Energy distribution method and device

Abstract

The invention discloses an energy distribution method and device, and relates to the technical field of new energy. The energy distribution method comprises the following steps: acquiring a target energy consumption curve and a target energy supply and demand map of industrial silicon; based on the target energy consumption curve and the target energy supply and demand map, establishing a multi-target mixed integer programming model; based on the multi-target mixed integer programming model, dynamic optimization is carried out through a genetic algorithm or reinforcement learning, and a target energy distribution strategy is obtained; and according to the target energy distribution strategy, first target equipment in the energy system is controlled to execute a control instruction corresponding to the target energy distribution strategy, so that energy distribution of the first target equipment in the energy system is realized. According to the embodiment of the invention, the target energy distribution strategy determined by the method can give consideration to the environmental protection performance and the economic feasibility, so that the energy distribution of the first target equipment in the energy system is realized according to the target energy distribution strategy, the environmental protection performance and the economic feasibility can be taken into consideration, and the reasonability of energy distribution can be improved.

Description

Technical Field

[0001] This application belongs to the field of new energy technology, specifically relating to an energy distribution method and device. Background Technology

[0002] With increasing global emphasis on environmental protection and sustainable development, the proportion of new energy sources in the energy structure is gradually increasing. As a high-energy-consuming industry, industrial silicon production has made energy consumption and carbon emissions a research hotspot in this field.

[0003] Traditional industrial silicon production relies heavily on fossil fuels, resulting in significant carbon emissions and low energy efficiency. Integrating a high proportion of renewable energy into industrial silicon production systems can facilitate a low-carbon transformation of this energy-intensive industry, while simultaneously improving the local utilization efficiency of renewable energy. A high proportion of renewable energy refers to a significant and dominant role of renewable energy (such as solar and wind power) in the energy system (power generation, energy consumption, and power grid). According to the International Energy Agency (IEA) standards, this typically means: a power generation share ≥ 60%; an annual penetration rate growth rate ≥ 5 percentage points; and a peak instantaneous power output share ≥ 80%.

[0004] In existing technologies, after a high proportion of new energy sources are connected to the industrial silicon production system, the energy allocation strategy is usually determined with the goal of minimizing the direct cost of purchasing electricity from the main power source such as the grid, and energy is allocated according to the energy allocation strategy, which results in the problem of unreasonable energy allocation. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide an energy allocation method and apparatus to address the above-mentioned shortcomings of the existing technology. Using this energy allocation method, carbon emission intensity and comprehensive energy efficiency are taken as hard constraints, and carbon trading costs and grid purchase costs are taken as core economic decision variables, so that the target energy allocation strategy determined based on a multi-objective mixed integer programming model can take into account both environmental performance and economic feasibility.

[0006] In a first aspect, embodiments of this application provide an energy distribution method applied to an energy system, the energy distribution method comprising:

[0007] The target energy consumption curve and target energy supply and demand map of industrial silicon are obtained. The target energy consumption curve is the energy consumption curve of different processes in the production of industrial silicon within a future preset period. The target energy supply and demand map is the energy supply and demand map within the future preset period. The energy supply and demand map is used to characterize the data matching relationship between the supply and demand sides of the energy system in the process of industrial silicon production.

[0008] Based on the target energy consumption curve and the target energy supply and demand map, a multi-objective mixed integer programming model is established. The multi-objective mixed integer programming model includes target constraints and target decision variables. The target constraints include carbon emission intensity constraints and comprehensive energy efficiency constraints. The target decision variables include carbon trading cost economic indicators and grid electricity purchase cost economic indicators.

[0009] Based on a multi-objective mixed integer programming model, the target energy allocation strategy is obtained through dynamic optimization using genetic algorithms or reinforcement learning.

[0010] The control system executes control commands corresponding to the target energy allocation strategy for the first target device in the energy system to achieve energy allocation for the first target device in the energy system. The first target device includes photovoltaic / wind power arrays, electrochemical energy storage devices, electric arc furnaces, and waste heat power generation equipment.

[0011] In some embodiments of the first aspect, obtaining the target energy consumption curve for industrial silicon specifically includes:

[0012] Obtain current production data for industrial silicon, including current new energy power generation data, current silicon production process parameters, and current power grid operation data;

[0013] Based on current new energy power generation data, current silicon production process parameters, current power grid operation data, and machine learning algorithms, the target energy consumption curve is predicted.

[0014] In some embodiments of the first aspect, obtaining the target energy supply and demand map for industrial silicon specifically includes:

[0015] Acquire current meteorological data, current power generation equipment status, and current equipment operation data corresponding to industrial silicon;

[0016] Based on the new energy power generation prediction model, the target energy supply and demand map is determined according to the current meteorological data, the current status of power generation equipment, and the current equipment operation data.

[0017] In some implementations of the first aspect, based on a new energy power generation forecasting model, and according to current meteorological data, current power generation equipment status, and current equipment operation data, the target energy supply and demand map for industrial silicon is determined, specifically including:

[0018] Based on the new energy power generation prediction model, the target new energy power generation capacity is determined according to current meteorological data, current power generation equipment status, and current equipment operation data. The new energy power generation prediction model is trained based on historical meteorological data, historical power generation equipment status, historical equipment operation data, and deep learning algorithms.

[0019] Based on the target new energy power generation capacity, determine the target energy supply and demand map.

[0020] In some embodiments of the first aspect, the control commands corresponding to the target energy allocation strategy include multiple real-time control commands for devices.

[0021] The control command for the first target device in the energy system to execute the control command corresponding to the target energy distribution strategy includes:

[0022] Send corresponding real-time control commands to multiple first target devices that correspond one-to-one with the real-time control commands of multiple devices, so that the first target devices corresponding to the real-time control commands of the devices execute their corresponding real-time control commands.

[0023] In some embodiments of the first aspect, the multi-objective mixed-integer programming model further includes an objective function;

[0024] The method also includes:

[0025] Obtain current production data for industrial silicon, including current new energy power generation data, current silicon production process parameters, and current power grid operation data;

[0026] Based on current new energy power generation data, current silicon production process parameters, current power grid operation data, multi-objective optimization algorithms and objective functions, target control instructions are generated. The target control instructions include at least one of the following: power allocation instructions, energy storage system control instructions, process parameter adjustment instructions, carbon capture and utilization instructions, and system safety boundary instructions.

[0027] Sending target control commands to a second target device in the energy system to enable the second target device to execute the target control commands, thereby achieving energy supply and demand balance in the energy system. The second target device includes at least one of photovoltaic / wind power arrays, electrochemical energy storage devices, electric arc furnaces, carbon processing equipment, and safe operation control equipment.

[0028] In some embodiments of the first aspect, the target control commands include power allocation commands, energy storage system control commands, process parameter adjustment commands, carbon capture and utilization commands, and system safety boundary commands;

[0029] After generating the target control instructions, the method further includes:

[0030] The target new energy power generation capacity, target industrial silicon active load, target industrial silicon active load change rate, target energy storage system surplus energy, and target energy storage system energy change amount are obtained for industrial silicon.

[0031] Based on the target new energy power generation capacity, target industrial active load, target industrial silicon active load change rate, target energy storage system remaining energy, target energy storage system energy change, and target control commands, control the power output of photovoltaic / wind power arrays, the charging and discharging capacity of electrochemical energy storage devices, the operating power and process parameters of electric arc furnaces, and the power generation of waste heat power generation equipment, and adjust the operating status of carbon treatment equipment in the energy system while maintaining safety boundary parameters, so as to optimize the operating status and parameters of each equipment in the cogeneration process of electricity and silicon. The safety boundary parameters include at least one of the grid interface voltage and equipment operating load.

[0032] Based on the same inventive concept, in a second aspect, embodiments of this application provide an energy distribution device applied to an energy system, the energy distribution device comprising:

[0033] The first acquisition module is used to acquire the target energy consumption curve and the target energy supply and demand map of industrial silicon. The target energy consumption curve is the energy consumption curve of different processes in the production of industrial silicon within a future preset period. The target energy supply and demand map is the energy supply and demand map within the future preset period. The energy supply and demand map is used to characterize the data matching relationship between the supply and demand sides of the energy system in the process of industrial silicon production.

[0034] The module is established and connected to the first acquisition module. It is used to establish a multi-objective mixed integer programming model based on the target energy consumption curve and the target energy supply and demand map. The multi-objective mixed integer programming model includes target constraints, target decision variables and target functions. The target constraints include carbon emission intensity constraints and comprehensive energy efficiency constraints. The target decision variables include carbon trading cost economic indicators and grid electricity purchase cost economic indicators.

[0035] The optimization module and the connection establishment module are used to dynamically optimize the target energy allocation strategy based on a multi-objective mixed integer programming model through genetic algorithms or reinforcement learning.

[0036] The first control module, connected to the optimization module, is used to control the first target device in the energy system to execute the control commands corresponding to the target energy allocation strategy, so as to realize the energy allocation of the first target device. The first target device includes a photovoltaic / wind power array, an electrochemical energy storage device, a submerged arc furnace, and a waste heat power generation device.

[0037] In some embodiments of the second aspect, the first acquisition module is specifically used for:

[0038] Obtain current production data for industrial silicon, including current new energy power generation data, current silicon production process parameters, and current power grid operation data;

[0039] Based on current new energy power generation data, current silicon production process parameters, current power grid operation data, and machine learning algorithms, the target energy consumption curve is predicted.

[0040] In some embodiments of the second aspect, the first acquisition module is specifically used for:

[0041] Acquire current meteorological data, current power generation equipment status, and current equipment operation data corresponding to industrial silicon;

[0042] Based on the new energy power generation prediction model, the target energy supply and demand map is determined according to the current meteorological data, the current status of power generation equipment, and the current equipment operation data.

[0043] In some embodiments of the second aspect, the control commands corresponding to the target energy allocation strategy include multiple real-time control commands for devices.

[0044] The first control module is specifically used for:

[0045] Send corresponding real-time control commands to multiple first target devices that correspond one-to-one with the real-time control commands of multiple devices, so that the first target devices corresponding to the real-time control commands of the devices execute their corresponding real-time control commands.

[0046] According to the energy allocation method and apparatus provided in this application, the target energy consumption curve and target energy supply and demand map of industrial silicon are first obtained; then, based on the target energy consumption curve and target energy supply and demand map, a multi-objective mixed integer programming model is established; then, based on the multi-objective mixed integer programming model, dynamic optimization is performed through genetic algorithm or reinforcement learning to obtain the target energy allocation strategy; then, the first target device in the energy system is controlled to execute the control command corresponding to the target energy allocation strategy to realize the energy allocation of the first target device. Since the multi-objective mixed integer programming model includes target constraints and target decision variables, the target constraints include carbon emission intensity constraints and comprehensive energy efficiency constraints, and the target decision variables include carbon trading cost economic indicators and grid electricity purchase cost economic indicators. That is, carbon emission intensity and comprehensive energy efficiency are used as hard constraints, and carbon trading cost and grid electricity purchase cost are used as core economic decision variables. This allows the target energy allocation strategy determined based on the multi-objective mixed integer programming model to take into account both environmental performance and economic feasibility. Therefore, according to the target energy allocation strategy, energy is allocated to the first target device in the energy system, which can take into account both environmental performance and economic feasibility, thereby improving the rationality of energy allocation.

[0047] Furthermore, based on a multi-objective mixed integer programming model, dynamic optimization is performed through genetic algorithms or reinforcement learning to obtain the target energy allocation strategy, which can overcome the limitations of linear programming and adapt to the nonlinear energy consumption characteristics of industrial silicon (such as integer decisions for electric furnace start-up and shutdown and raw material switching). Attached Figure Description

[0048] Figure 1This illustration shows a flowchart of an energy distribution method provided in an embodiment of this application;

[0049] Figure 2 This illustration shows a structural diagram of a high-proportion new energy cogeneration system with high efficiency and low carbon operation provided in an embodiment of this application.

[0050] Figure 3 This is a schematic diagram of an energy distribution device provided in an embodiment of this application. Detailed Implementation

[0051] To enable those skilled in the art to better understand the technical solutions of this application, the application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0052] The features and exemplary embodiments of various aspects of this application will now be described in detail. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain this application and are not configured to limit this application. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.

[0053] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0054] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0055] Example 1

[0056] The energy distribution method provided in this application can be applied to energy systems and also to the production process of industrial silicon. This energy distribution method can be executed by an energy distribution device and electronic equipment. The following description uses the execution of this energy distribution method by electronic equipment as an example.

[0057] like Figure 1 As shown, the energy distribution method provided in this application embodiment may include steps S110 to S140.

[0058] S110. Obtain the target energy consumption curve and target energy supply and demand map of industrial silicon. The target energy consumption curve is the energy consumption curve of different processes in the production of industrial silicon within a future preset period. The target energy supply and demand map is the energy supply and demand map within a future preset period. The energy supply and demand map is used to characterize the data matching relationship between the supply side and the demand side of the energy system in the process of industrial silicon production.

[0059] S120. Based on the target energy consumption curve and the target energy supply and demand map, establish a multi-objective mixed integer programming model. The multi-objective mixed integer programming model includes target constraints, target decision variables and objective functions. The target constraints include carbon emission intensity constraints and comprehensive energy efficiency constraints. The target decision variables include carbon trading cost economic indicators and grid electricity purchase cost economic indicators.

[0060] S130. Based on a multi-objective mixed integer programming model, the target energy allocation strategy is obtained through dynamic optimization using genetic algorithms or reinforcement learning.

[0061] S140. The first target device in the control energy system executes the control command corresponding to the target energy allocation strategy to realize the energy allocation of the first target device. The first target device includes a photovoltaic / wind power array, an electrochemical energy storage device, a submerged arc furnace, and a waste heat power generation device.

[0062] According to the energy allocation method provided in this application, the target energy consumption curve and target energy supply and demand map of industrial silicon are first obtained; then, based on the target energy consumption curve and target energy supply and demand map, a multi-objective mixed integer programming model is established; then, based on the multi-objective mixed integer programming model, dynamic optimization is performed through genetic algorithm or reinforcement learning to obtain the target energy allocation strategy; then, the first target device in the energy system is controlled to execute the control command corresponding to the target energy allocation strategy to realize the energy allocation of the first target device. Since the multi-objective mixed integer programming model includes target constraints and target decision variables, the target constraints include carbon emission intensity constraints and comprehensive energy efficiency constraints, and the target decision variables include carbon trading cost economic indicators and grid electricity purchase cost economic indicators. That is, carbon emission intensity and comprehensive energy efficiency are used as hard constraints, and carbon trading cost and grid electricity purchase cost are used as core economic decision variables. This allows the target energy allocation strategy determined based on the multi-objective mixed integer programming model to take into account both environmental performance and economic feasibility. Therefore, according to the target energy allocation strategy, energy is allocated to the first target device in the energy system, which can take into account both environmental performance and economic feasibility, thereby improving the rationality of energy allocation.

[0063] Furthermore, based on a multi-objective mixed integer programming model, dynamic optimization is performed through genetic algorithms or reinforcement learning to obtain the target energy allocation strategy, which can overcome the limitations of linear programming and adapt to the nonlinear energy consumption characteristics of industrial silicon (such as integer decisions for electric furnace start-up and shutdown and raw material switching).

[0064] The specific implementation methods for each of the above steps are described below.

[0065] In step S110, the future preset time period can be set according to the actual situation and is not limited here. For example, the future preset time period can be the next 24 hours, etc.

[0066] For example, data on the supply side of the energy system may include photovoltaic power generation, wind power, hydropower and other renewable energy sources and traditional energy sources; data on the demand side of the energy system may include electricity demand by time period and by region.

[0067] In some implementations, obtaining the target energy consumption curve for industrial silicon specifically includes:

[0068] Obtain current production data for industrial silicon, including current new energy power generation data, current silicon production process parameters, and current power grid operation data;

[0069] Based on current new energy power generation data, current silicon production process parameters, current power grid operation data, and machine learning algorithms, the target energy consumption curve for industrial silicon is predicted.

[0070] For example, current new energy power generation data may include real-time data on the power generation of new energy sources such as photovoltaics and wind power.

[0071] For example, current silicon production process parameters may include the current energy consumption, current load requirements, and current process parameters of the industrial silicon production equipment. These current silicon production process parameters can be acquired in real time through smart sensors.

[0072] For example, current power grid operation data may include current power grid price fluctuations and current meteorological data. The current meteorological data includes various meteorological information for a specific time period, specifically including temperature, humidity, precipitation, wind speed, and air pressure parameters. These parameters comprehensively reflect the weather conditions for a given time period.

[0073] For example, machine learning algorithms include decision tree algorithms and random forest algorithms.

[0074] In some implementations, obtaining the target energy supply and demand profile for industrial silicon specifically includes:

[0075] Acquire current meteorological data, current power generation equipment status, and current equipment operation data corresponding to industrial silicon;

[0076] Based on the new energy power generation prediction model, the target energy supply and demand map is determined according to the current meteorological data, the current status of power generation equipment, and the current equipment operation data.

[0077] For example, the current status of the power generation equipment is used to reflect the current operating condition and health of the power generation equipment, specifically including the current equipment temperature, current vibration, current current and voltage parameters, etc. These parameters can reflect the operating status of the equipment in real time.

[0078] For example, current equipment operation data refers to various data generated by the power generation equipment during operation, specifically including current power generation, current power generation efficiency, and current equipment operation time indicators.

[0079] In some examples, based on new energy power generation forecasting models, and according to current meteorological data, current power generation equipment status, and current equipment operation data, the target energy supply and demand map for industrial silicon is determined, specifically including:

[0080] Based on the new energy power generation prediction model, the target new energy power generation capacity is determined according to current meteorological data, current power generation equipment status, and current equipment operation data. The new energy power generation prediction model is trained based on historical meteorological data, historical power generation equipment status, historical equipment operation data, and deep learning algorithms.

[0081] Based on the target new energy power generation capacity, determine the target energy supply and demand map.

[0082] For example, deep learning algorithms include neural networks and attention mechanisms, among others.

[0083] For example, the new energy power generation forecasting model predicts the target new energy power generation capacity based on current meteorological data, current power generation equipment status, and current equipment operation data. The target new energy power generation capacity can be the new energy power generation capacity at multiple different times in the future. Based on the target new energy power generation capacity, a new energy power generation capacity curve can be generated; the new energy power generation capacity curve is aligned with the time axis of a pre-stored load forecast curve to generate a time-segmented supply and demand balance sheet; and based on the time-segmented supply and demand balance sheet, the target new energy supply and demand map for industrial silicon is determined.

[0084] In step S120, after acquiring the target energy consumption curve and target energy supply and demand map of industrial silicon, the electronic device can also establish a multi-objective mixed integer programming model based on the target energy consumption curve and target energy supply and demand map.

[0085] For example, a multi-objective mixed-integer programming model can be used to balance the renewable energy absorption rate with production stability. The multi-objective mixed-integer programming model also includes an objective function.

[0086] For example, the carbon emission intensity constraint includes: system carbon emission intensity ≤ X kg / kWh; X can be set according to actual conditions.

[0087] For example, the comprehensive energy efficiency constraint includes: comprehensive energy efficiency ≥ Y%. Y can be set according to actual conditions.

[0088] For example, the objective function may include:

[0089]

[0090] Where t is the time step, P renewable (t) represents the output power of the new energy source at time t, P demand (t) represents the power demand of the industrial silicon smelting process at time t, P assign (t) represents the new energy power allocated to the industrial silicon smelting process; T is the total number of time steps.

[0091] The objective function described above aims to ensure that the renewable energy power allocated to industrial silicon smelting is as close as possible to the renewable energy output power, thereby reducing the waste of renewable energy electricity. The objective function calculates the sum of the smaller of the allocated renewable energy power and the actual renewable energy output power over the entire time period; a larger sum indicates that more renewable energy is being utilized.

[0092] For example, in a specific implementation, when establishing a multi-objective mixed-integer programming model based on the target energy consumption curve and the target energy supply and demand map, the target constraints are first clarified, including carbon emission intensity constraints (e.g., system carbon emission intensity ≤ X kg / kWh) and comprehensive energy efficiency constraints (comprehensive energy efficiency ≥ Y%), to ensure environmental compliance. Next, target decision variables are determined, with carbon trading costs and grid purchase costs as core economic indicators to measure economic feasibility. Regarding the objective function, for example, with the goal of maximizing renewable energy consumption, a function is constructed that calculates the sum of the smaller values ​​of the renewable energy power allocated to industrial silicon smelting and the renewable energy output power, prompting the allocated renewable energy power to be as close as possible to the output power, reducing waste.

[0093] Taking industrial silicon production as an example, assuming the target energy consumption curve shows the peak energy consumption during a certain period, and combining the predicted power generation of new energy sources in the target energy supply and demand map, constraints are set in the model, such as the carbon emission intensity during this period not exceeding 300 kg / kWh and the comprehensive energy efficiency not less than 85%. The decision variables are the carbon trading cost and the grid purchase cost during this period. The allocation of new energy sources is optimized through the objective function to achieve a balance between environmental protection and the economy.

[0094] In step S130, after establishing a multi-objective mixed integer programming model based on the target energy consumption curve and the target energy supply and demand map, the electronic device can also dynamically optimize the target energy allocation strategy based on the multi-objective mixed integer programming model through genetic algorithms or reinforcement learning.

[0095] For example, the target energy allocation strategy can be an energy allocation strategy that can balance environmental compliance and economic feasibility.

[0096] For example, in a specific implementation, the target energy allocation strategy aims to balance environmental compliance and economic feasibility. For instance, in the industrial silicon production process, the target energy consumption curve and target energy supply and demand map are first obtained. A multi-objective mixed-integer programming model is established, including hard constraints such as carbon emission intensity (e.g., system carbon emission intensity ≤ 300 kg / kWh) and comprehensive energy efficiency (e.g., comprehensive energy efficiency ≥ 85%), as well as carbon trading costs and grid purchase costs as core decision variables. After dynamic optimization using a genetic algorithm, a strategy is obtained that prioritizes the consumption of renewable energy, with any shortfall supplemented by energy storage systems or the grid. Specifically, when the photovoltaic array's power generation is 500 kW during a certain period, and the industrial silicon smelting demand is 600 kW, 500 kW of renewable energy is allocated, and the remaining 100 kW is supplemented by the energy storage system's discharge. Simultaneously, it is ensured that the carbon emission intensity during this period does not exceed the constraint value. This strategy is decomposed into real-time control commands such as photovoltaic array power adjustment and energy storage system discharge through the EMS system and sent to the corresponding equipment for execution.

[0097] In some implementations, the multi-objective mixed-integer programming model also includes an objective function;

[0098] The method further includes:

[0099] Obtaining the current production data of metallurgical grade silicon, where the current production data includes current new energy power generation data, current silicon production process parameters, and current power grid operation data;

[0100] Generating a target control instruction according to the current new energy power generation data, current silicon production process parameters, current power grid operation data, a multi-objective optimization algorithm, and an objective function, where the target control instruction includes at least one of a power distribution instruction, an energy storage system control instruction, a process parameter adjustment instruction, a carbon capture and utilization instruction, and a system safety boundary instruction;

[0101] Sending the target control instruction to a second target device of the energy system so that the second target device executes the target control instruction, thereby achieving the energy supply-demand balance of the energy system, where the second target device includes at least one of a photovoltaic / wind power array, an electrochemical energy storage device, a submerged arc furnace, a carbon treatment device, and a safety operation control device.

[0102] Exemplarily, the target control instruction can be an optimal control instruction, and the optimal control instruction can be used to characterize the core control signal of the coordinated regulation strategy and parameter setting for each subsystem and device under the goal of realizing the efficient and low-carbon operation of a high-proportion new energy power-silicon co-production system.

[0103] Exemplarily, the carbon capture and utilization instruction (CCUD) is a mandatory legal framework established by the EU to achieve the carbon neutrality goal, aiming to systematically regulate the whole process of carbon capture, resource utilization, and geological sequestration of industrial carbon emissions.

[0104] Exemplarily, the system safety boundary instruction generally refers to the mandatory operation rules and technical standards formulated in the fields of power systems, industrial control, or critical infrastructure to ensure the safe and stable operation of the system, and its core is to define the physical limit values of the system's safe operation and the overlimit handling process.

[0105] Exemplarily, a power distribution instruction can be sent to the photovoltaic / wind power array; an energy storage system control instruction can be sent to the electrochemical energy storage device; a process parameter condition instruction can be sent to the submerged arc furnace; a carbon capture and utilization instruction can be sent to the carbon treatment device; a system safety boundary instruction can be sent to the safety operation control device. Among them, the carbon treatment device can be a carbon capture-related device, that is, the carbon treatment device supporting the production of metallurgical grade silicon; the safety operation control device can be a control device responsible for safe operation in the energy system, such as a power grid interface protection device and an equipment overload protection device, etc.

[0106] Understandably, by integrating multi-dimensional data such as new energy power generation data and production process parameters, and based on multi-objective optimization algorithms and objective functions, comprehensive instructions covering power allocation, energy storage control, process regulation, carbon capture, and safety boundaries are generated. This achieves synergistic optimization of environmental performance (carbon emission intensity constraints) and economic feasibility (carbon trading costs and grid purchase costs), breaking through the traditional energy allocation model that only takes purchase cost as a single objective, and improving the rationality of energy allocation and the efficiency of system operation.

[0107] For example, by acquiring current production data of industrial silicon, and based on the objective function mentioned above and existing multi-objective optimization algorithms, optimal control commands can be generated, enabling real-time adjustment of energy supply and demand balance.

[0108] For example, current production data of industrial silicon can be combined with intelligent decision-making algorithms to generate target control commands, thereby improving or resolving the contradiction between the volatility of new energy sources and the stability of industrial load, and achieving real-time adjustment of energy supply and demand balance. The intelligent decision-making algorithm includes a reinforcement learning algorithm, the specific formula of which is:

[0109] Q(s t ,a t )←Q(s t ,a t )+α[r t+1 +γmax a Q(s t+1 ,a)-Q(s t ,a t )]

[0110] Among them, s t It is the state at time step t, such as the state vector composed of parameters like the current renewable energy generation power, grid voltage, and industrial silicon production temperature. t The action taken at time step t is the control instruction, and α is the learning rate (0 < α ≤ 1), which controls the degree to which new information overwrites old information; the larger α is, the greater the impact of new experience on the Q value; the smaller α is, the slower the Q value is updated; r t+1 Action a is taken at time step t. t Then, the immediate reward obtained at time step t+1; γ is a discount factor (0≤γ≤1), reflecting the importance attached to future rewards. The closer γ is to 1, the more the agent values ​​long-term future rewards; the closer γ is to 0, the more the agent focuses on immediate rewards; max a Q(s t+1 a) represents the state s at time step t+1. t+1 Given the maximum Q value that can be obtained by taking all possible actions, where Q is the expected cumulative return.

[0111] In some implementations, the target control commands include power allocation commands, energy storage system control commands, process parameter adjustment commands, carbon capture and utilization commands, and system safety boundary commands;

[0112] After generating the target control instructions, the method further includes:

[0113] The target new energy power generation capacity, target industrial silicon active load, target industrial silicon active load change rate, target energy storage system surplus energy, and target energy storage system energy change amount are obtained for industrial silicon.

[0114] Based on the target new energy power generation capacity, target industrial active load, target industrial silicon active load change rate, target energy storage system remaining energy, target energy storage system energy change, and target control commands, control the power output of photovoltaic / wind power arrays, the charging and discharging capacity of electrochemical energy storage devices, the operating power and process parameters of electric arc furnaces, and the power generation of waste heat power generation equipment, and adjust the operating status of carbon treatment equipment in the energy system while maintaining safety boundary parameters, so as to optimize the operating status and parameters of each equipment in the cogeneration process of electricity and silicon. The safety boundary parameters include at least one of the grid interface voltage and equipment operating load.

[0115] In other words, it drives the integrated optimization control of cogeneration (cogeneration of electricity and silicon). Integrated optimization control of cogeneration of electricity and silicon refers to building a data-driven closed-loop control system by integrating power generation and silicon material manufacturing processes to achieve global coordinated optimization control of energy flow, material flow, and process parameters. Specifically, based on the target renewable energy generation capacity, target industrial silicon active load, target industrial silicon active load change rate, target energy storage system remaining energy, target energy storage system energy change, as well as power allocation commands, energy storage system control commands, process parameter condition commands, carbon capture and utilization commands, and system safety boundary commands, it controls the power output of photovoltaic / wind power arrays, the charging and discharging of electrochemical energy storage devices, the operating power and process parameters of the electric arc furnace, and the power generation of waste heat power generation equipment. Simultaneously, it adjusts the operating status of carbon capture equipment and maintains system safety boundary parameters such as grid interface voltage and equipment operating load, thereby achieving dynamic optimization of the operating status and key parameters of each piece of equipment in the cogeneration process.

[0116] For example, the target renewable energy power generation capacity for industrial silicon can be obtained through a renewable energy power generation prediction model. This involves inputting current meteorological data, current power generation equipment status, and current equipment operation data into the renewable energy power generation prediction model, and then using the model to make a prediction to obtain the target renewable energy power generation capacity.

[0117] For example, the target industrial active load and the rate of change of the target industrial active load for industrial silicon can be obtained through a dynamic characteristic model of industrial silicon load. That is, the load reference setpoint P... LS,refThe system frequency deviation Δf is input into the dynamic characteristic model of industrial silicon load, and the target industrial silicon active power load P is calculated. LS The dynamic model of industrial silicon load includes:

[0118]

[0119] Among them, P LS P represents the current active power load of industrial silicon, which is also the target active power load of industrial silicon, expressed in MW. LS,ref This is a reference setpoint for industrial silicon load, in MW, given by production process requirements or scheduling instructions; T LS K represents the industrial silicon load response time constant, measured in seconds. It characterizes the load's response speed to changes in a reference value and is related to the inertia of the electric furnace equipment and the regulation characteristics of the control system. LS The frequency regulation coefficient for industrial silicon load, in MW / Hz, reflects the degree of influence of system frequency fluctuation Δf on the load; Δf is the system frequency deviation, in Hz, which is the difference between the actual frequency and the rated frequency. The rate of change of industrial silicon load, also known as the rate of change of target industrial silicon active power load, is expressed in MW / s and describes the dynamic adjustment process of the load over time.

[0120] For example, the remaining energy of the target energy storage system can be obtained through an energy storage system model. That is, the time interval Δt; control command: charging power P ch,t or discharge power P dis,t Input the energy storage system model to calculate the remaining energy of the target energy storage system. The energy storage system model includes:

[0121]

[0122] Among them, E t E represents the remaining energy of the energy storage system at time t, which is also the remaining energy of the target energy storage system; t-1 η represents the remaining energy of the energy storage system at time t-1; ch The charging efficiency of the energy storage system; P ch,t Let P be the charging power at time t, and P be the charging power when the system is in a discharging state. ch,t =0; Δt is the time interval used to discretize the time series; η dis P represents the discharge efficiency of the energy storage system. dis,t Let P be the discharge power at time t, and P be the discharge power when the system is in a charging state. dis,t =0; L t The energy loss of the energy storage system during the time period t is related to the system's resting time and environmental conditions.

[0123] For example, the energy change of the target energy storage system can be obtained through a grid interaction model. That is, the active power input from the grid, the active power output from the grid, the power generation of the photovoltaic power plant, the power generation of the wind farm, the non-silicon production load within the system, the power consumption of the silicon production process, and the calculation time interval are input into the grid interaction model to calculate the energy change of the target energy storage system. The grid interaction model includes:

[0124]

[0125] Among them, P grid,in P represents the active power input to the system from the power grid. grid,out P represents the active power output of the system to the power grid. pv P represents the power generation capacity of a photovoltaic power station. wt P represents the power generation capacity of the wind farm. lood For non-silicon production electrical loads within the system; P si-productio Power consumption for silicon manufacturing processes; ΔE storage This represents the energy change of the energy storage system within a time interval Δt; Δt is the calculation time interval.

[0126] In this embodiment, a structured real-time model is used to drive the integrated optimization control of cogeneration of electricity and silicon by using the target new energy power generation, the target industrial active load, the target industrial silicon active load change rate, the target energy storage system remaining energy, the target energy storage system energy change, and the target control command, thereby realizing the dynamic updating and optimization of the multi-dimensional digital twin system.

[0127] It should be noted that by integrating power generation and silicon material manufacturing processes, a data-driven closed-loop control system is constructed to achieve global collaborative optimization control of energy flow, material flow, and process parameters. This innovation emphasizes systematic optimization control and breaks through the limitations of separating energy and material management in traditional industrial silicon production. By combining multi-dimensional parameters such as target new energy power generation, target industrial silicon active load and its rate of change, target energy storage system remaining energy, and target energy change, and working in conjunction with target control commands, the integrated optimization control of power-silicon cogeneration is driven. This strategy improves the accuracy and adaptability of energy allocation and production scheduling, while taking into account both environmental performance and economic feasibility.

[0128] For example, the system integration scheme can be adjusted according to the geographical conditions, energy structure and industrial silicon production capacity demand of the demonstration project to ensure the efficient and stable operation of the high-proportion new energy cogeneration system with high efficiency and low carbon emissions.

[0129] Specifically, geographical conditions mainly include sunlight conditions, wind resources, topography, and climate factors; energy structure mainly includes the proportion of new energy sources, the use of traditional energy sources, and the stability of energy supply; and industrial silicon production capacity demand mainly includes production capacity scale, production process requirements, and load characteristics. The system integration solution here includes: configuring a high proportion of photovoltaic power plants in areas with abundant sunlight, coupled with energy storage systems to mitigate power fluctuations; and employing waste heat recovery technology in the industrial silicon smelting process to convert waste heat into steam or electricity for plant heating or supplementing power needs.

[0130] For example, advanced heat exchange technology can be used to efficiently capture the heat energy in the high-temperature waste gas generated during industrial silicon smelting and convert it into usable heat or electricity. Furthermore, a multi-energy complementary strategy can be employed to integrate the recovered waste heat with other renewable energy sources, forming a complementary energy supply system that maximizes energy efficiency and reduces dependence on traditional fossil fuels. This multi-energy complementary strategy includes coordinating wind, solar, and energy storage with traditional energy sources; waste heat recovery and utilization; combined power, heating, and cooling (CHP); and coupling hydrogen energy storage. In other words, through waste heat recovery and a multi-energy complementary strategy, energy efficiency is improved, and dependence on traditional fossil fuels is reduced.

[0131] For example, the energy management system monitors and analyzes the system's energy efficiency, carbon emissions, and environmental impact in real time. Through data analysis and intelligent prediction, this high-proportion new energy cogeneration high-efficiency low-carbon operating system can promptly identify energy waste and potential environmental risks, propose improvement measures, and further optimize the operating efficiency and environmental performance of the high-proportion new energy cogeneration high-efficiency low-carbon operating system.

[0132] In step S140, after the electronic device obtains the target energy allocation strategy by dynamically optimizing through genetic algorithm or reinforcement learning based on a multi-objective mixed integer programming model, it can also allocate energy to the first target device in the energy system according to the target energy allocation strategy.

[0133] Specifically, the allocation process is as follows: The electricity generated by the photovoltaic / wind power array is prioritized for direct supply to the high-efficiency ferroelectric furnace; the EMS monitors the power generation of wind and solar power and the electricity demand of the ferroelectric furnace in real time. When the wind and solar power generation exceeds the immediate demand of the ferroelectric furnace, the surplus electricity is transferred to the electrochemical energy storage device for storage; if the wind and solar power generation is insufficient, the system activates other backup power sources or the energy storage device to discharge and supplement, ensuring the stable operation of the ferroelectric furnace; the electrochemical energy storage device plays a "peak shaving and valley filling" role in the system; during peak wind and solar power generation periods, the energy storage device charges and stores excess electricity; during off-peak wind and solar power generation periods, at night, or when the electricity load of the ferroelectric furnace suddenly increases, the energy storage device discharges... Electricity is released to ensure a stable power supply for the ferroelectric furnace, reducing reliance on the traditional power grid. Waste heat generated during the operation of the high-efficiency ferroelectric furnace is recovered and used to generate electricity by waste heat power generation equipment. The EMS (Electric Power Management System) rationally allocates the electricity generated by waste heat power generation based on the operating status of the ferroelectric furnace, the power output of waste heat power generation, and the overall power demand of the system. If the power demand of the ferroelectric furnace is not saturated at this time, waste heat power generation can prioritize supplying the ferroelectric furnace. If the power demand of the ferroelectric furnace is met, the electricity generated by waste heat power generation can be used to charge the energy storage device or connected to the grid. Based on weather forecasts, ferroelectric furnace production plans, and other data, the EMS predicts the power output of wind and solar power generation and the power load of the ferroelectric furnace in advance, and formulates equipment operation optimization strategies.

[0134] In the embodiments of this application, the operation of photovoltaic / wind power arrays, electrochemical energy storage devices, high-efficiency electric arc furnaces and waste heat power generation equipment is coordinated according to the target energy allocation strategy, which can achieve efficient synergy between energy flow and material flow.

[0135] In some implementations, the control commands corresponding to the target energy distribution strategy include multiple real-time control commands for devices;

[0136] The control command for the first target device in the energy system to execute the control command corresponding to the target energy distribution strategy includes:

[0137] Send corresponding real-time control commands to multiple first target devices that correspond one-to-one with the real-time control commands of multiple devices, so that the first target devices corresponding to the real-time control commands of the devices execute their corresponding real-time control commands.

[0138] For example, an energy management system (EMS) can be used to decompose the distribution strategy into real-time control instructions for each device, prioritizing the consumption of renewable energy power, with any shortfall supplemented by energy storage systems or the power grid.

[0139] For example, a real-time monitoring node can be deployed to compare the actual energy consumption with the target energy allocation strategy every 15 minutes. When the deviation is large, an adaptive adjustment mechanism is triggered to determine the execution accuracy of the target energy allocation strategy.

[0140] To better understand the energy distribution method provided in the embodiments of this application, a specific implementation method will be described below.

[0141] like Figure 2 As shown, a high-proportion new energy power-silicon cogeneration high-efficiency low-carbon operation system is provided. This system can be used to implement the above-mentioned energy distribution method. The system includes a dynamic matching control module, a real-time control module, a structured model driven control platform, a source-grid-load-storage integrated module, a customized engineering demonstration application module, and a waste heat cascade utilization module.

[0142] The dynamic matching control module constructs a dynamic matching model between the new energy power generation-thermal power coupling and the industrial silicon production load, targeting the periodic and high energy consumption characteristics of large-scale industrial silicon loads.

[0143] The real-time control module is used to provide a distributed real-time control system for high-proportion new energy cogeneration projects. It adopts an "edge computing + cloud platform" architecture to achieve millisecond-level response for energy supply and demand balance regulation.

[0144] The structured model-driven control platform is a structured real-time model-driven platform for integrated optimization control of cogeneration of electricity and silicon. It establishes a multi-dimensional digital twin system that includes a new energy power generation prediction model, an industrial silicon load dynamic characteristic model, an energy storage system model, and a grid interaction model.

[0145] The integrated module of source-grid-load-storage optimizes the system topology and integrates large-scale photovoltaic / wind power arrays, electrochemical energy storage devices, high-efficiency electric arc furnaces and waste heat power generation equipment to achieve efficient coordination of energy flow and material flow.

[0146] Customized engineering demonstration application modules combine the geographical conditions, energy structure, and industrial silicon production capacity requirements of demonstration projects to design customized system integration solutions;

[0147] The waste heat cascade utilization module further improves energy utilization efficiency through waste heat recovery and multi-energy complementarity.

[0148] This application discloses a high-proportion, high-efficiency, low-carbon cogeneration system for renewable energy and silicon, comprising a dynamic matching control module, a real-time control module, a structured model-driven control platform, an integrated module for source-grid-load-storage, a customized engineering demonstration application module, and a waste heat cascade utilization module. By integrating renewable energy power generation forecasting, dynamic characteristic analysis of industrial silicon load, energy storage system optimization, and a grid interaction model, it achieves precise matching and efficient utilization of renewable energy power and industrial silicon production load. The synergistic effect of the dynamic matching control module and the real-time control module effectively resolves the contradiction between the volatility of renewable energy and the stability of industrial load, ensuring stable system operation and maximized energy utilization.

[0149] Optionally, the dynamic matching control module uses a multi-objective optimization algorithm to sense the fluctuations in renewable energy output and the power demand of the industrial silicon smelting process in real time, dynamically adjusting the energy allocation strategy to maximize the absorption of renewable energy power while ensuring the stability of industrial silicon production; the multi-objective optimization algorithm formula is:

[0150] Let t be the time step, P renewable (t) represents the output power of the new energy source at time t, P demand (t) represents the power demand of the industrial silicon smelting process at time t, P assign (t) represents the renewable energy power allocated to the industrial silicon smelting process; the objective is to make the renewable energy power allocated to the industrial silicon smelting process as close as possible to the output power of the renewable energy, thereby reducing the waste of renewable energy power; the objective function is defined as:

[0151]

[0152] Where T is the total number of time steps; the formula calculates the sum of the smaller of the allocated renewable energy power and the actual renewable energy output power over the entire time range. The larger the sum, the more renewable energy is absorbed.

[0153] Optionally, the real-time control module integrates a multi-source data acquisition module, including new energy power generation data, grid operation parameters, and industrial silicon production process parameters. Combined with an intelligent decision-making algorithm, it generates optimal control commands to resolve the conflict between the volatility of new energy sources and the stability of industrial load. The intelligent decision-making algorithm is a reinforcement learning algorithm, and its specific formula is as follows:

[0154] Q(s t ,a t )←Q(s t ,a t )+α[r t+1 +γmax a Q(s t+1 ,a)-Q(s t ,a t )]

[0155] Among them, s t It is the state at time step t, such as the state vector composed of parameters like the current renewable energy generation power, grid voltage, and industrial silicon production temperature. t The action taken at time step t is the control instruction, and α is the learning rate (0 < α ≤ 1), which controls the degree to which new information overwrites old information; the larger α is, the greater the impact of new experience on the Q value; the smaller α is, the slower the Q value is updated; r t+1 Action a is taken at time step t. tThen, the immediate reward obtained at time step t+1; γ is a discount factor (0≤γ≤1), reflecting the importance attached to future rewards. The closer γ is to 1, the more the agent values ​​long-term future rewards; the closer γ is to 0, the more the agent focuses on immediate rewards; max a Q(s t+1 a) represents the state s at time step t+1. t+1 Under these conditions, the maximum Q value that can be obtained by taking all possible actions.

[0156] Optionally, the methods for establishing the new energy power generation prediction model, industrial silicon load dynamic characteristic model, energy storage system model, and grid interaction model in the structured model-driven control platform are as follows: The new energy power generation prediction model adopts a deep learning algorithm, using historical meteorological data, power generation equipment status, and operating data as input features. Historical meteorological data includes various meteorological information over a past period, specifically including temperature, humidity, precipitation, wind speed, and air pressure parameters. These parameters comprehensively reflect the weather conditions over a past period. Power generation equipment status data reflects the operating status and health of the power generation equipment, specifically including equipment temperature, vibration, and current and voltage parameters. These parameters reflect the operating status of the equipment in real time. Power generation equipment operating data refers to various data generated by the power generation equipment during operation, specifically including power generation, power generation efficiency, and equipment operating time indicators. The model for predicting new energy power generation is then trained.

[0157] The dynamic characteristic model of industrial silicon load is based on the physicochemical principles of the industrial silicon smelting process, combined with real-time collected production data, mainly including: energy production data, silicon production data, system operating parameters, environmental monitoring data, and energy storage and dispatch data. A dynamic equation describing the changes in industrial silicon load is established, expressed as:

[0158]

[0159] Among them, P LS P represents the actual active power load of industrial silicon at the current moment, in MW. LS,ref This is a reference setpoint for industrial silicon load, in MW, given by production process requirements or scheduling instructions; T LS K represents the industrial silicon load response time constant, measured in seconds. It characterizes the load's response speed to changes in a reference value and is related to the inertia of the electric furnace equipment and the regulation characteristics of the control system. LS The frequency regulation coefficient for industrial silicon load, in MW / Hz, reflects the degree of influence of system frequency fluctuation Δf on the load; Δf is the system frequency deviation, in Hz, which is the difference between the actual frequency and the rated frequency. The rate of change of industrial silicon load, in MW / s, describes the dynamic adjustment process of the load over time; Inputs and outputs of the dynamic characteristic model of industrial silicon load: Input: Load reference setpoint P LS,ref System frequency deviation Δf; Output: Industrial silicon active load P LS and its rate of change over time It is used to reflect the dynamic response of industrial silicon load in real time under external commands and power grid frequency disturbances.

[0160] Based on the charging and discharging characteristics, capacity limitations, and efficiency factors of the energy storage device, the energy storage system model constructs the state transition equations of the energy storage system, expressed as follows:

[0161]

[0162] Among them, E t E represents the remaining energy of the energy storage system at time t. t-1 η represents the remaining energy of the energy storage system at time t-1; ch The charging efficiency of the energy storage system; P ch,t Let P be the charging power at time t, and P be the charging power when the system is in a discharging state. ch,t =0; Δt is the time interval used to discretize the time series; η dis P represents the discharge efficiency of the energy storage system. dis,t Let P be the discharge power at time t, and P be the discharge power when the system is in a charging state. dis,t =0; L t The energy loss of the energy storage system during time interval t is related to the system's resting time and environmental conditions; the input parameters of the energy storage system model are: time interval Δt; and the control command is: charging power P. ch,t or discharge power P dis,t Output parameter: Current remaining energy E of the energy storage system t .

[0163] The grid interaction model considers grid structure, transmission losses, and dispatch strategies (mainly including priority dispatch of new energy sources, load forecasting and coordinated dispatch, optimized dispatch of energy storage systems, low-carbon target-oriented dispatch, and dynamic response dispatch) to simulate the energy exchange process between the grid and new energy power generation, industrial silicon loads, and energy storage systems. The expression for the grid interaction model is:

[0164]

[0165] Among them, P grid,in P represents the active power input to the system from the power grid. grid,out P represents the active power output of the system to the power grid. pv P represents the power generation capacity of a photovoltaic power station. wt P represents the power generation capacity of the wind farm. loodFor non-silicon production electrical loads within the system; P si-production Power consumption for silicon manufacturing processes; ΔE storage This represents the energy change of the energy storage system within a time interval Δt; Δt is the calculation time interval.

[0166] Optionally, optimizing the system topology involves adopting a hierarchical distributed structure, modularizing key components such as photovoltaic / wind power arrays, electrochemical energy storage devices, high-efficiency electric arc furnaces, and waste heat power generation equipment. These modules interact and coordinate control via a high-speed communication network. This design not only improves the system's flexibility and scalability but also facilitates future maintenance and upgrades. Furthermore, by integrating advanced power electronic devices, including DC / DC converters and DC / AC inverters, efficient energy conversion and flexible scheduling are achieved, further enhancing the overall system performance.

[0167] Optional, customized engineering demonstration application modules include configuring a high proportion of photovoltaic power stations in areas with sufficient sunlight, and pairing them with energy storage systems to smooth out power fluctuations; and using waste heat recovery technology in the industrial silicon smelting process to convert waste heat in the smelting process into steam or electricity for heating the plant area or supplementing power demand.

[0168] Optionally, in the waste heat cascade utilization module, the waste heat recovery system adopts advanced heat exchange technology to efficiently capture the heat energy in the high-temperature waste gas generated during industrial silicon smelting and convert it into usable heat energy or electrical energy. This waste heat cascade utilization module also incorporates a multi-energy complementary strategy, mainly including: wind, solar and energy storage synergy with traditional energy sources, waste heat recovery and utilization, combined power, heat and cooling supply and hydrogen energy storage coupling, so as to form a complementary energy supply system with other renewable energy sources, thereby maximizing energy utilization efficiency and reducing dependence on traditional fossil energy.

[0169] Optionally, the system also includes a comprehensive energy efficiency management system for real-time monitoring and analysis of the system's energy efficiency, carbon emissions, and environmental impact. Through data analysis and intelligent prediction, the system can promptly identify energy waste and potential environmental risks, propose improvement measures, and further optimize the system's operating efficiency and environmental performance.

[0170] Optionally, the energy efficiency management system can also be integrated with other relevant systems or platforms, such as smart grids and the Industrial Internet of Things (IIoT), to achieve broader data sharing and collaborative control. This integration not only enhances the system's intelligence level but also promotes the development of the energy internet, providing strong support for achieving broader and more efficient energy utilization.

[0171] An operation method (i.e., energy distribution method) for a high-proportion new energy-silicon cogeneration high-efficiency low-carbon operation system includes the following operation steps:

[0172] Step 1: System initialization, configuring parameters for each module, and establishing the initial states of the new energy power generation prediction model, the industrial silicon load dynamic characteristic model, the energy storage system model, and the grid interaction model;

[0173] Step Two: The dynamic matching control module formulates an energy allocation strategy based on the new energy power generation forecast results to achieve dynamic matching between new energy power and industrial silicon production. The specific process of formulating the energy allocation strategy is as follows: real-time collection of new energy power generation data such as photovoltaic and wind power (i.e., current new energy power generation data); real-time energy consumption, load demand, and process parameters of electrolytic silicon production equipment (i.e., current industrial silicon production process parameters) are obtained through intelligent sensors; an energy data center is established to aggregate grid electricity price fluctuations and meteorological forecast data (i.e., current grid operating parameters) to form a multi-source data fusion energy information database; based on historical production data (i.e., current production data) and machine learning algorithms, the energy consumption curves of different processes in electrolytic silicon production are predicted; combined with the new energy power generation forecast model, a dynamic energy supply and demand map for the next 24 hours is generated and marked. Peak and valley periods and power gaps; with system carbon emission intensity ≤ X kg / kWh and comprehensive energy efficiency ≥ Y% as constraints, a multi-objective mixed integer programming model is established to balance the renewable energy absorption rate and production stability; carbon trading costs and grid power purchase costs are introduced as economic indicators, and the optimal energy allocation scheme is output through dynamic optimization using genetic algorithms or reinforcement learning; the allocation strategy is decomposed into real-time control commands for each device using the energy management system (EMS), prioritizing the absorption of renewable energy power, with the shortfall supplemented by energy storage systems or the grid; real-time monitoring nodes are deployed to compare the deviation between actual energy consumption and allocation strategy every 15 minutes, triggering an adaptive adjustment mechanism to ensure the accuracy of strategy execution; the dynamic matching control module formulates energy allocation strategies based on renewable energy power generation forecasts and industrial silicon production load demands to achieve dynamic matching between renewable energy power and industrial silicon production;

[0174] Step 3: The real-time control module acquires new energy power generation data, grid operation parameters, and industrial silicon production process parameters through the multi-source data acquisition module. Based on the objective function mentioned above and existing multi-objective optimization algorithms, it generates optimal control commands. Optimal control commands are core control signals characterizing the coordinated control strategies and parameter settings of various subsystems and equipment in a high-proportion new energy-silicon cogeneration system to achieve the goal of high-efficiency and low-carbon operation. Optimal control commands include power allocation commands, energy storage system control commands, process parameter adjustment commands, carbon capture and utilization (CCU) commands, and system safety boundary commands, adjusting the energy supply and demand balance in real time. The real-time control module acquires new energy power generation data, grid operation parameters, and industrial silicon production process parameters through the multi-source data acquisition module, and combines this with intelligent decision-making algorithms to generate optimal control commands, adjusting the energy supply and demand balance in real time.

[0175] Step 4: The structured model-driven control platform uses the real-time data corresponding to the optimal control command in Step 3 and the prediction results of the four models to drive the integrated optimization control of the cogeneration of electricity and silicon in a structured real-time model, thereby realizing the dynamic updating and optimization of the multi-dimensional digital twin system.

[0176] Step 5: The integrated source-grid-load-storage module coordinates the operation of photovoltaic / wind power arrays, electrochemical energy storage devices, high-efficiency submerged arc furnaces, and waste heat power generation equipment based on the optimized system topology and energy distribution strategy, achieving efficient synergy between energy flow and material flow. The specific coordination process is as follows: The electricity generated by the photovoltaic / wind power arrays is preferentially supplied directly to the high-efficiency submerged arc furnace; the EMS monitors the power generation of wind and solar power and the power demand of the submerged arc furnace in real time. When the wind and solar power generation exceeds the immediate demand of the submerged arc furnace, the surplus electricity will be transferred to the electrochemical energy storage device for storage; if the wind and solar power generation is insufficient, the system will start other backup power sources or the energy storage device to discharge and supplement, so as to ensure the stable operation of the submerged arc furnace; the electrochemical energy storage device plays a "peak shaving and valley filling" role in the system; during the peak period of wind and solar power generation, the energy storage device is charged to store excess electricity; during the off-peak period of wind and solar power generation, at night, or when the power load of the submerged arc furnace suddenly increases, the storage device... The energy storage device discharges, releasing electrical energy to ensure a stable power supply to the electric arc furnace and reduce dependence on the traditional power grid. Waste heat generated during the operation of the high-efficiency electric arc furnace is recovered and used to generate electricity by waste heat power generation equipment. The energy management system (EMS) rationally allocates the electrical energy generated by waste heat power generation based on the operating status of the electric arc furnace, the power output of waste heat power generation, and the overall power demand of the system. If the power demand of the electric arc furnace is not saturated at this time, waste heat power generation can prioritize supplying the electric arc furnace. If the power demand of the electric arc furnace is already met, the electrical energy generated by waste heat power generation can be used to charge the energy storage device or connected to the grid. Based on weather forecasts, electric arc furnace production plans, and other data, the EMS predicts the power output of wind and solar power generation and the power load of the electric arc furnace in advance, and formulates equipment operation optimization strategies. The integrated module of source-grid-load-storage coordinates the efficient operation of the photovoltaic / wind power array, electrochemical energy storage device, and electric arc furnace according to the optimized system topology and energy allocation strategy, achieving efficient synergy between energy flow and material flow.

[0177] Step Six: Customized Engineering Demonstration Application Module. Based on the geographical conditions, energy structure, and industrial silicon production capacity requirements of the demonstration project, the system integration scheme is adjusted to ensure the efficient and stable operation of the system.

[0178] Geographical conditions mainly include sunlight conditions, wind resources, topography and climate factors; energy structure mainly includes the proportion of new energy sources, the use of traditional energy sources and the stability of energy supply; industrial silicon capacity demand mainly includes capacity scale, production process requirements and load characteristics.

[0179] The system integration solution here includes "configuring a high proportion of photovoltaic power plants in areas with abundant sunshine, coupled with energy storage systems to mitigate power fluctuations; and employing waste heat recovery technology in the industrial silicon smelting process to convert waste heat from the smelting process into steam or electricity for plant heating or to supplement power demand."

[0180] Step 7: The waste heat cascade utilization module improves energy utilization efficiency and reduces dependence on traditional fossil fuels through waste heat recovery and multi-energy complementarity strategies.

[0181] Step 8: The energy efficiency management system monitors and analyzes the system's energy efficiency, carbon emissions, and environmental impact in real time, proposes improvement measures, and optimizes the system's operating efficiency and environmental performance.

[0182] Step 9: Integrate the system with other relevant systems or platforms to achieve broader data sharing and collaborative control, thereby improving the system's intelligence level and energy efficiency.

[0183] Compared with existing technologies, the beneficial effects of this application's embodiments are as follows: By integrating new energy power generation forecasting, dynamic characteristic analysis of industrial silicon load, energy storage system optimization, and grid interaction models, the system achieves precise matching and efficient utilization of new energy power and industrial silicon production load. The synergistic effect of the dynamic matching control module and the real-time control module effectively resolves the contradiction between the volatility of new energy and the stability of industrial load, ensuring the stable operation of the system and the maximization of energy utilization.

[0184] The application of a structured model-driven control platform provides a structured real-time model-driven approach for the integrated optimization control of cogeneration, enabling dynamic updates and optimization of the multi-dimensional digital twin system, thereby improving the system's intelligence level and decision-making efficiency. The optimized design of the integrated source-grid-load-storage module not only enhances the system's flexibility and scalability but also promotes efficient coordination of energy and material flows, further improving energy utilization efficiency.

[0185] The implementation of customized engineering demonstration application modules, by adjusting the system integration scheme according to the actual situation of the demonstration projects, ensured the efficient and stable operation of the system. The waste heat cascade utilization module maximizes energy utilization efficiency through waste heat recovery and multi-energy complementarity strategies, reduces dependence on traditional fossil fuels, and helps promote the green transformation of industrial silicon production.

[0186] Furthermore, the real-time monitoring and analysis functions of the energy efficiency management system provide comprehensive data support for the system's energy efficiency, carbon emissions, and environmental impact. This helps to promptly identify energy waste and potential environmental risks, propose improvement measures, and further optimize the system's operational efficiency and environmental performance. The system's integration capabilities enable broader data sharing and collaborative control, enhancing its intelligence level and energy utilization efficiency, and providing strong support for the construction and development of the energy internet.

[0187] The embodiments of this application enable low-carbon and economical production of industrial silicon, reduce production costs, and improve the economic benefits and market competitiveness of enterprises.

[0188] In practical use, users can select and combine appropriate modules according to actual production needs to adapt to different industrial silicon production scenarios. For example, in areas with abundant solar resources, photovoltaic power stations and energy storage systems can be prioritized to maximize the utilization of solar energy resources; while in areas with abundant wind resources, the proportion of wind turbine arrays can be increased to improve wind energy utilization efficiency. Simultaneously, through real-time monitoring and analysis by the energy efficiency management system, users can keep track of the system's operating status and energy utilization efficiency at any time, promptly identify problems, and take improvement measures to ensure the system's continuous and efficient operation.

[0189] Furthermore, the high-proportion new energy cogeneration system for silicon production in this application embodiment also possesses excellent scalability and flexibility. With the continuous development of new energy technologies and changes in industrial silicon production demands, users can upgrade and expand the system according to actual needs to meet future production requirements. For example, new energy types, such as biomass energy and geothermal energy, can be added to further enrich the system's energy structure; or the existing energy storage system can be expanded to enhance its energy storage and regulation capabilities.

[0190] In summary, the high-proportion new energy-silicon cogeneration system of this invention is an innovative energy utilization solution that combines new energy power generation, industrial silicon production, energy storage system optimization, and grid interaction, achieving efficient energy utilization and low carbon emissions. The application of this system can not only reduce the cost of industrial silicon production and improve the economic benefits and market competitiveness of enterprises, but also promote the optimization and upgrading of the energy structure, contributing to sustainable development.

[0191] The purpose of this application is to address the technical pain points of high energy consumption and high carbon emissions in the industrial silicon production field by constructing a high-proportion, high-efficiency, low-carbon operating system for cogeneration of new energy and silicon, integrating multi-energy complementarity and intelligent scheduling. By integrating fluctuating new energy sources such as photovoltaics and wind power, an innovative multi-source coupling and collaborative control technology of new energy, thermal power, and energy storage is developed to achieve flexible scheduling and optimized configuration of multiple energy flows, overcoming the challenge of dynamically matching power supply and load demand in the silicon smelting process. The system employs electrothermal decoupling and multi-timescale energy scheduling strategies, combined with cascade recovery technology of waste heat from silicon furnace flue gas, to achieve flexible absorption of fluctuating new energy power and improve energy efficiency throughout the entire industrial silicon production process.

[0192] Example 2

[0193] This application provides an energy distribution device. This energy distribution device can be applied to an energy system. For example... Figure 3 As shown, the energy distribution device provided in this application embodiment may include a first acquisition module 201, an establishment module 202, an optimization module 203, and a first control module 204.

[0194] The first acquisition module 201 is used to acquire the target energy consumption curve and the target energy supply and demand map of industrial silicon. The target energy consumption curve is the energy consumption curve of different processes in the production of industrial silicon within a future preset period. The target energy supply and demand map is the energy supply and demand map within a future preset period. The energy supply and demand map is used to characterize the data matching relationship between the supply side and the demand side of the energy system in the process of industrial silicon production.

[0195] Establishment module 202, connected to the first acquisition module 201, is used to establish a multi-objective mixed integer programming model based on the target energy consumption curve and the target energy supply and demand map. The multi-objective mixed integer programming model includes target constraints and target decision variables. The target constraints include carbon emission intensity constraints and comprehensive energy efficiency constraints. The target decision variables include carbon trading cost economic indicators and grid electricity purchase cost economic indicators.

[0196] The optimization module 203 and the connection establishment module 202 are used to dynamically optimize based on a multi-objective mixed integer programming model through genetic algorithms or reinforcement learning to obtain the target energy allocation strategy.

[0197] The first control module 204, connected to the optimization module 203, is used to control the first target device in the energy system to execute the control instructions corresponding to the target energy allocation strategy, so as to realize the energy allocation of the first target device in the energy system. The first target device includes a photovoltaic / wind power array, an electrochemical energy storage device, a submerged arc furnace, and a waste heat power generation device.

[0198] According to the energy distribution device provided in this application embodiment, the target energy consumption curve and target energy supply and demand map of industrial silicon are first obtained; then, based on the target energy consumption curve and target energy supply and demand map, a multi-objective mixed integer programming model is established; then, based on the multi-objective mixed integer programming model, dynamic optimization is performed through genetic algorithm or reinforcement learning to obtain the target energy distribution strategy; then, the first target device in the energy system is controlled to execute the control command corresponding to the target energy distribution strategy to realize the energy distribution of the first target device. Since the multi-objective mixed integer programming model includes target constraints and target decision variables, the target constraints include carbon emission intensity constraints and comprehensive energy efficiency constraints, and the target decision variables include carbon trading cost economic indicators and grid electricity purchase cost economic indicators. That is, carbon emission intensity and comprehensive energy efficiency are used as hard constraints, and carbon trading cost and grid electricity purchase cost are used as core economic decision variables. This allows the target energy distribution strategy determined based on the multi-objective mixed integer programming model to take into account both environmental performance and economic feasibility. Therefore, according to the target energy distribution strategy, energy is allocated to the first target device in the energy system, which can take into account both environmental performance and economic feasibility, thereby improving the rationality of energy distribution.

[0199] Furthermore, based on a multi-objective mixed integer programming model, dynamic optimization is performed through genetic algorithms or reinforcement learning to obtain the target energy allocation strategy, which can overcome the limitations of linear programming and adapt to the nonlinear energy consumption characteristics of industrial silicon (such as integer decisions for electric furnace start-up and shutdown and raw material switching).

[0200] In some implementations, the first acquisition module 201 is specifically used for:

[0201] Obtain current production data for industrial silicon, including current new energy power generation data, current silicon production process parameters, and current power grid operation data;

[0202] Based on current new energy power generation data, current silicon production process parameters, current power grid operation data, and machine learning algorithms, the target energy consumption curve is predicted.

[0203] In some implementations, the first acquisition module 201 is specifically used for:

[0204] Acquire current meteorological data, current power generation equipment status, and current equipment operation data corresponding to industrial silicon;

[0205] Based on the new energy power generation prediction model, the target energy supply and demand map is determined according to the current meteorological data, the current status of power generation equipment, and the current equipment operation data.

[0206] In some implementations, the first acquisition module 201 is specifically used for:

[0207] Based on the new energy power generation prediction model, the target new energy power generation capacity is determined according to current meteorological data, current power generation equipment status, and current equipment operation data. The new energy power generation prediction model is trained based on historical meteorological data, historical power generation equipment status, historical equipment operation data, and deep learning algorithms.

[0208] Based on the target new energy power generation capacity, determine the target energy supply and demand map for industrial silicon.

[0209] In some implementations, the control commands corresponding to the target energy distribution strategy include multiple real-time control commands for devices;

[0210] The first control module 204 is specifically used for:

[0211] Send corresponding real-time control commands to multiple first target devices that correspond one-to-one with the real-time control commands of multiple devices, so that the first target devices corresponding to the real-time control commands of the devices execute their corresponding real-time control commands.

[0212] In some embodiments, the device further includes:

[0213] The second acquisition module is used to acquire the current production data of industrial silicon, which includes current new energy power generation data, current silicon production process parameters, and current power grid operation data.

[0214] The generation module, connected to the second acquisition module, is used to generate target control instructions based on current new energy power generation data, current silicon production process parameters, current power grid operation data, multi-objective optimization algorithms and objective functions. The target control instructions include at least one of the following: power allocation instructions, energy storage system control instructions, process parameter adjustment instructions, carbon capture and utilization instructions, and system safety boundary instructions.

[0215] The sending module, connected to the generation module, is used to send target control commands to the second target device of the energy system, so that the second target device executes the target control commands, thereby achieving energy supply and demand balance of the energy system. The second target device includes at least one of photovoltaic / wind power array, electrochemical energy storage device, electric arc furnace, carbon processing equipment and safe operation control equipment.

[0216] In some implementations, the target control commands include power allocation commands, energy storage system control commands, process parameter adjustment commands, carbon capture and utilization commands, and system safety boundary commands;

[0217] The device also includes:

[0218] The third acquisition module is used to acquire the target new energy power generation capacity of industrial silicon, the target active load of industrial silicon, the rate of change of the target active load of industrial silicon, the remaining energy of the target energy storage system, and the energy change of the target energy storage system.

[0219] The second control module, connected to the third acquisition module, is used to control the power output of the photovoltaic / wind power array, the charging and discharging of the electrochemical energy storage device, the operating power and process parameters of the electric arc furnace, and the power generation of the waste heat power generation equipment based on the target new energy power generation, target industrial active load, target industrial silicon active load change rate, target energy storage system remaining energy, target energy storage system energy change, and target control commands. It also adjusts the operating status of the carbon treatment equipment in the energy system and maintains safety boundary parameters to optimize the operating status and parameters of each device in the cogeneration process of electro-silicon. The safety boundary parameters include at least one of the grid interface voltage and equipment operating load.

[0220] The energy distribution device provided in this application has the beneficial effects and implementation methods of the energy distribution method provided in Embodiment 1 of this application. For details, please refer to the specific description of the energy distribution method in Embodiment 1 above. This embodiment will not repeat the description here.

[0221] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. An energy distribution method, characterized in that, The energy distribution method, applied to energy systems, includes: The target energy consumption curve and target energy supply and demand map of industrial silicon are obtained. The target energy consumption curve is the energy consumption curve of different processes in the production of industrial silicon within a future preset period. The target energy supply and demand map is the energy supply and demand map within the future preset period. The energy supply and demand map is used to characterize the data matching relationship between the supply and demand sides of the energy system in the process of industrial silicon production. Based on the target energy consumption curve and the target energy supply and demand map, a multi-objective mixed integer programming model is established. The multi-objective mixed integer programming model includes target constraints and target decision variables. The target constraints include carbon emission intensity constraints and comprehensive energy efficiency constraints. The target decision variables include carbon trading cost economic indicators and grid electricity purchase cost economic indicators. Based on the aforementioned multi-objective mixed integer programming model, the target energy allocation strategy is obtained through dynamic optimization using genetic algorithms or reinforcement learning. The first target device in the energy system is controlled to execute the control command corresponding to the target energy allocation strategy in order to realize the energy allocation of the first target device. The first target device includes a photovoltaic / wind power array, an electrochemical energy storage device, a submerged arc furnace, and a waste heat power generation device.

2. The method according to claim 1, characterized in that, The target energy consumption curve for obtaining industrial silicon specifically includes: Obtain the current production data of the industrial silicon, which includes current new energy power generation data, current silicon production process parameters, and current power grid operation data; Based on the current new energy power generation data, the current silicon production process parameters, the current power grid operation data, and machine learning algorithms, the target energy consumption curve is predicted.

3. The method according to claim 1, characterized in that, Obtain the target energy supply and demand map for industrial silicon, specifically including: Acquire the current meteorological data, current power generation equipment status, and current equipment operation data corresponding to the industrial silicon; Based on the new energy power generation prediction model, the target energy supply and demand map is determined according to the current meteorological data, the current status of the power generation equipment, and the current equipment operation data.

4. The method according to claim 3, characterized in that, The method for determining the target energy supply and demand map based on the new energy power generation prediction model, according to the current meteorological data, the current status of the power generation equipment, and the current equipment operation data, specifically includes: Based on the aforementioned new energy power generation prediction model, the target new energy power generation capacity is determined according to the current meteorological data, the current status of the power generation equipment, and the current equipment operation data; the new energy power generation prediction model is trained based on historical meteorological data, historical power generation equipment status, historical equipment operation data, and deep learning algorithms. Based on the target new energy power generation capacity, the target energy supply and demand map is determined.

5. The method according to claim 1, characterized in that, The control commands corresponding to the target energy distribution strategy include multiple real-time control commands for devices. The control instructions for controlling the first target device in the energy system to execute the target energy allocation strategy specifically include: Send corresponding real-time control commands to a plurality of first target devices that correspond one-to-one with the plurality of real-time control commands, so that the first target device corresponding to the real-time control command executes its corresponding real-time control command.

6. The method according to any one of claims 1-5, characterized in that, The multi-objective mixed-integer programming model also includes an objective function; The method further includes: Obtain current production data for industrial silicon, including current new energy power generation data, current silicon production process parameters, and current power grid operation data; Based on the current new energy power generation data, the current silicon production process parameters, the current power grid operation data, the multi-objective optimization algorithm and the objective function, target control instructions are generated. The target control instructions include at least one of the following: power allocation instructions, energy storage system control instructions, process parameter adjustment instructions, carbon capture and utilization instructions and system safety boundary instructions. The target control command is sent to a second target device in the energy system so that the second target device executes the target control command, thereby achieving energy supply and demand balance in the energy system. The second target device includes at least one of the photovoltaic / wind power array, the electrochemical energy storage device, the electric arc furnace, the carbon processing equipment, and the safe operation control equipment.

7. The method according to claim 6, characterized in that, The target control commands include the power allocation command, the energy storage system control command, the process parameter adjustment command, the carbon capture and utilization command, and the system safety boundary command; After generating the target control command, the method further includes: The target new energy power generation capacity, target industrial silicon active load, target industrial silicon active load change rate, target energy storage system surplus energy, and target energy storage system energy change amount are obtained for industrial silicon. Based on the target new energy power generation, the target industrial active load, the target industrial silicon active load change rate, the target energy storage system remaining energy, the target energy storage system energy change, and the target control command, the power output of the photovoltaic / wind power array, the charging and discharging of the electrochemical energy storage device, the operating power and process parameters of the electric arc furnace, and the power generation of the waste heat power generation equipment are controlled. The operating status of the carbon treatment equipment in the energy system is also adjusted, and safety boundary parameters are maintained to optimize the operating status and parameters of each device in the cogeneration process of electro-silicon. The safety boundary parameters include at least one of the grid interface voltage and the equipment operating load.

8. An energy distribution device, characterized in that, The energy distribution device, applied to an energy system, includes: The first acquisition module is used to acquire the target energy consumption curve and the target energy supply and demand map of industrial silicon. The target energy consumption curve is the energy consumption curve of different processes in the production of industrial silicon within a future preset period. The target energy supply and demand map is the energy supply and demand map within the future preset period. The energy supply and demand map is used to characterize the data matching relationship between the supply side and the demand side of the energy system in the process of industrial silicon production. A module is established, connected to the first acquisition module, for establishing a multi-objective mixed integer programming model based on the target energy consumption curve and the target energy supply and demand map. The multi-objective mixed integer programming model includes target constraints and target decision variables. The target constraints include carbon emission intensity constraints and comprehensive energy efficiency constraints. The target decision variables include carbon trading cost economic indicators and grid electricity purchase cost economic indicators. An optimization module, connected to the establishment module, is used to dynamically optimize the target energy allocation strategy based on the multi-objective mixed integer programming model through genetic algorithms or reinforcement learning. The first control module, connected to the optimization module, is used to control the first target device in the energy system to execute the control command corresponding to the target energy allocation strategy, so as to realize the energy allocation of the first target device. The first target device includes a photovoltaic / wind power array, an electrochemical energy storage device, a submerged arc furnace, and a waste heat power generation device.

9. The apparatus according to claim 8, characterized in that, The first acquisition module is specifically used for: Obtain the current production data of the industrial silicon, which includes current new energy power generation data, current silicon production process parameters, and current power grid operation data; Based on the current new energy power generation data, the current silicon production process parameters, the current power grid operation data, and machine learning algorithms, the target energy consumption curve is predicted.

10. The apparatus according to claim 8, characterized in that, The first acquisition module is specifically used for: Acquire the current meteorological data, current power generation equipment status, and current equipment operation data corresponding to the industrial silicon; Based on the new energy power generation prediction model, the target energy supply and demand map is determined according to the current meteorological data, the current status of the power generation equipment, and the current equipment operation data.

11. The apparatus according to claim 8, characterized in that, The control commands corresponding to the target energy distribution strategy include multiple real-time control commands for devices. The first control module is specifically used for: Send corresponding real-time control commands to a plurality of first target devices that correspond one-to-one with the plurality of real-time control commands, so that the first target device corresponding to the real-time control command executes its corresponding real-time control command.

Images