Household optical storage and charging countercurrent prevention and grid-connected and off-grid intelligent switching system based on closed-loop algorithm

Abstract

The invention discloses a household light storage and charging countercurrent prevention and grid-connected and off-grid intelligent switching system based on a closed-loop algorithm. The system comprises the following units: a data acquisition unit, a prediction model construction unit, a dynamic mode adaptive adjustment unit, a risk calculation unit and a grid-connected and off-grid control unit. The data acquisition unit is used for acquiring multi-source real-time operation data of the household optical storage and charging system; and the prediction model construction unit is used for constructing an energy demand prediction model and a photovoltaic generating capacity prediction model based on an intelligent algorithm and outputting a prediction result. According to the household optical storage and charging countercurrent prevention and grid-connected and off-grid intelligent switching system based on the closed-loop algorithm, the closed-loop algorithm is adopted, the operation mode of the system can be intelligently judged through real-time prediction and dynamic mode adjustment, self-adaptive adjustment is achieved, efficient operation of the optical storage and charging system is ensured, and the service life of the system is prolonged. And energy waste caused by excessive dependence on a fixed rule of the system can be avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent power systems, more particularly, to a household light storage and charging anti-backflow and on-off grid intelligent switching system based on a closed-loop algorithm. BACKGROUND

[0002] With the rapid development of distributed photovoltaic power generation technology, household light storage and charging systems have gradually become one of the energy solutions for people's self-sufficiency. A household light storage and charging system is usually composed of a photovoltaic power generation unit, an energy storage unit and a load management unit. In practical applications, how to ensure the effective interaction of photovoltaic power generation and energy storage systems with the power grid and avoid the reverse flow of photovoltaic power generation systems to the power grid has become a key problem in design and operation.

[0003] At present, in the common household light storage and charging system, the anti-backflow protection usually relies on simple current sensor detection and traditional control logic, but with the complication of the system and the diversification of the use environment, this traditional method has limitations in dynamic response, load priority adjustment and intelligent control. The traditional on-grid and off-grid switching mechanism is relatively fixed and difficult to adaptively adjust according to real-time operation data, resulting in insufficient energy utilization efficiency and system operation stability.

[0004] In order to overcome these problems, a household light storage and charging anti-backflow and on-off grid intelligent switching system based on a closed-loop algorithm is proposed. Based on real-time data acquisition and intelligent prediction model, the system can dynamically adjust the operation mode of the light storage and charging system to ensure the reasonable distribution of energy and effectively avoid the reverse flow phenomenon, while improving the operation efficiency and intelligent level of the system. SUMMARY

[0005] The present application aims to provide a household light storage and charging anti-backflow and on-off grid intelligent switching system based on a closed-loop algorithm, which solves the problem of the existing on-grid and off-grid switching mechanism being relatively fixed and difficult to adaptively adjust according to real-time operation data, resulting in insufficient energy utilization efficiency and system operation stability.

[0006] The present application achieves the above-mentioned purpose through the following technical solutions: a household light storage and charging anti-backflow and on-off grid intelligent switching system based on a closed-loop algorithm, the system comprising the following units: a data acquisition unit, a prediction model construction unit, a dynamic mode adaptive adjustment unit, a risk calculation unit and an on-off grid control unit; The data acquisition unit is used to acquire multi-source real-time operation data of the household light storage and charging system. The prediction model construction unit is used to construct an energy demand prediction model and a photovoltaic power generation capacity prediction model based on intelligent algorithms, and output the prediction results. The dynamic mode adaptive adjustment unit is configured to adjust the grid-connected or off-grid operation mode of the system by dividing the electricity load priority and dynamically allocating energy according to the prediction result and the multi-source real-time operation data through a closed-loop algorithm. The risk calculation unit is configured to optimize the charging and discharging strategy of the energy storage device and calculate the reverse flow risk coefficient in real time based on the system operation mode and the load demand. The grid-connected and off-grid control unit is configured to trigger the anti-reverse flow control logic and the grid-connected and off-grid intelligent switching mechanism according to the reverse flow risk coefficient and the grid operation state, so as to ensure that the system operates without reverse flow and maximizes the operation efficiency.

[0007] Further, the multi-source real-time operation data includes photovoltaic power generation related data, household electricity load related data, energy storage device operation state data, and grid interaction related data.

[0008] Further, the input features of the intelligent prediction model constructed by the prediction model construction unit include historical operation data and environmental influence parameters, and the environmental influence parameters include at least one of temperature, humidity, irradiance, and solar elevation angle.

[0009] Further, the electricity load divided by the dynamic mode adaptive adjustment unit includes critical load and non-critical load, and the load priority is determined based on the evaluation index related to the load importance and energy consumption proportion.

[0010] Further, the closed-loop algorithm calculates the mode adjustment coefficient related to the feedback error and the grid interaction parameter to determine whether to maintain the current operation mode, adjust the energy storage power, or switch the operation mode.

[0011] Further, the charging and discharging strategy of the energy storage device is based on the operation state parameter interval of the energy storage device and the charging and discharging power constraint condition to adaptively adjust the charging and discharging power.

[0012] Further, the reverse flow risk coefficient is calculated based on the photovoltaic power generation power, the energy storage charging and discharging power, the household electricity load power, and the grid allowed interaction power.

[0013] Further, the anti-reverse flow control logic adopts a hierarchical response mechanism, and according to different intervals of the reverse flow risk coefficient, the operation of adjusting the energy storage charging and discharging power, reducing the non-critical load power supply, or switching the operation mode is executed.

[0014] Further, the intelligent prediction model introduces a prediction error correction term to dynamically correct the prediction result based on the historical prediction error and a preset correction coefficient.

[0015] Further, in the off-grid intelligent switching mechanism, the trigger condition for switching from the off-grid mode back to the grid-connected mode includes that the grid operation parameter is in the rated range and the reverse flow risk coefficient is maintained below the safety threshold for a preset stable time length.

[0016] The present application has the following advantages: 1. The system uses a closed-loop algorithm, which can intelligently determine the operation mode of the system through real-time prediction and dynamic mode adjustment, and adaptively adjusts to ensure efficient operation of the light storage and charging system, which helps to avoid energy waste caused by excessive reliance on fixed rules.

[0017] 2. By calculating the reverse flow risk coefficient, using anti-reverse flow control logic and hierarchical response mechanism, the system can automatically adjust the energy storage charging and discharging power, reduce non-critical loads or switch operation modes according to different risk intervals, thereby effectively avoiding reverse flow of the photovoltaic power generation system to the grid and ensuring grid safety.

[0018] 3. By building an intelligent prediction model, the system can predict photovoltaic power generation and household electricity demand, dynamically allocate energy according to load priority, optimize the charging and discharging strategy of energy storage devices, and improve the overall energy utilization efficiency of the household light storage and charging system.

[0019] 4. The system can intelligently adjust the energy storage power or switch the operation mode when the grid state changes by calculating the reverse flow risk coefficient in real time and combining the grid interaction parameters, ensuring stable operation of the system and minimizing reverse flow risk.

[0020] 5. Through the intelligent switching mechanism, the system can dynamically adjust according to the grid operation state and reverse flow risk coefficient to avoid unnecessary downtime or system instability, improving the reliability and long-term stability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of this application and illustrate certain illustrative embodiments of the present application and its description, which do not constitute an improper limitation on the present application. In the drawings: Fig. 1 The present application is a system overall architecture flowchart; Fig. 2 The present application is a dynamic mode adaptive adjustment flowchart; Fig. 3 The present application is an anti-reverse flow control logic flowchart. DETAILED DESCRIPTION

[0022] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0023] Embodiment 1: see Figs. 1-3 The present application provides a technical solution: a household light storage and charging anti-backflow and on-off grid intelligent switching system based on a closed-loop algorithm, which comprises the following units: a data acquisition unit, a prediction model construction unit, a dynamic mode adaptive adjustment unit, a risk calculation unit, and an on-off grid control unit; The data acquisition unit is used to acquire multi-source real-time data of the household light storage and charging system, and the multi-source real-time data includes photovoltaic power generation data, household electricity load data, energy storage device operation data, and grid interaction data. Among the multi-source real-time data, the photovoltaic power generation data reflects the real-time power generation and power generation of the household photovoltaic power generation system, and is used to understand the real-time situation of photovoltaic power generation; the household electricity load data records the real-time power consumption and power consumption of various electrical equipment in the household, and reflects the household electricity demand situation; the energy storage device operation data includes the real-time charging and discharging power, residual power, and health status of the energy storage device, and is used to master the operation status of the energy storage device; the grid interaction data is the power, power, voltage, and frequency data related to the real-time interaction between the household and the grid, and reflects the energy exchange between the household and the grid. The prediction model construction unit is used to construct a household energy demand prediction model and a photovoltaic power generation prediction model based on a double intelligent load scheduling and adaptive energy management algorithm, and output a prediction result. The double intelligent load scheduling and adaptive energy management algorithm is a complex algorithm that combines intelligent load scheduling and adaptive energy management concepts and comprehensively considers various factors to predict household energy demand and photovoltaic power generation; the household energy demand prediction model is used to predict the energy demand of the household in a future period of time, helping to plan energy distribution in advance; the photovoltaic power generation prediction model is used to predict the power generation of the household photovoltaic power generation system in a future period of time, providing a basis for energy management; the prediction result includes a household energy demand prediction value and a photovoltaic power generation prediction value. The dynamic mode adaptive adjustment unit is used to divide the household electricity load priority and dynamically allocate energy through a closed-loop algorithm according to the prediction result and the multi-source real-time data, and adaptively adjust the on-grid operation mode or off-grid operation mode of the system. The closed-loop algorithm is a feedback control algorithm that continuously compares the system output with the expected target and adjusts the system input to make the system output as close as possible to the expected target. The priority of household electrical loads is determined by the importance and usage demand of household electrical equipment, and the electrical loads are divided into different priorities to meet the electricity demand of high-priority loads first in energy distribution. Dynamic energy allocation is to adjust the energy supply to different loads in real time according to the load priority and real-time energy status. The grid-connected operation mode is to connect the household photovoltaic storage and charging system with the power grid for energy exchange. In this mode, the excess electricity of the household can be sold to the grid, and the insufficient electricity can be purchased from the grid. The off-grid operation mode is to disconnect the household photovoltaic storage and charging system from the power grid for independent operation, relying on its own photovoltaic power generation and energy storage equipment to meet the household electricity demand. The risk calculation unit is used to optimize the charging and discharging strategy of the energy storage device based on the operation mode and load demand, and to calculate the reverse flow risk coefficient in real time. The operation mode is grid-connected operation mode or off-grid operation mode. The load demand is the real-time demand of household electrical loads for electricity. The charging and discharging strategy of the energy storage device is a strategy that determines when the energy storage device charges and discharges, and the size of the charging and discharging power according to the system operation mode and load demand. The reverse flow risk coefficient is an index for measuring the possibility of the household photovoltaic storage and charging system sending electricity to the grid in reverse direction. The larger the value, the higher the reverse flow risk. The on-off grid control unit is used to trigger the anti-reverse flow control logic and the on-off grid intelligent switching mechanism according to the reverse flow risk coefficient and the grid operation state, to ensure that the system runs without reverse flow and maximizes the operation efficiency. The grid operation state includes whether the voltage, frequency, power and other parameters of the grid are normal, and whether the grid has faults and other conditions. The anti-reverse flow control logic is a series of control measures taken when reverse flow risk is detected, such as adjusting the charging and discharging of the energy storage device, limiting the output of photovoltaic power generation, etc., to prevent reverse flow of electricity back to the grid. The on-off grid intelligent switching mechanism is a mechanism that automatically and intelligently switches between grid-connected operation mode and off-grid operation mode according to the reverse flow risk coefficient and the grid operation state, to ensure safe and efficient operation of the system.

[0024] It should be noted that in use, the data acquisition unit acquires multi-source real-time data to provide a comprehensive and accurate information base for the system, the prediction model construction unit constructs a prediction model based on advanced algorithms to know the household energy demand and photovoltaic power generation in advance, which is beneficial to advance planning, the dynamic mode adaptive adjustment unit dynamically allocates energy and adjusts the operation mode according to the prediction and real-time data through a closed-loop algorithm, improves the flexibility and efficiency of energy utilization, the risk calculation unit optimizes the charging and discharging strategy of energy storage and calculates the reverse flow risk coefficient, effectively prevents reverse flow problems, and the off-grid control unit triggers the anti-reverse flow logic and intelligent switching mechanism according to the risk coefficient and the state of the power grid, which can not only avoid the influence of reverse flow on the power grid, but also ensure the efficient operation of the system under different working conditions, realize the safe, stable and efficient operation of the household photovoltaic storage and charging system, and improve the intelligent level of energy management.

[0025] In an embodiment, multi-source real-time data of a household photovoltaic storage and charging system is acquired and a prediction model is constructed, including: acquiring a time series data set of multi-source real-time data , , is the data acquisition time length, is the photovoltaic power at time t, is the total household power consumption at time t, is the remaining energy of the energy storage device at time t, is the power grid interaction power at time t; constructing a household energy demand prediction model based on LSTM, input feature vector: , wherein is the historical data window length, is the ambient temperature at time t, is the ambient humidity at time t, is the date type at time t, specifically weekdays / holidays, and the model output the demand prediction value at time t , and the calculation formula is: wherein is the model weight matrix, is the bias vector; constructing a photovoltaic power generation prediction model based on LSTM, input feature vector: , wherein is the solar irradiance at time t, is the temperature of the photovoltaic module, The model output is the solar elevation angle The photovoltaic power generation prediction value at the moment The calculation formula is: , Wherein is the model weight matrix, is the bias vector; The prediction error correction term is introduced, and the correction formula is: , Wherein is the demand prediction error at the moment, is the photovoltaic prediction error at the moment, , is the correction coefficient, and the value range is .

[0026] In this way, by collecting multi-source time series data, a household energy demand and photovoltaic power generation prediction model based on LSTM is constructed based on historical data, environmental factors, etc. and a prediction error correction term is introduced. The LSTM model can capture long-term data dependencies and improve prediction accuracy. Multi-source data considers comprehensively and covers key factors affecting energy supply and demand. The introduction of error correction term can dynamically adjust the prediction result, reduce the prediction bias, and accurate prediction can let the system plan energy distribution in advance, provide reliable basis for subsequent dynamic mode adjustment, risk calculation, etc. Make the household light storage and charging system more efficient and stable operation, effectively deal with the change of energy supply and demand.

[0027] In an embodiment, the specific calculation process of double intelligent load scheduling includes: Divide the household electricity load into key load and non-key load , calculate the load priority weight , the formula is: Wherein, is the load importance score, taking 1-5 points, is the load energy consumption proportion, , , is the weight coefficient, satisfying , , ; Calculate the energy supply and demand balance coefficient based on the prediction value and real-time data , the formula is: Wherein, is the energy storage and charging power at the moment; When When the priority is assigned to the photovoltaic energy, the critical load is assigned power , and the non-critical load is assigned power ; When , the critical load is fully powered, and the non-critical load is assigned the remaining energy according to the weight, and the formula is: .

[0028] In this way, the load is divided into critical and non-critical, the priority weight is calculated, and the energy is distributed in combination with the energy supply and demand balance coefficient. The priority of the load can ensure the stable power supply of the critical load, improve the reliability and safety of household electricity, and through the calculation of the energy supply and demand balance coefficient, the energy status can be understood in real time, the energy can be reasonably distributed, and energy waste or shortage can be avoided. When the energy is sufficient, it is distributed according to the priority, and when the energy is insufficient, the critical load is prioritized, so that the energy utilization is more reasonable, the household electricity demand under different working conditions is adapted, and the flexibility and adaptability of the system are improved.

[0029] In an embodiment, the closed-loop algorithm adaptively adjusts the calculation process of the off-grid mode, and includes: Define the closed-loop feedback error , and the calculation formula is: , wherein is the net output of the energy storage power; Calculate the mode adjustment coefficient , and the formula is: , wherein is the error weight coefficient, , is the maximum interactive power allowed by the power grid; When , is the grid-connected threshold, and when the value is 0.15, the grid-connected mode is maintained; When , is the off-grid threshold, and when the value is 0.3, it is switched to the off-grid mode; When , the energy storage charging and discharging power is adjusted for dynamic balance, and the formula is: .

[0030] Thus designed, the closed-loop feedback error and the mode adjustment coefficient are defined, the coefficient value is determined to switch between the off-grid mode and the energy storage power adjustment, the closed-loop feedback error can reflect the matching degree of energy supply and demand and load in real time, the mode adjustment coefficient comprehensively considers the error and the grid interaction power, the mode switching decision is more scientific, by setting the threshold, the system can automatically and accurately switch between the grid-connected mode and the off-grid mode, or adjust the energy storage power in the intermediate state to realize dynamic balance, improve the adaptability of the system to different operation scenes, ensure stable operation of the system, and reduce the risk caused by improper mode switching.

[0031] In an embodiment, the calculation process of the energy storage device charging and discharging optimization strategy includes: Setting the SOC operation interval of the energy storage device , charging and discharging power constraint , ; In the grid-connected mode, the energy storage charging power calculation is: ; Energy storage discharging power calculation: ; Wherein , is the charging and discharging efficiency coefficient, taking the value of 0.9-0.95; In the off-grid mode, the energy storage charging and discharging power calculation is: , ; The SOC balance correction term is introduced, when or , wherein is the buffer threshold, taking the value of 5%, then the charging and discharging power is linearly attenuated, the formula is: , .

[0032] Thus designed, the SOC operation interval and the power constraint of the energy storage device are set, the charging and discharging power is calculated in different modes, and the SOC balance correction term is introduced. The setting of the operation interval and the power constraint can prevent the overcharging and overdischarging of the energy storage device, prolong the service life thereof, the charging and discharging power is calculated in different modes, which can better adapt to the energy management needs of the grid-connected and off-grid scenes, the SOC balance correction term is introduced, which can linearly attenuate the charging and discharging power when the energy of the energy storage device approaches the boundary, avoid damage to the device caused by sudden change of the energy, and ensure stable operation of the system in different energy states, improve the use efficiency and reliability of the energy storage device.

[0033] In an embodiment, The real-time reverse flow risk power is calculated: , wherein is Grid allows input power at this moment; Risk coefficient of reverse current: , When ,Maintain the current mode; When ,Start energy storage charging or reduce non-critical load, adjustment amount ; When ,Trigger emergency reverse current protection switching, if the current is grid-connected mode, switch to off-grid mode immediately, switching response time , while calculating the load distribution adjustment amount after switching: , after switching, real-time monitoring of grid voltage and frequency , When and , ( , Grid rated voltage, frequency), and Last , Switch back to grid-connected mode. In this way, real-time reverse current risk power and risk coefficient are calculated, different reverse current protection measures are taken according to the coefficient value, and whether to switch back to grid-connected mode is determined by monitoring the grid parameters after switching. Real-time calculation of reverse current risk can discover potential reverse current problems in time, and by setting different risk thresholds, hierarchical measures can be taken to effectively avoid the impact of reverse current on the grid. Starting energy storage charging or reducing non-critical load can quickly reduce the risk of reverse current, and emergency reverse current protection switching can ensure system safety. Monitoring the grid parameters after switching and setting the conditions for switching back to grid-connected mode can switch back to grid-connected mode in time when the grid returns to normal and the risk of reverse current is low, improve the operation efficiency and flexibility of the system, and ensure the coordinated operation of the home light storage charging system and the grid. Those of ordinary skill in the art can understand that all or part of the steps in the above-mentioned embodiment methods can be completed by programs instructing related hardware, therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer usable program code.

[0034]

[0035]

[0036] The above embodiments have been described in detail, and the principles and embodiments of the present application have been described by applying specific examples. The above examples are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific embodiments and application scope will be changed, and the above description should not be understood as a limitation of the present application.

Claims

1. A closed-loop algorithm-based home light storage and charging anti-backflow and on-grid and off-grid intelligent switching system, characterized in that, The system comprises the following units: a data acquisition unit, a prediction model construction unit, a dynamic mode adaptive adjustment unit, a risk calculation unit, and an on-grid and off-grid control unit; The data acquisition unit is configured to acquire multi-source real-time operation data of the household light storage and charging system. The prediction model construction unit is configured to construct an energy demand prediction model and a photovoltaic power generation prediction model based on an intelligent algorithm, and output a prediction result. The dynamic mode adaptive adjustment unit is configured to divide the electricity load priority and dynamically allocate energy according to the prediction result and the multi-source real-time operation data, and adjust the on-grid or off-grid operation mode of the system through a closed-loop algorithm. The risk calculation unit is configured to optimize the charging and discharging strategy of the energy storage device and calculate the reverse flow risk coefficient in real time based on the system operation mode and load demand. The on-grid and off-grid control unit is configured to trigger the anti-reverse flow control logic and the on-grid and off-grid intelligent switching mechanism according to the reverse flow risk coefficient and the grid operation state, to ensure that the system operates without reverse flow and maximizes the operation efficiency.

2. The household light storage and charging anti-reverse flow and on-grid and off-grid intelligent switching system based on a closed-loop algorithm according to claim 1, wherein: The multi-source real-time operation data includes photovoltaic power generation related data, household electricity load related data, energy storage device operation state data, and grid interaction related data.

3. The household light storage and charging anti-reverse flow and on-grid and off-grid intelligent switching system based on a closed-loop algorithm according to claim 1, wherein: The intelligent prediction model constructed by the prediction model construction unit has input features including historical operation data and environmental influence parameters, and the environmental influence parameters include at least one of temperature, humidity, irradiance, and solar elevation angle.

4. The household light storage and charging anti-reverse flow and on-grid and off-grid intelligent switching system based on a closed-loop algorithm according to claim 1, wherein: The electricity load divided by the dynamic mode adaptive adjustment unit includes critical load and non-critical load, and the load priority is determined based on evaluation indexes related to load importance and energy consumption proportion.

5. The household light storage and charging anti-reverse flow and on-grid and off-grid intelligent switching system based on a closed-loop algorithm according to claim 1, wherein: The closed-loop algorithm calculates the mode adjustment coefficient related to the feedback error and the grid interaction parameter to determine whether to maintain the current operation mode, adjust the energy storage power, or switch the operation mode.

6. The household light storage and charging anti-reverse flow and on-grid and off-grid intelligent switching system based on a closed-loop algorithm according to claim 1, wherein: The charging and discharging strategy of the energy storage device is based on the operating state parameter interval of the energy storage device and the charging and discharging power constraint condition to adaptively adjust the charging and discharging power.

7. The household light storage and charging anti-reverse flow and on-grid and off-grid intelligent switching system based on a closed-loop algorithm according to claim 1, wherein: The reverse flow risk coefficient is calculated based on the photovoltaic power generation power, the energy storage charging and discharging power, the household electricity load power, and the grid allowed interaction power.

8. The household light storage and charging anti-reverse flow and on-grid and off-grid intelligent switching system based on a closed-loop algorithm according to claim 1, wherein: The anti-backflow control logic adopts a hierarchical response mechanism, and according to different intervals of the backflow risk coefficient, corresponding operations of adjusting the energy storage charge and discharge power, reducing the power supply of non-critical loads or switching the operation mode are performed. 9.The home light storage charging anti-backflow and grid-connected / off-grid intelligent switching system based on a closed-loop algorithm according to claim 3, characterized in that: The intelligent prediction model introduces a prediction error correction term, and dynamically corrects the prediction result based on the historical prediction error and a preset correction coefficient. 10.The home light storage charging anti-backflow and grid-connected / off-grid intelligent switching system based on a closed-loop algorithm according to claim 1, characterized in that: In the grid-connected / off-grid intelligent switching mechanism, the trigger condition for switching from the off-grid mode to the grid-connected mode includes that the grid operation parameters are in a rated range and the backflow risk coefficient is maintained below a safety threshold for a preset stable time length.

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