A method for assessing the safety and economy of a load demand response process

Abstract

The application discloses a kind of method and system for evaluating the security and economy of load demand response process, belong to electric power market demand response technical field.The method of the present application comprises: based on industrial and commercial user load, the analysis model of load demand response process is established;For the production process of target industrial and commercial user load, the correlation between the production process is established, the correlation is divided into multiple minimum subcut sets;Determine the economic influence of the multiple minimum subcut sets on target industrial and commercial user load demand response process;Determine the security and economy of target industrial and commercial user load demand response process.The present application can be flexibly organized according to spot market price, the adjustment amplitude and duration, so as to maximize the benefit.

Description

Technical Field

[0001] This invention relates to the field of electricity market demand response technology, and more specifically, to a method for assessing the safety and economy of load demand response processes. Background Technology

[0002] With the proposal of the new power system goals, a modern energy system with new energy as the mainstay is gradually being established. In the future, the proportion of clean energy in end-use energy consumption will continue to increase, and the power system operation mode will also shift towards coordinated interaction between power generation, grid, load, and storage. Previous research results indicate that, assuming no power supply shortages and no curtailment of new energy sources, actual simulation calculations show that the installed capacity of thermal power in 2025 needs to reach 104.5 GW. Furthermore, frequent start-ups and shutdowns of thermal power units, coupled with peak shaving, result in poor grid operation economics. However, if demand-side management is adopted to ensure orderly power supply, increasing controllable load and electrochemical energy storage by 12 GW, the corresponding installed capacity of thermal power at this point is calculated to be roughly equivalent to the current scale. This demonstrates the increasingly prominent economic benefits of implementing power load demand response. Industrial and commercial users are the main force in demand-side response, but their participation may cause interruptions in one or more production processes, long-term declines in product quality and production efficiency, reduced asset utilization, or shortened asset lifespan, leading to equipment damage.

[0003] The National Energy Administration's "Notice on Further Improving the Time-of-Use Electricity Pricing Mechanism" proposes measures such as rationally determining the peak-valley electricity price difference, establishing a peak-load electricity price mechanism, and improving the seasonal electricity price mechanism. With the advancement of electricity price marketization reform, all industrial and commercial users have entered the electricity market, the industrial and commercial catalog sales price has been abolished, and the time-of-use electricity pricing mechanism has been improved. Previous research and evaluation of industrial and commercial user load participation in the electricity market demand-side response still have many problems. Some scholars have analyzed the situation of the US electrolytic aluminum industry, summarized the results of experimental projects on ancillary services provided by the electrolytic aluminum industry, and pointed out that electrolytic aluminum loads have the ability to provide frequency regulation services. Some literature has proposed considering the adjustable capacity and participation time factors of industrial loads participating in grid dispatch. Under the condition of a determined grid structure and cross-sectional power flow, offline simulation methods are used to calculate the upper limit of wind / solar power output before and after industrial loads participate in grid dispatch, and then a method for evaluating the role of industrial loads in improving the grid's renewable energy absorption capacity is proposed. Other literature suggests that for the electrolytic aluminum industry, direct load power control (including interruption control, timing control, and continuous control) can achieve rapid dynamic response of regulating resources, which helps to achieve grid-load coordinated control and maintain frequency stability. Simultaneously, using an improved gray target method, a quantitative analysis of the load regulation potential of large users under business scenarios such as maintenance, rotation, shifting, and peak shaving can be conducted to obtain the quantitative demand response potential under each scenario. While the above literature analyzes and models the adjustable potential of industrial loads and the role of participating in the frequency regulation ancillary service market, none of them consider the safety and economics of industrial and commercial users' load participation in the electricity market demand response process without changing the industrial production process. Summary of the Invention

[0004] To address the above problems, this invention proposes a method for evaluating the safety and economy of load demand response processes, comprising:

[0005] An analytical model for the load demand response process is established based on the load of industrial and commercial users.

[0006] Based on fault tree analysis theory, for the production process of the target industrial and commercial user load, the correlation between the production processes is established, and the correlation is divided into multiple minimal subsets.

[0007] Using the multiple minimal subsets as input data for the analysis model, and based on the analysis model, determine the economic impact of the multiple minimal subsets on the target industrial and commercial user load demand response process;

[0008] Based on the aforementioned economic impact, the flexible power supply for the target industrial and commercial user load to participate in the demand response process is determined. Based on the flexible power supply, the safety and economy of the target industrial and commercial user load demand response process are determined.

[0009] Optionally, based on the load of industrial and commercial users, an analytical model for the load demand response process is established, including:

[0010] Based on fault tree analysis theory, for the production process of industrial and commercial users, the correlation between the production processes is established, and the correlation is divided into multiple minimal subsets.

[0011] Based on process immunology theory, the immune time PIT of the equipment demand response process in the multiple minimum sub-cutting sets is determined. For different demand response amplitudes, and based on machine learning algorithms and multiple production parameters, a PIT feature library is established for the immune time PIT.

[0012] An analytical model of the load demand response process is established based on the PIT feature library.

[0013] Optionally, the relationships can be the relationships between the generation structure, production sub-processes and production flow, as well as the relationships between production flows, wherein the production flows are associated using AND and OR logic.

[0014] Optionally, the association relationships can be described and partitioned using AND and OR gates to generate multiple minimal subcut sets.

[0015] Optionally, based on process immunology theory, determining the immune time PIT of the equipment demand response process in the plurality of minimum sub-cut sets includes: determining the maximization boundary of the equipment demand response based on process immunology theory, and determining the immune time PIT of the equipment demand response process in the plurality of minimum sub-cut sets according to the maximization boundary.

[0016] Optional, multiple production parameters, including at least one of the following: equipment model parameters, production status parameters, meteorological parameters, and temperature parameters.

[0017] Optionally, the immunity time PIT of the equipment demand response process in multiple minimum sub-cutting sets is the minimum immunity time PIT of the equipment in the production process of industrial and commercial users.

[0018] Optional principles for establishing analytical models include:

[0019] The underlying events are independent of each other;

[0020] Bottom events and top events include normal equipment status or out-of-limit status of equipment physical parameters.

[0021] Optionally, the probability of the top time occurring can be determined based on the PIT feature library, and an analysis model can be established based on the probability of the top time occurring.

[0022] Optional economic impacts include at least one of the following:

[0023] All or part of the loss caused by the interruption of one or more production processes;

[0024] Economic losses resulting from long-term decline in product quality and reduced production efficiency;

[0025] The additional costs incurred due to equipment damage caused by reduced asset utilization or shortened lifespan.

[0026] Furthermore, this invention also proposes a system for evaluating the safety and economy of load demand response processes, comprising:

[0027] The model building unit establishes an analytical model of the load demand response process based on the load of industrial and commercial users;

[0028] The computing unit, based on fault tree analysis theory, establishes the correlation between the production processes of the target industrial and commercial users' load, and divides the correlation into multiple minimal subsets.

[0029] The analysis unit takes the multiple minimal subsets as input data to the analysis model, and determines the economic impact of the multiple minimal subsets on the target industrial and commercial user load demand response process based on the analysis model.

[0030] The output unit determines the flexible power supply for the target industrial and commercial user load to participate in the demand response process based on the economic impact, and determines the safety and economy of the target industrial and commercial user load demand response process based on the flexible power supply.

[0031] Optionally, based on the load of industrial and commercial users, an analytical model for the load demand response process is established, including:

[0032] Based on fault tree analysis theory, for the production process of industrial and commercial users, the correlation between the production processes is established, and the correlation is divided into multiple minimal subsets.

[0033] Based on process immunology theory, the immune time PIT of the equipment demand response process in the multiple minimum sub-cutting sets is determined. For different demand response amplitudes, and based on machine learning algorithms and multiple production parameters, a PIT feature library is established for the immune time PIT.

[0034] An analytical model of the load demand response process is established based on the PIT feature library.

[0035] Optionally, the relationships can be the relationships between the generation structure, production sub-processes and production flow, as well as the relationships between production flows, wherein the production flows are associated using AND and OR logic.

[0036] Optionally, the association relationships can be described and partitioned using AND and OR gates to generate multiple minimal subcut sets.

[0037] Optionally, based on process immunology theory, determining the immune time PIT of the equipment demand response process in the plurality of minimum sub-cut sets includes: determining the maximization boundary of the equipment demand response based on process immunology theory, and determining the immune time PIT of the equipment demand response process in the plurality of minimum sub-cut sets according to the maximization boundary.

[0038] Optional, multiple production parameters, including at least one of the following: equipment model parameters, production status parameters, meteorological parameters, and temperature parameters.

[0039] Optionally, the immunity time PIT of the equipment demand response process in multiple minimum sub-cutting sets is the minimum immunity time PIT of the equipment in the production process of industrial and commercial users.

[0040] Optional principles for establishing analytical models include:

[0041] The underlying events are independent of each other;

[0042] Bottom events and top events include normal equipment status or out-of-limit status of equipment physical parameters.

[0043] Optionally, the probability of the top time occurring can be determined based on the PIT feature library, and an analysis model can be established based on the probability of the top time occurring.

[0044] Optional economic impacts include at least one of the following:

[0045] All or part of the loss caused by the interruption of one or more production processes;

[0046] Economic losses resulting from long-term decline in product quality and reduced production efficiency;

[0047] The additional costs incurred due to equipment damage caused by reduced asset utilization or shortened lifespan.

[0048] In another aspect, the present invention also provides a computing device, comprising: one or more processors;

[0049] A processor is used to execute one or more programs;

[0050] When the one or more programs are executed by the one or more processors, the method described above is implemented.

[0051] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the method described above.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] This invention provides a method for evaluating the safety and economy of load demand response processes, comprising: establishing an analytical model of the load demand response process based on industrial and commercial user loads; establishing the correlation between the production processes of the target industrial and commercial user load based on fault tree analysis theory, and dividing the correlation into multiple minimal subsets; using the multiple minimal subsets as input data of the analytical model, and determining the economic impact of the multiple minimal subsets on the target industrial and commercial user load demand response process based on the analytical model; determining the flexible electricity consumption of the target industrial and commercial user load participating in the demand response process based on the economic impact; and determining the safety and economy of the target industrial and commercial user load demand response process based on the flexible electricity consumption. This invention first uses fault tree analysis theory to reasonably and effectively characterize and model the structural and functional relationships of industrial and commercial production processes; secondly, for different demand response amplitudes, it uses machine learning algorithms to establish a process-immune PIT feature library to evaluate safety; by constructing an analytical model, it evaluates the economic impact of each minimal subset and the entire industrial and commercial demand response process on users; and outputs the electricity consumption results participating in the demand response based on spot market prices. Its advantages are: users can control the risks in the demand response process; and the adjustment range and duration can be flexibly organized according to spot market prices to maximize benefits. Attached Figure Description

[0054] Figure 1 This is a flowchart of the method of the present invention;

[0055] Figure 2 This is a schematic diagram of the analytical model of the method of the present invention;

[0056] Figure 3 This is an immune time curve of the user demand response process in the method of the present invention;

[0057] Figure 4 This is a schematic diagram of the uncertainty region in the immunization time curve of the method of the present invention;

[0058] Figure 5 This is an analytical model diagram illustrating the method of the present invention using an automobile manufacturing plant as an example;

[0059] Figure 6 This is a structural diagram of the system of the present invention. Detailed Implementation

[0060] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0061] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0062] Example 1:

[0063] This invention proposes a method for evaluating the safety and economy of load demand response processes, such as... Figure 1 As shown, it includes:

[0064] Step 1: Based on the load of industrial and commercial users, establish an analytical model for the load demand response process;

[0065] Step 2: Based on fault tree analysis theory, establish the correlation between the production processes for the target industrial and commercial user load, and divide the correlation into multiple minimal subsets.

[0066] Step 3: Use the multiple minimal subsets as input data for the analysis model, and based on the analysis model, determine the economic impact of the multiple minimal subsets on the target industrial and commercial user load demand response process;

[0067] Step 4: Determine the flexible power supply for the target industrial and commercial user load to participate in the demand response process based on the economic impact. Based on the flexible power supply, determine the safety and economy of the target industrial and commercial user load demand response process.

[0068] In step 1, an analytical model for the load demand response process is established based on the load of industrial and commercial users, including:

[0069] Based on fault tree analysis theory, for the production process of industrial and commercial users, the correlation between the production processes is established, and the correlation is divided into multiple minimal subsets.

[0070] Based on process immunology theory, the immune time PIT of the equipment demand response process in the multiple minimum sub-cutting sets is determined. For different demand response amplitudes, and based on machine learning algorithms and multiple production parameters, a PIT feature library is established for the immune time PIT.

[0071] An analytical model of the load demand response process is established based on the PIT feature library.

[0072] The relationships are the relationships between the generated structure, production sub-processes and production flow, as well as the relationships between production flows. The production flows are associated using AND and OR logic.

[0073] Multiple minimal subcut sets are generated by describing and partitioning the association relationships using AND and OR gates.

[0074] Based on process immunology theory, the immune time PIT of the equipment demand response process in the plurality of minimum subcut sets is determined, including: based on process immunology theory, determining the maximization boundary of equipment demand response, and determining the immune time PIT of the equipment demand response process in the plurality of minimum subcut sets according to the maximization boundary.

[0075] Multiple production parameters, including at least one of the following: equipment model parameters, production status parameters, meteorological parameters, and temperature parameters.

[0076] The immunity time PIT of the equipment demand response process in multiple minimum sub-cutting sets is the minimum immunity time PIT of the equipment in the production process of industrial and commercial users.

[0077] The principles for establishing an analytical model include:

[0078] The underlying events are independent of each other;

[0079] Bottom events and top events include normal equipment status or out-of-limit status of equipment physical parameters.

[0080] The probability of the top time occurrence is determined based on the PIT feature library, and an analysis model is established based on the probability of the top time occurrence.

[0081] Economic impacts include at least one of the following:

[0082] All or part of the loss caused by the interruption of one or more production processes;

[0083] Economic losses resulting from long-term decline in product quality and reduced production efficiency;

[0084] The additional costs incurred due to equipment damage caused by reduced asset utilization or shortened lifespan.

[0085] In step 1, the production process and the relationships between each process are characterized based on fault tree analysis theory, such as... Figure 2 As shown.

[0086] Using fault tree theory, which focuses on the consequences of a single equipment interruption throughout the entire production process, we can rationally characterize the entire production structure, the functions of subprocesses, and the relationships between them. This provides a basis for subsequent analysis of various combinations of production process interruptions and for evaluating their safety and economy. Each production process is connected using AND and OR logic, and the minimum subset K is defined based on the connection relationships described by AND and OR gates.

[0087] Based on the minimum subset K partitioned by the connection relationships in step 1, and based on the principle of process immunology, the equipment demand response process immune time (PIT) in each minimum subset K is determined, such as... Figure 3 As shown, a PIT feature library is established using machine learning algorithms for different demand response amplitudes. This includes two steps:

[0088] 1. Determining the duration of the immunization process;

[0089] In load demand response control, the time during which the physical parameters of an industrial process exceed permissible limits after experiencing a power reduction of a given magnitude is defined. Where P... limit P is the critical value of the physical parameter. nom t1 represents the rated value of the physical parameters; t1, t1+Δt, and t2 represent the start time of the demand response control process, the time when the physical parameters deviate from the rated value, and the time when they exceed the critical value, respectively. When the duration of participating in the electricity market demand response is T < Δt, the production process is completely normal as long as the parameters do not exceed the limit; when the duration of participating in the electricity market demand response is Δt < T < PIT, the production process can automatically return to normal; when T > PIT, the process is interrupted.

[0090] The variation patterns of physical parameters in industrial processes are jointly determined by the type of equipment used, their connections, and their current operating status. The process immunity time (PIT) has a certain degree of uncertainty, such as... Figure 4 As shown, at time t∈[t1+T] min ,t1+T max During the demand response control process, the physical parameters of the equipment can be [t1+T]. min ,t1+T max The deviation from the rated value can begin at any time within the specified range, and the physical parameters can exceed the limit value at any time t∈[t2+T]. min ,t2+T max At any given time within the range [PITmin, PITmax], according to the definition of process immunity time, the process immunity time has an uncertain interval. When the duration of demand response in the electricity market is within this uncertain interval, the process physical parameters may exceed the limits. Therefore, the process immunity time curve can be any PIT curve within the regions PIT1 and PIT1′.

[0091] 2. Dynamic aggregation characteristics of multi-source data inputs such as equipment type, production status, weather, and environment are fitted by deep learning. Under the condition of limited actual sample size, a PIT feature library is established for different demand response amplitudes.

[0092] Point Intake (PIT) is a safety indicator that measures demand response capability. The PIT varies depending on the load response amplitude: a longer PIT indicates a longer participation time in the electricity market demand response and stronger process immunity. Theoretically, with a sufficiently large sample size, the PIT for any load demand response amplitude can be determined. Therefore, deep learning is used to fit the dynamic aggregation characteristics of multi-source data inputs, including equipment type, production status, meteorological, and environmental data, to different demand response amplitudes. Under the condition of a limited actual sample size, a PIT feature library is established for different demand response amplitudes.

[0093] The process immunity time (PIT) of each sub-cut set is equal to the minimum PIT of each device under the sub-process, that is:

[0094] K iPIT =min{X 1PIT X 2PIT , ..., X mPIT}

[0095] In the formula: K i It is a minimal cut set, X i The devices included in the minimal cut set.

[0096] The duration of the demand response immune process (PIT) of each minimum subset K is accurately evaluated, and an analysis model of the entire industrial demand response process is established.

[0097] Establishment principle: First, the underlying events are independent of each other;

[0098] Second, bottom events and top events only consider two states: normal or exceeding the limits of the equipment's physical parameters.

[0099] Given n minimal cut sets K i (1≤i≤n), system fault events are represented as:

[0100] T = K1 + K2 + ... + K n

[0101] Each minimal cut set K i Is the bottom event X i The product of events (1≤i≤m, where m is the number of base events), i.e.:

[0102]

[0103] According to the probability calculation formula for compatible events, the probability of the top event occurring is P(T), and the unreliability of the system is F(t), as shown in the following formula:

[0104]

[0105] Events at each level are connected using AND and OR logic. The relationship between the hierarchical events connected by AND and OR gates is defined as follows:

[0106] P a (AND)P b =P a ·P b

[0107] P a (OR)P b =1-(1-P) a (1-P) b )

[0108] In the formula: P a and P b Let be the probabilities of events a and b, respectively. In this analytical model, the subprocesses under the Level 1 OR gate are independent of each other; the interruption of any subprocess will lead to a complete process interruption. The subprocesses under the Level 1 AND gate are mutually redundant; a complete process interruption only occurs when all subprocesses under the Level 1 AND gate are interrupted. Devices and subprocesses are connected via Level 2 OR gates; any device exceeding its controlled physical parameter limits will cause a subprocess interruption. In actual industrial processes, there may be mutually redundant devices within the same subprocess; these can be treated as a single device for process immunization analysis.

[0109] If a sub-procedure and a device are connected via a level 2 OR gate, then the interruption probability of the sub-procedure is:

[0110]

[0111]

[0112] In the formula: Let num be the interruption probability of the i-th subprocess; i Let be the number of devices in the i-th subprocess. This represents the probability of the physical parameters exceeding the limit controlled by the j-th device under the i-th subprocess.

[0113] If a process and its subprocesses are connected by a level 1 AND gate and a level 1 OR gate, then the process interruption probability is:

[0114]

[0115] In the formula: p P P represents the process interruption probability.i os Let be the interrupt probability of the i-th subprocess directly connected to the level 1 OR gate; n is the interruption probability of the k-th subprocess directly connected to the j-th level 1 AND gate; m1 is the number of subprocesses directly connected to the level 1 OR gate; m2 is the number of level 1 AND gates. j Let be the number of subprocesses under the j-th level 1 AND gate.

[0116] The present invention proposes that step 3 specifically involves: evaluating the economic impact on users of each minimum subset and the entire industrial demand response process based on the analysis model of the entire industrial demand response process established in step 1.

[0117] The economic impact on users can be categorized into the following three types:

[0118] (1) All or part of the loss caused by the interruption of one or more production processes;

[0119] (2) Economic losses caused by long-term decline in product quality and reduction in production efficiency;

[0120] (3) Additional costs incurred due to equipment damage caused by reduced asset utilization or shortened service life.

[0121] IEEE Standard 100-1992 defines risk as: a measure of the probability and consequences of an undesirable event, generally expressed as the product of probability and consequence, calculated as follows:

[0122] Risk = P r ×S ev

[0123] Risk is a risk indicator for the studied subjects, and P is a risk factor for the study subjects. r S is the probability of the subject of study occurring. ev This refers to the severity of the consequences to the system after the occurrence of the event under study. As can be seen from the formula, the two main aspects of risk assessment methods are the establishment and solution of models for the probability and severity of accidents, as shown in the following formula:

[0124]

[0125] In the formula: Let be the interruption probability of the j-th subprocess; p is the interruption loss of the j-th subprocess; P C represents the process interruption probability; p For process interruption losses, c sag The economic loss caused by a single incident.

[0126] Considering the different production characteristics of different users, the cost of a single production interruption is as follows: scrap loss, downtime loss, production profit loss, restart cost, equipment cost, other costs, and cost savings.

[0127] The present invention proposes that step 4 specifically involves: based on the economic assessment results and electricity market prices in step 3, outputting the flexible electricity volume results for participating in demand response before and during the day.

[0128] Utilizing the Euclidean principle of shortest distance, and based on weather, predicted next-day load levels, and peak-valley electricity pricing in the power market, this algorithm outputs flexible electricity allocation results for participation in demand response, both before and during the day. It allows for flexible combinations based on production and consumption requirements and the saturation levels of various minimum cut sets, enabling participation in the electricity market.

[0129] c VR >c sag

[0130] In the formula: c VR Demand response profit.

[0131] To make the technical problems, technical solutions and advantages of this invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and the painting production process of an automobile manufacturing plant.

[0132] The painting process in automobile manufacturing plants can generally be divided into two main parts: first, the surface treatment of the metal before painting, also known as pretreatment technology; and second, the painting application process. Surface treatment mainly includes removing oil, dust, rust, and old paint layers from the workpiece surface during repair work to improve the surface condition of the workpiece. This includes mechanical processing and chemical treatment of the workpiece surface, such as phosphating, oxidation, and passivation, depending on various specific circumstances.

[0133] Surface pretreatment (also known as pre-treatment): The purpose of surface pretreatment before coating is to remove all foreign matter from the components of the workpiece, providing a good base suitable for coating requirements to ensure that the coating has good corrosion resistance and decorative properties. The core process parameters affecting pretreatment are degreasing temperature and phosphating temperature. Every degreasing solution has an optimal degreasing temperature; if the temperature is below the process requirements, the degreasing effect cannot be fully realized; if the temperature is too high, surfactants will precipitate onto the surface, affecting the degreasing effect and causing uneven blooming of the phosphating film. Phosphating temperature is a key factor determining whether a complete phosphating film can be formed. Once the phosphating formula is selected in production, the temperature should be strictly controlled within the process range. Too high a temperature will produce a large amount of sludge, and the phosphating solution will lose its original balance; too low a temperature will prevent the film-forming ion concentration from reaching the concentration product, thus failing to produce a complete phosphating film.

[0134] Electrophoretic coating is a coating method in which a conductive substrate is immersed in a coating bath as the anode (or cathode), and a separate cathode (or anode) is placed opposite it in the bath. A direct current is applied between the two electrodes for a certain period of time, resulting in the deposition of a uniform, water-insoluble coating film on the substrate. The core process parameters affecting electrophoresis are the electrophoresis voltage and the temperature of the electrophoresis bath. Generally, the electrophoresis process is carried out within a certain voltage range to obtain a coating with excellent appearance. Exceeding a certain upper limit of the electrophoresis voltage will cause a violent reaction on the deposition electrodes, generating a large amount of gas, causing the deposited coating to crack, damaging insulation, and resulting in abnormal adhesion. Below a certain lower limit of the electrophoresis voltage, almost no coating can be formed. Simultaneously, the electrophoresis temperature is controlled within the range of 28℃±1℃. If the bath temperature is too high, the coating becomes too thick, which is detrimental to the stability of the bath; if the temperature is too low, the coating becomes too thin, easily leading to coating defects.

[0135] Intermediate coat: Improves the balance between the workpiece surface and the primer layer, providing a good base for the topcoat to enhance the overall finish and weather resistance (gloss, fullness, smoothness, UV resistance, etc.). In other words, the purpose of automotive intermediate coat application is to protect the primer and improve the finish of the topcoat. Key process parameters affecting the intermediate coat include air temperature, humidity, wind speed, cleanliness, paint temperature, and the spraying flow rate accuracy and pressure of the spraying robot.

[0136] Topcoat: This coating decorates and protects the vehicle's exterior panels, while also protecting the primer and topcoat coatings themselves. The topcoat is the final step in the painting process, and its first-pass yield has a significant impact on production efficiency and coating quality. High rework rates keep painting costs high. The core process parameters affecting the topcoat include air temperature, humidity, wind speed, cleanliness, paint temperature, and the spraying robot's flow rate accuracy and pressure. These are basically the same as the core process parameters for the intermediate coat, but the specific requirements for each parameter may differ.

[0137] The core process parameters involved in the coating process are shown in Table 1:

[0138] Each step will be described in further detail below with reference to the embodiments:

[0139] In step 1: Based on fault tree analysis theory, the painting production process and relationships of the automobile manufacturing plant are described, and the plant is divided into 4 minimum subsets K=4, such as... Figure 5 As shown. Each subset is connected by an OR gate.

[0140] In step 2: Based on the principle of process immunology, the process immunotime (PIT) of equipment demand response in each minimum subset K of the painting process in the automobile manufacturing plant is determined. For different demand response amplitudes, a PIT feature library is established using machine learning algorithms.

[0141] The variation patterns of physical parameters for each piece of equipment in the painting process of an automobile manufacturing plant are jointly determined by the equipment type, ambient temperature, and the equipment's current operating status. The magnitude and duration of the demand response power reduction are inversely proportional. The PIT (Power Intake Time) of the equipment is analyzed when the power reduction during the painting process is 5% and 10% of the rated power.

[0142] For example, in the pretreatment of coating surfaces: as shown in Table 1, under the premise of meeting the quality requirements, the temperature of the degreasing workshop is maintained at 40℃-60℃ and the temperature of the phosphating workshop is maintained at 30℃-40℃ using a fresh air system.

[0143] To calculate the cooling load loss of the workshop building, the following model is derived from the above influencing factors:

[0144] Q c =Q1+Q2+Q3+Q4+Q5;

[0145] In the formula: Q1 is a fixed parameter of its own properties; Q2 is the heat transfer caused by the temperature difference between indoors and outdoors, in W / m². 2 Q3 represents the solar radiation absorbed by the building, in W / m³. 2 Q4 represents heat loss due to ventilation with outside air, in W / m³. 2 Q5 represents the building's overall heat dissipation capacity, in W / m². 2 Q2 to Q5 vary with temperature, weather, and internal heat source factors.

[0146] Calculate Q per hour within the time period c The sum of these amounts represents the total cooling load loss during that time period.

[0147]

[0148] Where: Q2 = βkA(Tout - Tin); Q3 = ART;

[0149] Q4 = C air ρ air NSH(T out -T in )

[0150] In the formula, β is the temperature difference correction coefficient of the building envelope; K is the heat transfer coefficient of the building envelope; A is the area of ​​the building envelope; Tin is the indoor temperature; Tout is the outdoor temperature; RT is the actual light radiation intensity; C air ρ is the specific heat capacity of air. air N is the air density; S is the number of air changes per minute; H is the room area; and H is the room height.

[0151] The parameters β, k, A, RT, Cair, and ρair have different values ​​depending on the orientation and function of the house. However, the greater the temperature difference between indoors and outdoors, the shorter the heat preservation time.

[0152] Constraints: The indoor temperature in the degreasing workshop shall be maintained between 40℃ and 60℃; the temperature in the phosphating workshop shall be between 30℃ and 40℃.

[0153] Calculate the immunization time for each sub-process sequentially, and take the immunization time of the shortest sub-process as the normal immunization time for the entire production process.

[0154] Step 3: Establish an analysis model for the entire coating production process.

[0155] The fault tree for the painting production process has 4 minimum cut sets K. i (1≤i≤4), system fault events are represented as:

[0156] T = K1 + K2 + ... + K4

[0157] Each minimal cut set K i There are different bottom events X i The product of events (1≤i≤m, where m is the number of base events), i.e.:

[0158]

[0159] According to the probability calculation formula for compatible events, the probability of the top event is P(T), and the unreliability of the system is F(t).

[0160] In step 4: Evaluate the economic impact on users of each minimum subset and the entire industrial demand response process, including the impact of process interruptions, as shown in Table 2;

[0161] Risk = P r ×S ev

[0162] Step 5: Output the day-ahead and mid-day flexible power results for participating in demand response.

[0163] Based on weather forecasts, predicted next day load levels, and peak-valley electricity pricing in the electricity market, the system outputs flexible electricity volumes for participation in demand response, both day-ahead and mid-day. This allows for flexible allocation based on production and consumption requirements, the saturation levels of various minimum cut sets, and participation in the electricity market.

[0164] c VR >c sag

[0165] Table 1

[0166]

[0167] Table 2

[0168] Interruption level Economic loss / 10,000 yuan Interruption during painting process 78.69 Preprocessing interrupt 10.82 Electrophoresis interruption 7.54 Intermediate coating interruption 41.23 Topcoat interruption 19.10

[0169] Example 2:

[0170] This invention also proposes a system 200 for evaluating the safety and economy of load demand response processes, such as... Figure 6 As shown, it includes:

[0171] Model building unit 201 establishes an analytical model of the load demand response process based on the load of industrial and commercial users;

[0172] The computing unit 202, based on fault tree analysis theory, establishes the correlation between the production processes of the target industrial and commercial users' load, and divides the correlation into multiple minimal subsets.

[0173] Analysis unit 203 uses the plurality of minimum subcut sets as input data for the analysis model, and determines the economic impact of the plurality of minimum subcut sets on the target industrial and commercial user load demand response process based on the analysis model;

[0174] Output unit 204 determines the flexible power supply for the target industrial and commercial user load to participate in the demand response process based on the economic impact, and determines the safety and economy of the target industrial and commercial user load demand response process based on the flexible power supply.

[0175] Among them, an analytical model for the load demand response process is established based on the load of industrial and commercial users, including:

[0176] Based on fault tree analysis theory, for the production process of industrial and commercial users, the correlation between the production processes is established, and the correlation is divided into multiple minimal subsets.

[0177] Based on process immunology theory, the immune time PIT of the equipment demand response process in the multiple minimum sub-cutting sets is determined. For different demand response amplitudes, and based on machine learning algorithms and multiple production parameters, a PIT feature library is established for the immune time PIT.

[0178] An analytical model of the load demand response process is established based on the PIT feature library.

[0179] The relationships are the relationships between the generation structure, production sub-processes and production flow, as well as the relationships between production flows. The production flows are associated using AND and OR logic.

[0180] In this process, AND gates and OR gates are used to describe and divide the association relationships to generate multiple minimal subcut sets.

[0181] Specifically, determining the immune time PIT of the equipment demand response process in the plurality of minimum subcut sets based on process immunology theory includes: determining the maximization boundary of the equipment demand response based on process immunology theory, and determining the immune time PIT of the equipment demand response process in the plurality of minimum subcut sets according to the maximization boundary.

[0182] Among them, multiple production parameters include at least one of the following: equipment model parameters, production status parameters, meteorological parameters, and temperature parameters.

[0183] Among them, the immunity time PIT of the equipment demand response process in multiple minimum sub-cutting sets is the minimum value of the immunity time PIT of the equipment in the production process of industrial and commercial users.

[0184] The principles for establishing the analytical model include:

[0185] The underlying events are independent of each other;

[0186] Bottom events and top events include normal equipment status or out-of-limit status of equipment physical parameters.

[0187] Specifically, the probability of the top time occurrence is determined based on the PIT feature library, and an analysis model is established based on the probability of the top time occurrence.

[0188] The economic impact includes at least one of the following:

[0189] All or part of the loss caused by the interruption of one or more production processes;

[0190] Economic losses resulting from long-term decline in product quality and reduced production efficiency;

[0191] The additional costs incurred due to equipment damage caused by reduced asset utilization or shortened lifespan.

[0192] This invention first uses fault tree analysis theory to rationally and effectively characterize and model the structural-functional relationships of industrial and commercial production processes. Second, for different demand response amplitudes, it utilizes machine learning algorithms to establish a process-immune PIT feature library to assess safety. By constructing an analytical model, it evaluates the economic impact on users of each minimum subset and the entire industrial and commercial demand response process. Based on spot market prices, it outputs the electricity consumption results for participating in demand response. Its advantages are: user risk is controllable in the demand response process; and the amplitude and duration of adjustments can be flexibly organized based on spot market prices to maximize benefits.

[0193] Example 3:

[0194] Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby implementing the steps of the methods in the above embodiments.

[0195] Example 4:

[0196] Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiments.

[0197] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0198] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0199] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0200] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0201] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0202] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for evaluating the safety and economy of a load demand response process, characterized in that, The method includes: An analytical model for the load demand response process is established based on the load of industrial and commercial users. Based on fault tree analysis theory, for the production process of the target industrial and commercial user load, the correlation between the production processes is established, and the correlation is divided into multiple minimal subsets. Using the multiple minimum subsets as input data for the analysis model, and based on the analysis model, determine the economic impact of the multiple minimum subsets on the target industrial and commercial user load demand response process; Based on the aforementioned economic impact, the flexible power supply for the target industrial and commercial user load to participate in the demand response process is determined. Based on the flexible power supply, the safety and economy of the target industrial and commercial user load demand response process are determined. The analytical model for establishing the load demand response process based on industrial and commercial user load includes: Based on fault tree analysis theory, for the production process of industrial and commercial users, the correlation between the production processes is established, and the correlation is divided into multiple minimal subsets. Based on process immunology theory, the immune time PIT of the equipment demand response process in the multiple minimum sub-cutting sets is determined. For different demand response amplitudes, and based on machine learning algorithms and multiple production parameters, a PIT feature library is established for the immune time PIT. An analytical model of the load demand response process was established based on the PIT feature library; The relationships are the relationships between the generation structure, production sub-processes and production flow, as well as the relationships between production flows. The production flows are associated using AND and OR logic. Its characteristic is that it describes and divides the association relationship through AND and OR gates to generate multiple minimal subcut sets; Based on process immunology theory, the immune time PIT of the equipment demand response process in the plurality of minimum subcut sets is determined, including: based on process immunology theory, determining the maximization boundary of equipment demand response, and determining the immune time PIT of the equipment demand response process in the plurality of minimum subcut sets according to the maximization boundary. The multiple production parameters include at least one of the following: equipment model parameters, production status parameters, meteorological parameters, and temperature parameters; The immunity time PIT of the equipment demand response process in the multiple minimum sub-cutting sets is the minimum immunity time PIT of the equipment in the production process of industrial and commercial users.

2. The method according to claim 1, characterized in that, The principles for establishing the analytical model include: The underlying events are independent of each other; Bottom events and top events include normal equipment status or out-of-limit status of equipment physical parameters.

3. The method according to claim 2, characterized in that, The probability of the top time occurrence is determined based on the PIT feature library, and an analysis model is established based on the probability of the top time occurrence.

4. The method according to claim 1, characterized in that, The economic impact includes at least one of the following: All or part of the loss caused by the interruption of one or more production processes; Economic losses resulting from long-term decline in product quality and reduced production efficiency; The additional costs incurred due to equipment damage caused by reduced asset utilization or shortened lifespan.

5. A system for evaluating the safety and economy of load demand response processes, characterized in that, The system includes: The model building unit establishes an analytical model of the load demand response process based on the load of industrial and commercial users; The computing unit, based on fault tree analysis theory, establishes the correlation between the production processes of the target industrial and commercial users' load, and divides the correlation into multiple minimal subsets. The analysis unit takes the multiple minimal subsets as input data to the analysis model, and determines the economic impact of the multiple minimal subsets on the target industrial and commercial user load demand response process based on the analysis model. The output unit determines the flexible power supply for the target industrial and commercial user load to participate in the demand response process based on the economic impact, and determines the safety and economy of the target industrial and commercial user load demand response process based on the flexible power supply. The analytical model for establishing the load demand response process based on industrial and commercial user load includes: Based on fault tree analysis theory, for the production process of industrial and commercial users, the correlation between the production processes is established, and the correlation is divided into multiple minimal subsets. Based on process immunology theory, the immune time PIT of the equipment demand response process in the multiple minimum sub-cutting sets is determined. For different demand response amplitudes, and based on machine learning algorithms and multiple production parameters, a PIT feature library is established for the immune time PIT. An analytical model of the load demand response process was established based on the PIT feature library; The relationships are the relationships between the generation structure, production sub-processes and production flow, as well as the relationships between production flows. The production flows are associated using AND and OR logic. Multiple minimal subcut sets are generated by describing and partitioning the association relationships using AND and OR gates. Based on process immunology theory, the immune time PIT of the equipment demand response process in the plurality of minimum subcut sets is determined, including: based on process immunology theory, determining the maximization boundary of equipment demand response, and determining the immune time PIT of the equipment demand response process in the plurality of minimum subcut sets according to the maximization boundary. The multiple production parameters include at least one of the following: equipment model parameters, production status parameters, meteorological parameters, and temperature parameters; The immunity time PIT of the equipment demand response process in the multiple minimum sub-cutting sets is the minimum immunity time PIT of the equipment in the production process of industrial and commercial users.

6. The system according to claim 5, characterized in that, The principles for establishing the analytical model include: The underlying events are independent of each other; Bottom events and top events include normal equipment status or out-of-limit status of equipment physical parameters.

7. The system according to claim 6, characterized in that, The probability of the top time occurrence is determined based on the PIT feature library, and an analysis model is established based on the probability of the top time occurrence.

8. The system according to claim 5, characterized in that, The economic impact includes at least one of the following: All or part of the loss caused by the interruption of one or more production processes; Economic losses resulting from long-term decline in product quality and reduced production efficiency; The additional costs incurred due to equipment damage caused by reduced asset utilization or shortened lifespan.

9. A computer device, characterized in that, include: One or more processors; A processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method described in any one of claims 1-4 is implemented.

10. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the method as described in any one of claims 1-4.

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