Electric energy interaction method and system of bidirectional photovoltaic energy storage inverter

Abstract

The invention relates to an electric energy interaction method and system of a bidirectional photovoltaic energy storage inverter, relates to the technical field of electric energy storage, and aims to solve the problems that in the prior art, photovoltaic output prediction precision is insufficient, a power supply priority strategy is rigid, and environment and power grid state changes cannot be dynamically adapted. The method comprises the steps of collecting photovoltaic irradiation intensity, energy storage battery monomer voltage, power grid frequency and an environment air pressure value, correcting photovoltaic irradiation intensity data based on the environment air pressure value, generating actual irradiation intensity, determining a battery charge state based on the energy storage battery monomer voltage, and determining the power grid frequency based on the actual irradiation intensity, historical irradiation intensity data and the battery charge state. The method comprises the steps of predicting a photovoltaic output volatility and a load demand, generating a dynamic weight matrix, comparing the dynamic weight matrix with a preset strategy, generating a power supply priority instruction, and controlling bidirectional electric energy flow based on the power supply priority instruction and a power grid frequency.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, specifically to an energy interaction method and system for a bidirectional photovoltaic energy storage inverter. Background Technology

[0002] With the development of photovoltaic power generation technology, bidirectional photovoltaic energy storage inverters, as core equipment connecting photovoltaic modules, energy storage batteries, AC power grids, and loads, directly affect the stability and economy of the system due to their energy interaction efficiency. In existing technologies, photovoltaic output prediction often ignores the impact of environmental factors on irradiance, resulting in insufficient prediction accuracy. Simultaneously, power supply priority strategies are mostly fixed, unable to dynamically adjust according to photovoltaic output fluctuations, battery status, and grid frequency, easily leading to energy waste or power outages.

[0003] For example, in traditional methods, photovoltaic irradiance intensity is directly based on raw data collected without considering the differences in atmospheric transparency caused by changes in air pressure, which may overestimate or underestimate the actual power generation capacity; the power supply strategy is simply executed according to the fixed priority of grid power or solar power, and when the grid frequency is abnormal or the battery state of charge is too low, the power supply mode cannot be switched in time, affecting the reliability of the system.

[0004] Therefore, there is an urgent need for a power interaction method that can combine environmental factors to modify and dynamically optimize power supply strategies in order to improve the adaptability and efficiency of photovoltaic energy storage systems. Summary of the Invention

[0005] This invention provides a method and system for power interaction in a bidirectional photovoltaic energy storage inverter, solving the problems of insufficient photovoltaic output prediction accuracy, rigid power supply priority strategies, and inability to dynamically adapt to changes in the environment and grid conditions in existing technologies. The technical solution adopted in this application is as follows:

[0006] Firstly, a power interaction method for a bidirectional photovoltaic energy storage inverter can be specifically implemented as follows: collecting photovoltaic irradiance, energy storage battery cell voltage, grid frequency, and ambient air pressure; correcting the photovoltaic irradiance data based on the ambient air pressure to generate the actual irradiance; determining the battery state of charge based on the energy storage battery cell voltage; predicting the photovoltaic output fluctuation rate and load demand based on the actual irradiance, historical irradiance data, and battery state of charge; generating a dynamic weight matrix; comparing the dynamic weight matrix with a preset strategy to generate a power supply priority command; and controlling the bidirectional power flow based on the power supply priority command and grid frequency.

[0007] By correcting the photovoltaic irradiance intensity using ambient air pressure values, the accuracy of actual irradiance data is improved, laying the foundation for photovoltaic power output prediction. A dynamic weight matrix is ​​generated by combining actual irradiance, historical data, and battery state of charge to achieve dynamic adjustment of power supply priority, adapting to photovoltaic power output fluctuations and load demand. Power interaction is controlled according to the grid frequency status to ensure the stability of the system during grid anomalies, improve energy utilization efficiency, extend battery life, and ensure reliable power supply to the load.

[0008] Another method involves correcting photovoltaic (PV) irradiance data based on ambient air pressure to generate the actual irradiance. Specifically, this can be achieved by: determining the atmospheric transparency coefficient based on the ambient air pressure; correcting the PV irradiance data based on this coefficient to obtain corrected PV irradiance data; and then compensating the corrected PV irradiance data with the ambient air pressure to obtain the actual irradiance. By determining the atmospheric transparency coefficient using ambient air pressure and specifically correcting the PV irradiance data, the accuracy of the basic data is improved. Further compensation using air pressure eliminates the influence of air pressure on light propagation, making the actual irradiance more closely reflect the real environment. This provides precise data support for subsequent PV output prediction, dynamic weight allocation, and power supply control, enhancing the system's adaptability to environmental changes and ensuring efficient and stable power interaction.

[0009] Another implementation method involves pre-setting a strategy that includes at least one priority mode among grid priority mode, solar priority mode, SBU priority mode, and SUF priority mode. Each priority mode corresponds to the priority order of power supply from at least one photovoltaic module, energy storage battery, and AC grid to the load. By pre-setting multiple power supply priority modes, the power supply sequence requirements of photovoltaic modules, energy storage batteries, and AC grid under different scenarios are covered. This not only satisfies the stable power supply requirements of grid priority but also achieves efficient utilization of solar energy priority. Furthermore, it can balance charging and power supply through SBU, SUF, and other modes, flexibly adapting to the system's operating status under different energy conditions, improving the rationality of energy allocation, and ensuring the continuity and stability of power supply to the load.

[0010] Another implementation method, when the power supply priority command is in solar priority mode, the control logic can be specifically implemented as follows: photovoltaic modules are given priority in supplying power to the load; if the output of the photovoltaic modules is insufficient, the energy storage battery supplements the power supply; and the system switches to AC grid power supply only when the photovoltaic modules are unavailable and the energy storage battery voltage drops to a preset low-level threshold. In solar priority mode, photovoltaic modules are used first to maximize the utilization rate of clean energy; when the photovoltaic output is insufficient, the energy storage battery supplements the power supply to ensure continuous power supply to the load; and the system switches to grid power only when the photovoltaic modules are unavailable and the battery voltage reaches the threshold, reducing dependence on grid power. This logic improves the efficiency of photovoltaic energy utilization, avoids frequent switching through battery transition, and enhances system stability and economy by protecting the battery with a preset low-level threshold.

[0011] Another approach involves predicting photovoltaic (PV) output volatility and load demand based on historical irradiance data and battery state of charge (SOC), generating a dynamic weight matrix. Specifically, this can be achieved by: acquiring historical irradiance data; determining the rate of change of actual irradiance per unit time based on the actual irradiance; determining the battery charge / discharge capacity coefficient based on the rate of change of irradiance per unit time, battery SOC, and preset battery type parameters; analyzing the correlation between the rate of change of irradiance and battery SOC; predicting the PV output volatility based on the correlation analysis results; matching the predicted PV output volatility with historical load demand data in a historical database to generate a PV-load supply-demand deviation value; and generating a dynamic weight matrix based on this deviation value. By combining historical and actual irradiance data to determine the rate of change, and correlating it with battery SOC and type parameters, the PV output volatility is accurately predicted. The supply-demand deviation value generated by matching historical load data is used to construct the dynamic weight matrix, ensuring that the weight allocation aligns with actual energy supply and demand. This provides a scientific basis for power supply priority commands, improves the system's ability to predict energy fluctuations and its load adaptability, and ensures efficient and coordinated power interaction.

[0012] Another approach involves generating a dynamic weight matrix based on the photovoltaic-load supply-demand deviation. Specifically, this can be achieved by determining photovoltaic (PV), energy storage (ESS), and grid weight factors based on the PV-load supply-demand deviation. These factors are then normalized to generate a dynamic weight matrix. By determining the weight factors for PV, ESS, and the grid through the PV-load supply-demand deviation, and then normalizing them to generate the dynamic weight matrix, the weight allocation of each energy source accurately matches actual supply and demand. This approach not only prioritizes PV utilization but also flexibly adjusts the participation of ESS and the grid based on the deviation, providing a quantitative basis for power supply priority decisions, improving the system's response flexibility to energy fluctuations, and ensuring efficient and rational power allocation.

[0013] Another implementation method compares the dynamic weight matrix with a preset strategy to generate power supply priority commands. Specifically, this involves comparing the photovoltaic (PV), energy storage (ESD), and grid weight factors in the dynamic weight matrix with the PV, ESD, and grid power supply thresholds in the preset strategy, respectively. Based on the comparison results, the corresponding power supply priority commands are triggered. By comparing each factor in the dynamic weight matrix with the preset strategy thresholds, the power supply compatibility of PV, ESD, and the grid is quantitatively assessed, and the corresponding priority commands are accurately triggered. This ensures that strategy selection aligns with real-time energy conditions while avoiding subjective decision-making biases, making power supply mode switching more scientific and efficient, improving the system's response speed to complex operating conditions, ensuring dynamic matching of energy allocation and load demand, and enhancing operational stability.

[0014] Another implementation method, based on power supply priority commands and grid frequency, controls bidirectional power flow. Specifically, it involves detecting the grid frequency state; when the grid frequency state is in the first state, a first bidirectional power flow is initiated; when the grid frequency state is in the second state, a first bidirectional power flow is initiated based on power supply priority commands. The grid distinguishes between the first and second states by detecting the grid frequency state, and executes bidirectional power flow control accordingly. When the grid frequency is normal, priority commands are executed; when abnormal, adjustments are made flexibly. This ensures efficient energy distribution under normal operating conditions and guarantees power supply safety when the grid is unstable, preventing abnormal grid conditions from affecting system operation, improving adaptability to grid fluctuations, and enhancing the continuity and reliability of load power supply.

[0015] Secondly, a power interaction system for a bidirectional photovoltaic energy storage inverter is provided, which can be specifically implemented as follows: a data acquisition module for acquiring photovoltaic irradiance, energy storage battery cell voltage, grid frequency, and ambient air pressure; a correction module for correcting photovoltaic irradiance data based on ambient air pressure to generate actual irradiance; a prediction module for determining battery state of charge based on energy storage battery cell voltage, predicting photovoltaic output fluctuation rate and load demand based on actual irradiance, historical irradiance data, and battery state of charge, and generating a dynamic weight matrix; an instruction generation module for comparing the dynamic weight matrix with a preset strategy to generate power supply priority instructions; and a control module for controlling bidirectional power flow based on power supply priority instructions and grid frequency.

[0016] It should be noted that the technical effects of any implementation method in the second aspect can be found in the technical effects of the corresponding implementation method in the first aspect, and will not be repeated here.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0019] Figure 1 This is a schematic diagram of a bidirectional photovoltaic energy storage inverter and a power interaction method and system of a bidirectional photovoltaic energy storage inverter, according to an exemplary embodiment.

[0020] Figure 2 This is a flowchart illustrating a power interaction method for a bidirectional photovoltaic energy storage inverter according to an exemplary embodiment;

[0021] Figure 3This is a schematic diagram of the power interaction system of a bidirectional photovoltaic energy storage inverter according to an exemplary embodiment. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0023] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with some aspects of this application as detailed in the appended claims.

[0024] In the embodiments of this application, the words "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0025] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0026] This application provides a method and system for bidirectional photovoltaic energy storage inverters to achieve bidirectional energy interaction between photovoltaic modules, energy storage batteries, AC grids and loads in hybrid solar inverters / chargers.

[0027] Specifically, this application applies to scenarios where photovoltaic modules are connected via a photovoltaic input interface, energy storage batteries are connected via a battery input interface, and AC grid and loads are connected via AC input / output interfaces. It can dynamically adjust power supply priorities based on collected data such as photovoltaic irradiance, individual energy storage battery voltage, grid frequency, and ambient air pressure. This allows for bidirectional power flow processes, including photovoltaic modules supplying power to the load or energy storage batteries, energy storage batteries supplying power to the load, and the AC grid supplying power to the load or energy storage batteries. This ensures that the system can efficiently and stably supply power to the load under different operating conditions, and rationally manages the charging and discharging of the energy storage batteries. Simultaneously, it adapts to grid frequency conditions to ensure power safety and grid stability, meeting the practical application requirements of hybrid solar inverters / chargers in photovoltaic energy storage systems.

[0028] like Figure 1 As shown in the figure, this application provides an architecture diagram of a bidirectional photovoltaic energy storage inverter, which includes: a control unit, a data acquisition unit, and a display and operation unit.

[0029] The control unit is integrated into the inverter housing and is connected to the photovoltaic input interface, battery input interface, AC input / output interface and display panel. It can receive data such as photovoltaic irradiance, energy storage battery cell voltage, grid frequency and ambient air pressure collected by each interface, and perform operations such as data correction, prediction, weight matrix generation, command generation and power control to realize bidirectional power interaction between photovoltaic module, energy storage battery, AC grid and load.

[0030] The data acquisition unit is used to collect data such as photovoltaic irradiance, individual cell voltage of energy storage batteries, grid frequency and ambient air pressure, providing basic information for the calculation of the control unit.

[0031] The display and operation unit includes an LCD screen, status indicator lights, charging indicator lights, fault indicator lights, and function buttons. It can display the operating status and parameter information, and can also perform setting operations through the function buttons, such as configuring power supply priority mode.

[0032] A bidirectional photovoltaic energy storage inverter is a power conversion and management device that integrates control, data acquisition, display and operation units. It can be connected to photovoltaic input, battery input, and AC input / output interfaces, and can collect data such as photovoltaic irradiance and battery voltage. It realizes bidirectional power interaction between photovoltaic, energy storage, grid and load, and has data processing, power control and status display setting functions.

[0033] like Figure 2 As shown, this application provides a flowchart of a power interaction method for a bidirectional photovoltaic energy storage inverter. This method is executed by the control unit of the bidirectional photovoltaic energy storage inverter, and the specific steps are as follows:

[0034] S201: Collects photovoltaic irradiance, energy storage battery cell voltage, grid frequency, and ambient air pressure.

[0035] Photovoltaic irradiance refers to the solar radiation energy projected onto a unit area per unit time, reflecting the intensity of solar energy resources that photovoltaic modules can utilize.

[0036] The voltage of a single cell in an energy storage battery pack refers to the terminal voltage of a single cell in the pack, which is used to determine the state of charge and health status of the energy storage battery.

[0037] Power grid frequency refers to the alternating frequency of voltage or current in an AC power grid, and it is an important indicator for measuring the stability of power grid operation.

[0038] Ambient air pressure refers to the atmospheric pressure of the environment in which the inverter is located. Its changes can affect the propagation of solar radiation, and thus affect the accuracy of photovoltaic irradiance.

[0039] Specifically, the collection of photovoltaic irradiance intensity, energy storage battery cell voltage, grid frequency, and ambient air pressure can be achieved as follows: photovoltaic irradiance intensity is collected through an irradiance sensor installed at the photovoltaic input interface; the voltage of the energy storage battery cell is collected through a voltage detection circuit connected to the battery input interface; the grid frequency is collected through a frequency detection module at the AC input / output interface; and the ambient air pressure is collected through an air pressure sensor installed outside the inverter housing. All collected data are transmitted to the control unit in real time.

[0040] S202: Based on the ambient air pressure value, correct the photovoltaic irradiance data to generate the actual irradiance.

[0041] In some embodiments, the atmospheric transparency coefficient is determined based on the ambient air pressure value, the photovoltaic irradiance data is corrected based on the atmospheric transparency coefficient to obtain the corrected photovoltaic irradiance data, and the corrected photovoltaic irradiance data is compensated based on the ambient air pressure value to obtain the actual irradiance.

[0042] Specifically, the atmospheric transparency coefficient is determined based on the ambient air pressure value, as shown in the following formula:

[0043]

[0044] Where k is the atmospheric transparency coefficient, P is the collected ambient air pressure, P0 is the standard atmospheric pressure, and n is an empirical constant, which is determined based on regional climate characteristics and is generally between 0.2 and 0.3. The atmospheric transparency coefficient reflects the degree of light absorption and attenuation by the air; the lower the air pressure, the higher the transparency.

[0045] Specifically, in arid plateau regions: low air pressure, thin air, low humidity, few aerosols, and weak light attenuation, the n value is taken as 0.20 to 0.23.

[0046] Inland semi-arid region: moderate air pressure, moderate humidity, more dust, moderate light attenuation, n value is 0.23 to 0.25.

[0047] Coastal humid areas: air pressure is close to standard atmospheric pressure, humidity is high, water vapor and aerosols are dense, light attenuation is strong, and the n value is 0.25 to 0.28.

[0048] Tropical rainforest area: high humidity, frequent clouds and fog, significant light scattering and absorption, n value ranges from 0.28 to 0.30.

[0049] Specifically, the corrected photovoltaic irradiance data is obtained by correcting the photovoltaic irradiance data based on the atmospheric transparency coefficient:

[0050] G1 = G0 × k

[0051] The original photovoltaic irradiance intensity G0 is corrected to G1, where k is the aforementioned atmospheric transparency coefficient. This step can eliminate the irradiance intensity attenuation caused by changes in air pressure, making the photovoltaic data closer to the actual usable energy.

[0052] Specifically, the actual irradiance is obtained by compensating the corrected photovoltaic irradiance data based on the ambient air pressure value:

[0053] G actual =G1×[1+α·(P-P0)]

[0054] Where: α is the pressure compensation coefficient, typically taken as 1×10⁻⁶. -4 ~5×10 -4 P and P0 are the same as before.

[0055] S203: Determine the state of charge of the battery based on the voltage of the individual energy storage battery cells, and predict the photovoltaic power output fluctuation rate and load demand based on the actual irradiance, historical irradiance data and battery state of charge, and generate a dynamic weight matrix.

[0056] Specifically, the state of charge (SOC) of the energy storage battery is determined based on the individual cell voltage: by collecting the individual cell voltage V and combining it with the full-charge voltage V0. full and discharge voltage V empty Calculate the battery state of charge:

[0057]

[0058] In some embodiments, based on historical irradiance data and battery state of charge, the photovoltaic output volatility and load demand are predicted, and a dynamic weight matrix is ​​generated. Specifically, this can be achieved by: acquiring historical irradiance data, determining the actual irradiance change rate per unit time based on the actual irradiance, determining the battery charge / discharge capacity coefficient based on the irradiance change rate per unit time, battery state of charge, and preset battery type parameters, analyzing the correlation between the irradiance change rate and battery state of charge, predicting the photovoltaic output volatility based on the correlation analysis results, matching historical load demand data in the historical database based on the predicted photovoltaic output volatility, generating a photovoltaic-load supply-demand deviation value, and generating a dynamic weight matrix based on the photovoltaic-load supply-demand deviation value.

[0059] This involves acquiring historical irradiance data and combining it with actual irradiance to determine the rate of change of actual irradiance per unit time.

[0060] By extracting the photovoltaic irradiance G for a preset period from the historical database history Based on the current actual irradiance G actual The rate of change of irradiance intensity per unit time was obtained:

[0061]

[0062] Where Δt represents the time interval. This indicator is used to describe the real-time fluctuation trend of photovoltaic irradiance.

[0063] The battery charge / discharge capacity coefficient is determined based on the rate of change of irradiance per unit time, the battery state of charge (SOC), and preset battery type parameters. This is combined with the rate of change of irradiance per unit time (λ), the battery SOC, and the battery type parameter C. rate The charge / discharge capacity coefficient K is calculated as follows:

[0064] K = α1λ + βSOC + γC rate +δT+εD

[0065] Where K ranges from 0 to 1, a larger value indicates a stronger adaptability of the battery to charging and discharging; T is the temperature of the photovoltaic module; and D is the degradation rate.

[0066] α1,β,γ,δ,ε are weighting coefficients that satisfy α1+β+γ+δ+ε=1. The parameters in the above formula have all been standardized.

[0067] Among them, the degradation rate refers to the power degradation rate of photovoltaic modules. It represents the degree to which the output power of photovoltaic modules gradually decreases as the usage time increases, and is one of the key indicators for measuring the long-term performance stability of photovoltaic modules.

[0068] A common calculation method is to measure the initial power P0 of the photovoltaic module under standard test conditions at the beginning of its use, and then measure its power Pt again under the same standard test conditions after a certain period of operation. The degradation rate D is then calculated according to the following formula:

[0069]

[0070] Specifically, analyzing the correlation between the rate of change of irradiation intensity and the state of charge of the battery includes: calculating the correlation between the rate of change of irradiation intensity and the state of charge of the battery through Pearson correlation coefficient or regression analysis, determining whether the two are positively correlated, negatively correlated or not significantly correlated, and establishing a mathematical correlation model between the two through scatter plot or trend line fitting.

[0071] Based on the correlation analysis results, the fluctuation rate of photovoltaic output is predicted. Using the above correlation model, combined with the current rate of change of irradiance, the fluctuation range of photovoltaic output in the future within a preset period is predicted. It is usually represented by the fluctuation rate σ, that is, σ=k×|λ|×(1-SOC / 100), where k is a proportionality coefficient, reflecting the degree of influence of correlation on fluctuation. The larger the value of σ, the more severe the fluctuation of photovoltaic output.

[0072] Based on the predicted photovoltaic output volatility, historical load demand data is matched with historical database data to generate a photovoltaic-load supply-demand deviation value:

[0073] Match the load data L corresponding to the predicted volatility σ in the historical database. history Combined with predicted photovoltaic output:

[0074] P pv =G actual ·S·η

[0075] Calculate the supply-demand deviation:

[0076] Δ=P pv -L history

[0077] Δ>0 indicates excess photovoltaic output, Δ<0 indicates insufficient photovoltaic output, S represents the effective area of ​​the photovoltaic panel, that is, the surface area of ​​the photovoltaic module used to receive solar radiation, in square meters, η represents the dimensionless photovoltaic conversion efficiency, which refers to the efficiency of the photovoltaic module in converting the received solar radiation energy into electrical energy, G actual The unit is W / m², P pv The units are W and L. history It matches the load data corresponding to the predicted volatility σ in the historical database. Essentially, it represents the load's electrical power consumption at a specific moment, equivalent to instantaneous power, L. history The unit is W.

[0078] A dynamic weight matrix is ​​generated based on the photovoltaic-load supply-demand deviation value, including: generating weight factors for photovoltaic, energy storage, and power grid based on the supply-demand deviation Δ.

[0079] W pv W bat W grid When Δ>0, W pv and W bat Increase (prioritize energy storage), W grid Decrease; when Δ<0, W pv Decrease, W bat As |Δ| increases (preferential discharge), W grid Increase. This is achieved through normalization (W) pv +W bat +W grid =1), thus obtaining the dynamic weight matrix [[W pv W bat W grid This provides a quantitative basis for power supply priority decisions.

[0080] S204: Compare the dynamic weight matrix with the preset strategy to generate power supply priority instructions.

[0081] In some embodiments, the preset strategy includes at least one priority mode among grid priority mode, solar priority mode, SBU priority mode and SUF priority mode; the priority mode corresponds to the priority order of at least one photovoltaic module, energy storage battery and AC grid supplying power to the load.

[0082] When the power supply priority instruction is in solar priority mode, the control logic includes: prioritizing power supply from the photovoltaic module to the load; if the output of the photovoltaic module is insufficient, supplementing the power supply from the energy storage battery; and switching to AC grid power supply only when the photovoltaic module is unavailable and the energy storage battery voltage drops to a preset low-level threshold.

[0083] In some embodiments, the photovoltaic weight factor, energy storage weight factor and grid weight factor in the dynamic weight matrix are compared with the photovoltaic power supply threshold, energy storage power supply threshold and grid power supply threshold in the preset strategy, respectively, and the corresponding power supply priority instruction is triggered based on the comparison result.

[0084] Specifically, the corresponding power supply priority command is triggered based on the comparison result, and the specific rules are as follows:

[0085] When the photovoltaic weight factor is greater than or equal to the photovoltaic power supply threshold, the "photovoltaic priority power supply" command is triggered.

[0086] When the energy storage weight factor is greater than or equal to the energy storage power supply threshold and the photovoltaic weight factor is less than the photovoltaic power supply threshold, the "energy storage priority power supply" command is triggered.

[0087] When the grid weight factor is greater than or equal to the grid power supply threshold (e.g., the grid frequency deviation exceeds ±0.5Hz), the "grid priority power supply" command is triggered.

[0088] When all weight factors are below their corresponding thresholds, a "mixed power supply" command is triggered, and electrical energy is allocated according to the weight ratio.

[0089] S205: Controls bidirectional power flow based on power supply priority commands and grid frequency.

[0090] In some embodiments, the state of the power grid frequency is detected, and a first bidirectional power flow is performed when the power grid frequency state is a first state, and a first bidirectional power flow is performed based on a power supply priority command when the power grid frequency state is a second state.

[0091] The first state refers to the grid frequency being within the normal operating range, such as 50Hz±0.5Hz or 60Hz±0.5Hz, depending on the local grid standard. At this time, the grid is operating stably and has the ability to supply or receive power normally.

[0092] The second state refers to the power grid frequency being outside the normal range and in an abnormal fluctuation state, which may pose a risk of power supply instability.

[0093] The first bidirectional power flow refers to the flexible power interaction between photovoltaic modules, energy storage batteries, AC grid and load according to power supply priority instructions when the grid frequency is normal. This includes photovoltaic power supply to load / battery, battery discharge to load, grid supplementary power supply or receiving surplus power, etc.

[0094] The second bidirectional power flow refers to suspending grid participation in power interaction when the grid frequency is abnormal, and supplying power to the load only through photovoltaic modules and energy storage batteries, thus avoiding the impact of abnormal grid on system stability.

[0095] Specifically, when the grid frequency is detected to be in the first state, the control unit prioritizes the photovoltaic module to supply power to the load according to the power supply priority instruction (such as solar priority). The surplus power is stored in the energy storage battery. If there is still a shortage, the grid will supplement it. When the grid frequency is in the second state, the grid connection is immediately disconnected, and the load is supplied only through the photovoltaic module and the energy storage battery. At the same time, the fault indicator light will indicate the grid abnormality.

[0096] For example, when the grid frequency is 50.2Hz (first state) and the power supply priority is solar power, the photovoltaic output directly supplies the load, and the excess power charges the battery through the inverter; if the grid frequency suddenly changes to 51.5Hz (second state), the inverter automatically disconnects from the grid, and only the photovoltaic and battery supply power to the load, ensuring that the load is not affected by grid anomalies.

[0097] Figure 3This is a schematic diagram of the power interaction system of a bidirectional photovoltaic energy storage inverter provided by the present invention.

[0098] like Figure 3 As shown, a power interaction system for a bidirectional photovoltaic energy storage inverter includes: a data acquisition module, a correction module, a prediction module, an instruction generation module, and a control module.

[0099] Data acquisition module: Used to collect data on photovoltaic irradiance, individual cell voltage of energy storage batteries, grid frequency, and ambient air pressure.

[0100] Correction module: Used to correct photovoltaic irradiance data based on ambient air pressure to generate actual irradiance;

[0101] Prediction module: Used to determine the state of charge of the battery based on the voltage of the individual energy storage battery cells, and to predict the fluctuation rate of photovoltaic output and load demand based on actual irradiance, historical irradiance data and battery state of charge, and generate a dynamic weight matrix.

[0102] Instruction generation module: compares the dynamic weight matrix with the preset strategy to generate power supply priority instructions;

[0103] Control module: Controls bidirectional power flow based on power supply priority commands and grid frequency.

[0104] It should be noted that those skilled in the art will understand that Figure 3 The structures shown do not constitute a limitation on electronic devices, which may include more than [other types of devices]. Figure 3 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0105] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above.

[0106] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of systems or units may be electrical, mechanical, or other forms.

[0107] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0110] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for power interaction in a bidirectional photovoltaic energy storage inverter, characterized in that, The method includes: Collect data on photovoltaic irradiance, individual cell voltage of energy storage batteries, grid frequency, and ambient air pressure. The photovoltaic irradiance data is corrected based on the ambient air pressure value to generate the actual irradiance. The state of charge of the battery is determined based on the voltage of the individual energy storage battery cells. Based on the actual irradiance, historical irradiance data and the state of charge of the battery cells, the photovoltaic power output fluctuation rate and load demand are predicted, and a dynamic weight matrix is ​​generated. The dynamic weight matrix is ​​compared with a preset strategy to generate a power supply priority instruction; Based on the power supply priority command and the power grid frequency, bidirectional power flow is controlled.

2. The method according to claim 1, characterized in that, The step of correcting the photovoltaic irradiance data based on the ambient air pressure value to generate the actual irradiance includes: The atmospheric transparency coefficient is determined based on the ambient air pressure value. The photovoltaic irradiance data is corrected based on the atmospheric transparency coefficient to obtain the corrected photovoltaic irradiance data. The actual irradiance is obtained by compensating the corrected photovoltaic irradiance data based on the ambient air pressure value.

3. The method according to claim 1, characterized in that, The preset strategy includes at least one of the following priority modes: grid priority mode, solar priority mode, SBU priority mode, and SUF priority mode; the priority mode corresponds to the priority order of power supply from at least one photovoltaic module, energy storage battery, and AC grid to the load.

4. The method according to claim 3, characterized in that, When the power supply priority command is in solar priority mode, the control logic includes: The photovoltaic module is given priority in supplying power to the load; if the output of the photovoltaic module is insufficient, the energy storage battery will supplement the power supply; only when the photovoltaic module is unavailable and the voltage of the energy storage battery drops to a preset low level threshold will the AC grid power supply be switched.

5. The method according to claim 1, characterized in that, The method of predicting photovoltaic power output volatility and load demand based on historical irradiance data and battery state of charge, and generating a dynamic weight matrix, includes: Obtain historical irradiance data and determine the rate of change of actual irradiance per unit time by combining the actual irradiance data; The battery charge / discharge capacity coefficient is determined based on the rate of change of irradiance per unit time, the battery state of charge, and preset battery type parameters. Analyze the correlation between the rate of change of irradiation intensity and the state of charge of the battery; Predict photovoltaic power output volatility based on the correlation analysis results; Based on the predicted photovoltaic output volatility, historical load demand data is matched with historical data in the historical database to generate a photovoltaic-load supply-demand deviation value. A dynamic weight matrix is ​​generated based on the photovoltaic-load supply-demand deviation value.

6. The method according to claim 5, characterized in that, The generation of a dynamic weight matrix based on the photovoltaic-load supply-demand deviation value includes: Based on the photovoltaic-load supply and demand deviation value, determine the photovoltaic weight factor, energy storage weight factor and grid weight factor; The photovoltaic weight factor, energy storage weight factor, and power grid weight factor are normalized to generate a dynamic weight matrix.

7. The method according to claim 1, characterized in that, The dynamic weight matrix is ​​compared with the preset strategy to generate power supply priority instructions, including: The photovoltaic weight factor, energy storage weight factor, and grid weight factor in the dynamic weight matrix are compared with the photovoltaic power supply threshold, energy storage power supply threshold, and grid power supply threshold in the preset strategy, respectively. The corresponding power supply priority command is triggered based on the comparison result.

8. The method according to claim 1, characterized in that, The control of bidirectional power flow based on power supply priority commands and the grid frequency includes: Detect the state of the power grid frequency; When the power grid frequency state is in the first state, a first bidirectional power flow is performed; When the power grid frequency state is in the second state, a first bidirectional power flow is performed based on the power supply priority command.

9. A power interaction system for a bidirectional photovoltaic energy storage inverter, characterized in that, The system includes: Data acquisition module: used to collect photovoltaic irradiance, individual cell voltage of energy storage batteries, grid frequency and ambient air pressure; Correction module: used to correct the photovoltaic irradiance data based on the ambient air pressure value to generate the actual irradiance; Prediction module: used to determine the state of charge of the battery based on the voltage of the individual energy storage battery cells, and to predict the photovoltaic power output fluctuation rate and load demand based on the actual irradiance, historical irradiance data and the state of charge of the battery cells, and to generate a dynamic weight matrix. Instruction generation module: compares the dynamic weight matrix with the preset strategy to generate power supply priority instructions; Control module: Controls bidirectional power flow based on power supply priority commands and the grid frequency.

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