Industrial flexible load regulation and control method, system and equipment based on time-of-use electricity price and medium
By collecting equipment information and electricity price data, performing equipment classification and load prediction, establishing optimization objective functions, using genetic algorithms to solve the optimal operation strategy, generating control instructions and executing them through PLC controllers, the problem of power load regulation in industrial enterprises is solved, and the power consumption cost is reduced and production stability is improved.
Patent Information
- Application Number
- CN202510540429.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-27
- Publication Date
- 2025-08-15
AI Technical Summary
Industrial enterprises lack effective methods to reasonably regulate electricity load, resulting in the inability to fully utilize the cost difference in time-sharing electricity prices, affecting electricity costs and production stability.
By collecting equipment information and electricity price data, performing equipment classification and load prediction, establishing optimization objective functions, using genetic algorithms to solve the optimal operation strategy, generating control instructions and executing them through the PLC controller.
It has realized the intelligent regulation of equipment operation according to changes in electricity prices, reduce the electricity costs of enterprises, improve energy utilization efficiency, reduce the risks of equipment failures and production interruptions, and improve the level of electricity management.
Smart Images
Figure CN120497951A_ABST
Abstract
Description
Technical Field
[0001] The present application belongs to the technical field of industrial flexible load management and control, and specifically relates to an industrial flexible load control method, system, equipment and medium based on time-of-use electricity prices. Background Art
[0002] Industrial electricity consumption accounts for a significant portion of total electricity consumption in industrial production. Traditional industrial electricity consumption models prioritize fixed equipment operation schedules, ignoring cost differences during different periods of power supply. With the development of the electricity market, time-of-use pricing is gaining popularity as an effective means of regulating power supply and demand. However, most industrial enterprises lack the technical means to rationally regulate their electricity load.
[0003] Industrial production processes are highly continuous and complex, with each production link interconnected. Different equipment has different operating times, power requirements, and importance to production. There are two main existing load control methods: one is manual adjustment of equipment start and stop times, which is inefficient and difficult to achieve precise control; the other is a timing control strategy, which cannot adapt to the dynamic changes in the production process and real-time fluctuations in electricity prices. These traditional methods have failed to fully tap the regulation potential of flexible loads in industrial enterprises, nor have they considered the negative impact of frequent equipment start and stop on equipment life and production stability. Therefore, there is an urgent need to develop an intelligent, automated, time-of-use electricity price-based industrial flexible load control method that can comprehensively consider multiple factors such as time-of-use electricity prices, industrial production processes, and equipment characteristics, in order to achieve the goals of reducing enterprise electricity costs, ensuring normal production, and improving the efficiency of power resource utilization. Summary of the Invention
[0004] In a first aspect, an embodiment of the present application provides an industrial flexible load control method based on time-of-use electricity prices, comprising the following steps: S1. Collect basic information and operating parameters of various industrial equipment at the production site, as well as time-of-use electricity price information; S2. Classify industrial equipment according to its energy consumption characteristics and production process requirements, and characterize the flexible adjustment capabilities of each type of industrial equipment using load characteristic curves; S3. Use time series analysis algorithms to predict the total load of industrial equipment in future periods; S4. Establish an optimization objective function with the goal of minimizing the enterprise's electricity costs. Define constraints based on the flexible adjustment capabilities of industrial equipment and the predicted total load. Use a genetic algorithm to solve for the optimal operation strategy. S5. Generate control instructions based on the optimal operating strategy and issue them to the corresponding industrial equipment through the PLC controller. Through data collection, equipment classification, load forecasting, strategy optimization, and instruction issuance, industrial flexible load regulation is achieved, ensuring the integrity and consistency from information acquisition to actual regulation.
[0005] Furthermore, the specific steps of step S1 are as follows: S11. Collect basic information of various industrial equipment at the production site, including the rated power, minimum allowable power, and maximum allowable power of the equipment; S12. Functional sensors are set on industrial equipment at the production site to collect operating parameters of industrial equipment in real time; the functional sensors include power sensors, current sensors, and voltage sensors; the operating parameters of the industrial equipment include the power, current, voltage, and operating status of the equipment; S13. Collect time-of-use electricity price information at different time periods; S14. Build a database to store and manage the collected data. By collecting basic equipment information, operating parameters, and time-of-use electricity price information, and building a database for storage and management, a data foundation is provided for equipment analysis, load forecasting, and strategy optimization.
[0006] Furthermore, the specific steps of step S2 are as follows: S21. Analyze the collected operating parameters of the industrial equipment and classify the industrial equipment into interruptible load equipment, adjustable load equipment, and rigid load equipment based on the equipment's energy consumption characteristics and production process requirements; S22. Establish a load characteristic curve for each adjustable load device and determine the corresponding power adjustment range and adjustment speed. By classifying devices into interruptible, adjustable, and rigid load devices and establishing load characteristic curves for adjustable load devices, targeted control strategies can be formulated.
[0007] Furthermore, the specific steps of step S3 are as follows: S31. Collect the historical total load of industrial equipment and the historical total load time series; S32. Define a time window for the historical total load time series, determine input and output, and construct a data set; S33. Build an ARIMA model and train it using the dataset to obtain a load forecasting model. S34. Use a load forecasting model to predict the total load of industrial equipment in future periods. Analyzing and training historical total load data using the ARIMA model improves the accuracy of load forecasts. Accurate load forecasts can provide a reference for optimization strategies, making them more aligned with actual load demand and avoiding regulatory errors caused by inaccurate load forecasts.
[0008] Furthermore, the specific steps of step S4 are as follows: S41. With the goal of minimizing electricity costs, establish an optimization function that takes into account the comprehensive time-of-use electricity price, equipment operating costs, power adjustment costs of adjustable load devices, and state switching costs of interruptible load devices; S42. The constraints of the optimization function are set to the power adjustment range, power adjustment rate and total load of the adjustable load device in the future period; S43. Encode the operating status of the adjustable load device and the power adjustment amount of the interruptible load device as the initial population; S44. Use the optimization function to calculate the fitness of each individual, and iterate the population through genetic operations such as selection, crossover, and mutation until the optimal solution is obtained; S45. The optimal operating state of the interruptible load devices at different times and the optimal power adjustment of the adjustable load devices at different times corresponding to the optimal solution are used as the optimal operating strategy. By establishing an optimization objective function and constraints and using a genetic algorithm to solve the optimal operating strategy, the company's electricity costs are minimized. This also takes into account the physical characteristics and operating requirements of the equipment, ensuring the feasibility and effectiveness of the optimization strategy.
[0009] Furthermore, the specific steps of step S44 are as follows: S441. Calculate the fitness of each individual in the initial population according to the optimization function; S442. Based on the fitness value of each individual and using the roulette wheel method to calculate the probability of the individual being selected, select the individual to enter the next generation population according to the probability of the individual being selected; S443. Perform single-point crossover on the selected individuals according to a preset crossover probability to generate offspring individuals, and then randomly flip the gene positions of the offspring individuals according to a preset mutation probability; S444. When the maximum number of iterations is reached or the fitness converges, the iterations are stopped and the best individual is output as the optimal solution. Roulette wheel selection, single-point crossover, and random gene mutation ensure the effective execution of the genetic algorithm, improve the efficiency and accuracy of finding the optimal solution, and bring the optimization strategy closer to the global optimum.
[0010] Furthermore, the specific steps of step S5 are as follows: S51. Generate a first control instruction sequence based on the optimal operating state of the interruptible load device at different times in the optimal operating strategy, and issue the first control instruction sequence to the corresponding interruptible load device through the PLC controller; S52. Generate a second control instruction sequence based on the optimal power adjustment amount of the adjustable load device at different times in the optimal operation strategy, and issue the second control instruction sequence item corresponding to the adjustable load device through the PLC controller; S53. Analyze the real-time collected operating parameters of industrial equipment and the load characteristic curves of adjustable load devices, monitor the control effects, and dynamically adjust the optimal operating strategy based on the control effects. Generating and issuing control instructions based on the optimal operating strategy, as well as monitoring and dynamically adjusting the control effects, ensures that the optimal operating strategy is applied to industrial equipment. Monitoring and adjustment ensure the sustainability and adaptability of the control effects, enabling the control system to be optimized in a timely manner based on actual conditions.
[0011] In a second aspect, an embodiment of the present application further provides an industrial flexible load control system based on time-of-use electricity prices, comprising: Data acquisition module, used to collect basic information and operating parameters of various industrial equipment at the production site, as well as time-of-use electricity price information; The equipment classification and load curve characterization module is used to classify industrial equipment according to its energy consumption characteristics and production process requirements, and to characterize the flexible adjustment capability of each type of industrial equipment using load characteristic curves; The load forecasting module is used to predict the total load of various industrial equipment in the future period using time series analysis algorithms; The operation strategy determination module is used to establish an optimization objective function with the goal of minimizing the enterprise's electricity costs, determine constraints based on the flexible adjustment capabilities of industrial equipment and the predicted total load, and use a genetic algorithm to solve the optimal operation strategy; The load control module generates control instructions based on the optimal operating strategy and issues them to the corresponding industrial equipment through the PLC controller. Through the interactive collaboration of the data acquisition module, the equipment classification and load curve characterization module, the load forecasting module, the operating strategy determination module, and the load control module, functional integration from data acquisition to load control is achieved, enabling effective regulation of industrial flexible loads and saving corporate electricity costs.
[0012] In a third aspect, an embodiment of the present application further provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the industrial flexible load control method based on time-of-use electricity prices as described in the first aspect are implemented.
[0013] In a fourth aspect, an embodiment of the present application further provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the industrial flexible load control method based on time-of-use electricity prices as described in the first aspect.
[0014] It can be seen from the above technical solutions that this application has the following advantages: The industrial flexible load control method, system, equipment and medium based on time-of-use electricity prices provided in this application can fully utilize the low-price period to increase equipment operation, reduce the load during peak periods, and save the company's electricity costs by collecting time-of-use electricity price information and combining equipment classification, load forecasting and optimization strategies; by managing equipment operation, reasonably allocating load according to the flexible adjustment capability of the equipment, reducing energy waste and improving energy utilization efficiency; real-time monitoring of equipment operation data, adjusting equipment status according to load forecasting and optimization strategies, reducing the risk of equipment failure and production interruption caused by load fluctuations, reducing equipment failure rate, and ensuring production continuity and product quality stability; from data collection and analysis to load control, realizing intelligent and automated control, reducing manual intervention, improving control efficiency and accuracy, and improving the level of electricity management of industrial enterprises. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
[0016] Figure 1 The figure is a flow chart of the industrial flexible load control method based on time-of-use electricity price of the present invention.
[0017] Figure 2 Schematic diagram of the industrial flexible load control system based on time-of-use electricity price of the present invention. DETAILED DESCRIPTION
[0018] The various embodiments of the present disclosure will be described in more detail below in the specific steps of the industrial flexible load control method based on time-of-use electricity pricing. The present disclosure can have various embodiments, and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but rather that the present disclosure should be understood to cover all adjustments, equivalents, and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.
[0019] For example, in the industrial sector, industrial electricity consumption accounts for a significant proportion of total electricity consumption in society. In traditional industrial electricity consumption models, companies' power equipment operation plans are relatively fixed, often failing to fully consider the cost differences of electricity supply at different times. With the continuous development of the electricity market, time-of-use electricity pricing has gradually become widely used as an effective tool for regulating electricity supply and demand. However, most industrial enterprises currently lack effective technical means to rationally regulate electricity load.
[0020] Industrial production processes are typically highly continuous and complex, with interdependent production links. Different equipment has varying operating times, power requirements, and importance to the production process. Currently, there are two common load control methods: manual adjustment of equipment start and stop times is inefficient and difficult to achieve precise control; and timing control strategies are employed, which are unable to adapt to dynamic production processes and real-time fluctuations in electricity prices. These traditional methods fail to fully tap the regulatory potential of flexible loads in industrial enterprises and fail to consider the potential negative impact of frequent equipment starts and stops on equipment life and production stability. Therefore, there is an urgent need to develop an intelligent, automated control method that comprehensively considers multiple factors, such as time-of-use electricity prices, industrial production processes, and equipment characteristics, in order to reduce enterprise electricity costs, ensure normal production, and improve the efficiency of power resource utilization.
[0021] To address the above issues, this embodiment provides an industrial flexible load control method based on time-of-use electricity prices. Through data collection, equipment classification, load characteristic analysis, load forecasting, optimization strategy and control execution, it achieves full process optimization from data collection to equipment control, reducing enterprise electricity costs and improving energy utilization efficiency and production stability.
[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0023] See also Figure 1 FIG. 1 is a flow chart of an industrial flexible load control method based on time-of-use electricity pricing in a specific embodiment, the method comprising the following steps: S1. Collect basic information and operating parameters of various industrial equipment at the production site, as well as time-of-use electricity price information; It should be noted that by collecting basic information, operating parameters, and time-of-use electricity price information for various industrial equipment, a data foundation is provided for analysis and decision-making. Obtaining equipment parameters can reveal equipment performance and capacity boundaries. Operating parameters reflect the real-time status of equipment, and time-of-use electricity price information serves as the basis for cost optimization. S2. Classify industrial equipment according to its energy consumption characteristics and production process requirements, and characterize the flexible adjustment capabilities of each type of industrial equipment using load characteristic curves; It should be noted that by classifying industrial equipment, the control characteristics of different equipment can be determined, so that differentiated control strategies can be formulated for different types of equipment. Using load characteristic curves to characterize flexible control capabilities can quantify the equipment's control range and speed, providing a basis for optimization strategies. S3. Use time series analysis algorithms to predict the total load of industrial equipment in future periods; It should be noted that using time series analysis algorithms to predict the total load of industrial equipment in future periods enables enterprises to understand load change trends in advance. Based on load forecasts, they can reasonably arrange equipment operation to avoid power shortages during peak load periods and energy waste during low load periods. S4. Establish an optimization objective function with the goal of minimizing the enterprise's electricity costs. Define constraints based on the flexible adjustment capabilities of industrial equipment and the predicted total load. Use a genetic algorithm to solve for the optimal operation strategy. It should be noted that the optimization objective function is established with the goal of minimizing the enterprise's electricity costs. By comprehensively considering multiple cost factors, the control strategy can guarantee the enterprise's costs. The constraints are determined based on the equipment's flexible adjustment capabilities and the predicted total load, ensuring the feasibility of the optimization strategy. The genetic algorithm is used to solve the optimal operation strategy, which improves the efficiency and accuracy of the solution. S5. Generate control instructions based on the optimal operation strategy and issue them to the corresponding industrial equipment through the PLC controller; It should be noted that control instructions are generated based on the optimal operation strategy and issued through the PLC controller, thus achieving accurate regulation of industrial equipment.
[0024] This embodiment realizes the regulation of industrial flexible loads through data collection, equipment classification, load forecasting, strategy optimization and instruction issuance, ensuring the integrity and consistency from information acquisition to actual regulation.
[0025] Furthermore, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another industrial flexible load control method based on time-of-use electricity prices is provided, which includes the following steps: S1. Collect basic information and operating parameters of various industrial equipment at the production site, as well as time-of-use electricity price information; the specific steps of step S1 are as follows: S11. Collect basic information of various industrial equipment at the production site, including the rated power of the equipment , minimum allowable power and maximum allowable power ; Where i represents the i-th industrial equipment; S12. Functional sensors are set on industrial equipment at the production site to collect operating parameters of industrial equipment in real time; the functional sensors include power sensors, current sensors and voltage sensors; the operating parameters of the industrial equipment include the power of the equipment , current ,Voltage and operating status ; It should be noted that It can include on, off, and standby states, where t represents time; S13. Collect time-of-use electricity price information at different time periods. Specifically, represents the electricity price at time t; S14. Build a database to store and manage the collected data; It should be noted that by collecting basic equipment information, operating parameters and time-of-use electricity price information and building a database for storage and management, a data foundation is provided for equipment analysis, load forecasting and strategy optimization; S2. Classify industrial equipment according to its energy consumption characteristics and production process requirements, and characterize the flexible adjustment capability of each type of industrial equipment using load characteristic curves. The specific steps of step S2 are as follows: S21. Analyze the collected operating parameters of the industrial equipment and classify the industrial equipment into interruptible load equipment, adjustable load equipment, and rigid load equipment based on the equipment's energy consumption characteristics and production process requirements; It should be noted that interruptible load equipment is characterized by not causing serious impact on production if it stops running for a certain period of time, such as some auxiliary equipment in non-critical production links; adjustable load equipment can adjust the power output within a certain range, such as some variable frequency drive equipment; rigid load equipment refers to equipment that must continue to operate stably and with a basically fixed power during the production process; S22 establishes a load characteristic curve for each adjustable load device, and determines the corresponding power adjustment range and adjustment speed; The specific load characteristic curve is as follows:
[0026] in, Represents the load characteristic function of the equipment; Based on the load characteristic curve of the adjustable load equipment, the flexible adjustment capability of the adjustable load equipment is evaluated. According to the physical characteristics and operation requirements of the equipment, the adjustment range and adjustment speed of the adjustable load equipment at different power levels are determined, which can be expressed as and ,in Indicates the power regulation speed of device i; It should be noted that by classifying the equipment into interruptible, adjustable and rigid load equipment, and establishing load characteristic curves for adjustable load equipment, targeted control strategies can be formulated; S3. Use a time series analysis algorithm to predict the total load of industrial equipment in the future period. The specific steps of step S3 are as follows: S31. Collect the historical total load of industrial equipment and the historical total load time series; The historical total load time series is as follows: , ,……,
[0027] S32. Define a time window for the historical total load time series, determine input and output, and construct a data set; S33. Build an ARIMA model and train it using the dataset to obtain a load forecasting model. t) Among them, B is the backshift operator, , and is a polynomial, d is the degree of difference, t) is a white noise sequence; During the training process, the model parameters are continuously adjusted to improve the prediction accuracy through modeling and parameter estimation of historical data; S34. Use the load forecasting model to predict the total load of industrial equipment in the future period; Using historical total load time series , ,……, Predict the total load for the next k periods , where n is the length of historical data, j=1,2,…,k; It should be noted that the analysis and training of historical total load data using the ARIMA model improves the accuracy of load forecasting. Accurate load forecasting can provide a reference for optimization strategies, making the control strategy more in line with actual load demand and avoiding control errors caused by inaccurate load forecasting. S4. Establish an optimization objective function with the goal of minimizing the enterprise's electricity costs, determine constraints based on the flexible adjustment capabilities of industrial equipment and the predicted total load, and use a genetic algorithm to solve the optimal operation strategy. The specific steps of step S4 are as follows: S41. With the goal of minimizing electricity costs, establish an optimization function that takes into account the comprehensive time-of-use electricity price, equipment operating costs, power adjustment costs of adjustable load devices, and state switching costs of interruptible load devices;
[0028] Where T is the regulation period, N is the total number of devices, α and β are weight coefficients used to balance energy cost, power adjustment cost and device state switching cost. is the target operating state of device i; S42. The constraints of the optimization function are set to the power adjustment range, power adjustment rate and total load of the adjustable load device in the future period; Specifically, the constraints are as follows:
[0029] in, is the minimum allowed power of device i, is the maximum allowed power of device i;
[0030] in, and is the adjustment range at different power levels;
[0031] For example, if device i has only two states, on and off, 0 means off and 1 means on. S43. Encode the operating status of the adjustable load device and the power adjustment amount of the interruptible load device as the initial population; Specifically, the power adjustment amount of the adjustable load device and the status of interruptible load equipment Perform binary encoding to generate the initial population , where each individual Indicates a possible operation strategy:
[0032] Among them, M represents the number of adjustable load devices, K represents the number of interruptible load devices, and T represents the total number of control periods; S44. Calculate the fitness of each individual using the optimization function, and iterate the population through genetic operations such as selection, crossover, and mutation until the optimal solution is obtained. The specific steps of step S44 are as follows: S441. Calculate the fitness of each individual in the initial population according to the optimization function; According to the optimization function Calculate the fitness of each individual :
[0033] It should be noted that the fitness function is the inverse of the optimization function, ensuring that the lower the cost, the higher the fitness; S442. Based on the fitness value of each individual And use the roulette method to calculate the probability of an individual being selected , according to the probability of an individual being selected Select individuals to enter the next generation of the population;
[0034] It should be noted that the roulette wheel method is a selection method based on the ratio of individual fitness. Individuals with higher fitness have a greater probability of being selected. In flexible load regulation, fitness is related to the optimization objectives of equipment operating cost, power adjustment cost, and state switching cost. Selecting individuals through the roulette wheel method can ensure that individuals with high fitness, that is, operating strategies with lower costs and better regulation effects, have a higher probability of entering the next generation of population. The roulette wheel method can quickly screen out operating strategies with lower costs under current time-of-use electricity prices and equipment operating conditions, thereby quickly converging to a better solution. Even if some individuals have low fitness, there is still a certain probability of being selected, avoiding premature convergence to a local optimal solution, thereby improving global search capabilities. In industrial production, equipment operating status and electricity price information are dynamically changing. The roulette wheel method can dynamically adjust the selection probability based on real-time fitness to ensure that the optimization strategy can adapt to these changes. S443. Select individuals according to the preset crossover probability Perform single-point crossover to generate offspring individuals, and then perform mutation on the offspring individuals according to the preset mutation probability. Randomly flip the gene bit, for example Changes from 1 to 0; for example, the crossover probability The value can be between 0.6-0.9, the probability of mutation The value can be between 0.01-0.1; It should be noted that single-point crossover generates new offspring individuals by selecting two parent individuals and exchanging some of their genetic information at a random point. In flexible load control, by exchanging the genetic information of different individuals, new operating strategy combinations are generated, thereby exploring a wider solution space and discovering more optimal control schemes. During the crossover process, single-point crossover can retain the operating characteristics of some equipment, such as power adjustment range and adjustment speed, so that the generated offspring individuals still meet the physical and process constraints of the equipment. Through effective genetic combination, single-point crossover can quickly generate offspring individuals that are more suitable for the current optimization goal, thereby accelerating the convergence process of the algorithm. Random locus mutation introduces new genetic variation by randomly changing individual loci. In flexible load regulation, random mutation can introduce new gene combinations, disrupting the stability of local optimal solutions, increasing population diversity, and thus improving the probability of finding the global optimal solution. In industrial production, the operating status and environmental conditions of equipment change dynamically. Random mutation can enable optimization strategies to better adapt to these dynamic changes, ensuring the usability of control strategies. By randomly changing certain loci of individuals, such as the power adjustment amount or operating status of equipment, optimization strategies can be fine-tuned locally, further improving optimization accuracy. S444. When the maximum number of iterations is reached Or when the fitness converges, stop the iteration and output the best individual as the optimal solution; It should be noted that the use of roulette wheel method to select individuals, single-point crossover and random flipping of gene position mutations ensures the effective execution of the genetic algorithm, improves the efficiency and accuracy of finding the optimal solution, and makes the optimization strategy closer to the global optimum; S45. The optimal operating state of the interruptible load device corresponding to the optimal solution at different times and the optimal power adjustment amount of the adjustable load device at different times are used as the optimal operating strategy; It should be noted that by establishing the optimization objective function and constraints and using a genetic algorithm to solve the optimal operation strategy, the company's electricity costs were minimized. At the same time, the physical characteristics and operation requirements of the equipment were taken into account, ensuring the feasibility and effectiveness of the optimization strategy. S5. Generate control instructions based on the optimal operation strategy and send them to the corresponding industrial equipment through the PLC controller; the specific steps of step S5 are as follows: S51. Generate a first control instruction sequence based on the optimal operating state of the interruptible load device at different times in the optimal operating strategy, and issue the first control instruction sequence to the corresponding interruptible load device through the PLC controller; S52. Generate a second control instruction sequence based on the optimal power adjustment amount of the adjustable load device at different times in the optimal operation strategy, and issue the second control instruction sequence item corresponding to the adjustable load device through the PLC controller; S53. Analyze the real-time collected operating parameters of industrial equipment and the load characteristic curves of adjustable load equipment, monitor the control effect, and dynamically adjust the optimal operating strategy based on the control effect; It should be noted that the generation and issuance of control instructions based on the optimal operating strategy, as well as the monitoring and dynamic adjustment of the control effects, ensure that the optimal operating strategy can act on industrial equipment. At the same time, through monitoring and adjustment, the continuity and adaptability of the control effects are guaranteed, so that the control system can be optimized in a timely manner according to actual conditions.
[0035] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
[0036] like Figure 2 As shown, the following is an embodiment of the industrial flexible load control system based on time-of-use electricity price provided by the embodiment of the present disclosure. This system and the industrial flexible load control method based on time-of-use electricity price in the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the industrial flexible load control system based on time-of-use electricity price, please refer to the embodiment of the above-mentioned industrial flexible load control method based on time-of-use electricity price.
[0037] The system includes: Data acquisition module, used to collect basic information and operating parameters of various industrial equipment at the production site, as well as time-of-use electricity price information; The equipment classification and load curve characterization module is used to classify industrial equipment according to its energy consumption characteristics and production process requirements, and to characterize the flexible adjustment capability of each type of industrial equipment using load characteristic curves; The load forecasting module is used to predict the total load of various industrial equipment in the future period using time series analysis algorithms; The operation strategy determination module is used to establish an optimization objective function with the goal of minimizing the enterprise's electricity costs, determine constraints based on the flexible adjustment capabilities of industrial equipment and the predicted total load, and use a genetic algorithm to solve the optimal operation strategy; The load control module is used to generate control instructions based on the optimal operation strategy and send them to the corresponding industrial equipment through the PLC controller.
[0038] This embodiment realizes the functional integration from data collection to load control through the interactive collaboration of the data acquisition module, the equipment category classification and load curve characterization module, the load forecasting module, the operation strategy determination module and the load control module, realizes the effective control of industrial flexible loads, and saves the enterprise's electricity costs.
[0039] The industrial flexible load regulation method based on time-of-use electricity price provided in the embodiment of the present application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiment of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In the embodiment of the present invention, the electronic device includes but is not limited to laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.
[0040] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, a button, a camera, a display, and a SIM card interface, etc.
[0041] It is understood that the structures illustrated in the embodiments of the present application do not constitute specific limitations on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than shown, or combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
[0042] A processor may include one or more processing units, such as a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.
[0043] The processor can be the nerve center and command center of the electronic device. The controller can generate operation control signals based on the instruction opcode and timing signal to complete the control of instruction fetching and execution.
[0044] The processor may also include a memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or is reusing. If the processor needs to use the instruction or data again, it can directly call it from the memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.
[0045] The above-mentioned electronic equipment realizes the industrial flexible load control method based on time-of-use electricity prices of this application, which collects basic information and operating parameters of various industrial equipment at the production site, as well as time-of-use electricity price information; categorizes industrial equipment according to equipment energy consumption characteristics and production process requirements, and uses load characteristic curves to characterize the flexible adjustment capabilities of each type of industrial equipment; uses time series analysis algorithms to predict the total load of industrial equipment in future time periods; establishes an optimization objective function with the goal of minimizing the enterprise's electricity cost, determines constraints based on the flexible adjustment capabilities of industrial equipment and the predicted total load, and uses genetic algorithms to solve the optimal operation strategy; generates control instructions based on the optimal operation strategy, and issues them to the corresponding industrial equipment through a PLC controller. The technical solution achieves the beneficial effect of collecting time-of-use electricity price information, combining equipment classification, load forecasting and optimization strategies, making full use of low electricity price periods to increase equipment operation, reduce load during peak periods, and save enterprise electricity costs.
[0046] The storage medium provided in the present application stores a program product that can implement an industrial flexible load control method based on time-of-use electricity prices.
[0047] The industrial flexible load control method based on time-of-use electricity prices includes: collecting basic information and operating parameters of various industrial equipment at the production site, as well as collecting time-of-use electricity price information; classifying industrial equipment according to the equipment energy consumption characteristics and production process requirements, and using load characteristic curves to characterize the flexible adjustment capabilities of each type of industrial equipment; using time series analysis algorithms to predict the total load of industrial equipment in future time periods; establishing an optimization objective function with the goal of minimizing the enterprise's electricity costs, determining constraints based on the flexible adjustment capabilities of industrial equipment and the predicted total load, and using genetic algorithms to solve the optimal operation strategy; generating control instructions based on the optimal operation strategy, and issuing them to the corresponding industrial equipment through the PLC controller.
[0048] In some possible embodiments, the industrial flexible load regulation method based on time-of-use electricity prices disclosed herein can be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification.
[0049] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.
[0050] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for industrial flexible load control based on time-of-use electricity price, characterized in that: The steps include: S1. Collect basic information and operating parameters of various industrial equipment at the production site, as well as time-of-use electricity price information; S2. Classify industrial equipment according to its energy consumption characteristics and production process requirements, and characterize the flexible adjustment capabilities of each type of industrial equipment using load characteristic curves; S3. Use time series analysis algorithms to predict the total load of industrial equipment in future periods; S4. Establish an optimization objective function with the goal of minimizing the enterprise's electricity costs. Define constraints based on the flexible adjustment capabilities of industrial equipment and the predicted total load. Use a genetic algorithm to solve for the optimal operation strategy. S5. Generate control instructions based on the optimal operation strategy and send them to the corresponding industrial equipment through the PLC controller.
2. The industrial flexible load control method based on time-of-use electricity price according to claim 1 is characterized in that: The specific steps of step S1 are as follows: S11. Collect basic information of various industrial equipment at the production site, including the rated power, minimum allowable power, and maximum allowable power of the equipment; S12. Functional sensors are set on industrial equipment at the production site to collect operating parameters of industrial equipment in real time; the functional sensors include power sensors, current sensors, and voltage sensors; the operating parameters of the industrial equipment include the power, current, voltage, and operating status of the equipment; S13. Collect time-of-use electricity price information at different time periods; S14. Build a database to store and manage the collected data.
3. The industrial flexible load control method based on time-of-use electricity price according to claim 2 is characterized in that: The specific steps of step S2 are as follows: S21. Analyze the collected operating parameters of the industrial equipment and classify the industrial equipment into interruptible load equipment, adjustable load equipment, and rigid load equipment based on the equipment's energy consumption characteristics and production process requirements; S22. Establish a load characteristic curve for each adjustable load device and determine the corresponding power adjustment range and adjustment speed.
4. The industrial flexible load control method based on time-of-use electricity price according to claim 3 is characterized in that: The specific steps of step S3 are as follows: S31. Collect the historical total load of industrial equipment and the historical total load time series; S32. Define a time window for the historical total load time series, determine input and output, and construct a data set; S33. Build an ARIMA model and train it using the dataset to obtain a load forecasting model. S34. Use the load forecasting model to predict the total load of industrial equipment in the future period.
5. The industrial flexible load control method based on time-of-use electricity price according to claim 4 is characterized in that: The specific steps of step S4 are as follows: S41. With the goal of minimizing electricity costs, establish an optimization function that takes into account the comprehensive time-of-use electricity price, equipment operating costs, power adjustment costs of adjustable load devices, and state switching costs of interruptible load devices; S42. The constraints of the optimization function are set to the power adjustment range, power adjustment rate and total load of the adjustable load device in the future period; S43. Encode the operating status of the adjustable load device and the power adjustment amount of the interruptible load device as the initial population; S44. Use the optimization function to calculate the fitness of each individual, and iterate the population through genetic operations such as selection, crossover, and mutation until the optimal solution is obtained; S45. The optimal operating states of the interruptible load devices at different times and the optimal power adjustment amounts of the adjustable load devices at different times corresponding to the optimal solution are used as the optimal operating strategy.
6. The industrial flexible load control method based on time-of-use electricity price according to claim 5 is characterized in that: The specific steps of step S44 are as follows: S441. Calculate the fitness of each individual in the initial population according to the optimization function; S442. Based on the fitness value of each individual and using the roulette wheel method to calculate the probability of the individual being selected, select the individual to enter the next generation population according to the probability of the individual being selected; S443. Perform single-point crossover on the selected individuals according to a preset crossover probability to generate offspring individuals, and then randomly flip the gene positions of the offspring individuals according to a preset mutation probability; S444. When the maximum number of iterations is reached or the fitness converges, stop the iteration and output the best individual as the optimal solution.
7. The industrial flexible load control method based on time-of-use electricity price according to claim 5 is characterized in that: The specific steps of step S5 are as follows: S51. Generate a first control instruction sequence based on the optimal operating state of the interruptible load device at different times in the optimal operating strategy, and issue the first control instruction sequence to the corresponding interruptible load device through the PLC controller; S52. Generate a second control instruction sequence based on the optimal power adjustment amount of the adjustable load device at different times in the optimal operation strategy, and issue the second control instruction sequence item corresponding to the adjustable load device through the PLC controller; S53. Analyze the real-time collected operating parameters of industrial equipment and the load characteristic curves of adjustable load equipment, monitor the control effect, and dynamically adjust the optimal operating strategy based on the control effect.
8. An industrial flexible load control system based on time-of-use electricity price, characterized in that: include: Data acquisition module, used to collect basic information and operating parameters of various industrial equipment at the production site, as well as time-of-use electricity price information; The equipment classification and load curve characterization module is used to classify industrial equipment according to its energy consumption characteristics and production process requirements, and to characterize the flexible adjustment capability of each type of industrial equipment using load characteristic curves; The load forecasting module is used to predict the total load of various industrial equipment in the future period using time series analysis algorithms; The operation strategy determination module is used to establish an optimization objective function with the goal of minimizing the enterprise's electricity costs, determine constraints based on the flexible adjustment capabilities of industrial equipment and the predicted total load, and use a genetic algorithm to solve the optimal operation strategy; The load control module is used to generate control instructions based on the optimal operation strategy and send them to the corresponding industrial equipment through the PLC controller.
9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method implements the steps of the industrial flexible load control method based on time-of-use electricity price as claimed in any one of claims 1 to 7.
10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the industrial flexible load control method based on time-of-use electricity price as claimed in any one of claims 1 to 7 are implemented.