Distribution and micro collaborative simulation test platform and control method

Abstract

The invention discloses a distribution and micro co-simulation test platform and a control method, the distribution and micro co-simulation test platform is established by adopting a complete simulation mode, and the distribution and micro co-simulation test platform comprises the steps of constructing a plurality of simulation test bench areas, constructing a bench area simulation circuit and basic control equipment, being provided with equipment such as a simulation load, a power supply and an energy storage equipment, and being provided with edge calculation control equipment. According to the method, a full-real transformer area communication network is established, a simulation control software platform is established, a multi-source load storage equipment cooperative control program can be downloaded from edge computing control equipment, a distribution network and microgrid cooperative control program can be developed on the simulation control software platform, and management and control of the whole distribution network cooperative simulation test platform are achieved. According to the method, an environment which is completely equivalent to the reality is constructed, various practical problems and control schemes can be reproduced and tested, safe and efficient test conditions are provided, and effective support is provided for technical research related to the micro power grid.

Description

Technical Field

[0001] The present invention relates to the field of distribution network and microgrid simulation testing, and in particular to a distribution-microgrid collaborative simulation testing platform and a control method. Background Art

[0002] With the advancement of the construction of new power systems and smart distribution networks, a large number of various types of equipment, including distributed photovoltaics, energy storage, charging piles, and other source-grid-load-storage devices, are connected to the distribution network, and microgrids are formed in local power grids. The traditional distribution network has gradually shifted from a simple power receiving end to an active one, forming an active distribution network. In the local distribution network, due to the existence of distributed power sources, energy storage, loads and other elements, the conditions for forming a microgrid are met, and it may become an off-grid microgrid, operating without the grid. After the distribution network and microgrid are activated, in order to ensure the normal operation of the distribution network microgrid, various source-load-storage devices must be operated in coordination to ensure power balance and voltage stability in the area, and to ensure power supply quality and reliability.

[0003] However, the coordinated control of various distributed power sources and energy storage is very difficult. The power of a single device is small, the cost and necessity of communication need to be discussed, and the lack of necessary coordinated control between multiple devices makes the stable operation of distribution networks and microgrids difficult. Various distribution network and microgrid operation control studies have been carried out, but it is very difficult to verify the feasibility, effectiveness, economy and safety of the control scheme. Generally speaking, considering the reliability of power supply and the safety of equipment, it is impossible to carry out trial operation directly in the actual power grid, resulting in a large number of studies that can only remain in the theoretical stage and cannot be verified in the real environment, and it is difficult to promote and apply.

[0004] In view of the above-mentioned background, it is necessary to build a highly simulated test platform to promote the research and application of various control algorithms for distribution networks and microgrids. Summary of the invention

[0005] In view of the above problems, the present invention provides a micro-cooperative simulation test platform and a control method.

[0006] The technical solution of the present invention is: a micro-cooperative simulation test platform, comprising:

[0007] Multiple simulation test benches, each with independent power supply, and flexible power transfer control between the benches through connecting switches or multi-port flexible interconnection devices to adapt to real-time needs and maximize energy efficiency;

[0008] The substation simulates the lines and basic control facilities. There is a main switch at the exit of the substation, and multiple line branches are set under the main switch. Each branch line is equipped with a switch, and high-power adjustable resistors and reactors are used to simulate the equivalent impedance of each line, providing a solid foundation for simulation testing;

[0009] Multi-source load storage equipment, including distributed photovoltaic, energy storage system, simulated controllable load and charging pile, etc., fully covering the key components of distribution microgrid;

[0010] Edge computing control equipment (i.e., intelligent fusion terminal in the substation area), as the core control unit of the platform, is used to realize comprehensive data collection and precise control of various switch controllers and distributed source-load-storage equipment, and supports dynamic program download function, so that the system can flexibly respond to various complex scenarios and changes in demand;

[0011] The full-scale area communication network integrates advanced technologies such as low-voltage high-speed carrier (HPLC) and long-distance wireless communication (LORA) to ensure efficient and stable data transmission;

[0012] The simulation control software platform (i.e., the operating console) is equipped with a distribution network and microgrid collaborative control program to realize the management and control of the entire distribution network collaborative simulation test platform, including in-depth mining and analysis of edge computing control device data on the platform, and intelligent generation of power and voltage control instructions based on real-time operation data, and sending them to edge computing control devices for precise regulation;

[0013] Among them, the edge computing control device supports multiple communication protocols, can efficiently generate and parse communication messages, and store device data in the database in real time. It also has powerful adjustment and control functions, and can generate detailed control instructions for multi-source load storage devices, including but not limited to the settings of active power, reactive power, power factor and operating mode, as well as remote switch control, etc.;

[0014] The simulation control software platform can intelligently analyze and optimize the control strategy based on the real-time operation data of the distribution microgrid, ensure that the source, load and storage equipment in the substation can work together, realize the fine control of power and voltage, and thus improve the operation efficiency and stability of the distribution microgrid.

[0015] A control method for a micro-computer collaborative simulation test platform, comprising:

[0016] Based on the communication network of the substation, seamless communication between multi-source load storage equipment, switch equipment and edge computing control equipment is established;

[0017] Develop programs on edge computing control devices to realize device data collection, adjustment and control functions;

[0018] The data of edge computing control equipment is obtained on the simulation control software platform, and power and voltage control instructions are intelligently generated according to the real-time operation data of the distribution microgrid. The instructions are optimized based on the real-time perception of the substation equipment status by multi-sensor fusion technology and the operation trend predicted by the prediction model;

[0019] The generated control instructions are sent to the edge computing control device, which regulates the source, load and storage equipment in each substation to achieve precise control of the power and voltage of the substation.

[0020] in,

[0021] Multi-sensor data fusion: Use multiple sensors to collect equipment status data, including voltage, current, power factor, ambient temperature, load, etc.

[0022] Prediction model: Based on historical data and sensor data, forward-looking predictions are made using methods such as machine learning or regression analysis to evaluate the operating trends of the distribution network.

[0023] Energy dispatch and optimization control: optimize power distribution between stations and adjust the control strategy of source, load and storage equipment according to real-time demand and forecast data;

[0024] Among them, the energy utilization rate of the real-time calculation system is:

[0025]

[0026] Among them, P load (t): load demand at time t; P pv (t): photovoltaic power generation at time t; P stor (t): power output of the energy storage device at time t; P battery (t): battery power at time t; P grid (t): grid power at time t;

[0027] P load (t)-P pv (t)-P stor (t)-P battery (t)-P grid (t) This part represents the difference between the power demand at the current moment and the photovoltaic power generation, energy storage power output, battery power, and grid power. It is the difference between the actual load demand of the substation and the current supply.

[0028] This is an exponential decay function, which is used to express the decay process of power demand over time. k1 is a constant related to the dynamic response speed of the system, t is the time, ensuring that the formula takes into account the timing dynamics; △t: time interval;

[0029] This part involves the fusion and weighted processing of multi-sensor data. For each sensor, the weight λ is adjusted based on the error between its reading and the predicted device state. i , α iis the sensor sensitivity coefficient. In this way, the fusion result is to adjust the accuracy of the control command according to the current device state. n represents the total number of sensors;

[0030] The future power forecast value (i.e. the predicted power output in Watts (W)) calculated based on historical data and prediction models. This part is calculated through multi-sensor fusion and prediction models to make forward-looking decisions on future power scheduling.

[0031] U(t): The final output dispatch instruction, which represents the control instruction set at the current moment. The output of the formula is based on the power status of all devices, including photovoltaic, energy storage, load, battery and power grid, and is comprehensively dispatched and optimized through edge computing control devices. Specifically, U(t): The output of the control instruction, in units of power or voltage control, reflects the platform's dispatch command for source, load and storage devices; it indicates how to dispatch each device (including batteries and power grids) to achieve system load balance, in units of watts (W) or voltage control.

[0032] S sensor (t): device status data (device status data vector collected by multiple sensors at time t), including current, voltage, temperature, etc., in units of corresponding physical quantities (such as volts, amperes, degrees Celsius, etc.).

[0033] The real-time measurement value of the sensor at time t is used to describe the monitoring parameters of the sensor at a specific moment, such as temperature, pressure, power factor, load demand, etc., depending on the system application scenario.

[0034] dτ: integral variable; represents the small time increment from time t to t+Δt. It is used to gradually accumulate the energy balance changes over time, reflecting the nature of the dynamic time integral in the formula.

[0035] η j : Efficiency factor; represents the efficiency factor of the jth energy device or dispatch unit (usually a dimensionless parameter, ranging from 0<η j ≤10). It is used to weigh the energy transfer loss or conversion efficiency of different devices. In practice, if the device includes a battery, energy storage device or grid conversion unit, η j The charging and discharging efficiency, power conversion efficiency or power conversion efficiency of these units can be respectively represented.

[0036] m: the total number of all multi-source load-storage devices in multiple areas;

[0037] λ i , α i : The weight and sensitivity coefficient of the sensor are adjusted according to the device accuracy and historical data.

[0038] The data acquisition function on the edge computing control device includes supporting multiple communication protocols, being able to generate and parse communication messages, and storing device data in the database; the adjustment and control functions on the edge computing control device include generating detailed control instructions for multi-source load storage devices, specifically, adjusting active power, reactive power, power factor, operating mode, etc.; as well as remote switch control, specifically, remotely controlling the closing and opening of switch devices.

[0039] The present invention has the following beneficial effects:

[0040] 1. The present invention uses a complete simulation method to establish a micro-distribution collaborative simulation test platform, which has branch line and line impedance simulation functions. The full-scale source-load-storage and switch equipment can send data to the edge computing control equipment and simulation control software platform through the communication network. This platform builds an environment that is completely equivalent to the actual micro-distribution network under laboratory conditions, which can reproduce and test various practical problems and control schemes, and provide safe and efficient test conditions.

[0041] 2. The present invention supports control program development, downloading, data display, advanced control strategy execution and other functions that are exactly the same as on-site, and can completely simulate the collaborative control algorithm in distribution network and microgrid scenarios, providing effective support for technical research related to distribution and microgrid.

[0042] 3. The present invention realizes power optimization of the system, can effectively improve the energy management capability of the system, and provide stable power output under high load demand or photovoltaic power fluctuation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 creative labor. Among them:

[0044] Figure 1 Schematic diagram of the micro-cooperative panoramic verification platform in the present invention;

[0045] Figure 2 This is a communication architecture diagram of the low-voltage distribution station area in the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0049] Reference Figure 1 The present invention provides a distribution-micro network collaborative simulation test platform, including constructing multiple simulation test areas, building area simulation lines and basic control equipment, equipped with simulated loads, power supplies, energy storage and other equipment, equipped with edge computing control equipment, building a full-scale area communication network, building a simulation control software platform, and being able to download multi-source load and storage equipment collaborative control programs on edge computing control devices, and developing distribution-micro network collaborative control programs on the simulation control software platform to achieve management and control of the entire distribution network collaborative simulation test platform.

[0050] The micro-electronics collaborative simulation test platform includes at least two or more simulation test benches. The power supply of the simulation test benches is controlled by a separate switch. Connecting switches or multi-port flexible interconnection devices are set between the benches to realize power transfer control between the benches.

[0051] Reference Figure 1 The present invention provides two symmetrical low-voltage distribution stations, namely, station A and station B, which respectively draw electricity from the distribution room to the wall to meet the needs of two independent power supplies. All equipment adopts a single-phase design, including: a 10kW photovoltaic power supply, a 5kW load, a 5kW energy storage battery, a 7kW charging pile, an 8kW DC power supply, and a device fan, a communication module and a socket, etc., which together consume a small amount of electricity. In addition, capacitors and reactors of 3kVar are also equipped to provide reactive power.

[0052] In the substation design, the load is connected to phase A, while the distributed photovoltaic, energy storage and charging piles are connected to phase C. The control power supply of the DC power supply, device fan and communication module and other equipment is also connected to phase C. This design ensures that phase A is only connected to resistive loads, while phase C is dedicated to connecting photovoltaic storage and charging equipment, thereby eliminating the interference of irrelevant factors and providing higher accuracy for system testing.

[0053] The power of the substation is introduced into the distribution micro-cooperative panoramic verification platform through the main molded case circuit breaker, and then distributed to various switches and equipment. The equipment in the substation adopts three-level switch control to ensure the flexibility and safety of power supply. In the middle link, the substation flexible interconnection and phase shifter can be selected to realize power transfer between substations.

[0054] like Figure 1 As shown, the present invention is equipped with low-voltage distribution stations A and B. Station area A and station area B are both equipped with low-voltage distribution systems. Four molded case circuit breakers A2-1, A2-2, A2-3, and A2-4 are arranged under the main molded case circuit breaker A1-1 of station area A, which respectively control different equipment and branches. Among them, A2-1 controls the reactor and capacitor, A2-2 controls the load, and A2-3 controls the charging pile, energy storage, and photovoltaic. The configuration of station area B is similar to that of station area A, but the numbering of the molded case circuit breakers and branch switches is different.

[0055] In order to simulate line loss, a resistor and an inductor are connected in series under the A-phase main molded case circuit breaker of area A and area B respectively.

[0056] In the distribution-micro-cooperative simulation test platform, source-load-storage equipment includes distributed photovoltaic power sources, distributed energy storage, simulated controllable loads and charging piles, etc. Both area A and area B are equipped with 10kW distributed photovoltaic and 5kW distributed energy storage.

[0057] Low-voltage distribution substations A and B respectively collect data from five intelligent branch switches, photovoltaic inverters, energy storage inverters, etc. The substation intelligent fusion terminal based on edge computing capabilities is an edge IoT node for distribution substations and power consumption. It adopts a modular structure design, with built-in standard core boards and intelligent connection units, integrating base station functions with terminal equipment. It has massive data storage and powerful edge computing capabilities, can share the main station function, realize the management and control of users and consumption terminals including electricity meters, realize real-time interconnection of information, and promote the integration of marketing and distribution.

[0058] For equipment control, the end equipment is connected to the fusion terminal and the complete process of information transmission and command issuance is realized. The remote signal status of the current equipment switch is read to remotely control the switch. The voltage, current, power and other telemetry data of the equipment are read to remotely control the equipment.

[0059] In order to optimize the energy balance and power scheduling of the system, the following formula is introduced to calculate the energy utilization of the system in real time:

[0060]

[0061] Where, U(t): the final output scheduling instruction, P load (t): load demand at time t; P pv(t): photovoltaic power generation at time t; P stor (t): power output of the energy storage device at time t; P battery (t): battery power at time t; P grid (t): grid power at time t; Future power forecasts calculated based on historical data and forecast models; Exponential decay function;

[0062] Fusion and weighted processing of multi-sensor data; n represents the total number of sensors;

[0063] dτ: integration variable; η j : efficiency factor; m: the total number of all multi-source load-storage devices in multiple areas;

[0064] S sensor (t): device status data; The real-time measurement value of the sensor at time t;

[0065] λ i , α i : Weight and sensitivity coefficient of the sensor.

[0066] Through this formula, the system can balance the energy distribution between different power sources and loads in real time, and dynamically adjust the scheduling strategy of energy storage and loads according to current sensor data and predicted power demand. This method can effectively optimize the coordinated work of various devices in the substation area and improve the energy efficiency and stability of the system.

[0067] The energy utilization rate value represents the dynamic balance efficiency index between various energy supplies and demands in the system over a period of time, and is used to characterize the real-time utilization of the comprehensive energy flow; specifically: it represents the optimal power output of the system's photovoltaic power generation, battery energy storage, and grid regulation to jointly meet the load demand.

[0068] In practical applications,

[0069] Data collection: Through the system's intelligent fusion terminal, the power output data of each device (including load, photovoltaic, energy storage, battery, power grid, etc.) is collected in real time, and the data of environmental sensors is obtained;

[0070] Power balance calculation: In each time interval Δt, the collected data is substituted into the above formula to calculate the energy utilization of the system. This calculation takes into account the power attenuation of the system and the attenuation of the sensor data.

[0071] Dynamic scheduling: Based on the calculated energy utilization value, the system will adjust the power output of each device, prioritize photovoltaic and energy storage equipment to meet load demand, and reduce dependence on grid power.

[0072] Future power forecast: Based on historical data, predict the power demand at future times And perform weighted superposition in power calculation to ensure that the system can maintain power balance at all times in the future.

[0073] Example application scenario

[0074] In actual applications, when the photovoltaic power generation is reduced (such as rainy weather) and the load demand is high, the system will calculate U(t) to obtain the power balance state and give priority to calling the power of energy storage and batteries to maintain a stable power supply for the load. At the same time, the system will dynamically adjust the power scheduling strategy based on sensor data and future load forecasts to ensure that the system does not experience power shortage or overload in the following period of time.

[0075] In the present invention, multi-device collaborative control can be performed, such as load access and input of capacitors, photovoltaic access and input of reactors, and photovoltaic storage and charging collaborative control.

[0076] The micro-cooperative simulation test platform constructs a full-scale area communication network, which at least includes a low-voltage high-speed carrier (HPLC) and a long-distance wireless communication (LORA) network.

[0077] In the present invention, low voltage distribution station areas A and B are equipped with low voltage high speed carrier (HPLC) and long distance wireless communication (LORA) network. Compare the two methods of HPLC and LORA:

[0078] (1) High speed power line communication

[0079] HPLC technology is a high-speed power line communication technology. Power line communication technology refers to a communication technology that uses power lines as a communication medium for data transmission. Since power lines are the most popular and widely covered physical media, using power lines to transmit data information is extremely convenient. Without rewiring, all electrical appliances connected to the power lines can be formed into a communication network for information exchange and communication. This method is simple to implement and easy to maintain. It can effectively reduce operating costs and reduce the expenditure on building new communication networks. Therefore, it has become the main communication means for applications such as smart grids, energy management, smart homes, photovoltaic power generation, and electric vehicle charging.

[0080] Power line communication can be divided into narrowband low-speed power line communication, narrowband high-speed power line communication, and broadband high-speed power line communication according to the working frequency band. The frequency range of narrowband power line communication is 3kHz to 500kHz. Due to the relatively narrow bandwidth, it can only provide communication services with lower transmission rates, and its anti-interference ability is weak. The success rate of meter reading is difficult to exceed 90%. The working frequency range of broadband high-speed power line communication for power meter reading includes 2.4MHz~5.6MHz, 2MHz~12MHz, 0.7MHz~3MHz, and 1.7MHz~3MHz. It has a relatively wide bandwidth and can provide data transmission rates from hundreds of kbps to several Mbps. The noise of the power line in the high frequency band is relatively weak. Compared with narrowband power line communication, the communication reliability and stability are significantly improved.

[0081] Based on HPLC technology, it can realize functions such as high-frequency data collection, active reporting of power outages, precise clock management, phase topology recognition, automatic identification of substations, unified ID management, automatic file synchronization, communication performance monitoring and network optimization.

[0082] At present, power line carrier communication technology has been successfully applied in the power system, mainly used for collecting residential electricity consumption information on a per-station basis.

[0083] The centralized meter reading system is an information collection and processing system based on computer application technology, modern digital communication technology, power line carrier data transmission technology, etc. It consists of a master station, a concentrator, a collector, an electric energy meter, and a data transmission channel between the master station and the concentrator, the concentrator and the collector or the electric energy meter.

[0084] (2) LORA (Long Range Radio)

[0085] LoRa (Long Range radio) is a type of low power wide area network (LPWAN) communication technology. It is an ultra-long-distance wireless transmission solution based on spread spectrum technology adopted and promoted by Semtech in the United States. This solution changes the previous compromise between transmission distance and power consumption, and provides users with a simple system that can achieve long distance, long battery life, and large capacity, thereby expanding the sensor network. At present, LoRa mainly operates in the global free frequency band.

[0086] Features of LoRa:

[0087] Long transmission distance: up to 2-5Km in towns and up to 15Km in suburbs.

[0088] Working frequency: ISM band includes 433, 868, 915M, etc. LoRa works in 433 / 470 / 868 / 915MHz / 2.4GHz band

[0089] Standard: IEEE 802.15.4g.

[0090] Modulation mode: Based on spread spectrum technology, a variant of linear modulation spread spectrum (CSS), with forward error correction (FEC) capability and strong anti-interference ability.

[0091] Capacity: One LoRa gateway can connect thousands or tens of thousands of LoRa nodes.

[0092] Transmission rate: several hundred to several tens of Kbps. The lower the rate, the longer the transmission distance.

[0093] LoRa technology has the characteristics of long distance, low power consumption (long battery life), multi-node and low cost.

[0094] The distribution network collaborative simulation test platform is provided with a simulation control software platform on which a distribution network collaborative control program can be developed to achieve control over the entire distribution network collaborative simulation test platform.

[0095] The present invention is equipped with an operating console to read equipment data and perform regulation. The simulation control software platform is characterized by its cross-platform compatibility, efficient data processing capabilities, powerful interface display and configuration functions, rich functional modules, powerful simulation and optimization functions, remote monitoring and debugging capabilities, and high security and reliability. The software integrates advanced algorithms and data processing technologies, and can automatically collect and process a large amount of photovoltaic system data to ensure the accuracy and timeliness of the data, providing strong support for the optimization and management of the micro-distribution collaborative panoramic verification system. By simulating and analyzing data, the software can simulate the functions of low-voltage distribution substations and provide a scientific decision-making basis for testing. At the same time, the software also has optimization functions, which can maximize system performance while meeting test requirements.

[0096] The control method of the distribution-micro collaborative simulation test platform includes realizing communication between various source-load storage devices, switch devices and edge computing control devices based on the substation communication network, and developing programs on the edge computing control devices to realize data collection, adjustment and control of the above-mentioned devices.

[0097] like Figure 2 As shown, the network communication architecture of the substation area includes an operation console, a fusion terminal, a plug-and-play communication unit, adapting the communication media and protocols between edge devices, a unified information model (i.e., a unified information point table between the fusion terminal and the device), realizing information transmission between edge devices, and sending the edge device information to the operation console under the conditions of a unified point table.

[0098] The substation area communication uses HPLC, LORA and serial ports to collect distributed photovoltaic information. The communication protocols commonly used are MODBUS, DLT 645, DLT 698, etc. This time, the low-voltage distribution substation A is connected to five branch switches and two inverters. Currently, they are directly connected using 485, and carrier and LORA communication modes are also reserved. The fusion terminal in the substation area is connected to the operation console using 104 communication and a direct network cable.

[0099] The data acquisition function on the edge computing control device includes supporting multiple communication protocols, generating communication messages, parsing communication messages, and obtaining device data and storing it in a database.

[0100] The communication protocols include MODBUS, DLT 645, DLT 698, etc. The device data access work includes data collection, registration model and registration device. Data collection means completing the communication and message parsing with the distributed power supply equipment to obtain data; registration model means dynamically registering the device model in the fusion terminal data center according to the information of the connected device; registration device means generating a specific device instance through the corresponding device model according to the specific situation of the connected device, such as the model, the number of devices, etc.

[0101] The regulation and control functions on the edge computing control device include generating control instructions for multi-source load storage devices, including the setting of active power, reactive power, power factor, and operating mode, and can remotely control the opening and closing of switches, etc.

[0102] Read the real-time data of low-voltage distribution station areas A and B, including the status, current, voltage, active power, reactive power and other values ​​of intelligent branch switches, energy storage inverters and photovoltaic inverters.

[0103] The present invention uses NJ39 intelligent switch, which has short circuit, overload, leakage, over-voltage, under-voltage, phase loss and temperature monitoring functions. The opening and closing operation can be remotely controlled, and the current, voltage, leakage current, temperature, subdivided electricity and fault alarm data on the user side are detected in real time by using measurement technology. The communication adopts RS-485 communication interface with ESD protection circuit, Bluetooth, Zigbee, WIFI, LOLA and 4G and NB-IoT and other Internet of Things technologies, combined with the smart energy service cloud platform, the current, voltage leakage current, temperature, subdivided electricity and fault alarm data on the user side are integrated with intelligent safety supervision and other systems, and the intelligent power safety management of each application site is realized through remote control, real-time alarm, statistical calculation, energy service, energy-saving management and big data analysis. The present invention can remotely open and close the switch. Enter the administrator password before opening and closing to ensure the safety of its opening and closing.

[0104] The photovoltaic inverter and energy storage inverter can be remotely controlled. The photovoltaic inverter has a capacity of 10kW and the energy storage inverter has a capacity of 5kW. When regulating the active power of photovoltaics, the power limit percentage can be adjusted. When regulating the reactive power of photovoltaics, the power factor of photovoltaics can be adjusted, and the power factor range is plus or minus 0.8-1. The apparent power of photovoltaics can run at 1.1 times the rated power. When the power factor is greater than 0.9, it has almost no effect on the active power.

[0105] A control method for a distribution micro-network collaborative simulation test platform includes obtaining data of an edge computing control device on a simulation control software platform, generating power and voltage control instructions according to the operation data of the distribution micro-network, and then sending them to the edge computing control device.

[0106] After calculation by the edge computing control device, the source, load and storage equipment in the substation are adjusted separately to achieve precise control of the power and voltage in the substation.

[0107] The simulation control software realizes the collection of distributed power information and the issuance of control instructions. Based on the data of the intelligent fusion terminal edge computing control device, the substation microgrid interacts with the microgrid group collaborative control substation upward, uploads the substation microgrid information and receives collaborative control instructions; it interacts with the plug-and-play device downward to collect the distributed power information of the substation microgrid and issue control instructions. The microgrid group develops a microgrid collaborative control module based on the distribution network-substation micro-collaborative control substation, collects substation microgrid information and issues control instructions for the microgrid group. Through the regulation of the substation microgrid source-load-storage equipment, the distributed power sources of the substation microgrid can be efficiently utilized and economically operated, ensuring the overall economic and reliability of the distribution network.

[0108] The distribution-micro-cooperative simulation test platform and control method proposed in the present invention establish a distribution-micro-cooperative simulation test platform in a fully simulated manner, simulating the actual line topology and impedance. The full-scale source-load storage equipment, switch equipment, edge computing control equipment and simulation control software platform can transmit measurement and control data to each other through the substation communication network. The simulation test platform supports control program development, downloading, data display, advanced control strategy execution and other functions that are exactly the same as those on site, and can fully simulate the collaborative control algorithm in the distribution network and microgrid scenarios. This platform constructs an environment that is completely equivalent to the actual distribution micro-grid under laboratory conditions, which can reproduce and test various practical problems and control schemes, provide safe and efficient experimental conditions, and provide effective support for technical research related to distribution micro-grids.

[0109] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be covered by the scope of the claims of the present invention.

Claims

1. A micro-cooperative simulation test platform, characterized in that: include: At least two simulation test benches, with adjacent benches interconnected; The station area includes a simulation control software platform, edge computing control equipment, switch equipment and multi-source load storage equipment. The simulation control software platform is connected to the edge computing control device, The edge computing control device is used to realize data collection and control of the switch device and the multi-source load storage device; The switch device includes a main switch, which is connected to the edge computing control device. A plurality of line branches are arranged under the main switch. The plurality of line branches are correspondingly connected to a plurality of source load storage devices, and a switch is arranged on each branch line.

2. A micro-cooperative simulation test platform according to claim 1, characterized in that: The power supply of the substation is controlled by a separate switch, and connecting switches or multi-port flexible interconnection devices are arranged between adjacent substations.

3. The micro-computer collaborative simulation test platform according to claim 1, characterized in that: The multi-source load storage equipment includes distributed photovoltaic, energy storage system, simulated controllable load and charging pile.

4. The micro-computer collaborative simulation test platform according to claim 1, characterized in that: The edge computing control device is connected to multiple source and load storage devices through a plug-and-play unit.

5. The micro-computer collaborative simulation test platform according to claim 1, characterized in that: The simulation control software platform and the edge computing control device use 104 communication; The edge computing control device and the plug-and-play unit use low-voltage high-speed carrier or long-distance wireless communication.

6. A control method based on the micro-cooperative simulation test platform according to any one of claims 1 to 5, characterized in that: include: Based on the communication network of the substation, seamless communication between multi-source load storage equipment, switch equipment and edge computing control equipment is established; Realize equipment data collection, adjustment and control functions on edge computing control equipment; The data of edge computing control equipment is obtained on the simulation control software platform, and power and voltage control instructions are intelligently generated according to the real-time operation data of the distribution microgrid. The instructions are optimized based on the real-time perception of the substation equipment status by multi-sensor fusion technology and the operation trend predicted by the prediction model; The generated control instructions are sent to the edge computing control device, which regulates the source, load and storage equipment in each substation to achieve precise control of the power and voltage of the substation.

7. The control method of a micro-computer collaborative simulation test platform according to claim 6 is characterized in that: Multi-sensor data fusion includes: using multiple sensors to collect equipment status data, including voltage, current, power factor, ambient temperature, and load; Predictive models include: using machine learning or regression analysis methods to make predictions based on historical data and sensor data.

8. The control method of a micro-computer collaborative simulation test platform according to claim 6 is characterized in that: It also includes real-time calculation of energy utilization values ​​and adjustment of power output of each device; The energy utilization rate is calculated as follows: Where, U(t): the final output scheduling instruction, P load (t): load demand at time t; P pv (t): photovoltaic power generation at time t; P stor (t): power output of the energy storage device at time t; P battery (t): battery power at time t; P grid (t): grid power at time t; Future power forecasts calculated based on historical data and forecast models; Exponential decay function; Fusion and weighted processing of multi-sensor data; n represents the total number of sensors; dτ: integration variable; η j : efficiency factor; m: the total number of all multi-source load-storage devices in multiple areas; S sensor (t): device status data; The real-time measurement value of the sensor at time t; λ i , α i : Weight and sensitivity coefficient of the sensor.

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