Method and system for controlling energy flow of ac-dc hybrid microgrid

Abstract

The application discloses an AC-DC hybrid micro-grid energy flow control method and system, and belongs to the technical field of micro-grid new energy dispatching and power stability control. In view of the industry pain points of poor adaptability of existing fixed droop control, imbalance of AC-DC sub-network coupling, large switching impact of working condition, solidification of energy storage distribution, and low new energy consumption rate, the application proposes a fine full-working-condition self-adaptive energy management and control scheme. The application collects micro-grid global operation parameters at a high frequency, quantitatively divides four types of subdivided operation conditions, adopts parameter normalization combined with a segmented variable droop algorithm, corrects the regulation coefficient of the converter, realizes bidirectional precise mutual aid of sub-network energy, and distributes the energy storage output relying on multi-parameter fuzzy rules to suppress high and low frequency power fluctuations in layers. At the same time, a gradient smooth switching and hierarchical fault-tolerant protection mechanism is configured to suppress parameter oscillation. The application solves the defects of traditional strategy control lag, low energy storage utilization rate and poor working condition stability, improves the power supply reliability and new energy consumption capacity of the micro-grid, and is suitable for high penetration rate new energy micro-grid operation and maintenance scenes.

Description

Technical Field

[0001] This invention relates to the fields of microgrid energy control and new energy power dispatching, specifically to an AC / DC hybrid microgrid energy flow control method and system. Background Technology

[0002] With the widespread adoption of distributed renewable energy sources such as photovoltaics and wind power, hybrid AC / DC microgrids have become a core carrier for smart grids and distributed energy utilization due to their ability to simultaneously adapt to AC and DC loads and accommodate multiple distributed power sources. A hybrid AC / DC microgrid consists of an AC subgrid, a DC subgrid, bidirectional interconnected converters, distributed power sources, a hybrid energy storage system, and AC / DC loads. Through energy sharing between subgrids, it achieves local consumption of renewable energy and reliable power supply to loads.

[0003] Most existing publicly available AC / DC hybrid microgrid energy control schemes employ fixed-coefficient droop control, fixed-ratio energy storage allocation, and single-condition regulation logic, which have significant technical shortcomings in scenarios with high penetration of renewable energy into the grid. Current mainstream microgrid energy regulation schemes generally adopt static droop control strategies. Once the droop coefficient is set, it cannot adaptively adjust with load conditions. In scenarios with random fluctuations in photovoltaic and wind power output and dynamic load switching, problems such as light-load power oscillations and heavy-load regulation lags are easily encountered, resulting in AC bus frequency deviations exceeding 1Hz and DC bus voltage distortion exceeding 5%, making it difficult to meet national standards for power supply quality.

[0004] Secondly, existing technologies do not distinguish the dimensional differences of AC and DC electrical parameters and lack a parameter normalization calibration mechanism, which directly leads to an imbalance in the power matching ratio of AC and DC subgrids, resulting in differentiated control defects such as excessive AC frequency regulation and insufficient DC voltage regulation, and poor subgrid coupling and collaborative control capabilities.

[0005] Meanwhile, traditional energy storage control often adopts a fixed threshold charging and discharging strategy, which cannot adaptively allocate power output according to the frequency and amplitude of power fluctuations. This results in frequent deep charging and discharging of batteries, a significant reduction in cycle life, long-term idle supercapacitors, and energy storage comprehensive utilization efficiency generally below 60%, leading to high equipment operation and maintenance costs.

[0006] More importantly, the existing solution has a rigid switching logic and no smooth transition mechanism. The power surge during grid-connected and off-grid switching can reach more than 15% of the rated power, which can easily cause faults such as converter overcurrent tripping and bus voltage spikes, resulting in insufficient system robustness.

[0007] Furthermore, existing protection systems are mostly single-fault trip protections, lacking tiered fault tolerance and correction capabilities. Even slight parameter fluctuations trigger protection actions, resulting in poor system fault tolerance and an inability to adapt to complex and dynamic microgrid operating scenarios. Simultaneously, most existing technologies lack differentiated control logic for specific operating conditions such as grid-connected fluctuations and off-grid profitability, resulting in coarse-grained condition adaptation. This fails to balance renewable energy absorption efficiency with system operational stability, highlighting significant technological limitations and room for optimization in industrial and commercial microgrid scenarios with high penetration rates of distributed renewable energy.

[0008] Existing technologies struggle to balance the dynamic stability of microgrids, the accuracy of energy dispatch, and the efficiency of energy storage utilization. They are also ill-suited for complex operating scenarios involving high-penetration renewable energy sources. Therefore, there is an urgent need for an AC / DC hybrid microgrid energy flow control scheme that is highly adaptive, has high control accuracy, and good operational stability. Summary of the Invention

[0009] The purpose of this invention is to address the shortcomings of existing technologies by providing an AC / DC hybrid microgrid energy flow control method and system, thereby solving the technical problems of large power fluctuations, subgrid energy coupling misalignment, unstable operating condition switching, low energy storage utilization, and poor new energy absorption in existing microgrids.

[0010] To achieve the above objectives, the present invention provides the following technical solution: An energy flow control method for AC / DC hybrid microgrids is provided for adaptive energy regulation under all operating conditions in a photovoltaic-storage AC / DC hybrid microgrid. The AC / DC hybrid microgrid includes an AC subgrid, a DC subgrid, bidirectional interconnected converters, distributed renewable energy sources, AC / DC loads, and a hybrid energy storage system. The control method includes the following steps: S1. Synchronous Acquisition and Filtering Preprocessing of Global Parameters: The operating parameters of the AC / DC hybrid microgrid are acquired in real time at a sampling frequency of 100Hz. These operating parameters include the AC subgrid bus frequency, three-phase AC voltage, AC active / reactive power, wind power output, AC load power, DC subgrid bus voltage, photovoltaic output power, DC load power, state of charge, charging and discharging current, and remaining capacity of the hybrid energy storage, as well as the real-time transmission power and operating status parameters of the bidirectional interconnected converter. The acquired raw parameters are then subjected to Kalman filtering to remove abrupt interference data and obtain effective steady-state operating parameters. S2. Multi-threshold refined operating condition determination: Based on the pre-processed effective operating parameters, combined with the microgrid's grid-connected / off-grid operating status, system source-load power difference threshold, and energy storage operating threshold, four mutually exclusive operating conditions are accurately divided. These operating conditions include grid-connected power balance, grid-connected power fluctuation, off-grid power surplus, and off-grid power deficit. Each condition is determined independently without overlap, achieving accurate identification of the system's operating status. S3. Segmented Adaptive Droop Converter Control: Construct an improved segmented droop control model with normalized AC and DC parameters, perform dimensionless unified quantization of AC frequency and DC voltage, divide the system into three operating ranges (light load, rated load, and heavy load) based on the real-time load rate of the system, dynamically match the droop adjustment slope of the corresponding range, adaptively correct the power transmission coefficient, voltage compensation coefficient, and frequency compensation coefficient of the bidirectional interconnected converter, and accurately control the bidirectional energy flow direction and transmission power between AC and DC subgrids to achieve dynamic power balance of the subgrid; S4. Hierarchical Fuzzy Adaptive Energy Storage Power Allocation: Using system power fluctuation amplitude, power fluctuation rate, and hybrid energy storage state of charge as fuzzy inputs, the system adaptively outputs the low-frequency power allocation coefficient of the battery and the high-frequency response weight of the supercapacitor based on a preset fuzzy rule base; the supercapacitor is used to quickly smooth out short-term high-frequency power fluctuations, and the battery is used to compensate for long-term low-frequency power deficits. At the same time, the energy storage charging and discharging constraint thresholds are bound to avoid overcharging and over-discharging of the energy storage. S5. Gradient smooth switching and hierarchical closed-loop protection: Real-time monitoring of system operating condition switching signals, buffering all control parameters before switching, and using a gradient smooth transition algorithm to gradually correct parameters and eliminate power impact during operating condition switching; Simultaneously enabling multi-level protection logic such as parameter pre-correction, power limiting, and over-limit blocking to form a fully closed-loop adaptive control of microgrid energy flow.

[0011] Furthermore, the quantitative judgment criteria for the four operating conditions in step S2 are as follows: When a microgrid is connected to the grid, if the difference between the total power generation of the AC / DC subgrid and the total load power is within the preset ±5kW balance threshold range, it is determined to be a grid-connected power balance condition; if the power difference exceeds the ±5kW threshold range, it is determined to be a grid-connected power fluctuation condition. When a microgrid is off-grid, if the total power generation of the system is greater than the total load power, and the battery state of charge is ≤90% and the supercapacitor is not fully charged, it is determined to be an off-grid power surplus condition; if the total power generation of the system is less than the total load power, and the battery state of charge is ≥20% and the supercapacitor has remaining response capacity, it is determined to be an off-grid power deficit condition. The judgment conditions for each condition are independent of each other, with no logical overlap or blank intervals.

[0012] Furthermore, the method for constructing the AC / DC parameter normalization improvement piecewise droop control model in step S3 includes: First, the frequency range of 49Hz-51Hz for the AC bus and the range of 0.9-1.1 times the rated voltage for the DC bus are uniformly mapped to a dimensionless quantization range of 0-1, eliminating the dimensional differences between AC and DC electrical parameters and establishing a unified power regulation benchmark. Second, based on the system load rate, light load, rated load, and heavy load ranges are set. The light load range corresponds to a load rate of less than or equal to 30%, the rated load range corresponds to a load rate of greater than 30% and less than or equal to 70%, and the heavy load range corresponds to a load rate of greater than 70%. The light load range is configured with a small droop slope of 0.02 to achieve high-precision micro-adjustment and eliminate power overshoot oscillation. The rated load range is configured with a standard droop slope of 0.05 to balance regulation accuracy and response speed. The heavy load range is configured with a large droop slope of 0.08 to improve the dynamic power response speed and solve the lag defect of heavy load regulation.

[0013] Furthermore, the specific implementation of the fuzzy adaptive power allocation strategy in step S4 includes: A dual-input dual-output fuzzy control rule base is constructed, classifying system power fluctuation amplitude into five levels: small, medium, large, and extremely large, and energy storage state of charge into five levels: low, medium-low, medium, medium-high, and high, forming 25 sets of one-to-one adaptive control rules. For high-frequency short-term power fluctuations lasting 0 to 10 seconds, the supercapacitor response weight is adaptively adjusted to 80%-90% to prioritize the smoothing of instantaneous fluctuations. For low-frequency continuous power deviations lasting more than 10 seconds, the battery power distribution coefficient is adaptively adjusted to 70%-85% to handle steady-state power compensation. At the same time, the energy storage threshold is locked, with the battery state of charge of 20%-90% being the effective operating range, and charging and discharging are forcibly locked outside the range.

[0014] Furthermore, the gradient smoothing seamless switching control logic in step S5 includes: The system caches the droop slope, converter transmission power, and energy storage output parameters in real time before the operating condition switch. It presets a 20ms single iteration cycle and a 10-step gradient correction strategy, and gradually updates the control parameters during the switching process, replacing the instantaneous abrupt switching mode. It monitors the bus voltage and frequency fluctuations throughout the process, locking the voltage fluctuations within ±2% and the frequency fluctuations within ±0.5Hz, completely suppressing the power surge and parameter oscillations at the moment of operating condition switching.

[0015] An AC / DC hybrid microgrid energy flow control system is used to execute the AC / DC hybrid microgrid energy flow control method according to any one of claims 1-5, comprising a parameter acquisition and preprocessing module, an operating condition quantification and judgment module, a core algorithm control module, an energy storage adaptive adjustment module, a converter power control module, and a seamless switching and multi-level protection module. The parameter acquisition and preprocessing module is used to acquire the full-domain operating parameters of the microgrid at a high frequency of 100Hz, and to complete the Kalman filter noise reduction preprocessing to output effective steady-state parameters. The working condition quantification and judgment module and the electrical connection parameter acquisition and preprocessing module accurately identify four types of mutually exclusive operating conditions based on preset multi-threshold judgment logic and output working condition identification signals. The core algorithm control module has an improved segmented droop control algorithm and a fuzzy adaptive power allocation algorithm, which are respectively connected to the operating condition quantification judgment module, the energy storage adaptive adjustment module, and the converter power control module, and output precise control commands according to real-time operating conditions. The converter power control module is connected to the bidirectional interconnected converter and dynamically corrects the converter regulation coefficient according to the control command, thereby controlling the direction and power of bidirectional energy flow in the AC / DC subgrid. The energy storage adaptive adjustment module is used to control the charging and discharging output of the battery and supercapacitor in a layered manner, so as to achieve layered smoothing of high and low frequency power fluctuations, while executing energy storage threshold protection constraints. The seamless switching and multi-level protection module has built-in gradient smooth switching logic and hierarchical protection mechanism to achieve shock-free switching of operating conditions and system over-limit protection, ensuring stable closed-loop operation of the system.

[0016] Furthermore, the parameter acquisition and preprocessing module includes a high-precision voltage sensor, a frequency sensor, a power detection unit, an energy storage status acquisition unit, and a Kalman filter processing unit. All sensors have a sampling accuracy of no less than 0.01Hz, 0.1V, and 1%SOC, and the sampling frequency is fixed at 100Hz, which can realize synchronous high-precision acquisition and noise reduction optimization of parameters across the entire domain.

[0017] Furthermore, the bidirectional interconnected converter is a four-quadrant bidirectional AC / DC converter with a rated power of 400kW, supporting bidirectional energy transmission between AC and DC subgrids. It has a built-in power limiting unit and a coefficient adjustable unit, and can respond to the dynamic coefficient correction command of the core algorithm control module.

[0018] Furthermore, the hybrid energy storage system includes a 200Ah battery pack and a 50F supercapacitor pack. The battery pack is responsible for low-frequency steady-state power compensation, while the supercapacitor pack is responsible for high-frequency instantaneous power smoothing. The two work together and their outputs are adaptively matched to achieve optimal allocation of energy storage resources.

[0019] Furthermore, the seamless switching and multi-level protection module integrates a gradient smooth switching unit, a voltage over-limit protection unit, a frequency over-limit protection unit, an overcurrent power limiting unit, and a fault blocking unit. Minor parameter over-limits trigger pre-correction control, while severe faults trigger blocking protection, thus balancing system operational stability and equipment safety.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention adopts an improved segmented droop control strategy, which is suitable for the entire operating range of microgrids under light load, rated load, and heavy load. It dynamically optimizes the energy mutual assistance accuracy of AC and DC subgrids, effectively suppresses bus voltage and frequency fluctuations, and significantly improves power supply stability. 2. By using a fuzzy adaptive hybrid energy storage power allocation strategy, the battery and supercapacitor can work together to output power, accurately smooth out power fluctuations of different frequencies, balance the working state of energy storage, extend the service life of energy storage equipment, and improve the efficiency of energy storage utilization. 3. Set up multi-condition accurate judgment and seamless switching logic to solve the power surge problem during grid-connected and off-grid switching, adapt to the complex dynamic operation scenario of microgrids, and improve the system's anti-interference capability; 4. Construct a fully closed-loop energy management system to maximize the local consumption of new energy, reduce power interaction fluctuations between microgrids and the main grid, adapt to high-penetration distributed new energy access scenarios, and have a wide range of applications. Attached Figure Description

[0021] Figure 1 This is a block diagram of the overall architecture of the AC / DC hybrid microgrid energy flow control system of the present invention; Figure 2 This is a schematic diagram of the overall process of the AC / DC hybrid microgrid energy flow control method of the present invention; Figure 3 This is a schematic diagram of the fuzzy adaptive energy storage power allocation control logic of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0023] An energy flow control method for AC / DC hybrid microgrids is provided for adaptive energy regulation under all operating conditions in a photovoltaic-storage AC / DC hybrid microgrid. The AC / DC hybrid microgrid includes an AC subgrid, a DC subgrid, bidirectional interconnected converters, distributed renewable energy sources, AC / DC loads, and a hybrid energy storage system. The control method includes the following steps: S1. Synchronous Acquisition and Filtering Preprocessing of Global Parameters: The operating parameters of the AC / DC hybrid microgrid are acquired in real time at a sampling frequency of 100Hz. These operating parameters include the AC subgrid bus frequency, three-phase AC voltage, AC active / reactive power, wind power output, AC load power, DC subgrid bus voltage, photovoltaic output power, DC load power, state of charge, charging and discharging current, and remaining capacity of the hybrid energy storage, as well as the real-time transmission power and operating status parameters of the bidirectional interconnected converter. The acquired raw parameters are then subjected to Kalman filtering to remove abrupt interference data and obtain effective steady-state operating parameters. S2. Multi-threshold refined operating condition determination: Based on the pre-processed effective operating parameters, combined with the microgrid's grid-connected / off-grid operating status, system source-load power difference threshold, and energy storage operating threshold, four mutually exclusive operating conditions are accurately divided. These operating conditions include grid-connected power balance, grid-connected power fluctuation, off-grid power surplus, and off-grid power deficit. Each condition is determined independently without overlap, achieving accurate identification of the system's operating status. S3. Segmented Adaptive Droop Converter Control: Construct an improved segmented droop control model with normalized AC and DC parameters, perform dimensionless unified quantization of AC frequency and DC voltage, divide the system into three operating ranges (light load, rated load, and heavy load) based on the real-time load rate of the system, dynamically match the droop adjustment slope of the corresponding range, adaptively correct the power transmission coefficient, voltage compensation coefficient, and frequency compensation coefficient of the bidirectional interconnected converter, and accurately control the bidirectional energy flow direction and transmission power between AC and DC subgrids to achieve dynamic power balance of the subgrid; S4. Hierarchical Fuzzy Adaptive Energy Storage Power Allocation: Using system power fluctuation amplitude, power fluctuation rate, and hybrid energy storage state of charge as fuzzy inputs, the system adaptively outputs the low-frequency power allocation coefficient of the battery and the high-frequency response weight of the supercapacitor based on a preset fuzzy rule base; the supercapacitor is used to quickly smooth out short-term high-frequency power fluctuations, and the battery is used to compensate for long-term low-frequency power deficits. At the same time, the energy storage charging and discharging constraint thresholds are bound to avoid overcharging and over-discharging of the energy storage. S5. Gradient smooth switching and hierarchical closed-loop protection: Real-time monitoring of system operating condition switching signals, buffering all control parameters before switching, and using a gradient smooth transition algorithm to gradually correct parameters and eliminate power impact during operating condition switching; Simultaneously enabling multi-level protection logic such as parameter pre-correction, power limiting, and over-limit blocking to form a fully closed-loop adaptive control of microgrid energy flow.

[0024] Furthermore, the quantitative judgment criteria for the four operating conditions in step S2 are as follows: When a microgrid is connected to the grid, if the difference between the total power generation of the AC / DC subgrid and the total load power is within the preset ±5kW balance threshold range, it is determined to be a grid-connected power balance condition; if the power difference exceeds the ±5kW threshold range, it is determined to be a grid-connected power fluctuation condition. When a microgrid is off-grid, if the total power generation of the system is greater than the total load power, and the battery state of charge is ≤90% and the supercapacitor is not fully charged, it is determined to be an off-grid power surplus condition; if the total power generation of the system is less than the total load power, and the battery state of charge is ≥20% and the supercapacitor has remaining response capacity, it is determined to be an off-grid power deficit condition. The judgment conditions for each condition are independent of each other, with no logical overlap or blank intervals.

[0025] Furthermore, the method for constructing the AC / DC parameter normalization improvement piecewise droop control model in step S3 includes: First, the frequency range of 49Hz-51Hz for the AC bus and the range of 0.9-1.1 times the rated voltage for the DC bus are uniformly mapped to a dimensionless quantization range of 0-1, eliminating the dimensional differences between AC and DC electrical parameters and establishing a unified power regulation benchmark. Second, based on the system load rate, light load, rated load, and heavy load ranges are set. The light load range corresponds to a load rate of less than or equal to 30%, the rated load range corresponds to a load rate of greater than 30% and less than or equal to 70%, and the heavy load range corresponds to a load rate of greater than 70%. The light load range is configured with a small droop slope of 0.02 to achieve high-precision micro-adjustment and eliminate power overshoot oscillation. The rated load range is configured with a standard droop slope of 0.05 to balance regulation accuracy and response speed. The heavy load range is configured with a large droop slope of 0.08 to improve the dynamic power response speed and solve the lag defect of heavy load regulation.

[0026] Furthermore, the specific implementation of the fuzzy adaptive power allocation strategy in step S4 includes: A dual-input dual-output fuzzy control rule base is constructed, classifying system power fluctuation amplitude into five levels: small, medium, large, and extremely large, and energy storage state of charge into five levels: low, medium-low, medium, medium-high, and high, forming 25 sets of one-to-one adaptive control rules. For high-frequency short-term power fluctuations lasting 0 to 10 seconds, the supercapacitor response weight is adaptively adjusted to 80%-90% to prioritize the smoothing of instantaneous fluctuations. For low-frequency continuous power deviations lasting more than 10 seconds, the battery power distribution coefficient is adaptively adjusted to 70%-85% to handle steady-state power compensation. At the same time, the energy storage threshold is locked, with the battery state of charge of 20%-90% being the effective operating range, and charging and discharging are forcibly locked outside the range.

[0027] Furthermore, the gradient smoothing seamless switching control logic in step S5 includes: The system caches the droop slope, converter transmission power, and energy storage output parameters in real time before the operating condition switch. It presets a 20ms single iteration cycle and a 10-step gradient correction strategy, and gradually updates the control parameters during the switching process, replacing the instantaneous abrupt switching mode. It monitors the bus voltage and frequency fluctuations throughout the process, locking the voltage fluctuations within ±2% and the frequency fluctuations within ±0.5Hz, completely suppressing the power surge and parameter oscillations at the moment of operating condition switching.

[0028] An AC / DC hybrid microgrid energy flow control system is used to execute the AC / DC hybrid microgrid energy flow control method according to any one of claims 1-5, comprising a parameter acquisition and preprocessing module, an operating condition quantification and judgment module, a core algorithm control module, an energy storage adaptive adjustment module, a converter power control module, and a seamless switching and multi-level protection module. The parameter acquisition and preprocessing module is used to acquire the full-domain operating parameters of the microgrid at a high frequency of 100Hz, and to complete the Kalman filter noise reduction preprocessing to output effective steady-state parameters. The working condition quantification and judgment module and the electrical connection parameter acquisition and preprocessing module accurately identify four types of mutually exclusive operating conditions based on preset multi-threshold judgment logic and output working condition identification signals. The core algorithm control module has an improved segmented droop control algorithm and a fuzzy adaptive power allocation algorithm, which are respectively connected to the operating condition quantification judgment module, the energy storage adaptive adjustment module, and the converter power control module, and output precise control commands according to real-time operating conditions. The converter power control module is connected to the bidirectional interconnected converter and dynamically corrects the converter regulation coefficient according to the control command, thereby controlling the direction and power of bidirectional energy flow in the AC / DC subgrid. The energy storage adaptive adjustment module is used to control the charging and discharging output of the battery and supercapacitor in a layered manner, so as to achieve layered smoothing of high and low frequency power fluctuations, while executing energy storage threshold protection constraints. The seamless switching and multi-level protection module has built-in gradient smooth switching logic and hierarchical protection mechanism to achieve shock-free switching of operating conditions and system over-limit protection, ensuring stable closed-loop operation of the system.

[0029] Furthermore, the parameter acquisition and preprocessing module includes a high-precision voltage sensor, a frequency sensor, a power detection unit, an energy storage status acquisition unit, and a Kalman filter processing unit. All sensors have a sampling accuracy of no less than 0.01Hz, 0.1V, and 1%SOC, and the sampling frequency is fixed at 100Hz, which can realize synchronous high-precision acquisition and noise reduction optimization of parameters across the entire domain.

[0030] Furthermore, the bidirectional interconnected converter is a four-quadrant bidirectional AC / DC converter with a rated power of 400kW, supporting bidirectional energy transmission between AC and DC subgrids. It has a built-in power limiting unit and a coefficient adjustable unit, and can respond to the dynamic coefficient correction command of the core algorithm control module.

[0031] Furthermore, the hybrid energy storage system includes a 200Ah battery pack and a 50F supercapacitor pack. The battery pack is responsible for low-frequency steady-state power compensation, while the supercapacitor pack is responsible for high-frequency instantaneous power smoothing. The two work together and their outputs are adaptively matched to achieve optimal allocation of energy storage resources.

[0032] Furthermore, the seamless switching and multi-level protection module integrates a gradient smooth switching unit, a voltage over-limit protection unit, a frequency over-limit protection unit, an overcurrent power limiting unit, and a fault blocking unit. Minor parameter over-limits trigger pre-correction control, while severe faults trigger blocking protection, thus balancing system operational stability and equipment safety.

[0033] Addressing the five core technical shortcomings of existing technologies—static control, parameter mismatch, inefficient energy storage utilization, poor switching stability, and weak fault tolerance—this invention proposes a full-condition adaptive refined control scheme that differs from traditional fixed control logic, representing substantial innovation and significant technological advancement.

[0034] Existing technologies generally employ single, fixed control parameters and unified regulation logic, which cannot match the complex dynamic operating characteristics of AC / DC hybrid microgrids with multiple sources, loads, and operating conditions. The regulation strategies are coarse and lack versatility, making them unsuitable for practical engineering scenarios involving random disturbances in renewable energy output and frequent load switching. To completely overcome these common industry technical bottlenecks, this invention implements a holistic technological innovation across five dimensions: unified parameter benchmarks, dynamic adaptive regulation, refined energy storage coordination, smooth operating condition transitions, and system fault-tolerant protection. This forms a new energy control system with layered coupling and closed-loop linkage, offering significant advantages in systemic and holistic approaches compared to traditional single-module optimization schemes. The core innovations of this invention are reflected in four major improvement dimensions: First, it pioneered a normalized calibration mechanism for AC and DC parameters. For the normal fluctuation range of AC frequency 49-51Hz and DC voltage 0.9-1.1 times the rated voltage, it completes the unified quantitative mapping of electrical parameters with different dimensions, completely solves the inherent defects of existing technologies such as inconsistent AC and DC control benchmarks and unbalanced power ratios, realizes coordinated and balanced control of AC and DC sub-grids, and eliminates AC and DC coupling control deviations from the underlying principle.

[0035] Second, abandoning the traditional single fixed droop coefficient, we innovatively designed a three-stage variable droop control model for light load, rated load, and heavy load. By matching different load conditions with differentiated slopes, we achieve high-precision anti-vibration under light load and high-speed response to prevent hysteresis under heavy load. This fully covers and adapts to the dynamic load scenarios of microgrids, solving the technical problem of poor adaptability of traditional droop control and significantly improving the control accuracy and dynamic response capability across the entire load range.

[0036] Third, it breaks through the limitations of traditional fixed-ratio energy storage allocation, and constructs a fuzzy adaptive energy storage control rule library with three input variables: power fluctuation amplitude, fluctuation rate, and energy storage SOC state. It accurately distinguishes between instantaneous high-frequency fluctuations and long-term low-frequency steady-state deviations, realizes layered collaborative work of energy storage, completely changes the traditional extensive output mode of energy storage, and significantly improves the utilization efficiency and service life of energy storage.

[0037] Fourth, establish a gradient-based gradual switching of operating conditions and a four-level hierarchical protection system. Through parameter caching and step-by-step gradient correction, achieve shock-free switching of operating conditions. Improve the system's fault tolerance capability through pre-correction, amplitude limiting, blocking, and tripping hierarchical protection, and fundamentally solve the industry pain points of switching power surges and low system fault tolerance.

[0038] In addition, this invention constructs a fully closed-loop operation logic of condition prediction, dynamic control, and fault self-correction, which breaks through the technical shortcomings of traditional control schemes such as open-loop control and no feedback correction. It can dynamically iterate and optimize control parameters according to the real-time operating status of the microgrid, and has extremely strong adaptive learning and dynamic adaptation capabilities.

[0039] Through the aforementioned differentiated and holistic technical improvements, this invention forms a complete, closed-loop, and engineering-implementable microgrid energy regulation system. Compared with existing technologies, it has outstanding substantive features and significant technological advancements, effectively filling the technological gap in refined adaptive control of high-penetration new energy AC / DC hybrid microgrids under all operating conditions.

[0040] This invention also proposes an AC / DC hybrid microgrid energy flow control system, including a parameter acquisition module, an operating condition determination module, a core control module, an energy storage regulation module, a converter regulation module, and a protection switching module. The modules work together to form a complete closed-loop system for energy detection, determination, regulation, and protection, which can automatically complete the energy flow management and control of the microgrid under all operating conditions.

[0041] To enable those skilled in the art to fully and clearly understand and reproduce the technical solution of the present invention, the present invention will be further described in detail below in conjunction with specific quantitative embodiments, hardware parameters, algorithm logic, simulation conditions and comparative data. This embodiment is only used to explain the present invention and is not intended to limit the scope of protection of the present invention.

[0042] The test prototype built in this invention is a 500kW photovoltaic-storage hybrid AC / DC microgrid system, fully compatible with the standard configuration of industrial and commercial distributed new energy microgrids. The quantitative configuration of the system hardware parameters is as follows: AC subnet: connects to a 200kW permanent magnet wind turbine generator set and a 300kW industrial AC adjustable load, with a rated power frequency of 50Hz and an allowable fluctuation range of 49-51Hz; DC subgrid: Connects to a 300kW monocrystalline silicon photovoltaic array, a 200kW DC lighting and charging pile load, with a rated DC bus voltage of 800V and an allowable fluctuation range of 720V-880V; Bidirectional interconnected converter: rated power 400kW, four-quadrant operation, response iteration cycle 10ms; Hybrid energy storage system: 200Ah lead-carbon battery pack (rated voltage 400V, effective operating SOC range 20%-90%), 50F supercapacitor module (rated voltage 450V). The core controller adopts the DSP28335 industrial controller, the sensors have a uniform sampling frequency of 100Hz, and the overall system complies with the national standard GB / T 33593-2017 for microgrid operation control.

[0043] The energy flow control method for AC / DC hybrid microgrids disclosed in this invention includes the following specific implementation steps, parameter configuration, and control logic: The first step is to synchronously acquire parameters across the entire domain and perform Kalman filtering preprocessing.

[0044] This system employs an industrial-grade high-precision sensor array to synchronously acquire all dimensions of operating parameters. The acquisition accuracy strictly matches national standards: AC frequency acquisition accuracy ±0.01Hz, AC / DC voltage acquisition accuracy ±0.1V, power acquisition accuracy ±0.5%FS, and energy storage SOC detection accuracy ±1%. Real-time acquired parameters include: AC subgrid three-phase bus voltage, real-time frequency, wind power real-time output, and AC load active / reactive power; DC subgrid bus voltage, photovoltaic real-time output, and DC load power consumption; battery terminal voltage, charging / discharging current, real-time SOC, and maximum allowable charging / discharging power; supercapacitor terminal voltage, remaining capacity, and instantaneous response power; and bidirectional interconnected converter transmission power, switching status, and operating temperature. All data is uploaded to the core controller in real-time via the CAN2.0 bus. The controller incorporates a standard Kalman filter algorithm, sets state equations and observation equations to denoise the raw sampled data, filters out high-frequency interference pulses and random abrupt changes, and outputs a smooth and effective steady-state parameter sequence. This provides accurate data support for subsequent operating condition judgment and algorithm control, completely avoiding control malfunctions caused by data interference.

[0045] The second step is to quantitatively determine the mutually exclusive operating conditions with multiple thresholds.

[0046] This embodiment presets a system power balance threshold of ±5kW. Combining grid-connected / off-grid status, source-load power difference, and effective energy storage operating range, it achieves precise, non-overlapping determination of four operating conditions: Grid-connected power balance condition: The microgrid operates in grid-connected mode, with a source-load power difference of [-5kW, 5kW]. The system maintains steady-state operation, with only minor parameter corrections. Grid-connected power fluctuation condition: The microgrid operates in grid-connected mode, with an absolute value of the source-load power difference > 5kW. A rapid voltage and frequency regulation strategy using converters and energy storage-assisted smoothing is initiated. Off-grid power surplus condition: The microgrid operates in isolation, with total power generation > total load power, and battery SOC ≤ 90%, and supercapacitors not fully charged. Energy storage is prioritized to absorb redundant power and absorb excess renewable energy. Off-grid power deficit condition: The microgrid operates in isolation, with total power generation < total load power, and battery SOC ≥ 20%, and supercapacitors are responsive. Energy storage-based tiered discharge compensation is initiated, prioritizing power supply to core loads. If energy storage reaches upper or lower thresholds, flexible load tiered shelving logic is executed to prevent system collapse.

[0047] The third step is to normalize the segmented adaptive droop converter energy regulation.

[0048] This step is one of the core innovations of the invention. First, dimensionless normalization of AC and DC parameters is performed: the AC frequency of 49-51Hz is linearly mapped to the 0-1 range, and the DC voltage of 720V-880V is linearly mapped to the 0-1 range, eliminating the difference in parameter dimensions and establishing a unified power regulation benchmark. Then, based on the real-time load rate of the system, three control ranges are divided, each with a dedicated droop coefficient: in the light load range (load rate ≤ 30%), the droop slope is 0.02, focusing on high-precision fine-tuning to prevent power oscillations and parameter overshoot under light load conditions; in the rated range (30% < load rate ≤ 70%), the droop slope is 0.05, balancing... The response speed and control precision are adapted to the normal operating conditions of the system; in the heavy load range of >70%, the droop slope is 0.08, which greatly improves the power response speed and solves the problems of lag in adjustment and large voltage and frequency deviation under heavy load conditions; the core controller iterates the load rate judgment and coefficient update every 10ms, dynamically correcting the power transmission coefficient and voltage / frequency compensation coefficient of the bidirectional interconnected converter: when the AC side frequency is low and the power is insufficient, the DC subgrid is controlled to provide positive energy to the AC subgrid; when the DC side voltage is high and the power is surplus, the AC subgrid is controlled to absorb redundant power in reverse, realizing dynamic power balance between AC and DC subgrids.

[0049] The fourth step is multi-parameter fuzzy adaptive energy storage hierarchical power allocation.

[0050] This invention differs from traditional single-input fuzzy control by constructing a three-input fuzzy control model consisting of power fluctuation amplitude, fluctuation rate, and energy storage SOC. It employs a 5×5 refined fuzzy rule library with 25 sets of fully covered control rules, eliminating blind spots. The control is layered according to the time scale of power fluctuations: for 0-10s instantaneous high-frequency fluctuations (light and load disturbances), the supercapacitor response weight is adaptively increased to 80%-90%, leveraging the supercapacitor's millisecond-level response speed and long cycle life to quickly smooth fluctuations without frequent battery start-stop cycles; for 10s... The above-mentioned continuous low-frequency power deviation (new energy steady-state output deviation, base load variation) will increase the battery power distribution coefficient to 70%-85%, with the battery undertaking steady-state power compensation to ensure long-term system power balance; at the same time, it will be bound with hard constraint protection logic: battery SOC < 20% will be forcibly locked for discharge, SOC > 90% will be forcibly locked for charging, and supercapacitor voltage will immediately stop adjustment if it exceeds the 400V-450V range, completely eliminating overcharging, over-discharging, and overload operation of energy storage, balancing the workload of the two sets of energy storage equipment, and effectively extending the cycle life of energy storage.

[0051] The fifth step is gradient smooth switching and four-level closed-loop fault-tolerant protection control.

[0052] The system monitors the grid-connected switch status, source load power, and changes in electrical parameters in real time throughout the entire process, accurately capturing the trigger signal for operating condition switching. When an operating condition switching requirement is identified, the core controller immediately caches all core parameters such as the droop coefficient, converter transmission power, and energy storage output at the current moment, and initiates a 20ms cycle, 10-step gradient smoothing iterative algorithm to gradually correct the control parameters in stages. This abandons the abrupt change mode of traditional instantaneous switching and suppresses power surges, voltage spikes, and frequency oscillations at the moment of switching from the root. This invention sets up a four-level hierarchical protection closed-loop mechanism, strictly adapting to the practicality and stability requirements of the authorization review. Level 1 pre-correction protection automatically fine-tunes parameters without stopping the machine when parameters slightly exceed limits (voltage ±1%, frequency ±0.3Hz); The secondary power limiting protection limits the maximum output of the converter and energy storage when parameters slightly exceed the limits (voltage ±2%, frequency ±0.5Hz) to prevent the fault from escalating. Level 3 fault interlocking protection: When parameters seriously exceed the limit, the corresponding equipment branch is interlocked to isolate the fault point; Level 4 emergency trip protection enables rapid shutdown in the event of major faults such as system short circuits and overcurrents, ensuring equipment safety.

[0053] The system completes a closed-loop iteration of "acquisition-computation-control-verification" every 10ms, continuously correcting the operating status and achieving stable and controllable operation under all working conditions.

[0054] The working process of the AC / DC hybrid microgrid energy flow control system of the present invention is as follows: The parameter acquisition module collects system operating parameters in real time and transmits them to the operating condition determination module; After the working condition determination module completes the working condition identification, it sends the working condition signal to the core control module; The core control module calls the built-in improved droop control algorithm and fuzzy adaptive allocation algorithm to send control commands to the converter control module and the energy storage regulation module, respectively. The converter control module regulates the power transmission status of the bidirectional interconnected converter, and the energy storage control module controls the charging and discharging output of the hybrid energy storage. The protection switching module monitors the system status throughout the process, enabling seamless switching of operating conditions and fault protection, forming a continuous closed-loop control.

[0055] Verification of beneficial effects: To objectively verify the technological advancement and practicality of this invention, a comparative simulation platform under identical operating conditions was built using MATLAB / Simulink. The proposed solution was compared with traditional fixed droop control and fixed-ratio energy storage allocation schemes across multiple dimensions. The simulation lasted 24 hours, covering all scenarios including light fluctuations, load switching, and grid-connected / off-grid switching. The quantitative test results are as follows: 1. Stability indicators: The bus voltage fluctuation of this invention is controlled within ±2%, and the frequency fluctuation is controlled within ±0.5Hz. Compared with the traditional solution, the fluctuation is reduced by 62%, and there is no continuous oscillation or overshoot phenomenon, which fully meets the national standard requirements for microgrid power supply. 2. Energy storage utilization indicators: The balance of energy storage charging and discharging is greatly improved, the number of deep charge and discharge cycles of batteries is reduced by 45%, the overall cycle life of energy storage is increased by 18%, and the comprehensive utilization efficiency of energy storage is increased to over 92%. 3. Switching stability indicators: There is no significant power surge during grid-connected / off-grid switching, and the instantaneous power fluctuation is controlled within 3% of the rated power, which is far lower than the 15% surge of traditional solutions. There are no tripping or shutdown faults. 4. Renewable Energy Consumption Indicators: The renewable energy curtailment rate is reduced to below 2%, and the local consumption rate is increased to over 98%. Quantitative data fully demonstrate that this invention represents a significant technological advancement compared to existing technologies, effectively addressing various shortcomings of existing technologies, and possessing strong engineering practicality and promotional value.

Claims

1. A method for controlling energy flow in an AC / DC hybrid microgrid, characterized in that, Full-condition adaptive energy regulation for photovoltaic-storage hybrid AC / DC microgrids, wherein the hybrid AC / DC microgrid includes an AC subgrid, a DC subgrid, bidirectional interconnected converters, distributed renewable energy sources, AC / DC loads, and a hybrid energy storage system; the control method includes the following steps: S1. Synchronous Acquisition and Filtering Preprocessing of Global Parameters: The operating parameters of the AC / DC hybrid microgrid are acquired in real time at a sampling frequency of 100Hz. These operating parameters include the AC subgrid bus frequency, three-phase AC voltage, AC active / reactive power, wind power output, AC load power, DC subgrid bus voltage, photovoltaic output power, DC load power, state of charge, charging and discharging current, and remaining capacity of the hybrid energy storage, as well as the real-time transmission power and operating status parameters of the bidirectional interconnected converter. The acquired raw parameters are then subjected to Kalman filtering to remove abrupt interference data and obtain effective steady-state operating parameters. S2. Multi-threshold refined operating condition determination: Based on the pre-processed effective operating parameters, combined with the microgrid's grid-connected / off-grid operating status, system source-load power difference threshold, and energy storage operating threshold, four mutually exclusive operating conditions are accurately divided. These operating conditions include grid-connected power balance, grid-connected power fluctuation, off-grid power surplus, and off-grid power deficit. Each condition is determined independently without overlap, achieving accurate identification of the system's operating status. S3. Segmented Adaptive Droop Converter Control: Construct an improved segmented droop control model with normalized AC and DC parameters, perform dimensionless unified quantization of AC frequency and DC voltage, divide the system into three operating ranges (light load, rated load, and heavy load) based on the real-time load rate of the system, dynamically match the droop adjustment slope of the corresponding range, adaptively correct the power transmission coefficient, voltage compensation coefficient, and frequency compensation coefficient of the bidirectional interconnected converter, and accurately control the bidirectional energy flow direction and transmission power between AC and DC subgrids to achieve dynamic power balance of the subgrid; S4. Hierarchical Fuzzy Adaptive Energy Storage Power Allocation: Using system power fluctuation amplitude, power fluctuation rate, and hybrid energy storage state of charge as fuzzy inputs, the system adaptively outputs the low-frequency power allocation coefficient of the battery and the high-frequency response weight of the supercapacitor based on a preset fuzzy rule base; the supercapacitor is used to quickly smooth out short-term high-frequency power fluctuations, and the battery is used to compensate for long-term low-frequency power deficits. At the same time, the energy storage charging and discharging constraint thresholds are bound to avoid overcharging and over-discharging of the energy storage. S5. Gradient smooth switching and hierarchical closed-loop protection: Real-time monitoring of system operating condition switching signals, buffering all control parameters before switching, and using a gradient smooth transition algorithm to gradually correct parameters and eliminate power impact during operating condition switching; Simultaneously enabling multi-level protection logic such as parameter pre-correction, power limiting, and over-limit blocking to form a fully closed-loop adaptive control of microgrid energy flow.

2. The AC / DC hybrid microgrid energy flow control method according to claim 1, characterized in that, The specific quantitative judgment criteria for the four operating conditions in step S2 are as follows: When a microgrid is connected to the grid, if the difference between the total power generation of the AC / DC subgrid and the total load power is within the preset ±5kW balance threshold range, it is determined to be a grid-connected power balance condition; if the power difference exceeds the ±5kW threshold range, it is determined to be a grid-connected power fluctuation condition. When a microgrid is off-grid, if the total power generation of the system is greater than the total load power, and the battery state of charge is ≤90% and the supercapacitor is not fully charged, it is determined to be an off-grid power surplus condition; if the total power generation of the system is less than the total load power, and the battery state of charge is ≥20% and the supercapacitor has remaining response capacity, it is determined to be an off-grid power deficit condition. The judgment conditions for each condition are independent of each other, with no logical overlap or blank intervals.

3. The AC / DC hybrid microgrid energy flow control method according to claim 1, characterized in that, The method for constructing the improved piecewise droop control model by normalizing AC / DC parameters in step S3 includes: First, the frequency range of 49Hz-51Hz for the AC bus and the range of 0.9-1.1 times the rated voltage for the DC bus are uniformly mapped to a dimensionless quantization range of 0-1, eliminating the dimensional differences between AC and DC electrical parameters and establishing a unified power regulation benchmark. Second, based on the system load rate, light load, rated load, and heavy load ranges are set. The light load range corresponds to a load rate of less than or equal to 30%, the rated load range corresponds to a load rate of greater than 30% and less than or equal to 70%, and the heavy load range corresponds to a load rate of greater than 70%. The light load range is configured with a small droop slope of 0.02 to achieve high-precision micro-adjustment and eliminate power overshoot oscillation. The rated load range is configured with a standard droop slope of 0.05 to balance regulation accuracy and response speed. The heavy load range is configured with a large droop slope of 0.08 to improve the dynamic power response speed and solve the lag defect of heavy load regulation.

4. The AC / DC hybrid microgrid energy flow control method according to claim 1, characterized in that, The specific implementation of the fuzzy adaptive power allocation strategy in step S4 includes: A dual-input dual-output fuzzy control rule base is constructed, classifying system power fluctuation amplitude into five levels: small, medium, large, and extremely large, and energy storage state of charge into five levels: low, medium-low, medium, medium-high, and high, forming 25 sets of one-to-one adaptive control rules. For high-frequency short-term power fluctuations lasting 0 to 10 seconds, the supercapacitor response weight is adaptively adjusted to 80%-90% to prioritize the smoothing of instantaneous fluctuations. For low-frequency continuous power deviations lasting more than 10 seconds, the battery power distribution coefficient is adaptively adjusted to 70%-85% to handle steady-state power compensation. At the same time, the energy storage threshold is locked, with the battery state of charge of 20%-90% being the effective operating range, and charging and discharging are forcibly locked outside the range.

5. The AC / DC hybrid microgrid energy flow control method according to claim 1, characterized in that, The gradient smoothing seamless switching control logic in step S5 includes: The system caches the droop slope, converter transmission power, and energy storage output parameters in real time before the operating condition switch. It presets a 20ms single iteration cycle and a 10-step gradient correction strategy, and gradually updates the control parameters during the switching process, replacing the instantaneous abrupt switching mode. It monitors the bus voltage and frequency fluctuations throughout the process, locking the voltage fluctuations within ±2% and the frequency fluctuations within ±0.5Hz, completely suppressing the power surge and parameter oscillations at the moment of operating condition switching.

6. An AC / DC hybrid microgrid energy flow control system, used to execute the AC / DC hybrid microgrid energy flow control method according to any one of claims 1-5, characterized in that, It includes a parameter acquisition and preprocessing module, an operating condition quantification and judgment module, a core algorithm control module, an energy storage adaptive adjustment module, a converter power control module, and a seamless switching and multi-level protection module. The parameter acquisition and preprocessing module is used to acquire the full-domain operating parameters of the microgrid at a high frequency of 100Hz, and to complete the Kalman filter noise reduction preprocessing to output effective steady-state parameters. The working condition quantification and judgment module and the electrical connection parameter acquisition and preprocessing module accurately identify four types of mutually exclusive operating conditions based on preset multi-threshold judgment logic and output working condition identification signals. The core algorithm control module has an improved segmented droop control algorithm and a fuzzy adaptive power allocation algorithm, which are respectively connected to the operating condition quantification judgment module, the energy storage adaptive adjustment module, and the converter power control module, and output precise control commands according to real-time operating conditions. The converter power control module is connected to the bidirectional interconnected converter and dynamically corrects the converter regulation coefficient according to the control command, thereby controlling the direction and power of bidirectional energy flow in the AC / DC subgrid. The energy storage adaptive adjustment module is used to control the charging and discharging output of the battery and supercapacitor in a layered manner, so as to achieve layered smoothing of high and low frequency power fluctuations, while executing energy storage threshold protection constraints. The seamless switching and multi-level protection module has built-in gradient smooth switching logic and hierarchical protection mechanism to achieve shock-free switching of operating conditions and system over-limit protection, ensuring stable closed-loop operation of the system.

7. The AC / DC hybrid microgrid energy flow control system according to claim 6, characterized in that, The parameter acquisition and preprocessing module includes a high-precision voltage sensor, a frequency sensor, a power detection unit, an energy storage status acquisition unit, and a Kalman filter processing unit. All sensors have a sampling accuracy of no less than 0.01Hz, 0.1V, and 1%SOC, and the sampling frequency is fixed at 100Hz, which can realize synchronous high-precision acquisition and noise reduction optimization of parameters across the entire domain.

8. The AC / DC hybrid microgrid energy flow control system according to claim 6, characterized in that, The bidirectional interconnected converter is a four-quadrant bidirectional AC / DC converter with a rated power of 400kW. It supports bidirectional energy transmission between AC and DC subgrids and has a built-in power limiting unit and coefficient adjustable unit, which can respond to the dynamic coefficient correction commands of the core algorithm control module.

9. The AC / DC hybrid microgrid energy flow control system according to claim 6, characterized in that, The hybrid energy storage system includes a 200Ah battery pack and a 50F supercapacitor pack. The battery pack is responsible for low-frequency steady-state power compensation, while the supercapacitor pack is responsible for high-frequency instantaneous power smoothing. The two work together and their outputs are adaptively matched to achieve optimal allocation of energy storage resources.

10. The AC / DC hybrid microgrid energy flow control system according to claim 6, characterized in that, The seamless switching and multi-level protection module integrates a gradient smooth switching unit, a voltage over-limit protection unit, a frequency over-limit protection unit, an overcurrent power limiting unit, and a fault blocking unit. Minor parameter over-limits trigger pre-correction control, while severe faults trigger blocking protection, balancing system operational stability and equipment safety.

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