Intelligent energy consumption regulation and control steel smelting system based on multi-energy complementation

Abstract

The embodiment of the invention discloses an intelligent energy consumption regulation and control steel smelting system based on multi-energy complementation, and the system comprises a multi-energy complementation energy supply system which is used for integrating various energy forms, forming a dynamic cooperative energy supply network, and carrying out the energy cooperative operation according to an energy priority strategy and a dynamic matching mechanism; the intelligent regulation and control platform adopts a three-layer framework of'perception-decision-execution 'to coordinate energy flow and process operation between the multi-energy complementary energy supply system and the multi-energy coupling process equipment; the multi-energy coupling process equipment comprises hydrogen-based direct reduction ironmaking equipment, and hydrogen is used for replacing traditional coke to serve as a reducing agent for ironmaking; comprehensive energy supply monitoring and optimization can be carried out on the steel smelting process, various renewable energy sources are combined, and the problems that a single renewable energy source is intermittent, depends on a power grid and the like are effectively solved; meanwhile, the multi-energy complementary hydrogen storage process is coupled with the hydrogen metallurgy process, and the multi-energy cooperation problems of whole-process green power supply, waste heat utilization and the like are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of steel production, and in particular to a steel smelting system with intelligent energy consumption control based on multi-energy complementarity. Background Art

[0002] Steel is a vital foundational material for modern industry, widely used in infrastructure, manufacturing, construction, transportation, and other fields. However, its production process consumes significant amounts of energy and produces high carbon emissions per unit of product. The steel industry is a typical major energy consumer and carbon emitter in my country.

[0003] The traditional steelmaking energy supply system, centered around pulverized coal and purchased electricity, employs a single energy input and distributed power supply model, relying primarily on fossil fuel combustion and grid power to meet energy needs throughout the entire smelting process. Its core goal is to ensure continuous production of processes such as blast furnaces, converters, and steel rolling mills through low-cost energy. However, this system suffers from high carbon emissions, low energy efficiency, and limited flexibility. Pulverized coal primarily provides heat and acts as a reducing agent for high-temperature processes such as blast furnace ironmaking, coking, and sintering. Structural electricity primarily powers equipment such as electric arc furnace steelmaking, rolling mills, fans, and pumps.

[0004] Currently, steelmaking relies primarily on fossil fuels such as pulverized coal and purchased electricity. These energy sources are not only costly but also generate significant environmental damage through pollutant emissions. Furthermore, traditional energy supply systems lack flexibility and are unable to adapt to the volatility of renewable energy, resulting in low energy efficiency and insufficient energy supply stability.

[0005] Therefore, a new steel smelting technology is urgently needed to further improve energy utilization efficiency, reduce environmental pollution, and enhance the flexibility and stability of the energy supply system. Summary of the Invention

[0006] The main purpose of the present invention is to provide a steel smelting system with intelligent energy consumption control based on multi-energy complementarity, which can realize green steel smelting, continuously optimize control strategies, more reasonably perform energy supply control, and further improve smelting efficiency and stability.

[0007] To achieve the above objectives, the present application provides a steelmaking system for intelligently controlling energy consumption based on multi-energy complementarity, comprising:

[0008] Multi-energy complementary energy supply system, used to integrate multiple energy forms to form a dynamic collaborative energy supply network, and perform energy collaborative operation according to energy priority strategy and dynamic matching mechanism;

[0009] An intelligent control platform, which adopts a three-layer "perception-decision-execution" architecture to coordinate energy flow and process operations between the multi-energy complementary energy supply system and the multi-energy coupling process equipment;

[0010] Multi-energy coupling process equipment, including hydrogen-based direct reduction ironmaking equipment, uses hydrogen instead of traditional coke as a reducing agent for ironmaking.

[0011] The present application provides an intelligent energy consumption control steel smelting system based on multi-energy complementarity, including: a multi-energy complementary energy supply system, which is used to integrate multiple energy forms to form a dynamic collaborative energy supply network, and perform energy collaborative operation according to energy priority strategy and dynamic matching mechanism; an intelligent control platform, which adopts a "perception-decision-execution" three-layer architecture to coordinate the energy flow and process operation between the multi-energy complementary energy supply system and the multi-energy coupling process equipment; multi-energy coupling process equipment, including hydrogen-based direct reduction ironmaking equipment, which uses hydrogen instead of traditional coke as a reducing agent for ironmaking; it can comprehensively monitor and optimize the energy supply of the steel smelting process, combine multiple renewable energy sources, and effectively solve the problems of intermittent and dependence on the power grid for single renewable energy (such as photovoltaic or wind power); at the same time, the multi-energy complementary hydrogen storage process is coupled with the hydrogen metallurgical process to solve multi-energy coordination problems such as green electricity supply and waste heat utilization in the whole process. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0013] in:

[0014] Figure 1 A schematic diagram of the structure of a steelmaking system for intelligently controlling energy consumption based on multi-energy complementarity provided in an embodiment of the present application;

[0015] Figure 2 A schematic diagram of a multi-energy complementary dynamic collaborative energy supply network provided in an embodiment of the present application;

[0016] Figure 3 A flow chart of a method for dynamically adjusting active power and reactive power on the power side provided in an embodiment of the present application;

[0017] Figure 4 A schematic diagram of the control process of an intelligent control platform provided in an embodiment of the present application;

[0018] Figure 5 A schematic diagram of a method for calculating theoretical power of a wind turbine provided in an embodiment of the present application;

[0019] Figure 6A schematic diagram of a wind power curve provided in an embodiment of the present application;

[0020] Figure 7 A schematic diagram of a comprehensive evaluation model for a green power system provided in an embodiment of the present application;

[0021] Figure 8 A schematic diagram of a risk assessment process provided in an embodiment of the present application;

[0022] Figure 9 A schematic diagram of a hydrogen-based direct reduction ironmaking process based on multi-energy complementarity provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0024] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0025] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0026] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0027] See also Figure 1 , is a structural diagram of a steel smelting system based on intelligent energy consumption control and multi-energy complementarity provided in an embodiment of the present application, such as Figure 1 As shown, the energy consumption intelligent control steel smelting system 100 based on multi-energy complementarity includes:

[0028] The multi-energy complementary energy supply system 110 is used to integrate multiple energy forms to form a dynamic collaborative energy supply network, and to perform energy collaborative operation according to energy priority strategies and dynamic matching mechanisms;

[0029] Intelligent control platform 120, which adopts a three-layer "perception-decision-execution" architecture to coordinate energy flow and process operations between the multi-energy complementary energy supply system and the multi-energy coupling process equipment;

[0030] The multi-energy coupling process equipment 130 includes hydrogen-based direct reduction ironmaking equipment, which uses hydrogen instead of traditional coke as a reducing agent for ironmaking.

[0031] The embodiment of the present application proposes a green steelmaking technology with intelligent energy consumption control based on multi-energy complementarity, wherein the steelmaking system with intelligent energy consumption control based on multi-energy complementarity integrates a multi-energy complementary energy supply system, an intelligent control platform and multi-energy coupling process equipment to conduct comprehensive energy supply monitoring and optimization of the steelmaking process.

[0032] First, the multi-energy complementary energy supply system 110 is introduced.

[0033] The multi-energy complementary energy supply system 110 in the embodiment of the present application integrates multiple energy forms to form a dynamic collaborative energy supply network. In an optional embodiment, the multiple energy forms include but are not limited to green electricity, hydrogen energy, energy storage, electrochemical energy storage, thermal storage, and purchased electricity. The green electricity (green electricity) includes wind power and photovoltaic power, and the energy storage includes electrochemical energy storage and thermal storage systems.

[0034] See Figure 2 , Figure 2 A schematic diagram of a multi-energy complementary dynamic collaborative energy supply network provided in an embodiment of the present application.

[0035] like Figure 2 As shown, green electricity is configured based on the steel plant's load characteristics, using rooftop photovoltaics and on-site wind power to cover the daytime base load. It is connected to the plant's microgrid via a bidirectional converter, supporting on-grid and off-grid switching. Hydrogen energy is produced using surplus green electricity or off-peak electricity. The hydrogen production unit uses a PEM or alkaline electrolyzer, with high-pressure hydrogen storage tanks (35-70 MPa) and liquid hydrogen storage tanks (-253°C) storing hydrogen, with a capacity to meet 3-7 days of hydrogen demand. Hydrogen fuel cells are used for power generation (backup power) and hydrogen direct reduction ironmaking (instead of coke). The energy storage system includes electrochemical and thermal storage systems. The electrochemical storage primarily relies on lithium-ion batteries (response time ≤ 100ms, cycle efficiency ≥ 95%) and flow batteries (long-term energy storage, 4-8 hours). The thermal storage system uses molten salt thermal storage tanks (operating temperature 300-565°C) to store high-temperature waste heat for nighttime power supply.

[0036] In an optional embodiment, the energy priority strategy includes:

[0037] Prioritize the use of green electricity generated in real time;

[0038] When there is a surplus of green electricity, the energy storage system is used to store the excess energy, or when there is a shortage of green electricity, the energy is released to balance supply and demand;

[0039] In the case of excess green electricity, the excess electricity can be used to produce hydrogen or charge batteries;

[0040] The battery system is used to smooth out short-term fluctuations in power supply to ensure the stability of power supply;

[0041] Use the above-mentioned thermal or hydrogen storage systems to meet energy needs during extended periods when electricity demand exceeds supply;

[0042] In special circumstances or emergencies when all other means of energy supply cannot meet demand, the gap will be filled by purchasing electricity as mentioned above.

[0043] In an optional implementation, the dynamic matching mechanism includes:

[0044] During high-load periods, the green electricity mentioned above and the battery energy storage system are used to provide power.

[0045] If the power supply capacity of the aforementioned green electricity and the aforementioned battery energy storage system cannot meet demand, hydrogen fuel cells and the aforementioned purchased electricity will be used to make up the remaining power gap;

[0046] Utilize wind power and thermal storage systems to supply power during low-load periods;

[0047] If the electricity generated by the above-mentioned wind power and the above-mentioned heat storage system exceeds the current demand, the surplus electricity will be used to produce hydrogen, and the hydrogen will be stored.

[0048] Specifically, the multi-energy complementary energy supply system 110 is composed of an energy priority strategy and a dynamic matching mechanism to form an energy collaborative operation logic. The priority strategy is to prioritize green electricity, adjust energy storage and purchased electricity, fully absorb the real-time output of photovoltaic / wind power, use surplus electricity to produce hydrogen or charge energy storage, use batteries to smooth short-term fluctuations in seconds, and use heat / hydrogen storage to cope with long-term gaps in hours. In special circumstances, purchases are used to fill the gap. A dynamic matching mechanism is established to use green electricity + battery power supply during high-load periods such as arc furnace startup, and the power gap is supplemented by hydrogen fuel cells and purchased electricity. Wind power + heat storage is used for energy supply during low-load periods, and surplus electricity is used to produce hydrogen for storage.

[0049] In an optional embodiment, the above-mentioned multi-energy complementary energy supply system 110 has the function of dynamic regulation of active power and reactive power on the power generation side, wherein a hierarchical control architecture and power distribution strategy are used for active power regulation, and a distributed reactive power compensation method and a multi-energy collaborative voltage regulation method are used for reactive power regulation.

[0050] Further optionally, the hierarchical control architecture is configured to automatically respond to frequency deviations based on droop control to perform primary frequency modulation, and the central controller dynamically adjusts to eliminate steady-state errors to perform secondary frequency modulation;

[0051] The above power allocation strategy uses a low-pass filtering algorithm to use battery energy storage to smooth and compensate for wind power / photovoltaic fluctuations.

[0052] See Figure 3 , Figure 3 A flow chart of a method for dynamically adjusting active power and reactive power on the power side provided in an embodiment of the present application.

[0053] like Figure 3 As shown, the following method is used to balance the active power between the generation and load sides in real time and ensure frequency stability (50±0.2Hz). A hierarchical control architecture is used, where the energy storage system (battery, hydrogen fuel cell) automatically responds to frequency deviations through primary frequency regulation using droop control. A central controller dynamically adjusts to eliminate steady-state errors for secondary frequency regulation. The power allocation strategy uses a low-pass filtering algorithm to allow battery energy storage to smooth and compensate for wind / photovoltaic power fluctuations. Based on electricity price signals and load forecasts, the energy storage system charges during off-peak hours and discharges during peak hours. Hydrogen-grid collaboration is used to rapidly reduce / increase load when grid frequency regulation is required, allowing participation in the ancillary services market. To maintain the voltage stability of the factory microgrid (±5% of rated voltage) and reduce network losses, a distributed reactive power compensation method is used to deploy SVG (static VAR generator) at the outlets of photovoltaic inverters and wind power converters to adjust reactive power in real time (response time ≤ 10ms). STATCOM (static synchronous compensator) is used for centralized compensation at the main grid connection point to support the system voltage. Multi-energy coordinated voltage regulation is adopted, and the hydrogen fuel cell inverter and energy storage converter have reactive power output capability (power factor adjustable from -0.9 to +0.9). Based on sensitivity analysis optimization, the reactive output of each node is dynamically allocated according to the voltage deviation. The neural network predicts the reactive power demand in the future time period (for example, 15 minutes) and adjusts the reactive power setting value of the energy storage and hydrogen energy equipment in advance.

[0054] The intelligent control platform 120 is introduced below.

[0055] In an optional embodiment, the intelligent control platform 120 includes:

[0056] The data perception layer includes three units: data acquisition, data transmission, and data preprocessing, which are used to collect and preprocess energy supply and demand data;

[0057] Optimize the decision-making layer, including multi-objective dynamic optimization models, machine learning modules, and risk assessment modules;

[0058] The control execution layer is used to generate and update the scheduling plan in real time based on the results of the above optimization decision layer.

[0059] In the embodiment of the present application, the intelligent control platform 120 can adopt a three-layer architecture of "perception-decision-execution" to achieve dynamic matching of energy supply and demand and optimization of process parameters.

[0060] Figure 4 This is a schematic diagram of the control process of an intelligent control platform provided in an embodiment of the present application. It mainly includes historical data analysis, real-time control, alarm service, data collection, and stability analysis. The three-tier architecture is described in detail as follows:

[0061] 1. The data perception layer primarily consists of three systems: data acquisition, data transmission, and data preprocessing. Data acquisition is achieved through the deployment of monitoring devices at multiple locations. Smart meters and small weather stations are deployed in green power systems to monitor power generation, irradiance, and wind speed. BMS (battery management systems) and pressure gauges are deployed in energy and hydrogen storage systems to monitor SOC (±1%) and hydrogen tank pressure (35-70 MPa). Power quality analyzers are deployed at grid access points to monitor voltage / frequency deviation and harmonic content.

[0062] Industrial Internet of Things (IIoT) edge computing nodes (such as the Huawei AR502H) are used to locally process high-frequency data (sampling rate ≥ 1kHz) to reduce transmission latency. Redundant sensors can be used to ensure reliability for critical process parameters. Real-time data is transmitted to a central server via 5G / Industrial Ethernet with a latency of ≤ 50ms. Non-real-time data (such as equipment health status) is synchronized daily via the OPC UA protocol.

[0063] Data preprocessing mainly includes outlier filtering and weighted fusion of multi-sensor data. The Gaussian weighted moving average filtering method is used to remove high-frequency random noise, and the time series data x(t) is windowed and weighted averaged to suppress high-frequency fluctuations. The specific formula is:

[0064]

[0065] Where n represents the half-width of the window (for example, n=5 corresponds to a window of 11 sampling points), w(k) represents the Gaussian weight coefficient, and σ represents the control attenuation speed.

[0066] Alternatively, wavelet threshold denoising can be used to remove non-stationary noise and pulse interference, such as transient spikes in current and voltage signals. The basic principle is that noise energy is distributed throughout the wavelet domain. After wavelet decomposition, the amplitude of the signal's wavelet coefficients must be greater than the amplitude of the noise coefficients. It can be assumed that wavelet coefficients with larger amplitudes are generally signal-dominated, while coefficients with smaller amplitudes are largely noise. Therefore, a threshold determination method is used to reduce most noise coefficients to zero. Coefficients below the threshold are treated as interfering noise and then wavelet reconstruction is performed to achieve noise reduction.

[0067] 2. The core algorithm framework of the optimization decision layer includes a multi-objective dynamic optimization model, a machine learning module, and a risk assessment module. The endpoint of the multi-objective dynamic optimization can be quantitatively described by establishing an objective function.

[0068] In an optional embodiment, the multi-objective dynamic optimization model is used to dynamically adjust the energy supply and demand balance by comprehensively considering energy costs, carbon emissions, and equipment losses;

[0069] The above energy costs are determined based on the total energy required by the current system, the energy supplied by the current wind power system, the energy supplied by the current photovoltaic system, the energy supplied by the energy storage system, and the price of purchased electricity in the current period.

[0070] Specifically, the objective function of the multi-objective dynamic optimization model can be expressed as follows:

[0071] min(αf(a)+βf(b)+γf(c))

[0072] The weight coefficients α:β:γ can be adjusted dynamically as needed, for example, α:β:γ = 0.5:0.3:0.2. f(a), f(b), and f(c) represent energy costs, carbon emissions, and equipment losses, respectively. Carbon emissions and equipment losses can be obtained from historical data.

[0073] The energy cost of this system comes from purchased electricity, so f(a) can be calculated as follows:

[0074] f(a)=(a 总 -a 风 -a 光 -a 储 )×r

[0075] where a 总 、a 风 、a 光 and a 储where represents the total energy required by the current system, the energy supplied by the wind power system, the energy supplied by the photovoltaic system, and the energy supplied by the energy storage system, respectively. r represents the price of purchased electricity during the current period. To calculate the green power output, the generated power during both normal and emergency periods must be considered. These are described in detail below.

[0076] Further, Figure 5 This is a schematic diagram of a method for calculating the theoretical power of a wind turbine provided in an embodiment of the present application. Figure 5 As shown, for the wind power system, that is, a 风 The calculation can be specifically:

[0077] A mapping relationship is established between the nacelle anemometer measurement data during normal wind turbine operation and the actual output power of the wind turbine. During abnormal wind turbine operation periods, such as when clean energy is limited or the wind turbine is shut down due to a fault, the expected output power of the wind turbine, i.e., the theoretical power, is calculated based on the nacelle anemometer measurement data and this mapping relationship. The theoretical power of the entire wind farm can be obtained by accumulating the theoretical power of the wind turbines.

[0078] Optionally, the modeling process of this method is as follows:

[0079] (1) Establish a nacelle wind speed power curve. Select the normal operation period of the wind turbine, extract the nacelle anemometer measurement data and the actual output power of the wind turbine during these periods, and establish a mapping relationship between the two, namely the power curve. The power curve diagram can be as follows: Figure 6 shown.

[0080] Based on wind speed and power data, a piecewise function is used to fit the mathematical expression of the power curve. This method can accurately describe the relationship between wind speed and power in the entire section without errors caused by interpolation or averaging. The power curve needs to be fitted in sections, where the wind speed ramp-up phase is fitted according to the wind energy conversion formula. The phase below the cut-in wind speed uses a value of 0, the phase above the rated wind speed and below the cut-out wind speed uses the rated wind speed, and the phase above the cut-out wind speed uses a value of 0, as shown below:

[0081]

[0082] where v in 、v rated 、v out and v s Represents cut-in wind speed, rated wind speed, cut-off wind speed and measured wind speed respectively. rated is the rated output power.

[0083] To ensure that the power curve is consistent with the actual situation and relatively smooth, the intersections of the first three power curves need to be connected using a smooth curve based on actual data.

[0084] The wind energy conversion formula, that is, the wind turbine output power and the wind speed in the wind direction of the wind turbine rotor satisfy:

[0085]

[0086] Where p represents the output power of the wind wheel, C p represents the wind energy utilization coefficient, ρ is the air density, R is the rotor size, and v represents the wind speed.

[0087] The wind speed measured by the nacelle anemometer is the wind speed downwind of the wind turbine rotor, so it needs to be corrected. The wind energy conversion formula is transformed into

[0088]

[0089] Where s represents the cabin wind speed correction factor, v s Represents the nacelle anemometer measuring wind speed.

[0090] (2) Calculate the theoretical power of the wind turbine during abnormal operation. During abnormal operation of the wind turbine, the expected output power of the wind turbine is calculated based on the nacelle anemometer measurement data and the obtained mapping relationship, which is also called the theoretical power of the wind turbine.

[0091] Due to the wind turbine wake effect, rotor rotation during normal operation has a certain impact on the nacelle anemometer's measurements, whereas this effect does not occur during turbine shutdown. Therefore, applying the mapping relationship established during normal operation to abnormal operation requires correction. Due to differences in nacelle anemometer installation location and nacelle shape, the correction parameters vary between wind turbine models and must be calculated based on measured data.

[0092] (3) Calculate the theoretical power of the wind farm during abnormal operation. If a wind turbine in the wind farm is abnormally operating at a certain moment, the theoretical power of all wind turbines in the wind farm is accumulated to obtain the theoretical power of the wind farm at that moment, that is,

[0093]

[0094] where p i It represents the theoretical power of the wind turbine at a certain moment, n represents the number of wind turbines in the wind farm, and P represents the theoretical power of the wind farm at that moment.

[0095] For photovoltaic systems, that is, a 光The calculation method can be based on meteorological monitoring data. The physical method is used to convert the measured horizontal irradiation intensity into the inclined irradiation intensity of the photovoltaic module, and the ambient temperature is converted into the panel surface temperature. Taking into account the location of the photovoltaic power station, the characteristics of different photovoltaic modules and the installation method, etc., a photoelectric conversion model of the photovoltaic cell is established. Combined with the autoregressive correction mathematical model of the photovoltaic power station, the theoretical power of the photovoltaic power station is obtained.

[0096] The theoretical power calculation of a photovoltaic power station based on physical methods mainly includes four steps: conversion of meteorological monitoring data, calculation of DC power output by photovoltaic modules, calculation of AC power at the photovoltaic power station grid connection point, and regular parameter calibration. The details are as follows:

[0097] (1) Conversion of meteorological monitoring data. According to the measured horizontal irradiance intensity and ambient temperature of the meteorological monitoring equipment during the calculation time, the horizontal irradiance intensity is converted into the effective irradiance intensity of the PV module slope. The calculation formula is:

[0098]

[0099] Among them G e , G dn , G dif and G t They represent the effective irradiance intensity of the PV module slope, the direct irradiance intensity measured by the meteorological monitoring equipment, the scattered irradiance intensity measured by the meteorological monitoring equipment, and the total irradiance intensity measured by the meteorological monitoring equipment, respectively. Z' is the inclination angle of the PV module, θ is the solar incidence angle, and ρ is the ground reflection coefficient.

[0100] The ambient temperature is converted into the surface temperature of the photovoltaic module. The calculation formula is:

[0101] T m =T a +KG e

[0102] Where T m Represents the surface temperature of the photovoltaic module, T a is the ambient temperature.

[0103] The K value can be corrected every year by collecting actual operating data and using the autoregressive method.

[0104] (2) Calculation of DC power output by photovoltaic modules. Based on the equipment parameters of photovoltaic modules under standard working conditions, calculate the optimal output current I of the modules under current meteorological conditions. MPP And the optimal output voltage U MPP ,Right now

[0105]

[0106] U MPP =Umref ln(e+bΔG)(1-cΔT)

[0107] Among them, G ref Indicates the standard solar radiation intensity, which is 1000W / m2. mref The optimal output current of photovoltaic modules under standard working conditions, U mref The optimal output voltage of a PV module under standard operating conditions, ΔG represents the difference between the actual irradiance and the standard irradiance, and ΔT represents the difference between the actual module temperature and the standard module temperature. a, b, and c are compensation coefficients, which can be fitted based on experimental PV module data and regularly revised based on measured data.

[0108] Calculate the DC output power P of the photovoltaic module as follows:

[0109] P dc =U MPP I MPP

[0110] (3) Calculation of AC power at the grid connection point of the photovoltaic power station. Taking into account the effective number of photovoltaic modules, aging of photovoltaic modules, mismatch loss of photovoltaic modules, dust blocking on the surface of photovoltaic modules, line transmission from photovoltaic panels to grid connection point and power loss of the station, inverter efficiency and other factors, the AC power P at the grid connection point of the photovoltaic power station is obtained as follows:

[0111] P ac =nP dc K1K2K3K4η inv

[0112] K1=1-ky a

[0113] Where n represents the effective number of photovoltaic modules in power generation operation, P ac is the AC output power of the PV module. K1, K2, K3, and K4 are dimensionless numbers, representing the PV module aging loss coefficient, which decreases at a certain rate each year, the PV module mismatch loss coefficient, the dust shielding loss coefficient, and the line transmission and station power loss coefficient, respectively. inv The grid-connected inverter efficiency is dimensionless and can be equivalent to the European standard EN50530. The actual operation data is collected every year and the autoregressive method is used to correct K1, K2, K3 and K4. a is the annual attenuation rate of different photovoltaic cell materials, based on the relevant attenuation rate parameters provided by photovoltaic cell manufacturers, and k is the number of years the grid-connected photovoltaic power station has been in operation.

[0114] (4) Regular parameter calibration: Based on the least squares linear regression method, the theoretical power calculation parameter values ​​can be calibrated annually using the historical data of the photovoltaic power station during the uncontrolled period.

[0115] The minimum value of f(a) obtained using the above method is the minimum energy cost under the current conditions. Furthermore, during system operation, the instability of clean energy power can affect system safety. Therefore, risk assessment is necessary while calculating the minimum energy cost.

[0116] Figure 7 This is a schematic diagram of a comprehensive evaluation model for a green power system provided in an embodiment of the present application. Figure 6 As shown in the figure, a comprehensive model for evaluating green power systems (such as wind power and photovoltaic systems) is presented. The model aims to optimize energy supply through a dynamic matching mechanism while performing risk assessment to ensure system stability and reliability. The overall risk assessment process may include:

[0117] Input Condition Assessment: The model first receives inputs regarding wind speed and temperature. These conditions are used to assess the potential output capacity of the green power system. By determining whether the wind speed is greater than the cut-in wind speed and the temperature is below a set threshold (e.g., 40°C), the model determines whether the wind turbine and photovoltaic panels are capable of operating normally.

[0118] Wind power generation assessment: If wind speed conditions are met, the model will evaluate the output of the wind power system. This includes using wind speed data to calculate the theoretical power output. Wind power generation assessment may involve the power curve of the wind turbine and consider the impact of factors such as wake effects on the actual output power.

[0119] Photovoltaic power generation assessment: If temperature conditions are met, the model will evaluate the output of the photovoltaic system. This involves converting the measured irradiance and temperature into the effective irradiance and panel temperature of the photovoltaic modules. Photovoltaic power generation assessment involves building a photovoltaic cell photoelectric conversion model, taking into account factors such as the characteristics of the photovoltaic modules and their installation method.

[0120] Risk Assessment: For wind and photovoltaic systems, the model performs risk assessments to identify and quantify potential operational risks. Risk assessments may include evaluations of extreme weather events, equipment failures, or other factors that could lead to unstable energy supply.

[0121] Comprehensive Assessment: The model combines wind and photovoltaic power generation assessment results with risk assessment results to provide a holistic energy supply assessment. This comprehensive assessment will guide energy scheduling decisions and ensure the stability and economic efficiency of energy supply.

[0122] Output optimal results: Based on comprehensive evaluation, the model outputs the optimal energy scheduling plan to maximize the utilization of green electricity while minimizing risks.

[0123] In an optional embodiment, the risk assessment module is specifically configured to:

[0124] Calculate the order matrix in advance based on the historical data of clean energy power station power;

[0125] When conducting risk assessment on the power system, Latin hypercube sampling is performed based on the power probability prediction results of the clean energy power station, and the samples of the Latin hypercube sampling are sorted using the above-mentioned order matrix to construct a clean energy power station power scenario that takes correlation into account;

[0126] In the extreme value scenarios extracted according to the above sequence matrix, risk assessment calculation is performed on the above extreme value scenarios. If no dangerous scenarios are found, the system is considered to have no operation risk, the assessment result is output and the system exits; if a dangerous scenario is found, the next step is executed;

[0127] Find clean energy power plant power scenarios adjacent to the above-mentioned dangerous scenarios and perform risk assessment calculations on those scenarios that have not been assessed. If a new dangerous scenario is found, repeat this step. If no new dangerous scenario is found, proceed to the next step.

[0128] The above-mentioned unevaluated clean energy power plant power scenarios are regarded as safe scenarios, and the risk assessment results are statistically analyzed and output.

[0129] Figure 8 A risk assessment process diagram provided in an embodiment of the present application is as follows: Figure 8 As shown, the specific process may include:

[0130] (1) Input the historical data of clean energy power station power and calculate the sequence matrix in advance.

[0131] (2) When conducting risk assessment on the power system, LHS sampling (Latin Hypercube sampling) is performed based on the input clean energy power plant power probability prediction results, and the LHS sampling samples are sorted using a pre-calculated sequence matrix to quickly construct a clean energy power plant power scenario that takes correlation into account.

[0132] (3) Extract extreme scenarios based on the sequential matrix: and perform risk assessment calculations on the extreme scenarios. If no dangerous scenarios are found, the system is considered to have no operational risk, outputs the assessment results and exits; if a dangerous scenario is found, execute the next step.

[0133] (4) Find the clean energy power plant power scenarios adjacent to the dangerous scenario and perform risk assessment calculations on the scenarios that have not been assessed. If a new dangerous scenario is found, repeat this step; if no new dangerous scenario is found, proceed to the next step.

[0134] (5) Treat the unevaluated clean energy power plant power scenarios as safe scenarios, and perform risk assessment statistics and output the results.

[0135] 3. Implementation details for the control execution layer can be tailored to individual needs, primarily including time control for plan generation and updates, fault tolerance, and backup mechanisms. For example, a 24-hour scheduling plan can be generated at midnight daily based on weather forecasts and production plans, with real-time rolling updates and optimizations every five minutes to respond to unexpected operating conditions (such as power grid failures). A safety fault-tolerant mechanism can be implemented to ensure safe system operation, with dual controllers for hot backup of key energy storage converter equipment. A CRC-32 checksum is used to prevent transmission errors, and erroneous instructions are automatically retransmitted.

[0136] The multi-energy coupling process equipment 130 is introduced below.

[0137] Hydrogen-based Direct Reduced Iron (HDRI) uses hydrogen instead of traditional coke as a reducing agent to directly reduce iron ore (iron oxide) to metallic iron (sponge iron) at high temperatures. The core chemical reaction is as follows:

[0138]

[0139] Side reaction (carbon impurity removal):

[0140] C+2H2→CH4

[0141] In an optional embodiment, the hydrogen-based direct reduction ironmaking process includes:

[0142] Using green electricity to electrolyze water to produce hydrogen, the generated hydrogen is fed into the blast furnace as a reducing agent to replace pulverized coal, and the water vapor is condensed, separated, and recycled;

[0143] Dynamically calculate hydrogen demand based on ore feed rate and metallization rate feedback to achieve real-time hydrogen flow and pressure control;

[0144] The hydrogen concentration is detected in real time, and an alarm is triggered when the hydrogen concentration reaches a preset threshold.

[0145] Figure 9 A schematic diagram of a hydrogen-based direct reduction ironmaking process based on multi-energy complementation is provided in the embodiment of this application. Figure 9 As shown in the figure, the process specifically includes:

[0146] Hydrogen is produced by electrolyzing water with green electricity (PEM electrolyzer) and the hydrogen is introduced into the blast furnace to replace pulverized coal as a reducing agent. The water vapor is separated by condensation and then recycled. Based on the dynamic adjustment model and the feedback of the ore feed rate and metallization rate, the hydrogen demand is dynamically calculated and the real-time hydrogen flow and pressure control are carried out. Equipped with a safety interlock system, a laser gas sensor (TDLAS) is used to detect the H2 concentration in real time, and a threshold alarm (≥1% volume concentration) is issued. When the leakage exceeds the standard, the hydrogen valve is closed within 0.5 seconds and the nitrogen purge is started. Reactor explosion venting discs (bursting pressure 3.0MPa) and explosion suppression coatings (temperature resistance 1500℃) are used to deal with dangerous situations.

[0147] This application essentially proposes a system for the utilization and intelligent control of multiple renewable energy sources and energy supply, which combines multiple renewable energy sources to effectively solve the problems of intermittent and grid-dependent single renewable energy (such as photovoltaic or wind power). Based on the real-time output of wind power and photovoltaic power, it establishes the optimal energy supply composition when green electricity is unstable, reduces the impact of green electricity fluctuations on the power system, and establishes a risk assessment system to assess system risks in real time and ensure the safe and stable operation of the system. In addition, this application couples the multi-energy complementary hydrogen storage process with the hydrogen metallurgical process to solve the multi-energy coordination problems such as green electricity supply and waste heat utilization in the whole process.

[0148] The following effects can be achieved through the steel smelting system with intelligent energy consumption control based on multi-energy complementarity in the embodiments of the present application:

[0149] 1. Improved energy utilization efficiency and production efficiency:

[0150] Through the multi-energy complementarity and functional control system, CO2 emissions per ton of steel are reduced and multi-energy complementarity avoids the risks of coal price fluctuations and power grid interruptions, breaking through the dependence on a single energy source, solving the problems of green electricity consumption and stability, and improving energy resilience. Through the coordination of wind, solar, and hydrogen storage, green electricity fluctuations are smoothed and dynamically matched with multiple energies. Through valley electricity storage + hydrogen energy peak regulation, the overall energy cost is reduced.

[0151] 2. Reduced production costs and pollution control costs:

[0152] Using green electricity instead of purchased electricity can significantly reduce production costs. Through the energy supply optimization algorithm during unstable periods of green electricity, the most reasonable and economical energy supply ratio can be achieved, which can effectively reduce production costs. By significantly introducing green electricity, the use of pulverized coal can be reduced, and the emissions of pollutants such as nitrogen oxides and sulfur oxides can be reduced, thereby reducing pollution control costs.

[0153] The traditional steelmaking energy supply system may have the following deficiencies:

[0154] High carbon emissions and pollution: According to statistics, carbon emissions per ton of steel are about 1.8-2.2 tons, of which pulverized coal combustion contributes more than 70%. Sulfur oxides (SO x ), nitrogen oxides (NOx ) emissions exceed standards, necessitating the installation of additional desulfurization and denitrification equipment. Furthermore, purchased electricity continues to rely primarily on thermal power generation, increasing indirect carbon emissions and exacerbating environmental pressures.

[0155] Low energy efficiency: Direct combustion of pulverized coal has a low thermal energy utilization rate (typically <50%), and insufficient high-temperature waste heat recovery results in energy waste. When electricity is directly used in equipment such as blast furnaces and electric arc furnaces, there is a lack of multi-energy synergy and optimization, resulting in low overall energy efficiency.

[0156] Energy supply fluctuation risk: Coal price fluctuations, grid curtailment, or peak electricity price periods directly impact production costs. Without energy storage as a buffer, sudden power outages could lead to major accidents such as blast furnace freezes. Renewable energy is not being utilized, failing to alleviate fossil fuel shortages.

[0157] Insufficient flexibility: Renewable energy sources (such as wind power and photovoltaics) are not being utilized, failing to alleviate fossil fuel shortages. A lack of energy storage technology prevents grid load fluctuations or the storage of surplus energy.

[0158] To address the above issues, the present invention proposes an intelligent energy consumption control steelmaking system based on multi-energy complementarity, which can achieve the following goals:

[0159] 1. Build a low-carbon energy system: By replacing pulverized coal and thermal power with complementary energy sources such as green electricity (wind power, photovoltaics), hydrogen energy, and biomass energy, carbon emissions per ton of steel produced will be reduced, driving steelmaking to near-zero emissions. This will form a closed loop of "energy supply-emission reduction-regeneration" and reduce pollutant emissions.

[0160] 2. Enhanced energy supply stability and affordability: This system integrates a multi-energy system, including wind, solar, and energy storage, hydrogen storage, and natural gas peak-shaving, supporting both off-grid and grid-connected operation, significantly improving power supply reliability. By utilizing direct green electricity supply, off-peak electricity storage, and hydrogen energy to replace pulverized coal, it reduces overall energy costs and mitigates the risk of fossil energy price fluctuations.

[0161] 3. Support process innovation and intelligent upgrades: Adapting new processes such as hydrogen-based direct reduction ironmaking and green electric arc furnaces will promote the transformation of steelmaking technology towards low-carbon and high-efficiency. A real-time perception-prediction-decision-making closed-loop system will be established to achieve intelligent coordinated control of multiple energy sources, reducing manual intervention by over 90%.

[0162] 4. Promote renewable energy consumption and industrial collaboration: Reduce curtailment by consuming distributed wind and solar resources locally at steel mills and using surplus green electricity to produce hydrogen. This will be coupled with grid demand-side response, with steel mills acting as virtual power plants to participate in peak load regulation and improve the overall economic efficiency of the energy system.

[0163] In one embodiment, a computer-readable storage medium is further provided. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor executes the method steps executed by the above system.

[0164] Those skilled in the art will appreciate that all or part of the processes in the method executed by the above-mentioned system can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0165] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0166] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. An intelligent energy consumption control steel smelting system based on multi-energy complementarity, characterized in that: include: Multi-energy complementary energy supply system, used to integrate multiple energy forms to form a dynamic collaborative energy supply network, and perform energy collaborative operation according to energy priority strategy and dynamic matching mechanism; An intelligent control platform, which adopts a three-layer "perception-decision-execution" architecture to coordinate energy flow and process operations between the multi-energy complementary energy supply system and the multi-energy coupling process equipment; Multi-energy coupling process equipment, including hydrogen-based direct reduction ironmaking equipment, uses hydrogen instead of traditional coke as a reducing agent for ironmaking.

2. The energy consumption intelligent control steel smelting system based on multi-energy complementarity according to claim 1 is characterized in that: The various energy forms include but are not limited to green electricity, hydrogen energy, energy storage, electrochemical energy storage, heat storage and purchased electricity. The green electricity includes wind power and photovoltaic power, and the energy storage includes electrochemical energy storage and heat storage systems.

3. The energy consumption intelligent control steel smelting system based on multi-energy complementarity according to claim 2 is characterized in that: The energy prioritization strategy includes: Prioritize the use of green electricity generated in real time; When the green power is in excess, the energy storage system is used to store the excess energy, or when the green power is insufficient, the energy is released to balance supply and demand; When there is excess green electricity, the excess electricity is used to produce hydrogen or charge batteries; The battery system is used to smooth out short-term fluctuations in power supply to ensure the stability of power supply; using the thermal storage system or hydrogen storage system to meet energy demand during extended periods when electricity demand exceeds supply; In special circumstances or emergencies where all other means of energy supply cannot meet demand, the gap will be filled by purchasing electricity as described above.

4. The energy consumption intelligent control steel smelting system based on multi-energy complementarity according to claim 2 is characterized in that: The dynamic matching mechanism includes: During high-load periods, the green electricity and the battery energy storage system are used to supply power; If the power supply capacity of the green electricity and the battery energy storage system cannot meet the demand, the hydrogen fuel cell and the purchased electricity are used to make up the remaining power gap; Utilize wind power and thermal storage systems to supply power during low-load periods; If the electricity generated by the wind power and the heat storage system exceeds the current demand, the surplus electricity is used to produce hydrogen, and the hydrogen is stored.

5. The energy consumption intelligent control steel smelting system based on multi-energy complementarity according to claim 1 is characterized in that: The multi-energy complementary energy supply system has the function of dynamic regulation of active power and reactive power on the power generation side, wherein a hierarchical control architecture and a power distribution strategy are used for active power regulation, and a distributed reactive power compensation method and a multi-energy coordinated voltage regulation method are used for reactive power regulation.

6. The energy consumption intelligent control steel smelting system based on multi-energy complementarity according to claim 5 is characterized in that: The hierarchical control architecture is used for primary frequency modulation based on droop control automatically responding to frequency deviations, and secondary frequency modulation based on dynamic adjustment of the central controller to eliminate steady-state errors; The power allocation strategy adopts a low-pass filtering algorithm to use battery energy storage to smooth and compensate for wind power / photovoltaic fluctuations.

7. The energy consumption intelligent control steel smelting system based on multi-energy complementarity according to claim 1 is characterized in that: The intelligent control platform includes: The data perception layer includes three units: data acquisition, data transmission, and data preprocessing, which are used to collect and preprocess energy supply and demand data; Optimize the decision-making layer, including multi-objective dynamic optimization models, machine learning modules, and risk assessment modules; The control execution layer is used to generate and update the scheduling plan in real time according to the results of the optimization decision layer.

8. The energy consumption intelligent control steel smelting system based on multi-energy complementarity according to claim 7 is characterized in that: The multi-objective dynamic optimization model is used to comprehensively consider energy costs, carbon emissions and equipment losses, and dynamically adjust the energy supply and demand balance; The energy cost is determined based on the total energy required by the current system, the energy supplied by the current wind power system, the energy supplied by the current photovoltaic system, the energy supplied by the energy storage system, and the price of purchased electricity in the current period.

9. The energy consumption intelligent control steel smelting system based on multi-energy complementarity according to claim 8 is characterized in that: The risk assessment module is specifically used to: Calculate the order matrix in advance based on the historical data of clean energy power station power; When performing risk assessment on the power system, Latin hypercube sampling is performed based on the power probability prediction results of the clean energy power station, and the samples of the Latin hypercube sampling are sorted using the order matrix to construct a clean energy power station power scenario that takes correlation into account; In extracting extreme value scenarios according to the sequential matrix, performing risk assessment calculations on the extreme value scenarios; if no dangerous scenarios are found, the system is deemed to have no operational risk, outputs the assessment results, and exits; If a dangerous scenario is found, proceed to the next step; Search for clean energy power plant power scenarios adjacent to the hazardous scenario and perform risk assessment calculations on those scenarios that have not been assessed. If a new hazardous scenario is found, repeat this step. If no new hazardous scenario is found, proceed to the next step. The unevaluated clean energy power plant power scenario is regarded as a safe scenario, and risk assessment results are statistically analyzed and output.

10. The energy consumption intelligent control steel smelting system based on multi-energy complementarity according to claim 1 is characterized in that: The hydrogen-based direct reduction ironmaking process includes: Using green electricity to electrolyze water to produce hydrogen, the generated hydrogen is fed into the blast furnace as a reducing agent to replace pulverized coal, and the water vapor is condensed, separated, and recycled; Dynamically calculate hydrogen demand based on ore feed rate and metallization rate feedback to achieve real-time hydrogen flow and pressure control; The hydrogen concentration is detected in real time, and an alarm is triggered when the hydrogen concentration reaches a preset threshold.

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