A microgrid energy interaction system and method based on distributed energy storage devices

Abstract

The application discloses a kind of microgrid energy interaction system and method based on distributed energy storage equipment, it is related to microgrid energy interaction technical field, including processor and loss monitoring module, the loss monitoring module is used to monitor the energy loss generated in the process of power generation, storage, transport and use of energy, and information is transmitted to processor by baseband transmission and bluetooth transmission, the processor constructs energy loss model according to energy loss information, the loss monitoring module includes: power generation monitoring component, storage monitoring component, transport monitoring component and use monitoring component.The application realizes the function of real-time acquisition of energy loss by designing loss monitoring module, solves the problem of insufficient energy loss monitoring precision and the coupling of energy loss information in each link, can carry out cross-module collaborative control to each link energy loss coupling analysis, improves energy utilization efficiency, enhances system operation safety and stability.

Description

Technical Field

[0001] This invention relates to the field of microgrid energy control technology, specifically to a microgrid energy interaction system and method based on distributed energy storage devices. Background Technology

[0002] The large-scale integration of distributed renewable energy sources such as wind power and photovoltaics has led to increased power fluctuations in the power grid. The randomness and intermittency of these fluctuations pose a challenge to the stability of the power grid. Distributed energy storage systems can suppress these fluctuations through real-time charging and discharging, thereby improving the grid's ability to absorb new energy sources with high penetration rates. The spatial separation between my country's energy centers and load centers makes long-distance power transmission costly and poses safety risks. Developing a localized microgrid model of "distributed new energy + energy storage" can reduce transmission losses and enable local energy production and consumption.

[0003] The existing microgrid energy interaction system has insufficient accuracy in monitoring energy loss and poor dynamic adaptability. The total loss statistical deviation of the system is amplified by the superposition of errors from multiple nodes, and the energy loss of each link cannot be coupled and analyzed after collection.

[0004] Patent CN110112775B discloses a micro energy grid system with distributed energy storage. The above patent realizes the smooth access and stable use of new energy sources, as well as the efficient and economical utilization of new energy sources.

[0005] The aforementioned patent highlights the high flexibility of scheduling in microgrid systems incorporating distributed energy storage. During energy scheduling, multiple factors, such as demand-side load and energy devices, can be considered simultaneously. Therefore, it can effectively improve the overall energy utilization efficiency of the entire microgrid system incorporating distributed energy storage, as well as the supply-demand matching degree of the distributed microgrid system composed of distributed energy storage. There is also room for optimization in monitoring energy loss during energy collection, storage, transmission, and utilization.

[0006] Therefore, this application proposes a microgrid energy interaction system and method based on distributed energy storage devices for real-time acquisition of energy loss. Summary of the Invention

[0007] The purpose of this invention is to provide a microgrid energy interaction system and method based on distributed energy storage devices, so as to solve the technical problem of collecting, analyzing and utilizing energy loss information at each stage during system operation as mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a microgrid energy interaction system based on distributed energy storage devices, comprising a processor and a loss monitoring module. The loss monitoring module is used to monitor energy losses generated during power generation, storage, transmission, and use, and transmits the information to the processor via baseband transmission and Bluetooth transmission. After receiving the energy loss information, the processor aligns the energy loss data time axis through dynamic time warping. After outlier removal and data filling, it inputs the SARIMA algorithm to decompose the seasonal cycle of line loss data, quantifies the contribution through random forest feature importance analysis, inputs the voltage, current, temperature, and historical battery aging coefficient of the energy storage module, processes the nonlinear time-series characteristics of battery polarization loss through an LSTM network, and constructs an energy loss model by automatically adjusting network hyperparameters through Bayesian optimization and allocating multi-node coupling losses through a Shapley-value-based alliance algorithm. The loss monitoring module includes: a power generation monitoring component, a storage monitoring component, a transmission monitoring component, and a usage monitoring component. The power generation monitoring component monitors the power generation module, the storage monitoring component monitors the energy storage module, the transmission monitoring component monitors the transmission module, and the usage monitoring component monitors the usage module.

[0009] The power generation monitoring component includes: a Hall effect sensor for acquiring output DC current and voltage values, a smart meter for monitoring the AC output power of the inverter, and an environmental monitoring unit for quantifying power generation efficiency deviations caused by environmental factors.

[0010] The storage monitoring component includes: a voltage sensor and a current sensor for monitoring ohmic loss and polarization loss during charging and discharging; a temperature sensor for detecting the internal temperature difference of the battery cluster; and a bidirectional converter efficiency analyzer for measuring the charging and discharging conversion efficiency of the energy storage AC.

[0011] The transmission monitoring component includes a bus monitoring unit for capturing line loss and contact loss, a power analyzer for analyzing the power difference between the input and output of the connected converter, and a switch status recorder for recording the instantaneous energy loss when switching from grid connection to islanding.

[0012] The monitoring components include a cluster of smart meters for monitoring the actual energy consumption of DC and AC loads, and a non-intrusive load identification unit that analyzes load types through current waveform analysis.

[0013] Preferably, the environmental monitoring unit includes: an irradiance sensor, a wind speed sensor, and a temperature sensor. The irradiance sensor and the wind speed sensor are used to quantify the power generation efficiency deviation caused by environmental factors, and the temperature sensor is used to monitor the power generation attenuation caused by the temperature rise of the photovoltaic panel.

[0014] Preferably, the power generation monitoring component is connected to the power generation module via Bluetooth transmission. The power generation module includes: a photovoltaic power generation unit, a wind power generation unit, a micro gas turbine, and a fuel cell.

[0015] The photovoltaic power generation unit includes: silicon-based thin-film batteries, MPPT controllers, and inverters. It uses the photovoltaic effect to directly convert solar energy into electrical energy, and then converts it into alternating current through the inverter.

[0016] The wind power generation unit includes: blades, gearbox, permanent magnet synchronous generator and converter. The blades capture wind energy to drive the rotor to rotate, and the gearbox increases the speed to drive the generator to output AC power.

[0017] The micro gas turbine unit includes a centrifugal compressor, a regenerator, a combustion chamber, and a radial turbine. It utilizes the combustion of gas in the combustion chamber to drive the turbine to rotate and generate electricity.

[0018] A fuel cell includes a motor, an electrolyte membrane, bipolar plates, and a hydrogen supply unit. It utilizes the electrochemical reaction of hydrogen and oxygen through a proton exchange membrane to convert chemical energy into direct current.

[0019] Preferably, the storage monitoring component is connected to the energy storage module via baseband transmission, and the energy storage module includes: an energy storage component, a battery management unit, and a thermal management unit;

[0020] Energy storage components include: lithium-ion batteries, flow batteries, lead-acid batteries, supercapacitors, and flywheel energy storage units, used to store electrical energy converted by the power generation module;

[0021] The battery management unit is used to monitor the voltage, temperature and state of charge of individual cells in real time;

[0022] The thermal management unit is used to maintain the operating temperature of the energy storage module.

[0023] Preferably, the output of the power generation module is connected to the input of the energy storage module through a switching module, the switching module including: an energy router and a power unit;

[0024] The energy router includes a multi-port power converter and an FPGA high-speed switching array for real-time monitoring of the output power of the power generation module and the state of charge of the energy storage module battery, and dynamically switches the power generation-energy storage connection path through a solid-state relay matrix.

[0025] The power unit connects to the delivery module and the usage module to obtain the load demand and automatically allocate the power flow according to the load demand.

[0026] Preferably, the output end of the energy storage module is connected to a transmission module via a switching module. The transmission module includes: a power distribution network, a power electronic conversion unit, a grid connection and protection unit, and a local controller.

[0027] The power distribution network includes AC busbars, DC busbars, and AC / DC hybrid busbar systems, forming the main channels for power transmission;

[0028] The power electronic conversion unit includes converters and inverters that convert DC power output from photovoltaic and energy storage into AC power, rectifiers that convert AC power into DC power, and bidirectional converters that support the bidirectional flow of electrical energy between AC and DC buses.

[0029] The grid connection and protection unit includes: an automatic transfer switch for switching between microgrid grid-connected operation and islanded operation, and circuit breakers and protective relays for providing electrical protection;

[0030] The local controller is used to execute real-time scheduling instructions.

[0031] Preferably, the energy storage module is connected to the user module via a transmission line, and the user module is the user-end load.

[0032] Preferably, the power generation module and the energy storage module are connected via an energy router.

[0033] Preferably, the microgrid energy interaction method is as follows:

[0034] S1: Electrical energy is generated through photovoltaic power generation units, wind power generation units, micro gas turbine units, and fuel cells and stored in the energy storage module;

[0035] S2: The electrical energy stored in the energy storage module is transmitted to the user module through the transmission line to power the user's load.

[0036] S3: The loss monitoring module collects energy loss information during power generation, storage, transmission and use, transmits the loss information to the processor, the processor builds an energy loss model based on the received energy loss information, and generates an adjustment scheme based on the energy loss information.

[0037] S4: The processor adjusts the power generation module, energy storage module, transmission module and usage module according to the generated adjustment scheme to compensate for energy consumption and maintain the stability of the microgrid energy interaction system.

[0038] S5: During the energy regulation process, the loss monitoring module collects energy loss data a second time and constructs a regulation loss model;

[0039] S6: The processor optimizes the energy loss model by adjusting the loss model.

[0040] Preferably, S3 specifically includes:

[0041] S31: Collect energy losses during power generation, storage, transmission and interaction through the energy loss module, and transmit the collected energy loss information to the processor;

[0042] S32: After receiving energy loss information, the processor aligns the energy loss data time axis through dynamic time warping and identifies outliers through the isolated forest algorithm. When data is missing after outlier removal, the processor fills in the missing data with linear interpolation and then decomposes the seasonal cycle of the line loss data through the input SARIMA algorithm.

[0043] S33: The contribution of each factor is quantified by random forest feature importance analysis. The processor processes the nonlinear time-series characteristics of battery polarization loss through LSTM network by inputting the voltage, current, temperature and historical battery aging coefficient of the energy storage module during system operation. The network hyperparameters are automatically adjusted by running Bayesian optimization and multi-node coupling loss is allocated by Shapley value-based alliance algorithm to complete the construction of energy loss model.

[0044] S34: After inputting irradiance trends and component aging rates, the processor can predict the photovoltaic degradation rate in the next 1 hour through an LSTM network. The processor calculates the power deviation and dynamically adjusts the MPPT operating point through a PID controller. The SARIMA model decomposes the load cycle pattern and pre-generates transformer tap adjustment schemes. The processor acquires electricity price signals, battery life loss and carbon emission intensity as features, quantifies decision weights through a random forest algorithm, and generates an economical power dispatch scheme.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] 1. This invention, through the design of a loss monitoring module, realizes the function of real-time acquisition of energy loss status, solves the problems of insufficient energy loss monitoring accuracy and the inability to couple energy loss information in various links, enables cross-module collaborative control of energy loss coupling analysis in various links, improves energy utilization efficiency, enhances system operation safety and stability, and optimizes economic operation benefits.

[0047] 2. This invention, by incorporating a power generation module, achieves the function of multi-energy coordinated dispatch, solving the problems of reliance on a single energy source, low energy utilization, and low system reliability. It can switch energy supply modes according to different load demands, reducing system operating costs and improving the system's environmental performance and operational stability.

[0048] 3. This invention, through the design of a switching module, realizes the function of dynamically adjusting the energy transmission path, solves the problems of grid instability, long-term energy supply interruption and insufficient system resilience caused by energy fluctuations, avoids equipment damage caused by power fluctuations, improves system operation stability, and optimizes user experience;

[0049] 4. This invention, by designing a transmission monitoring component, a transmission module, a usage monitoring component, and a usage module, achieves the function of optimizing the energy supply to the user-end load, solves the problems of low energy transmission efficiency, mismatch between intermittent power supply and load, and poor adaptability to complex scenarios, can dynamically compensate for losses during energy transmission, improve the response speed of energy transmission mode switching, reduce energy transmission losses, increase the renewable energy consumption rate, and reduce equipment failure rate. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the microgrid energy interaction system of the present invention;

[0051] Figure 2 This is a schematic diagram of the power generation module of the present invention;

[0052] Figure 3 This is a schematic diagram of the energy storage module of the present invention;

[0053] Figure 4 This is a schematic diagram of the conveying module of the present invention;

[0054] Figure 5 This is a schematic diagram of the switching module of the present invention;

[0055] Figure 6 This is a schematic diagram of the loss monitoring module of the present invention;

[0056] Figure 7 This is a schematic diagram of the system information flow of the present invention;

[0057] Figure 8 This is a schematic diagram illustrating the energy loss model construction process of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1: Please refer to Figure 1 , Figure 6 and Figure 8A microgrid energy interaction system based on distributed energy storage devices includes a processor and a loss monitoring module. The loss monitoring module monitors energy losses generated during power generation, storage, transmission, and use, and transmits the information to the processor via baseband and Bluetooth. After receiving the energy loss information, the processor aligns the energy loss data time axis through dynamic time warping, removes outliers, and fills in the missing data. It then inputs the SARIMA algorithm to decompose the seasonal cycle of the line loss data, quantifies the contribution through random forest feature importance analysis, and inputs the voltage, current, temperature, and historical battery aging coefficient of the energy storage module. The nonlinear time-series characteristics of battery polarization loss are processed through an LSTM network. The system automatically adjusts the network hyperparameters through Bayesian optimization and allocates multi-node coupling losses through a Shapley-value-based alliance algorithm to construct an energy loss model. The loss monitoring module includes a power generation monitoring component, a storage monitoring component, a transmission monitoring component, and a usage monitoring component. The power generation monitoring component monitors the power generation module, the storage monitoring component monitors the energy storage module, the transmission monitoring component monitors the transmission module, and the usage monitoring component monitors the usage module.

[0060] The power generation monitoring component includes: a Hall effect sensor for acquiring output DC current and voltage values, a smart meter for monitoring the AC output power of the inverter, and an environmental monitoring unit for quantifying power generation efficiency deviations caused by environmental factors.

[0061] The storage monitoring component includes: a voltage sensor and a current sensor for monitoring ohmic loss and polarization loss during charging and discharging; a temperature sensor for detecting the internal temperature difference of the battery cluster; and a bidirectional converter efficiency analyzer for measuring the charging and discharging conversion efficiency of the energy storage AC.

[0062] The transmission monitoring component includes a bus monitoring unit for capturing line loss and contact loss, a power analyzer for analyzing the power difference between the input and output of the connected converter, and a switch status recorder for recording the instantaneous energy loss when switching from grid connection to islanding.

[0063] The monitoring components include a cluster of smart meters for monitoring the actual energy consumption of DC and AC loads and a non-intrusive load identification unit that analyzes load types through current waveform analysis.

[0064] The environmental monitoring unit includes an irradiance sensor, a wind speed sensor, and a temperature sensor. The irradiance sensor and wind speed sensor are used to quantify the power generation efficiency deviation caused by environmental factors, and the temperature sensor is used to monitor the power generation attenuation caused by the temperature rise of the photovoltaic panel.

[0065] Furthermore, during the operation of the microgrid energy interaction system, the power generation module generates electricity and stores it in the energy storage module. The transmission module then delivers the electricity to the user modules to power the loads. During this process, the loss monitoring module collects information on energy losses during power generation, storage, transmission, and consumption, transmitting this information to the processor. The processor receives energy loss data from these four stages (power generation, storage, transmission, and consumption), aligns the timeline of the energy loss data using dynamic time warping, and identifies outliers using the isolated forest algorithm. When missing data appears after outlier removal, the processor uses linear interpolation to fill in the missing data. The SARIMA algorithm is then used to decompose the seasonal cycle of the line loss data, and random forest feature importance analysis is used to quantify the contribution of each factor. Finally, the processor uses an LSTM network to process the nonlinear time-varying battery polarization loss by inputting the voltage, current, temperature, and historical battery aging coefficients from the energy storage module during system operation. This system utilizes Bayesian optimization to automatically adjust network hyperparameters and a Shapley-value-based consortium algorithm to allocate multi-node coupling losses, thus constructing an energy loss model. After inputting irradiance trends and component aging rates, the processor can predict the photovoltaic degradation rate over the next hour using an LSTM network. The processor calculates power deviations and dynamically adjusts the MPPT operating point using a PID controller. A SARIMA model decomposes load cycle patterns and pre-generates transformer tap adjustment schemes. The processor acquires electricity price signals, battery life loss, and carbon emission intensity as features, quantifies decision weights using a random forest algorithm, and generates an economically optimal power dispatch scheme. This system achieves real-time energy loss monitoring, solving the problems of insufficient energy loss monitoring accuracy and the inability to couple energy loss information across different stages. It enables cross-module collaborative control of energy loss coupling analysis across different stages, improving energy utilization efficiency, enhancing system operational safety and stability, and optimizing economic benefits.

[0066] Example 2: Please refer to Figure 1 and Figure 2 A microgrid energy interaction system based on distributed energy storage devices, wherein the power generation monitoring component is connected to the power generation module via Bluetooth transmission, and the power generation module includes: a photovoltaic power generation unit, a wind power generation unit, a micro gas turbine, and a fuel cell;

[0067] The photovoltaic power generation unit includes: silicon-based thin-film batteries, MPPT controllers, and inverters. It uses the photovoltaic effect to directly convert solar energy into electrical energy, and then converts it into alternating current through the inverter.

[0068] The wind power generation unit includes: blades, gearbox, permanent magnet synchronous generator and converter. The blades capture wind energy to drive the rotor to rotate, and the gearbox increases the speed to drive the generator to output AC power.

[0069] The micro gas turbine unit includes a centrifugal compressor, a regenerator, a combustion chamber, and a radial turbine. It utilizes the combustion of gas in the combustion chamber to drive the turbine to rotate and generate electricity.

[0070] Fuel cells include: an electric motor, an electrolyte membrane, bipolar plates, and a hydrogen supply unit. They utilize the electrochemical reaction of hydrogen and oxygen through a proton exchange membrane to convert chemical energy into direct current.

[0071] Furthermore, in the microgrid energy interaction system, multiple power generation measures are employed. Solar radiation is converted into direct current (DC) electricity using the photovoltaic effect through silicon-based thin-film batteries. The generated DC power is optimized for maximum power point tracking (MPPT) using a controller to improve energy capture efficiency. An inverter then converts the DC power into alternating current (AC) that meets system standards. Wind energy is captured by blades and used to drive the rotor. Mechanical energy is used to increase the rotational speed via a gearbox. A synchronous motor converts the high-speed rotation into AC power. A converter further adjusts the voltage and frequency to ensure stable AC output. Air is compressed by a centrifugal compressor and sent to the combustion chamber, where fuel mixes and combusts with the air, generating high-temperature, high-pressure gas that drives a centrifugal turbine to generate electricity. A regenerator recovers waste heat. It can improve the overall power generation efficiency and output AC power. The fuel cell is supplied with hydrogen fuel through the hydrogen supply unit. It reacts with oxygen through the electrolyte membrane. The bipolar plate collects the DC current generated by the reaction. The output DC power is converted into AC power. Since the photovoltaic power generation unit and the wind power generation unit are intermittent, when the power generation of the photovoltaic power generation unit and the wind power generation unit drops sharply, the processor can generate electricity through the micro gas turbine and fuel cell to maintain the stable operation of the system. It realizes the function of multi-energy coordinated scheduling, solves the problems of single energy dependence, low energy utilization and low system reliability. It can switch the energy supply mode according to different load requirements, reduce the system operating cost, and improve the system's environmental performance and operational stability.

[0072] Example 3: Please refer to Figure 1 , Figure 2 , Figure 3 and Figure 5 A microgrid energy interaction system based on distributed energy storage devices, wherein the power generation monitoring component is connected to the power generation module via Bluetooth transmission, and the power generation module includes: a photovoltaic power generation unit, a wind power generation unit, a micro gas turbine, and a fuel cell;

[0073] The photovoltaic power generation unit includes: silicon-based thin-film batteries, MPPT controllers, and inverters. It uses the photovoltaic effect to directly convert solar energy into electrical energy, and then converts it into alternating current through the inverter.

[0074] The wind power generation unit includes: blades, gearbox, permanent magnet synchronous generator and converter. The blades capture wind energy to drive the rotor to rotate, and the gearbox increases the speed to drive the generator to output AC power.

[0075] The micro gas turbine unit includes a centrifugal compressor, a regenerator, a combustion chamber, and a radial turbine. It utilizes the combustion of gas in the combustion chamber to drive the turbine to rotate and generate electricity.

[0076] Fuel cells include: an electric motor, an electrolyte membrane, bipolar plates, and a hydrogen supply unit. They utilize the electrochemical reaction of hydrogen and oxygen through a proton exchange membrane to convert chemical energy into direct current.

[0077] The storage monitoring component is connected to the energy storage module via baseband transmission. The energy storage module includes: an energy storage component, a battery management unit, and a thermal management unit.

[0078] Energy storage components include: lithium-ion batteries, flow batteries, lead-acid batteries, supercapacitors, and flywheel energy storage units, used to store electrical energy converted by the power generation module;

[0079] The battery management unit is used to monitor the voltage, temperature and state of charge of individual cells in real time;

[0080] The thermal management unit is used to maintain the operating temperature of the energy storage module;

[0081] The power generation module and the energy storage module are connected via a switching module, which includes an energy router and a power unit.

[0082] The energy router includes a multi-port power converter and an FPGA high-speed switching array for real-time monitoring of the output power of the power generation module and the state of charge of the energy storage module battery, and dynamically switches the power generation-energy storage connection path through a solid-state relay matrix.

[0083] The power unit connects to the delivery module and the user module to obtain the load demand and automatically allocate the power flow according to the load demand;

[0084] The power generation module and the energy storage module are connected via an energy router;

[0085] Furthermore, during the operation of the power generation module, the power generation monitoring component acquires real-time data such as photovoltaic inverter power and wind turbine speed via Bluetooth transmission. The storage monitoring component simultaneously collects the battery state of charge, temperature, and health status of each component in the storage component. Different connections between the power generation components in the power generation module and the components in the storage component have different effects. The photovoltaic power generation unit is directly connected to the DC bus to buffer instantaneous fluctuations and is connected to the supercapacitor without conversion, resulting in a short response time and reduced conversion losses. The wind power generation unit can process the converted wind power fluctuations through the supercapacitor and flywheel energy storage unit. The micro gas turbine can be connected to lithium-ion batteries, lead-acid batteries, and flow batteries. The fuel cell DC output is compatible with DC interface energy storage. The power generation monitoring component captures real-time data from the photovoltaic power generation unit and the wind power generation unit. The energy storage monitoring component monitors the battery state of charge in real time to mitigate fluctuations in the unit's energy supply. When losses occur, the processor switches the connection between the power generation module and the energy storage module via the energy router to reduce energy loss. After receiving the switching command, the FPGA high-speed switching array reconstructs the power flow topology. The solid-state relay matrix replaces mechanical contacts to achieve zero-arc switching. When there is surplus photovoltaic power, the bidirectional converter converts AC bus power into DC power and stores it in the energy storage module. When the load demand surges, it converts the DC power stored in the energy storage module into AC output, realizing the function of dynamically adjusting the energy transmission path. This solves the problems of grid instability, long-term energy supply interruption, and insufficient system resilience caused by energy fluctuations. It can avoid equipment damage caused by power fluctuations, improve system operation stability, and optimize user experience.

[0086] Example 4: Please refer to Figure 1 , Figure 4 , Figure 5 and Figure 7 A microgrid energy interaction system based on distributed energy storage devices, wherein the output end of the power generation module is connected to the input end of the energy storage module through a switching module, and the switching module includes: an energy router and a power unit;

[0087] The energy router includes a multi-port power converter and an FPGA high-speed switching array for real-time monitoring of the output power of the power generation module and the state of charge of the energy storage module battery, and dynamically switches the power generation-energy storage connection path through a solid-state relay matrix.

[0088] The power unit connects to the delivery module and the user module to obtain the load demand and automatically allocate the power flow according to the load demand;

[0089] The output of the energy storage module is connected to a transmission module via a switching module. The transmission module includes: a power distribution network, a power electronic conversion unit, a grid connection and protection unit, and a local controller.

[0090] The power distribution network includes AC busbars, DC busbars, and AC / DC hybrid busbar systems, forming the main channels for power transmission;

[0091] The power electronic conversion unit includes converters and inverters that convert DC power output from photovoltaic and energy storage into AC power, rectifiers that convert AC power into DC power, and bidirectional converters that support the bidirectional flow of electrical energy between AC and DC buses.

[0092] The grid connection and protection unit includes: an automatic transfer switch for switching between microgrid grid-connected operation and islanded operation, and circuit breakers and protective relays for providing electrical protection;

[0093] The local controller is used to execute real-time scheduling instructions;

[0094] The energy storage module is connected to the user module via a transmission line, and the user module is the user-end load.

[0095] Furthermore, during the operation of the transmission module supplying electrical energy from the energy storage module to the user-end load, the transmission monitoring component can capture line losses, contact losses, and converter conversion efficiency losses in real time. After receiving energy loss information, the processor can dynamically adjust the power conversion method through the power electronic conversion unit to reduce energy losses in the energy transmission process. During grid-connected and islanded switching, the switch status recorder can capture the instantaneous energy loss during the switchover and trigger automatic switching action, thereby ensuring uninterrupted power supply to critical loads. When the user-end load uses electrical energy, the monitoring component identifies the load type through a non-intrusive load identification unit and obtains actual energy consumption data through the smart meter cluster, transmitting the information... After being transmitted to the processor, the processor can construct an energy consumption profile for the user end. During peak electricity price periods, it can prioritize the use of stored energy in the energy storage module through the energy router to supply power, thereby reducing electricity costs. When the transmission monitoring component detects abnormal temperature rise at the contact point, the processor can use the local controller to link the circuit breaker to isolate the risk section in advance to avoid equipment damage. This realizes the function of optimizing the energy supply to the user end load, solves the problems of low energy transmission efficiency, mismatch between intermittent power supply and load, and poor adaptability to complex scenarios. It can dynamically compensate for losses during energy transmission, improve the response speed of energy transmission mode switching, reduce energy transmission losses, increase the renewable energy consumption rate, and reduce equipment failure rate.

[0096] Example 5: Please refer to Figure 1 , Figure 7 and Figure 8A microgrid energy interaction system based on distributed energy storage devices includes a processor and a loss monitoring module. The loss monitoring module monitors energy losses generated during power generation, storage, transmission, and use, and transmits the information to the processor via baseband and Bluetooth. After receiving the energy loss information, the processor aligns the energy loss data time axis through dynamic time warping, removes outliers, and fills in the missing data. It then inputs the SARIMA algorithm to decompose the seasonal cycle of the line loss data, quantifies the contribution through random forest feature importance analysis, and inputs the voltage, current, temperature, and historical battery aging coefficient of the energy storage module. The nonlinear time-series characteristics of battery polarization loss are processed through an LSTM network. The system automatically adjusts the network hyperparameters through Bayesian optimization and allocates multi-node coupling losses through a Shapley-value-based alliance algorithm to construct an energy loss model. The loss monitoring module includes a power generation monitoring component, a storage monitoring component, a transmission monitoring component, and a usage monitoring component. The power generation monitoring component monitors the power generation module, the storage monitoring component monitors the energy storage module, the transmission monitoring component monitors the transmission module, and the usage monitoring component monitors the usage module.

[0097] The power generation monitoring component includes: a Hall effect sensor for acquiring output DC current and voltage values, a smart meter for monitoring the AC output power of the inverter, and an environmental monitoring unit for quantifying power generation efficiency deviations caused by environmental factors.

[0098] The storage monitoring component includes: a voltage sensor and a current sensor for monitoring ohmic loss and polarization loss during charging and discharging; a temperature sensor for detecting the internal temperature difference of the battery cluster; and a bidirectional converter efficiency analyzer for measuring the charging and discharging conversion efficiency of the energy storage AC.

[0099] The transmission monitoring component includes a bus monitoring unit for capturing line loss and contact loss, a power analyzer for analyzing the power difference between the input and output of the connected converter, and a switch status recorder for recording the instantaneous energy loss when switching from grid connection to islanding.

[0100] The monitoring components include a cluster of smart meters for monitoring the actual energy consumption of DC and AC loads and a non-intrusive load identification unit that analyzes load types through current waveform analysis.

[0101] The environmental monitoring unit includes an irradiance sensor, a wind speed sensor, and a temperature sensor. The irradiance sensor and wind speed sensor are used to quantify the power generation efficiency deviation caused by environmental factors, and the temperature sensor is used to monitor the power generation attenuation caused by the temperature rise of the photovoltaic panel.

[0102] Furthermore, after the energy loss model is constructed, the processor inputs relevant data based on the specific operating conditions of the system to predict potential energy losses during subsequent energy conversion, storage, transmission, and use. It then adjusts the power generation module, energy storage module, transmission module, and usage module. During adjustment, the power fluctuations, system equipment status changes, and control signal timing are recorded in real time by the power generation monitoring unit, storage monitoring unit, transmission monitoring unit, and usage monitoring unit. Dynamic parameters such as transient loss indicators and adjustment execution delay losses are extracted. Second-level response losses, minute-level adjustment losses, and hour-level adjustment losses are modeled hierarchically. The steady-state parameters and adjustment loss variables of the energy loss model are linked through state transition equations, merging the initial energy loss model with the adjustment loss model. This dynamically couples battery cycle aging with transient losses caused by adjustment, extending the lifespan of the energy storage module while reducing system operating costs. The energy loss model after merging the adjustment loss model can incorporate adjustment losses as parameters to optimize the original adjustment scheme, thereby further reducing energy loss.

[0103] Working Principle: In the microgrid energy interaction system, multiple power generation measures are employed. Silicon-based thin-film batteries utilize the photovoltaic effect to convert solar radiation into direct current (DC) electricity. The generated DC power is optimized for maximum power point tracking (MPPT) by a controller to improve energy capture efficiency. An inverter then converts the DC power into alternating current (AC) that meets system standards. Wind energy is captured by blades and used to drive the rotor. Mechanical energy is used to increase the rotational speed via a gearbox. A synchronous motor converts the high-speed rotation into AC power. A converter further adjusts the voltage and frequency to ensure stable AC output. Air is compressed by a centrifugal compressor and then delivered to the combustion chamber for combustion. The fuel mixes and explodes with air in the combustion chamber, generating high-temperature and high-pressure gas that drives the centripetal turbine to rotate and drive the generator to generate electricity. The regenerator recovers waste heat energy to improve the overall power generation efficiency and outputs alternating current. The fuel cell provides hydrogen fuel through the hydrogen supply unit, which reacts with oxygen through the electrolyte membrane. The bipolar plate collects the direct current generated by the reaction, and the output direct current is converted into alternating current. Since the photovoltaic power generation unit and the wind power generation unit are intermittent, when the power generation of the photovoltaic power generation unit and the wind power generation unit drops sharply, the processor can generate electricity through the micro gas turbine and the fuel cell to maintain the stable operation of the system.

[0104] During the operation of the power generation module, the power generation monitoring component acquires real-time data such as photovoltaic inverter power and wind turbine speed via Bluetooth transmission. The storage monitoring component synchronously collects the battery state of charge, temperature, and health status of each component in the storage component. Different connections between the power generation components in the power generation module and the components in the storage component have different effects. The photovoltaic power generation unit is directly connected to the DC bus to buffer instantaneous fluctuations and is connected to the supercapacitor without conversion, resulting in a short response time and reduced conversion losses. The wind power generation unit can process the converted wind power fluctuations through the supercapacitor and flywheel energy storage unit. The micro gas turbine can be connected to lithium-ion batteries, lead-acid batteries, and flow batteries. The fuel cell DC output is compatible with DC interface energy storage. The power generation monitoring component captures the fluctuations of the photovoltaic power generation unit and the wind power generation unit in real time, and the energy storage monitoring component monitors the battery state of charge in real time. When losses occur, the processor switches the connection mode between the power generation module and the energy storage module through the energy router to reduce energy loss. After receiving the switching command, the FPGA high-speed switching array reconstructs the power flow topology. The solid-state relay matrix replaces the mechanical contacts to achieve zero-arc switching. When there is surplus photovoltaic power, the bidirectional converter converts the AC bus power into DC power and stores it in the energy storage module. When the load demand surges, the DC power stored in the energy storage module is converted into AC output.

[0105] When the transmission module delivers electrical energy from the energy storage module to the user module to supply the user-end load, the transmission monitoring component can capture line loss, contact loss, and converter conversion efficiency loss in real time. After receiving energy loss information, the processor can dynamically adjust the power conversion mode through the power electronic conversion unit to reduce energy loss in the energy transmission process. When switching between grid connection and islanding, the processor can capture the instantaneous energy loss when switching from grid connection to islanding through the switch status recorder and trigger the automatic switching action to ensure uninterrupted power supply to critical loads. When the user-end load uses electrical energy, the monitoring component uses the non-intrusive load identification unit to identify the load type and obtains the actual energy consumption data through the smart meter cluster. After transmitting the information to the processor, the processor can build an energy consumption profile of the user end. During peak electricity price periods, the processor can prioritize the use of electrical energy stored in the energy storage module for power supply through the energy router, thereby reducing electricity costs. When the transmission monitoring component detects abnormal temperature rise at the contact point, the processor can use the local controller to link the circuit breaker to isolate the risk section in advance to avoid equipment damage.

[0106] During the operation of the microgrid energy interaction system, the power generation module generates electricity and stores it in the energy storage module. The transmission module then delivers the electricity to the user modules to power the loads. Throughout this process, the loss monitoring module collects information on energy losses during power generation, storage, transmission, and consumption, transmitting this information to the processor. The processor receives energy loss data from these four stages (power generation, storage, transmission, and consumption), aligns the energy loss data along the timeline using dynamic time warping, and identifies outliers using the isolated forest algorithm. After outlier removal, the processor fills in the missing data using linear interpolation before inputting it into the SARIMA algorithm for decomposition. The seasonal cycle of the loss data is analyzed using random forest feature importance analysis to quantify the contribution of each factor. The processor, by inputting voltage, current, temperature, and historical battery aging coefficients from the energy storage module during system operation, processes the nonlinear temporal characteristics of battery polarization loss through an LSTM network. It then automatically adjusts network hyperparameters using Bayesian optimization and allocates multi-node coupling losses using a Shapley-value-based consortium algorithm, thus constructing the energy loss model. After inputting irradiance trends and component aging rates, the processor can predict the photovoltaic degradation rate for the next 1 hour using an LSTM network. The processor calculates power deviation and dynamically adjusts the MPPT operating point using a PID controller. SARIMA... The model decomposes the load cycle pattern and pre-generates transformer tap changer adjustment schemes. The processor acquires electricity price signals, battery life loss, and carbon emission intensity as features, quantifies decision weights using a random forest algorithm, and generates an economically optimal power dispatch scheme. After the energy loss model is constructed, the processor inputs relevant data based on the specific operating conditions of the system to predict potential energy losses during subsequent energy conversion, storage, transmission, and use. It then adjusts the generation, storage, transmission, and usage modules. During the adjustment process, power is recorded in real time by the generation monitoring unit, storage monitoring unit, transmission monitoring unit, and usage monitoring unit. By analyzing fluctuations, changes in system equipment status, and control signal timing, dynamic parameters such as transient loss indicators and adjustment execution delay losses are extracted. Second-level response loss, minute-level adjustment loss, and hour-level adjustment loss are modeled hierarchically. The steady-state parameters and adjustment loss variables of the energy loss model are linked through state transition equations. The initial energy loss model is then fused with the adjustment loss model, dynamically coupling battery cycle aging and transient losses caused by adjustment. This approach can extend the lifespan of energy storage modules while reducing system operating costs. Furthermore, the energy loss model after fusing the adjustment loss model can incorporate adjustment losses as parameters to optimize the original adjustment scheme, thereby further reducing energy loss.

[0107] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A microgrid energy interaction system based on distributed energy storage devices, characterized in that: The system includes a processor and a loss monitoring module. The loss monitoring module monitors energy losses generated during power generation, storage, transmission, and use, and transmits the information to the processor via baseband and Bluetooth. Upon receiving the energy loss information, the processor aligns the energy loss data timeline using dynamic time warping. After outlier removal and data filling, it inputs the SARIMA algorithm to decompose the seasonal cycle of the line loss data, quantifies the contribution through random forest feature importance analysis, and processes the nonlinear time-series characteristics of battery polarization loss using an LSTM network based on the voltage, current, temperature, and historical battery aging coefficients of the energy storage module. It then automatically adjusts the network hyperparameters using Bayesian optimization and allocates multi-node coupling losses using a Shapley-based alliance algorithm to construct an energy loss model. The loss monitoring module includes: a power generation monitoring component, a storage monitoring component, a transmission monitoring component, and a usage monitoring component. The power generation monitoring component monitors the power generation module, the storage monitoring component monitors the energy storage module, the transmission monitoring component monitors the transmission module, and the usage monitoring component monitors the usage module. The power generation monitoring component includes: a Hall effect sensor for acquiring output DC current and voltage values, a smart meter for monitoring the AC output power of the inverter, and an environmental monitoring unit for quantifying power generation efficiency deviations caused by environmental factors. The storage monitoring component includes: a voltage sensor and a current sensor for monitoring ohmic loss and polarization loss during charging and discharging; a temperature sensor for detecting the internal temperature difference of the battery cluster; and a bidirectional converter efficiency analyzer for measuring the charging and discharging conversion efficiency of the energy storage AC. The transmission monitoring component includes a bus monitoring unit for capturing line loss and contact loss, a power analyzer for analyzing the power difference between the input and output of the connected converter, and a switch status recorder for recording the instantaneous energy loss when switching from grid connection to islanding. The monitoring components include a cluster of smart meters for monitoring the actual energy consumption of DC and AC loads, and a non-intrusive load identification unit that analyzes load types through current waveform analysis.

2. The microgrid energy interaction system based on distributed energy storage devices according to claim 1, characterized in that: The environmental monitoring unit includes an irradiance sensor, a wind speed sensor, and a temperature sensor. The irradiance sensor and wind speed sensor are used to quantify the power generation efficiency deviation caused by environmental factors, and the temperature sensor is used to monitor the power generation attenuation caused by the temperature rise of the photovoltaic panel.

3. A microgrid energy interaction system based on distributed energy storage devices according to claim 1, characterized in that: The power generation monitoring component is connected to the power generation module via Bluetooth transmission. The power generation module includes: a photovoltaic power generation unit, a wind power generation unit, a micro gas turbine, and a fuel cell. The photovoltaic power generation unit includes: silicon-based thin-film batteries, MPPT controllers, and inverters. It uses the photovoltaic effect to directly convert solar energy into electrical energy, and then converts it into alternating current through the inverter. The wind power generation unit includes: blades, gearbox, permanent magnet synchronous generator and converter. The blades capture wind energy to drive the rotor to rotate, and the gearbox increases the speed to drive the generator to output AC power. The micro gas turbine unit includes a centrifugal compressor, a regenerator, a combustion chamber, and a radial turbine. It utilizes the combustion of gas in the combustion chamber to drive the turbine to rotate and generate electricity. A fuel cell includes a motor, an electrolyte membrane, bipolar plates, and a hydrogen supply unit. It utilizes the electrochemical reaction of hydrogen and oxygen through a proton exchange membrane to convert chemical energy into direct current.

4. A microgrid energy interaction system based on distributed energy storage devices according to claim 1, characterized in that: The storage monitoring component is connected to the energy storage module via baseband transmission. The energy storage module includes: an energy storage component, a battery management unit, and a thermal management unit. Energy storage components include: lithium-ion batteries, flow batteries, lead-acid batteries, supercapacitors, and flywheel energy storage units, used to store electrical energy converted by the power generation module; The battery management unit is used to monitor the voltage, temperature and state of charge of individual cells in real time; The thermal management unit is used to maintain the operating temperature of the energy storage module.

5. A microgrid energy interaction system based on distributed energy storage devices according to claim 3, characterized in that: The output of the power generation module is connected to the input of the energy storage module through a switching module. The switching module includes an energy router and a power unit. The energy router includes a multi-port power converter and an FPGA high-speed switching array for real-time monitoring of the output power of the power generation module and the state of charge of the energy storage module battery, and dynamically switches the power generation-energy storage connection path through a solid-state relay matrix. The power unit connects to the delivery module and the usage module to obtain the load demand and automatically allocate the power flow according to the load demand.

6. A microgrid energy interaction system based on distributed energy storage devices according to claim 4, characterized in that: The output of the energy storage module is connected to a transmission module via a switching module. The transmission module includes: a power distribution network, a power electronic conversion unit, a grid connection and protection unit, and a local controller. The power distribution network includes AC busbars, DC busbars, and AC / DC hybrid busbar systems, forming the main channels for power transmission; The power electronic conversion unit includes converters and inverters that convert DC power output from photovoltaic and energy storage into AC power, rectifiers that convert AC power into DC power, and bidirectional converters that support the bidirectional flow of electrical energy between AC and DC buses. The grid connection and protection unit includes: an automatic transfer switch for switching between microgrid grid-connected operation and islanded operation, and circuit breakers and protective relays for providing electrical protection; The local controller is used to execute real-time scheduling instructions.

7. A microgrid energy interaction system based on distributed energy storage devices according to claim 4, characterized in that: The energy storage module is connected to the user module via a transmission line, and the user module is the user-end load.

8. A microgrid energy interaction system based on distributed energy storage devices according to claim 3, characterized in that: The power generation module and the energy storage module are connected via an energy router.

9. A microgrid energy interaction method based on distributed energy storage devices, applicable to a microgrid energy interaction system based on distributed energy storage devices as described in any one of claims 1-8, characterized in that: The microgrid energy interaction method is as follows: S1: Electrical energy is generated through photovoltaic power generation units, wind power generation units, micro gas turbine units, and fuel cells and stored in the energy storage module; S2: The electrical energy stored in the energy storage module is transmitted to the user module through the transmission line to power the user's load. S3: The loss monitoring module collects energy loss information during power generation, storage, transmission and use, transmits the loss information to the processor, the processor builds an energy loss model based on the received energy loss information, and generates an adjustment scheme based on the energy loss information. S4: The processor adjusts the power generation module, energy storage module, transmission module and usage module according to the generated adjustment scheme to compensate for energy consumption and maintain the stability of the microgrid energy interaction system. S5: During the energy regulation process, the loss monitoring module collects energy loss data a second time and constructs a regulation loss model; S6: The processor optimizes the energy loss model by adjusting the loss model.

10. A microgrid energy interaction method based on distributed energy storage devices according to claim 9, characterized in that: Specifically, S3 is: S31: Collect energy losses during power generation, storage, transmission and interaction through the energy loss module, and transmit the collected energy loss information to the processor; S32: After receiving energy loss information, the processor aligns the energy loss data time axis through dynamic time warping and identifies outliers through the isolated forest algorithm. When data is missing after outlier removal, the processor fills in the missing data with linear interpolation and then decomposes the seasonal cycle of the line loss data through the input SARIMA algorithm. S33: The contribution of each factor is quantified by random forest feature importance analysis. The processor processes the nonlinear time-series characteristics of battery polarization loss through LSTM network by inputting the voltage, current, temperature and historical battery aging coefficient of the energy storage module during system operation. The network hyperparameters are automatically adjusted by running Bayesian optimization and multi-node coupling loss is allocated by Shapley value-based alliance algorithm to complete the construction of energy loss model. S34: After inputting irradiance trends and component aging rates, the processor can predict the photovoltaic degradation rate in the next 1 hour through an LSTM network. The processor calculates the power deviation and dynamically adjusts the MPPT operating point through a PID controller. The SARIMA model decomposes the load cycle pattern and pre-generates transformer tap adjustment schemes. The processor acquires electricity price signals, battery life loss and carbon emission intensity as features, quantifies decision weights through a random forest algorithm, and generates an economical power dispatch scheme.

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