A grid-connected control method for a bidirectional energy storage inverter

Abstract

The present invention provides a grid-connected control method for a bidirectional energy storage inverter, which relates to the field of power conversion technology, including: predicting the load demand of a target power grid, the target power grid includes a grid-connected main power grid and a microgrid, and the load demand includes distributed DC load and AC load; reading the basic principles of the energy storage inverter, including the topological structure, filtering circuit and operation mechanism; building a conversion scheduling decision model based on the basic principles, making grid scheduling decisions and bidirectional conversion decisions for load demand, and determining a pre-control strategy; tuning based on a benchmark control characteristic to determine a target control strategy; based on the target control strategy, acting on the energy storage inverter to perform grid-connected power control of the target power grid. The present invention solves the technical problems that traditional power grid control cannot effectively cope with diverse load demands and lacks adaptability to dynamic changes in the power grid, resulting in instability and low efficiency in power grid control.

Description

Technical Field

[0001] The present invention relates to the technical field of power conversion, and in particular to a grid-connected control method for a bidirectional energy storage inverter. Background Art

[0002] With the rapid development and popularization of renewable energy, the demand for grid-connected management of multiple energy resources is increasing. In this context, it is necessary to effectively manage and dispatch the grid-connected operation of multiple energy sources to achieve stable operation of the power grid and efficient use of energy. However, there are still some problems with the existing technology. On the one hand, in traditional power grids, load demand may come from multiple sources, including the main grid and microgrids, and involves distributed DC loads and AC loads. Traditional energy storage systems cannot effectively manage and meet such diverse and complex load demands; on the other hand, traditional control strategies lack optimization and adaptability for dynamic changes in the power grid, resulting in poor performance in actual operation and inability to effectively cope with grid load fluctuations and system changes. Summary of the invention

[0003] The present application provides a grid-connected control method for a bidirectional energy storage inverter, aiming to solve the technical problems that traditional power grid control cannot effectively cope with diverse load demands and lacks adaptability to dynamic changes in the power grid, resulting in instability and inefficiency in power grid control.

[0004] In view of the above problems, the present application provides a grid-connected control method for a bidirectional energy storage inverter.

[0005] The first aspect disclosed in the present application provides a grid-connected control method for a bidirectional energy storage inverter, the method comprising: predicting the load demand of a target power grid, wherein the target power grid comprises a grid-connected main power grid and a microgrid, and the load demand comprises distributed DC load and AC load; reading the basic principle of the energy storage inverter, the basic principle comprises a topological structure, a filtering circuit and an operating mechanism, and the energy storage inverter is a single device or a parallel device group; building an energy conversion scheduling decision model based on the basic principle, making a grid scheduling decision and a bidirectional energy conversion decision for the load demand, and determining a pre-control strategy; based on the benchmark control characteristics of the central control system, optimizing the pre-control strategy and determining a target control strategy, the benchmark control characteristics being determined based on the dynamic and static characteristics of the system, the distortion rate and the harmonic amount; based on the target control strategy, acting on the energy storage inverter to perform grid-connected power control of the target power grid.

[0006] The second aspect disclosed in the present application provides a grid-connected control system for a bidirectional energy storage inverter, the system being used for the above-mentioned grid-connected control method for a bidirectional energy storage inverter, the system comprising: a load demand acquisition module, the load demand acquisition module being used to predict the load demand of a target power grid, wherein the target power grid comprises a grid-connected main power grid and a microgrid, and the load demand comprises distributed DC load and AC load; a basic principle acquisition module, the basic principle acquisition module being used to read the basic principle of the energy storage inverter, the basic principle comprising a topological structure, a filtering circuit and an operating mechanism, and the energy storage inverter is a single device or a parallel device group; a prediction module; a ... being used to read the basic principle of the energy storage inverter, wherein the basic principle comprises a topological structure, a filtering circuit and an operating mechanism, and the energy storage inverter is a single device or a parallel device group; a prediction module; a load demand acquisition module being used to predict the load demand of a target power grid, wherein the target power grid comprises a grid-connected main power grid and a microgrid, and the load demand comprises distributed DC load and AC load; a basic principle acquisition module being used to read the basic principle of the energy storage inverter, wherein the basic principle comprises a topological structure, a filtering circuit and an operating mechanism, and the energy storage inverter is a single device or a parallel device group; a prediction module; a load demand acquisition module being used to predict the load demand of a target power grid, wherein the target power grid comprises A control strategy acquisition module, wherein the pre-control strategy acquisition module is used to build an energy conversion scheduling decision model based on the basic principle, make grid scheduling decisions and bidirectional energy conversion decisions for the load demand, and determine the pre-control strategy; a pre-control strategy tuning module, wherein the pre-control strategy tuning module is used to tune the pre-control strategy based on the benchmark control characteristics of the central control system, and determine the target control strategy, wherein the benchmark control characteristics are determined based on the dynamic and static characteristics of the system, the distortion rate, and the harmonic amount; a grid-connected power control module, wherein the grid-connected power control module is used to act on the energy storage inverter based on the target control strategy to perform grid-connected power control of the target power grid.

[0007] The third aspect disclosed in the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program: predicting the load demand of a target power grid, wherein the target power grid includes a main power grid and a microgrid connected to the grid, and the load demand includes distributed DC load and AC load; reading the basic principles of the energy storage inverter, wherein the basic principles include a topological structure, a filtering circuit and an operating mechanism, and the energy storage inverter is a single device or a parallel device group; building an energy conversion scheduling decision model based on the basic principles, making a grid scheduling decision and a bidirectional energy conversion decision for the load demand, and determining a pre-control strategy; based on the benchmark control characteristics of the central control system, tuning the pre-control strategy and determining a target control strategy, wherein the benchmark control characteristics are determined based on the dynamic and static characteristics of the system, the distortion rate and the harmonic amount; based on the target control strategy, acting on the energy storage inverter to perform grid-connected power control of the target power grid.

[0008] The fourth aspect disclosed in the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the following steps when executed by a processor: predicting the load demand of a target power grid, wherein the target power grid includes a main power grid and a microgrid connected to the grid, and the load demand includes distributed DC loads and AC loads; reading the basic principles of the energy storage inverter, wherein the basic principles include a topological structure, a filtering circuit, and an operating mechanism, and the energy storage inverter is a single device or a parallel device group; building an energy conversion scheduling decision model based on the basic principles, making a grid scheduling decision and a bidirectional energy conversion decision for the load demand, and determining a pre-control strategy; based on the benchmark control characteristics of the central control system, optimizing the pre-control strategy and determining a target control strategy, wherein the benchmark control characteristics are determined based on the dynamic and static characteristics of the system, the distortion rate, and the harmonic amount; based on the target control strategy, acting on the energy storage inverter to perform grid-connected power control of the target power grid.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By predicting the load demand of the target power grid and combining the basic principles of the energy storage inverter, a conversion scheduling decision model is established. The model can effectively make grid scheduling decisions and bidirectional conversion decisions for load demand, thereby determining the pre-control strategy; by tuning the pre-control strategy based on the benchmark control characteristics of the central control system, the target control strategy is determined. The formulation of this control strategy is based on a comprehensive consideration of factors such as system dynamic characteristics, distortion rate and harmonic quantity, thereby improving the optimization degree and adaptability of the control strategy; based on the target control strategy, the energy storage inverter is acted on to achieve the grid-connected power control of the target power grid. Through the optimized control strategy, more stable and efficient power control is achieved, and the operation efficiency and stability of the power grid are improved. In summary, the grid-connected control method of the bidirectional energy storage inverter achieves the technical effect of improving the operation efficiency, stability and adaptability of the power grid by comprehensively considering the load demand of the power grid, the characteristics of the energy storage inverter and the control optimization strategy.

[0011] 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic flow chart of a grid-connected control method for a bidirectional energy storage inverter provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of a grid-connected control system structure of a bidirectional energy storage inverter provided in an embodiment of the present application;

[0014] Figure 3 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application.

[0015] Explanation of reference numerals: load demand acquisition module 10 , basic principle acquisition module 20 , pre-control strategy acquisition module 30 , pre-control strategy tuning module 40 , grid-connected power control module 50 . DETAILED DESCRIPTION

[0016] The embodiment of the present application provides a grid-connected control method for a bidirectional energy storage inverter, thereby solving the technical problems that traditional power grid control cannot effectively cope with diverse load demands and lacks adaptability to dynamic changes in the power grid, resulting in instability and inefficiency in power grid control.

[0017] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] like Figure 1 As shown, an embodiment of the present application provides a grid-connected control method for a bidirectional energy storage inverter, the method comprising:

[0019] Predicting the load demand of the target power grid, wherein the target power grid includes a main grid and a microgrid connected to the grid, and the load demand includes distributed DC load and AC load;

[0020] Collect historical load data and other relevant data of the target power grid, such as weather conditions, seasonal changes, etc., analyze these data, for example, through time series analysis, use historical data as input, and generate forecast results for future load demand to determine trends and patterns in load demand.

[0021] The target grid includes the main grid and microgrid. The main grid is usually a large power network that provides power to a wide range of users. The microgrid is a small power system that is connected to the main grid and is usually used to meet the energy needs of a specific area or building. Loads are divided into DC loads and AC loads. DC loads refer to loads that use DC power, such as solar photovoltaic systems, DC motors, etc.; AC loads refer to loads that use AC power, such as household electricity, industrial equipment, etc.

[0022] Read the basic principle of the energy storage inverter, which includes the topology structure, filtering circuit and operation mechanism. The energy storage inverter is a single device or a parallel device group;

[0023] Read the basic principles of energy storage inverters for subsequent control and scheduling decisions. Energy storage inverters are used to convert DC power to AC power, or AC power to DC power, to achieve energy storage and regulation. Energy storage inverters can be used as a single device, or multiple inverters can be connected in parallel to increase power capacity or achieve redundant backup.

[0024] Among them, the topological structure of the energy storage inverter refers to its circuit connection method. Common topological structures include single-phase full-bridge, three-phase full-bridge, half-bridge, etc. These structures determine the working performance, efficiency and control method of the inverter; the filter circuit is used to reduce the harmonics and electromagnetic interference at the output end of the inverter and improve the waveform quality of the output voltage to meet the power quality requirements of the power grid. The filter circuit includes capacitors, inductors, filters and other components; the operating mechanism of the energy storage inverter refers to its working mode and control strategy. According to the application requirements and the type of inverter, the operating mechanism may include constant voltage, constant current, pulse width modulation and other control methods. The choice of the operating mechanism affects the output characteristics and efficiency of the inverter.

[0025] Based on the basic principle, a conversion scheduling decision model is built to make grid scheduling decisions and bidirectional conversion decisions for the load demand, and determine the pre-control strategy;

[0026] Based on the topological structure, filtering circuit and operating mechanism of the energy storage inverter, an energy conversion scheduling decision model is built. The model adopts a hierarchical structure, including a scheduling decision layer and an energy conversion decision layer. The scheduling decision layer is used to perform power scheduling analysis based on the load demand and determine the scheduling strategy to meet the load demand and operation requirements of the power grid; the energy conversion decision layer is used for bidirectional energy conversion decisions to determine when to release energy from the energy storage device to the power grid or absorb energy from the power grid to the energy storage device. The pre-control strategy is determined by combining the power grid scheduling decision and the bidirectional energy conversion decision. This strategy can achieve effective control and optimized operation of the power grid in actual operation.

[0027] Based on the benchmark control characteristics of the central control system, the pre-control strategy is optimized to determine the target control strategy, wherein the benchmark control characteristics are determined based on the dynamic and static characteristics, distortion rate and harmonic amount of the system;

[0028] According to the characteristics and requirements of the target power grid, the parameters of the benchmark control characteristics are determined, including the dynamic and static characteristics of the system, distortion rate and harmonic content. Among them, the dynamic characteristics refer to the response speed and stability of the system when subjected to external disturbances, usually including characteristics such as voltage response time and frequency response; static characteristics refer to the performance of the system in steady-state operation, such as voltage stability, power factor, etc.; distortion rate and harmonic content are indicators to measure the power quality of the system, affecting the stability of the system and the working performance of the equipment. The distortion rate is used to describe the degree of distortion of the system waveform, and the harmonic content is used to describe the content of each harmonic in the system.

[0029] Apply the pre-control strategy to the power grid system and evaluate it based on the benchmark control characteristic parameters, such as using simulation tools or actual operation data to evaluate the performance of the pre-control strategy in meeting system requirements. Based on the evaluation results, tune the pre-control strategy, including adjusting control parameters, optimizing control algorithms, improving control strategies, etc. The goal of tuning is to enable the pre-control strategy to better meet the requirements of the benchmark control characteristic parameters. During the tuning process, determine the final target control strategy, which is the best strategy that can achieve effective management and optimization of the load demand of the power grid system under the premise of meeting the benchmark control characteristic parameters.

[0030] Based on the target control strategy, the energy storage inverter is acted on to perform grid-connected power control of the target power grid.

[0031] The determined target control strategy is converted into specific control instructions applicable to the energy storage inverter. According to the target control strategy, the control parameters of the energy storage inverter are set, including the output power, voltage and other parameters of the inverter, so that it can operate in accordance with the requirements of the target control strategy. The converted control instructions and set control parameters are applied to the energy storage inverter to implement the target control strategy. The inverter will adjust its output according to these instructions and parameters, effectively participate in the grid-connected power control of the target power grid, and realize the dispatch and regulation of power to meet the load demand and operation requirements of the power grid.

[0032] Furthermore, the energy conversion scheduling decision model includes a scheduling decision layer and an energy conversion decision layer, and performs a power grid scheduling decision on the load demand. The method further includes:

[0033] Based on the scheduling decision layer, perform power scheduling analysis based on the load demand and determine the scheduling strategy, wherein the scheduling strategy includes power scheduling targets and power scheduling amounts, and the power scheduling targets include power station-power load, energy storage equipment-power load, power station capacity-energy storage equipment, and main grid-microgrid bidirectional scheduling;

[0034] The dispatching strategy is compensated based on the real-time power generation and the energy storage status of the equipment.

[0035] Through the scheduling decision layer, an electric energy scheduling strategy is formulated according to the load demand data, wherein the electric energy scheduling strategy includes an electric energy scheduling target and a corresponding electric energy scheduling amount. The electric energy scheduling target is the amount of electric energy injection or extraction that needs to be achieved within a specific time period, and the electric energy scheduling amount is the actual amount of electric energy that needs to be adjusted.

[0036] The dispatching decision layer is the level for formulating electric energy dispatching strategies. The specific process of conducting electric energy dispatching analysis on load demand through the dispatching decision layer is as follows: the dispatching decision layer first conducts a detailed analysis of the load demand, including determining the system's power consumption pattern, load peak and valley characteristics, and load expectations in the future. Based on the load demand analysis results and system operating status, the energy utilization method is optimized, including adjusting the output power of each energy device, controlling energy storage and release, etc., to meet the load demand and maximize energy utilization efficiency. According to the load demand analysis and energy utilization optimization results, an electric energy dispatching strategy is formulated. The dispatching strategy includes determining the energy exchange method and electric energy dispatching amount between various parts in different time periods to ensure the load balance of the system and the balance of energy supply and demand.

[0037] Among them, the power dispatch targets between different parts include, power station-power load, that is, adjusting the power generation of the power station to meet specific power demand; energy storage equipment-power load, that is, adjusting the charging and discharging of the energy storage equipment to meet the power demand and provide backup power when needed; power station capacity-energy storage equipment, that is, determining the energy exchange strategy between the power station and the energy storage equipment according to the power station capacity and the charging and discharging efficiency of the energy storage equipment; main grid-microgrid bidirectional dispatch, that is, adjusting the energy exchange amount between the main grid and the microgrid to balance the system load and improve the stability of the grid.

[0038] By installing sensors or using monitoring systems, the power generation of power generation equipment, such as solar photovoltaic panels, wind turbines, etc., is monitored in real time to obtain the available energy of the current system; the energy storage status of energy storage equipment is monitored in real time, such as batteries, energy storage systems, etc., to monitor their charging and discharging status, capacity, remaining power and other information to obtain the available storage energy information in the current system.

[0039] The obtained scheduling strategy is adjusted and compensated according to the real-time monitored power generation and equipment energy storage status. Specifically, if the real-time power generation is sufficient, but the energy storage status of the energy storage device is low, the excess electric energy can be stored in the energy storage device first to improve the energy utilization efficiency of the system; if the real-time power generation is not enough to meet the load demand, but the energy storage status of the energy storage device is high, energy can be released from the energy storage device to make up for the energy shortage; in the case of insufficient energy storage status of the energy storage device, local scheduling can be considered to transfer energy from energy storage devices in other areas to meet the needs of the current area.

[0040] Through the above method, the compensation of the scheduling strategy based on the real-time power generation and equipment energy storage status can effectively optimize the energy scheduling, improve the energy utilization efficiency of the system, and ensure the stable operation of the system.

[0041] Furthermore, the energy scheduling priority is set, and the method further includes:

[0042] In the peak phase, in terms of electricity dispatch, renewable energy is higher than traditional energy; in terms of energy storage dispatch, regional storage dispatch is carried out based on energy consumption ratio;

[0043] In the valley phase, in terms of electricity dispatch, grid-connected electricity is higher than stored electricity. The grid-connected electricity dispatch includes microgrid-main grid, main grid-microgrid, and microgrid-microgrid.

[0044] During the peak period, the power generation of renewable energy (such as solar energy, wind energy, etc.) is often high, while the power generation of traditional energy (such as coal, natural gas, etc.) is relatively low. In terms of power dispatch, priority is given to using renewable energy to meet load demand in order to minimize dependence on traditional energy, that is, priority is given to using renewable energy to generate electricity to meet current load demand, including adjusting the output power of renewable energy power generation equipment and adjusting the operating mode of the generator set.

[0045] For energy storage equipment, regional storage scheduling based on energy consumption ratio means storing electric energy in areas close to load demand to reduce transmission losses and improve energy utilization efficiency. When the load demand in a certain area is low and the renewable energy production capacity near the area is high, the energy storage equipment can store excess electric energy; when the load demand increases, the energy storage equipment can release the stored electric energy from the nearest energy storage area to meet the load demand in the area and reduce dependence on remote power generation equipment.

[0046] By comprehensively considering the dimensions of electricity dispatch and energy storage dispatch, renewable energy can be effectively utilized during peak periods, and local storage and allocation of electricity can be achieved through energy storage equipment to improve the system's energy utilization efficiency and operational flexibility, while reducing dependence on traditional energy.

[0047] During the valley phase, the grid-connected power is higher than the stored power, so it is necessary to reasonably dispatch the grid-connected power to ensure that the system can fully utilize the grid-connected power during the valley period and flexibly dispatch the stored power when needed. The grid-connected power dispatch includes the power dispatch between the microgrid and the main grid, the main grid and the microgrid, and between microgrids.

[0048] Specifically, when the energy supply of the microgrid is insufficient and the main grid has surplus electricity, the remaining electricity of the main grid is preferentially dispatched to meet the needs of the microgrid; for scheduling between microgrids, if the energy supply of one microgrid is insufficient and the other microgrid has surplus electricity, the demand can be balanced through energy exchange between microgrids.

[0049] Furthermore, when conducting power dispatch between microgrids and main grids, and between microgrids, the conversion between AC and DC is involved. According to the current demand and the topology of the system, the conversion between AC and DC can be achieved through inverters or converters to ensure the flow of energy between different parts, thereby achieving flexible power dispatch and meeting the energy supply and demand relationship between different parts.

[0050] Furthermore, the method further comprises:

[0051] Determine a power grid energy conversion direction, wherein the power grid energy conversion direction includes direct current-alternating current and alternating current-direct current;

[0052] Based on the energy conversion decision layer and in combination with the energy conversion direction of the power grid, the scheduling strategy is configured and identified, and the pre-control strategy is output.

[0053] The grid energy conversion direction includes the conversion from DC to AC and from AC to DC. For DC to AC, the output of the DC power supply is converted into AC to meet the needs of the AC load; for AC to DC, the output of the AC power supply is converted into DC to supply the DC load or charging energy storage equipment. The choice of grid energy conversion direction depends on the needs of the system and the energy conversion path between different parts. By selecting the appropriate grid energy conversion direction, efficient conversion and utilization of energy can be achieved, ensuring the stable operation of the system and flexible scheduling of energy.

[0054] Based on the energy conversion decision layer and in combination with the energy conversion direction of the power grid, the parts of the system that need to perform power conversion are matched with the scheduling strategy to determine the power scheduling requirements and power conversion methods of each part. Specifically, for the parts that need to perform direct current to alternating current conversion, their power scheduling strategy and conversion method are determined; for the parts that need to perform alternating current to direct current conversion, their power scheduling strategy and conversion method are also determined. The scheduling strategies after configuration identification are integrated to form a pre-control strategy, which includes information such as the power scheduling requirements of each part, the power conversion method, and the configuration of the power conversion equipment, so as to ensure that the system can operate according to the pre-set power conversion direction and scheduling strategy under different circumstances.

[0055] Furthermore, the method of optimizing the pre-control strategy based on the benchmark control characteristics of the central control system includes:

[0056] Determining a strategy parameter constraint threshold based on the baseline control characteristic;

[0057] Determine whether the pre-control strategy satisfies the strategy parameter constraint threshold, and if so, use the pre-control strategy as the initial strategy and perform random perturbations to determine a backup strategy set;

[0058] Based on the golden section ratio, the backup strategy set is divided to determine the fitness of the segmentation strategy, and the fitness of the search strategy is determined;

[0059] Check the fitness of the search strategy and the fitness of the segmentation strategy, reduce the backup strategy set, and determine an optimal set;

[0060] Based on the primary optimization set, iterative optimization is repeatedly performed until a convergence condition is met, and the target control strategy is determined.

[0061] Based on the system's dynamic characteristics, static characteristics, distortion rate and harmonic content, the constraint thresholds of the strategy parameters are determined. These constraint thresholds are restrictions on system response time, voltage stability, distortion rate, harmonic content, etc., to ensure that the system meets certain performance requirements and quality standards during operation.

[0062] The pre-control strategy is evaluated to check whether it meets the determined strategy parameter constraint threshold, that is, whether the pre-control strategy meets the constraints such as system dynamic characteristics, static characteristics, distortion rate and harmonic quantity. If the pre-control strategy meets the requirements in these aspects, the pre-control strategy is determined as the initial strategy as the starting point for optimized scheduling.

[0063] The initial strategy is randomly perturbed to determine the backup strategy set. Random perturbation can be achieved by randomly changing the parameters of the initial strategy or adding random interference. The perturbation involves slight adjustments to the parameters, partial changes to the strategy, or adding new elements. The purpose of the perturbation is to explore a wider strategy space to find a possible better scheduling solution. The strategy obtained after random perturbation forms a backup strategy set, which contains multiple possible scheduling solutions and can be used as candidate solutions for further optimization and selection.

[0064] The golden ratio is a number approximately equal to 0.618. The search interval of the backup strategy set is divided according to the golden ratio, that is, the search interval is divided into two sub-intervals in proportion, where the ratio of the length of one sub-interval to the length of the previous sub-interval is equal to the golden ratio, and the ratio of the length of the other sub-interval to the length of the previous sub-interval is 1 minus the golden ratio.

[0065] Find the middle split point, which is located in the middle of the middle strategy set. Divide the backup strategy set into two parts according to the split point, one part contains the strategy from the initial point to the split point, and the other part contains the strategy from the split point to the end point. According to the split strategy set, calculate the fitness of the split point. The fitness can be defined according to the objective function and optimization goal of the system. It is usually an indicator to measure the quality of the strategy. Obtain the fitness of the split strategy. According to the division of the golden section ratio, determine the position of the search strategy, calculate its fitness, and obtain the fitness of the search strategy.

[0066] Compare the fitness of the search strategy with the fitness of the split strategy. If the search point is smaller than the split point, reduce the strategy set to the strategy from the search point to the end point; if the search point is larger than the split point, reduce the strategy set to the strategy from the initial point to the search point.

[0067] According to the proofreading results of the fitness of the search strategy and the fitness of the segmentation strategy, the set of alternative strategies is reduced, and the strategies that perform better in the current situation are screened out and included in the primary optimization set. The primary optimization set is the set of alternative strategies obtained in the current optimization process, which provides alternative strategy solutions for the next round of optimization scheduling process.

[0068] The strategies in the optimization set are used as the initial set for iterative optimization. In each round of iteration, the current strategy set is evaluated and adjusted to seek a better control strategy. After each round of iteration, it is evaluated whether the convergence conditions are met. The convergence conditions can be that the fitness of the strategy set tends to be stable, or that a preset number of iterations is reached. If the convergence conditions are met, the iteration is stopped and the current strategy set is used as the final target control strategy; otherwise, the next round of iterations is continued until the final target control strategy is obtained. This target control strategy is obtained after multiple rounds of iterative optimization and can be regarded as the optimal control solution under the current conditions.

[0069] Furthermore, the method comprises:

[0070] If the pre-control strategy does not meet the strategy parameter constraint threshold, perform a power grid big data search to determine multiple adjustment plans;

[0071] Based on the multiple adjustment schemes, the pre-control strategy is adjusted to determine multiple adjustment control strategies;

[0072] The multiple adjustment control strategies are used as the initial generation strategies.

[0073] If the pre-control strategy does not meet the set strategy parameter constraint threshold, the power grid big data is used to collect real-time power grid data such as power grid operation status, load conditions, energy production conditions, and working conditions of various equipment. The collected power grid data is analyzed, including a comprehensive analysis of load demand, energy production conditions, equipment status, etc., to determine the reasons why the current pre-control strategy is not met, and find the direction and goal of adjustment. According to the results of the power grid data analysis, multiple possible adjustment plans are determined, including adjustment of strategy parameters, adjustment of equipment operation mode, load scheduling, etc.

[0074] According to multiple adjustment schemes, the pre-control strategy is adjusted accordingly to form multiple adjustment control strategies. Each adjustment control strategy corresponds to an adjustment scheme, reflecting different grid dispatching and optimization ideas.

[0075] The obtained multiple adjustment control strategies are set as new initial strategies. In the further optimization process, these initial strategies will be used as the initial solution set of the optimization algorithm. The optimization algorithm continuously searches and adjusts the strategy according to the preset optimization objectives and constraints to find the optimal control solution.

[0076] Furthermore, after performing grid-connected power control of the target power grid, the method further includes:

[0077] With the execution of the target control strategy, grid-connected control monitoring is performed synchronously, and strategy control data is transmitted back;

[0078] Identify the strategic control data, perform control status evaluation, and determine a grid-connected control index, wherein the evaluation criteria include at least response fluctuation and power quality;

[0079] If the grid-connected control index does not meet the index threshold, the generalized abnormal control features are mined and traced, and the energy conversion scheduling decision model is incrementally learned.

[0080] Execute the determined target control strategy and conduct grid-connected control monitoring simultaneously, that is, conduct real-time monitoring of the operating status of the power grid, the working conditions of the equipment, and the execution effect of the control strategy. The monitoring content includes the grid frequency, voltage stability, the operating status of the energy storage inverter, etc. The real-time monitored data is transmitted back and sent to the central control system or data processing center.

[0081] The returned strategy control data is evaluated to analyze the actual operation status of the power grid and the execution effect of the control strategy. The evaluation content includes the fluctuation of power grid frequency and voltage, the working stability of equipment, the regulation capacity of power grid load, etc. According to the evaluation results, the grid connection control index is determined, which is used to evaluate the influence of the control strategy on the grid connection.

[0082] Among them, the evaluation criteria include response fluctuation and power quality. Response fluctuation is used to evaluate the response speed and stability of the control strategy to grid load fluctuations, that is, the system's adjustment ability when the load changes; power quality is used to evaluate whether the power quality after AC-DC conversion meets the standards, including voltage stability, degree of distortion of current waveform, power factor of the system, etc.

[0083] If the grid control index does not reach the preset index threshold, analyze the reasons why the grid control index does not reach the index threshold, identify abnormal control features, including grid load fluctuations, equipment failures, communication failures, etc., and further, perform source tracing analysis on the abnormal control features, such as analyzing historical data, equipment logs and other information to determine the time of occurrence, duration and scope of impact of the abnormality.

[0084] Based on the analysis results of abnormal control characteristics, incremental learning is performed on the energy conversion scheduling decision model to improve the adaptability of the model to the current power grid. Incremental learning includes updating model parameters, adjusting control logic, optimizing algorithm parameters, etc., so that the model can better adapt to the dynamic changes of the power grid.

[0085] In summary, the grid-connected control method of a bidirectional energy storage inverter provided in the embodiment of the present application has the following technical effects:

[0086] 1. By predicting the load demand of the target power grid and combining the basic principles of energy storage inverters, a conversion scheduling decision model is established. This model can effectively make grid scheduling decisions and bidirectional conversion decisions based on load demand, thereby determining the pre-control strategy;

[0087] 2. Based on the benchmark control characteristics of the central control system, the pre-control strategy is tuned and the target control strategy is determined. The formulation of this control strategy is based on comprehensive consideration of factors such as system dynamic characteristics, distortion rate and harmonic quantity, thereby improving the optimization degree and adaptability of the control strategy;

[0088] 3. Based on the target control strategy, the energy storage inverter is acted on to realize the grid-connected power control of the target power grid. Through the optimized control strategy, more stable and efficient power control is achieved, which improves the operation efficiency and stability of the power grid.

[0089] In summary, the grid-connected control method of the bidirectional energy storage inverter achieves the technical effect of improving the operation efficiency, stability and adaptability of the power grid by comprehensively considering the power grid load demand, energy storage inverter characteristics and control optimization strategy.

[0090] Based on the same inventive concept as the grid-connected control method of a bidirectional energy storage inverter in the aforementioned embodiment, Figure 2 As shown, the present application provides a grid-connected control system of a bidirectional energy storage inverter, the system comprising:

[0091] A load demand acquisition module 10, wherein the load demand acquisition module 10 is used to predict the load demand of a target power grid, wherein the target power grid includes a main grid and a microgrid connected to the grid, and the load demand includes a distributed DC load and an AC load;

[0092] A basic principle acquisition module 20, the basic principle acquisition module 20 is used to read the basic principle of the energy storage inverter, the basic principle includes the topology structure, the filter circuit and the operation mechanism, the energy storage inverter is a single device or a parallel device group;

[0093] A pre-control strategy acquisition module 30, the pre-control strategy acquisition module 30 is used to build a conversion scheduling decision model based on the basic principle, perform power grid scheduling decision and bidirectional conversion decision on the load demand, and determine the pre-control strategy;

[0094] A pre-control strategy tuning module 40, the pre-control strategy tuning module 40 is used to tune the pre-control strategy based on the benchmark control characteristics of the central control system to determine the target control strategy, the benchmark control characteristics are determined based on the dynamic and static characteristics of the system, the distortion rate and the harmonic amount;

[0095] The grid-connected power control module 50 is used to act on the energy storage inverter based on the target control strategy to perform grid-connected power control of the target power grid.

[0096] Furthermore, the system further includes a scheduling strategy compensation module to perform the following operation steps:

[0097] Based on the scheduling decision layer, perform power scheduling analysis based on the load demand and determine the scheduling strategy, wherein the scheduling strategy includes power scheduling targets and power scheduling amounts, and the power scheduling targets include power station-power load, energy storage equipment-power load, power station capacity-energy storage equipment, and main grid-microgrid bidirectional scheduling;

[0098] The dispatching strategy is compensated based on the real-time power generation and the energy storage status of the equipment.

[0099] Furthermore, the system further includes an energy scheduling priority setting module to perform the following operation steps:

[0100] In the peak phase, in terms of electricity dispatch, renewable energy is higher than traditional energy; in terms of energy storage dispatch, regional storage dispatch is carried out based on energy consumption ratio;

[0101] In the valley phase, in terms of electricity dispatch, grid-connected electricity is higher than stored electricity. The grid-connected electricity dispatch includes microgrid-main grid, main grid-microgrid, and microgrid-microgrid.

[0102] Furthermore, the system further includes a pre-control strategy acquisition module to perform the following operation steps:

[0103] Determine a power grid energy conversion direction, wherein the power grid energy conversion direction includes direct current-alternating current and alternating current-direct current;

[0104] Based on the energy conversion decision layer and in combination with the energy conversion direction of the power grid, the scheduling strategy is configured and identified, and the pre-control strategy is output.

[0105] Furthermore, the system further includes a target control strategy determination module to perform the following operation steps:

[0106] Determining a strategy parameter constraint threshold based on the baseline control characteristic;

[0107] Determine whether the pre-control strategy satisfies the strategy parameter constraint threshold, and if so, use the pre-control strategy as the initial strategy and perform random perturbations to determine a backup strategy set;

[0108] Based on the golden section ratio, the backup strategy set is divided to determine the fitness of the segmentation strategy, and the fitness of the search strategy is determined;

[0109] Check the fitness of the search strategy and the fitness of the segmentation strategy, reduce the backup strategy set, and determine an optimal set;

[0110] Based on the primary optimization set, iterative optimization is repeatedly performed until a convergence condition is met, and the target control strategy is determined.

[0111] Furthermore, the system further includes a primary strategy acquisition module to perform the following operation steps:

[0112] If the pre-control strategy does not meet the strategy parameter constraint threshold, perform a power grid big data search to determine multiple adjustment plans;

[0113] Based on the multiple adjustment schemes, the pre-control strategy is adjusted to determine multiple adjustment control strategies;

[0114] The multiple adjustment control strategies are used as the initial generation strategies.

[0115] Furthermore, the system further includes an incremental learning module to perform the following steps:

[0116] With the execution of the target control strategy, grid-connected control monitoring is performed synchronously, and strategy control data is transmitted back;

[0117] Identify the strategic control data, perform control status evaluation, and determine a grid-connected control index, wherein the evaluation criteria include at least response fluctuation and power quality;

[0118] If the grid-connected control index does not meet the index threshold, the generalized abnormal control features are mined and traced, and the energy conversion scheduling decision model is incrementally learned.

[0119] Through the above-mentioned detailed description of the grid-connected control method of a bidirectional energy storage inverter in this specification, those skilled in the art can clearly know the grid-connected control system of a bidirectional energy storage inverter in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0120] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store news data and data such as time attenuation factors. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a grid-connected control method for a bidirectional energy storage inverter.

[0121] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0122] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: predicting the load demand of a target power grid, wherein the target power grid includes a main power grid and a microgrid connected to the grid, and the load demand includes distributed DC load and AC load; reading the basic principle of an energy storage inverter, wherein the basic principle includes a topological structure, a filtering circuit and an operating mechanism, and the energy storage inverter is a single device or a parallel device group; building an energy conversion scheduling decision model based on the basic principle, making a grid scheduling decision and a bidirectional energy conversion decision for the load demand, and determining a pre-control strategy; based on the benchmark control characteristics of a central control system, optimizing the pre-control strategy and determining a target control strategy, wherein the benchmark control characteristics are determined based on the dynamic and static characteristics of the system, the distortion rate and the harmonic amount; based on the target control strategy, acting on the energy storage inverter to perform grid-connected power control of the target power grid.

[0123] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: predicting the load demand of a target power grid, wherein the target power grid includes a main power grid and a microgrid connected to the grid, and the load demand includes distributed DC load and AC load; reading the basic principle of the energy storage inverter, wherein the basic principle includes a topological structure, a filtering circuit and an operating mechanism, and the energy storage inverter is a single device or a parallel device group; building an energy conversion scheduling decision model based on the basic principle, making a grid scheduling decision and a bidirectional energy conversion decision for the load demand, and determining a pre-control strategy; based on the benchmark control characteristics of the central control system, tuning the pre-control strategy and determining a target control strategy, wherein the benchmark control characteristics are determined based on the dynamic and static characteristics of the system, the distortion rate and the harmonic amount; based on the target control strategy, acting on the energy storage inverter to perform grid-connected power control of the target power grid.

[0124] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0125] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A grid-connected control method for a bidirectional energy storage inverter, characterized in that: The method comprises: Predicting the load demand of the target power grid, wherein the target power grid includes a main grid and a microgrid connected to the grid, and the load demand includes distributed DC load and AC load; Read the basic principle of the energy storage inverter, which includes the topology structure, filtering circuit and operation mechanism. The energy storage inverter is a single device or a parallel device group; Based on the basic principle, a conversion scheduling decision model is built to make grid scheduling decisions and bidirectional conversion decisions for the load demand, and determine the pre-control strategy; Based on the benchmark control characteristics of the central control system, the pre-control strategy is tuned to determine the target control strategy, wherein the benchmark control characteristics are determined based on the dynamic and static characteristics, distortion rate and harmonic amount of the system; Based on the target control strategy, the energy storage inverter is acted on to perform grid-connected power control of the target power grid; The energy conversion scheduling decision model includes a scheduling decision layer and an energy conversion decision layer, and performs power grid scheduling decision on the load demand, including: Based on the scheduling decision layer, perform power scheduling analysis based on the load demand and determine the scheduling strategy, wherein the scheduling strategy includes power scheduling targets and power scheduling amounts, and the power scheduling targets include power station-power load, energy storage equipment-power load, power station capacity-energy storage equipment, and main grid-microgrid bidirectional scheduling; Compensating the scheduling strategy based on real-time power generation and equipment energy storage status; The step of optimizing the pre-control strategy based on the benchmark control characteristics of the central control system includes: Determining a strategy parameter constraint threshold based on the baseline control characteristic; Determine whether the pre-control strategy satisfies the strategy parameter constraint threshold, and if so, use the pre-control strategy as the initial strategy and perform random perturbations to determine a backup strategy set; Based on the golden section ratio, the backup strategy set is divided to determine the fitness of the segmentation strategy, and the fitness of the search strategy is determined; Check the fitness of the search strategy and the fitness of the segmentation strategy, reduce the backup strategy set, and determine an optimal set; Based on the primary optimization set, iterative optimization is repeatedly performed until a convergence condition is met, and the target control strategy is determined.

2. The method according to claim 1, characterized in that Setting energy scheduling priorities, the method further includes: In the peak phase, in terms of electricity dispatch, renewable energy is higher than traditional energy; in terms of energy storage dispatch, regional storage dispatch is carried out based on energy consumption ratio; In the valley phase, in terms of electricity dispatch, grid-connected electricity is higher than stored electricity. The grid-connected electricity dispatch includes microgrid-main grid, main grid-microgrid, and microgrid-microgrid.

3. The method according to claim 1, characterized in that The method further comprises: Determine a power grid energy conversion direction, wherein the power grid energy conversion direction includes direct current-alternating current and alternating current-direct current; Based on the energy conversion decision layer and in combination with the energy conversion direction of the power grid, the scheduling strategy is configured and identified, and the pre-control strategy is output.

4. The method according to claim 1, characterized in that The method comprises: If the pre-control strategy does not meet the strategy parameter constraint threshold, perform a power grid big data search to determine multiple adjustment plans; Based on the multiple adjustment schemes, the pre-control strategy is adjusted to determine multiple adjustment control strategies; The multiple adjustment control strategies are used as the initial generation strategies.

5. The method according to claim 1, characterized in that After performing grid-connected power control of the target power grid, the method further includes: With the execution of the target control strategy, grid-connected control monitoring is performed synchronously, and strategy control data is transmitted back; Identify the strategic control data, perform control status evaluation, and determine a grid-connected control index, wherein the evaluation criteria include at least response fluctuation and power quality; If the grid-connected control index does not meet the index threshold, the generalized abnormal control features are mined and traced, and the energy conversion scheduling decision model is incrementally learned.

6. A grid-connected control system for a bidirectional energy storage inverter, characterized in that: A grid-connected control method for a bidirectional energy storage inverter for implementing any one of claims 1 to 5, the system comprising: A load demand acquisition module, the load demand acquisition module is used to predict the load demand of the target power grid, wherein the target power grid includes a main grid and a microgrid connected to the grid, and the load demand includes a distributed DC load and an AC load; A basic principle acquisition module, which is used to read the basic principle of the energy storage inverter, wherein the basic principle includes a topological structure, a filtering circuit and an operating mechanism, and the energy storage inverter is a single device or a parallel device group; A pre-control strategy acquisition module, the pre-control strategy acquisition module is used to build a conversion scheduling decision model based on the basic principle, make a power grid scheduling decision and a bidirectional conversion decision for the load demand, and determine the pre-control strategy; A pre-control strategy tuning module, the pre-control strategy tuning module is used to tune the pre-control strategy based on the benchmark control characteristics of the central control system to determine the target control strategy, the benchmark control characteristics are determined based on the dynamic and static characteristics of the system, the distortion rate and the harmonic amount; A grid-connected power control module is used to act on the energy storage inverter based on the target control strategy to perform grid-connected power control of the target power grid.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of a grid-connected control method for a bidirectional energy storage inverter according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a grid-connected control method for a bidirectional energy storage inverter according to any one of claims 1 to 5 are implemented.

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