Energy scheduling method, device and equipment for multi-grate furnace

Abstract

The invention provides an energy scheduling method, device and equipment for a multi-grate furnace. The method comprises the following steps: acquiring a scheduling period of the multi-grate furnace; dividing the scheduling period into a plurality of preset scheduling windows; obtaining green power output data and network power sequence data in each preset scheduling window; wherein the green power output data is clean energy data for supplying power to the multi-grate furnace, and the grid power sequence data is grid data for supplying power to the multi-grate furnace; according to the load power, the green power output data and the grid power sequence data of the multi-grate furnace in each preset scheduling window, determining an energy scheduling scheme of multiple groups of energy consumption units; and scheduling the multi-grate furnace according to the energy scheduling scheme of the multi-grate furnace. According to the invention, green electricity can be consumed to the maximum extent, the electricity abandoning rate is effectively reduced, the proportion of new energy in total energy consumption is improved, and the utilization efficiency of the new energy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy dispatching, and in particular to an energy dispatching method, device and equipment for a multi-grate furnace. Background Art

[0002] Multi-grate furnaces are commonly used in steel smelting, non-ferrous metal smelting and silicon material production. They are characterized by high energy consumption and continuous production cycles. Once the furnace is started, it is necessary to maintain a stable power supply for several hours to dozens of hours. Wind power and photovoltaic power are intermittent and random, and the power generation output fluctuates significantly from hour to hour, daytime / nighttime, and seasonally. There are also minimum / maximum grid-connected power restrictions on the grid side, and it is impossible to break through its peak-shaving space to use electricity.

[0003] The traditional simple power supply mode or manual scheduling method is difficult to take into account the "furnace cycle demand", "grid power upper and lower limit scheduling" and "green electricity substitution rate" goals. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an energy scheduling method, device and equipment for a multi-grate furnace, which can maximize the consumption of green electricity, effectively reduce the power abandonment rate, increase the proportion of new energy in total energy consumption, and improve the utilization efficiency of new energy.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows: an energy scheduling method for a multi-grate furnace, comprising:

[0006] Get the scheduling period of a multi-grate furnace;

[0007] Dividing the scheduling period into a plurality of preset scheduling windows;

[0008] Acquire green power output data and grid power sequence data in each of the preset scheduling windows; wherein the green power output data is clean energy data for supplying power to the multi-grate furnace, and the grid power sequence data is grid power data for supplying power to the multi-grate furnace;

[0009] Determine energy scheduling plans for multiple groups of energy consumption units according to the load power, green power output data and grid power sequence data of the multi-grate furnace in each of the preset scheduling windows;

[0010] The multi-grate furnace is scheduled according to the energy scheduling plan of the multi-grate furnace.

[0011] Optionally, the scheduling period is divided into a plurality of preset scheduling windows, including:

[0012] The scheduling period is divided into a plurality of preset scheduling windows of the same length.

[0013] Optionally, obtaining green power output data in each of the preset scheduling windows includes:

[0014] Get the time period Ω corresponding to each of the preset scheduling windows k Wind and solar power output data Data; wherein k is the number of preset scheduling windows, and t is the length of the time period corresponding to the preset scheduling window.

[0015] Optionally, obtaining the grid power sequence data in each of the preset scheduling windows includes:

[0016] Randomly generate multiple network power sequences G, the length of each network power sequence G is equal to the number of time units in the window H, and the value range of each network power sequence G is [G min ,G max ]; said G min is the minimum value of the grid power, G max is the maximum value of the off-grid power;

[0017] According to each grid power sequence G, multiple fitness values ​​are obtained;

[0018] According to multiple fitness values, the grid power sequence data G in the preset scheduling window is determined * .

[0019] Optionally, multiple fitness values ​​are obtained according to each grid power sequence G, including:

[0020] according to Get multiple fitness values;

[0021] Among them, Fitness(G) is the fitness of the grid power sequence G, α is the target value of the green power substitution rate, R h is the wind and solar power output data of the hth time unit, G h is the grid power sequence data of the hth time unit.

[0022] Optionally, determining an energy scheduling scheme for the multi-grate furnace according to the load power, green power output data and grid power sequence data of the multi-grate furnace in each of the preset scheduling windows includes:

[0023] If the remaining power M=R t +G t - If ∑(load power of the running furnace) is greater than 0, try to start a new furnace;

[0024] And according to ∑+f(1)≤R t +G t , and ∑(operating furnace load power)+f(2)≤R t+1 +G t+1,…determine whether M is sufficient to support the full operation of the furnace in the next L hours. If all conditions are met, the furnace can be started; otherwise, the furnace is kept idle. Among them, f(1) is the load power of the furnace in the first time unit in the preset scheduling window, and f(2) is the load power of the furnace in the second time unit in the preset scheduling window.

[0025] Optionally, if the remaining power is insufficient to start the new furnace, first t Downgrade to max(G min ,G t - Surplus electricity);

[0026] If it is impossible to further reduce the grid power, this part will be counted as abandoned power D t .

[0027] The present invention also provides an energy dispatching device for a multi-grate furnace, comprising:

[0028] An acquisition module is used to acquire the scheduling cycle of the multi-grate furnace; acquire the green power output data and the grid power sequence data in each of the preset scheduling windows; wherein the green power output data is the clean energy data for supplying power to the multi-grate furnace, and the grid power sequence data is the grid data for supplying power to the multi-grate furnace;

[0029] A processing module is used to divide the scheduling cycle into multiple preset scheduling windows; determine the energy scheduling plan of multiple groups of energy-consuming units according to the load power, green power output data and grid power sequence data of the multi-grate furnace in each of the preset scheduling windows; and schedule the multi-grate furnace according to the energy scheduling plan of the multi-grate furnace.

[0030] The present invention also provides a computing device, comprising: a processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed.

[0031] The present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above.

[0032] The above solution of the present invention includes at least the following beneficial effects:

[0033] The above scheme of the present invention can more precisely match the intermittent and random output of green electricity such as wind power and photovoltaic power by dividing the scheduling cycle into multiple preset scheduling windows. The energy scheduling scheme is determined according to the green power output data and grid power sequence data in each window, and green power is used preferentially. When the green power output is high, the grid power use is automatically reduced or the number of furnaces turned on is increased, thereby maximizing the consumption of green power, effectively reducing the power abandonment rate, increasing the proportion of new energy in total energy consumption, and improving the utilization efficiency of new energy.

[0034] The multi-grate furnace has the characteristics of high energy consumption and continuous production cycle, and requires stable power supply once started. This scheduling method fully considers the load power of the multi-grate furnace when determining the energy scheduling plan. Through the forward-looking furnace startup verification, it checks whether the power in the future period is sufficient before starting the furnace, ensuring that the furnace will not be interrupted due to insufficient power during the entire production cycle, ensuring the continuity and stability of the multi-grate furnace production, and avoiding product scrapping or equipment damage due to power outages.

[0035] This scheduling method introduces the green electricity substitution rate target into the fitness function of the genetic algorithm, which can actively approach the set green electricity substitution rate target, so that during the energy scheduling process, enterprises can meet the policy requirements on the proportion of green electricity use by reasonably allocating grid electricity and green electricity, helping enterprises achieve energy conservation and emission reduction goals.

[0036] Traditional grid power dispatching adopts fixed or binary strategies and lacks flexibility. This method can flexibly adjust the grid power sequence data within the minimum / maximum grid power limit according to the actual situation of each preset dispatching window, realize the fine distribution of grid power, avoid the waste or power shortage caused by traditional "binary power supply" or "fixed dispatching", and improve the flexibility and adaptability of grid power dispatching.

[0037] Compared with the traditional simple power supply mode or manual scheduling method, which is difficult to take into account multiple goals, this method of energy scheduling based on multi-window division and combining green electricity and grid electricity data comprehensively considers the "furnace cycle demand", "grid electricity upper and lower limit scheduling" and "green electricity replacement rate" goals, and realizes the comprehensive and scientific management of multi-grate furnace energy use, improves the energy management level of the entire industrial production process, and provides strong support for enterprises to reduce energy costs and improve production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the energy scheduling method of the multi-grate furnace of the present invention;

[0039] Figure 2 It is a module schematic diagram of the energy dispatching device of the multi-grate furnace of the present invention.

[0040] Figure 3 It is the overall flow chart of Example 1 of the present invention.

[0041] Figure 4 It is a sub-flow chart of the genetic algorithm (GA) in Example 1 of the present invention.

[0042] Figure 5 It is a sub-flow chart of hourly scheduling of a multi-grate furnace in Example 1 of the present invention. DETAILED DESCRIPTION

[0043] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0044] like Figure 1 As shown, an embodiment of the present invention provides an energy scheduling method for a multi-grate furnace, comprising:

[0045] Step 11, obtaining the scheduling cycle of the multi-grate furnace;

[0046] Step 12, dividing the scheduling period into a plurality of preset scheduling windows;

[0047] Step 13, obtaining green power output data and grid power sequence data in each of the preset scheduling windows; wherein the green power output data is clean energy data for supplying power to the multi-grate furnace, and the grid power sequence data is grid data for supplying power to the multi-grate furnace;

[0048] Step 14, determining energy scheduling plans for multiple groups of energy consumption units according to the load power, green power output data and grid power sequence data of the multi-grate furnace in each of the preset scheduling windows;

[0049] Step 15: Scheduling the multi-grate furnace according to the energy scheduling plan of the multi-grate furnace.

[0050] Specifically, the multi-grate furnace refers to one or more groups of industrial furnaces (such as grate furnaces, heating furnaces, electric furnaces, etc.) with a fixed production cycle. Once started, it must run continuously for several hours without power outages or power failures. Each furnace requires a fixed length of L hours from startup to completion of production; different types of furnaces may have different cycle lengths and consume different power in each hour. The power demand of each furnace during operation is distributed according to a pre-given power curve, f_l (furnace power curve): represents the power (MW) required by the furnace in the lth hour of the production cycle; for example, if l=1 represents the power demand in the first hour after startup, l=2 represents the second hour.

[0051] If a furnace is at time t 0 Start, then the power demand in the first hour (l = 1, 2, ..., L) can be expressed as

[0052] f(l)(l=1,2,…,L)

[0053] Where f(l) is a pre-given power curve corresponding to the heating or insulation requirements of the furnace at different stages. When the furnace is in the lth hour of operation at the tth hour, it needs to consume If the furnace has not been started or has completed the production cycle, the power consumption is 0.

[0054] The grid power sequence G is the power supplied by the grid to the multi-grate furnace. There is a minimum G min With maximum G max The off-grid power limit cannot be exceeded due to the limited peak-shaving resources of the power grid.

[0055] The present invention can more precisely match the intermittent and random output of green electricity such as wind power and photovoltaic power by dividing the scheduling cycle into multiple preset scheduling windows. The energy scheduling plan is determined according to the green power output data and grid power sequence data in each window, and green power is used first. When the green power output is high, the grid power use is automatically reduced or the number of furnaces turned on is increased, thereby maximizing the consumption of green power, effectively reducing the power abandonment rate, increasing the proportion of new energy in total energy consumption, and improving the utilization efficiency of new energy.

[0056] The multi-grate furnace has the characteristics of high energy consumption and continuous production cycle, and requires stable power supply once started. This scheduling method fully considers the load power of the multi-grate furnace when determining the energy scheduling plan. Through the forward-looking furnace startup verification, it checks whether the power in the future period is sufficient before starting the furnace, ensuring that the furnace will not be interrupted due to insufficient power during the entire production cycle, ensuring the continuity and stability of the multi-grate furnace production, and avoiding product scrapping or equipment damage due to power outages.

[0057] This scheduling method introduces the green electricity substitution rate target into the fitness function of the genetic algorithm, which can actively approach the set green electricity substitution rate target, so that during the energy scheduling process, enterprises can meet the policy requirements on the proportion of green electricity use by reasonably allocating grid electricity and green electricity, helping enterprises achieve energy conservation and emission reduction goals.

[0058] Traditional grid power dispatching adopts fixed or binary strategies and lacks flexibility. This method can flexibly adjust the grid power sequence data within the minimum / maximum grid power limit according to the actual situation of each preset dispatching window, realize the fine distribution of grid power, avoid the waste or power shortage caused by traditional "binary power supply" or "fixed dispatching", and improve the flexibility and adaptability of grid power dispatching.

[0059] Compared with the traditional simple power supply mode or manual scheduling method, which is difficult to take into account multiple goals, this method of energy scheduling based on multi-window division and combining green electricity and grid electricity data comprehensively considers the "furnace cycle demand", "grid electricity upper and lower limit scheduling" and "green electricity replacement rate" goals, and realizes the comprehensive and scientific management of multi-grate furnace energy use, improves the energy management level of the entire industrial production process, and provides strong support for enterprises to reduce energy costs and improve production efficiency.

[0060] In an optional embodiment of the present invention, in step 11, the scheduling period of the multi-grate furnace is obtained, and the scheduling period is specifically 8760 hours throughout the year.

[0061] In an optional embodiment of the present invention, in step 12, the scheduling period is divided into a plurality of preset scheduling windows, including:

[0062] The scheduling period is divided into a plurality of preset scheduling windows of the same length.

[0063] Specifically, the 8760 hours of the whole year are divided into several small windows (such as 24 hours a day).

[0064] In this example, the dispatch cycle is divided into multiple preset dispatch windows of equal length, such as 24 hours per day, so that the dispatch can more closely track the real-time changes in green power output and multi-grate furnace load. The formulation and adjustment of the dispatch plan on a daily basis can more promptly respond to the daytime / nighttime and hourly fluctuations of wind power and photovoltaic power, as well as the power demand of multi-grate furnaces at different production stages, thereby achieving accurate energy allocation and further improving energy utilization efficiency and production stability.

[0065] The complex scheduling problem of 8,760 hours a year is divided into multiple small windows, and independent optimization calculations are performed in each small window, which greatly reduces the calculation dimension and complexity of the overall problem. Compared with dealing with the energy scheduling problem of the whole year at one time, this block-by-block solution method reduces the demand for computing resources and computing time, allowing optimization technologies such as genetic algorithms to run more efficiently, and improving the operability and practicality of the scheduling method.

[0066] In an optional embodiment of the present invention, in step 13, obtaining green power output data in each of the preset scheduling windows includes:

[0067] Step 1301: Obtain the time period Ω corresponding to each of the preset scheduling windows. k Wind and solar power output data Data; wherein k is the number of preset scheduling windows, and t is the length of the time period corresponding to the preset scheduling window.

[0068] In this example, obtaining the wind and solar power output data within the time period corresponding to each preset scheduling window can accurately capture the changing pattern of green power output and then cleverly adjust the green power consumption strategy.

[0069] In an optional embodiment of the present invention, in step 13, obtaining the network power sequence data in each of the preset scheduling windows includes:

[0070] Step 1311: randomly generate multiple network power sequences G, the length of each network power sequence G is equal to the number of time units in the window H, and the value range of each network power sequence G is [G min ,G max ]; said G min is the minimum value of the grid power, G max is the maximum value of the off-grid power;

[0071] Step 1312: Obtain multiple fitness values ​​according to each grid power sequence G;

[0072] Step 1313: Determine the grid power sequence data G in the preset scheduling window according to multiple fitness values. * .

[0073] In this example, by randomly generating multiple grid power sequences and screening based on fitness values, it is possible to flexibly explore various possible grid power allocation schemes within the minimum and maximum range of grid power, breaking through the limitations of traditional fixed modes, accurately matching power demand at different times, and avoiding waste or power shortages. Such operations are performed within each preset dispatch window, allowing the grid power dispatch to dynamically adjust the grid power supply according to the real-time wind and solar output and the load changes of the multi-grate furnace, greatly improving the adaptability of the grid power dispatch to complex power environments.

[0074] Integrating green electricity substitution rate-related indicators into the calculation of fitness values ​​allows the genetic algorithm to always be guided by approaching the preset green electricity substitution rate target when searching for the optimal grid electricity sequence. This helps companies to effectively increase the proportion of new energy in total electricity consumption and achieve energy conservation and emission reduction goals while meeting the production needs of multi-grate furnaces. The grid electricity sequence data determined in this way can reasonably adjust the proportion of grid electricity and green electricity. When the wind and solar power output is sufficient, green electricity is used first to reduce the use of grid electricity; when the wind and solar power are insufficient, grid electricity is supplemented reasonably to ensure that the normal production of multi-grate furnaces is not affected while achieving the green electricity substitution rate target.

[0075] This method can make more effective use of green electricity. When the output of green electricity is high, it can automatically reduce the use of grid electricity by optimizing the grid electricity sequence, making room for green electricity consumption, thereby improving the utilization rate of green electricity and reducing the abandonment rate. Combined with the production cycle characteristics of the multi-grate furnace, the balance between electricity supply and demand is fully considered when determining the grid electricity sequence. On the premise of ensuring the continuous and stable operation of the furnace, green electricity is used first to avoid the waste of green electricity due to unreasonable power distribution, and further reduce the abandonment rate.

[0076] By utilizing the characteristics of genetic algorithms, randomly generating grid power sequences and screening processes based on fitness values, the natural selection mechanism is imitated to intelligently search for the optimal grid power dispatching scheme under current conditions, thus improving the intelligence level of the dispatching system. In different preset dispatching windows, the grid power sequence data is dynamically adjusted according to factors such as real-time wind and solar power output, grid power restrictions, and multi-grate furnace status, so that the dispatching system can adapt to various complex and changeable industrial production scenarios and power supply conditions.

[0077] In an optional embodiment of the present invention, in step 1312, multiple fitness values ​​are obtained according to each grid power sequence G, including:

[0078] according to Get multiple fitness values;

[0079] Among them, Fitness(G) is the fitness of the grid power sequence G, α is the target value of the green power substitution rate, R h is the wind and solar power output data of the hth time unit, G h is the grid power sequence data of the hth time unit.

[0080] The negative sign is taken to allow the genetic algorithm GA to "maximize" the fitness during the evolution process, that is, to "minimize" the square deviation between the green electricity substitution rate and the target α.

[0081] By directly incorporating the target value of the green electricity substitution rate into the fitness function, the genetic algorithm is always guided by minimizing the square deviation between the green electricity substitution rate and the target value in the process of searching for the optimal grid power sequence. This sets a clear and precise optimization direction for the entire dispatching system, ensuring that the final dispatching plan can be closely centered around the company's preset green electricity substitution rate target, effectively solving the problem that traditional dispatching methods are difficult to accurately track the proportion of new energy.

[0082] The fitness function incorporates the wind and solar power output data R of the hth time unit h and network power sequence data G h This enables the genetic algorithm to dynamically adjust the grid power distribution strategy based on the real-time renewable energy power generation and grid power usage. When wind and solar power generation is sufficient, the algorithm tends to reduce grid power usage and make full use of green power resources; when wind and solar power generation is insufficient, it reasonably increases grid power supply to ensure the stable production of multi-grate furnaces, thus avoiding power waste or power shortage caused by traditional "dual power supply" or "fixed scheduling".

[0083] Due to the fitness function, the genetic algorithm will give priority to maximizing the proportion of green electricity during the evolution process. When the wind and solar output is high, the algorithm will automatically reduce the use of grid electricity or increase the number of furnaces turned on to give priority to consuming green electricity, thereby effectively reducing the power abandonment rate, increasing the proportion of new energy in total energy consumption, and maximizing the use of new energy.

[0084] The fitness function cooperates with rolling optimization, forward-looking furnace start-up verification and other mechanisms to form an organic whole. Under the premise of ensuring the continuous and stable production of the multi-grate furnace, the waste of green electricity caused by unreasonable power distribution is avoided by rationally allocating grid power and green electricity, further improving energy utilization efficiency.

[0085] Taking a negative sign maximizes the fitness of the genetic algorithm during the evolution process. This design imitates the mechanism of natural selection, allowing the algorithm to intelligently search for the grid power dispatching scheme that is most conducive to achieving the green power substitution rate target among many possible grid power sequences. This intelligent search capability greatly improves the intelligence level of the dispatching system, enabling it to quickly find a better solution in a complex power environment and under multiple constraints.

[0086] In each preset dispatch window, the fitness function will evaluate and optimize the grid power sequence based on factors such as real-time wind and solar output, grid power restrictions, and multi-grate furnace status. This enables the dispatch system to quickly adapt to changes in power supply and demand at different times. Whether it is the hourly fluctuations in wind and solar output or the changes in power demand at different production stages of multi-grate furnaces, the grid power dispatch strategy can be adjusted in a timely manner through the continuous iteration of the genetic algorithm to ensure the stable operation of the system and the realization of the green power substitution rate target.

[0087] In an optional embodiment of the present invention, in step 1313, the network power sequence data G in the preset scheduling window is determined according to multiple fitness values. * ,include:

[0088] The selection operation is performed according to the fitness value, and individuals with higher fitness are retained, and individuals with lower fitness are eliminated. Then the retained individuals are crossover operated, and the gene fragments of different individuals are combined to generate new offspring individuals, hoping to fuse excellent genes and produce better grid power sequences. At the same time, a small probability mutation operation is performed on some individuals to randomly change the values ​​of individual genes to increase the diversity of the population and avoid the algorithm falling into a local optimal solution.

[0089] After multiple rounds of selection, crossover and mutation operations, when the genetic algorithm reaches the preset termination condition (such as reaching the maximum number of iterations or fitness value convergence), the grid power sequence corresponding to the individual with the highest fitness at this time is the grid power sequence data determined in the preset scheduling window. This grid power sequence can optimize the set goals to the greatest extent, such as increasing the green power substitution rate and reducing the power abandonment rate, while meeting the upper and lower limit constraints of the power grid and the load requirements of the multi-grate furnace.

[0090] The individual is a coded representation of the grid power per hour in the preset scheduling window, which is the basic unit in the genetic algorithm and contains a series of genes, each of which corresponds to the grid power data at different times in the preset scheduling window, such as the grid power supply value at a certain time, whether it is powered on, and other key information.

[0091] Through multiple rounds of genetic algorithm operation, guided by the fitness value, the final determined grid electricity sequence data can optimize the green electricity substitution rate to the greatest extent under various constraints. Since the fitness function is closely related to the green electricity substitution rate, the algorithm continuously adjusts the grid electricity allocation during the search process, pushing the proportion of green electricity in total energy consumption closer to the target value, effectively solving the problem that traditional methods are difficult to accurately track the proportion of new energy, and meeting the needs of enterprises to regulate the proportion of green electricity use under the background of "dual carbon".

[0092] Under the premise of meeting the load demand of multi-grate furnaces and the upper and lower limits of the power grid, the optimized grid power sequence can reasonably allocate the use of grid power and green power. When the wind and solar power output is high, green power is consumed first to reduce the occurrence of power abandonment; when the wind and solar power are insufficient, the grid power supply is reasonably arranged to ensure production continuity while avoiding the waste of green power, thereby effectively reducing the power abandonment rate and improving the utilization efficiency of new energy.

[0093] The mutation operation randomly changes the value of individual genes with a small probability, introduces new gene combinations into the population, and increases the diversity of the population. This allows the genetic algorithm to not be limited to the local optimal solution, but to search in a wider solution space, which helps to find a better grid power sequence, further improve the quality of the dispatch plan, make the grid power allocation more reasonable, and be more conducive to achieving the goals of green power substitution rate and reducing power abandonment rate.

[0094] The crossover operation combines the gene fragments of different individuals to generate new offspring individuals, hoping to integrate excellent genes. In this way, the algorithm can quickly accumulate and disseminate the excellent grid power distribution mode found in the optimization process, accelerate convergence to a better solution, and make the final grid power sequence able to combine the advantages of multiple better modes and better adapt to the complex situation of multi-grate furnace production and wind and solar fluctuations.

[0095] The determined grid power sequence data meets the upper and lower limit constraints of the grid and the load requirements of the multi-grate furnace, which shows that this method can effectively handle the complex constraints of the power system in actual industrial scenarios. On the basis of considering the peak-shaving capacity of the grid and the production stability of the multi-grate furnace, the reasonable dispatch of grid power is realized to avoid production interruptions or grid operation problems caused by unreasonable power supply.

[0096] Rolling optimization combined with genetic algorithms enables the dispatching system to adapt to the dynamic changes in wind and solar output. Each preset dispatching window is independently optimized with genetic algorithms, which can timely adjust the grid power sequence according to real-time wind and solar forecasts and multi-grate furnace status, ensuring efficient energy dispatching at different times, and improving the system's adaptability to complex and changing power environments.

[0097] In an optional embodiment of the present invention, step 1313 further includes: obtaining the grid power sequence data G in the preset scheduling window * Integrate into the global energy dispatch system, including:

[0098] G * Middle (G t ,…,G t+H-1 ) is written into the corresponding position of the global network power array,

[0099] Enter the next windowΩ k+1 , determine the grid power sequence data G in the next dispatch window * .

[0100] Integrating the grid power sequence data determined by each preset scheduling window into the global grid power array helps to build a complete and continuous energy scheduling plan from the perspective of the whole year or longer time span. In this way, the energy supply and demand in different time periods can be fully considered, avoiding the problem of unreasonable overall energy scheduling caused by focusing only on local time periods, thereby realizing the overall planning of energy in the entire production cycle.

[0101] As the grid power sequence data of each window is written into the global array in sequence, the energy scheduling becomes coherent in time. This coherence ensures the stability of power supply during the production process of the multi-grate furnace, avoids power supply interruption or fluctuation due to improper scheduling connection between windows, and ensures that the multi-grate furnace can operate continuously and stably according to a fixed production cycle.

[0102] In an optional embodiment of the present invention, in step 14, the energy scheduling scheme of the multi-grate furnace is determined according to the load power, green power output data and grid power sequence data of the multi-grate furnace in each of the preset scheduling windows, including:

[0103] Step 141: If the remaining power M = R t +G t- If ∑(load power of the running furnace) is greater than 0, try to start a new furnace;

[0104] And according to ∑+f(1)≤R t +G t , and ∑(operating furnace load power)+f(2)≤R t+1 +G t+1 ,…determine whether M is sufficient to support the full operation of the furnace in the next L hours. If all conditions are met, the furnace can be started; otherwise, the furnace is kept idle. Among them, f(1) is the load power of the furnace in the first time unit in the preset scheduling window, and f(2) is the load power of the furnace in the second time unit in the preset scheduling window.

[0105] When the remaining power M is greater than 0, try to start the new furnace, making full use of the excess power and avoiding energy waste. At the same time, based on the unit load power of the furnace at different times, proactively check whether the power in the next L hours is sufficient to support the full operation of the new furnace, ensure the continuity of the production of the new furnace, and achieve reasonable allocation and efficient use of energy.

[0106] When the wind and solar power output is high and more surplus electricity is generated, the rational start-up of new furnaces can effectively absorb the excess green electricity and reduce the rate of power abandonment, which is in line with the goal of maximizing the use of new energy and reducing power abandonment.

[0107] Before starting the furnace, the power supply in the future period is fully considered to avoid power outages in the furnace due to insufficient electricity, thus ensuring the stability and safety of multi-grate furnace production and reducing economic losses caused by production interruptions.

[0108] The energy dispatching scheme is improved by working in coordination with the grid power sequence data determined by the genetic algorithm. Comprehensive decisions are made based on grid power, green power and load conditions to better achieve the green power substitution rate target and achieve coordinated dispatch of grid power call and furnace start and stop.

[0109] In an optional embodiment of the present invention, in step 141, if the remaining power is insufficient to start a new furnace, the grid power G t Downgrade to max(G min ,G t - Surplus electricity);

[0110] If it is impossible to further reduce the grid power, this part will be counted as abandoned power D t .

[0111] When the remaining power is insufficient to start a new furnace, priority is given to reducing grid power and using green power to meet current load demand as much as possible, thereby increasing the proportion of green power in energy consumption and helping to achieve the green power replacement rate target. For example, when wind and solar power generation is sufficient but the remaining power is insufficient to start a new furnace, reducing grid power use allows more green power to be put into use, which is in line with the purpose of maximizing the use of new energy.

[0112] First try to reduce the grid power, and give priority to using the excess green power to meet the current load. Only when the grid power cannot be further reduced will this part be counted as abandoned power. This strategy effectively reduces the occurrence of abandoned power. Compared with the traditional dispatching method, it can make fuller use of green power resources, avoid the waste of green power, and thus significantly reduce the abandoned power rate.

[0113] This operation is coordinated with the overall energy dispatching system to dynamically adjust the grid power usage according to the real-time surplus power situation, making energy dispatching more reasonable and flexible. While ensuring the stable operation of the multi-grate furnace, it achieves the optimal configuration of energy and improves the efficiency of the entire energy dispatching system.

[0114] In the process of adjusting the grid power, full consideration was given to the load demand of the multi-grate furnace to ensure that the normal operation of the furnace would not be affected while reducing the grid power, thereby ensuring the continuity and stability of production and avoiding losses caused by production interruptions due to power supply problems.

[0115] An optional embodiment of the present invention further includes step 16 of calculating and outputting index data for energy scheduling of the multi-grate furnace.

[0116] The step 16 comprises:

[0117] Step 161: calculate the actual grid power usage G per hour. t , Load power of multi-grate furnace t And the amount of power wasted D t =max(0,R t +G t -Load t ) to keep accurate records;

[0118] Step 162, accumulate the data recorded every hour. For the total load, add the load power of each multi-grate furnace every hour. As time goes by, continue to accumulate the total load power every hour to get the annual total load. The calculation of the total amount of grid power is similar. The actual grid power used every hour is accumulated hour by hour to get the total grid power used for the whole year. The statistics of abandoned power are to accumulate the abandoned power every hour. When there is abandoned power in a certain hour, this part of the power is added to the total abandoned power statistics;

[0119] Step 163: After obtaining the annual “total load”, “total grid power” and “total power abandonment”, the green power substitution rate and power abandonment rate can be calculated;

[0120] Green electricity substitution rate: in, is the total annual electricity consumption of the multi-grate furnace, The total amount of grid electricity used in the whole year;

[0121] Abandonment rate: in, The amount of electricity wasted throughout the year, The total wind and solar power generation for the whole year;

[0122] Step 164: The green electricity substitution rate and the power abandonment rate are used as indicator data for energy dispatch of the multi-grate furnace and fed back to the energy dispatch decision system in real time.

[0123] The green power replacement rate and power abandonment rate are fed back to the energy dispatch decision system in real time, providing managers with intuitive data basis. Decision makers can adjust the dispatch strategy in time according to these indicators. For example, if the green power replacement rate is found to be below the target, the allocation ratio of grid power and green power can be optimized, thereby improving the scientificity and effectiveness of energy dispatch.

[0124] By accurately calculating the green electricity substitution rate and the power abandonment rate, we can clearly evaluate the actual utilization of new energy in energy dispatch. The green electricity substitution rate reflects the proportion of new energy in total electricity consumption, and the power abandonment rate reflects the degree of new energy waste, which helps enterprises to intuitively understand the effectiveness of energy utilization and find out the problems and improvement directions in the energy utilization process.

[0125] Clear data on green power replacement rate and power abandonment rate can help enterprises intuitively judge whether they are approaching or reaching the preset green power replacement rate target. If the target is not reached, the subsequent dispatch plan can be adjusted in a targeted manner to meet policy requirements and the company's own green development plan, providing strong support for achieving energy conservation and emission reduction goals.

[0126] This forms a closed loop with the genetic algorithm, rolling optimization, furnace start-up verification and other links mentioned above. Through the quantitative analysis of energy scheduling results, data support is provided for the optimization algorithm and scheduling strategy, and the entire intelligent scheduling and production scheduling mechanism is further improved to enhance the overall performance of the system.

[0127] like Figure 2 As shown, the present invention also provides an energy dispatching device 20 for a multi-grate furnace, comprising:

[0128] The acquisition module 21 is used to acquire the scheduling cycle of the multi-grate furnace; acquire the green power output data and the grid power sequence data in each of the preset scheduling windows; wherein the green power output data is the clean energy data for supplying power to the multi-grate furnace, and the grid power sequence data is the grid data for supplying power to the multi-grate furnace;

[0129] The processing module 22 is used to divide the scheduling cycle into multiple preset scheduling windows; determine the energy scheduling plan of multiple groups of energy-consuming units according to the load power, green power output data and grid power sequence data of the multi-grate furnace in each of the preset scheduling windows; and schedule the multi-grate furnace according to the energy scheduling plan of the multi-grate furnace.

[0130] Optionally, the scheduling period is divided into a plurality of preset scheduling windows, including:

[0131] The scheduling period is divided into a plurality of preset scheduling windows of the same length.

[0132] Optionally, obtaining green power output data in each of the preset scheduling windows includes:

[0133] Get the time period Ω corresponding to each of the preset scheduling windows k Wind and solar power output data Data; wherein k is the number of preset scheduling windows, and t is the length of the time period corresponding to the preset scheduling window.

[0134] Optionally, obtaining the grid power sequence data in each of the preset scheduling windows includes:

[0135] Randomly generate multiple network power sequences G, the length of each network power sequence G is equal to the number of time units in the window H, and the value range of each network power sequence G is [G min ,G max ]; said G min is the minimum value of the grid power, G max is the maximum value of the off-grid power;

[0136] According to each grid power sequence G, multiple fitness values ​​are obtained;

[0137] According to multiple fitness values, the grid power sequence data G in the preset scheduling window is determined * .

[0138] Optionally, multiple fitness values ​​are obtained according to each grid power sequence G, including:

[0139] according to Get multiple fitness values;

[0140] Among them, Fitness(G) is the fitness of the grid power sequence G, α is the target value of the green power substitution rate, R h is the wind and solar power output data of the hth time unit, G h is the grid power sequence data of the hth time unit.

[0141] Optionally, determining an energy scheduling scheme for the multi-grate furnace according to the load power, green power output data and grid power sequence data of the multi-grate furnace in each of the preset scheduling windows includes:

[0142] If the remaining power M=R t +G t - If ∑(load power of the running furnace) is greater than 0, try to start a new furnace;

[0143] And according to v+f(1)≤R t +G t , and v(operating furnace load power)+f(2)≤R t+1 +G t+1 ,…determine whether M is sufficient to support the full operation of the furnace in the next L hours. If all conditions are met, the furnace can be started; otherwise, the furnace is kept idle. Among them, f(1) is the load power of the furnace in the first time unit in the preset scheduling window, and f(2) is the load power of the furnace in the second time unit in the preset scheduling window.

[0144] Optionally, if the remaining power is insufficient to start the new furnace, first t Downgrade to max(G min ,G t - Surplus electricity);

[0145] If it is impossible to further reduce the grid power, this part will be counted as abandoned power D t .

[0146] It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0147] Example 1

[0148] like Figure 3-5 As shown, this embodiment 1 provides an energy scheduling method for a multi-grate furnace, comprising the following steps:

[0149] Step 101, reading data and parameters such as wind and solar power generation output, furnace type and quantity, furnace operation power curve, grid power range and target green power replacement rate;

[0150] Step 102, determine whether the scrolling window is traversed, if not, enter "end scrolling" and the process ends; if yes, enter "genetic algorithm (GA) optimizes the grid power supply of the current window" step;

[0151] Step 103, optimizing the grid power supply by using a genetic algorithm, optimizing the grid power supply in the current window by using a genetic algorithm;

[0152] Step 104, the multi-grate furnace is scheduled hourly, the furnace is started proactively, and after the green electricity substitution rate is met, the grid electricity is reduced first, and the remaining electricity is abandoned.

[0153] The step 103 comprises the following steps:

[0154] Step 1031, initialize the population, create a population containing multiple grid power sequences (G_t), and the range is between (G_{min}) and (G_{max});

[0155] Step 1032: Evaluate the matching degree between each grid electricity sequence and the target green electricity substitution rate (α);

[0156] Step 1033: determine whether the termination condition is met, that is, whether the maximum number of generations is reached or the result converges. If not, perform selection, crossover, and mutation operations in sequence; if so, the process ends.

[0157] The selection operation: select individuals with higher fitness. Crossover operation: combine two individuals to generate a new individual. Mutation operation: randomly change some genes of an individual. Generate a new population: generate a new population to replace the previous generation population.

[0158] The step 104 includes the following steps:

[0159] Step 1041, update the status of the running furnace, if the remaining time of the furnace cycle is greater than 0, supply power according to the power operation curve;

[0160] Step 1041, calculate the total wind and solar power output and the power consumption of the running furnace at the current moment, and determine whether there is surplus power. If not, directly proceed to the next hour; if yes, proceed to the "try to start a new furnace" step.

[0161] Step 1042: Proactively check whether the power supply for the subsequent cycles is sufficient. If it cannot be started, proceed to the step of "still having surplus power: reduce grid power or determine to abandon power"; if it can be started, start the furnace and update the demand, reduce the surplus power, and then proceed to the step of "still having surplus power: reduce grid power or determine to abandon power".

[0162] Step 1043, reduce grid power or determine power abandonment: determine whether grid power can be further reduced. If so, reduce grid power until it is the minimum grid-connected; if not, determine whether there is still surplus power. If so, record the amount of abandoned power; if not, set both surplus power and abandoned power to 0.

[0163] Step 1044, the current hour scheduling ends.

[0164] The embodiment of the present invention further provides a computing device, including: a processor, a memory storing a computer program, and when the computer program is executed by the processor, the method described in the above embodiment is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0165] In an embodiment of the present invention, a computer-readable storage medium is further provided, which stores instructions, and when the instructions are executed on a computer, the computer executes the method described in the above embodiment. All implementations in the above method embodiment are applicable to this embodiment, and can also achieve the same technical effect.

[0166] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0167] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0168] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0169] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0171] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0172] In addition, it should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0173] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0174] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for energy scheduling of a multi-grate furnace, characterized in that: include: Get the scheduling period of a multi-grate furnace; Dividing the scheduling period into a plurality of preset scheduling windows; Acquire green power output data and grid power sequence data in each of the preset scheduling windows; wherein the green power output data is clean energy data for supplying power to the multi-grate furnace, and the grid power sequence data is grid power data for supplying power to the multi-grate furnace; Determine energy scheduling plans for multiple groups of energy consumption units according to the load power, green power output data and grid power sequence data of the multi-grate furnace in each of the preset scheduling windows; The multi-grate furnace is scheduled according to the energy scheduling plan of the multi-grate furnace.

2. The energy scheduling method for a multi-grate furnace according to claim 1, characterized in that: The scheduling period is divided into a plurality of preset scheduling windows, including: The scheduling period is divided into a plurality of preset scheduling windows of the same length.

3. The energy scheduling method for a multi-grate furnace according to claim 1, characterized in that: Obtaining green power output data in each of the preset scheduling windows includes: Get the time period Ω corresponding to each of the preset scheduling windows k Wind and solar output data Data; wherein k is the number of preset scheduling windows, and t is the length of the time period corresponding to the preset scheduling window.

4. The energy scheduling method for a multi-grate furnace according to claim 1, characterized in that: Obtaining grid power sequence data in each of the preset scheduling windows includes: Randomly generate multiple network power sequences G, the length of each network power sequence G is equal to the number of time units in the window H, and the value range of each network power sequence G is [G min ,G max ]; said G min is the minimum value of the grid power, G max is the maximum value of the off-grid power; According to each grid power sequence G, multiple fitness values ​​are obtained; According to multiple fitness values, the grid power sequence data G in the preset scheduling window is determined * .

5. The energy scheduling method for a multi-grate furnace according to claim 4, characterized in that: According to each grid power sequence G, multiple fitness values ​​are obtained, including: according to Get multiple fitness values; Among them, Fitness(G) is the fitness of the grid power sequence G, α is the target value of the green power substitution rate, R h is the wind and solar power output data of the hth time unit, G h is the grid power sequence data of the hth time unit.

6. The energy scheduling method for a multi-grate furnace according to claim 1, characterized in that: According to the load power, green power output data and grid power sequence data of the multi-grate furnace in each of the preset scheduling windows, an energy scheduling scheme for the multi-grate furnace is determined, including: If the remaining power M=R t +G t - If ∑(load power of the running furnace) is greater than 0, try to start a new furnace; And according to ∑+f(1)≤R t +G t , and ∑(operating furnace load power)+f(2)≤R t+1 +G t+1 ,…determine whether M is sufficient to support the full operation of the furnace in the next L hours. If all conditions are met, the furnace can be started; otherwise, the furnace is kept idle. Among them, f(1) is the load power of the furnace in the first time unit in the preset scheduling window, and f(2) is the load power of the furnace in the second time unit in the preset scheduling window.

7. The energy dispatching method of a multi-grate furnace according to claim 6, characterized in that: If the remaining power is not enough to start the new furnace, first t Adjust down to max(G min ,G t - Surplus electricity); If it is impossible to further reduce the grid power, this part will be counted as abandoned power D t .

8. An energy dispatching device for a multi-grate furnace, comprising: An acquisition module, used to acquire the scheduling cycle of the multi-grate furnace; Acquire green power output data and grid power sequence data in each of the preset scheduling windows; wherein the green power output data is clean energy data for supplying power to the multi-grate furnace, and the grid power sequence data is grid power data for supplying power to the multi-grate furnace; A processing module is used to divide the scheduling cycle into multiple preset scheduling windows; determine the energy scheduling plan of multiple groups of energy-consuming units according to the load power, green power output data and grid power sequence data of the multi-grate furnace in each of the preset scheduling windows; and schedule the multi-grate furnace according to the energy scheduling plan of the multi-grate furnace.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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