A two-stage scheduling method considering arc furnace regulation and wind power modal decomposition

Abstract

This application discloses a two-stage scheduling method that takes into account arc furnace regulation and wind power modal decomposition. By controlling the arc furnace load as a representative high-energy load, the arc furnace load, thermal power plant, and BESS are jointly scheduled to solve the problems of wind power consumption and carbon emission reduction of thermal power plants. In the day-ahead scheduling, this application maximizes the day-ahead grid-connected ratio of wind power and minimizes the carbon emissions of thermal power plants by scheduling the arc furnace load. In the intraday adjustment, the wind power components of various frequencies are dispatched by the thermal power plant and BESS to reduce the wind abandonment within the day. This application reduces the carbon emissions of thermal power plants, which is of great significance to the development of low-carbon green electricity and the implementation of sustainable development strategies.

Description

Technical Field

[0001] This patent relates to the field of power system dispatching, especially to a two-stage dispatching method of day-ahead and day-intraday considering arc furnace regulation and wind power modal decomposition. Background Art

[0002] In the prior art, with the continuous increase of wind power generation, the system faces significant peak-to-valley differences and rapid fluctuations in wind power, and the problem of overcapacity emerges. Not all generated wind energy is absorbed, resulting in wind abandonment, which significantly affects the revenue flow of wind farms (WF) and endangers the financial feasibility of wind energy projects.

[0003] On the issue of flexibility resources in the power system, existing research mainly focuses on demand response, energy storage systems and thermal power plants. However, as the scale of wind power expands and its impact on the power grid becomes increasingly obvious, relying solely on a single flexibility resource may not be enough to meet the needs of the system. Although many academic studies have focused on the day-ahead dispatch problem, this may lead to inaccurate wind power forecasts and waste of wind power.

[0004] At the same time, the global climate change problem remains serious. A small number of thermal power plants contribute most of the carbon emissions. Therefore, urgent measures are needed to reduce carbon dioxide emissions from thermal power plants.

[0005] For example, the prior art CN 115204702 discloses a day-ahead intraday scheduling method based on dynamic partitioning, comprising the following steps; Step S1: Evaluate the flexibility resources on the power side, load side and grid side of the distribution network, and define the flexibility shortage rate based on dynamic partitioning; Step S2: Construct a day-ahead scheduling model based on dynamic partitioning, formulate the next day's electricity purchase plan according to the day-ahead intermittent distributed power supply power and load demand forecast results, coordinate various controllable devices to achieve the optimal operation of the system, and make the day-ahead scheduling results guide the intraday scheduling; Step S3: Construct an intraday scheduling model based on dynamic partitioning, and make rolling adjustments to the day-ahead scheduling plan based on the actual operation of the distribution network access device and the ultra-short-term forecast results according to the errors in the intermittent distributed power generation and load demand forecasts in the system, so that the intraday scheduling can adjust the day-ahead scheduling and improve the utilization efficiency of the flexibility resources within the partition. However, existing technologies mainly focus on the distribution network level, especially considering the internal structure and related constraints of the distribution network. Its goal is to optimize the stability and economy of the local power grid. The main participants in demand response are the users of the distribution network. The load capacity of these users is relatively small, which is limited to maximizing the absorption of wind power and minimizing the cost of purchasing electricity, and does not involve consideration of CO2 emissions. Summary of the invention

[0006] The purpose of the present invention is to address the problems of the prior art. This application proposes a two-stage scheduling method that takes into account electric arc furnace regulation and wind power modal decomposition. In order to achieve the coordinated optimization of wind power absorption and carbon dioxide emissions from thermal power plants, this application establishes an electric arc furnace model based on the load operation characteristics of the electric arc furnace (EAF), and comprehensively utilizes the strategy of these flexible resources, aiming to achieve optimal wind power absorption and reduce emissions from thermal power plants.

[0007] The specific application plan is as follows:

[0008] A two-stage scheduling method considering arc furnace control and wind power modal decomposition specifically includes the following steps:

[0009] Step 1, establishing a high energy load model of an electric arc furnace;

[0010] Step 2: construct a day-ahead and intraday double-layer scheduling model, which includes a day-ahead upper-layer model and a lower-layer model, wherein the lower-layer model includes a first day-ahead lower-layer model, a second day-ahead lower-layer model, a first day-intraday lower-layer model, and a second day-intraday lower-layer model;

[0011] Step 3: Introduce arc furnace load and thermal power plant to coordinate the dispatch and consumption of wind power, establish the objective function of the day-ahead upper model, optimize the day-ahead wind energy utilization and minimize wind power curtailment through the constraints in the day-ahead upper model;

[0012] Step 4: Establish the first day-ahead lower-layer model, and calculate the day-ahead active power input to the grid by each wind farm according to the proportion of the historical power data input to the grid by each wind farm and the day-ahead total active power actually input to the grid by all wind farms in period t;

[0013] Step 5: The total power P of all thermal power plants on the day before is obtained according to the upper model on the day before. G t d , to minimize the CO 2 Taking the emission as the target, the lower-level model of the day before the second day is established, and the day-ahead active power of each thermal power plant is calculated based on the constraints of the lower-level model of the day before the second day;

[0014] Step 6: Establish the first objective function and the second objective function of the lower-level model within the first day, perform variable modal decomposition on the day-ahead power of each wind farm obtained by the day-ahead scale lower-level model, calculate the high-frequency component of the day-ahead power of each wind farm absorbed by the battery energy storage system based on the constraint conditions, and absorb the low-frequency component of the day-ahead power of all wind farms by compressing the power of all thermal power plants;

[0015] Step 7: Establish the lower-level model for the second day to minimize the daily CO 2 Taking emission as the target, solve the daily power of each thermal power plant.

[0016] Step 1 specifically includes the following steps:

[0017] Based on the operation characteristics of the electric arc furnace, a high-energy load model of the electric arc furnace is established. During the analysis process, the actual operation of the electric arc furnace is considered to ensure accurate description of the load characteristics of the electric arc furnace. This specific process provides a basis and guidance for the subsequent formulation of control strategies;

[0018] Modeling high energy loads for electric arc furnaces:

[0019]

[0020] represents the active power of the kth electric arc furnace (EAF) in period t, and They represent the melting voltage and current of the kth arc furnace in time period t, represents the power factor of the kth arc furnace; It represents the total power of all arc furnace loads in the power system during period t. The total power of all arc furnace loads during period t is the sum of the rated power of all arc furnaces running in the system and the regulated power (adjustable control quantity) of all adjustable arc furnaces; Indicates the basic power of the electric arc furnace during period t (the average operating capacity of a single electric arc furnace during period t); N EAF represents the number of arc furnaces operating in the power system, N EAF adj Indicates the number of adjustable arc furnaces in the power system. The regulated power of the kth electric arc furnace in period t; if ΔP t EAF，k >0, indicating that the kth arc furnace is increasing its load; if ΔP t Mg base <0, indicating that the kth arc furnace is reducing its load. is a state variable indicating whether the kth arc furnace adjusts the active power during period t, where Indicates dynamic power adjustment, Indicates that dynamic power is not adjusted; P EAF.kmin represents the minimum active power of the kth electric arc furnace (EAF), P EAF.kmax Indicates the maximum value of the active power of the kth electric arc furnace (EAF); the value of k ranges from 1 to N EAF +N EAF adj .

[0021] The day-ahead upper model outputs the day-ahead decision on wind power consumption, including the dispatch plan for arc furnace load and the day-ahead total power planning for wind power plants and thermal power plants; the first day-ahead lower model is used to formulate the day-ahead power planning for each wind farm, and the second day-ahead lower model is used to formulate the day-ahead power planning for each thermal power plant; the first intra-day lower model is used to formulate the intra-day power planning for each wind farm and battery energy storage system, and the second day-ahead lower model is used to formulate the intra-day power planning for each thermal power plant.

[0022] Step S3 specifically includes the following steps:

[0023] The day-ahead upper model calculates the total predicted active power output of the wind farm on the bus during period t Normal load during period t Rated power of electric arc furnace (rated load size) As the known input quantity, under the premise of satisfying the constraints of thermal power plants, wind power and load, the day-ahead wind curtailment is minimized, and the day-ahead dispatching strategy of the total power of thermal power plants, the total power of wind power plants and the load regulation power of arc furnaces is obtained.

[0024] The objective function of the day-ahead upper model is:

[0025]

[0026] Among them, T d is the number of time periods in the day-ahead scheduling cycle, which is 96 in this embodiment; is the day-ahead total active power actually input to the grid by all wind power plants in period t; is the sum of the day-ahead active power output of all thermal power plants in period t; is the normal load during period t; N EAF represents the number of electric arc furnaces operating in the power system, represents the active power of the kth electric arc furnace (EAF) during period t, ΔP t EAF，k is the regulated power of the kth arc furnace in period t, minE abon It means that the wind abandonment amount is the smallest in the day-ahead stage.

[0027] The constraints in the day-ahead upper model include the day-ahead power balance constraint, the day-ahead wind power output constraint, and the day-ahead thermal power plant output constraint.

[0028] (301) Day-ahead power balance constraint:

[0029] Formula (6) is used for the day-ahead power balance constraint of the entire power system, that is, in each period of the day-ahead stage, the sum of the power generated by the wind farm and the thermal power plant on the source side is equal to the sum of the power consumed by the arc furnace load and the conventional load on the load side.

[0030]

[0031] (302) The wind power output constraint for the day ahead is:

[0032]

[0033] In formula (7), represents the total predicted active power output of the wind farms on the bus during period t. The day-ahead power balance constraint ensures that the predicted power output of each wind farm does not exceed its respective upper limit and remains above the lower limit. By incorporating the day-ahead power balance constraint into the optimization model, the day-ahead dispatch process takes into account the inherent variability and uncertainty associated with wind power generation.

[0034] (303) The constraints on the day-ahead output of a thermal power plant include the upper and lower limits of the day-ahead power output of the thermal power plant, the ramping constraints of the day-ahead power output of the thermal power plant, and the power adjustment limit constraints on the arc furnace load;

[0035] (3031) The upper and lower limits of the day-ahead power output of a thermal power plant are:

[0036]

[0037] P Gd.min and P Gd.max They represent the lower and upper limits of the day-ahead power output of all thermal power plants. The upper limit constraint prevents the power output from exceeding the maximum capacity of the thermal power plant, thereby avoiding potential operational problems and ensuring system reliability. The lower limit constraint ensures that the thermal power plant generates the minimum power to meet the needs and functional requirements of the power system.

[0038] (3032) The ramp constraint of the day-ahead power output of a thermal power plant is:

[0039]

[0040] P t-1 Gd represents the sum of the active power output of all thermal power plants in the period (t-1) on the previous day, P Gd cli.max and P Gd cli.min denote the maximum and minimum slopes of the day-ahead active power output of all thermal power plants, respectively. By incorporating the slope constraint into the optimization process, the day-ahead dispatch model aims to ensure a smooth transition of the power output of thermal power plants, taking into account their operating capabilities and limitations. The ramping constraint of the day-ahead power output of thermal power plants contributes to the reliable and efficient operation of the power system, allowing for appropriate adjustment of the generation level while maintaining grid stability and complying with technical constraints.

[0041] (3033) The arc furnace load power adjustment limit constraints are:

[0042]

[0043] Among them, ΔP t EAFmax and ΔP t EAFmin They represent the upper and lower limits of the power adjustment of the high-energy load of the arc furnace, respectively. The power adjustment limit constraint of the arc furnace load is incorporated into the optimization framework. The day-ahead dispatch model considers the operation limit of the high-energy load and ensures that its power adjustment is within the feasible range, thereby achieving adequate supply and demand coordination and promoting grid stability and efficient use of energy resources.

[0044] Step 4 specifically includes the following steps:

[0045] The day-ahead active power input to the grid by each wind farm is:

[0046]

[0047] is the day-ahead active power input to the grid by the j-th wind farm in period t, is the historical active power input to the grid by the j-th wind farm in period t, N W represents the number of wind farms; is the total active power actually input to the grid by all wind power plants during period t.

[0048] The total power of all thermal power plants on the day before is calculated according to the upper model on the day before To minimize the CO 2 The lower-layer model of the second day before the day is established to calculate the day-ahead power of each thermal power plant. 2 The total emission is minimized and optimized so that the total active power output of all thermal power plants in the day before the t period is P t Gd The day-ahead power of each thermal power plant is obtained. 2 The greater the emission, the greater the power size of the thermal power plant and CO 2 There is a quadratic relationship between emissions. The objective function of the lower-level model before the first day is:

[0049]

[0050] Among them, N G Indicates the number of units in the thermal power plant group, T d is the number of time periods in the day-ahead scheduling cycle, represents the total carbon dioxide emissions of thermal power plants during the day-ahead dispatch period, represents the binary start / stop variable of thermal power plant i in period t, where U t Gi =0 means that the thermal power plant is in maintenance state during time interval t, U t Gi =1 indicates that the thermal power plant is in operation during period t, represents the day-ahead power generation of thermal power plant i in period t, and the coefficient a i 、b i and c i They respectively represent the quadratic carbon emission coefficient, linear carbon emission coefficient and constant carbon emission coefficient of thermal power plant i.

[0051] The constraints of the second day-ahead lower-level model include the day-ahead power balance constraint of thermal power plants, the day-ahead ramping constraint of a single thermal power plant, and the day-ahead power generation limit of a single thermal power plant;

[0052] The constraints of the second day-ahead lower-layer model include the day-ahead power balance constraints of the thermal power plants, i.e., formula (13), to ensure that the total power generation matches the system demand. The ramp constraint limits the rate of change of the power output of each thermal power plant in a continuous time interval, i.e., formula (14). The day-ahead power of each thermal power plant is subject to upper and lower limits, i.e., formula (15), to maintain the operational feasibility of the power plant. Through the constraints of the second day-ahead lower-layer model, the second day-ahead lower-layer model ensures that the day-ahead power generation plan of the thermal power plant meets the operational requirements while taking into account the carbon emission characteristics of each power plant. This approach contributes to the efficient and environmentally friendly operation of the power system.

[0053] (501) Day-ahead power balance constraints of thermal power plants:

[0054] The day-ahead power balance constraint of thermal power plants to ensure that the total power generation of all thermal power plants matches the system demand:

[0055]

[0056] Where N G Indicates the number of units in the thermal power plant group, It represents the day-ahead power generation of thermal power plant i in period t.

[0057] (502) Day-ahead ramping constraints for a single thermal power plant:

[0058] The ramping constraint limits the rate of change of the day-ahead power generation of each thermal power plant between adjacent time intervals.

[0059]

[0060] Where P t-1 Gd，i represents the day-ahead power generation of thermal power plant i within the time interval (t-1), PGdcli.imax and P Gdcli.imin They represent the maximum slope limit and minimum slope limit of the day-ahead power generation of thermal power plant i respectively.

[0061] (503) Day-ahead power generation limit for a single thermal power plant:

[0062] The day-ahead generation capacity of each thermal power plant is subject to upper and lower limits to ensure operational feasibility:

[0063]

[0064] Where P Gd.imax and P Gd.imin P Gd，imin They represent the upper and lower limits of the day-ahead power generation of thermal power plant i respectively.

[0065] Step 6 specifically includes the following steps:

[0066] The mth subcomponent of the wind power input to the grid by the jth wind farm a day ago is obtained by performing variable modal decomposition on the day-ahead power of each wind farm:

[0067]

[0068] m is the number of subcomponents to be decomposed, and the size of m can be specified manually. t Wd out，j，m Is P t Wd out，j The mth subcomponent of the wind power input to the grid by the jth wind farm a day ago is obtained after variable modal decomposition. e represents the number of iterations, starting from 0; ω is the frequency of the wind power component, ω j.s.m.e+1 is the frequency of the mth subcomponent of the wind power input to the grid by the jth wind farm a day before at the e+1th iteration; It represents the mth subcomponent of the wind power input to the grid at the jth wind farm with a frequency of ω in period t obtained after variable modal decomposition during the eth generation iteration; α is a regularization parameter used to balance the weight between signal reconstruction and modal bandwidth minimization. is the Lagrange multiplier of frequency ω at the e-th iteration, which is used to constrain the optimization problem.

[0069] Formulas (17) and (18) aim to find the component set {P t Wd out，j，m，ω}={P t Wd out，j，1，ω ...,P t Wd out，j，m，ω} and the corresponding center frequency set {ω j，s,m}={ω j，s，1 ,...,ω j，s，m}, so that these component signals can reconstruct the original signal P as well as possible t Wd out，j , while each mode has the narrowest possible bandwidth.

[0070] The day-ahead active power input to the grid by the j-th wind farm obtained by the lower model on the first day before the day in period t is Perform variable mode decomposition to decompose m fixed components and convert the high frequency components (i.e. ) and low-frequency components (i.e. ) are summed up to obtain the power of the battery energy storage system (BESS) connected to each wind farm and the total daily power of the thermal power plant. Finally, the power of the battery energy storage system (BESS) connected to each wind farm is added to the daily power of each wind farm to obtain the day-ahead power of each wind farm.

[0071] The lower-level model within the first day maximizes wind power consumption during the intraday stage by utilizing the regulation capabilities of two flexible resources, BESS and thermal power plants.

[0072] Compared with traditional empirical mode decomposition (EMD), variable mode decomposition has better robustness and anti-noise performance. The input signal can be decomposed into discrete data of sub-band signals. This method first inputs and initializes the parameters, then establishes the Lagrangian function, further iterates and optimizes and checks whether the convergence conditions are met. If so, each sub-component and the center frequency of each sub-component are output. If not, the iteration continues until the convergence conditions are met and the output is output.

[0073] On the first day, the lower model adopts the method of frequency division and absorption, and processes the wind power components in different frequency ranges through the battery energy storage system and the thermal power plant respectively. Specifically, the battery energy storage system is mainly responsible for absorbing the high-frequency wind power components, while the thermal power plant corresponds to the low-frequency wind power components. This allocation needs to be based on a key parameter: the average slope of the decomposed wind power components. The average slope is a measure of the instantaneous change of wind power. The average slope can accurately allocate wind power components of different frequencies to the power system that best suits its characteristics, thereby optimizing the operating efficiency and stability of the entire power system.

[0074] The specific allocation strategy of wind power is as follows:

[0075]

[0076] The average slope of the decomposed wind power component is determined by judging the range of the slope change Δf of the average slope of the decomposed wind power component to determine whether the slope change Δf is absorbed by the thermal power plant or the BESS. low Δf is the low-frequency upper limit of the slope change of the average slope; high is the upper frequency limit of the high-frequency band of the slope change of the average slope; U low Represents the set of low-frequency wind power components; U high Represents the set of high-frequency wind power components. After the variable modal decomposition of wind power is completed, in order to make the high-frequency components of the day-ahead power of each wind farm absorbed by the battery energy storage system as much as possible, the objective function of the lower-level model within the day is first established;

[0077] The first objective function of the lower model on the first day is:

[0078]

[0079] In formula (20), It is the sum of all high-frequency components of the day-ahead power that the j-th wind farm finally outputs to the system during period t. It has been solved according to the variable mode algorithm, that is, all of formula (19) The sum of is a known quantity; is the charging and discharging power of the battery energy storage system (BESS) connected to the j-th wind farm in period t, and is the unknown quantity to be determined. t high，j For period t and The absolute value of the difference;

[0080] The constraints of the first objective function of the lower model on the first day include the intraday power balance constraint of the j-th wind farm, the equation constraint of the conservation of the charge and discharge capacity of the battery energy storage system, the upper and lower limit constraints of the charge and discharge power of the battery energy storage system, and the upper and lower limit constraints of the state of charge of the battery energy storage system:

[0081] (6011) Intraday power balance constraint of the j-th wind farm.

[0082]

[0083] (6012)Equality constraints for conservation of charge and discharge capacity of BESS.

[0084]

[0085] Where: T i is the total number of time periods per T0 hour in the intraday phase, t=1,2…T i . Here Δt is defined as the time of each period within the day.

[0086] (6013)BESS charging and discharging power upper and lower limit constraints:

[0087]

[0088] Where: P BESS jmax is the power upper limit of the BESS connected to the jth wind farm, P BESS jmin is the lower power limit of the BESS connected to the j-th wind farm.

[0089] (6014) Upper and lower limit constraints of BESS state of charge:

[0090]

[0091] Where: is the state of charge of the BESS connected to the jth wind farm in period t, SOC max is the upper limit of the state of charge of BESS, SOC min is the lower limit of the state of charge of BESS. According to the relationship between the state of charge of BESS and the power of BESS, the change of power during the charging and discharging process of BESS is:

[0092]

[0093] in, is the state of charge of the energy storage device connected to the jth wind farm during the period (t+1), η c and η d are the charging efficiency and discharging efficiency of BESS respectively, and Δt is the charging and discharging time interval. BESS.j is the capacity of the BESS connected to the j-th wind farm, is the charging and discharging power of the battery energy storage system connected to the j-th wind farm during period t.

[0094] In order to absorb the low-frequency components of the day-ahead power of all wind farms by compressing the power of all thermal power plants, the second objective function of the lower model within the first day is established:

[0095]

[0096] in, It is the sum of all low-frequency components of the day-ahead power actually output to the system by all wind farms during period t, which has been solved by the variable mode algorithm and is a known quantity; It is the regulation quantity of the daily power of all thermal power plants in period t, and is the unknown quantity to be determined. t low is the absolute value of the difference between the two in period t; the sum of the daily power regulation of all thermal power plants ΔPt Gintr The total active power of all thermal power plants in the day before t The subtraction is the total daily power of all thermal power plants P t Gintr , calculate the total daily power P of all thermal power plants t Gintr ;

[0097]

[0098] Total daily power of all thermal power plants The constraints include the upper and lower limits of the thermal power output power during the day and the ramp speed constraints of the thermal power power during the day:

[0099] (6021) The upper and lower limits of thermal power output power on the day before:

[0100]

[0101] (6022) Ramp-up speed constraints of thermal power generation during the day:

[0102]

[0103] Among them, P Gintr cli，max and P Gintr cli，min They are respectively the maximum and minimum values ​​of the ramp rate of the total power of all thermal power plants during the day.

[0104] Step 7 specifically includes the following steps: taking all the thermal power plants’ daily CO 2 The minimum emission is taken as the objective function to reasonably allocate the total daily power of all thermal power plants and obtain the daily power of each thermal power plant.

[0105] The daily CO 2 The minimum emission is the objective function, which realizes the total daily power of all thermal power plants solved by the lower model on the first day. (That is, all the The sum of the two) is reasonably distributed to obtain the daily power of each thermal power plant.

[0106] The objective function of the lower model on the second day is:

[0107]

[0108] Where: CO in one cycle of all thermal power plants in a day 2 Total emissions; U t Gintr，iis the start / stop state variable of the ith thermal power plant in period t, U t Gintr，i =0 means that the ith thermal power plant is under maintenance during period t. t Gintr，i =1 means that the i-th thermal power plant is in operation during the t period of the day;

[0109] The constraints of the objective function of the lower model on the second day include the equality constraint of the power of the thermal power plant on a daily basis and the ramp limit of each thermal power plant on a daily basis:

[0110] (701)Equality constraints for the daily power of a thermal power plant:

[0111]

[0112] (702) Daily ramp limit for each thermal power plant:

[0113]

[0114] Where ΔP t Gintr，i represents the daily regulation output of the ith thermal power plant within the time interval t-1, P t Gintrcli，imax and P t Gintrcli，imin It is the symbol used to indicate the maximum and minimum slope limits of the daily power output of thermal power plant i.

[0115] (703) The upper and lower limits of each thermal power plant’s daily power generation are:

[0116]

[0117] P t Gintr，imax and P t Gintr，imin They represent the maximum and minimum values ​​of the daily power output of thermal power plant i within the time interval t respectively.

[0118] Compared with the prior art, this application has the following beneficial effects:

[0119] This application discloses a two-stage scheduling method that considers arc furnace regulation and wind power modal decomposition. By controlling the arc furnace load as a representative high-energy load, the arc furnace load, thermal power plant, and BESS are jointly scheduled to solve the problems of wind power consumption and carbon emission reduction of thermal power plants. In the day-ahead scheduling, this application maximizes the day-ahead grid-connected ratio of wind power and minimizes the carbon emissions of thermal power plants by scheduling the arc furnace load. In the intraday adjustment, by scheduling thermal power plants and BESS to consume wind power components of various frequencies, the wind curtailment within the day is reduced, and the carbon emissions of thermal power plants are reduced;

[0120] In order to achieve the coordinated optimization of wind power absorption and CO2 emissions from thermal power plants, this application establishes a model of electric arc furnace (EAF) based on the load operation characteristics of the EAF, and comprehensively utilizes the strategy of these flexibility resources, aiming to achieve optimal wind power absorption and reduce emissions from thermal power plants;

[0121] This application focuses on the overall power system, with an emphasis on the stability of large-scale power grids;

[0122] The main load in this application is the electric arc furnace, and the high-energy load consumes huge amounts of electricity. Therefore, it is more effective in reducing wind curtailment.

[0123] This application is based on the perspective of reducing CO2 emissions from thermal power plants, which is more conducive to achieving carbon peak and carbon neutrality goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0124] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0125] Figure 1 It is a flow chart of the solution of the present invention for the two-stage scheduling of day-ahead and intraday considering the control of electric arc furnace and the modal decomposition of wind power;

[0126] Figure 2 It is a schematic diagram of a power system rich in various flexibility resources to absorb a large amount of wind power in the present invention;

[0127] Figure 3 It is a schematic diagram of the time frame of dual time scale scheduling in the present invention;

[0128] Figure 4 It is the conventional load and wind power forecast power curve in the verification example of the present invention and the arc furnace load power curve obtained by solving the upper model a few days ago;

[0129] Figure 5 is a schematic diagram comparing the day-ahead power and wind abandonment of wind power before and after the EAF high energy load is involved in an embodiment of the present invention;

[0130] Figure 6 It is a comparison of the total CO2 emissions of all thermal power plants under different optimization schemes in the verification example of the present invention. DETAILED DESCRIPTION

[0131] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0132] The core idea of ​​the present invention is to design a suitable algorithm for image preprocessing and enhancement according to the characteristics of the fundus image itself, and then perform retinal vascular segmentation based on the information migration fundus image segmentation network, and finally use a neural network combined with ordered classification for intelligent analysis and prediction to achieve the purpose of intelligent analysis of fundus images.

[0133] A two-stage scheduling method considering arc furnace control and wind power modal decomposition. The solution flow chart is as follows: Figure 1 As shown, the specific steps include:

[0134] Step 1, establishing a high energy load model of an electric arc furnace;

[0135] Step 2, construct a day-ahead-intraday double-layer scheduling model, which includes a day-ahead upper model and a lower model, wherein the lower model includes a first day-ahead lower model, a second day-ahead lower model, a first day-intraday lower model, and a second day-intraday lower model;

[0136] Step 3: Introduce arc furnace load and thermal power plant to coordinate the dispatch and consumption of wind power, establish the objective function of the day-ahead upper model, optimize the day-ahead wind energy utilization and minimize wind power curtailment through the constraints in the day-ahead upper model;

[0137] Step 4: Establish the first day-ahead lower-layer model, and calculate the day-ahead active power input to the grid by each wind farm according to the proportion of the historical power data input to the grid by each wind farm and the day-ahead total active power actually input to the grid by all wind farms in period t;

[0138] Step 5: The total power P of all thermal power plants on the day before is obtained according to the upper model on the day before. G t d , to minimize the CO 2 Taking the emission as the target, the lower-level model of the day before the second day is established, and the day-ahead active power of each thermal power plant is calculated based on the constraints of the lower-level model of the day before the second day;

[0139] Step 6: Establish the first objective function and the second objective function of the lower-level model within the first day, perform variable modal decomposition on the day-ahead power of each wind farm obtained by the day-ahead scale lower-level model, calculate the high-frequency component of the day-ahead power of each wind farm absorbed by the battery energy storage system based on the constraint conditions, and absorb the low-frequency component of the day-ahead power of all wind farms by compressing the power of all thermal power plants;

[0140] Step 7: Establish the lower-level model for the second day to minimize the daily CO 2 Taking emission as the target, solve the daily power of each thermal power plant.

[0141] Step 1 specifically includes the following steps: Establish a high-energy load model for the electric arc furnace based on the operating characteristics of the electric arc furnace. During the analysis process, the actual operating conditions of the electric arc furnace are considered to ensure that the load characteristics of the electric arc furnace are accurately described. This specific process provides a basis and guidance for the subsequent formulation of control strategies;

[0142] Modeling high energy loads for electric arc furnaces:

[0143]

[0144]

[0145] represents the active power of the kth electric arc furnace (EAF) in period t, and They represent the melting voltage and current of the kth arc furnace in time period t, represents the power factor of the kth arc furnace; It represents the total power of all arc furnace loads in the power system during period t. The total power of all arc furnace loads during period t is the sum of the rated power of all arc furnaces running in the system and the regulated power (adjustable control quantity) of all adjustable arc furnaces; Indicates the basic power of the electric arc furnace during period t (the average operating capacity of a single electric arc furnace during period t); N EAF represents the number of arc furnaces operating in the power system, N EAF adj Indicates the number of adjustable arc furnaces in the power system. The regulated power of the kth electric arc furnace in period t; if ΔP t EAF，k >0, indicating that the kth arc furnace is increasing its load; if ΔP t Mg base <0, indicating that the kth arc furnace is reducing its load. is a state variable indicating whether the kth arc furnace adjusts the active power during period t, where Indicates dynamic power adjustment, Indicates that dynamic power is not adjusted; P EAF.kmin represents the minimum active power of the kth electric arc furnace (EAF), P EAF.kmax Indicates the maximum value of the active power of the kth electric arc furnace (EAF); the value of k ranges from 1 to N EAF +N EAF adj .

[0146] This embodiment proposes a power system rich in multiple flexibility resources, such as Figure 2, which aims to achieve the maximum absorption of wind power by jointly dispatching arc furnace high-energy loads, battery energy storage systems (BESS), and thermal power plants. The day-ahead dispatch plan of each thermal power plant and wind farm is determined by allocating the day-ahead total power of thermal power and wind power. The entire dispatch process involves a two-scale two-layer model, which involves two scales, day-ahead and intraday. Due to the interdependence between the upper and lower models, iterative calculations are required to obtain the best solution. In order to manage these complexities, a two-layer optimization model at two time scales is used to solve the problem. The two-layer optimization model effectively solves the challenge of optimizing load coordination in the context of large-scale wind power grid connection and thermal power generation emission reduction.

[0147] Establish a day-ahead and day-intraday dual-layer dispatch model for absorbing wind power from multiple flexible resources to optimize the day-ahead wind energy utilization and minimize the day-ahead wind power abandonment;

[0148] A day-ahead-intraday double-layer dispatching model is established. The day-ahead-intraday double-layer dispatching model for the day-ahead-intraday double-layer dispatching model for the day-ahead-intraday double-layer dispatching model for the wind power consumption of multiple flexible resources is a double-layer hierarchical structure. The double-layer model includes a day-ahead upper model and a lower model. The lower model is the first day-ahead lower model, the second day-ahead lower model, the first day-intraday lower model, and the second day-intraday lower model. The day-ahead upper model outputs the decision of the day-ahead wind power consumption, including the dispatching plan of the arc furnace load and the day-ahead total power planning of the wind power plant and the thermal power plant. The first day-ahead lower model is used to formulate the day-ahead power planning of each wind farm, and the second day-ahead lower model is used to formulate the day-ahead power planning of each thermal power plant. The first day-intraday lower model is used to formulate the day-ahead power planning of each wind farm and battery energy storage system, and the second day-intraday lower model is used to formulate the day-ahead power planning of each thermal power plant. This double-layer structure enables the model to handle complex multi-objective optimization tasks and provide more balanced and efficient power system operations.

[0149] Introduce arc furnace load and thermal power plant to coordinate the dispatch of wind power, establish a day-ahead upper-layer model to optimize the day-ahead wind energy utilization to minimize wind power curtailment; the day-ahead-intraday double-layer dispatch model established in this study for the absorption of wind power by multiple flexible resources is a complex power system optimization model aimed at reducing wind power curtailment and reducing carbon emissions from thermal power. The day-ahead-intraday double-layer dispatch model operates on two time scales, including the day-ahead scale and the intraday scale. The time frame of the dual-time scale dispatch is as follows: Figure 3 shown.

[0150] like Figure 3As shown in the figure, the day-ahead scheduling is carried out one day before the actual operation (i.e., the cycle is 24 hours), and the time interval is 15 minutes. The day-ahead scheduling involves the prediction of the future state of the system one day in advance, including the day-ahead predicted total power of the thermal power plant, the day-ahead predicted total power of the wind power plant, the arc furnace operating load, the day-ahead predicted power of each wind power plant, and the day-ahead predicted power of each thermal power plant. The intraday scale involves shorter-cycle operational decisions. The intraday-scale scheduling strategy is based on the results of the day-ahead scheduling strategy. Scheduling is carried out every four hours on the actual operation day to formulate the operation strategy for the next four hours. The time interval of the intraday-scale scheduling strategy is also 15 minutes. The intraday-scale scheduling strategy includes the intraday charging and discharging status of each wind farm connected to the BESS, the intraday predicted power of each wind power plant, and the intraday predicted power of each thermal power plant.

[0151] Step S3 specifically includes the following steps:

[0152] The day-ahead upper model calculates the total predicted active power output of the wind farm on the bus during period t Normal load during period t Rated power of electric arc furnace (rated load size) As a known input, the day-ahead wind curtailment is minimized under the premise of satisfying the constraints of thermal power plants, wind power and load. The day-ahead dispatch strategy of the total power of thermal power plants, total power of wind power plants and load regulation power of arc furnaces is obtained.

[0153] The objective function of the day-ahead upper model is:

[0154]

[0155] Among them, T d is the number of time periods in the day-ahead scheduling cycle, which is 96 in this embodiment; is the day-ahead total active power actually input to the grid by all wind power plants in period t; is the sum of the day-ahead active power output of all thermal power plants in period t; is the normal load during period t; N EAF represents the number of electric arc furnaces operating in the power system, represents the active power of the kth electric arc furnace (EAF) during period t, ΔP t EAF，k is the regulated power of the kth arc furnace in period t, minE abon It means that the wind abandonment amount is the smallest in the day-ahead stage.

[0156] The constraints in the day-ahead upper model include the day-ahead power balance constraint, the day-ahead wind power output constraint, and the day-ahead thermal power plant output constraint.

[0157] (301) Day-ahead power balance constraint:

[0158] Formula (6) is used for the day-ahead power balance constraint of the entire power system, that is, in each period of the day-ahead stage, the sum of the power generated by the wind farm and the thermal power plant on the source side is equal to the sum of the power consumed by the arc furnace load and the conventional load on the load side.

[0159]

[0160] (302) The wind power output constraint for the day ahead is:

[0161]

[0162] In formula (7), represents the total predicted active power output of the wind farms on the bus during period t. The day-ahead power balance constraint ensures that the predicted power output of each wind farm does not exceed its respective upper limit and remains above the lower limit. By incorporating the day-ahead power balance constraint into the optimization model, the day-ahead dispatch process takes into account the inherent variability and uncertainty associated with wind power generation.

[0163] (303) The constraints on the day-ahead output of a thermal power plant include the upper and lower limits of the day-ahead power output of the thermal power plant, the ramping constraints of the day-ahead power output of the thermal power plant, and the power adjustment limit constraints on the arc furnace load;

[0164] In order to ensure the reliable and efficient operation of the power system, the day-ahead power output of the thermal power plant is subject to constraints. The day-ahead output constraints of the thermal power plant include equations (8)-(10):

[0165] (3031) The upper and lower limits of the day-ahead power output of a thermal power plant are:

[0166]

[0167] Formula (8) ensures that the day-ahead power output of the thermal power plant remains within a specific range. Gd.min and P Gd.max They represent the lower and upper limits of the day-ahead power output of all thermal power plants. The upper limit constraint prevents the power output from exceeding the maximum capacity of the thermal power plant, thereby avoiding potential operational problems and ensuring system reliability. The lower limit constraint ensures that the thermal power plant generates the minimum power to meet the needs and functional requirements of the power system.

[0168] (3032) The ramp constraint of the day-ahead power output of a thermal power plant is:

[0169]

[0170] P t-1 Gd represents the sum of the active power output of all thermal power plants in the period (t-1) on the previous day, PGd cli.max and P Gd cli.min denote the maximum and minimum slopes of the day-ahead active power output of all thermal power plants, respectively. By incorporating the slope constraint into the optimization process, the day-ahead dispatch model aims to ensure a smooth transition of the power output of thermal power plants, taking into account their operating capabilities and limitations. The ramping constraint of the day-ahead power output of thermal power plants contributes to the reliable and efficient operation of the power system, allowing for appropriate adjustment of the generation level while maintaining grid stability and complying with technical constraints.

[0171] (3033) The arc furnace load power adjustment limit constraints are:

[0172]

[0173] In formula (10), ΔP t EAFmax and ΔP t EAFmin They represent the upper and lower limits of the power adjustment of the high-energy load of the arc furnace, respectively. The power adjustment limit constraint of the arc furnace load is incorporated into the optimization framework. The day-ahead dispatch model considers the operation limit of the high-energy load and ensures that its power adjustment is within the feasible range, thereby achieving adequate supply and demand coordination and promoting grid stability and efficient use of energy resources.

[0174] Step 4 specifically includes the following steps:

[0175] The day-ahead active power input to the grid by each wind farm is:

[0176]

[0177] is the day-ahead active power input to the grid by the j-th wind farm in period t, is the historical active power input to the grid by the j-th wind farm in period t, N W represents the number of wind farms; is the total active power actually input to the grid by all wind power plants during period t.

[0178] This step is based on the proportion of historical power data of each wind farm input to the grid. The total active power actually input to the grid by all wind power plants during period t Obtain the day-ahead active power input to the grid by each wind power plant to ensure that the power of each wind power plant in the day-ahead stage meets the distribution characteristics of its historical data;

[0179] Step 5 specifically includes the following steps: 1. The total power of all thermal power plants on the day before is calculated according to the upper model on the day before. To minimize the CO2 The lower-layer model of the second day before the day is established to calculate the day-ahead power of each thermal power plant. 2 The total emission is minimized and optimized so that the total active power output of all thermal power plants in the day before the t period is P t Gd The day-ahead power of each thermal power plant is obtained. 2 The greater the emission, the greater the power size of the thermal power plant and CO 2 There is a quadratic relationship between emissions. The objective function of the lower-level model before the first day is:

[0180]

[0181] Among them, N G Indicates the number of units in the thermal power plant group, T d is the number of time periods in the day-ahead scheduling cycle, represents the total carbon dioxide emissions of thermal power plants during the day-ahead dispatch period, represents the binary start / stop variable of thermal power plant i in period t, where U t Gi =0 means that the thermal power plant is in maintenance state during time interval t, U t Gi =1 indicates that the thermal power plant is in operation during period t, represents the day-ahead power generation of thermal power plant i in period t, and the coefficient a i 、b i and c i They respectively represent the quadratic carbon emission coefficient, linear carbon emission coefficient and constant carbon emission coefficient of thermal power plant i.

[0182] The constraints of the second day-ahead lower-level model include the day-ahead power balance constraint of thermal power plants, the day-ahead ramping constraint of a single thermal power plant, and the day-ahead power generation limit of a single thermal power plant;

[0183] The constraints of the second day-ahead lower-layer model include the day-ahead power balance constraints of the thermal power plants, i.e., formula (13), to ensure that the total power generation matches the system demand. The ramp constraint limits the rate of change of the power output of each thermal power plant in a continuous time interval, i.e., formula (14). The day-ahead power of each thermal power plant is subject to upper and lower limits, i.e., formula (15), to maintain the operational feasibility of the power plant. Through the constraints of the second day-ahead lower-layer model, the second day-ahead lower-layer model ensures that the day-ahead power generation plan of the thermal power plant meets the operational requirements while taking into account the carbon emission characteristics of each power plant. This approach contributes to the efficient and environmentally friendly operation of the power system.

[0184] (501) Day-ahead power balance constraints of thermal power plants:

[0185] The day-ahead power balance constraint of thermal power plants to ensure that the total power generation of all thermal power plants matches the system demand:

[0186]

[0187] Where N G Indicates the number of units in the thermal power plant group, It represents the day-ahead power generation of thermal power plant i in period t.

[0188] (502) Day-ahead ramping constraints for a single thermal power plant:

[0189] The ramping constraint limits the rate of change of the day-ahead power generation of each thermal power plant between adjacent time intervals.

[0190]

[0191] Where P t-1 Gd，i represents the day-ahead power generation of thermal power plant i within the time interval (t-1), P Gdcli.imax and P Gdcli.imin They represent the maximum slope limit and minimum slope limit of the day-ahead power generation of thermal power plant i respectively.

[0192] (503) Day-ahead power generation limit for a single thermal power plant:

[0193] The day-ahead generation capacity of each thermal power plant is subject to upper and lower limits to ensure operational feasibility:

[0194]

[0195] Where P Gd.imax and P Gd.imin P Gd，imin They represent the upper and lower limits of the day-ahead power generation of thermal power plant i respectively.

[0196] Step 6 specifically includes the following steps:

[0197] The mth subcomponent of the wind power input to the grid by the jth wind farm a day ago is obtained by performing variable modal decomposition on the day-ahead power of each wind farm:

[0198] In formulas (17)-(18), m is the number of subcomponents to be decomposed, and the size of m can be specified artificially. t Wd out，j，m Is P t Wd out，jThe mth subcomponent of the wind power input to the grid by the jth wind farm a day ago is obtained after variable modal decomposition. e represents the number of iterations, starting from 0; ω is the frequency of the wind power component, ω j.s.m.e+1 is the frequency of the mth subcomponent of the wind power input to the grid by the jth wind farm a day before at the e+1th iteration; It represents the mth subcomponent of the wind power input to the grid at the jth wind farm with a frequency of ω in period t obtained after variable modal decomposition during the eth generation iteration; α is a regularization parameter used to balance the weight between signal reconstruction and modal bandwidth minimization. is the Lagrange multiplier of frequency ω at the e-th iteration, which is used to constrain the optimization problem.

[0199] Formulas (17) and (18) aim to find the component set {P t Wd out，j，m，ω}={P t Wd out，j，1，ω ...,P t Wd out，j，m，ω} and the corresponding center frequency set {ω j，s,m}={ω j，s，1 ,...,ω j，s，m}, so that these component signals can reconstruct the original signal P as well as possible t Wd out，j , while each mode has the narrowest possible bandwidth.

[0200] The day-ahead active power input to the grid by the j-th wind farm obtained by the lower model on the first day before the day in period t is Perform variable mode decomposition to decompose m fixed components and convert the high frequency components (i.e. ) and low-frequency components (i.e. ) are summed up to obtain the power of the battery energy storage system (BESS) connected to each wind farm and the total daily power of the thermal power plant. Finally, the power of the battery energy storage system (BESS) connected to each wind farm is added to the daily power of each wind farm to obtain the day-ahead power of each wind farm.

[0201] The lower-level model within the first day maximizes wind power consumption during the intraday stage by utilizing the regulation capabilities of two flexible resources, BESS and thermal power plants.

[0202] Compared with traditional empirical mode decomposition (EMD), variable mode decomposition has better robustness and anti-noise performance. The input signal can be decomposed into discrete data of sub-band signals. This method first inputs and initializes the parameters, then establishes the Lagrangian function, further iterates and optimizes and checks whether the convergence conditions are met. If so, each sub-component and the center frequency of each sub-component are output. If not, the iteration continues until the convergence conditions are met and the output is output.

[0203] On the first day, the lower model adopts the method of frequency division and absorption, and processes the wind power components in different frequency ranges through the battery energy storage system and the thermal power plant respectively. Specifically, the battery energy storage system is mainly responsible for absorbing the high-frequency wind power components, while the thermal power plant corresponds to the low-frequency wind power components. This allocation needs to be based on a key parameter: the average slope of the decomposed wind power components. The average slope is a measure of the instantaneous change of wind power. The average slope can accurately allocate wind power components of different frequencies to the power system that best suits its characteristics, thereby optimizing the operating efficiency and stability of the entire power system.

[0204] The specific allocation strategy of wind power is as follows:

[0205]

[0206] The average slope of the decomposed wind power component is determined by judging the range of the slope change Δf of the average slope of the decomposed wind power component to determine whether the slope change Δf is absorbed by the thermal power plant or the BESS. low Δf is the low-frequency upper limit of the slope change of the average slope; high is the upper frequency limit of the high-frequency band of the slope change of the average slope; U low Represents the set of low-frequency wind power components; U high Represents the set of high-frequency wind power components. After the variable modal decomposition of wind power is completed, in order to make the high-frequency components of the day-ahead power of each wind farm absorbed by the battery energy storage system as much as possible, the objective function of the lower-level model within the day is first established;

[0207] The first objective function of the lower model on the first day is:

[0208]

[0209] In formula (20), It is the sum of all high-frequency components of the day-ahead power that the j-th wind farm finally outputs to the system during period t. It has been solved according to the variable mode algorithm, that is, all of formula (19) The sum of is a known quantity; is the charging and discharging power of the battery energy storage system (BESS) connected to the j-th wind farm in period t, and is the unknown quantity to be determined. t high，j For period t and The absolute value of the difference;

[0210] The constraints of the first objective function of the lower model on the first day include the intraday power balance constraint of the j-th wind farm, the equation constraint of the conservation of the charge and discharge capacity of the battery energy storage system, the upper and lower limit constraints of the charge and discharge power of the battery energy storage system, and the upper and lower limit constraints of the state of charge of the battery energy storage system:

[0211] (6011) Intraday power balance constraint of the j-th wind farm.

[0212]

[0213] (6012)Equality constraints for conservation of charge and discharge capacity of BESS.

[0214]

[0215] Where: T i The total number of time periods in each four-hour period during the day, set the value to 16, t=1,2…T i . Here, Δt is defined as the time of each period within the day, which is also 15 minutes.

[0216] (6013)BESS charging and discharging power upper and lower limit constraints:

[0217]

[0218] Where: P BESS jmax is the power upper limit of the BESS connected to the jth wind farm, P BESS jmin is the lower power limit of the BESS connected to the j-th wind farm.

[0219] (6014) Upper and lower limit constraints of BESS state of charge:

[0220]

[0221] Where: is the state of charge of the BESS connected to the jth wind farm in period t, SOC max is the upper limit of the state of charge of BESS, SOC min is the lower limit of the state of charge of BESS. According to the relationship between the state of charge of BESS and the power of BESS, the change of power during the charging and discharging process of BESS is:

[0222]

[0223] Among them, SOC t+1 j is the state of charge of the energy storage device connected to the jth wind farm during the period (t+1), η c and η d are the charging efficiency and discharging efficiency of BESS respectively, and Δt is the charging and discharging time interval. In this embodiment, Δt is 15min, and the value is 0.25h. BESS.j is the capacity of the BESS connected to the jth wind farm.

[0224] In order to absorb the low-frequency components of the day-ahead power of all wind farms by compressing the power of all thermal power plants, the second objective function of the lower model within the first day is established:

[0225]

[0226] in, It is the sum of all low-frequency components of the day-ahead power actually output to the system by all wind farms during period t, which has been solved by the variable mode algorithm and is a known quantity; It is the regulation quantity of the daily power of all thermal power plants in period t, and is the unknown quantity to be determined. t low is the absolute value of the difference between the two in period t; the sum of the daily power regulation of all thermal power plants ΔP t Gintr The total active power of all thermal power plants in the day before t The subtraction is the total daily power of all thermal power plants P t Gintr , calculate the total daily power P of all thermal power plants t Gintr ;

[0227]

[0228] Total daily power of all thermal power plants The constraints include the upper and lower limits of the thermal power output power during the day and the ramp speed constraints of the thermal power power during the day:

[0229] (6021) The upper and lower limits of thermal power output power on the day before:

[0230]

[0231] (6022) Ramp-up speed constraints of thermal power generation during the day:

[0232]

[0233] Among them, P Gintrcli，max and P Gintr cli，min They are respectively the maximum and minimum values ​​of the ramp rate of the total power of all thermal power plants during the day.

[0234] Step 7 specifically includes the following steps: taking all the thermal power plants’ daily CO 2 The minimum emission is taken as the objective function to reasonably allocate the total daily power of all thermal power plants and obtain the daily power of each thermal power plant.

[0235] The daily CO 2 The minimum emission is the objective function, which realizes the total daily power of all thermal power plants solved by the lower model on the first day. (That is, all the The sum of the two) is reasonably distributed to obtain the daily power of each thermal power plant.

[0236] The objective function of the lower model on the second day is:

[0237]

[0238] Where: CO in one cycle of all thermal power plants in a day 2 Total emissions; U t Gintr，i is the start / stop state variable of the ith thermal power plant in period t, U t Gintr，i =0 means that the ith thermal power plant is under maintenance during period t. t Gintr，i =1 means that the i-th thermal power plant is in operation during the t period of the day;

[0239] The constraints of the objective function of the lower model on the second day include the equality constraint of the power of the thermal power plant on a daily basis and the ramp limit of each thermal power plant on a daily basis:

[0240] (701)Equality constraints for the daily power of a thermal power plant:

[0241]

[0242] (702) Daily ramp limit for each thermal power plant:

[0243]

[0244] Where ΔP t Gintr，i represents the daily regulation output of the ith thermal power plant i within the time interval t-1, P t Gintrcli，imax and P tGintrcli，imin It is the symbol used to indicate the maximum and minimum slope limits of the daily power output of thermal power plant i.

[0245] (703) The upper and lower limits of each thermal power plant’s daily power generation are:

[0246]

[0247] P t Gintr，imax and P t Gintr，imin They represent the maximum and minimum values ​​of the daily power output of thermal power plant i within the time interval t respectively.

[0248] This embodiment is a specific low-carbon power system, which includes four thermal power plants, four wind power plants and four BESS. This embodiment is based on the actual data of the power system where the steel smelter is located. The total capacity of all wind farms is 800MW, the total capacity of conventional load is 1000MW, the minimum value PGd,min of the sum of the total power of all thermal power plants the day before is 150MW; the maximum value PGd,max of the sum of the total power of all thermal power plants the day before is 1500MW; the maximum value PGd cli,max and the minimum value PGd cli,min of the total power ramp power of all thermal power plants the day before are 500MW and 0MW respectively.

[0249] Figure 4 The known wind power forecast curve, the known conventional load curve, and the dispatch curve of the arc furnace load developed according to the upper-level strategy on the day-ahead scale in the example are shown. Figure 4 As shown in the figure, the total power of the wind power plant has obvious fluctuation characteristics, and the power range varies within the range of 686 to 996MW. The trough of the total wind power occurs in the 20-55 period and the 66-91 period, while the conventional load power forecast curve is at its peak during this period; at the same time, in the 1-19 period, 55-65 period and 91-96 period when the conventional load power is at a low point, the wind power is at its peak. It can be seen that the wind power forecast curve and the conventional load curve usually show a certain anti-peak regulation, resulting in a mismatch between the supply and demand of the power system. Since the minimum power of the total power of the thermal power plant is 600MW, the minimum total power of thermal power is relatively large. According to the balance of supply and demand of the system, when the wind power is high, there may be an oversupply of power, which may lead to the need to take additional measures (such as increasing production capacity of high-load enterprises and reducing power of thermal power plants) to deal with excess power, such as Figure 4 During the periods 1 to 19, 55 to 65, and 91 to 96, the arc furnace load is at maximum capacity. When the wind power is low, it may not be able to meet the system load demand, and it is necessary to rely on conventional power generation sources (i.e. thermal power plants increase power) to make up for the gap or reduce demand (such as high-load enterprises reducing production capacity, such as Figure 4 The above parameters are substituted into the upper-level strategy of the day-ahead scale, and the objective function is to minimize the amount of wind power abandoned. The CPLEX solver is used to adopt the genetic algorithm (the maximum number of iterations of the genetic algorithm is set to 100 times, the population size is set to 500, the mutation probability is set to 0.8, and the crossover probability is set to 0.3) to solve the dispatch curve of the arc furnace load (see Figure 4 ), the day-ahead total power curve of all thermal power plants after adjustment by electric arc furnaces, and the day-ahead total power curve of all wind power plants after adjustment by electric arc furnaces.

[0250] In order to verify the effectiveness of introducing EAF high energy load into the day-ahead upper model established in this study to achieve wind power consumption, a comparative analysis is conducted by setting the following two day-ahead schemes.

[0251] (1) Scheme 1: Only thermal power plants are used for the day-ahead upper model and day-ahead lower model 1, and the arc furnace load is not adjusted up or down.

[0252] (2) Scheme 2: Consider both the upper-level strategy and the lower-level model for the day-ahead regulation of high-energy loads of thermal power plants and arc furnaces. Other conditions are consistent with the strategy proposed in Scheme 1.

[0253] The wind power plant operation and wind power consumption conditions obtained by the two day-ahead optimization schemes are shown in the figure below: Figure 5 shown.

[0254] exist Figure 5 In the figure, the yellow boxes correspond to the time periods when wind power is abandoned during EAF regulation, namely, time periods 1 to 19, 55 to 65, and 91 to 96. Figure 4 It can be seen that during these periods, wind power is at its peak. Even if the electric arc furnace is adjusted upward as much as possible, there is still a small amount of wind abandonment. The area of ​​the pink shaded area (Region Ⅰ) represents the amount of wind abandoned the day before when the EAF load is not adjusted up or down, and the area of ​​the green area (Region Ⅱ) represents the amount of wind abandoned the day before when the EAF load is adjusted. The abandoned wind volume of the two schemes is made into a bar chart for easy comparison. The pink and green bar charts are the abandoned wind volume of Scheme Ⅰ and Scheme 2, respectively. Figure 5It can be seen that when Scheme 1 is adopted, the predicted power of wind power is restricted in 41 of the 96 time periods in one day, the maximum blocked power in a single period is 181.69MW, and the total wind power abandoned is 755.15MW·h; when Scheme 2 is adopted, the predicted power of wind power is restricted in 23 time periods, the maximum blocked power in a single period is 145.49MW, and the total restricted power is 449.42MW·h. Scheme 2 reduces the amount of wind abandoned by 40.49% compared with Scheme 1, achieving a good effect of reducing wind abandoned. This is because Scheme 1 does not consider the regulation capacity of EAF high energy load, and only relies on the very limited regulation capacity of thermal power plants to regulate the fluctuation of wind power on the day before. However, due to the limited regulation capacity of thermal power plants, the system abandons wind seriously. When Scheme 2 is adopted, it not only relies on the regulation capacity of thermal power plants on the day before, but also considers the regulation capacity of EAF high energy load to absorb wind power. Therefore, on a day-ahead scale, after adopting EAF high-energy load control, the wind power consumption level has been significantly improved, effectively reducing the amount of wind abandonment.

[0255] In addition, in this embodiment, the specific parameters of the four thermal power plants are selected from the actual four thermal power plants in the city where a smelting enterprise in China is located. The specific parameters of each thermal power plant are shown in Table 1 and Table 2. In addition, a continuous flue gas emission monitoring system (CEMS) device is selected to measure the CO2 flow rate and concentration of each thermal power plant every 15 minutes, and the CO2 emissions of each thermal power plant are calculated according to formula (37). According to the calculation formula of CEMS monitoring carbon dioxide, CO2 emissions are equal to the product of each flue gas flow rate and CO2 concentration and then integrated over the period of time.

[0256]

[0257] E t CO2，i is the CO of thermal power plant i at time t 2 Emissions, in tons (t); C CO2 is the concentration of carbon dioxide emitted by thermal power plants (t / m 3 );F CO2 It is the flue gas from thermal power plants (including CO 2 )Flow (m 3 / h); t k represents the kth time period, t k+1 Indicates the k+1th time period. k+1 With t k The difference between two adjacent time periods is 15 minutes, i.e. 0.25 hours. Then the power of each thermal power plant in each time period is compared with the CO 2 The scattered points of the emission are fitted quadratically. The coefficients of the quadratic function obtained from the results of the quadratic fitting are shown in Table 3. These coefficients of the quadratic function will be used in the objective function of the lower model.

[0258] Table 1

[0259]

[0260] Table 2

[0261]

[0262] Table 3

[0263]

[0264] Compared with the upper-level scheduling model, the number of variables in the lower-level model on the second day before the day is significantly reduced. Therefore, the maximum number of iterations in the lower-level scheduling model on the second day before the day is set to 50 times, the population size is 100, the mutation probability is 0.7, and the crossover probability is 0.3. The model repeatedly iterates through the genetic algorithm to find the optimal solution, and realizes the formulation of the power plan of each thermal power plant on the day before and within the day, as well as the CO 2 Comprehensive optimization of emissions.

[0265] Depend on Figure 4 It can be seen that during the period of 21 to 90, the amount of wind power access is very different from the conventional load. Therefore, during these periods, regardless of whether the high energy load of the electric arc furnace is adjusted up or down, the thermal power power needs to be adjusted in the day-ahead stage. During the period of 21 to 90, most of the thermal power plant 4 in Scheme 2 is at minimum power, and thermal power plant 3 undertakes most of the adjustment tasks of the thermal power plant. This is because the participation of the EAF high energy load reduces the adjustment pressure of the thermal power plant, and each thermal power plant can flexibly change its own power size according to the goal of minimizing carbon emissions. And because for the same increase in thermal power power, the CO of thermal power plant 4 2 The emissions of thermal power plants are the largest among all thermal power plants. 2 The emission is the smallest among all thermal power plants (Table 2). Therefore, the total power of thermal power plants will be allocated to thermal power plant 3, which has the smallest carbon emission per unit power, as much as possible, and will not be allocated to thermal power plant 4, which has the largest carbon emission per unit power.

[0266] Finally, according to the total power of all thermal power plants in the day-ahead upper model, there are also some periods in which the total thermal power of Scheme II is greater than that of Scheme I (i.e., the period in which Area II is located), such as Figure 6 In the periods 30-33, 49-58, and 84-86, the CO2 emissions of Scheme II are greater than those of Scheme I. However, from the perspective of the entire day-ahead cycle, the carbon emissions of thermal power plants are reduced after EAF participates in the regulation.

[0267] Depend on Figure 6It can be seen that compared with Scheme 1, the total CO2 emissions of the thermal power plants in Scheme 2 based on the technical solution of this application are smaller. 2 The emission is 6.552×10 5 tons, the day-ahead CO2 of all thermal power plants in Scenario 2 2 The emission is 6.386×10 5 Tons. The day-ahead CO2 emissions of all thermal power plants in Scheme 2 relative to Scheme 1 2 Emissions were reduced by 2.5%. In addition, the CO emissions per unit of power of each thermal power plant were reduced by 2.5%. 2 The emission comparison is shown in Table 4.

[0268] Table 4

[0269]

[0270]

[0271] As shown in Table 4, the CO2 emissions per unit day-ahead power of the thermal power plants in Scheme 2 are reduced compared to those in Scheme 1. In particular, the carbon emissions per kilowatt-hour of thermal power plants 3 and 4 are reduced the most, because the dispatch of the thermal power plants in the day-ahead stage is mainly adjusted by compressing the power of thermal power plant 4 and increasing the power of thermal power plant 3.

[0272] In this embodiment, the total thermal power curve of Scheme 2 is smoother than the total thermal power curve of Scheme 1. This is because the high energy load of the arc furnace, as a dispatchable resource in the system, can effectively reduce the regulation pressure of the thermal power plant, make the overall power curve of the thermal power plant smoother, and reduce the regulation pressure of the thermal power plant. In summary, the introduction of the high energy load of the arc furnace in the upper model of the day-ahead scale and the lower model of the day-ahead scale established in this paper is conducive to reducing the emissions of thermal power plants, enhancing the system's wind power consumption level, and making the overall power curve of thermal power plants smoother.

[0273] In addition, in order to verify the effectiveness of the lower-level model at the intraday and day-ahead scales in the model proposed in this paper, this implementation column uses the total output power data of four wind power plants with a total capacity of 800MW on a typical day in 2022 for simulation analysis. The capacities of the four wind power plants are 100MW, 300MW, 200MW and 200MW respectively. The rated capacities of the BESSs connected to these three wind farms are 10MW, 30MW, 20MW and 20MW respectively; see Tables 5 and 6 for specific parameters.

[0274] Table 5

[0275]

[0276]

[0277] Table 6

[0278]

[0279] The lower model 1 of the day-ahead scale is used to distribute power to wind farms. Referring to the historical operation data of each wind farm, the total wind power obtained by the upper model of the day-ahead scale is specifically distributed according to the proportion of each wind farm relative to the total in each period, and finally the day-ahead power curve of each wind farm is determined.

[0280] According to the lower-level strategy 1, the day-ahead power curve of each wind farm is subjected to variable mode decomposition. In order to ensure the accuracy and applicability of the decomposition, the number of decomposed components is set to 6. This means that the day-ahead wind power curve of each wind farm is carefully decomposed into six day-ahead wind power components with different frequencies. In order to optimize the operation of the system, the sum of the high-frequency day-ahead wind power components is delivered to the BESS directly connected to the wind farm for consumption. After this step, the specific charging and discharging status of the BESS connected to each wind farm is obtained. Combining the charging and discharging power of the BESS with the day-ahead power of the wind farm, the intraday power curve of each wind farm is obtained.

[0281] In this embodiment, the energy storage devices in each wind farm station are continuously and rapidly adjusted throughout the daily cycle to absorb the sum of high-frequency wind power components. In addition, according to the day-ahead forecast wind power of each wind farm (i.e. Figure 6 The blue curve in the middle) has great randomness and volatility. The maximum fluctuation of wind power active power every 15 minutes a day before the event in these four wind farms reaches 33.64MW. This will seriously affect the stable operation of the power system. The lower-level strategy 1 at the intraday scale uses BESS to absorb the high-frequency components of the wind power of each wind farm a day before the event, resulting in the smoothing of the wind power of each wind farm after absorption. The maximum fluctuation of wind power active power within the day is 11.12MW, which is 66.94% less than the change of wind power a day before the event. Therefore, the lower-level model 1 at the intraday scale in the model established in this paper can make full use of the flexibility of BESS, absorb high-frequency wind power, improve the utilization rate of wind power, and smooth the fluctuation of wind power, which is conducive to the grid connection of wind power.

[0282] For the low-frequency day-ahead wind power components of all wind farms, the low-frequency day-ahead wind power components of all wind farms are added up, and by implementing the lower-level model of the intraday stage, the power of each thermal power plant is minimized in the intraday stage, and the low-frequency day-ahead wind power components are consumed as much as possible. The goal is to minimize the carbon emissions of the thermal power plants within the day, and finally determine the intraday power situation of each thermal power plant. In this embodiment, thermal power plant 4 is at minimum power for most of the time period on the intraday scale, while thermal power plant 3 undertakes most of the regulation tasks of the thermal power plants. This is because for the same increase in thermal power, the CO 2 The emissions of thermal power plants are the largest among all thermal power plants.2 The emissions are the smallest among all thermal power plants.

[0283] In summary, the two-stage scheduling strategy of day-ahead and day-intraday considering the high-energy load regulation of arc furnace and wind power modal decomposition proposed in this invention can ensure the maximum consumption of wind power, reduce wind power fluctuations, and effectively reduce the CO 2 Emissions.

[0284] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0285] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0286] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in less than all of the features of the individual embodiments previously disclosed. Therefore, the claims that follow the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the present invention.

[0287] Those skilled in the art will appreciate that the modules or units or groups of the devices in the examples disclosed herein may be arranged in the device as described in the embodiment, or alternatively may be located in one or more devices different from the devices in the example. The modules in the foregoing examples may be combined into one module or may be divided into multiple submodules.

[0288] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or groups in the embodiments may be combined into one module or unit or group, and in addition they may be divided into a plurality of submodules or subunits or subgroups. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device so disclosed may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0289] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0290] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions. Therefore, a processor with necessary instructions for implementing the method or method elements forms a device for implementing the method or method elements. In addition, the elements described herein of the device embodiments are examples of devices for implementing the functions performed by the elements for the purpose of implementing the invention.

[0291] The various techniques described herein may be implemented in combination with hardware or software, or a combination thereof. Thus, the method and apparatus of the present invention, or some aspects or portions of the method and apparatus of the present invention may be in the form of program codes (i.e., instructions) embedded in a tangible medium, such as a floppy disk, a CD-ROM, a hard disk drive, or any other machine-readable storage medium, wherein when the program is loaded into a machine such as a computer and executed by the machine, the machine becomes a device for practicing the present invention.

[0292] In the case where the program code is executed on a programmable computer, the computing device generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The memory is configured to store the program code; the processor is configured to execute the method of the present invention according to the instructions in the program code stored in the memory.

[0293] By way of example and not limitation, computer readable media include computer storage media and communication media. Computer readable media include computer storage media and communication media. Computer storage media stores information such as computer readable instructions, data structures, program modules or other data. Communication media generally embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and includes any information delivery medium. Any combination of the above is also included within the scope of computer readable media.

[0294] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved, and is not intended to imply that the objects so described must have a given order in time, space, order, or in any other manner.

[0295] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is selected primarily for readability and teaching purposes, rather than for explaining or defining the subject matter of the present invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is illustrative, not restrictive, with respect to the scope of the present invention, which is defined by the appended claims.

Claims

1. A two-stage scheduling method considering arc furnace control and wind power modal decomposition, characterized in that: The specific steps include: Step 1, establishing a high energy load model of an electric arc furnace; Step 2: construct a day-ahead-intraday double-layer scheduling model, which includes a day-ahead upper-layer model and a lower-layer model, wherein the lower-layer model includes a first day-ahead lower-layer model, a second day-ahead lower-layer model, a first day-intraday lower-layer model, and a second day-intraday lower-layer model; Step 3: Introduce arc furnace load and thermal power plant to coordinate the dispatch and consumption of wind power, establish the objective function of the day-ahead upper model, optimize the day-ahead wind energy utilization and minimize wind power curtailment through the constraints in the day-ahead upper model; Step 4: Establish the first day-ahead lower-layer model, and calculate the day-ahead active power input to the grid by each wind farm according to the proportion of the historical power data input to the grid by each wind farm and the day-ahead total active power actually input to the grid by all wind farms in period t; Step 5: The total power of all thermal power plants on the day before is calculated based on the upper model on the day before. With the goal of minimizing the CO2 emissions of thermal power plants the day before, a lower-level model the day before the second day is established. Based on the constraints of the lower-level model the day before the second day, the day before active power of each thermal power plant is calculated. Step 6: Establish the first objective function and the second objective function of the lower-level model within the first day, perform variable modal decomposition on the day-ahead power of each wind farm obtained by the day-ahead scale lower-level model, calculate the high-frequency component of the day-ahead power of each wind farm absorbed by the battery energy storage system based on the constraint conditions, and absorb the low-frequency component of the day-ahead power of the wind farm by compressing the power of the thermal power plant; Step 7: Establish the lower-level model for the second day, with the goal of minimizing the daily CO2 emissions of all thermal power plants, and solve the daily power of each thermal power plant; Wherein, step 6 specifically includes the following steps: The mth subcomponent of the wind power input to the grid by the jth wind farm a day ago is obtained by performing variable modal decomposition on the day-ahead power of each wind farm: Where m is the number of subcomponents to be decomposed, P t Wdout，j，m Is P t Wdout，j The mth subcomponent of the wind power input to the grid by the jth wind farm a day ago obtained after variable modal decomposition; e represents the number of iterations, starting from 0; ω is the frequency of the wind power component, ω j.s.m.e+1 is the frequency of the mth subcomponent of the wind power input to the grid by the jth wind farm a day before at the e+1th iteration; It represents the mth subcomponent of the wind power input to the grid at the jth wind farm with a frequency of ω in period t obtained after variable modal decomposition during the eth generation iteration; α is a regularization parameter used to balance the weight between signal reconstruction and modal bandwidth minimization; is the Lagrange multiplier of frequency ω at the e-th iteration; The day-ahead active power input to the grid by the j-th wind farm obtained by the lower model on the first day before the day in period t is Perform variable mode decomposition to decompose m fixed components, sum the high-frequency components and low-frequency components respectively to obtain the power of the battery energy storage system connected to each wind farm and the total daily power of the thermal power plant; add the power of the battery energy storage system connected to each wind farm and the daily power of each wind farm to obtain the day-ahead power of each wind farm; The specific allocation strategy of wind power is as follows: is the average slope of the decomposed wind power component. By judging the range of the slope change Δf of the average slope of the decomposed wind power component, it is determined whether the slope change Δf is absorbed by the thermal power plant or the BESS; Δf low Δf is the low-frequency upper limit of the slope change of the average slope; high is the upper frequency limit of the high-frequency band of the slope change of the average slope; U low Represents the set of low-frequency wind power components; U high Represents the set of high-frequency wind power components; The first objective function of the lower model on the first day is: in, It is the sum of all high-frequency components of the day-ahead power that the j-th wind farm finally outputs to the system during period t. is the charging and discharging power of the battery energy storage system connected to the j-th wind farm during period t; D t high，j For period t and The absolute value of the difference; The constraints of the first objective function of the lower model on the first day include the intraday power balance constraint of the j-th wind farm, the equation constraint of the conservation of the charge and discharge capacity of the battery energy storage system, the upper and lower limit constraints of the charge and discharge power of the battery energy storage system, and the upper and lower limit constraints of the state of charge of the battery energy storage system: (6011) Intraday power balance constraint of the jth wind farm; (6012) Equality constraints for conservation of charge and discharge capacity of BESS; Where: T i The total number of time periods within the set time period of the day, t=1,2…T i ; Δt is defined as the time of each period within the day; (6013) The upper and lower limits of the charging and discharging power of the battery energy storage system are as follows: Where: P BESSjmax is the power upper limit of the BESS connected to the jth wind farm, P BESSjmin is the lower power limit of the BESS connected to the jth wind farm; (6014) Upper and lower limit constraints of the state of charge of the battery energy storage system: in, is the state of charge of the BESS connected to the jth wind farm in period t, SOC max is the upper limit of the state of charge of BESS, SOC min is the lower limit of the state of charge of the battery energy storage system; according to the relationship between the state of charge of the battery energy storage system and the power of the battery energy storage system, the change of the amount of electricity during the charging and discharging process of the battery energy storage system is: Among them, SOC t+1 j is the state of charge of the energy storage device connected to the jth wind farm at time t+1, η c and η d are the charging efficiency and discharging efficiency of BESS respectively, Δt is the charging and discharging time interval; C BESS.j is the capacity of the BESS connected to the jth wind farm.

2. A two-stage scheduling method considering arc furnace control and wind power modal decomposition according to claim 1, characterized in that: Step 1 specifically includes the following steps: Modeling the high energy load of an electric arc furnace: represents the active power of the kth arc furnace in period t, and They represent the melting voltage and current of the kth arc furnace in time period t, represents the power factor of the kth arc furnace; It represents the total power of all arc furnace loads in the power system during period t. The total power of all arc furnace loads during period t is the sum of the rated power of all arc furnaces running in the system and the regulated power of all adjustable arc furnaces; Indicates the basic power of the arc furnace during period t; N EAF represents the number of arc furnaces operating in the power system, N EAFadj Indicates the number of adjustable electric arc furnaces in the power system; The regulated power of the kth electric arc furnace in period t; if ΔP t EAF，k >0, indicating that the kth arc furnace is increasing its load; if ΔP t Mgbase <0, indicating that the kth arc furnace is reducing its load; is a state variable, indicating whether the kth arc furnace adjusts the active power in period t; P EAF.kmin represents the minimum active power of the kth arc furnace, P EAF.kmax Indicates the maximum value of the active power of the kth arc furnace; the value range of k is 1 to N EAFadj .

3. A two-stage scheduling method considering arc furnace control and wind power modal decomposition according to claim 2, characterized in that: in Indicates dynamic power adjustment, Indicates that there is no dynamic power adjustment.

4. A two-stage scheduling method considering arc furnace control and wind power modal decomposition according to claim 3, characterized in that: The day-ahead upper model outputs the day-ahead decision on wind power consumption, including the dispatch plan for arc furnace load and the day-ahead total power planning for wind power plants and thermal power plants; the first day-ahead lower model is used to formulate the day-ahead power planning for each wind farm, and the second day-ahead lower model is used to formulate the day-ahead power planning for each thermal power plant; the first intra-day lower model is used to formulate the intra-day power planning for each wind farm and battery energy storage system, and the second day-ahead lower model is used to formulate the intra-day power planning for each thermal power plant.

5. A two-stage scheduling method considering arc furnace control and wind power modal decomposition according to claim 4, characterized in that: Step S3 specifically includes the following steps: The objective function of the day-ahead upper model is: Among them, T d is the number of time periods in the day-ahead scheduling cycle; is the day-ahead total active power actually input to the grid by all wind power plants in period t; is the sum of the day-ahead active power output of all thermal power plants in period t; is the normal load during period t; N EAF represents the number of electric arc furnaces operating in the power system, represents the active power of the kth arc furnace in period t, ΔP t EAF，k is the regulated power of the kth arc furnace in period t, minE abon Indicates that the amount of wind abandoned in the day-ahead stage is the smallest; The constraints in the day-ahead upper model include the day-ahead power balance constraint, the day-ahead wind power output constraint, and the day-ahead thermal power plant output constraint. (301) Day-ahead power balance constraint: (302) The wind power output constraint is: in, It represents the total predicted active power output of the wind farm on the bus during the period t; (303) The constraints on the day-ahead output of a thermal power plant include the upper and lower limits of the day-ahead power output of the thermal power plant, the ramping constraints of the day-ahead power output of the thermal power plant, and the power adjustment limit constraints on the arc furnace load; (3031) The upper and lower limits of the day-ahead power output of a thermal power plant are: P Gd.min and P Gd.max They represent the lower and upper limits of the day-ahead power output of all thermal power plants respectively; (3032) The ramp constraint of the day-ahead power output of a thermal power plant is: P t-1 Gd represents the sum of the active power output of all thermal power plants in the period (t-1) on the previous day, P Gdcli.max and P Gdcli.min They represent the maximum slope and minimum slope of the day-ahead active power output of all thermal power plants respectively; (3033) The arc furnace load power adjustment limit constraints are: Among them, ΔP t EAFmax and ΔP t EAFmin They respectively represent the upper and lower limits of power adjustment for high energy load of electric arc furnace.

6. A two-stage scheduling method considering arc furnace control and wind power modal decomposition according to claim 5, characterized in that: Step 4 specifically includes the following steps: The day-ahead active power input to the grid by each wind farm is: is the day-ahead active power input to the grid by the j-th wind farm in period t, is the historical active power input to the grid by the j-th wind farm in period t, N W represents the number of wind farms; is the total active power actually input to the grid by all wind power plants during period t.

7. A two-stage scheduling method considering arc furnace control and wind power modal decomposition according to claim 6, characterized in that: Step 5 specifically includes the following steps: The objective function of the lower layer model before the second day is: Among them, N G Indicates the number of units in the thermal power plant group, T d is the number of time periods in the day-ahead scheduling cycle, represents the total carbon dioxide emissions of thermal power plants during the day-ahead dispatch period, represents the binary start / stop variable of thermal power plant i in period t, where U t Gi =0 means that the thermal power plant is in maintenance state during period t, U t Gi =1 indicates that the thermal power plant is in operation during period t, represents the day-ahead power generation of thermal power plant i in period t, and the coefficient a i 、b i and c i They represent the quadratic carbon emission coefficient, linear carbon emission coefficient and constant carbon emission coefficient of thermal power plant i respectively; The constraints of the second day-ahead lower-level model include the day-ahead power balance constraint of thermal power plants, the day-ahead ramping constraint of a single thermal power plant, and the day-ahead power generation limit of a single thermal power plant; (501) Day-ahead power balance constraints of thermal power plants: Where N G represents the number of units in the thermal power plant group, represents the day-ahead power generation of thermal power plant i in period t; (502) Day-ahead ramping constraints for a single thermal power plant: Among them, P t-1 Gd，i represents the day-ahead power generation of thermal power plant i in period t-1, P Gdcli.imax and P Gdcli.imin They represent the maximum slope limit and minimum slope limit of the day-ahead power generation of thermal power plant i respectively; (503) Day-ahead power generation limit for a single thermal power plant: The day-ahead generation capacity of each thermal power plant is subject to upper and lower limits to ensure operational feasibility: Where P Gd.imax and P Gd.imin They represent the upper and lower limits of the day-ahead power generation of thermal power plant i respectively.

8. A two-stage scheduling method considering arc furnace control and wind power modal decomposition according to claim 7, characterized in that: Establish the second objective function of the lower model on the first day: in, It is the sum of all low-frequency components of the day-ahead power actually output to the system by all wind farms during period t; It is the regulation amount of the daily power of all thermal power plants during period t; D t low is the absolute value of the difference between the two in period t; the total active power of all thermal power plants in period t before the day is The sum of the daily power regulation of all thermal power plants ΔP t Gintr The subtraction is the total daily power of all thermal power plants P t Gintr , calculate the total daily power P of all thermal power plants t Gintr ; Total daily power of all thermal power plants The constraints include the upper and lower limits of the thermal power output power during the day and the ramp speed constraints of the thermal power power during the day: (6021) Constraints on upper and lower limits of thermal power daily output power: (6022) Ramp-up speed constraints of thermal power generation during the day: Among them, P Gintrcli，max and P Gintrcli，min They are respectively the maximum and minimum values ​​of the ramp rate of the total power of all thermal power plants during the day.

9. A two-stage scheduling method considering arc furnace control and wind power modal decomposition according to claim 8, characterized in that: Step 7 specifically includes the following steps: Establish the objective function of the lower model on the second day: Where: is the total CO2 emissions of all thermal power plants in one cycle per day; U t Gintr，i is the start / stop state variable of the ith thermal power plant in period t, U t Gintr，i =0 means that the ith thermal power plant is under maintenance during period t. t Gintr，i =1 means that the i-th thermal power plant is in operation during the t period of the day; The constraints of the objective function of the lower model on the second day include the equality constraint of the power of the thermal power plant on a daily basis and the ramp limit of each thermal power plant on a daily basis: (701)Equality constraints for the daily power of a thermal power plant: (702) Daily ramp limit for each thermal power plant: Where ΔP t Gintr，i represents the daily regulation output of the i-th thermal power plant within the time interval t, ΔP t Gintr，i-1 represents the daily regulation output of the i-1th thermal power plant within the time interval t-1, P t Gintrcli，imax and P t Gintrcli，imin are the maximum and minimum power ramp rates of the daily power output of thermal power plant i, respectively; (703) The upper and lower limits of each thermal power plant’s daily power generation are: P t Gintr，imax It represents the maximum value of the daily power output of thermal power plant i during period t.

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