Control method and storage medium for off-grid operation of a home light storage system

Abstract

This invention provides a control method and storage medium for off-grid operation of a home photovoltaic-storage system. The method includes, in response to the home photovoltaic-storage system switching to off-grid operation, determining the overload support domain of the energy storage battery in the system; determining the evolution trend of comfort indices of each controllable load at various times within the prediction time domain; determining the comfort feasible domain of each controllable load during load shedding; determining the power allocation amount of the power deficit borne by the energy storage battery and each controllable load; and, based on the power allocation amount, performing load shedding operations on the controllable loads, and / or controlling the energy storage battery to increase power output according to its allocated power within a preset overload duration. By introducing the overload support domain of the energy storage battery, the short-term overload capacity of the energy storage battery and the load shedding capacity of the controllable loads are coordinated for decision-making, ensuring that the power deficit is shared by the energy storage battery and each controllable load, thereby improving the continuity of off-grid power supply.

Description

Technical Field

[0001] This invention relates to home energy management technology, and in particular to control methods and storage media for off-grid operation of home photovoltaic and energy storage systems. Background Technology

[0002] Home photovoltaic and energy storage systems can switch to off-grid operation mode during grid faults. In off-grid operation control, if a power deficit is detected, loads are typically shed step-by-step according to a pre-set fixed priority order until power balance is achieved. In this mode, the load shedding sequence is static and does not adjust with changes in users' real-time comfort requirements for various loads. Simultaneously, the system only addresses the power deficit through unidirectional adjustment on the load side, limiting the power output of the energy storage batteries to within the rated range. If photovoltaic output or load power fluctuates after load shedding, a new round of load shedding needs to be recalculated and executed. This load shedding process involves multiple switching operations, leading to frequent power outages. Summary of the Invention

[0003] One objective of this invention is to improve the continuity of power supply and user comfort of home photovoltaic and energy storage systems in off-grid operation.

[0004] Specifically, the present invention provides a control method for off-grid operation of a home photovoltaic energy storage system, comprising: In response to the home photovoltaic energy storage system switching to off-grid operation, the overload support domain of the energy storage battery in the home photovoltaic energy storage system is determined; the overload support domain characterizes the range of power support that the energy storage battery can provide beyond the rated power within a preset overload duration. Based on the current operating parameters of each controllable load in the household, the local environmental parameters of each controllable load, and the overall environmental parameters of the household, the evolution trend of the comfort index of each controllable load at each moment in the prediction time domain is determined. Based on the comparison between the predicted comfort index values ​​at each moment in the evolution trend and the preset comfort threshold, the feasible comfort range for each controllable load during the load reduction process is determined. Based on the overload support domain and the comfort feasible domain, the power allocation amount in the power deficit to be borne by the energy storage battery and to be borne by each of the controllable loads is determined. The power deficit is the difference between the sum of the power of each of the controllable loads and the real-time photovoltaic power generation. Based on the power allocation, a load reduction operation is performed on the controllable load, and / or the energy storage battery is controlled to increase its power output according to its assigned power allocation during the preset overload duration.

[0005] Optionally, determining the overload support domain of the energy storage battery in the home photovoltaic-energy storage system includes: Obtain the rated power, preset overload factor, current state of charge, and current operating temperature of the energy storage battery; The upper limit of the basic overload power of the energy storage battery is determined based on the product of the rated power and the preset overload coefficient. A SOC correction factor is determined based on the current state of charge, and the SOC correction factor decreases as the current state of charge decreases; Obtain a temperature correction coefficient for derating the upper limit of the basic overload power based on temperature. If the current operating temperature is greater than or equal to a preset temperature threshold, the temperature correction coefficient is determined to be a preset derating value less than 1; if the current operating temperature is less than the preset temperature threshold, the temperature correction coefficient is determined to be 1. The product of the basic overload power limit, the SOC correction factor, and the temperature correction factor is used as the short-term overload power of the energy storage battery based on the current state. The overload support domain is formed by using the rated power of the energy storage battery as the lower limit and the short-term overload power as the upper limit.

[0006] Optionally, determining the SOC correction factor based on the current state of charge includes: If the current state of charge is greater than or equal to a preset first state of charge threshold, the SOC correction coefficient is set to 1. If the current state of charge is less than or equal to a preset second state of charge threshold, the SOC correction coefficient is a preset lower limit value; If the current state of charge is between the preset second state of charge threshold and the preset first state of charge threshold, the SOC correction coefficient is determined by linear interpolation between the preset lower limit and 1; wherein the preset first state of charge threshold is greater than the preset second state of charge threshold.

[0007] Optionally, determining the evolution trend of the comfort index of each of the controllable loads at each time point in the prediction time domain includes: If the controllable load is a temperature-controlled load, the current operating parameters of the temperature-controlled load include the current indoor temperature and the set temperature. The local environmental parameters of the temperature-controlled load include the indoor humidity, indoor wind speed and indoor radiation temperature of the area where the temperature-controlled load is located. The overall home environmental parameters of the temperature-controlled load include the outdoor ambient temperature outside the building where the temperature-controlled load is located. Based on the current indoor temperature, indoor humidity, indoor wind speed, indoor radiant temperature, and outdoor ambient temperature, an equivalent thermal parameter model is constructed for the space where the temperature-controlled load is located; the equivalent thermal parameter model is used to describe the dynamic change of the indoor temperature in the space where the temperature-controlled load is located within the prediction time domain; Using the equivalent thermal parameter model and the preset time step, the predicted indoor temperature value at each moment in the prediction time domain is calculated recursively. Based on the set temperature and preset human thermal comfort evaluation index, the predicted indoor temperature value at each time moment is mapped to the corresponding comfort index value, and the comfort index value at each time moment constitutes the evolution trend of the comfort index of the temperature control load at each time moment in the prediction time domain.

[0008] Optionally, determining the evolution trend of the comfort index of each of the controllable loads at each time point in the prediction time domain includes: If the controllable load is a lighting load, the current operating parameters of the lighting load include the current dimming level and the current color temperature; the local environmental parameters of the lighting load include the local natural illuminance and the local color temperature; and the overall household environmental parameters of the lighting load include the outdoor light intensity and the outdoor color temperature change rate. The illuminance contribution value of artificial lighting at each moment is determined based on the current dimming level; the natural illuminance contribution value at each moment is calculated recursively based on the outdoor light intensity, using the local natural illuminance as a benchmark; the illuminance contribution value of artificial lighting at the same moment is superimposed with the natural illuminance contribution value to obtain the comprehensive illuminance of the space where the lighting load is located at each moment in the predicted time domain. The color temperature contribution of artificial lighting at each moment is determined based on the current color temperature; the color temperature contribution of natural light at each moment is calculated recursively based on the outdoor color temperature change rate, using the local color temperature as a reference; the color temperature contribution of artificial lighting at the same moment is superimposed with the color temperature contribution of natural light to obtain the comprehensive color temperature of the space where the lighting load is located at each moment in the prediction time domain. Based on the comprehensive illuminance and comprehensive color temperature at each time point, the lighting comfort index value at each time point is determined; the lighting comfort index value at each time point constitutes the evolution trend of the lighting load comfort index at each time point in the prediction time domain.

[0009] Optionally, determining the evolution trend of the comfort index of each of the controllable loads at each time point in the prediction time domain includes: If the controllable load is a transferable load, the current operating parameters of the transferable load include the waiting time, the expected running time, and the rated power. The overall household environmental parameters of the transferable load include the current time-of-use electricity price information and the household electricity priority. Based on the waiting time, the expected running time, and the rated power, determine the remaining running time of the movable load at each time point in the predicted time domain; Based on the current time-of-use electricity price information and the household electricity priority, the offset at each time is determined, and the remaining runtime at each time is added to the offset at the corresponding time to obtain the adjusted remaining runtime at each time. Based on the adjusted remaining runtime at each time point, the movable load comfort index value at each time point is determined. The movable load comfort index value is used to characterize the tolerance of the movable load being delayed in execution at the corresponding time point, and is positively correlated with the adjusted remaining runtime. The comfort index of the movable load at each moment is used to construct the evolution trend of the comfort index of the movable load at each moment in the prediction time domain.

[0010] Optionally, determining the comfort feasible domain for each of the controllable loads during the load reduction process includes: Obtain the comparison results between the predicted comfort index values ​​at each moment in the evolution trend and the preset comfort threshold; Based on the comparison results, determine the maximum allowable load reduction amount at each moment during the load reduction process for each of the controllable loads; The maximum load reduction at each time point is used as the upper limit of the load reduction, and 0 is used as the lower limit of the load reduction. These constitute the load reduction constraint boundaries for each controllable load at each time point in the prediction time domain. The range of values ​​defined by the load reduction constraint boundaries is the comfort feasible domain.

[0011] Optionally, based on the overload support domain and the comfort feasible domain, determining the power allocation amount in the power deficit borne by the energy storage battery and each of the controllable loads includes: Based on the load type and / or user preset priority of each controllable load, determine the comfort loss weight corresponding to each controllable load; Under the conditions of satisfying the power support upper limit constraint of the overload support domain, the load reduction constraint defined by the comfort feasible domain, and the power balance constraint that the power deficit is equal to the sum of the first power allocation and each second power allocation, the first power allocation borne by the energy storage battery and the second power allocation borne by each controllable load in the power deficit are determined with the objective of minimizing the sum of the weighted cumulative comfort loss values ​​of each controllable load at each moment in the prediction time domain. The weighted comfort loss is the sum of the products of the comfort loss value of each controllable load and the corresponding comfort loss weight. The comfort loss value is determined based on the deviation between the predicted comfort index value at each moment in the evolution trend and the preset comfort benchmark value. The power deficit is the difference obtained by subtracting the real-time photovoltaic power generation from the sum of the power of each controllable load. A load shedding control sequence is generated based on each of the second power allocation values, wherein the load shedding control sequence includes the load shedding power value of each controllable load at each time in the prediction time domain.

[0012] Optionally, determining the comfort loss weight corresponding to each of the controllable loads includes: Obtain a preset load type-basic weight mapping table, which stores the basic weight values ​​corresponding to temperature-controlled loads, lighting loads, and movable loads. The load type-basic weight mapping table is queried according to the load type of each controllable load to determine the basic weight of each controllable load; Obtain the user preset priority of each controllable load, and determine the priority level corresponding to each controllable load based on the user preset priority of each controllable load; wherein, the priority level includes three levels: high priority, medium priority and low priority; Based on the priority level of each controllable load, a priority coefficient corresponding to each controllable load is determined; wherein, the priority coefficient corresponding to high priority is a first preset value, the priority coefficient corresponding to medium priority is a second preset value, and the priority coefficient corresponding to low priority is a third preset value, and the first preset value is greater than the second preset value, and the second preset value is greater than the third preset value; The initial weight of each controllable load is the product of its basic weight and its corresponding priority coefficient. The initial weights of each controllable load are normalized, and the normalized initial weights are used as the comfort loss weights corresponding to each controllable load.

[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the control method for off-grid operation of any of the above-described home optical storage systems are implemented.

[0014] In the control method and storage medium for off-grid operation of a home photovoltaic-storage system of the present invention, by introducing an overload support domain for the energy storage battery, the short-term overload capacity of the energy storage battery and the load reduction capacity of controllable loads are incorporated into the same optimization framework for collaborative decision-making. This allows the power deficit to be shared by the energy storage battery and each controllable load, effectively reducing load reduction while meeting power balance constraints and improving the continuity of off-grid power supply. While reducing load reduction, the evolution trend of comfort indices for each controllable load in the prediction time domain is constructed, and the feasible comfort domain for each load is determined. This allows load reduction decisions to be based on future comfort change trends, prioritizing loads with higher comfort indices in the current and prediction time domains for load reduction operations. This avoids frequent start-stop cycles and large temperature fluctuations caused by decisions based solely on current comfort levels, reducing user discomfort caused by load reduction. The optimization framework is solved progressively based on real-time operating status and environmental parameters, enabling the load reduction strategy to adaptively track changes in photovoltaic output and load, avoiding power imbalance. By limiting the preset overload duration, the energy storage battery will only increase its power according to the allocated amount within that duration. If the power balance is not restored after the time limit, the load shedding protection will be executed and the overload call will be stopped, ensuring the stable operation of the home photovoltaic and energy storage system.

[0015] The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0016] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings: Figure 1 This is a schematic flowchart of a control method for off-grid operation of a home photovoltaic energy storage system according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of a control method for off-grid operation of a home photovoltaic energy storage system according to an embodiment of the present invention; Figure 3 This is a schematic flowchart of a control method for off-grid operation of a home photovoltaic energy storage system according to an embodiment of the present invention; Figure 4 This is a schematic flowchart of a control method for off-grid operation of a home photovoltaic energy storage system according to an embodiment of the present invention; Figure 5 This is a schematic flowchart of a control method for off-grid operation of a home photovoltaic energy storage system according to an embodiment of the present invention; Figure 6 This is a schematic flowchart of a control method for off-grid operation of a home photovoltaic energy storage system according to an embodiment of the present invention; Figure 7 This is a schematic flowchart of a control method for off-grid operation of a home photovoltaic energy storage system according to an embodiment of the present invention; Figure 8 This is a schematic flowchart of a control method for off-grid operation of a home photovoltaic energy storage system according to an embodiment of the present invention; Figure 9 This is a schematic diagram of a computer program product according to an embodiment of the present invention; Figure 10 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention; Figure 11 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0017] Figure 1 This is a schematic flowchart of a control method for off-grid operation of a home photovoltaic energy storage system according to an embodiment of the present invention. The method generally includes: Step S100: In response to the home photovoltaic and energy storage system switching to off-grid operation, determine the overload support domain of the energy storage battery in the home photovoltaic and energy storage system; the overload support domain characterizes the range of power support that the energy storage battery can provide beyond the rated power within a preset overload duration. Step S200: Based on the current operating parameters of each controllable load in the household, the local environmental parameters of each controllable load, and the overall environmental parameters of the household, determine the evolution trend of the comfort index of each controllable load at each moment in the prediction time domain. Step S300: Based on the comparison results between the predicted comfort index values ​​at each moment in the evolution trend and the preset comfort threshold, determine the feasible comfort range for each controllable load during the load reduction process. Step S400: Based on the overload support domain and the comfort feasible domain, determine the power allocation amount in the power deficit to be borne by the energy storage battery and to be borne by each controllable load. The power deficit is the difference between the sum of the power of each controllable load and the real-time photovoltaic power generation. Step S500: Based on the power allocation, perform a load reduction operation on the controllable load, and / or control the energy storage battery to increase its power output according to its allocated power within a preset overload duration.

[0018] In this embodiment, at the instant the home photovoltaic energy storage system switches to off-grid operation, the overload support domain of the energy storage battery is first established to provide a brief power buffer for the system. Simultaneously, based on the current operating parameters of each controllable load, local environmental parameters, and overall home environmental parameters, the changing trend of the comfort index of each load at various moments in the future prediction time domain is deduced and compared with a preset comfort threshold, limiting the feasible comfort domain for each controllable load during load reduction. Subsequently, the real-time calculated power deficit is used as the total amount to be allocated, and the power allocation on the battery side and the power allocation corresponding to each controllable load are considered collaboratively. The power allocation on the battery side can be referred to as the first power allocation, and the power allocation corresponding to each controllable load can be referred to as the second power allocation. Within the preset overload duration, the output power of the energy storage battery is set according to the first power allocation, and a load reduction control sequence is generated based on the second power allocation. The load reduction control sequence includes the load reduction power value of each controllable load at each moment in the prediction time domain. During execution, the load reduction operation is performed using the load reduction power value corresponding to the current moment. Upon entering the next time step, the power deficit is redefined based on the real-time photovoltaic output and load status. Under the constraints of the overload support domain and the comfort feasible domain, the power allocation within the deficit, determined as to be borne by the energy storage battery and by each controllable load, is then updated. The load shedding control sequence is updated accordingly, and the load shedding operation is executed using the updated current power value. This rolling process allows the load shedding strategy to adaptively track changes in photovoltaic output and load, and synchronously executes short-term overload discharge of the energy storage battery and graded load shedding operations for each controllable load according to the first power allocation and each of the second power allocations.

[0019] By introducing the overload support domain of energy storage batteries, the short-term overload capacity of energy storage batteries and the load reduction capacity of controllable loads are incorporated into the same optimization framework for collaborative decision-making. This allows the power deficit to be shared by the energy storage batteries and each controllable load, effectively reducing load reduction while meeting power balance constraints and improving off-grid power continuity. While reducing load reduction, the evolution trend of comfort indices for each controllable load in the prediction time domain is constructed, and the feasible comfort domain for each load is determined. This enables load reduction decisions to be based on future comfort trends, prioritizing loads with higher comfort indices in the current and prediction time domains. This avoids frequent start-stop cycles and large temperature fluctuations caused by decisions based solely on current comfort levels, reducing user discomfort caused by load reduction. The optimization framework is solved progressively based on real-time operating status and environmental parameters, enabling the load reduction strategy to adaptively track changes in photovoltaic output and load, avoiding power imbalance. By limiting the preset overload duration, the energy storage battery will only increase its power according to the allocated amount within that duration. If the power balance is not restored after the time limit, the load shedding protection will be executed and the overload call will be stopped, ensuring the stable operation of the home photovoltaic and energy storage system.

[0020] In some embodiments of the present invention, the home photovoltaic and energy storage system includes photovoltaics, energy storage batteries, and an inverter. The home photovoltaic and energy storage system also integrates an off-grid operation control system, which includes: a grid status detection module, a load monitoring module, a comfort sensing module, a load shedding decision module, and an execution module. The grid status detection module includes a grid-connected relay and a voltage transformer. The load monitoring module includes a smart meter. The comfort sensing module includes a temperature sensor and a human body sensor. The load shedding decision module includes an edge computing gateway. The execution module includes a smart socket, an infrared remote control, and a communication interface.

[0021] In some embodiments of the present invention, such as Figure 2 As shown, the overload support domain of the energy storage battery in the home photovoltaic-energy storage system determined in step S100 includes: Step S110: Obtain the rated power, preset overload factor, current state of charge, and current operating temperature of the energy storage battery; Step S120: Determine the upper limit of the basic overload power of the energy storage battery based on the product of the rated power and the preset overload coefficient. Step S130: Determine the SOC correction factor based on the current state of charge. The SOC correction factor decreases as the current state of charge decreases. Step S140: Obtain the temperature correction coefficient for derating the upper limit of the basic overload power based on the temperature. If the current operating temperature is greater than or equal to the preset temperature threshold, the temperature correction coefficient is determined to be a preset derating value less than 1; if the current operating temperature is less than the preset temperature threshold, the temperature correction coefficient is determined to be 1. Step S150: The product of the basic overload power limit, the SOC correction factor, and the temperature correction factor is used as the short-term overload power of the energy storage battery based on the current state. Step S160: The rated power of the energy storage battery is used as the lower limit and the short-term overload power is used as the upper limit to form an overload support domain.

[0022] In this embodiment, by acquiring the rated power, preset overload coefficient, current state of charge, and current operating temperature of the energy storage battery, and comprehensively considering the rated capacity of the equipment, the user-defined overload strategy, and the real-time status of the battery, the upper limit of the short-term overload power of the energy storage battery under the current operating conditions is accurately calculated. An overload support domain is constructed with the rated power as the lower limit and the short-term overload power as the upper limit, providing a clear feasible boundary for power allocation.

[0023] In some embodiments of the present invention, determining the SOC correction factor based on the current state of charge in step S130 includes: If the current state of charge is greater than or equal to the preset first state of charge threshold, the SOC correction coefficient is set to 1. If the current state of charge is less than or equal to the preset second state of charge threshold, the SOC correction coefficient is set to the preset lower limit value. If the current state of charge is between a preset second state of charge and a preset first state of charge, the SOC correction coefficient is determined by linear interpolation between a preset lower limit and 1; wherein the preset first state of charge is greater than the preset second state of charge.

[0024] The corrected overload power is neither a fixed value nor a sudden value, but changes continuously and smoothly with the SOC, ensuring the stability of the control system and avoiding a step jump in power command due to abrupt changes in the correction coefficient.

[0025] In some embodiments of the present invention, such as Figure 3 As shown, step S200, determining the evolution trend of the comfort index of each controllable load at each moment in the prediction time domain, includes: If the controllable load is a temperature-controlled load, the current operating parameters of the temperature-controlled load include the current indoor temperature and the set temperature. The local environmental parameters of the temperature-controlled load include the indoor humidity, indoor wind speed and indoor radiant temperature of the area where the temperature-controlled load is located. The overall environmental parameters of the temperature-controlled load include the outdoor ambient temperature outside the building where the temperature-controlled load is located. Step S201: Based on the current indoor temperature, indoor humidity, indoor wind speed, indoor radiant temperature and outdoor ambient temperature, construct an equivalent thermal parameter model of the space where the temperature control load is located; the equivalent thermal parameter model is used to describe the dynamic change law of the indoor temperature of the space where the temperature control load is located in the prediction time domain. Step S202: Using the equivalent thermal parameter model and the preset time step, calculate the predicted indoor temperature value at each moment in the prediction time domain. Step S203: Based on the set temperature and preset human thermal comfort evaluation index, the predicted indoor temperature value at each time moment is mapped to the corresponding comfort index value, and the comfort index value at each time moment constitutes the evolution trend of the comfort index of the temperature control load at each time moment in the prediction time domain.

[0026] In this embodiment, the temperature-controlled load includes air conditioners and water heaters, etc. First, the current indoor temperature, set temperature, indoor humidity, indoor wind speed, indoor radiant temperature, and outdoor ambient temperature of the temperature-controlled load are obtained. Based on these parameters, an equivalent thermal parameter model of the space where the temperature-controlled load is located is constructed. This model describes the dynamic change of indoor temperature with outdoor ambient temperature within the prediction time domain. Then, using the current indoor temperature as the initial condition, the predicted indoor temperature value at each moment within the prediction time domain is calculated recursively with a preset time step, obtaining the complete trajectory of indoor temperature evolution over time. Finally, the predicted indoor temperature value at each moment is combined with the set temperature and preset human thermal comfort evaluation indicators to establish a mapping relationship between the temperature prediction value and the comfort index value. When the predicted temperature is within the user-set comfort temperature range, a higher comfort index value is assigned; when it deviates, the value decreases as the degree of deviation increases. Arranging the comfort index values ​​calculated at each moment in a time series constitutes the evolution trend of the comfort index of the temperature-controlled load within the prediction time domain.

[0027] By constructing an equivalent thermal parameter model, which comprehensively considers multi-dimensional environmental parameters such as current indoor temperature, indoor humidity, indoor wind speed, indoor radiation temperature and outdoor ambient temperature, the model is used to recursively calculate the predicted indoor temperature values ​​at each moment in the prediction time domain. The predicted temperature values ​​are then mapped to comfort index values, enabling load reduction decisions to predict the actual impact of temperature control load on human thermal comfort at each moment in the future.

[0028] In some embodiments of the present invention, such as Figure 4 As shown, step S200, determining the evolution trend of the comfort index of each controllable load at each moment in the prediction time domain, further includes: If the controllable load is a lighting load, the current operating parameters of the lighting load include the current dimming level and the current color temperature. The local environmental parameters of the lighting load include the local natural illuminance and the local color temperature. The overall household environmental parameters of the lighting load include the outdoor illuminance and the outdoor color temperature change rate. Step S204: Determine the illuminance contribution value of artificial lighting at each moment based on the current dimming level; calculate the natural illuminance contribution value at each moment based on the local natural illuminance and the outdoor light intensity; superimpose the artificial lighting illuminance contribution value and the natural illuminance contribution value at the same moment to obtain the comprehensive illuminance of the space where the lighting load is located at each moment in the prediction time domain. Step S205: Determine the color temperature contribution value of artificial lighting at each moment based on the current color temperature; calculate the natural light color temperature contribution value at each moment based on the outdoor color temperature change rate, using the local color temperature as a benchmark; and superimpose the artificial lighting color temperature contribution value and the natural light color temperature contribution value at the same moment to obtain the comprehensive color temperature of the space where the lighting load is located at each moment in the prediction time domain. Step S206: Determine the lighting comfort index value for each moment based on the comprehensive illuminance and comprehensive color temperature; use the lighting comfort index value for each moment to construct the evolution trend of the lighting load comfort index at each moment in the prediction time domain.

[0029] In this embodiment, the lighting load includes intelligent lighting devices that support dimming and color temperature adjustment. First, the current dimming level and color temperature of the lighting load, the local natural illuminance and local color temperature of the area, and the outdoor light intensity and outdoor color temperature change rate are obtained. Based on the current dimming level, the illuminance contribution value of artificial lighting at each moment in the prediction time domain is determined. Simultaneously, using the measured local natural illuminance at the current moment as the initial reference value, the natural illuminance contribution value at each moment is recursively calculated based on the predicted change trend of outdoor light intensity. The artificial lighting illuminance contribution value at the same moment is then superimposed with the natural illuminance contribution value to obtain the comprehensive illuminance prediction value of the space where the lighting load is located at each moment in the prediction time domain. For color temperature, the contribution of artificial lighting to color temperature at each moment is determined based on the current color temperature. Using the measured local color temperature at the current moment as the initial benchmark, the contribution of natural light to color temperature at each moment is calculated recursively based on the outdoor color temperature change rate. The contribution of artificial lighting to color temperature at the same moment is then superimposed with the contribution of natural light to obtain the comprehensive color temperature prediction value for the space at each moment within the prediction time domain. Illuminance and color temperature recursion are calculated in parallel. Finally, based on the comprehensive illuminance and comprehensive color temperature at each moment, combined with a pre-set lighting comfort evaluation model, the lighting comfort index value for each moment is determined. Arranging the calculated lighting comfort index values ​​at each moment in a time series constitutes the evolution trend of the lighting load comfort index within the prediction time domain. This evolution trend reflects how the visual comfort of the lighting load will change at future moments without load reduction intervention, providing a forward-looking basis for load reduction decisions.

[0030] By acquiring local natural illuminance and local color temperature as local environmental parameters, and acquiring outdoor illuminance and outdoor color temperature change rate as overall home environmental parameters, the time-varying characteristics of natural light are incorporated into the prediction model to achieve dynamic prediction of the lighting environment in the entire prediction time domain, enabling load reduction decisions to predict the actual lighting demand of the lighting load at various times in the future.

[0031] In some embodiments of the present invention, such as Figure 5 As shown, step S200, determining the evolution trend of the comfort index of each controllable load at each moment in the prediction time domain, further includes: If the controllable load is a transferable load, the current operating parameters of the transferable load include the waiting time, the expected running time, and the rated power. The overall household environmental parameters of the transferable load include the current time-of-use electricity price information and the household electricity priority. Step S207: Based on the waiting time, the expected running time, and the rated power, determine the remaining running time of the transferable load at each moment in the prediction time domain; Step S208: Based on the current time-of-use electricity price information and household electricity priority, determine the offset at each time point, and add the remaining runtime at each time point to the offset at the corresponding time point to obtain the adjusted remaining runtime at each time point. Step S209: Based on the adjusted remaining runtime at each time, determine the transferable load comfort index value at each time. The transferable load comfort index value is used to characterize the tolerance of the transferable load being delayed at the corresponding time and is positively correlated with the adjusted remaining runtime. Step S210: The comfort index of the movable load at each moment is used to construct the evolution trend of the comfort index of the movable load at each moment in the prediction time domain.

[0032] In this embodiment, the transferable load refers to appliances such as washing machines and dishwashers. First, the current operating parameters of the transferable load and the overall household environmental parameters are obtained. Based on the waiting time, expected runtime, and rated power, the remaining runtime of the transferable load at each moment within the prediction time domain is determined. Specifically, a longer waiting time results in a shorter remaining runtime, indicating that the load is closer to its latest executable deadline; a shorter expected runtime results in a longer remaining runtime, indicating that the load can be started closer to its latest deadline. Simultaneously, the offset at each moment is determined based on the current time-of-use electricity price information and the household's electricity priority. Higher current time-of-use electricity prices correspond to negative offsets, lower prices to positive offsets, and higher household electricity priority to positive offsets. The remaining runtime at each moment is then summed with the corresponding offset to obtain the adjusted remaining runtime at each moment. Subsequently, the comfort index value of the transferable load at each time point is determined based on the adjusted remaining runtime. The longer the adjusted remaining runtime, the higher the tolerance for delaying the execution of the load at the current time, and the higher the comfort index value, for example, it can be close to 1. Conversely, the shorter the adjusted remaining runtime, the smaller the delay space, and the lower the comfort index value, for example, it can be close to 0. This mapping relationship is implemented using a positive correlation function. Arranging the comfort index values ​​calculated at each time point in a time series constitutes the evolution trend of the comfort index of the transferable load in the prediction time domain. This evolution trend reflects how the tolerance for continued delay of the execution of the transferable load at future times will change if it is not started, providing a forward-looking basis for determining when it must be started for load reduction decisions. When the comfort index drops to the preset lower limit, it indicates that the load can no longer be delayed and power supply must be restored before the corresponding time.

[0033] By acquiring the waiting time, estimated running time, and rated power as current operating parameters, and acquiring the current time-of-use electricity price information and household electricity priority as overall household environmental parameters, the adjusted remaining running time is determined based on the above parameters. This allows the comfort index of the load that can be shifted to comprehensively reflect the combined impact of electricity price incentives and waiting tolerance on users' acceptance of delays.

[0034] In some embodiments of the present invention, such as Figure 6 As shown, step S300, determining the comfort feasible domain for each controllable load during the load reduction process, includes: Step S310: Obtain the comparison results between the predicted value of the comfort index at each moment in the evolution trend and the preset comfort threshold. Step S320: Determine the maximum allowable load reduction at each moment during the load reduction process for each controllable load based on the comparison results; Step S330: The maximum load reduction at each time moment is used as the upper limit of the load reduction, and 0 is used as the lower limit of the load reduction to form the load reduction constraint boundary for each controllable load at each time moment in the prediction time domain. The range of values ​​defined by the load reduction constraint boundary is the comfort feasible domain.

[0035] In this embodiment, when the predicted value of the comfort index at a certain moment is higher than the threshold, it indicates that reducing the load to a certain extent at that moment will not cause the user's comfort to exceed the limit; conversely, if the predicted value of the comfort index is close to or lower than the threshold, it indicates that the load reduction space at that moment is extremely limited or even zero. The higher the predicted value of the comfort index is than the preset threshold, the greater the "comfort redundancy" of the load at that moment, and the greater the maximum allowable load reduction; conversely, the closer the predicted value of the comfort index is to or lower than the threshold, the smaller the maximum allowable load reduction, until it is zero. This mapping relationship can be implemented using a positive correlation function, such as a linear proportional relationship or a piecewise function. Using the maximum load reduction at each moment as the upper limit of the load reduction and 0 as the lower limit of the load reduction, the load reduction constraint boundary of each controllable load at each moment in the prediction time domain is constructed, and the range of values ​​defined by this boundary is the comfort feasible domain of the load.

[0036] By comparing the predicted comfort index values ​​at each moment in the evolution trend with the preset comfort threshold, the subjective requirement that the comfort level should not be lower than the threshold is transformed into an objective constraint boundary that the load reduction should not exceed the upper limit.

[0037] In some embodiments of the present invention, such as Figure 7 As shown, in step S400, based on the overload support domain and the comfort feasible domain, the power allocation amount determined for the power deficit to be borne by the energy storage battery and by each controllable load includes: Step S410: Determine the comfort loss weight corresponding to each controllable load based on the load type and / or user preset priority of each controllable load; Step S420: Under the conditions of satisfying the power support upper limit constraint of the overload support domain, the load reduction constraint limited by the comfort feasible domain, and the power balance constraint that the power deficit is equal to the sum of the first power allocation and each second power allocation, the first power allocation undertaken by the energy storage battery and the second power allocation undertaken by each controllable load are determined with the goal of minimizing the sum of the cumulative weighted comfort loss of each controllable load at each moment in the prediction time domain. The weighted comfort loss is the sum of the products of the comfort loss value of each controllable load and the corresponding comfort loss weight. The comfort loss value is determined based on the deviation between the predicted value of the comfort index at each moment in the evolution trend and the preset comfort benchmark value. The power deficit is the difference obtained by subtracting the real-time photovoltaic power generation from the sum of the power of each controllable load. Step S430: Generate a load reduction control sequence based on each second power allocation, wherein the load reduction control sequence includes the load reduction power value of each controllable load at each time in the prediction time domain.

[0038] Based on the load type and / or user-preset priority of each controllable load, the comfort loss weight corresponding to each controllable load is determined. This weight reflects the difference in the degree of impact of different loads on user experience when the load is reduced. For example, temperature control loads such as air conditioning are directly related to human thermal comfort, so their weight is usually higher, meaning that the comfort loss caused by load reduction is greater. Lighting loads are next, while loads that can be shifted only involve a delay in usage time and do not directly affect immediate physical comfort, so their weight is relatively lower. User-preset priority provides an adjustment entry point for personalized preferences. Users can set specific loads, such as study room lighting and aquarium oxygenation equipment, as high priority through apps, increasing their load reduction weight and thus being prioritized for retention in the optimization solution. Under the premise of satisfying three types of constraints, the optimal allocation scheme of power deficit is solved through optimization algorithms. The first type of constraint is the overload support domain constraint, that is, the first power allocation amount, that is, the power undertaken by the energy storage battery must not exceed the difference between the short-term overload power limit and the rated power, ensuring that the battery's increased power is within the safe overload range allowed in its current state. The second type of constraint is the comfort feasible region constraint, which is the second power allocation of each controllable load. This means the power reduction undertaken by each load must not exceed the upper limit of load reduction at any given time, and must not be lower than 0, ensuring that load reduction for any load at any time does not exceed the comfort red line. The third type of constraint is the power balance constraint, which means the sum of the first power allocation and each second power allocation must equal the current power deficit, ensuring that load reduction decisions meet the system's power balance requirements from the source. Under these constraints, the objective function of the optimization algorithm is to minimize the sum of the weighted cumulative comfort loss of each controllable load at each time in the prediction time domain. The comfort loss value is the deviation between the preset comfort benchmark value (i.e., the maximum value of the comfort index corresponding to the user's complete satisfaction state) and the predicted value of the comfort index at each time. The larger the deviation, the greater the damage to user comfort caused by load reduction at that time. Based on the second power allocation obtained from the optimization solution, a load reduction control sequence is generated. This load reduction control sequence contains the load reduction power value of each controllable load at each time in the prediction time domain, thus specifying which load reduces power by how much at which time.

[0039] By constructing an optimization framework aimed at minimizing weighted comfort loss, the overload power generation capacity of the energy storage battery and the load reduction capacity of the controllable load are treated as two parallel decision variables. Under the dual boundary constraints of power balance constraints and overload support domain and comfort feasible domain, the optimal power allocation scheme that minimizes weighted comfort loss is determined, thereby achieving the globally optimal allocation of power deficit between the energy storage battery and each controllable load.

[0040] In some embodiments of the present invention, such as Figure 8 As shown, the comfort loss weights corresponding to each controllable load in step S410 include: Step S411: Obtain the preset load type-basic weight mapping table. The load type-basic weight mapping table stores the basic weight values ​​corresponding to temperature control load, lighting load and movable load. Step S412: Query the load type-basic weight mapping table according to the load type of each controllable load to determine the basic weight of each controllable load; Step S413: Obtain the user preset priority of each controllable load, and determine the priority level corresponding to each controllable load according to the user preset priority of each controllable load; wherein, the priority level includes three levels: high priority, medium priority and low priority. Step S414: Determine the priority coefficient corresponding to each controllable load according to the priority level of each controllable load; wherein, the priority coefficient corresponding to high priority is the first preset value, the priority coefficient corresponding to medium priority is the second preset value, and the priority coefficient corresponding to low priority is the third preset value, and the first preset value is greater than the second preset value, and the second preset value is greater than the third preset value. Step S415: The product of the basic weight of each controllable load and the corresponding priority coefficient is used as the initial weight of each controllable load. Step S416: Normalize the initial weights of each controllable load, and use the normalized initial weights as the comfort loss weights corresponding to each controllable load.

[0041] In this embodiment, the short-term overload capacity of the energy storage battery and the controllable load shedding capacity are coordinated, so that the power deficit is shared by the increased energy storage generation and load shedding, effectively reducing the amount of load shedding and extending the off-grid power supply time. By constructing a predictive evolution trend of the load comfort index and defining a feasible region, the load shedding decision is forward-looking, selecting load shedding targets based on future comfort changes rather than just current values, avoiding frequent start-stops and large temperature fluctuations. Based on the standardized basic weights of the load type mapping table, combined with user-preset priorities, personalized adjustments are achieved, and normalization processing ensures cross-type comparability. A preset overload duration limits the time for increased energy storage generation; if the timeout is exceeded, load shedding protection is executed, forming a layered safety system to ensure equipment safety.

[0042] The flowchart provided in this embodiment is not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in every case. Furthermore, the method may include additional operations. Within the scope of the technical concept provided by the method in this embodiment, additional variations can be made to the above method.

[0043] It should be understood that in some embodiments, the components may be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods may be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0044] This embodiment also provides a computer program product 10, a computer-readable storage medium 20, and a computer device 30. Figure 9 This is a schematic diagram of a computer program product 10 according to an embodiment of the present invention. Figure 10 This is a schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention. Figure 11 This is a schematic diagram of a computer device 30 according to an embodiment of the present invention. The computer program product 10 includes a computer program 11, which, when executed by the processor 32, implements the steps of the control method for off-grid operation of any of the above-described home optical storage systems. A computer-readable storage medium 20 stores the computer program 11 thereon, which, when executed by the processor 32, implements the steps of the control method for off-grid operation of any of the above-described home optical storage systems. The computer device 30 may include a memory 31, a processor 32, and the computer program 11 stored in the memory 31 and running on the processor 32.

[0045] The computer program 11 used to perform the operations of this invention may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages. The computer program 11 may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer. In some embodiments, to perform aspects of this invention, electronic circuits, including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may execute computer-readable program instructions to personalize the electronic circuits by utilizing status information of the computer-readable program instructions.

[0046] For the purposes of this embodiment, computer program product 10 is a related product that includes computer program 11.

[0047] For the purposes of this embodiment, computer-readable storage medium 20 is a tangible device capable of holding and storing a computer program 11. It can be any device capable of containing, storing, communicating, propagating, or transmitting the program 11 for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable storage medium 20 include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical encoding device, and any suitable combination thereof.

[0048] Computer device 30 can be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer device 30 can be a cloud computing node. Computer device 30 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. Computer device 30 can be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can reside on local or remote computing system storage media, including storage devices.

[0049] Computer device 30 may include a processor 32 adapted to execute stored instructions and a memory 31 that provides temporary storage space for the operation of said instructions during operation. The processor 32 may be a single-core processor, a multi-core processor, a computing cluster, or any other configuration. The memory 31 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0050] Computer device 30 may also include a network adapter / interface and an input / output (I / O) interface. The I / O interface allows external devices that can be connected to the computer device to input and output data. The network adapter / interface provides communication between the computer device and a network, typically represented as a communication network.

[0051] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.

Claims

1. A control method for off-grid operation of a home photovoltaic storage system, characterized in that, include: In response to the home photovoltaic and energy storage system switching to off-grid operation, the overload support domain of the energy storage battery in the home photovoltaic and energy storage system is determined. The overload support domain characterizes the range of power support beyond the rated power that the energy storage battery can provide within a preset overload duration. Specifically, the rated power, preset overload factor, current state of charge (SOC), and current operating temperature of the energy storage battery are obtained. The basic overload power upper limit of the energy storage battery is determined based on the product of the rated power and the preset overload factor. A SOC correction factor is determined based on the current SOC, and the SOC correction factor decreases as the current SOC decreases. The following steps are taken: Obtain a temperature correction coefficient for derating the upper limit of the basic overload power based on temperature; if the current operating temperature is greater than or equal to a preset temperature threshold, determine the temperature correction coefficient as a preset derating value less than 1; if the current operating temperature is less than the preset temperature threshold, determine the temperature correction coefficient as 1; use the product of the upper limit of the basic overload power, the SOC correction coefficient, and the temperature correction coefficient as the short-term overload power of the energy storage battery based on the current state; use the rated power of the energy storage battery as the lower limit and the short-term overload power as the upper limit to construct the overload support domain. Based on the current operating parameters of each controllable load in the household, the local environmental parameters of each controllable load, and the overall environmental parameters of the household, the evolution trend of the comfort index of each controllable load at each moment in the prediction time domain is determined. Based on the comparison between the predicted comfort index values ​​at each moment in the evolution trend and the preset comfort threshold, the feasible comfort range for each controllable load during the load reduction process is determined. Based on the overload support domain and the comfort feasible domain, the power allocation amount in the power deficit to be borne by the energy storage battery and to be borne by each of the controllable loads is determined. The power deficit is the difference between the sum of the power of each of the controllable loads and the real-time photovoltaic power generation. Based on the power allocation, a load reduction operation is performed on the controllable load, and / or the energy storage battery is controlled to increase its power output according to its assigned power allocation during the preset overload duration.

2. The control method for off-grid operation of a home photovoltaic storage system according to claim 1, characterized in that, Determining the SOC correction factor based on the current state of charge includes: If the current state of charge is greater than or equal to a preset first state of charge threshold, the SOC correction coefficient is set to 1. If the current state of charge is less than or equal to a preset second state of charge threshold, the SOC correction coefficient is a preset lower limit value; If the current state of charge is between the preset second state of charge threshold and the preset first state of charge threshold, the SOC correction coefficient is determined by linear interpolation between the preset lower limit and 1; wherein the preset first state of charge threshold is greater than the preset second state of charge threshold.

3. The control method for off-grid operation of a home photovoltaic and energy storage system according to claim 1, characterized in that, Determining the evolution trend of comfort indices for each of the controllable loads at different times in the prediction time domain includes: If the controllable load is a temperature-controlled load, the current operating parameters of the temperature-controlled load include the current indoor temperature and the set temperature. The local environmental parameters of the temperature-controlled load include the indoor humidity, indoor wind speed and indoor radiation temperature of the area where the temperature-controlled load is located. The overall home environmental parameters of the temperature-controlled load include the outdoor ambient temperature outside the building where the temperature-controlled load is located. Based on the current indoor temperature, indoor humidity, indoor wind speed, indoor radiant temperature, and outdoor ambient temperature, an equivalent thermal parameter model is constructed for the space where the temperature-controlled load is located; the equivalent thermal parameter model is used to describe the dynamic change of the indoor temperature in the space where the temperature-controlled load is located within the prediction time domain; Using the equivalent thermal parameter model and the preset time step, the predicted indoor temperature value at each moment in the prediction time domain is calculated recursively. Based on the set temperature and preset human thermal comfort evaluation index, the predicted indoor temperature value at each time moment is mapped to the corresponding comfort index value, and the comfort index value at each time moment constitutes the evolution trend of the comfort index of the temperature control load at each time moment in the prediction time domain.

4. The control method for off-grid operation of a home photovoltaic storage system according to claim 1, characterized in that, Determining the evolution trend of comfort indices for each of the controllable loads at different times in the prediction time domain includes: If the controllable load is a lighting load, the current operating parameters of the lighting load include the current dimming level and the current color temperature; the local environmental parameters of the lighting load include the local natural illuminance and the local color temperature; and the overall household environmental parameters of the lighting load include the outdoor light intensity and the outdoor color temperature change rate. The illuminance contribution value of artificial lighting at each moment is determined based on the current dimming level; the natural illuminance contribution value at each moment is calculated recursively based on the outdoor light intensity, using the local natural illuminance as a benchmark; the illuminance contribution value of artificial lighting at the same moment is superimposed with the natural illuminance contribution value to obtain the comprehensive illuminance of the space where the lighting load is located at each moment in the predicted time domain. The color temperature contribution of artificial lighting at each moment is determined based on the current color temperature; the color temperature contribution of natural light at each moment is calculated recursively based on the outdoor color temperature change rate, using the local color temperature as a reference; the color temperature contribution of artificial lighting at the same moment is superimposed with the color temperature contribution of natural light to obtain the comprehensive color temperature of the space where the lighting load is located at each moment in the prediction time domain. Based on the comprehensive illuminance and comprehensive color temperature at each time point, the lighting comfort index value at each time point is determined; the lighting comfort index value at each time point constitutes the evolution trend of the lighting load comfort index at each time point in the prediction time domain.

5. The control method for off-grid operation of a home photovoltaic and energy storage system according to claim 1, characterized in that, Determining the evolution trend of comfort indices for each of the controllable loads at different times in the prediction time domain includes: If the controllable load is a transferable load, the current operating parameters of the transferable load include the waiting time, the expected running time, and the rated power. The overall household environmental parameters of the transferable load include the current time-of-use electricity price information and the household electricity priority. Based on the waiting time, the expected running time, and the rated power, determine the remaining running time of the movable load at each time point in the predicted time domain; Based on the current time-of-use electricity price information and the household electricity priority, the offset at each time is determined, and the remaining runtime at each time is added to the offset at the corresponding time to obtain the adjusted remaining runtime at each time. Based on the adjusted remaining runtime at each time point, the movable load comfort index value at each time point is determined. The movable load comfort index value is used to characterize the tolerance of the movable load being delayed in execution at the corresponding time point, and is positively correlated with the adjusted remaining runtime. The comfort index of the movable load at each moment is used to construct the evolution trend of the comfort index of the movable load at each moment in the prediction time domain.

6. The control method for off-grid operation of a home photovoltaic and energy storage system according to claim 1, characterized in that, Determining the comfort feasible domain for each of the aforementioned controllable loads during the load reduction process includes: Obtain the comparison results between the predicted comfort index values ​​at each moment in the evolution trend and the preset comfort threshold; Based on the comparison results, determine the maximum allowable load reduction amount at each moment during the load reduction process for each of the controllable loads; The maximum load reduction at each time point is used as the upper limit of the load reduction, and 0 is used as the lower limit of the load reduction. These constitute the load reduction constraint boundaries for each controllable load at each time point in the prediction time domain. The range of values ​​defined by the load reduction constraint boundaries is the comfort feasible domain.

7. The control method for off-grid operation of a home photovoltaic storage system according to claim 1, characterized in that, Based on the overload support domain and the comfort feasible domain, the power allocation amount determined in the power deficit to be borne by the energy storage battery and to be borne by each of the controllable loads includes: Based on the load type and / or user preset priority of each controllable load, determine the comfort loss weight corresponding to each controllable load; Under the conditions of satisfying the power support upper limit constraint of the overload support domain, the load reduction constraint defined by the comfort feasible domain, and the power balance constraint that the power deficit is equal to the sum of the first power allocation and each second power allocation, the first power allocation borne by the energy storage battery and the second power allocation borne by each controllable load in the power deficit are determined with the objective of minimizing the sum of the weighted cumulative comfort loss values ​​of each controllable load at each moment in the prediction time domain. The weighted comfort loss is the sum of the products of the comfort loss value of each controllable load and the corresponding comfort loss weight. The comfort loss value is determined based on the deviation between the predicted comfort index value at each moment in the evolution trend and the preset comfort benchmark value. The power deficit is the difference obtained by subtracting the real-time photovoltaic power generation from the sum of the power of each controllable load. A load shedding control sequence is generated based on each of the second power allocation values, wherein the load shedding control sequence includes the load shedding power value of each controllable load at each time in the prediction time domain.

8. The control method for off-grid operation of a home photovoltaic storage system according to claim 7, characterized in that, Determining the comfort loss weights corresponding to each of the controllable loads includes: Obtain a preset load type-basic weight mapping table, which stores the basic weight values ​​corresponding to temperature-controlled loads, lighting loads, and movable loads. The load type-basic weight mapping table is queried according to the load type of each controllable load to determine the basic weight of each controllable load; Obtain the user preset priority of each controllable load, and determine the priority level corresponding to each controllable load based on the user preset priority of each controllable load; wherein, the priority level includes three levels: high priority, medium priority and low priority; Based on the priority level of each controllable load, a priority coefficient corresponding to each controllable load is determined; wherein, the priority coefficient corresponding to high priority is a first preset value, the priority coefficient corresponding to medium priority is a second preset value, and the priority coefficient corresponding to low priority is a third preset value, and the first preset value is greater than the second preset value, and the second preset value is greater than the third preset value; The initial weight of each controllable load is the product of its basic weight and its corresponding priority coefficient. The initial weights of each controllable load are normalized, and the normalized initial weights are used as the comfort loss weights corresponding to each controllable load.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the control method for off-grid operation of the home photovoltaic storage system as described in any one of claims 1 to 8.

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