in a rural distribution network described in Example 7 are implemented.

[0175] Example 9

[0176] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for intelligent control of photovoltaic power generation and electric vehicles in rural distribution networks described in Example 7 are implemented.

[0177] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0178] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0179] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. Rural distribution network photovoltaic power generation and electric vehicle intelligent control system, which is characterized by: including: A rural distribution network bus unit, a rural electric vehicle charging unit, a rural distributed photovoltaic unit, a smart energy storage unit and a rural household load unit; the rural distribution network bus unit includes: a power conversion module; the rural distribution network bus unit is coupled with a 230V distribution network and a rural microgrid through the power conversion module; the rural electric vehicle charging unit includes: a charging status monitoring module and a charging module; the rural distributed photovoltaic unit includes: a photovoltaic intelligent monitoring module, a photovoltaic component module and a power control module, wherein the photovoltaic component module is connected to the photovoltaic intelligent monitoring module, the photovoltaic intelligent monitoring module is connected to the power control module, the photovoltaic component module realizes energy conversion from solar energy to electrical energy, the power control module controls the photovoltaic output power, and adjusts the voltage level to the rural distribution network bus unit voltage; the smart energy storage unit includes: a battery module, a battery monitoring module and a battery management module, the smart energy storage unit is connected to the rural distribution network bus unit through a bidirectional DC / DC converter, and manages the storage and release of electrical energy according to current control and mode switching.

2. The rural distribution network photovoltaic power generation and electric vehicle intelligent control system according to claim 1 is characterized by: The power conversion module includes four IGBT drive circuits, and the power conversion unit controls the coupling between the 230V distribution network and the rural microgrid; The charging status monitoring module is used to realize the reading of the electric vehicle status, and the charging module is used to charge the electric vehicle; the charging status monitoring module uses the CAN communication protocol to obtain the electric vehicle charging information in real time through the electric vehicle charging pile, and the charging module realizes the discharge operation of the electric vehicle.

3. The rural distribution network photovoltaic power generation and electric vehicle intelligent control system according to claim 1 is characterized by: The photovoltaic intelligent monitoring module includes: a current sensor, a voltage sensor, a microcontroller, a temperature sensor, a radiation intensity meter, a data logger and a WIFI transceiver; wherein the microcontroller in the photovoltaic intelligent monitoring module connects to a remote server according to configuration information, reads light and temperature values, and monitors the output current and voltage of each photovoltaic module to realize the monitoring and data recording of the photovoltaic module; the temperature sensor in the photovoltaic intelligent monitoring module is used to collect the temperature value of each photovoltaic module in real time; the radiation intensity meter in the photovoltaic intelligent monitoring module is used to measure the light intensity in real time to help the monitoring system collect the input value of each photovoltaic module; the data logger is used to collect solar radiation and temperature data, and record the output power of each photovoltaic module; the WIFI transceiver is used to record the information of the data logger and send it to the remote database regularly, and save it to the cloud; The current sensor is connected to the microcontroller and is used to accurately record the current value in the circuit in real time; the voltage sensor is connected to the microcontroller and maps the measured value to the required analog-to-digital converter input through a voltage divider to measure the voltage value; the temperature sensor, radiation intensity meter, data recorder, and WIFI transceiver are respectively connected to the microcontroller.

4. The rural distribution network photovoltaic power generation and electric vehicle intelligent control system according to claim 1 is characterized by: The smart energy storage unit is composed of an output end of a voltage input submodule connected to an input end of a voltage control submodule, an output end of a voltage control submodule connected to an input end of a PMA algorithm submodule, an output end of a power input submodule connected to an input end of a voltage control submodule and an input end of a PMA algorithm submodule, an output end of a battery state SOC input submodule connected to an input end of a PMA algorithm submodule, an output end of a PMA algorithm submodule connected to an input end of a PI control submodule, and an output end of a PI control submodule connected to an input end of a PWM switch submodule; The battery module is responsible for power transfer, obtaining power from rural distributed photovoltaic units, or discharging power to rural electric vehicle charging units and rural household load units; The battery monitoring module evaluates the battery capacity of the second-life battery and controls the charging and discharging of the battery; The battery management module determines the operating state of the smart energy storage unit by considering the load power and production power available in the system; The battery management module includes: a voltage input submodule, a battery SOC state input submodule, a power input submodule, a PMA algorithm submodule, a PI control submodule, a PWM switch submodule, and a voltage control submodule; wherein the voltage input submodule is connected to the voltage control submodule, the power input submodule is connected to the voltage control submodule and the PMA algorithm submodule, the battery state SOC state input submodule is connected to the PMA algorithm submodule, the voltage control submodule is connected to the PMA algorithm submodule, the PMA algorithm submodule is connected to the PI control submodule, and the PI control submodule is connected to the PMA switch submodule; The voltage input submodule uses the DC bus deviation voltage and DC bus voltage obtained by the voltage collector as the voltage input of the system; The power input submodule, i.e., the power monitor obtains the relevant power parameters, including: average power component, transient power component, power monitored from the photovoltaic system and power monitored from the load, as the power input of the battery management unit; The battery SOC state input submodule, that is, the battery capacity percentage monitored by the battery monitoring unit in the battery energy storage system, is input into the PMA algorithm submodule; The voltage control submodule is responsible for generating the average value and transient value of the operating current flowing through the DC bus according to the voltage of the voltage input module; The PMA algorithm submodule includes: a current management algorithm for creating a reference current and controlling different current converters; The PI control submodule includes: a proportional link and an integral link; The PWM switch submodule performs a current control link according to the reference current generated by the PMA, and controls the operation of other components of the rural microgrid by controlling the switching frequency of the PWM modulation of other circuits.

5. The rural distribution network photovoltaic power generation and electric vehicle intelligent control system according to claim 4 is characterized by: The PMA algorithm in the PMA algorithm submodule includes: Step 1. Input the battery power P PV 、Load power P load And battery capacity SOC b ; Step 2. Determine the battery power P PV Is it greater than or equal to the load power P? load , if it is greater than or equal to, go to step 3; if it is less than or equal to, go to step 4; Step 3: Over power mode EPM or floating power mode FPM, including: (1) If the battery capacity SOC b Less than the upper capacity limit MAX, then I bref * =I b,ch Among them, I bref * is the reference current of the energy storage system, I gref * is the reference current of the grid, is the average current of the grid, I' t is the instantaneous current of the grid, I b,ch is the rated charging current of the battery; (2) If the battery capacity SOC b Not less than the upper capacity limit MAX, then: I bref * ＝0 Step 4. Under-power mode, including: (1) If the battery capacity SOC b If it is greater than the capacity lower limit MIN, then: Where λ is the battery power sharing coefficient, which is determined by SOC b It is determined by the percentage of battery capacity represented; (2) If the battery capacity SOC b Not greater than the capacity lower limit MIN, then: I bref * ＝0 Step 5. Generate reference current for the grid and current converter; The modes of the PMA algorithm include: Under-power mode: The rural distribution network bus unit, rural distributed photovoltaic unit and smart energy storage unit are responsible for meeting the average power gap demand. Before the SOC capacity of the battery reaches the predetermined value, the battery discharges to meet the total load demand; when the SOC capacity is lower than the lower limit MIN, the battery is idle; the rural distribution network bus unit is responsible for the transient power part; Overpower mode: The remaining power is used to charge the smart energy storage unit until the battery reaches the maximum SOC capacity limit; when the battery reaches a higher SOC capacity limit, the excess power will be supplied to the rural distribution network bus unit through the VSC; Floating power mode: The power of the rural distributed photovoltaic unit is equal to the power required by the load. If the rural distributed photovoltaic unit generates additional electricity, the electricity is used to charge the smart energy storage unit until a higher SOC capacity limit is reached; the excess electricity is transmitted to the rural distribution network bus unit.

6. The rural distribution network photovoltaic power generation and electric vehicle intelligent control system according to claim 1 is characterized by: The execution process of the photovoltaic intelligent monitoring module includes: The photovoltaic intelligent monitoring module reads the configuration information, including the information of all connected photovoltaic modules; The photovoltaic intelligent monitoring module attempts to connect to the remote server through the WiFi transceiver; if the connection is successful, the photovoltaic intelligent monitoring module synchronizes the existing data with the remote database and updates the existing configuration parameters; The photovoltaic intelligent monitoring module reads the irradiance and temperature values ​​of the pyrometer and temperature sensor; the photovoltaic intelligent monitoring module reads the output of each photovoltaic module in the system according to the provisions in the configuration; For each PV module, the PV intelligent monitoring module reads the output current and output voltage from the current and voltage sensors respectively; if the current reading of the PV module differs from the previous reading, the sensing data is recorded offline for local storage and transmitted to the remote database when the network connection is activated; The photovoltaic intelligent monitoring module uses the characteristics of the photovoltaic cell module and the input of the photovoltaic cell module to determine whether the photovoltaic module is operating normally, and uses the artificial neural network reference module to predict the reference output power of the tested photovoltaic module; For the PV modules under test, the actual sensed PV module power is compared with the predicted reference PV module power, the data is recorded and a maintenance notification is sent; when the difference between the PV module power and the reference PV module power is greater than or equal to 10%, a notification is sent to the remote communication device.

7. A method for intelligent control of photovoltaic power generation and electric vehicles in a rural distribution network, characterized by: The method is implemented by using the rural distribution network photovoltaic power generation and electric vehicle intelligent control system according to any one of claims 1 to 6, wherein the PMA algorithm of the PMA algorithm submodule includes: Step 1. Input the battery power P PV 、Load power P load And battery capacity SOC b ; Step 2. Determine the battery power P PV Is it greater than or equal to the load power P? load , if it is greater than or equal to, go to step 3; if it is less than or equal to, go to step 4; Step 3: Over power mode EPM or floating power mode FPM, including: (1) If the battery capacity SOC b Less than the upper capacity limit MAX, then I bref * =I b,ch Among them, I bref * is the reference current of the energy storage system, I gref * is the reference current of the grid, is the average current of the grid, I' t is the instantaneous current of the grid, I b,ch is the rated charging current of the battery; (2) If the battery capacity SOC b Not less than the upper capacity limit MAX, then: I bref * ＝0 Step 4. Under-power mode, including: (1) If the battery capacity SOC b If it is greater than the capacity lower limit MIN, then: Where λ is the battery power sharing coefficient, which is determined by SOC b It is determined by the percentage of battery capacity represented; (2) If the battery capacity SOC b Not greater than the capacity lower limit MIN, then: I bref * ＝0 Step 5. Generate reference current for the grid and current converter; The modes of the PMA algorithm include: Under-power mode: The rural distribution network bus unit, rural distributed photovoltaic unit and smart energy storage unit are responsible for meeting the average power gap demand. Before the SOC capacity of the battery reaches the predetermined value, the battery discharges to meet the total load demand; when the SOC capacity is lower than the lower limit MIN, the battery is idle; the rural distribution network bus unit is responsible for the transient power part; Overpower mode: The remaining power is used to charge the smart energy storage unit until the battery reaches the maximum SOC capacity limit; when the battery reaches a higher SOC capacity limit, the excess power will be supplied to the rural distribution network bus unit through the VSC; Floating power mode: The power of the rural distributed photovoltaic unit is equal to the power required by the load. If the rural distributed photovoltaic unit generates additional electricity, the electricity is used to charge the smart energy storage unit until a higher SOC capacity limit is reached; the excess electricity is transmitted to the rural distribution network bus unit.

8. The method for intelligent control of photovoltaic power generation and electric vehicles in a rural distribution network according to claim 7 is characterized by: The execution process of the photovoltaic intelligent monitoring module includes: The photovoltaic intelligent monitoring module reads the configuration information, including the information of all connected photovoltaic modules; The photovoltaic intelligent monitoring module attempts to connect to the remote server through the WiFi transceiver; if the connection is successful, the photovoltaic intelligent monitoring module synchronizes the existing data with the remote database and updates the existing configuration parameters; The photovoltaic intelligent monitoring module reads the irradiance and temperature values ​​of the pyrometer and temperature sensor; the photovoltaic intelligent monitoring module reads the output of each photovoltaic module in the system according to the provisions in the configuration; For each PV module, the PV intelligent monitoring module reads the output current and output voltage from the current and voltage sensors respectively; if the current reading of the PV module differs from the previous reading, the sensing data is recorded offline for local storage and transmitted to the remote database when the network connection is activated; The photovoltaic intelligent monitoring module uses the characteristics of the photovoltaic cell module and the input of the photovoltaic cell module to determine whether the photovoltaic module is operating normally, and uses the artificial neural network reference module to predict the reference output power of the tested photovoltaic module; For the PV modules under test, the actual sensed PV module power is compared with the predicted reference PV module power, the data is recorded and a maintenance notification is sent; when the difference between the PV module power and the reference PV module power is greater than or equal to 10%, a notification is sent to the remote communication device.

9. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for intelligent control of photovoltaic power generation and electric vehicles in a rural distribution network described in any one of claims 7-8 are implemented.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of the rural distribution network photovoltaic power generation and electric vehicle intelligent control method described in any one of claims 7-8.

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