Optical storage DC microgrid anti-interference control system and method based on hybrid energy storage

Abstract

The invention discloses an optical storage DC microgrid anti-interference control system and method based on hybrid energy storage, and relates to the related field of power supply parallel operation, and the method comprises the steps: an energy storage module obtaining unit is used for obtaining a first energy storage module and a second energy storage module of hybrid energy storage; the bidirectional transducer construction unit is used for constructing a bidirectional transducer and performing energy conversion between the first energy storage module and the second energy storage module; the disturbance monitoring unit is used for monitoring the first energy storage module and the second energy storage module; the disturbance risk prediction unit is used for inputting the first disturbance monitoring data set and the second disturbance monitoring data set into a disturbance risk prediction model; and the energy conversion unit is used for inputting the first risk index and the second risk index into the bidirectional converter to determine an energy conversion strategy. The technical problem that a system is unstable due to the fact that disturbance conditions are difficult to effectively deal with in existing light storage direct current micro-grid disturbance rejection is solved, and the technical effects of effectively restraining voltage fluctuation and improving system stability and reliability are achieved.

Description

Technical Field

[0001] The present application relates to the field related to parallel operation of power supplies, and in particular to an anti-disturbance control system and method for a photovoltaic-storage-DC microgrid based on hybrid energy storage. Background Art

[0002] With the rapid development and widespread application of renewable energy (such as solar energy and wind energy), DC microgrids, as an efficient and flexible distributed energy system architecture, can directly supply power to DC loads, reduce energy losses caused by AC conversion, and improve system reliability and efficiency. However, the intermittent and uncertain nature of renewable energy has brought challenges to the stable operation of microgrids, especially in the face of disturbances such as load mutations and power failures. How to maintain the energy balance and stable operation of microgrids has become an urgent problem to be solved. Hybrid energy storage systems combine the advantages of different types of energy storage technologies, such as the high energy density of battery energy storage and the high power density of supercapacitors, and can quickly respond to power fluctuations while ensuring long-term energy storage. However, the application of existing hybrid energy storage systems in microgrids mostly relies on simple control strategies, such as fixed charging and discharging rules or switching control based on fixed thresholds. These strategies are difficult to effectively cope with complex and changeable disturbances and cannot achieve efficient and optimized energy allocation.

[0003] Among the current related technologies, the anti-disturbance control of hybrid energy storage photovoltaic and DC microgrids has the technical problem of being unable to effectively cope with complex and changeable disturbances, leading to system instability. Summary of the invention

[0004] The present application provides a photovoltaic-storage DC microgrid anti-disturbance control system and method based on hybrid energy storage, adopts technical means such as real-time monitoring of the status of the first energy storage module and the second energy storage module, predicting disturbance risks, and dynamically adjusting the energy conversion strategy according to risk indicators. By adjusting the energy conversion strategy in advance and responding quickly to power fluctuations, the technical effect of effectively suppressing microgrid voltage fluctuations and improving the stability and reliability of the system is achieved.

[0005] The present application provides a hybrid energy storage-based photovoltaic-storage-DC microgrid anti-disturbance control system, including:

[0006] An energy storage module acquisition unit, the energy storage module acquisition unit is used to acquire a first energy storage module and a second energy storage module of a hybrid energy storage, wherein the energy of the first energy storage module and the second energy storage module is convertible; a bidirectional converter construction unit, the bidirectional converter construction unit is used to construct a bidirectional converter, the first energy storage module and the second energy storage module are connected to a microgrid through the bidirectional converter, and is used to perform energy conversion between the first energy storage module and the second energy storage module; a disturbance monitoring unit, the disturbance monitoring unit is used to monitor the first energy storage module and the second energy storage module based on a disturbance observer, and output a first disturbance monitoring data set and a second disturbance monitoring data set; a disturbance risk prediction unit, the disturbance risk prediction unit is used to input the first disturbance monitoring data set and the second disturbance monitoring data set into a disturbance risk prediction model, and output a first risk indicator and a second risk indicator; an energy conversion unit, the energy conversion unit is used to input the first risk indicator and the second risk indicator into the bidirectional converter, determine an energy conversion strategy according to the bidirectional converter, and perform energy conversion on the first energy storage module and the second energy storage module according to the energy conversion strategy.

[0007] In a possible implementation, the energy conversion unit is used to:

[0008] Construct an objective function and energy storage safety constraints; solve the objective function based on a gradient descent algorithm to determine an energy conversion strategy that meets the energy storage safety constraints, wherein the energy conversion strategy includes input / output voltage and current, power flow direction, size, and energy conversion efficiency; the bidirectional converter performs energy conversion on the first energy storage module and the second energy storage module using the energy conversion strategy.

[0009] In a possible implementation, the objective function expression is: Among them, J is the objective function, which represents the combination of the total energy loss and voltage deviation of the system; V bus (t) is the DC bus voltage at time t; V ref is the target voltage value of the DC bus; P conv (t) is the energy conversion power at time t, Q and R are weight matrices, representing the weights of voltage deviation and energy conversion power in the objective function, respectively, k is the current time step, and N is the optimization time step.

[0010] In a possible implementation, the energy conversion unit is used to:

[0011] Collecting an energy conversion sample set of the bidirectional converter; performing energy conversion loss evaluation on the energy conversion sample set and outputting an energy conversion loss index; if the energy conversion loss index is greater than a preset energy conversion loss index, optimizing the energy conversion strategy based on the energy conversion loss index and outputting the optimized energy conversion strategy.

[0012] In a possible implementation, the energy conversion unit is used to:

[0013] The objective function is updated based on the energy conversion loss index, and the updated objective function is as follows: Among them, J is the objective function, which represents the combination of the total energy loss and voltage deviation of the system; V bus (t) is the DC bus voltage at time t; V ref is the target voltage value of the DC bus; p conv (t) is the energy conversion power at time t, Q and R are weight matrices, which represent the weights of voltage deviation and energy conversion power in the objective function, respectively, k is the current time step, N is the optimization time step, and P loss (t) = (1-η conv ) conv (t), P loss (t) is the energy conversion loss index at time t, λ is the weight coefficient of the energy conversion loss index, which is used to adjust the impact of the loss term on the objective function, η conv is the energy conversion efficiency of the bidirectional converter, which is between 0 and 1.

[0014] In a possible implementation, the disturbance risk prediction unit is used to:

[0015] The working modes of the first energy storage module and the second energy storage module are identified, wherein the working modes include a charging mode and a discharging mode; a charging disturbance sample set and a discharging disturbance sample set are collected respectively according to the charging mode and the discharging mode; a charging-disturbance risk prediction model is output by training with the charging disturbance sample set and identification information of the identification risk index; a discharging-disturbance risk prediction model is output by training with the discharging disturbance sample set and identification information of the identification risk index.

[0016] In a possible implementation, the energy conversion unit is used to:

[0017] The first risk indicator and the second risk indicator are input into the bidirectional converter to determine a conversion energy storage module and a converted energy storage module, wherein the risk indicator of the conversion energy storage module is smaller than the risk indicator of the converted energy storage module; a conversion path is determined according to the conversion energy storage module and the converted energy storage module, and the bidirectional converter executes the energy conversion strategy based on the conversion path.

[0018] In a possible implementation, the energy storage module acquisition unit is used to:

[0019] Determine the number of energy storage modules in the hybrid energy storage; if the number of energy storage modules in the hybrid energy storage is greater than a preset number, obtain a distributed energy storage module, analyze the distributed energy storage module, and generate a plurality of complementary energy storage groups; wherein each complementary energy storage group is connected to the microgrid through the bidirectional converter, and each complementary energy storage group consists of two energy storage modules with convertible energy.

[0020] The present application also provides a method for anti-disturbance control of a photovoltaic-storage DC microgrid based on hybrid energy storage, including:

[0021] Acquire a first energy storage module and a second energy storage module of hybrid energy storage, wherein the energy of the first energy storage module and the second energy storage module is convertible; construct a bidirectional converter, through which the first energy storage module and the second energy storage module are connected to a microgrid, for performing energy conversion between the first energy storage module and the second energy storage module; monitor the first energy storage module and the second energy storage module based on a disturbance observer, and output a first disturbance monitoring data set and a second disturbance monitoring data set; input the first disturbance monitoring data set and the second disturbance monitoring data set into a disturbance risk prediction model, and output a first risk indicator and a second risk indicator; input the first risk indicator and the second risk indicator into the bidirectional converter, determine an energy conversion strategy according to the bidirectional converter, and perform energy conversion on the first energy storage module and the second energy storage module according to the energy conversion strategy.

[0022] It is intended to adopt the anti-disturbance control system and method for a photovoltaic-storage DC microgrid based on hybrid energy storage proposed in the present application, obtain the first energy storage module and the second energy storage module of the hybrid energy storage through the energy storage module acquisition unit, wherein the energy of the first energy storage module and the second energy storage module is convertible, construct a bidirectional converter through a bidirectional converter construction unit, the first energy storage module and the second energy storage module are connected to the microgrid through the bidirectional converter, and are used to perform energy conversion between the first energy storage module and the second energy storage module, monitor the first energy storage module and the second energy storage module based on the disturbance observer through the disturbance monitoring unit, output the first disturbance monitoring data set and the second disturbance monitoring data set, input the first disturbance monitoring data set and the second disturbance monitoring data set into the disturbance risk prediction model through the disturbance risk prediction unit, output the first risk index and the second risk index, input the first risk index and the second risk index into the bidirectional converter through the energy conversion unit, determine the energy conversion strategy according to the bidirectional converter, and perform energy conversion on the first energy storage module and the second energy storage module according to the energy conversion strategy, thereby achieving the technical effect of effectively suppressing the voltage fluctuation of the microgrid and improving the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0024] Figure 1 A schematic diagram of the structure of a photovoltaic-storage-DC microgrid anti-disturbance control system based on hybrid energy storage provided in an embodiment of the present application.

[0025] Figure 2 A flow chart of a method for anti-disturbance control of a photovoltaic-DC microgrid based on hybrid energy storage provided in an embodiment of the present application.

[0026] Explanation of the reference numerals: energy storage module acquisition unit 10 , bidirectional converter construction unit 20 , disturbance monitoring unit 30 , disturbance risk prediction unit 40 , energy conversion unit 50 . DETAILED DESCRIPTION

[0027] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0028] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0029] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0030] The embodiment of the present application provides a photovoltaic storage DC microgrid anti-disturbance control system based on hybrid energy storage, such as Figure 1 As shown, the system comprises:

[0031] The energy storage module acquisition unit 10 is used to acquire the first energy storage module and the second energy storage module of the hybrid energy storage, wherein the energy of the first energy storage module and the second energy storage module is convertible. Specifically, when the system starts, the connected energy storage module is identified and initialized, that is, the existence and type (such as lithium battery, super capacitor, etc.) of the first energy storage module and the second energy storage module are confirmed through the communication interface or physical connection detection. Among them, hybrid energy storage refers to the simultaneous existence of multiple types of energy storage devices in the system, which can work together to optimize the energy storage effect. The energy storage module refers to an independent energy storage unit, and the energy conversion between different energy storage modules can be achieved through a bidirectional converter. The bidirectional converter is a power electronic device that can realize the bidirectional flow of electric energy, which is used to connect the energy storage module and the microgrid to realize the bidirectional flow and control of energy. A microgrid refers to a small power system composed of distributed power sources, energy storage devices, loads, control devices, and protection devices, which can operate independently or in parallel with the main power grid.

[0032] In a possible implementation, the energy storage module acquisition unit 10 is used to: determine the number of energy storage modules in the hybrid energy storage; if the number of energy storage modules in the hybrid energy storage is greater than a preset number, acquire a distributed energy storage module, analyze the distributed energy storage module, and generate a plurality of complementary energy storage groups; wherein each complementary energy storage group is connected to the microgrid through the bidirectional converter, and each complementary energy storage group is composed of two energy storage modules with convertible energy.

[0033] Specifically, after the system is started, the energy storage module acquisition unit 10 first performs an initial detection to identify the number of energy storage modules connected in the current hybrid energy storage. The number of energy storage modules detected is compared with a preset threshold (i.e., a preset number). If the number of energy storage modules detected is greater than the preset number, it means that there are sufficient energy storage resources for a more optimized configuration. At this time, the energy storage module acquisition unit 10 further searches and acquires available distributed energy storage modules, which are located at different locations of the microgrid system and are connected to the central control system through a communication network. The distributed energy storage modules can work independently or in coordination with other energy storage modules. After acquiring the distributed energy storage modules, the energy storage module acquisition unit 10 performs a detailed analysis of these modules, including their type, capacity, charge and discharge characteristics, remaining power and other key parameters. Based on the results of the module analysis, the energy storage module acquisition unit 10 combines the distributed energy storage modules into multiple complementary energy storage groups. Each complementary energy storage group consists of two energy-convertible energy storage modules, which can complement each other in performance and jointly improve the overall efficiency and stability of the energy storage system. Each complementary energy storage group is connected to the microgrid through a bidirectional converter to achieve a bidirectional flow of energy. Finally, all the generated complementary energy storage groups are integrated into the entire microgrid system to form a more efficient and stable energy storage network. This implementation method fully utilizes the advantages of each energy storage module by combining them into complementary energy storage groups when there are a large number of energy storage modules in the hybrid energy storage system, which helps reduce the impact of a single energy storage module failure on the entire system and improves the stability and reliability of the system.

[0034] A bidirectional converter construction unit 20, the bidirectional converter construction unit 20 is used to construct a bidirectional converter, the first energy storage module and the second energy storage module are connected to the microgrid through the bidirectional converter, and is used to convert energy between the first energy storage module and the second energy storage module. Specifically, according to the characteristics of the energy storage module and the system requirements, a suitable bidirectional converter model and specification are selected. The bidirectional converter is physically connected to the first energy storage module and the second energy storage module to ensure that energy can flow freely between them. The control parameters and working mode of the bidirectional converter are configured to adapt to the real-time requirements of the system. The configured bidirectional converter is connected to the microgrid system to achieve integration with the entire system.

[0035] The disturbance monitoring unit 30 is used to monitor the first energy storage module and the second energy storage module based on the disturbance observer, and output the first disturbance monitoring data set and the second disturbance monitoring data set. Specifically, the disturbance observer is a device for real-time monitoring and identifying system disturbances. Disturbance refers to various external or internal factors that affect the normal operation of the system, such as sudden load changes, power supply fluctuations, etc. Specifically, the operating data of the energy storage module is collected in real time through sensors and other equipment, and the collected data is pre-processed by filtering, denoising and other operations. The disturbance observer is used to analyze the processed data to identify the type of disturbance and its degree to which the energy storage module may be subjected. The identified disturbance information is organized into a first disturbance monitoring data set and a second disturbance monitoring data set.

[0036] The disturbance risk prediction unit 40 is used to input the first disturbance monitoring data set and the second disturbance monitoring data set into the disturbance risk prediction model, and output the first risk index and the second risk index. Specifically, the risk prediction model is a model established based on historical data (historical disturbance data and risk index data) and a machine learning algorithm, and is used to predict risks that may occur in the future. The first disturbance monitoring data set and the second disturbance monitoring data set are input into the trained disturbance risk prediction model, and the model outputs the first risk index and the second risk index according to the input data, indicating the risk level of the energy storage module under the current disturbance.

[0037] In a possible implementation, the disturbance risk prediction unit 40 is used to: identify the working modes of the first energy storage module and the second energy storage module, wherein the working modes include a charging mode and a discharging mode; collect a charging disturbance sample set and a discharging disturbance sample set according to the charging mode and the discharging mode, respectively; train with the charging disturbance sample set and identification information of the identification risk index, and output a charging-disturbance risk prediction model; train with the discharge disturbance sample set and identification information of the identification risk index, and output a discharge-disturbance risk prediction model.

[0038] Specifically, the disturbance risk prediction unit 40 first monitors the current working state of the first energy storage module and the second energy storage module, and determines whether they are currently in charging mode or discharging mode by reading the current, voltage, temperature and other parameters of these energy storage modules, as well as the system control instructions. When it is identified that the energy storage module is in charging mode, various operating data related to the mode are collected, including but not limited to charging rate, charging voltage, charging current, battery temperature, etc. At the same time, any possible disturbance events (such as overcharging, overheating, charging interruption, etc.) and their corresponding risk indicators (such as risk level, impact range, etc.) are recorded. Similarly, in the discharge mode, various data during the discharge process are collected, and disturbance events and risk indicators that may occur during the discharge process are recorded. Using the collected charging disturbance sample set, combined with the machine learning algorithm, the model is trained with the identification information of the risk indicator (such as a label) as the training target. The goal of model learning is to predict the risk of disturbance of the energy storage module under given charging conditions. The same method is used, but training is performed based on the discharge disturbance sample set to predict the disturbance risk during the discharge process. After the training is completed, the disturbance risk prediction unit 40 outputs two prediction models: a charging-disturbance risk prediction model and a discharging-disturbance risk prediction model. These two models are used to predict disturbance risks based on real-time data during the operation of the energy storage system, and provide early warning and decision support for the system. This implementation method identifies the working mode of the energy storage module and collects the corresponding disturbance sample set, and establishes a special prediction model for different working states, so that these models can more accurately predict the risk of disturbance of the energy storage module under specific conditions, and then adjust the operation strategy in advance to avoid or reduce the impact of disturbance events on the system.

[0039] The energy conversion unit 50 is used to input the first risk indicator and the second risk indicator into the bidirectional converter, determine the energy conversion strategy according to the bidirectional converter, and perform energy conversion on the first energy storage module and the second energy storage module according to the energy conversion strategy. Specifically, the energy conversion unit 50 receives the first risk indicator and the second risk indicator from the disturbance risk prediction unit 40, and these risk indicators reflect the degree of disturbance risk that the first energy storage module and the second energy storage module may face under the current operating state. The received risk indicators are analyzed, the risk levels of the two energy storage modules are compared, and it is determined which energy storage module needs energy adjustment more to cope with potential disturbances. According to the risk analysis results, combined with the current power state of the energy storage module, the charge and discharge capacity and other factors, an energy conversion strategy is formulated, including transferring energy from a high-risk energy storage module to a low-risk energy storage module or adjusting the charge and discharge state of the energy storage module according to the overall needs of the microgrid. The energy conversion strategy is converted into specific control instructions and sent to the bidirectional converter. These instructions detail how energy conversion should be performed between energy storage modules, including the conversion rate, direction and time. During the energy conversion process, the execution of the bidirectional converter is monitored to ensure that the energy conversion proceeds smoothly according to the predetermined strategy. At the same time, the status of the energy storage module is continuously monitored so that the strategy can be adjusted in time to deal with new disturbances. The embodiment of the present application adopts technical means such as real-time monitoring of the status of the first energy storage module and the second energy storage module, predicting disturbance risks, and dynamically adjusting the energy conversion strategy according to risk indicators. By adjusting the energy conversion strategy in advance and responding quickly to power fluctuations, the technical effect of effectively suppressing microgrid voltage fluctuations and improving the stability and reliability of the system is achieved.

[0040] In one possible implementation, the energy conversion unit 50 is used to: construct an objective function and energy storage safety constraints; solve the objective function based on a gradient descent algorithm to determine an energy conversion strategy that satisfies the energy storage safety constraints, wherein the energy conversion strategy includes input / output voltage and current, power flow direction, size, and energy conversion efficiency; and the bidirectional converter performs energy conversion on the first energy storage module and the second energy storage module using the energy conversion strategy.

[0041] Specifically, one or more goals are defined according to the actual needs of the hybrid energy storage system (such as maximizing system efficiency, minimizing energy loss, balancing the state of the energy storage module, etc.), and based on these goals, a mathematical expression is constructed as the objective function. Determine the safe upper and lower limits of the charging and discharging voltage, current, temperature and other parameters of the energy storage module, consider the overall stability requirements of the system, such as avoiding overcharging and overdischarging, preventing thermal runaway, etc., set the corresponding constraints to ensure that at any time, the input power and output power of the system are balanced to avoid system collapse or instability. Set the initial value of the energy conversion strategy (voltage, current, power flow, etc.), calculate the gradient (i.e. partial derivative) of the objective function with respect to each parameter in each iteration, and then update the parameter value of the energy conversion strategy according to the gradient direction to gradually approach the optimal solution. When the change in the objective function value is less than the preset threshold or reaches the maximum number of iterations, it is considered that the algorithm has converged and the optimal or near-optimal energy conversion strategy is obtained. The energy conversion strategy obtained by solving (including input / output voltage and current, power flow, size and energy conversion efficiency) is sent to the bidirectional converter. The bidirectional converter converts energy between the first energy storage module and the second energy storage module according to the received instructions to achieve efficient and stable energy scheduling. This implementation method ensures that the bidirectional converter achieves efficient and stable energy scheduling and conversion in the hybrid energy storage system by constructing the objective function and energy storage safety constraints and solving the optimal energy conversion strategy based on the gradient descent algorithm, thereby improving the system's anti-interference ability and overall performance.

[0042] In a possible implementation, the objective function expression is:

[0043]

[0044] Among them, J is the objective function, which represents the combination of the total energy loss and voltage deviation of the system; V bus (t) is the DC bus voltage at time t; V ref is the target voltage value of the DC bus; P conv (t) is the energy conversion power at time t, Q and R are weight matrices, representing the weights of voltage deviation and energy conversion power in the objective function, respectively, k is the current time step, and N is the optimization time step.

[0045] In one possible implementation, the energy conversion unit 50 is used to: collect an energy conversion sample set of the bidirectional converter; perform energy conversion loss evaluation on the energy conversion sample set and output an energy conversion loss index; if the energy conversion loss index is greater than a preset energy conversion loss index, optimize the energy conversion strategy based on the energy conversion loss index and output the optimized energy conversion strategy.

[0046] Specifically, it is connected to the bidirectional converter, and various data of the bidirectional converter in the process of executing energy conversion are collected in real time or regularly, including but not limited to the input energy type (such as solar energy, wind energy, electric energy, etc.), input power, output power, conversion efficiency, ambient temperature, equipment operation status, etc., and the collected data are organized into an energy conversion sample set. The data in the energy conversion sample set is processed and analyzed using a preset statistical model to calculate the actual loss amount in each energy conversion process. Based on the actual loss amount, combined with parameters such as conversion efficiency and equipment rated power, an energy conversion loss index is calculated to intuitively reflect the degree of loss in the energy conversion process. The calculated energy conversion loss index is compared with a preset energy conversion loss index (set according to system requirements, industry standards and historical data). If the actual loss index is greater than the preset index, the optimization process is triggered, including adjusting the control parameters of the bidirectional converter (such as voltage and current setting values), replacing more efficient conversion elements, optimizing the equipment layout to reduce heat loss, adjusting the energy input ratio, etc. The optimized energy conversion strategy is output to the bidirectional converter to guide it to adjust parameters or optimize operations. This implementation reduces energy waste and improves energy efficiency by evaluating and optimizing losses during energy conversion.

[0047] In a possible implementation, the energy conversion unit 50 is used to update the objective function based on the energy conversion loss index, and the updated objective function is as follows:

[0048]

[0049] Among them, J is the objective function, which represents the combination of the total energy loss and voltage deviation of the system; V bus (t) is the DC bus voltage at time t; V ref is the target voltage value of the DC bus; p conv (t) is the energy conversion power at time t, Q and R are weight matrices, which represent the weights of voltage deviation and energy conversion power in the objective function, respectively, k is the current time step, N is the optimization time step, and P loss (t) = (1-η conv ) conv (t), P loss (t) is the energy conversion loss index at time t, λ is the weight coefficient of the energy conversion loss index, which is used to adjust the impact of the loss term on the objective function, η conv is the energy conversion efficiency of the bidirectional converter, which is between 0 and 1.

[0050] In one possible implementation, the energy conversion unit 50 is used to: input the first risk indicator and the second risk indicator into the bidirectional converter to determine a conversion energy storage module and a converted energy storage module, wherein the risk indicator of the conversion energy storage module is less than the risk indicator of the converted energy storage module; determine a conversion path based on the conversion energy storage module and the converted energy storage module, and the bidirectional converter executes the energy conversion strategy based on the conversion path.

[0051] Specifically, the first risk index and the second risk index are used as input parameters and passed to the bidirectional converter. The bidirectional converter compares the size of the first risk index and the second risk index to determine which energy storage module has a lower risk (i.e., the conversion energy storage module) and which has a higher risk (i.e., the converted energy storage module). Based on the comparison result, the energy storage module with a smaller risk index is selected as the conversion energy storage module, and the energy storage module with a larger risk index is selected as the converted energy storage module. Once the conversion energy storage module and the converted energy storage module are determined, the bidirectional converter plans an energy conversion path, which includes multiple intermediate links or devices, such as power electronic converters, filters, protection switches, etc., to ensure that energy is efficiently and safely transferred from the converted energy storage module to the conversion energy storage module. While planning the path, relevant conversion parameters, such as voltage, current, power, etc., are set to ensure that the conversion process meets system requirements and achieves the best effect. According to the determined conversion path and parameter settings, the bidirectional converter starts the energy conversion process. This implementation method optimizes the risk level of the entire energy storage system by comparing the risk indicators of different energy storage modules and selecting the module with a lower risk as the conversion target, which helps to extend the service life of the energy storage system, reduce maintenance costs, and improve the reliability and stability of the system.

[0052] In the above, refer to Figure 1 The anti-disturbance control system of a photovoltaic-storage-DC microgrid based on a hybrid energy storage system according to an embodiment of the present invention is described in detail. Figure 2 The anti-disturbance control method of a photovoltaic-storage-DC microgrid based on hybrid energy storage according to an embodiment of the present invention is described.

[0053] The anti-disturbance control method for a photovoltaic DC microgrid based on hybrid energy storage according to an embodiment of the present invention is used to solve the technical problem that the anti-disturbance control of a photovoltaic DC microgrid with hybrid energy storage is difficult to effectively cope with complex and changeable disturbances, resulting in system instability, and achieves the technical effect of effectively suppressing microgrid voltage fluctuations and improving system stability and reliability. The anti-disturbance control method for a photovoltaic DC microgrid based on hybrid energy storage includes:

[0054] Acquire a first energy storage module and a second energy storage module of hybrid energy storage, wherein the energy of the first energy storage module and the second energy storage module is convertible; construct a bidirectional converter, through which the first energy storage module and the second energy storage module are connected to a microgrid, for performing energy conversion between the first energy storage module and the second energy storage module; monitor the first energy storage module and the second energy storage module based on a disturbance observer, and output a first disturbance monitoring data set and a second disturbance monitoring data set; input the first disturbance monitoring data set and the second disturbance monitoring data set into a disturbance risk prediction model, and output a first risk indicator and a second risk indicator; input the first risk indicator and the second risk indicator into the bidirectional converter, determine an energy conversion strategy according to the bidirectional converter, and perform energy conversion on the first energy storage module and the second energy storage module according to the energy conversion strategy.

[0055] Among them, performing energy conversion on the first energy storage module and the second energy storage module according to the energy conversion strategy may further include: constructing an objective function and energy storage safety constraints; solving the objective function based on a gradient descent algorithm to determine an energy conversion strategy that satisfies the energy storage safety constraints, wherein the energy conversion strategy includes input / output voltage and current, power flow direction, size, and energy conversion efficiency; and the bidirectional converter performs energy conversion on the first energy storage module and the second energy storage module according to the energy conversion strategy.

[0056] Wherein, the objective function expression is:

[0057] Among them, J is the objective function, which represents the combination of the total energy loss and voltage deviation of the system; V bus (t) is the DC bus voltage at time t; V ref is the target voltage value of the DC bus; P conv (t) is the energy conversion power at time t, Q and R are weight matrices, representing the weights of voltage deviation and energy conversion power in the objective function, respectively, k is the current time step, and N is the optimization time step.

[0058] Among them, the method may further include: collecting an energy conversion sample set of the bidirectional converter; performing energy conversion loss evaluation on the energy conversion sample set and outputting an energy conversion loss index; if the energy conversion loss index is greater than a preset energy conversion loss index, optimizing the energy conversion strategy based on the energy conversion loss index and outputting the optimized energy conversion strategy.

[0059] Optimizing the energy conversion strategy based on the energy conversion loss index may further include: updating the objective function based on the energy conversion loss index, and the updated objective function is as follows:

[0060] Among them, J is the objective function, which represents the combination of the total energy loss and voltage deviation of the system; V bus (t) is the DC bus voltage at time t; V ref is the target voltage value of the DC bus; p conv (t) is the energy conversion power at time t, Q and R are weight matrices, which represent the weights of voltage deviation and energy conversion power in the objective function, respectively, k is the current time step, N is the optimization time step, and P loss (t) = (1-η conv ) conv (t), P loss (t) is the energy conversion loss index at time t, λ is the weight coefficient of the energy conversion loss index, which is used to adjust the impact of the loss term on the objective function, η conv is the energy conversion efficiency of the bidirectional converter, which is between 0 and 1.

[0061] The method may further include: identifying the working modes of the first energy storage module and the second energy storage module, wherein the working modes include a charging mode and a discharging mode; collecting a charging disturbance sample set and a discharging disturbance sample set according to the charging mode and the discharging mode, respectively; training with the charging disturbance sample set and identification information of the identification risk index, and outputting a charging-disturbance risk prediction model; training with the discharge disturbance sample set and identification information of the identification risk index, and outputting a discharge-disturbance risk prediction model.

[0062] Among them, inputting the first risk indicator and the second risk indicator into the bidirectional converter may further include: inputting the first risk indicator and the second risk indicator into the bidirectional converter, determining a conversion energy storage module and a converted energy storage module, wherein the risk indicator of the conversion energy storage module is less than the risk indicator of the converted energy storage module; determining a conversion path according to the conversion energy storage module and the converted energy storage module, and the bidirectional converter executes the energy conversion strategy based on the conversion path.

[0063] Wherein, the method may further include: determining the number of energy storage modules in the hybrid energy storage; if the number of energy storage modules in the hybrid energy storage is greater than a preset number, obtaining a distributed energy storage module, analyzing the distributed energy storage module, and generating a plurality of complementary energy storage groups; wherein each complementary energy storage group is connected to the microgrid through the bidirectional converter, and each complementary energy storage group is composed of two energy storage modules with convertible energy.

[0064] The photovoltaic-DC microgrid anti-disturbance control system based on hybrid energy storage provided in an embodiment of the present invention can execute the photovoltaic-DC microgrid anti-disturbance control method based on hybrid energy storage provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0065] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0066] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A photovoltaic-storage-DC microgrid anti-disturbance control system based on hybrid energy storage, characterized in that: The system comprises: An energy storage module acquisition unit, the energy storage module acquisition unit is used to acquire a first energy storage module and a second energy storage module for hybrid energy storage, wherein the energy sources of the first energy storage module and the second energy storage module are convertible; A bidirectional converter construction unit, the bidirectional converter construction unit is used to construct a bidirectional converter, the first energy storage module and the second energy storage module are connected to the microgrid through the bidirectional converter, and is used to perform energy conversion between the first energy storage module and the second energy storage module; A disturbance monitoring unit, the disturbance monitoring unit is used to monitor the first energy storage module and the second energy storage module based on a disturbance observer, and output a first disturbance monitoring data set and a second disturbance monitoring data set; A disturbance risk prediction unit, the disturbance risk prediction unit is used to input the first disturbance monitoring data set and the second disturbance monitoring data set into a disturbance risk prediction model, and output a first risk index and a second risk index; An energy conversion unit, wherein the energy conversion unit is used to input the first risk indicator and the second risk indicator into the bidirectional converter, determine an energy conversion strategy based on the bidirectional converter, and perform energy conversion on the first energy storage module and the second energy storage module according to the energy conversion strategy.

2. The anti-disturbance control system of the photovoltaic-storage-DC microgrid based on hybrid energy storage according to claim 1 is characterized in that: The energy conversion unit is used for: Construct objective functions and energy storage safety constraints; Solving the objective function based on a gradient descent algorithm to determine an energy conversion strategy that satisfies the energy storage safety constraints, wherein the energy conversion strategy includes input / output voltage and current, power flow direction, magnitude, and energy conversion efficiency; The bidirectional converter performs energy conversion on the first energy storage module and the second energy storage module according to the energy conversion strategy.

3. The anti-disturbance control system of the photovoltaic-storage-DC microgrid based on hybrid energy storage as claimed in claim 2 is characterized in that: The objective function expression is: Among them, J is the objective function, which represents the combination of the total energy loss and voltage deviation of the system; V bus (t) is the DC bus voltage at time t; V ref is the target voltage value of the DC bus; P conv (t) is the energy conversion power at time t, Q and R are weight matrices, which respectively represent the weights of voltage deviation and energy conversion power in the objective function, k is the current time step, and N is the optimization time step.

4. The anti-disturbance control system of the photovoltaic-storage-DC microgrid based on hybrid energy storage as claimed in claim 2, characterized in that: The energy conversion unit is used for: Collecting an energy conversion sample set of the bidirectional converter; Performing energy conversion loss evaluation on the energy conversion sample set and outputting an energy conversion loss index; If the energy conversion loss index is greater than a preset energy conversion loss index, the energy conversion strategy is optimized based on the energy conversion loss index, and the optimized energy conversion strategy is output.

5. The anti-disturbance control system of the photovoltaic-storage-DC microgrid based on hybrid energy storage as claimed in claim 4 is characterized in that: The energy conversion unit is used for: The objective function is updated based on the energy conversion loss index, and the updated objective function is as follows: Among them, J is the objective function, which represents the combination of the total energy loss and voltage deviation of the system; V bus (t) is the DC bus voltage at time t; V ref is the target voltage value of the DC bus; p conv (t) is the energy conversion power at time t, Q and R are weight matrices, representing the weights of voltage deviation and energy conversion power in the objective function, respectively, k is the current time step, N is the optimization time step, and P loss (t) = (1-η conv ) conv (t), P loss (t) is the energy conversion loss index at time t, λ is the weight coefficient of the energy conversion loss index, which is used to adjust the impact of the loss term on the objective function, η conv is the energy conversion efficiency of the bidirectional converter, between 0 and 1.

6. The anti-disturbance control system of the photovoltaic-storage-DC microgrid based on hybrid energy storage according to claim 1 is characterized in that: The disturbance risk prediction unit is used for: Identifying the working modes of the first energy storage module and the second energy storage module, wherein the working modes include a charging mode and a discharging mode; According to the charging mode and the discharging mode, respectively collecting a charging disturbance sample set and a discharging disturbance sample set; Training is performed using the charging disturbance sample set and identification information of the identification risk index, and outputting a charging-disturbance risk prediction model; The discharge disturbance sample set and identification information of the identification risk index are used for training, and a discharge-disturbance risk prediction model is output.

7. The anti-disturbance control system of the photovoltaic-storage-DC microgrid based on hybrid energy storage according to claim 1 is characterized in that: The energy conversion unit is used for: Inputting the first risk index and the second risk index into the bidirectional converter to determine a conversion energy storage module and a converted energy storage module, wherein the risk index of the conversion energy storage module is smaller than the risk index of the converted energy storage module; A conversion path is determined according to the conversion energy storage module and the converted energy storage module, and the bidirectional converter executes the energy conversion strategy based on the conversion path.

8. The anti-disturbance control system of the photovoltaic-storage-DC microgrid based on hybrid energy storage according to claim 1 is characterized in that: The energy storage module acquisition unit is used for: Determining the number of energy storage modules in the hybrid energy storage; If the number of energy storage modules in the hybrid energy storage is greater than a preset number, obtaining a distributed energy storage module, analyzing the distributed energy storage module, and generating a plurality of complementary energy storage groups; Wherein, each complementary energy storage group is connected to the microgrid via the bidirectional converter, and each complementary energy storage group is composed of two energy storage modules with convertible energy.

9. A method for anti-disturbance control of a photovoltaic-storage DC microgrid based on hybrid energy storage, characterized in that: The method is implemented by executing the anti-disturbance control system of the photovoltaic-storage-DC microgrid based on hybrid energy storage according to any one of claims 1 to 8, and the method comprises: Acquire a first energy storage module and a second energy storage module for hybrid energy storage, wherein energy sources of the first energy storage module and the second energy storage module are convertible; Constructing a bidirectional converter, wherein the first energy storage module and the second energy storage module are connected to a microgrid through the bidirectional converter, and are used for performing energy conversion between the first energy storage module and the second energy storage module; Monitoring the first energy storage module and the second energy storage module based on a disturbance observer, and outputting a first disturbance monitoring data set and a second disturbance monitoring data set; Inputting the first disturbance monitoring data set and the second disturbance monitoring data set into a disturbance risk prediction model, and outputting a first risk index and a second risk index; The first risk indicator and the second risk indicator are input into the bidirectional converter, an energy conversion strategy is determined according to the bidirectional converter, and energy conversion is performed on the first energy storage module and the second energy storage module according to the energy conversion strategy.

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