Method and system for peak shaving optimization scheduling of steel industry load based on network load game

Abstract

The present application relates to the technical field of steel industry load participating in power grid interaction, and provides a method and system for peak shaving optimization scheduling of a power grid game of a steel industry load, the method comprising: based on the load characteristics specific to the steel industry load, analyzing and obtaining the adjustable potential interval of key production equipment; then, performing fine modeling on the adjustable potential of the steel industry load, considering the production factor constraint condition, and analyzing to obtain the adjustable capacity of different equipment and multi-production line combinations; finally, analyzing the behaviors of peak shaving strategy participants and formulators, and using the Stackelberg game to construct a master-slave game optimization scheduling model and solve it. The present application can reasonably mobilize the enthusiasm of short-process steel industry load participating in power grid peak shaving, the proposed peak shaving optimization scheduling method can effectively reduce the peak-valley difference of power grid load while increasing the revenue of steel industry load, and can provide theoretical guidance and reference for steel industry load participating in demand response, interacting with power grid, and optimization scheduling.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steel industry load participating in power grid interaction, and particularly relates to a method and system for peak shaving optimization scheduling of steel industry load participating in power grid interaction. BACKGROUND

[0002] Demand response, as an important demand side resource management method, guides users to change power consumption characteristics and tap the potential of resource scheduling on the load side through measures such as electricity price or economic incentives, so as to alleviate the adverse effects of new energy and load fluctuation on the power grid to cope with peak load problems. The short process steel industry load, with electric arc furnace as the core equipment and electricity as the main energy carrier, has great potential to participate in demand response as a high energy-consuming industry.

[0003] The basis for the short process steel industry load to participate in demand response lies in the in-depth analysis of its adjustable potential and the accurate modeling of its response behavior. In the research on the short process steel industry load participating in demand response regulation, the influence of its own load characteristics on the demand response capability must be considered comprehensively to achieve more accurate and effective supply and demand balance regulation. The modeling and analysis of the response behavior of the short process steel industry load is also crucial. The operation time of high-power production equipment such as electric arc furnace can be controlled without affecting the order quantity to balance the load fluctuation of the power grid. However, there is currently a lack of fine power consumption models suitable for smelting loads such as short process steel industry loads, and it is necessary to conduct more detailed modeling and analysis of the short process steel load to optimize its power consumption behavior while ensuring safe and economic operation of the system. How to encourage the steel industry load to participate in power dispatching and peak shaving by designing a reasonable incentive mechanism while meeting the economic benefits of the steel industry load to achieve a balance of interests between the power supply and demand is still a key direction for current and future technical research. The existing short process steel industry load participating in power grid peak shaving scheduling has the following problems: (1) lack of comprehensive consideration of the influence of its own load characteristics on the demand response capability; (2) lack of fine power consumption models suitable for smelting loads such as short process steel industry loads; (3) lack of a comprehensive perspective considering the interests of both supply and demand. SUMMARY

[0004] The present application aims to solve at least one of the technical problems in the background art, and provides a method and system for peak shaving optimization scheduling of steel industry load participating in power grid interaction.

[0005] To achieve the above-mentioned purpose, the present application provides a method for peak shaving optimization scheduling of steel industry load participating in power grid interaction, comprising:

[0006] Collecting data information for peak shaving optimization scheduling of power grid interaction;

[0007] Constructing a short process steel industry load characteristic analysis model based on the data information for peak shaving optimization scheduling of power grid interaction;

[0008] Based on the short process steel industry load characteristic analysis model, a production line electric arc furnace demand response adjustable capacity model is constructed;

[0009] Based on the short process steel industry load characteristic analysis model, a production line hot rolling mill demand response adjustable capacity model is constructed;

[0010] The production line electric arc furnace demand response adjustable capacity model and the production line hot rolling mill demand response adjustable capacity model are superimposed to form an industrial load demand response adjustable capacity calculation model, and according to different adjustment modes, an industrial load demand response adjustable capacity calculation model under multiple types of adjustment modes is formed;

[0011] Based on the industrial load demand response adjustable capacity calculation model under multiple types of adjustment modes, a lower-layer steel industry load daily production revenue maximum optimization objective model is constructed for the load side;

[0012] The lower-layer steel industry load daily production revenue maximum optimization objective model is set with lower-layer steel industry load daily production operation constraint conditions;

[0013] Based on the industrial load demand response adjustable capacity calculation model under multiple types of adjustment modes, an upper-layer power grid dispatching center equivalent load variance minimum optimization objective model is constructed for the grid side;

[0014] The upper-layer power grid dispatching center equivalent load variance minimum optimization objective model is set with upper-layer power grid dispatching center equivalent load variance minimum operation constraint conditions;

[0015] Based on the lower-layer steel industry load daily production revenue maximum optimization objective model and the upper-layer power grid dispatching center equivalent load variance minimum optimization objective model, a Nash-Cournot game model is constructed, in which the dispatching center is the leader and the steel industry load is the participant;

[0016] A grid-load game peak regulation optimization scheduling solving method is proposed for the Nash-Cournot game model, and the grid-load game peak regulation optimization scheduling is solved;

[0017] The grid-load game peak regulation optimization scheduling result information is output.

[0018] According to an aspect of the present application, the grid-load game peak regulation optimization scheduling data information comprises:

[0019] The production line productivity, the production line cycle, the number of steel production lines, the production line power, the total number of electric arc furnaces on all production lines, the current gear position of the equipment, the stable working power of the hot rolling mill, the total number of hot rolling mills on all production lines, the total number of electric arc furnace equipment gears, the total time period of the optimization scheduling cycle, the time-of-use electricity price, the carbon treatment price, the unit control cost coefficient, the optimization scheduling data transmission step, the unit benefit of participating in demand response, the start time of participating in demand response, the end time of participating in demand response, the transformer gear adjustment percentage, the total number of transformer taps, the electric arc furnace daily maximum working time length, and the maximum number of adjustable hot rolling mill data information.

[0020] According to an aspect of the present application, the short-process steel industry load characteristic analysis model comprises:

[0021] The steel production rate model expression of a single steel production line in T n The steel production rate model expression of a single steel production line in T

[0022]

[0023] In the formula, m n is the production rate of the nth production line; T n is the cycle of the nth production line;

[0024] For a steel industry load with N steel production lines, the total production amount model expression is:

[0025]

[0026] In the formula, m t is the total production amount of N production lines; m n is the production rate of the nth production line; and N is the number of steel production lines.

[0027] For a steel industry load with N steel production lines, the total production load power model expression is:

[0028]

[0029] In the formula, P t is the total production power of N production lines; P n is the production power of the nth production line; and N is the number of steel production lines.

[0030] According to an aspect of the present application, the short-process steel industry load characteristic analysis model is used to construct a production line electric arc furnace demand response adjustable capacity model.

[0031] In combination with the data information of peak regulation and optimization scheduling of the net load game, on the basis of the load characteristic analysis model of the short-process steel industry, the load characteristic of the short-process steel industry is refined and focused, the production line in the load characteristic analysis model of the short-process steel industry is described by the electric arc furnace, and a demand response adjustable capacity model of the production line electric arc furnace is established;

[0032] An expression of the demand response adjustable capacity model of the production line electric arc furnace is as follows:

[0033]

[0034] In the formula, Pijt represents the power of the i th electric arc furnace at the t th moment; d Pijt represents the power of the i th electric arc furnace at the t th moment; d i,t,k Pijt represents the power of the i th electric arc furnace at the t th moment; d Lc,k Pijt represents the power of the i th electric arc furnace at the t th moment; d Pijt represents the power of the i th electric arc furnace at the t th moment; d Pijt represents the power of the i th electric arc furnace at the t th moment; d i,t,ko Pijt represents the power of the i th electric arc furnace at the t th moment; d Lc,ko Pijt represents the power of the i th electric arc furnace at the t th moment; d Pijt represents the power of the i th electric arc furnace at the t th moment; d

[0035] According to one aspect of the present application, the demand response adjustable capacity model of the production line hot rolling mill is constructed based on the load characteristic analysis model of the short-process steel industry as follows:

[0036] In combination with the data information of peak regulation and optimization scheduling of the net load game, on the basis of the load characteristic analysis model of the short-process steel industry, the load characteristic of the short-process steel industry is refined and focused, the production line in the load characteristic analysis model of the short-process steel industry is described by the electric arc furnace, and a demand response adjustable capacity model of the production line electric arc furnace is established;

[0037] An expression of the demand response adjustable capacity model of the production line electric arc furnace is as follows:

[0038]

[0039] In the formula, Pijt represents the power of the i th electric arc furnace at the t th moment; d Pijt represents the power of the i th electric arc furnace at the t th moment; d Pijt represents the power of the i th electric arc furnace at the t th moment; d L,j Pijt represents the power of the i th electric arc furnace at the t th moment; d Pijt represents the power of the i th electric arc furnace at the t th moment; dL,j,o is the power of the i th electric arc furnace at t time before participating in the regulation; is the total demand response adjustable capacity that can be provided by all the hot rolling mills.

[0040] According to an aspect of the present application, the industrial load demand response adjustable capacity calculation model in the multi-type regulation mode comprises:

[0041] The total demand response adjustable capacity calculation model expression that can be provided in the equipment regulation mode is:

[0042]

[0043] In the formula: is the total demand response adjustable capacity that can be provided by the steel industry load at t time in the equipment regulation mode; I is the total number of electric arc furnaces on all production lines; K is the total number of electric arc furnace gearshifts; d i,t,k is the gearshift position k of the i th equipment at t time; P Lc,k is the power of the electric arc furnace at gearshift k; d i,t,ko is the gearshift position ko of the i th equipment at t time when not participating in the demand response, that is, the gearshift position of the equipment when the steel industry load does not participate in the demand response; P Lc,ko is the power of the electric arc furnace at gearshift ko when not participating in the demand response; is a different 0-1 variable, which is 1 when turned on at t time and 0 when turned off at t time; P L,j is the stable working power of the j th hot rolling mill; J is the total number of hot rolling mills on all production lines; P L,j,o is the power of the j th hot rolling mill at t time before participating in the regulation;

[0044] The total demand response adjustable capacity calculation model expression that can be provided in the production line regulation mode is:

[0045]

[0046] In the formula: is the total demand response adjustable capacity that can be provided by the steel industry load at t time in the production line regulation mode; N is the number of steelmaking production lines; P n is the production power of a single production line; is a 0-1 variable, which is 1 when the production line is turned on at t time and 0 when the production line is turned off at t time;

[0047] The total demand response adjustable capacity calculation model expression that can be provided in the combined regulation mode is:

[0048]

[0049] In the formula: total demand response adjustable capacity available for the steel industry load at time t in the combined regulation mode; total demand response adjustable capacity available for the steel industry load at time t in the equipment regulation mode; total demand response adjustable capacity available for the steel industry load at time t in the production line regulation mode; and μ is a regulation mode weight coefficient.

[0050] According to an aspect of the present application, the lower steel industry load daily production benefit maximum optimization target model expression is:

[0051] C = max (E DR - ΔC u + ΔC t + ΔC c + ΔC s ) ;

[0052] In the formula, C is the lower steel industry load daily production benefit; E DR is the benefit obtained by participating in demand response; ΔC u is the power cost change amount; ΔC t is the carbon emission cost change amount; ΔC c is the equipment wear cost change amount; ΔC s is the production benefit change amount;

[0053] In the formula, the power cost change amount expression is:

[0054]

[0055] In the formula, ΔC u is the power cost change amount; C is the time-of-use electricity price at time t; and T is the total number of time periods of the optimization scheduling period. total demand response adjustable capacity available for the steel industry load at time t in the combined regulation mode;

[0056] The carbon emission cost change amount expression is:

[0057]

[0058] In the formula, ΔC t is the carbon emission cost change amount; λ c is the carbon treatment price; I is the total number of electric arc furnaces on all production lines; β c is the carbon emission coefficient of the electric arc furnace, indicating the equivalent carbon emission weight for producing 1 ton of crude steel; K c is the crude steel production rate of the electric arc furnace; and Δt is the optimization scheduling data transmission step length.

[0059] The device wear cost change amount expression is:

[0060]

[0061] In the formula, ΔC c is a device wear cost change amount; T is a total time period number of an optimized scheduling period; I is a total number of electric arc furnaces on all production lines; K is a total number of electric arc furnace device gears; J is a total number of hot rolling mills on all production lines; C sg is a gear switching cost; d i,t,k is a gear position k of the i-th device at a t time; d i,t-1,k is a gear position k of the i-th device at a t-1 time; d i,t,ko is a gear position ko of the i-th device at the t time when the device does not participate in demand response, that is, a gear position of the device when a steel industry load does not participate in demand response; d i,t-1,ko is a gear position ko of the i-th device at the t-1 time when the device does not participate in demand response, that is, a gear position of the device when a steel industry load does not participate in demand response; C sr is a hot rolling mill switching cost; is a 0-1 variable, which is 1 when turned on at the t time and 0 when turned off at the t time; is a 0-1 variable, which is 1 when turned on at the t-1 time and 0 when turned off at the t-1 time;

[0062] The production benefit change amount expression is:

[0063]

[0064] In the formula, ΔC s is a production benefit change amount; C ea is a unit control cost coefficient; is a total demand response adjustable capacity that can be provided by a steel industry load at the t time in a combined adjustment mode; Δt is an optimized scheduling data transmission step length; F p is a unit product sales price; F c is a unit product production cost; α is a proportion of an electricity consumption cost in the production cost; C u is a unit output electricity consumption; η is a production efficiency;

[0065] The benefit obtained by participating in demand response expression is:

[0066]

[0067] In the formula, E DR is a benefit obtained by participating in demand response; C DR is a unit benefit of participating in demand response; t strat is a start time of participating in demand response; tend The end point of participation in demand response; This refers to the total demand response adjustable capacity that the steel industry load can provide at time t under the combined regulation mode.

[0068] According to one aspect of the present invention, the daily production operation constraints of the lower-level steel industry load include: power constraints of the electric arc furnace, transformer adjustment frequency constraints, transformer over-level adjustment constraints, constraints to ensure that the participation of the electric arc furnace in demand response does not affect the daily production plan of the steel industry load, hot rolling mill adjustment frequency constraints, and storage capacity constraints.

[0069] The power constraint condition expression for the electric arc furnace is as follows:

[0070]

[0071] In the formula: The power of the i-th electric arc furnace at time t; γ is the transformer tap adjustment percentage; d i,t,k Let k be the position of the gear lever of the i-th device at time t; P i E D represents the rated power of the transformer at its initial tap; D represents the total number of taps on the transformer.

[0072] The expression for the constraint condition of the number of transformer adjustments is:

[0073]

[0074] In the formula: u i,t This represents the adjustment status of the transformer tap changer; a value of 0 indicates that the transformer tap changer is not adjusted, and a value of 1 indicates that the transformer tap changer is adjusted. i,t,k Let k be the position of the gear lever of the i-th device at time t; d i,t-1,k Let k be the position of the gear lever of the i-th device at time t-1; T is the total number of time periods in the optimized scheduling cycle; U i,max This refers to the maximum number of taps that can be connected to the adjustable transformer.

[0075] The expression for the transformer over-level regulation constraint condition is as follows:

[0076] d i,t,k -d i,t-1,k ≤1;

[0077] In the formula: d i,t,k Let k be the position of the gear lever of the i-th device at time t; d i,t-1,k Let k be the position of the gear lever of the i-th device at time t-1;

[0078] The constraint expression for ensuring that the participation of electric arc furnaces in demand response does not affect the daily production plan of the steel industry is as follows:

[0079]

[0080] T is the total time period of the optimized scheduling period; is the power of all electric arc furnaces at t moment; is the power of all electric arc furnaces at t moment without participating in demand response; I is the total number of electric arc furnaces on all production lines; τ i is the production time change caused by the adjustment power of the i th electric arc furnace; T max is the daily maximum working time of the electric arc furnace;

[0081] The expression of the adjustment frequency constraint condition of the hot rolling mill is:

[0082]

[0083] v j,t is the adjustment state of the hot rolling mill, and the value of 0 indicates that the hot rolling mill is not switched on or off, and the value of 1 indicates that the hot rolling mill is switched on or off; is a 0-1 variable, which is 1 when turned on at t moment and 0 when turned off at t moment; is a 0-1 variable, which is 1 when turned on at t-1 moment and 0 when turned off at t-1 moment; T is the total time period of the optimized scheduling period; V j,max is the maximum number of adjustable hot rolling mills;

[0084] The expression of the storage capacity constraint condition is:

[0085]

[0086] s t is the output rate of all electric arc furnaces; I is the total number of electric arc furnaces on all production lines; K is the total number of electric arc furnace equipment grades; d i,t,k is the position k of the i th equipment at t moment; s c is the output rate of the electric arc furnace on the k grade of the transformer tap; s f is the consumption rate of all hot rolling mills; J is the total number of hot rolling mills on all production lines; is a 0-1 variable, which is 1 when turned on at t moment and 0 when turned off at t moment; s x is the consumption rate of the hot rolling mill when working normally; R t is the storage capacity at t moment; R t-1 is the storage capacity at t-1 moment; R max is the maximum storage capacity.

[0087] According to one aspect of the present application, the expression of the equivalent load variance minimum optimization objective model of the upper-layer power grid dispatching center is:

[0088]

[0089] In the formula, F is the equivalent load variance of the steel industry load participating in demand response and connected to the power distribution network; T is the total time period of the optimization scheduling period; is the total demand response adjustable capacity provided by the steel industry load at time t in the combined regulation mode; P av (t) is the average value of the load.

[0090] According to an aspect of the present application, the equivalent load variance minimum operation constraint condition of the upper-layer power grid dispatching center comprises: a steel industry load equipment electric energy balance constraint condition connected to a power grid bus;

[0091] The expression of the steel industry load equipment electric energy balance constraint condition connected to the power grid bus is:

[0092] P b + P d + P c = P m .

[0093] In the formula, P b is the power purchased by the steel industry load; P d is the power of the self-provided power plant of the steel industry load; P c is the discharging power of the energy storage of the steel industry load; and P m is the power consumed by the steel industry load.

[0094] According to an aspect of the present application, the expression of the Nash-Cournot game model is:

[0095]

[0096] In the formula, F(*) is an equivalent load variance function; C(*) is a daily production revenue function of the steel industry load; and c p are respectively the equilibrium solution and the non-equilibrium solution of the incentive price of the dispatching center; and P DR are respectively the equilibrium solution and the non-equilibrium solution of the demand response capacity reported by the steel industry load.

[0097] According to an aspect of the present application, the Nash game peak shaving optimization scheduling solving method comprises:

[0098] S1. setting the population size n pop , the maximum iteration number M, the population crossover rate, the mutation rate, the convergence error Δε, and the iteration calculation number m=0;

[0099] S2. initializing the population, and the dispatching center randomly generates an incentive price and transmits the parameter to the steel industry load;

[0100] S3. Update iteration calculation times m = m + 1;

[0101] S4. After the steel industry load receives the incentive price specified by the dispatching center, the general solver tool is used to calculate the demand response capacity under different adjustment modes, and the maximum benefit C is compared under different adjustment modes m , and the demand response capacity corresponding to the maximum benefit is reported to the dispatching center;

[0102] S5. After the dispatching center receives the demand response capacity reported by the steel industry load, the peak shaving benefit is calculated, and the current peak shaving benefit F m and the incentive price are reserved;

[0103] S6. If the reported demand response capacity does not meet the peak shaving demand of the dispatching center, a new incentive price is generated by using the genetic algorithm to select, cross and mutate, and steps S3 to S5 are repeated to calculate the steel industry load benefit C and the peak shaving benefit F

[0104] S7. If in the next iteration, let otherwise F m+1 = F m ,

[0105] C m+1 = C m ;

[0106] S8. If F m+1 - F m ≤ Δε and C m+1 - C m ≤ Δε, it is judged that the game reaches equilibrium, and the iteration ends, otherwise, return to step S3.

[0107] According to one aspect of the application, the net load game peak shaving optimization dispatching result information comprises:

[0108] The power of each electric arc furnace at different times, the demand response capacity that all electric arc furnaces can provide at different times, the power of each hot rolling mill at different times, the demand response adjustable capacity that all hot rolling mills can provide, the total demand response adjustable capacity that the steel industry load can provide in the equipment adjustment mode at different times, the total demand response adjustable capacity that the steel industry load can provide in the production line adjustment mode at different times, the total demand response adjustable capacity that the steel industry load can provide in the combined adjustment mode at different times, the daily production income of the lower steel industry load, the income obtained by participating in demand response, the change amount of electricity cost, the change amount of carbon emission cost, the change amount of equipment loss cost, the change amount of production income, and the equivalent load variance result information of the steel industry load participating in demand response and connecting to the power distribution network.

[0109] To achieve the above-mentioned purpose, the application further provides a net load game peak regulation optimization scheduling system for a steel industry load, comprising:

[0110] A data information acquisition module acquires net load game peak regulation optimization scheduling data information.

[0111] A short-process steel industry load characteristic analysis model construction module constructs a short-process steel industry load characteristic analysis model based on the net load game peak regulation optimization scheduling data information.

[0112] A production line electric arc furnace demand response adjustable capacity model construction module constructs a production line electric arc furnace demand response adjustable capacity model based on the short-process steel industry load characteristic analysis model.

[0113] A production line hot rolling mill demand response adjustable capacity model construction module constructs a production line hot rolling mill demand response adjustable capacity model based on the short-process steel industry load characteristic analysis model.

[0114] A multi-type adjustment mode industrial load demand response adjustable capacity calculation model construction module superimposes the production line electric arc furnace demand response adjustable capacity model and the production line hot rolling mill demand response adjustable capacity model to form an industrial load demand response adjustable capacity calculation model, and forms a multi-type adjustment mode industrial load demand response adjustable capacity calculation model according to different adjustment modes.

[0115] A lower steel industry load daily production income maximum optimization target model construction module constructs a lower steel industry load daily production income maximum optimization target model for the load side based on the multi-type adjustment mode industrial load demand response adjustable capacity calculation model.

[0116] A lower steel industry load daily production operation constraint condition setting module sets lower steel industry load daily production operation constraint conditions for the lower steel industry load daily production income maximum optimization target model.

[0117] The upper power grid dispatching center equivalent load variance minimum optimization objective model construction module, based on the industrial load demand response adjustable capacity calculation model under multiple types of regulation modes, constructs an upper power grid dispatching center equivalent load variance minimum optimization objective model for the grid side;

[0118] The upper power grid dispatching center equivalent load variance minimum operation constraint condition setting module sets the upper power grid dispatching center equivalent load variance minimum operation constraint condition for the upper power grid dispatching center equivalent load variance minimum optimization objective model;

[0119] The Nash-Cournot game model construction module constructs a Nash-Cournot game model based on the lower steel industrial load daily production maximum optimization objective model and the upper power grid dispatching center equivalent load variance minimum optimization objective model, wherein the dispatching center is the leader and the steel industrial load is the participant;

[0120] The Nash-Cournot game peak regulation optimization scheduling solving module proposes a Nash-Cournot game peak regulation optimization scheduling solving method for the Nash-Cournot game model, and solves the Nash-Cournot game peak regulation optimization scheduling;

[0121] The optimization scheduling result output module outputs the Nash-Cournot game peak regulation optimization scheduling result information.

[0122] To achieve the above object, the application further provides an electronic device, which comprises a processor, a memory and a computer program stored in the memory and executable on the processor, and the computer program is executed by the processor to realize the Nash-Cournot game peak regulation optimization scheduling method of the steel industrial load.

[0123] To achieve the above object, the application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the Nash-Cournot game peak regulation optimization scheduling method of the steel industrial load.

[0124] According to the scheme of the application, the application aims to provide a Nash-Cournot game peak regulation optimization scheduling method of a multi-production-line steel industrial load, so as to reasonably mobilize the short-process steel industrial load resources to participate in power grid peak regulation; through adjustable potential analysis of key production equipment, the adjustable interval of each equipment is obtained, so as to ensure the accuracy of the steel industrial load participating in demand response; a refined model is constructed for the response capacity of the steel industrial load, and the influence of production factor constraints is fully considered, so as to better reflect the real adjustable capacity of the steel industrial load; a multi-production-line short-process steel industrial load game peak regulation optimization scheduling strategy is obtained based on a 1-N Stackelberg game model, so as to meet the peak regulation demand of the dispatching center while increasing the income of the steel industrial load, and realize better economy and feasibility.

[0125] According to the scheme of the present application, the present application can accurately evaluate the adjustable potential interval of each production equipment by analyzing the load characteristics of the iron and steel industry through fine modeling, realize the accurate regulation and control of the load of the short process iron and steel industry, not only effectively reduce the load peak-valley difference of the power grid, but also improve the overall peak shaving efficiency and stability of the power grid; the Stackelberg game model constructed by the present application fully considers the interests of both supply and demand, encourages the iron and steel industry load to participate in the peak shaving of the power grid through a reasonable incentive mechanism, not only provides an additional income opportunity for the iron and steel industry load, but also reduces its electricity cost and enhances its economic benefit through optimized scheduling strategy; the optimized scheduling method proposed by the present application provides a theoretical guidance and practical path for the participation of the iron and steel industry load in demand response, strengthens the interaction between the industrial load and the power grid, not only improves the flexibility and reliability of the power grid, but also provides support for the sustainable development of the iron and steel industry load, realizes the optimization of energy utilization and double benefits of social economy. BRIEF DESCRIPTION OF DRAWINGS

[0126] Figure 1 A flow chart schematically showing a net load game peak shaving optimized scheduling method for an iron and steel industrial load according to an embodiment of the present application. DETAILED DESCRIPTION

[0127] The present application will now be discussed with reference to example embodiments. It should be understood that the discussed embodiments are merely for the purpose of enabling those of ordinary skill in the art to better understand and therefore implement the present application, and are not intended to imply any limitation on the scope of the present application.

[0128] As used herein, the term "comprising" and variations thereof are to be construed as open-ended terms that mean "including, but not limited to". The term "based on" is to be construed as "based at least in part on". The terms "one embodiment" and "an embodiment" are to be construed as "at least one embodiment".

[0129] Figure 1 A flow chart schematically showing a net load game peak shaving optimized scheduling method for an iron and steel industrial load according to an embodiment of the present application. As shown in Figure 1 In the present embodiment, the net load game peak shaving optimized scheduling method for the iron and steel industrial load comprises:

[0130] Collecting net load game peak shaving optimized scheduling data information;

[0131] Constructing a short process iron and steel industrial load characteristic analysis model based on the net load game peak shaving optimized scheduling data information;

[0132] Based on the short process iron and steel industrial load characteristic analysis model, a production line electric arc furnace demand response adjustable capacity model is constructed;

[0133] Based on the short process steel industry load characteristic analysis model, a production line hot rolling mill demand response adjustable capacity model is constructed;

[0134] The production line electric arc furnace demand response adjustable capacity model and the production line hot rolling mill demand response adjustable capacity model are superimposed to form an industrial load demand response adjustable capacity calculation model, and according to different adjustment modes, an industrial load demand response adjustable capacity calculation model under multiple types of adjustment modes is formed;

[0135] Based on the industrial load demand response adjustable capacity calculation model under multiple types of adjustment modes, a lower-layer steel industry load daily production revenue maximum optimization objective model is constructed for the load side;

[0136] The lower-layer steel industry load daily production revenue maximum optimization objective model is set with lower-layer steel industry load daily production operation constraint conditions;

[0137] Based on the industrial load demand response adjustable capacity calculation model under multiple types of adjustment modes, an upper-layer power grid dispatching center equivalent load variance minimum optimization objective model is constructed for the grid side;

[0138] The upper-layer power grid dispatching center equivalent load variance minimum optimization objective model is set with upper-layer power grid dispatching center equivalent load variance minimum operation constraint conditions;

[0139] Based on the lower-layer steel industry load daily production revenue maximum optimization objective model and the upper-layer power grid dispatching center equivalent load variance minimum optimization objective model, a Bertrand Stackelberg game model is constructed, in which the dispatching center is the leader and the steel industry load is the participant;

[0140] A grid-load game peak regulation optimization scheduling solution method is proposed for the Bertrand Stackelberg game model, and the grid-load game peak regulation optimization scheduling is solved;

[0141] The grid-load game peak regulation optimization scheduling result information is output.

[0142] Further, according to an embodiment of the present application, the grid-load game peak regulation optimization scheduling data information comprises:

[0143] The production rate of each production line, the working cycle of each production line, the number of steelmaking production lines, the production power of each production line, the total number of electric arc furnaces on all production lines, the current gear position of the equipment, the stable working power of the hot rolling mill, the total number of hot rolling mills on all production lines, the total number of electric arc furnace device gears, the total time period number of the optimization scheduling period, the time-of-use electricity price, the carbon treatment price, the unit control cost coefficient, the optimization scheduling data transmission step length, the unit income of participating in demand response, the start time of participating in demand response, the end time of participating in demand response, the transformer gear adjustment percentage, the total number of transformer taps, the electric arc furnace daily maximum working time length, and the maximum number of adjustable hot rolling mills.

[0144] Furthermore, according to one embodiment of the present invention, in order to cope with rapidly changing order volumes and diverse order varieties, the short-process steel industry is equipped with multiple production lines, and different production lines often have different production tasks.

[0145] Specifically, the load characteristic analysis model for the short-process steel industry includes:

[0146] A single steelmaking production line in T n The expression for the steel productivity model within a cycle is:

[0147]

[0148] Where: m n T represents the productivity of the nth production line. n The working cycle of the nth production line;

[0149] For a steel industry with N steelmaking production lines, the total production capacity model expression is:

[0150]

[0151] Where: m t Let m be the total production capacity of N production lines; n Let N be the productivity of the nth production line; N is the number of steelmaking production lines.

[0152] For a steel industry with N steelmaking production lines, the total production load power model expression is:

[0153]

[0154] In the formula: P t P represents the total production power of N production lines. n Let N be the production power of the nth production line; N is the number of steelmaking production lines.

[0155] Furthermore, according to one embodiment of the present invention, based on the load characteristic analysis model of the short-process steel industry, a demand response adjustable capacity model for the electric arc furnace in the production line is constructed as follows:

[0156] Based on the load characteristic analysis model of the short-process steel industry, and combining the peak-shaving and optimization scheduling data information of the grid-load game, this paper refines and focuses on the load characteristics of electric arc furnaces in the short-process steel industry. The production line in the load characteristic analysis model of the short-process steel industry is described by electric arc furnaces, and an adjustable capacity model for the demand response of electric arc furnaces in the production line is established.

[0157] Among them, the production line electric arc furnace changes its power by controlling the transformer joints;

[0158] The expression for the adjustable capacity model of the production line electric arc furnace demand response is as follows:

[0159]

[0160] In the formula: Let d be the power of the i-th electric arc furnace at time t; i,t,k Let k be the position of the gear lever of the i-th device at time t; P Lc,k The power of the electric arc furnace at gear k; Let I be the power of all electric arc furnaces at time t; let I be the total number of electric arc furnaces on all production lines; and let K be the total number of gears for electric arc furnace equipment. d represents the power at time t when all electric arc furnaces are not participating in demand response; i,t,ko Let ko be the gear position at time t when the i-th device is not participating in demand response, which is also the gear position of the device when the steel industry load is not participating in demand response; P Lc,ko The power of the electric arc furnace at gear KO when it is not participating in demand response; The demand response capacity available for all electric arc furnaces at time t.

[0161] Furthermore, according to one embodiment of the present invention, based on the load characteristic analysis model of the short-process steel industry, a demand response adjustable capacity model for the hot rolling mill of the production line is constructed as follows:

[0162] Based on the load characteristic analysis model of the short-process steel industry, and combining the peak-shaving and optimization scheduling data of the grid-load game, this paper focuses on the load characteristics of hot rolling mills in the short-process steel industry, describes the production line in the short-process steel industry load characteristic analysis model with hot rolling mills, and establishes an adjustable capacity model for the demand response of hot rolling mills in the production line.

[0163] Among them, the power of the hot rolling mill is changed by switching equipment;

[0164] The adjustable capacity model for the hot rolling mill's demand response is as follows:

[0165]

[0166] In the formula: Let be the power of the j-th hot rolling mill at time t; For different 0-1 variables, P is 1 when the circuit is turned on at time t and 0 when the circuit is turned off at time t; L,j Let be the stable operating power of the j-th hot rolling mill; P represents the power of the hot rolling mill at time t before it participates in the control process; J represents the total number of hot rolling mills on all production lines; P represents the power of the hot rolling mill at time t before it participates in the control process. L,j,o Let be the power at time t before the j-th hot rolling mill participates in the control; Adjustable capacity to meet demand response for all hot rolling mills.

[0167] Further, according to an embodiment of the present application, the industrial load demand response adjustable capacity calculation model in the multi-type regulation mode comprises:

[0168] The total demand response adjustable capacity calculation model expression provided by the equipment regulation mode is:

[0169]

[0170] In the formula: is the total demand response adjustable capacity provided by the steel industry load at time t in the equipment regulation mode; I is the total number of electric arc furnaces on all production lines; K is the total number of electric arc furnace gearshifts; d i,t,k is the gearshift position k of the i-th equipment at time t; P Lc,k is the power of the electric arc furnace at the gearshift k; d i,t,ko is the gearshift position ko of the i-th equipment at time t when it does not participate in the demand response, that is, the gearshift position of the equipment when the steel industry load does not participate in the demand response; P Lc,ko is the power of the electric arc furnace at the gearshift ko when it does not participate in the demand response; is a different 0-1 variable, which is 1 when turned on at time t and 0 when turned off at time t; P L,j is the stable working power of the j-th hot rolling mill; J is the total number of hot rolling mills on all production lines; P L,j,o is the power of the j-th hot rolling mill at time t before participating in the regulation and control;

[0171] The total demand response adjustable capacity calculation model expression provided by the production line regulation mode is:

[0172]

[0173] In the formula: is the total demand response adjustable capacity provided by the steel industry load at time t in the production line regulation mode; N is the number of steelmaking production lines; P n is the production power of a single production line; is a 0-1 variable, which is 1 when the production line is turned on at time t and 0 when it is turned off at time t;

[0174] The total demand response adjustable capacity calculation model expression provided by the combined regulation mode is:

[0175]

[0176] In the formula: is the total demand response adjustable capacity provided by the steel industry load at time t in the combined regulation mode; Total demand response adjustable capacity available for the steel industry load at time t in the equipment adjustment mode; Total demand response adjustable capacity available for the steel industry load at time t in the production line adjustment mode; μ is the adjustment mode weight coefficient.

[0177] Further, according to an embodiment of the present application, the steel industry load takes the maximum daily production benefit as the objective function, and the daily production benefit is composed of five parts, i.e., the change amount of electricity cost, the change amount of carbon emission cost, the change amount of equipment wear cost, the change amount of production benefit, and the benefit obtained by participating in demand response; specifically, the expression of the lower steel industry load daily production benefit maximum optimization objective model is as follows:

[0178] C = max (E DR - ΔC u + ΔC t + ΔC c + ΔC s ) ;

[0179] In the formula, C is the daily production benefit of the lower steel industry load; E DR is the benefit obtained by participating in demand response; ΔC u is the change amount of electricity cost; ΔC t is the change amount of carbon emission cost; ΔC c is the change amount of equipment wear cost; ΔC s is the change amount of production benefit;

[0180] Wherein, the expression of the change amount of electricity cost is as follows:

[0181]

[0182] In the formula, ΔC u is the change amount of electricity cost; is the time-of-use electricity price at time t; T is the total time period number of the optimization scheduling period; is the total demand response adjustable capacity available for the steel industry load at time t in the combined adjustment mode;

[0183] The expression of the change amount of carbon emission cost is as follows:

[0184]

[0185] In the formula, ΔC t is the change amount of carbon emission cost; λ c is the carbon treatment price; I is the total number of electric arc furnaces on all production lines; β c is the carbon emission coefficient of the electric arc furnace, representing the equivalent carbon emission weight for producing 1 ton of crude steel; K c is the crude steel production rate of the electric arc furnace; Δt is the optimization scheduling data transmission step length;

[0186] The expression of the device wear cost change amount is:

[0187]

[0188] ΔCwear=∑i=1I∑k=1K∑t=1TΔCweariktd c ΔCwearis the device wear cost change amount; T is the total time period of the optimized scheduling period; I is the total number of electric arc furnaces on all production lines; K is the total number of electric arc furnace gearshifts; J is the total number of hot rolling mills on all production lines; Cweariktdis the cost of switching the gearshift of the i-th device at the t-th time period; sg Cweariktdis the cost of switching the gearshift of the i-th device at the t-th time period; i,t,k ki is the position of the gearshift of the i-th device at the t-th time period; d i,t-1,k ki-1 is the position of the gearshift of the i-th device at the (t-1)-th time period; d i,t,ko ko is the position of the gearshift of the i-th device at the t-th time period when the steel industry load does not participate in demand response, i.e., the gearshift position of the device when the steel industry load does not participate in demand response; d i,t-1,ko ko-1 is the position of the gearshift of the i-th device at the (t-1)-th time period when the steel industry load does not participate in demand response, i.e., the gearshift position of the device when the steel industry load does not participate in demand response; Cweariktdis the cost of switching the gearshift of the i-th device at the t-th time period; sr Cswitch is the cost of switching the hot rolling mill; xj is a 0-1 variable, which is 1 when the j-th hot rolling mill is turned on at the t-th time period and is 0 when the j-th hot rolling mill is turned off at the t-th time period; xj-1 is a 0-1 variable, which is 1 when the j-th hot rolling mill is turned on at the (t-1)-th time period and is 0 when the j-th hot rolling mill is turned off at the (t-1)-th time period;

[0189] The expression of the production revenue change amount is:

[0190]

[0191] ΔCprod=∑i=1I∑k=1K∑t=1TΔCprodiktd s ΔCprod is the production revenue change amount; Cprod is the production cost per unit; ea Cprod is the production cost per unit; α is the proportion of power consumption cost in the production cost; Cprod is the power consumption per unit of production; η is the production efficiency; Ft is the total demand response adjustable capacity that the steel industry load can provide at the t-th time period under the combined regulation mode; Δt is the optimized scheduling data transmission step; F is the unit product sales price; p F is the unit product sales price; F is the unit product production cost; α is the proportion of power consumption cost in the production cost; Cprod is the power consumption per unit of production; η is the production efficiency; c F is the unit product sales price; F is the unit product production cost; α is the proportion of power consumption cost in the production cost; Cprod is the power consumption per unit of production; η is the production efficiency; u F is the unit product sales price; F is the unit product production cost; α is the proportion of power consumption cost in the production cost; Cprod is the power consumption per unit of production; η is the production efficiency;

[0192] The expression of the revenue obtained by participating in demand response is:

[0193]

[0194] E is the revenue obtained by participating in demand response; C is the unit revenue obtained by participating in demand response; t is the start time of participating in demand response; t is the end time of participating in demand response; DR E is the revenue obtained by participating in demand response; C is the unit revenue obtained by participating in demand response; t is the start time of participating in demand response; t is the end time of participating in demand response; DR E is the revenue obtained by participating in demand response; C is the unit revenue obtained by participating in demand response; t is the start time of participating in demand response; t is the end time of participating in demand response; strat E is the revenue obtained by participating in demand response; C is the unit revenue obtained by participating in demand response; t is the start time of participating in demand response; t is the end time of participating in demand response;end is the end time of participating in demand response; is the total demand response adjustable capacity that the steel industry load can provide at time t in the combined regulation mode.

[0195] Further, according to an embodiment of the present application, the lower-layer steel industry load daily production operation constraint conditions include: an electric arc furnace power constraint condition, a transformer regulation frequency constraint condition, a transformer step regulation constraint condition, a constraint condition for ensuring that the electric arc furnace participating in demand response does not affect the steel industry load daily production plan, a hot rolling mill regulation frequency constraint condition, and a storage capacity constraint condition;

[0196] In the present embodiment, the electric arc furnace power remains unchanged during smelting, and the electric arc furnace power is controlled by regulating the transformer tap before and after smelting is completed; the electric arc furnace power constraint condition expression is:

[0197]

[0198] In the formula: is the power of the i th electric arc furnace at time t; γ is the transformer tap regulation percentage; d i,t,k is the position k of the i th device at time t; P i E is the rated power of the transformer at the initial tap; D is the total number of transformer taps;

[0199] When the electric arc furnace participates in demand response regulation, frequent regulation of the transformer tap can cause the transformer tap to age and reduce the service life of the transformer; the transformer regulation frequency constraint condition expression is:

[0200]

[0201] In the formula: u i,t is the adjustment state of the transformer tap, and its value of 0 indicates that the transformer tap is not regulated, and its value of 1 indicates that the transformer tap is regulated; d i,t,k is the position k of the i th device at time t; d i,t-1,k is the position k of the i th device at time t-1; T is the total time period number of the optimized scheduling period; U i,max is the maximum number of adjustable transformer taps;

[0202] In actual operation, to prevent the electric arc furnace power from changing greatly and damaging the equipment, the transformer tap cannot be regulated step by step; the transformer step regulation constraint condition expression is:

[0203] d i,t,k -d i,t-1,k ≤1;

[0204] In the formula: di,t,k is the position of the i-th device at time t; i,t-1,k is the position of the i-th device at time t-1;

[0205] Changing the transformer tap position to change the power of the electric arc furnace will cause the production time to change. When the power of the electric arc furnace is reduced, the production time will increase, but the total heat required for smelting 1 ton of crude steel is the same. The constraint condition expression for ensuring that the electric arc furnace participating in demand response does not affect the daily production plan of the steel industry load is:

[0206]

[0207] T is the total time period of the optimization scheduling period; is the power of all electric arc furnaces at time t; is the power of all electric arc furnaces at time t when they do not participate in demand response; I is the total number of electric arc furnaces on all production lines; τ i is the production time change caused by the adjustment of the i-th electric arc furnace power; T max is the daily maximum working time of the electric arc furnace;

[0208] If the hot rolling mill frequently starts and stops when participating in demand response regulation, it will cause equipment aging and even damage, resulting in the loss of the base load of the steel industry. The constraint condition expression for the adjustment frequency of the hot rolling mill is:

[0209]

[0210] v j,t is the adjustment state of the hot rolling mill, and its value is 0, indicating that the hot rolling mill is not started and stopped, and its value is 1, indicating that the hot rolling mill is started and stopped; is a 0-1 variable, which is 1 when turned on at time t, and 0 when turned off at time t; is a 0-1 variable, which is 1 when turned on at time t-1, and 0 when turned off at time t-1; T is the total time period of the optimization scheduling period; V j,max is the maximum number of adjustable hot rolling mills;

[0211] There is a problem of transportation and storage in the process of transporting the steel after refining to the continuous casting machine for casting, and then transporting the slab after casting to the hot rolling mill for rolling and forming. When the steel industry load participates in demand response regulation, if only the power of the electric arc furnace is regulated and the hot rolling mill remains in a normal working state, the output rate will be less than the consumption rate, which will reduce the production efficiency of the steel industry load. If only the hot rolling mill is regulated and the electric arc furnace remains in a normal working state, the output rate will be greater than the consumption rate, and the storage capacity needs to be considered. When the number of produced slabs is greater than the maximum storage capacity, it will cause the storage cost of the steel industry load to increase. The constraint condition expression for the storage capacity is:

[0212]

[0213] wherein s t is the production rate of all electric arc furnaces; I is the total number of electric arc furnaces on all production lines; K is the total number of electric arc furnace equipment shifts; d i,t,k is the position k of the i-th equipment at time t; s c is the production rate of the electric arc furnace on k shift of the transformer tap; s f is the consumption rate of all hot rolling mills; J is the total number of hot rolling mills on all production lines; is a 0-1 variable, which is 1 when turned on at time t and 0 when turned off at time t; s x is the consumption rate of the hot rolling mill when working normally; R t is the storage capacity at time t; R t-1 is the storage capacity at time t-1; R max is the maximum storage capacity.

[0214] Further, according to an embodiment of the present application, when the power grid dispatching center gives the steel industry load a drop incentive price, the steel industry load performs equipment regulation and control according to a daily production plan, and reports the demand response adjustable capacity to the dispatching center. The dispatching center takes the minimum equivalent load variance of the steel industry load connected to the distribution network after participating in the demand response regulation and control as an objective function, and the minimum equivalent load variance optimization objective model expression of the upper power grid dispatching center is:

[0215]

[0216] wherein F is the equivalent load variance of the steel industry load connected to the distribution network after participating in the demand response; T is the total number of time periods of the optimization dispatching period; is the total demand response adjustable capacity that the steel industry load can provide at time t in the combined regulation mode; P av (t) is the average value of the load.

[0217] Further, according to an embodiment of the present application, the minimum equivalent load variance operation constraint condition of the upper power grid dispatching center includes: an electric energy balance constraint condition of the steel industry load equipment connected to the power grid bus;

[0218] The expression of the electric energy balance constraint condition of the steel industry load equipment connected to the power grid bus is:

[0219] P b + P d + P c = P m ;

[0220] wherein P b is the power purchase power of the steel industry load; P dPower for steel industry load self-provided power plant; P c Power for steel industry load energy storage discharge; m Power for steel industry load electricity consumption.

[0221] Further, according to an embodiment of the present application, compared with the residential load, the steel industry load as a high energy consumption industry can better meet the peak shaving demand of the dispatch center; on the one hand, as a formulator of regulation and control strategy, the dispatch center tends to achieve a relatively stable peak shaving effect with the least economic cost, and the steel industry load reports the adjustable capacity to the dispatch center according to the production plan of the previous day, the dispatch center reasonably formulates the incentive price according to the peak shaving demand, and guides the steel industry load to participate in the demand response regulation and control; on the other hand, as a participant of the regulation and control strategy of the dispatch center, the steel industry load will pay more attention to its own benefits when participating in the demand response regulation and control, and considers factors such as electricity cost, carbon emission cost, production benefit and demand response benefit, etc. When the steel industry load participates in the regulation and control and leads to an increase in cost and a decrease in benefit, the steel industry load has reason not to participate in the demand response regulation and control or to reduce its adjustable capacity to ensure the benefit; therefore, the steel industry load and the dispatch center constitute a game decision problem, that is, the dispatch center is the leader and the steel industry load is the participant, both sides change their own strategies according to the strategy of the other side to achieve the goal and finally obtain the equilibrium solution. Based on this, a Stackelberg game model is constructed to describe the regulation and control relationship between the dispatch center and the steel industry load, and a 1-N Stackelberg game model is constructed to obtain the interaction information; the dispatch center is the leader and the steel industry load is the follower; the constructed Stackelberg game is an ordered and dynamic process, and the game process is as follows:

[0222] (Process #1) The steel industry load reports its demand response capacity to the dispatch center one day in advance according to its production plan;

[0223] (Process #2) The dispatch center sends an incentive price to the steel industry load according to the demand response capacity reported by the steel industry load and the peak shaving demand, and encourages the steel industry load to actively participate in the regulation and control;

[0224] (Process #3) The steel industry load calculates according to the cost benefit function according to the incentive price sent by the dispatch center, participates in the regulation and control or changes the demand response capacity and reports it to the dispatch center again;

[0225] (Process #4) The dispatch center calculates the peak shaving effect according to the demand response capacity returned by the steel industry load, and adjusts the incentive price and sends it to the steel industry load again;

[0226] (Process #5) Repeat (process #1) to (process #4) until the strategy of either the dispatch center or the steel industry load changes, which will make the overall result worse, that is, meet the strategies of both sides.

[0227] The judgment model expression for reaching equilibrium of the Nash game is:

[0228]

[0229] In the formula, F(*) is an equivalent load variance function; C(*) is a daily production income function of the steel industry load; and c p are respectively an equilibrium solution and a non-equilibrium solution of the incentive price of the dispatching center; and P DR are respectively an equilibrium solution and a non-equilibrium solution of the load demand response capacity reported by the steel industry.

[0230] Further, according to an embodiment of the present application, the difficulty in solving lies in that the daily production income function C(*) of the lower-layer steel industry load is constrained by discrete switching actions and cannot be treated as a continuous function to transform the double-layer model into a single-layer model for solving by derivation; based on this, a genetic algorithm is combined with a general solver tool to solve, and the specific steps of the peak shaving optimization scheduling solving method of the net load game are as follows:

[0231] S1. Setting a population size n pop , a maximum iteration number M, a population crossover rate, a mutation rate, a convergence error Δε, and an iteration calculation number m=0;

[0232] S2. Initializing the population, and the dispatching center randomly generates an incentive electricity price The parameters are transmitted to the steel industry load;

[0233] S3. Updating the iteration calculation number m=m+1;

[0234] S4. After the steel industry load receives the incentive electricity price specified by the dispatching center, the demand response capacity under different adjustment modes is calculated by using the general solver tool, the maximum income C m under different adjustment modes is compared, and the demand response capacity corresponding to the maximum income is reported to the dispatching center;

[0235] S5. After the dispatching center receives the demand response capacity reported by the steel industry load, the peak shaving income is calculated, and the current peak shaving income F m and the incentive electricity price are reserved;

[0236] S6. If the reported demand response capacity does not meet the peak shaving demand of the dispatching center, a new incentive electricity price is generated by using the genetic algorithm to select, cross and mutate, and steps S3 to S5 are repeated to calculate the steel industry load income and the peak shaving income

[0237] S7. If In the next iteration, let Otherwise F m+1 = F m ,

[0238] C m+1 = C m ;

[0239] S8.If F m+1 -F m ≤ Δε and C m+1 - C m ≤ Δε, it is determined that the game reaches equilibrium, the iteration is ended, otherwise, return to step S3.

[0240] Further, according to an embodiment of the present application, the web load game peak regulation optimization scheduling result information comprises:

[0241] The power of each electric arc furnace at different times, the demand response capacity that can be provided by all electric arc furnaces at different times, the power of each hot rolling mill at different times, the demand response adjustable capacity that can be provided by all hot rolling mills, the total demand response adjustable capacity that can be provided by the steel industry load in the equipment regulation mode at different times, the total demand response adjustable capacity that can be provided by the steel industry load in the production line regulation mode at different times, the total demand response adjustable capacity that can be provided by the steel industry load in the combined regulation mode at different times, the daily production income of the lower steel industry load, the income obtained by participating in demand response, the change amount of electricity cost, the change amount of carbon emission cost, the change amount of equipment loss cost, the change amount of production income, and the equivalent load variance result information of the steel industry load participating in demand response and connecting to the power distribution network.

[0242] According to the above scheme of the present application, the present application aims to provide a web load game peak regulation optimization scheduling method considering the multi-production line steel industry load, so as to reasonably mobilize the short process steel industry load resource to participate in the power grid peak regulation; through adjustable potential analysis of the key production equipment, the adjustable interval of each equipment is obtained, so as to ensure the accuracy of the steel industry load participating in demand response; a refined model is constructed according to the response ability of the steel industry load, and the influence of the production factor constraint is fully considered, so as to better reflect the real adjustable capacity of the steel industry load; based on the 1-N Stackelberg game model, the short process steel industry load game peak regulation optimization scheduling strategy facing the multi-production line is obtained, so as to meet the peak regulation demand of the scheduling center while increasing the income of the steel industry load, and realize better economy and feasibility.

[0243] According to the above scheme of the present application, the present application can accurately evaluate the adjustable potential interval of each production equipment by analyzing the load characteristics of the steel industry through fine modeling, realize the accurate regulation and control of the short process steel industry load, not only effectively reduce the load peak-valley difference of the power grid, but also improve the overall peak shaving efficiency and stability of the power grid; the Stackelberg game model constructed by the present application fully considers the interests of both supply and demand sides, encourages the steel industry load to participate in the peak shaving of the power grid through a reasonable incentive mechanism, not only provides additional income opportunities for the steel industry load, but also reduces the electricity cost and enhances the economic benefit of the steel industry load through the optimization scheduling strategy; the optimization scheduling method proposed by the present application provides theoretical guidance and practical path for the steel industry load to participate in demand response, strengthens the interaction between the industrial load and the power grid, not only improves the flexibility and reliability of the power grid, but also provides support for the sustainable development of the steel industry load, realizes the optimization of energy utilization and double benefits of social economy.

[0244] Further, in order to achieve the above object, the present application also provides a net load game peak shaving optimization scheduling system for steel industry load, comprising:

[0245] a data information acquisition module for acquiring net load game peak shaving optimization scheduling data information;

[0246] a short process steel industry load characteristic analysis model construction module for constructing a short process steel industry load characteristic analysis model based on the net load game peak shaving optimization scheduling data information;

[0247] a production line electric arc furnace demand response adjustable capacity model construction module for constructing a production line electric arc furnace demand response adjustable capacity model based on the short process steel industry load characteristic analysis model;

[0248] a production line hot rolling mill demand response adjustable capacity model construction module for constructing a production line hot rolling mill demand response adjustable capacity model based on the short process steel industry load characteristic analysis model;

[0249] a multi-type regulation mode industrial load demand response adjustable capacity calculation model construction module for superimposing the production line electric arc furnace demand response adjustable capacity model and the production line hot rolling mill demand response adjustable capacity model to form an industrial load demand response adjustable capacity calculation model, and forming a multi-type regulation mode industrial load demand response adjustable capacity calculation model according to different regulation modes;

[0250] a lower layer steel industry load daily production maximum profit optimization target model construction module for constructing a lower layer steel industry load daily production maximum profit optimization target model based on the multi-type regulation mode industrial load demand response adjustable capacity calculation model for the load side;

[0251] The lower steel industry load daily production operation constraint condition setting module sets lower steel industry load daily production operation constraint conditions for the lower steel industry load daily production maximum yield optimization target model;

[0252] The upper grid dispatching center equivalent load variance minimum optimization target model construction module, based on the industrial load demand response adjustable capacity calculation model under the multi-type regulation mode, constructs the upper grid dispatching center equivalent load variance minimum optimization target model for the grid side;

[0253] The upper grid dispatching center equivalent load variance minimum operation constraint condition setting module sets upper grid dispatching center equivalent load variance minimum operation constraint conditions for the upper grid dispatching center equivalent load variance minimum optimization target model;

[0254] The Nash-Cournot game model construction module constructs a Nash-Cournot game model based on the lower steel industry load daily production maximum yield optimization target model and the upper grid dispatching center equivalent load variance minimum optimization target model, wherein the dispatching center is the leader and the steel industry load is the participant;

[0255] The Nash game peak regulation optimization dispatching solution module proposes a Nash game peak regulation optimization dispatching solution method for the Nash-Cournot game model, and solves the Nash game peak regulation optimization dispatching.

[0256] The optimization dispatching result output module outputs the Nash game peak regulation optimization dispatching result information.

[0257] The steel industry load Nash game peak regulation optimization dispatching system according to the application can realize the above-mentioned steel industry load Nash game peak regulation optimization dispatching method, and the specific method steps are as described above, which will not be repeated.

[0258] Further, to achieve the above-mentioned purpose, the application further provides an electronic device, which comprises a processor, a memory, and a computer program stored on the memory and executable on the processor, and the computer program is executed by the processor to realize the above-mentioned steel industry load Nash game peak regulation optimization dispatching method.

[0259] Further, to achieve the above-mentioned purpose, the application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor to realize the above-mentioned steel industry load Nash game peak regulation optimization dispatching method.

[0260] Those of skill in the art would understand that the modules and algorithms described herein can be implemented in electronic hardware, computer software, or combinations of both. The disclosure encompasses both hardware and software implementations of a technique. The technique could be implemented using software programs or computer-executable instructions in combination with general purpose computers, general purpose processors, special purpose processors, or the like. The disclosure encompasses a computer program product on a computer readable medium with computer-executable instructions.

[0261] Those of skill in the art would understand that, for the described convenience and conciseness, the specific working processes of the apparatus and device described above can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0262] In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed modules can be indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0263] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, that is, they can be located in one place, or can be distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0264] In addition, each functional module in the embodiments of the present application can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0265] 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 solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the energy-saving signal transmission / reception method of the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, and various media that can store program codes.

[0266] The above description is merely preferred embodiments of the present application and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and 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 can be replaced with the technical features disclosed in the present application (but not limited to) having similar functions to form technical solutions.

[0267] It should be understood that the sequence of the steps in the summary and embodiments of the present application does not absolutely mean the order of execution, and the execution order of the processes should be determined according to their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Claims

1. A method for peak shaving optimization scheduling of steel industry load based on network load game, characterized in that, The method comprises the following steps: Collecting network-load game peak regulation optimization scheduling data information; Constructing a short-process steel industry load characteristic analysis model based on the network-load game peak regulation optimization scheduling data information; Based on the short-process steel industry load characteristic analysis model, a production line electric arc furnace demand response adjustable capacity model is constructed; Based on the short-process steel industry load characteristic analysis model, a production line hot rolling mill demand response adjustable capacity model is constructed; The production line electric arc furnace demand response adjustable capacity model and the production line hot rolling mill demand response adjustable capacity model are superimposed to form an industrial load demand response adjustable capacity calculation model, and according to different regulation modes, an industrial load demand response adjustable capacity calculation model under multiple types of regulation modes is formed; Based on the industrial load demand response adjustable capacity calculation model under multiple types of regulation modes, a lower steel industry load daily production maximum profit optimization objective model is constructed for the load side; The lower steel industry load daily production maximum profit optimization objective model is set with lower steel industry load daily production operation constraint conditions; Based on the industrial load demand response adjustable capacity calculation model under multiple types of regulation modes, an upper grid dispatching center equivalent load variance minimum optimization objective model is constructed for the grid side; The upper grid dispatching center equivalent load variance minimum optimization objective model is set with upper grid dispatching center equivalent load variance minimum operation constraint conditions; Based on the lower steel industry load daily production maximum profit optimization objective model and the upper grid dispatching center equivalent load variance minimum optimization objective model, a network-load Stackelberg game model is constructed, in which the dispatching center is the leader and the steel industry load is the participant; A network-load game peak regulation optimization scheduling solution method is proposed for the network-load Stackelberg game model, and the network-load game peak regulation optimization scheduling is solved; The network-load game peak regulation optimization scheduling result information is output.

2. The method according to claim 1, wherein, The network-load game peak regulation optimization scheduling data information comprises: The production rate of each production line, the working cycle of each production line, the number of steelmaking production lines, the production power of each production line, the total number of electric arc furnaces on all production lines, the current gear position of the equipment, the stable working power of the hot rolling mill, the total number of hot rolling mills on all production lines, the total number of electric arc furnace gear positions, the total time period of the optimization scheduling period, the time-of-use electricity price, the carbon treatment price, the unit control cost coefficient, the optimization scheduling data transmission step length, the unit income of participating in demand response, the start time of participating in demand response, the end time of participating in demand response, the transformer gear adjustment percentage, the total number of transformer tap positions, the electric arc furnace daily maximum working time length, and the maximum number of adjustable hot rolling mills data information.

3. The method according to claim 1, wherein, The short-process steel industry load characteristic analysis model comprises: A single steel production line in T n The model expression for the steel production rate in a cycle is wherein: m n is the production rate of the nth production line; T n is the cycle time of the nth production line; For an N-line steelmaking production line steel industry load, the total production quantity model expression is: wherein: m t is the total production of N production lines; m n is the production rate of the nth production line; N is the number of steelmaking production lines; For an N-line steelmaking production line steel industry load, the total production load power model expression is: In the formula: P t is the total production power of N production lines; P n is the production power of the nth production line; N is the number of steelmaking production lines.

4. The method according to claim 1, wherein, Based on the short-process steel industry load characteristic analysis model, a production line electric arc furnace demand response adjustable capacity model is constructed as follows: In combination with the data information of the peak regulation optimization scheduling of the network load game, on the basis of the load characteristic analysis model of the short-process steel industry, the load characteristic of the short-process steel industry is refined and focused, the production line in the load characteristic analysis model of the short-process steel industry is described by an electric arc furnace, and a demand response adjustable capacity model of the production line electric arc furnace is established; An expression of the demand response adjustable capacity model of the production line electric arc furnace is: wherein: Pit is the power of the ith electric arc furnace at time t; d i,t,k Pi(k,t) is the position of the ith device at time t in gear k; P Lc,k Pf(k) is the power of the electric arc furnace at gear k; It(k) is the power of all electric arc furnaces at time t; I is the total number of electric arc furnaces on all production lines; K is the total number of gears of the electric arc furnace device; Id(k) is the power of all electric arc furnaces at time t when they do not participate in demand response; d i,t,ko Pi(ko,t) is the position of the ith device at time t in gear ko when it does not participate in demand response, i.e., the gear of the device when the load of the steel industry does not participate in demand response; P Lc,ko Pf(ko) is the power of the electric arc furnace at gear ko when it does not participate in demand response; It(k) is the power of all electric arc furnaces at time t; I is the total number of electric arc furnaces on all production lines; K is the total number of gears of the electric arc furnace device; 5. The method according to claim 1, wherein, Based on the load characteristic analysis model of the short-process steel industry, a demand response adjustable capacity model of a hot rolling mill of a production line is constructed as: In combination with the data information of the peak regulation optimization scheduling of the network load game, on the basis of the load characteristic analysis model of the short-process steel industry, the load characteristic of the short-process steel industry is refined and focused, the production line in the load characteristic analysis model of the short-process steel industry is described by a hot rolling mill, and a demand response adjustable capacity model of the production line hot rolling mill is established; An expression of the demand response adjustable capacity model of the production line hot rolling mill is: In the formula: Pj(t) is the power of the jth hot rolling mill at time t; is a different 0-1 variable, which is 1 when turned on at time t and 0 when turned off at time t; P L,j Pj(t) is the stable working power of the jth hot rolling mill; Pj(t) is the power of the jth hot rolling mill at time t; J is the total number of hot rolling mills on all production lines; P L,j,o Pj(t) is the power of the jth hot rolling mill at time t; J is the total number of hot rolling mills on all production lines; P Pj(t) is the power of the jth hot rolling mill at time t; J is the total number of hot rolling mills on all production lines; P 6. The method of claim 1, wherein, The demand response adjustable capacity calculation model of the industrial load under the multi-type regulation mode comprises: An expression of the total demand response adjustable capacity calculation model that can be provided under the equipment regulation mode is: In the formula: is the total demand response adjustable capacity that the steel industry load can provide at time t in the equipment adjustment mode; I is the total number of electric arc furnaces on all production lines; K is the total number of electric arc furnace equipment gears; d i,t,k is the gear position k of the i th equipment at time t; P Lc,k is the power of the electric arc furnace at gear k; d i,t,ko is the gear position ko of the i th equipment at time t when the equipment does not participate in demand response, that is, the gear position of the equipment when the steel industry load does not participate in demand response; P Lc,ko is the power of the electric arc furnace at gear ko when the equipment does not participate in demand response; is a different 0-1 variable, which is 1 when turned on at time t and 0 when turned off at time t; P L,j is the stable working power of the j th hot rolling mill; J is the total number of hot rolling mills on all production lines; P L,j,o is the power of the j th hot rolling mill at time t before participating in regulation and control; An expression of the total demand response adjustable capacity calculation model that can be provided under the production line regulation mode is: wherein: is the total demand response adjustable capacity available at time t for the steel industry load; N is the number of steelmaking production lines; P n is the production power of a single production line; is a 0-1 variable, equal to 1 when the production line is on at time t and equal to 0 when the production line is off at time t. An expression of the total demand response adjustable capacity calculation model that can be provided under the combined regulation mode is: wherein: is the total demand response adjustable capacity available to the steel industry load at time t in the combined regulation mode; is the total demand response adjustable capacity available to the steel industry load at time t in the equipment regulation mode; is the total demand response adjustable capacity available to the steel industry load at time t in the production line regulation mode; and μ is a regulation mode weight coefficient.

7. The method according to claim 1, wherein, An expression of the optimization target model of the daily production revenue maximization of the lower-layer steel industry load is: C = max(E DR - ΔC u + ΔC t + ΔC c + ΔC s ); In the formula, C is the daily production income of the lower steel industry; E DR is the income obtained by participating in demand response; ΔC u is the change amount of electricity cost; ΔC t is the change amount of carbon emission cost; ΔC c is the change amount of equipment loss cost; and ΔC s is the change amount of production income. An expression of the change amount of the electricity cost is: wherein: ΔC u is the variation of electricity cost; is the time-of-use electricity price at time t; T is the total number of time periods of the optimization scheduling period; is the total demand response adjustable capacity that the steel industry load can provide at time t in the combined regulation mode; An expression of the change amount of the carbon emission cost is: wherein: ΔC t is the carbon emission cost variation; λ c is the carbon treatment price; I is the total number of electric arc furnaces on all production lines; β c is the carbon emission coefficient of the electric arc furnace, representing the equivalent carbon emission weight for producing 1 ton of crude steel; K c is the electric arc furnace crude steel production rate; Δt is the optimal scheduling data transmission step length; An expression of the change amount of the equipment loss cost is: wherein: ΔC c is the change of equipment wear cost; T is the total number of time periods of the optimization scheduling; I is the total number of electric arc furnaces on all production lines; K is the total number of electric arc furnace gear shifts; J is the total number of hot rolling mills on all production lines; C sg is the cost of switching gear shifts; d i,t,k is the gear shift position k of the i-th equipment at time t; d i,t-1,k is the gear shift position k of the i-th equipment at time t-1; d i,t,ko is the gear shift position ko of the i-th equipment at time t when the steel industry load does not participate in demand response, i.e., the gear shift position of the equipment when the steel industry load does not participate in demand response; d i,t-1,ko is the gear shift position ko of the i-th equipment at time t-1 when the steel industry load does not participate in demand response, i.e., the gear shift position of the equipment when the steel industry load does not participate in demand response; C sr is the cost of switching the hot rolling mill; is a 0-1 variable, which is 1 when the hot rolling mill is turned on at time t and 0 when the hot rolling mill is turned off at time t; is a 0-1 variable, which is 1 when the hot rolling mill is turned on at time t-1 and 0 when the hot rolling mill is turned off at time t-1. An expression of the change amount of the production revenue is: In the formula: ΔC s is the production benefit change amount; C ea is the unit control cost coefficient; is the total demand response adjustable capacity that the steel industry load can provide at time t in the combined regulation mode; Δt is the optimization scheduling data transmission step; F p is the unit product sales price; F c is the unit product production cost; α is the proportion of power consumption cost in the production cost; C u is the unit output power consumption; η is the production efficiency; An expression of the revenue obtained by participating in the demand response is: wherein: E DR is the benefit obtained for participating in demand response; C DR is the unit benefit for participating in demand response; t strat is the starting time instant for participating in demand response; t end is the ending time instant for participating in demand response; is the total demand response adjustable capacity available at time instant t for the steel industry load in the combined regulation mode. 8.The method of claim 1, wherein, The daily production operation constraint condition of the lower-layer steel industry load comprises: a power constraint condition of the electric arc furnace, a transformer regulation frequency constraint condition, a transformer over-regulation constraint condition, a constraint condition for ensuring that the electric arc furnace participating in the demand response does not affect the daily production plan of the steel industry load, a hot rolling mill regulation frequency constraint condition, and a storage capacity constraint condition. An expression of the power constraint condition of the electric arc furnace is: wherein: Pit is the power of the ith electric arc furnace at time t; γ is the transformer tap adjustment percentage; d i,t,k k is the position of the shift lever of the ith device at time t; P0 is the rated power of the transformer at the initial tap; D is the total number of transformer taps. An expression of the transformer regulation frequency constraint condition is: In the formula: u i,t is the adjustment state of the transformer tap, and a value of 0 indicates that the transformer tap is not adjusted, and a value of 1 indicates that the transformer tap is adjusted; d i,t,k is the gear position k at which the ith device is located at the time t; d i,t-1,k is the gear position k at which the ith device is located at the time t-1; T is the total number of time periods of the optimized scheduling period; U i,max is the maximum number of times of the adjustable transformer tap; An expression of the transformer over-regulation constraint condition is: d i,t,k -d i,t-1,k ≤1; wherein: d i,t,k is the position k of the i-th device at the time t; d i,t-1,k is the position k of the i-th device at the time t-1; An expression of the constraint condition for ensuring that the electric arc furnace participating in the demand response does not affect the daily production plan of the steel industry load is: In the formula, T is the total number of the optimized scheduling period; P(t) is the power of all electric arc furnaces at time t; P(t) is the power of all electric arc furnaces at time t; I is the total number of electric arc furnaces on all production lines; τ i is the production time change caused by the i-th electric arc furnace adjustment power; T max is the daily maximum working time of the electric arc furnace; An expression of the hot rolling mill regulation frequency constraint condition is: In the formula: v j,t is the adjustment state of the hot rolling mill, a value of 0 indicates that the hot rolling mill is not switched on, and a value of 1 indicates that the hot rolling mill is switched on; is a 0-1 variable, which is 1 when turned on at time t and 0 when turned off at time t; is a 0-1 variable, which is 1 when turned on at time t-1 and 0 when turned off at time t-1; T is the total number of time periods of the optimized scheduling period; V j,max is the maximum number of times the hot rolling mill can be adjusted; An expression of the storage capacity constraint condition is: where: s t is the production rate of all electric arc furnaces; I is the total number of electric arc furnaces on all production lines; K is the total number of electric arc furnace equipment shifts; d i,t,k is the position k of the ith equipment at time t in the shift; s c is the production rate of the electric arc furnace on the kth shift of the transformer tap; s f is the consumption rate of all hot rolling mills; J is the total number of hot rolling mills on all production lines; is a 0-1 variable, which is 1 when turned on at time t and 0 when turned off at time t; s x is the consumption rate of the hot rolling mill when working normally; R t is the storage capacity at time t; R t-1 is the storage capacity at time t-1; R max is the maximum storage capacity. 9.The method of claim 1, wherein, An expression of the optimization target model of the minimum equivalent load variance of the upper-layer power grid dispatching center is: In the formula, F is the equivalent load variance of the steel industry load connected to the distribution network after participating in demand response; T is the total number of total time periods of the optimization scheduling period; is the total demand response adjustable capacity that the steel industry load can provide at time t in the combined regulation mode; P av (t) is the load average value.

10. The method of claim 1, wherein, The minimum equivalent load variance operation constraint condition of the upper-layer power grid dispatching center comprises: a steel industry load equipment electric energy balance constraint condition of an access power grid bus. An expression of the steel industry load equipment electric energy balance constraint condition of the access power grid bus is: P b +P d +P c = P m ; where: P b P is the power of the steel industry load purchased from the power grid; P d P is the power of the steel industry load from the self-provided power plant; P c P is the power of the steel industry load discharged from the energy storage; P m P is the power of the steel industry load.

11. The method of claim 1, wherein the method further comprises: An expression of the network load Stackelberg game model is: Where F(*) is the equivalent load variance function; C(*) is the daily production revenue function of the steel industry under load; and c p are the equilibrium and non-equilibrium solutions of the dispatch center incentive price, respectively; and P DR are the equilibrium and non-equilibrium solutions of the steel industry load demand response capacity reporting, respectively.

12. The method of claim 1, wherein, The peak regulation optimization scheduling solution method of the network load game comprises: S1. Set the population size n pop , the maximum number of iterations M, the population crossover rate, the mutation rate, the convergence error Δε, and the iteration count m = 0; S2. Initialize population, dispatch center randomly generates incentive price Pass parameters to steel industry load; S3. Update the iteration calculation number m = m + 1; S4. Steel industry load receives incentive price designated by dispatching center, uses general solver tool to calculate demand response capacity under different adjustment modes, compares different adjustment modes to take maximum benefit C m At the same time, the maximum benefit corresponding demand response capacity is reported to the dispatching center; S5. After the dispatch center receives the demand response capacity reported by the steel industry, it calculates the peak shaving benefit and reserves the current peak shaving benefit F m and incentive electricity price; S6. If the reported demand response capacity does not meet the peak shaving demand of the dispatch center, a new incentive price is generated by using genetic algorithm selection, crossover and mutation, and steps S3 to S5 are repeated to calculate the load benefit of the steel industry and peak shaving benefit S7. If At the next iteration, let Otherwise F m+1 = F m , C m+1 = C m ; S8. If F m+1 - F m ≤ Δε and C m+1 - C m ≤ Δε, then the game is determined to reach equilibrium, the iteration ends, otherwise return to step S3.

13. The method of claim 1-12, wherein, The peak regulation optimization scheduling result information of the network load game comprises: Power of each electric arc furnace at different time, demand response capacity that all electric arc furnaces can provide at different time, power of each hot rolling mill at different time, demand response adjustable capacity that all hot rolling mills can provide, total demand response adjustable capacity that steel industry load can provide in equipment regulation mode at different time, total demand response adjustable capacity that steel industry load can provide in production line regulation mode at different time, total demand response adjustable capacity that steel industry load can provide in combined regulation mode at different time, daily production income of lower steel industry load, income obtained by participating in demand response, change amount of electricity cost, change amount of carbon emission cost, change amount of equipment loss cost, change amount of production income, and equivalent load variance result information of steel industry load after participating in demand response and connecting to power distribution network.

14. A web load game peak shaving optimization scheduling system for the iron and steel industry, characterized in that, Comprise: A data information acquisition module for acquiring data information of peak regulation optimization scheduling of network and load game; A short-process steel industry load characteristic analysis model construction module for constructing a short-process steel industry load characteristic analysis model based on the data information of peak regulation optimization scheduling of network and load game; A production line electric arc furnace demand response adjustable capacity model construction module for constructing a production line electric arc furnace demand response adjustable capacity model based on the short-process steel industry load characteristic analysis model; A production line hot rolling mill demand response adjustable capacity model construction module for constructing a production line hot rolling mill demand response adjustable capacity model based on the short-process steel industry load characteristic analysis model; A multi-type regulation mode industrial load demand response adjustable capacity calculation model construction module for superimposing the production line electric arc furnace demand response adjustable capacity model and the production line hot rolling mill demand response adjustable capacity model to form an industrial load demand response adjustable capacity calculation model, and forming a multi-type regulation mode industrial load demand response adjustable capacity calculation model according to different regulation modes; A lower steel industry load daily production income maximum optimization objective model construction module for constructing a lower steel industry load daily production income maximum optimization objective model for the load side based on the multi-type regulation mode industrial load demand response adjustable capacity calculation model; A lower steel industry load daily production operation constraint condition setting module for setting lower steel industry load daily production operation constraint conditions for the lower steel industry load daily production income maximum optimization objective model; An upper grid dispatching center equivalent load variance minimum optimization objective model construction module for constructing an upper grid dispatching center equivalent load variance minimum optimization objective model for the grid side based on the multi-type regulation mode industrial load demand response adjustable capacity calculation model; An upper grid dispatching center equivalent load variance minimum operation constraint condition setting module for setting upper grid dispatching center equivalent load variance minimum operation constraint conditions for the upper grid dispatching center equivalent load variance minimum optimization objective model; A network and load Stackelberg game model construction module for constructing a network and load Stackelberg game model based on the lower steel industry load daily production income maximum optimization objective model and the upper grid dispatching center equivalent load variance minimum optimization objective model, wherein the dispatching center is the leader and the steel industry load is the participant. The network load game peak regulation optimization scheduling solving module proposes a network load game peak regulation optimization scheduling solving method for the network load game peak regulation optimization scheduling. The optimization scheduling result output module outputs network load game peak regulation optimization scheduling result information.

15. An electronic device, characterized by The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the network load game peak regulation optimization scheduling method of the steel industry load as claimed in any one of claims 1-13.

16. A computer readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the network load game peak regulation optimization scheduling method of the steel industry load as claimed in any one of claims 1-13.

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