Building energy dynamic control method and system based on distributed photovoltaic direct-current driving

Abstract

The application provides a kind of based on distributed photovoltaic direct current drive building energy dynamic control method and system, the method comprises: detecting several direct current loads, and creating digital twin model;According to the digital twin model, the target photovoltaic parameters corresponding to the output of the photovoltaic array are collected in real time, and the photovoltaic distribution proportion of each direct current load in the photovoltaic array is calculated in real time according to the target photovoltaic parameters;According to the photovoltaic distribution proportion, the initial energy distribution function adapted to the photovoltaic array is created in real time, and the initial energy distribution function is optimized by the preset particle swarm algorithm, to generate the corresponding target energy distribution function in real time;The energy value corresponding to each direct current load is calculated in real time by the target energy distribution function, and each direct current load is allocated corresponding electric energy according to the size of the energy value.The application can improve energy utilization rate, and improve control efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy control, and particularly relates to a building energy dynamic control method and system based on distributed photovoltaic direct current driving. BACKGROUND

[0002] With the increasing application of distributed photovoltaic in building energy systems, the existing mainstream technology adopts an alternating current driving mode: after converting solar energy into direct current, the photovoltaic panel converts the direct current into alternating current in real time through an inverter, and supplies the building alternating current load, and is connected to the power grid or stored in the alternating current energy storage to reduce the dependence of the building on the power grid.

[0003] Among them, with the increasing proportion of LED lighting, direct current charging piles and direct current air conditioners and other direct current loads in modern buildings, the existing technology needs to first convert photovoltaic direct current into alternating current during alternating current driving, and then converts alternating current back to direct current through a converter to supply direct current loads to complete corresponding energy control.

[0004] Further, in the process of real-time conversion of photovoltaic direct current, the inverter will generate primary energy loss, and in the process of converting alternating current back to direct current to supply direct current loads, the converter will generate secondary energy loss, thereby causing a lot of energy waste, which reduces the energy use efficiency. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a building energy dynamic control method and system based on distributed photovoltaic direct current driving to solve the problem of a lot of energy waste in the process of building energy dynamic control in the prior art, which reduces the energy use efficiency.

[0006] The first aspect of the embodiment of the present application provides:

[0007] A building energy dynamic control method based on distributed photovoltaic direct current driving, wherein the method comprises:

[0008] Real-time detection of a plurality of direct current loads corresponding to a target building, and real-time creation of a digital twin model of a photovoltaic array corresponding to the target building;

[0009] Real-time acquisition of target photovoltaic parameters corresponding to the output of the photovoltaic array according to the digital twin model, and real-time calculation of a photovoltaic distribution ratio of each direct current load in the photovoltaic array according to the target photovoltaic parameters;

[0010] Real-time creation of an initial energy distribution function adapted to the photovoltaic array according to the photovoltaic distribution ratio, and optimization of the initial energy distribution function through a preset particle swarm algorithm to generate a corresponding target energy distribution function in real time;

[0011] The energy value corresponding to each of the direct current loads is calculated in real time by the target energy distribution function, and the corresponding electric energy is distributed to each of the direct current loads according to the size of the energy value.

[0012] The beneficial effects of the present application are that the target photovoltaic parameters corresponding to the actual working process of the current photovoltaic array can be obtained in real time by creating a digital twin model corresponding to the photovoltaic array inside the target building in real time, and based on this, the photovoltaic distribution ratio of each direct current load in the current photovoltaic array can be calculated in real time according to the current target photovoltaic parameters, that is, the proportion of the energy required by each direct current load, and based on this, the initial energy distribution function required can be created in real time, and finally optimized into the target energy distribution function required, and based on this, the real-time electric energy required by each direct current load can be calculated in real time through the target energy distribution function, so that the direct energy supply of each direct current load can be realized, the redundant conversion process is saved, and the control efficiency is improved.

[0013] Further, the step of collecting the target photovoltaic parameters corresponding to the output of the photovoltaic array in real time according to the digital twin model, and calculating the photovoltaic distribution ratio of each of the direct current loads in the photovoltaic array in real time according to the target photovoltaic parameters comprises:

[0014] a plurality of unit regions corresponding to the photovoltaic array are divided in the digital twin model in real time, and real-time electrical parameters corresponding to each of the unit regions are collected every preset time;

[0015] The real-time solar irradiance of the roof of the target building and the surface temperature of the photovoltaic component are collected in real time to create a corresponding target photovoltaic parameter matrix in real time, and historical energy consumption parameters corresponding to each of the direct current loads generated in a preset time period are extracted in real time in a historical database;

[0016] The photovoltaic distribution ratio of each of the direct current loads is calculated according to the real-time electrical parameters, the target photovoltaic parameter matrix and the historical energy consumption parameters based on the analytic hierarchy process.

[0017] Further, the step of calculating the photovoltaic distribution ratio of each of the direct current loads according to the real-time electrical parameters, the target photovoltaic parameter matrix and the historical energy consumption parameters based on the analytic hierarchy process comprises:

[0018] The photovoltaic distribution ratio of each of the direct current loads is set as a target layer, and the real-time electrical parameters, the target photovoltaic parameter matrix and the historical energy consumption parameters are set as a criterion layer;

[0019] constructing a corresponding judgment matrix according to the criteria layer in real time based on a preset scale method, and performing correlation assignment on the target layer to generate a corresponding assignment matrix in real time;

[0020] calculating a characteristic vector corresponding to each of the DC loads in real time according to the judgment matrix and the assignment matrix, and converting the characteristic vector into a photovoltaic distribution ratio corresponding to each of the DC loads in real time.

[0021] Further, the step of creating an initial energy distribution function adapted to the photovoltaic array according to the photovoltaic distribution ratio in real time comprises:

[0022] taking the real-time total power of the photovoltaic array, the number of DC loads, and the photovoltaic distribution ratio of each DC load as input variables output by the digital twin model;

[0023] setting the initial distribution power of each DC load as an output variable, and extracting a series-parallel loss coefficient corresponding to the photovoltaic array in the digital twin model in real time;

[0024] correspondingly fusing the input variables, the output variable, and the series-parallel loss coefficient into a preset distribution function to form the initial energy distribution function.

[0025] Further, the step of correspondingly fusing the input variables, the output variable, and the series-parallel loss coefficient into a preset distribution function to form the initial energy distribution function comprises:

[0026] obtaining a series-parallel topology structure of each sub-array in the photovoltaic array in real time, and determining a series-parallel loss coefficient weight value corresponding to each sub-array based on the topology structure;

[0027] According to the photovoltaic distribution ratio of each DC load, the real-time total power of the photovoltaic array is decomposed into a preliminary distribution power corresponding to each DC load, and the preliminary distribution power is taken as a reference value of the output variable;

[0028] The real-time total power of the photovoltaic array, the number of DC loads, and the photovoltaic distribution ratio in the input variables, the reference value of the output variable, the series-parallel loss coefficient, and its weight value are sequentially substituted into the variable corresponding positions of the preset distribution function, and the correlation between the parameters is fused through multiplication operation to form the initial energy distribution function.

[0029] Further, the step of optimizing the initial energy distribution function by a preset particle swarm algorithm to generate a corresponding target energy distribution function in real time comprises:

[0030] The deviation value of the output result of the initial energy distribution function from the rated power of each direct current load is taken as a fitness function core parameter of the preset particle swarm algorithm, and a total photovoltaic power constraint threshold and a single load power fluctuation threshold are set as position boundary conditions of the particle swarm;

[0031] The position parameter and the speed parameter of each particle in the particle swarm are initialized, the position parameter corresponds to the correction coefficient of each variable in the initial energy distribution function, the speed parameter corresponds to the iteration step of the correction coefficient, and the real-time photovoltaic parameter fed back by the digital twin model is taken as a dynamic input quantity of the iteration of the particle swarm;

[0032] The preset particle swarm algorithm is run according to a preset iteration number, the particle position and the speed are updated by comparing the individual optimal solution and the global optimal solution of the particle in each iteration process, and when the iteration result meets the convergence condition of the fitness function, the current particle position parameter is substituted into the initial energy distribution function to obtain the target energy distribution function.

[0033] Further, the step of calculating the energy value corresponding to each direct current load in real time through the target energy distribution function comprises:

[0034] The correction coefficient corresponding to each direct current load is output in real time through the target energy distribution function, and the correction coefficient is multiplied by the output voltage of the photovoltaic array collected in real time by the digital twin model to calculate the reference voltage value corresponding to each direct current load in real time;

[0035] The reference voltage value is subjected to power calculation with the rated resistance of the direct current load to calculate the intermediate energy value corresponding thereto in real time;

[0036] The real-time power factor corresponding to each direct current load is collected, and the real-time power factor is multiplied by the intermediate energy value to calculate the energy value corresponding to the direct current load in real time.

[0037] The second aspect of the embodiment of the application provides:

[0038] A building energy dynamic control system based on distributed photovoltaic direct current driving, wherein the system comprises:

[0039] A detection module is configured to detect a plurality of direct current loads corresponding to a target building in real time, and create a digital twin model of a photovoltaic array corresponding to the target building in real time.

[0040] a calculation module, configured to collect target photovoltaic parameters corresponding to the output of the photovoltaic array according to the digital twin model, and calculate photovoltaic distribution ratios of each direct-current load in the photovoltaic array according to the target photovoltaic parameters;

[0041] a creation module, configured to create an initial energy distribution function adapted to the photovoltaic array according to the photovoltaic distribution ratios, and optimize the initial energy distribution function through a preset particle swarm algorithm to generate a target energy distribution function in real time;

[0042] a distribution module, configured to calculate energy values corresponding to each direct-current load through the target energy distribution function, and distribute corresponding electric energy to each direct-current load according to the size of the energy values.

[0043] Further, the calculation module is specifically configured to:

[0044] divide a plurality of series-parallel unit regions corresponding to the photovoltaic array in the digital twin model in real time, and collect real-time electrical parameters corresponding to each unit region every preset time;

[0045] collect real-time solar irradiance of the target building roof and surface temperature of the photovoltaic component in real time to create a target photovoltaic parameter matrix in real time, and extract historical energy consumption parameters corresponding to each direct-current load in a preset time period in a historical database in real time;

[0046] calculate photovoltaic distribution ratios of each direct-current load based on an analytic hierarchy process according to the real-time electrical parameters, the target photovoltaic parameter matrix, and the historical energy consumption parameters.

[0047] Further, the calculation module is specifically configured to:

[0048] set the photovoltaic distribution ratio of each direct-current load as a target layer, and set the real-time electrical parameters, the target photovoltaic parameter matrix, and the historical energy consumption parameters as a criterion layer;

[0049] construct a corresponding judgment matrix based on a preset scale method according to the criterion layer, and perform correlation assignment on the target layer to generate a corresponding assignment matrix in real time;

[0050] calculate a feature vector corresponding to each direct-current load according to the judgment matrix and the assignment matrix, and convert the feature vector into a photovoltaic distribution ratio corresponding to each direct-current load in real time.

[0051] Further, the creation module is specifically configured to:

[0052] the real-time total power of the photovoltaic array, the number of the direct current loads, and the photovoltaic allocation proportion of each direct current load as input variables;

[0053] set the initial allocation power of each direct current load as an output variable, and extract the series-parallel loss coefficient corresponding to the photovoltaic array in the digital twin model in real time;

[0054] correspondingly fuse the input variables, the output variable, and the series-parallel loss coefficient into a preset allocation function to correspondingly form the initial energy allocation function.

[0055] Further, the creating module is specifically configured to:

[0056] obtain the series-parallel topology structure of each sub-array in the photovoltaic array in real time, and determine the series-parallel loss coefficient weight value corresponding to each sub-array based on the topology structure;

[0057] According to the photovoltaic allocation proportion of each direct current load, the real-time total power of the photovoltaic array is decomposed into the preliminary allocation power corresponding to each direct current load, and the preliminary allocation power is taken as the reference value of the output variable;

[0058] The real-time total power of the photovoltaic array, the number of the direct current loads, and the photovoltaic allocation proportion in the input variables, the reference value of the output variable, the series-parallel loss coefficient, and the weight value thereof are sequentially substituted into the variable corresponding positions of the preset allocation function, and the correlation between parameters is fused through multiplication operation to form the initial energy allocation function.

[0059] Further, the distribution module is specifically configured to:

[0060] take the deviation value between the output result of the initial energy allocation function and the rated power of each direct current load as the fitness function core parameter of the preset particle swarm algorithm, and set the photovoltaic total power constraint threshold and the single load power fluctuation threshold as the position boundary condition of the particle swarm;

[0061] initialize the position parameter and the speed parameter of each particle in the particle swarm, wherein the position parameter corresponds to the correction coefficient of each variable in the initial energy allocation function, and the speed parameter corresponds to the iteration step length of the correction coefficient, and take the real-time photovoltaic parameters fed back by the digital twin model as the dynamic input quantity of the iteration of the particle swarm;

[0062] running the preset particle swarm algorithm according to a preset iteration number, updating the particle position and speed by comparing the individual optimal solution and the global optimal solution in each iteration process, and when the iteration result meets the convergence condition of the fitness function, substituting the current particle position parameter into the initial energy distribution function to obtain the target energy distribution function.

[0063] Further, the distribution module is specifically used for:

[0064] The target energy distribution function is used to output a correction coefficient corresponding to each direct current load in real time, and the correction coefficient is multiplied by the output voltage of the photovoltaic array collected by the digital twin model in real time to calculate a reference voltage value corresponding to each direct current load in real time.

[0065] The reference voltage value is used to perform power calculation on the rated resistance of the direct current load to calculate a corresponding intermediate energy value in real time.

[0066] A real-time power factor corresponding to each direct current load is collected, and the real-time power factor is multiplied by the intermediate energy value to calculate an energy value corresponding to the direct current load in real time.

[0067] The third aspect of the embodiment of the present application provides:

[0068] A computer comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the building energy dynamic control method based on distributed photovoltaic direct current driving as described above when executing the computer program.

[0069] The fourth aspect of the embodiment of the present application provides:

[0070] A readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the building energy dynamic control method based on distributed photovoltaic direct current driving as described above.

[0071] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0072] Figure 1 A flowchart of the building energy dynamic control method based on distributed photovoltaic direct current driving provided by the first embodiment of the present application is provided.

[0073] Figure 2 A structural block diagram of the building energy dynamic control system based on distributed photovoltaic direct current driving provided by the third embodiment of the present application is provided.

[0074] The following detailed description will further explain the present application with reference to the above mentioned drawings. DETAILED DESCRIPTION

[0075] For the purpose of promoting an understanding of the present application, the present application will be described with reference to the drawings. The present application is illustrated by a number of embodiments. However, the present application can be realized in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It will be apparent that the scope of the present application is not limited to the embodiments set forth herein.

[0076] It should be noted that when an element is referred to as being "on" another element, it can be directly on the other element or intervening elements can also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements can also be present. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0077] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0078] Referring to Figure 1 , a building energy dynamic control method based on distributed photovoltaic direct current driving provided by the first embodiment of the present application is shown. The building energy dynamic control method based on distributed photovoltaic direct current driving provided by the embodiment can accurately allocate energy corresponding to each direct current load, can save the energy conversion process, eliminates energy loss, and improves energy control efficiency.

[0079] Specifically, the embodiment provides:

[0080] A building energy dynamic control method based on distributed photovoltaic direct current driving, specifically comprising the following steps:

[0081] Step S10, real-time detection of a plurality of direct current loads corresponding to a target building, and real-time creation of a digital twin model of a photovoltaic array corresponding to the target building;

[0082] It should be noted that, in order to accurately allocate the energy required by each direct current load, first of all, the sensor network needs to identify all direct current loads (such as LED lighting, direct current air conditioning, charging piles, etc.) in the target building in real time, record parameters such as type, power demand, operating state, etc. At the same time, based on the physical structure, component parameters (such as model, number, arrangement method) and installation environment of the photovoltaic array, a digital twin model is constructed. The model can dynamically map the actual operating state of the photovoltaic array, provide a virtual simulation platform for subsequent parameter collection and energy allocation, and realize real-time interaction between the physical entity and the virtual model. Through the above-mentioned mode, subsequent data collection can be facilitated for subsequent processing. It should be noted that, in order to accurately create a digital twin model that is adapted to the photovoltaic array of the target building, the CAD drawing of the current target building needs to be obtained first, and the 3D geometric model of the target building is constructed in the digital twin platform (Unity). During the construction process, the appearance, internal structure and electrical topology of the building can be accurately restored. Based on this, the static parameters of the photovoltaic array of the current target building are also obtained. Specifically, the static parameters include the rated power, open circuit voltage, short circuit current, size and temperature coefficient of the photovoltaic array. Based on this, the photovoltaic components are arranged in the target building 3D geometric model according to the physical installation position 1:1, and the positions of the photovoltaic inverter and the current box (such as the roof inverter room) are added in the model, and the corresponding electrical connection lines are drawn. Based on this, the core operating parameters of the physical photovoltaic array are collected and directly mapped to the corresponding devices in the digital model. Specifically, the real-time power, temperature and open circuit voltage of the single photovoltaic component are collected, the input current and total current of the photovoltaic current box are collected, the input power, voltage and current of the photovoltaic inverter are collected, the real-time total power generation and cumulative power generation of the array total state are collected. Based on this, in the digital twin platform, a unique "digital ID" is assigned to each physical device (component, current box, inverter), which corresponds to the ID of the collection device. When the collection device uploads real-time data, the platform matches the ID and writes the data into the "attribute field" of the corresponding digital device in real time, and visualizes the display in the 3D model, thereby generating the above-mentioned data twin model for subsequent processing.

[0083] Step S20, according to the digital twin model, real-time collection of the target photovoltaic parameters corresponding to the output of the photovoltaic array, and real-time calculation of the photovoltaic allocation ratio of each direct current load in the photovoltaic array according to the target photovoltaic parameters;

[0084] It should be noted that the digital twin model collects the output parameters of the photovoltaic array (such as total power, voltage, current, etc.) in real time through an integrated sensor data interface. Combined with these parameters and the energy consumption characteristics of the DC load, the proportion of photovoltaic energy allocated to each load is calculated. This step is the basis of energy allocation, ensuring that the initial allocation meets the matching relationship between photovoltaic power generation and load demand. Based on this, the accuracy of subsequent allocation can be improved to facilitate subsequent processing.

[0085] Step S30, according to the photovoltaic allocation proportion, an initial energy allocation function adapted to the photovoltaic array is created in real time, and the initial energy allocation function is optimized by a preset particle swarm algorithm to generate a corresponding target energy allocation function in real time;

[0086] It should be noted that according to the photovoltaic allocation proportion, an initial energy allocation function is constructed, which needs to consider the real-time power generation of the photovoltaic array, the number of loads and their respective allocation weights. To improve the allocation accuracy, the initial function is optimized using a preset particle swarm algorithm. Specifically, by simulating the swarm intelligence behavior of the particle swarm, the function parameters are iteratively adjusted to make the energy allocation more in line with the actual demand (such as minimizing energy waste and balancing energy allocation between loads), and finally a target energy allocation function is generated. Through the above method, a function for subsequent allocation can be quickly and effectively generated to facilitate subsequent processing.

[0087] Step S40, the energy value corresponding to each DC load is calculated in real time by the target energy allocation function, and the corresponding electric energy is allocated to each DC load according to the size of the energy value.

[0088] It should be noted that the target energy allocation function outputs the specific energy value of each DC load, and the control system allocates electric energy to each load according to the value. The whole process dynamically responds to photovoltaic power generation fluctuations and load demand changes, realizing efficient use of energy. Based on this, the process of energy conversion can be effectively avoided, thereby reducing energy loss and improving energy control efficiency.

[0089] Second embodiment

[0090] Further, the step of collecting the target photovoltaic parameters corresponding to the output of the photovoltaic array in real time according to the digital twin model, and calculating the photovoltaic allocation proportion of each DC load in the photovoltaic array in real time according to the target photovoltaic parameters comprises:

[0091] In the digital twin model, a plurality of series-parallel unit regions corresponding to the photovoltaic array are divided in real time, and real-time electrical parameters corresponding to each unit region are collected every preset time;

[0092] Real-time solar irradiance and surface temperature of the target building roof are collected to create a corresponding target photovoltaic parameter matrix in real time, and historical energy consumption parameters corresponding to each direct current load in a preset time period are extracted in real time from a historical database;

[0093] The photovoltaic distribution ratio of each direct current load is calculated based on the real-time electrical parameters, the target photovoltaic parameter matrix, and the historical energy consumption parameters.

[0094] It should be noted that in the digital twin model, the photovoltaic array is divided into several unit regions according to the series-parallel structure (for example, each photovoltaic component or each string of components is a unit). Real-time electrical parameters such as voltage, current, and power of each unit are collected every preset time (for example, 1 minute) to accurately grasp the power generation capacity of each part of the array and provide fine-grained data support for subsequent distribution. Based on this, the solar irradiance (a key environmental factor affecting photovoltaic efficiency) and the surface temperature of the photovoltaic component (high temperature will reduce the conversion efficiency) of the building roof are collected in real time. These parameters are combined with the time dimension to construct a target photovoltaic parameter matrix, reflecting the dynamic power generation potential of the photovoltaic array. At the same time, the historical energy consumption parameters of each direct current load in a similar time period (such as the same season or time period) are extracted from the historical database as a reference basis for predicting current demand. Finally, the existing analytic hierarchy process is called. Specifically, the analytic hierarchy process (AHP) is a multi-criteria decision-making method that divides complex problems into layers (target layer, criterion layer, and scheme layer) and quantifies the influence weight of each factor. Here, the "photovoltaic distribution ratio of each direct current load" is the target layer, and the "real-time electrical parameters, target photovoltaic parameter matrix, and historical energy consumption parameters" are the criterion layer. By constructing a judgment matrix and calculating the weight, a scientific and reasonable distribution ratio is obtained to ensure that the distribution result takes into account both photovoltaic capacity and load demand. Through the above method, the energy distribution idea can be clearly determined for subsequent processing.

[0095] Further, the step of calculating the photovoltaic distribution ratio of each direct current load based on the real-time electrical parameters, the target photovoltaic parameter matrix, and the historical energy consumption parameters includes:

[0096] The photovoltaic distribution ratio of each direct current load is set as the target layer, and the real-time electrical parameters, the target photovoltaic parameter matrix, and the historical energy consumption parameters are set as the criterion layer;

[0097] A corresponding judgment matrix is constructed based on a preset scale method according to the criterion layer, and the target layer is associated with a value to generate a corresponding value matrix in real time;

[0098] Real-time calculate a feature vector corresponding to each of the DC loads according to the judgment matrix and the assignment matrix, and convert the feature vector into a photovoltaic distribution ratio corresponding to each of the DC loads in real time.

[0099] Need to be explained is that the application will explicitly three-layer structure of the analytic hierarchy process: the target layer is "the photovoltaic distribution ratio of each DC load"; the criterion layer includes "real-time electrical parameters" (reflecting the current power generation capacity of the photovoltaic unit), "target photovoltaic parameter matrix" (reflecting the influence of the environment on the overall capacity), and "historical energy consumption parameters" (reflecting the energy consumption law of the load). The hierarchical division makes the complex problem more organized and facilitates quantitative analysis. Based on this, the importance of each parameter in the criterion layer is compared based on a preset scale method (such as the 1-9 scale method), and a judgment matrix is constructed (such as "solar irradiance is more important than component temperature in affecting capacity"). At the same time, according to the relevance of each DC load and the parameters in the criterion layer (such as the strong correlation between the energy consumption of a certain load and solar irradiance), the target layer is assigned relevance to generate an assignment matrix. This step converts qualitative judgments into quantitative data, laying the foundation for subsequent calculations. Finally, the weight vector (feature vector) of each DC load in the target layer is calculated through matrix operations (such as finding the eigenvector corresponding to the maximum eigenvalue). After normalizing the vector, the photovoltaic distribution ratio of each load can be obtained. This process ensures that the distribution ratio takes into account multiple factors and is consistent with mathematical logic. Through the above method, the photovoltaic distribution ratio of each DC load can be finally determined for subsequent processing.

[0100] Further, the step of creating an initial energy distribution function adapted to the photovoltaic array according to the photovoltaic distribution ratio includes:

[0101] The real-time total power of the photovoltaic array, the number of DC loads, and the photovoltaic distribution ratio of each DC load output by the digital twin model are taken as input variables;

[0102] The initial distribution power of each DC load is set as an output variable, and the series-parallel loss coefficient corresponding to the photovoltaic array in the digital twin model is extracted in real time;

[0103] The input variables, the output variables, and the series-parallel loss coefficient are fused into a preset distribution function to form the initial energy distribution function.

[0104] It should be noted that before creating the required function, this invention first determines the input variables, output variables, and related coefficients corresponding to the current function. Based on this, the input variables provided by this invention include: the real-time total power of the photovoltaic array (the current total capacity output by the digital twin model), the number of DC loads (reflecting the scale of the allocation objects), and the photovoltaic allocation ratio of each load. Correspondingly, the output variable provided by this invention is the initial allocated power of each DC load. Simultaneously, the series and parallel loss coefficients adapted to the current photovoltaic array are extracted in real-time within the aforementioned digital twin model. Based on this, the input variables, output variables, and series and parallel loss coefficients are substituted into a preset allocation function (such as a mathematical model based on power conservation). The correlation between each parameter is expressed through formulas (e.g., total power equals the sum of the allocated power of each load plus losses), forming the initial energy allocation function. This function is the basis for subsequent optimization and must fully reflect the balance between photovoltaic capacity, load demand, and losses. Through the above method, various parameters can be reasonably allocated to facilitate subsequent processing.

[0105] It should be noted that the expression for calculating the series loss coefficient provided by this invention is as follows:

[0106]

[0107] in, N S Indicates the number of components connected in series. I mp，i Indicates the first i The maximum power point current of each component R S Indicates series resistance. V mp，i Indicates the first i The maximum power point voltage of each component; furthermore, the expression for calculating the parallel loss coefficient provided by this invention is as follows:

[0108]

[0109] in, N P Indicates the number of components connected in parallel. R P Indicates parallel resistance. N S Indicates the number of components connected in series. I mp，i This represents the maximum power point current of the i-th component. V mp，i Indicates the first i The maximum power point voltage of each component.

[0110] Furthermore, the step of integrating the input variables, the output variables, and the series-parallel loss coefficients into a preset allocation function to form the initial energy allocation function includes:

[0111] The series and parallel topology of each subarray in the photovoltaic array is acquired in real time, and the series and parallel loss coefficient weight value corresponding to each subarray is determined based on the topology.

[0112] Based on the photovoltaic allocation ratio of each DC load, the real-time total power of the photovoltaic array is decomposed into preliminary allocated power corresponding to each DC load, and the preliminary allocated power is used as the reference value of the output variable.

[0113] The real-time total power of the photovoltaic array, the number of DC loads, and the photovoltaic allocation ratio in the input variables are substituted sequentially with the baseline value of the output variables, the series and parallel loss coefficients and their weight values ​​into the corresponding positions of the variables in the preset allocation function, and the correlation between the parameters is fused through multiplication to form the initial energy allocation function.

[0114] It should be noted that the photovoltaic array is composed of multiple subarrays connected in series and parallel. The topology of different subarrays (such as the number of series connections and the number of parallel branches) leads to differences in loss coefficients. Based on topology analysis, weights are assigned to the loss coefficients of each subarray (e.g., subarrays with higher losses have higher weights) to make loss calculations more accurate. Based on this, according to the photovoltaic allocation ratio of each DC load, the real-time total power of the photovoltaic array is proportionally decomposed to obtain the preliminary allocated power for each load, which serves as the baseline value for the output variable. For example, if the total power is 100kW and the allocation ratio of a certain load is 20%, then its preliminary allocated power is 20kW. Finally, the input variables (total power, number of loads, allocation ratio), the baseline value of the output variable, the loss coefficient, and their weights are substituted into the corresponding variable positions in the preset allocation function. Multiplication operations are used to fuse the correlation of various parameters (e.g., the preliminary allocated power needs to be multiplied by the loss coefficient weight for correction), ultimately forming the initial energy allocation function. This process ensures that the function considers both power allocation logic and actual losses, improving the rationality of the initial allocation. This method improves the accuracy of energy allocation, facilitating subsequent processing.

[0115] The algorithm for calculating the weighting of the loss coefficients of each subarray, provided by this invention, is expressed as follows:

[0116]

[0117] in, α , β , gamma These are the weighting coefficients. W kFor the first k The weight values ​​of the series and parallel loss coefficients of each subarray T k A represents the structural coefficient of each subarray. i For the first i The topological coefficients of each subarray W P，K The percentage of the total power to the rated power of each subarray. W L，K The loss sensitivity coefficient for each subarray.

[0118] Furthermore, the expression for the initial energy allocation function provided by this invention is as follows:

[0119]

[0120] in, For the first j Initial power allocation for each DC load, lambda j For the first j Photovoltaic allocation ratio for each DC load This represents the real-time total power of the photovoltaic array. M The total number of subarrays, W k For the first k The weight values ​​of the loss coefficients of each subarray For the first k The combined series-parallel loss coefficient of each subarray n This refers to the number of DC loads.

[0121] Furthermore, the step of optimizing the initial energy allocation function using a preset particle swarm optimization algorithm to generate the corresponding target energy allocation function in real time includes:

[0122] The deviation between the output of the initial energy allocation function and the rated power of each DC load is used as the core parameter of the fitness function of the preset particle swarm algorithm, and the total photovoltaic power constraint threshold and the single load power fluctuation threshold are set as the position boundary conditions of the particle swarm.

[0123] Initialize the position and velocity parameters of each particle in the particle swarm, wherein the position parameters correspond to the correction coefficients of each variable in the initial energy allocation function, the velocity parameters correspond to the iteration step size of the correction coefficients, and the real-time photovoltaic parameters fed back by the digital twin model are used as the dynamic input of the particle swarm iteration.

[0124] running the preset particle swarm algorithm according to a preset iteration number, updating the particle position and speed by comparing the particle individual optimal solution and the global optimal solution in each iteration process, and when the iteration result meets the fitness function convergence condition, substituting the current particle position parameter into the initial energy distribution function to obtain the target energy distribution function.

[0125] It should be noted that after the required initial energy distribution function is obtained in real time by the above-mentioned manner, in order to further improve the energy distribution accuracy of the current initial energy distribution function, the particle swarm algorithm adapted to the current initial energy distribution function is called out, specifically, in order to facilitate implementation, the output result of the current initial energy distribution function and the deviation value of the rated power of each direct current load are taken as the fitness function core parameters of the current preset particle swarm algorithm, and the required total power constraint threshold of the photovoltaic array and the single load power fluctuation threshold are simultaneously set as the position boundary conditions of the current particle swarm, based on which, after the above-mentioned preconditions are completed, the current particle swarm algorithm is also adjusted correspondingly, specifically, in the particle swarm algorithm, each particle represents a group of optimization parameters: the position parameter corresponds to the correction coefficient of each variable in the initial function (such as the correction value of the loss coefficient), and the speed parameter corresponds to the iteration step of the correction coefficient. When initialized, these parameters are randomly set, and the real-time photovoltaic parameters (such as real-time total power and irradiance) fed back by the digital twin model are taken as dynamic inputs, so that the optimization process adapts to the real-time changes of photovoltaic capacity. Finally, the algorithm is run according to a preset iteration number (such as 100 times): in each iteration, the particle updates the position and speed by comparing the historical optimal solution and the global optimal solution (moves towards a better solution). When the iteration result meets the convergence condition (such as the fitness value change of continuous multiple iterations is less than a threshold value), the position parameter (optimal correction coefficient) of the current particle is substituted into the initial energy distribution function to obtain the target energy distribution function. This process searches for the global optimal solution through swarm intelligence, significantly improving the accuracy of energy distribution. Through the above-mentioned manner, the target energy distribution function for subsequent distribution can be finally obtained, so that the energy distribution task can be accurately completed for subsequent processing.

[0126] Further, the step of calculating the energy value corresponding to each direct current load in real time through the target energy distribution function comprises:

[0127] The correction coefficient corresponding to each direct current load is output in real time through the target energy distribution function, and the correction coefficient is multiplied by the output voltage of the photovoltaic array collected in real time by the digital twin model to calculate the reference voltage value corresponding to each direct current load in real time;

[0128] The reference voltage value is power calculated with the rated resistance of the direct current load to calculate the corresponding intermediate energy value in real time;

[0129] The real-time power factor corresponding to each direct current load is collected, and the real-time power factor is multiplied by the intermediate energy value to calculate the energy value corresponding to the direct current load in real time.

[0130] It should be noted that the target energy distribution function outputs the correction coefficient of each direct current load (reflecting the optimized voltage adjustment ratio), and the coefficient is multiplied by the output voltage of the photovoltaic array collected by the digital twin model to obtain the reference voltage value of each load. For example, the photovoltaic output voltage is 380V, and the correction coefficient of a certain load is 0.9, so the reference voltage is 342V. Based on this, according to the power calculation formula (power = voltage squared / resistance), the reference voltage value and the rated resistance of the direct current load are substituted to calculate the intermediate energy value (i.e. the theoretical power without considering the power factor). This step is based on the basic principle of circuit to ensure the physical rationality of energy calculation. Finally, the real-time power factor of each direct current load (reflecting the utilization efficiency of the load to electrical energy, such as the power factor of motor load) is collected, and it is multiplied by the intermediate energy value to obtain the final energy value. The value takes into account the influence of voltage, resistance and power factor, and accurately reflects the effective energy consumed by the load, providing the final basis for energy distribution. Through the above-mentioned manner, the dynamic distribution and control of building energy can be objectively and effectively completed, and the control efficiency of energy is improved.

[0131] Please refer to Figure 2 The third embodiment of the present application provides:

[0132] A building energy dynamic control system based on distributed photovoltaic direct current driving, wherein the system comprises:

[0133] A detection module is configured to detect in real time a plurality of direct current loads contained in a target building, and create in real time a digital twin model of a photovoltaic array corresponding to the target building;

[0134] A calculation module is configured to collect in real time a target photovoltaic parameter corresponding to an output of the photovoltaic array according to the digital twin model, and calculate in real time a photovoltaic distribution ratio of each direct current load in the photovoltaic array according to the target photovoltaic parameter;

[0135] A creation module is configured to create in real time an initial energy distribution function adapted to the photovoltaic array according to the photovoltaic distribution ratio, and optimize the initial energy distribution function by a preset particle swarm algorithm to generate in real time a target energy distribution function;

[0136] an allocation module, configured to calculate energy values corresponding to each of the DC loads respectively in real time through the target energy source allocation function, and allocate corresponding electric energy to each of the DC loads according to the sizes of the energy values.

[0137] Further, the calculation module is specifically configured to:

[0138] a plurality of series-parallel unit regions corresponding to the photovoltaic array are divided in the digital twin model in real time, and real-time electrical parameters generated by each of the unit regions are collected every preset time;

[0139] real-time solar irradiance of the target building roof and surface temperature of the photovoltaic component are collected in real time to create a corresponding target photovoltaic parameter matrix in real time, and historical energy consumption parameters generated by each of the DC loads in a preset time period are extracted in real time from a historical database;

[0140] a photovoltaic allocation proportion of each of the DC loads is calculated based on an analytic hierarchy process according to the real-time electrical parameters, the target photovoltaic parameter matrix and the historical energy consumption parameters.

[0141] Further, the calculation module is specifically configured to:

[0142] the photovoltaic allocation proportion of each of the DC loads is set as a target layer, and the real-time electrical parameters, the target photovoltaic parameter matrix and the historical energy consumption parameters are set as criterion layers;

[0143] a judgment matrix corresponding to the criterion layers is constructed in real time based on a preset scale method, and the target layer is associatedly valued to generate a corresponding value matrix in real time;

[0144] a feature vector corresponding to each of the DC loads is calculated in real time according to the judgment matrix and the value matrix, and the feature vector is converted into a photovoltaic allocation proportion corresponding to each of the DC loads in real time.

[0145] Further, the creation module is specifically configured to:

[0146] the real-time total power of the photovoltaic array, the number of the DC loads and the photovoltaic allocation proportion of each of the DC loads output by the digital twin model are taken as input variables;

[0147] an initial allocation power of each of the DC loads is taken as an output variable, and a series-parallel loss coefficient corresponding to the photovoltaic array in the digital twin model is extracted in real time;

[0148] correspondingly fuse the input variables, the output variables and the series-parallel loss coefficients into a preset allocation function to correspondingly form the initial energy allocation function.

[0149] Further, the creating module is specifically configured to:

[0150] acquire the series-parallel topology structure of each sub-array in the photovoltaic array in real time, and determine the series-parallel loss coefficient weight value corresponding to each sub-array based on the topology structure;

[0151] According to the photovoltaic allocation proportion of each direct-current load, the real-time total power of the photovoltaic array is decomposed into a preliminary allocation power corresponding to each direct-current load, and the preliminary allocation power is taken as the reference value of the output variable;

[0152] The real-time total power of the photovoltaic array, the number of direct-current loads, the photovoltaic allocation proportion in the input variables, the reference value of the output variable, the series-parallel loss coefficient and its weight value are sequentially substituted into the variable corresponding position of the preset allocation function, and the correlation between each parameter is fused through multiplication operation to form the initial energy allocation function.

[0153] Further, the distribution module is specifically configured to:

[0154] The deviation value between the output result of the initial energy allocation function and the rated power of each direct-current load is taken as the fitness function core parameter of the preset particle swarm algorithm, and the photovoltaic total power constraint threshold and the single load power fluctuation threshold are set as the position boundary condition of the particle swarm;

[0155] Initialize the position parameter and the speed parameter of each particle in the particle swarm, wherein the position parameter corresponds to the correction coefficient of each variable in the initial energy allocation function, the speed parameter corresponds to the iteration step length of the correction coefficient, and the real-time photovoltaic parameter fed back by the digital twin model is taken as the dynamic input quantity of the iteration of the particle swarm;

[0156] Run the preset particle swarm algorithm according to the preset iteration number, update the particle position and speed by comparing the individual optimal solution and the global optimal solution of the particle in each iteration process, and when the iteration result meets the convergence condition of the fitness function, substitute the current particle position parameter into the initial energy allocation function to obtain the target energy allocation function.

[0157] Further, the distribution module is specifically configured to:

[0158] The target energy distribution function is used to output a correction coefficient corresponding to each direct current load in real time, and the correction coefficient is multiplied by an output voltage of the photovoltaic array collected by the digital twin model in real time to calculate a reference voltage value corresponding to each direct current load in real time.

[0159] The reference voltage value is subjected to power calculation with a rated resistance of the direct current load to calculate a corresponding intermediate energy value in real time.

[0160] A real-time power factor corresponding to each direct current load is collected, and the real-time power factor is multiplied by the intermediate energy value to calculate an energy value corresponding to the direct current load in real time.

[0161] The fourth embodiment of the present application provides a computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the building energy dynamic control method based on distributed photovoltaic direct current driving as described above when executing the computer program.

[0162] The fifth embodiment of the present application provides a readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the building energy dynamic control method based on distributed photovoltaic direct current driving as described above.

[0163] In summary, the building energy dynamic control method and system based on distributed photovoltaic direct current driving provided by the above embodiments of the present application can accurately allocate corresponding energy to each direct current load, and can save the energy conversion process, thereby improving the energy control efficiency.

[0164] It should be noted that the above-mentioned modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above-mentioned modules can be located in the same processor, or the above-mentioned modules can be located in different processors in any combination.

[0165] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be embodied in non-transitory computer-readable media, executed by one or more computing devices, and / or in any other way. The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer. In this context, the computer-readable medium can be paper or another suitable medium that can act to transfer a program, for example, from the Internet, over a wired or wireless network, over the airwaves, etc., in a cartridged, downloadable, or other format. Thus, a computer-readable medium can take many forms of hardware and software.

[0166] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer. In this context, the computer-readable medium can be paper or another suitable medium that can act to transfer a program, for example, from the Internet, over a wired or wireless network, over the airwaves, etc., in a cartridged, downloadable, or other format. Thus, a computer-readable medium can take many forms of hardware and software.

[0167] It should be understood that aspects of the application can be implemented in hardware, software, firmware, or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and so forth.

[0168] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The illustrative descriptions of the above terms in this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0169] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the present application. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A building energy dynamic control method based on distributed photovoltaic direct current direct drive, characterized in that, The method comprises: Real-time detection of a plurality of DC loads contained in the target building, and real-time creation of a digital twin model of a photovoltaic array corresponding to the target building; Real-time acquisition of target photovoltaic parameters corresponding to the output of the photovoltaic array according to the digital twin model, and real-time calculation of a photovoltaic distribution ratio of each DC load in the photovoltaic array according to the target photovoltaic parameters; Real-time creation of an initial energy distribution function adapted to the photovoltaic array according to the photovoltaic distribution ratio, and optimization of the initial energy distribution function by a preset particle swarm algorithm to generate a corresponding target energy distribution function in real time; Real-time calculation of an energy value corresponding to each DC load by the target energy distribution function, and distribution of corresponding electric energy to each DC load according to the size of the energy value.

2. The method for building energy dynamic control based on distributed photovoltaic direct current direct drive according to claim 1, characterized in that: The step of real-time acquisition of target photovoltaic parameters corresponding to the output of the photovoltaic array according to the digital twin model, and real-time calculation of a photovoltaic distribution ratio of each DC load in the photovoltaic array according to the target photovoltaic parameters comprises: Real-time division of a plurality of series-parallel unit regions corresponding to the photovoltaic array in the digital twin model, and real-time acquisition of real-time electrical parameters generated by each unit region at a preset time interval; Real-time acquisition of real-time solar irradiance of the roof of the target building and surface temperature of the photovoltaic component to create a corresponding target photovoltaic parameter matrix in real time, and real-time extraction of historical energy consumption parameters corresponding to each DC load generated in a preset time period from a historical database; Based on the analytic hierarchy process, the photovoltaic distribution ratio of each DC load is calculated according to the real-time electrical parameters, the target photovoltaic parameter matrix, and the historical energy consumption parameters.

3. The method of claim 2, wherein the method is a method of building energy dynamic control based on distributed photovoltaic direct current direct drive. The step of calculating the photovoltaic distribution ratio of each DC load according to the real-time electrical parameters, the target photovoltaic parameter matrix, and the historical energy consumption parameters based on the analytic hierarchy process comprises: The photovoltaic distribution ratio of each DC load is set as a target layer, and the real-time electrical parameters, the target photovoltaic parameter matrix, and the historical energy consumption parameters are set as a criterion layer; Based on a preset scale method, a corresponding judgment matrix is constructed in real time according to the criterion layer, and the target layer is associated with a value to generate a corresponding value matrix in real time; According to the judgment matrix and the value matrix, a feature vector corresponding to each DC load is calculated in real time, and the feature vector is converted into a photovoltaic distribution ratio corresponding to each DC load in real time.

4. The method for building energy dynamic control based on distributed photovoltaic DC direct drive according to claim 1, characterized in that: The step of real-time creation of an initial energy distribution function adapted to the photovoltaic array according to the photovoltaic distribution ratio comprises: The real-time total power of the photovoltaic array, the number of DC loads, and the photovoltaic distribution ratio of each DC load output by the digital twin model are used as input variables; The preliminary distribution power of each DC load is set as an output variable, and the series-parallel loss coefficient corresponding to the photovoltaic array in the digital twin model is extracted in real time; The input variable, the output variable and the series-parallel loss coefficient are fused into a preset distribution function to form the initial energy distribution function.

5. The method for building energy dynamic control based on distributed photovoltaic direct current direct drive according to claim 1, characterized in that: The step of optimizing the initial energy distribution function by the preset particle swarm algorithm to generate a corresponding target energy distribution function in real time comprises: The deviation value between the output result of the initial energy distribution function and the rated power of each direct current load is taken as the fitness function core parameter of the preset particle swarm algorithm, and the total power constraint threshold of photovoltaic and the single load power fluctuation threshold are set as the position boundary condition of the particle swarm; The position parameter and the speed parameter of each particle in the particle swarm are initialized, wherein the position parameter corresponds to the correction coefficient of each variable in the initial energy distribution function, and the speed parameter corresponds to the iteration step of the correction coefficient, and the real-time photovoltaic parameter fed back by the digital twin model is taken as the dynamic input quantity of the iteration of the particle swarm; The preset particle swarm algorithm is run according to a preset iteration number, the particle position and speed are updated by comparing the individual optimal solution and the global optimal solution of the particle in each iteration process, and when the iteration result meets the convergence condition of the fitness function, the current particle position parameter is substituted into the initial energy distribution function to obtain the target energy distribution function.

6. The method for building energy dynamic control based on distributed photovoltaic direct current direct drive according to claim 5, characterized in that: The step of calculating the energy value corresponding to each direct current load by the target energy distribution function in real time comprises: The correction coefficient corresponding to each direct current load is output in real time by the target energy distribution function, and the correction coefficient is multiplied by the output voltage of the photovoltaic array collected by the digital twin model in real time to calculate the reference voltage value corresponding to each direct current load in real time; The reference voltage value is calculated with the rated resistance of the direct current load to calculate the intermediate energy value in real time; The real-time power factor corresponding to each direct current load is collected, and the real-time power factor is multiplied by the intermediate energy value to calculate the energy value corresponding to the direct current load in real time.

7. A building energy dynamic control system based on distributed photovoltaic direct current direct drive, characterized in that, The system comprises: A detection module is configured to detect a plurality of direct current loads corresponding to a target building in real time, and create a digital twin model of a photovoltaic array corresponding to the target building in real time; A calculation module is configured to collect target photovoltaic parameters output by the photovoltaic array according to the digital twin model in real time, and calculate a photovoltaic distribution ratio of each direct current load in the photovoltaic array according to the target photovoltaic parameters in real time; A creation module is configured to create an initial energy distribution function adapted to the photovoltaic array according to the photovoltaic distribution ratio in real time, and optimize the initial energy distribution function by a preset particle swarm algorithm to generate a corresponding target energy distribution function in real time; An allocation module is configured to calculate an energy value corresponding to each direct current load by the target energy distribution function in real time, and allocate corresponding electric energy to each direct current load according to the size of the energy value.

8. A computer comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the building energy dynamic control method based on distributed photovoltaic direct current direct drive as claimed in any one of claims 1 to 6.

9. A readable storage medium, having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the building energy dynamic control method based on distributed photovoltaic direct current direct drive as claimed in any one of claims 1 to 6.

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