CN112668751B - Method and device for establishing unit optimization scheduling model - Google Patents

Method and device for establishing unit optimization scheduling model Download PDF

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CN112668751B
CN112668751B CN202011344914.1A CN202011344914A CN112668751B CN 112668751 B CN112668751 B CN 112668751B CN 202011344914 A CN202011344914 A CN 202011344914A CN 112668751 B CN112668751 B CN 112668751B
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subinterval
interval
unit
constraint
output
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CN112668751A (en
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黎静华
徐逸夫
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Guangxi University
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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Abstract

The invention discloses a method and a device for establishing a unit optimization scheduling model, belonging to the field of scheduling of new energy units, wherein the establishing method comprises the following steps: constructing a random energy output fluctuation subinterval based on an interval segmented robust optimization method; setting segmented robust optimization parameters of each subinterval by using the historical predicted output data and actual output data of the random energy station; and establishing a unit optimization scheduling model by using each subinterval segmented robust optimization parameter, taking the minimum combustion cost and start-stop cost of the thermal power generating unit as a target function and combining constraint conditions. The method can better balance the robustness and the economy of the operation of the power system while considering the random energy fluctuation condition in the subinterval. Meanwhile, the invention adopts the historical sample probability information to carry out the method for setting the subinterval segmentation robust optimization parameters, and the method can determine the maximum value and the minimum value of the number of uncertain variables in each subinterval only by setting the segmentation coefficient, thereby leading the optimization result to be more objective.

Description

Method and device for establishing unit optimization scheduling model
Technical Field
The invention belongs to the field of scheduling of new energy units, and particularly relates to a method and a device for establishing a unit optimization scheduling model.
Background
At present, large-scale new energy grid connection brings great challenges to operation and scheduling of an electric power system, and a power system unit combination model and a method capable of dealing with new energy power generation uncertainty are researched, so that the method has important significance for large-scale new energy power generation grid connection and guarantee of safe and stable operation of the electric power system. The random energy randomness processing method mainly comprises two types, one type is a method based on random energy power prediction, and the other type is a method based on an optimization problem containing uncertain variables.
Stochastic programming is a method of solving an optimization problem with uncertain variables. The method considers that the change rule of the uncertain quantity obeys certain probability distribution, and solves the problem through probabilistic modeling of constraint or objective functions. Conventional probability distribution functions have difficulty accurately describing the distribution characteristics of wind speed. The scene generation method and the scene reduction method provide a new idea for the description and modeling of uncertain factors such as random energy in the power system. Although the scene-based uncertainty modeling method can describe uncertainty factors in the system, the scene-based uncertainty modeling method is limited by the calculation scale, and the expression degree of the clearly reduced scene to the uncertainty is difficult to be explained.
Robust optimization is another mature theory for solving optimization problems with uncertain variables. The basic idea for solving the optimization problem of the power system with uncertain variables is to give an uncertain set containing all possible values of the uncertain variables and then find a solution that is feasible for all possible values in the uncertain set. At present, only the limit condition of random energy fluctuation interval can be considered in solving the scheduling problem of the unit by the existing robust optimization, although an uncertainty parameter gamma can be adoptediAdjusting the robustness of the optimization results, but with an uncertainty parameter ΓiThe setting of (2) is too subjective, and the robustness and the economical efficiency of the operation of the power system cannot be effectively considered. Therefore, a robust optimization method capable of simultaneously considering the limit condition and the general condition in the random energy fluctuation interval is needed, so that the unit is more objectively scheduled, and the robustness and the economy of the operation of the power system are both considered.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method and a device for establishing a unit optimization scheduling model, and aims to solve the problems that in the current unit optimization scheduling considering the uncertainty of random energy, only the extreme condition of output fluctuation of the random energy can be considered, and the robustness and the economy of the operation of a power system cannot be objectively adjusted.
In order to achieve the above object, the present invention provides a method for establishing a unit optimization scheduling model, which comprises the following steps:
s1: based on an interval segmented robust optimization method, the output fluctuation interval of the random energy is segmented and represented, and a random energy output fluctuation sub-interval is constructed;
s2: setting sub-interval segmented robust optimization parameters by using historical predicted output and historical actual output data of the random energy station;
s3: and establishing a linearization constraint condition by utilizing the subsection robust optimization parameters and the dual variables of each subinterval, and establishing a unit optimization scheduling model by taking the minimum sum of the combustion cost and the start-stop cost of the thermal power unit as a target function.
Preferably, step S3 specifically includes:
establishing a target function with the minimum sum of fuel cost and start-stop cost of the thermal power generating unit, and establishing a deterministic constraint condition by utilizing a subinterval segmented robust optimization parameter;
adopting a piecewise linearization method to linearize the target function;
and (3) converting the positive rotation standby constraint and the negative rotation standby constraint into linearization by introducing a dual variable, and finishing the unit optimization scheduling model.
Preferably, the subinterval segment robust optimization parameters include an upper limit and a lower limit of the number of deviation multiples of each subinterval.
Preferably, the method for obtaining the subinterval segment robust optimization parameters includes the following steps:
obtaining the historical deviation multiple of the output of the random energy station according to the ratio of the historical actual output to the historical predicted output of the random energy station;
comparing the number of the deviation multiples in each time interval in the subinterval segment, and screening out the maximum value and the minimum value of the number of the deviation multiples corresponding to each subinterval;
and dividing the maximum value and the minimum value of the number of the deviation multiples by the time interval to obtain the upper limit and the lower limit of the number of the deviation multiples of each subinterval. Preferably, the constraint conditions include a power balance constraint, a unit output upper and lower limit constraint, a unit climbing constraint, a unit start and stop logic constraint, a positive rotation standby constraint and a negative rotation standby constraint.
Preferably, the output fluctuation interval segment of the random energy source is represented as:
Figure GDA0003591752620000031
wherein the content of the first and second substances,
Figure GDA0003591752620000032
Figure GDA0003591752620000033
the output deviation of the kth random energy station in the t period is satisfied, and the condition is as follows:
Figure GDA0003591752620000034
Figure GDA0003591752620000035
actual output of the kth random energy station in the t period;
Figure GDA0003591752620000036
predicted capacity for the kth stochastic energy site for the t-th time period;
Figure GDA0003591752620000037
m is a section coefficient of the interval section robust optimization method; q is a number set of m; n is a radical ofwThe number of random energy stations in the system; t is the total scheduling period.
Preferably, the linearized objective function is:
Figure GDA0003591752620000038
Figure GDA0003591752620000039
Figure GDA00035917526200000310
Figure GDA00035917526200000311
δl,g,t≤Hl,g-Hl-1,g,l∈NLg,
Figure GDA00035917526200000312
Figure GDA00035917526200000313
δl,g,t≥0,l∈NLg,
Figure GDA00035917526200000314
wherein, deltal,g,tRepresenting the output power of the unit g in the l section of the time period t as an additional variable; NLgThe number of sections for section linearization of the unit g fuel cost characteristic curve; a. thegThe minimum fuel cost for the unit g in the starting state; fl,gThe slope of a fuel cost secondary curve of the unit g in the l section is obtained; hl,gIs a segmentation point of the l section of the unit g;
Figure GDA0003591752620000041
the lower limit of the output of the unit g;
Figure GDA0003591752620000042
the upper limit of the output of the unit g; f. ofGThe unit fuel cost; n is a radical ofGA thermoelectric generator set in the system is collected; a isg、bg、cgSecondary, primary and constant cost coefficients of the thermal power generating unit are obtained;
Figure GDA0003591752620000043
the output of the g thermal power generating unit at the t moment is obtained; u. ofg,tAnd the operating state of the g-th thermal power generating unit at the t-th moment is shown.
Preferably, the positive rotation standby constraint that translates into a deterministic constraint is:
Figure GDA0003591752620000044
Figure GDA0003591752620000045
Figure GDA0003591752620000046
Figure GDA0003591752620000047
wherein the content of the first and second substances,
Figure GDA0003591752620000048
is a dual variable;
Figure GDA0003591752620000049
is the positive rotation reserve capacity requirement of the system during the period t;
Figure GDA00035917526200000410
the load requirement of the whole system is time t; u. ofg,tThe operating state of the g-th thermal power generating unit at the t-th moment is shown;
Figure GDA00035917526200000411
the output deviation of the kth random energy station in the t period is obtained;
Figure GDA00035917526200000412
predicted capacity for the kth stochastic energy site for the t-th time period;
Figure GDA00035917526200000413
the upper limit of the output of the unit g;
Figure GDA00035917526200000414
and
Figure GDA00035917526200000415
the upper limit and the lower limit of the number of the sub-interval sections of the deviation multiple are respectively.
Based on the method for establishing the unit optimized scheduling model, the invention provides a corresponding device for establishing the unit optimized scheduling model, which comprises the following steps: the system comprises a subinterval building module, a parameter setting module and a model building module which are connected in sequence;
the subinterval construction module is used for performing segmented representation on the output fluctuation interval of the random energy based on an interval segmented robust optimization method, and constructing a random energy output fluctuation subinterval;
the parameter setting module is used for setting segmented robust optimization parameters of all subintervals by utilizing historical predicted output data of the random energy station and historical actual output data of the random energy station;
the model establishing module is used for establishing a linearization constraint condition by utilizing the subsection robust optimization parameters and the dual variables of each subinterval, and establishing a unit optimization scheduling model by taking the minimum sum of the combustion cost and the start-stop cost of the thermal power generating unit as a target function.
Preferably, the model building module comprises an objective function building unit, a constraint condition processor, a linearization processor and a converter; the target function establishing unit is connected with the linearization processor; the converter is connected with the constraint condition processor;
the target function establishing module is used for establishing a target function with the minimum sum of fuel cost and start-stop cost of the thermal power generating unit;
the constraint condition processor is used for establishing a deterministic constraint condition by utilizing the subsection robust optimization parameters of each subinterval;
the linearization processor is used for linearizing the target function by adopting a piecewise linearization method;
the converter is used for linearizing the positive rotation standby constraint and the negative rotation standby constraint by introducing a dual variable and establishing a unit optimization scheduling model.
Preferably, the subinterval segment robust optimization parameters include an upper limit and a lower limit of the number of deviation multiples of each subinterval.
Preferably, the constraint conditions include a power balance constraint, a unit output upper and lower limit constraint, a unit climbing constraint, a unit start and stop logic constraint, a positive rotation standby constraint and a negative rotation standby constraint.
Preferably, the output fluctuation interval segment of the random energy source is represented as:
Figure GDA0003591752620000051
wherein the content of the first and second substances,
Figure GDA0003591752620000052
Figure GDA0003591752620000053
and the output deviation of the kth random energy station in the t time period meets the following conditions:
Figure GDA0003591752620000054
Figure GDA0003591752620000055
actual output of the kth random energy station in the t period;
Figure GDA0003591752620000056
predicting the output of the kth random energy station in the t period;
Figure GDA0003591752620000057
preferably, the linearized objective function is:
Figure GDA0003591752620000058
Figure GDA0003591752620000061
Figure GDA0003591752620000062
Figure GDA0003591752620000063
δl,g,t≤Hl,g-Hl-1,g,l∈NLg,
Figure GDA0003591752620000064
Figure GDA0003591752620000065
δl,g,t≥0,l∈NLg,
Figure GDA0003591752620000066
wherein, deltal,g,tRepresenting the output power of the unit g in the l section of the time period t as an additional variable; NLgThe number of sections for section linearization of the unit g fuel cost characteristic curve; a. thegThe minimum fuel cost of the unit g in the starting state; fl,gThe slope of a fuel cost secondary curve of the unit g in the l section is obtained; hl,gIs a segmentation point of the l section of the unit g;
Figure GDA0003591752620000067
the lower limit of the output of the unit g;
Figure GDA0003591752620000068
the upper limit of the output of the unit g; f. ofGThe unit fuel cost; n is a radical ofGA thermoelectric generator set in the system is collected; a is ag、bg、cgSecondary, primary and constant cost coefficients of the thermal power generating unit are obtained;
Figure GDA0003591752620000069
for the g-th thermal power generating unit at the t-th momentThe output of (2); u. ofg,tAnd the operating state of the g-th thermal power generating unit at the t-th moment is shown.
Preferably, the forward rotation standby constraint that translates to a deterministic constraint is:
Figure GDA00035917526200000610
Figure GDA00035917526200000611
Figure GDA00035917526200000612
Figure GDA00035917526200000613
wherein the content of the first and second substances,
Figure GDA00035917526200000614
is a dual variable;
Figure GDA00035917526200000615
is the positive rotation reserve capacity requirement of the system during the period t;
Figure GDA00035917526200000616
the load requirement of the whole system is time t; u. ofg,tThe operating state of the g-th thermal power generating unit at the t-th moment is shown;
Figure GDA00035917526200000617
the output deviation of the kth random energy station in the t period is obtained;
Figure GDA00035917526200000618
predicting the output of the kth random energy station in the t period;
Figure GDA00035917526200000619
the upper limit of the output of the unit g;
Figure GDA00035917526200000620
and
Figure GDA00035917526200000621
the upper limit and the lower limit of the number of the sub-interval sections of the deviation multiple are respectively.
The method for establishing the unit optimization scheduling model can be stored in a computer readable storage medium, and the method for establishing the unit optimization scheduling model can be realized when a computer program is executed by a processor.
Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:
the invention provides a method for establishing a unit optimization scheduling model, which is based on an interval segmentation robust optimization method and is used for carrying out segmentation representation on an output fluctuation interval of random energy to construct a random energy output fluctuation sub-interval; meanwhile, the predicted output and the actual output of the historical random energy field station are utilized to set sub-interval segmented robust optimization parameters, and positive rotation standby and negative rotation standby constraints based on the output fluctuation sub-interval of the random energy field station are established by utilizing the sub-interval segmented robust optimization parameters, wherein the output fluctuation sub-interval of the random energy field station is adopted to replace the output of the random energy field station in the positive rotation standby constraints and the negative rotation standby constraints, so that the uncertain rotation standby constraints are converted into the deterministic rotation standby constraints. In addition, a dual variable is introduced based on the dual transformation, a non-linear part in the deterministic rotating standby constraint is converted into a linear part, and the conversion of the deterministic rotating standby constraint with the non-linear part into the deterministic linear rotating standby constraint is realized. Compared with the existing uncertain rotary standby constraint, the deterministic linear rotary standby constraint can avoid the waste of fuel cost of the thermal power generating unit caused by excessive rotary standby and effectively improve the operating economy of the system; in addition, the deterministic linear rotation standby constraint can avoid system load loss and wind and light abandon caused by insufficient rotation standby, and the operation robustness of the system is effectively improved. Therefore, the method can better balance the robustness and the economy of the operation of the power system while considering the random energy fluctuation condition in the subinterval.
The invention is based on the interval segmentation robust optimization method, carries out segmentation representation on the output fluctuation interval of the random energy, simultaneously covers the extreme condition and the general condition of the fluctuation of the random energy, and makes up the defect that the extreme fluctuation condition of the random energy can only be considered in the extreme robust optimization and the Seng-Cheol Kang robust optimization.
The method for setting the subinterval segmentation robust optimization parameters by adopting the historical sample probability information can determine the maximum value and the minimum value of the number of the uncertain variables in each subinterval by only setting the segmentation coefficient, avoids the defect that the traditional interval segmentation robust optimization needs to set too many parameters, and enables the optimization result to be more objective.
Drawings
FIG. 1 is a flow chart of a method for establishing a unit optimization scheduling model provided by the invention;
FIG. 2 is a schematic diagram showing the comparison of the unit output of SCK-RO and MBU-RO in the first scheme provided by the embodiment;
FIG. 3 is a system rotation standby diagram of SCK-RO and MBU-RO in the first solution provided by the embodiment;
FIG. 4 is a schematic diagram comparing the system operation costs of CRO, SCK-RO and MBU-RO in the first solution provided by the embodiment;
FIG. 5(a) is a graph comparing the average air rejection of CRO, SCK-RO and MBU-RO in the first solution provided by the example;
FIG. 5(b) is a graph comparing the average wind curtailment times of CRO, SCK-RO and MBU-RO in the first solution provided by the embodiment;
FIG. 5(c) is a graph comparing the average adjustment amounts of CRO, SCK-RO and MBU-RO in the first embodiment;
FIG. 6 is a schematic diagram comparing the system operation cost of SCK-RO and MBU-RO in the second scheme provided by the embodiment;
FIG. 7(a) is a graph comparing the average air rejection of CRO, SCK-RO and MBU-RO in the second scheme provided by the example;
FIG. 7(b) is a graph comparing the average wind curtailment times of CRO, SCK-RO and MBU-RO in the second scheme provided by the example;
FIG. 7(c) is a graph comparing the average adjustment amounts of CRO, SCK-RO and MBU-RO in the second scheme provided in the example.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the present invention provides a method for establishing a unit optimization scheduling model, which includes the following steps:
s1: based on an interval segmented robust optimization method, the output fluctuation interval of the random energy is segmented and represented, and a random energy output fluctuation sub-interval is constructed;
the method specifically comprises the following steps:
the random energy output may be expressed as:
Figure GDA0003591752620000091
wherein N iswThe number of random energy stations; t is a total scheduling time interval;
Figure GDA0003591752620000092
actual output of the kth random energy station in the t period;
Figure GDA0003591752620000093
predicting the output of the kth random energy station in the t period;
Figure GDA0003591752620000094
is composed of
Figure GDA0003591752620000095
Relative to
Figure GDA0003591752620000096
Deviation multiple of (2);
Figure GDA0003591752620000097
d-Mis a negative deviation percentage; d is a radical ofMIs a positive deviation percentage;
Figure GDA0003591752620000098
the interval form of (a) is represented as follows:
Figure GDA0003591752620000099
wherein [ d ]-M,dM]Is called as
Figure GDA00035917526200000910
Relative to
Figure GDA00035917526200000911
A deviation percentage interval of (d); robust optimization method based on interval segmentation, [ d ]-M,dM]Can be expressed in a plurality of subinterval forms as follows:
d-M<…<dm-1<dm<…<dM
wherein, M is a segmentation coefficient of the interval segmentation robust optimization method, M is the number of each subinterval, and M belongs to { Q | -M, …, -1,0,1, …, M }, and Q is a number set of M;
when M is-M, the subintervals are a single number d-M(ii) a When M ∈ { -M +1, …, -1,0,1, …, M }, the subinterval is (d)m-1,dm](ii) a In particular, when m is 0, d0Is equal to 0, i.e.
Figure GDA00035917526200000912
For the
Figure GDA00035917526200000913
No deviation occurs; based on the above, it is possible to provide a display device,
Figure GDA00035917526200000914
can be expressed as:
Figure GDA00035917526200000915
wherein the content of the first and second substances,
Figure GDA00035917526200000916
when in use
Figure GDA00035917526200000917
When the utility model is used, the water is discharged,
Figure GDA00035917526200000918
relative to
Figure GDA00035917526200000919
No deviation occurs; when in use
Figure GDA00035917526200000920
When the temperature of the water is higher than the set temperature,
Figure GDA0003591752620000101
relative to
Figure GDA0003591752620000102
A deviation occurs and, therefore,
Figure GDA0003591752620000103
can be simplified to be represented as:
Figure GDA0003591752620000104
wherein the content of the first and second substances,
Figure GDA0003591752620000105
Figure GDA0003591752620000106
the output deviation upper limit of the kth random energy station in the t period is satisfiedConditions are as follows:
Figure GDA0003591752620000107
the output fluctuation interval segmentation of the random energy is completed, and the output subinterval of the random energy is constructed;
s2: setting parameters of the subinterval segmented robust optimization:
segmentation coefficient M will [ d-M,dM]Dividing into 2M +1 sub-intervals, defining lmRepresents
Figure GDA0003591752620000108
Belonging to the sub-interval (1+ d)m-1,1+dm]Lower limit of the number of deviation multiples of (1), define umRepresents
Figure GDA0003591752620000109
Belongs to the sub-interval (1+ d)m-1,1+dm]And has an upper limit of 0<lm<um<NWT; based on
Figure GDA00035917526200001010
History data of (2)
Figure GDA00035917526200001011
Each subinterval (1+ d) may be determinedm-1,1+dm]L ofmAnd umA value; suppose that
Figure GDA00035917526200001012
Is that
Figure GDA00035917526200001013
A history sample of (a) of (b),
Figure GDA00035917526200001014
is that
Figure GDA00035917526200001015
D is the sample dayD is the set of sample days, determine lmAnd umThe method comprises the following specific steps:
s2.1: based on
Figure GDA00035917526200001016
And
Figure GDA00035917526200001017
the value of (A) is calculated by
Figure GDA00035917526200001018
History value of
Figure GDA00035917526200001019
Figure GDA00035917526200001020
S2.2: calculate each subinterval (1+ d)m-1,1+dm]In each time interval
Figure GDA00035917526200001021
Number N ofd,mAnd screening out the maximum value of the number of deviation multiples corresponding to each subinterval
Figure GDA00035917526200001022
And minimum value
Figure GDA00035917526200001023
Figure GDA00035917526200001024
And
Figure GDA00035917526200001025
represents each subinterval (1+ d)m -1,1+dm]In the time period
Figure GDA00035917526200001026
Upper limit and lower limit of the number of (2); general time periodOne day as a period;
s2.3: calculate each subinterval (1+ d)m-1,1+dm]Maximum probability of occurrence within a time period
Figure GDA00035917526200001027
And minimum probability of occurrence
Figure GDA00035917526200001028
If the time interval is based on one day, T is 24 h;
Figure GDA00035917526200001029
Figure GDA00035917526200001030
s2.4: calculate each subinterval (1+ d)m-1,1+dm]Of multiples of deviation ofmAnd umA value;
Figure GDA0003591752620000111
Figure GDA0003591752620000112
from the steps, based on the historical data of the random energy, the parameter setting of the interval segmentation robust optimization is completed, the parameter setting can be found visually, and l can be determined only by setting the segmentation coefficient MmAnd umA value of (d); furthermore, lmAnd umThe method is determined by the historical probability information of the random energy, so that the result of robust optimization is more objective;
s3: establishing an interval segmented robust model based on the random energy output uncertainty of the interval segmented robust optimization method;
based on the uncertainty of the random energy output of the interval segmented robust optimization method, the objective function of the set optimization model is established by taking the minimum fuel cost and start-stop cost of the thermal power generating unit as the target; the constraint conditions of the objective function comprise power balance constraint, unit output upper and lower limit constraint, unit climbing constraint, unit start-stop logic constraint and positive and negative rotation standby constraint;
the objective function is as follows:
fUC＝min(fG+fC)
Figure GDA0003591752620000113
Figure GDA0003591752620000114
in the above objective function, fUCThe total cost of system operation; f. ofGThe unit fuel cost; f. ofCThe unit start-stop cost;
Figure GDA0003591752620000115
a thermoelectric generator set in the system is collected; a isg、bg、cgSecondary, primary and constant cost coefficients of the thermal power generating unit are obtained;
Figure GDA0003591752620000116
the output of the g thermal power generating unit at the t moment is obtained; u. ofg,tThe operating state of the g-th thermal power generating unit at the t-th moment is shown;
Figure GDA0003591752620000117
the total starting times of the g-th thermal power generating unit are obtained;
Figure GDA0003591752620000118
starting up cost of the g-th thermal power generating unit at the t-th moment;
Figure GDA0003591752620000119
the total shutdown times of the ith thermal power generating unit;
Figure GDA00035917526200001110
the shutdown cost of the gtth thermal power generating unit at the t-th moment;
in order to reduce the solving difficulty of the unit optimization scheduling model, a piecewise linearization method is adopted to linearize an objective function, and a quadratic objective function is reduced to a primary objective function;
Figure GDA0003591752620000121
Figure GDA0003591752620000122
Figure GDA0003591752620000123
Figure GDA0003591752620000124
δl,g,t≤Hl,g-Hl-1,g,l∈NLg,
Figure GDA0003591752620000125
Figure GDA0003591752620000126
δl,g,t≥0,l∈NLg,
Figure GDA0003591752620000127
wherein, deltal,g,tRepresenting the output power of the unit g in the l section of the time period t as an additional variable; NL is a linear arraygThe number of sections for section linearization of the unit g fuel cost characteristic curve; a. thegThe minimum fuel cost of the unit g in the starting state is obtained; fl,gWith fuel cost quadratic curve in section l for unit gA slope; hl,gIs a segmentation point of the l section of the unit g;
Figure GDA0003591752620000128
the lower limit of the output of the unit g;
Figure GDA0003591752620000129
the upper limit of the output of the unit g; f. ofGThe unit fuel cost; n is a radical ofGA thermoelectric generator set in the system is collected; a isg、bg、cgSecondary, primary and constant cost coefficients of the thermal power generating unit are obtained;
Figure GDA00035917526200001210
the output of the g thermal power generating unit at the t moment is obtained; u. ofg,tAnd the operating state of the g-th thermal power generating unit at the t-th moment is shown.
Constraint conditions are as follows:
and power balance constraint:
Figure GDA00035917526200001211
wherein the content of the first and second substances,
Figure GDA00035917526200001212
the load requirement of the whole system is time t;
the power balance constraint requires that in a time period t, the sum of the total output of the thermoelectric generator set and the total output of the random energy in the system is equal to the system load requirement;
and (3) restraining the upper and lower limits of the unit output:
Figure GDA00035917526200001213
wherein the content of the first and second substances,
Figure GDA0003591752620000131
the lower limit of the output of the unit g;
Figure GDA0003591752620000132
the upper limit of the output of the unit g; the constraint condition requires that the output of the thermal power generating unit meets the output upper and lower limits of the unit;
unit climbing restraint:
Figure GDA0003591752620000133
wherein the content of the first and second substances,
Figure GDA0003591752620000134
the power is the downward climbing power of the thermal power generating unit g;
Figure GDA0003591752620000135
the power is the upward climbing power of the thermal power generating unit g; the physical meaning of the unit climbing constraint is as follows: the power variation of the unit in the front and rear periods meets the climbing power of the unit;
unit start-stop logic constraint:
Figure GDA0003591752620000136
wherein the content of the first and second substances,
Figure GDA0003591752620000137
the continuous running time of the unit g is obtained;
Figure GDA0003591752620000138
the minimum continuous operation time of the unit g;
Figure GDA0003591752620000139
continuous downtime of the unit g;
Figure GDA00035917526200001310
the minimum continuous downtime of the unit g; the physical meaning of the unit start-stop logic constraint is as follows: in that
Figure GDA00035917526200001311
When the unit is started, the unit is started; in that
Figure GDA00035917526200001312
When the machine set is in operation, the machine set is shut down; in addition, the state of the unit is not limited by the conditions;
positive rotation standby constraint (indeterminate constraint):
Figure GDA00035917526200001313
wherein the content of the first and second substances,
Figure GDA00035917526200001314
is the positive spinning reserve capacity requirement of the system for the period t; positive rotation standby restraint including random energy output
Figure GDA00035917526200001315
The positive rotation reserve constraint represents the uncertainty of random energy, and the physical meaning of the positive rotation reserve constraint is that the sum of the output upper limit of all on-line thermal power generating units and the output of the random energy is greater than the sum of the load and the positive rotation reserve capacity requirement;
negative rotation standby constraint (indeterminate constraint):
Figure GDA00035917526200001316
wherein the content of the first and second substances,
Figure GDA00035917526200001317
the demand of the negative rotation reserve capacity of the system in the period t; negative rotation standby constraint including random energy output
Figure GDA00035917526200001318
The negative spinning reserve constraint thus characterizes the uncertainty of the random energy source; the physical meaning of the negative rotation standby constraint is: the sum of the output lower limit of all the online thermal power generating units and the random energy output is smaller than the difference between the load and the negative rotation reserve capacity requirement;
s4: converting positive rotation standby constraint and negative rotation standby constraint containing random energy into deterministic constraint based on an interval segmented robust optimization method;
taking the positive rotation standby constraint as an example, will
Figure GDA0003591752620000141
Figure GDA0003591752620000142
Bringing in
Figure GDA0003591752620000143
The following equation is obtained:
Figure GDA0003591752620000144
the premise of interval segmented robust optimization is that the formula can be ensured under the worst fluctuation condition of random energy, so that the interval segmented robust optimization method is used for solving the problem that the interval segmented robust optimization method can not only solve the problem of low energy consumption but also solve the problem of low energy consumption
Figure GDA0003591752620000145
The above equation holds when taking the minimum value, and will thus
Figure GDA0003591752620000146
The equivalence is as follows:
Figure GDA0003591752620000147
Figure GDA0003591752620000148
Figure GDA0003591752620000149
Figure GDA00035917526200001410
Figure GDA00035917526200001411
wherein, DEVtIs the maximum total deviation of the random energy fluctuations; satisfying the constraint condition of formula (1) can realize each subinterval
Figure GDA00035917526200001412
The number of the middle random energy stations meets the upper limit and the lower limit of the number of the random energy stations in the interval;
introduction of
Figure GDA00035917526200001413
And (3) respectively representing dual variables of formulas (1) to (3), and acquiring a linearized form of the positive rotation standby constraint:
Figure GDA00035917526200001414
Figure GDA00035917526200001415
Figure GDA00035917526200001416
Figure GDA0003591752620000151
the same principle, introducing dual variables
Figure GDA0003591752620000152
Obtain linearized form of negative rotation standby constraint:
Figure GDA0003591752620000153
Figure GDA0003591752620000154
Figure GDA0003591752620000155
Figure GDA0003591752620000156
after positive and negative rotation standby constraint linearization, acquiring an interval segmented robust model;
s5: and solving the unit optimization scheduling model for unit scheduling.
Based on the method for establishing the unit optimized scheduling model, the invention provides a corresponding device for establishing the unit optimized scheduling model, which comprises the following steps: the system comprises a subinterval building module, a parameter setting module and a model building module which are connected in sequence;
the subinterval construction module is used for performing segmented representation on the output fluctuation interval of the random energy based on an interval segmented robust optimization method, and constructing a random energy output fluctuation subinterval;
the parameter setting module is used for setting segmented robust optimization parameters of all subintervals by utilizing historical predicted output data of the random energy station and historical actual output data of the random energy station;
the model establishing module is used for establishing a linearization constraint condition by utilizing the subsection robust optimization parameters and the dual variables of each subinterval, and establishing a unit optimization scheduling model by taking the minimum combustion cost and start-stop cost of the thermal power unit as a target function.
Preferably, the model building module comprises an objective function building unit, a constraint condition processor, a linearization processor and a converter; the target function establishing unit is connected with the linearization processor; the converter is connected with the constraint condition processor;
the target function establishing module is used for establishing a target function with the minimum sum of fuel cost and start-stop cost of the thermal power generating unit;
the constraint condition processor is used for establishing a deterministic constraint condition by utilizing the subsection robust optimization parameters of each subinterval;
the linearization processor is used for linearizing the target function by adopting a piecewise linearization method;
the converter is used for linearizing the positive rotation standby constraint and the negative rotation standby constraint by introducing a dual variable and establishing a unit optimization scheduling model.
Preferably, the subinterval segment robust optimization parameters include an upper limit and a lower limit of the number of deviation multiples of each subinterval. Preferably, the constraint conditions include a power balance constraint, a unit output upper and lower limit constraint, a unit climbing constraint, a unit start and stop logic constraint, a positive rotation standby constraint and a negative rotation standby constraint.
Preferably, the output fluctuation interval segment of the random energy source is represented as:
Figure GDA0003591752620000161
wherein the content of the first and second substances,
Figure GDA0003591752620000162
Figure GDA0003591752620000163
the output deviation of the kth random energy station in the t period is satisfied, and the condition is as follows:
Figure GDA0003591752620000164
Figure GDA0003591752620000165
actual output of the kth random energy station in the t period;
Figure GDA0003591752620000166
predicting the output of the kth random energy station in the t period;
Figure GDA0003591752620000167
preferably, the linearized objective function is:
Figure GDA0003591752620000168
Figure GDA0003591752620000169
Figure GDA00035917526200001610
Figure GDA00035917526200001611
δl,g,t≤Hl,g-Hl-1,g,l∈NLg,
Figure GDA00035917526200001612
Figure GDA00035917526200001613
δl,g,t≥0,l∈NLg,
Figure GDA00035917526200001614
wherein, deltal,g,tRepresenting the output power of the unit g in the l section of the time t as an additional variable; NLgThe number of sections for section linearization of the unit g fuel cost characteristic curve; a. thegThe minimum fuel cost of the unit g in the starting state is obtained; fl,gThe slope of a fuel cost secondary curve of the unit g in the l section is obtained; hl,gIs a segmentation point of the l section of the unit g;
Figure GDA0003591752620000171
the lower limit of the output of the unit g;
Figure GDA0003591752620000172
the upper limit of the output of the unit g; f. ofGThe unit fuel cost; n is a radical ofGA thermoelectric generator set in the system is collected; a isg、bg、cgSecondary, primary and constant cost coefficients of the thermal power generating unit;
Figure GDA0003591752620000173
the output of the g thermal power generating unit at the t moment is obtained; u. ofg,tAnd the operating state of the g-th thermal power generating unit at the t-th moment is shown.
Preferably, the forward rotation standby constraint that translates to a deterministic constraint is:
Figure GDA0003591752620000174
Figure GDA0003591752620000175
Figure GDA0003591752620000176
Figure GDA0003591752620000177
wherein the content of the first and second substances,
Figure GDA0003591752620000178
is a dual variable;
Figure GDA0003591752620000179
is the positive rotation reserve capacity requirement of the system during the period t;
Figure GDA00035917526200001710
for the load requirement of the whole system of time period tSolving; u. ug,tThe operating state of the gth thermal power generating unit at the t moment is shown;
Figure GDA00035917526200001711
the output deviation of the kth random energy station in the t period is obtained;
Figure GDA00035917526200001712
predicting the output of the kth random energy station in the t period;
Figure GDA00035917526200001713
the upper limit of the output of the unit g;
Figure GDA00035917526200001714
and
Figure GDA00035917526200001715
the upper limit and the lower limit of the number of the sub-interval sections of the deviation multiple are respectively.
The method for establishing the unit optimization scheduling model can be stored in a computer readable storage medium, and the method for establishing the unit optimization scheduling model can be realized when a computer program is executed by a processor.
Detailed description of the preferred embodiments
In order to reduce the conservative property of the limit robust optimization and consider the economical efficiency of the operation of the power system, the document 'research on the operation optimization method of the power system including the large-scale wind farm' applies the Seng-Cheol Kang robust optimization to the scheduling of the power system unit, but the Seng-Cheol Kang robust optimization model can only consider the limit condition of each uncertain variable and cannot consider the general condition in the fluctuation interval of the uncertain variables.
The invention considers the limit condition and the general condition of the random energy output fluctuation in detail, divides the traditional robust interval and carries out robust optimization based on the random energy output fluctuation sub-interval. The constructed subinterval effectively reduces the conservatism of the traditional robust optimization method, and is more favorable for finding out the balance point of the robustness and the economy of the robust unit scheduling result; on the basis, the conversion from a nonlinear model to a linearized model is realized by adopting a dual theory and a quadratic function piecewise linearization method. The interval segmentation robust model based on the interval segmentation robust optimization provided by the invention is compared and analyzed with the scheduling results of the traditional extreme robust optimization model and the Seng-Cheol Kang robust optimization model, so that the effectiveness of the interval segmentation robust optimization method is further verified.
Based on the establishing method of the interval segmentation robust model provided by the invention, two simulation comparison schemes are provided:
scheme I, scheme 10 for scheduling unit of 39-node-1 wind power plant system
The scheme adopts an extreme robust optimization model, a Seng-Cheol Kang robust optimization model and the interval segmentation robust model provided by the invention to compare and analyze the system operation cost and the system robustness after the system unit is scheduled. The robustness comprises testing the average air abandon quantity, the average air abandon times and the average regulating quantity of the scheduling results of the three model units; and the system operation cost represents the economy of the scheduling result.
Scheme II, scheme 54 for scheduling wind power plant system unit at 118 node 3
The scheme is based on a large-scale multi-unit power system comparison and analysis Seng-Cheol Kang robust optimization model and the unit scheduling result of the interval segmentation robust model provided by the invention. And analyzing the robustness adjusting capability of the Seng-Cheol Kang robust optimization model and the interval segmentation robust model by emphasis.
The present invention will be described in further detail with reference to examples.
The embodiment is based on MATLAB combined with a CPLEX solver to perform simulation. In the simulation, the random energy is set as wind power, the prediction error of the wind power is 20%, namely the actual wind power fluctuates in the interval of 0.8-1.2 times of the predicted wind power, and the specific simulation scheme is shown in table 1. Table 2 is an example of interval segmentation of the interval segmentation robust optimization method, where the segmentation coefficient is set to M-2, and there are 5 subintervals 2M +1, and the value of each subinterval is shown in table 2; table 3 shows the subjective parameter corresponding relation between the Seng-Cheol Kang robust model (SCK-RO) and the interval segmentation robust model (MBU-RO) in the first scheme. The Seng-Cheol Kang robust model (SCK-RO) and the interval subsection robust model (MBU-RO) in the scheme (1) are subjected to simulation experiments for 20 times; in the second scheme, 3 times of simulation experiments of the Seng-Cheol Kang robust model (SCK-RO) and 20 times of simulation experiments of the interval segmentation robust model (MBU-RO).
TABLE 1
Figure GDA0003591752620000191
TABLE 2
Figure GDA0003591752620000192
TABLE 3
Figure GDA0003591752620000193
FIG. 2 is a unit output force comparison diagram of a Seng-Cheol Kang robust model (SCK-RO) and an interval segmentation robust model (MBU-RO) in the scheme, wherein an uncertainty parameter gamma of the Seng-Cheol Kang robust model (SCK-RO)tThe segmentation coefficient M of the interval segmentation robust model (MBU-RO) is 20, and both robust models are the most conservative cases. As can be seen from FIG. 2, the unit output of the Seng-Cheol Kang robust model (SCK-RO) and the block-section robust model (MBU-RO) under the most conservative condition is very different, wherein the generator G4 and the generator G5 are most obvious.
FIG. 3 is a diagram of a comparison between the Seng-Cheol Kang robust model (SCK-RO) and the span segmentation robust model (MBU-RO) in scenario one. As can be seen from FIG. 3, the spinning spares of the Seng-Cheol Kang robust model (SCK-RO) and the span segmentation robust model (MBU-RO) are very close, but there is a certain gap in the individual time intervals. Therefore, the Seng-Cheol Kang robust model (SCK-RO) and the interval segmentation robust model (MBU-RO) have different rotation standby under the respective most conservative condition, namely the capacity of the unit scheduling result under the most conservative condition of the Seng-Cheol Kang robust model (SCK-RO) and the interval segmentation robust model (MBU-RO) is different when the wind power fluctuation is responded.
FIG. 4 is a schematic diagram of comparing system operation cost of a limit robust model (CRO), a Seng-Cheol Kang robust model (SCK-RO) and an interval segmentation robust model (MBU-RO). FIG. 5 is a graph showing the comparison of the robustness of the scheduling schemes of the extreme robust model (CRO), the Seng-Cheol Kang robust model (SCK-RO) and the interval segmentation robust model (MBU-RO) (FIG. 5(a) is a graph showing the comparison of the average air curtailment;
FIG. 5(b) is a graph comparing the average wind curtailment times; fig. 5(c) is a graph comparing the average adjustment amounts). As can be seen from fig. 4 and 5 (fig. 5(a), 5(b) and 5(c)), the unit scheduling result of the extreme robust model (CRO) is the unit scheduling result of the seg-Cheol Kang robust model (SCK-RO) under the most conservative condition, and by comparing the scheduling results of the seg-Cheol Kang robust model (SCK-RO) and the block segmentation robust model (MBU-RO), although the seg-Cheol Kang robust model (SCK-RO) can adjust the economy and robustness of the optimization result, the same balance point of the economy and robustness as the block segmentation robust model (MBU-RO) can still not be obtained.
Similarly, fig. 6 is a schematic diagram of system operation cost comparison between the Seng-Cheol Kang robust model (SCK-RO) and the block segmentation robust model (MBU-RO) in the second scenario, fig. 7 is a schematic diagram of scheduling method robustness comparison between the Seng-Cheol Kang robust model (SCK-RO) and the block segmentation robust model (MBU-RO) in the second scenario (fig. 7(a) is a comparison diagram of average wind curtailment amount, fig. 7(b) is a comparison diagram of average wind curtailment times, and fig. 7(c) is a comparison diagram of average adjustment amount). Uncertainty parameter Γ of Seng-Cheol Kang robust model (SCK-RO) in case of multiple wind farmstAlthough adjustable, in the most conserved ΓtCost waste still occurs when the value is 3. The interval segmentation robust model (MBU-RO) gives consideration to the economy and the robustness of the unit scheduling result, a balance point exists between the economy and the robustness of the unit scheduling result and is between the T and the T in the Seng-Cheol Kang robust model (SCK-RO)t2 with rtBest operating point between 3.
Through the analysis of the economy and the robustness of the scheduling result in the simulation, the situation that the operation cost of the power system is wasted can be found if the interval segmentation is not carried out.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (3)

1. A method for establishing a unit optimization scheduling model is characterized by comprising the following steps:
s1: constructing a random energy output fluctuation subinterval based on an interval segmented robust optimization method;
s2: setting segmented robust optimization parameters of each subinterval by using historical predicted output data and historical actual output data of the random energy station;
s3: establishing a linearization constraint condition by utilizing the subsection robust optimization parameters and the dual variables of each subinterval, and establishing a unit optimization scheduling model by taking the minimum combustion cost and start-stop cost of the thermal power unit as a target function;
the step S3 specifically includes:
establishing a target function with the minimum sum of fuel cost and start-stop cost of the thermal power generating unit, and establishing a deterministic constraint condition by utilizing a subinterval segmented robust optimization parameter;
adopting a piecewise linearization method to linearize the target function;
by introducing a dual variable, linearizing a positive rotation standby constraint and a negative rotation standby constraint to complete a unit optimization scheduling model;
the constraint conditions comprise power balance constraint, unit output upper and lower limit constraint, unit climbing constraint, unit start and stop logic constraint, positive rotation standby constraint and negative rotation standby constraint;
s1 is specifically based on an interval segmentation robust optimization method, and the output fluctuation interval of the random energy is segmented and represented to construct a random energy output fluctuation subinterval;
the random energy output is expressed as:
Figure FDA0003591752610000011
wherein N iswThe number of random energy stations; t is a total scheduling time interval;
Figure FDA0003591752610000012
actual output of the kth random energy station in the t period;
Figure FDA0003591752610000013
predicting the output of the kth random energy station in the t period;
Figure FDA0003591752610000014
is composed of
Figure FDA0003591752610000015
Relative to
Figure FDA0003591752610000016
A deviation multiple of (d);
Figure FDA0003591752610000017
d-Mis a negative deviation percentage; dMIs a positive deviation percentage;
Figure FDA0003591752610000018
the interval form of (a) is represented as follows:
Figure FDA0003591752610000021
wherein [ d ]-M，dM]Is called as
Figure FDA0003591752610000022
Relative to
Figure FDA0003591752610000023
A deviation percentage interval of (d); robust optimization method based on interval segmentation, [ d ]-M，dM]Expressed in a number of subinterval forms:
d-M＜…＜dm-1＜dm＜…＜dM
the method comprises the following steps that M is a segmentation coefficient of an interval segmentation robust optimization method, M is the number of each subinterval, M belongs to { Q | -M,. eta., -1,0,1,. eta.,. M }, and Q is the number set of M;
when M is-M, the subintervals are a single number d-M(ii) a When M { -M + 1., -1,0, 1.,. M } the subinterval is (d)m -1，dm]；
Figure FDA0003591752610000024
Is expressed as:
Figure FDA0003591752610000025
wherein the content of the first and second substances,
Figure FDA0003591752610000026
when in use
Figure FDA0003591752610000027
When the temperature of the water is higher than the set temperature,
Figure FDA0003591752610000028
relative to
Figure FDA0003591752610000029
No deviation occurs; when in use
Figure FDA00035917526100000210
When the temperature of the water is higher than the set temperature,
Figure FDA00035917526100000211
relative to
Figure FDA00035917526100000212
A deviation occurs in the form of a deviation,
Figure FDA00035917526100000213
expressed as:
Figure FDA00035917526100000214
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA00035917526100000215
Figure FDA00035917526100000216
the output deviation upper limit of the kth random energy station in the t period is satisfied, and the following conditions are satisfied:
Figure FDA00035917526100000217
the output fluctuation interval segmentation of the random energy is completed, and the output subinterval of the random energy is constructed;
step S2 sets the subinterval segment robust optimization parameters, which includes the following specific steps:
segmentation coefficient M will [ d-M，dM]Dividing into 2M +1 sub-intervals, defining lmRepresents
Figure FDA00035917526100000218
Belongs to the sub-interval (1+ d)m-1，1+dm]Lower limit of the number of deviation multiples of (1), define umRepresents
Figure FDA00035917526100000219
Belongs to the sub-interval (1+ d)m-1，1+dm]And has an upper limit of 0 < lm＜um＜NWT; based on
Figure FDA00035917526100000220
History data of
Figure FDA00035917526100000221
Determining each subinterval (1+ d)m-1，1+dm]L ofmAnd umA value; suppose that
Figure FDA0003591752610000031
Is that
Figure FDA0003591752610000032
A history sample of (a) of (b),
Figure FDA0003591752610000033
is that
Figure FDA0003591752610000034
D is the number of the sample day, D is the set of sample days, determine lmAnd umThe method comprises the following specific steps:
s2.1: based on
Figure FDA0003591752610000035
And
Figure FDA0003591752610000036
the values of (A) were calculated as follows
Figure FDA0003591752610000037
History value of
Figure FDA0003591752610000038
Figure FDA0003591752610000039
S2.2: calculate each subinterval (1+ d)m-1，1+dm]In each time interval
Figure FDA00035917526100000310
Number N ofd，mAnd screening out the maximum value of the number of deviation multiples corresponding to each subinterval
Figure FDA00035917526100000311
And minimum value
Figure FDA00035917526100000312
And
Figure FDA00035917526100000313
represents each subinterval (1+ d)m-1，1+dm]In the time period
Figure FDA00035917526100000314
Upper limit and lower limit of the number of (2);
s2.3: calculate each subinterval (1+ d)m-1，1+dm]Maximum probability of occurrence within a time period
Figure FDA00035917526100000315
And minimum probability of occurrence
Figure FDA00035917526100000316
Figure FDA00035917526100000317
Figure FDA00035917526100000318
S2.4: calculate each subinterval (1+ d)m-1，1+dm]Of multiples of deviation ofmAnd umA value;
Figure FDA00035917526100000319
Figure FDA00035917526100000320
in which dual variables are introduced
Figure FDA00035917526100000321
Obtain linearized form of the positive rotation standby constraint:
Figure FDA00035917526100000322
Figure FDA00035917526100000323
Figure FDA00035917526100000324
Figure FDA00035917526100000325
introducing dual variables
Figure FDA00035917526100000326
Obtaining a linearized form of the negative rotation standby constraint:
Figure FDA00035917526100000327
Figure FDA00035917526100000328
Figure FDA00035917526100000329
Figure FDA00035917526100000330
after positive and negative rotation standby constraint linearization, acquiring an interval segmented robust model;
wherein N isGA thermoelectric generator set in the system is collected; u. ug，tThe operating state of the gth thermal power generating unit at the t moment is shown;
Figure FDA0003591752610000041
the load requirement of the whole system is time t; g is a thermal power generating unit;
Figure FDA0003591752610000042
the lower limit of the output of the unit g;
Figure FDA0003591752610000043
the upper limit of the output of the unit g;
Figure FDA0003591752610000044
and
Figure FDA0003591752610000045
respectively the upper limit and the lower limit of the number of the sub-interval sections of the deviation multiple;
Figure FDA0003591752610000046
predicting the output of the kth random energy station in the t period;
Figure FDA0003591752610000047
is the positive rotation reserve capacity requirement of the system during the period t;
Figure FDA0003591752610000048
the reserve capacity requirement for negative rotation of the system for the period t.
2. The device for establishing the establishing method based on the unit optimization scheduling model is characterized by comprising a subinterval establishing module, a parameter setting module and a model establishing module which are connected in sequence;
the subinterval construction module is used for performing segmented representation on the output fluctuation interval of the random energy based on an interval segmented robust optimization method, and constructing a random energy output fluctuation subinterval;
the parameter setting module is used for setting segmented robust optimization parameters of all subintervals by utilizing historical predicted output data and historical actual output data of the random energy station;
the model establishing module is used for establishing a linearization constraint condition by utilizing the subsection robust optimization parameters and the dual variables of each subinterval, and establishing a unit optimization scheduling model by taking the minimum sum of the combustion cost and the start-stop cost of the thermal power unit as a target function;
the model building module comprises an objective function building unit, a constraint condition processor, a linearization processor and a converter;
the target function establishing unit is connected with the linearization processor; the converter is connected with the constraint processor;
the target function establishing module is used for establishing a target function with the minimum sum of the fuel cost and the start-stop cost of the thermal power generating unit;
the constraint condition processor is used for establishing a deterministic constraint condition by utilizing the subsection robust optimization parameters of each subinterval;
the linearization processor is used for linearizing the target function by adopting a piecewise linearization method;
the converter is used for linearizing positive rotation standby constraint and negative rotation standby constraint by introducing dual variables and establishing a unit optimization scheduling model; the constraint conditions comprise power balance constraint, unit output upper and lower limit constraint, unit climbing constraint, unit start and stop logic constraint, positive rotation standby constraint and negative rotation standby constraint;
the output fluctuation interval of the random energy is expressed in a segmented mode based on an interval segmented robust optimization method, and a random energy output fluctuation sub-interval is constructed;
the random energy output is expressed as:
Figure FDA0003591752610000051
wherein N iswThe number of random energy stations; t is a total scheduling time interval;
Figure FDA0003591752610000052
actual output of the kth random energy station in the t period;
Figure FDA0003591752610000053
predicting the output of the kth random energy station in the t period;
Figure FDA0003591752610000054
is composed of
Figure FDA0003591752610000055
Relative to
Figure FDA0003591752610000056
A deviation multiple of (d);
Figure FDA0003591752610000057
d-Mis a negative deviation percentage; dMIs a positive deviation percentage;
Figure FDA0003591752610000058
the interval form of (a) is represented as follows:
Figure FDA0003591752610000059
wherein [ d ]-M，dM]Is called as
Figure FDA00035917526100000510
Relative to
Figure FDA00035917526100000511
A deviation percentage interval of (d); robust optimization method based on interval segmentation, [ d ]-M，dM]Expressed in a number of subinterval forms:
d-M＜…＜dm-1＜dm＜…＜dM
the method comprises the following steps that M is a segmentation coefficient of an interval segmentation robust optimization method, M is the number of each subinterval, M belongs to { Q | -M, ·, -1,0,1,..,. M }, and Q is a number set of M;
when M is-M, the subintervals are a single number d-M(ii) a When M { -M + 1., -1,0, 1.,. M } the subinterval is (d)m -1，dm]；
Figure FDA00035917526100000512
Is expressed as:
Figure FDA00035917526100000513
wherein the content of the first and second substances,
Figure FDA00035917526100000514
when in use
Figure FDA00035917526100000515
When the temperature of the water is higher than the set temperature,
Figure FDA00035917526100000516
relative to
Figure FDA00035917526100000517
No deviation occurs; when in use
Figure FDA00035917526100000518
When the temperature of the water is higher than the set temperature,
Figure FDA00035917526100000519
relative to
Figure FDA00035917526100000520
A deviation occurs in the form of a deviation,
Figure FDA00035917526100000521
expressed as:
Figure FDA00035917526100000522
wherein the content of the first and second substances,
Figure FDA00035917526100000523
Figure FDA00035917526100000524
the output deviation upper limit of the kth random energy station in the t period is satisfied, and the following conditions are satisfied:
Figure FDA0003591752610000061
the output fluctuation interval segmentation of the random energy is completed, and the output subinterval of the random energy is constructed;
the method comprises the following steps of setting sub-interval segmentation robust optimization parameters, wherein the specific steps are as follows:
segmentation coefficient M will [ d-M，dM]Divided into 2M +1 sub-intervals, definedlmRepresents
Figure FDA0003591752610000062
Belongs to the sub-interval (1+ d)m-1，1+dm]Lower limit of the number of deviation multiples of (1), define umRepresents
Figure FDA0003591752610000063
Belonging to the sub-interval (1+ d)m-1，1+dm]And has an upper limit of 0 < lm＜um＜NWT; based on
Figure FDA0003591752610000064
History data of
Figure FDA0003591752610000065
Determining each subinterval (1+ d)m-1，1+dm]L ofmAnd umA value; suppose that
Figure FDA0003591752610000066
Is that
Figure FDA0003591752610000067
A history sample of (a) of (b),
Figure FDA0003591752610000068
is that
Figure FDA0003591752610000069
D is the number of the sample day, D is the set of sample days, determine lmAnd umThe method comprises the following specific steps:
based on
Figure FDA00035917526100000610
And
Figure FDA00035917526100000611
is calculated byBy the following calculation
Figure FDA00035917526100000612
History value of
Figure FDA00035917526100000613
Figure FDA00035917526100000614
Calculate each subinterval (1+ d)m-1，1+dm]In each time interval
Figure FDA00035917526100000615
Number N ofd，mAnd screening out the maximum value of the number of deviation multiples corresponding to each subinterval
Figure FDA00035917526100000616
And minimum value
Figure FDA00035917526100000617
And
Figure FDA00035917526100000618
represents each subinterval (1+ d)m-1，1+dm]In the time period
Figure FDA00035917526100000619
Upper limit and lower limit of the number of (2);
calculate each subinterval (1+ d)m-1，1+dm]Maximum probability of occurrence within a time period
Figure FDA00035917526100000620
And minimum probability of occurrence
Figure FDA00035917526100000621
Figure FDA00035917526100000622
Figure FDA00035917526100000623
Calculate each subinterval (1+ d)m-1，1+dm]Of multiples of deviation ofmAnd umA value;
Figure FDA00035917526100000624
Figure FDA00035917526100000625
in which dual variables are introduced
Figure FDA00035917526100000626
Obtain linearized form of the positive rotation standby constraint:
Figure FDA00035917526100000627
Figure FDA0003591752610000071
Figure FDA0003591752610000072
Figure FDA0003591752610000073
introducing dual variables
Figure FDA0003591752610000074
Obtain linearized form of negative rotation standby constraint:
Figure FDA0003591752610000075
Figure FDA0003591752610000076
Figure FDA0003591752610000077
Figure FDA0003591752610000078
after positive and negative rotation standby constraint linearization, acquiring an interval segmented robust model;
wherein N isGA thermoelectric generator set in the system is collected; u. ofg，tThe operating state of the gth thermal power generating unit at the t moment is shown;
Figure FDA0003591752610000079
the load requirement of the whole system is time t; g is a thermal power generating unit;
Figure FDA00035917526100000710
the lower limit of the output of the unit g;
Figure FDA00035917526100000711
the upper limit of the output of the unit g;
Figure FDA00035917526100000712
and
Figure FDA00035917526100000713
respectively the upper limit and the lower limit of the number of the sub-interval sections of the deviation multiple;
Figure FDA00035917526100000714
predicting the output of the kth random energy station in the t period;
Figure FDA00035917526100000715
is the positive rotation reserve capacity requirement of the system during the period t;
Figure FDA00035917526100000716
the reserve capacity requirement for negative rotation of the system for the period t.
3. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the set-up method of claim 1.
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