Electrolytic aluminum load regulation evaluation method and device, medium and electronic equipment

Abstract

The invention discloses an electrolytic aluminum load regulation evaluation method and device, a medium and electronic equipment, and relates to the field of power systems. Data modeling, index system construction and multi-index comprehensive evaluation can be fused, and accurate quantification of the adjustable potential is achieved. The electrolytic aluminum load regulation evaluation method comprises the following steps: acquiring multi-dimensional data of each load device in the electrolytic aluminum industry; encoding the multi-dimensional data according to the equipment dimension, the time dimension and the index dimension to generate a three-dimensional data matrix; calculating the adjustable capacity, the response speed and the duration of each load device and the adjustment income and the adjustment cost of the electrolytic aluminum industry through the three-dimensional data matrix; and calculating an adjustable index of each load device in combination with the adjustable capacity, the response speed, the duration time, the adjustment income and the adjustment cost.

Description

Technical Field

[0001] This application relates to the field of power systems, and in particular to a method, apparatus, medium and electronic equipment for evaluating load regulation of electrolytic aluminum. Background Technology

[0002] Electrolytic aluminum, as a typical high-energy-consuming industry, has a large load scale, strong operational continuity, and high energy consumption concentration, playing an important role in the power grid operation regulation and demand response market. However, electrolytic aluminum load also exhibits characteristics such as strict process constraints, complex equipment operation, and uneven distribution of adjustable space, making it difficult to accurately assess its adjustable potential.

[0003] Currently, the potential assessment of high-energy-consuming industrial loads still mainly relies on static capacity calculations and empirical parameter settings, often neglecting the dynamic characteristics and multi-dimensional constraints of the load under different operating conditions. For example, the adjustment of electrolytic cell load is limited by cell temperature and anode effect; overhead crane operation load is random and intermittent; fans and compressors have minimum flow rates and operational safety boundaries; and auxiliary loads such as air conditioning and lighting, although highly flexible, account for a small overall proportion. Traditional assessment methods fail to comprehensively consider these differences, resulting in one-sided and biased results that are difficult to meet the power grid's requirements for accuracy and operability. In addition, most existing methods focus on single indicators, lacking a unified quantification of economic benefits, making it difficult to adapt to the development needs of electricity marketization and carbon trading mechanisms. Summary of the Invention

[0004] This application provides a method, apparatus, medium, and electronic equipment for evaluating the load regulation of electrolytic aluminum, which can integrate data modeling, indicator system construction, and multi-indicator comprehensive evaluation to achieve accurate quantification and application promotion of adjustable potential.

[0005] In a first aspect, this application provides a method for evaluating the load adjustment of electrolytic aluminum, including: Acquire multidimensional data of various load equipment in the electrolytic aluminum industry; Multidimensional data is encoded according to device dimension, time dimension, and indicator dimension to generate a three-dimensional data matrix; The adjustable capacity, response speed, and duration of each load device are calculated using the three-dimensional data matrix, as well as the adjustment benefits and adjustment costs of the electrolytic aluminum industry. The adjustable parameters of each load device are calculated by combining the adjustable capacity, response speed, duration, adjustment benefits, and adjustment costs.

[0006] According to the electrolytic aluminum load regulation method provided in this embodiment, multi-source data is collected from the electrolytic aluminum industry, and a data model is constructed. The response capacity, speed, and economic benefits are calculated based on the operating modes of the main load equipment in the electrolytic aluminum industry. Through a comprehensive evaluation of multiple indicators, the load adjustability of the electrolytic aluminum industry is precisely quantified. This solution can comprehensively assess the load regulation capability of the electrolytic aluminum industry, improving the scientific rigor and operability of high-energy-consuming industries in grid dispatching, virtual power plant aggregation, and the demand response market.

[0007] Secondly, this application provides an electrolytic aluminum load regulation and assessment device, comprising: The multi-source data acquisition module is used to acquire multi-dimensional data of various load equipment in the electrolytic aluminum industry; The data processing module is used to encode multidimensional data according to device dimension, time dimension, and indicator dimension to generate a three-dimensional data matrix; The index calculation module is used to calculate the adjustable capacity, response speed and duration of each load device, as well as the adjustment benefits and adjustment costs of the electrolytic aluminum industry, through the three-dimensional data matrix. The adjustable evaluation module is used to calculate the adjustable indicators of each load device by combining the adjustable capacity, response speed, duration, adjustment benefits and adjustment costs.

[0008] Thirdly, this application provides an electronic device including a memory and one or more processors. The memory stores one or more computer programs, each including instructions that, when executed by the processor, cause the electronic device to perform the electrolytic aluminum load regulation evaluation method as described in the first aspect.

[0009] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the electrolytic aluminum load regulation assessment method as described in the first aspect.

[0010] Fifthly, this application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the electrolytic aluminum load regulation assessment method as described in the first aspect.

[0011] Understandably, the beneficial effects that the electrolytic aluminum load adjustment and evaluation device, electronic equipment, computer-readable storage medium, and computer program products provided above can be referred to the beneficial effects in the first aspect, and will not be repeated here. Attached Figure Description

[0012] Figure 1 A schematic flowchart illustrating the electrolytic aluminum load adjustment evaluation method provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of the electrolytic aluminum load regulation and evaluation device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] To facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. For example, "first chip" and "second chip" are only used to distinguish different chips and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" do not necessarily imply that they are different. It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner. In the embodiments of this application, "at least one" means one or more, and "more than one" means two or more.

[0014] It should be noted that "at the time of..." in the embodiments of this application can be either at the instant when a certain situation occurs, or for a period of time after the occurrence of a certain situation. The embodiments of this application do not make specific limitations on this.

[0015] The implementation of this embodiment will now be described in detail with reference to the accompanying drawings.

[0016] This embodiment provides a method for evaluating the load adjustment of electrolytic aluminum. For example, this method can be applied to various electronic devices such as computers (PCs), tablets, virtual reality / augmented reality devices, wearable devices, industrial computers, and vehicle-mounted systems; it can also be applied to servers, cloud computing, server clusters, etc. This embodiment does not impose any special limitations on it.

[0017] Figure 1 A schematic flowchart of the electrolytic aluminum load adjustment evaluation method provided in the embodiments of this application is shown.

[0018] like Figure 1 As shown, the electrolytic aluminum load adjustment assessment method may include the following steps: Step 101: Obtain multidimensional data of various load equipment in the electrolytic aluminum industry.

[0019] Step 102: Encode the multidimensional data according to the device dimension, time dimension, and indicator dimension to generate a three-dimensional data matrix.

[0020] Step 103: Calculate the adjustable capacity, response speed, and duration of each load device, as well as the adjustment benefits and adjustment costs of the electrolytic aluminum industry, using the three-dimensional data matrix.

[0021] Step 104: Calculate the adjustable parameters of each load device by combining the adjustable capacity, response speed, duration, adjustment benefits, and adjustment costs.

[0022] This embodiment integrates key technologies such as industrial load modeling, multi-source operation data acquisition and preprocessing, construction of an adjustable potential index system, and comprehensive evaluation of multiple indicators. It achieves a unified quantitative assessment of the dynamic response capabilities, process safety constraints, and economic benefits of major loads such as electrolytic cells, overhead cranes, fans, air conditioning, and lighting in the electrolytic aluminum production process. This results in a scientific, comparable, and operable potential evaluation result, providing a reliable decision-making basis for grid dispatch, virtual power plant aggregation, and the demand response market.

[0023] Next, we will go into the specific details of the above steps.

[0024] In step 101, the overall load of the electrolytic aluminum industry consists of the main production load and auxiliary common loads, exhibiting characteristics of large capacity, strong continuity, and high operational stability requirements. Exemplarily, the load equipment includes electrolytic cells, multi-functional overhead crane loads, fans and compressors, lighting loads, air conditioning loads, etc., but this embodiment is not limited to these. Data from these load equipment are collected separately to obtain multi-dimensional data. Different loads differ significantly in terms of capacity proportion, controllable channels, and process constraints, requiring classification modeling to clarify the boundaries and range of adjustable potential. First: Electrolyzer load (main production and core energy-consuming unit) Electrolyzers use high-current direct current to drive the electrolytic reaction, and their power constitutes a major part of the enterprise's total load. Their adjustability directly determines the upper limit of the overall adjustable potential. The formula for the DC-side power versus external characteristic curve can be expressed as: (1) (2) (3) in, This refers to the active power of the electrolytic cell. and These are DC voltage and DC current, respectively. This is the DC bus voltage. Equivalent resistance This is the back electromotive force. The DC voltage and high-voltage side adjustable parameters, i.e., the high-voltage bus voltage, turns ratio, and reactance voltage drop, must meet the following requirements: (4) in, This refers to the voltage of the high-voltage bus on the load side. To set the turns ratio of the voltage regulating transformer, This is the equivalent voltage drop of the saturated reactor. At the operating point... to Performing a first-order expansion, we obtain the power-voltage sensitivity relationship under small perturbations: (5) And further by For each control quantity A linear mapping yields the form of a superposition of the three-channel gains: (6) in, , , They are respectively , , The disturbance to power, i.e.: (7) (8) (9) This allows for the formation of a joint constraint between the power adjustable range and the control physical boundary: (10) Provided that the process thermal balance and cell temperature window (e.g., 940–960℃) are met, the above three channels provide the physical basis for rapid up / down adjustment of the electrolytic cell; large disturbances or abnormal anode effects should be eliminated by derating factors or unavailable periods.

[0025] Second: Multifunctional overhead crane load (intermittent impact unit) The overhead crane performs operations such as feeding, electrode changing, and slag removal, and its operating characteristics are intermittent and impulsive. Its operating power can be modeled as a gated superposition: (11) in, For a moment The power of the overhead crane This represents the number of tasks completed before the specified time. Rated power, This is the start time node for the task. This refers to the duration of a single operation. If the operation reaches a certain intensity... Then the expected power can be approximated as: (12) in, Given the average duration of the tasks, the adjustable strategy mainly involves shifting task timing (shifting non-urgent tasks within non-critical windows). Let's assume the time is... The translational order is Then the upper bound of the short-term equivalent downregulation potential can be approximated as: (13) in, Represented as time The equivalent downward adjustment potential. At this point, the power adjustment meets the process constraints of no interruption for critical operations.

[0026] Third: Fan and compressor group load (continuous operation and variable frequency unit) Ventilation and air supply systems are typically equipped with variable frequency drives (VFDs) to provide continuous adjustability while meeting minimum service requirements. Based on the fluid similarity law: (14) (15) (16) in, For rotational speed, For traffic, For the pressure head, This represents the shaft-side power of the fan / compressor. Given the prevailing speed. and feasible interval The instantaneous adjustable power can be obtained as follows: (17) (18) in, To lower the potential, To increase potential and within the time window The reachability within is affected by the slope rate constraint: (19) And it needs to simultaneously meet the minimum service requirement boundary. and Process constraints (corresponding to the minimum allowable speed, the value of which depends on the ventilation and air supply conditions of the industrial plant).

[0027] Third: Office lighting and public loads (small capacity, high flexibility units) Office lighting includes common low-power electrical equipment such as sockets and lights, which can be adjusted down through grouping or dimming. In addition, temperature-controlled loads (TCL) such as air conditioners have the thermal inertia characteristic of "peak shifting and compensation" and need to be considered separately.

[0028] Lighting section: Assuming it exists Circuit, single-circuit rated power is The dimming coefficient is Then the total lighting power can be calculated. for: (20) The reduced power can be further calculated as follows: (twenty one) in, The requirements are determined by industrial lighting standards, industrial occupancy scenarios, and process arrangements. If only individual group switches are supported, the off status and constraints can be selected via 0-1.

[0029] Air Conditioning (TCL): The air conditioning unit can be equivalent to a first-order RC model, analyzed as a separate area, to form a temperature control model. (twenty two) in, In start / stop mode. Comfort zone specified. With expandable dead zone For air conditioning users The short-term reduction in power load can be approximated as follows: (twenty three) in, Total number of devices Average power per unit It represents the decrease in duty cycle; its upper limit is jointly limited by thermal inertia and temperature comfort boundary, and it can compensate for the cooling capacity during off-peak periods to achieve peak shifting.

[0030] Based on the modeling of the aforementioned load equipment, data collection is performed to obtain comprehensive multidimensional data. To meet the needs of the electrolytic aluminum industry for full-element, full-dimensional, and traceable data, this implementation method adopts a four-layer architecture of "plant-level - workshop-level - equipment-component-level" to construct multi-point collaborative collection and time-unified multidimensional data.

[0031] For energy consumption data within electrolytic aluminum plants, a hierarchical and domain-based acquisition architecture is adopted, gradually refining the data from general to specific points, to achieve unified acquisition of multi-source and multi-dimensional data.

[0032] Plant Level (L1): Total incoming line / bus electrical parameters, plant level EMS / SCADA summary, power quality (harmonics, flicker), electricity price and daily / real-time market data.

[0033] Workshop level (L2): Electrical parameters of the busbar in the electrolysis workshop, output of the rectifier / voltage regulating unit, power and operating conditions of the fan / compressor group.

[0034] Equipment level (L3): Single-slot DC side key quantities ("U" _"DC", "I" _"DC", "V" _"B"), anode effect event markers, crane motor power / status, lighting group circuit power, air conditioning TCL settings and start / stop status.

[0035] Component level (L4): Temperature points (tank temperature, shell temperature), pressure / flow points (air supply / ventilation), vibration / noise points (rotating machine health), field events and operation logs.

[0036] The types of signals collected include: electrical quantities, such as voltage, current, power, power factor, frequency, and harmonics; process quantities, such as temperature, anode effect, electrolyte ratio, feeding time, tank age, and electrode change records; auxiliary quantities, such as fan and compressor speed, valve position, flow / head, lighting dimming factor, and air conditioning set temperature and duty cycle; and exogenous quantities, such as electricity price (including day-ahead, real-time, and ancillary service prices), meteorological data (outdoor temperature, humidity, and wind speed), and production shifts / plans.

[0037] The above hierarchical division ensures full coverage from macro-system operation to micro-device status, providing complete data support for the multi-dimensional extraction of subsequent potential indicators.

[0038] To ensure the accuracy of potential assessment across different time scales, data sampling follows the principle of capturing dynamics at high frequencies and statistical trends at low frequencies. This ensures the ability to observe rectified pulsations, load abrupt changes, and adjustable responses, and the sampling rate... The following conditions must be met: (twenty four) in, This corresponds to the highest characteristic frequency of the acquired signal. These are the oversampling coefficients. For DC-side ripple characteristics, if the rectified ripple fundamental frequency... ( If the rectifier pulse number factor is used, then it is recommended that... For workshop / plant-level energy statistics, data granularity of 15s to 1min meets the calculation input requirements. Specific deployment can be divided into standardized acquisition of high-frequency, mid-frequency, and low-frequency data.

[0039] For the time synchronization scheme, the acquisition ports are uniformly based on PTP (IEEE 1588v2) for high-precision time synchronization, and the cross-level timestamp error meets the following requirements: (25) Ensure that the timestamp deviation of data at different levels does not exceed 10ms. For high-frequency data that requires cross-device event correlation, adopt a time synchronization redundancy mechanism to further ensure the time consistency of the entire system.

[0040] In step 102, to ensure the consistency of multi-source data across different levels and acquisition devices, the multidimensional data is encoded. The specific process mainly includes four steps: data denoising, missing data compensation, outlier identification and correction, and multidimensional matrix organization.

[0041] (1) Data denoising processing: Acquired signals such as DC voltage of the electrolytic cell DC current Bus voltage and crane power It is often affected by electromagnetic interference and measurement noise. To address this, a combined method of wavelet threshold denoising and moving average filtering is adopted.

[0042] Wavelet thresholding denoising will remove noise from the original signal. The wavelet transform decomposes the approximate coefficients into different scales. With detail coefficient : (26) in, This represents the wavelet decomposition level. For detail coefficients... Set threshold A soft threshold function is used: (27) Where sgn is the sign function. Indicates the first Layer detail factor, Indicates the first Layered thresholding, reconstructing the denoised signal for: (28) Moving average filtering, for low-frequency data such as plant-level bus power, uses a window length... The moving average method, the denoised signal for: (29) in, This is the length of the moving average window.

[0043] The signal set obtained after processing While maintaining the main dynamic characteristics, it effectively reduced noise interference.

[0044] (2) Missing data compensation Due to communication interruptions or sensor malfunctions, short-term gaps in the acquired signals may occur. This invention employs a combination of linear interpolation and spline interpolation methods to address both short gaps (less than three sampling points) and minute-level gaps, respectively.

[0045] Linear interpolation: (30) in, , These are the valid moments before and after the missing segment.

[0046] Spline interpolation method for constructing cubic spline functions ,satisfy: (31) In addition, the compensation results retain missing markers in the database to ensure traceability for subsequent analysis.

[0047] (3) Outlier identification and correction The original multidimensional data may contain outliers, abrupt changes, or values ​​inconsistent with physical laws, such as abnormal current pulses or sudden drops in wind turbine power under heavy load. Therefore, the CUSUM method is first used to define the cumulative sum statistic: (32) (33) when or When the value exceeds the manual threshold h, it is considered an anomaly. Corrections for these anomalies are made by replacing the average of adjacent time points, or by interpolation within the physical boundaries.

[0048] (4) Multidimensional matrix organization: To facilitate subsequent indicator extraction and potential assessment, the preprocessed data is organized into a three-dimensional data matrix based on the equipment dimension (e), time dimension (t), and indicator dimension (k), as shown below: (34) in: Time index set, Equipment assembly (electrolytic cell, overhead crane, fan, lighting, air conditioning, etc.) A set of indicators. Each data point x can also be accompanied by a quality flag Q. (35) in, Indicates whether noise reduction has been performed. Indicate whether there is any lack of compensation. This indicates whether there is an abnormal correction.

[0049] In step 103, the obtained three-dimensional data matrix is ​​processed to calculate indicators including adjustable capacity, response speed, response duration, adjustment benefits, and adjustment costs.

[0050] Adjustable capacity includes both the upward and downward adjustment capacity of the load equipment: (36) (37) in, For the operating point power, These are the maximum and minimum allowable power for the load equipment, respectively.

[0051] The response speed value is the power change rate: (38) in, The time required to reach the target adjustment range.

[0052] Duration refers to the maximum length of time that the load equipment can maintain an adjustable state: (39) The regulation revenue is calculated based on the product of the regulation power and the electricity price: (40) in, For the time period The amount of power regulation, The market electricity price for that period. For time intervals.

[0053] Adjustment costs refer to production losses and maintenance costs caused by adjustments. (41) in, To incur production loss costs, Additional maintenance costs can be preset.

[0054] For example, when calculating adjustable indicators, it is also necessary to consider the constraints of the electrolytic aluminum industry, specifically including: obtaining the constraints of the electrolytic aluminum industry, which include cell temperature safety constraints, flow constraints, illuminance constraints, and equipment loss rate; and combining the cell temperature safety constraints, flow constraints, illuminance constraints, equipment loss rate, adjustable capacity, response speed, duration, and adjustment benefits and adjustment costs to calculate the adjustable indicators of each load equipment in the time dimension.

[0055] The constraints are expressed as follows: (42) in, The temperature of the electrolytic cell. For gas supply flow rate, Illuminance.

[0056] Equipment loss rate refers to the impact of adjustment behavior on equipment life. This indicator can be coded from 0 to 100, and the loss rate of each adjustment behavior can be determined by setting an adjustment threshold.

[0057] In summary, the three-dimensional data matrix can be used to calculate multiple indicators for each time period, including the capacity to be adjusted upwards, the capacity to be adjusted downwards, the response speed, the duration, the tank temperature safety constraint, the flow rate, the illuminance constraint, the equipment loss rate, the adjustment benefits, and the adjustment costs. These indicators can then be used to evaluate the adjustable indicators of the load equipment for each time period.

[0058] In step 104, the adjustable index also includes whether the load equipment is adjustable; specifically, it includes: identifying the operating status of each load equipment at each time moment through the three-dimensional data matrix; determining whether the load equipment is adjustable based on the operating status; and calculating the adjustable index of the load equipment if the load equipment is adjustable.

[0059] The operating status of the equipment is identified through the following steps: extracting the feature vector of the sliding time window at each moment from the three-dimensional data matrix; detecting the state change points when the load equipment switches operating conditions using the feature vector; dividing the time period into operating condition segments according to the state change points, and calculating the segment-average feature within each operating condition segment based on the feature vector; using a Gaussian mixture model to cluster the segment-average feature to obtain a status label, which is used to represent the operating status of the load equipment.

[0060] Operating status refers to whether the load is in a certain spatial or operational condition. The state change point is the time point at which different operating conditions are switched.

[0061] The process of identifying state change points is as follows: determine the linear indicator of the feature vector at each time step; accumulate the linear indicator at each time step based on the mean and sensitivity coefficient of the linear indicator, and obtain the time when the accumulation exceeds a preset threshold to obtain the state change point.

[0062] In the sliding time window Inside, for the equipment key channel Extract statistical and dynamic features, and construct feature vectors: (43) in: surface This represents the mean of the e-th index of the k-th load device within time t; Standard deviation The rate of change is expressed as:

[0063] L is the length of the time window. For bandwidth power density:

[0064] in, This represents the power spectral density.

[0065] Duty cycle, expressed as:

[0066] in, This is the switch state function.

[0067] This feature vector can be used to distinguish between the steady-state and anode-effect states of an electrolytic cell, the idle and operational states of an overhead crane, and the high and low load ranges of a blower.

[0068] To avoid statistical feature distortion during operating condition switching, a change point detection method based on cumulative sums is adopted. The linear indicator is determined as follows: (44) The mean of its linear indicator With sensitivity coefficient As a baseline, construct the cumulative sum statistic: (45) (46) when or When the time exceeds the preset threshold h, it is determined that at time h... These are state transition points. The time dimension is divided into a set of operating condition segments based on these state transition points. Each segment corresponds to a relatively stable load operating state.

[0069] In each working condition segment The segment-average features of the feature vectors are calculated internally, and clustering is performed using a Gaussian Mixture Model (GMM) to obtain state labels and probabilities: (47) in, For mixed weights, , For the first The mean and variance of the class. Its probability is: (48) The state determination is obtained based on the maximum a posteriori principle: (49) Then add the status label Backfill to fragment At each time point, the time-by-time running state vector is obtained. The final output includes: Running status label: For example, the operating state vector: electrolytic cell {steady state, up, down, anode effect}, overhead crane {idle, working}, fan {low load, high load}.

[0070] Confidence distribution values: This reflects the uncertainty in identifying the operational status.

[0071] This operational status, as one of the indicators, together with the aforementioned indicators such as capacity increase, capacity decrease, response speed, and duration, constitutes a multi-dimensional indicator set. The set of load devices to be evaluated is... The set of indicators is The matrix that can be constructed is: (50) To eliminate the dimensional differences between different indexes of the support, all indexes can be made dimensionless.

[0072] The adjustable index calculation process specifically includes: normalizing the tank temperature safety constraints, flow rate constraints, illuminance constraints, equipment loss rate, adjustable capacity, response speed, duration, adjustment benefits, and adjustment costs to obtain an index matrix; using grey relational analysis to calculate the grey relational coefficient of each normalized index in the index matrix, as well as the average relational coefficient; calculating the weight vector of the index matrix using the grey relational coefficient and the average relational coefficient; and determining the adjustable index of each load device in the time dimension based on the index matrix and the weight vector of the index matrix.

[0073] The step of determining the adjustable index of each load device based on the index matrix and the weight vector of the index matrix includes: combining the weight vector and the index matrix to obtain a weighted matrix for each load device; determining the distance of the weighted matrix to the positive ideal solution and the negative ideal solution respectively; and calculating the adjustable index based on the distance of the weighted matrix to the positive ideal solution and the negative ideal solution respectively.

[0074] First, the above multidimensional indicator set is normalized. Normalization is done in the following two ways: For efficiency-type indicators: (51) b. For cost-related indicators: (52) The normalized index matrix is ​​obtained after calculation. .

[0075] To objectively determine the weights of the indicators, grey relational analysis (GRA) was used. First, a reference sequence was constructed. ,in Calculate the first... Grey relational coefficients of the indicators: (53) in, , , The resolution coefficient (typically taken as 0.5). Indicator The average correlation coefficient is: (54) Finally, the indicator weight vector can be calculated: (55) By introducing the indicator weight vector, a weighted matrix is ​​constructed for each device: (56) Define positive and negative ideal solutions: (57) (58) Calculate the first Distance from each device to the positive / negative ideal solution: (59) (60) Calculate the posting progress, then adjust the indicators: (61) in, The closer it is to 1, the better the equipment. The higher the regulatory potential, the better.

[0076] Upon receiving a regulation request, the system dynamically adjusts each load device over time based on its adjustable parameters for each time period (t). This reduces energy consumption and costs while ensuring the production needs of the electrolytic aluminum industry are met. Furthermore, the calculated evaluation results... It can not only compare and rank the adjustability potential of different types of loads, but also map it to the multi-timescale adjustment needs of the power system. For example, a higher short-term potential score can correspond to grid frequency regulation and inertia support applications, a higher medium-term potential score can serve peak shaving and load shifting, and a long-term potential score can provide quantitative support for demand response planning.

[0077] In this embodiment, firstly, a unified modeling mechanism for multi-source data in the electrolytic aluminum industry is used to integrate DC voltage and current of electrolytic cells, crane operation status, fan and compressor power, air conditioning and lighting loads, and external market electricity prices to construct a complete time-series data matrix, providing a comprehensive and traceable data foundation for potential assessment; secondly, a multi-dimensional indicator system is proposed, including response capacity and speed, process safety and lifespan, and economic benefits, breaking through the limitations of traditional methods that only consider the adjustable power range, and forming a scientific and reasonable comprehensive constraint framework; thirdly, a GRA–TOPSIS combined method is introduced to achieve comprehensive scoring of multiple indicators, considering both the correlation and weight allocation between indicators, and obtaining a unified potential evaluation result through proximity calculation, providing a quantitative basis for horizontal comparison and ranking of different types of loads.

[0078] The calculation results obtained by analyzing actual data from the main load equipment of a certain electrolytic aluminum plant are shown in Table 1: Table 1. Load Regulation Assessment Results of an Aluminum Electrolysis Plant

[0079] The results show that electrolytic cells, as the main production load, account for approximately 80% and possess an adjustable potential of 15-25% under process safety constraints, forming the core of the overall regulation capacity. Auxiliary production loads such as multi-functional overhead cranes and fans, although accounting for a relatively low proportion, contribute 2-4% of the adjustable space. Non-production loads such as office lighting, air conditioning, and residential electricity, while having a small individual proportion, still have an overall potential of 2% for flexible regulation. These results demonstrate that this method can accurately identify the regulation space under multiple load types and time scales, comprehensively revealing the value of the electrolytic aluminum industry in grid frequency regulation, peak shaving, and demand response.

[0080] Furthermore, this embodiment also provides an electrolytic aluminum load adjustment assessment device, which can be used to perform the above-described electrolytic aluminum load adjustment assessment method. For example... Figure 2 As shown, the electrolytic aluminum load regulation assessment device 200 specifically includes: a multi-source data acquisition module 201, used to acquire multi-dimensional data of various load equipment in the electrolytic aluminum industry; a data processing module 202, which encodes the multi-dimensional data according to equipment dimension, time dimension, and index dimension to generate a three-dimensional data matrix; an index calculation module 203, used to calculate the adjustable capacity, response speed, and duration of each load equipment, as well as the regulation benefits and regulation costs of the electrolytic aluminum industry, through the three-dimensional data matrix; and an adjustable assessment module 204, used to calculate the adjustable index of each load equipment by combining the adjustable capacity, response speed, duration, regulation benefits, and regulation costs.

[0081] The specific details of each module or unit in the above-mentioned electrolytic aluminum load regulation and evaluation device have been described in detail in the corresponding electrolytic aluminum load regulation and evaluation method, so they will not be repeated here.

[0082] This application also provides an electronic device. Figure 3 A schematic diagram of the structure of an electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 3 The electronic device 600 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0083] like Figure 3 As shown, the electronic device 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 602 or a program loaded from a storage section 608 into a random access memory (RAM) 603. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0084] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0085] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined in the embodiments of this application.

[0086] For example, when the computer program is executed by the central processing unit (CPU) 601, it can perform the following: acquire multi-dimensional data of each load device in the electrolytic aluminum industry; encode the multi-dimensional data according to the device dimension, time dimension, and index dimension to generate a three-dimensional data matrix; calculate the adjustable capacity, response speed, and duration of each load device, as well as the adjustment benefits and adjustment costs of the electrolytic aluminum industry, through the three-dimensional data matrix; and calculate the adjustable index of each load device by combining the adjustable capacity, response speed, duration, adjustment benefits, and adjustment costs.

[0087] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0088] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0089] The units described in the embodiments of this disclosure can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the unit itself.

[0090] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which include instructions that, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0091] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0092] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating the load adjustment of electrolytic aluminum, characterized in that, include: Acquire multidimensional data of various load equipment in the electrolytic aluminum industry; Multidimensional data is encoded according to device dimension, time dimension, and indicator dimension to generate a three-dimensional data matrix; The adjustable capacity, response speed, and duration of each load device are calculated using the three-dimensional data matrix, as well as the adjustment benefits and adjustment costs of the electrolytic aluminum industry. The adjustable parameters of each load device are calculated by combining the adjustable capacity, response speed, duration, adjustment benefits, and adjustment costs.

2. The electrolytic aluminum load adjustment evaluation method according to claim 1, characterized in that, The adjustable index also includes whether the load equipment is adjustable; the method further includes: The operating status of each load device at each moment is identified through the three-dimensional data matrix; Determine whether the load equipment is adjustable based on the operating status.

3. The electrolytic aluminum load adjustment evaluation method according to claim 1, characterized in that, The process of identifying the operating status of each load device at each moment using the three-dimensional data matrix includes: Extract the feature vector of the sliding time window at each moment from the three-dimensional data matrix; The feature vector is used to detect the state change points when the load equipment switches operating conditions. The time period is divided into working condition segments according to the state change points, and the segment-average feature within each working condition segment is calculated based on the feature vector. The segment average features are clustered using a Gaussian mixture model to obtain state labels, which are used to represent the operating status of the load equipment.

4. The electrolytic aluminum load adjustment evaluation method according to claim 3, characterized in that, The step of detecting the state change points during the switching of the load equipment operating conditions using the feature vector includes: Determine the linear indicator of the eigenvector at each time step; The linear indicator is accumulated at each time step based on the mean and sensitivity coefficient of the linear indicator, and the time when the accumulated value exceeds a preset threshold is obtained to obtain the state change point.

5. The electrolytic aluminum load adjustment evaluation method according to claim 1, characterized in that, The calculation of the adjustable indicators of each load device in the time dimension, combining the adjustable capacity, response speed, duration, adjustment benefits, and adjustment costs, includes: Obtain the constraints of the electrolytic aluminum industry, including cell temperature safety constraints, flow rate constraints, illuminance constraints, and equipment loss rate; By combining tank temperature safety constraints, flow rate constraints, illuminance constraints, equipment loss rate, adjustable capacity, response speed, duration, and adjustment benefits and costs, the adjustable indicators of each load equipment in the time dimension are calculated.

6. The electrolytic aluminum load adjustment evaluation method according to claim 5, characterized in that, The adjustable indicators of each load device in the time dimension are calculated by combining tank temperature safety constraints, flow rate constraints, illuminance constraints, equipment loss rate, adjustable capacity, response speed, duration, and adjustment benefits and costs, including: The temperature safety constraint, flow rate constraint, illuminance constraint, equipment loss rate, adjustable capacity, response speed, duration, adjustment benefits and adjustment costs are normalized to obtain an index matrix. The grey relational analysis method is used to calculate the grey relational coefficient of each normalized index in the index matrix, as well as the average relational coefficient. The weight vector of the index matrix is ​​calculated using the grey correlation coefficient and the average correlation coefficient. The adjustable indicators of each load device in the time dimension are determined based on the indicator matrix and the weight vector of the indicator matrix.

7. The electrolytic aluminum load adjustment evaluation method according to claim 6, characterized in that, The step of determining the adjustable indicators of each load device based on the indicator matrix and the weight vector of the indicator matrix includes: The weighted matrix for each load device is obtained by combining the weight vector and the index matrix; The distances of the weighted matrix to the positive and negative ideal solutions are determined, and the adjustable index is calculated based on the distances of the weighted matrix to the positive and negative ideal solutions.

8. An electrolytic aluminum load regulation and evaluation device, characterized in that, include: The multi-source data acquisition module is used to acquire multi-dimensional data of various load equipment in the electrolytic aluminum industry; The data processing module is used to encode multidimensional data according to device dimension, time dimension, and indicator dimension to generate a three-dimensional data matrix; The index calculation module is used to calculate the adjustable capacity, response speed and duration of each load device, as well as the adjustment benefits and adjustment costs of the electrolytic aluminum industry, through the three-dimensional data matrix. The adjustable evaluation module is used to calculate the adjustable indicators of each load device by combining the adjustable capacity, response speed, duration, adjustment benefits and adjustment costs.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the electrolytic aluminum load regulation assessment method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing one or more computer programs, the one or more computer programs including instructions that, when executed by the electronic device, cause the electronic device to perform the electrolytic aluminum load regulation evaluation method according to any one of claims 1-7.

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