Intelligent regulation and optimization system for energy consumption data of photovoltaic lithium battery factories based on cloud computing

Abstract

The present invention relates to the field of intelligent energy consumption control, and in particular to an intelligent regulation and optimization system for energy consumption data of a photovoltaic lithium battery factory based on cloud computing. The system comprises a photovoltaic lithium battery data acquisition unit, which acquires relevant parameters of the photovoltaic lithium battery factory based on sensors of photovoltaic components, lithium batteries, and power equipment; a photovoltaic system power generation prediction unit, which establishes a photovoltaic system power generation prediction neural network model based on the acquired photovoltaic lithium battery factory data parameters to obtain predicted power generation conditions of the photovoltaic power generation system; calculates the energy self-sufficiency rate and divides it into levels, and sets a production plan variation; an intelligent regulation and optimization unit, which is used to combine real-time energy consumption data, power generation prediction results, and energy cost factors to optimize energy utilization efficiency and realize intelligent regulation and optimization of energy consumption; and a photovoltaic lithium battery factory energy consumption regulation and monitoring unit, which establishes a photovoltaic lithium battery factory energy consumption regulation and monitoring model based on the acquired photovoltaic lithium battery factory production plan variation and the minimum photovoltaic lithium battery factory energy cost.

Description

Technical Field

[0001] The present invention relates to the field of intelligent energy consumption control, and in particular to a cloud computing-based intelligent regulation and optimization system for energy consumption data of photovoltaic lithium battery plants. Background Art

[0002] As the global energy crisis and environmental issues grow more severe, energy structures are gradually shifting. Photovoltaic power generation and lithium-ion battery technologies, as integral components of clean energy and energy storage, have gained widespread adoption. However, photovoltaic and lithium-ion battery production processes consume high amounts of energy, and are affected by multiple factors, including weather, equipment status, and production schedules. This results in significant energy consumption fluctuations and low efficiency.

[0003] Traditional energy management methods often rely on manual experience and simple data analysis, making it difficult to accurately predict and optimize energy consumption. The development of cloud computing technology has opened up new possibilities for the real-time collection, storage, and analysis of energy consumption data, gradually enabling intelligent energy consumption optimization and regulation in photovoltaic and lithium-ion battery plants based on big data. However, existing technologies still have the following problems: They fail to integrate real-time energy consumption with the photovoltaic system's power generation to clearly define energy supply conditions, making it impossible to efficiently and accurately set and control production plan changes; they fail to consider multiple factors influencing photovoltaic and lithium-ion battery plant energy consumption, resulting in discrepancies between production plan adjustments and actual conditions, increasing energy consumption and preventing further optimization; because they fail to align lithium-ion battery status with real-time photovoltaic power generation, energy costs deviate, failing to minimize the photovoltaic and lithium-ion battery plant's energy costs and interfering with energy regulation; and they lack effective real-time monitoring of photovoltaic and lithium-ion battery plant energy consumption, making the energy regulation and optimization system unstable and resulting in energy waste.

[0004] Therefore, a cloud computing-based intelligent adjustment and optimization system for energy consumption data of photovoltaic lithium battery factories is needed. Summary of the Invention

[0005] The cloud computing-based intelligent regulation and optimization system for energy consumption data of photovoltaic lithium battery factories provided by the present invention aims to utilize cloud computing technology to efficiently and accurately set and regulate production plan changes based on the real-time power generation of the photovoltaic system in combination with the energy supply situation, so as to optimize energy utilization to the greatest extent; consider the influencing factors related to the energy consumption of photovoltaic lithium battery factories from multiple angles to ensure that the regulation of production plans is consistent with the actual situation, avoid energy consumption increase, and further optimize the energy utilization efficiency of photovoltaic lithium battery factories; consider the status of lithium batteries and the real-time situation of photovoltaic power generation, so that the system can accurately regulate energy costs, reduce energy waste, and improve the accuracy and efficiency of energy consumption regulation; and effectively monitor the energy consumption of photovoltaic lithium battery factories in real time to ensure the stability of the energy consumption regulation and optimization system.

[0006] The technical solutions of the present invention are as follows:

[0007] The cloud computing-based intelligent regulation and optimization system for energy consumption data in photovoltaic and lithium battery factories includes the following:

[0008] Photovoltaic lithium battery data acquisition unit, photovoltaic system power generation prediction unit, intelligent regulation and optimization unit, photovoltaic lithium battery factory energy consumption regulation and monitoring unit;

[0009] The photovoltaic lithium battery data acquisition unit obtains relevant parameters of the photovoltaic lithium battery factory based on the sensors of photovoltaic modules, lithium batteries, and power equipment, and transmits the data to the photovoltaic system power generation prediction unit;

[0010] The photovoltaic system power generation prediction unit establishes a photovoltaic system power generation prediction neural network model based on the relevant data parameters obtained from the photovoltaic lithium battery factory, and obtains the predicted power generation of the photovoltaic power generation system; calculates the energy self-sufficiency rate and divides it into levels, and sets the production plan change amount;

[0011] Intelligent adjustment and optimization unit, which is used to optimize energy utilization efficiency and realize intelligent adjustment and optimization of energy consumption by combining real-time energy consumption data, power generation forecast results and energy cost factors;

[0012] The photovoltaic lithium battery factory energy consumption regulation and monitoring unit establishes a photovoltaic lithium battery factory energy consumption regulation and monitoring model based on the obtained photovoltaic lithium battery factory production plan change and the minimum photovoltaic lithium battery factory energy cost;

[0013] The intelligent adjustment and optimization unit includes a first-level adjustment module, a second-level adjustment module, and a third-level adjustment module;

[0014] The first-level adjustment module calculates the correction coefficient of the production plan change based on carbon emissions, production plan flexibility, and error terms;

[0015] The secondary adjustment module analyzes and compares the charge and discharge loss of lithium batteries with the charge and discharge loss of standard lithium batteries, and determines whether to make secondary adjustments to the production plan changes based on the comparison results;

[0016] The three-level regulation module adjusts the energy cost of the photovoltaic lithium battery factory according to the status of the lithium battery and the real-time situation of photovoltaic power generation.

[0017] Furthermore, the photovoltaic system power generation prediction unit specifically includes:

[0018] The photovoltaic system power generation prediction neural network model includes an input layer, an evaluation layer, a prediction layer, and an output layer. The input layer transmits the photovoltaic system operation data to the evaluation layer for measurement, the prediction layer analyzes the photovoltaic system power generation situation, and the output layer outputs the prediction results. .

[0019] Furthermore, in the photovoltaic system power generation prediction unit, according to the predicted power generation of the photovoltaic power generation system Real-time energy demand monitoring , calculate energy self-sufficiency rate ,definition Indicates the thresholds for low and medium energy self-sufficiency; Indicates the threshold between medium and high energy self-sufficiency rates, classifies the energy self-sufficiency rates of photovoltaic and lithium battery factories, and sets the initial production plan change amount The specific process is as follows:

[0020] when When the level is A, the preset first production plan change is , , Indicates the production plan adjustment coefficient corresponding to level A;

[0021] when When the level is B, the preset first production plan change is , , Indicates the production plan adjustment coefficient corresponding to level B;

[0022] when When the level is C, the preset first production plan change is , , Indicates the production plan adjustment coefficient corresponding to level C.

[0023] Furthermore, the production plan change correction coefficient is calculated in the first-level adjustment module The specific process is as follows:

[0024] ;

[0025] in, Indicates the low carbon emission correction factor; Indicates the high carbon emission correction factor; Indicates the maximum carbon emission; represents the production plan flexibility impact coefficient. Specifically, , represents the production planning flexibility coefficient, represents the flexibility correction factor, , The instructions are completely flexible. Description: Complete rigidity; represents the error term influence coefficient, specifically, , represents the photovoltaic power generation prediction error, Indicates the maximum allowable error; represents the error correction coefficient; represents carbon emissions; Indicates the threshold value of carbon emissions.

[0026] Furthermore, in the first-level adjustment module, the production plan change correction coefficient is Comparison value with the preset production plan change correction coefficient Compare and adjust the production plan changes based on the comparison results Make adjustments, ; The specific process is as follows:

[0027] if , then the production plan change Adjust to , then define ;

[0028] if , then the production plan change Adjust to , then define ;

[0029] if , then the production plan change Adjust to , then define .

[0030] Furthermore, in the secondary regulation module, Indicates the charge and discharge loss of lithium batteries. Indicates the charge and discharge loss of standard lithium batteries:

[0031] ;

[0032] in, Indicates the charge and discharge loss comparison coefficient of lithium battery; if , it means that the actual charge and discharge loss is higher than the standard loss, and secondary adjustment is required;

[0033] if , it means that the actual charge and discharge loss is lower than or equal to the standard loss, indicating that the energy consumption is currently within a reasonable range and no secondary adjustment is required.

[0034] Furthermore, when performing secondary adjustment, calculate the lithium battery charge and discharge loss comparison coefficient Comparison coefficient with preset lithium battery charge and discharge loss Ratio , ; Set the first ratio parameter at the same time and the second ratio parameter ,and , respectively and and For comparison; the specific process is as follows:

[0035] if , then adjust the production plan change to ;

[0036] if , then adjust the production plan change to ;

[0037] if , then adjust the production plan change to ;in, Indicates the corresponding control coefficients, which are used to control the adjustment amplitude.

[0038] Furthermore, the three-level adjustment module specifically includes:

[0039] The energy cost of a photovoltaic lithium battery factory is defined as , in, represents the cost of photovoltaic power generation; represents the grid electricity price; Indicates that only the electricity purchased from the grid is calculated; Represents photovoltaic power generation power;

[0040] Introducing health status factors The energy cost of the photovoltaic lithium battery factory is corrected. The specific process is as follows:

[0041] ;

[0042] in, Represents the revised energy cost of photovoltaic lithium battery factories.

[0043] Furthermore, in the photovoltaic lithium battery factory energy consumption regulation monitoring unit, the photovoltaic lithium battery factory energy consumption regulation monitoring model is established through adaptive learning. The adaptive network model is combined with the photovoltaic lithium battery factory production plan change Minimum energy cost of photovoltaic lithium battery factory At the same time, the attention mechanism optimization algorithm is introduced to monitor the energy consumption of the factory in real time and adjust it dynamically according to the actual situation:

[0044] ;

[0045] in, Represents the output of the energy consumption regulation monitoring model for photovoltaic lithium battery plants; represents the learning factor; represents the focus factor; represents the discount factor; Indicates the monitoring time; Represents the optimized attention mechanism; Represents complexity; represents the number of neurons; Indicates the The weights of neurons; A feature matrix representing the input user power consumption forecast data information; Represents the additional attention guidance matrix introduced.

[0046] Beneficial effects

[0047] 1. The present invention predicts photovoltaic power generation through a photovoltaic system power generation prediction neural network model, which can understand the power generation situation in advance, optimize energy distribution, and calculate the energy self-sufficiency rate in combination with real-time energy demand; by calculating the energy self-sufficiency rate and dividing it into levels, the energy supply situation is clarified, the production plan change is set according to different corresponding levels, and the production schedule is adjusted. The production plan is adjusted according to actual conditions to better match energy supply and demand, avoid energy waste, improve energy utilization efficiency, provide data support for managers, and optimize production plans and energy scheduling decisions; at the same time, based on the cloud computing platform, real-time data collection, processing and analysis are realized to improve management efficiency.

[0048] 2. The present invention can more accurately adjust the production plan to adapt to the actual energy consumption data and production conditions by calculating the correction coefficient of the production plan change. At the same time, considering the carbon emission factor, it can reduce carbon emissions, which is helpful to achieve the factory's carbon neutrality and environmental protection goals. Moreover, by calculating the correction coefficient through the error term, it can reduce the impact of the prediction error on the production plan, improve the accuracy of the plan, and realize the adjustment and optimization of the energy consumption of the photovoltaic lithium battery factory; according to the flexibility of the production plan, it can quickly respond to changes in market demand and improve resource utilization efficiency; by dynamically adjusting the production plan change, the production plan is more in line with the actual situation, which is more conducive to the intelligent regulation of the energy consumption of the photovoltaic lithium battery factory.

[0049] 3. The present invention makes adjustments based on the status of lithium batteries and the real-time situation of photovoltaic power generation, which can maximize the use of renewable energy and improve the energy utilization efficiency of photovoltaic power generation systems. By intelligently adjusting energy costs, factories can reasonably adjust energy use during peak and off-peak periods, reduce energy costs during high-energy consumption periods, and thus reduce overall energy costs. According to the real-time situation of photovoltaic power generation, energy supply can be flexibly adjusted to better respond to market demand and price fluctuations, optimize energy procurement strategies, and provide data support for the subsequent photovoltaic lithium battery factory energy consumption adjustment and monitoring process.

[0050] 4. The present invention can monitor the energy consumption of photovoltaic lithium battery factories in real time through monitoring models, and make adjustments based on actual production plan changes and minimum energy costs to ensure efficient and economical energy utilization and optimal energy consumption, thereby avoiding energy waste. Energy consumption can be dynamically adjusted through the model to reduce energy consumption fluctuations, improve the stability of the energy consumption adjustment and optimization system, reduce the operating costs of photovoltaic lithium battery factories, and improve competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a structural diagram of the cloud computing-based intelligent adjustment and optimization system for energy consumption data of photovoltaic lithium battery plants of the present invention;

[0052] Figure 2 is a schematic diagram of the regulation optimization unit of the present invention;

[0053] Figure 3 This is a flow chart of the cloud computing-based intelligent adjustment and optimization method for energy consumption data of photovoltaic lithium battery factories of the present invention. DETAILED DESCRIPTION

[0054] In order to better understand the above technical solution, the following will be described in detail with reference to the accompanying drawings and specific implementation methods. It should also be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] Refer to the attached Figure 1 This embodiment provides a cloud computing-based intelligent adjustment and optimization system for energy consumption data of photovoltaic lithium battery factories, including the following contents:

[0056] Photovoltaic lithium battery data acquisition unit, photovoltaic system power generation prediction unit, intelligent regulation and optimization unit, photovoltaic lithium battery factory energy consumption regulation and monitoring unit;

[0057] The photovoltaic lithium battery data acquisition unit obtains relevant parameters of the photovoltaic lithium battery factory based on the sensors of photovoltaic modules, lithium batteries, and power equipment, and transmits the data to the photovoltaic system power generation prediction unit;

[0058] The photovoltaic system power generation prediction unit establishes a photovoltaic system power generation prediction neural network model based on the relevant data parameters obtained from the photovoltaic lithium battery factory, and obtains the predicted power generation of the photovoltaic power generation system; calculates the energy self-sufficiency rate and divides it into levels, and sets the production plan change amount;

[0059] Intelligent adjustment and optimization unit, which is used to optimize energy utilization efficiency and realize intelligent adjustment and optimization of energy consumption by combining real-time energy consumption data, power generation forecast results, energy cost and other factors;

[0060] The photovoltaic lithium battery factory energy consumption regulation and monitoring unit establishes a photovoltaic lithium battery factory energy consumption regulation and monitoring model based on the obtained photovoltaic lithium battery factory production plan changes and the minimum photovoltaic lithium battery factory energy cost, and finds the optimal energy consumption regulation solution;

[0061] Refer to the attached Figure 2 ,The regulation and optimization unit includes a primary regulation module, a secondary regulation module, and a tertiary regulation module;

[0062] The first-level adjustment module calculates the production plan change correction coefficient based on carbon emissions, production plan flexibility, and error terms; compares the production plan change correction coefficient with the preset production plan change correction coefficient comparison value, and adjusts the production plan change based on the comparison result;

[0063] The secondary adjustment module compares the charge and discharge loss of lithium batteries with the charge and discharge loss of standard lithium batteries, and determines whether to make secondary adjustments to the production plan changes based on the comparison results;

[0064] The three-level regulation module adjusts the energy cost of the photovoltaic lithium battery factory according to the status of the lithium battery and the real-time situation of photovoltaic power generation.

[0065] Refer to the attached Figure 3 This embodiment provides a method for intelligently adjusting and optimizing energy consumption data of a photovoltaic lithium battery factory based on cloud computing, comprising the following steps:

[0066] S1. Utilize sensors from photovoltaic modules, lithium batteries, and electrical equipment to obtain relevant parameters of the photovoltaic lithium battery plant. Build a neural network model for photovoltaic system power generation prediction, obtain the predicted power generation of the photovoltaic power generation system, calculate and rank the energy self-sufficiency rate, and set production plan changes.

[0067] High-precision sensors are installed at key locations such as photovoltaic modules, lithium batteries, and electrical equipment. These sensors include, but are not limited to, current sensors, voltage sensors, temperature sensors, humidity sensors, and fiber optic sensors. These sensors collect and monitor relevant parameters in real time, including photovoltaic power generation data, lithium battery energy storage data, power consumption data, environmental data, and equipment operation data. Data acquisition terminals receive, process, and store the data collected by the sensors. These sensors are then transmitted to a cloud computing platform using either wired (e.g., Ethernet, RS485) or wireless (e.g., Wi-Fi, LoRa, ZigBee) communication technologies. Wireless communication technologies are particularly well-suited for distributed photovoltaic and lithium battery plants, reducing wiring costs. In embodiments of the present invention, edge computing capabilities are integrated into data acquisition terminals or gateway devices. These data are then processed using existing technologies, such as filtering, compression, and anomaly detection, to reduce data transmission volume and improve real-time performance.

[0068] In this embodiment of the present invention, a neural network model for predicting photovoltaic system power generation is established. Pre-processed historical photovoltaic system operating data is used as training samples and input data for the neural network model. The model is trained through deep learning and includes an input layer, an evaluation layer, a prediction layer, and an output layer.

[0069] The training samples are represented as , Represents the photovoltaic system operation data obtained at any moment, where , Indicates the different moments of acquisition. Types of data information, represented as ,in The input of the neural network model for photovoltaic system power generation prediction is neurons. The input layer will Pass it to the evaluation layer for analysis and measurement. The specific process is as follows:

[0070] ;

[0071] ;

[0072] in, Represents the input of the evaluation layer; Represents the connection weight between the input layer and the evaluation layer; Represents the output result of the input layer; represents the bias of the evaluation layer; Represents the output of the evaluation layer; Indicates the influence coefficient of light intensity on photovoltaic system power generation; Indicates the influence coefficient of temperature on photovoltaic system power generation; Indicates The light intensity information obtained when Indicates the average light intensity during the recording period; Indicates Temperature information obtained when Indicates the maximum value of temperature difference during the recording period; Indicates The wind speed information obtained at Indicates the rate of change of wind speed; Represents a function of change over time; Indicates the impact coefficient of changes in environmental parameters during the evaluation process; Indicates the total period of historical records; express PV panel performance status; Indicates the balance coefficient.

[0073] In the prediction layer, the photovoltaic system power generation situation is predicted and analyzed. The specific process is as follows:

[0074] ;

[0075] ;

[0076] in, Represents the input of the prediction layer; Represents the connection weight value between the evaluation layer and the prediction layer; Represents the bias of the prediction layer; Indicates light intensity; Indicates the orientation angle of the photovoltaic angle, that is, the angle between the direction facing the photovoltaic panel and the south direction; Indicates the solar azimuth; Indicates the tilt angle of the photovoltaic panel; Indicates the dynamic change coefficient of cloud cover in photovoltaic system; Indicates The cloud cover obtained at Indicates The cloud cover obtained at Represents the model's early stopping strategy coefficient; Indicates Energy when Indicates Expected energy when Represents the output of the prediction layer; Indicates the stability demand correction coefficient of the photovoltaic system power generation process; Indicates the total period of historical records.

[0077] The results are output in the output layer, specifically expressed as: ;

[0078] in, Represents the output result of the output layer; Represents the connection weight between the prediction layer and the output layer; Represents the bias of the prediction layer. Finally, the output layer outputs the predicted power generation of the photovoltaic power generation system.

[0079] According to the predicted power generation of the photovoltaic power generation system Real-time energy demand monitoring , calculate energy self-sufficiency rate In the embodiment of the present invention, the energy self-sufficiency rate represents the ratio of the predicted power generation of the photovoltaic power generation system to the real-time energy demand, reflecting the coverage degree of the photovoltaic power generation system to the energy demand; the energy self-sufficiency rate is divided into levels; the specific process is as follows:

[0080] ;

[0081] in, Indicates prediction; Indicates the real-time energy demand obtained through monitoring; Indicates that lithium batteries are The discharge amount when Indicates that lithium batteries are Charge capacity at time Indicates energy utilization efficiency.

[0082] Now according to The value and actual experience of Indicates the thresholds for low and medium energy self-sufficiency; The threshold between medium and high energy self-sufficiency rates is used to classify the energy self-sufficiency rates of photovoltaic and lithium battery factories. The specific process is as follows:

[0083] when When the level is A, it means that the photovoltaic power generation is lower than the energy demand and the energy supply is insufficient. In the embodiment of the present invention, The value is 0.6, The value is 0.3, when , indicating that the energy self-sufficiency rate is low; when When , it indicates that the energy self-sufficiency rate is extremely low;

[0084] when When the level is B, it means that the photovoltaic power generation is close to the energy demand and the energy supply is basically balanced;

[0085] when When the level is C, it means that the photovoltaic power generation capacity is close to fully meeting the energy demand and the energy supply is sufficient. In the embodiment of the present invention, The value is 0.9, The value is 0.3, when , indicating that the energy self-sufficiency rate is high; when When , it indicates that it is completely self-sufficient and basically in an ideal state;

[0086] Set the initial production plan variation according to different corresponding levels , adjust the production schedule to reduce dependence on the external power grid and optimize energy consumption, optimize energy utilization efficiency, and thus intelligently regulate energy consumption.

[0087] When the level is A, the preset first production plan change is , , Indicates the production plan adjustment coefficient corresponding to level A;

[0088] When the level is B, the preset first production plan change is , , Indicates the production plan adjustment coefficient corresponding to level B;

[0089] When the level is C, the preset first production plan change is , , Indicates the production plan adjustment coefficient corresponding to level C.

[0090] The present invention predicts photovoltaic power generation through a photovoltaic system power generation prediction neural network model, which can understand the power generation situation in advance, optimize energy distribution, and calculate the energy self-sufficiency rate in combination with real-time energy demand; by calculating the energy self-sufficiency rate and dividing it into levels, the energy supply situation is clarified, the production plan change is set according to different corresponding levels, and the production schedule is adjusted. The production plan is adjusted according to actual conditions to better match energy supply and demand, avoid energy waste, improve energy utilization efficiency, provide data support for managers, optimize production plans and energy scheduling decisions; at the same time, based on the cloud computing platform, real-time data collection, processing and analysis are realized to improve management efficiency.

[0091] S2. Based on carbon emissions , production planning flexibility , error term , calculate the production plan change correction coefficient; compare the production plan change correction coefficient with the preset production plan change correction coefficient comparison value, and adjust the production plan change according to the comparison result. Make adjustments.

[0092] In the first-level regulation module, according to carbon emissions , production planning flexibility , error term , thereby calculating the production plan change correction coefficient Specifically, the error term refers to the errors in historical data and the forecasting process, which may affect the accuracy of the energy self-sufficiency rate and thus affect the adjustment of the production plan change;

[0093] In the embodiment of the present invention, the carbon emissions are obtained by using the existing technology. , ;

[0094] in, Represents direct carbon emissions generated during the production process; Represents indirect carbon emissions from purchased energy, such as grid electricity;

[0095] Define carbon emission thresholds based on historical experience and experiments ,if , indicating low carbon emissions; if , indicating high carbon emissions.

[0096] Calculate the correction coefficient of production plan variation The specific process is as follows:

[0097]

[0098] in, Indicates the low carbon emission correction factor; Indicates the high carbon emission correction factor; Indicates the maximum carbon emission; represents the production plan flexibility impact coefficient. Specifically, , It represents the production plan flexibility coefficient, which represents the production plan flexibility coefficient. represents the flexibility correction factor, , The instructions are completely flexible. Description: Complete rigidity; represents the error term influence coefficient, specifically, , represents the photovoltaic power generation prediction error, Indicates the maximum allowable error; represents the error correction coefficient;

[0099] By using the production plan change correction coefficient Comparison value with the preset production plan change correction coefficient Compare and adjust the production plan changes based on the comparison results Make adjustments,

[0100] ; The specific process is as follows:

[0101] if , then the production plan change Adjust to , then define ;

[0102] if , then the production plan change Adjust to , then define ;

[0103] if , then the production plan change Adjust to , then define .

[0104] In the secondary regulation module, according to the lithium battery charge and discharge loss , compared with the charge and discharge loss of standard lithium batteries Analyze and compare, and determine whether to adjust the production plan according to the comparison results Perform secondary adjustment; in the embodiment of the present invention, the charge and discharge loss of the battery is inferred by analyzing the historical charge and discharge data through existing technology, the change trend and loss of battery performance are understood, and the battery characteristic curve is combined for analysis to obtain the real-time charge and discharge loss. The specific process is as follows:

[0105]

[0106] in, Indicates the charge and discharge loss comparison coefficient of lithium battery; if , it means that the actual charge and discharge loss is higher than the standard loss, indicating that the energy consumption is high or the battery performance is degraded, and secondary adjustment is needed;

[0107] if , it means that the actual charge and discharge loss is lower than or equal to the standard loss, indicating that the energy consumption is currently within a reasonable range and no secondary adjustment is required;

[0108] Specifically, the preset lithium battery charge and discharge loss comparison coefficient is , calculate the comparison coefficient of lithium battery charge and discharge loss Comparison coefficient with preset lithium battery charge and discharge loss Ratio , ; Set the first ratio parameter at the same time and the second ratio parameter ,and , respectively and and For comparison; the specific process is as follows:

[0109] if , then adjust the production plan change to ;

[0110] if , then adjust the production plan change to ;

[0111] if , then adjust the production plan change to ;in, Indicates the corresponding control coefficients, which are used to control the adjustment amplitude.

[0112] The present invention can more accurately adjust the production plan to adapt to actual energy consumption data and production conditions by calculating the correction coefficient of the production plan change. At the same time, considering the carbon emission factor, it can reduce carbon emissions, which is helpful to achieve the factory's carbon neutrality and environmental protection goals. Moreover, by calculating the correction coefficient through the error term, it can reduce the impact of the prediction error on the production plan, improve the accuracy of the plan, and realize the regulation and optimization of the energy consumption of the photovoltaic lithium battery factory; according to the flexibility of the production plan, it can quickly respond to changes in market demand and improve resource utilization efficiency; by dynamically adjusting the production plan change, the production plan is made more in line with the actual situation, which is more conducive to the intelligent regulation of the energy consumption of the photovoltaic lithium battery factory.

[0113] S3. In the three-level regulation module, the energy cost of the photovoltaic lithium battery factory is adjusted according to the status of the lithium battery and the real-time situation of photovoltaic power generation.

[0114] The charge and discharge power of lithium batteries is defined as , ,in, Indicates rated capacity. Indicates the charging and discharging efficiency of lithium batteries, Indicates the current power level. Indicates the health status of the lithium battery; specifically, if , indicating that the lithium battery is charging; if , indicating that the lithium battery is discharging;

[0115] Define grid power distribution , ,in, Indicates the factory load power, Represents photovoltaic power generation power;

[0116] The energy cost of a photovoltaic lithium battery factory is defined as ,in, represents the cost of photovoltaic power generation; represents the grid electricity price; Indicates that only the electricity purchased from the grid is calculated.

[0117] Lithium battery health status Affects its charging and discharging efficiency and lifespan, and thus affects energy costs. The health status factor is now introduced The energy cost of the photovoltaic lithium battery factory is corrected. The specific process is as follows:

[0118]

[0119] in, represents the revised energy cost of photovoltaic lithium battery factories;

[0120] At the same time, the goal of regulation optimization is to minimize the energy cost of the photovoltaic lithium battery plant, namely: , while meeting the factory load requirements and lithium battery status constraints;

[0121] Constraints:

[0122] in, Indicates the maximum charge and discharge power of lithium battery; Indicates the maximum allowable power of the power grid; finally, use represents the revised optimal energy cost of the photovoltaic lithium battery factory.

[0123] The present invention makes adjustments based on the status of lithium batteries and the real-time situation of photovoltaic power generation, which can maximize the use of renewable energy and improve the energy utilization efficiency of photovoltaic power generation systems. By intelligently adjusting energy costs, factories can reasonably adjust energy use during peak and off-peak periods, reduce energy costs during high-energy consumption periods, and thus reduce overall energy costs. According to the real-time situation of photovoltaic power generation, energy supply can be flexibly adjusted to better respond to market demand and price fluctuations, optimize energy procurement strategies, and provide data support for the subsequent photovoltaic lithium battery factory energy consumption adjustment and monitoring process.

[0124] S4. Based on the obtained change in the production plan of the photovoltaic lithium battery factory , Minimum energy cost of photovoltaic lithium battery factory , establish an energy consumption regulation monitoring model for photovoltaic lithium battery factories and find the optimal energy consumption regulation solution.

[0125] Through adaptive learning, the energy consumption regulation monitoring model of photovoltaic lithium battery factory is established. The adaptive network model is combined with the production plan change of photovoltaic lithium battery factory. Minimum energy cost of photovoltaic lithium battery factory At the same time, the attention mechanism optimization algorithm is introduced to make the model pay more attention to key input information, monitor the energy consumption of the factory in real time, and dynamically adjust according to the actual situation to make energy utilization more efficient.

[0126] By learning attention weights, we can focus more on important features to better understand the working conditions of photovoltaic lithium battery factories and extract useful information. The specific adjustment and optimization process is as follows:

[0127] ;

[0128] ;

[0129] in, Represents the output of the energy consumption regulation monitoring model for photovoltaic lithium battery plants; represents the learning factor; represents the focus factor; represents the discount factor, which is used to measure the importance of future costs or rewards in current decisions; Indicates the monitoring time; Represents the optimized attention mechanism; Indicates complexity, which is used to measure the complexity of nonlinearity; represents the number of neurons; Indicates the The weights of neurons; A feature matrix representing the input user power consumption forecast data information; Represents the additional attention guidance matrix introduced; represents the activation function; represents the weight matrix used to map input features to the attention score space; represents the bias term, which is used to adjust the baseline value of the attention score; represents the value matrix; Indicates the dimension of the key vector.

[0130] The present invention can monitor the energy consumption of photovoltaic lithium battery factories in real time through the monitoring model, and make adjustments based on the actual changes in production plans and the minimum energy cost, thereby ensuring the high efficiency and economy of energy utilization and optimizing energy consumption, and avoiding energy waste; the present invention can dynamically adjust energy consumption through the model, reduce energy consumption fluctuations, improve the stability of the energy consumption adjustment and optimization system, reduce the operating costs of photovoltaic lithium battery factories, and improve competitiveness.

[0131] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0134] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0135] The above content is only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. The cloud computing-based intelligent adjustment and optimization system for energy consumption data of photovoltaic lithium battery factories is characterized by: Includes the following: Photovoltaic lithium battery data acquisition unit, photovoltaic system power generation prediction unit, intelligent regulation and optimization unit, photovoltaic lithium battery factory energy consumption regulation and monitoring unit; The photovoltaic lithium battery data acquisition unit obtains relevant parameters of the photovoltaic lithium battery factory based on sensors of photovoltaic components, lithium batteries, and power equipment, and transmits the data to the photovoltaic system power generation prediction unit; The photovoltaic system power generation prediction unit establishes a photovoltaic system power generation prediction neural network model based on the obtained photovoltaic lithium battery factory related data parameters to obtain the predicted power generation of the photovoltaic power generation system; calculates the energy self-sufficiency rate and divides it into levels, and sets the production plan change amount; The intelligent adjustment and optimization unit is used to optimize energy utilization efficiency and realize intelligent adjustment and optimization of energy consumption by combining real-time energy consumption data, power generation forecast results and energy cost factors; The photovoltaic lithium battery factory energy consumption adjustment and monitoring unit establishes a photovoltaic lithium battery factory energy consumption adjustment and monitoring model based on the obtained photovoltaic lithium battery factory production plan change and the photovoltaic lithium battery factory energy cost; The intelligent adjustment and optimization unit includes a primary adjustment module, a secondary adjustment module, and a tertiary adjustment module; The first-level adjustment module calculates the production plan change correction coefficient based on carbon emissions, production plan flexibility, and error terms; The secondary adjustment module analyzes and compares the charge and discharge loss of the lithium battery with the charge and discharge loss of the standard lithium battery, and determines whether to make a secondary adjustment to the production plan change according to the comparison result; The three-level adjustment module adjusts the energy cost of the photovoltaic lithium battery factory according to the status of the lithium battery and the real-time situation of photovoltaic power generation.

2. The cloud computing-based intelligent adjustment and optimization system for energy consumption data of photovoltaic lithium battery plants according to claim 1 is characterized in that: The photovoltaic system power generation prediction unit specifically includes: The photovoltaic system power generation prediction neural network model includes an input layer, an evaluation layer, a prediction layer, and an output layer; the input layer transmits the photovoltaic system operation data to the evaluation layer for measurement, the prediction layer analyzes the photovoltaic system power generation situation, and the output layer outputs the prediction results. .

3. The cloud computing-based intelligent adjustment and optimization system for energy consumption data of photovoltaic lithium battery plants according to claim 2 is characterized in that: In the photovoltaic system power generation prediction unit, according to the predicted power generation of the photovoltaic power generation system Real-time energy demand monitoring , calculate energy self-sufficiency rate ,definition Indicates the thresholds for low and medium energy self-sufficiency; Indicates the threshold between medium and high energy self-sufficiency rates, classifies the energy self-sufficiency rates of photovoltaic and lithium battery factories, and sets the initial production plan change amount The specific process is as follows: when When the level is A, the preset first production plan change is , , Indicates the production plan adjustment coefficient corresponding to level A; when When the level is B, the preset first production plan change is , , Indicates the production plan adjustment coefficient corresponding to level B; when When the level is C, the preset first production plan change is , , Indicates the production plan adjustment coefficient corresponding to level C.

4. The cloud computing-based photovoltaic lithium battery factory energy consumption data intelligent adjustment and optimization system according to claim 1 is characterized in that: Calculate the production plan change correction coefficient in the first-level adjustment module The specific process is as follows: ； in, Indicates the low carbon emission correction factor; Indicates the high carbon emission correction factor; Indicates the maximum carbon emission; represents the production plan flexibility impact coefficient. Specifically, , represents the production planning flexibility coefficient, represents the flexibility correction factor, , The instructions are completely flexible. Description: Complete rigidity; represents the error term influence coefficient, specifically, , represents the photovoltaic power generation prediction error, Indicates the maximum allowable error; represents the error correction coefficient; represents carbon emissions; Indicates the threshold value of carbon emissions.

5. The photovoltaic lithium battery plant energy consumption data intelligent adjustment and optimization system based on cloud computing according to any one of claims 3 or 4, characterized in that: In the first-level adjustment module, the production plan change correction coefficient is Comparison value with the preset production plan change correction coefficient Compare and adjust the production plan changes based on the comparison results Make adjustments, ; The specific process is as follows: if , then the production plan change Adjust to , then define ; if , then the production plan change Adjust to , then define ; if , then the production plan change Adjust to , then define .

6. The cloud computing-based photovoltaic lithium battery factory energy consumption data intelligent adjustment and optimization system according to claim 1 is characterized in that: In the secondary regulation module, Indicates the charge and discharge loss of lithium batteries. Indicates the charge and discharge loss of standard lithium batteries: ； in, Indicates the charge and discharge loss comparison coefficient of lithium battery; if , it means that the actual charge and discharge loss is higher than the standard loss, and secondary adjustment is required; if , it means that the actual charge and discharge loss is lower than or equal to the standard loss, indicating that the energy consumption is currently within a reasonable range and no secondary adjustment is required.

7. The cloud computing-based intelligent adjustment and optimization system for energy consumption data of photovoltaic lithium battery plants according to claim 6 is characterized in that: When performing secondary adjustment, calculate the comparison coefficient of lithium battery charge and discharge loss Comparison coefficient with preset lithium battery charge and discharge loss Ratio , ; Set the first ratio parameter at the same time and the second ratio parameter ,and , respectively and and For comparison; the specific process is as follows: if , then adjust the production plan change to ; if , then adjust the production plan change to ; if , then adjust the production plan change to ;in, Indicates the corresponding control coefficients, which are used to control the adjustment amplitude.

8. The cloud computing-based intelligent adjustment and optimization system for energy consumption data of photovoltaic lithium battery plants according to claim 1 is characterized in that: The three-level regulation module specifically includes: The energy cost of a photovoltaic lithium battery factory is defined as , ,in, represents the cost of photovoltaic power generation; represents the grid electricity price; Indicates that only the electricity purchased from the grid is calculated; Represents photovoltaic power generation power; Introducing health status factors The energy cost of the photovoltaic lithium battery factory is corrected. The specific process is as follows: ； in, Represents the revised energy cost of photovoltaic lithium battery factories.

9. The photovoltaic lithium battery factory energy consumption data intelligent adjustment and optimization system based on cloud computing according to any one of claims 2 or 7, characterized in that: In the photovoltaic lithium battery factory energy consumption regulation monitoring unit, the photovoltaic lithium battery factory energy consumption regulation monitoring model is established through adaptive learning. The adaptive network model is combined with the photovoltaic lithium battery factory production plan change Energy costs of photovoltaic lithium battery factories At the same time, the attention mechanism optimization algorithm is introduced to monitor the energy consumption of the factory in real time and adjust it dynamically according to the actual situation: ； in, Represents the output of the energy consumption regulation monitoring model for photovoltaic lithium battery plants; represents the learning factor; represents the focus factor; represents the discount factor; Indicates the monitoring time; Represents the optimized attention mechanism; Represents complexity; represents the number of neurons; Indicates the The weights of neurons; A feature matrix representing the input user power consumption forecast data information; Represents the additional attention guidance matrix introduced.

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