Chemical industrial park production scheme generation method and system considering new energy uncertainty

Abstract

The present application belongs to the technical field of production simulation of chemical industry park, and discloses a method and system for generating production scheme of chemical industry park considering uncertainty of new energy, which comprises: obtaining wind power output scene, photovoltaic output scene, load scene and attribute data adapted to production cycle to generate production scene of chemical industry park; determining output scheme in current optimization interval based on production scene of chemical industry park, combining the output scheme with power grid operation mode determined through power balance strategy to calculate consumption index in current optimization interval; rolling updating current optimization interval; integrating and analyzing consumption index in each optimization interval to obtain cycle consumption index; repeatedly executing the process of generating production scene and cycle consumption index until stopping when meeting convergence criterion to obtain production scheme of chemical industry park in production cycle. The present application realizes dynamic coordination between new energy side and chemical side of chemical industry park by accurately analyzing new energy output characteristics and combining power balance strategy.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and system for generating production plans for chemical industrial parks that takes into account the uncertainties of new energy sources. Background Technology

[0002] Power system production simulation is a method that uses mathematical models and computer simulation technology to simulate and analyze the operating status and dynamic changes of components such as power generation equipment, transmission networks, and loads within a certain time scale. Production simulation technology is widely used in scenarios such as power balance analysis, dispatching operation simulation, and power market simulation, providing a theoretical basis for power system planning, layout, and dispatching operations.

[0003] The integrated power-chemical energy system of a chemical industrial park can be viewed as a "source-grid-load-storage" system: the power source of the system is composed of wind and photovoltaic power generation; the small microgrid formed by the entire system is weakly connected to the main power grid, requiring that power cannot be fed back to the grid, but power can be drawn from the grid according to certain requirements; the load is mainly hydrogen production by electricity and ammonia synthesis, where hydrogen production by electricity includes supporting cooling and heat dissipation equipment, and ammonia synthesis includes supporting air separation and compression equipment; the energy storage and material storage parts of the system are composed of electricity storage and hydrogen storage, respectively used for electricity buffering and hydrogen buffering.

[0004] The output of new energy sources is characterized by uncertainty, such as randomness, fluctuation, and intermittency. The duration and power range are difficult to measure directly, and there is a contradiction between the high stability of the chemical industry side and the uncertainty of the new energy side.

[0005] Therefore, how to accurately analyze the output characteristics of new energy sources and optimize the production plan of chemical industrial parks accordingly in order to achieve dynamic coordination between the new energy side and the chemical side is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0006] The present invention provides a method and system for generating production plans for chemical industrial parks that take into account the uncertainties of new energy sources, in order to solve the problems mentioned above in the prior art.

[0007] According to a first aspect of the present invention, a method for generating production plans for chemical industrial parks that takes into account the uncertainty of new energy sources is provided.

[0008] In one embodiment, the method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources includes: setting a production cycle and optimization interval corresponding to the production plan of the chemical industrial park; acquiring wind power output scenarios, photovoltaic power output scenarios, load scenarios and attribute data that are suitable for the production cycle, and combining each scenario and attribute data to generate a chemical industrial park production scenario covering the entire production cycle; taking maximizing the acceptance capacity and maximizing the absorption capacity of new energy sources as optimization objectives, determining the output scheme within the current optimization interval based on the chemical industrial park production scenario, combining the output scheme with the power grid operation mode determined by the power balance strategy, and calculating the absorption index within the current optimization interval; continuously updating the current optimization interval until the optimization of the entire production cycle is completed, and obtaining the absorption index within each optimization interval; integrating and analyzing the absorption index within each optimization interval to obtain the periodic absorption index; repeatedly executing the process of generating production scenarios and periodic absorption indices until the convergence criterion is met, and summarizing the output scheme within each optimization interval and the power grid operation mode determined by the power balance strategy to obtain the chemical industrial park production plan within the production cycle.

[0009] According to a second aspect of the present invention, a system for generating production plans for chemical industrial parks that takes into account the uncertainty of new energy sources is provided.

[0010] In one embodiment, the chemical industrial park production scheme generation system taking into account the uncertainty of new energy sources includes: a parameter configuration module for setting the production cycle and optimization interval corresponding to the production plan of the chemical industrial park; a scenario determination module for acquiring wind power output scenarios, photovoltaic power output scenarios, load scenarios and attribute data suitable for the production cycle, and combining each scenario and attribute data to generate a chemical industrial park production scenario covering the entire production cycle; and a scheme generation module for determining the output scheme within the current optimization interval based on the chemical industrial park production scenario, taking maximizing the new energy acceptance capacity and maximizing the new energy absorption capacity as optimization objectives, combining the output scheme with the grid operation mode determined by the power balance strategy, calculating the absorption index within the current optimization interval; continuously updating the current optimization interval until the optimization of the entire production cycle is completed, obtaining the absorption index within each optimization interval; integrating and analyzing the absorption index within each optimization interval to obtain the periodic absorption index; repeatedly executing the process of generating production scenarios and periodic absorption indices until the convergence criterion is met, and summarizing the output scheme within each optimization interval and the grid operation mode determined by the power balance strategy to obtain the chemical industrial park production scheme within the production cycle.

[0011] According to a third aspect of the present invention, a computer device is provided. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0012] According to a fourth aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the above-described method.

[0013] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0014] (1) By accurately analyzing the output characteristics of new energy sources and combining them with power balance strategies, production plans were optimized, and dynamic coordination between the new energy side and the chemical side of the chemical industrial park was achieved.

[0015] (2) By clustering months through SOM neural network, the fluctuation characteristics of similar months are merged, avoiding the error of uniform modeling throughout the year; Markov chain is used to simulate the state transition of fluctuations, so that the wind power scenario can reflect the temporal correlation of fluctuations; by joint, one-dimensional probability distribution sequential sampling and random power output addition, the generated wind power scenario not only conforms to the statistical law of wind power output, but also contains random fluctuations, which improves the realism of the wind power scenario.

[0016] (3) The influence of the sun's position is eliminated by the net clearance model conversion; the photovoltaic power output fluctuation characteristics of different weather conditions are quantified by weather type clustering and mixed Gaussian distribution; and the temporal continuity of weather type and photovoltaic power output sequence is ensured by Markov chain and sequential sampling.

[0017] (4) By decomposing the optimization target and output plan, the optimization division of the supply side and demand side is clarified to maximize the new energy consumption capacity; by quantifying the consumption contribution, the first day plan is extracted, and the interval optimization is transformed into a fine-grained daily execution plan; by transmitting the initial conditions, the unit output and chemical load output are alternately optimized to ensure the continuity and real-time nature of the production plan.

[0018] (5) By adopting a power balancing strategy that covers multiple modes and scenarios, the consumption of new energy is maximized when wind and solar power are abundant; when wind and solar power are moderate, the consumption of new energy and energy storage are balanced; and when wind and solar power are scarce, production continuity is ensured. Priority is given to the use of flexible loads in the chemical industry, followed by the use of energy storage, and finally grid power supplementation. This is in line with the actual load characteristics of the chemical industrial park, and the power balancing strategy also provides a continuous guarantee for rolling optimization and alternating optimization.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0021] Figure 1This is a flowchart illustrating a method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources, according to an exemplary embodiment.

[0022] Figure 2 This is a schematic diagram illustrating a process for obtaining a wind power output scenario adapted to a production cycle, according to an exemplary embodiment.

[0023] Figure 3 This is a schematic diagram illustrating a process for obtaining a photovoltaic power output scenario that is adapted to the production cycle, according to an exemplary embodiment.

[0024] Figure 4 This is a schematic diagram illustrating a power balancing strategy according to an exemplary embodiment;

[0025] Figure 5 This is a schematic diagram illustrating a process for jointly supplying power to an energy storage unit that includes renewable energy generation, according to an exemplary embodiment.

[0026] Figure 6 This is a flowchart illustrating a power balancing strategy according to an exemplary embodiment;

[0027] Figure 7 This is a schematic diagram of a chemical industrial park production scheme generation system that takes into account the uncertainty of new energy sources, according to an exemplary embodiment.

[0028] Figure 8 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment. Detailed Implementation

[0029] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some portions and features of certain embodiments may be included in or replace portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0030] Figure 1 An embodiment of the method for generating production plans for chemical industrial parks that takes into account the uncertainty of new energy sources according to the present invention is shown.

[0031] In this optional embodiment, the method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources includes: S101, setting a production cycle and optimization interval corresponding to the production plan of the chemical industrial park; S102, acquiring wind power output scenarios, photovoltaic power output scenarios, load scenarios and attribute data that are suitable for the production cycle, and combining each scenario and attribute data to generate a production scenario for the chemical industrial park covering the entire production cycle; S103, taking maximizing the acceptance capacity and maximizing the absorption capacity of new energy sources as optimization objectives, determining the output scheme within the current optimization interval based on the production scenario of the chemical industrial park, combining the output scheme with the grid operation mode determined by the power balance strategy, and calculating the absorption index within the current optimization interval; S104, continuously updating the current optimization interval until the optimization of the entire production cycle is completed, and obtaining the absorption index within each optimization interval; S105, integrating and analyzing the absorption index within each optimization interval to obtain the periodic absorption index; S106, repeatedly executing the process of generating production scenarios and periodic absorption indexes until the convergence criterion is met, and summarizing the output schemes within each optimization interval and the grid operation mode determined by the power balance strategy to obtain the production plan for the chemical industrial park within the production cycle.

[0032] In this optional embodiment, acquiring wind power output scenarios, photovoltaic output scenarios, load scenarios, and attribute data adapted to the production cycle, and combining each scenario with the attribute data to generate a chemical industrial park production scenario covering the entire production cycle includes: based on pre-acquired historical wind power output data, analyzing and combining the probability distribution of various wind power output fluctuations and the transition probability matrix between various wind power output fluctuations to generate a wind power output scenario adapted to the production cycle; based on pre-acquired historical photovoltaic power output data, analyzing and combining the probability distribution of various weather conditions and the transition probability matrix between various weather conditions to generate a photovoltaic output scenario adapted to the production cycle; generating a load scenario adapted to the production cycle by analyzing pre-acquired historical load data; and combining the wind power output scenario, photovoltaic output scenario, load scenario, and pre-acquired attribute data to generate a chemical industrial park production scenario covering the entire production cycle.

[0033] In this optional embodiment, based on pre-acquired historical wind power output data, analyzing and combining the probability distribution of various wind power output fluctuations and the transition probability matrix between various wind power output fluctuations to generate a wind power output scenario adapted to the production cycle includes: acquiring historical wind power output data for the corresponding production cycle; classifying fluctuation categories according to the fluctuation amplitude and duration of wind power output, including large fluctuations, medium fluctuations, small fluctuations, and low output fluctuations; extracting wind power output features from the historical wind power output data and inputting them into a SOM neural network for clustering to obtain monthly categories and determine the wind power fluctuation sequence for each category of month; using Markov chains to analyze the wind power fluctuation sequence for each category of month to obtain the transition probability matrix between various fluctuations and determine the probability distribution of various fluctuations in each category of month; and performing sequential sampling based on the probability distribution of various fluctuations in each category of month and the transition probability matrix between various fluctuations to obtain a wind power output scenario adapted to the production cycle.

[0034] In this optional embodiment, Markov chain analysis is used to analyze the wind power fluctuation sequence of each type of month to obtain the transition probability matrix between different types of fluctuations. The probability distribution of different types of fluctuations in each type of month is determined by: counting the occurrence and transition times of different types of fluctuations in each type of month, calculating the transition probability, and generating the transition probability matrix between different types of fluctuations in each type of month based on the transition probability; counting the Gaussian fitting function parameters and peak interval duration of wind power fluctuation segments of large, medium, and small fluctuation categories to obtain the joint probability distribution; and counting the frequency distribution of the duration of wind power fluctuation segments of low output fluctuation category to obtain the one-dimensional probability distribution.

[0035] In this optional embodiment, sequential sampling is performed based on the probability distribution of various fluctuations in each month and the transition probability matrix between various fluctuations to obtain a wind power output scenario suitable for the production cycle. This includes: randomly sampling according to the proportion of various fluctuations in the first month of the production cycle to determine the first fluctuation category; sequential sampling according to the transition probability matrix between various fluctuations in each month, combined with the month transition rules, to determine the subsequent fluctuation categories; for large, medium, and small fluctuation categories, Gaussian fitting function parameters and peak interval duration are sampled from the corresponding joint probability distribution to determine the output value at each point. For low-output fluctuation categories, the duration is sampled from the corresponding one-dimensional probability distribution, and the output value at each point is determined by random sampling. According to the preset fluctuation connection rules, the output sequences of various fluctuations are sequentially connected to obtain the standard output sequence for each month. Based on the fluctuation category to which each point in the standard output sequence of each month belongs, random sampling is performed sequentially from the probability distribution of the corresponding random output to obtain the random output sequence for each month. The random output sequences of each month are superimposed on the standard output sequence to obtain the wind power output sequence for each month. The wind power output sequences of each month are sequentially connected to obtain the wind power output scenario adapted to the production cycle.

[0036] In this optional embodiment, the wave connection rule includes: if two non-low-output waves are adjacent, the output value of each point in the rising segment on the left side of the latter wave is compared with the output value of each point in the falling segment on the right side of the former wave, and the larger output value is taken as the output of that point; if adjacent low-output waves have been merged, and there is a low-output wave between two non-low-output waves, the duration of the low-output wave is evenly distributed to the two non-low-output waves, and the output values ​​of each point in the falling segment on the right side of the former wave and the output values ​​of each point in the rising segment on the left side of the latter wave are calculated according to their respective Gaussian fitting functions; for the first wave, if it is a non-low-output wave, the output value of each point in the rising segment on the left side of the wave is calculated only according to its Gaussian fitting function, and if it is a low-output wave, the final output of each point inside is determined by the randomly sampled output.

[0037] In this optional embodiment, based on pre-acquired historical photovoltaic power generation output data, analyzing and combining the probability distribution of various weather types and the transition probability matrix between various weather types to generate a photovoltaic power generation scenario adapted to the production cycle includes: acquiring historical photovoltaic power generation output data for the corresponding production cycle; inputting the historical photovoltaic power generation output data into a preset airspace model, using the airspace model to calculate the daily relative output sequence, where the relative output includes a power baseline value and a fluctuation coefficient; extracting photovoltaic output features from the daily relative output sequence, forming an output feature vector and inputting it into a SOM neural network for clustering to obtain weather types and determine the relative output sequence under various weather types; using Gaussian mixture distribution and Markov chain analysis to obtain the probability distribution of various weather types and the transition probability matrix between various weather types; performing sequential sampling based on the probability distribution of various weather types and the transition probability matrix between various weather types to obtain the photovoltaic relative output sequence; and using the airspace model to reconstruct the photovoltaic relative output sequence to obtain a photovoltaic power generation scenario adapted to the production cycle.

[0038] In this optional embodiment, sequential sampling based on the probability distribution of various types of weather and the transition probability matrix between various types of weather to obtain the photovoltaic relative output sequence includes: determining the weather type of each day based on the transition probability matrix between various types of weather to form a weather type sequence table; sampling the power benchmark value and fluctuation coefficient from the one-dimensional probability distribution and mixed probability distribution corresponding to each type of weather in the weather type sequence table to determine the output value at each point and generate the photovoltaic relative output sequence for each day; and connecting the photovoltaic relative output sequences of each day in sequence to obtain the photovoltaic relative output sequence.

[0039] In this optional embodiment, maximizing both renewable energy acceptance and renewable energy absorption are the optimization objectives. Based on the production scenario of the chemical industrial park, the power output scheme within the current optimization range is determined. This power output scheme is then combined with the grid operation mode determined through a power balance strategy to calculate the absorption index within the current optimization range. This includes: taking maximizing renewable energy acceptance as the first optimization objective, and combining it with constraints including unit ramp rate, minimum start-up and shutdown time, maximum / minimum technical output, and power balance, to solve for the unit output scheme within the range; and taking maximizing renewable energy absorption as the second optimization objective, and combining it with constraints including chemical production process constraints, maximum power absorption capacity, and maximum power absorption capacity, to solve for the maximum power absorption capacity within the range. The constraints of minimum / maximum starting load and adjustment rate are used to solve for the power output scheme of the chemical load in the interval. The power output scheme of the first day is extracted from the power output scheme of the interval units, and the first day grid operation mode in the current optimization interval determined by the power balance strategy is used to calculate the first absorption index. The first day grid operation mode in the current optimization interval is used as the initial condition for the next optimization interval, and the power output of the units and / or the chemical load output are alternately optimized. The power output scheme of the first day is extracted from the power output scheme of the interval chemical load, and the second absorption index is calculated by combining the first day grid operation mode in the current optimization interval determined by the power balance strategy.

[0040] In this optional embodiment, the grid operation mode of the first day within the current optimization interval is used as the initial condition for the next optimization interval. Alternating optimization of unit output and / or chemical load output includes: determining the curtailment rate on the unit side and the curtailment rate on the chemical load side based on the grid operation mode of the first day within the current optimization interval, and calculating the absorption contribution based on the curtailment rate on the unit side and the curtailment rate on the chemical load side; calculating a first dynamic threshold based on the energy storage charge state; calculating a second dynamic threshold based on the chemical load state; and determining the optimization subject of the next optimization interval based on the comparison result of the absorption contribution with the first dynamic threshold and the second dynamic threshold, so that the switching decision between unit output optimization and chemical load output optimization changes from time-triggered to state-triggered.

[0041] In this optional embodiment, the power balancing strategy includes: obtaining the preset minimum operating load and preset maximum operating load of the daily renewable energy power generation and chemical load; comparing and analyzing the daily renewable energy power generation and the preset maximum operating load to determine a first operating mode; based on the first operating mode, setting the chemical load to the preset maximum operating load, calculating the renewable energy power generation margin, and storing the electricity according to the storage order; analyzing the renewable energy power generation margin and the electricity storage amount to obtain the renewable energy power abandonment; comparing and analyzing the daily renewable energy power generation and the preset minimum operating load and the preset maximum operating load to determine a second operating mode; based on the second operating mode, jointly supplying power to renewable energy power generation and energy storage units; comparing and analyzing the daily renewable energy power generation and the preset minimum operating load to determine a third operating mode; based on the third operating mode, prioritizing the supply of power to renewable energy power generation and energy storage units, and jointly supplying power to the grid.

[0042] In this optional embodiment, the joint power supply of new energy power generation and energy storage unit based on the second operating mode includes: analyzing the energy release of new energy power generation and energy storage unit and the preset maximum start-up load to determine the first scenario and the second scenario of the second operating mode; combining the first scenario of the second operating mode, setting the chemical load to operate at the preset maximum start-up load and using the surplus of energy storage unit for supplementary power supply; combining the second scenario of the second operating mode, setting the chemical load to operate at the preset minimum start-up load and storing the surplus of new energy power generation.

[0043] Figure 7 An embodiment of the chemical industrial park production scheme generation system of the present invention, which takes into account the uncertainty of new energy sources, is shown.

[0044] In this optional embodiment, the chemical industrial park production plan generation system considering the uncertainty of new energy sources includes: a parameter configuration module 201, used to set the production cycle and optimization interval corresponding to the production plan of the chemical industrial park; a scenario determination module 202, used to acquire wind power output scenarios, photovoltaic power output scenarios, load scenarios and attribute data that are suitable for the production cycle, and combine each scenario and attribute data to generate a chemical industrial park production scenario covering the entire production cycle; and a plan generation module 203, used to determine the output plan within the current optimization interval based on the chemical industrial park production scenario with the optimization goal of maximizing the new energy acceptance capacity and maximizing the new energy absorption capacity, and combine the output plan with the power grid operation mode determined by the power balance strategy to calculate the absorption index within the current optimization interval; continuously update the current optimization interval until the optimization of the entire production cycle is completed, and obtain the absorption index within each optimization interval; integrate and analyze the absorption index within each optimization interval to obtain the periodic absorption index; repeatedly execute the process of generating production scenarios and periodic absorption indexes until the convergence criterion is met, and then summarize the output plan within each optimization interval and the power grid operation mode determined by the power balance strategy to obtain the chemical industrial park production plan within the production cycle.

[0045] To facilitate understanding of the above technical solutions of the present invention, the following further explains the above technical solutions of the present invention from the perspective of architecture and principle, as follows:

[0046] Common techniques for power system production simulation can be broadly categorized into two types: stochastic production simulation and time-series production simulation. Stochastic production simulation typically employs methods such as the equivalent power function method, Monte Carlo method, standard convolution method, and sequence operation method. It considers the impact of random factors such as system source-load fluctuations and equipment failures, arranges unit commissioning modes based on continuous load curves, and calculates and analyzes system economic and reliability indicators. It uses simplified grid operation constraints, has lower computational complexity, and can better characterize the uncertainty characteristics of the power grid, but it struggles to reflect the time-series characteristics of the grid and cannot obtain specific operational results. It is suitable for medium- and long-term production simulation, focusing on power balance and probabilistic statistical indicators such as economic efficiency and reliability over long time scales.

[0047] Time-series production simulations typically construct unit combination models based on mixed-integer programming, employing methods such as branch-and-bound and data-driven approaches for solution. Acceleration methods include redundancy constraint identification, heuristic algorithms, and decomposition algorithms. They consider relatively detailed grid operation constraints, can reflect the time-series characteristics of the grid well, and have strong scalability; however, the models are relatively complex, and solution efficiency needs improvement in complex problems or long-term scenarios. They are suitable for short-term and ultra-short-term production simulations, focusing on power balance at short timescales and the operational status and details of various elements in the grid under typical scenarios.

[0048] This invention fully considers the uncertainty of new energy output and, based on this, combines power balancing strategies to formulate and optimize production plans for chemical industrial parks, achieving high absorption and low curtailment of new energy sources. Figure 1 As shown, the present invention includes the following steps:

[0049] The process involves defining a production cycle and optimization interval; acquiring wind power output scenarios, photovoltaic power output scenarios, load scenarios, and attribute data suitable for the production cycle; determining the power output scheme within the current optimization interval based on the production scenario and optimization objectives; determining the grid operation mode and providing absorption indicators based on power balancing strategies; continuously updating the current optimization interval until the entire production cycle is optimized; integrating and analyzing the absorption indicators within each optimization interval to provide periodic absorption indicators; repeating the process of providing production scenarios and periodic absorption indicators until convergence criteria are met, and providing the production plan for the chemical industrial park within the production cycle. By accurately analyzing the characteristics of new energy power output and combining them with power balancing strategies to optimize the production plan, dynamic coordination between the new energy side and the chemical side of the chemical industrial park is achieved.

[0050] Understandably, the production cycle is a medium- to long-term timeframe (e.g., January to December, February to July), corresponding to the park's production plan; the optimization interval is a rolling short-term window (e.g., 3 days, 7 days). Wind power and solar power output scenarios are time series of wind and solar power output, while load scenarios are time series of load output. The duration of each scenario is consistent with the production cycle (e.g., one year), ensuring coverage of annual new energy and load changes. Load scenarios can be simulated by analyzing historical load data, which refers to the historical records of electricity load within the park, primarily including the production load of chemical enterprises (e.g., the electricity demand of heavy chemical plants).

[0051] Attribute data includes unit data, chemical load data, and energy storage data. Unit data includes heating units; unit data refers to the technical parameters of traditional power units (such as thermal power units and heating units), used to describe the unit's operating characteristics, including installed capacity, ramp rate, minimum start-up and shutdown time, maximum technical output, minimum technical output, and heating parameters and load of heating units. Chemical load data includes parameters such as maximum and minimum chemical load output. Energy storage data includes technical parameters such as capacity, power, efficiency, and charge / discharge constraints of various thermal, hydrogen, and electrical storage devices, used to describe the operating characteristics of energy storage devices. By combining wind and solar power output scenarios, load scenarios, and attribute data, production scenarios can be determined for production simulation. The absorption indicators are indicators that quantify the effectiveness of renewable energy absorption, including a first absorption indicator and a second absorption indicator. The first absorption indicator is renewable energy power generation and renewable energy curtailment, and the second absorption indicator is renewable energy acceptance and renewable energy curtailment. By summarizing and analyzing the absorption indices of all optimization intervals within the production cycle, the cycle absorption index for a single production scenario is obtained, and the absorption effect of a single production scenario is evaluated. The convergence criterion is that the variance coefficient of the absorption indices of multiple production scenarios is less than a set value (preferably 0.05), at which point the results are stable and will not fluctuate significantly due to different scenarios. By combining the unit and chemical load output schemes within each optimization interval, along with the energy storage dispatching method determined through the power balance strategy and the grid operation mode, the production scheme of the chemical industrial park within the production cycle can be obtained.

[0052] By generating wind and solar power scenarios and implementing power balancing strategies that cover various uncertainties, the capacity of chemical industrial parks to absorb new energy sources is improved, power curtailment is reduced, and dependence on traditional energy sources is decreased. Through multiple scenario simulations, production plans for chemical loads, unit operation plans, and energy storage scheduling plans are generated. Combined with convergence criteria, robust production solutions are obtained to guide chemical industrial parks in optimizing resource allocation.

[0053] like Figure 2 As shown, obtaining wind power output scenarios suitable for the production cycle specifically includes: acquiring historical wind power output data for the corresponding production cycle; determining fluctuation categories, extracting wind power output features from the historical output data, inputting them into a SOM neural network for clustering, assigning monthly categories, and determining the wind power fluctuation sequence for each category of months; using Markov chain analysis to analyze the wind power fluctuation sequence for each category of months, providing the transition probability matrix between different types of fluctuations, and determining the probability distribution of different types of fluctuations within each category of months; and performing sequential sampling based on the probability distribution of different types of fluctuations within each category of months and the transition probability matrix between different types of fluctuations to provide wind power output scenarios. It can be understood that, to formulate a production plan for February to July of this year, the actual power generation records of wind farms from February to July of previous years, in megawatts (MW), are obtained.

[0054] Specifically, fluctuation categories include large fluctuations, medium fluctuations, small fluctuations, and low-output fluctuations. These fluctuation categories are based on the amplitude and duration of wind power output fluctuations, used to quantify the randomness and fluctuation characteristics of wind power output. For example, fluctuation identification algorithms such as threshold methods and slope methods can be used to classify fluctuations: fluctuations with an output change amplitude ≥30% of rated installed capacity and a duration ≥2 hours are classified as large fluctuations; fluctuations with an output change amplitude between 15% and 30% of rated capacity and a duration of 1 to 2 hours are classified as medium fluctuations; fluctuations with an output change amplitude between 5% and 15% of rated capacity and a duration of 0.5 to 1 hour are classified as small fluctuations; and fluctuations with a continuous output ≤10% of rated capacity are classified as low-output fluctuations. Fluctuations with output >10% of rated capacity and exhibiting a clear fluctuation pattern (large, medium, and small fluctuations) are referred to as non-low-output fluctuations. By distinguishing these four fluctuation categories, the amplitude differences and temporal characteristics of wind power can be accurately characterized, improving the realism of wind power scenario generation.

[0055] Because wind power output exhibits seasonal variations, with significant differences in output levels across different calendar months, primarily reflected in the duration of various fluctuations, a uniform modeling approach for the entire year would fail to simulate these monthly differences. Conversely, modeling on a shorter timescale, such as a week, would not reflect the model's characteristics due to the limited number of fluctuations within that period. Therefore, a monthly modeling period is adopted. Since some calendar months exhibit similar fluctuation characteristics, months can be categorized. Compared to the number of fluctuations, the total duration of each type of fluctuation better reflects the monthly differences. The proportion of the duration of each type of non-low-output fluctuation is extracted from historical wind power output data as a characteristic of wind power output.

[0056] The Self-Organizing Map (SOM) neural network is a type of neural network. High-dimensional wind power output characteristics (the proportion of large, medium, and small fluctuations) are input into the SOM neural network, which can then be clustered into low-dimensional monthly categories (high, medium, and low output months), thereby determining the fluctuation sequence of wind power output under different monthly categories. Monthly clustering merges the fluctuation characteristics of similar months, reducing modeling complexity and avoiding the errors of uniform modeling throughout the year. Different probability distributions are used for different categories of months, and the generated scenarios can reflect the seasonal differences in wind power output.

[0057] Specifically, Markov chain analysis is used to analyze the wind power fluctuation sequences for each type of month, providing the transition probability matrix between different types of fluctuations, and determining the probability distribution of each type of fluctuation within each type of month, including:

[0058] The occurrence and transition frequency of each type of fluctuation in each month are statistically analyzed, the transition probability is calculated, and the transition probability matrix between each type of fluctuation in each month is given. For wind power fluctuation segments of large, medium and small fluctuation categories, the parameters of their Gaussian fitting function and the duration of the peak interval are statistically analyzed, and the joint probability distribution is given. For wind power fluctuation segments of low output fluctuation category, the frequency distribution of their duration is statistically analyzed, and the one-dimensional probability distribution is given.

[0059] For the fluctuation sequence of each type of month, the occurrence and transition times of each type of fluctuation can be counted, the transition probability can be calculated, and a 4×4 transition probability matrix can be formed to quantify the state transition pattern of fluctuations, making the generated scenario more consistent with the actual fluctuation characteristics.

[0060] The wind power fluctuation sequence is segmented into segments corresponding to various fluctuation types, which are called wind power fluctuation segments. For non-low-output fluctuations such as large, medium, and small fluctuations, due to their obvious fluctuation patterns, Gaussian functions are used to fit the corresponding wind power fluctuation segments to quantify the fluctuation process, and the peak value, standard deviation, mean, and other parameters of each Gaussian fitting function are statistically analyzed. The peak interval duration refers to the duration between the peak of a fluctuation and the peak of the previous non-low-output fluctuation. The calculation method is as follows: if there is a low-output fluctuation between the two fluctuations, the duration is the difference between the duration between the peak of the current fluctuation and the peak of the previous non-low-output fluctuation and the duration of the low-output fluctuation between the two. If there is no low-output fluctuation between the two fluctuations, the number of points (time difference) between the peaks is directly calculated.

[0061] Each non-low-output fluctuation's Gaussian fitting function parameters and peak interval duration are multidimensional random variables. Therefore, it is necessary to statistically analyze their multidimensional probability distributions and establish a joint probability distribution for each fluctuation category. For low-output fluctuations, due to their low output level and lack of obvious fluctuation regularity, only the one-dimensional probability distribution of their duration in the three types of months is statistically analyzed to describe the simple randomness of low-output fluctuations. Their final internal output value is determined by the randomly sampled output. The joint probability distribution accurately describes the complexity of non-low-output fluctuations through parameter correlation and peak intervals, while the one-dimensional probability distribution accurately describes the simplicity of low-output fluctuations through duration, jointly improving the statistical accuracy of wind power scenarios.

[0062] It should be noted that, to facilitate subsequent sequential sampling and generation of wind power output sequences, if low-output fluctuations occur consecutively, their durations are summed and merged into a single low-output fluctuation process. This merging of low-output fluctuations avoids fragmented sequences and aligns with real-world weak wind scenarios.

[0063] Since the fluctuation statistics of each month within each category are similar, the transition probability matrix and joint probability distribution of each month can be replaced by the transition probability matrix and joint probability distribution of the month category to which it belongs, and then sequential sampling can be performed to generate wind power output scenarios.

[0064] Specifically, based on the probability distribution of various fluctuations in each month and the transition probability matrix between different fluctuations, sequential sampling is performed to provide wind power output scenarios, including:

[0065] The first fluctuation category is determined by random sampling based on the proportion of various fluctuations in the first month of the production cycle. Subsequent fluctuation categories are determined by sequential sampling based on the transition probability matrix between various fluctuations within each month category, combined with month transition rules. For large, medium, and small fluctuation categories, Gaussian fitting function parameters and peak interval durations are sampled from the corresponding joint probability distribution to determine the power output value at each point. For low-output fluctuation categories, the duration is sampled from the corresponding one-dimensional probability distribution, and random sampling is used to determine the power output value at each point. The power output sequences of various fluctuations are sequentially connected according to preset fluctuation connection rules to obtain the standard power output sequence for each month. Based on the fluctuation category to which each point belongs in the standard power output sequence for each month, random sampling is sequentially performed from the corresponding random power output probability distribution to give the random power output sequence for each month. The random power output sequences for each month are superimposed on the standard power output sequence to obtain the wind power output sequence for each month. Finally, the wind power output sequences for each month are sequentially connected to obtain the wind power output scenario.

[0066] Specifically, sequential sampling is a method of drawing continuous samples from a probability distribution in chronological order. It uses conditional probability to transform multidimensional distribution sampling into one-dimensional distribution sampling, simplifying the sampling complexity. In a certain example, sampling is done sequentially month by month. If an annual plan is formulated, a total of 12 samples are taken, and the sampling distribution for each month is the historical statistical distribution of its corresponding month category. The sampling process is as follows: first, the fluctuation category is determined based on the transition probability matrix between different fluctuation categories; then, fluctuation parameters are sampled from the probability distribution corresponding to each fluctuation category. Specifically, for the first month (e.g., January), random sampling is performed based on the proportion of each fluctuation category in that month: if January is a high-output month, and high-output months have a 40% proportion of large fluctuations, then there is a 40% probability of drawing a large fluctuation, which is taken as the first fluctuation category. Then, sequential sampling is performed based on the transition probability matrix between different fluctuation categories in each month category: if the current fluctuation is a large fluctuation, there is a 30% probability of transitioning to a medium fluctuation, then the probability of the next fluctuation being drawn as a medium fluctuation is 30%.

[0067] The rule for transitioning between adjacent months is set as follows: if the total duration of wind power output accumulated after sampling the (i-1)th fluctuation in a month is less than the total duration of the month, while the total duration of wind power output accumulated after sampling the ith fluctuation is greater than or equal to the total duration of the month, the extra duration of the ith fluctuation in that month needs to be extended to the next month. The first fluctuation category of the first month is randomly sampled from the proportion of each fluctuation category in that month, while the first fluctuation category of each subsequent month is determined to be the category of fluctuations spanning the current month and the previous month.

[0068] If the fluctuation category belongs to any of the large, medium, and small fluctuations, then based on the multidimensional joint probability distribution of that fluctuation category, the parameters of the Gaussian fitting function and the duration of the peak interval of the fluctuation are randomly sampled, and the output value at each point on the fluctuation is calculated according to the Gaussian fitting function. If the fluctuation category belongs to the low-output fluctuation, then based on its one-dimensional probability distribution, the duration is randomly sampled, and its internal output is determined by random sampling, such as randomly selecting values ​​between 0 and 10% of the rated output.

[0069] The rules for wave connection are as follows: a) If two non-low-output waves are adjacent, the output value of each point on the left rising segment of the latter wave is compared with the output value of each point on the right falling segment of the former wave, and the larger output value is taken as the output value of that point to avoid output overlap and better conform to the physical laws of wind power output.

[0070] b) Since adjacent low-output fluctuations have been merged, if there is a low-output fluctuation between two non-low-output fluctuations, there must be only one. The duration of the low-output fluctuation is evenly distributed among the two non-low-output fluctuations, and the output values ​​of each point on the right side of the descending segment of the previous fluctuation and the output values ​​of each point on the left side of the ascending segment of the subsequent fluctuation are calculated according to their respective Gaussian fitting functions.

[0071] c) For the first fluctuation, if it is not a low-output fluctuation, the output value of each point on the left rising segment of the fluctuation can be calculated only based on its Gaussian fitting function. If it is a low-output fluctuation, the final output of each point inside is determined by the random output obtained by sampling.

[0072] By handling the output of adjacent fluctuations through fluctuation connection rules, "jumps" in output are avoided, which better reflects the physical laws of actual wind power output. The output sequences of various fluctuations are sequentially concatenated to obtain the standard output sequences for each month, free from random noise. Since random output is concentrated in the high-frequency domain, its essence is random high-frequency noise, and it has no seasonal differences. Therefore, it is classified according to the fluctuation category to which each point in the wind power fluctuation sequence belongs, resulting in four corresponding categories of random output. The probability distribution of each type of random output approximately follows a normal distribution. Parameter estimation is performed on the normal distribution probability density function of the four types of random output samples to determine the normal distribution they follow. Then, according to the fluctuation category to which each point in the standard output sequence belongs, random output sequences are randomly sampled from the normal distribution followed by that type of random output to obtain random output sequences. The random output sequences are of equal length to the standard output sequences. Adding them to the standard output sequences yields the wind power output sequence. Finally, the wind power output sequences of each month are sequentially concatenated to obtain the wind power output scenario, i.e., the wind power output sequence within the production cycle. The generated wind power output scenario can accurately reflect the seasonality, volatility and randomness of wind power, providing a reliable input for the generation and optimization of production plans in chemical industrial parks, and improving the robustness and accuracy of production plans.

[0073] like Figure 3As shown, obtaining a photovoltaic (PV) power output scenario suitable for the production cycle specifically includes: acquiring historical PV power output data for the corresponding production cycle; processing the historical PV power output data based on a preset airspace model to provide a daily relative power output sequence; the relative power output includes a power baseline value and a fluctuation coefficient; extracting PV power output features from the daily relative power output sequence to form a power output feature vector, inputting it into a SOM neural network for clustering, providing weather types, and determining the relative power output sequence under various weather conditions; weather types include sunny, cloudy, overcast, and changing weather; using Gaussian mixture distribution and Markov chain analysis to analyze the relative power output sequence under various weather conditions, providing the probability distribution of various weather conditions and the transition probability matrix between various weather conditions; performing sequential sampling based on the probability distribution of various weather conditions and the transition probability matrix between various weather conditions to provide a PV relative power output sequence; and reconstructing the PV relative power output sequence using the airspace model to obtain the PV power output scenario.

[0074] It is understandable that historical photovoltaic (PV) power output data refers to the actual power generation record of a PV power plant over a past period (e.g., February to August), with a time resolution of 15 minutes and the unit being megawatts (MW). The overhead range model refers to an ideal unshaded model calculated using an astronomical model; the theoretical overhead range output is the ideal unshaded output calculated using this model. The historical PV power output data includes the PV power output for each day and time period during the daytime. It can be transformed and applied to the theory of clear space. By comparison, the relative output based on the net air volume model can be obtained for each day. :

[0075]

[0076] In the formula, For the relative output at time t on day i, For the actual output at time t on day i, This contributes to the net airspace theory at time t on day i.

[0077] Will It is decomposed into the sum of the power reference value and the uncertain output (fluctuation coefficient):

[0078]

[0079]

[0080] In the formula, Δ Let be the fluctuation coefficient at time t on day i; This is the power baseline value for day i, reflecting the relative level of photovoltaic output on that day; Let n be the relative output at time t on day i, and n be the total number of time periods during the day.

[0081] Ultimately, the relative output of photovoltaic power is converted into a power baseline value plus the uncertain output caused by different cloud layers and weather conditions.

[0082] Traditional classification algorithms, such as Bayesian methods, decision trees, artificial neural networks, and association rules, primarily classify high-dimensional vectors using Euclidean distance, which is not suitable for distinguishing the fluctuation characteristics of photovoltaic (PV) power output sequence curves. Since PV power plant data is recorded at a 15-minute scale, totaling 96 times per day, the variable dimensionality is high. Directly classifying the power output variable would be computationally intensive, and the differences in relative power output fluctuation under different weather types would not be well reflected. Therefore, this invention employs a feature extraction method, selecting five features that reflect the relative power output fluctuation of PV: the average relative power output of the PV power plant for each day, the average of the absolute values ​​of the first-order differences of the relative power output matrix for each day, the standard deviation of the relative power output for each day, the standard deviation of the absolute values ​​of the first-order differences of the relative power output matrix for each day, and the maximum value of the absolute values ​​of the first-order differences of the relative power output matrix for each day. The feature vector composed of these five features is then clustered in a SOM neural network to obtain classification results, with each category corresponding to a weather type.

[0083] Specifically, the relative output sequences under various weather conditions are analyzed using Gaussian mixture distribution and Markov chain analysis, and the probability distributions of various weather conditions and the transition probability matrices between them are given:

[0084] The relative power output during sunrise and sunset in the relative power output sequence under clear weather conditions is corrected; the fluctuation coefficients of the relative power output sequences under various weather conditions are fitted using a three-component mixed Gaussian distribution, and the mixed probability distribution of the fluctuation coefficients under various weather conditions is given; the one-dimensional probability distribution of the power benchmark value under various weather conditions is determined; the occurrence frequency of various weather conditions and the number of times they shift to other weather types are counted, the shift probability is calculated, and the shift probability matrix between various weather types is given.

[0085] Since the relative power output sequence on sunny days follows a certain pattern, if sunrise and sunset times are excluded, the relative power output curve approximates a straight line. Therefore, we can fit the properties of the sunrise and sunset times to find the starting time t of each sunny day's relative power output linear time period. i,on Termination time t i,off and the corresponding relative output value P i,on P i,off The time periods before the start time and after the end time can be considered as sunrise and sunset times (generally 4-6 points, i.e., 1-1.5 hours). Calculate the relative output and P at sunrise on a clear day. i,on The ratio of the relative output at sunset on a sunny day to P i,off The ratio of t to t is used as a reference output, and then the sunrise time and t are calculated separately. i,on The difference between the time of sunset and t i,offThe difference is the relative time. Then, the relative time of sunrise / sunset and the reference output are fitted with linear least squares to obtain the fitting characteristic line of sunrise and sunset on a clear day.

[0086] Specifically, based on the probability distribution of various weather types and the transition probability matrix between them, sequential sampling is performed to give the photovoltaic relative power output sequence, including:

[0087] Based on the transition probability matrix between various weather types, the weather type for each day is determined, forming a weather type sequence table. Power output values ​​at each point are determined by sampling the benchmark power value and fluctuation coefficient from the one-dimensional probability distribution and mixed probability distribution corresponding to each weather type in the weather type sequence table, providing the relative photovoltaic power output sequence for each day. The relative photovoltaic power output sequences for each day are then sequentially connected to obtain the total relative photovoltaic power output sequence. By effectively handling the uncertainty of photovoltaics, this provides a high-precision photovoltaic power output scenario for chemical industrial park production plans, supporting the assessment of new energy absorption capacity and the optimization of energy storage scheduling.

[0088] Specifically, the optimization objectives include a first optimization objective and a second optimization objective; the output schemes include unit output schemes and chemical load output schemes; and, in conjunction with the optimization objectives, the output schemes within the current optimization range are determined as follows: based on the first optimization objective, the unit output schemes within the current optimization range are optimized to provide the unit output scheme for the range; and based on the second optimization objective, the chemical load output schemes within the current optimization range are optimized to provide the chemical load output scheme for the range.

[0089] The first optimization objective is to maximize the capacity to accommodate new energy sources. This involves adjusting the output of thermal power units (e.g., reducing it to minimum technical output) to free up system capacity, allowing more space for new energy sources (wind and solar power) and avoiding power curtailment due to unit capacity limitations. Unit output optimization is a classic unit combination problem in power systems. It can be solved using optimization algorithms such as linear programming, mixed-integer linear programming (MILP), and genetic algorithms, taking into account constraints such as unit ramp-up rate, minimum start-up and shutdown time, maximum / minimum technical output, and power balance, to find the optimal unit output plan that satisfies the constraints and maximizes the capacity to accommodate new energy sources. Specific limitations are not specified here. The second optimization objective is to maximize the capacity to absorb new energy sources. This involves adjusting the output of chemical loads (e.g., increasing electricity consumption during periods of high wind and solar power generation) to better absorb new energy sources, adapt to fluctuations in new energy supply, and avoid power curtailment due to insufficient load demand, taking into account constraints such as chemical load output limitations. Among these, optimizing the output of chemical plants falls under the scope of power system demand response. It can be achieved by combining constraints such as chemical production process constraints (e.g., reactor temperatures must be maintained within a certain range and cannot be frequently adjusted), minimum / maximum operating loads, and adjustment rates. Algorithms such as Model Predictive Control (MPC), Particle Swarm Optimization (PSO), and Reinforcement Learning (RL) can be employed to solve for the optimal chemical plant output plan that satisfies these constraints, maximizing the absorption capacity of new energy sources. No specific limitations are specified here. By coordinating the adjustment characteristics of the generating units and the load side, the limitations of single-mode optimization are avoided, thereby improving the overall absorption capacity of the industrial park.

[0090] Specifically, the absorption indicators include a first absorption indicator and a second absorption indicator. Combined with the power balance strategy, the grid operation mode is determined, and the absorption indicators are given as follows: The first-day unit output plan is extracted from the interval unit output plan; combined with the power balance strategy, the first-day grid operation mode within the optimized interval is given, and the first absorption indicator is calculated; the first-day chemical load output plan is extracted from the interval chemical load output plan; combined with the power balance strategy, the first-day grid operation mode within the optimized interval is given, and the second absorption indicator is calculated.

[0091] For example, the power output scheme for the interval is a [1,3]-day unit operation plan. The power output plan for the first day (day 1) is extracted. Combined with the power balance strategy, the operating mode of each unit, load, energy storage, and other equipment in the power grid on that day is determined. Then, the renewable energy generation and renewable energy curtailment are calculated to characterize the absorption contribution of the unit output scheme. The power output scheme for the interval is a [2,4]-day chemical load operation plan. The power output plan for the first day (day 2) is extracted. Combined with the power balance strategy, the operating mode of each unit, load, energy storage, and other equipment in the power grid on that day is determined. Then, the renewable energy absorption and renewable energy curtailment are calculated to characterize the absorption contribution of the chemical load output scheme.

[0092] The system statistically analyzes the daily consumption indicators within the production cycle, calculating the total renewable energy generation, total renewable energy curtailment, total renewable energy absorption, and total renewable energy restriction, serving as the cycle-specific consumption indicators for the entire production cycle. Further calculations of the curtailment rate, utilization rate, and restriction rate for the production cycle can be performed to assess the feasibility of the production plan. This process transforms interval optimization into daily executable plans, avoiding rigidity caused by excessively long interval periods and improving the accuracy and flexibility of the plans.

[0093] Specifically, after calculating the first absorption index, the process also includes: using the grid operation mode of the first day within the current optimization interval as the initial condition for the next optimization interval, and alternately optimizing the unit output and / or chemical load output. The grid operation mode of the first day (day 1) of the current optimization interval (e.g., [1,3] days) is transferred to the next optimization interval [2,4] days as its initial condition for optimization. Based on the initial condition, the unit output and chemical load output are alternately optimized (e.g., optimizing unit output on day 1, optimizing chemical load output on day 2, and optimizing unit output again on day 3, cycling through the entire production cycle). The transfer of initial conditions ensures that the daily plan can connect with the previous day's state, avoiding plan "jumps". Unit adjustments have low flexibility and require advance planning, while chemical load adjustments have high flexibility and can be adjusted on the same day. Therefore, alternating optimization can take into account the characteristics of both. In addition, alternating optimization can quickly respond to fluctuations in new energy sources and improve the timeliness of absorption.

[0094] Specifically, the grid operation mode of the first day within the current optimization interval is used as the initial condition for the next optimization interval. Alternating optimization of unit output and / or chemical load output includes: determining the curtailment rate on the unit side and the curtailment rate on the chemical load side based on the grid operation mode of the first day within the current optimization interval, and giving the absorption contribution; determining the first dynamic threshold based on the energy storage charge status; determining the second dynamic threshold based on the chemical load status; analyzing the relationship between the absorption contribution and the first and second dynamic thresholds, and determining the optimization subject of the next optimization interval.

[0095] In a specific example, the first day's curtailment and power restriction are determined based on the grid operation mode on the first day. Then, combined with the renewable energy generation, the curtailment rate on the computer unit side, and the power restriction rate on the chemical load side, the consumption contribution is given. The consumption contribution is specifically expressed as follows:

[0096]

[0097] In the formula, R is the power consumption contribution, η1 is the power curtailment rate on the unit side, η2 is the power curtailment rate on the chemical load side, and є is a very small constant to prevent division by zero.

[0098] It is understandable that the curtailment rate on the generating unit side is the proportion of renewable energy that is forcibly abandoned due to insufficient regulating capacity of the generating units, while the curtailment rate on the chemical load side refers to the proportion of renewable energy that cannot be utilized due to insufficient absorption capacity of chemical loads. The absorption contribution rate is a relative indicator used to quantify the sources of bottlenecks in renewable energy absorption. When the absorption contribution rate is greater than 1, it indicates that the current obstruction to renewable energy absorption is mainly due to curtailment losses caused by limited regulating capacity on the supply side (generating unit side); when the absorption contribution rate is less than 1, it indicates that the current obstruction to renewable energy absorption is mainly due to curtailment losses caused by limited response capacity on the demand side (chemical load side); when the absorption contribution rate is equal to or close to 1, it indicates that both supply-side regulating capacity and demand-side absorption capacity are limited.

[0099] By quantifying the abstract state of hindered new energy consumption into a calculable ratio index through the contribution of new energy consumption, the bottlenecks on the supply side and the demand side can be identified and located, avoiding the mismatch of regulation caused by the misalignment between the optimization subject and the consumption bottleneck in the fixed-cycle alternating optimization.

[0100] The first dynamic threshold is used to determine whether to initiate unit-side optimization (reducing unit output to increase the capacity for renewable energy absorption). As a buffer link between the supply and demand sides, the state of charge (SOC) of energy storage units directly determines the system's renewable energy absorption margin without adjusting unit output. When the energy storage SOC is high, it indicates that the energy storage charging and regulation capacity is approaching saturation. If there is still curtailment, it is necessary to reduce unit output to achieve absorption. Conversely, when the energy storage SOC is low, the energy storage has sufficient charging and absorption capacity and can prioritize the function of smoothing renewable energy fluctuations, without the need for emergency unit adjustments. Therefore, the first dynamic threshold needs to be dynamically adjusted with the energy storage SOC to reflect the urgency of unit adjustments.

[0101] The first dynamic threshold is specifically expressed as:

[0102] ;

[0103] In the formula, α is the first dynamic threshold, α0 is the baseline threshold, which can be set according to actual conditions (such as 2.0, 1.5, etc.), and SOC is the current (i.e., after implementing the grid operation mode on the first day) energy storage state of charge (%). ref For reference state of charge, k can be set according to actual conditions (e.g., 50%). SOC This is the first adjustment coefficient, used to control the adjustment range of SOC on the first dynamic threshold. It can be set according to actual conditions (e.g., 0.2). The lower the SOC of the energy storage, the more sufficient the energy storage has to absorb energy. The higher the first dynamic threshold, the less likely it is to trigger unit output optimization.

[0104] The second dynamic threshold is used to determine whether to initiate demand-side optimization (increasing chemical load output to enhance renewable energy absorption). The adjustability margin of the chemical load directly determines the effectiveness of the demand-side response. When the current output of the chemical load has a large margin from its maximum technical output, it indicates that the chemical load has the potential for rapid increase. In this case, if there are power curtailment losses, renewable energy can be absorbed by optimizing the chemical load output. Conversely, when the chemical load is close to its maximum output (small adjustability margin), even if there are power curtailments, it is difficult to solve the problem through chemical load optimization. Therefore, the second dynamic threshold reflects the actual adjustability of the chemical load.

[0105] The second dynamic threshold is specifically expressed as follows:

[0106]

[0107]

[0108] In the formula, The first dynamic threshold, This is the baseline threshold, which can be set according to the actual situation (such as 0.5, 0.67, etc.). This represents the current adjustable margin of the chemical load (i.e., after implementing the power grid operation mode on the first day), indicating how much adjustment space the chemical load currently has. To achieve maximum output for chemical load, Minimum output for chemical load, For the current output of the chemical load (i.e., after the first day of grid operation), This is the second adjustment coefficient, used to control the adjustment range of the adjustable margin on the second dynamic threshold. It can be set according to actual conditions (e.g., 0.3). The larger the chemical load adjustable margin flex is, the stronger the chemical load adjustment capability, the higher the second dynamic threshold, and the easier it is to trigger chemical load output optimization.

[0109] It is understandable that the optimization subject is either the unit output or the chemical load output. By analyzing the relationship between the absorption contribution and the first and second dynamic thresholds, the optimization subject of the next optimization interval is determined: if R > α, the unit side is determined to be the current main bottleneck, and the unit output is optimized in the next optimization interval (the first optimization objective is executed, and the new energy absorption capacity is improved by adjusting the unit output); if R < β, the load side is determined to be the current main bottleneck, and the chemical load output is optimized in the next optimization interval (the second optimization objective is executed, and the new energy absorption capacity is improved by adjusting the chemical load); if β ≤ R ≤ α, the bottlenecks on both sides are determined to be balanced, and the original alternation order is maintained (that is, optimization is carried out in turn according to the preset cycle, for example, if the unit was optimized in the previous interval, the load is optimized in this interval, and vice versa).

[0110] Based on the comparison results of absorption contribution and dynamic threshold, the optimization subject of the next optimization interval is determined, so that the switching decision between unit output optimization and chemical load output optimization is changed from time-triggered to state-triggered, which significantly improves the responsiveness of rolling optimization to the randomness of new energy output.

[0111] like Figure 4 As shown, the power balancing strategy specifically includes: obtaining the preset minimum and maximum operating loads of the daily renewable energy generation and chemical loads; comparing and analyzing the daily renewable energy generation and the preset maximum operating loads to determine the first operating mode; based on the first operating mode, setting the chemical load to the preset maximum operating load, providing a reserve of renewable energy generation power, and storing electricity according to the storage order; analyzing the renewable energy generation power reserve and electricity storage to provide the renewable energy generation curtailment; comparing and analyzing the daily renewable energy generation and the preset minimum and maximum operating loads to determine the second operating mode; based on the second operating mode, jointly supplying power to renewable energy generation and energy storage units; comparing and analyzing the daily renewable energy generation and the preset minimum operating loads to determine the third operating mode; based on the third operating mode, prioritizing the supply of power to renewable energy generation and energy storage units, and jointly supplying power to the grid.

[0112] like Figure 5 As shown, the joint power supply of energy storage units containing new energy power generation includes the following steps: Based on the analysis of the energy release of new energy power generation and energy storage units and the preset maximum operating load, the first scenario and the second scenario of the second operating mode are determined; combined with the first scenario of the second operating mode, the chemical load is set to operate at the preset maximum operating load, and the reserve of the energy storage unit is given for supplementary power supply; combined with the second scenario of the second operating mode, the chemical load is set to operate at the preset minimum operating load, and the reserve of new energy power generation is stored as electricity.

[0113] like Figure 6 As shown, this invention designs three operating modes by coordinating the output of new energy sources, the output of chemical loads, energy storage operation, and external power grid connection. The energy storage unit includes a thermal storage device, a hydrogen storage device, and an electric storage device. Specifically, the preset maximum and minimum operating loads Pmax and Pmin of the chemical load are first determined; based on the daily production scenario, the sum of the total output of wind power and photovoltaic power on that day is obtained as the new energy power generation Pre. If the new energy power generation exceeds the maximum operating load of the chemical load, it indicates a large wind and solar power generation, corresponding to the first operating mode, where the chemical load is set to the preset maximum operating load to ensure full-load operation of chemical production and maximize the absorption of new energy. The surplus new energy power generation ΔP = Pre - Pmax is calculated, and the excess power is stored in the order of thermal storage (e.g., heating molten salt), hydrogen storage (e.g., electrolysis of water to produce hydrogen), and electric storage (battery energy storage). If the total capacity for thermal, hydrogen, and electricity storage is exceeded, the excess will not be returned and will be discarded. The amount of electricity discarded will be calculated based on the surplus generated and the stored electricity.

[0114] If the renewable energy power generation capacity is between the preset minimum and maximum operating load of the chemical plant, corresponding to the second operating mode, renewable energy power generation and energy storage units are used for combined power supply. Renewable energy power is directly supplied to the chemical plant load, and any shortfall is released by energy storage. If renewable energy + energy storage release can meet the maximum operating load of the chemical plant, corresponding to the first scenario, the chemical plant load operates at the maximum operating load, renewable energy power is directly supplied to the chemical plant load, and any shortfall (surplus) is released by energy storage. If renewable energy + energy storage release cannot meet the maximum operating load of the chemical plant, corresponding to the second scenario, the chemical plant load is set to the preset minimum operating load, the renewable energy power generation surplus ΔP=Pre-Pmin is calculated, and the surplus is stored in the order of thermal storage, hydrogen storage, and electricity storage for future backup when wind and solar power are insufficient.

[0115] If the renewable energy generation capacity is lower than the minimum operating load preset for the chemical industry, indicating insufficient wind and solar power, the third operating mode is adopted, prioritizing renewable energy and energy storage release, supplemented by grid power: renewable energy power is directly supplied to the chemical load, and the insufficient portion is supplemented by energy storage units in the order of thermal storage, hydrogen storage, and electricity storage; if renewable energy and energy storage release still cannot meet the minimum operating load of the chemical industry, grid power is used to supplement, ensuring uninterrupted chemical production; if renewable energy and energy storage release can meet the minimum operating load of the chemical industry, the chemical industry operates at the minimum operating load, avoiding dependence on grid power. Through a power balancing strategy covering multiple modes and scenarios, renewable energy consumption is maximized during periods of high wind and solar power generation; renewable energy consumption and energy storage are balanced during periods of moderate wind and solar power generation; and production continuity is ensured during periods of low wind and solar power generation. Prioritizing the use of flexible chemical loads, followed by energy storage, and finally grid power supplementation, conforms to the actual load characteristics of the chemical industrial park. The power balancing strategy also provides continuous assurance for rolling optimization and alternating optimization.

[0116] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0117] Those skilled in the art will understand that Figure 8 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer device to which the present invention is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. Furthermore, the present invention also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. Additionally, the present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0118] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0119] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A method for generating production plans for chemical industrial parks that takes into account the uncertainties of new energy sources, characterized in that, The method includes: Set the production cycle and optimization range corresponding to the production plan of the chemical industrial park; Acquire wind power output scenarios, photovoltaic power output scenarios, load scenarios and attribute data that are suitable for the production cycle, and combine each scenario and attribute data to generate a chemical industrial park production scenario covering the entire production cycle; With the optimization objectives of maximizing the acceptance and absorption capacity of new energy sources, the power output scheme within the current optimization range is determined based on the production scenario of the chemical industrial park. The power output scheme is then combined with the power grid operation mode determined through the power balance strategy to calculate the absorption index within the current optimization range. The current optimization interval is continuously updated until the optimization of the entire production cycle is completed, and the consumption index within each optimization interval is obtained. By integrating and analyzing the absorption indicators within each optimization interval, the periodic absorption indicators are obtained. The process of generating production scenarios and periodic consumption indicators is repeated until the convergence criterion is met. The output schemes in each optimization interval and the power grid operation mode determined by the power balance strategy are summarized to obtain the production scheme of the chemical industrial park within the production cycle.

2. The method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources, as described in claim 1, is characterized in that... The process of acquiring wind power output scenarios, photovoltaic power output scenarios, load scenarios, and attribute data adapted to the production cycle, and combining each scenario and attribute data to generate a chemical industrial park production scenario covering the entire production cycle includes: Based on the historical wind power output data obtained in advance, the probability distribution of various wind power output fluctuations and the transition probability matrix between various wind power output fluctuations are analyzed and combined to generate wind power output scenarios that are adapted to the production cycle. Based on the historical output data of photovoltaic power generation obtained in advance, the probability distribution of various types of weather and the transition probability matrix between various types of weather are analyzed and combined to generate photovoltaic output scenarios that are suitable for the production cycle. By analyzing pre-acquired historical load data, load scenarios adapted to the production cycle are generated; By combining wind power output scenarios, photovoltaic power output scenarios, load scenarios, and pre-acquired attribute data, a chemical industrial park production scenario covering the entire production cycle is generated.

3. The method for generating a chemical industrial park production plan taking into account the uncertainty of new energy sources according to claim 2, characterized in that, The attribute data includes unit data, chemical load data, and energy storage data; The unit includes a heating unit; The unit data includes the unit's installed capacity, ramp rate, minimum start-up and shutdown time, maximum technical output, minimum technical output, and heating parameters and heating load of the heating unit; The chemical load data includes the maximum and minimum output of the chemical load. The energy storage data includes the capacity, power, efficiency, and charge / discharge constraints of each thermal storage, hydrogen storage, and electrical storage device.

4. The method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources, as described in claim 2, is characterized in that... The process of generating wind power output scenarios adapted to the production cycle by analyzing and combining the probability distribution of various wind power output fluctuations and the transition probability matrix between various wind power output fluctuations, based on pre-acquired historical wind power output data, includes: Obtain historical wind power output data for the corresponding production cycle, and classify the fluctuation categories according to the fluctuation amplitude and duration of wind power output. The fluctuation categories include large fluctuation, medium fluctuation, small fluctuation and low output fluctuation. Wind power output features are extracted from historical wind power output data and input into the SOM neural network for clustering to obtain monthly categories and determine the wind power fluctuation sequence for each category of month. By using Markov chains to analyze the wind power fluctuation sequence for each type of month, the transition probability matrix between different types of fluctuations is obtained, and the probability distribution of each type of fluctuation in each type of month is determined. Sequential sampling is performed based on the probability distribution of various fluctuations in each month and the transition probability matrix between various fluctuations to obtain wind power output scenarios that are suitable for the production cycle.

5. The method for generating a chemical industrial park production plan taking into account the uncertainty of new energy sources according to claim 4, characterized in that, The method of using Markov chains to analyze the wind power fluctuation sequence for each type of month yields the transition probability matrix between different types of fluctuations, and determines the probability distribution of each type of fluctuation within each type of month, including: The number of occurrences and transitions of various fluctuations in each type of month is counted, the transition probability is calculated, and a transition probability matrix between various fluctuations in each type of month is generated based on the transition probability. By statistically analyzing the Gaussian fitting function parameters and peak interval durations of wind power fluctuation segments categorized into large, medium, and small fluctuations, a joint probability distribution is obtained. By statistically analyzing the frequency distribution of the duration of wind power fluctuation segments in the low-output fluctuation category, a one-dimensional probability distribution is obtained.

6. The method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources, as described in claim 5, is characterized in that... The sequential sampling based on the probability distribution of various fluctuations in each month and the transition probability matrix between various fluctuations yields wind power output scenarios adapted to the production cycle, including: The first fluctuation category is determined by random sampling based on the proportion of various fluctuations in the first month of the production cycle. Subsequent fluctuation categories are determined by sequential sampling based on the transition probability matrix between various fluctuations in each month category, combined with the month transition rules. For large, medium and small fluctuation categories, the Gaussian fitting function parameters and peak interval duration are sampled from the corresponding joint probability distribution to determine the output value at each point; For low-output fluctuation categories, the duration is sampled from the corresponding one-dimensional probability distribution, and random sampling is used to determine the output value at each point; According to the preset fluctuation connection rules, the output sequences of various fluctuations are connected sequentially to obtain the standard output sequence for each month; Based on the fluctuation category of each point in the standard output sequence for each month, random samples are randomly drawn from the probability distribution of the corresponding random output to obtain the random output sequence for each month. The random power output sequences for each month are superimposed onto the standard power output sequence to obtain the wind power output sequence for each month; By sequentially connecting the wind power output sequences of each month, a wind power output scenario adapted to the production cycle can be obtained.

7. The method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources, as described in claim 6, is characterized in that... The fluctuation connection rules include: If two non-low-output fluctuations are adjacent, the output value of each point in the rising segment on the left side of the latter fluctuation is compared with the output value of each point in the falling segment on the right side of the former fluctuation, and the larger output value is taken as the output of that point. If adjacent low-output fluctuations have been merged, and there is a low-output fluctuation between two non-low-output fluctuations, then the duration of the low-output fluctuation is equally distributed to the two non-low-output fluctuations, and the output value of each point on the right side of the previous fluctuation and the output value of each point on the left side of the subsequent fluctuation are calculated according to their respective Gaussian fitting functions. For the first fluctuation, if it is not a low-output fluctuation, the output value of each point on the left rising segment of the fluctuation is calculated only based on its Gaussian fitting function. If it is a low-output fluctuation, the final output of each point inside is determined by the random output obtained by sampling.

8. The method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources, as described in claim 2, is characterized in that... The process of generating photovoltaic output scenarios adapted to the production cycle by analyzing and combining the probability distribution of various weather conditions and the transition probability matrix between different weather conditions, based on pre-acquired historical photovoltaic power output data, includes: Obtain historical photovoltaic power generation output data for the corresponding production cycle; Historical photovoltaic power generation output data is input into a preset airspace model, and the relative output sequence for each day is calculated using the airspace model. The relative output includes a power reference value and a fluctuation coefficient. Photovoltaic power output features are extracted from the daily relative power output sequence to form a power output feature vector, which is then input into the SOM neural network for clustering to obtain the weather type and determine the relative power output sequence under each type of weather. By using Gaussian mixture distribution and Markov chain analysis, the relative output sequence under various weather conditions is obtained, and the probability distribution of various weather conditions and the transition probability matrix between various weather conditions are obtained. Sequential sampling is performed based on the probability distribution of various weather types and the transition probability matrix between various weather types to obtain the photovoltaic relative output sequence; By using the headroom model to reconstruct the relative output sequence of photovoltaic power, a photovoltaic output scenario that is adapted to the production cycle can be obtained.

9. The method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources, as described in claim 8, is characterized in that... The sequential sampling based on the probability distribution of various weather types and the transition probability matrix between different weather types yields the following photovoltaic relative output sequence: Based on the transition probability matrix between various weather types, the weather type for each day is determined, and a weather type sequence list is formed. The power benchmark value and fluctuation coefficient are sampled from the one-dimensional probability distribution and mixed probability distribution corresponding to each type of weather in the weather type sequence table to determine the output value at each point and generate the photovoltaic relative output sequence for each day. By sequentially connecting the daily photovoltaic relative output sequences, a photovoltaic relative output sequence is obtained.

10. The method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources, as described in claim 1, is characterized in that... The optimization objective is to maximize both the acceptance and absorption capacity of new energy sources. Based on the production scenario of the chemical industrial park, the power output scheme within the current optimization range is determined. This power output scheme is then combined with the power grid operation mode determined through a power balance strategy to calculate the absorption index within the current optimization range, including: Maximizing the acceptance of new energy sources is taken as the primary optimization objective. Combined with constraints including unit ramp rate, minimum start-up and shutdown time, maximum / minimum technical output, and power balance, the unit output scheme for the interval is obtained. Maximizing the absorption capacity of new energy sources is taken as the second optimization objective. Combined with constraints including chemical production process constraints, minimum / maximum start-up load, and adjustment rate, the power output scheme of the chemical load in the interval is obtained. The first-day unit output plan is extracted from the unit output plan of the interval, and the first-day grid operation mode in the current optimized interval is determined by the power balance strategy to calculate the first absorption index. The power grid operation mode on the first day within the current optimization interval is used as the initial condition for the next optimization interval, and the unit output and / or chemical load output are alternately optimized. The first day's chemical load output plan is extracted from the interval chemical load output plan, and the second absorption index is calculated by combining it with the first day's power grid operation mode in the current optimized interval determined by the power balance strategy.

11. The method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources, as described in claim 10, is characterized in that... The step of using the grid operation mode of the first day within the current optimization interval as the initial condition for the next optimization interval, and alternately optimizing the unit output and / or chemical load output, includes: Based on the grid operation mode on the first day within the current optimization interval, the curtailment rate on the generating unit side and the curtailment rate on the chemical load side are determined, and the consumption contribution is calculated by combining the curtailment rate on the generating unit side and the curtailment rate on the chemical load side. Calculate the first dynamic threshold based on the energy storage state of charge; Calculate the second dynamic threshold based on the chemical load status; Based on the comparison results of the absorption contribution with the first dynamic threshold and the second dynamic threshold, the optimization subject of the next optimization interval is determined, so that the switching decision between unit output optimization and chemical load output optimization is changed from time-triggered to state-triggered.

12. The method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources, as described in claim 10, is characterized in that... The power balancing strategy includes: Obtain the preset minimum operating load and preset maximum operating load of the new energy power generation and chemical load for the day; By comparing and analyzing the daily renewable energy power generation and the preset maximum operating load, the first operating mode is determined; Based on the first operating mode, the chemical load is set to the preset maximum start-up load, the reserve of new energy power generation is calculated, and the power is stored according to the storage order; By analyzing the surplus power and energy storage of new energy generation, the amount of abandoned new energy generation is obtained; By comparing and analyzing the daily renewable energy power generation capacity with the preset minimum operating load and the preset maximum operating load, the second operating mode is determined. Based on the second operating mode, the power supply is jointly provided by new energy power generation and energy storage units; By comparing and analyzing the daily renewable energy power generation and the preset minimum operating load, the third operating mode is determined. Based on the third operating mode, priority is given to power supply from new energy power generation and energy storage units, and combined power supply is provided by grid power supplementation.

13. The method for generating a production plan for a chemical industrial park that takes into account the uncertainty of new energy sources, as described in claim 12, is characterized in that... The combined power supply based on the second operating mode, involving new energy power generation and energy storage units, includes: Analyze the energy release of the new energy power generation and energy storage units and the preset maximum start-up load to determine the first and second scenarios of the second operating mode; In conjunction with the first scenario of the second operating mode, the chemical load is set to operate at the preset maximum start-up load, and the surplus of the energy storage unit is used to supplement power supply; In conjunction with the second scenario of the second operating mode, the chemical load is set to operate at the preset minimum operating load, and the surplus of new energy power generation is stored as electricity.

14. A production plan generation system for chemical industrial parks that takes into account the uncertainties of new energy sources, characterized in that, The system includes: The parameter configuration module is used to set the production cycle and optimization range corresponding to the production plan of the chemical industrial park; The scenario determination module is used to acquire wind power output scenarios, photovoltaic power output scenarios, load scenarios and attribute data that are suitable for the production cycle, and combine each scenario and attribute data to generate a chemical industrial park production scenario covering the entire production cycle. The scheme generation module is used to determine the power output scheme within the current optimization range based on the production scenario of the chemical industrial park, with the optimization objectives of maximizing the acceptance and absorption capacity of new energy sources. It combines the power output scheme with the power grid operation mode determined by the power balance strategy to calculate the absorption index within the current optimization range. The current optimization interval is continuously updated until the optimization of the entire production cycle is completed, and the consumption index within each optimization interval is obtained. By integrating and analyzing the absorption indicators within each optimization interval, the periodic absorption indicators are obtained. The process of generating production scenarios and periodic consumption indicators is repeated until the convergence criterion is met. The output schemes in each optimization interval and the power grid operation mode determined by the power balance strategy are summarized to obtain the production scheme of the chemical industrial park within the production cycle.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 13.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 13.

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