Energy storage equipment grid-connected control method and device based on distributed energy storage system

Abstract

The invention relates to the technical field of grid-connected control of energy storage equipment, and discloses a grid-connected control method and device for energy storage equipment based on a distributed energy storage system. The method comprises: initializing and collecting operation data of a distributed energy storage system integrating an inverter and multiple energy storage equipment to obtain system operation status data; performing multi-scale decomposition of the output power of renewable energy to obtain power component data; calculating the target power value of each energy storage equipment and constructing an energy storage power allocation scheme; constructing a nonlinear state feedback controller to adjust the DC bus voltage to obtain converter control parameters; dynamically selecting an optimal PWM scheme and generating a PWM control instruction for controlling the inverter; inputting the PWM control instruction into a model prediction controller to calculate the charging and discharging power instruction of the energy storage equipment and the grid-connected control signal to realize the grid-connected operation control of the distributed energy storage system. The invention realizes precise control and optimized scheduling of the distributed energy storage system.

Description

Technical Field

[0001] The present invention relates to the technical field of grid-connected control of energy storage equipment, and in particular to a grid-connected control method and device for energy storage equipment based on a distributed energy storage system. Background Art

[0002] As the proportion of renewable energy in the power system continues to increase, its intermittency and volatility have brought severe challenges to the stable operation of the power grid. As an effective solution, distributed energy storage systems play an important role in smoothing the fluctuations in renewable energy output and improving the stability of the power grid. However, traditional energy storage equipment grid-connected control methods often have difficulty coping with the complex and changing power grid environment and the diverse characteristics of energy storage equipment.

[0003] Existing control strategies usually use a single control algorithm, which makes it difficult to fully consider the multi-scale characteristics of renewable energy output power, the dynamic response capabilities of energy storage equipment, and the real-time status of the power grid. This leads to insufficient control accuracy of the charge and discharge of energy storage equipment, and the inability to fully utilize the regulation capabilities of the energy storage system. In addition, the performance of traditional PWM control methods varies greatly under different working conditions, making it difficult to achieve the optimal grid-connected control effect. Summary of the invention

[0004] The present invention provides a method and device for controlling grid connection of energy storage equipment based on a distributed energy storage system, and the present invention realizes accurate control and optimized scheduling of the distributed energy storage system.

[0005] In a first aspect, the present invention provides a method for controlling a grid-connected energy storage device based on a distributed energy storage system, the method comprising:

[0006] Initialize and collect operation data of the distributed energy storage system integrating inverters and multiple energy storage devices to obtain system operation status data;

[0007] Perform multi-scale decomposition of the renewable energy output power based on the system operation status data to obtain power component data;

[0008] According to the power component data, a two-layer fuzzy controller is used to calculate the target power value of each energy storage device and construct an energy storage power allocation plan;

[0009] Based on the energy storage power allocation scheme, a nonlinear state feedback controller is constructed to adjust the DC bus voltage and obtain converter control parameters;

[0010] Dynamically selecting an optimal PWM scheme according to the converter control parameters, and generating a PWM control instruction for controlling the inverter;

[0011] The PWM control instruction is input into the model predictive controller to calculate the energy storage device charging and discharging power instruction and the grid-connected control signal to realize the grid-connected operation control of the distributed energy storage system.

[0012] In a second aspect, the present invention provides a distributed energy storage system-based energy storage device grid-connected control device, the distributed energy storage system-based energy storage device grid-connected control device comprising:

[0013] An initialization module is used to initialize and collect operation data of a distributed energy storage system integrating an inverter and multiple energy storage devices to obtain system operation status data;

[0014] A decomposition module, used for performing multi-scale decomposition of the output power of renewable energy based on the system operation status data to obtain power component data;

[0015] A calculation module, used to calculate the target power value of each energy storage device according to the power component data using a two-layer fuzzy controller, and to construct an energy storage power allocation plan;

[0016] A regulating module, used to construct a nonlinear state feedback controller based on the energy storage power allocation scheme, adjust the DC bus voltage, and obtain converter control parameters;

[0017] A generating module, used for dynamically selecting an optimal PWM scheme according to the converter control parameters, and generating a PWM control instruction for controlling the inverter;

[0018] The control module is used to input the PWM control instruction into the model predictive controller, calculate the energy storage device charging and discharging power instruction and the grid-connected control signal, so as to realize the grid-connected operation control of the distributed energy storage system.

[0019] The third aspect of the present invention provides a computer device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned energy storage device grid-connected control method based on the distributed energy storage system.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored, and when the computer-readable storage medium is run on a computer, the computer executes the above-mentioned energy storage device grid-connected control method based on a distributed energy storage system.

[0021] In the technical solution provided by the present invention, by adopting the wavelet packet decomposition algorithm to perform multi-scale decomposition on the output power of renewable energy, the time-frequency characteristics of power fluctuation can be captured more accurately, and a two-layer fuzzy controller is introduced to calculate the target power value of the energy storage device, so as to realize the refined allocation of different frequency components, and at the same time consider the real-time state of the energy storage device, thereby improving the rationality and reliability of power allocation. The nonlinear state feedback controller is adopted to adjust the DC bus voltage, so as to enhance the system's resistance to external disturbances and improve the control accuracy and dynamic response performance of the bidirectional converter. An adaptive PWM selection algorithm is designed, which can dynamically select the optimal PWM scheme according to the real-time operating conditions, effectively reduce the DC side voltage ripple, and improve the grid-connected quality of the inverter. The model predictive controller is introduced to perform overall coordinated control of the system, so as to realize the forward-looking optimization of the charging and discharging power and grid-connected control of the energy storage device, and improve the overall operation efficiency and stability of the system. The present invention realizes the coordinated control of the distributed energy storage system, and significantly improves the absorption capacity of renewable energy and the stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 A schematic diagram of the steps of a method for controlling grid connection of energy storage equipment based on a distributed energy storage system in an embodiment of the present invention;

[0024] Figure 2 It is a structural schematic diagram of a grid-connected control device for energy storage equipment based on a distributed energy storage system in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The embodiment of the present invention provides a method and device for grid-connected control of energy storage equipment based on a distributed energy storage system. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of a method for controlling grid connection of energy storage equipment based on a distributed energy storage system in an embodiment of the present invention includes:

[0027] Step S1, initializing and collecting operation data of a distributed energy storage system integrating an inverter and multiple energy storage devices to obtain system operation status data;

[0028] It is understandable that the execution subject of the present invention may be a distributed energy storage system-based energy storage device grid-connected control device, or a terminal or a server, which is not specifically limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0029] Specifically, the inverter, communication module and control unit in the distributed energy storage system are started to ensure that all devices are in the initial working state and the system initialization state is obtained. This step not only includes the startup and initialization of the hardware equipment, but also includes the loading of the control algorithm and the establishment of the system communication network to ensure that the various parts of the distributed energy storage system can work efficiently and collaboratively. In the system initialization state, the pre-installed sensor network is used to collect real-time data of the renewable energy power generation equipment in the distributed energy storage system to obtain the real-time output power of the renewable energy. The sensor network is distributed on renewable energy power generation equipment such as solar panels and wind turbines, and can continuously monitor the voltage, current and other parameters of the power generation equipment, and then calculate the output power of the renewable energy in real time. Through real-time data, the fluctuation of renewable energy power generation is obtained. At the same time, based on the initialization state of the system, the real-time parameters of the power grid are collected using the power grid monitoring equipment, including the voltage, frequency and current power demand of the power grid. Through real-time monitoring of the power grid parameters, the distributed energy storage system responds quickly according to the needs of the power grid and adjusts the output power of the inverter to ensure the stable operation of the entire system. The state of each energy storage device in the distributed energy storage system is monitored. Through the sensors installed on the energy storage device, the state of charge (SOC), temperature, voltage and other information of the energy storage device are collected in real time. The state of charge is an important indicator reflecting the current remaining power of the energy storage device, while the monitoring of temperature and voltage helps to ensure the safe operation of the energy storage device and extend its service life. Through real-time monitoring of these status data, the charging and discharging behavior of each energy storage device is adjusted according to the status of each energy storage device to ensure the coordination and efficient operation between the energy storage units and avoid failure or damage of a single device due to excessive charging and discharging. Based on the energy storage device status data, the real-time power demand data of the user end is collected to obtain the load demand situation. The output power of renewable energy, grid parameters, energy storage device status data and load demand data are input into the central control unit for data preprocessing. Data preprocessing includes steps such as filtering abnormal data, smoothing and standardizing data to obtain system operation status data.

[0030] Step S2: performing multi-scale decomposition of the renewable energy output power based on the system operation status data to obtain power component data;

[0031] Specifically, the output power of renewable energy in the system operation status data is analyzed in time series to obtain power time series data. Time series analysis is the process of modeling and processing the output power signal of renewable energy in the time domain, so as to identify the change pattern, trend and periodic characteristics of power data and describe the fluctuation behavior of renewable energy power generation. Based on the power time series data, a multi-scale decomposition operation is performed using the preset wavelet basis function and decomposition layer number to extract the characteristics of the power signal at different time scales. By setting appropriate wavelet basis functions and reasonable decomposition layers, the power time series signal is decomposed into wavelet packet coefficients covering different scales such as high frequency, medium frequency and low frequency, effectively capturing the short-term fluctuation, periodic change and overall trend of renewable energy output power. Energy analysis is performed on the wavelet packet coefficients to calculate the energy percentage of each frequency range and obtain the energy distribution characteristics, which reflects the proportion of different frequency characteristics in the output power of renewable energy and helps to identify which frequency components are the main sources of fluctuations and which are low-frequency trends or high-frequency disturbances in the system. Based on the results obtained by energy analysis, the cutoff frequencies of high-frequency components, medium-frequency components and low-frequency components are determined, and the wavelet packet coefficients are classified according to different frequency ranges. By setting a reasonable cutoff frequency, the high-frequency component representing fast-changing components, the medium-frequency component representing medium-time-scale fluctuations, and the low-frequency component describing the overall change trend can be effectively distinguished. Based on the cutoff frequency, the wavelet packet coefficients are reconstructed to obtain high-frequency power components, medium-frequency power components, and low-frequency power components. The decomposed wavelet packet coefficients are recombined to restore the original power signals of each frequency band. The high-frequency power component, the medium-frequency power component, and the low-frequency power component are reconstructed and normalized in the time domain to obtain the final power component data. Time domain reconstruction is to restore the decomposed signal in the time domain so that it can be consistent with the original power signal and have the same time characteristics. In the reconstruction process, ensure that the time alignment between the various frequency components is ensured so that the subsequent control algorithm can simultaneously consider the power change characteristics of each time scale. Normalization eliminates the difference in amplitude between different frequency components, making each frequency component comparable in the subsequent control strategy, improving the control accuracy and the stability of the system response.

[0032] Step S3: Calculate the target power value of each energy storage device using a two-layer fuzzy controller according to the power component data, and construct an energy storage power allocation plan;

[0033] Specifically, the high-frequency power component, the medium-frequency power component and the low-frequency power component in the power component data are respectively input into the first-layer fuzzy controller to obtain the preliminary power allocation result. Based on the preliminary power allocation result, combined with the real-time monitoring data such as the state of charge, temperature and voltage of each energy storage device, the power adjustment coefficient of each energy storage device is calculated to obtain the power adjustment parameter. The state of charge (SOC) is a key indicator for evaluating the current power level of the energy storage device, and the temperature and voltage are used to determine whether the device is in a safe operating range. On the basis of the preliminary power allocation result, the power adjustment coefficient is calculated through these state parameters to achieve a comprehensive assessment of the health status of the energy storage device, so as to consider the actual availability and safety of each energy storage unit when allocating power. The power adjustment coefficient gives a correction value for the preliminary allocation result by fuzzy reasoning on the device status, ensuring that the operation of each energy storage device is always in the best state, which can achieve power balance without causing additional burden on the device. The power adjustment parameter and the preliminary power allocation result are input into the second-layer fuzzy controller for refined power allocation calculation to obtain the target power value of each energy storage device. According to the target power value of each energy storage device, the charging and discharging power and power change rate of each energy storage device are calculated to obtain the operating parameters of the energy storage device. The operating parameters are used to guide the actual charging and discharging process of each energy storage device, and to evaluate the power change of the device in a short period of time, so as to avoid the negative impact of excessive power fluctuations on the equipment and system. Based on the operating parameters of the energy storage device, combined with the real-time voltage and frequency data of the power grid, the grid-connected power balance calculation of the energy storage device is performed to obtain the grid-connected power balance value. The changes in the voltage and frequency of the power grid directly reflect the dynamic balance of power supply and demand. By analyzing these data, it is determined whether the current grid-connected power needs to be adjusted. Through the grid-connected power balance calculation, it is ensured that the power output of the entire energy storage system is always consistent with the demand of the power grid, thereby maintaining the stability and reliability of the system. The obtained grid-connected power balance value reflects the degree of power matching between the energy storage system and the power grid. If there is a deviation, further measures need to be taken to adjust it. The grid-connected power balance value is compared with the preset grid-connected power limit to determine whether it exceeds the limit range. If the grid-connected power exceeds the limit range, a corresponding power adjustment instruction is generated to make necessary corrections to the target power of the energy storage device to bring the system back to the safe operating range. According to the power adjustment instruction, the target power value of each energy storage device is corrected, and the corresponding charging and discharging tasks are assigned to each energy storage device to generate the final energy storage power allocation plan.

[0034] Step S4: Based on the energy storage power allocation scheme, a nonlinear state feedback controller is constructed to adjust the DC bus voltage and obtain converter control parameters;

[0035] Specifically, according to the energy storage power allocation scheme, a mathematical model of a bidirectional converter including the voltage and current relationship between the DC side and the AC side is established. The mathematical model of the bidirectional converter is used to describe the energy conversion process between DC and AC in the energy storage system. The model includes variables such as voltage, current, impedance, etc. in the system. Through the coupling relationship of these variables, the dynamic characteristics between the DC side and the AC side are described. A state feedback control law is designed based on the mathematical model of the bidirectional converter. The state feedback control law uses the state variables of the system (such as voltage and current) to design a feedback control strategy to achieve precise control of the system output. In the design process, in order to enhance the dynamic performance of the system and the ability to adapt to complex working conditions, a nonlinear feedback term is introduced into the state feedback control law to obtain an enhanced control law. The introduction of nonlinear feedback terms effectively copes with the nonlinear characteristics in the energy storage system and improves the robustness of the controller when facing complex system disturbances and changes. Through the enhanced control law, the change of the DC bus voltage is more accurately adjusted to ensure that the system can remain stable under different operating conditions. Based on the enhanced control law, a super-twist observer is designed, and the generalized disturbance estimate is calculated. The supertwist observer is an observation tool that can observe the state of the system in real time when there are uncertainties and disturbances, and effectively estimate the disturbances of the system. By calculating the generalized disturbance estimate, the uncertainty factors and external disturbances in the energy storage system are monitored in real time. The introduction of the generalized disturbance estimate enables the controller to respond to various external disturbances (such as grid fluctuations, load changes, etc.) in real time, improving the stability and anti-interference ability of the entire system. The generalized disturbance estimate is introduced into the enhanced control law to construct a complete nonlinear state feedback controller. The controller achieves comprehensive control of the dynamic behavior of the system by integrating the system state variables, feedback control laws and disturbance estimates. The core goal of the nonlinear state feedback controller is to monitor and adjust the DC bus voltage in real time to ensure that the converter can always maintain the best working state under different operating conditions. By real-time monitoring of the DC bus voltage, the voltage fluctuation and change trend can be discovered in time, so that corresponding adjustment measures can be taken to enable the system to maintain stable output when responding to the fluctuating demand of the power grid. The controller parameters are dynamically adjusted based on the voltage regulation data to obtain adaptive control parameters. The calculation of adaptive control parameters is based on the real-time voltage and current feedback of the system. Through real-time analysis of the system response, the controller gain, feedback coefficient and other parameters are automatically adjusted to enable the system to adapt to different working environments and working conditions. The adaptive control parameters are applied to the nonlinear state feedback controller to generate the control parameters of the converter. These control parameters are used to regulate the operation of the converter, including adjusting the switching frequency of the converter, controlling the direction of power flow, etc., to ensure the stable operation of the system.

[0036] Step S5: dynamically select the optimal PWM scheme according to the converter control parameters, and generate a PWM control instruction for controlling the inverter;

[0037] Specifically, based on the converter control parameters, a PWM evaluation model of DC capacitor current, DC voltage ripple and DC power supply current ripple is established. The PWM evaluation model is used to describe the dynamic behavior and characteristics of the key variables on the DC side of the system under different PWM schemes. By modeling the capacitor current and the ripple of the DC side voltage and current, the influence of different PWM modulation methods on the system operation, especially the influence on the ripple, is analyzed. Based on the PWM evaluation model, error voltage vector analysis is performed to calculate the DC side ripple characteristics under different PWM schemes and obtain the PWM performance index. The purpose of error voltage vector analysis is to quantify the performance of the PWM scheme under different working conditions. By calculating the voltage ripple under different PWM modulation methods, their influence on the DC side stability and power quality is obtained. The PWM performance indicators mainly include the DC side ripple amplitude, ripple frequency, etc. These indicators reflect the actual effects of different PWM schemes in the modulation process. According to the PWM performance indicators, a comparative analysis is conducted between the continuous PWM scheme and the discontinuous PWM scheme to obtain the minimum DC voltage ripple area on the modulation index and load angle plane. The continuous PWM scheme usually has a higher voltage modulation accuracy, while the discontinuous PWM scheme produces smaller switching losses under certain working conditions. By conducting a comprehensive performance comparison and analysis of these PWM schemes, the area with the minimum DC voltage ripple is found on the plane of the modulation index and load angle, so as to determine the best PWM scheme suitable for different working conditions. Based on the determined minimum DC voltage ripple area, a PWM scheme selection lookup table is constructed to quickly find the optimal PWM scheme under different operating conditions. The PWM scheme selection lookup table is an optimization tool based on actual operating data. By classifying and sorting the PWM performance under different modulation indices and load angles, a quick search database is obtained, so as to realize real-time PWM scheme selection during system operation. In the actual operation of the system, the real-time operating parameters are input into the PWM scheme selection lookup table to obtain the candidate PWM scheme under the current working condition. The real-time operating parameters mainly include the modulation index and the load angle, which directly affect the operating state and load characteristics of the inverter. By inputting the real-time parameters into the lookup table, the candidate PWM scheme suitable for the current working condition is obtained. The switching loss and harmonic distortion of the candidate PWM scheme under the current working condition are evaluated to determine the PWM scheme with the best comprehensive performance. The switching loss evaluation mainly considers the switching frequency and switching time of the inverter under different PWM schemes to reduce the energy loss of the system; the harmonic distortion evaluation analyzes the harmonic components generated by different PWM schemes on the load side to ensure that the power quality of the system output meets the requirements. Through the comprehensive evaluation of the two indicators of switching loss and harmonic distortion, the PWM scheme with the best comprehensive performance under the current working conditions is found. Based on the PWM scheme with the best comprehensive performance, the PWM switching sequence and switching time are calculated to obtain the PWM control timing.The calculation of PWM switching sequence and switching time is based on the inverter's operating requirements and load characteristics. Through precise control timing design, each switch device is ensured to be turned on or off at the appropriate time to achieve the best power modulation effect. The PWM control timing is converted into a digital control signal to generate a PWM control instruction for controlling the inverter. The digital control signal is generated by the controller in the control system. These signals are used to drive the inverter's power switch tube to achieve precise control of the AC output.

[0038] Step S6: Input the PWM control command into the model predictive controller to calculate the energy storage device charging and discharging power command and the grid-connected control signal to achieve grid-connected operation control of the distributed energy storage system.

[0039] Specifically, the PWM control instruction and the current state data of the system are input into the model predictive controller to obtain the initial optimized control sequence. The model predictive controller obtains the state data of the system, including variables such as voltage and current, and analyzes the behavior of the system in the future prediction time domain in combination with the PWM control instruction to form an initial optimized control sequence. Based on the initial control sequence, the change of the system state in the prediction time domain, that is, the system state prediction value, is calculated. Through the prediction process, the model predictive controller can effectively predict and regulate the future behavior of the energy storage system. According to the system state prediction value and the preset multi-objective optimization function, a rolling optimization calculation is performed to obtain the optimal control sequence. The setting of the multi-objective optimization function is to achieve multiple control objectives of the system at the same time, including minimizing system losses, optimizing power balance, and improving power quality. In the rolling optimization process, the model predictive controller continuously updates the state information of the system to achieve rolling prediction of future behavior, and optimizes the control sequence at each moment to ensure that the system can respond to changes in the external environment in real time. Through the optimization calculation, the optimal control quantity at the current moment is obtained, and the control quantity is extracted from the optimal control sequence to obtain the charging and discharging power instructions and grid-connected control signals of the energy storage device. The charge and discharge power instructions of the energy storage device are converted into the charge and discharge current and voltage setting values ​​of each energy storage device to obtain the control parameters of the energy storage device. The setting of the charge and discharge current and voltage is directly related to the charge and discharge process of the energy storage device. Reasonable current and voltage settings can ensure that the energy storage device is charged and discharged within a safe range, thereby improving the service life and charge and discharge efficiency of the equipment. At the same time, based on the grid-connected control signal, the grid-connected voltage and frequency control instructions of the inverter are generated to obtain the grid-connected operation parameters to ensure that the energy storage system can keep consistent with the voltage and frequency of the power grid when it is connected to the grid, thereby ensuring the stability of the grid connection and the stable operation of the system. The control parameters of the energy storage device are sent to the power conversion system of each energy storage device to achieve precise charge and discharge control of the energy storage device. As a bridge between the energy storage device and the power grid, the power conversion system has the main function of adjusting the charge and discharge state of the energy storage device according to the control parameters, thereby achieving effective energy transfer. At the same time, the grid-connected operation parameters are sent to the grid-connected inverter, and the output voltage and frequency of the inverter are adjusted to ensure that the energy storage system is synchronized with the power grid, so as to achieve the grid-connected operation control of the distributed energy storage system. The inverter converts DC power into AC power and needs to adjust the output voltage and frequency to keep consistent with the grid. Through the PWM control instructions generated by the model predictive controller, the inverter can dynamically adjust its switching sequence according to the real-time working conditions to achieve precise control of the output power. By adjusting the output voltage and frequency, the inverter can achieve seamless connection with the grid voltage and frequency, improving the stability of grid connection and the quality of power supply.

[0040] In the embodiment of the present invention, by adopting the wavelet packet decomposition algorithm to perform multi-scale decomposition on the output power of renewable energy, the time-frequency characteristics of power fluctuation can be captured more accurately, and a two-layer fuzzy controller is introduced to calculate the target power value of the energy storage device, so as to realize the refined allocation of different frequency components, and at the same time consider the real-time state of the energy storage device, thereby improving the rationality and reliability of power allocation. The nonlinear state feedback controller is adopted to adjust the DC bus voltage, so as to enhance the system's resistance to external disturbances and improve the control accuracy and dynamic response performance of the bidirectional converter. An adaptive PWM selection algorithm is designed, which can dynamically select the optimal PWM scheme according to the real-time operating conditions, effectively reduce the DC side voltage ripple, and improve the grid-connected quality of the inverter. The model predictive controller is introduced to perform the overall coordinated control of the system, so as to realize the forward-looking optimization of the charging and discharging power and grid-connected control of the energy storage device, and improve the overall operation efficiency and stability of the system. The present invention realizes the coordinated control of the distributed energy storage system, and significantly improves the absorption capacity of renewable energy and the stability of the power grid.

[0041] In a specific embodiment, the process of executing step S1 may specifically include the following steps:

[0042] Start the inverter, communication module and control unit in the distributed energy storage system to obtain the system initialization state;

[0043] Based on the system initialization state, the real-time output power data of the renewable energy power generation equipment is collected through the pre-installed sensor network to obtain the renewable energy output power;

[0044] According to the output power of renewable energy, the real-time voltage, frequency and power demand data of the power grid are collected by power grid monitoring equipment to obtain power grid parameters. Based on the power grid parameters, the state of charge, temperature and voltage of each energy storage device are monitored in real time to obtain the status data of the energy storage device.

[0045] According to the energy storage device status data, collect the real-time power demand data of the user end to obtain the load demand data;

[0046] The renewable energy output power, grid parameters, energy storage equipment status data and load demand data are input into the central control unit for data preprocessing to obtain system operation status data.

[0047] Specifically, the inverter, communication module and control unit in the distributed energy storage system are started. During the startup process, the inverter, as an important device for power conversion, needs to ensure that the voltage on the DC side and the AC side is within the specified safety range to avoid overcurrent or overvoltage during the startup process. The communication module starts and establishes a communication link with other devices to ensure that the system can exchange data normally; at the same time, the initialization of the control unit means loading the control algorithm and scheduling strategy of the system so that the entire system can coordinate and operate according to the predetermined process. When all components have completed the startup and successfully established communication, the entire distributed energy storage system enters the system initialization state. Based on the initialization state of the system, the real-time output power data of the renewable energy power generation equipment is collected through the sensor network pre-installed in the system to obtain the output power of the renewable energy. The sensor network usually includes voltage and current sensors, etc., which are used to monitor the output of renewable energy equipment such as solar panels and wind turbines. For example, the output power of a solar power generation system can be measured by measuring its output current and voltage To calculate the output power , the formula is:

[0048] ;

[0049] in, Represents the output voltage of the solar panel, Represents the output current, It is the instantaneous output power of the power generation system. By collecting these data in real time, the actual power output of the solar power generation system is obtained, reflecting the current power generation capacity of renewable energy. Based on the output power data of renewable energy, the real-time parameters of the power grid are collected using power grid monitoring equipment, including the voltage of the power grid. ,frequency and power requirements The real-time voltage and frequency of the power grid directly reflect the operating status and load conditions of the power grid, while the power demand indicates the current user's demand for electricity. By collecting these parameters in real time, the state of the power grid can be understood, and it can be determined whether the energy storage device needs to supply power to the grid or obtain electricity from the grid. At the same time, based on the collected grid parameters, the status of each energy storage device in the distributed energy storage system is monitored, including real-time monitoring of the state of charge (SOC), temperature and voltage of each energy storage device. For example, the state of charge It is expressed by the formula:

[0050] ;

[0051] in, For energy storage devices The current stored energy, The maximum energy capacity of the energy storage device. A reasonable state of charge can extend the service life of the energy storage device and avoid overcharging or over-discharging. At the same time, temperature and voltage monitoring can effectively prevent failures of the energy storage device due to overheating or voltage abnormalities, and ensure the stability of the system. According to the status data of the energy storage device, the real-time power demand data of the user end is collected to obtain the load demand data. The power demand of the user end is monitored by the smart meter installed at the user end. The smart meter measures the user's current and voltage in real time, thereby calculating the current user's instantaneous power demand. , the formula is:

[0052] ;

[0053] in, Indicates the voltage at the user end, Indicates the current at the user end, is the instantaneous load demand of the user. By collecting load data, the user's electricity consumption in different time periods can be understood, and the energy storage system can be reasonably scheduled accordingly. For example, during periods of high electricity demand, the discharge power of the energy storage device can be appropriately increased according to the load demand to meet the user's electricity demand. The collected renewable energy output power, grid parameters, energy storage device status data and load demand data are input into the central control unit for data preprocessing to obtain the system's operating status data. The data preprocessing process includes operations such as filtering, smoothing and standardization of the data. For example, for the signal noise of voltage and current, data smoothing is performed through a low-pass filter to eliminate the noise caused by sensor errors or external interference. At the same time, the data is standardized so that different types of data can be compared and analyzed on a unified scale. After data preprocessing, the system operating status data obtained reflects the current comprehensive operation of the system, including the power generation capacity of renewable energy, the operating status of the grid, the health of the energy storage device and the user's electricity demand.

[0054] In a specific embodiment, the process of executing step S2 may specifically include the following steps:

[0055] Perform time series analysis on the renewable energy output power in the system operation status data to obtain power time series data;

[0056] Based on the preset wavelet basis function and decomposition layer number, multi-scale decomposition operation is performed on the power time series data to obtain wavelet packet coefficients in different frequency ranges;

[0057] Perform energy analysis on the wavelet packet coefficients, calculate the energy percentage of each frequency range, obtain the energy distribution characteristics, and determine the cutoff frequencies of the high-frequency component, the medium-frequency component, and the low-frequency component based on the energy distribution characteristics;

[0058] Based on the cutoff frequency, the wavelet packet coefficients are reconstructed to obtain high-frequency power components, intermediate-frequency power components and low-frequency power components, and the high-frequency power components, intermediate-frequency power components and low-frequency power components are reconstructed and normalized in the time domain to obtain power component data.

[0059] Specifically, the output power of renewable energy is sampled and a time series model is constructed. The output power of renewable energy (such as wind power or solar power generation) has significant fluctuations, which are affected by factors such as weather conditions, sunshine intensity, wind speed, etc., and its output power data changes over time. Assume Represents the output power at a certain moment. After continuous time sampling, a set of data points is obtained , forming the time series data of power. By modeling the time series of these power data, the time characteristics of power generation fluctuations are revealed. Based on the preset wavelet basis function and decomposition level, the power time series data is decomposed at multiple scales to obtain wavelet packet coefficients in different frequency ranges. Wavelet transform is a time-frequency analysis tool. By selecting a suitable wavelet basis function, the time signal can be decomposed into components of different frequencies, which is convenient for analyzing the fluctuation characteristics of power signals at different scales. Set the wavelet basis function to , the number of decomposition levels is Through wavelet transform, time series data Decomposed into signal components of different scales, its wavelet packet coefficients It is expressed as:

[0060] ;

[0061] in, Indicated in scale and location The wavelet packet coefficients under is obtained by transforming the mother wavelet function It is obtained by stretching and translating. In this process, the wavelet packet coefficients represent the contribution of different frequency components in the signal. Through these coefficients, the energy distribution and frequency characteristics of the signal are analyzed. Energy analysis is performed on the wavelet packet coefficients to calculate the energy percentage of each frequency range to obtain the energy distribution characteristics. The purpose of energy analysis is to quantify the energy distribution of the power signal in different frequency ranges to help determine the main source of fluctuations and characteristics of the signal. Assume that in the In the layer decomposition, the wavelet packet coefficients are , then The total energy of the layer It is expressed as:

[0062] ;

[0063] By calculating the energy of each layer, the energy percentage is calculated , which means The proportion of layer frequency components in the total energy is given by:

[0064] ;

[0065] in, For the The energy percentage of the layer, is the sum of the energies of all layers. By calculating the energy percentage, the energy distribution characteristics are obtained, thereby identifying the main energy contributions of the high-frequency, medium-frequency and low-frequency components in the power signal. Based on the energy distribution characteristics, the cutoff frequencies of the high-frequency component, the medium-frequency component and the low-frequency component are determined. For example, if the energy percentage of a layer is large and the frequency of the layer is high, it is classified as a high-frequency component; correspondingly, if the frequency of a layer is low and the energy contribution is large, it is classified as a low-frequency component. Based on the cutoff frequencies of high frequency, medium frequency and low frequency, the wavelet packet coefficients are reconstructed to obtain high-frequency power components, medium-frequency power components and low-frequency power components. The process of signal reconstruction is to recombine the wavelet packet coefficients to restore the signal parts in different frequency ranges. Assume that the wavelet packet coefficients of the high-frequency part are , the wavelet packet coefficient of the intermediate frequency part is , the wavelet packet coefficient of the low-frequency part is , then the high-frequency, medium-frequency and low-frequency power components are obtained by inverse wavelet transform. The reconstructed high-frequency component It mainly reflects the rapid fluctuations in the output power of renewable energy, which are caused by short-term disturbances in the external environment, such as sudden changes in wind speed or rapid changes in solar radiation. represents relatively smooth fluctuations, such as power fluctuations caused by gradually changing weather conditions, while low-frequency components It mainly describes the long-term trend of the power signal. The high-frequency, medium-frequency and low-frequency power components are reconstructed and normalized in the time domain to obtain the final power component data. In the process of time domain reconstruction, the alignment of each component in time is ensured so that the subsequent power scheduling and control can be carried out in a unified manner. At the same time, the normalization process is used to eliminate the difference in amplitude between different components so that they can be compared and analyzed at the same level.

[0066] In a specific embodiment, the process of executing step S3 may specifically include the following steps:

[0067] The high-frequency power component, the medium-frequency power component and the low-frequency power component in the power component data are input into the first-layer fuzzy controller respectively to obtain the preliminary power allocation result;

[0068] Based on the preliminary power allocation results, combined with the state of charge, temperature and voltage data of each energy storage device, the power regulation coefficient of each energy storage device is calculated to obtain the power regulation parameters;

[0069] The power regulation parameters and the preliminary power allocation results are input into the second-layer fuzzy controller to perform refined power allocation calculations to obtain the target power value of each energy storage device;

[0070] According to the target power value of each energy storage device, the charging and discharging power and the power change rate of each energy storage device are calculated to obtain the operating parameters of the energy storage device;

[0071] Based on the operating parameters of the energy storage equipment and the real-time voltage and frequency data of the power grid, the grid-connected power balance calculation of the energy storage equipment is performed to obtain the grid-connected power balance value;

[0072] Compare the grid-connected power balance value with the preset grid-connected power limit value to determine whether it exceeds the limit range and obtain a power adjustment instruction;

[0073] According to the power adjustment instruction, the target power value of each energy storage device is corrected, and the corresponding charging and discharging tasks are assigned to each energy storage device to generate an energy storage power allocation plan.

[0074] Specifically, different frequency components of the power signal are effectively controlled and allocated. The high-frequency power component, medium-frequency power component and low-frequency power component in the power component data are respectively input into the first-layer fuzzy controller, and the fuzzification, reasoning and defuzzification process of fuzzy logic are used to obtain preliminary power allocation results. The preliminary allocation results determine how different energy storage devices should respond to these fluctuations of different frequencies to achieve preliminary stable control of the system. For example, fast-responding energy storage devices will be allocated more high-frequency components to cope with rapid fluctuations in a short period of time, while slower-responding devices will deal with more low-frequency components. Based on the preliminary power allocation results, combined with the state of charge (SOC), temperature, voltage and other data of each energy storage device, the power regulation coefficient of each energy storage device is calculated to obtain the power regulation parameters. The state of charge reflects the current remaining power of the energy storage device and is used to measure whether the device can continue to charge or discharge. For energy storage devices The state of charge is usually between 0 and 1; temperature and voltage are used to determine the safe operating range of the device. The calculation formula is expressed as:

[0075] ;

[0076] in, and Respectively represent energy storage devices Temperature and voltage, function Represents a fuzzy reasoning process that combines multiple factors such as state of charge, temperature and voltage to obtain a power regulation coefficient. The calculated power regulation coefficient is used to reasonably adjust the preliminary power allocation results to ensure that each energy storage device operates within its safety range and can optimally respond to the power demand of the system. The power regulation parameters and the preliminary power allocation results are input into the second-layer fuzzy controller to perform refined power allocation calculations to obtain the target power value of each energy storage device. The role of the second-layer fuzzy controller is to optimize and refine the power allocation based on the status of each device. For example, for energy storage devices with a low state of charge, the second-layer controller reduces its discharge task according to the adjustment parameters to protect the device and extend its service life. For devices with higher temperatures, the controller will appropriately reduce its load to prevent the device from being damaged by overheating. By considering the comprehensive characteristics of each energy storage device, the second-layer fuzzy controller can achieve accurate power scheduling, making the power balance of the system more robust and efficient. According to the target power value of each energy storage device, the charging and discharging power of each energy storage device is calculated and power change rate , and obtain the operating parameters of the energy storage device. The charging and discharging power is directly determined by the target power value. Assuming the target power is , then the charge and discharge power is expressed as:

[0077] ;

[0078] The power change rate It reflects the power change of the energy storage device in the current time interval. The formula is:

[0079] ;

[0080] in, represents the power value of the previous time step, Indicates a time interval. The calculation of the power change rate helps to avoid excessive power changes in the equipment, which can cause overheating or excessive current stress, thereby damaging the life of the equipment. Based on the operating parameters of the energy storage device and the real-time voltage of the power grid and frequency Data, to calculate the grid-connected power balance of energy storage equipment, and obtain the grid-connected power balance value The real-time voltage and frequency of the power grid are key indicators of grid stability. By calculating the difference between the total power output of the energy storage device and the grid demand, we can determine whether the energy storage system can effectively balance the power of the grid. For example, assuming the total power output is , the power demand of the grid is , then the grid-connected power balance value is expressed as:

[0081] ;

[0082] if If it is a positive value, it means that the power output of the energy storage system exceeds the demand of the power grid. At this time, the output power of some energy storage devices needs to be reduced; if it is a negative value, it means that there is a power gap in the power grid, and the energy storage system needs to further increase the output power to maintain the stability of the power grid. Compare the grid-connected power balance value with the preset grid-connected power limit to determine whether it exceeds the limit range, and obtain a power adjustment instruction. If the grid-connected power balance value exceeds the limit range, it means that the current operating status of the system will affect the stability of the power grid. At this time, it is necessary to generate a power adjustment instruction to correct the target power value of each energy storage device. By adjusting the target power value, the charging and discharging tasks of each energy storage device are redistributed to ensure that the overall power output of the system meets the requirements of the power grid, thereby avoiding interference to the power grid due to excessive or insufficient power output.

[0083] In a specific embodiment, the process of executing step S4 may specifically include the following steps:

[0084] According to the energy storage power allocation scheme, a mathematical model of the bidirectional converter including the voltage and current relationship between the DC side and the AC side is established;

[0085] Based on the mathematical model of bidirectional converter, the state feedback control law is designed, and nonlinear feedback terms are introduced into the state feedback control law to obtain an enhanced control law.

[0086] Based on the enhanced control law, a super-twist observer is designed and the generalized disturbance estimate is calculated.

[0087] The generalized disturbance estimate is introduced into the enhanced control law to construct a complete nonlinear state feedback controller;

[0088] Monitor and adjust the DC bus voltage of the nonlinear state feedback controller in real time to obtain voltage regulation data;

[0089] The controller parameters are dynamically adjusted based on the voltage regulation data to obtain adaptive control parameters, and the adaptive control parameters are applied to the nonlinear state feedback controller to generate converter control parameters.

[0090] Specifically, according to the energy storage power allocation scheme, a mathematical model of a bidirectional converter including the voltage and current relationship between the DC side and the AC side is established. The main function of the bidirectional converter is to realize the bidirectional flow of electric energy between the DC side energy storage device and the AC power grid. It can convert DC power into AC power to supply the power grid, and convert AC power into DC power to charge the energy storage device. To establish this mathematical model, the DC side voltage is described. , AC side voltage And DC current , AC current The bidirectional converter is considered as a nonlinear system with input and output, and its dynamic behavior is modeled by circuit equations. Assume that the converter contains an inductor and a capacitor , and adjusts the power flow through the control signal. Its mathematical model is expressed as the following two state equations:

[0091] ;

[0092] ;

[0093] In the above equation, is the DC side inductance, is the DC side capacitance, is the DC bus voltage, is the AC side voltage, is the DC side current, is the load current. Through these equations, the dynamic behavior of the bidirectional converter and the electrical relationship between the DC and AC sides are described. Based on the mathematical model of the bidirectional converter, a state feedback control law is designed to achieve accurate control of the system state. The purpose of the state feedback control law is to use the state information of the system to adjust the behavior of the converter to ensure the stability and dynamic response characteristics of the system. Assume that the state variable of the system is , input is the control variable , the state feedback control law is expressed as follows:

[0094] ;

[0095] in, is the control gain matrix, by choosing the appropriate gain , so that the closed-loop performance of the system reaches the expected goal. On the basis of the state feedback control law, the nonlinear feedback term is introduced to obtain the enhanced control law. The form of the enhanced control law is:

[0096] ;

[0097] in, is a nonlinear feedback term used to compensate for the nonlinear part of the system. By introducing nonlinear terms, the response performance and robustness of the control system in dealing with complex dynamic processes are effectively improved, so that the system can better cope with voltage and current fluctuations. Based on the enhanced control law, a supertwist observer is designed to estimate the generalized disturbance in the system in real time. The supertwist observer is a robust state observer that can effectively observe the system state in the presence of uncertainty and disturbance. Assume that the disturbance is , system status The dynamic behavior of is expressed in the following form:

[0098] ;

[0099] By designing a supertwist observer, the disturbance Make an estimate, denoted as , so that the control system can adjust the control strategy in time when facing disturbances to maintain the stability of the system. The introduction of generalized disturbance estimates makes the controller more robust when dealing with uncertainties and external disturbances. Introduced into the enhanced control law, a complete nonlinear state feedback controller is constructed. The controller takes into account the state feedback of the system and introduces compensation for nonlinearity and estimation of disturbances, so that the output of the converter can be effectively controlled under various operating conditions. The output of the controller is used to adjust the working state of the converter in real time to ensure that the system can operate stably and maintain the DC bus voltage In order to realize the real-time monitoring and adjustment of the DC bus voltage of the nonlinear state feedback controller, Continuous sampling and analysis are performed. By real-time monitoring of the DC bus voltage, the current voltage regulation data is obtained to determine whether it deviates from the set value. When the voltage is detected to deviate from the target value, the controller will automatically adjust the control parameters according to the size and direction of the deviation to restore the voltage to the set range. Based on the voltage regulation data obtained in real time, the controller parameters are dynamically adjusted to obtain adaptive control parameters. Adaptive control parameters are used to enhance the adaptability of the controller to different operating conditions. For example, assuming the adaptive control gain is It performs dynamic calculations based on voltage deviation and current changes to better cope with instantaneous changes in the system. The calculation formula for the adaptive control parameter is expressed as:

[0100] ;

[0101] in, is the initial gain, is the adaptive gain adjustment coefficient, is the voltage deviation, is the reference voltage of the DC bus. By adjusting the adaptive gain, the controller is ensured to provide stronger regulation ability when the voltage deviation is large, and to maintain the stability of the system when the voltage deviation is small. The adaptive control parameters are applied to the nonlinear state feedback controller to generate the control parameters of the converter. These control parameters are used to adjust the switching frequency and switching sequence of the bidirectional converter to achieve precise control of the flow of electric energy between AC and DC. For example, when the grid load is high, the control parameters will drive the converter to supply the energy released by the energy storage device on the DC side to the grid through the AC side to support the stable operation of the grid; when the load is low or there is excess power generation from renewable energy, the converter will convert the excess energy on the AC side into DC power and store it in the energy storage device.

[0102] In a specific embodiment, the process of executing step S5 may specifically include the following steps:

[0103] Based on the converter control parameters, a PWM evaluation model of DC capacitor current, DC voltage ripple and DC power supply current ripple is established;

[0104] Perform error voltage vector analysis on the PWM evaluation model, calculate the DC side ripple characteristics under different PWM schemes, and obtain PWM performance indicators;

[0105] According to the PWM performance index, the continuous PWM scheme and the discontinuous PWM scheme are compared and analyzed, and the minimum DC voltage ripple area on the modulation index and load angle plane is obtained;

[0106] Based on the minimum DC voltage ripple area, a PWM scheme selection lookup table is constructed to obtain the optimal PWM scheme under different working conditions;

[0107] Input the real-time operating parameters into the PWM scheme selection lookup table to obtain the candidate PWM scheme under the current working condition, wherein the real-time operating parameters include the modulation index and the load angle;

[0108] Evaluate the switching loss and harmonic distortion of candidate PWM schemes under current working conditions to obtain the PWM scheme with the best overall performance;

[0109] Based on the PWM scheme with the best comprehensive performance, the PWM switching sequence and switching time are calculated to obtain the PWM control timing, and the PWM control timing is converted into a digital control signal to generate a PWM control instruction for controlling the inverter.

[0110] Specifically, based on the converter control parameters, a PWM evaluation model of DC capacitor current, DC voltage ripple and DC power supply current ripple is established. PWM (Pulse Width Modulation) technology controls the waveform of voltage and current by adjusting the on and off time of the switching device, thereby adjusting the output power of the converter. , DC voltage ripple and DC supply current ripple It is a key parameter that affects the performance of the energy storage system. Establishing a PWM evaluation model requires describing the behavior of these parameters under different PWM modulation schemes. , which mainly comes from the change of DC bus voltage and the fluctuation of load. In the PWM control process, the charging and discharging of the capacitor determines the voltage stability of the DC bus, and the capacitor current is expressed as:

[0111] ;

[0112] in, is the capacitance value on the DC side, is the DC bus voltage, is the rate of change of voltage. The size of capacitor current directly reflects the degree of voltage fluctuation. When evaluating PWM schemes, capacitor current is an important reference indicator. In order to evaluate the DC side voltage ripple , using the instantaneous value changes of the DC side voltage under different PWM schemes for analysis. The size of the ripple voltage depends on the switching frequency, load conditions and the size of the capacitor. The ripple voltage is approximately expressed by the following formula:

[0113] ;

[0114] in, is the current on the DC side, is the DC capacitance value, is the switching frequency. The choice of switching frequency will affect the amplitude of voltage ripple. A higher switching frequency can usually reduce ripple, but it will increase switching loss. Therefore, a trade-off needs to be made between reducing ripple and reducing loss. , which depends on the change of DC side current and load characteristics. The magnitude of current ripple is approximately described by the following formula:

[0115] ;

[0116] in, is the duty cycle, which indicates the proportion of time the switch is on. is the inductance value on the DC side, is the switching frequency. Duty cycle It is directly related to the ratio of output voltage to input voltage. By calculating the current ripple, the fluctuation of DC current under different PWM schemes is evaluated. Based on the PWM evaluation model established above, error voltage vector analysis is performed to calculate the DC side ripple characteristics under different PWM schemes and obtain the PWM performance index. Error voltage vector analysis is to evaluate the performance of the PWM control scheme by analyzing the deviation between the ideal voltage vector and the actual voltage vector. Different PWM schemes, such as continuous PWM (CPWM) and discontinuous PWM (DPWM), have different performances in the voltage and current ripple characteristics on the DC side. Continuous PWM usually has lower harmonic distortion but higher switching loss, while discontinuous PWM reduces switching loss but increases ripple. The selection of performance indicators requires comprehensive consideration of factors such as ripple, voltage deviation, and harmonic distortion. According to the PWM performance index, the continuous PWM scheme and the discontinuous PWM scheme are compared and analyzed to obtain the minimum DC voltage ripple area on the modulation index and load angle plane. Modulation index Describes the ratio of the output voltage to the input DC voltage, while the load angle It reflects the power factor angle of the load. By analyzing the DC voltage ripple characteristics under different modulation indexes and load angles, the area where the DC voltage ripple is minimized is found. Based on the minimum DC voltage ripple area, a PWM scheme selection lookup table is constructed to quickly find the optimal PWM scheme under different working conditions. The PWM scheme selection lookup table classifies and organizes the optimal PWM schemes under different modulation indexes and load angles to form a database, so that during the operation of the system, the appropriate PWM scheme can be quickly selected according to the real-time operating parameters. The lookup table records the performance of continuous PWM and discontinuous PWM under different working conditions, so that the system can make quick choices under different operating states. In the actual operation of the system, the real-time operating parameters (such as modulation index and load angle) are input into the PWM scheme selection lookup table to obtain the candidate PWM schemes under the current working conditions. The continuous PWM or discontinuous PWM scheme suitable for the current operating conditions is quickly obtained through the lookup table. The switching loss and harmonic distortion of the candidate PWM schemes are evaluated to determine the PWM scheme with the best comprehensive performance. The switching loss evaluation mainly considers the switching frequency and switching time of the inverter under different PWM schemes to reduce the energy loss of the system. Harmonic distortion assessment is used to analyze the harmonic components generated on the load side to ensure that the power quality of the system output meets the requirements of the power grid. Through the comprehensive evaluation of the two indicators of switching loss and harmonic distortion, the PWM scheme with the best performance under the current working conditions is obtained. Based on the PWM scheme with the best comprehensive performance, the PWM switching sequence and switching time are calculated to obtain the PWM control timing. The design of the switching sequence is based on the control requirements and load characteristics of the converter. Through the precise switching timing design, it is ensured that each power switch device is turned on or off at the right time, thereby achieving precise control of the output voltage and current. The calculation of the PWM control timing is completed by calculating the switching state in each switching cycle in real time, and finally converting the control timing into a digital control signal to generate a PWM control instruction for controlling the inverter. These digital control signals drive the switch tube of the inverter through the controller to achieve the required power modulation.

[0117] In a specific embodiment, the process of executing step S6 may specifically include the following steps:

[0118] Input the PWM control instruction and the current state data of the system into the model predictive controller to obtain the initial optimized control sequence, and calculate the predicted value of the system state in the prediction time domain based on the initial optimized control sequence;

[0119] According to the system state prediction value and the preset multi-objective optimization function, rolling optimization calculation is performed to obtain the optimal control sequence, and the control quantity at the current moment is extracted from the optimal control sequence to obtain the energy storage device charging and discharging power instructions and grid-connected control signals;

[0120] Convert the charging and discharging power instructions of the energy storage device into the charging and discharging current and voltage setting values ​​of each energy storage device to obtain the control parameters of the energy storage device, and generate the grid-connected voltage and frequency control instructions of the inverter based on the grid-connected control signal to obtain the grid-connected operation parameters;

[0121] The control parameters of the energy storage equipment are sent to the power conversion system of each energy storage equipment to achieve precise charging and discharging control of the energy storage equipment. At the same time, the grid-connected operation parameters are sent to the grid-connected inverter to adjust the output voltage and frequency to achieve grid-connected operation control of the distributed energy storage system.

[0122] Specifically, the control requirements and real-time status of the system are comprehensively modeled. Model predictive controller is a control method that predicts the dynamics of the system within a limited time prediction range and performs rolling optimization to obtain the optimal control strategy, so that complex dynamic systems can be accurately adjusted. Taking the PWM control instructions and the current state data of the system (such as DC voltage, DC current, AC voltage, etc.) as input, the model predictive controller generates an initial optimized control sequence. This sequence describes how the system should change in the next few time steps to achieve a stable control goal. Based on the initial optimized control sequence, the model predictive controller calculates the predicted value of the system state in the prediction time domain. Assume that the system state is , the control input is , model predictive control predicts the future state of the system through the following state equation:

[0123] ;

[0124] in, and They are the state matrix and control matrix of the system, respectively, which describe the relationship between the system state and the control input. By continuously updating and predicting the state of the system in the future time domain, the model predictive controller obtains a complete prediction sequence of the future behavior of the system, which is used to further optimize the control decision. After obtaining the predicted value of the system state, a rolling optimization calculation is performed according to the preset multi-objective optimization function to find the optimal control sequence. The multi-objective optimization function is the core of the model predictive control, and its goal is to simultaneously minimize the system deviation, the change of the control input, and other performance indicators. For example, assuming that the optimization objective includes the control error , Control input change And energy loss, set the following optimization function:

[0125] ;

[0126] in, is the total cost function to be minimized, , , are the weight coefficients of different target items, is the error between the system output and the target value, is the change in the control input, is the energy loss at the current time step. Through rolling optimization of the optimization function, the model predictive controller finds a set of optimal control sequences in each prediction time domain. After obtaining the optimal control sequence, the model predictive controller extracts the control quantity at the current moment from the sequence to guide the charging and discharging process of the energy storage device and the grid-connected control of the system. The extracted control quantity includes the charging and discharging power instructions of the energy storage device and the grid-connected control signal. For example, if the optimal control quantity Including the specific power requirements for each energy storage device, it directly determines the charging and discharging operations that each energy storage device should perform at the current moment. Convert the charging and discharging power instructions of the energy storage device into the charging and discharging current and voltage setting values ​​of each energy storage device to obtain the control parameters of the energy storage device. Assume that the energy storage device The target charge and discharge power is The voltage setting value is , then the corresponding charge and discharge current Calculated by the following formula:

[0127] ;

[0128] in, is the target power value allocated to the energy storage device by the system, is the DC voltage of the energy storage device, is the corresponding charge and discharge current. Through this process, the power command is accurately converted into the current and voltage control parameters required by each energy storage device to achieve precise control of the energy storage device. At the same time, based on the grid-connected control signal, the grid-connected voltage and frequency control instructions of the inverter are generated to obtain the grid-connected operation parameters. The control of the grid-connected voltage and frequency is to ensure that the energy storage system can be seamlessly connected to the power grid, so as to stably transmit electric energy to the power grid. Assume that the grid-connected control signal is and , which represent the voltage and frequency of the AC power grid respectively. After obtaining these control signals, the inverter will output electric energy according to the set voltage and frequency to ensure that it is synchronized with the power grid. The above energy storage device control parameters are sent to the power conversion system of each energy storage device to achieve precise charging and discharging control of the energy storage device. For example, when the energy storage device needs to be charged, the power conversion system controls the charging rate and time according to the set voltage and current to ensure that the charging efficiency is maximized without exceeding the safety threshold. When the energy storage device needs to be discharged, the power conversion system releases energy to the power grid or load according to the target power and system status. At the same time, the grid-connected operation parameters are sent to the grid-connected inverter to adjust the output voltage and frequency of the inverter to achieve grid-connected operation control of the distributed energy storage system. According to the PWM control instructions given by the model predictive controller, the inverter adjusts the waveform of the output electric energy by accurately controlling the on and off time of the switch tube so that its voltage amplitude and frequency are completely consistent with the power grid, thereby achieving grid-connected operation.

[0129] The above describes the energy storage device grid-connected control method based on the distributed energy storage system in the embodiment of the present invention. The following describes the energy storage device grid-connected control device based on the distributed energy storage system in the embodiment of the present invention. Figure 2 In an embodiment of the present invention, an energy storage device grid-connected control device based on a distributed energy storage system includes:

[0130] An initialization module is used to initialize and collect operation data of a distributed energy storage system integrating an inverter and multiple energy storage devices to obtain system operation status data;

[0131] A decomposition module, used for performing multi-scale decomposition of renewable energy output power based on system operation status data to obtain power component data;

[0132] A calculation module is used to calculate the target power value of each energy storage device based on the power component data using a two-layer fuzzy controller and to construct an energy storage power allocation plan;

[0133] A regulation module is used to construct a nonlinear state feedback controller based on the energy storage power allocation scheme, adjust the DC bus voltage, and obtain the converter control parameters;

[0134] A generation module, used for dynamically selecting an optimal PWM scheme according to converter control parameters, and generating PWM control instructions for controlling the inverter;

[0135] The control module is used to input the PWM control command into the model predictive controller, calculate the energy storage device charging and discharging power command and grid-connected control signal, so as to realize the grid-connected operation control of the distributed energy storage system.

[0136] Through the cooperation of the above components, the output power of renewable energy is decomposed at multiple scales by using the wavelet packet decomposition algorithm, which can more accurately capture the time-frequency characteristics of power fluctuations. The two-layer fuzzy controller is introduced to calculate the target power value of the energy storage device, which realizes the refined allocation of different frequency components. At the same time, the real-time state of the energy storage device is considered, which improves the rationality and reliability of power distribution. The nonlinear state feedback controller is used to adjust the DC bus voltage, which enhances the system's resistance to external disturbances and improves the control accuracy and dynamic response performance of the bidirectional converter. An adaptive PWM selection algorithm is designed, which can dynamically select the optimal PWM scheme according to the real-time operating conditions, effectively reduce the DC side voltage ripple, and improve the grid-connected quality of the inverter. The model predictive controller is introduced to carry out the overall coordinated control of the system, which realizes the forward-looking optimization of the charging and discharging power and grid-connected control of the energy storage device, and improves the overall operation efficiency and stability of the system. The present invention realizes the coordinated control of the distributed energy storage system, significantly improving the absorption capacity of renewable energy and the stability of the power grid.

[0137] The present invention also provides a computer device, which includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the energy storage device grid-connected control method based on the distributed energy storage system in the above-mentioned embodiments.

[0138] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the method for controlling the grid connection of energy storage equipment based on a distributed energy storage system.

[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0140] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0141] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling grid connection of energy storage equipment based on a distributed energy storage system, characterized in that: The method comprises: Initialize and collect operation data of the distributed energy storage system integrating inverters and multiple energy storage devices to obtain system operation status data; Based on the system operation status data, multi-scale decomposition of the renewable energy output power is performed to obtain power component data; specifically, the method includes: performing time series analysis on the renewable energy output power in the system operation status data to obtain power time series data; performing multi-scale decomposition operation on the power time series data based on a preset wavelet basis function and a decomposition layer number to obtain wavelet packet coefficients in different frequency ranges; performing energy analysis on the wavelet packet coefficients to calculate the energy percentage of each frequency range to obtain energy distribution characteristics, and determining the cutoff frequencies of high-frequency components, medium-frequency components and low-frequency components according to the energy distribution characteristics; reconstructing the wavelet packet coefficients based on the cutoff frequencies to obtain high-frequency power components, medium-frequency power components and low-frequency power components, and performing time domain reconstruction and normalization processing on the high-frequency power components, the medium-frequency power components and the low-frequency power components to obtain power component data; According to the power component data, a two-layer fuzzy controller is used to calculate the target power value of each energy storage device and construct an energy storage power allocation plan; Based on the energy storage power allocation scheme, a nonlinear state feedback controller is constructed to adjust the DC bus voltage and obtain converter control parameters; Dynamically selecting an optimal PWM scheme according to the converter control parameters, and generating a PWM control instruction for controlling the inverter; The PWM control instruction is input into the model predictive controller to calculate the energy storage device charging and discharging power instruction and the grid-connected control signal to realize the grid-connected operation control of the distributed energy storage system.

2. The method for controlling grid connection of energy storage equipment based on a distributed energy storage system according to claim 1, characterized in that: The distributed energy storage system integrating the inverter and multiple energy storage devices is initialized and operation data is collected to obtain system operation status data, including: Starting the inverter, the communication module and the control unit in the distributed energy storage system to obtain a system initialization state; Based on the system initialization state, real-time output power data of the renewable energy power generation equipment is collected through a pre-installed sensor network to obtain the renewable energy output power; According to the output power of the renewable energy, the real-time voltage, frequency and power demand data of the power grid are collected by using the power grid monitoring equipment to obtain the power grid parameters, and based on the power grid parameters, the state of charge, temperature and voltage of each energy storage device are monitored in real time to obtain the energy storage device status data; According to the energy storage device status data, real-time power demand data of the user end is collected to obtain load demand data; The renewable energy output power, the grid parameters, the energy storage device status data and the load demand data are input into a central control unit for data preprocessing to obtain system operation status data.

3. The method for controlling grid connection of energy storage equipment based on a distributed energy storage system according to claim 1, characterized in that: The target power value of each energy storage device is calculated using a two-layer fuzzy controller according to the power component data, and an energy storage power allocation scheme is constructed, including: Inputting the high-frequency power component, the medium-frequency power component and the low-frequency power component in the power component data into the first-layer fuzzy controller respectively to obtain a preliminary power allocation result; Based on the preliminary power allocation result, combined with the state of charge, temperature and voltage data of each energy storage device, a power regulation coefficient of each energy storage device is calculated to obtain a power regulation parameter; Input the power adjustment parameter and the preliminary power allocation result into the second-layer fuzzy controller to perform refined power allocation calculation to obtain the target power value of each energy storage device; According to the target power value of each energy storage device, the charging and discharging power and the power change rate of each energy storage device are calculated to obtain the operating parameters of the energy storage device; Based on the operating parameters of the energy storage device and in combination with the real-time voltage and frequency data of the power grid, a grid-connected power balance calculation of the energy storage device is performed to obtain a grid-connected power balance value; Compare the grid-connected power balance value with the preset grid-connected power limit value, determine whether it exceeds the limit range, and obtain a power adjustment instruction; According to the power adjustment instruction, the target power value of each energy storage device is corrected, and a corresponding charging and discharging task is allocated to each energy storage device to generate an energy storage power allocation plan.

4. The method for controlling grid connection of energy storage equipment based on a distributed energy storage system according to claim 1, characterized in that: The method of constructing a nonlinear state feedback controller based on the energy storage power allocation scheme, adjusting the DC bus voltage, and obtaining the converter control parameters includes: According to the energy storage power allocation scheme, a mathematical model of a bidirectional converter including the voltage and current relationship between the DC side and the AC side is established; Based on the mathematical model of the bidirectional converter, a state feedback control law is designed, and a nonlinear feedback term is introduced into the state feedback control law to obtain an enhanced control law; Based on the enhanced control law, a super twist observer is designed, and a generalized disturbance estimate is calculated; Introducing the generalized disturbance estimate into the enhanced control law to construct a complete nonlinear state feedback controller; Performing real-time monitoring and regulation on the DC bus voltage of the nonlinear state feedback controller to obtain voltage regulation data; Dynamically adjust controller parameters based on the voltage regulation data to obtain adaptive control parameters, and apply the adaptive control parameters to the nonlinear state feedback controller to generate converter control parameters.

5. The method for controlling grid connection of energy storage equipment based on a distributed energy storage system according to claim 1, characterized in that: The step of dynamically selecting an optimal PWM scheme according to the converter control parameters and generating a PWM control instruction for controlling the inverter comprises: Based on the converter control parameters, a PWM evaluation model of DC capacitor current, DC voltage ripple and DC power supply current ripple is established; Performing error voltage vector analysis on the PWM evaluation model, calculating DC side ripple characteristics under different PWM schemes, and obtaining PWM performance indicators; According to the PWM performance index, a continuous PWM scheme and a discontinuous PWM scheme are compared and analyzed to obtain a minimum DC voltage ripple region on a modulation index and load angle plane; Based on the minimum DC voltage ripple region, a PWM scheme selection lookup table is constructed to obtain the optimal PWM scheme under different working conditions; Inputting real-time operating parameters into the PWM scheme selection lookup table to obtain candidate PWM schemes under the current working condition, wherein the real-time operating parameters include a modulation index and a load angle; Performing switching loss and harmonic distortion evaluation on candidate PWM schemes under the current working condition to obtain a PWM scheme with the best comprehensive performance; Based on the PWM scheme with the best comprehensive performance, the PWM switching sequence and the switching time are calculated to obtain the PWM control timing, and the PWM control timing is converted into a digital control signal to generate a PWM control instruction for controlling the inverter.

6. The method for controlling grid connection of energy storage equipment based on a distributed energy storage system according to claim 1, characterized in that: The step of inputting the PWM control instruction into a model predictive controller and calculating the energy storage device charging and discharging power instruction and the grid-connected control signal to realize the grid-connected operation control of the distributed energy storage system includes: Inputting the PWM control instruction and the current state data of the system into a model predictive controller to obtain an initial optimized control sequence, and calculating a predicted value of the system state in a prediction time domain based on the initial optimized control sequence; According to the system state prediction value and the preset multi-objective optimization function, a rolling optimization calculation is performed to obtain an optimal control sequence, and the control amount at the current moment is extracted from the optimal control sequence to obtain the energy storage device charging and discharging power instruction and the grid-connected control signal; Convert the energy storage device charge and discharge power instructions into charge and discharge current and voltage setting values ​​of each energy storage device to obtain energy storage device control parameters, and generate grid-connected voltage and frequency control instructions for the inverter based on the grid-connected control signal to obtain grid-connected operation parameters; The energy storage device control parameters are sent to the power conversion system of each energy storage device to achieve precise charge and discharge control of the energy storage device. At the same time, the grid-connected operation parameters are sent to the grid-connected inverter to adjust the output voltage and frequency to achieve grid-connected operation control of the distributed energy storage system.

7. A grid-connected control device for energy storage equipment based on a distributed energy storage system, characterized in that: The device is used to execute the energy storage device grid connection control method based on the distributed energy storage system according to any one of claims 1 to 6, and the device comprises: An initialization module is used to initialize and collect operation data of a distributed energy storage system integrating an inverter and multiple energy storage devices to obtain system operation status data; A decomposition module, used for performing multi-scale decomposition of the output power of renewable energy based on the system operation status data to obtain power component data; A calculation module, used to calculate the target power value of each energy storage device according to the power component data using a two-layer fuzzy controller, and to construct an energy storage power allocation plan; A regulating module, used to construct a nonlinear state feedback controller based on the energy storage power allocation scheme, adjust the DC bus voltage, and obtain converter control parameters; A generating module, used for dynamically selecting an optimal PWM scheme according to the converter control parameters, and generating a PWM control instruction for controlling the inverter; The control module is used to input the PWM control instruction into the model predictive controller, calculate the energy storage device charging and discharging power instruction and the grid-connected control signal, so as to realize the grid-connected operation control of the distributed energy storage system.

8. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, the grid-connected control method for energy storage equipment based on a distributed energy storage system described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor executes the method for controlling grid connection of energy storage devices based on a distributed energy storage system according to any one of claims 1 to 6.

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