Method and system for optimizing deep peak load regulation of heating units

Abstract

The present invention provides a method and system for optimizing the deep peak load regulation of heating units, which belongs to the field of data processing technology. The present invention obtains the optimal strategy for the deep peak load regulation of heating units by constructing multiple matrices for calculation, and can obtain accurate solutions for the lowest coal consumption of autonomous deep regulation and joint deep regulation at each gear. In addition, when the method is used for scenarios with many units and many deep regulation gears, the operating speed will be significantly improved compared with the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for optimizing deep peak regulation of a heating unit. Background Art

[0002] Deep peak regulation, abbreviated as deep regulation, is a mode of operation in which the thermal power plants are forced to reduce their output due to the large difference in peak-to-valley load of the power grid, and the generator sets exceed the basic peak regulation range for peak regulation. Generally, the load rate of deep peak regulation is 40% to 30%.

[0003] There are two main ways for heating units to participate in deep peak regulation: autonomous deep regulation (peak regulation is performed by the generator set itself, and the electric boiler does not participate in peak regulation) and joint deep regulation (the electric boiler assists the generator set in peak regulation). How to accurately and efficiently obtain the generator load and electric boiler load corresponding to the minimum coal consumption at each deep regulation gear is an important purpose of deep peak regulation;

[0004] To address this problem, when calculating the deep peak-shaving gear amount, it is currently necessary to cyclically determine whether it is in a certain gear according to the situation, and then calculate the deep regulation amount of the gear.

[0005] However, when the number of gears or units increases gradually, the code volume will be redundant and the calculation speed will be greatly reduced. Therefore, an efficient optimization method is urgently needed. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] In view of the deficiencies in the prior art, the present invention provides a method and system for optimizing deep peak load regulation of a heating unit, which solves the problem of time-consuming calculations.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] In a first aspect, a method for optimizing deep peak load regulation of a heating unit is provided, the method comprising:

[0011] Get the rated capacity p of each generator n , each deep adjustment gear benchmark db j 、The power generation sharing ratio of each generator f n , the rated power of the electric boiler d, the coal consumption coefficient matrix α corresponding to each generator n , the minimum load constraint coefficient matrix β corresponding to each generator n , the maximum load constraint coefficient matrix γ corresponding to each generator n 、Plant power load factor matrix δ corresponding to each generator n, total heating load h, electric boiler efficiency η, deep adjustment gear reference difference θ;

[0012] Based on the rated capacity of each generator p n , the rated power d of the electric boiler, construct the matrix PM and the matrix DM;

[0013] The matrix PM is:

[0014] PM=[pm1 … pm m … pm M ] M

[0015] The matrix DM is:

[0016] DM=[dm1 … dm m …dm M ] M

[0017] Wherein, M = K × I; I = d + 1;

[0018] It represents the sum of the rated capacities of all generators;

[0019] p n represents the rated capacity of the nth generator;

[0020] N represents the number of generators;

[0021] Pm m represents the mth data in the matrix PM; and pm m =pl m％K ;

[0022] m%K represents remainder calculation, m%K=1, 2, ..., K;

[0023] pl k Represents the kth data in the matrix PL;

[0024] The matrix PL = [pl1 … pl k …pl K ] K ; and pl1=0, pl k -pl k-1 =1;

[0025] dm m represents the mth data in the matrix DM; and

[0026] Indicates rounding up calculation;

[0027] dl i Represents the i-th data in the matrix DL;

[0028] Matrix DL = [dl1 … dl i …dl I ] I ; and dl1=0, dl i -dl i-1 =1;

[0029] Based on each deep adjustment gear benchmark db j , construct the matrix DBM;

[0030] The matrix DBM is:

[0031]

[0032] Among them, dBm m，j represents the data in the mth row and jth column of the matrix DBM; and dbm m，j =db j ;

[0033] db j represents the jth deep adjustment gear position benchmark;

[0034] J represents the number of deep adjustment gears;

[0035] Based on the matrix PM, matrix DM and the coal consumption coefficient matrix α corresponding to each generator n , construct matrix C;

[0036] The matrix C is:

[0037] C=[c1 … c m … c M ] M

[0038] Among them, c m Represents the mth data in matrix C;

[0039]

[0040] α n represents the coal consumption coefficient matrix corresponding to the nth generator;

[0041] E represents the coal consumption calculation formula;

[0042] Based on the total heating load h, the efficiency of the electric boiler η, the matrix DM, and the power generation allocation ratio f of each generator n , the minimum load constraint coefficient matrix β corresponding to each generator n , the maximum load constraint coefficient matrix γ corresponding to each generator n, construct matrix Pmin and matrix Pmax;

[0043] The matrix Pmin is:

[0044]

[0045] The matrix Pmax is:

[0046]

[0047] Among them, pmin m,n Represents the data of the mth row and nth column of the matrix Pmin, and pmin m,n =Q(β n , dm m );

[0048] f n Indicates the power generation sharing ratio of the nth generator;

[0049] β n represents the minimum load constraint coefficient matrix of the nth generator;

[0050] Q represents the minimum load calculation formula;

[0051] pmax m，n Represents the data of the mth row and nth column of the matrix Pmax; pmax m，n =H(γ n , dm m );

[0052] γ n represents the maximum load constraint coefficient matrix of the nth generator;

[0053] H represents the maximum load calculation formula;

[0054] Based on the matrix PM, matrix DM, and the power generation allocation ratio f of each generator n And the plant power load factor matrix δ corresponding to each generator n , construct the matrix PG;

[0055] The matrix PG is:

[0056] PG=[pg1 … pg m … Pg M ] M

[0057] Among them, pg m Represents the mth data in the matrix PG;

[0058]

[0059] δn =[δ n，1 , δ n，2 , δ n，3 ] represents the auxiliary power load factor matrix of the nth generator;

[0060] R represents the calculation formula for the plant power load;

[0061] Based on the matrix L corresponding to each deep adjustment gear j and the matrix LL j , construct the matrix LD corresponding to each deep adjustment gear j ;

[0062] LD j =[ld 1,j …ld m,j …ld M,j ] M

[0063] Among them, ld m,j Representation matrix LD j The mth data in ;

[0064] ld m,j =MAX(ll m，j -l m，j ,0)

[0065] Get the index of the data less than 0 in the matrix PG; and get the matrix Pump f The index of the data that is less than the matrix Pmin or greater than the matrix Pmax; the matrix PM, the matrix DM, the matrix C, the matrix L j and the matrix LD j Delete the value at the corresponding index position in the matrix to obtain the modified matrix PM, matrix DM, matrix C, matrix L j and the matrix LD j ;

[0066] The modified matrix PM is:

[0067] PM=[pm1 … pm m″ … pm M″ ] M″

[0068] The modified matrix DM is:

[0069] DM=[dm1 … dm m″ …dm M″ ] M″

[0070] The modified matrix C is:

[0071] C=[c1 … cm″ … c M″ ] M″

[0072] The modified matrix L j for:

[0073] L j =[l 1，j … l m″，j … l M″，j ] M″

[0074] The modified matrix LD j for:

[0075] LD j =[ld 1,j …ld m″,j …ld M″,j ] M″

[0076] Wherein, M″=M-neg; neg represents the number of obtained indexes after deduplication;

[0077] Based on the modified matrix PM, matrix DM, matrix C, matrix L j and the matrix LD j , construct the result matrix RE;

[0078] The result matrix RE is:

[0079]

[0080] Based on the result matrix RE, the minimum coal consumption strategy for each deep adjustment gear is screened out.

[0081] Furthermore, the coal consumption coefficient matrix α corresponding to the nth generator n =[α n,1 … α n,10 ];

[0082] And the coal consumption calculation formula E is:

[0083]

[0084] Furthermore, the minimum load constraint coefficient matrix β of the nth generator is n =[β n，1 , β n，2 , β n，3 ];

[0085] And the minimum load calculation formula Q is:

[0086] Q(β n , dmm )=β n，1 *(h-dm m *η*3.6×f n ) 2 +β n，2 *(h-dm m *η*3.6×f n )+β n，3

[0087] The maximum load constraint coefficient matrix γ of the nth generator n =[γ n，1 , γ n，2 , γ n,3 ];

[0088] And the maximum load calculation formula H is:

[0089] H(β n , dm m )=γ n，1 *(h-dm m *η*3.6×f n ) 2 +γ n，2 *(h-dm m *η*3.6×f n )+γ n,3 .

[0090] Furthermore, the auxiliary power load factor matrix δ of the nth generator is n =[δ n，1 , δ n，2 , δ n，3 ];

[0091] And the factory power load calculation formula R is:

[0092] R(δ n , f n , p.m. m )=δ n，1 ×(pm m ×f n ) 3 +δ n，2 ×(pm m ×f n ) 2 +δ n，3 ×(pm m ×f n ).

[0093] Furthermore, the minimum coal consumption strategy for each deep adjustment gear position is screened out based on the result matrix RE, including:

[0094] For the minimum coal consumption strategy of the jth gear under autonomous deep adjustment:

[0095] Filter out dm from the result matrix RE m″ = Data of all rows corresponding to 0;

[0096] Determine whether there is a value greater than 0 in the column corresponding to the j-th level of the autonomous deep adjustment;

[0097] If it exists, when j≠J, filter out the data with a value of 0 in the corresponding column of the j+1th file, and then filter out the row number m" corresponding to the minimum coal consumption value in the corresponding column of the coal consumption value minc , and then the generator load corresponds to the m″th column minc The data of the row is used as the generator load corresponding to the j-th gear of autonomous deep adjustment;

[0098] When j=J, the number of rows corresponding to the minimum coal consumption value in the corresponding column of coal consumption value is selected from m″ minc , and then the generator load corresponds to the m″th column minc The data of the row is used as the generator load corresponding to the j-th gear of autonomous deep adjustment;

[0099] If it does not exist, there will be no autonomous deep adjustment of the jth level.

[0100] Furthermore, the method of screening out the minimum coal consumption strategy for each deep adjustment gear position based on the result matrix RE also includes:

[0101] For the minimum coal consumption strategy of the jth gear under joint deep regulation:

[0102] Filter out dm from the result matrix RE m″ ≠0 corresponding to all rows of data;

[0103] Determine whether there is a value greater than 0 in the column corresponding to the j-th level of the joint deep adjustment;

[0104] If it exists, when j≠J, filter out the data with a value of 0 in the corresponding column of the j+1th level of the joint deep adjustment; then filter out the row number m″ corresponding to the minimum coal consumption value in the corresponding column of the coal consumption value. minc , and then the generator load corresponds to the m″th column minc The data in the row is used as the generator load corresponding to the jth level of joint deep adjustment; and the electric boiler load is corresponding to the m″th level in the column minc The data of the row is used as the electric boiler load corresponding to the j-th level of joint deep adjustment;

[0105] When j=J, select the row number m" corresponding to the minimum coal consumption value in the corresponding column of coal consumption value minc , and then the generator load corresponds to the m″th column minc The data in the row is used as the generator load corresponding to the jth level of joint deep adjustment; and the electric boiler load is corresponding to the m″th level in the columnminc The data of the row is used as the electric boiler load corresponding to the j-th level of joint deep adjustment;

[0106] If it does not exist, there will be no autonomous deep adjustment of the jth level.

[0107] In a second aspect, a system for optimizing deep peak load regulation of a heating unit is provided, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.

[0108] (III) Beneficial effects

[0109] The present invention provides a method and system for optimizing deep peak load regulation of a heating unit. Compared with the prior art, the method has the following beneficial effects:

[0110] The present invention obtains the optimal strategy for the heating units to participate in deep peak regulation by constructing multiple matrices for calculation, and can obtain accurate solutions for the lowest coal consumption of autonomous deep regulation and joint deep regulation at each gear. In addition, when the method is used in scenarios with many units and many deep regulation gears, the operating speed will be significantly improved compared with the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0112] Figure 1 is a flow chart of an embodiment of the present invention;

[0113] Figure 2 A schematic diagram of a result matrix constructed for an embodiment of the present invention. DETAILED DESCRIPTION

[0114] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0115] The embodiments of the present application solve the problem of time-consuming calculations by providing a method and system for optimizing deep peak load regulation of a heating unit.

[0116] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0117] Embodiment 1:

[0118] like Figure 1 As shown, the present invention provides a method for optimizing deep peak load regulation of a heating unit, which is executed by a computer and includes:

[0119] Get the rated capacity p of each generator n , each deep adjustment gear benchmark db j 、The power generation sharing ratio of each generator f n , the rated power of the electric boiler d, the coal consumption coefficient matrix α corresponding to each generator n , the minimum load constraint coefficient matrix β corresponding to each generator n , the maximum load constraint coefficient matrix γ corresponding to each generator n 、Plant power load factor matrix δ corresponding to each generator n , total heating load h, electric boiler efficiency η, deep adjustment gear reference difference θ;

[0120] Based on the rated capacity of each generator p n , the rated power d of the electric boiler, construct the matrix PM and the matrix DM;

[0121] Based on each deep adjustment gear benchmark db j , construct the matrix DBM;

[0122] Based on the matrix PM, matrix DM and the coal consumption coefficient matrix α corresponding to each generator n , construct matrix C;

[0123] Based on the total heating load h, the efficiency of the electric boiler η, the matrix DM, and the power generation allocation ratio f of each generator n , the minimum load constraint coefficient matrix β corresponding to each generator n , the maximum load constraint coefficient matrix γ corresponding to each generator n , construct matrix Pmin and matrix Pmax;

[0124] Based on the matrix PM, matrix DM, and the power generation allocation ratio f of each generator n And the plant power load factor matrix δ corresponding to each generator n , construct the matrix PG;

[0125] Based on the matrix PM and the matrix DBM, construct the matrix L′ corresponding to each deep adjustment gear j ; and based on the matrix L′ corresponding to each deep adjustment gear jand the deep adjustment gear reference difference θ, construct the matrix L corresponding to each deep adjustment gear j ;

[0126] Based on the matrix PM, the matrix DM and the matrix DBM, the matrix LL′ corresponding to each deep adjustment gear is constructed; based on the matrix LL′ corresponding to each deep adjustment gear j and the deep adjustment gear reference difference θ, construct the matrix LL corresponding to each deep adjustment gear j ;

[0127] Based on the matrix L corresponding to each deep adjustment gear j and the matrix LL j , construct the matrix LD corresponding to each deep adjustment gear j ;

[0128] Get the index of the data less than 0 in the matrix PG; and get the matrix Pump f The index of the data that is less than the matrix Pmin or greater than the matrix Pmax; the matrix PM, the matrix DM, the matrix C, the matrix L j and the matrix LD j Delete the value at the corresponding index position in the matrix to obtain the modified matrix PM, matrix DM, matrix C, matrix L j and the matrix LD j ;

[0129] Based on the modified matrix PM, matrix DM, matrix C, matrix L j and the matrix LD j , construct the result matrix RE;

[0130] Based on the result matrix RE, the minimum coal consumption strategy for each deep adjustment gear is screened out.

[0131] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0132] The embodiment of the present invention obtains the optimal strategy for the heating units to participate in deep peak regulation by constructing multiple matrices for calculation, and can obtain accurate solutions for the lowest coal consumption of autonomous deep regulation and joint deep regulation at each gear. In addition, when the method is used in scenarios with many units and many deep regulation gears, the operating speed will be significantly improved compared with the prior art.

[0133] The specific implementation process of this embodiment is described in detail below:

[0134] The known quantities include:

[0135] Get the rated capacity p of each generator n , n=1,2,…N;

[0136] Each deep adjustment gear benchmark db j, j = 1, 2, ... J;

[0137] The power generation sharing ratio of each generator is f n , n = 1, 2, ... N;

[0138] The coal consumption coefficient matrix α corresponding to each generator n , n=1,2,…N;

[0139] The minimum load constraint coefficient matrix β corresponding to each generator n , n=1,2,…N;

[0140] The maximum load constraint coefficient matrix γ corresponding to each generator n , n=1,2,…N;

[0141] The plant power load factor matrix δ corresponding to each generator n , n=1,2,…N;

[0142] Rated power of electric boiler d;

[0143] Total heating load h;

[0144] Electric boiler efficiency η;

[0145] S1, based on the rated capacity p of each generator n , the rated power d of the electric boiler, construct the matrix PM and the matrix DM;

[0146] In specific implementation,

[0147] The matrix PM is used to represent the generator load and can be expressed as:

[0148] PM=[pm1 … pm m … pm M ] M

[0149] The matrix DM is used to represent the load of the electric boiler and can be expressed as:

[0150] DM=[dm1 … dm m …dm M ] M

[0151] in,

[0152] M = K × I; I = d + 1;

[0153] It represents the sum of the rated capacities of all generators;

[0154] p nIt represents the rated capacity of the nth generator, in MW;

[0155] N represents the number of generators;

[0156] pm m represents the mth data in the matrix PM, in MW; and pm m =pl m％K ;

[0157] m%K represents remainder calculation, m%K=1, 2, ..., K;

[0158] pl k Represents the kth data in the matrix PL, unit MW;

[0159] The matrix PL = [pl1 … pl k …pl K ] K ; and pl1=0, pl k -pl k-1 =1;

[0160] dm m represents the mth data in the matrix DM, in units of MW; and

[0161] Indicates rounding up calculation;

[0162] dl i Represents the i-th data in the matrix DL, unit MW;

[0163] Matrix DL = [dl1 … dl i …dl I ] I ; and dl1=0, dl i -dl i-1 =1.

[0164] Obviously, the data in the matrix PM is composed of the K data in the matrix PL repeated I times in sequence, that is, pl1~pl K Repeat I times; PM = [pl1 … pl K … … pl1 … pl K ] M ;

[0165] The data in the matrix DM is composed of I data in the matrix DL repeated K times one by one and arranged in sequence, that is, dl1 is repeated K times, then dl2 is repeated K times, and so on, until dl I Repeat K times; that is, DM = [dl1 … dl1 … … dl I …dlI ] M .

[0166] For example:

[0167] Take 4 generators as an example: the rated capacity of each generator, in MW, can be expressed as:

[0168] P = [600,600,330,330]

[0169] Through calculation, we can get K = 1861; then the matrix PL can be expressed as:

[0170] PL=[0 1 2 … 1860] 1861

[0171] The power of the electric boiler is d = 240MW, so I = 241; then the matrix DL can be expressed as:

[0172] DL=[0 1 2 … 240] 241

[0173] Then M = 241*1861 = 448501;

[0174] The matrix PM is obtained by looping the data in the matrix PL, i.e., 0 to 1860, 241 times, and can be expressed as:

[0175] PM=[0…1860……0…1860] 448501

[0176] The matrix DM is obtained by cycling the data in the matrix DL, i.e., 0 to 240, one by one for 1861 times. That is, the first 1861 data are all 0, the next 1861 data are all 1, and so on, until the last 1861 data are all 240. Therefore, it can be expressed as:

[0177] DM=[0…0…1…1…240…240] 448501

[0178] S2, based on each deep adjustment gear benchmark db j , construct the matrix DBM;

[0179] In specific implementation, the matrix DBM is used to represent each deep adjustment gear reference, which can be expressed as:

[0180]

[0181] in,

[0182] dbm m，j represents the data in the mth row and jth column of the matrix DBM, in MW; and dbmm，j =db j ;

[0183] db j represents the jth deep adjustment gear position benchmark;

[0184] J represents the number of deep adjustment gears;

[0185] For example:

[0186] For example, if there are 5 gears for deep peak regulation, that is, J=5, the known deep peak regulation gear standard is:

[0187] DB = [930 744 558 372 186] 5

[0188] Among them, 930MW is the benchmark for the first tier, 744MW is the benchmark for the second tier, and so on.

[0189] Then the constructed matrix DBM is:

[0190]

[0191] In the matrix DBM, the data in each row is DB=[930 744 558 372 186]5, and there are 448501 rows in total.

[0192] S3, based on the matrix PM, matrix DM and the coal consumption coefficient matrix α corresponding to each generator n , construct matrix C;

[0193] In a specific implementation, the matrix C is used to represent the coal consumption value of the corresponding matrix PM and matrix DM, which can be expressed as:

[0194] C=[c1 … c m … c M ] M

[0195] Among them, c m Represents the mth data in matrix C, unit t / h;

[0196]

[0197] α n represents the coal consumption coefficient matrix corresponding to the nth generator; α n =[α n,1 … α n,10 ];

[0198] E represents the coal consumption calculation formula; and

[0199]

[0200] For example:

[0201] Since there are 4 generators in total, the coal consumption coefficient matrix corresponding to each generator is:

[0202] α1=[α 1,1 … α 1，10 ]

[0203] α2=[α 1，1 … α 1，10 ]

[0204] α3=[α 1，1 … α 1，10 ]

[0205] α4=[α 1，1 … α 1，10 ]

[0206] For example, taking m=1, pm1=0, dm1=0;

[0207] Then the coal consumption coefficient corresponding to the first generator is: α 1，1 ~α 1，10 ; Substitute into the coal consumption calculation formula E to get:

[0208] c 1，1 =E(α1, pm1, dm1)

[0209] The remaining generators correspond to c 2，1 , C 3，1 、c 4,1 It can be calculated in the same way;

[0210] Then calculate c1 = c 1,1 +c 2,1 +c 3,1 +c 4,1 , and so on, we can get any c m As a result, we get the matrix C:

[0211] C=[67.18 67.44 67.70 … 563.4 563.68 563.95] 448501

[0212] S4, based on the total heating load h, electric boiler efficiency η, matrix DM, and the power generation allocation ratio f of each generator n , the minimum load constraint coefficient matrix β corresponding to each generator n , the maximum load constraint coefficient matrix γ corresponding to each generator n , construct matrix Pmin and matrix Pmax;

[0213] In a specific implementation, the matrix Pmin is used for the lower limit constraint of the generator load with respect to the extraction steam load, which can be expressed as:

[0214]

[0215] The matrix Pmax is used to represent the upper limit constraint of the generator load with respect to the extraction load, which can be expressed as:

[0216]

[0217] in,

[0218] pmin m,n Represents the data of the mth row and nth column of the matrix Pmin, in units of MW, and pmin m,n =Q(β n , dm m );

[0219] f n Indicates the power generation sharing ratio of the nth generator;

[0220] β n =[β n，1 , β n，2 , β n，3 ] represents the minimum load constraint coefficient matrix of the nth generator;

[0221] Q represents the minimum load calculation formula;

[0222] Q(β n , dm m )=β n，1 *(h-dm m *η*3.6×f n ) 2 +β n，2 *(h-dm m *η*3.6×f n )+β n，3

[0223] pmax m，n Represents the data of the mth row and nth column of the matrix Pmax, in MW; pmax m，n =H(γ n , dm m );

[0224] γ n =[γ n，1 , γ n，2 , γ n，3 ] represents the maximum load constraint coefficient matrix of the nth generator;

[0225] H represents the maximum load calculation formula;

[0226] H(β n , dm m )=γ n，1 *(h-dm m *η*3.6×f n ) 2 +γ n,2 *(h-dm m *η*3.6×f n )+γ n，3

[0227] For example: Through calculation, we finally get:

[0228]

[0229]

[0230] S5, based on matrix PM, matrix DM, and the power generation allocation ratio of each generator f n And the plant power load factor matrix δ corresponding to each generator n , construct the matrix PG;

[0231] In a specific implementation, the matrix PG is used to represent the amount of power online, which can be expressed as:

[0232] PG=[pg1 … pg m … pg M ] M

[0233] Among them, pg m Represents the mth data in the matrix PG;

[0234]

[0235] δ n =[δ n，1 , δ n，2 , δ n，3 ] represents the auxiliary power load factor matrix of the nth generator;

[0236] R represents the calculation formula for the plant power load;

[0237] R(δ n , f n , p.m. m )=δ n，1 ×(pm m ×f n ) 3 +δ n，2 ×(pm m ×f n ) 2 +δn，3 ×(pm m ×f n )

[0238] For example, according to the above formula, we finally get:

[0239] PG=[0 0.92 1.84 … 1477.8 1478.7 1479.6] 448501

[0240] S6. Based on the matrix PM and the matrix DBM, construct the matrix L′ corresponding to each deep adjustment gear j ; and based on the matrix L′ corresponding to each deep adjustment gear j and the deep adjustment gear reference difference θ, construct the matrix L corresponding to each deep adjustment gear j ;

[0241] The matrix L′ j for:

[0242] L′ j =[l′ 1，j … l′ m，j … l′ M，j ] M

[0243] Among them, L′ j represents the matrix corresponding to the jth deep adjustment position;

[0244] l′ m，j Denote the matrix L′ j The mth data in; and l′ m，j =MAX(dbm m，j -pm m , 0);

[0245] The matrix L j Used to indicate the autonomous deep adjustment amount of a certain gear:

[0246] L j =[l 1，j … l m，j … l M，j ] M

[0247] Among them, l m，j Represents the matrix L j The mth data in; and the l m，j for:

[0248]

[0249] θ represents the deep adjustment gear position reference difference, which is a preset constant.

[0250] For example:

[0251] The deep adjustment gear position reference difference θ can be calculated in advance. Taking db1 and db2 as an example, it is calculated that θ=930-744=186.

[0252]

[0253]

[0254] PM=[0…1860……0…1860] 448501

[0255] When j = 1, m = 4; dBm 4，1 =930, pm4=3,

[0256] Then l′ 4，1 =MAX(dbm m，j -pm m , 0) = 927;

[0257] When j = 2, m = 4; l' 4，2 =741;

[0258] When j = 3, m = 4; l' 4，3 =555;

[0259] When j = 4, m = 4, l' 4，4 =369;

[0260] When j = 5, m = 4, l' 4，5 =183;

[0261] At this time, l′ 4，5 =183>0; then l′ is satisfied m，j ≥θ, so l 4，5 =186;

[0262] Finally, according to the formula, we can get:

[0263] L′1=[930 929 928 927 … 0 0 0] 448501

[0264] L'2=[744 743 742 741 ... 0 0 0] 448501

[0265] L′3=[558 557 556 555 … 0 0 0] 448501

[0266] L′4=[372 371 370 369 … 0 0 0] 448501

[0267] L′5=[186 185 184 183 … 0 0 0] 448501

[0268] After correction, we can get:

[0269] L1=[186 186 186 186 … 0 0 0] 448501

[0270] L2=[186 186 186 186 … 0 0 0] 448501

[0271] L3=[186 186 186 186 … 0 0 0] 448501

[0272] L4=[186 186 186 186 … 0 0 0] 448501

[0273] L5=[186 185 184 183 … 0 0 0] 448501

[0274] S7, based on the matrix PM, the matrix DM and the matrix DBM, construct the matrix LL′ corresponding to each deep adjustment gear position; based on the matrix LL′ corresponding to each deep adjustment gear position j and the deep adjustment gear reference difference θ, construct the matrix LL corresponding to each deep adjustment gear j ;

[0275] The matrix LL′ is:

[0276] LL′ j =[ll′ 1,j … ll′ m,j … ll′ M,j ] M

[0277] Among them, ll′ m，j Denote the matrix LL′ j The mth data in ;

[0278] ll′ m，j =MAX(dbm m，j -pm m -dm m ,0)

[0279] The matrix LL j It is used to represent the combined deep adjustment of each gear, which can be expressed as:

[0280] LL j =[ll1，j …ll m，j …ll M，j ] M

[0281] Among them, ll m，j Represents the matrix LL j The mth data in; and the ll m，j for:

[0282]

[0283] For example:

[0284] Similar to the example in S6, the final formula can be calculated as follows:

[0285] LL'1=[930 929 928 927 ... 0 0 0] 448501

[0286] LL'2=[744 743 742 741 ... 0 0 0] 448501

[0287] LL′3=[558 557 556 555 … 0 0 0] 448501

[0288] LL′4=[372 371 370 369 … 0 0 0] 448501

[0289] LL′5=[186 185 184 183 … 0 0 0] 448501

[0290] After correction, we can get:

[0291] LL1=[186 186 186 186 … 0 0 0] 448501

[0292] LL2=[186 186 186 186 … 0 0 0] 448501

[0293] LL3=[186 186 186 186 … 0 0 0] 448501

[0294] LL4=[186 186 186 186 … 0 0 0] 448501

[0295] LL5=[186 185 184 183 … 0 0 0] 448501

[0296] S8, based on the matrix L corresponding to each deep adjustment gear j and the matrix LL j , construct the matrix LD corresponding to each deep adjustment gear j ;

[0297] In a specific implementation, the LD j It is used to indicate the deep adjustment amount of electric boilers in each gear, which can be expressed as;

[0298] LD j =[ld 1，j …ld m，j …ld M，j ] M

[0299] Among them, ld m，j Representation matrix LD j The mth data in ;

[0300] ld m，j =MAX(ll m，j -l m，j ,0)

[0301] For example: By calculation, we can get:

[0302] LD1=[0 0 0 0 … 0 0 0] 448501

[0303] LD2=[0 0 0 0 … 0 0 0] 448501

[0304] LD3=[0 0 0 0 … 0 0 0] 448501

[0305] LD4=[0 0 0 0 … 0 0 0] 448501

[0306] LD5=[0 0 0 0 … 0 0 0] 448501

[0307] S9, obtain the index of the data less than 0 in the matrix PG; and obtain the matrix Pump f The index of the data that is less than the matrix Pmin or greater than the matrix Pmax; the matrix PM, the matrix DM, the matrix C, the matrix L j and the matrix LD j Delete the value at the corresponding index position in the matrix to obtain the modified matrix PM, matrix DM, matrix C, matrix L j and the matrix LD j ;

[0308] The modified matrix PM is:

[0309] PM=[pm1 … pm m″ … pm M″ ] M″

[0310] The modified matrix DM is:

[0311] DM=[dm1 … dm m″ …dm M″ ] M″

[0312] The modified matrix C is:

[0313] C=[c1 … c m″ … c M″ ] M″

[0314] The modified matrix L j for:

[0315] L j =[l 1，j … l m″，j … l M″，j ] M″

[0316] The modified matrix LD j for:

[0317] LD j =[ld 1,j …ld m″,j …ld M″，j ] M″

[0318] Wherein, M″=M-neg; neg represents the number of obtained indexes after deduplication;

[0319] S10, based on the modified matrix PM, matrix DM, matrix C, matrix L j and the matrix LD j , construct the result matrix RE;

[0320] The result matrix RE is:

[0321]

[0322] For example:

[0323] The final result matrix is ​​as follows Figure 2As shown in the figure, the first column is all possible situations of the generator load after screening out some data that do not meet the constraints, the second column is all possible situations of the electric boiler load, the third column is the coal consumption corresponding to the generator load and the electric boiler load in this row, the 4th to 8th columns are the deep adjustment amounts corresponding to the autonomous deep adjustment 1st to 5th gears respectively; the 9th to 13th columns are the amounts of the electric boiler participating in the deep adjustment corresponding to the joint deep adjustment 1st to 5th gears respectively.

[0324] S11. Based on the result matrix RE, the minimum coal consumption strategy for each deep adjustment gear is selected;

[0325] The specific implementation includes the following steps:

[0326] (1) For the minimum coal consumption strategy of the jth gear under autonomous deep adjustment:

[0327] Filter out dm from the result matrix RE m″ = Data of all rows corresponding to 0;

[0328] Determine the data in the column corresponding to the jth level of the autonomous deep adjustment (i.e., the j+3th column): 1，j ~l M″，j Is there a value greater than 0 in ?

[0329] If it exists, when j≠J, filter out the data with a value of 0 in the corresponding column of the j+1th level (i.e., the (j+1)+3th column), and then filter out the row number m" corresponding to the minimum coal consumption value in the corresponding column of the coal consumption value (i.e., the 3rd column). minc , and then the m″th column in the generator load corresponding column (i.e. column 1) minc The data of the row is used as the generator load corresponding to the j-th gear of autonomous deep adjustment;

[0330] When j=J, the number of rows corresponding to the minimum coal consumption value in the corresponding column of coal consumption value (i.e., the third column) is selected. minc , and then the m″th column in the generator load corresponding column (i.e. column 1) minc The data of the row is used as the generator load corresponding to the j-th gear of autonomous deep adjustment;

[0331] If it does not exist, there will be no autonomous deep adjustment of the jth level.

[0332] (2) For the minimum coal consumption strategy of the jth gear under joint deep regulation:

[0333] Filter out dm from the result matrix RE m″ ≠0 corresponding to the data of all rows;

[0334] Determine the data in the column corresponding to the jth level of the joint deep adjustment (i.e., the J+j+3th column): 1，j ~ld M″，j Is there a value greater than 0 in ?

[0335] If it exists, when j≠J, filter out the data with a value of 0 in the corresponding column of the j+1th level of the joint deep adjustment; then filter out the row number m″ corresponding to the minimum coal consumption value in the corresponding column of the coal consumption value (i.e., the third column) minc , and then the m″th column in the generator load corresponding column (i.e. column 1) minc The data in the row is used as the generator load corresponding to the jth gear of the joint deep adjustment; and the data in the m″th gear in the column corresponding to the electric boiler load (i.e., the second column) is used as the generator load corresponding to the jth gear of the joint deep adjustment; minc The data of the row is used as the electric boiler load corresponding to the j-th level of joint deep adjustment;

[0336] When j=J, select the row number m" corresponding to the minimum coal consumption value in the corresponding column of coal consumption value minc , and then the m″th column in the generator load corresponding column (i.e. column 1) minc The data in the row is used as the generator load corresponding to the Jth level of the joint deep adjustment; and the data in the m″th column corresponding to the electric boiler load (i.e., the second column) is used as the generator load corresponding to the Jth level of the joint deep adjustment; minc The data of the row is used as the electric boiler load corresponding to the j-th level of joint deep adjustment;

[0337] If it does not exist, there will be no autonomous deep adjustment of the jth level.

[0338] For example:

[0339] (1) Autonomous deep adjustment:

[0340] First filter out dm m″ =0;

[0341] Deep adjustment by one gear: Check whether the value in the 4th column of the result matrix RE is greater than 0. If not, there is no deep adjustment by one gear; if yes, filter the case where the 5th column is 0, select the smallest index in the remaining 3rd column values, and the data in the 1st column corresponding to the index is the generator load corresponding to the autonomous deep adjustment by one gear;

[0342] Deep adjustment to the second gear: Check whether the value in the 5th column of the result matrix RE is greater than 0. If not, there is no deep adjustment to the second gear; if yes, filter the value of 0 in the 6th column, select the smallest index in the remaining 3rd column values, and the data in the 1st column corresponding to the index is the generator load corresponding to the autonomous deep adjustment to the second gear;

[0343] Deepen the adjustment by three or four levels and so on;

[0344] Deep adjustment to fifth gear: Check whether the value in the 8th column is greater than 0. If not, there is no deep adjustment to fifth gear. If yes, select the smallest index among the remaining values ​​in the 3rd column. The data in the 1st column corresponding to the index is the generator load corresponding to the autonomous deep adjustment to fifth gear.

[0345] (2) Joint deep adjustment,

[0346] First filter dm m″ ≠0;

[0347] Deepen one level: Check whether the value of column 9 is greater than 0. If not, there is no joint deep adjustment.

[0348] If yes, filter the case where the 10th column is equal to 0, select the smallest index among the remaining 3rd column values, and the data in the 1st column corresponding to the index is the generator load corresponding to the joint deep adjustment of the first gear; the data in the 2nd column corresponding to the index is the electric boiler load corresponding to the joint deep adjustment of the first gear;

[0349] Deepen the adjustment by two to four levels and so on;

[0350] Deep adjustment to five levels: Check whether the value of the last column is greater than 0. If not, there is no joint deep adjustment to five levels;

[0351] If so, select the smallest index among the remaining values ​​in the third column. The data in the first column corresponding to the index is the generator load corresponding to the fifth gear of the combined deep adjustment; the data in the second column corresponding to the index is the electric boiler load corresponding to the fifth gear of the combined deep adjustment.

[0352] The final minimum coal consumption strategy for each deep adjustment gear is shown in Table 1:

[0353] Table 1

[0354]

[0355]

[0356] The effect verification experiment is shown in Table 2. Obviously, when there are more units and more gears, the operating efficiency of the method of this embodiment is greatly improved.

[0357] Table 2

[0358] Model Original algorithm Matrix Algorithms 1 unit 2 gears deep adjustment 56ms 99ms 2 units, 2 levels of depth adjustment 231ms 168ms 4 units, 5 levels of depth adjustment 5s 497ms

[0359] Embodiment 2:

[0360] A system for optimizing deep peak load regulation of a heating unit, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0361] Get the rated capacity p of each generator n , each deep adjustment gear base db j 、The power generation sharing ratio of each generator f n , the rated power of the electric boiler d, the coal consumption coefficient matrix α corresponding to each generator n, the minimum load constraint coefficient matrix β corresponding to each generator n , the maximum load constraint coefficient matrix γ corresponding to each generator n 、Plant power load factor matrix δ corresponding to each generator n , total heating load h, electric boiler efficiency η, deep adjustment gear reference difference θ;

[0362] Based on the rated capacity of each generator p n , the rated power d of the electric boiler, construct the matrix PM and the matrix DM;

[0363] Based on each deep adjustment gear benchmark db j , construct the matrix DBM;

[0364] Based on the matrix PM, matrix DM and the coal consumption coefficient matrix α corresponding to each generator n , construct matrix C;

[0365] Based on the total heating load h, the efficiency of the electric boiler η, the matrix DM, and the power generation allocation ratio f of each generator n , the minimum load constraint coefficient matrix β corresponding to each generator n , the maximum load constraint coefficient matrix γ corresponding to each generator n , construct matrix Pmin and matrix Pmax;

[0366] Based on the matrix PM, matrix DM, and the power generation allocation ratio f of each generator n And the plant power load factor matrix δ corresponding to each generator n , construct the matrix PG;

[0367] Based on the matrix PM and the matrix DBM, construct the matrix L′ corresponding to each deep adjustment gear j ; and based on the matrix L′ corresponding to each deep adjustment gear j and the deep adjustment gear reference difference θ, construct the matrix L corresponding to each deep adjustment gear j ;

[0368] Based on the matrix PM, the matrix DM and the matrix DBM, the matrix LL′ corresponding to each deep adjustment gear is constructed; based on the matrix LL′ corresponding to each deep adjustment gear j and the deep adjustment gear reference difference θ, construct the matrix LL corresponding to each deep adjustment gear j ;

[0369] Based on the matrix L corresponding to each deep adjustment gear j and the matrix LL j , construct the matrix LD corresponding to each deep adjustment gear j ;

[0370] Get the index of the data less than 0 in the matrix PG; and get the matrix Pump f The index of the data that is less than the matrix Pmin or greater than the matrix Pmax; the matrix PM, the matrix DM, the matrix C, the matrix L j and the matrix LD j Delete the value at the corresponding index position in the matrix to obtain the modified matrix PM, matrix DM, matrix C, matrix L j and the matrix LD j ;

[0371] Based on the modified matrix PM, matrix DM, matrix C, matrix L j and the matrix LD j , construct the result matrix RE;

[0372] Based on the result matrix RE, the minimum coal consumption strategy for each deep adjustment gear is screened out.

[0373] It can be understood that the heating unit participation in deep peak-shaving optimization system provided in the embodiment of the present invention corresponds to the above-mentioned heating unit participation in deep peak-shaving optimization method. The explanations, examples, beneficial effects and other parts of its relevant contents can refer to the corresponding contents in the heating unit participation in deep peak-shaving optimization method, and will not be repeated here.

[0374] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0375] The present invention obtains the optimal strategy for the heating units to participate in deep peak regulation by constructing multiple matrices for calculation, and can obtain accurate solutions for the lowest coal consumption of autonomous deep regulation and joint deep regulation at each gear. In addition, when the method is used in scenarios with many units and many deep regulation gears, the operating speed will be significantly improved compared with the existing technology.

[0376] It should be noted that, through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment. In this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0377] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing deep peak load regulation of a heating unit, characterized in that: The method includes: Get the rated capacity p of each generator n , each deep adjustment gear benchmark db j 、The power generation sharing ratio of each generator f n , the rated power of the electric boiler d, the coal consumption coefficient matrix α corresponding to each generator n , the minimum load constraint coefficient matrix β corresponding to each generator n , the maximum load constraint coefficient matrix γ corresponding to each generator n 、Plant power load factor matrix δ corresponding to each generator n , total heating load h, electric boiler efficiency η, deep adjustment gear reference difference θ; Based on the rated capacity p of each generator n , the rated power d of the electric boiler, construct the matrix PM and the matrix DM; The matrix PM is: PM=[pm1…pm m …pm M ] M The matrix DM is: DM=[dm1…dm m …dm M ] M Wherein, M = K × I; I = d + 1; It represents the sum of the rated capacities of all generators; p n represents the rated capacity of the nth generator; N represents the number of generators; pm m represents the mth data in the matrix PM; and pm m =pl m％K ; m%K represents remainder calculation, m%K=1, 2, ..., K; pl k Represents the kth data in the matrix PL; Matrix PL = [pl1…pl k …pl K ] K ; and pl1=0, pl k -pl k-1 =1; dm m represents the mth data in the matrix DM; and Indicates rounding up calculation; dl i Represents the i-th data in the matrix DL; Matrix DL = [dl1…dl i …dl I ] I ; and dl1=0, dl i -dl i-1 =1; Based on each deep adjustment gear benchmark db j , construct the matrix DBM; The matrix DBM is: Among them, dBm m,j represents the data in the mth row and jth column of the matrix DBM; and dbm m,j =db j ; db j represents the jth deep adjustment gear position benchmark; J represents the number of deep adjustment gears; Based on the matrix PM, matrix DM and the coal consumption coefficient matrix α corresponding to each generator n , construct matrix C; The matrix C is: C=[c1…c m …c M ] M Among them, c m Represents the mth data in matrix C; α n represents the coal consumption coefficient matrix corresponding to the nth generator; E represents the coal consumption calculation formula; Based on the total heating load h, the efficiency of the electric boiler η, the matrix DM, and the power generation allocation ratio f of each generator n , the minimum load constraint coefficient matrix β corresponding to each generator n , the maximum load constraint coefficient matrix γ corresponding to each generator n , construct matrix Pmin and matrix Pmax; The matrix Pmin is: The matrix Pmax is: Among them, pmin m,n Represents the data of the mth row and nth column of the matrix Pmin, and pmin m，n =Q(β n , dm m ); f n Indicates the power generation sharing ratio of the nth generator; β n represents the minimum load constraint coefficient matrix of the nth generator; Q represents the minimum load calculation formula; pmax m,n Represents the data of the mth row and nth column of the matrix Pmax; pmax m,n =H(γ n , dm m ); γ n represents the maximum load constraint coefficient matrix of the nth generator; H represents the maximum load calculation formula; Based on the matrix PM, matrix DM, and the power generation allocation ratio f of each generator n And the plant power load factor matrix δ corresponding to each generator n , construct the matrix PG; The matrix PG is: PG=[pg1…pg m …pg M ] M Among them, pg m Represents the mth data in the matrix PG; δ n =[δ n，1 , δ n，2 , δ n，3 ] represents the auxiliary power load factor matrix of the nth generator; R represents the calculation formula for the plant power load; Based on the matrix L corresponding to each deep adjustment gear j and the matrix LL j , construct the matrix LD corresponding to each deep adjustment gear j ; LD j =[ld 1，j ...old m，j ...old M，j ] M Among them, ld m，j Representation matrix LD j The mth data in ; ld m，j =MAX(ll m，j -l m，j ,0) Get the index of the data less than 0 in the matrix PG; and get the matrix Pump f The index of the data that is less than the matrix Pmin or greater than the matrix Pmax; the matrix PM, the matrix DM, the matrix C, the matrix L j and the matrix LD j Delete the value at the corresponding index position in the matrix to obtain the modified matrix PM, matrix DM, matrix C, matrix L j and the matrix LD j ; The modified matrix PM is: PM=[pm1…prn m″ …pm M″ ] M″ The modified matrix DM is: DM=[dm1…dm m″ …dm M″ ] M″ The modified matrix C is: C=[c1…c m″ …c M″ ] M″ The modified matrix L j for: L j ＝[l 1，j …l m″，j …l M″，j ] M″ The modified matrix LD j for: LD j =[ld 1,j ...old m″,j ...old M″,j ] M″ Wherein, M″=M-neg; neg represents the number of obtained indexes after deduplication; Based on the modified matrix PM, matrix DM, matrix C, matrix L j and the matrix LD j , construct the result matrix RE: The result matrix RE is: Based on the result matrix RE, the minimum coal consumption strategy for each deep adjustment gear is screened out.

2. A method for optimizing deep peak load regulation of a heating unit according to claim 1, characterized in that: The coal consumption coefficient matrix α corresponding to the nth generator n =[α n,1 …α n,10 ]; And the coal consumption calculation formula E is:

3. A method for optimizing deep peak load regulation of a heating unit according to claim 1, characterized in that: The minimum load constraint coefficient matrix β of the nth generator n =[β n，1 , β n，2 , β n，3 ]; And the minimum load calculation formula Q is: Q(β n ,dm m )=β n，1 *(h-dm m *η*3.6×f n ) 2 +b n，2 *(h-dm m *η*3.6×f n )+b n，3 The maximum load constraint coefficient matrix γ of the nth generator n =[γ n，1 , γ n,2 , γ n,3 ]; And the maximum load calculation formula H is: H(β n ,dm m )=γ n,1 *(h-dm m *η*3.6×f n ) 2 +g n，2 *(h-dm m *η*3.6×f n )+c n,3 。 4. A method for optimizing deep peak load regulation of a heating unit according to claim 1, characterized in that: The auxiliary power load factor matrix δ of the nth generator n =[δ n，1 , δ n，2 , δ n，3 ]; And the factory power load calculation formula R is: R(δ n ,f n ,pm m )=δ n，1 ×(pm m ×f n ) 3 +δ n，2 ×(pm m ×f n ) 2 +δ n，3 ×(pm m ×f n )。 5. A method for optimizing deep peak load regulation of a heating unit according to claim 1, characterized in that: The minimum coal consumption strategy for each deep adjustment gear position is screened out based on the result matrix RE, including: For the minimum coal consumption strategy of the jth gear under autonomous deep adjustment: Filter out dm from the result matrix RE m″ = Data of all rows corresponding to 0; Determine whether there is a value greater than 0 in the column corresponding to the j-th level of the autonomous deep adjustment; If it exists, when j≠J, filter out the data with a value of 0 in the corresponding column of the j+1th file, and then filter out the row number m" corresponding to the minimum coal consumption value in the corresponding column of the coal consumption value minc , and then the generator load corresponds to the m″th column minc The data of the row is used as the generator load corresponding to the j-th gear of autonomous deep adjustment; When j=J, the number of rows corresponding to the minimum coal consumption value in the corresponding column of coal consumption value is selected from m″ minc , and then the generator load corresponds to the m″th column minc The data of the row is used as the generator load corresponding to the j-th gear of autonomous deep adjustment; If it does not exist, there will be no autonomous deep adjustment of the jth level.

6. A method for optimizing deep peak load regulation of a heating unit according to claim 5, characterized in that: The method of screening out the minimum coal consumption strategy for each deep adjustment gear position based on the result matrix RE also includes: For the minimum coal consumption strategy of the hth gear under joint deep regulation: Filter out dm from the result matrix RE m″ ≠0 corresponding to all rows of data; Determine whether there is a value greater than 0 in the column corresponding to the j-th level of the joint deep adjustment; If it exists, when j≠J, filter out the data with a value of 0 in the corresponding column of the j+1th level of the joint deep adjustment; then filter out the row number m″ corresponding to the minimum coal consumption value in the corresponding column of the coal consumption value. minc , and then the generator load corresponds to the m″th column minc The data in the row is used as the generator load corresponding to the jth level of joint deep adjustment; and the electric boiler load is corresponding to the m″th level in the column minc The data of the row is used as the electric boiler load corresponding to the j-th level of joint deep adjustment; When j=J, select the row number m" corresponding to the minimum coal consumption value in the corresponding column of coal consumption value minc , and then the generator load corresponds to the m″th column minc The data in the row is used as the generator load corresponding to the jth level of joint deep adjustment; and the electric boiler load is corresponding to the mth level in the column. minc The data of the row is used as the electric boiler load corresponding to the j-th level of joint deep adjustment; If it does not exist, there will be no autonomous deep adjustment of the jth level.

7. A system for optimizing deep peak load regulation of a heating unit, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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