A steel load and multi-energy system collaborative optimization scheduling method and device

Abstract

The application discloses a steel load and multi-energy system collaborative optimization scheduling method and device, relates to the technical field of multi-energy collaborative optimization scheduling, and provides a steel load model and a load regulation potential model; a regulation cost model including a steel load, a thermal power unit and an energy storage system is constructed, and operation and economy constraint conditions are set; a steel enterprise power balance equation is established, and a probability statistical model of wind speed and illumination is introduced to process the uncertainty of wind and light output; a multi-objective optimization model with the minimum net load fluctuation and the lowest total regulation cost as the targets is established; the multi-objective optimization model is solved based on multi-scene wind and light output samples, and a regulation strategy of multiple time scales is obtained. The device includes a scheduling control module and a display interface which are composed of a memory, a processor and a computer program stored in the memory and capable of being run on the processor; and the processor realizes the method of the application when the computer program is executed.

Description

Technical Field

[0001] This invention relates to the field of multi-energy collaborative optimization scheduling technology, specifically to a method and apparatus for collaborative optimization scheduling of steel load and multi-energy systems. Background Technology

[0002] As a basic industrial sector, steel enterprises have huge energy consumption and high carbon emission intensity in their production processes, facing severe pressure to reduce emissions. Steel enterprises are generally equipped with their own power plants, possess large areas of factory rooftop resources, have large electricity loads and strong renewable energy absorption capacity, and have good conditions for developing wind, solar and energy storage. In some areas with abundant wind resources, small wind turbines can be installed to form a multi-energy complementary scenario of wind, solar, thermal and energy storage. Building a multi-energy collaborative optimization scheduling model has become an industry trend.

[0003] The inherent volatility and intermittency of wind and solar power output lead to unstable power supply, while thermal power units have strong regulation capabilities and mature technology, serving as a reliable support for voltage and frequency regulation. Configuring energy storage systems can achieve the dual benefits of low-price storage and high-generation arbitrage and demand management: charging during off-peak hours and discharging during peak hours reduces peak electricity purchase costs; discharging reduces peak load power, lowering the maximum demand costs for large industrial users.

[0004] Existing technologies mainly employ salt cavern energy storage and data-driven time-sharing strategies to smooth out load peak-valley differences by collecting electricity consumption data, cleaning the data, and dividing it into time periods. However, this method has the following shortcomings: First, it ignores the impact and nonlinearity of steel loads, which are characteristic of process fluctuations; second, it lacks a complete scheduling strategy that deeply integrates steel load regulation capabilities with wind and solar load fluctuations, making it difficult to balance economy and stability; and third, the page display and page switching response of existing steel load and multi-energy system collaborative optimization scheduling devices are not fast enough. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method and apparatus for coordinated optimization scheduling of steel load and multi-energy system.

[0006] The technical solution of this invention is:

[0007] The first aspect of this invention provides a method for coordinated optimization scheduling of steel load and multi-energy system, the method comprising the following steps:

[0008] A steel load model is constructed based on actual steel load data of steel enterprises and distributed wind and photovoltaic power generation data;

[0009] Based on the steel load model, a steel load adjustment potential model is constructed;

[0010] A regulation cost model is constructed based on the steel load regulation potential model. The regulation cost model includes a constraint system, a regulation cost model for thermal power units, a regulation cost model for energy storage systems, and a regulation cost model for steel load. The constraint system includes: power balance constraints for steel enterprises, power constraints and start-up / shutdown time constraints for thermal power units, and state of charge constraints and power constraints for energy storage devices.

[0011] By combining the adjustment cost model, a multi-objective optimization model is established with the objectives of minimizing total adjustment cost and net load fluctuation.

[0012] Solve the multi-objective optimization model to obtain the collaborative optimization scheduling strategy for steel load and multi-energy system.

[0013] Optionally, according to the aforementioned method for coordinated optimization scheduling of steel load and multi-energy system, the construction of the steel load model includes:

[0014] (a) Construct a simplified load model of the refining furnace and the electric arc furnace as represented by equation (1):

[0015]

[0016] In the formula, This indicates that electric arc furnaces and refining furnaces are in... Power at any given moment; The arc voltage of the electric arc furnace and the refining furnace; The arc current of the electric arc furnace and the refining furnace;

[0017] (b) Construct a simplified load model of the rolling mill according to equation (2):

[0018]

[0019] In the formula, Indicates the rolling mill is in Power at any given moment; Indicates mill efficiency; Indicates the angular velocity of the rolling mill; The rolling mill torque, and the periodic variation model of the rolling mill load torque are as follows:

[0020]

[0021] in, This indicates the no-load torque or basic load torque of the rolling mill; This indicates the change in load torque; This is the moment when the load torque begins to change; This is the moment when the load torque reaches its maximum value; This is the moment when the load torque begins to decrease; The moment when the torque is restored to no-load torque; Indicates the period of load torque variation;

[0022] (c) Construct a simplified constant power model for the blast furnace blower according to equation (4):

[0023]

[0024] In the formula, This indicates the power of the blast furnace blower; For the efficiency of blast furnace blowers; This refers to the gas volumetric flow rate; The pressure difference generated by the blast furnace blower;

[0025] (d) The load model of the oxygen generator unit is constructed according to equation (5):

[0026]

[0027] In the formula, Indicates the power of the oxygen generator unit; This indicates the specific enthalpy change during the compression process of the oxygen generator unit; This indicates the mass flow rate of gas within the oxygen generator unit; This indicates the efficiency of the oxygen generator unit's motor; This indicates the isentropic efficiency of the oxygen generator unit during compression.

[0028] (e) The load model for dust removal and environmental protection load is constructed according to equation (6):

[0029]

[0030] In the formula, Power required for dust removal and environmental protection load; The efficiency of the fan indicates its dust removal and environmental protection load. The fan / pump pressure difference represents the dust removal and environmental protection load.

[0031] (f) By combining the simplified load models of the refining furnace and electric arc furnace, the simplified load model of the rolling mill, the simplified constant power model of the blast furnace blower, the load model of the oxygen generator unit, and the load models of dust removal and environmental protection loads, the steel load model is obtained as follows:

[0032]

[0033] in For steel companies Total load at any given moment.

[0034] Optionally, based on the aforementioned method for coordinated optimization scheduling of steel load and multi-energy systems, a steel load regulation potential model is constructed using the steel load model:

[0035]

[0036] In the formula, This represents the change in electricity prices; Based on steel companies Total load at any time Adjustable load of steel enterprises determined under changes in electricity prices;

[0037] Optionally, based on the steel load and multi-energy system collaborative optimization scheduling method, the steel load adjustment cost model constructed based on the steel load adjustment potential model is as follows:

[0038]

[0039] In the formula, This indicates the cost of steel load adjustment; This indicates the electricity price before the fluctuation in steel load. This indicates the steel load adjustment time.

[0040] Optionally, based on the aforementioned steel load and multi-energy system coordinated optimization scheduling method, and combined with the adjustment cost model, a multi-objective optimization model is established with the objectives of minimizing total adjustment cost and net load fluctuation, including:

[0041] Establish an objective function that aims to minimize net load fluctuation:

[0042]

[0043] in, This indicates the net load fluctuation after the power grid absorbs energy output; This indicates that steel companies are Total load at any given time; Indicates that the energy storage system is in The charging power at any given time; Indicates that the energy storage system is in Discharge power at any given moment; Indicates wind force at Real-time wind speed The power generation capacity below; Indicates photovoltaics in Time of day, light intensity The power generation capacity; Indicates in Total power of thermal power units at any given time; Indicates the total number of thermal power units; The average power of a steel enterprise is calculated using the following formula:

[0044]

[0045] The objective function aimed at minimizing total adjustment cost is as follows:

[0046]

[0047] in, This represents the total adjustment cost of steel enterprises; This represents the regulation cost of the energy storage system as determined by the regulation cost model of the energy storage system; The first is determined by the adjustment cost model of thermal power units. The adjustment cost of each thermal power unit.

[0048] Optionally, based on the aforementioned method for coordinated optimization scheduling of steel load and multi-energy system, a multi-objective optimization model is solved to obtain a coordinated optimization scheduling strategy for steel load and multi-energy system, including:

[0049] Step 5.1: Obtain the wind speed and solar intensity data for the day, determine the wind power output through the wind-solar piecewise function, and then incorporate the wind and solar power output into the power balance constraints of the steel enterprise;

[0050] Step 5.2: Based on the steel load model, determine the decision variables and obtain the initial decision variable set. :

[0051]

[0052] Step 5.3: Determine whether the load fluctuation exceeds the capacity of the energy storage device. If not, then in a short timescale of minutes, only consider using the energy storage system to regulate the load of the steel enterprise and proceed to step 5.8; if yes, then in a long timescale of hours, proceed to step 5.4.

[0053] Step 5.4 assesses the regulation capabilities of the steel load, thermal power units, and energy storage system, including: determining the adjustable power range and the adjustable power range of the steel load based on its minimum and maximum operating power; for grid-connected thermal power units, assessing their feasibility to participate in steel enterprise load regulation based on their adjustable output range; for thermal power units in a shutdown state, assessing their feasibility to participate in steel enterprise load regulation based on their minimum start-stop time constraints; and assessing whether the current state of charge, maximum charging / discharging power, and charging / discharging efficiency of the energy storage system are feasible for participating in steel enterprise load regulation.

[0054] Step 5.5: Based on the determined steel load, the regulation capacity of thermal power units and energy storage systems, and in conjunction with the constraint system, the NSGA-II algorithm is used to solve the multi-objective optimization model, outputting a Pareto solution set. Each solution in this set corresponds to a trade-off between net load fluctuation and total regulation cost, thus obtaining different trade-offs between net load fluctuation and total regulation cost. The total regulation cost includes the regulation cost of thermal power units, the regulation cost of energy storage systems, and the regulation cost of steel load.

[0055] Step 5.6: Determine whether the load of the steel enterprise is within the preset fluctuation range. If yes, proceed to step 5.7; otherwise, return to step 5.4.

[0056] Step 5.7: Output the final decision variable set. Each decision variable set corresponds to a set of thermal power output, energy storage system charging and discharging power, and steel load power. Steel companies determine adjustment strategies at different time scales based on the decision variable set.

[0057] A second aspect of the present invention provides a steel load and multi-energy system collaborative optimization scheduling device. The device includes a scheduling control module, which includes a data acquisition interface module, a memory, a processor, and a computer program stored in the memory and executable on the processor. The data acquisition interface module is used to acquire actual steel load data of steel enterprises and distributed wind power and photovoltaic power generation data. When the processor executes the computer program, it implements the above-mentioned steel load and multi-energy system collaborative optimization scheduling method.

[0058] Optionally, according to the steel load and multi-energy system collaborative optimization scheduling device, the device further includes a display interface, which is divided into the following sub-interfaces according to the query function:

[0059] A flexible interactive interface is used to provide load energy consumption data query, using a line graph to display the energy consumption trend of load changes over time, and supporting real-time data viewing and historical data query;

[0060] The performance monitoring data interface is used to display time-series data of load performance, showing the performance changes of the load at different time periods through line graphs;

[0061] The energy efficiency monitoring data interface is used to display the real-time monitoring status of control commands and their execution effects, helping users monitor the execution results of load, energy storage, and thermal power.

[0062] The alarm query data interface is used for querying, filtering, and managing alarm information. It supports filtering and querying by alarm time, alarm object, alarm content, and alarm type.

[0063] The carbon efficiency management data interface is used to display the carbon efficiency monitoring and optimization of steel production lines, and provides enterprise carbon efficiency level, carbon efficiency level, carbon efficiency analysis and real-time carbon emission data monitoring.

[0064] The energy efficiency management interface provides real-time monitoring information and energy efficiency analysis for the production line, displaying energy efficiency levels, industry benchmark comparisons, and energy consumption-related data.

[0065] Optionally, according to the steel load and multi-energy system collaborative optimization scheduling device, the display interface introduces a "Star Island" feature, which displays other pages simultaneously on the current display page via a floating window, enabling rapid switching between multiple pages.

[0066] A third aspect of the present invention provides an electronic device comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the above-described method for coordinated optimization scheduling of steel load and multi-energy system.

[0067] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the above-described method for coordinated optimization scheduling of steel load and multi-energy system.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] (1) A precise steel load model is constructed for key loads such as electric arc furnace, rolling mill, blast furnace blower, oxygen generator, dust removal and environmental protection load. Based on this model, a load adjustment potential model and a type-based adjustment cost model are established to fully characterize the electricity consumption characteristics of steel enterprises and provide a reliable foundation for multi-energy coordinated scheduling.

[0070] (2) Establish a multi-objective optimization model with the goal of minimizing total regulation cost and net load fluctuation. Use the NSGA-II algorithm to obtain the Pareto compromise solution set, forming a mechanism for rapid regulation of energy storage at the minute-level short time scale and multi-energy coordinated regulation at the hour-level long time scale. Without the need for accurate prediction of future wind and solar power output, steel companies can flexibly choose regulation strategies according to actual operating conditions, taking into account both operational economy and grid stability.

[0071] (3) The dispatching device includes a dedicated dispatching control module and six functional display interfaces. The floating window of the Star Island enables multi-page simultaneous display and quick switching, resulting in efficient and smooth operation. The interface can display real-time operating data in all dimensions, such as energy consumption, energy efficiency, carbon efficiency, and alarms, improving monitoring and management efficiency. At the same time, the Star Island has good scalability, supports subsequent secondary development, and facilitates the integration of more pages, ensuring efficient management and operation of the steel load and multi-energy system collaborative optimization dispatching device when its functions are expanded. Attached Figure Description

[0072] Figure 1 This is a flowchart of the steel load and multi-energy system collaborative optimization scheduling method in this embodiment;

[0073] Figure 2 This is a flowchart of the steel production process of a certain enterprise in a certain location in this embodiment;

[0074] Figure 3 This is a diagram illustrating the periodic variation model of the mill load torque in this embodiment;

[0075] Figure 4 This is a schematic diagram of the single-load regulation capacity model in this embodiment;

[0076] Figure 5 This is a flowchart illustrating the solution process for the collaborative optimization scheduling strategy in this implementation.

[0077] Figure 6 This is a schematic diagram of the structure of the steel load and multi-energy system collaborative optimization scheduling device in this embodiment;

[0078] Figure 7 This is an external view of the steel load and multi-energy collaborative optimization scheduling device in this embodiment;

[0079] Figure 8 This is a schematic diagram of the flexible interactive interface of the steel load and multi-energy collaborative optimization scheduling device in this embodiment;

[0080] Figure 9 This is a schematic diagram of the performance monitoring interface of the steel load and multi-energy collaborative optimization scheduling device in this embodiment;

[0081] Figure 10This is a schematic diagram of the energy efficiency monitoring interface of the steel load and multi-energy collaborative optimization scheduling device in this embodiment;

[0082] Figure 11 This is a schematic diagram of the alarm query interface of the steel load and multi-energy collaborative optimization scheduling device in this embodiment;

[0083] Figure 12 This is a schematic diagram of the carbon efficiency management interface of the steel load and multi-energy collaborative optimization scheduling device in this embodiment;

[0084] Figure 13 This is a schematic diagram of the energy efficiency management interface of the steel load and multi-energy collaborative optimization scheduling device in this embodiment. Detailed Implementation

[0085] To facilitate understanding of this application, a more comprehensive description of this application will be provided below with reference to the accompanying drawings.

[0086] The core idea of ​​this invention is as follows: A steel load model is constructed for typical steel loads such as electric arc furnaces, rolling mills, blast furnace blowers, oxygen generators, dust removal, and environmental protection loads. Based on this model, a load regulation potential model and a regulation cost model including thermal power units, energy storage systems, and steel loads are further established. Combined with the regulation cost model, a multi-objective optimization model is established with the objectives of minimizing total regulation cost and net load fluctuation. This model is solved using the NSGA-II algorithm, forming a scheduling mechanism that combines minute-level short-term energy storage rapid regulation with hour-level long-term multi-energy collaborative regulation, outputting a collaborative optimization scheduling strategy for steel load and multi-energy systems. The corresponding device includes a scheduling control module and a display interface. The module can execute the aforementioned scheduling method, and the display interface provides six functional interfaces: flexible interaction, performance detection, energy efficiency monitoring, alarm query, carbon efficiency management, and energy efficiency management. A floating window, known as a "Star Island," is introduced to achieve multi-page simultaneous display and rapid switching, ultimately achieving dual-objective optimization of net load fluctuation and regulation cost, improving the energy flexibility and operational stability of steel enterprises.

[0087] The first aspect of this embodiment provides a method for coordinated optimization scheduling of steel load and multi-energy systems. Figure 1 Here is a flowchart of the method, such as Figure 1 As shown, the steel load and multi-energy system collaborative optimization scheduling method of this embodiment includes the following steps:

[0088] Step 1: Construct a steel load model based on actual steel load data of steel enterprises and distributed wind and photovoltaic power generation data;

[0089] Figure 2 This is a steel production flow chart of a certain enterprise in a certain location, as described in an embodiment of the present invention. Figure 2As shown, the steel load includes: electric arc furnace, refining furnace (LF refining furnace is a special form of electric arc furnace and can be considered as a low-power electric arc furnace), rolling mill, blast furnace blower, and oxygen generator unit. Multiple energy sources include: distributed wind power, distributed photovoltaic power, thermal power units, and energy storage systems. Steel smelting mainly includes blast furnace converter and electric arc furnace steelmaking processes. Large steel enterprises using electric arc furnace steelmaking processes mainly include the "electric arc furnace steelmaking - refining furnace secondary steelmaking - rolling" electricity consumption steps. Electric arc furnace steelmaking mainly uses scrap steel of similar steel grades as the main raw material, adjusting the chemical composition and alloy element content by adding ferroalloys, and smelting using the high temperature of the electric arc; then, secondary steelmaking in the refining furnace involves pouring the molten steel initially treated in the electric arc furnace into the refining furnace for secondary refining treatment according to requirements, adjusting the steel composition; finally, continuous casting turns the molten steel into steel billets, which are then rolled to obtain steel products. The blast furnace-converter production process uses iron ore as raw material. In the blast furnace, coke is used as fuel and reducing agent (coal is often used to partially replace coke) to reduce and smelt the iron ore into liquid pig iron. Then, in the converter, the liquid pig iron and scrap steel are smelted into liquid steel. After secondary refining, qualified finished steel liquid is obtained.

[0090] Furthermore, based on the key steel loads involved in the steel production process, load models are established as follows:

[0091] (a) Construct a simplified load model of the refining furnace and the electric arc furnace as represented by equation (14):

[0092]

[0093] In the formula, Indicates that the electric arc furnace is in Power at any given moment; The arc voltage of the electric arc furnace; The arc current of the electric arc furnace;

[0094] In this embodiment, the arc voltage is taken. 33kV, arc current 1850A, electric arc furnace load model at this time:

[0095]

[0096] (b) Construct a simplified load model of the rolling mill according to equation (15):

[0097]

[0098] In the formula, Indicates the rolling mill is in Power at any given moment; For mill efficiency (generally between 85% and 95%); This refers to the mill torque. This refers to the angular velocity of the rolling mill.

[0099] Analyze the torque of the rolling mill at different stages, such as Figure 3 As shown, This indicates the no-load torque or basic load torque of the rolling mill; This indicates the change in load torque; The load torque begins to change at that moment; The load torque reaches its maximum value at any given moment; The load torque begins to decrease at that moment; The torque should be restored to no-load torque at any time; This indicates the period of load torque variation.

[0100] The rolling mill load torque periodic variation model is as follows:

[0101]

[0102] In this embodiment, the relevant data of the rolling mill are as follows:

[0103] Table 1. Rolling Mill Related Data

[0104] Substituting the data in Table 1 into equation (3) yields the mill load torque periodic variation model:

[0105]

[0106] This leads to a simplified load model for the rolling mill:

[0107]

[0108] (c) Construct a simplified constant power model for the blast furnace blower according to equation (17):

[0109]

[0110] In the formula, This indicates the power of the blast furnace blower; For the efficiency of blast furnace blowers; This refers to the gas volumetric flow rate; The pressure difference generated by the blast furnace blower;

[0111] In this embodiment, the relevant data for the blast furnace blower are as follows:

[0112] Table 2. Relevant data for blast furnace blowers.

[0113] Simplified constant power model of blast furnace blower:

[0114]

[0115] (d) The load model of the oxygen generator unit is constructed according to equation (18):

[0116]

[0117] In the formula, Indicates the power of the oxygen generator unit; This indicates the specific enthalpy change during the compression process of the oxygen generator unit; This indicates the mass flow rate of gas within the oxygen generator unit; This indicates the efficiency of the oxygen generator unit's motor; This indicates the isentropic efficiency of the oxygen generator unit during compression.

[0118] In this embodiment, the relevant data of the oxygen generator unit are as follows:

[0119] Table 3. Relevant data for oxygen generator units

[0120] Load model of oxygen generator unit:

[0121]

[0122] (e) The load model for dust removal and environmental protection load is constructed according to equation (19):

[0123]

[0124] In the formula, Power required for dust removal and environmental protection load; The efficiency of the fan indicates its dust removal and environmental protection load. The fan / pump pressure difference represents the dust removal and environmental protection load.

[0125] In this embodiment, the relevant data of the dust removal and environmental protection equipment are as follows:

[0126] Table 4. Relevant data on dust removal and environmental protection equipment

[0127] Load model for dust removal and environmental protection equipment:

[0128]

[0129] (f) By combining the simplified load models of the refining furnace and electric arc furnace, the simplified load model of the rolling mill, the simplified constant power model of the blast furnace blower, the load model of the oxygen generator unit, and the load models of dust removal and environmental protection loads, the steel load model is obtained as follows:

[0130]

[0131] in For steel companies Total load at any given moment.

[0132] Step 2: Construct a steel load adjustment potential model based on the steel load model;

[0133] The single-load regulation method involves switching on / off loads or changing load modes based on electricity prices; therefore, its load regulation capacity model is similar. Figure 4 The stepped curve shown is an example. Figure 4 As shown, the horizontal axis The vertical axis represents the change in electricity prices. Based on steel companies Total load at any time The adjustable load of steel enterprises is determined under changes in electricity prices. By summing up the curves of different load adjustment capabilities, a continuous curve will be obtained when the number of curves is sufficiently large; this is the steel load adjustment potential model.

[0134]

[0135]

[0136] In the formula, and Since they are inverse functions, in practical applications, it is sufficient to obtain the total single-load regulation capacity and form a two-dimensional table corresponding to the electricity price and regulation capacity. There is no need for curve fitting calculation, as shown in Table 5.

[0137] Table 5. Two-dimensional table of electricity price-regulation capacity

[0138] Step 3: Construct a regulation cost model based on the steel load regulation potential model, including a constraint system, a regulation cost model for thermal power units, a regulation cost model for energy storage systems, and a regulation cost model for steel load; the constraint system includes: power balance constraints for steel enterprises, power constraints and start-up / shutdown time constraints for thermal power units, and state of charge constraints and power constraints for energy storage devices.

[0139] Step 3.1: Construct a steel load adjustment cost model based on the steel load adjustment potential model;

[0140] The steel load adjustment cost model constructed based on the steel load adjustment potential model in this implementation is as follows:

[0141]

[0142] In the formula, This indicates the cost of steel load adjustment; This indicates the electricity price before the fluctuation in steel load. Indicates fluctuating electricity prices; This indicates the adjustable load capacity under the corresponding change in electricity price, i.e., the steel load regulation capacity. This indicates the steel load adjustment time.

[0143] Step 3.2: Construct a regulation cost model for thermal power units based on the steel load regulation potential model, including the regulation cost relationship and constraints of thermal power units;

[0144] The coal consumption loss of thermal power units is:

[0145]

[0146] In the formula, For thermal power units exist Additional coal consumption costs during certain periods; For 0-1 variables, This indicates that the thermal power unit is in a state of deep peak shaving; This indicates that the thermal power unit is in normal output or shut-down state; For thermal power units Coal consumption rate coefficient; For thermal power units Under normal minimum technical output conditions; For thermal power units Coal consumption rate at rated output; For thermal power units At any moment contribution; for arrive The time period is set to 1 hour. Price per unit of coal.

[0147] The lifespan loss cost of thermal power units during deep peak shaving is:

[0148]

[0149] In the formula, For thermal power units exist Lifetime depreciation costs over a period of time; This is the operating loss coefficient for thermal power units; For thermal power units The cost of purchasing the equipment; For thermal power units The mathematical relationship for the number of rotor-induced cracking cycles is as follows:

[0150]

[0151] In the formula, For thermal power units During the period Total strain amplitude; , , , All are correlation coefficients inherent to the rotor material; The plastic strain concentration factor; It is the elastic modulus; and These are the thermal stress and centrifugal tangential stress of the rotor, respectively.

[0152] thermal power units During the period The adjustment cost is:

[0153]

[0154] Step 3.3: Construct a regulation cost model for the energy storage system based on the steel load regulation potential model;

[0155] The formula for calculating the charging regulation cost of constructing an energy storage system is as follows:

[0156]

[0157] In the formula, Indicates that the energy storage system is in The cost of adjusting charging speed at any time; This represents the charging regulation cost coefficient of the energy storage system; Indicates energy storage system Real-time charging power;

[0158] The formula for calculating the discharge regulation cost of constructing an energy storage system is as follows:

[0159]

[0160] In the formula, Indicates the energy storage system at time Discharge regulation cost; This represents the discharge regulation cost coefficient of the energy storage system; Indicates energy storage system Discharge power at any given moment;

[0161]

[0162] In the formula, This indicates the charging and discharging regulation cost of the energy storage system. , Indicates that the energy storage system is in The operating status at any given time, 1 indicates power on, 0 indicates power off.

[0163] Step 3.4: Construct a constraint system; the constraint system includes: power balance constraints for steel enterprises, power constraints and start-up / shutdown time constraints for thermal power units, and state-of-charge constraints and power constraints for energy storage devices;

[0164] The power constraints of thermal power units are:

[0165]

[0166] In the formula, Indicates the first Each thermal power unit at time The operating status is indicated by 1 for power-on and 0 for power-off. ,

[0167] They represent thermal power units Minimum and maximum output; For thermal power units At any moment The active power; This indicates the minimum load factor.

[0168] The minimum start-stop time constraint is:

[0169]

[0170] In the formula, Indicates the first Each thermal power unit at time The operating status is indicated by 1 for power-on and 0 for power-off. , They represent thermal power units exist Continuous running time and downtime; , They represent thermal power units Minimum allowed continuous running time and downtime.

[0171] The state of charge constraint of the energy storage system is:

[0172]

[0173] In the formula, Indicates that the energy storage system is in The state of charge at any given moment; Indicates that the energy storage system is in The state of charge at any given moment; , , These represent the self-discharge rate, charging efficiency, and discharging efficiency of the energy storage system, respectively. Indicates the rated energy capacity of the energy storage system; , These represent the upper and lower limits of the state of charge of the energy storage system, respectively. , These represent the initial and final states of the energy storage system, respectively.

[0174] The power constraint of the energy storage system is:

[0175]

[0176] In the formula, , These represent the energy storage system in The charging and discharging state at any given moment; , These represent the minimum and maximum charging power of the energy storage system, respectively. , These represent the minimum and maximum discharge power of the energy storage system, respectively.

[0177] The power balance constraints for steel enterprises are as follows:

[0178]

[0179] In the formula, Indicates the total number of thermal power units; Indicates the output of the main power grid; Indicates photovoltaics in Time of day, light intensity The power generation capacity; Indicates wind force at Real-time wind speed The power generation capacity below.

[0180] Wind power output can be represented by the following piecewise function:

[0181]

[0182] In the formula, The cut-in wind speed for the fan. To cut off the wind speed for the fan. The rated wind speed of the fan. Indicates wind force at Real-time wind speed The power generation capacity below.

[0183] In this embodiment, the rated output power of the wind turbine is... Cut into wind speed Fan cutoff speed Rated wind speed of the fan Substituting the wind speed data for a particular day in this embodiment shown in Table 6 into equation (36) yields the output power shown in Table 7.

[0184] Table 6 Wind speed data for a certain day

[0185] Output power corresponding to Table 7 and Table 6

[0186] Photovoltaic output power can be represented by the following piecewise function:

[0187]

[0188] In the formula, Indicates photovoltaics in Time of day, light intensity The power generation capacity; The illumination value is the standard test condition. This is a reference illumination value.

[0189] In this embodiment, the rated output power of photovoltaic power generation is taken. Standard test conditions light value Reference light value Substituting the illumination data for a particular day in this embodiment shown in Table 8 into equation (37) yields the output power shown in Table 8.

[0190] Table 8 Light data for a specific day

[0191] Table 9 shows the output power corresponding to Table 8.

[0192] Step 4: Combine the adjustment cost model to establish a multi-objective optimization model with the objectives of minimizing total adjustment cost and net load fluctuation;

[0193] The objective function for minimizing net load fluctuations after energy consumption within the power grid is:

[0194]

[0195] The formula for calculating average power is as follows:

[0196]

[0197] The objective function aimed at minimizing total adjustment cost is as follows:

[0198]

[0199] in, This represents the total adjustment cost of steel enterprises; This represents the regulation cost of the energy storage system as determined by the regulation cost model of the energy storage system; The first is determined by the adjustment cost model of thermal power units. The adjustment cost of each thermal power unit.

[0200] Step 5: Solve the multi-objective optimization model to obtain the collaborative optimization scheduling strategy of steel load and multi-energy system.

[0201] Figure 5 This is a flowchart of the collaborative optimization scheduling strategy solution in this embodiment, such as... Figure 5 As shown, this step specifically includes the following steps:

[0202] Step 5.1: Obtain the wind speed data and solar intensity data for the day, determine the wind power output power through the wind-solar piecewise function, and then incorporate the wind-solar power output power into the power balance constraints of the steel enterprise.

[0203] Step 5.2: Determine the decision variables and obtain the initial set of decision variables. :

[0204]

[0205] in, Indicates the output of the thermal power unit;

[0206] Step 5.3: Determine whether the load fluctuation exceeds the capacity of the energy storage device. If not, then in a short timescale of minutes, only consider using the energy storage system to regulate the load of the steel enterprise and proceed to step 5.7; if yes, then in a long timescale of hours, proceed to step 5.4.

[0207] Step 5.4 assesses the regulation capabilities of the steel load, thermal power units, and energy storage system, including: determining the adjustable power range and the adjustable power range of the steel load based on its minimum and maximum operating power; for grid-connected thermal power units, assessing their feasibility to participate in steel enterprise load regulation based on their adjustable output range; for thermal power units in a shutdown state, assessing their feasibility to participate in steel enterprise load regulation based on their minimum start-stop time constraints; and assessing whether the current state of charge, maximum charging / discharging power, and charging / discharging efficiency of the energy storage system are feasible for participating in steel enterprise load regulation.

[0208] Step 5.5: Based on the determined steel load, the regulation capacity of thermal power units and energy storage systems, and in conjunction with the constraint system, the NSGA-II algorithm is used to solve the multi-objective optimization model, outputting a Pareto solution set. Each solution in this set corresponds to a trade-off between net load fluctuation and total regulation cost, thus obtaining different trade-offs between net load fluctuation and total regulation cost. The total regulation cost includes the regulation cost of thermal power units, the regulation cost of energy storage systems, and the regulation cost of steel load.

[0209] Step 5.6: Determine whether the load of the steel enterprise is within the preset fluctuation range, that is, determine whether the expected stabilization effect has been achieved. If not, proceed to step 5.7; otherwise, return to step 5.4.

[0210] Step 5.7: Output the final decision variable set. Each decision variable set corresponds to a set of thermal power output, energy storage system charging and discharging power, and steel load power. Steel companies determine adjustment strategies at different time scales based on the decision variable set.

[0211] The second aspect of this embodiment provides a steel load and multi-energy collaborative optimization scheduling device. Figure 6 This is a structural diagram of the device. For example... Figure 6As shown, the steel load and multi-energy coordinated optimization scheduling device includes a scheduling control module and a display interface. The scheduling control module includes a data acquisition interface module, a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned steel load and multi-energy system coordinated optimization scheduling method. Specifically, it constructs a steel load regulation potential model based on the characteristics of typical impact loads such as electric arc furnaces and rolling mills in steel enterprises. Combining the coordinated regulation capabilities of wind, solar, thermal, and energy storage systems, it establishes a multi-objective optimization model with the objectives of minimizing net load fluctuations and minimizing regulation costs. The NSGA-II algorithm is used to solve the multi-objective optimization model, forming a regulation mechanism that combines rapid energy storage regulation on a short time scale with coordinated optimization of multi-energy systems on a long time scale. Ultimately, it achieves dual-objective optimization, improving the flexibility and stability of enterprises in complex energy environments. The data acquisition interface module is used to acquire actual steel load data and distributed wind power and photovoltaic power generation data of steel enterprises; the memory is also used to store the collaborative optimization scheduling strategy of steel load and multi-energy system obtained after the processor executes the computer program to implement the above-mentioned collaborative optimization scheduling method of steel load and multi-energy system; the display interface introduces the Star Island, which can display other pages at the same time through the floating window on the current display page, reducing page jump time, enabling the collaborative optimization scheduling device of steel load and multi-energy system to realize fast switching of multiple pages and improve the smoothness of operation.

[0212] The appearance design of the steel load and multi-energy collaborative optimization scheduling device in this embodiment is as follows: Figure 7 As shown, the interface provides a load operation status query function, which can display the operation data of a specified load or all loads at a specified time according to actual needs. The interface can perform six function queries: flexible interactive status query, performance detection status query, energy efficiency monitoring status query, alarm status query, carbon efficiency management status query, and energy efficiency management status query, so that users can better understand the load operation status.

[0213] The flexible interactive interface diagram of this embodiment is shown below. Figure 8 As shown, the flexible interactive interface provides a function to query load energy consumption data, displaying the collected load data in a line graph format. The horizontal axis represents time, and the vertical axis represents the load's energy consumption data; the line graph shows the energy consumption trend of the load over time. Real-time data is also provided, including current load, operating capacity, and compensation amount, allowing users to quickly view the current load's energy consumption and status. The interface also supports historical data querying, allowing users to view historical data according to their actual needs. Through this interface, users can view the real-time energy consumption data of a specified load, facilitating timely understanding of the load's energy consumption and corresponding adjustments.

[0214] The performance testing data interface diagram of this embodiment is shown below. Figure 9 As shown, the performance monitoring data interface provides real-time monitoring and analysis of load performance fluctuations. The graph displays time-series data of load performance, showing the trend of performance monitoring values ​​through a line graph. The horizontal axis represents the data point number, and the vertical axis represents the monitored performance value, reflecting the performance changes of the load over different time periods. The right-hand interface provides a real-time monitoring data display. On this interface, users can perform energy efficiency monitoring accuracy calculations and view energy monitoring data, using this data to analyze the load's operating efficiency and status. Users can update the performance monitoring data as needed, or obtain the latest monitoring results by clicking the "Update" button. This interface provides users with intuitive performance fluctuation analysis, helping them understand the load's operating status.

[0215] The energy efficiency monitoring data interface diagram of this embodiment is shown below. Figure 10 As shown, the energy efficiency monitoring data interface displays real-time monitoring of control commands and their execution effects. The top of the interface displays the execution status of control commands. The horizontal axis in the graph represents time, and the vertical axis represents the effect or result of control execution. These graphs illustrate the actual impact of each control command on the load. The right-hand area displays current control command information, including received commands, command events, and execution status. Users can clearly see the execution effect of each control command and its corresponding load changes. The charts below show the trends in load execution effect and energy storage execution effect, helping users monitor the execution results of load, energy storage, and thermal power.

[0216] The alarm query data interface diagram of this embodiment is shown below. Figure 11 As shown, the alarm query data interface is used to display and manage alarm information. The interface provides multiple query items, including alarm time, alarm object, alarm content, and alarm type. Users can fill in the corresponding information to query specific alarm records as needed. In this interface, users can see the recorded alarm events. In the example, the device (load 01 switch state) alarmed due to a change in position; the alarm content is displayed as "from 0 to 1," and the alarm type is "change in position." This information helps users quickly understand the specific circumstances of the alarm and provides a basis for subsequent processing. The interface also supports filtering queries based on alarm time, object, content, and type, making it convenient for users to locate specific alarm events for handling. By clicking the query button, users can view historical alarm records and take necessary measures in a timely manner to ensure load safety and normal production.

[0217] The carbon efficiency management data interface diagram in this embodiment is shown below. Figure 12As shown, the carbon efficiency management data interface is used to display the carbon efficiency monitoring and optimization of a steel production line. The flowchart in the figure shows the main process steps of the steel production line, through which users can view the implementation process of carbon efficiency management. Several analysis items are provided at the bottom of the interface, including enterprise carbon efficiency level, carbon efficiency rating, and carbon efficiency. Users can select to view the carbon efficiency level (e.g., high, medium, low) and assess the carbon emissions during the production process based on this data. The right-hand area displays real-time monitoring data, including load carbon emission information, carbon efficiency control data, etc., allowing users to obtain key carbon efficiency data. By clicking the "Update" button, users can view the latest carbon emission situation and optimize the production process based on the carbon efficiency analysis results to reduce carbon emissions.

[0218] The energy efficiency management interface diagram of this embodiment is as follows: Figure 13 As shown, the energy efficiency management interface provides real-time monitoring information and energy efficiency analysis functions for the production line. The diagram horizontally displays the main stages of the steel production line, allowing users to quickly understand the production status of each stage. The bottom of the interface provides energy efficiency analysis data for the production line, including the company's energy efficiency level (high, medium, low) and whether it meets industry benchmark levels. Based on the actual situation, users can select the corresponding energy efficiency level and perform relevant analysis to help optimize the production process. On the right, the interface displays real-time monitoring data, including total input power and total output power, allowing users to view the current energy consumption and related data of the production line. The interface also provides further analysis of the production line's energy efficiency. If the production line does not meet industry benchmark levels, the device will suggest ways to improve energy efficiency by modifying production methods.

[0219] A third aspect of the present invention provides an electronic device comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the above-described method for coordinated optimization scheduling of steel load and multi-energy system.

[0220] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for coordinated optimization scheduling of steel load and multi-energy system.

[0221] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0222] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0223] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0224] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0225] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0226] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for coordinated optimization scheduling of steel load and multiple energy systems, characterized in that, The method includes the following steps: A steel load model is constructed based on actual steel load data of steel enterprises and distributed wind and photovoltaic power generation data; Based on the steel load model, a steel load adjustment potential model is constructed; A regulation cost model is constructed based on the steel load regulation potential model. The regulation cost model includes a constraint system, a regulation cost model for thermal power units, a regulation cost model for energy storage systems, and a regulation cost model for steel load. The constraint system includes: power balance constraints for steel enterprises, power constraints for thermal power units, start-up and shutdown time constraints, and state of charge constraints and power constraints for energy storage devices. By combining the adjustment cost model, a multi-objective optimization model is established with the objectives of minimizing total adjustment cost and net load fluctuation. Solve the multi-objective optimization model to obtain the collaborative optimization scheduling strategy for steel load and multi-energy system.

2. The method for coordinated optimization scheduling of steel load and multi-energy system according to claim 1, characterized in that, The steel load model includes: (a) constructing a simplified load model of the refining furnace and the electric arc furnace as considered as an electric arc furnace according to equation (1): ； In the formula, This indicates that electric arc furnaces and refining furnaces are in... Power at any given moment; The arc voltage of the electric arc furnace and the refining furnace; The arc current of the electric arc furnace and the refining furnace; (b) Construct a simplified load model of the rolling mill according to equation (2): ； In the formula, Indicates the rolling mill is in Power at any given moment; Indicates mill efficiency; Indicates the angular velocity of the rolling mill; The rolling mill torque, and the periodic variation model of the rolling mill load torque are as follows: ； in, This indicates the no-load torque or basic load torque of the rolling mill; Indicates the change in load torque; This is the moment when the load torque begins to change; This is the moment when the load torque reaches its maximum value; This is the moment when the load torque begins to decrease; The moment when the torque is restored to no-load torque; Indicates the period of load torque variation; (c) Construct a simplified constant power model for the blast furnace blower according to equation (4): ； In the formula, This indicates the power of the blast furnace blower; For the efficiency of blast furnace blowers; This refers to the gas volumetric flow rate; The pressure difference generated by the blast furnace blower; (d) The load model of the oxygen generator unit is constructed according to equation (5): ； In the formula, Indicates the power of the oxygen generator unit; This indicates the specific enthalpy change during the compression process of the oxygen generator unit; This indicates the mass flow rate of gas within the oxygen generator unit; This indicates the efficiency of the oxygen generator unit's motor; This indicates the isentropic efficiency of the oxygen generator unit during compression. (e) The load model for dust removal and environmental protection load is constructed according to equation (6): ； In the formula, Power required for dust removal and environmental protection load; The efficiency of the fan indicates its dust removal and environmental protection load. The fan / pump pressure difference represents the dust removal and environmental protection load. (f) By combining the simplified load models of the refining furnace and electric arc furnace, the simplified load model of the rolling mill, the simplified constant power model of the blast furnace blower, the load model of the oxygen generator unit, and the load models of dust removal and environmental protection loads, the steel load model is obtained as follows: ； in For steel companies Total load at any given moment.

3. The method for coordinated optimization scheduling of steel load and multi-energy system according to claim 2, characterized in that, Based on the steel load model, a steel load adjustment potential model is constructed: ； In the formula, This represents the change in electricity prices; Based on steel companies Total load at any time Adjustable load of steel enterprises determined under changes in electricity prices.

4. The method for coordinated optimization scheduling of steel load and multi-energy system according to claim 3, characterized in that, The steel load adjustment cost model constructed based on the steel load adjustment potential model is as follows: ； In the formula, This indicates the cost of steel load adjustment; This indicates the electricity price before the fluctuation in steel load. This indicates the steel load adjustment time.

5. The method for coordinated optimization scheduling of steel load and multi-energy system according to claim 4, characterized in that, Based on the adjustment cost model, a multi-objective optimization model is established with the objectives of minimizing total adjustment cost and net load fluctuation, including: Establish an objective function that aims to minimize net load fluctuation: ； in, This indicates the net load fluctuation after the power grid absorbs energy output; This indicates that steel companies are Total load at any given time; Indicates that the energy storage system is in The charging power at any given time; Indicates that the energy storage system is in Discharge power at any given moment; Indicates wind force at Real-time wind speed The power generation capacity below; Indicates photovoltaics in Time of day, light intensity The power generation capacity; Indicates in Total power of thermal power units at any given time; Indicates the total number of thermal power units; The average power of a steel enterprise is calculated using the following formula: ； The objective function aimed at minimizing total adjustment cost is as follows: ； in, This represents the total adjustment cost of steel enterprises; This represents the adjustment cost of the energy storage system as determined by the adjustment cost model of the energy storage system; The first is determined by the adjustment cost model of thermal power units. The adjustment cost of each thermal power unit.

6. The method for coordinated optimization scheduling of steel load and multi-energy system according to claim 1, characterized in that, Solving the multi-objective optimization model yields a collaborative optimization scheduling strategy for steel load and multiple energy systems, including: Step 5.1: Obtain the wind speed and solar intensity data for the day, determine the wind and solar output power through the wind and solar piecewise function, and then incorporate the wind and solar output power into the power balance constraints of the steel enterprise. Step 5.2: Based on the steel load model, determine the decision variables and obtain the initial decision variable set. : ； Step 5.3: Determine whether the load fluctuation exceeds the capacity of the energy storage device. If not, then in a short timescale of minutes, only consider using the energy storage system to regulate the load of the steel enterprise and proceed to step 5.8; if yes, then in a long timescale of hours, proceed to step 5.

4. Step 5.4 assesses the regulation capabilities of the steel load, thermal power units, and energy storage system, including: determining the adjustable power range and the adjustable power range of the steel load based on its minimum and maximum operating power; for grid-connected thermal power units, assessing their feasibility to participate in steel enterprise load regulation based on their adjustable output range; for thermal power units in a shutdown state, assessing their feasibility to participate in steel enterprise load regulation based on their minimum start-stop time constraints; and assessing whether the current state of charge, maximum charging / discharging power, and charging / discharging efficiency of the energy storage system are feasible for participating in steel enterprise load regulation. Step 5.5: Based on the determined steel load, the regulation capacity of thermal power units and energy storage systems, and in conjunction with the constraint system, the NSGA-II algorithm is used to solve the multi-objective optimization model, outputting a Pareto solution set. Each solution in this set corresponds to a trade-off between net load fluctuation and total regulation cost, thus obtaining different trade-offs between net load fluctuation and total regulation cost. The total regulation cost includes the regulation cost of thermal power units, the regulation cost of energy storage systems, and the regulation cost of steel load. Step 5.6: Determine whether the load of the steel enterprise is within the preset fluctuation range. If yes, proceed to step 5.7; otherwise, return to step 5.

4. Step 5.7: Output the final decision variable set. Each decision variable set corresponds to a set of thermal power output, energy storage system charging and discharging power, and steel load power. Steel companies determine adjustment strategies at different time scales based on the decision variable set.

7. A steel load and multi-energy system collaborative optimization scheduling device, characterized in that, The device includes a scheduling and control module, which includes a data acquisition interface module, a memory, a processor, and a computer program stored in the memory and executable on the processor. The data acquisition interface module is used to acquire actual steel load data of steel enterprises and distributed wind power and photovoltaic power generation data. When the processor executes the computer program, it implements the steel load and multi-energy system collaborative optimization scheduling method as described in any one of claims 1 to 6.

8. The steel load and multi-energy system collaborative optimization scheduling device according to claim 7, characterized in that, The device also includes a display interface, which is divided into the following sub-interfaces according to the query function: A flexible interactive interface is used to provide load energy consumption data query, using a line graph to display the energy consumption trend of load changes over time, and supporting real-time data viewing and historical data query; The performance monitoring data interface is used to display time-series data of load performance, showing the performance changes of the load at different time periods through line graphs; The energy efficiency monitoring data interface is used to display the real-time monitoring status of control commands and their execution effects, helping users monitor the execution results of load, energy storage, and thermal power. The alarm query data interface is used for querying, filtering, and managing alarm information. It supports filtering and querying by alarm time, alarm object, alarm content, and alarm type. The carbon efficiency management data interface is used to display the carbon efficiency monitoring and optimization of steel production lines, and provides enterprise carbon efficiency level, carbon efficiency level, carbon efficiency analysis and real-time carbon emission data monitoring. The energy efficiency management interface provides real-time monitoring information and energy efficiency analysis for the production line, displaying energy efficiency levels, industry benchmark comparisons, and energy consumption-related data.

9. The steel load and multi-energy system collaborative optimization scheduling device according to claim 8, characterized in that, The display interface incorporates Star Island, which allows other pages to be displayed simultaneously on the current page via a floating window, enabling quick switching between multiple pages.

10. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the steel load and multi-energy system collaborative optimization scheduling method as described in any one of claims 1 to 6.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steel load and multi-energy system collaborative optimization scheduling method as described in any one of claims 1 to 6.

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