Network load game peak regulation optimization scheduling method and system for iron and steel industry load

Abstract

The invention relates to the technical field of participation of iron and steel industry loads in power grid interaction, and provides a network load game peak regulation optimization scheduling method and system for iron and steel industry loads, and the method comprises the steps: analyzing and obtaining an adjustable potential interval of key production equipment based on the specific load characteristics of the iron and steel industry loads; fine modeling is carried out on the adjustable potential of the iron and steel industry load, production factor constraint conditions are considered, and the adjustable capacity of different devices and multi-production-line combinations is obtained through analysis; and finally, analyzing behaviors of peak regulation strategy participants and formulators, and constructing a master-slave game optimization scheduling model by using the Stackelberg game and solving the model. According to the method, the enthusiasm of the short-process iron and steel industry load participating in power grid peak regulation can be reasonably mobilized, the load income of the iron and steel industry is increased while the peak-valley difference of the power grid load is effectively reduced, and theoretical guidance and reference can be provided for participation of the iron and steel industry load in demand response, interaction with the power grid, optimal scheduling and the like.

Description

Technical Field

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

[0002] As an important resource management method on the demand side, demand response guides users to change their electricity consumption characteristics and tap the resource scheduling potential on the load side through measures such as electricity prices or economic incentives, thereby alleviating the adverse effects of new energy and load fluctuations on the power grid to deal with peak load problems. The short-process steel industry load uses arc furnaces as core equipment and electricity as the main energy carrier. As a high-energy consumption industry, its electricity cost accounts for a high proportion, and it has great potential to participate in demand response.

[0003] The basis for the participation of short-process steel industry loads in demand response is the in-depth analysis of their adjustable potential and the accurate modeling of their response behavior. In the study of the participation of short-process steel industry loads in demand response regulation, the impact of their own load characteristics on demand response capabilities must be comprehensively considered to achieve more accurate and effective supply and demand balance regulation. Modeling and analysis of the response behavior of short-process steel industry loads is also crucial. It can balance the load fluctuations of the power grid by controlling the operating time of high-power production equipment such as arc furnaces without affecting the order output. However, there is currently a lack of refined electricity consumption models suitable for smelting loads such as short-process steel industry loads. It is necessary to conduct more refined modeling and analysis of short-process steel loads to optimize their electricity consumption behavior while ensuring the safe and economic operation of the system. How to encourage steel industry loads to participate in power dispatching and peak regulation by designing a reasonable incentive mechanism under the premise of meeting the economic benefits of steel industry loads, so as to achieve a balance of interests between power supply and demand, is still a key direction for current and future technical research. The following problems exist in the current short-process steel industry load participation in power grid peak load regulation: (1) Lack of comprehensive consideration of the impact of its own load characteristics on demand response capabilities; (2) Lack of a sophisticated electricity consumption model suitable for short-process steel industry loads and other smelting loads; (3) Lack of a comprehensive perspective that considers the interests of both supply and demand sides at the same time. Summary of the invention

[0004] The purpose of the present invention is to solve at least one technical problem in the background technology and to provide a network-load game peak-shaving optimization scheduling method and system for steel industry load.

[0005] To achieve the above object, the present invention provides a network load game peak load optimization scheduling method for steel industry load, comprising:

[0006] Collect network load game peak load optimization dispatch data information;

[0007] Construct a load characteristic analysis model for the short-process steel industry based on the peak load optimization scheduling data information of the network load game;

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

[0009] 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;

[0010] The production line 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 adjustable capacity calculation model of industrial load demand response under multi-type regulation mode, a maximum optimization target model for the daily production revenue of the lower-level steel industry load is constructed for the load side;

[0012] The lower-level steel industry load daily production operation constraint conditions are set for the lower-level steel industry load daily production revenue maximum optimization target model;

[0013] Based on the adjustable capacity calculation model of industrial load demand response under multi-type regulation mode, a minimum optimization target model of equivalent load variance of the upper power grid dispatching center is constructed for the grid side;

[0014] The upper power grid dispatching center equivalent load variance minimum optimization target model is set with the upper power grid dispatching center equivalent load variance minimum operation constraint condition;

[0015] Based on the maximum optimization target model of the daily production revenue of the lower-level steel industry load and the minimum optimization target model of the equivalent load variance of the upper-level power grid dispatching center, a grid-Host Stackelberg game model is constructed, in which the dispatching center is the leader and the steel industry load is the participant.

[0016] A solution method for the grid-load Stackelberg game model is proposed to solve the grid-load game peak-shaving optimization scheduling.

[0017] Output the network load game peak load optimization scheduling result information.

[0018] According to one aspect of the present invention, the network load game peak load optimization scheduling data information includes:

[0019] The productivity 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 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 gears of the arc furnace equipment, the total number of time periods in the optimized scheduling cycle, time-of-use electricity price, carbon treatment price, unit control cost coefficient, optimized scheduling data transmission step, 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 gears of the transformer tap, the maximum daily working time of the arc furnace and the maximum number of adjustable hot rolling mill data information.

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

[0021] A single steelmaking production line in T n The steel productivity model expression within the cycle is:

[0022]

[0023] Where: m n is the productivity of the nth production line; T n is the working cycle of the nth production line;

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

[0025]

[0026] Where: m t is the total production volume of N production lines; m n is the productivity of the nth production line; N is the number of steelmaking production lines;

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

[0028]

[0029] Where: 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.

[0030] According to one aspect of the present invention, the production line electric arc furnace demand response adjustable capacity model is constructed based on the short-process steel industry load characteristic analysis model as follows:

[0031] Combined with the peak load optimization dispatching data information of the network load game, based on the load characteristic analysis model of the short-process steel industry, the load characteristics of the short-process steel industry of the electric arc furnace are refined and focused, the production line in the short-process steel industry load characteristic analysis model is described by the electric arc furnace, and the demand response adjustable capacity model of the electric arc furnace of the production line is established;

[0032] The expression of the production line arc furnace demand response adjustable capacity model is:

[0033]

[0034] Where: is the power of the ith arc furnace at time t; d i,t,k is the position k of the gear handle of the i-th device at time t; P Lc,k is the power of the electric arc furnace at gear k; is the power of all arc furnaces at time t; I is the total number of arc furnaces on all production lines; K is the total number of gears of the arc furnace equipment; is the power of all arc furnaces at time t when they do not participate in demand response; d i,t,ko is the position of the gear lever at time t when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; P Lc,ko The power of the arc furnace when it is in gear KO when it does not participate in demand response; is the demand response capacity available for all arc furnaces at time t.

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

[0036] Combined with the peak load optimization scheduling data information of the network load game, based on the short-process steel industry load characteristic analysis model, the short-process steel industry load characteristics of the hot rolling mill are refined and focused on, the production line in the short-process steel industry load characteristic analysis model is described by the hot rolling mill, and the production line hot rolling mill demand response adjustable capacity model is established;

[0037] The demand response adjustable capacity model of the hot rolling mill of the production line is:

[0038]

[0039] Where: is the power of the jth hot rolling mill at time t; is a different 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; P L,j is the stable operating power of the jth hot rolling mill; is the power of the hot rolling mill at time t before it participates in regulation; J is the total number of hot rolling mills on all production lines; PL,j,o is the power of the jth hot rolling mill at time t before it participates in regulation; Demand-responsive adjustable capacity available for all hot rolling mills.

[0040] According to one aspect of the present invention, the industrial load demand response adjustable capacity calculation model under the multi-type regulation mode includes:

[0041] The calculation model expression of the total demand response adjustable capacity available in the equipment regulation mode is:

[0042]

[0043] Where: is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the equipment regulation mode; I is the total number of arc furnaces on all production lines; K is the total number of arc furnace equipment gears; d i,t,k is the position k of the gear handle of the i-th device at time t; P Lc,k is the power of the electric arc furnace when the gear is in k; d i,t,ko is the position of the gear lever at time t when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; P Lc,ko The power of the arc furnace when it is in gear KO when it does not participate in demand response; is a different 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; P L,j is the stable working power of the jth 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 jth hot rolling mill at time t before it participates in regulation;

[0044] The calculation model expression of the total demand response adjustable capacity available in the production line regulation mode is:

[0045]

[0046] Where: is the total demand response adjustable capacity that the steel industry load can provide at time t in the production line adjustment mode; N is the number of steel 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;

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

[0048]

[0049] Where: is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the combined regulation mode; is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the equipment regulation mode; is the total demand response adjustable capacity that can be provided by the steel industry load at time t under the production line regulation mode; μ is the regulation mode weight coefficient.

[0050] According to one aspect of the present invention, the maximum optimization target model expression of the lower iron and steel industry load daily production revenue is:

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

[0052] Where: C is the daily production revenue of the lower iron and steel industry load; E DR The benefits obtained from participating in demand response; ΔC u is the change in electricity cost; ΔC t is the change in carbon emission cost; ΔC c is the change in equipment loss cost; ΔC s is the change in production revenue;

[0053] The expression of the change in electricity cost is:

[0054]

[0055] Where: ΔC u is the change in electricity cost; is the time-of-use electricity price at time t; T is the total number of time periods in the optimization scheduling cycle; is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the combined regulation mode;

[0056] The carbon emission cost change expression is:

[0057]

[0058] Where: ΔC t is the change in carbon emission cost; c is the carbon treatment price; I is the total number of electric arc furnaces in all production lines; β c is the carbon emission coefficient of the electric arc furnace, which represents the equivalent carbon emission weight of producing 1 ton of crude steel; K c is the crude steel production rate of the electric arc furnace; Δt is the optimal scheduling data transmission step;

[0059] The equipment loss cost change expression is:

[0060]

[0061] Where: ΔC c is the change in equipment loss cost; T is the total number of time periods in the optimal scheduling cycle; I is the total number of arc furnaces on all production lines; K is the total number of arc furnace equipment gears; J is the total number of hot rolling mills on all production lines; C sg The cost of switching the gear lever; d i,t,k is the position k of the gear handle of the i-th device at time t; d i,t-1,k is the position k of the gear handle of the i-th device at time t-1; d i,t,ko is the position of the gear lever at time t when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; d i,t-1,ko is the position of the gear lever at time t-1 when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; C sr The cost of switching on and off the hot rolling mill; is a 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; is a 0-1 variable, which is 1 when it is turned on at time t-1 and 0 when it is turned off at time t-1;

[0062] The expression of the production income change is:

[0063]

[0064] Where: ΔC s is the change in production revenue; 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 optimal scheduling data transmission step; F p is the unit product price; F c is the unit product production cost; α is the proportion of electricity consumption cost in production cost; C u is the power consumption per unit output; η is the production efficiency;

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

[0066]

[0067] Where: E DR The benefits obtained from participating in demand response; C DR is the unit income of participating in demand response; strat is the start time of participating in demand response; tend The end moment of participation in demand response; It is the total demand response adjustable capacity that can be provided by the steel industry load at time t under the combined regulation mode.

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

[0069] The power constraint condition expression of the arc furnace is:

[0070]

[0071] Where: is the power of the i-th arc furnace at time t; γ is the transformer gear adjustment percentage; d i,t,k is the position k of the gear handle 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;

[0072] The transformer adjustment times constraint condition expression is:

[0073]

[0074] Where: u i,t is the adjustment status of the transformer tap, its value 0 means no adjustment of the transformer tap, its value 1 means adjustment of the transformer tap; d i,t,k is the position k of the gear handle of the i-th device at time t; d i,t-1,k is the position k of the gear handle of the i-th device at time t-1; T is the total number of time periods in the optimization scheduling cycle; U i,max is the maximum number of adjustable transformer taps;

[0075] The transformer skip-level regulation constraint condition expression is:

[0076] d i,t,k -d i,t-1,k ≤1;

[0077] Where: d i,t,k is the position k of the gear handle of the i-th device at time t; d i,t-1,k is the position k of the gear handle of the i-th device at time t-1;

[0078] The constraint expression for ensuring that the arc furnace's participation in demand response does not affect the daily production plan of the steel industry load is:

[0079]

[0080] Where: T is the total number of time periods in the optimization scheduling cycle; is the power of all arc furnaces at time t; is the power of all arc furnaces at time t when they do not participate in demand response; I is the total number of arc furnaces on all production lines; τ i is the production time change caused by adjusting the power of the i-th arc furnace; T max The maximum daily working time of the electric arc furnace;

[0081] The constraint expression for the number of hot rolling mill adjustments is:

[0082]

[0083] Where: v j,t The adjustment state of the hot rolling mill, its value 0 means the hot rolling mill is not turned on or off, and its value 1 means the hot rolling mill is turned on or off; is a 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; is a 0-1 variable, which is 1 when it is turned on at time t-1 and 0 when it is turned off at time t-1; T is the total number of time periods in the optimization scheduling cycle; V j,max is the maximum number of times the hot rolling mill can be adjusted;

[0084] The storage capacity constraint condition expression is:

[0085]

[0086] Where: 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 gears; d i,t,k is the position k of the gear handle of the i-th device at time t; s c is the output rate of the electric arc furnace at the transformer tap level k; 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 it is turned on at time t and 0 when it is turned off at time t; s x is the consumption rate of the hot rolling mill during normal operation; R t is the storage capacity at time t; R t-1 is the storage capacity at time t-1; R max Maximum storage capacity.

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

[0088]

[0089] Where: 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 time periods in the optimization scheduling cycle; 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 load value.

[0090] According to one aspect of the present invention, the minimum operation constraint condition of equivalent load variance of the upper power grid dispatching center includes: power balance constraint conditions of steel industry load equipment connected to the power grid bus;

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

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

[0093] Where: P b Power purchased for steel industry load; P d Power of self-provided power plant for steel industry load; P c P is the energy storage discharge power of the steel industry load; m It is the electrical power consumed by the steel industry load.

[0094] According to one aspect of the present invention, the network Stackelberg game model expression is:

[0095]

[0096] Where: F(*) is the equivalent load variance function; C(*) is the daily production revenue function of the steel industry load; and c p They are the dispatch center incentive price equilibrium solution and non-equilibrium solution respectively; and P DR They are the equilibrium solution and non-equilibrium solution of the steel industry load demand response capacity reporting.

[0097] According to one aspect of the present invention, the method for solving the peak load optimization scheduling of the network load game includes:

[0098] S1. Set the population size n pop , maximum number of iterations M, population crossover rate, mutation rate, convergence error Δε, number of iterative calculations m = 0;

[0099] S2. Initialize the population, and the dispatch center randomly generates incentive electricity prices Transmit parameters to steel industry loads;

[0100] S3. Update the number of iterative calculations m = m + 1;

[0101] S4. After the steel industry load receives the incentive electricity price specified by the dispatch center, the general solver tool is used to calculate the demand response capacity under different regulation methods, and the maximum benefit C is obtained by comparing different regulation methods. m , and at the same time report the demand response capacity corresponding to the maximum benefit to the dispatch center;

[0102] S5. After receiving the demand response capacity reported by the steel industry load, the dispatch center calculates the peak-shaving revenue and retains the current peak-shaving revenue F m and incentive electricity prices;

[0103] S6. If the reported demand response capacity does not meet the peak load demand of the dispatch center, use the genetic algorithm to select, crossover, and mutate to generate a new incentive electricity price, repeat steps S3 to S5, and calculate the load revenue of the steel industry and peak load benefits

[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, the game is determined to have reached equilibrium and the iteration ends, otherwise it returns to step S3.

[0107] According to one aspect of the present invention, the network load game peak load optimization scheduling result information includes:

[0108] The power of each arc furnace at different times, the demand response capacity that all 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 at different times under the equipment adjustment mode, the total demand response adjustable capacity that the steel industry load can provide at different times under the production line adjustment mode, the total demand response adjustable capacity that the steel industry load can provide at different times under the combined adjustment mode, the daily production income of the lower-level steel industry load, the income obtained from participating in demand response, the change in electricity cost, the change in carbon emission cost, the change in equipment loss cost, the change in production income and the equivalent load variance result information of the steel industry load connected to the distribution network after participating in demand response.

[0109] To achieve the above-mentioned purpose, the present invention also provides a network load game peak load optimization dispatching system for steel industry load, comprising:

[0110] Data information collection module, collecting network load game peak load optimization scheduling data information;

[0111] The module for constructing a load characteristic analysis model for the short-process steel industry is based on the peak load optimization scheduling data information of the network load game to construct a load characteristic analysis model for the short-process steel industry;

[0112] The module for building a production line electric arc furnace demand response adjustable capacity model is based on the short-process steel industry load characteristic analysis model to build a production line electric arc furnace demand response adjustable capacity model;

[0113] The production line hot rolling mill demand response adjustable capacity model construction module is based on the short-process steel industry load characteristic analysis model to build the production line hot rolling mill demand response adjustable capacity model;

[0114] The module for constructing the industrial load demand response adjustable capacity calculation model under multiple types of regulation modes is used to superimpose the production line 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 to form an industrial load demand response adjustable capacity calculation model under multiple types of regulation modes according to different regulation modes;

[0115] The module for constructing the maximum optimization target model of the daily production revenue of the lower-level steel industry load is based on the adjustable capacity calculation model of the industrial load demand response under the multi-type regulation mode. For the load side, the maximum optimization target model of the daily production revenue of the lower-level steel industry load is constructed;

[0116] A module for setting the daily production and operation constraints of the lower-level steel industry load is used to set the daily production and operation constraints of the lower-level steel industry load for the maximum optimization target model of the daily production revenue of the lower-level steel industry load;

[0117] The upper power grid dispatching center equivalent load variance minimum optimization target model construction module is based on the industrial load demand response adjustable capacity calculation model under multiple types of regulation modes. For the grid side, the upper power grid dispatching center equivalent load variance minimum optimization target model is constructed;

[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 target model;

[0119] The grid-host Stackelberg game model construction module builds a grid-host Stackelberg game model based on the maximum optimization target model of the daily production revenue of the lower-level steel industry load and the minimum optimization target model of the equivalent load variance of the upper-level power grid dispatching center, in which the dispatching center is the leader and the steel industry load is the participant;

[0120] The network-load game peak-shaving optimization scheduling solution module proposes a network-load game peak-shaving optimization scheduling solution method for the network-load Stackelberg game model, and solves the network-load game peak-shaving optimization scheduling;

[0121] The optimization scheduling result output module outputs the network load game peak-shaving optimization scheduling result information.

[0122] To achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the network-load game peak-shaving optimization scheduling method for steel industry load is implemented as described above.

[0123] To achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the network-load game peak-shaving optimization scheduling method for the steel industry load is implemented as described above.

[0124] According to the scheme of the present invention, the present invention aims to provide a network-load game peak-shaving optimization scheduling method taking into account the load of the steel industry of multiple production lines, in order to reasonably mobilize the load resources of the short-process steel industry to participate in the peak-shaving of the power grid; by analyzing the adjustable potential of key production equipment, the adjustable range of each equipment is obtained, in order to ensure the accuracy of the steel industry load participating in the demand response; a refined model is constructed for the response capability of the steel industry load, and the influence of production factor constraints is fully considered, in order to better reflect the real adjustable capacity of the steel industry load; based on the 1-N Stackelberg game model, a short-process steel industry load game peak-shaving optimization scheduling strategy for multiple production lines is obtained, in order to increase the benefits of the steel industry load while meeting the peak-shaving needs of the dispatching center and achieve better economy and feasibility.

[0125] According to the solution of the present invention, the present invention analyzes the load characteristics of the steel industry through refined modeling, can accurately evaluate the adjustable potential range of each production equipment, and realize precise regulation of the short-process steel industry load, which not only effectively reduces the peak-to-valley difference of the load of the power grid, but also improves the overall peak-shaving efficiency and stability of the power grid; the Stackelberg game model constructed by the present invention fully considers the interests of both supply and demand, and encourages the steel industry load to participate in the peak-shaving of the power grid through a reasonable incentive mechanism, which not only provides additional profit opportunities for the steel industry load, but also reduces its electricity cost and enhances economic benefits through optimized scheduling strategies; the optimized scheduling method proposed in the present invention provides theoretical guidance and practical paths for the steel industry load to participate in demand response, strengthens the interaction between industrial loads 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, and realizes the optimization of energy utilization and the dual benefits of social economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0126] Figure 1 A flow chart schematically shows a method for optimizing the peak load regulation of the steel industry load by means of network-load game according to an embodiment of the present invention. DETAILED DESCRIPTION

[0127] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only to enable those skilled in the art to better understand and thus implement the present invention, rather than implying any limitation on the scope of the present invention.

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

[0129] Figure 1 A flowchart schematically shows a method for optimizing the peak load regulation of the steel industry load according to an embodiment of the present invention. Figure 1 As shown, in this embodiment, the network load game peak load optimization scheduling method of the steel industry load includes:

[0130] Collect network load game peak load optimization and dispatching data information;

[0131] Construct a load characteristic analysis model for the short-process steel industry based on the peak load optimization scheduling data information of the network load game;

[0132] Based on the load characteristic analysis model of the short-process steel industry, a demand response adjustable capacity model for the electric arc furnace of the production line is constructed;

[0133] 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;

[0134] The production line 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 adjustable capacity calculation model of industrial load demand response under multi-type regulation mode, a maximum optimization target model for the daily production revenue of the lower-level steel industry load is constructed for the load side;

[0136] The lower-level steel industry load daily production operation constraint conditions are set for the lower-level steel industry load daily production revenue maximum optimization target model;

[0137] Based on the adjustable capacity calculation model of industrial load demand response under multi-type regulation mode, a minimum optimization target model of equivalent load variance of the upper power grid dispatching center is constructed for the grid side;

[0138] The upper power grid dispatching center equivalent load variance minimum optimization target model is set with the upper power grid dispatching center equivalent load variance minimum operation constraint condition;

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

[0140] A solution method for the grid-load Stackelberg game model is proposed to solve the grid-load game peak-shaving optimization scheduling.

[0141] Output the network load game peak load optimization scheduling result information.

[0142] Further, according to an embodiment of the present invention, the network load game peak load optimization scheduling data information includes:

[0143] The productivity 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 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 gears of the arc furnace equipment, the total number of time periods in the optimized scheduling cycle, time-of-use electricity price, carbon treatment price, unit control cost coefficient, optimized scheduling data transmission step, 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 gears of the transformer tap, the maximum daily working time of the arc furnace and the maximum number of adjustable hot rolling mill data information.

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

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

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

[0147]

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

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

[0150]

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

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

[0153]

[0154] Where: 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.

[0155] Further, according to an embodiment of the present invention, 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:

[0156] Combined with the peak load optimization dispatching data information of the network load game, based on the load characteristic analysis model of the short-process steel industry, the load characteristics of the short-process steel industry of the electric arc furnace are refined and focused, the production line in the short-process steel industry load characteristic analysis model is described by the electric arc furnace, and the demand response adjustable capacity model of the electric arc furnace of the production line is established;

[0157] Among them, the arc furnace of the production line changes the power by controlling the transformer joint;

[0158] The expression of the production line electric arc furnace demand response adjustable capacity model that can be provided by the production line electric arc furnace is:

[0159]

[0160] Where: is the power of the ith arc furnace at time t; d i,t,k is the position k of the gear handle of the i-th device at time t; P Lc,k is the power of the electric arc furnace at gear k; is the power of all arc furnaces at time t; I is the total number of arc furnaces on all production lines; K is the total number of gears of the arc furnace equipment; is the power of all arc furnaces at time t when they do not participate in demand response; d i,t,ko is the position of the gear lever at time t when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; P Lc,ko The power of the arc furnace when it is in gear KO when it does not participate in demand response; is the demand response capacity available for all arc furnaces at time t.

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

[0162] Combined with the peak load optimization scheduling data information of the network load game, based on the short-process steel industry load characteristic analysis model, the short-process steel industry load characteristics of the hot rolling mill are refined and focused on, the production line in the short-process steel industry load characteristic analysis model is described by the hot rolling mill, and the production line hot rolling mill demand response adjustable capacity model is established;

[0163] Among them, the hot rolling mill changes power through switchgear;

[0164] The production line hot rolling mill demand response adjustable capacity model that the hot rolling mill can provide is:

[0165]

[0166] Where: is the power of the jth hot rolling mill at time t; is a different 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; P L,j is the stable operating power of the jth hot rolling mill; is the power of the hot rolling mill at time t before it participates in regulation; J is the total number of hot rolling mills on all production lines; P L,j,o is the power of the jth hot rolling mill at time t before it participates in regulation; Demand-responsive adjustable capacity available for all hot rolling mills.

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

[0168] The calculation model expression of the total demand response adjustable capacity available in the equipment regulation mode is:

[0169]

[0170] Where: is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the equipment regulation mode; I is the total number of arc furnaces on all production lines; K is the total number of arc furnace equipment gears; d i,t,k is the position k of the gear handle of the i-th device at time t; P Lc,k is the power of the electric arc furnace when the gear is in k; d i,t,ko is the position of the gear lever at time t when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; P Lc,ko The power of the arc furnace when it is in gear KO when it does not participate in demand response; is a different 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; P L,j is the stable working power of the jth 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 jth hot rolling mill at time t before it participates in regulation;

[0171] The calculation model expression of the total demand response adjustable capacity available in the production line regulation mode is:

[0172]

[0173] Where: is the total demand response adjustable capacity that the steel industry load can provide at time t in the production line adjustment mode; N is the number of steel 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 calculation model expression of the total demand response adjustable capacity that can be provided in the combined regulation mode is:

[0175]

[0176] Where: is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the combined regulation mode; is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the equipment regulation mode; is the total demand response adjustable capacity that can be provided by the steel industry load at time t under the production line regulation mode; μ is the regulation mode weight coefficient.

[0177] Further, according to an embodiment of the present invention, the steel industry load takes the maximum daily production revenue as the objective function, and the daily production revenue is composed of five parts: the change in electricity cost, the change in carbon emission cost, the change in equipment loss cost, the change in production revenue and the revenue from participating in demand response; specifically, the expression of the maximum optimization objective model of the lower-level steel industry load daily production revenue is:

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

[0179] Where: C is the daily production revenue of the lower iron and steel industry load; E DR The benefits obtained from participating in demand response; ΔC u is the change in electricity cost; ΔC t is the change in carbon emission cost; ΔC c is the change in equipment loss cost; ΔC s is the change in production revenue;

[0180] The expression of the change in electricity cost is:

[0181]

[0182] Where: ΔC u is the change in electricity cost; is the time-of-use electricity price at time t; T is the total number of time periods in the optimization scheduling cycle; is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the combined regulation mode;

[0183] The expression of carbon emission cost change is:

[0184]

[0185] Where: ΔC t is the change in carbon emission cost; c is the carbon treatment price; I is the total number of electric arc furnaces in all production lines; β c is the carbon emission coefficient of the electric arc furnace, which represents the equivalent carbon emission weight of producing 1 ton of crude steel; K c is the crude steel production rate of the electric arc furnace; Δt is the optimal scheduling data transmission step;

[0186] The expression of equipment loss cost change is:

[0187]

[0188] Where: ΔC c is the change in equipment loss cost; T is the total number of time periods in the optimal scheduling cycle; I is the total number of arc furnaces on all production lines; K is the total number of arc furnace equipment gears; J is the total number of hot rolling mills on all production lines; C sg The cost of switching the gear lever; d i,t,k is the position k of the gear handle of the i-th device at time t; d i,t-1,k is the position k of the gear handle of the i-th device at time t-1; d i,t,ko is the position of the gear lever at time t when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; d i,t-1,ko is the position of the gear lever at time t-1 when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; C sr The cost of switching on and off the hot rolling mill; is a 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; is a 0-1 variable, which is 1 when it is turned on at time t-1 and 0 when it is turned off at time t-1;

[0189] The expression of production revenue change is:

[0190]

[0191] Where: ΔC s is the change in production revenue; 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 optimal scheduling data transmission step; F p is the unit product price; F c is the unit product production cost; α is the proportion of electricity consumption cost in production cost; C u is the power consumption per unit output; η is the production efficiency;

[0192] The profit expression of participating in demand response is:

[0193]

[0194] Where: E DR The benefits obtained from participating in demand response; C DR is the unit income of participating in demand response; strat is the start time of participating in demand response; tend The end moment of participation in demand response; It is the total demand response adjustable capacity that can be provided by the steel industry load at time t under the combined regulation mode.

[0195] Further, according to an embodiment of the present invention, the lower-level steel industry load daily production operation constraints include: arc furnace power constraints, transformer adjustment times constraints, transformer leapfrogging constraints, constraints to ensure that arc furnace participation in demand response does not affect the steel industry load daily production plan, hot rolling mill adjustment times constraints and storage capacity constraints;

[0196] In this embodiment, the power of the arc furnace is kept constant during the smelting process, and the power of the arc furnace is controlled by adjusting the transformer tap before and after the smelting is completed; the power constraint condition expression of the arc furnace is:

[0197]

[0198] Where: is the power of the i-th arc furnace at time t; γ is the transformer gear adjustment percentage; d i,t,k is the position k of the gear handle 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 arc furnace participates in demand response regulation, frequent adjustment of the transformer tap will cause the transformer tap to age and reduce the life of the transformer; the transformer adjustment times constraint expression is:

[0200]

[0201] Where: u i,t is the adjustment status of the transformer tap, its value 0 means no adjustment of the transformer tap, its value 1 means adjustment of the transformer tap; d i,t,k is the position k of the gear handle of the i-th device at time t; d i,t-1,k is the position k of the gear handle of the i-th device at time t-1; T is the total number of time periods in the optimization scheduling cycle; U i,max is the maximum number of adjustable transformer taps;

[0202] In the actual operation process, in order to prevent the arc furnace power fluctuation from damaging the equipment, the transformer tap cannot be adjusted across levels; the transformer across-level adjustment constraint expression is:

[0203] d i,t,k -d i,t-1,k ≤1;

[0204] Where: di,t,k is the position k of the gear handle of the i-th device at time t; d i,t-1,k is the position k of the gear handle of the i-th device at time t-1;

[0205] Changing the transformer tap position to change the arc furnace power will cause changes in production time. When the arc furnace power is reduced, the production time will increase, but the total heat required to smelt 1 ton of crude steel is the same; the constraint expression to ensure that the arc furnace's participation in demand response does not affect the daily production plan of the steel industry load is:

[0206]

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

[0208] If the hot rolling mill is frequently turned on and off when participating in demand response regulation, it will lead to equipment aging or even damage, resulting in increased cost losses in the steel industry load; the constraint expression for the number of hot rolling mill adjustments is:

[0209]

[0210] Where: v j,t The adjustment state of the hot rolling mill, its value 0 means the hot rolling mill is not turned on or off, and its value 1 means the hot rolling mill is turned on or off; is a 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; is a 0-1 variable, which is 1 when it is turned on at time t-1 and 0 when it is turned off at time t-1; T is the total number of time periods in the optimization scheduling cycle; V j,max is the maximum number of times the hot rolling mill can be adjusted;

[0211] After steel refining, the continuous casting machine is transported for casting. The cast slabs are transported to the hot rolling mill for rolling and forming. There are transportation and storage problems in the process. When the steel industry load participates in demand response regulation, if only the arc furnace power is regulated, the hot rolling mill remains in normal working state, and the output rate is less than the consumption rate, the production efficiency of the steel industry load will be reduced; if only the hot rolling mill is regulated, the arc furnace remains in normal working state, and the output rate is greater than the consumption rate, the storage capacity problem needs to be considered. When the number of billets produced is greater than the maximum storage capacity, the storage cost of the steel industry load will increase; the storage capacity constraint condition expression is:

[0212]

[0213] Where: 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 gears; d i,t,k is the position k of the gear handle of the i-th device at time t; s c is the output rate of the electric arc furnace at the transformer tap level k; 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 it is turned on at time t and 0 when it is turned off at time t; s x is the consumption rate of the hot rolling mill during normal operation; R t is the storage capacity at time t; R t-1 is the storage capacity at time t-1; R max Maximum storage capacity.

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

[0215]

[0216] Where: 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 time periods in the optimization scheduling cycle; 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 load value.

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

[0218] The expression of the power 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] Where: P b Power purchased for the steel industry load; P dPower of self-provided power plant for steel industry load; P c P is the energy storage discharge power of the steel industry load; m It is the electrical power consumed by the steel industry load.

[0221] Further, according to an embodiment of the present invention, compared with residential load, the steel industry load, as a high-energy-consuming industry, can better meet the peak-shaving demand of the dispatching center; on the one hand, as the maker of the control strategy, the dispatching center tends to achieve a relatively stable peak-shaving effect with the least economic cost. The steel industry load reports the adjustable capacity to the dispatching center according to its own production plan the day before. The dispatching center reasonably formulates incentive electricity prices according to the peak-shaving demand to guide the steel industry load to participate in demand response regulation; on the other hand, as a participant in the control strategy of the dispatching center, the steel industry load will pay more attention to its own benefits when participating in demand response regulation. Considering factors such as electricity cost, carbon emission cost, production benefit and benefit of participating in demand response, when the steel industry load participates in regulation and control, resulting in increased costs and reduced benefits, the steel industry load has reason not to participate in demand response regulation or reduce its own adjustable capacity to ensure benefits; therefore, the steel industry load and the dispatching center constitute a game decision-making problem, that is, the dispatching center is the leader and the steel industry load is the participant. Both parties change their own strategies according to the strategies of the other party to achieve their goals and finally obtain an equilibrium solution. Based on this, a Stackelberg game model is constructed to describe the regulation relationship between the dispatch center and the steel industry load, and a 1-N Stackelberg game model is constructed to obtain interactive information; the dispatch center is the leader and the steel industry load is the follower; the constructed Stackelberg game is an orderly 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 based on its own production plan;

[0223] (Process #2) The dispatch center sends incentive electricity prices to the steel industry loads based on the demand response capacity and peak load demand reported by the steel industry loads, encouraging the steel industry loads to actively participate in the regulation;

[0224] (Process #3) The steel industry load is calculated based on the incentive electricity price issued by the dispatch center according to the cost-benefit function, and participates in regulation or changes the demand response capacity and reports it to the dispatch center again;

[0225] (Process #4) The dispatch center calculates the peak load regulation effect based on the demand response capacity returned by the steel industry load, and adjusts the incentive electricity price based on the peak load regulation effect and re-sends it to the steel industry load;

[0226] (Process #5) Repeat (Process #1) to (Process #4) until any change in the strategy of the dispatch center and the steel industry load will make the overall result worse, that is, the strategies of both parties are satisfied;

[0227] The judgment model expression of the Stackelberg game reaching equilibrium, that is, the ideal result, is:

[0228]

[0229] Where: F(*) is the equivalent load variance function; C(*) is the daily production revenue function of the steel industry load; and c p They are the dispatch center incentive price equilibrium solution and non-equilibrium solution respectively; and P DR They are the equilibrium solution and non-equilibrium solution of the steel industry load demand response capacity reporting.

[0230] Further, according to an embodiment of the present invention, the difficulty in solving the problem lies in that the daily production revenue function C(*) of the lower iron and steel industry load is constrained by discrete switching actions and cannot be treated as a continuous function and the double-layer model is converted into a single-layer model by the method of taking its derivative for solving; based on this, a genetic algorithm is used in combination with a general solver tool for solving the problem. The specific steps of the grid-load game peak-shaving optimization scheduling solution method are as follows:

[0231] S1. Set the population size n pop , maximum number of iterations M, population crossover rate, mutation rate, convergence error Δε, number of iterative calculations m = 0;

[0232] S2. Initialize the population, and the dispatch center randomly generates incentive electricity prices Transmit parameters to steel industry loads;

[0233] S3. Update the number of iterative calculations m = m + 1;

[0234] S4. After the steel industry load receives the incentive electricity price specified by the dispatch center, the general solver tool is used to calculate the demand response capacity under different regulation methods, and the maximum benefit C is obtained by comparing different regulation methods. m , and at the same time report the demand response capacity corresponding to the maximum benefit to the dispatch center;

[0235] S5. After receiving the demand response capacity reported by the steel industry load, the dispatch center calculates the peak-shaving revenue and retains the current peak-shaving revenue F m and incentive electricity prices;

[0236] S6. If the reported demand response capacity does not meet the peak load demand of the dispatch center, use the genetic algorithm to select, crossover, and mutate to generate a new incentive electricity price, repeat steps S3 to S5, and calculate the load revenue of the steel industry and peak load benefits

[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, the game is determined to have reached equilibrium and the iteration ends, otherwise it returns to step S3.

[0240] Further, according to an embodiment of the present invention, the network load game peak load optimization scheduling result information includes:

[0241] The power of each arc furnace at different times, the demand response capacity that all 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 at different times under the equipment adjustment mode, the total demand response adjustable capacity that the steel industry load can provide at different times under the production line adjustment mode, the total demand response adjustable capacity that the steel industry load can provide at different times under the combined adjustment mode, the daily production income of the lower-level steel industry load, the income obtained from participating in demand response, the change in electricity cost, the change in carbon emission cost, the change in equipment loss cost, the change in production income and the equivalent load variance result information of the steel industry load connected to the distribution network after participating in demand response.

[0242] According to the above scheme of the present invention, the present invention aims to provide a network-load game peak-shaving optimization scheduling method taking into account the load of the steel industry of multiple production lines, in order to reasonably mobilize the load resources of the short-process steel industry to participate in the peak-shaving of the power grid; by analyzing the adjustable potential of key production equipment, the adjustable range of each equipment is obtained, in order to ensure the accuracy of the steel industry load participating in the demand response; a refined model is constructed for the response capability of the steel industry load, and the influence of production factor constraints is fully considered, in order to better reflect the real adjustable capacity of the steel industry load; based on the 1-N Stackelberg game model, a short-process steel industry load game peak-shaving optimization scheduling strategy for multiple production lines is obtained, in order to increase the benefits of the steel industry load while meeting the peak-shaving needs of the dispatching center and achieve better economy and feasibility.

[0243] According to the above scheme of the present invention, the present invention analyzes the load characteristics of the steel industry through refined modeling, can accurately evaluate the adjustable potential range of each production equipment, and realize precise regulation of the short-process steel industry load, which not only effectively reduces the peak-to-valley difference of the load of the power grid, but also improves the overall peak-shaving efficiency and stability of the power grid; the Stackelberg game model constructed by the present invention fully considers the interests of both supply and demand, and encourages the steel industry load to participate in the peak-shaving of the power grid through a reasonable incentive mechanism, which not only provides additional profit opportunities for the steel industry load, but also reduces its electricity cost and enhances economic benefits through optimized scheduling strategies; the optimized scheduling method proposed in the present invention provides theoretical guidance and practical paths for the steel industry load to participate in demand response, strengthens the interaction between industrial loads 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, and realizes the optimization of energy utilization and the dual benefits of social economy.

[0244] Furthermore, in order to achieve the above-mentioned purpose, the present invention also provides a network load game peak load optimization scheduling system for steel industry load, comprising:

[0245] Data information collection module, collecting network load game peak load optimization scheduling data information;

[0246] The module for constructing a load characteristic analysis model for the short-process steel industry is based on the peak load optimization scheduling data information of the network load game to construct a load characteristic analysis model for the short-process steel industry;

[0247] The module for building a production line electric arc furnace demand response adjustable capacity model is based on the short-process steel industry load characteristic analysis model to build a production line electric arc furnace demand response adjustable capacity model;

[0248] The production line hot rolling mill demand response adjustable capacity model construction module is based on the short-process steel industry load characteristic analysis model to build the production line hot rolling mill demand response adjustable capacity model;

[0249] The module for constructing the industrial load demand response adjustable capacity calculation model under multiple types of regulation modes is used to superimpose the production line 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 to form an industrial load demand response adjustable capacity calculation model under multiple types of regulation modes according to different regulation modes;

[0250] The module for constructing the maximum optimization target model of the daily production revenue of the lower-level steel industry load is based on the adjustable capacity calculation model of the industrial load demand response under the multi-type regulation mode. For the load side, the maximum optimization target model of the daily production revenue of the lower-level steel industry load is constructed;

[0251] A module for setting the daily production and operation constraints of the lower-level steel industry load is used to set the daily production and operation constraints of the lower-level steel industry load for the maximum optimization target model of the daily production revenue of the lower-level steel industry load;

[0252] The upper power grid dispatching center equivalent load variance minimum optimization target model construction module is based on the industrial load demand response adjustable capacity calculation model under multiple types of regulation modes. For the grid side, the upper power grid dispatching center equivalent load variance minimum optimization target model is constructed;

[0253] 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 target model;

[0254] The grid-host Stackelberg game model construction module builds a grid-host Stackelberg game model based on the maximum optimization target model of the daily production revenue of the lower-level steel industry load and the minimum optimization target model of the equivalent load variance of the upper-level power grid dispatching center, in which the dispatching center is the leader and the steel industry load is the participant;

[0255] The network-load game peak-shaving optimization scheduling solution module proposes a network-load game peak-shaving optimization scheduling solution method for the network-load Stackelberg game model, and solves the network-load game peak-shaving optimization scheduling;

[0256] The optimization scheduling result output module outputs the network load game peak-shaving optimization scheduling result information.

[0257] The network load game peak-shaving optimization scheduling system for the steel industry load according to the present invention can realize the network load game peak-shaving optimization scheduling method for the steel industry load. The specific method steps are as described above and will not be repeated here.

[0258] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the network-load game peak-shaving optimization scheduling method for the steel industry load as described above is implemented.

[0259] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the network-load game peak-shaving optimization scheduling method for the steel industry load as described above is implemented.

[0260] Those skilled in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0261] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method implementation methods, and will not be repeated here.

[0262] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

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

[0264] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[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 solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the energy-saving signal sending / receiving method of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0266] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other.

[0267] It should be understood that the size of the serial numbers of each step in the content of the invention and the implementation methods of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of the present invention.

Claims

1. A network-load game peak-shaving optimization scheduling method for steel industry load, characterized in that: include: Collect network load game peak load optimization and dispatching data information; Construct a load characteristic analysis model for the short-process steel industry based on the peak load optimization scheduling data information of the network load game; Based on the load characteristic analysis model of the short-process steel industry, a demand response adjustable capacity model for the electric arc furnace of the production line is constructed; 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; The production line 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; Based on the adjustable capacity calculation model of industrial load demand response under multi-type regulation mode, a maximum optimization target model for the daily production revenue of the lower-level steel industry load is constructed for the load side; The lower-level steel industry load daily production operation constraint conditions are set for the lower-level steel industry load daily production revenue maximum optimization target model; Based on the adjustable capacity calculation model of industrial load demand response under multi-type regulation mode, a minimum optimization target model of equivalent load variance of the upper power grid dispatching center is constructed for the grid side; The upper power grid dispatching center equivalent load variance minimum optimization target model is set with the upper power grid dispatching center equivalent load variance minimum operation constraint condition; Based on the maximum optimization target model of the daily production revenue of the lower-level steel industry load and the minimum optimization target model of the equivalent load variance of the upper-level power grid dispatching center, a grid-Host Stackelberg game model is constructed, in which the dispatching center is the leader and the steel industry load is the participant. A solution method for the grid-load Stackelberg game model is proposed to solve the grid-load game peak-shaving optimization scheduling. Output the network load game peak load optimization scheduling result information.

2. The method for optimizing the peak load regulation of the steel industry load by using network load game according to claim 1 is characterized in that: The network load game peak load optimization scheduling data information includes: The productivity 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 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 gears of the arc furnace equipment, the total number of time periods in the optimized scheduling cycle, time-of-use electricity price, carbon treatment price, unit control cost coefficient, optimized scheduling data transmission step, 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 gears of the transformer tap, the maximum daily working time of the arc furnace and the maximum number of adjustable hot rolling mill data information.

3. The method for optimizing the peak load regulation of the steel industry load by using network load game according to claim 1 is characterized in that: The short-process steel industry load characteristic analysis model includes: A single steelmaking production line in T n The steel productivity model expression within the cycle is: Where: m n is the productivity of the nth production line; T n is the working cycle of the nth production line; For a steel industry load with N steel production lines, the total production model expression is: Where: m t is the total production volume of N production lines; m n is the productivity of the nth production line; N is the number of steelmaking production lines; For the steel industry load with N steel production lines, the total production load power model expression is: Where: 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 for optimizing the peak load regulation of the steel industry load by using network load game according to claim 1 is characterized in that: The adjustable capacity model of arc furnace demand response for production line based on the short-process steel industry load characteristic analysis model is constructed as follows: Combined with the peak load optimization dispatching data information of the network load game, based on the load characteristic analysis model of the short-process steel industry, the load characteristics of the short-process steel industry of the electric arc furnace are refined and focused, the production line in the short-process steel industry load characteristic analysis model is described by the electric arc furnace, and the demand response adjustable capacity model of the electric arc furnace of the production line is established; The expression of the production line arc furnace demand response adjustable capacity model is: Where: is the power of the i-th arc furnace at time t; d i,t,k is the position k of the gear handle of the i-th device at time t; P Lc,k is the power of the electric arc furnace at gear k; is the power of all arc furnaces at time t; I is the total number of arc furnaces on all production lines; K is the total number of gears of the arc furnace equipment; is the power of all arc furnaces at time t when they do not participate in demand response; d i,t,ko is the position of the gear lever at time t when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; P Lc,ko The power of the arc furnace when it is in gear KO when it does not participate in demand response; is the demand response capacity available for all arc furnaces at time t.

5. The network load game peak load optimization scheduling method of the steel industry load according to claim 1 is characterized in that: The short-process steel industry load characteristic analysis model is used to construct a production line hot rolling mill demand response adjustable capacity model: Combined with the peak load optimization scheduling data information of the network load game, based on the short-process steel industry load characteristic analysis model, the short-process steel industry load characteristics of the hot rolling mill are refined and focused on, the production line in the short-process steel industry load characteristic analysis model is described by the hot rolling mill, and the production line hot rolling mill demand response adjustable capacity model is established; The demand response adjustable capacity model of the hot rolling mill of the production line is: Where: is the power of the jth hot rolling mill at time t; is a different 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; P L,j is the stable operating power of the jth hot rolling mill; is the power of the hot rolling mill at time t before it participates in regulation; J is the total number of hot rolling mills on all production lines; P L,j,o is the power of the jth hot rolling mill at time t before it participates in regulation; Demand-responsive adjustable capacity available for all hot rolling mills.

6. The method for optimizing the peak load regulation of the steel industry load by using network-load game according to claim 1 is characterized in that: The industrial load demand response adjustable capacity calculation model in the multi-type regulation mode includes: The calculation model expression of the total demand response adjustable capacity available in the equipment regulation mode is: Where: is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the equipment regulation mode; I is the total number of arc furnaces on all production lines; K is the total number of arc furnace equipment gears; d i,t,k is the position k of the gear handle of the i-th device at time t; P Lc,k is the power of the electric arc furnace when the gear is in k; d i,t,ko is the position of the gear lever at time t when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; P Lc,ko The power of the arc furnace when it is in gear KO when it does not participate in demand response; is a different 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; P L,j is the stable working power of the jth 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 jth hot rolling mill at time t before it participates in regulation; The calculation model expression of the total demand response adjustable capacity available in the production line regulation mode is: Where: is the total demand response adjustable capacity that the steel industry load can provide at time t in the production line adjustment mode; N is the number of steel 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; The calculation model expression of the total demand response adjustable capacity that can be provided in the combined regulation mode is: Where: is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the combined regulation mode; is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the equipment regulation mode; is the total demand response adjustable capacity that can be provided by the steel industry load at time t under the production line regulation mode; μ is the regulation mode weight coefficient.

7. The method for optimizing the peak load regulation of the steel industry load by using network load game according to claim 1 is characterized in that: The expression of the maximum optimization target model of the lower iron and steel industry load daily production revenue is: C=max(E DR -ΔC u +ΔC t +ΔC c +ΔC s ); Where: C is the daily production revenue of the lower iron and steel industry load; E DR The benefits obtained from participating in demand response; ΔC u is the change in electricity cost; ΔC t is the change in carbon emission cost; ΔC c is the change in equipment loss cost; ΔC s is the change in production revenue; The expression of the change in electricity cost is: Where: ΔC u is the change in electricity cost; is the time-of-use electricity price at time t; T is the total number of time periods in the optimization scheduling cycle; is the total demand response adjustable capacity that can be provided by the steel industry load at time t in the combined regulation mode; The carbon emission cost change expression is: Where: ΔC t is the change in carbon emission cost; c is the carbon treatment price; I is the total number of electric arc furnaces in all production lines; β c is the carbon emission coefficient of the electric arc furnace, which represents the equivalent carbon emission weight of producing 1 ton of crude steel; K c is the crude steel production rate of the electric arc furnace; Δt is the optimal scheduling data transmission step; The equipment loss cost change expression is: Where: ΔC c is the change in equipment loss cost; T is the total number of time periods in the optimal scheduling cycle; I is the total number of arc furnaces on all production lines; K is the total number of arc furnace equipment gears; J is the total number of hot rolling mills on all production lines; C sg The cost of switching the gear lever; d i,t,k is the position k of the gear handle of the i-th device at time t; d i,t-1,k is the position k of the gear handle of the i-th device at time t-1; d i,t,ko is the position of the gear lever at time t when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; d i,t-1,ko is the position of the gear lever at time t-1 when the i-th device does not participate in demand response, that is, the gear position of the device when the steel industry load does not participate in demand response; C sr The cost of switching on and off the hot rolling mill; is a 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; is a 0-1 variable, which is 1 when it is turned on at time t-1 and 0 when it is turned off at time t-1; The expression of the production income change is: Where: ΔC s is the change in production revenue; 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 optimal scheduling data transmission step; F p is the unit product price; F c is the unit product production cost; α is the proportion of electricity consumption cost in production cost; C u is the power consumption per unit output; η is the production efficiency; The profit expression obtained by participating in demand response is: Where: E DR The benefits obtained from participating in demand response; C DR is the unit income of participating in demand response; strat is the start time of participating in demand response; t end The end moment of participation in demand response; It is the total demand response adjustable capacity that can be provided by the steel industry load at time t under the combined regulation mode.

8. The method for optimizing the peak load regulation of the steel industry load by using network-load game according to claim 1 is characterized in that: The lower-level steel industry load daily production and operation constraints include: arc furnace power constraints, transformer adjustment times constraints, transformer leapfrogging constraints, constraints to ensure that arc furnace participation in demand response does not affect the steel industry load daily production plan, hot rolling mill adjustment times constraints and storage capacity constraints; The power constraint condition expression of the arc furnace is: Where: is the power of the i-th arc furnace at time t; γ is the transformer gear adjustment percentage; d i,t,k is the position k of the gear handle of the i-th device at time t; is the rated power of the transformer at the initial tap; D is the total number of transformer taps; The transformer adjustment times constraint condition expression is: Where: u i,t is the adjustment status of the transformer tap, its value 0 means no adjustment of the transformer tap, its value 1 means adjustment of the transformer tap; d i,t,k is the position k of the gear handle of the i-th device at time t; d i,t-1,k is the position k of the gear handle of the i-th device at time t-1; T is the total number of time periods in the optimization scheduling cycle; U i,max is the maximum number of adjustable transformer taps; The transformer skip-level regulation constraint condition expression is: d i,t,k -d i,t-1,k ≤1; Where: d i,t,k is the position k of the gear handle of the i-th device at time t; d i,t-1,k is the position k of the gear handle of the i-th device at time t-1; The constraint expression for ensuring that the arc furnace's participation in demand response does not affect the daily production plan of the steel industry load is: Where: T is the total number of time periods in the optimization scheduling cycle; is the power of all arc furnaces at time t; is the power of all arc furnaces at time t when they do not participate in demand response; I is the total number of arc furnaces on all production lines; τ i is the production time change caused by adjusting the power of the i-th arc furnace; T max The maximum daily working time of the electric arc furnace; The constraint expression for the number of hot rolling mill adjustments is: Where: v j,t The adjustment state of the hot rolling mill, its value 0 means the hot rolling mill is not turned on or off, and its value 1 means the hot rolling mill is turned on or off; is a 0-1 variable, which is 1 when it is turned on at time t and 0 when it is turned off at time t; is a 0-1 variable, which is 1 when it is turned on at time t-1 and 0 when it is turned off at time t-1; T is the total number of time periods in the optimization scheduling cycle; V j,max is the maximum number of times the hot rolling mill can be adjusted; The storage capacity constraint condition expression is: Where: 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 gears; d i,t,k is the position k of the gear handle of the i-th device at time t; s c is the output rate of the electric arc furnace at the transformer tap level k; 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 it is turned on at time t and 0 when it is turned off at time t; s x is the consumption rate of the hot rolling mill during normal operation; R t is the storage capacity at time t; R t-1 is the storage capacity at time t-1; R max Maximum storage capacity.

9. The method for optimizing the peak load regulation of the steel industry load by using network-load game according to claim 1 is characterized in that: The expression of the optimization objective model for the minimum variance of equivalent load of the upper power grid dispatching center is: Where: 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 time periods in the optimization scheduling cycle; 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 load value.

10. The method for optimizing the peak load regulation of the steel industry load by using network load game according to claim 1 is characterized in that: The minimum operation constraint condition of equivalent load variance of the upper power grid dispatching center includes: power balance constraint condition of steel industry load equipment connected to the power grid bus; The expression of the electric energy balance constraint condition of the steel industry load equipment connected to the power grid bus is: P b +P d +P c =P m ; Where: P b Power purchased for steel industry load; P d Power of self-provided power plant for steel industry load; P c P is the energy storage discharge power of the steel industry load; m It is the electrical power consumed by the steel industry load.

11. The method for optimizing the peak load regulation of the steel industry load by using network load game according to claim 1 is characterized in that: The network Stackelberg game model expression is: Where: F(*) is the equivalent load variance function; C(*) is the daily production revenue function of the steel industry load; and c p They are the dispatch center incentive price equilibrium solution and non-equilibrium solution respectively; and P DR They are the equilibrium solution and non-equilibrium solution of the steel industry load demand response capacity reporting.

12. The method for optimizing the peak load regulation of the steel industry load by using network load game according to claim 1 is characterized in that: The grid-load game peak load optimization scheduling solution method comprises: S1. Set the population size n pop , maximum number of iterations M, population crossover rate, mutation rate, convergence error Δε, number of iterative calculations m = 0; S2. Initialize the population, and the dispatch center randomly generates incentive electricity prices Transmit parameters to steel industry loads; S3. Update the number of iterative calculations m = m + 1; S4. After the steel industry load receives the incentive electricity price specified by the dispatch center, the general solver tool is used to calculate the demand response capacity under different regulation methods, and the maximum benefit C is obtained by comparing different regulation methods. m , and at the same time report the demand response capacity corresponding to the maximum benefit to the dispatch center; S5. After receiving the demand response capacity reported by the steel industry load, the dispatch center calculates the peak-shaving revenue and retains the current peak-shaving revenue F m and incentive electricity prices; S6. If the reported demand response capacity does not meet the peak load demand of the dispatch center, use the genetic algorithm to select, crossover, and mutate to generate a new incentive electricity price, repeat steps S3 to S5, and calculate the load revenue of the steel industry and peak load benefits S7. If In 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, the game is determined to have reached equilibrium and the iteration ends, otherwise it returns to step S3.

13. The method for optimizing peak load regulation of the steel industry load by network-load game according to any one of claims 1 to 12, characterized in that: The network load game peak load optimization scheduling result information includes: The power of each arc furnace at different times, the demand response capacity that all 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 at different times under the equipment adjustment mode, the total demand response adjustable capacity that the steel industry load can provide at different times under the production line adjustment mode, the total demand response adjustable capacity that the steel industry load can provide at different times under the combined adjustment mode, the daily production income of the lower-level steel industry load, the income obtained from participating in demand response, the change in electricity cost, the change in carbon emission cost, the change in equipment loss cost, the change in production income and the equivalent load variance result information of the steel industry load connected to the distribution network after participating in demand response.

14. The network load game peak load optimization dispatching system of the steel industry load is characterized by: include: Data information collection module, collecting network load game peak load optimization scheduling data information; The module for constructing a load characteristic analysis model for the short-process steel industry is based on the peak load optimization scheduling data information of the network load game to construct a load characteristic analysis model for the short-process steel industry; The module for building a production line electric arc furnace demand response adjustable capacity model is based on the short-process steel industry load characteristic analysis model to build a production line electric arc furnace demand response adjustable capacity model; The production line hot rolling mill demand response adjustable capacity model construction module is based on the short-process steel industry load characteristic analysis model to build the production line hot rolling mill demand response adjustable capacity model; The module for constructing the industrial load demand response adjustable capacity calculation model under multiple types of regulation modes is used to superimpose the production line 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 to form an industrial load demand response adjustable capacity calculation model under multiple types of regulation modes according to different regulation modes; The module for constructing the maximum optimization target model of the daily production revenue of the lower-level steel industry load is based on the adjustable capacity calculation model of the industrial load demand response under the multi-type regulation mode. For the load side, the maximum optimization target model of the daily production revenue of the lower-level steel industry load is constructed; A module for setting the daily production and operation constraints of the lower-level steel industry load is used to set the daily production and operation constraints of the lower-level steel industry load for the maximum optimization target model of the daily production revenue of the lower-level steel industry load; The upper power grid dispatching center equivalent load variance minimum optimization target model construction module is based on the industrial load demand response adjustable capacity calculation model under multiple types of regulation modes. For the grid side, the upper power grid dispatching center equivalent load variance minimum optimization target model is constructed; 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 target model; The grid-host Stackelberg game model construction module builds a grid-host Stackelberg game model based on the maximum optimization target model of the daily production revenue of the lower-level steel industry load and the minimum optimization target model of the equivalent load variance of the upper-level power grid dispatching center, in which the dispatching center is the leader and the steel industry load is the participant; The network-load game peak-shaving optimization scheduling solution module proposes a network-load game peak-shaving optimization scheduling solution method for the network-load Stackelberg game model, and solves the network-load game peak-shaving optimization scheduling; The optimization scheduling result output module outputs the network load game peak-shaving optimization scheduling result information.

15. An electronic device, characterized in that The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for optimizing the peak load regulation of the steel industry load by network-load game is implemented.

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

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