Photovoltaic power generation off-grid power supply heating control method and system

Abstract

The application provides a photovoltaic power generation off-grid power supply and heating control method and system, comprising: collecting multi-dimensional parameters in the operation process of a photovoltaic power generation off-grid system, the multi-dimensional parameters at least including photovoltaic power generation parameters, energy storage parameters, load parameters and environmental parameters; processing the multi-dimensional parameters, analyzing the real-time correlation degree between any two types of parameters, constructing a parameter correlation matrix and determining a core influence parameter set of the current system; based on the core influence parameter set, respectively evaluating the power supply priority and the heating supply priority; combining the total allocatable energy of the system, determining the basic energy distribution ratio of power supply and heating supply; and according to the deviation of the actual demand of the load and the basic energy distribution ratio, dynamically adjusting the energy distribution ratio of power supply and heating supply. The method can effectively improve the intelligent level and response speed of system energy distribution, enhance the adaptability of the system to variable operating conditions, improve energy utilization efficiency, and ensure the stable operation of power supply and heating load.

Description

Technical Field

[0001] This disclosure generally relates to the field of energy system dispatching technology, and specifically to a method and system for controlling off-grid power supply and heating from photovoltaic power generation. Background Technology

[0002] Off-grid photovoltaic (PV) power generation technology does not rely on the public power grid. It directly converts solar energy into electricity through photovoltaic modules, and then uses energy storage devices to store the electricity to cope with fluctuations in sunlight. It can provide independent power support for various electrical devices in remote pastoral areas, field operation bases, emergency disaster relief sites, and other scenarios without grid coverage. Based on this, off-grid PV power generation systems have been further expanded to provide power and heat, providing electricity to electrical loads and converting electrical energy into heat to meet heating needs.

[0003] However, existing photovoltaic power generation systems rely solely on sunlight intensity or battery SOC to adjust power supply strategies without considering heating demand. This leads to an imbalance in the distribution of electricity between power supply and heating during operating condition switching or complex operating conditions, resulting in problems such as "insufficient heating when there is excess power supply" or "power shortage when heating consumes energy." It is difficult to achieve coordinated regulation of power supply and heating under multiple operating conditions and cannot fully adapt to the system's operational needs. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a photovoltaic power generation off-grid power supply and heating control method and system to solve the above problems.

[0005] The first aspect of this application provides a method for controlling off-grid power supply and heating from photovoltaic power generation, including:

[0006] Collect multi-dimensional parameters during the operation of the off-grid photovoltaic power generation system. These multi-dimensional parameters include at least photovoltaic power generation parameters, energy storage parameters, load parameters, and environmental parameters.

[0007] The multidimensional parameters are processed to analyze the real-time correlation between any two types of parameters, construct a parameter correlation matrix, and determine the core influencing parameter set of the current system based on the parameter correlation matrix.

[0008] Based on the aforementioned set of core influencing parameters, the priority of power supply and the priority of heating are evaluated respectively; combined with the total allocable energy of the system, the basic energy allocation ratio for power supply and heating is determined.

[0009] The energy allocation ratio for power supply and heating is dynamically adjusted based on the deviation between the actual load demand and the basic energy allocation ratio.

[0010] According to the technical solution provided in the embodiments of this application, the multidimensional parameters include:

[0011] Photovoltaic power generation parameters: output power, conversion efficiency, and module temperature;

[0012] Energy storage parameters: battery state of charge, battery temperature, phase change energy storage temperature, and energy storage capacity;

[0013] Load parameters: power supply load, power supply load, key load indicators;

[0014] Environmental parameters: light intensity, ambient temperature, humidity.

[0015] According to the technical solution provided in the embodiments of this application, the step of processing the multidimensional parameters, analyzing the real-time correlation between any two types of parameters, and constructing a parameter correlation matrix includes:

[0016] The multidimensional parameters are filtered and normalized using a sliding window filtering algorithm.

[0017] Based on the dynamic calculation of the relationship characteristic parameters of any two types of parameters under the working conditions, the relationship characteristic parameters are used to characterize the degree of physical correlation between the two parameters;

[0018] The real-time correlation degree is calculated based on the normalized values ​​of the two parameters and the corresponding relational characteristic parameters.

[0019] Iterate through the multidimensional parameters and construct a parameter correlation matrix based on the real-time correlation between any two parameters.

[0020] According to the technical solution provided in the embodiments of this application, the dynamic calculation of the relationship characteristic parameters between any two types of parameters based on the working condition scenario includes:

[0021] Collect continuous time-series data sequences of two types of parameters within a preset period, each of the continuous time-series data sequences including a set number of parameter values;

[0022] Based on continuous time-series data sequences of two types of parameters, we analyze and calculate the relational characteristic parameters that reflect the degree of linear correlation between the two types of parameters.

[0023] According to the technical solution provided in the embodiments of this application, determining the core influencing parameter set of the current system based on the parameter correlation matrix includes:

[0024] By combining the real-time correlation between any parameter and other parameters in the parameter correlation matrix, and the normalized values ​​of the other parameters, the influence index of the parameter on the system is calculated.

[0025] By iterating through the multidimensional parameters, the influence index of each parameter on the system is calculated.

[0026] The parameters whose influence index ranking falls within a set range are selected to form a core influence parameter set.

[0027] According to the technical solution provided in the embodiments of this application, the step of evaluating power supply priority and heating priority based on the core influence parameter set includes:

[0028] For each parameter in the core influencing parameter set, assign its influence weight to heating and power supply respectively;

[0029] By combining the influence index of each parameter, the power supply priority index and the heating priority index are obtained through weighted calculation.

[0030] According to the technical solution provided in the embodiments of this application, determining the basic energy allocation ratio for power supply and heating based on the total distributable energy of the system includes:

[0031] The total distributable energy of the system is calculated by combining the photovoltaic power generation output energy of the system with the energy that the battery can release, which is determined by the battery state of charge, battery capacity and battery discharge efficiency.

[0032] Based on the relative relationship between the power supply priority index and the heating priority index, the power supply basic allocation ratio is determined, so the sum of the power supply basic allocation ratio and the heating basic allocation ratio is 1.

[0033] According to the technical solution provided in the embodiments of this application, the step of dynamically adjusting the energy distribution ratio of power supply and heating based on the deviation between the actual load demand and the basic energy distribution ratio includes:

[0034] Calculate the power supply energy deviation and the heating energy deviation. The power supply energy deviation is the difference between the power supply load demand energy and the product of the power supply base allocation ratio and the remaining allocable energy. The heating energy deviation is the difference between the heating load demand energy and the product of the heating base allocation ratio and the remaining allocable energy.

[0035] The adjustment priorities for the power supply energy deviation and the heating energy deviation are ranked according to the importance of the load.

[0036] If either the power supply energy deviation or the heating energy deviation exceeds the preset range, the basic allocation ratio is adjusted according to the deviation and its corresponding basic allocation ratio and correction coefficient, and the allocation ratio corresponding to the other deviation is adjusted in reverse simultaneously.

[0037] If both the power supply energy deviation and the heating energy deviation exceed the preset range, the deviation with higher priority is corrected according to the adjustment priority, and the sum of the power supply and heating energy distribution ratio is kept at 1 during the correction process;

[0038] During the correction process, both the power supply distribution ratio and the heating distribution ratio should meet the set range.

[0039] According to the technical solution provided in the embodiments of this application, before the step of correcting the basic allocation ratio based on the deviation and its corresponding basic allocation ratio and correction coefficient, and simultaneously correcting the allocation ratio corresponding to the other deviation in the opposite direction if either the power supply energy deviation or the heating energy deviation exceeds a preset range, the method further includes:

[0040] The system acquires the ambient temperature deviation, the critical load power ratio, and the battery state of charge deviation. The ambient temperature deviation is the difference between the current ambient temperature and the average ambient temperature under the same historical operating conditions. The critical load power ratio is the ratio of the total power of the critical load in the power supply module / heating module to the total power of the corresponding load type. The battery state of charge deviation represents the difference between the current battery state of charge and the rated battery state of charge.

[0041] The acquired ambient temperature deviation, key load power ratio, and battery state of charge deviation are input into the dynamic adjustment model so that the dynamic adjustment model outputs a preset threshold for power supply energy deviation and a threshold for heating energy deviation.

[0042] The preset range is determined based on the preset threshold for power supply deviation and the preset threshold for heating deviation.

[0043] A second aspect of this application provides a photovoltaic power generation off-grid power supply and heating system, applied to the photovoltaic power generation off-grid power supply and heating control method described above, comprising:

[0044] The acquisition module is configured to acquire multi-dimensional parameters during the operation of the off-grid photovoltaic power generation system. The multi-dimensional parameters include at least photovoltaic power generation parameters, energy storage parameters, load parameters, and environmental parameters.

[0045] The processing module is configured to process the multidimensional parameters, analyze the real-time correlation between any two types of parameters, construct a parameter correlation matrix, and determine the core influencing parameter set of the current system based on the parameter correlation matrix.

[0046] A calculation module is configured to evaluate power supply priority and heating priority based on the core influence parameter set; and determine the basic energy allocation ratio between power supply and heating based on the total allocable energy of the system.

[0047] An adjustment module is configured to dynamically adjust the energy distribution ratio of power supply and heating based on the deviation between the actual load demand and the basic energy distribution ratio.

[0048] Compared with existing technologies, the advantages of this application are as follows: By collecting multi-dimensional parameters from the off-grid photovoltaic power generation system and calculating the real-time correlation between parameters, a parameter correlation matrix is ​​constructed to determine the core influencing parameter set. Then, based on this parameter set, the priority index for power supply and heating is dynamically calculated. Combined with the total allocable energy of the system, the basic energy allocation ratio is determined, and this ratio is dynamically adjusted according to actual load demand, ultimately achieving precise control and real-time fine-tuning of power supply and heating output. This method can effectively improve the intelligence level and response speed of system energy allocation, enhance the system's adaptability to changing operating conditions, improve energy utilization efficiency, and ensure the stable operation of power supply and heating loads. Attached Figure Description

[0049] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0050] Figure 1 A flowchart illustrating the steps of the photovoltaic power generation off-grid power supply and heating control method provided in Example 1;

[0051] Figure 2 This is a schematic diagram of the photovoltaic power generation off-grid power supply and heating system provided in Example 2.

[0052] The reference numerals are as follows: 10, Acquisition Module; 20, Processing Module; 30, Calculation Module; 40, Adjustment Module. Detailed Implementation

[0053] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0054] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0055] Example 1

[0056] Please refer to Figure 1 This embodiment provides a method for controlling off-grid power supply and heating from photovoltaic power generation, including:

[0057] S100: Collects multi-dimensional parameters during the operation of the off-grid photovoltaic power generation system. The multi-dimensional parameters include at least photovoltaic power generation parameters, energy storage parameters, load parameters, and environmental parameters.

[0058] Specifically, this embodiment is based on an off-grid photovoltaic power generation system. The system includes a photovoltaic power generation module, an energy storage module, a power supply module, a heating module, and a control module. The photovoltaic power generation module mainly includes photovoltaic modules, which are used to convert light energy into electrical energy and transmit it to the energy storage module. The energy storage module mainly includes a battery, which is used to store electrical energy and provide energy to the power supply module and the heating module. The power supply module connects to electrical loads and is used to transmit the electrical energy in the energy storage module to different electrical loads. The heating module mainly includes an electric heating device and a phase change energy storage material, which is used to generate heat from the electrical energy in the energy storage module and transmit it to the outside to meet external heating needs. In addition, the heating module is also used to heat the energy storage module to prevent the energy storage module from degrading in low-temperature scenarios.

[0059] Specifically, in step S100, during the operation of the off-grid photovoltaic power generation system, multi-dimensional parameters that can reflect the system's operating status are collected once every first set time interval. These parameters comprehensively cover key aspects such as power generation, energy storage, load, and environment during the system's operation.

[0060] Furthermore, the multidimensional parameters include:

[0061] Photovoltaic power generation parameters: output power, conversion efficiency, and module temperature;

[0062] Energy storage parameters: battery state of charge, battery temperature, phase change energy storage temperature, and energy storage capacity;

[0063] Load parameters: power supply load, power supply load, key load indicators;

[0064] Environmental parameters: light intensity, ambient temperature, humidity.

[0065] Specifically, among photovoltaic (PV) power generation parameters, output power is used to characterize the amount of electrical energy output by a PV module per unit time under current sunlight conditions. It directly reflects the real-time power generation capacity of the PV system. If the output power is high, the system has more readily available energy; otherwise, it needs to rely on energy storage to supplement it. Conversion efficiency is used to characterize the proportion of solar energy absorbed by the PV module that is converted into electrical energy. It reflects the performance and operating status of the PV module. Module temperature is used to characterize the temperature of the PV module itself when it is working. The performance of PV modules is sensitive to temperature. Excessive temperature will lead to a decrease in conversion efficiency. By collecting the module temperature, the current power generation potential of the PV module can be judged.

[0066] Among energy storage parameters, the state of charge (SOC) of a battery characterizes the percentage of its current remaining charge relative to its rated capacity. It is a key parameter reflecting the energy storage status of an energy storage device, and its value determines how much energy the energy storage device can replenish to the system. If the SOC is low, the system may not be able to meet power and heat supply needs when photovoltaic output is insufficient, and priority should be given to ensuring critical loads. Battery temperature characterizes the internal or surface temperature of the battery during operation. The charging and discharging efficiency and lifespan of the battery are closely related to temperature. Excessively high or low temperatures will reduce battery performance. Phase change energy storage temperature characterizes the current temperature state of the phase change energy storage material, which is directly related to its energy storage and release capacity. Phase change materials can help optimize energy distribution strategies.

[0067] Among the load parameters, the power supply load power is used to characterize the amount of electrical energy required by all electrical loads (such as lighting equipment, power equipment, etc., excluding heating loads) per unit time, directly reflecting the real-time energy demand on the power supply side. If the power supply load power is high, the system needs to prioritize allocating more energy to the power supply end to avoid the load from shutting down or malfunctioning due to insufficient power supply. The heating load power is used to characterize the amount of heat energy required by the heating system (such as heating equipment, hot water supply equipment, etc.) per unit time, reflecting the real-time energy demand on the heating side. This parameter determines the proportion of energy that the system needs to allocate to the heating end. If the heating load demand is high, the heating energy supply needs to be reasonably increased on the basis of ensuring power supply. The critical load identifier is used to distinguish between critical loads and non-critical loads in the power supply or heating load (such as medical equipment and emergency lighting are critical power supply loads, and core area heating is a critical heating load). This parameter provides a basis for judging the priority of energy allocation. When the system energy is insufficient, the energy demand of loads with critical load identifiers can be prioritized to ensure the stability of core functions.

[0068] Among environmental parameters, illuminance characterizes the amount of solar radiation received per unit area and is a core external factor affecting the output power of photovoltaic (PV) modules. Higher illuminance typically results in higher PV output power, and vice versa. Ambient temperature characterizes the temperature of the external environment in which the system operates, affecting both the conversion efficiency of PV modules and the power of the heating load. Humidity characterizes the water vapor content in the ambient air. High humidity can lead to condensation and corrosion on the surface of PV modules or affect the insulation performance of energy storage devices, thus indirectly impacting the system's power generation and energy storage efficiency.

[0069] S200: Process the multidimensional parameters, analyze the real-time correlation between any two types of parameters, construct a parameter correlation matrix, and determine the core influencing parameter set of the current system based on the parameter correlation matrix.

[0070] Specifically, in step S200, the multidimensional raw parameters collected in step S100 are preprocessed to eliminate data noise and outliers, ensuring the reliability of the parameter data. Then, based on the preprocessed parameters, the real-time correlation degree between any two types of parameters is calculated. This correlation degree can quantify the degree of mutual influence between different parameters. Subsequently, based on the real-time correlation degree of all parameters, a parameter correlation matrix that intuitively reflects the correlation between parameters is constructed. Finally, based on the parameter correlation matrix, the influence of each parameter on the overall system operation and energy distribution is analyzed, and parameters that play a key role in the system operation are selected to form the core influencing parameter set of the current system.

[0071] Further, in step S200, the processing of the multidimensional parameters, analyzing the real-time correlation between any two types of parameters, and constructing a parameter correlation matrix includes:

[0072] S210: The multidimensional parameters are filtered and normalized using a sliding window filtering algorithm.

[0073] Further, step S210 includes:

[0074] S211: The original parameters are filtered using a sliding window filtering algorithm, and the size of the sliding window is a second set duration.

[0075] Specifically, in this embodiment, a sliding window filtering algorithm is used to eliminate random noise generated during the multi-dimensional parameter acquisition process due to sensor errors, electromagnetic interference, etc. In this embodiment, the second preset duration is set to 5-10 minutes. The duration of the sliding window can be adjusted according to the actual scene. For example, it can be shortened to 3 minutes in outdoor scenes with drastic light fluctuations, and extended to 15 minutes in fixed scenes with stable light. The specific process of the sliding window filtering algorithm is prior art and will not be described in detail here.

[0076] S212: Normalize the filtered parameters and map them to... The normalization formula for the interval is:

[0077] Formula (1)

[0078] in, This represents the original value of the parameter. This represents the minimum value of the parameter. This indicates the maximum value of the parameter. This represents the normalized parameter value.

[0079] S220: Dynamically calculate the relationship characteristic parameter between any two types of parameters based on the working condition scenario. The relationship characteristic parameter is used to characterize the degree of physical correlation between the two parameters.

[0080] Specifically, in step S220, the current operating condition of the system is first clarified. The physical correlation logic between parameters differs under different operating conditions. For example, the correlation between "light intensity and photovoltaic output power" is close under sunny conditions, but weak under nighttime conditions; or the correlation between "heating load power and ambient temperature" is close under low temperature conditions in winter, but weak under high temperature conditions in summer.

[0081] Further, step S220 includes:

[0082] S221: Collect continuous time-series data sequences of two types of parameters within a preset period, each of the continuous time-series data sequences including a set number of parameter values.

[0083] Specifically, in step S221, the continuous time series data sequence is the same data type as the sliding window mentioned above. However, since the core function of the continuous time series data sequence is to analyze the long-term correlation characteristics of the two types of parameters, it needs to cover a sufficiently long time period to include typical changes in the working conditions. Therefore, the time length corresponding to the continuous time series data sequence is greater than the time length corresponding to the sliding window.

[0084] S222: Based on continuous time-series data sequences of two types of parameters, analyze and calculate the relational characteristic parameters that reflect the degree of linear correlation between the two types of parameters.

[0085] Specifically, in step S222, the continuous time-series data sequence obtained in step S221 is substituted into the calculation formula to calculate the correlation coefficient between the two types of parameters. The formula for calculating the correlation coefficient is as follows:

[0086] Formula (II)

[0087] in, This represents the relationship between two types of parameters. , They are respectively Time parameters , The normalized value, Indicates parameters The average value of all values ​​in the corresponding continuous time series data sequence. Indicates parameters The average value of all values ​​in the corresponding continuous time series data sequence.

[0088] S230: Calculate the real-time correlation degree based on the normalized values ​​of the two parameters and the corresponding relational characteristic parameters.

[0089] Specifically, in step S230, the normalized values ​​of the two selected parameters are substituted into the calculation model, combined with the relational characteristic parameters. The weighting effect of numerical differences is used to obtain the real-time correlation results.

[0090] The calculation model for the real-time correlation degree is shown in Formula (III):

[0091] Formula (3)

[0092] in, express Time parameters and parameters Real-time relevance, real-time relevance is determined by... and The numerical difference and the IoT correlation between them are calculated, which can effectively reflect the degree of influence of various parameters on the system operation; the range of real-time correlation is... Among them, "small numerical difference + strong physical correlation" will make the result approach 1, while "large numerical difference + weak physical correlation" will make the result approach 0.

[0093] S240: Traverse the multidimensional parameters and construct a parameter correlation matrix based on the real-time correlation between any two parameters.

[0094] Specifically, the method iterates through all types of parameters, calculates the real-time correlation between every two parameters, and constructs a parameter correlation matrix based on all real-time correlations. The following example illustrates a working condition characteristic based on a home scenario, along with a corresponding parameter correlation matrix. It should be noted that the control method provided in this embodiment can also be used in regional power supply and heating scenarios.

[0095] Operating conditions: Noon on a sunny winter day (light intensity of 800W / m²) 2 The ambient temperature is -5℃, the power of residential electrical load (lighting, small household appliances) is 2kW, the power of heating load (electric heater) is 3kW, the state of charge (SOC) of the battery is 80%, and the power of photovoltaic module transmission is 5kW.

[0096] The 12 parameters selected above, excluding humidity, are as follows:

[0097] Photovoltaic power generation parameters (P1-P3): P1 - output power, P2 - conversion efficiency, P3 - module temperature;

[0098] Energy storage parameters (S1-S4): S1 - battery state of charge, S2 - battery temperature, S3 - phase change energy storage temperature, S4 - energy storage capacity;

[0099] Load parameters (L1-L3): L1 - Power supply load power, L2 - Power supply load power, L3 - Key load identifier;

[0100] Environmental parameters (E1-E2): E1 - light intensity, E2 - ambient temperature.

[0101] Based on the above operating condition characteristics, the corresponding parameter correlation matrix is ​​obtained, as shown in Table 1 below:

[0102] Table 1

[0103]

[0104] Further, in step S200, determining the core influencing parameter set of the current system based on the parameter correlation matrix includes:

[0105] S250: By combining the real-time correlation degree between any parameter in the parameter correlation matrix and other parameters, as well as the normalized values ​​of the other parameters, the influence index of the parameter on the system is calculated.

[0106] Specifically, after establishing the parameter correlation matrix as shown in Table 1, select any type of parameter and obtain all its real-time correlation degrees from the parameter correlation matrix. At the same time, obtain the normalized values ​​of other types of parameters. Substitute the real-time correlation degree and the normalized values ​​of other types of parameters into the influence index calculation model. The model is shown in the following formula (IV):

[0107] Formula (IV)

[0108] in, express Time parameters The influence index; in the influence index formula, the degree of correlation of a single real-time parameter reflects the strong local correlation between the parameter and the system. By accumulating all the degrees of correlation of real-time parameters and the normalized values ​​of the corresponding parameters, the global linkage influence is achieved, avoiding the global imbalance of the system caused by local optima.

[0109] S260: Traverse the multidimensional parameters and calculate the influence index of all parameters on the system.

[0110] Specifically, in step S260, referring to the calculation method in step S250, the influence index of all parameters is calculated one by one.

[0111] S270: Select parameters whose influence index ranking is within the set range to form a core influence parameter set.

[0112] Specifically, in step S270, after obtaining the influence index of all parameters, all influence indices are sorted from largest to smallest. Influence indices within a set range are selected, and the parameters corresponding to these indices are used as core influence parameters, thus establishing a core influence parameter set. In this embodiment, the set range is the top five in the ranking from largest to smallest.

[0113] S300: Based on the core influence parameter set, evaluate the power supply priority and heating priority respectively; and determine the basic energy allocation ratio for power supply and heating by combining the total allocable energy of the system.

[0114] Specifically, in step S300, the power supply priority index and the heating priority index are calculated based on the different impacts of each core influencing parameter on power supply and heating. The power supply priority index measures the degree to which the system prioritizes power supply needs, while the heating priority index measures the degree to which the system prioritizes heating needs. At the same time, the total energy available for allocation in the system is calculated by combining the output energy of the photovoltaic power generation module and the releaseable energy of the energy storage module. Finally, based on the power supply priority index, the heating priority index, and the total allocated energy of the system, the basic energy allocation ratio for power supply and heating is determined and used for subsequent energy allocation.

[0115] Further, in step S300, the evaluation of power supply priority and heating priority based on the core influence parameter set includes:

[0116] S310: For each parameter in the core influencing parameter set, assign its influence weight on heating and power supply respectively.

[0117] Specifically, in step S310, the influence weight database is called according to the parameter type in the core influence parameter set. The influence weight database includes multiple normalized value ranges corresponding to each parameter in the multidimensional parameters. After selecting a normalized value range for each parameter, a set of corresponding weight coefficients can be obtained. This set of weight coefficients includes the heating influence weight and power supply influence weight corresponding to each parameter.

[0118] S320: Combining the influence index of each parameter, the power supply priority index and heating priority index are obtained through weighted calculation.

[0119] Specifically, in step S320, the five influence indices and their corresponding heating influence weights and power supply influence weights are input into the power supply priority index model and the heating priority index model, respectively. The power supply priority index is shown in formula (V) below, and the heating priority index is shown in formula (VI) below:

[0120] Formula (5)

[0121] Formula (VI)

[0122] in express Power supply priority index at any given time. express Heating priority index at any given time. Indicates parameters Weighting of the impact on power supply Indicates parameters The weight of the impact on heating supply, and .

[0123] Further, in step S300, the determination of the basic energy allocation ratio between power supply and heating based on the total distributable energy of the combined system includes:

[0124] S330: The total distributable energy of the system is calculated by combining the photovoltaic power generation output energy of the system with the energy that the battery can release, which is determined by the battery state of charge, battery capacity and battery discharge efficiency.

[0125] Specifically, in step S330, the total allocatable energy of the system is calculated according to the energy calculation model, which is shown in the following formula (VII):

[0126] Formula (VII)

[0127] in express The total energy that the system can allocate within the third set time period after the time point. The third set time period matches the parameter acquisition cycle and can be understood as being the same as the first set time period. express Within the third set time period after the initial time, the system's photovoltaic power generation output energy express Always monitor the battery's state of charge. Indicates battery capacity. Indicates the battery discharge efficiency; This indicates that the energy is intended for critical loads, such as emergency lighting.

[0128] S340: Based on the relative relationship between the power supply priority index and the heating priority index, the power supply basic allocation ratio is determined, so the sum of the power supply basic allocation ratio and the heating basic allocation ratio is 1.

[0129] Specifically, in step S340, the power supply priority index and heating priority index obtained in step S320 are used to calculate the power supply basic allocation ratio. The calculation of the power supply basic allocation ratio is shown in the following formula (VIII):

[0130] Formula (8)

[0131] in, express The basic power supply allocation ratio at all times;

[0132] Correspondingly, the allocation ratio of the heating infrastructure is shown in Formula (IX):

[0133] Formula (IX)

[0134] in, express The basic allocation ratio of heating supply at all times.

[0135] After obtaining the basic power supply allocation ratio and the basic heating allocation ratio, the system can rationally allocate the power supply and heating energy according to the basic power supply allocation ratio and the basic heating allocation ratio, effectively improving the intelligence level and response speed of the system's energy allocation, enhancing the system's adaptability to changing operating conditions, improving energy utilization efficiency, and ensuring the stable operation of power supply and heating loads.

[0136] S400: Dynamically adjust the energy distribution ratio of power supply and heating based on the deviation between the actual load demand and the basic energy distribution ratio.

[0137] Specifically, in step S400, when the system outputs power and heat according to the basic allocation ratio determined in step S340, it is also necessary to collect the actual demand data of the power supply load and the heat supply load in real time; compare the actual load demand with the theoretical supply calculated based on the energy allocation ratio, and analyze the magnitude and direction of the deviation between the two; based on the deviation and combined with the stability requirements of system operation, dynamically correct the energy allocation ratio of power supply and heat supply, so that the energy allocation ratio is more in line with the actual demand of the load, and avoid energy waste or abnormal load operation caused by supply and demand mismatch.

[0138] Further, step S400 includes:

[0139] S410: Calculate the power supply energy deviation and the heating energy deviation. The power supply energy deviation is the difference between the product of the power supply load demand energy and the power supply base allocation ratio and the remaining allocable energy. The heating energy deviation is the difference between the product of the heating load demand energy and the heating base allocation ratio and the remaining allocable energy.

[0140] Specifically, in step S410, during the process of allocating electrical energy according to the basic allocation ratio in step S320, the system acquires the remaining allocable energy in real time. Then, it calculates the available energy for power supply and heating based on the basic power supply allocation ratio and the basic heating allocation ratio, respectively. Finally, it calculates the power supply energy deviation based on the power load demand and the available power supply energy, and the heating energy deviation based on the heating load demand and the available heating energy. The power supply energy deviation and the heating energy deviation not only have numerical characteristics but also directional characteristics, which determine whether to adjust the basic energy allocation ratio in a positive or negative direction.

[0141] S420: Prioritize the adjustment of the power supply energy deviation and the heating energy deviation according to the importance of the load.

[0142] Specifically, in step S420, firstly, the key load identifiers collected in step S100 are retrieved to identify the load types with key identifiers among the power supply loads and heating loads; then, the current operating scenario (such as low-temperature winter operating conditions, emergency disaster relief operating conditions, and normal operating conditions) is determined based on the environmental parameters collected in step S100, and the sensitivity of power supply or heating demand under different operating conditions is determined; next, a priority ranking logic is established: if the power supply load contains a key identifier and the heating load is a non-key load, the priority of power supply energy deviation adjustment is higher than that of heating; if the heating load contains a key identifier and the power supply load is a non-key load, the priority of heating energy deviation adjustment is higher than that of power supply; if both types of loads contain key identifiers, the priority is further clarified based on the sensitivity of the operating conditions.

[0143] S430: If either the power supply energy deviation or the heating energy deviation exceeds the preset range, the basic allocation ratio is corrected according to the deviation and its corresponding basic allocation ratio and correction coefficient, and the allocation ratio corresponding to the other deviation is corrected in the opposite direction simultaneously.

[0144] Specifically, in step S430, the power supply energy deviation and heating energy deviation are first compared with the preset deviation thresholds to determine whether there is a single deviation exceeding the limit. When only one deviation exceeds the limit, the system's preset correction coefficient is retrieved to correct the basic allocation ratio. Here, the power supply energy deviation exceeding the limit is taken as an example, and the correction model is shown in the following formula (x):

[0145] Formula (10)

[0146] in, This indicates the corrected power distribution ratio. Indicates the actual power of the supplied load. This represents the correction factor, with a value range of [value missing]. ;

[0147] The corrected heating distribution ratio is now: .

[0148] S440: If both the power supply energy deviation and the heating energy deviation exceed the preset range, the deviation with higher priority is corrected according to the adjustment priority, and the sum of the power supply and heating energy distribution ratio is kept at 1 during the correction process.

[0149] Specifically, in step S440, when it is determined that both the power supply energy deviation and the heating energy deviation exceed the limit, the basic allocation ratio corresponding to the deviation with higher priority is adjusted according to the priority ranking in step S420 and the correction method in step S430, and the basic allocation ratio of the other deviation is adjusted accordingly.

[0150] It should be noted that during the correction process, both the power supply distribution ratio and the heating distribution ratio are maintained within a set range. In this embodiment, the set range is [missing information]. This is to avoid an excessively high proportion of power supply or heating, which could affect the normal operation of another function.

[0151] Furthermore, prior to step S430, the method includes determining a preset range, specifically including the following steps S430-1 to S430-3:

[0152] S430-1: Acquire ambient temperature deviation, critical load power percentage, and battery state of charge deviation.

[0153] The ambient temperature deviation is the difference between the current ambient temperature and the average ambient temperature under the same historical conditions. If the difference is negative, it means that the current temperature is lower than the historical average. The threshold for setting the heating energy deviation is tended to be reduced to improve the sensitivity of heating demand.

[0154] The critical load power ratio is the ratio of the total power of critical loads in the power supply module / heating module to the total power of the corresponding load type. The higher the ratio, the more likely it is to reduce the preset threshold of energy deviation of the corresponding load, so as to improve the sensitivity of critical load demand.

[0155] The battery state of charge deviation represents the difference between the current battery state of charge and the rated battery state of charge. If the difference is negative, it tends to reduce the preset threshold of the power supply and heating energy deviation at the same time to avoid excessive energy distribution leading to energy depletion.

[0156] S430-2: Input the acquired ambient temperature deviation, key load power ratio and battery state of charge deviation into the dynamic adjustment model so that the dynamic adjustment model outputs a preset threshold for power supply energy deviation and a threshold for heating energy deviation.

[0157] Specifically, based on the above-mentioned environmental temperature deviation, critical load power ratio, and battery state of charge deviation, the data are quantified and input into the dynamic adjustment model. The dynamic adjustment model is shown in formulas (XI) and (XII) below:

[0158] Formula (XI)

[0159] Formula (12)

[0160] in, This indicates the preset threshold for power supply deviation. This indicates the preset threshold for heating energy deviation. and This represents the initial fixed threshold. This represents the normalized value of the ambient temperature deviation. This represents the normalized value of the critical load power percentage. This represents the normalized value of the battery's state of charge deviation.

[0161] S430-3: Determine the preset range based on the preset threshold for power supply energy deviation and the preset threshold for heating energy deviation.

[0162] Specifically, based on calculations and Based on this, the preset range for power supply energy deviation is determined as follows: The preset range for heating energy deviation is: .

[0163] Example 2

[0164] Please refer to Figure 2 This embodiment provides a photovoltaic power generation off-grid power supply and heating system, applied to the photovoltaic power generation off-grid power supply and heating control method as described in Embodiment 1, including:

[0165] The acquisition module 10 is configured to acquire multi-dimensional parameters during the operation of the off-grid photovoltaic power generation system. The multi-dimensional parameters include at least photovoltaic power generation parameters, energy storage parameters, load parameters, and environmental parameters.

[0166] The processing module 20 is configured to process the multidimensional parameters, analyze the real-time correlation between any two types of parameters, construct a parameter correlation matrix, and determine the core influencing parameter set of the current system based on the parameter correlation matrix.

[0167] Calculation module 30 is configured to evaluate power supply priority and heating priority based on the core influence parameter set; and determine the basic energy allocation ratio between power supply and heating based on the total allocable energy of the system.

[0168] Adjustment module 40 is configured to dynamically adjust the energy distribution ratio of power supply and heating based on the deviation between the actual load demand and the basic energy distribution ratio.

[0169] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A photovoltaic power generation off-grid power supply heating control method, characterized in that, The application comprises the following steps: Collecting multi-dimensional parameters in the operation process of a photovoltaic power generation off-grid system, wherein the multi-dimensional parameters at least include photovoltaic power generation parameters, energy storage parameters, load parameters and environmental parameters; Processing the multi-dimensional parameters, analyzing the real-time correlation degree between any two types of parameters, constructing a parameter correlation matrix, and determining the core influence parameter set of the current system based on the parameter correlation matrix; Based on the core influence parameter set, the power supply priority and the heat supply priority are respectively evaluated; Combined with the total allocatable energy of the system, the basic energy distribution ratio of power supply and heat supply is determined; According to the deviation of the actual demand of the load and the basic energy distribution ratio, the energy distribution ratio of power supply and heat supply is dynamically adjusted; The determination of the core influence parameter set of the current system based on the parameter correlation matrix comprises the following steps: Combined with the real-time correlation degree of any parameter and other parameters in the parameter correlation matrix and the normalized value of the other parameters, the influence degree index of the parameter on the system is calculated; All parameters are traversed to calculate the influence degree index of the system; The parameters with the influence degree index ranking within a certain range are screened out to form the core influence parameter set; The evaluation of the power supply priority and the heat supply priority based on the core influence parameter set comprises the following steps: For each parameter in the core influence parameter set, the influence weight of the parameter on heat supply and power supply is respectively given; Combined with the influence degree index of each parameter, the power supply priority index and the heat supply priority index are respectively calculated by weighted calculation.

2. The photovoltaic off-grid power supply heating control method according to claim 1, characterized in that, The multi-dimensional parameters comprise: Photovoltaic power generation parameters: output power, conversion efficiency, component temperature; Energy storage parameters: battery state of charge, battery temperature, phase change energy storage temperature, energy storage capacity; Load parameters: power supply load power, heat supply load power, key load identification; Environmental parameters: light intensity, environmental temperature, humidity.

3. The photovoltaic off-grid power supply heating control method according to claim 2, characterized in that, The processing of the multi-dimensional parameters, the analysis of the real-time correlation degree between any two types of parameters, and the construction of the parameter correlation matrix comprise the following steps: The multi-dimensional parameters are filtered and normalized by using a sliding window filtering algorithm; The relationship characteristic parameters of any two types of parameters are dynamically calculated based on the working condition scene, and the relationship characteristic parameters are used to represent the physical correlation degree between the two parameters; The real-time correlation degree is calculated according to the normalized values of the two parameters and the corresponding relationship characteristic parameters; The parameter correlation matrix is constructed according to the real-time correlation degree of any two parameters.

4. The photovoltaic off-grid power supply heating control method according to claim 3, characterized in that, The dynamic calculation of the relationship characteristic parameters of any two types of parameters based on the working condition scene comprises the following steps: Collecting continuous time sequence data sequences of the two types of parameters within a preset period, each continuous time sequence data sequence comprising a certain number of parameter values; Based on the continuous time sequence data sequences of the two types of parameters, the relationship characteristic parameters reflecting the linear correlation degree between the two types of parameters are analyzed and calculated.

5. The photovoltaic off-grid power supply heating control method according to claim 4, characterized in that, Combined with the photovoltaic power generation output energy of the system and the releasable energy of the battery determined by the battery state of charge, battery capacity and battery discharge efficiency, the total allocatable energy of the system is calculated; ​ According to the relative relationship between the power supply priority index and the heat supply priority index, a power supply basic distribution ratio is determined, and a sum of the power supply basic distribution ratio and a heat supply basic distribution ratio is 1.

6. The photovoltaic off-grid power supply heating control method according to claim 5, characterized in that, The dynamic adjustment of the energy distribution ratio of power supply and heat supply according to the deviation of actual load demand from the basic energy distribution ratio comprises: a power supply energy deviation and a heat supply energy deviation are calculated, the power supply energy deviation being a difference between a power supply load demand energy and a product of a power supply basic distribution ratio and a remaining distributable energy, and the heat supply energy deviation being a difference between a heat supply load demand energy and a product of a heat supply basic distribution ratio and the remaining distributable energy; an adjustment priority of the power supply energy deviation and the heat supply energy deviation is determined according to load importance ranking; if one of the power supply energy deviation or the heat supply energy deviation exceeds a preset range, a basic distribution ratio is corrected according to the deviation, a corresponding basic distribution ratio and a correction coefficient, and a distribution ratio corresponding to the other deviation is synchronously and reversely corrected; if both the power supply energy deviation and the heat supply energy deviation exceed the preset range, a high-priority deviation is corrected according to the adjustment priority, and a sum of the power supply energy distribution ratio and the heat supply energy distribution ratio is kept as 1 during the correction process; during the correction process, the power supply distribution ratio and the heat supply distribution ratio are kept to satisfy a set interval.

7. The photovoltaic off-grid power supply heating control method according to claim 6, characterized in that, Before the if one of the power supply energy deviation or the heat supply energy deviation exceeds the preset range, a basic distribution ratio is corrected according to the deviation, a corresponding basic distribution ratio and a correction coefficient, and a distribution ratio corresponding to the other deviation is synchronously and reversely corrected, the method further comprises: an environment temperature deviation, a key load power proportion and a storage battery state of charge deviation are obtained, the environment temperature deviation being a difference between a current environment temperature and an average environment temperature in a same period and under a same working condition, the key load power proportion being a ratio of a total power of key loads in a power supply module / heat supply module to a total power of corresponding load types, and the storage battery state of charge deviation representing a difference between a current storage battery state of charge and a rated storage battery state of charge; the obtained environment temperature deviation, key load power proportion and storage battery state of charge deviation are input into a dynamic adjustment model, so that the dynamic adjustment model outputs a power supply energy deviation preset threshold and a heat supply energy deviation threshold; the preset range is determined based on the power supply energy deviation preset threshold and the heat supply energy deviation threshold.

8. A photovoltaic electricity generation off-grid power supply heating system, characterized in that, The method is applied to the photovoltaic power generation off-grid power supply and heat supply control method of any one of claims 1-7, and comprises: a collection module (10) configured to collect multi-dimensional parameters in a photovoltaic power generation off-grid system running process, the multi-dimensional parameters at least including photovoltaic power generation parameters, energy storage parameters, load parameters and environment parameters; a processing module (20) configured to process the multi-dimensional parameters, analyze a real-time correlation degree between any two types of parameters, construct a parameter correlation matrix, and determine a core influence parameter set of a current system based on the parameter correlation matrix. a computing module (30) configured to evaluate power supply priority and heat supply priority respectively based on the core influence parameter set, and determine a basic energy distribution ratio of power supply and heat supply in combination with total distributable energy of the system; an adjusting module (40) configured to dynamically adjust the energy distribution ratio of power supply and heat supply according to deviation of actual load demand and the basic energy distribution ratio.

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