CN109449974B - Comprehensive energy system optimization scheduling method based on variable mode decomposition and sample entropy theory - Google Patents

Comprehensive energy system optimization scheduling method based on variable mode decomposition and sample entropy theory Download PDF

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CN109449974B
CN109449974B CN201811365273.0A CN201811365273A CN109449974B CN 109449974 B CN109449974 B CN 109449974B CN 201811365273 A CN201811365273 A CN 201811365273A CN 109449974 B CN109449974 B CN 109449974B
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CN109449974A (en
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孙辉
丁长强
胡姝博
周玮
彭飞翔
高正男
孙立伟
王治
孙越峰
刘新宇
吴昊
于晓颖
高垚
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Dalian University of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H02J3/386—
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract

A comprehensive energy system optimal scheduling method based on variable mode decomposition and sample entropy theory belongs to the field of comprehensive energy system optimal scheduling. The method preferentially consumes the renewable energy, so the system load prediction curve and the renewable energy prediction output are subtracted to form a net load curve. And secondly, carrying out variable mode decomposition on the net load curve to form a plurality of mode curves. And then calculating the sample entropy value of each mode, carrying out complexity evaluation, and recombining the modes with similar complexity to form a typical net load mode curve. And finally, aiming at different net load mode curves, arranging corresponding units to carry out day-ahead optimal scheduling, and giving a day-ahead scheduling plan. The invention can avoid the problem of wind abandon; the net load mode curve with the sample entropy larger than the original curve is borne by the output of the gas turbine unit, and the net load mode curve with the sample entropy smaller than the original curve is borne by the output of the coal-fired thermal power unit, so that the climbing pressure and the frequent fluctuation of the output of the thermal power unit can be reduced.

Description

Comprehensive energy system optimization scheduling method based on variable mode decomposition and sample entropy theory
Technical Field
The invention belongs to the field of optimization scheduling of an integrated energy system. The method relates to the relevant theories of variable mode decomposition, sample entropy calculation and optimal scheduling of the power system, in particular to a comprehensive energy system optimal scheduling method based on the variable mode decomposition and the sample entropy theory.
Background
The comprehensive energy system has important significance for improving the utilization efficiency of social energy, promoting the large-scale development of renewable energy, improving the utilization rate of social infrastructure and the energy supply safety, and realizing the aims of energy conservation and emission reduction in China, and becomes an important strategic research direction in the international energy field. Along with the application of cogeneration units, gas turbines, electric boilers and electric gas conversion equipment in systems, the coupling of three energy systems of electricity, heat and gas is closer and closer. Moreover, in order to cope with the reduction of fossil energy, the proportion of renewable energy in the power system is increasing year by year. But renewable energy power generation has volatility and uncertainty and exhibits back-peaking characteristics. Especially during the heating period, a large amount of wind curtailment occurs during the night due to the coupling of the thermoelectric relationship. In addition, in order to deal with the output fluctuation of wind power, the output of a unit in the system can be frequently adjusted, so that great climbing pressure is brought, and certain influence is caused on the safe operation of the system. On the premise of ensuring the full consumption of renewable energy, the coal-fired thermal power generating unit with weaker system adjusting capacity is smooth in output, and the problem to be solved is to reduce large-scale climbing. At present, scholars apply variable mode decomposition and sample entropy theory to wind power prediction, but the application of the variable mode decomposition and the sample entropy theory to optimization scheduling of an integrated energy system is not common.
Disclosure of Invention
The invention aims to provide a comprehensive energy system optimization scheduling method based on variable mode decomposition and sample entropy theory. In order to ensure that renewable energy is preferentially consumed in the integrated energy system, the system load prediction curve is subtracted from the renewable energy prediction output to form a net load curve. And secondly, carrying out variable mode decomposition on the net load curve to form a plurality of mode curves. And then calculating the sample entropy value of each mode, carrying out complexity evaluation, and recombining the modes with similar complexity to form a typical net load mode curve. And finally, aiming at different net load mode curves, arranging corresponding units to carry out day-ahead optimal scheduling, and giving a day-ahead scheduling plan.
In order to achieve the purpose, the invention adopts the technical scheme that:
a comprehensive energy system optimization scheduling method based on variable mode decomposition and sample entropy theory comprises the following steps:
step 1, acquiring unit and equipment parameters, and predicting output of a renewable energy power source and predicting electric, heat and gas loads of a system.
And 2, subtracting the predicted output of the renewable energy power supply from the predicted electric load of the system to form a net load curve, wherein the net load is as shown in the formula (1):
Figure BDA0001868351450000011
wherein t is time and the unit is hour,
Figure BDA0001868351450000021
is a predicted value of the system load at the moment t,
Figure BDA0001868351450000022
is the predicted output of the wind power plant i in the period t, n _ w is the total number of the wind power plants,
Figure BDA0001868351450000023
and (4) predicting the system net load at the time t.
And 3, carrying out variable mode decomposition on the net load curve to form K mode curves.
The variable mode decomposition theory introduction and decomposition solving steps are as follows:
the variable mode decomposition is a new self-adaptive signal analysis method, and the input signal is decomposed into a certain number of omegakMode curve u as centerk. The mode decomposition is essentially a solution process of the variation problem, and is divided into two sub-processes of constructing and solving. The construction principle of the variation problem is as follows: assuming each mode has a finite bandwidth of the center frequency, making the sum of the modes equal to the constraint of the input signal, making k mode curves ukThe sum of the estimated bandwidths for each mode in (a) is minimal.
Figure BDA0001868351450000024
Wherein, { ukAnd { omega } andkthe sum of all the modes and their center frequencies, f is the signal to be decomposed, k is the mode serial number, the operation of convolution, and delta (t) isThe distribution of the dirac is such that,
Figure BDA0001868351450000025
is the sign of the gradient operation, j is the imaginary part.
The constraint variation problem can be processed into an unconstrained optimization problem through a secondary penalty term and a Lambertian multiplier lambda, and the expression is shown as a formula (3):
Figure BDA0001868351450000026
the detailed solving process is as follows:
(1) let n equal to 0, initialize
Figure BDA0001868351450000027
Typically 0;
(2) let n equal to 1 and K equal to 1: K, update
Figure BDA0001868351450000028
a. For ω ≧ 0, update
Figure BDA0001868351450000029
The iterative formula is:
Figure BDA00018683514500000210
b. update { omega [ omega ]kThe iteration formula is:
Figure BDA0001868351450000031
(3) updating
Figure BDA0001868351450000032
The iterative formula is:
Figure BDA0001868351450000033
(4) and (3) repeating the steps until the following conditions are met:
Figure BDA0001868351450000034
wherein K is the number of decomposition modes;
Figure BDA0001868351450000035
and
Figure BDA0001868351450000036
are respectively f and ukAnd the frequency spectrum of λ; tau is the update coefficient of the Lagrange multiplier; ε is the convergence accuracy; α represents a white noise constant; ω is the frequency.
And 4, calculating the sample entropies of the original net load curve and the K mode curves, and recombining according to a recombination rule. The method specifically comprises the following steps: recombining the mode curves with similar sample entropy values into M typical mode curves according to the power supply characteristics of the comprehensive energy system, wherein M is generally 2 or 3; wherein, the recombination rule is as follows: adding and combining the mode curves with the sample entropy values smaller than the original net load curve into a new mode curve, classifying the mode curves with the sample entropy values higher than the original net load curve according to the mutual distances of the mode curves, grouping the mode curves into a group if the distance between the mode curves is smaller than a certain set threshold, and respectively forming a mode if the distance between the mode curves is larger than the certain set threshold without recombination; it should be noted that this threshold is not fixed and may be changed according to actual conditions.
The solving steps of the sample entropies of the original net load curve and the K mode curves are as follows:
(1) given a time sequence, { ui-u (1), u (2), u (N), where N is the total number of samples;
(2) will time series uiSorting to generate m-dimensional subsequence u (i) ═ u (i), u (i +1),.., u (i + m-1)]Wherein i is 1, 2., N-m +1, typically m is 2;
(3) defining a distance d between U (i) and U (j)i m(U (i), U (j)) is the one with the largest difference value of the corresponding elements; for each value of i, the distance between u (i) and the remaining u (j) is calculated, where j is 1, 2. Namely:
Figure BDA0001868351450000037
(4) given a similar tolerance r (r)>0, usually 0.1-0.25 SD, SD is standard deviation of time series), corresponding to each i, counting
Figure BDA0001868351450000038
And calculating the ratio of the number of the sample data to the total distance N-m of the sample data, and counting the ratio
Figure BDA0001868351450000039
This process is called the template matching process of the subsequence u (i), then:
Figure BDA0001868351450000041
(5) to find
Figure BDA0001868351450000042
The average value of (a) is:
Figure BDA0001868351450000043
(6) increasing the dimensionality to m +1, and repeating the steps (1) to (5) to obtain
Figure BDA0001868351450000044
Average value of (B)m+1(r), then the sample entropy is defined as:
Figure BDA0001868351450000045
when N takes a finite value, the sample entropy estimation value is expressed by equation (5), as shown by equation (6):
SampEn(r,m,N)＝-ln(Bm+1(r)/Bm(r)) (12)
step 5, establishing a comprehensive energy system optimization scheduling model based on variable mode decomposition and sample entropy theory
(1) Objective function
The model takes the minimum operation cost of the comprehensive energy system as an objective function, including the power generation cost of the system and the cost of purchasing natural gas, as shown in the formula (7):
F＝F1+F2(13)
Figure BDA0001868351450000046
Figure BDA0001868351450000047
wherein F is the operating cost of the system, F1For the cost of electricity generation of coal-fired thermal power generating units, F2For the cost of purchasing natural gas, T is the total number of scheduling periods; n _ c is the number of the schedulable coal-fired thermal power units, and comprises a coal-fired cogeneration unit and a coal-fired pure condensation thermal power unit; n _ g is the number of schedulable gas turbine units;
Figure BDA0001868351450000048
is the active power output of a coal-fired thermal power generating unit i at the moment t, acoal,i、bcoal,iAnd ccoal,iThe coal consumption coefficient of a coal-fired thermal power generating unit i is obtained;
Figure BDA0001868351450000049
is the active power output, a, of the gas turbine unit i at time tgas,i、bgas,iAnd cgas,iThe gas consumption coefficient of the gas turbine unit i; price _ coal is the unit price of coal; price _ gas is the unit price of natural gas.
(2) Constraint of energy balance equation
a. Equality constraint of system electric energy balance
Figure BDA0001868351450000051
Wherein the content of the first and second substances,
Figure BDA0001868351450000052
for the mth net load pattern curve after recombination, m is 1.
Figure BDA0001868351450000053
Wherein the content of the first and second substances,
Figure BDA0001868351450000054
the mth net load mode curve after recombination, m is 2;
Figure BDA0001868351450000055
the power of the electric boiler i at the moment t;
Figure BDA0001868351450000056
the power of the electric gas conversion equipment i at the moment t; n _ b is the number of the electric boilers; n _ p is the number of the electric gas conversion equipment.
b. The equality constraint for the system thermal energy balance is shown as equation (18):
Figure BDA0001868351450000057
wherein n _ chp is the number of the coal-fired cogeneration units; etaehThe heat and power coefficient of the coal-fired cogeneration unit; etaboilerThe heat production efficiency of the electric boiler;
Figure BDA0001868351450000058
and
Figure BDA0001868351450000059
are respectively provided withThe heat charging and discharging power of the heat storage tank;
Figure BDA00018683514500000510
a predicted value for the thermal load of the system.
c. The equality constraint for the system natural gas flow balance is shown as equation (19):
Figure BDA00018683514500000511
wherein E isp2gIs the conversion constant of electric power and natural gas flow and has the unit of MW/km3h-1，
Figure BDA00018683514500000512
Is the output of the air source at the time t,
Figure BDA00018683514500000513
and predicting the air load of the system.
(2) Plant operating constraints
a. Output restraint for generator sets
Figure BDA00018683514500000514
Figure BDA00018683514500000515
Wherein the content of the first and second substances,
Figure BDA00018683514500000516
and
Figure BDA00018683514500000517
respectively the minimum and maximum output values of the coal-fired thermal power generating unit i;
Figure BDA00018683514500000518
and
Figure BDA00018683514500000519
the minimum and maximum output values of the gas turbine unit i are respectively.
b. Ramp rate constraint of generator set
Figure BDA00018683514500000520
Figure BDA00018683514500000521
Wherein, UPcoal,iAnd DNcoal,iThe upper limit and the lower limit of the climbing of the coal-fired thermal power generating unit i are respectively set; UPgas,iAnd DNgas,iThe upper limit and the lower limit of the climbing of the gas turbine unit i are respectively.
c. Thermal output constraint of coal-fired cogeneration unit
Figure BDA0001868351450000061
Wherein Hmax,iThe maximum value of the heat output of the coal-fired cogeneration unit i is determined by the capacity of the heat exchanger of the unit.
d. Electric boiler output restriction
Figure BDA0001868351450000062
Wherein the content of the first and second substances,
Figure BDA0001868351450000063
the maximum value of the output of the electric boiler i.
e. Output constraints for electric gas-to-gas equipment
Figure BDA0001868351450000064
Wherein the content of the first and second substances,
Figure BDA0001868351450000065
the maximum value of the output of the electric air converting device i.
f. Thermal storage device operational constraints
Figure BDA0001868351450000066
Smin≤St≤Smax (28)
S0＝ST (29)
Figure BDA0001868351450000067
Figure BDA0001868351450000068
Figure BDA0001868351450000069
Wherein S istThe capacity of the heat storage device at the moment t; etastoreAnd ηreleaseThe charge and discharge efficiency of the heat storage device; sminAnd SmaxRespectively the minimum capacity and the maximum capacity of the heat storage device; s0And STCapacity of the heat storage device in the first and last periods of a scheduling cycle respectively;
Figure BDA00018683514500000610
and
Figure BDA00018683514500000611
respectively the maximum charge-discharge power of the heat storage device.
And then, the comprehensive energy system optimization scheduling model based on the variable mode decomposition and the sample entropy theory is shown as a formula (33).
Figure BDA0001868351450000071
And 6, solving the comprehensive energy system optimization scheduling model by adopting an interior point method to obtain a comprehensive energy system optimization scheduling scheme based on variable mode decomposition and a sample entropy theory.
The invention has the advantages that: a comprehensive energy system optimization scheduling method based on variable mode decomposition and a sample entropy theory is provided. Aiming at the renewable energy source grid connection problem in the comprehensive energy source system, the system predicted load is subtracted from the renewable energy source power source predicted output to form a net load curve, and then the net load curve is processed, so that the problem of wind abandonment is avoided; aiming at the problems of frequent output adjustment and slow climbing of the coal-fired thermal power generating unit, a net load mode curve is provided after variable mode decomposition and sample entropy theory recombination. And (4) enabling the net load mode curve with the sample entropy larger than the original curve to be borne by the output of the gas turbine set. And the net load mode curve with the sample entropy smaller than the original curve is borne by the output of the coal-fired thermal power unit, so that the climbing pressure of the thermal power unit is reduced, and the frequent fluctuation of the output of the thermal power unit is reduced.
Drawings
FIG. 1 is a flowchart of a new energy-containing power system optimization scheduling method based on sample entropy.
Fig. 2 is a system electrical, thermal and gas load prediction curve, wherein (a) is a system electrical load prediction curve, (b) is a system thermal load prediction curve, and (c) is a system gas load prediction curve.
FIG. 3 is a system wind power output prediction curve.
Fig. 4 net load curve.
Fig. 5 shows the result of the mode-dependent decomposition of the net load curve, wherein (a) is mode 1, (b) is mode 2, and (c) is mode 3.
FIG. 6 is a sample entropy value for the original payload curve and sample entropy values for the respective decomposition modes, where (a) is the sample entropy value for the original payload curve, (b) is the sample entropy value for mode 2, (c) is the sample entropy value for mode 2, and (d) is the sample entropy value for mode 3.
FIG. 7 is a graph of net load patterns after reorganization, where (a) is pattern 1 and (b) is pattern 2.
FIG. 8 is a coal-fired cogeneration unit output optimization scheduling scheme in a conventional optimization scheduling model.
FIG. 9 is an output optimization scheduling scheme of a coal-fired straight condensing unit in a conventional optimization scheduling model.
FIG. 10 is a gas turbine set output optimized scheduling scheme in a conventional optimized scheduling model.
FIG. 11 is a coal-fired cogeneration unit output optimization scheduling scheme after considering variable mode decomposition and sample entropy theory.
FIG. 12 is a coal fired straight condensing unit output optimization scheduling scheme after considering variable mode decomposition and sample entropy theory.
FIG. 13 is a gas turbine plant contribution optimization scheduling scheme after consideration of variable mode decomposition and sample entropy theory.
FIG. 14 is an electric boiler and electric gas-to-gas plant output optimization scheduling scheme after considering variable mode decomposition and sample entropy theory.
FIG. 15 is a heat storage device output optimization scheduling scheme and capacity after consideration of variable mode decomposition and sample entropy theory.
Detailed Description
The following describes in detail a specific embodiment of the present invention by taking an improved ten-unit system as an example, and combining the technical scheme and the accompanying drawings. The electrical, thermal and gas predicted load curve of the system is shown in fig. 2. The total capacity of the coal-fired thermal power generating unit is 1850MW, wherein the total capacity of the coal-fired cogeneration unit is 850MW, the total capacity of the coal-fired straight condensing unit is 1000MW, and the capacity of the gas turbine unit is 500 MW. The unit parameters are shown in tables 1 and 2. The predicted output of a 900MW wind power plant with installed wind power capacity is shown in FIG. 3.
Fig. 1 is a flowchart of a new energy-containing power system optimal scheduling method based on sample entropy, and the specific steps are as follows:
firstly, acquiring the electricity, heat and gas predicted load curves of a unit and equipment parameters and a system, and the wind power output predicted curve.
And secondly, subtracting the predicted power load curve of the system from the predicted power output curve of the wind power to obtain a net load curve, wherein the curve is shown in fig. 4.
And thirdly, carrying out mode-variable decomposition on the obtained net load curve to obtain 3 mode curves, wherein the decomposition result is shown in fig. 5.
And fourthly, calculating the sample entropy value of the original net load curve and the sample entropy values under the 3 decomposition modes, wherein the obtained values are shown in fig. 6, and the obtained results show that the sample entropy of the original net load curve is 0.3509, the decomposed 3 modes are 0.2700, 0.8899 and 0.7563 respectively, according to the recombination rule, the first mode is a single mode, the last two modes are recombined into a single mode, and the recombined result is shown in fig. 7.
And fifthly, establishing a comprehensive energy system optimization scheduling model based on variable mode decomposition and sample entropy theory.
TABLE 1 coal-fired thermal power generating unit parameters
Figure BDA0001868351450000091
TABLE 2 gas turbine set parameters
Figure BDA0001868351450000092
And sixthly, solving by adopting an interior point method to obtain an optimized scheduling scheme. And comparing with the optimized scheduling scheme under the traditional optimized scheduling model.
As shown in fig. 8 and 9, the output optimization scheduling scheme of the coal-fired cogeneration unit and the output optimization scheduling scheme of the coal-fired straight condensing unit in the conventional optimization scheduling model are respectively shown. As shown in fig. 11 and fig. 12, the output optimization scheduling scheme of the coal-fired cogeneration unit and the output optimization scheduling scheme of the coal-fired straight condensing unit under the model proposed herein are respectively. From the figure, it can be seen that the output of the coal-fired cogeneration and straight condensing unit under the model provided by the invention is smoother, the output fluctuation is smaller, and a large amount of unit climbing time does not exist. The ramp rate of the thermal power generating unit under the two models is shown in table 3. As seen from the table, the ramp rate of the thermal power generating unit under the model provided by the invention is reduced by 2176.3171MWh and is reduced by 78.47% compared with that under the traditional model.
TABLE 3 climbing amount of thermal power generating unit under two models
Figure BDA0001868351450000101
As shown in FIGS. 10 and 13, the optimized scheduling scheme for gas turbine plant capacity under the conventional optimized scheduling model and the model presented herein, respectively. Compared with the two figures, it can be seen that the gas turbine under the traditional optimized scheduling model is used as a peak shaving power supply, and needs to output power when the thermal power generating unit is limited by output power and climbing capacity. Therefore, the system is frequently started and stopped. The output time period of the gas turbine under the model provided by the invention is more concentrated, and the start and stop of the unit are reduced.
As shown in fig. 14, an optimized scheduling scheme for electric boilers and electric gas-converting apparatuses under the model presented herein. It can be seen that both the electric boiler and the electric gas conversion equipment work in the time period of large heat load and easy wind abandonment of the system. This is because this part of the net load curve is negative and requires equipment to consume. It can also be seen that the electric boiler is given priority to output power, and the remaining power is consumed by the electric gas conversion equipment.
As shown in fig. 15, the scheduling scheme and the capacity variation condition are optimized for the charging and discharging energy of the heat storage device under the model provided herein. As can be seen from the figure, the heat storage device releases heat in the time period when the system has large heat load and the system is easy to abandon wind. This is because it can help to decouple the heat and power relationship, thereby reducing the necessary electrical power of the cogeneration unit, while increasing the output of the straight condensing unit and avoiding large-scale climbing with increased output during the daytime. And the output of the cogeneration unit is increased in the daytime, and the heat storage device is charged with heat, so that heat is released at night, the output of the cogeneration unit at night is reduced, the output of the straight condensing unit is not greatly reduced, and the output of the straight condensing unit is smoothed.
The comprehensive energy system optimization scheduling model based on the variable mode decomposition and the sample entropy theory not only avoids the phenomenon of wind abandonment, but also smoothes the output of the thermal power generating unit and increases the duration of stable operation of the thermal power generating unit. The scheduling method reduces frequent fluctuation of the output of the thermal power generating unit and improves the stability of the system.
The above-mentioned embodiments only express the embodiments of the present invention, but not should be understood as the limitation of the scope of the invention patent, it should be noted that, for those skilled in the art, many variations and modifications can be made without departing from the concept of the present invention, and these all fall into the protection scope of the present invention.

Claims (3)

1. A comprehensive energy system optimization scheduling method based on variable mode decomposition and sample entropy theory is characterized by comprising the following steps:
step 1, acquiring unit and equipment parameters, and predicting output of a renewable energy power supply and predicting electric, heat and gas loads of a system;
and 2, subtracting the predicted output of the renewable energy power supply from the predicted electric load of the system to form a net load curve, wherein the net load is as shown in the formula (1):
Figure FDA0003023438220000011
wherein t is time and the unit is hour,
Figure FDA0003023438220000012
is a predicted value of the system load at the moment t,
Figure FDA0003023438220000013
is the predicted output of the wind power plant i in the period t, n _ w is the total number of the wind power plants,
Figure FDA0003023438220000014
the predicted value of the system net load at the time t is obtained;
step 3, carrying out variable mode decomposition on the net load curve to form K mode curves;
step 4, calculating sample entropies of the original net load curve and the K mode curves, recombining the mode curves with similar sample entropy values according to a recombination rule according to the power supply characteristics of the comprehensive energy system, and recombining the mode curves into M typical mode curves; the recombination rules are as follows: adding and combining the mode curves with the sample entropy values smaller than the original net load curve into a new mode curve, classifying the mode curves with the sample entropy values higher than the original net load curve according to the mutual distances of the mode curves, grouping the mode curves into a group if the distance between the mode curves is smaller than a certain set threshold, and not recombining the mode curves if the distance between the mode curves is higher than the certain set threshold; the threshold value is determined according to the actual condition;
and 5, establishing a comprehensive energy system optimization scheduling model based on variable mode decomposition and a sample entropy theory, wherein the model is shown as a formula (33):
Figure FDA0003023438220000021
wherein i is 1, 2., N-m +1, and N is the total number of samples; t is the total number of scheduling periods; n _ c is the number of the schedulable coal-fired thermal power units, and comprises a coal-fired cogeneration unit and a coal-fired pure condensation thermal power unit; n _ g is the number of schedulable gas turbine units;
Figure FDA0003023438220000022
is the active power output of a coal-fired thermal power generating unit i at the moment t, acoal,i、bcoal,iAnd ccoal,iThe coal consumption coefficient of a coal-fired thermal power generating unit i is obtained;
Figure FDA0003023438220000023
is the active power output, a, of the gas turbine unit i at time tgas,i、bgas,iAnd cgas,iThe gas consumption coefficient of the gas turbine unit i; price _ coal is the unit price of coal; price _ gas is the unit price of natural gas;
Figure FDA0003023438220000024
is the mth net load mode curve after recombination;
Figure FDA0003023438220000025
the power of the electric boiler i at the moment t;
Figure FDA0003023438220000026
the power of the electric gas conversion equipment i at the moment t; n _ b is the number of the electric boilers; n _ p is the number of the electric gas conversion equipment; n _ chp is the number of the coal-fired cogeneration units; etaehThe heat and power coefficient of the coal-fired cogeneration unit; etaboilerThe heat production efficiency of the electric boiler;
Figure FDA0003023438220000031
and
Figure FDA0003023438220000032
respectively the heat charging and discharging power of the heat storage tank;
Figure FDA0003023438220000033
predicting a heat load of the system; ep2gIs the conversion constant of electric power and natural gas flow and has the unit of MW/km3h-1；
Figure FDA0003023438220000034
And
Figure FDA0003023438220000035
respectively the minimum and maximum output values of the coal-fired thermal power generating unit i;
Figure FDA0003023438220000036
and
Figure FDA0003023438220000037
respectively the minimum and maximum output values of the gas turbine unit i; UPcoal,iAnd DNcoal,iThe upper limit and the lower limit of the climbing of the coal-fired thermal power generating unit i are respectively set; UPgas,iAnd DNgas,iThe upper limit and the lower limit of the climbing of the gas turbine unit i are respectively set; hmax,iThe maximum value of the thermal output of the coal-fired cogeneration unit i is determined by the capacity of the heat exchanger of the unit;
Figure FDA0003023438220000038
the maximum value of the output of the electric gas conversion equipment i is obtained; (ii) a StThe capacity of the heat storage device at the moment t; sminAnd SmaxRespectively the minimum capacity and the maximum capacity of the heat storage device; etastoreAnd ηreleaseThe charge and discharge efficiency of the heat storage device;
Figure FDA0003023438220000039
and
Figure FDA00030234382200000310
the maximum charge and discharge power of the heat storage device is respectively;
and 6, solving the comprehensive energy system optimization scheduling model by adopting an interior point method to obtain a comprehensive energy system optimization scheduling scheme based on variable mode decomposition and a sample entropy theory.
2. The method for optimizing and scheduling the comprehensive energy system based on the variable mode decomposition and the sample entropy theory according to claim 1, wherein in the step 4, the solving steps of the sample entropies of the original payload curve and the K mode curves are as follows:
(1) given a time sequence, { ui-u (1), u (2), u (N), where N is the total number of samples;
(2) will time series uiSorting to generate m-dimensional subsequence u (i) ═ u (i), u (i +1),.., u (i + m-1)]Wherein, i is 1, 2., N-m + 1;
(3) defining the distance between U (i) and U (j)
Figure FDA00030234382200000311
The difference value of the two corresponding elements is the largest; calculating for each value of i the distance between u (i) and the remaining u (j), where j is 1, 2.., N-m +1, j ≠ i; namely:
Figure FDA00030234382200000312
(4) given a similar tolerance r, r>0, taking the value of 0.1-0.25 SD, wherein SD is the standard deviation of the time sequence, and counting corresponding to each i
Figure FDA00030234382200000313
And calculating the ratio of the number of the sample data to the total distance N-m of the sample data, and counting the ratio
Figure FDA00030234382200000314
This process is called the template matching process of the subsequence u (i), then:
Figure FDA00030234382200000315
(5) to find
Figure FDA00030234382200000316
The average value of (a) is:
Figure FDA0003023438220000041
(6) increasing the dimensionality to m +1, and repeating the steps (1) to (5) to obtain
Figure FDA0003023438220000042
Average value of (B)m+1(r), then the sample entropy is defined as:
Figure FDA0003023438220000043
when N takes a finite value, equation (11) represents the sample entropy estimation value, as shown in equation (12):
SampEn(r,m,N)＝-ln(Bm+1(r)/Bm(r)) (12)。
3. the method for optimizing and scheduling the comprehensive energy system based on the variable mode decomposition and the sample entropy theory according to claim 1, wherein the step of establishing the comprehensive energy system optimizing and scheduling model based on the variable mode decomposition and the sample entropy theory in the step 5 comprises the following steps:
(1) objective function
The model takes the minimum operation cost of the comprehensive energy system as an objective function, and is shown as the formula (13):
F＝F1+F2 (13)
Figure FDA0003023438220000044
Figure FDA0003023438220000045
wherein T is the total number of scheduling periods; n _ c is the number of the schedulable coal-fired thermal power units, and comprises a coal-fired cogeneration unit and a coal-fired pure condensation thermal power unit; n _ g is the number of schedulable gas turbine units;
Figure FDA0003023438220000046
is the active power output of a coal-fired thermal power generating unit i at the moment t, acoal,i、bcoal,iAnd ccoal,iThe coal consumption coefficient of a coal-fired thermal power generating unit i is obtained;
Figure FDA0003023438220000047
is the active power output, a, of the gas turbine unit i at time tgas,i、bgas,iAnd cgas,iThe gas consumption coefficient of the gas turbine unit i; price _ coal is the unit price of coal; price _ gas is the unit price of natural gas;
Figure FDA0003023438220000048
the output of the air source at the time t;
(2) constraint of energy balance equation
a. Equality constraint of system electric energy balance
Figure FDA0003023438220000049
Wherein the content of the first and second substances,
Figure FDA00030234382200000410
the mth net load mode curve after recombination, m is 1;
Figure FDA0003023438220000051
wherein the content of the first and second substances,
Figure FDA0003023438220000052
the mth net load mode curve after recombination, m is 2;
Figure FDA0003023438220000053
the power of the electric boiler i at the moment t;
Figure FDA0003023438220000054
the power of the electric gas conversion equipment i at the moment t; n _ b is the number of the electric boilers; n _ p is the number of the electric gas conversion equipment;
b. the equality constraint for the system thermal energy balance is shown as equation (18):
Figure FDA0003023438220000055
wherein n _ chp is the number of the coal-fired cogeneration units; etaehThe heat and power coefficient of the coal-fired cogeneration unit; etaboilerThe heat production efficiency of the electric boiler;
Figure FDA0003023438220000056
and
Figure FDA0003023438220000057
respectively the heat charging and discharging power of the heat storage tank;
Figure FDA0003023438220000058
predicting a heat load of the system;
c. the equality constraint for the system natural gas flow balance is shown as equation (19):
Figure FDA0003023438220000059
wherein E isp2gIs the conversion constant of electric power and natural gas flow and has the unit of MW/km3h-1；
(2) Plant operating constraints
a. Output restraint for generator sets
Figure FDA00030234382200000510
Figure FDA00030234382200000511
Wherein the content of the first and second substances,
Figure FDA00030234382200000512
and
Figure FDA00030234382200000513
respectively the minimum and maximum output values of the coal-fired thermal power generating unit i;
Figure FDA00030234382200000514
and
Figure FDA00030234382200000515
respectively the minimum and maximum output values of the gas turbine unit i;
b. ramp rate constraint of generator set
Figure FDA00030234382200000516
Figure FDA00030234382200000517
Wherein, UPcoal,iAnd DNcoal,iThe upper limit and the lower limit of the climbing of the coal-fired thermal power generating unit i are respectively set; UPgas,iAnd DNgas,iThe upper limit and the lower limit of the climbing of the gas turbine unit i are respectively set;
c. thermal output constraint of coal-fired cogeneration unit
Figure FDA00030234382200000518
Wherein Hmax,iThe maximum value of the thermal output of the coal-fired cogeneration unit i is determined by the capacity of the heat exchanger of the unit;
d. electric boiler output restriction
Figure FDA0003023438220000061
Wherein the content of the first and second substances,
Figure FDA0003023438220000062
the maximum value of the output of the electric boiler i is obtained;
e. output constraints for electric gas-to-gas equipment
Figure FDA0003023438220000063
Wherein the content of the first and second substances,
Figure FDA0003023438220000064
the maximum value of the output of the electric gas conversion equipment i is obtained;
f. thermal storage device operational constraints
Figure FDA0003023438220000065
Smin≤St≤Smax (28)
S0＝ST (29)
Figure FDA0003023438220000066
Figure FDA0003023438220000067
Figure FDA0003023438220000068
Wherein S istThe capacity of the heat storage device at the moment t; sminAnd SmaxRespectively the minimum capacity and the maximum capacity of the heat storage device; etastoreAnd ηreleaseThe charge and discharge efficiency of the heat storage device;
Figure FDA0003023438220000069
and
Figure FDA00030234382200000610
the maximum charge and discharge power of the heat storage device is respectively;
and then obtaining a comprehensive energy system optimization scheduling model based on variable mode decomposition and sample entropy theory as shown in a formula (33).
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