Power supply input power optimization method and system based on intelligent data analysis

Abstract

The invention relates to the technical field of intelligent control, and discloses a power supply input power optimization method and system based on intelligent data analysis, and the method comprises the steps: collecting multi-modal data, building a power optimization model according to the multi-modal data, and obtaining an input power set value; a fuzzy PID controller is adopted, and the input power is dynamically adjusted according to an input power set value; and obtaining a load prediction value through a load prediction algorithm, and adjusting an input power set value according to the load prediction value to realize feed-forward power adjustment. Compared with a traditional scheme which only depends on single feedback, the system stability, energy storage life protection and overall economic benefits can be better considered, and the comprehensive goals of peak load shifting, operation cost reduction and energy storage equipment utilization efficiency improvement are truly achieved; the method has remarkable popularization value and good application prospect for various micro-grids, power management and industrial energy application scenes.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a power supply input power optimization method and system based on intelligent data analysis. Background Art

[0002] With the rapid development of power electronics technology and intelligent control technology, emergency power supply (EPS) systems are increasingly used in the fields of construction, industry, and medical care. Traditional EPS systems mainly rely on fixed threshold control or simple feedback adjustment mechanisms to achieve switching and power distribution between mains and emergency power supplies. In recent years, with the popularization of the Internet of Things (IoT) and big data technology, intelligent power management systems have gradually become a research hotspot. In the existing technology, some studies have attempted to optimize the distribution efficiency of power input power by real-time monitoring of the grid status and load demand, combined with PID control algorithms or fuzzy control theory. In addition, the maturity of bidirectional inverter technology also provides technical support for energy recovery and reuse, so that the energy efficiency of EPS systems in non-emergency states can be improved. However, although the existing technology has made certain progress in optimizing power input power, it still has many limitations and is difficult to meet the requirements of efficient and stable operation in complex application scenarios.

[0003] Traditional power optimization methods mostly rely on a single data source and lack the comprehensive use of multimodal data (such as ambient temperature, battery status, load type, etc.), resulting in insufficient accuracy and adaptability of the optimization model. Secondly, when faced with nonlinear and time-varying load characteristics, existing control strategies often find it difficult to achieve fast and accurate power regulation, which can easily lead to overshoot or oscillation, affecting system stability. In addition, existing technologies generally lack the ability to predict load change trends and cannot achieve feedforward power regulation, resulting in delayed system response and reduced energy efficiency. These deficiencies limit the performance improvement of EPS systems in complex application scenarios, and a more intelligent and comprehensive power input power optimization method is urgently needed. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing technology has obvious deficiencies in multimodal data utilization, dynamic power regulation, load prediction and energy recovery, and it is difficult to meet the needs of efficient and stable power input power optimization.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a power supply input power optimization method based on intelligent data analysis, comprising:

[0007] Collect multimodal data, build a power optimization model based on the multimodal data, and obtain the input power setting value;

[0008] The fuzzy PID controller is used to dynamically adjust the input power according to the input power setting value;

[0009] The load prediction value is obtained through the load prediction algorithm, and the input power setting value is adjusted according to the load prediction value to realize feedforward power regulation.

[0010] As a preferred solution of the power input power optimization method based on intelligent data analysis described in the present invention, wherein: the acquisition of multimodal data includes acquiring real-time grid status data and load data, and introducing environmental data and battery status as auxiliary optimization parameters;

[0011] The real-time grid status data and load data include grid voltage, current, load forecast data, power cost data, and power exchange value between load and grid;

[0012] The auxiliary optimization parameters include the internal temperature of the battery, the equivalent life loss of the battery charge and discharge cycle, the battery Data and battery charge and discharge current data.

[0013] As a preferred solution of the power input power optimization method based on intelligent data analysis described in the present invention, wherein: the construction of the power optimization model based on multimodal data includes obtaining load prediction data and power cost data based on the multimodal data, and constructing a multi-objective optimization function, which is expressed as: ;

[0014] in, Indicates taking the minimum value; Indicates time Input power setting value; , , represents the weight coefficient; Indicates the operating cost; Represents the life attenuation cost of the energy storage device; Indicates the power grid impact index;

[0015] The running cost It is expressed as the weighted sum of the cost of obtaining electricity from the grid during the dispatch period: ;

[0016] in, Indicates the start time of the scheduling cycle; Indicates the length of the optimized rolling time domain; Indicates the power exchanged with the grid side; represents the time step; Represents the cost of exchanging electric energy with the grid; Time series index;

[0017] The equivalent life loss of the charge and discharge cycle is quantified through an empirical model to obtain the life attenuation cost of the energy storage device , expressed as: ;

[0018] in, Represents the cost function of battery life consumed by the current charging and discharging operation; Indicates time The state of charge; express changes; Indicates the battery charging and discharging current; Indicates the internal temperature of the battery;

[0019] The grid impact index The power exchange value between the load on the grid side and the grid is expressed as: ;

[0020] in, Indicates time The power exchange value between the grid-side load and the grid; Indicates time The power exchange value between the grid-side load and the grid.

[0021] As a preferred solution of the power input power optimization method based on intelligent data analysis described in the present invention, the empirical model includes: the average life loss caused by each unit charge and discharge under basic working conditions is expressed as the basic attenuation rate Indicates that the auxiliary optimization parameters are obtained through multimodal data, combined with the energy storage in each time step Calculate the life-time decay cost by changing the amount , expressed as: ;

[0022] in, represents the basic decay rate; Indicates the time step Amount of change; represents the temperature correction factor; Indicates the rate correction factor;

[0023] The temperature correction factor Describe the impact of temperature exceeding the basic operating conditions on battery aging, expressed as: ;

[0024] in, Indicates the sensitivity of temperature to the decay rate; Indicates the reference temperature under basic working conditions;

[0025] Ratio correction factor The effect of the difference between the actual charge and discharge current and the basic operating current on attenuation is expressed as: ;

[0026] in, Indicates the sensitivity of the charge and discharge current rate to the decay rate; Indicates the actual charge and discharge current; Indicates the reference current under basic working conditions; by introducing temperature correction factor and rate correction factor, the battery life attenuation is evaluated in real time in each scheduling cycle; when the temperature or rate deviation is too large, the life attenuation cost is adaptively increased , guiding the dispatch plan to balance power allocation and life protection.

[0027] As a preferred solution of the power input power optimization method based on intelligent data analysis described in the present invention, the fuzzy PID controller includes collecting basic input information, the basic input information includes input power setting value , Actual input power and energy storage life attenuation index The energy storage life decay index is based on the life decay cost Calculate acquisition;

[0028] Calculate the error between the input power setting value and the actual input power and error variation ;

[0029] Will , and Perform fuzzification, construct membership functions and language values;

[0030] Establish a fuzzy rule base, based on the two-dimensional rules of error and error change As a branch judgment, the judgment results of the two-dimensional rules of error and error change are corrected, and the results of the fuzzy rules are converted into specific values ​​through the centroid method, and the PID parameters are updated according to the specific values: ;

[0031] in, Indicates the final control amount; represents proportional gain; represents the integral gain; It means collecting and accumulating the sum of error changes over time; Represents the differential gain; the control quantity Converts to input power adjustment commands.

[0032] As a preferred solution of the power input power optimization method based on intelligent data analysis described in the present invention, wherein: the input power setting value is adjusted according to the load prediction value, including, according to the multimodal data, outputting the time series prediction in the future rolling window Predicted load curve within;

[0033] Get the input power setting value ,use Compare with the load prediction value to obtain the load deviation. According to the load deviation and energy storage constraint, a feedforward correction is generated to pre-adjust the input power setting value, which is expressed as: ;

[0034] in, Indicates the corrected power setting value; Represents the feedforward correction amount.

[0035] As a preferred solution of the power input power optimization method based on intelligent data analysis described in the present invention, the feedforward power regulation includes sending the input power setting sequence after feedforward correction to the fuzzy PID controller, and the fuzzy PID continues to fine-tune according to the actual error in a short time scale, forming a double guarantee of feedforward and feedback;

[0036] When the deviation between the actual load and the predicted load exceeds the allowable range, the actual input power is adjusted through fuzzy PID feedback control to quickly eliminate the deviation caused by the error, and the feedforward control is updated again when the next prediction cycle arrives.

[0037] A power input power optimization system based on intelligent data analysis using any of the methods described in the present invention, wherein: a collection module collects multi-modal data, builds a power optimization model based on the multi-modal data, and obtains an input power setting value;

[0038] The control module uses a fuzzy PID controller to dynamically adjust the input power according to the input power set value;

[0039] The regulation module obtains the load prediction value through the load prediction algorithm, adjusts the input power setting value according to the load prediction value, and realizes feedforward power regulation.

[0040] A computer device comprises: a memory and a processor; the memory stores a computer program, comprising: the steps of implementing any one of the methods of the present invention when the processor executes the computer program.

[0041] A computer-readable storage medium stores a computer program, comprising: when the computer program is executed by a processor, the steps of implementing any one of the methods of the present invention are implemented.

[0042] Beneficial effects of the present invention: The method of the present invention not only dynamically obtains the optimal input power setting value by introducing multimodal data analysis, load prediction and energy storage life attenuation model on the basis of conventional power scheduling, but also realizes fine adjustment of the actual input power by combining feedforward and feedback. The fuzzy PID controller responds to real-time errors at high speed, effectively avoiding power fluctuations caused by sudden changes in load; at the same time, through the energy management and feedforward scheduling of the bidirectional inverter, electricity can be reserved in advance before the load peak, or excess energy can be fed back in time when the load drops suddenly, greatly reducing the instantaneous impact on the power grid and energy storage system. Compared with the traditional solution that relies only on a single feedback, the present invention can better take into account system stability, energy storage life protection and overall economic benefits, and truly achieve the comprehensive goals of peak shaving and valley filling, reducing operating costs, and improving the utilization efficiency of energy storage equipment. It has significant promotion value and good application prospects for various microgrids, power management and industrial energy application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0044] Figure 1 An overall flow chart of a power supply input power optimization method based on intelligent data analysis provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0046] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a power supply input power optimization method based on intelligent data analysis, comprising:

[0047] S1: Collect multimodal data, build a power optimization model based on the multimodal data, and obtain an input power setting value.

[0048] Furthermore, the multimodal data collection includes collecting real-time grid status data and load data, and introducing environmental data and battery status as auxiliary optimization parameters. Specifically, multiple types of data collection devices are deployed on the system side and the load side to collect real-time grid status data and load data. The real-time grid status data and load data include basic electrical information such as grid voltage, current, harmonic content and load power demand. The battery status includes the temperature inside the battery, battery At the same time, external environmental sensors can also be used to obtain environmental data such as temperature, humidity, and light intensity to support the adaptive adjustment of subsequent models in different climates or locations. To improve data integrity and accuracy, a data fusion module can be set up in the main control platform to perform preliminary cleaning and formatting of multimodal data from electrical monitoring, energy storage management, and environmental perception to form a unified feature vector that can be called by subsequent power optimization models.

[0049] Furthermore, the core goal of constructing a power optimization model is to output a decision plan that can achieve the optimal input power setting value within a certain time domain based on multimodal data and system operation requirements. To this end, the problem can be abstracted into an optimization model with a multi-objective function and containing several physical and operational constraints, and prediction can be used to improve the foresight and real-time performance of the model. The construction of a power optimization model based on multimodal data includes obtaining load prediction data and electricity cost data based on multimodal data, and constructing a multi-objective optimization function, which is expressed as: ;

[0050] in, Indicates taking the minimum value; Indicates time Input power setting value; , , represents the weight coefficient; Indicates the operating cost; Represents the life attenuation cost of the energy storage device; Indicates the power grid impact index.

[0051] The running cost It is expressed as the weighted sum of the cost of obtaining electricity from the grid during the dispatch period: ;

[0052] in, Indicates the start time of the scheduling cycle; Indicates the length of the optimized rolling time domain; Indicates the power exchanged with the grid side; represents the time step; Represents the cost of exchanging electric energy with the grid; Time series index. It should be noted that if we consider selling energy to the grid (two-way trading), we need to add negative revenue terms to the above model, or model the purchase and sale of electricity as positive and negative power flows and corresponding costs / revenues respectively.

[0053] The life of energy storage devices (such as lithium batteries) is generally related to factors such as depth of discharge (DOD), charge and discharge rate (C-rate), and temperature. In practical applications, it is not necessary to calculate the life loss of energy storage devices very accurately, so it can be simplified and the equivalent life loss of the charge and discharge cycle can be quantified through an empirical model to obtain the life attenuation cost of the energy storage device. , expressed as: ;

[0054] in, Represents the cost function of battery life consumed by the current charging and discharging operation; Indicates time The state of charge; express changes; Indicates the battery charging and discharging current; Indicates the internal temperature of the battery.

[0055] The empirical model includes: the average life loss caused by each unit charge and discharge under basic operating conditions (normal temperature, normal charge and discharge rate) is expressed as the basic attenuation rate Indicates that the auxiliary optimization parameters are obtained through multimodal data, combined with the energy storage in each time step Calculate the life-time decay cost by changing the amount , expressed as: ;

[0056] in, represents the basic decay rate; Indicates the time step Amount of change; represents the temperature correction factor; Indicates the rate correction factor.

[0057] The temperature correction factor Describes the effect of temperature exceeding the basic operating conditions on battery aging. The chemical and physical properties of batteries or energy storage systems are very sensitive to temperature: Too high or too low temperature will accelerate the increase of internal resistance and the decay of active substances, resulting in a shortened life. Therefore, a correction factor is used to represent the effect of "the deviation of the current temperature from the standard temperature (or optimal temperature)" on the life decay rate. The temperature correction factor is expressed as: ;

[0058] in, Indicates the sensitivity of temperature to the decay rate; It indicates the reference temperature under basic working conditions. It is found through experiments that the exponential form is closer to the data law of many actual life acceleration tests (Arrhenius-type relationship), but considering that the life loss is calculated in the present invention for the purpose of adjusting the input power, it does not require an accurate life loss value, so a simpler linear form is used, which is convenient for rapid calculation in real-time control and can also be approximately applicable in occasions where the temperature change range is not large.

[0059] Similar to temperature, the effect of discharge rate on attenuation is also based on data (accelerated life test, manufacturer test curve, etc.). Battery aging is closely related to discharge rate (C-rate): when charging and discharging at high rates, the side reactions, heat generation, and material stress inside the battery are more serious. In order to punish the additional aging caused by excessive current during operation, a correction factor is usually defined relative to the standard rate (such as 1C). The rate correction factor The effect of the difference between the actual charge and discharge current and the basic operating current on attenuation is expressed as: ;

[0060] in, Indicates the sensitivity of the charge and discharge current rate to the decay rate; Indicates the actual charge and discharge current; Indicates the reference current under basic working conditions; by introducing temperature correction factor and rate correction factor, the battery life attenuation is evaluated in real time in each scheduling cycle; when the temperature or rate deviation is too large, the life attenuation cost is adaptively increased , guiding the dispatch plan to balance power allocation and life protection.

[0061] By introducing temperature / rate correction, the system will evaluate the battery life attenuation in real time during each scheduling cycle; if it is found that the temperature is too high or the rate is too large, the attenuation cost weight will be adaptively increased to guide the scheduling plan to better balance power allocation and life protection.

[0062] The grid impact index The power exchange value between the load on the grid side and the grid is expressed as: ;

[0063] in, Indicates time The power exchange value between the grid-side load and the grid; Indicates time The power exchange value between the grid-side load and the grid. The larger the grid impact index is, the more severe the power spike or fluctuation on the grid is; by introducing this index into the objective function or using it as a constraint, the instantaneous impact on the grid can be reduced.

[0064] It should be noted that the power optimization model also needs to be limited by constraints, so that when solving the optimal input power setting value, it can meet the actual engineering and system safety requirements, and avoid pursuing the optimal solution only from a mathematical perspective while ignoring physical or operational limits. Constraints include but are not limited to power balance constraints, energy storage state evolution constraints, charging and discharging power limits, and grid-side power limits.

[0065] S2: Use fuzzy PID controller to dynamically adjust the input power according to the input power set value.

[0066] Furthermore, the optimal input power setting value output by the multi-objective optimization model is used as the target input of the fuzzy PID. Through fuzzy reasoning and PID parameter adaptation, real-time adjustment of the actual input power is achieved, ensuring that the system maintains good tracking performance and has strong robustness and adaptability during operation.

[0067] The fuzzy PID controller includes collecting basic input information, wherein the basic input information includes an input power setting value , Actual input power and energy storage life attenuation index The energy storage life decay index is based on the life decay cost Calculate acquisition.

[0068] Specifically, life decay index In fuzzy control, an intuitive quantitative index such as 10 is used to express the tolerance of the current energy storage to rapid charging and discharging. The larger the value, the higher the risk of stress or aging of the battery / energy storage, and the more cautious high-power operation is required; A smaller value indicates that the battery is in good health and the power can be adjusted more aggressively.

[0069] Life-Decrease Cost By linear mapping to The domain of the decay cost When it is very high, it means that the battery is currently or recently experiencing high rate / high temperature / deep discharge, and the health pressure is actually accumulating or surging. The higher the value, the more cautious the controller scheduling and the less intense charging and discharging, thus achieving life protection. The mapping formula is implemented in the form of linear + saturation value, expressed as: ;

[0070] in, represents the average value of the decay cost during the control period; Indicates the low threshold of decay cost; Indicates the high threshold of decay cost. When the decay cost is lower than When the attenuation cost exceeds When the pressure is high, it is considered to be very high and intense operations should be reduced as much as possible.

[0071] Calculate the error between the input power setting value and the actual input power and error variation .

[0072] Will , and Perform fuzzification, construct membership functions and language values, and Use 7 or 5 levels such as NB (negative large), NM (negative medium), NS (negative small), Z (zero), PS (positive small), PM (positive medium), and PB (positive large), and express them with appropriate membership functions (such as trigonometric functions or trapezoidal functions). Represents the current health pressure of energy storage - the higher the value, the greater the risk of battery aging or the higher the temperature, etc. Define a reasonable range and divide it into three fuzzy subsets: L (Low), M (Medium), and H (High).

[0073] Establish a fuzzy rule base, based on the two-dimensional rules of error and error change As a branch judgment, the judgment results of the two-dimensional rules of error and error change are corrected, and the results of the fuzzy rules are converted into specific values ​​through the centroid method, and the PID parameters are updated according to the specific values: ;

[0074] in, Indicates the final control amount; represents proportional gain; represents the integral gain; It means collecting and accumulating the sum of error changes over time; Represents the differential gain; the control quantity Converts to input power adjustment commands.

[0075] S3: Obtain a load prediction value through a load prediction algorithm, adjust the input power setting value according to the load prediction value, and implement feedforward power regulation.

[0076] Further, adjusting the input power setting value according to the load prediction value includes, according to the multimodal data, outputting the load in the future rolling window through time series prediction. The predicted load curve within.

[0077] Get the input power setting value ,use Compare with the load prediction value to obtain the load deviation. According to the load deviation and energy storage constraint, a feedforward correction is generated to pre-adjust the input power setting value, which is expressed as: ;

[0078] in, Indicates the corrected power setting value; Represents the feedforward correction amount.

[0079] It should be noted that the energy storage system needs to be considered during adjustment. range, bidirectional inverter power cap / current cap, and grid safety limits to avoid violating physical and safety constraints. If the energy storage is close to full SoC, it will not be able to absorb excess energy in advance; conversely If the voltage is too low, it will be difficult to provide sufficient power before the peak load, and it will be necessary to charge in advance or coordinate with the power grid.

[0080] Furthermore, the charging and discharging strategy of the bidirectional inverter is adjusted according to the adjusted power setting value, and the charging and discharging strategy includes: when it is predicted that there will be a load peak and the current energy storage If the power supply is relatively insufficient, the energy storage can be charged in advance during periods of low load or low electricity prices. During this period, if the grid status permits or the electricity price is low, the purchased power can be increased and stored in the battery to prepare for discharge during subsequent peak periods.

[0081] When the load reaches its peak, the bidirectional inverter helps meet the peak load by discharging (releasing stored energy), thereby reducing the instantaneous demand peak from the grid side; reducing the significant impact on the grid, and also saving electricity purchase costs during peak electricity price periods (if there are peak and valley differences in the electricity price mechanism).

[0082] When the load forecast shows that it is about to drop or enter a period of extremely low power consumption, the discharge of energy storage can be reduced and the remaining energy can be retained; if the system allows two-way transactions and the electricity price is high, the excess energy can be fed back to the grid to generate profits.

[0083] Furthermore, the feedforward power regulation includes sending the feedforward corrected input power setting sequence to the fuzzy PID controller, and the fuzzy PID continues to make fine adjustments based on the actual error within a short time scale, forming a double guarantee of feedforward and feedback.

[0084] When the deviation between the actual load and the predicted load exceeds the allowable range, the actual input power is adjusted through fuzzy PID feedback control to quickly eliminate the deviation caused by the error, and the feedforward control is updated again when the next prediction cycle arrives.

[0085] Through the process design of prediction → feedforward scheduling → bidirectional inverter energy management → real-time execution → rolling update, the present invention can efficiently utilize the energy storage system and intelligently distribute the power of the power grid in scenarios with strong load fluctuations or large peak-to-valley differences, while ensuring load demand, greatly alleviating power grid shocks, reducing operating costs, and extending the life of energy storage. Compared with simple PID or post-passive control, this method is more forward-looking and flexible, and is an important technical means to achieve the goal of "feedforward power regulation" of the present invention.

[0086] Embodiment 2, in an exemplary embodiment, a power input power optimization system based on intelligent data analysis is also provided, including an acquisition module, a data management module, a power optimization model, a control module (fuzzy PID controller), a regulation module (feedforward regulation unit) and an execution module.

[0087] The acquisition module uses various sensors, metering devices and interfaces to obtain multi-modal data from the grid side, load side and energy storage side in real time, including voltage, current, load power, ambient temperature, humidity and battery SoC, etc. It can also collect historical operating conditions and fault information for subsequent model training and parameter correction.

[0088] The acquisition module uploads the collected raw data to the data management module for preliminary correction and storage; and provides the latest available data to the load prediction algorithm of the regulation module.

[0089] The data management module cleans, converts the format and extracts the features of the multimodal data uploaded by the acquisition module, providing high-quality, unified format input for the power optimization model or load prediction algorithm. It can include a database or cache for parallel management of historical data and real-time data.

[0090] After receiving the data from the acquisition module, the data management module outputs it to the power optimization model and the prediction unit of the regulation module to ensure the consistency and traceability of the data.

[0091] The power optimization model is based on the fused data provided by the data management module, comprehensively considers multiple objective factors such as energy cost, energy storage life, power fluctuation, etc., constructs and solves the power optimization problem, and obtains the input power setting value of the current or next cycle.

[0092] The power optimization model sends the optimization results (input power set value) to the control module and the regulation module, providing a target benchmark for subsequent feedback and feedforward regulation.

[0093] The control module (fuzzy PID controller) uses the input power setting value given by the power optimization model as a reference and performs real-time feedback control in seconds or shorter cycles. The current error is calculated through fuzzy PID, and the actual input power is dynamically adjusted to ensure rapid tracking of the target value and stable power output.

[0094] The control module (fuzzy PID controller) obtains the set value from the power optimization model or the regulation module, and outputs instructions to the inverter or energy storage management system in the "execution module" to achieve closed-loop regulation of the actual power.

[0095] The regulation module (feedforward regulation unit) includes a load prediction algorithm, which predicts subsequent load change trends by analyzing historical and real-time data. If it is predicted that the load is about to change significantly, the original set value of the power optimization model is corrected or updated to achieve feedforward power regulation, and the bidirectional inverter is used to store or release energy at appropriate times to further smooth the system operation.

[0096] The regulation module (feedforward regulation unit) interacts with the data management module to obtain the latest multimodal data, and shares or updates the set value with the power optimization model; and outputs the corrected input power reference value to the control module.

[0097] The execution module ultimately executes the control and regulation instructions, including hardware units such as inverters, energy storage management systems, and grid interaction interfaces; it completes actual charging and discharging or power conversion according to the power instructions issued by the control module or regulation module.

[0098] The execution module receives the adjustment commands output by the control module and feeds back the execution results (such as current power, SoC status, etc.) to the acquisition module and data management module in real time.

[0099] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0100] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0101] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0102] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A power input power optimization method based on intelligent data analysis, characterized in that: include: Collect multimodal data, build a power optimization model based on the multimodal data, and obtain the input power setting value; The fuzzy PID controller is used to dynamically adjust the input power according to the input power setting value; The load prediction value is obtained through the load prediction algorithm, and the input power setting value is adjusted according to the load prediction value to realize feedforward power regulation.

2. The power input power optimization method based on intelligent data analysis according to claim 1, characterized in that: The collecting of multimodal data includes collecting real-time grid status data and load data, and introducing environmental data and battery status as auxiliary optimization parameters; The real-time grid status data and load data include grid voltage, current, load forecast data, power cost data, and power exchange value between load and grid; The auxiliary optimization parameters include the internal temperature of the battery, the equivalent life loss of the battery charge and discharge cycle, the battery Data and battery charge and discharge current data.

3. The power input power optimization method based on intelligent data analysis according to claim 2, characterized in that: The constructing of the power optimization model according to the multimodal data includes acquiring load prediction data and power cost data according to the multimodal data, and constructing a multi-objective optimization function, which is expressed as: ； in, Indicates taking the minimum value; Indicates time Input power setting value; , , represents the weight coefficient; Indicates the operating cost; Represents the life attenuation cost of the energy storage device; Indicates the power grid impact index; The running costs It is expressed as the weighted sum of the cost of obtaining electricity from the grid during the dispatch period: ； in, Indicates the start time of the scheduling cycle; Indicates the length of the optimized rolling time domain; Indicates the power exchanged with the grid side; represents the time step; Represents the cost of exchanging electric energy with the grid; Time series index; The equivalent life loss of the charge and discharge cycle is quantified through an empirical model to obtain the life attenuation cost of the energy storage device , expressed as: ； in, Represents the cost function of battery life consumed by the current charging and discharging operation; Indicates time The state of charge; express changes; Indicates the battery charging and discharging current; Indicates the internal temperature of the battery; The grid impact index The power exchange value between the load on the grid side and the grid is expressed as: ； in, Indicates time The power exchange value between the grid-side load and the grid; Indicates time The power exchange value between the grid-side load and the grid.

4. The power input power optimization method based on intelligent data analysis according to claim 3, characterized in that: The empirical model includes the following steps: Indicates that the auxiliary optimization parameters are obtained through multimodal data, combined with the energy storage in each time step Calculate the life-time decay cost by changing the amount , expressed as: ； in, represents the basic decay rate; Indicates the time step Amount of change; represents the temperature correction factor; Indicates the rate correction factor; The temperature correction factor Describe the impact of temperature exceeding the basic operating conditions on battery aging, expressed as: ； in, Indicates the sensitivity of temperature to the decay rate; Indicates the reference temperature under basic working conditions; Ratio correction factor The effect of the difference between the actual charge and discharge current and the basic operating current on attenuation is expressed as: ； in, Indicates the sensitivity of the charge and discharge current rate to the decay rate; Indicates the actual charge and discharge current; Indicates the reference current under basic working conditions; by introducing temperature correction factor and rate correction factor, the battery life attenuation is evaluated in real time in each scheduling cycle; when the temperature or rate deviation is too large, the life attenuation cost is adaptively increased , guiding the dispatch plan to balance power allocation and life protection.

5. The power input power optimization method based on intelligent data analysis according to claim 4, characterized in that: The fuzzy PID controller includes collecting basic input information, wherein the basic input information includes an input power setting value , Actual input power and energy storage life attenuation index The energy storage life decay index is based on the life decay cost Calculate acquisition; Calculate the error between the input power setting value and the actual input power and error variation ; Will , and Perform fuzzification, construct membership functions and language values; Establish a fuzzy rule base, based on the two-dimensional rules of error and error change As a branch judgment, the judgment results of the two-dimensional rules of error and error change are corrected, and the results of the fuzzy rules are converted into specific values ​​through the centroid method, and the PID parameters are updated according to the specific values: ； in, Indicates the final control amount; represents proportional gain; represents the integral gain; It means collecting and accumulating the sum of error changes over time; Represents the differential gain; the control quantity Converts to input power adjustment commands.

6. The power input power optimization method based on intelligent data analysis according to claim 5, characterized in that: The step of adjusting the input power setting value according to the load prediction value includes: outputting the input power setting value in the future rolling window through time series prediction according to the multimodal data. Predicted load curve within; Get the input power setting value ,use Compare with the load prediction value to obtain the load deviation. According to the load deviation and energy storage constraint, a feedforward correction is generated to pre-adjust the input power setting value, which is expressed as: ； in, Indicates the corrected power setting value; Represents the feedforward correction amount.

7. The power input power optimization method based on intelligent data analysis according to claim 6, characterized in that: The feedforward power regulation includes sending the feedforward corrected input power setting sequence to the fuzzy PID controller, and the fuzzy PID continues to make fine adjustments according to the actual error in a short time scale, forming a double guarantee of feedforward and feedback; When the deviation between the actual load and the predicted load exceeds the allowable range, the actual input power is adjusted through fuzzy PID feedback control to quickly eliminate the deviation caused by the error, and the feedforward control is updated again when the next prediction cycle arrives.

8. A power supply input power optimization system based on intelligent data analysis, applied to a power supply input power optimization method based on intelligent data analysis as claimed in any one of claims 1 to 7, characterized in that: include, An acquisition module collects multimodal data, builds a power optimization model based on the multimodal data, and obtains an input power setting value; The control module uses a fuzzy PID controller to dynamically adjust the input power according to the input power set value; The regulation module obtains the load prediction value through the load prediction algorithm, adjusts the input power setting value according to the load prediction value, and realizes feedforward power regulation.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the power input power optimization method based on intelligent data analysis as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power supply input power optimization method based on intelligent data analysis as described in any one of claims 1 to 7 are implemented.

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