Double-layer optimal scheduling method and system for steel industry load

Abstract

The present invention relates to the technical field of steel industry load participation in power grid interaction, and provides a double-layer optimization scheduling method and system for steel industry load, the method comprising: constructing a continuous time interactive resource task network model based on double-layer optimization data information; constructing a resource task network model with production process constraints; constructing a complex order characteristic analysis model for steel industry load; setting dynamic maximum demand analysis model operation constraints for the complex order characteristic analysis model; constructing an upper-layer power grid scheduling optimization model that achieves optimal power grid economic cost, and the lower-layer steel industry load side full system model and the upper-layer power grid scheduling optimization model jointly form a double-layer optimization system; proposing a double-layer optimization algorithm for the double-layer optimization system, solving the double-layer optimization scheduling; and outputting double-layer optimization scheduling result information. The present invention significantly reduces the electricity cost of short-process steel industry loads, and allocates production orders in combination with steel order characteristics to achieve better peak shaving and valley filling effects.

Description

Technical Field

[0001] The present invention relates to the technical field of steel industry load participation in power grid interaction, and in particular to a double-layer optimization scheduling method and system for steel industry load. Background Art

[0002] As one of the important pillars of the economy, the order load management in the production process of the steel industry is of great significance for ensuring the stable operation of the power grid and reducing production costs. High-energy-consuming industries are large electricity users, among which the steel industry load has the advantages of high electricity consumption and large adjustable potential, and is the key target for the potential exploration of load-side demand response. In particular, short-process steel enterprises with arc furnaces as smelting heat sources and multi-batch production characteristics can quickly and flexibly change the power consumed by the arc furnaces to balance the fluctuation of new energy output without affecting the order output, so as to achieve peak shaving and valley filling of the power grid. In order to ensure that customer order requirements are completed on time and in quantity, the steel industry load participating in the demand response process must follow strict production process restrictions, and the start-up time and operating time of production equipment cannot be changed at will. Although the power grid has formulated policies such as electricity price incentives for steel enterprises, due to the influence of orders and business, the enthusiasm of steel enterprises to participate in demand response is still not high. Therefore, considering the complex electricity consumption characteristics of the production process and order requirements of short-process steel enterprises, considering the demand response cost, it is of great practical significance to propose an optimized scheduling strategy suitable for short-process steel enterprises to participate in power grid interaction. The following problems exist in the existing short-process steel enterprises participating in the peak-load dispatching of power grids: (1) The dispatching strategy does not take into account the production constraints of the enterprises, especially the time constraints involved in the interaction process between the power-consuming equipment in multiple production lines; (2) When short-process steel enterprises participate in the dispatching of power grids, the research on the demand-side electricity cost mainly focuses on the time-of-use electricity price, while the consideration of dynamic maximum demand is relatively small; (3) The existing dispatching strategy mainly focuses on load transfer, and has not yet carried out in-depth optimization of the production load allocation strategy based on orders; (4) Traditional steel industry production scheduling often relies on experience and lacks systematic optimization methods, resulting in unreasonable resource allocation, low production efficiency, and lack of optimization in production scheduling. In terms of load management, a fixed mode is usually adopted, which cannot be dynamically adjusted according to the actual situation on the power grid side and the industrial load side, and it is difficult to cope with power grid load fluctuations and changes in production demand. Summary of the invention

[0003] The purpose of the present invention is to solve at least one technical problem in the background technology and to provide a double-layer optimal scheduling method and system for steel industry load.

[0004] To achieve the above object, the present invention provides a double-layer optimization scheduling method for steel industry load, comprising:

[0005] Collect and calculate double-layer optimization data information on the industrial load side and the power grid side;

[0006] Construct a resource task network model with continuous time interaction based on two-layer optimization data information;

[0007] Setting constraints on the resource-task network model to form a resource-task network model with production process constraints;

[0008] Based on the resource task network model with production process constraints, complex order characteristic constraints are added to form a complex order characteristic analysis model for the steel industry load on a daily basis;

[0009] The dynamic maximum demand analysis model operation constraints are set for the complex order characteristic analysis model to form a full system model of the lower iron and steel industry load side;

[0010] While constructing the lower-level steel industry load-side full-system model, the upper-level power grid dispatch optimization model that achieves the optimal economic cost of the power grid is simultaneously constructed. The lower-level steel industry load-side full-system model and the upper-level power grid dispatch optimization model together form a double-layer optimization system.

[0011] Setting a safe and stable operating range for the double-layer optimization system;

[0012] Construct an optimization model for the lower iron and steel industry load of the two-layer optimization system;

[0013] A double-layer optimization algorithm is proposed for the double-layer optimization system to solve the double-layer optimization scheduling;

[0014] Output double-layer optimization scheduling result information.

[0015] According to one aspect of the present invention, the collecting and calculating of the dual-layer optimization data information on the industrial load side and the power grid side includes:

[0016] Collect and calculate grid-side optimization data information, including: the number of thermal power units on the grid side, the number of wind farms and photovoltaic power stations, the operating cost parameter information of thermal power units, the penetration rate of wind farms and power stations, the installed capacity of wind farms and power stations, the penetration rate of photovoltaic power stations, and the installed capacity of photovoltaic power stations;

[0017] Collect and calculate industrial load side optimization data information, including: rated power of electric arc furnace equipment, rated power of refining furnace equipment, rated power of continuous casting machine equipment, furnace processing and transportation time, average daily power demand, average daily steel volume, electricity price, basic electricity price calculated according to maximum demand, and basic electricity price calculated according to transformer capacity.

[0018] According to one aspect of the present invention, the method of constructing a continuous-time interactive resource task network model based on double-layer optimization data information includes:

[0019] According to the collected industrial load side optimization data information, resource nodes are formed and various resource nodes are labeled;

[0020] According to the collected industrial load side optimization data information, time nodes are formed, and the time nodes of each production equipment are labeled;

[0021] The relationship between time nodes and resource nodes forms a resource-task network model of continuous time interaction.

[0022] According to one aspect of the present invention, setting constraints on the resource task network model includes:

[0023] Set task continuity constraints and waiting time constraints for time nodes in the resource task network model;

[0024] Set resource quantity constraints for resource nodes in the resource task network model;

[0025] The task continuity constraint expression is:

[0026]

[0027] Where: are the available quantities of the intermediate product resource M in the resource node in time periods t and t-1 respectively; is the task T in time period t-θ T The available amount of resources; τ T is the operation period of task T; θ T is a relative time point; is task T at relative time point θ T The number of generated intermediate product resources M, whose value range is {-1, 0, 1}, -1 means consumption, 0 means unused, and 1 means production; A 0-1 variable that determines whether task T is running in time period t; is the power that the production line can generate in time period t; is task T at relative time point θ T The electrical power consumed;

[0028] The waiting time constraint expression is:

[0029]

[0030] Where: is the available quantity of intermediate product resource M in time period t; Γ is the optimization operation cycle; The maximum cooling time of the transport task furnace; d T The actual cooling time of the transport task furnace; Δ is the waiting time interval, which is set to 15 minutes;

[0031] The resource quantity constraint expression is:

[0032]

[0033] Where: is the available number of resources of the continuous casting machine in time period t; Γ is the optimized operation cycle; Q is the initial number of continuous casting machines.

[0034] According to one aspect of the present invention, the resource task network model based on the production process constraint conditions is supplemented with complex order characteristic constraints to form a complex order characteristic analysis model of the steel industry load in units of days:

[0035] Model and analyze the order execution of the steel industry load on a daily basis. For any order k, it is usually necessary to estimate the daily load EW caused by order k in the steel production workshop during the planning cycle. k ; For orders that have entered the order pool but have not been put into production, calculate the daily load UW caused by the orders that have not been put into production during the cycle M ; For orders that have been put into production, it is necessary to calculate the daily load RW caused by the orders that have been placed within the period t on the production system M ; The total daily load of the steel industry can provide GW M ; The complex order characteristic analysis model expression of the steel industry load is:

[0036]

[0037] Where: K is the number of production orders of the steel industry load during the production cycle; EW k is the daily load on the production system after order k is executed in the steel production workshop during the planning cycle; RW M The daily load on the production system caused by the orders placed; UW M The daily load on the production system caused by undelivered orders; RW k is the total daily load required for the steel industry load in order k; when RW k +EW k ≤GW M When RW k +EW k ≥GW M , the order load can be delivered every day.

[0038] According to one aspect of the present invention, the dynamic maximum demand analysis model expression is:

[0039]

[0040] Where: P max is the maximum demand; P demand,t is the demand in the measurement period t; RW M,t is the total work done by all order loads in time period t; τ is the demand calculation time span; x is the production line number of a casting; t j,α With t j,β are the start and end time of the jth period respectively; T is the task; S Tx,t is the status of task T of production line x at time t; P Tx,t represents the power of task T of production line x at time t; P t E The power that can be generated by the steel production line at time t.

[0041] According to one aspect of the present invention, the upper-layer power grid dispatch optimization model expression is:

[0042]

[0043] Where: F is the economic optimization target of the upper power grid dispatch; J is the total number of thermal power units; Γ is the optimized operation cycle; is the operating cost of thermal power unit i in period t; R is the total amount of new energy; is the operating cost of renewable energy i in period t; is the output of thermal power unit i in period t; a i 、b i 、c i is the coal consumption characteristic parameter of thermal power unit i; is the state variable of unit i in period t; is the state variable of unit i in period t-1; S i is the start-up and shutdown cost of unit i; C per.i is the electricity cost of new energy i; is the output forecast value of renewable energy i in period t; is the grid-connected power of renewable energy i in period t; Ξt is the optimization simulation calculation step.

[0044] According to one aspect of the present invention, the safe and stable operation range of the system is expressed as:

[0045]

[0046] Where: J is the total number of thermal power units; P G.i is the output of thermal power unit i; R is the total amount of renewable energy; P RES.i is the grid-connected power of new energy i; P L0 is the basic load of the steel industry load; P grid Power purchased for steel industry load; Ui is the state variable of unit i; A i , B i , C i is the coefficient matrix parameter in the inequality constraints.

[0047] According to one aspect of the present invention, the optimization model expression is:

[0048]

[0049] Where: f is the optimization target of the lower load of the steel industry; Γ is the optimization operation cycle; γ t The electricity price for the power grid during the period t; P t is the power consumption of the steel industry during load period t; η is the demand electricity price; P max is the maximum demand.

[0050] According to one aspect of the present invention, the two-layer optimization algorithm is a particle swarm optimization algorithm with a mutation operator.

[0051] According to one aspect of the present invention, the outputting of the double-layer optimization scheduling result information includes:

[0052] The change curve of electricity demand of steel industry load, the electricity cost of steel industry load under different processing furnace times, the peak shaving effect on the grid side, the operating cost on the grid side, the power curve at the grid connection line, the basic electricity consumption of steel industry load under time-of-use electricity price, and the total electricity consumption of steel industry load under time-of-use electricity price.

[0053] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a double-layer optimization scheduling system for steel industry load, comprising:

[0054] Data information acquisition module, which collects and calculates double-layer optimization data information on the industrial load side and the power grid side;

[0055] Resource task network model construction module, which builds a continuous-time interactive resource task network model based on double-layer optimization data information;

[0056] The resource task network model forming module sets constraints on the resource task network model to form a resource task network model with production process constraints;

[0057] The module for forming a complex order characteristic analysis model is based on a resource task network model with production process constraints, and adds complex order characteristic constraints to form a complex order characteristic analysis model for the steel industry load on a daily basis;

[0058] The whole system model forming module of the lower iron and steel industry load side sets the dynamic maximum demand analysis model operation constraint conditions for the complex order characteristic analysis model to form the whole system model of the lower iron and steel industry load side;

[0059] The double-layer optimization system forms a module. While constructing the lower-layer steel industry load-side full system model, it also constructs the upper-layer power grid dispatch optimization model that achieves the best economic cost of the power grid. The lower-layer steel industry load-side full system model and the upper-layer power grid dispatch optimization model together form a double-layer optimization system.

[0060] A safe and stable operation range setting module is used to set the safe and stable operation range of the double-layer optimization system;

[0061] The lower-level steel industry load optimization model building module builds an optimization model for the lower-level steel industry load of the double-level optimization system;

[0062] The two-layer optimization scheduling solution module proposes a two-layer optimization algorithm for the two-layer optimization system and solves the two-layer optimization scheduling;

[0063] The scheduling result output module outputs the double-layer optimization scheduling result information.

[0064] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the above-mentioned two-layer optimization scheduling method for steel industry load.

[0065] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the double-layer optimization scheduling method for steel industry load as described above is implemented.

[0066] According to the scheme of the present invention, the present invention combines the complex production order characteristics of short-process steel and fully considers the correlation of production processes and dynamic maximum demand. It aims to improve the production efficiency of the steel industry and the operating stability of the power grid, reduce production costs, and maximize economic and social benefits. The method of the present invention can significantly reduce the electricity cost of the short-process steel industry load, and allocate production orders in combination with the characteristics of steel orders to achieve better peak shaving and valley filling effects.

[0067] According to the above scheme of the present invention, the present invention comprehensively considers factors such as production cost, power grid supply and demand balance and electricity market price. Through overall production constraints and dynamic maximum demand management, the electricity cost of steel industry load can be more effectively reduced. The resource task network model with continuous time interaction is constructed to realize accurate simulation and optimal scheduling of steel production process, thereby improving production efficiency. The present invention realizes dynamic management and precise control of load based on the complex order characteristic analysis model and dynamic maximum demand analysis model of load management. By dividing the order load, its demand response potential can be brought into play to a greater extent, and better peak shaving and valley filling effect can be achieved for the power grid, thereby adapting to power grid load fluctuations and changes in production demand. The present invention flexibly handles various constraints in the production process by setting a series of constraints, thereby adapting to emergencies in the production process, and realizes effective coordination of power grid scheduling and industrial load scheduling by constructing an upper-level power grid scheduling optimization model and a lower-level steel industry load optimization model, thereby achieving full utilization of resources and achieving overall optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A flowchart schematically shows a method for double-layer optimal scheduling of loads in the steel industry according to an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only to enable those skilled in the art to better understand and thus implement the present invention, rather than implying any limitation on the scope of the present invention.

[0070] As used herein, the term “including” and variations thereof are to be interpreted as open-ended terms meaning “including, but not limited to.” The term “based on” is to be interpreted as “based, at least in part, on.” The terms “one embodiment” and “an embodiment” are to be interpreted as “at least one embodiment.”

[0071] Figure 1 The flowchart schematically shows a double-layer optimization scheduling method for steel industry load according to an embodiment of the present invention. Figure 1 As shown, in this embodiment, the double-layer optimization scheduling method for steel industry load includes:

[0072] Collect and calculate double-layer optimization data information on the industrial load side and the power grid side;

[0073] Construct a resource task network model with continuous time interaction based on two-layer optimization data information;

[0074] Setting constraints on the resource-task network model to form a resource-task network model with production process constraints;

[0075] Based on the resource task network model with production process constraints, complex order characteristic constraints are added to form a complex order characteristic analysis model for the steel industry load on a daily basis;

[0076] The dynamic maximum demand analysis model operation constraints are set for the complex order characteristic analysis model to form a full system model of the lower iron and steel industry load side;

[0077] While constructing the lower-level steel industry load-side full-system model, the upper-level power grid dispatch optimization model that achieves the optimal economic cost of the power grid is simultaneously constructed. The lower-level steel industry load-side full-system model and the upper-level power grid dispatch optimization model together form a double-layer optimization system.

[0078] Setting a safe and stable operating range for the double-layer optimization system;

[0079] Construct an optimization model for the lower iron and steel industry load of the two-layer optimization system;

[0080] A double-layer optimization algorithm is proposed for the double-layer optimization system to solve the double-layer optimization scheduling;

[0081] Output double-layer optimization scheduling result information.

[0082] Further, according to an embodiment of the present invention, collecting and calculating the two-layer optimization data information of the industrial load side and the power grid side includes:

[0083] Collect and calculate grid-side optimization data information, including: the number of thermal power units on the grid side, the number of wind farms and photovoltaic power stations, the operating cost parameter information of thermal power units, the penetration rate of wind farms and power stations, the installed capacity of wind farms and power stations, the penetration rate of photovoltaic power stations, and the installed capacity of photovoltaic power stations;

[0084] Collect and calculate industrial load side optimization data information, including: rated power of electric arc furnace equipment, rated power of refining furnace equipment, rated power of continuous casting machine equipment, furnace processing and transportation time, average daily power demand, average daily steel volume, electricity price, basic electricity price calculated according to maximum demand, and basic electricity price calculated according to transformer capacity.

[0085] Further, according to an embodiment of the present invention, a resource task network model of continuous time interaction is constructed based on the double-layer optimization data information, including:

[0086] According to the collected industrial load side optimization data information, resource nodes are formed and various resource nodes are labeled;

[0087] According to the collected industrial load side optimization data information, time nodes are formed, and the time nodes of each production equipment are labeled;

[0088] The relationship between time nodes and resource nodes forms a resource-task network model of continuous time interaction.

[0089] In this embodiment, the resource task network model is mainly composed of two types of nodes: resource nodes and time nodes (execution time nodes);

[0090] Among them, resource nodes cover various resources such as electricity and materials related to the process flow, including equipment resources such as arc furnace, refining furnace and continuous casting machine; intermediate product resources M, which represents the transportation process of materials in different production stages; and power resources E, which reflects the power consumption of characteristic production stages. In order to distinguish different production lines and furnaces, the present invention uses the subscript x to represent the production line number and h to represent the furnace number, such as M x,h It is the intermediate product resource of batch h produced by production line x;

[0091] The time node is used to describe in detail the time required for each production equipment in the production or processing process; specifically, the execution time of key links such as smelting, refining, and casting is represented by T E , T L , T C Indicates; Taking the electric arc furnace as an example, T Ex,h is the time consumed by the execution of the h heat under the production line x; in order to ensure the continuity of the production load, waiting time nodes are specially added to the model. These waiting time nodes exist between the electric arc furnace and the refining furnace, and between the refining furnace and the continuous casting machine, and are represented by T EL Indicates the waiting time between the electric arc furnace and the refining furnace, T LC Represents the waiting time between the refining furnace and the continuous casting machine; during these waiting time periods, the corresponding power resource consumption is set to 0, which more accurately reflects the continuity and intermittency of the actual production process.

[0092] Further, according to an embodiment of the present invention, the steel industry load is a typical process industry user, and its production process is subject to strict process restrictions; this means that the start-up time and operation time of the production equipment must follow the pre-established production plan; since the power supply cannot be interrupted at will once the production equipment starts working, the present invention sets a series of constraints when constructing a resource task network model based on continuous time interaction to ensure the accuracy and practicality of the model. Specifically, setting constraints on the resource task network model includes:

[0093] Set task continuity constraints and waiting time constraints for time nodes in the resource task network model;

[0094] Set resource quantity constraints for resource nodes in the resource task network model;

[0095] In this embodiment, under the continuous time representation, the optimal scheduling result of the steel industry load directly determines when each production task starts; for equipment or furnace resources that need to interact with the task continuously, the power consumption in time period t can be expressed by calculating the total power consumption of the task at the start time of time period t; the task continuity constraint ensures the continuity of the production process and avoids production interruption caused by power outage or improper startup of equipment;

[0096] The task continuity constraint expression is:

[0097]

[0098] Where: are the available quantities of the intermediate product resource M in the resource node in time periods t and t-1 respectively; is the task T in time period t-θ T The available amount of resources; τ T is the operation period of task T; θ T is a relative time point; is task T at relative time point θ T The number of generated intermediate product resources M, whose value range is {-1, 0, 1}, -1 means consumption, 0 means unused, and 1 means production; A 0-1 variable that determines whether task T is running in time period t; is the power that the production line can generate in time period t; is task T at relative time point θ T The electrical power consumed;

[0099] In this embodiment, in order to ensure the quality of the ingot during the steel production process, it is necessary to strictly control the residence time of the furnace outside the equipment; since the task resources are only generated and consumed at the starting time of each time period, the waiting time constraint between equipment operations can be converted into a time period limit of 1 for the furnace within the scheduling time range; it is required that the number of available time periods for each furnace within any given time period, that is, the number of time periods not occupied by other tasks, does not exceed 1; through the waiting time constraint, the present invention effectively prevents quality problems caused by long waiting time of the furnace, thereby ensuring the smoothness and efficiency of the production process,

[0100] The waiting time constraint expression is:

[0101]

[0102] Where: is the available quantity of intermediate product resource M in time period t; Γ is the optimization operation cycle; The maximum cooling time of the transport task furnace; d TThe actual cooling time of the transport task furnace; Δ is the waiting time interval, which is set to 15 minutes;

[0103] In this embodiment, during the steel production scheduling process, it is necessary to ensure that the available quantity of all resources in any period remains non-negative; at the same time, in order to ensure that all tasks can be successfully completed at the end of the scheduling cycle, it is necessary to ensure that each continuous casting machine is in an unoccupied state within the time Γ; the resource quantity constraint ensures the effective use of resources and the timely completion of tasks, which is of great significance for improving the production efficiency and economic benefits of the steel industry load;

[0104] The resource quantity constraint expression is:

[0105]

[0106] Where: is the available number of resources of the continuous casting machine in time period t; Γ is the optimized operation cycle; Q is the initial number of continuous casting machines.

[0107] Further, according to an embodiment of the present invention, based on the resource task network model with production process constraints, complex order characteristic constraints are added to form a complex order characteristic analysis model of the steel industry load in units of days:

[0108] Model and analyze the order execution of the steel industry load on a daily basis. For any order k, it is usually necessary to estimate the daily load EW caused by order k in the steel production workshop during the planning cycle. k ; For orders that have entered the order pool but have not been put into production, calculate the daily load UW caused by the orders that have not been put into production during the cycle M ; For orders that have been put into production, it is necessary to calculate the daily load RW caused by the orders that have been placed within the period t on the production system M ; The total daily load of the steel industry can provide GW M ; The complex order characteristic analysis model expression of the steel industry load is:

[0109]

[0110] Where: K is the number of production orders of the steel industry load during the production cycle; EW k is the daily load on the production system after order k is executed in the steel production workshop during the planning cycle; RW M The daily load on the production system caused by the orders placed; UW M The daily load on the production system caused by undelivered orders; RW k is the total daily load required for the steel industry load in order k; when RWk +EW k ≤GW M When RW k +EW k ≥GW M , the order load can be delivered every day.

[0111] Further, according to an embodiment of the present invention, the dynamic maximum demand analysis model expression is:

[0112]

[0113] Where: P max is the maximum demand; P demand,t is the demand in the measurement period t; RW M,t is the total work done by all order loads in time period t; τ is the demand calculation time span; x is the production line number of a casting; t j,α With t j,β are the start and end time of the jth period respectively; T is the task; S Tx,t is the status of task T of production line x at time t; P Tx,t represents the power of task T of production line x at time t; P t E The power that can be generated by the steel production line at time t.

[0114] Furthermore, according to an embodiment of the present invention, the upper-layer power grid dispatch optimization model is to achieve the optimal economic performance of the entire network under the premise of ensuring the safe and stable operation of the system. The economic cost includes the operating cost of thermal power units and the operating cost of new energy. The operating cost of thermal power units mainly includes coal burning costs and unit start-up and shutdown costs. The operating cost of new energy mainly depends on equipment maintenance and construction investment. After these two costs are apportioned to the power generation over the entire life cycle, the cost per kilowatt-hour is formed. In this embodiment, the upper-layer power grid dispatch optimization model is expressed as:

[0115]

[0116] Where: F is the economic optimization target of the upper power grid dispatch; J is the total number of thermal power units; Γ is the optimized operation cycle; is the operating cost of thermal power unit i in period t; R is the total amount of new energy; is the operating cost of renewable energy i in period t; is the output of thermal power unit i in period t; a i 、b i 、c i is the coal consumption characteristic parameter of thermal power unit i; is the state variable of unit i in period t; is the state variable of unit i in period t-1; S i is the start-up and shutdown cost of unit i; C per.i is the electricity cost of new energy i; is the output forecast value of renewable energy i in period t; is the grid-connected power of renewable energy i in period t; Ξt is the optimization simulation calculation step.

[0117] Further, according to an embodiment of the present invention, the safe and stable operation range of the system is expressed as:

[0118]

[0119] Where: J is the total number of thermal power units; P G.i is the output of thermal power unit i; R is the total amount of renewable energy; P RES.i is the grid-connected power of new energy i; P L0 is the basic load of the steel industry load; P grid Power purchased for steel industry load; U i is the state variable of unit i; A i , B i , C i is the coefficient matrix parameter in the inequality constraints.

[0120] Furthermore, according to an embodiment of the present invention, the main goal of the steel industry load participating in demand response is to reduce its own electricity cost; the lower-level optimization model ensures that it can complete the order task on time while minimizing the electricity purchase cost. In this embodiment, the optimization model expression is as follows:

[0121]

[0122] Where: f is the optimization target of the lower load of the steel industry; Γ is the optimization operation cycle; γ t The electricity price for the power grid during the period t; P t is the power consumption of the steel industry during load period t; η is the demand electricity price; P max is the maximum demand.

[0123] Further, according to an embodiment of the present invention, the two-layer optimization algorithm is a particle swarm optimization algorithm with a mutation operator. In this embodiment, in order to match the model solution process with the upper and lower layer interaction process, the present invention directly solves the two-layer optimization scheduling model of the steel industry load and the power grid based on the idea of ​​hierarchical iteration; since the iterative process involves two kinds of information, electricity price and purchased power, it is possible that the solution process falls into an infinite loop; to overcome this problem, the particle swarm optimization algorithm with a mutation operator proposed in the present invention is used as an iterative algorithm. The algorithm introduces a parameter δ in the D-dimensional search space. con(n)(n∈[1,D]) represents the current position x of each particle in the nth dimension. y,n (y = 1, 2, ..., N, N is the total number of particles in the particle group) to the center of mass D of the particle group in the nth dimension n The sum of the distances to the destination is expressed as follows:

[0124]

[0125] Where: con (n) is the convergence value of the particle swarm in n dimensions. The smaller it is, the greater the convergence degree of the particle swarm is. N is the total number of particles in the particle swarm. y,n is the current position of each particle in the nth dimension; D n is the center of mass of the particle group in the nth dimension;

[0126] To obtain D-dimensional δ con (n) is the dimension d where the minimum value is located, and the current position x of each particle in this dimension y,n Perform mutation operation according to the formula with a certain probability to make the positions of all particles in this dimension redistribute evenly; y,n The expression is as follows:

[0127]

[0128] Where: x y,d is the current position of each particle in the dth dimension; is a random number in the interval (0, 1); x max is the maximum position of all particles in d dimension.

[0129] Further, according to an embodiment of the present invention, outputting the double-layer optimization scheduling result information includes:

[0130] The change curve of electricity demand of steel industry load, the electricity cost of steel industry load under different processing furnace times, the peak shaving effect on the grid side, the operating cost on the grid side, the power curve at the grid connection line, the basic electricity consumption of steel industry load under time-of-use electricity price, and the total electricity consumption of steel industry load under time-of-use electricity price.

[0131] According to the above scheme of the present invention, the present invention combines the complex production order characteristics of short-process steel and fully considers the correlation of production processes and dynamic maximum demand. It aims to improve the production efficiency of the steel industry and the operating stability of the power grid, reduce production costs, and maximize economic and social benefits. The method of the present invention can significantly reduce the electricity cost of the short-process steel industry load, and allocate production orders in combination with the characteristics of steel orders to achieve better peak shaving and valley filling effects.

[0132] According to the above scheme of the present invention, the present invention comprehensively considers factors such as production cost, power grid supply and demand balance and electricity market price. Through overall production constraints and dynamic maximum demand management, the electricity cost of steel industry load can be more effectively reduced. The resource task network model with continuous time interaction is constructed to realize accurate simulation and optimal scheduling of steel production process, thereby improving production efficiency. The present invention realizes dynamic management and precise control of load based on the complex order characteristic analysis model and dynamic maximum demand analysis model of load management. By dividing the order load, its demand response potential can be brought into play to a greater extent, and better peak shaving and valley filling effect can be achieved for the power grid, thereby adapting to power grid load fluctuations and changes in production demand. The present invention flexibly handles various constraints in the production process by setting a series of constraints, thereby adapting to emergencies in the production process, and realizes effective coordination of power grid scheduling and industrial load scheduling by constructing an upper-level power grid scheduling optimization model and a lower-level steel industry load optimization model, thereby achieving full utilization of resources and achieving overall optimization.

[0133] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a double-layer optimization scheduling system for steel industry load, comprising:

[0134] Data information acquisition module, which collects and calculates double-layer optimization data information on the industrial load side and the power grid side;

[0135] Resource task network model construction module, which builds a continuous-time interactive resource task network model based on double-layer optimization data information;

[0136] The resource task network model forming module sets constraints on the resource task network model to form a resource task network model with production process constraints;

[0137] The module for forming a complex order characteristic analysis model is based on a resource task network model with production process constraints, and adds complex order characteristic constraints to form a complex order characteristic analysis model for the steel industry load on a daily basis;

[0138] The whole system model forming module of the lower iron and steel industry load side sets the dynamic maximum demand analysis model operation constraint conditions for the complex order characteristic analysis model to form the whole system model of the lower iron and steel industry load side;

[0139] The double-layer optimization system forms a module. While constructing the lower-layer steel industry load-side full system model, it also constructs the upper-layer power grid dispatch optimization model that achieves the best economic cost of the power grid. The lower-layer steel industry load-side full system model and the upper-layer power grid dispatch optimization model together form a double-layer optimization system.

[0140] A safe and stable operation range setting module is used to set the safe and stable operation range of the double-layer optimization system;

[0141] The lower-level steel industry load optimization model building module builds an optimization model for the lower-level steel industry load of the double-level optimization system;

[0142] The two-layer optimization scheduling solution module proposes a two-layer optimization algorithm for the two-layer optimization system and solves the two-layer optimization scheduling;

[0143] The scheduling result output module outputs the double-layer optimization scheduling result information.

[0144] The double-layer optimization scheduling system for the steel industry load according to the present invention can implement the above-mentioned double-layer optimization scheduling method for the steel industry load. The specific method steps are as described above and will not be repeated here.

[0145] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the double-layer optimization scheduling method for steel industry load as described above is implemented.

[0146] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the double-layer optimization scheduling method for steel industry load as described above is implemented.

[0147] Those skilled in the art will appreciate that the modules and algorithm steps 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.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method implementation methods, and will not be repeated here.

[0149] In the embodiments provided in the present application, 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 modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules 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 modules, which can be electrical, mechanical or other forms.

[0150] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0151] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0152] If the functions are implemented in the form of software function modules 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 can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the energy-saving signal sending / receiving method of 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.

[0153] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other.

[0154] It should be understood that the size of the serial numbers of each step in the content of the invention and the implementation methods of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of the present invention.

Claims

1. A double-layer optimization scheduling method for steel industry load, characterized in that: include: Collect and calculate double-layer optimization data information on the industrial load side and the power grid side; Construct a resource task network model with continuous time interaction based on two-layer optimization data information; Setting constraints on the resource-task network model to form a resource-task network model with production process constraints; Based on the resource task network model with production process constraints, complex order characteristic constraints are added to form a complex order characteristic analysis model for the steel industry load on a daily basis; The dynamic maximum demand analysis model operation constraints are set for the complex order characteristic analysis model to form a full system model of the lower iron and steel industry load side; While constructing the lower-level steel industry load-side full-system model, the upper-level power grid dispatch optimization model that achieves the optimal economic cost of the power grid is simultaneously constructed. The lower-level steel industry load-side full-system model and the upper-level power grid dispatch optimization model together form a double-layer optimization system. Setting a safe and stable operating range for the double-layer optimization system; Construct an optimization model for the lower iron and steel industry load of the two-layer optimization system; A double-layer optimization algorithm is proposed for the double-layer optimization system to solve the double-layer optimization scheduling; Output double-layer optimization scheduling result information.

2. The double-layer optimization scheduling method for steel industry load according to claim 1 is characterized in that: The collecting and calculating of the dual-layer optimization data information on the industrial load side and the power grid side includes: Collect and calculate grid-side optimization data information, including: the number of thermal power units on the grid side, the number of wind farms and photovoltaic power stations, the operating cost parameter information of thermal power units, the penetration rate of wind farms and power stations, the installed capacity of wind farms and power stations, the penetration rate of photovoltaic power stations, and the installed capacity of photovoltaic power stations; Collect and calculate industrial load side optimization data information, including: rated power of electric arc furnace equipment, rated power of refining furnace equipment, rated power of continuous casting machine equipment, furnace processing and transportation time, average daily power demand, average daily steel volume, electricity price, basic electricity price calculated according to maximum demand, and basic electricity price calculated according to transformer capacity.

3. The double-layer optimization scheduling method for steel industry load according to claim 1 is characterized in that: The resource task network model for continuous time interaction based on the double-layer optimization data information includes: According to the collected industrial load side optimization data information, resource nodes are formed and various resource nodes are labeled; According to the collected industrial load side optimization data information, time nodes are formed, and the time nodes of each production equipment are labeled; The relationship between time nodes and resource nodes forms a resource-task network model of continuous time interaction.

4. The double-layer optimization scheduling method for steel industry load according to claim 1 is characterized in that: The constraint conditions set for the resource task network model include: Set task continuity constraints and waiting time constraints for time nodes in the resource task network model; Set resource quantity constraints for resource nodes in the resource task network model; The task continuity constraint expression is: Where: are the available quantities of the intermediate product resource M in the resource node in time periods t and t-1 respectively; is the task T in time period t-θ T The available amount of resources; τ T is the operation period of task T; θ T is a relative time point; is task T at relative time point θ T The number of generated intermediate product resources M, whose value range is {-1, 0, 1}, -1 means consumption, 0 means unused, and 1 means production; A 0-1 variable that determines whether task T is running in time period t; is the power that the production line can generate in time period t; is task T at relative time point θ T The electrical power consumed; The waiting time constraint expression is: Where: is the available quantity of intermediate product resource M in time period t; Γ is the optimization operation cycle; The maximum cooling time of the transport task furnace; d T The actual cooling time of the transport task furnace; Δ is the waiting time interval, which is set to 15 minutes; The resource quantity constraint expression is: Where: is the available number of resources of the continuous casting machine in time period t; Γ is the optimized operation cycle; Q is the initial number of continuous casting machines.

5. The double-layer optimization scheduling method for steel industry load according to claim 1 is characterized in that: The resource task network model based on the production process constraint conditions is supplemented with complex order characteristic constraints to form a complex order characteristic analysis model for the steel industry load in units of days: Modeling and analyzing the order execution of the steel industry load on a daily basis. For any order k, it is usually necessary to estimate the daily load EW caused by order k in the steel production workshop during the planning cycle. k ; For orders that have entered the order pool but have not been put into production, calculate the daily load UW caused by the orders that have not been put into production during the cycle M ; For orders that have been put into production, it is necessary to calculate the daily load RW caused by the orders that have been placed within the period t on the production system M ; The total daily load of the steel industry can provide GW M ; The complex order characteristic analysis model expression of the steel industry load is: Where: K is the number of production orders of the steel industry load during the production cycle; EW k is the daily load on the production system after order k is executed in the steel production workshop during the planning cycle; RW M The daily load on the production system caused by the orders placed; UW M The daily load on the production system caused by undelivered orders; RW k is the total daily load required for the steel industry load in order k; when RW k +EW k ≤GW M When RW k +EW k ≥GW M , the order load can be delivered every day.

6. The double-layer optimization scheduling method for steel industry load according to claim 1 is characterized in that: The dynamic maximum demand analysis model expression is: Where: P max is the maximum demand; P demand,t is the demand in the measurement period t; RW M,t is the total work done by all order loads in time period t; τ is the demand calculation time span; x is the production line number of a casting; t j,α With t j,β are the start and end time of the jth period respectively; T is the task; S Tx,t is the status of task T of production line x at time t; P Tx,t represents the power of task T of production line x at time t; P t E The power that can be generated by the steel production line at time t.

7. The double-layer optimization scheduling method for steel industry load according to claim 1 is characterized in that: The upper power grid dispatch optimization model expression is: Where: F is the economic optimization target of the upper power grid dispatch; J is the total number of thermal power units; Γ is the optimized operation cycle; is the operating cost of thermal power unit i in period t; R is the total amount of new energy; is the operating cost of renewable energy i in period t; is the output of thermal power unit i in period t; a i 、b i 、c i is the coal consumption characteristic parameter of thermal power unit i; is the state variable of unit i in period t; is the state variable of unit i in period t-1; S i is the start-up and shutdown cost of unit i; C per.i is the electricity cost of new energy i; is the output forecast value of renewable energy i in period t; is the grid-connected power of renewable energy i in period t; Ξt is the optimization simulation calculation step.

8. The double-layer optimization scheduling method for steel industry load according to claim 1 is characterized in that: The expression of the safe and stable operation range of the system is: Where: J is the total number of thermal power units; P G.i is the output of thermal power unit i; R is the total amount of renewable energy; P RES.i is the grid-connected power of new energy i; P L0 is the basic load of the steel industry load; P grid Power purchased for steel industry load; U i is the state variable of unit i; A i , B i , C i is the coefficient matrix parameter in the inequality constraints.

9. The double-layer optimization scheduling method for steel industry load according to claim 1 is characterized in that: The optimization model expression is: Where: f is the optimization target of the lower load of the steel industry; Γ is the optimization operation cycle; γ t The electricity price for the power grid during the period t; P t is the power consumption of the steel industry during load period t; η is the demand electricity price; P max is the maximum demand.

10. The double-layer optimization scheduling method for steel industry load according to claim 1 is characterized in that: The two-layer optimization algorithm is a particle swarm optimization algorithm with a mutation operator.

11. The double-layer optimization scheduling method for steel industry load according to any one of claims 1 to 10, characterized in that: The output double-layer optimization scheduling result information includes: The change curve of electricity demand of steel industry load, the electricity cost of steel industry load under different processing furnace times, the peak shaving effect on the grid side, the operating cost on the grid side, the power curve at the grid connection line, the basic electricity consumption of steel industry load under time-of-use electricity price, and the total electricity consumption of steel industry load under time-of-use electricity price.

12. The double-layer optimization dispatching system for steel industry load is characterized by: include: Data information acquisition module, which collects and calculates double-layer optimization data information on the industrial load side and the power grid side; Resource task network model construction module, which builds a continuous-time interactive resource task network model based on double-layer optimization data information; The resource task network model forming module sets constraints on the resource task network model to form a resource task network model with production process constraints; The module for forming a complex order characteristic analysis model is based on a resource task network model with production process constraints, and adds complex order characteristic constraints to form a complex order characteristic analysis model for the steel industry load on a daily basis; The whole system model forming module of the lower iron and steel industry load side sets the dynamic maximum demand analysis model operation constraint conditions for the complex order characteristic analysis model to form the whole system model of the lower iron and steel industry load side; The double-layer optimization system forms a module. While constructing the lower-layer steel industry load-side full system model, it also constructs the upper-layer power grid dispatch optimization model that achieves the best economic cost of the power grid. The lower-layer steel industry load-side full system model and the upper-layer power grid dispatch optimization model together form a double-layer optimization system. A safe and stable operation range setting module is used to set the safe and stable operation range of the double-layer optimization system; The lower-level steel industry load optimization model building module builds an optimization model for the lower-level steel industry load of the double-level optimization system; The two-layer optimization scheduling solution module proposes a two-layer optimization algorithm for the two-layer optimization system and solves the two-layer optimization scheduling; The scheduling result output module outputs the double-layer optimization scheduling result information.

13. An electronic device, characterized in that The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for double-layer optimization scheduling of loads in the steel industry as described in any one of claims 1 to 10 is implemented.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the double-layer optimization scheduling method for load in the steel industry as described in any one of claims 1-10 is implemented.

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