CN117411083A

CN117411083A - Two-stage scheduling method considering electric arc furnace regulation and control and wind power modal decomposition

Info

Publication number: CN117411083A
Application number: CN202311433525.XA
Authority: CN
Inventors: 王艺博; 赵旭东; 刘闯; 蔡国伟; 王东哲; 熊健; 商晶茹
Original assignee: Northeast Dianli University
Current assignee: Northeast Electric Power University
Priority date: 2023-10-31
Filing date: 2023-10-31
Publication date: 2024-01-16

Abstract

The application discloses a two-stage scheduling method considering electric arc furnace regulation and control and wind power modal decomposition, wherein electric arc furnace load is controlled to serve as a representative high-energy load, three flexible resources of the electric arc furnace load, a thermal power plant and BESS are scheduled together, so that the problems of wind power consumption and carbon emission reduction of the thermal power plant are solved. In the adjustment of the day, wind power components of each frequency are consumed through scheduling the thermal power plant and the BESS, so that the daily waste wind is reduced, and the method reduces carbon emission of the thermal power plant and has important significance for developing low-carbon green power and implementing sustainable development strategy.

Description

Two-stage scheduling method considering electric arc furnace regulation and control and wind power modal decomposition

Technical Field

The patent relates to the field of power system dispatching, in particular to a day-day two-stage dispatching method considering electric arc furnace regulation and wind power modal decomposition

Background

In the prior art, with the continuous increase of wind power generation, under the condition of remarkable peak-valley difference and rapid fluctuation of wind power, the system has the problem of capacity surplus, not all generated wind energy is absorbed, wind abandoning is caused, the income flow of a Wind Farm (WF) is obviously influenced, and the financial feasibility of wind energy projects is endangered.

In terms of the flexible resource problem of the power system, the existing researches are mainly focused on demand response, an energy storage system and a thermal power plant. However, as wind power scales expand and the impact on the grid becomes increasingly apparent, relying on a single flexible resource alone may not be sufficient to meet the demands of the system. Although there are many academic studies focusing on the scheduling problem before date, this may lead to inaccuracy of wind power prediction and wind power waste.

At the same time, global climate change problems remain serious. A small number of thermal power plants contribute most of the carbon emissions. Therefore, emergency measures are required to reduce carbon dioxide emissions from thermal power plants.

For example, prior art CN 115204702 discloses a day-before-day scheduling method based on dynamic partitioning, comprising the following steps; step S1: the method comprises the steps of evaluating flexible resources of a power supply side, a load side and a power grid side of a power distribution network, and defining flexibility deficiency rate based on dynamic partition; step S2: a day-ahead scheduling model based on dynamic partitioning is constructed, a power purchase plan of the next day is formulated according to the day-ahead intermittent distributed power supply power and load demand prediction result, and various controllable devices are coordinated to realize the optimal operation of the system, so that the day-ahead scheduling result plays a role in guiding the day-ahead scheduling; step S3: and constructing an intra-day scheduling model based on dynamic partitioning, and rolling and adjusting a daily scheduling plan according to errors existing in the power generation and load demand prediction of the intermittent distributed power supply in the system and according to the actual running condition of the power distribution network access device and the ultra-short term prediction result, so that the daily scheduling can be adjusted and the utilization efficiency of flexible resources in the partitioning can be improved. However, the prior art mainly focuses on the power distribution network level, especially considers the internal structure and related constraints of the power distribution network, aims at optimizing the stability and economy of the local power grid, and takes part in demand response, wherein the main body of the power distribution network is the users of the power distribution network, the load capacity of the users is relatively small, and the users are limited to the maximized consumption of wind power and the minimized purchase cost, and the consideration of CO2 emission is not involved.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a two-stage scheduling method considering electric arc furnace regulation and control and wind power modal decomposition, and aims to realize the collaborative optimization of wind power absorption and carbon dioxide emission of a thermal power plant.

The specific scheme of the application is as follows:

a two-stage scheduling method considering electric arc furnace regulation and control and wind power modal decomposition specifically comprises the following steps:

step 1, establishing a high-energy load model of an electric arc furnace;

step 2, constructing a day-before-day double-layer scheduling model, wherein the day-before-day double-layer scheduling model comprises a day-before upper layer model and a day-before lower layer model, and the first day-before lower layer model, the second day-before lower layer model, the first day-in lower layer model and the second day-in lower layer model of the lower layer model;

step 3, introducing electric arc furnace load and a thermal power plant to cooperatively schedule and consume wind power, establishing an objective function of a day-ahead upper model, optimizing the day-ahead wind power utilization and minimizing wind power electricity limiting through constraint conditions in the day-ahead upper model;

Step 4, a first day-ahead lower layer model is established, and the day-ahead active power of each wind power plant input to the power grid is calculated according to the proportion of the historical power data of each wind power plant input to the power grid and the day-ahead total active power of all the wind power plants actually input to the power grid in the period t;

step 5, determining the total power of all the thermal power plants in the past according to the upper layer model in the pastTo minimize CO of thermal power plants before day ₂ The emission amount is used as a target, a second day-ahead lower model is established, and day-ahead active power of each thermal power plant is calculated based on constraint conditions of the second day-ahead lower model;

step 6, establishing a first objective function and a second objective function of the first day lower layer model, performing variable modal decomposition on the day-ahead power of each wind power plant obtained by the day-ahead scale lower layer model, calculating high-frequency components of the day-ahead power of each wind power plant absorbed by the battery energy storage system based on constraint conditions, and simultaneously, absorbing low-frequency components of the day-ahead power of all wind power plants by compressing the power of all thermal power plants;

step 7, establishing a second day lower layer model to minimize the day CO of all the thermal power plants ₂ And solving the daily power of each thermal power plant by taking the discharge amount as a target.

The step 1 specifically comprises the following steps:

And establishing a high-energy load model of the electric arc furnace based on the operation characteristics of the electric arc furnace, and considering the actual operation condition of the electric arc furnace in the analysis process so as to ensure that the load characteristics of the electric arc furnace are accurately described. The concrete process provides basis and guidance for the subsequent regulation strategy formulation;

establishing a high energy load model of the electric arc furnace:

representing the active power of the kth Electric Arc Furnace (EAF) t period, +.>And->Respectively representing the melting voltage and current of the kth electric arc furnace in the period t,/for the period t>Representing the power factor of the kth electric arc furnace; />Representing the total power of all electric arc furnace loads in the power system in the t period, wherein the total power of all electric arc furnace loads in the t period is the sum of rated power of electric arc furnaces running in all systems and regulated power (adjustable control quantity) of all adjustable electric arc furnaces; />Representing the base power of an arc furnace during period t (average operating capacity of a single arc furnace during period t); n (N) _EAF Representing the number of electric arc furnaces operating in said power system, N _EAF _adj Indicating the number of adjustable electric arc furnaces in the power system. />Regulating power of the kth electric arc furnace in a t period; if DeltaP ^t _EAF，k >0, meansThe kth electric arc furnace is increasing the load; if DeltaP ^t _Mg _base <0, indicating that the kth electric arc furnace is reducing the load. / >Is a state variable indicating whether the kth electric arc furnace is regulating active power in the period t, wherein +.>Representing dynamic power adjustment, +.>Indicating no adjustment of dynamic power; p (P) _EAF.kmin Representing the minimum value of the active power of the kth Electric Arc Furnace (EAF), P _EAF.kmax Representing the maximum value of the active power of the kth Electric Arc Furnace (EAF); k has a value ranging from 1 to N _EAF +N _{EAF adj} 。

Outputting a decision of wind power consumption before the day by the upper layer model before the day, wherein the decision comprises a scheduling plan of electric arc furnace load and a total power plan before the day of a wind power plant and a thermal power plant; the first day-ahead lower model is used for making day-ahead power planning of each wind power plant, and the second day-ahead lower model is used for making day-ahead power planning of each thermal power plant; the first day lower layer model is used for making day power planning of each wind power plant and each battery energy storage system, and the second day lower layer model is used for making day power planning of each thermal power plant.

The step S3 specifically comprises the following steps:

the day-ahead upper layer model sums the predicted active power output of the wind power plant on the bus in the period tRoutine load +.t period>Rated power (rated load size) of arc furnace>As known input quantity, on the premise of meeting the constraint conditions of thermal power plants, wind power and load And the daily waste air quantity is minimized, and a daily scheduling strategy of the total power of the thermal power plant, the total power of the wind power plant and the load regulation power of the electric arc furnace is obtained.

The objective function of the day-ahead upper model is (5):

wherein T is _d The number of time periods of the day-ahead scheduling period is 96;the method comprises the steps that the total active power of all wind power plants before the day actually input to a power grid in a period t is calculated; />The sum of the daily active power output of all the thermal power plants in the period t is calculated;is the normal load for period t; n (N) _EAF Indicating the number of electric arc furnaces operating in the power system, < >>Representing the active power, ΔP, of the kth Electric Arc Furnace (EAF) t period ^t _EAF，k For regulating the power of the kth electric arc furnace in the period t, minE _abon The air quantity is the smallest in the day-ahead stage.

Constraint conditions in the day-ahead upper model comprise day-ahead power balance constraint, wind power day-ahead output constraint and thermal power plant day-ahead output constraint

(301) Day-ahead power balance constraint:

equation (6) is used for the day-ahead power balance constraint of the whole power system, namely, the sum of the power emitted by the source side wind farm and the thermal power plant is equal to the sum of the load side electric arc furnace load and the power consumed by the conventional load in each period of the day-ahead stage.

(302) The wind power day-ahead output constraint is formula (7):

In the formula (7), the amino acid sequence of the compound,representing the sum of the predicted active power outputs of the wind farm on the bus bar during the t period. The day-ahead power balance constraint ensures that the predicted power output of each wind farm does not exceed the respective upper limit and remains above the lower limit. By incorporating the day-ahead power balance constraints into the optimization model, the day-ahead scheduling process accounts for inherent variability and uncertainty associated with wind power generation.

(303) The constraint of the day-ahead output of the thermal power plant comprises the constraint of the day-ahead power output upper limit and the day-ahead power output lower limit of the thermal power plant, the climbing constraint of the day-ahead power output of the thermal power plant and the power adjustment limit constraint of the load of the electric arc furnace;

(3031) The constraint of the upper and lower limits of the day-ahead power output of the thermal power plant is as follows:

P _Gd.min and P _Gd.max Representing the lower and upper limits of the day-ahead power output of all thermal power plants, respectively. 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 the reliability of the system. The lower limit constraint ensures that the thermal power plant generates the least power to meet the requirements and functional requirements of the power system.

(3032) The climbing constraint of the power output before the day of the thermal power plant is as follows:

P ^t-1 _Gd representing the sum of the daily active power output of all the thermal power plants in the period (t-1), P _{Gd cli.max} And P _{Gd cli.min} Representing the maximum slope and the minimum slope of the daily active power output of all the thermal power plants respectively. By incorporating slope constraints into the optimization process, the day-ahead scheduling model aims to ensure a smooth transition of the power output of the thermal power plant, taking into account its operational capabilities and limitations. The climbing constraint of the power output of the thermal power plant before the day contributes to the reliable and efficient operation of the power system, allowing the power generation level to be properly adjusted while keeping the power grid stable and adhering to the technical constraint.

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

wherein DeltaP ^t _{EAF max} And DeltaP ^t _{EAF min} The upper and lower limits of the power regulation of the high energy load of the electric arc furnace are indicated, respectively. The load power adjustment limit constraint of the electric arc furnace is included in an optimization framework, the operation limit of high-load energy load is considered by a day-ahead scheduling model, and the power adjustment of the electric arc furnace is ensured to be within a feasible range, so that sufficient supply and demand coordination is realized, and the stability of a power grid and the efficient utilization of energy resources are promoted.

The step 4 specifically comprises the following steps:

the active power of each wind power plant before day input to the power grid is:

for the j-th wind farm the active power before day, which is input to the grid during period t,/-, is input to the grid during period t>For the j-th wind power plant to input the historical active power to the power grid in the period t, N _W Representing the number of wind farms; />At time t for all wind power plantsThe actual input of the segments is the total real power of the grid before day.

The total power of all thermal power plants in the day according to the day upper modelTo minimize CO of thermal power plants before day ₂ The emission amount is taken as a target, a second day-ahead lower layer model is established, and day-ahead power of each thermal power plant is calculated; the second day front lower layer model is used for generating CO of a thermal power plant in the day front ₂ The total discharge is minimized and optimized to ensure that the sum P of the daily active power output of all the thermal power plants in the period t ^t _Gd And obtaining the distribution and the daily power of each thermal power plant. Because the higher the power of the thermal power plant, the CO of the thermal power plant ₂ The larger the discharge amount is, the power of the thermal power plant and CO ₂ There is a conic relationship between the emissions. The objective function of the first day before lower model is:

wherein N is _G Representing the number of units of a thermal power plant group, T _d For the number of time periods of the day-ahead schedule period,indicating total carbon dioxide emissions during a day-ahead schedule of a thermal power plant,/->A binary start-up/shut-down variable representing a thermal power plant i during a period t, where U ^t _Gi =0 indicates that the thermal power plant is in maintenance state at time interval t, U ^t _Gi =1 indicates that the thermal power plant is in operation during period t, +.>Representing the daily power generation amount of a thermal power plant i in a t period, and a coefficient a _i 、b _i And c _i The secondary carbon emission coefficient, the primary carbon emission coefficient and the constant carbon emission coefficient of the thermal power plant i are respectively represented.

Constraint conditions of the second day-ahead lower model comprise day-ahead power balance constraint of the thermal power plant, day-ahead climbing constraint of a single thermal power plant and day-ahead power generation capacity limit of the single thermal power plant;

constraints of the second day old lower model include the day old power balance constraint of the thermal power plant, equation (13), to ensure that the total power generation matches the system demand. The ramp-up constraint limits the rate of change of the power output of each thermal power plant over successive time intervals, equation (14). The day before power of each thermal power plant is limited by upper and lower limits, equation (15), to maintain the operational viability of the plant. And ensuring that a day-ahead power generation plan of the thermal power plant meets operation requirements through constraint conditions of a second day-ahead lower model, and simultaneously considering carbon emission characteristics of each power plant. This approach facilitates efficient and environmentally friendly operation of the power system.

(501) Day-ahead power balance constraint of thermal power plant:

day-ahead power balance constraint of thermal power plants to ensure that the total power generation of all thermal power plants is matched with system requirements:

wherein N is _G The number of units of the thermal power plant group is represented,and (5) representing the daily power generation amount of the thermal power plant i in the t period.

(502) Day-ahead climbing constraint of a single thermal power plant:

the ramp up constraint limits the rate of change of the daily power generation capacity of each thermal power plant between adjacent time intervals.

Wherein P is ^t-1 _Gd，i Representing the daily power generation amount, P, of a thermal power plant i within a time interval (t-1) _Gdcli.imax And P _Gdcli.imin Respectively represent the days of the thermal power plant iMaximum and minimum slope limits for the pre-generated power.

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

the daily power generation capacity of each thermal power plant is limited by upper and lower limits to ensure operational feasibility:

wherein P is _Gd.imax And P _Gd.imin P _Gd，imin The upper limit and the lower limit of the daily power generation amount of the thermal power plant i are respectively indicated.

The step 6 specifically comprises the following steps:

the method comprises the steps of performing variable modal decomposition on daily power of each wind farm to obtain an mth subcomponent of wind power input to a power grid before the daily of a jth wind farm:

m is the number of subcomponents that are decomposed and the size of m can be specified artificially. P (P) ^t _{Wd out，j，m} Is to make P ^t _{Wd out，j} And (3) inputting the mth subcomponent of wind power which is input to the power grid before the day of the jth wind power plant and is obtained after the variable modal decomposition. e represents the iteration number, starting from 0 times; omega is the frequency of the wind power component, omega _j.s.m.e+1 Is the frequency of the m-th sub-component of wind power input to the power grid before the day of the jth wind power plant in the e+1th iteration;representing the m-th sub-component of wind power input to a power grid before the day of the omega frequency of the t period of the j-th wind power plant obtained after variable mode decomposition when the e-th iteration is carried out; alpha is a regularization parameter for balancing weights between signal reconstruction and modal bandwidth minimization. />Is Lagrangian multiplier of omega frequency in the e-th iteration, and is used for constraint optimization.

Equations (17) and (18) aim to find the component sets { P } of different frequencies ^t _{Wd out，j，m，ω} }＝{P ^t _{Wd out，j，1，ω} ...,P ^t _{Wd out，j，m，ω} Sum { omega } of the corresponding center frequencies _j，s,m }＝{ω _j，s，1 ,...,ω _j，s，m So that these component signals reconstruct the original signal P as well as possible ^t _{Wd out，j} While each modality has as narrow a bandwidth as possible.

Inputting the jth wind power plant obtained by the first day front lower layer model into the day front active power of the power grid in the period tPerforming variable mode decomposition to obtain fixed m components, and separating high frequency component (i.e.)>) And a low frequency component (i.e +.>) And respectively summing to obtain the Battery Energy Storage System (BESS) power connected with each wind farm and the total daily power of the thermal power plant. Finally, adding the power of a Battery Energy Storage System (BESS) connected with each wind farm to the daily power of each wind farm to obtain daily power +. >

The lower layer model in the first day realizes the maximization of wind power consumption in the stage in the day by utilizing the adjustment capability of two flexible resources of the BESS and the thermal power plant.

The variable modal decomposition has better robustness and noise immunity than conventional Empirical Mode Decomposition (EMD). The input signal may be decomposed into discrete data of subband signals. The method comprises the steps of inputting and initializing parameters, establishing a Lagrangian function, further performing iterative optimization, checking whether convergence conditions are met, outputting all sub-components and the center frequency of each sub-component if the convergence conditions are met, and continuing to iterate if the convergence conditions are not met, and outputting the sub-components and the center frequency of each sub-component after the convergence conditions are met.

The lower layer model in the first day adopts a frequency division and absorption method, and wind power components in different frequency ranges are respectively processed through a battery energy storage system and a thermal power plant. Specifically, the battery energy storage system is mainly responsible for absorbing high-frequency wind power components, and the thermal power plant corresponds to low-frequency wind power components. This allocation is based on a key parameter: the average slope of the decomposed wind power component. The average slope is taken as a measure of the instantaneous change in wind power. By means of the measure of the average slope, wind power components with different frequencies can be accurately distributed to the power system which is most suitable for the characteristics of the wind power components, and therefore the operation efficiency and stability of the whole power system are optimized.

The specific distribution strategy of the wind power component is shown as a formula (19):

in order to determine the average slope of the decomposed wind power component, the range of the slope change amount delta f of the average slope of the decomposed wind power component is determined, and whether the slope change amount delta f is consumed by a thermal power plant or BESS is determined. Δf _low The upper limit of the low-frequency band is the slope variation of the average slope; Δf _high The upper limit of the high-frequency band is the slope variation of the average slope; u (U) _low Representing a set of low-frequency wind power components; u (U) _high Representing a collection of high-band wind power components. After the variable modal decomposition of wind power is completed, in order to enable the high-frequency component of the daily power of each wind power plant to be absorbed by the battery energy storage system as much as possible, a first establishmentAn objective function of the intra-day lower layer model;

the first objective function of the first day underlying model is:

in the formula (20), the amino acid sequence of the compound,is the sum of all high frequency components of the day-ahead power which is finally output to the system by the jth wind farm in the t period +.>Has been solved according to the variable modality algorithm, i.e. all of equation (19)The sum is a known amount; />The charging and discharging power of a Battery Energy Storage System (BESS) connected with the jth wind farm in the period t is unknown quantity to be solved. D (D) ^t _high，j For t period->And->Absolute value of the difference;

The constraint conditions of the first objective function of the first intra-day lower layer model comprise intra-day power balance constraint of the jth wind power plant, equation constraint conditions of charge and discharge conservation of the battery energy storage system, upper and lower limit constraint conditions of charge and discharge power of the battery energy storage system and upper and lower limit constraint conditions of charge state of the battery energy storage system:

(6011) And (5) the daily power balance constraint of the jth wind farm.

(6012) Equation constraint for conservation of charge and discharge amount of the BESS.

Wherein: t (T) _i T=1, 2 … T, the total number of time periods per T0 hours for the intra-day phase _i . Δt is defined herein as the time of each period in the day.

(6013) Upper and lower limit constraint conditions of the charge and discharge power of the BESS:

wherein: p (P) _{BESS jmax} The upper power limit, P, of the BESS for the jth wind farm connection _{BESS jmin} The lower power limit of the BESS for the jth wind farm connection.

(6014) The upper and lower limit constraints of the state of charge of the BESS:

wherein:state of charge, SOC, of BESS at time t for the jth wind farm connection _max The SOC is the upper limit of the state of charge of BESS _min Is the lower state of charge limit of the BESS. According to the relation between the charge state of the BESS and the power of the BESS, the change of the electric quantity in the charging and discharging process of the BESS is as follows:

wherein SOC is ^t+1 _j State of charge, η, for the (t+1) period of the energy storage device connected to the jth wind farm _c And eta _d The charge efficiency and the discharge efficiency of the BESS are respectively, and Δt is the charge-discharge time interval. C (C) _BESS.j For the capacity size of the BESS connected to the jth wind farm,the charging and discharging power of the battery energy storage system connected with the jth wind farm in the period t is calculated.

In order to enable the low-frequency component of all wind power plant day-ahead power to be absorbed by compressing all thermal power plant power, a second objective function of the first day-ahead lower layer model is established:

wherein,the sum of all low-frequency components of the day-ahead power actually output to the system by all wind farms in the t period is obtained by solving according to a variable mode algorithm and is a known quantity; />The adjustment quantity of the power in the period t and the day of all the thermal power plants is the unknown quantity to be solved. D (D) ^t _low Is the absolute value of the difference between the two t periods; the sum delta P of the adjustment quantity of the power in all thermal power plants ^t _Gintr Total active power before day in t period of all thermal power plants +>The subtraction is the total power P in all thermal power plants ^t _Gintr Calculating total power P in all thermal power plants ^t _Gintr ；

Total power of all thermal power plants in dayThe constraint conditions of (1) comprise upper and lower limit constraint of output power before thermal power day and climbing speed constraint of power in thermal power day:

(6021) Thermal power day-ahead output power upper and lower limit constraint:

(6022) Climbing speed constraint of power in thermal power day:

Wherein P is ^t-1 _Gd The total active power of all thermal power plants before the day of the t-1 period; p (P) _Gintr _cli，max And P _Gintr _cli，min And the maximum value and the minimum value of the climbing rate of the total power in all thermal power plants in the day are respectively.

The step 7 specifically comprises the following steps: with CO at all thermal power plant stages in the day ₂ The minimum emission is an objective function, so that the total daily power of all the thermal power plants is reasonably distributed, and the daily power of each thermal power plant is obtained.

With CO at all thermal power plant stages in the day ₂ The minimum discharge is an objective function, and the total power in the heat-engine plant in the day solved by the first-day lower model is realized(i.e., all +.in formula (19)>Sum) is reasonably distributed to obtain the daily power of each thermal power plant>

The objective function of the lower model in the second day is:

wherein:for CO in one period of all thermal power plants ₂ Total discharge amount; u (U) ^t _Gintr，i Is the start-stop state variable of the ith thermal power plant in the period t, U ^t _Gintr，i =0 indicates that the ith thermal power plant is in an overhaul state within the period t, U ^t _Gintr，i =1 indicates that the ith thermal power plant is in operation during the period t;

constraints of the objective function of the second intra-day lower layer model include equality constraints of intra-day power of thermal power plants and climbing limits of intra-day scales of each thermal power plant:

(701) Equation constraint of power in thermal power plant day:

(702) Climbing limit of daily scale of each thermal power plant:

wherein DeltaP ^t _Gintr，i Representing the daily regulation output, P, of a thermal power plant i within a time interval t-1 ^t _{Gintrcli，imax} And P ^t _{Gintrcli，imin} Is a symbol used to represent the maximum and minimum slope limits of the daily power output of the thermal power plant i.

(703) Upper and lower limit constraint of power generation in each thermal power plant day:

P ^t _Gintr，imax and P ^t _Gintr，imin Representing the maximum and minimum values of the daily power output of the thermal power plant i in the time interval t, respectively.

Compared with the prior art, the application has the following beneficial effects:

the application discloses a two-stage scheduling method considering electric arc furnace regulation and control and wind power modal decomposition, wherein electric arc furnace load is controlled to serve as a representative high-energy load, three flexible resources of the electric arc furnace load, a thermal power plant and BESS are scheduled together, so that the problems of wind power consumption and carbon emission reduction of the thermal power plant are solved. In the daily adjustment, wind power components of each frequency are consumed by dispatching the thermal power plant and the BESS, so that daily waste wind is reduced, and carbon emission of the thermal power plant is reduced;

in order to realize the cooperative optimization of wind power absorption and carbon dioxide emission of a thermal power plant, the method establishes a model of the electric arc furnace based on the load operation characteristic of the Electric Arc Furnace (EAF), comprehensively utilizes strategies of flexible resources, and aims to realize optimal wind power absorption and reduce the emission of the thermal power plant;

The application focuses on the overall power system, and focuses on the stability of a large-scale power grid;

the load main body in the application is an arc furnace, and the consumed electric energy of the high-load energy load is huge, so that the effect is obvious in the aspect of reducing the abandoned wind.

The method and the device are more beneficial to realizing the aim of double carbon from the perspective of reducing CO2 emission of the thermal power plant.

Drawings

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

FIG. 1 is a flow chart of a solution for a day-day two-phase schedule that takes into account electric arc furnace regulation and wind power modal decomposition in accordance with the present invention;

FIG. 2 is a schematic diagram of a power system enriched in a variety of flexible resources to absorb a large amount of wind power in the present invention;

FIG. 3 is a schematic diagram of a time frame of a dual time scale schedule in accordance with the present invention;

FIG. 4 is a graph of the load power of the electric arc furnace obtained by solving the model of the upper layer in the past in accordance with the conventional load and wind power predicted power curve in the verification example of the present invention;

FIG. 5 is a schematic diagram showing comparison of power and wind rejection before and after wind power day before participation of EAF high energy load in an embodiment of the invention;

fig. 6 is a comparison of the daily CO2 total emissions from all thermal power plants under different optimization schemes in a verification example of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on embodiments of the present invention, are within the scope of the present invention.

The invention has the core ideas that according to the self characteristics of fundus images, a proper algorithm is designed to perform image preprocessing enhancement, then retinal blood vessel segmentation is performed based on an information migration fundus image segmentation network, and finally, intelligent analysis and prediction are performed by using a neural network combined with ordered classification, so that the purpose of intelligent analysis of fundus images is achieved.

A two-stage scheduling method considering electric arc furnace regulation and control and wind power modal decomposition is provided, a solution flow chart is shown in fig. 1, and the method specifically comprises the following steps:

step 1, establishing a high-energy load model of an electric arc furnace;

step 5, rootTotal power of all thermal power plants before day calculated according to upper layer model before dayTo minimize CO of thermal power plants before day ₂ The emission amount is used as a target, a second day-ahead lower model is established, and day-ahead active power of each thermal power plant is calculated based on constraint conditions of the second day-ahead lower model;

The step 1 specifically comprises the following steps: and establishing a high-energy load model of the electric arc furnace based on the operation characteristics of the electric arc furnace, and considering the actual operation condition of the electric arc furnace in the analysis process so as to ensure that the load characteristics of the electric arc furnace are accurately described. The concrete process provides basis and guidance for the subsequent regulation strategy formulation;

establishing a high energy load model of the electric arc furnace:

representing the active power of the kth Electric Arc Furnace (EAF) t period, +.>And->Respectively representing the melting voltage and current of the kth electric arc furnace in the period t,/for the period t>Representing the power factor of the kth electric arc furnace; />Representing the total power of all electric arc furnace loads in the power system in the t period, wherein the total power of all electric arc furnace loads in the t period is the sum of rated power of electric arc furnaces running in all systems and regulated power (adjustable control quantity) of all adjustable electric arc furnaces; />Representing the base power of an arc furnace during period t (average operating capacity of a single arc furnace during period t); n (N) _EAF Representing the number of electric arc furnaces operating in said power system, N _{EAF adj} Indicating the number of adjustable electric arc furnaces in the power system. / >Regulating power of the kth electric arc furnace in a t period; if DeltaP ^t _EAF，k >0, indicating that the kth electric arc furnace is increasing load; if DeltaP ^t _{Mg base} <0, indicating that the kth electric arc furnace is reducing the load. />Is a state variable indicating whether the kth electric arc furnace is regulating active power in the period t, wherein +.>Representing dynamic power adjustment, +.>Indicating no adjustment of dynamic power; p (P) _EAF.kmin Representing the minimum value of the active power of the kth Electric Arc Furnace (EAF), P _EAF.kmax Representing the maximum value of the active power of the kth Electric Arc Furnace (EAF); k has a value ranging from 1 to N _EAF +N _{EAF adj} 。

The present embodiment proposes a power system rich in various flexible resources, as shown in fig. 2, aiming at achieving maximum wind power absorption by jointly scheduling high-load energy load of an arc furnace, a Battery Energy Storage System (BESS) and a thermal power plant. The day-ahead schedule of each thermal power plant and wind farm is determined by distributing the day-ahead total power of thermal power and wind power. The whole scheduling process involves a double-scale double-layer model, which involves both the day-ahead and day-in scales, requiring iterative calculations to arrive at the best solution due to the interdependence between the upper and lower models. To manage these complexities, a two-layer optimization model at a dual time scale is employed to solve the problem. The double-layer optimization model effectively solves the challenge of optimizing load coordination under the background of large-scale wind power grid connection and thermal power generation emission reduction.

Establishing a day-ahead-day double-layer scheduling model for absorbing wind power aiming at various flexible resources so as to optimize the utilization of the day-ahead wind power and minimize the wind abandoning of the day-ahead wind power;

establishing a day-ahead-day double-layer scheduling model, wherein the day-ahead-day double-layer scheduling model aims at a day-ahead-day double-layer scheduling model for absorbing wind power by various flexible resources and is of a double-layer hierarchical structure, and the double-layer model comprises a day-ahead upper layer model and a lower layer model, wherein the lower layer model comprises a first day-ahead lower layer model, a second day-ahead lower layer model, a first day-ahead lower layer model and a second day-ahead lower layer model; outputting a decision of wind power consumption before the day by the upper layer model before the day, wherein the decision comprises a scheduling plan of electric arc furnace load and a total power plan before the day of a wind power plant and a thermal power plant; the first day-ahead lower model is used for making day-ahead power planning of each wind power plant, and the second day-ahead lower model is used for making day-ahead power planning of each thermal power plant; the first day lower layer model is used for making day power planning of each wind power plant and each battery energy storage system, and the second day lower layer model is used for making day power planning of each thermal power plant. The double-layer structure enables the model to process complex multi-objective optimization tasks, and provides more balanced and efficient power system operation.

Introducing electric arc furnace load and a thermal power plant to cooperatively schedule and consume wind power, and establishing a daily upper model to optimize daily wind energy utilization so as to minimize wind power wind abandon; the day-ahead-day double-layer scheduling model for absorbing wind power by aiming at various flexible resources, which is established by the research, is a complex power system optimization model aiming at reducing wind power waste and thermal power carbon emission. The day-ahead-day dual layer scheduling model operates on two time scales, including a day-ahead scale and a day-in scale. The time frame of the dual time scale schedule is shown in fig. 3.

As shown in fig. 3, the day-ahead scheduling is performed the day before the actual operation (i.e., the period is 24 h), and the time interval is 15min. The day-ahead scale schedule involves a day-ahead forecast of the future state of the system, including the total day-ahead forecast power of the thermal power plant, the total day-ahead forecast power of the wind power plant, the operating load of the electric arc furnace, the day-ahead forecast power of each wind power plant, and the day-ahead forecast power of each thermal power plant. While intra-day scale involves shorter period operational decisions. The intra-day scale scheduling strategy is obtained based on the result of the pre-day scale scheduling strategy, and scheduling is carried out every four hours of the actual operation day to formulate the operation strategy of the next four hours. The time interval of the intra-day scale scheduling policy is also 15min. The scheduling strategy of the daily scale comprises daily charge and discharge conditions of connection BESS of each wind power plant, daily predicted power of each wind power plant and daily predicted power of each thermal power plant.

The step S3 specifically comprises the following steps:

the day-ahead upper layer model sums the predicted active power output of the wind power plant on the bus in the period tRoutine load +.t period>Rated power (rated load size) of arc furnace>As known input quantity, the method realizes the minimization of daily waste air quantity on the premise of meeting the constraint conditions of a thermal power plant, wind power and load. And obtaining a day-ahead scheduling strategy of the total power of the thermal power plant, the total power of the wind power plant and the load regulation power of the electric arc furnace.

The objective function of the day-ahead upper model is (5):

wherein T is _d The number of time periods of the day-ahead scheduling period is 96;the method comprises the steps that the total active power of all wind power plants before the day actually input to a power grid in a period t is calculated; />The sum of the daily active power output of all the thermal power plants in the period t is calculated;is the normal load for period t; n (N) _EAF Indicating the number of electric arc furnaces operating in the power system, < >>Representing the active power, ΔP, of the kth Electric Arc Furnace (EAF) t period ^t _EAF，k For regulating the power of the kth arc furnace in the period t, min E _abon The air quantity is the smallest in the day-ahead stage.

(301) Day-ahead power balance constraint:

(302) The wind power day-ahead output constraint is formula (7):

to ensure reliable and efficient operation of the power system, the day-ahead power output of the thermal power plant is constrained, and the constraints of the day-ahead power output of the thermal power plant include formulas (8) - (10):

equation (8) ensures that the day-ahead power output of the thermal power plant remains within a specified range. P (P) _Gd.min And P _Gd.max Representing the lower and upper limits of the day-ahead power output of all thermal power plants, respectively. 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 the reliability of the system. The lower limit constraint ensures that the thermal power plant generates the least power to meet the requirements and functional requirements of the power system.

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

in formula (10), ΔP ^t _{EAF max} And DeltaP ^t _{EAF min} The upper and lower limits of the power regulation of the high energy load of the electric arc furnace are indicated, respectively. The load power adjustment limit constraint of the electric arc furnace is included in an optimization framework, the operation limit of high-load energy load is considered by a day-ahead scheduling model, and the power adjustment of the electric arc furnace is ensured to be within a feasible range, so that sufficient supply and demand coordination is realized, and the stability of a power grid and the efficient utilization of energy resources are promoted.

The step 4 specifically comprises the following steps:

input for jth wind farm in period tDay-ahead active power to the grid, +.>For the j-th wind power plant to input the historical active power to the power grid in the period t, N _W Representing the number of wind farms; />The total active power of all wind power plants before the day actually input to the power grid in the period t.

The step is based on the proportion of the historical power data input to the power grid by each wind farmAnd total active power before day of all wind power plants actually input to the grid in period t +.>Obtaining daily active power input to a power grid by each wind power plant so as to ensure that the power of each wind power plant in the daily stage meets the distribution characteristics of historical data of each wind power plant;

the step 5 specifically comprises the following steps: the method comprises the steps of carrying out a first treatment on the surface of the The total power of all thermal power plants in the day according to the day upper modelTo minimize CO of thermal power plants before day ₂ The emission amount is taken as a target, a second day-ahead lower layer model is established, and day-ahead power of each thermal power plant is calculated; the second day front lower layer model is used for generating CO of a thermal power plant in the day front ₂ The total discharge is minimized and optimized to ensure that the sum P of the daily active power output of all the thermal power plants in the period t ^t _Gd And obtaining the distribution and the daily power of each thermal power plant. Because the higher the power of the thermal power plant, the CO of the thermal power plant ₂ The larger the discharge amount is, the power of the thermal power plant and CO ₂ There is a conic relationship between the emissions. The objective function of the first day before lower model is:

(501) Day-ahead power balance constraint of thermal power plant:

(502) Day-ahead climbing constraint of a single thermal power plant:

Wherein P is ^t-1 _Gd，i Representing the daily power generation amount, P, of a thermal power plant i within a time interval (t-1) _Gdcli.imax And P _Gdcli.imin The maximum slope limit and the minimum slope limit of the daily power generation amount of the thermal power plant i are respectively represented.

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

/>

The step 6 specifically comprises the following steps:

in formulas (17) - (18), m is the number of subcomponents that are decomposed, and the size of m can be specified artificially. P (P) ^t _{Wd out，j，m} Is to make P ^t _{Wd out，j} And (3) inputting the mth subcomponent of wind power which is input to the power grid before the day of the jth wind power plant and is obtained after the variable modal decomposition. e represents the iteration number, starting from 0 times; omega is the frequency of the wind power component, omega _j.s.m.e+1 Is the frequency of the m-th sub-component of wind power input to the power grid before the day of the jth wind power plant in the e+1th iteration;representing the m-th sub-component of wind power input to a power grid before the day of the omega frequency of the t period of the j-th wind power plant obtained after variable mode decomposition when the e-th iteration is carried out; alpha is a regularization parameter for balancing weights between signal reconstruction and modal bandwidth minimization. />Is Lagrangian multiplier of omega frequency in the e-th iteration, and is used for constraint optimization.

Inputting the jth wind power plant obtained by the first day front lower layer model into the day front active power of the power grid in the period tPerforming variable mode splittingSolution, decomposing fixed m components, separating high frequency components (i.e.) >) And a low frequency component (i.e +.>) And respectively summing to obtain the Battery Energy Storage System (BESS) power connected with each wind farm and the total daily power of the thermal power plant. Finally, adding the power of a Battery Energy Storage System (BESS) connected with each wind farm to the daily power of each wind farm to obtain daily power +.>

in order to determine the average slope of the decomposed wind power component, the range of the slope change amount delta f of the average slope of the decomposed wind power component is determined, and whether the slope change amount delta f is consumed by a thermal power plant or BESS is determined. Δf _low The upper limit of the low-frequency band is the slope variation of the average slope; Δf _high The upper limit of the high-frequency band is the slope variation of the average slope; u (U) _low Representing a set of low-frequency wind power components; u (U) _high Representing a collection of high-band wind power components. After the variable mode decomposition of wind power is completed, in order to enable the high-frequency component of the daily power of each wind power plant to be absorbed by a battery energy storage system as much as possible, an objective function of a daily lower layer model is first established;

the first objective function of the first day underlying model is:

(6011) And (5) the daily power balance constraint of the jth wind farm.

Wherein: t (T) _i For the total number of time periods of every four hours in the daily period, the value is set to be 16, t=1, 2 … T _i . Δt is defined herein as the time of each period of the day, which is also 15min.

wherein SOC is ^t+1 _j State of charge, η, for the (t+1) period of the energy storage device connected to the jth wind farm _c And eta _d The charge efficiency and the discharge efficiency of the BESS are respectively, and Δt is the charge-discharge time interval. In this example, Δt was 15min, and the value was 0.25h. C (C) _BESS.j The capacity size of the BESS connected to the jth wind farm.

wherein,the sum of all low-frequency components of the day-ahead power actually output to the system by all wind farms in the t period is obtained by solving according to a variable mode algorithm and is a known quantity; />Is the adjustment quantity of the power in the time t period and the day of all thermal power plants to be treatedThe unknown amount was obtained. D (D) ^t _low Is the absolute value of the difference between the two t periods; the sum delta P of the adjustment quantity of the power in all thermal power plants ^t _Gintr Total active power before day in t period of all thermal power plants +>The subtraction is the total power P in all thermal power plants ^t _Gintr Calculating total power P in all thermal power plants ^t _Gintr ；

Total power of all thermal power plants in dayThe constraint conditions of (1) comprise upper and lower limit constraint of output power before thermal power day and climbing speed constraint of power in thermal power day: />

(6021) Thermal power day-ahead output power upper and lower limit constraint:

(6022) Climbing speed constraint of power in thermal power day:

With all thermal power plant's daily inner stepsSegment CO ₂ The minimum discharge is an objective function, and the total power in the heat-engine plant in the day solved by the first-day lower model is realized(i.e., all +.in formula (19)>Sum) is reasonably distributed to obtain the daily power of each thermal power plant>

The objective function of the lower model in the second day is:

(701) Equation constraint of power in thermal power plant day:

(702) Climbing limit of daily scale of each thermal power plant:

This embodiment is a specific low-carbon power system including four thermal power plants, four wind power plants, and four BESS. According to actual data of a power system of a place where a steel smelting plant is located, the total capacity of all wind power plants is 800MW, the total capacity of conventional loads is 1000MW, and the minimum value PGd of the sum of the daily total power of all thermal power plants is 150MW in min; the maximum value PGd of the sum of the total power of all thermal power plants before the day, and max is 1500MW; the maximum value PGd cli, max and the minimum value PGd cli, min of the total power climbing power before the day of all the thermal power plants respectively take 500MW and 0MW.

FIG. 4 shows a known wind power prediction curve, a known conventional load curve, and a scheduling curve for electric arc furnace load formulated according to a day-ahead scale upper layer strategy in the examples. According to the graph shown in FIG. 4, the fluctuation characteristic of the total power of the wind power plant is obvious, the power range is changed in the 686-996 MW interval, the low valley of the total power of the wind power is generated in the 20-55 time period and the 66-91 time period, and the conventional load power prediction curve is in the peak stage in the time period; meanwhile, the wind power is just at the peak in the 1-19, 55-65 and 91-96 periods of the conventional load power at the valley. It can be seen that the wind power prediction curve and the conventional load curve generally show a certain anti-peak regulation performance, so that the supply and demand of the power system are not matched. Because the day-ahead minimum power of the total power of the thermal power plant is 600MW, the value of the day-ahead minimum power of the thermal power plant is larger. Depending on the system supply and demand balance, when the wind power is high, there may be an excess of power supply, which may result in additional measures (e.g., high energy enterprises increasing capacity, thermal power plants reducing power) being required to handle the excess power, such as periods 1-19, 55-65, and 91-96 of fig. 4, with the arc furnace load at maximum capacity. When the wind power is low, the load requirement of the system may not be met, and the conventional power generation source (i.e. the power of the thermal power plant is increased) needs to be relied on to compensate for the gap or reduce the requirement (e.g. the high-energy-carrying enterprise reduces the capacity, such as the 20-55 time period and the 66-91 time period of fig. 4). Substituting the parameters into an upper strategy of a day-ahead scale, taking the minimum wind power waste amount as an objective function, utilizing a CPLEX solver, adopting a genetic algorithm (the maximum iteration number of the genetic algorithm is set to be 100 times, the population scale is set to be 500, the variation probability is set to be 0.8, and the crossover probability is set to be 0.3), and solving a scheduling curve (see figure 4) of the electric arc furnace load, all thermal power day-ahead total power curves after electric arc furnace adjustment and all day-ahead total power curves of wind power plants after electric arc furnace adjustment.

In order to verify the effectiveness of wind power absorption by introducing EAF high-energy load into a day-ahead upper model established by the research, the following two day-ahead schemes are set for comparison analysis.

(1) Scheme 1: the load of the electric arc furnace is not adjusted up and down only by the upper layer model before the day and the lower layer model before the day 1 of the thermal power plant.

(2) Scheme 2: and simultaneously, an upper layer strategy and a day-ahead lower layer model for adjusting the high-energy load of the thermal power plant and the electric arc furnace are considered. Other conditions are consistent with the strategy presented in scheme 1.

The running condition and the wind power consumption condition of the wind power plant obtained by the two daily optimization schemes are shown in fig. 5.

In fig. 5, the periods corresponding to the yellow frames are the periods of the wind power waste wind, i.e., 1 to 19, 55 to 65 and 91 to 96 periods when EAF is adjusted. As can be seen in connection with fig. 4, during these periods the wind power is at a peak and even if the electric arc furnace is up-regulated as much as possible, there is still a small amount of wind curtailment. The area of the pink shadow Region (Region i) represents the daily air rejection when the EAF load is not adjusted up or down, and the area of the green Region (Region ii) represents the daily air rejection when the EAF load is adjusted. The waste air volumes of the two schemes are made into bar charts for comparison, and the pink and green bar charts are the waste air volumes of the scheme I and the scheme 2 respectively. As can be seen from fig. 5, when the scheme 1 is adopted, the predicted power before the wind power day is 41 in the limited time periods of 96 time periods in 1 day, the maximum blocked power in a single time period is 181.69MW, and the total wind-discarding electric quantity is 755.15 mw.h; when the scheme 2 is adopted, the predicted power limited time period before the wind power day is 23, the maximum blocked power of a single time period is 145.49MW, and the total limited electric quantity is 449.42 MW.h. Compared with the scheme 1, the air discarding quantity of the scheme 2 is reduced by 40.49%, and the effect of well reducing the air discarding quantity is achieved. This is because scheme 1 does not consider the capacity of the EAF to regulate high energy load, but only relies on the very limited capacity of the thermal power plant to regulate the fluctuation of wind power before day, but the limited capacity of the thermal power plant results in serious wind abandonment of the system. When the scheme 2 is adopted, wind power is consumed by not only relying on the day-ahead regulation capability of the thermal power plant, but also considering the regulation capability of the EAF high-energy load. Therefore, under the day-ahead scale, after EAF high-load energy load regulation is adopted, the wind power consumption level is obviously improved, and the air discarding quantity is effectively reduced.

In addition, in the embodiment, the specific parameters of the 4 thermal power plants are selected from the actual 4 thermal power plants in the city where a certain smelting enterprise is located in China, and the specific parameters of each thermal power plant are shown in tables 1 and 2. In addition, a continuous monitoring system for flue gas emission (CEMS) device is selected to measure the flow rate and the concentration of CO2 of each thermal power plant every 15min, and the CO2 emission of each thermal power plant is calculated according to a formula (37). According to a calculation formula of CEMS monitoring carbon dioxide, the CO2 emission amount is equal to the product of the respective flue gas flow and the CO2 concentration, and the integral of the period of time is carried out.

E ^t _CO2，i Is CO of thermal power plant i in t period ₂ Discharge amount per ton (t); c (C) _CO2 Is the concentration (t/m) of carbon dioxide discharged from a thermal power plant ³ )；F _CO2 Is the flue gas (comprising CO) of a thermal power plant ₂ ) Flow (m) ³ /h)；，t _k Represents the kth period, t _k+1 Representing the k+1th period. t is t _k+1 And t _k The difference between two adjacent time periods is 15min, i.e. 0.25h. And then measuring the power of each thermal power plant and the CO measured by CEMS in each period ₂ And carrying out secondary fitting on scattered points of the discharge. The coefficients of the quadratic function obtained from the result of the quadratic fit are shown in table 3. The coefficients of these quadratic functions will be used in the objective functions of the underlying model.

TABLE 1

TABLE 2

TABLE 3 Table 3

Compared with the upper layer scheduling model, the variable number in the lower layer model before the second day is obviously reduced, so that the maximum iteration number in the lower layer scheduling model with the second day scale is set to be 50 times, the population scale is 100, the variation probability is 0.7, and the crossover probability is 0.3. The model is repeatedly iterated through a genetic algorithm to find an optimal solution, so that the power plans of each thermal power plant before and during the day are formulated, and the CO of the thermal power plant is formulated ₂ And (5) comprehensive optimization of the emission.

As can be seen from fig. 4, the wind power cut-in amount is greatly different from the conventional load amount when the period of 21 to 90 is elapsed. Thus, during these periods, thermal power is required to be regulated during the day-ahead phase, whether or not the high energy load of the electric arc furnace is being regulated up and down. In the period of 21-90 hours, the thermal power plant 4 in scheme 2 is mostly at the minimum power, and the thermal power plant 3 bears the majority of the thermal power plant regulation tasks. This is because of the high energy loading of EAFThe load participates in, so that the regulating pressure of the thermal power plants is reduced, and each thermal power plant can flexibly change the power according to the minimum carbon emission target. And because for the same thermal power increment, CO of the thermal power plant 4 ₂ The emission is the largest in all thermal power plants, CO of thermal power plant 3 ₂ The emissions were the smallest in all thermal power plants (table 2). Therefore, the total power of the thermal power plant before the day is distributed to the thermal power plant 3 with the smallest carbon emission generated by the unit power as much as possible, and is not distributed to the thermal power plant 4 with the largest carbon emission generated by the unit power as much as possible.

Finally, according to the result of solving the total daily power of all thermal power plants in the upper-layer daily model, the total thermal power of the scheme II is larger than that of the scheme I (namely the scheme II is in the period), such as 30-33 periods, 49-58 periods and 84-86 periods in FIG. 6, and the CO2 emission amount of the scheme II is more than that of the scheme I, but the carbon emission amount of the thermal power plant is reduced after the EAF participates in regulation from the whole daily cycle.

As can be seen from fig. 6, the thermal power plant according to the embodiment 2 of the present application operates with a smaller total CO2 emission compared to the embodiment 1. Calculated, all thermal power plants of scheme 1 were CO before day ₂ The discharge amount was 6.552×10 ⁵ Ton, day-ahead CO of all thermal power plants of scheme 2 ₂ The discharge amount is 6.386 multiplied by 10 ⁵ Tons. Scheme 2 daily CO for all thermal power plants relative to scheme 1 ₂ The discharge amount is reduced by 2.5 percent. In addition, the daily CO per unit power of each thermal power plant ₂ The discharge amount pair is shown in table 4.

TABLE 4 Table 4

As can be seen from table 4, the CO2 emissions per unit of day power of the thermal power plant of scheme 2 are reduced relative to that of scheme 1. In particular, the power consumption of the thermal power plant 3 and the thermal power plant 4 is reduced most, since the individual thermal power plant schedules in the above-described day-ahead phase are mainly adjusted by compressing the thermal power plant 4 and increasing the power of the thermal power plant 3.

In this embodiment, the thermal power plant total power curve of scheme 2 is smoother than the thermal power plant total power curve of scheme 1. The high-energy load of the electric arc furnace is used as a schedulable resource in the system, so that the regulating pressure of the thermal power plant can be effectively reduced, the whole power curve of the thermal power plant is smoother, and the regulating pressure of the thermal power plant is reduced. In conclusion, the introduction of the high energy load of the electric arc furnace into the day-ahead upper model and the day-ahead lower model established in the method is beneficial to reducing the discharge amount of the thermal power plant, enhancing the wind power consumption level of the system and enabling the whole power curve of the thermal power plant to be smoother.

Furthermore, to verify the validity of the underlying model at the intra-day and pre-day scale in the model presented herein, the present embodiment uses total output power data for 4 wind plants 2022 years typical with a total capacity of 800MW for simulation analysis. The 4 wind farms had capacities of 100MW,300MW,200MW and 200MW, respectively. The rated capacities of the BESSs connected to the three wind farms were 10MW,30MW,20MW, respectively; the specific parameters are shown in tables 5 and 6.

TABLE 5

TABLE 6

And (5) carrying out power distribution on the wind farm by using the lower model 1 of the day-ahead scale. And referring to historical operation data of each wind power station, specifically distributing the total power of the wind power before day obtained by the upper layer model before day according to the proportion of each wind power station relative to the total power in each period, and finally determining the power curve before day of each wind power station.

And (3) performing variable modal decomposition on the daily power curve of each wind power plant according to the lower-layer strategy 1. In order to ensure the accuracy and applicability of the decomposition, the number of components decomposed was set to 6. This means that the wind power day-ahead curve of each wind farm is finely split into six wind power day-ahead components with different frequencies. In order to optimize the operation of the system, the sum of the high-frequency wind power components before the day is passed to the BESS directly connected to the wind farm for digestion. Through the step, the specific charge and discharge states of the BESS connected with each wind farm are obtained. And combining the charge and discharge power of the BESS and the day-ahead power of the wind power plants to obtain the day-ahead power curve of each wind power plant.

The energy storage devices in the wind power stations of the embodiment are continuously and rapidly adjusted in the whole daily period so as to absorb the sum of high-frequency wind power components. In addition, the wind power predicted by the day before of each wind power plant (namely, blue curve in fig. 6) has great randomness and fluctuation, and the variation quantity of the wind power active fluctuation of each 15min day before of the four wind power plants reaches 33.64MW at maximum. This will seriously affect the stable operation of the power system. And the lower layer strategy 1 of the intra-day scale is used for absorbing high-frequency components in wind power before the day of each wind power plant by using the BESS, so that the wind power in the day of each wind power plant obtained after the absorption becomes smooth. The maximum change amount of the active fluctuation of the wind power in the day is 11.12MW, and the change amount of the wind power is reduced by 66.94 percent relative to the change amount of the wind power before the day. Therefore, the daily-scale lower model 1 in the built model can fully utilize the flexibility of BESS, consume high-frequency wind power, improve the wind power utilization rate, smooth wind power fluctuation and be beneficial to wind power grid connection.

And adding the low-frequency day-ahead wind power components of all wind power plants, and finally determining the day-ahead power condition of each thermal power plant by implementing a lower model of the day-ahead stage, reducing the power of each thermal power plant as much as possible in the day-ahead stage, and eliminating the low-frequency day-ahead wind power components as much as possible with the aim of minimizing the carbon emission of the thermal power plant in the day. In this embodiment, most of the time period of the thermal power plant 4 is at the minimum power in the daily scale, and the thermal power plant 3 takes over most of the adjustment tasks of the thermal power plant. This is because for the same thermal power increment, the CO of the thermal power plant 4 ₂ The emission is the largest in all thermal power plants, CO of thermal power plant 3 ₂ The discharge is the smallest in all thermal power plants.

In the general sense, the following is a description of the present invention,the day-day two-stage scheduling strategy considering the high-load energy load regulation and the wind power modal decomposition of the electric arc furnace can ensure the maximum consumption of wind power, reduce wind power fluctuation and effectively reduce CO of a thermal power plant ₂ Discharge amount.

Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art will appreciate that the modules or units or groups of devices in the examples disclosed herein may be arranged in a device as described in this embodiment, or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into a plurality of sub-modules.

Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or groups of embodiments may be combined into one module or unit or group, and furthermore they may be divided into a plurality of sub-modules or sub-units or groups. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

Furthermore, some of the embodiments are described herein as methods or combinations of method elements that may be implemented by a processor of a computer system or by other means of performing the functions. Thus, a processor with the necessary instructions for implementing the described method or method element forms a means for implementing the method or method element. Furthermore, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is for carrying out the functions performed by the elements for carrying out the objects of the invention.

The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions of the methods and apparatus of the present invention, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.

In the case of program code execution on programmable computers, the computing device will generally include 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. Wherein the memory is configured to store program code; the processor is configured to perform the method of the invention in accordance with instructions in said program code stored in the memory.

By way of example, and not limitation, computer readable media comprise 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 typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of computer readable media.

As used herein, unless otherwise specified the use of the ordinal terms "first," "second," "third," etc., to describe a general object merely denote different instances of like objects, and are not intended to imply that the objects so described must have a given order, either temporally, spatially, in ranking, or in any other manner.

While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is defined by the appended claims.

Claims

1. The two-stage scheduling method considering electric arc furnace regulation and control and wind power modal decomposition is characterized by comprising the following steps of:

step 1, establishing a high-energy load model of an electric arc furnace;

Step 6, establishing a first objective function and a second objective function of the first day lower layer model, performing variable modal decomposition on the day-ahead power of each wind power plant obtained by the day-ahead scale lower layer model, and calculating a high-frequency component of the day-ahead power of each wind power plant absorbed by the battery energy storage system based on constraint conditions, wherein a low-frequency component of the day-ahead power of the wind power plant is absorbed by compressing the power of the thermal power plant;

2. A two-stage scheduling method taking into account electric arc furnace regulation and wind power modal decomposition according to claim 1,

the step 1 specifically comprises the following steps:

establishing a high energy load model of the electric arc furnace:

active power for the period t of the kth electric arc furnace,/->And->Respectively represent the kth arc furnaceMelting voltage and current during period t, +.>Representing the power factor of the kth electric arc furnace; />Representing the total power of all electric arc furnace loads in the power system in the t period, wherein the total power of all electric arc furnace loads in the t period is the sum of rated power of electric arc furnaces running in all systems and regulated power of all adjustable electric arc furnaces; / >Representing the base power of the arc furnace in the t period; n (N) _EAF Representing the number of electric arc furnaces operating in said power system, N _{EAF adj} Representing the number of adjustable electric arc furnaces in the power system; />Regulating power of the kth electric arc furnace in a t period; if DeltaP ^t _EAF，k >0, indicating that the kth electric arc furnace is increasing load; if DeltaP ^t _{Mg base} <0, indicating that the kth electric arc furnace is reducing load; />Is a state variable indicating whether the kth arc furnace is adjusting active power during the period t; p (P) _EAF.kmin Representing the minimum value of active power of the kth arc furnace, P _EAF.kmax Representing the maximum value of the active power of the kth electric arc furnace; k has a value ranging from 1 to N _EAF +N _{EAF adj} 。

3. A two-stage scheduling method taking into account electric arc furnace regulation and wind power modal decomposition according to claim 2,

wherein the method comprises the steps ofRepresenting dynamic power adjustment, +.>Indicating no adjustment of dynamic power.

4. A two-stage scheduling method taking into account electric arc furnace regulation and wind power modal decomposition according to claim 1,

5. A two-stage scheduling method taking into account electric arc furnace regulation and wind power modal decomposition according to claim 2,

the step S3 specifically comprises the following steps:

the objective function of the day-ahead upper model is (5):

wherein T is _d The time period number of the scheduling period before the day;the method comprises the steps that the total active power of all wind power plants before the day actually input to a power grid in a period t is calculated; />For all thermal power plants in period tSum of active power output before day; />Is the normal load for period t; n (N) _EAF Indicating the number of electric arc furnaces operating in the power system, < >>Representing the active power, ΔP, of the kth electric arc furnace t period ^t _EAF，k For regulating the power of the kth electric arc furnace in the period t, minE _abon The air discarding quantity at the day-ahead stage is minimum;

(301) Day-ahead power balance constraint:

(302) The wind power day-ahead output constraint is:

wherein,representing the predicted active power output sum of the wind farm on the bus in the t period;

P _Gd.min and P _Gd.max Respectively representing the lower limit and the upper limit of the daily power output of all the thermal power plants;

P ^t-1 _Gd representing the sum of the daily active power output of all the thermal power plants in the period (t-1), P _{Gd cli.max} And P _{Gd cli.min} Respectively representing the maximum slope and the minimum slope of the daily active power output of all the thermal power plants;

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

wherein DeltaP ^t _{EAF max} And DeltaP ^t _{EAF min} The upper and lower limits of the power regulation of the high energy load of the electric arc furnace are indicated, respectively.

6. A two-stage scheduling method taking into account electric arc furnace regulation and wind power modal decomposition according to claim 1,

the step 4 specifically comprises the following steps:

for the j-th wind farm to input the real power before day to the grid during period t,/>for the j-th wind power plant to input the historical active power to the power grid in the period t, N _W Representing the number of wind farms; />The total active power of all wind power plants before the day actually input to the power grid in the period t.

7. A two-stage scheduling method taking into account electric arc furnace regulation and wind power modal decomposition according to claim 1,

The step 5 specifically comprises the following steps: the objective function of the first day before lower model is:

wherein N is _G Representing the number of units of a thermal power plant group, T _d For the number of time periods of the day-ahead schedule period,indicating total carbon dioxide emissions during a day-ahead schedule of a thermal power plant,/->A binary start-up/shut-down variable representing a thermal power plant i during a period t, where U ^t _Gi =0 indicates that the thermal power plant is in maintenance state at time interval t, U ^t _Gi =1 indicates that the thermal power plant is in operation during period t, +.>Representing the daily power generation amount of a thermal power plant i in a t period, and a coefficient a _i 、b _i And c _i Representing the secondary carbon emission of the thermal power plant i respectivelyCoefficient, primary term carbon emission coefficient, and constant term carbon emission coefficient;

(501) Day-ahead power balance constraint of thermal power plant:

wherein N is _G The number of units of the thermal power plant group is represented,representing the daily power generation amount of the thermal power plant i in the t period;

(502) Day-ahead climbing constraint of a single thermal power plant:

wherein P is ^t-1 _Gd，i Representing the daily power generation amount, P, of a thermal power plant i within a time interval (t-1) _Gdcli.imax And P _Gdcli.imin Respectively representing the maximum slope limit and the minimum slope limit of the daily power generation amount of the thermal power plant i;

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

8. A two-stage scheduling method taking into account electric arc furnace regulation and wind power modal decomposition according to claim 1,

the step 6 specifically comprises the following steps:

where m is the number of subcomponents that are decomposed, P ^t _{Wd out，j，m} Is to make P ^t _{Wd out，j} The mth sub-component of wind power input to the power grid before the day of the jth wind power field is obtained after the variable modal decomposition is carried out; e represents the iteration number, starting from 0 times; omega is the frequency of the wind power component, omega _j.s.m.e+1 Is the frequency of the m-th sub-component of wind power input to the power grid before the day of the jth wind power plant in the e+1th iteration;representing the m-th sub-component of wind power input to a power grid before the day of the omega frequency of the t period of the j-th wind power plant obtained after variable mode decomposition when the e-th iteration is carried out; alpha is a regularization parameter for balancing weights between signal reconstruction and modal bandwidth minimization; / >Is Lagrangian multiplier of omega frequency in the e-th iteration;

inputting the jth wind power plant obtained by the first day front lower layer model into the day front active power of the power grid in the period tPerforming variable mode decomposition to decompose fixed m components, and respectively summing high-frequency components and low-frequency components to obtain battery energy storage system power connected with each wind power plant and total daily power of the thermal power plant; adding the power of the battery energy storage system connected with each wind farm to the daily front power of each wind farm to obtain daily power of each wind farm>

in order to determine the average slope of the decomposed wind power component, determining whether the slope change amount delta f is consumed by a thermal power plant or BESS by judging the range of the slope change amount delta f of the average slope of the decomposed wind power component; Δf _low The upper limit of the low-frequency band is the slope variation of the average slope; Δf _high The upper limit of the high-frequency band is the slope variation of the average slope; u (U) _low Representing a set of low-frequency wind power components; u (U) _high Representing a set of high-frequency wind power components;

the first objective function of the first day underlying model is:

wherein,is the sum of all high frequency components of the day-ahead power finally output to the system by the jth wind farm in the t period The charging and discharging power of the battery energy storage system connected with the jth wind power station in the period t; d (D) ^t _high，j For t period->And->Absolute value of the difference;

(6011) The daily power balance constraint of the jth wind power plant;

(6012) Equality constraints of conservation of charge and discharge amounts of the BESS;

wherein: t (T) _i For the total number of time periods within the day period setting time, t=1, 2 … T _i The method comprises the steps of carrying out a first treatment on the surface of the Δt is defined as the time of each period in the day;

(6013) Upper and lower limit constraint conditions of charge and discharge power of the battery energy storage system:

wherein: p (P) _{BESS jmax} The upper power limit, P, of the BESS for the jth wind farm connection _{BESS jmin} A lower power limit for the BESS for the jth wind farm connection;

(6014) Upper and lower limit constraint conditions of charge state of the battery energy storage system:

wherein,state of charge, SOC, of BESS at time t for the jth wind farm connection _max The SOC is the upper limit of the state of charge of BESS _min A lower state of charge limit for the battery energy storage system; according to the relation between the charge state of the battery energy storage system and the power of the battery energy storage system, the change of the electric quantity in the charge and discharge process of the battery energy storage system is as follows:

Wherein SOC is ^t+1 _j State of charge, η, for the (t+1) period of the energy storage device connected to the jth wind farm _c And eta _d Charging efficiency and discharging efficiency of the BESS are respectively, and delta t is a charging and discharging time interval; c (C) _BESS.j The capacity size of the BESS connected to the jth wind farm.

9. A two-stage scheduling method taking into account electric arc furnace regulation and wind power modal decomposition according to claim 1,

establishing a second objective function of the first day lower layer model:

wherein,is the sum of all low-frequency components of the day-ahead power actually output to the system by all wind farms in the period t;is the adjustment quantity of the power in the time period t and all thermal power plants; d (D) ^t _low Is the absolute value of the difference between the two t periods; the sum delta P of the adjustment quantity of the power in all thermal power plants ^t _Gintr Total active power before day in t period of all thermal power plants +>The subtraction is the total power P in all thermal power plants ^t _Gintr Calculating total power P in all thermal power plants ^t _Gintr ；

(6021) Thermal power day-ahead output power upper and lower limit constraint:

(6022) Climbing speed constraint of power in thermal power day:

wherein P is ^t-1 _Gd The total active power of all thermal power plants before the day of the t-1 period; p (P) _{Gintr cli，max} And P _{Gintr cli，min} And the maximum value and the minimum value of the climbing rate of the total power in all thermal power plants in the day are respectively.

10. A two-stage scheduling method taking into account electric arc furnace regulation and wind power modal decomposition according to claim 1,

the step 7 specifically comprises the following steps:

establishing an objective function of the lower layer model in the second day:

(701) Equation constraint of power in thermal power plant day:

(702) Climbing limit of daily scale of each thermal power plant:

wherein DeltaP ^t _Gintr，i Representing the daily regulation output, P, of a thermal power plant i within a time interval t-1 ^t _{Gintrcli，imax} And P ^t _{Gintrcli，imin} Is a symbol for representing the maximum and minimum slope limits of the daily power output of the thermal power plant i;