CN108876196B - Alternate iterative optimization scheduling method based on non-protrusion force characteristic of cogeneration unit - Google Patents

Alternate iterative optimization scheduling method based on non-protrusion force characteristic of cogeneration unit Download PDF

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CN108876196B
CN108876196B CN201810788403.5A CN201810788403A CN108876196B CN 108876196 B CN108876196 B CN 108876196B CN 201810788403 A CN201810788403 A CN 201810788403A CN 108876196 B CN108876196 B CN 108876196B
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陈建华
皇甫成
汪鸿
梁吉
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State Grid Corp of China SGCC
State Grid Jibei Electric Power Co Ltd
Qinhuangdao Power Supply Co of State Grid Jibei Electric Power Co Ltd
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State Grid Jibei Electric Power Co Ltd
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Abstract

The invention discloses an alternating iteration optimization scheduling method based on the non-protrusion force characteristic of a cogeneration unit, the method divides the non-protrusion force area of each cogeneration unit into a plurality of sub-areas, converts the segmented constraint of each sub-area into continuous linear constraint, further establishing an active economic dispatching model of the non-protrusion force characteristic of the cogeneration unit, simplifying the model into an optimized dispatching model aiming at a single cogeneration unit, further decomposing the model into two sub-models of heat supply optimized dispatching and active optimized dispatching, respectively solving by adopting a mixed integer quadratic programming algorithm and a quadratic programming algorithm, and simultaneously carrying out iterative optimization by adopting an alternating iteration method, the method and the device are suitable for the non-convex operation characteristic area of the cogeneration unit, and can realize real-time online scheduling of the cogeneration unit.

Description

Alternate iterative optimization scheduling method based on non-protrusion force characteristic of cogeneration unit
Technical Field
The invention belongs to the technical field of thermoelectricity, and particularly relates to an alternating iteration optimization scheduling method based on the non-protrusion force characteristic of a cogeneration unit.
Background
The cogeneration unit can meet the thermal and electric power requirements of users at the same time, and has higher energy utilization efficiency than the thermal power unit, so the cogeneration unit is applied to an electric power system on a large scale, is developed rapidly especially in the areas of north China, northeast China and northwest 'three-north' with heating requirements, basically all large thermal power units are cogeneration units, and the demand of rapidly scheduling electricity and heat production of the cogeneration unit is higher and higher along with the increasing influence of the cogeneration unit on the scheduling operation of the electric power system in China.
The operation characteristic region of the cogeneration unit comprises a convex operation characteristic region and a large number of non-convex operation characteristic regions, and the widely used optimization scheduling method and the commercial production optimization software package can only optimize the convex operation characteristic region and cannot be applied to the characteristics of the non-convex and non-linear coupling relation and the like between the heat output and the power output of the cogeneration unit, so that the existing active economic scheduling model is greatly challenged.
At present, no effective method is available for solving the problem, and artificial intelligence methods such as a genetic method and the like generally adopted comprise the following defects: the calculation time is too long, the optimal result cannot be achieved, the calculation process cannot be reproduced, and the actual scheduling application cannot be met.
In summary, neither the existing optimized scheduling method nor the existing artificial intelligence method can be completely applied to the characteristics of the non-convex and non-linear coupling relationship between the heat output and the power output of the cogeneration unit, and the scheduling requirements of stable operation results and high response speed cannot be met.
Disclosure of Invention
The invention aims to provide an alternate iterative optimization scheduling method based on the non-convex force characteristic of a cogeneration unit, which can be suitable for a non-convex operation characteristic region of the cogeneration unit, realize the rapid and accurate scheduling of power production, and achieve the level of online scheduling application, and the specific technical scheme is as follows:
an alternating iteration optimization scheduling method based on the non-protrusion force characteristic of a cogeneration unit comprises the following steps:
s1, dividing a non-protrusion force area in the output characteristic curve into a plurality of sub-areas according to the output characteristic curve of each cogeneration unit;
s2, converting the segmented constraint of each cogeneration unit in each subregion into continuous linear constraint of the region according to the output characteristic of each cogeneration unit in each subregion;
s3, establishing an active economic dispatching model of the non-protrusion force characteristic of the cogeneration unit based on the converted continuous linear constraint;
Figure BDA0001734235910000021
wherein,
Figure BDA0001734235910000022
in order to reduce the operating cost of the thermal power generating unit,
Figure BDA0001734235910000023
the running cost of the cogeneration unit; p is a radical ofiThe power output is the active power output of the thermal power generating unit i; p is a radical ofj、hjRespectively the active output and the heat supply output of the cogeneration unit j; n and m respectively represent the total number of the thermal power generating units and the cogeneration units; a isi,bi,ciThe cost coefficient is the cost coefficient of the thermal power generating unit i; a isj,bj,cj,dj,ej,fj,gjThe cost coefficient of the cogeneration unit j; d is the system load requirement; s is the heat supply requirement of the system;
Figure BDA0001734235910000024
pirespectively representing the upper limit and the lower limit of the active output of the thermal power generating unit i;
Figure BDA0001734235910000025
TLαrespectively the upper and lower limits of the alpha transmission capacity of the transmission section; l is the total number of the transmission sections; k is a radical ofαiThe sensitivity coefficient of the thermal power generating unit i to the power transmission section alpha is obtained; k is a radical ofαjThe sensitivity coefficient of the cogeneration unit j to the transmission section alpha is shown; the expression (6.1) represents that the active economic dispatching model aims at minimizing the sum of the operating costs of a thermal power unit and a cogeneration unit in the system, and the expression (6.2) represents the system power generation load balance constraint; expression (6.3) represents the system heating balance constraint; an expression (6.4) represents the upper and lower output limit constraints of the generator set; the expression (6.5) represents transmission capacity constraint of a power transmission section; expression (6.6) represents the output characteristic constraint of the cogeneration unit, namely expression (5);
s4, converting the active economic dispatching model into the sum of the single dispatching models of all the cogeneration units by adopting a Lagrange relaxation method;
s5, decomposing each single machine scheduling model into a heat supply optimization scheduling model and an active optimization scheduling model, respectively solving by adopting a mixed integer quadratic programming method and a quadratic programming method, and obtaining an optimal heat supply output value and an optimal active output value through an iteration process;
and S6, controlling each cogeneration unit to perform heating output and active output work by using the optimal heating output value and the optimal active output value, and obtaining an optimal active economic dispatching curve of the cogeneration unit.
Preferably, step S1 specifically includes:
s11, establishing a non-convex non-linear region expression of the cogeneration unit:
Figure BDA0001734235910000031
Figure BDA00017342359100000313
wherein,
Figure BDA0001734235910000032
respectively the upper and lower limits of the active output of the cogeneration unit j;
Figure BDA0001734235910000033
respectively the upper and lower limits of the heat output of the cogeneration unit j;
s12, dividing the output area of the cogeneration unit j into njA sub-region; wherein each subregion is regarded as a convex region; for the l-th zone, the output characteristics of the cogeneration unit are expressed in the form:
Figure BDA0001734235910000034
wherein k is,
Figure BDA0001734235910000035
β、
Figure BDA0001734235910000036
Are the coefficients of the thermoelectric coupling relationship, respectively;
Figure BDA0001734235910000037
the upper limit and the lower limit of the heating output of the first area are respectively set;
for all njFor the sub-regions, the output characteristics of the cogeneration unit j are expressed in the form:
Figure BDA0001734235910000038
preferably, step S2 specifically includes:
the piecewise linear constraint shown in expression (4) is further converted into a continuous linear constraint:
Figure BDA0001734235910000039
wherein M is a large positive number, and takes the value of
Figure BDA00017342359100000310
xj,lIs a binary variable from 0 to 1.
Preferably, the S4 is specifically:
expression (6) is simplified:
Figure BDA00017342359100000311
wherein inf { } is the infimum boundary of the expression; deRepresenting a standalone constraint, including constraint expressions (6.4) and (6.6);
Figure BDA00017342359100000312
αwv, η are lagrangian relaxation factors; c is a constant;
the right side of the expression (8) is a form of the sum of the optimization objective functions of all the combined heat and power units, and the active economic dispatching model of the combined heat and power units is finally equivalent to the optimal dispatching of a plurality of combined heat and power units.
Preferably, step S5 specifically includes:
the optimal scheduling models of the thermal power generating unit and the cogeneration unit are respectively expressed as expressions (9) and (10):
Figure BDA0001734235910000041
Figure BDA0001734235910000042
and (3) directly solving the optimal solution of the expression (9) by utilizing a quadratic programming method and comparing the target function symmetry axis with the upper limit and the lower limit of the active output of the unit, namely:
Figure BDA0001734235910000043
f (p) in expression (10)j,hj) The function containing bilinear terms pjhjIt is decomposed into two sub-optimization models of expressions (12) and (13):
Figure BDA0001734235910000044
Figure BDA0001734235910000045
the expression (12) is a heat supply optimization scheduling submodel, and the expression (13) is an active optimization scheduling submodel; solving the expressions (12) and (13) by adopting the following alternative iteration method:
in the k iteration process, the active output of the unit j is set as the k-1 suboptimal result
Figure BDA0001734235910000046
Solving an optimized model expression (12) by adopting a mixed integer quadratic programming method to obtain the optimal heat supply output of the unit j
Figure BDA0001734235910000047
And variable xj,lValue of (A)
Figure BDA0001734235910000048
Then, the optimal heat supply output of the unit and the binary variable xj,lAre respectively set as
Figure BDA0001734235910000049
And (3) for the optimization model expression (13), adopting a quadratic programming method, directly solving an optimal solution by comparing the symmetry axis of the objective function with the upper limit and the lower limit of the active output of the unit, and repeatedly carrying out the iterative process of solving the optimal heat supply output and the optimal active output until the optimization objective function is not reduced any more.
Preferably, step S6 specifically includes:
and controlling each cogeneration unit to work according to the final iterative optimization result of the active economic scheduling model, so that the active economic scheduling of the cogeneration units is optimal.
The invention has the beneficial effects that:
the invention applies the alternative iteration method to the active economic dispatching of the cogeneration unit, can be suitable for the non-convex and non-linear coupling characteristics existing between the heat output and the power output of the cogeneration unit, solves the problems of too long calculation time, incapability of obtaining an optimal result and irreproducible calculation process existing in the operation process of the existing method, improves the solving efficiency, can obtain a verifiable optimal result and realizes the aim of online application.
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FIG. 1 is a flow chart of an alternate iterative optimization scheduling method for the non-stick force characteristic of a cogeneration unit;
fig. 2 is a schematic diagram of the output characteristics of a 250MW cogeneration unit.
Detailed description of the invention
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides an alternating iterative optimization scheduling method based on the non-stick force characteristic of a cogeneration unit, comprising the following steps:
step (1): and dividing a non-convex force area in the output characteristic curve into a plurality of sub-areas according to the output characteristic curve of each cogeneration unit.
In this step, fig. 2 is a typical output characteristic curve of a certain type of 250MW cogeneration unit, where the output characteristic is a non-convex non-linear region, and a non-convex non-linear region expression of the cogeneration unit is established:
Figure BDA0001734235910000051
Figure BDA0001734235910000052
wherein p isj、hjRespectively the active output and the heating output of the cogeneration unit j.
Figure BDA0001734235910000053
Respectively the upper and lower limits of the active output of the cogeneration unit j;
Figure BDA0001734235910000054
respectively the upper and lower limits of the heating output of the cogeneration unit j.
The expressions (1) and (2) show that the active output limit value of the cogeneration unit j is a function of the heating output, and meanwhile, the heating output limit value is also a function of the active output.
Segmenting the output area of the combined heat and power generation unit j into njA sub-region. Wherein each sub-region is considered to be a convex region. For the l-th zone, the output characteristics of the cogeneration unit are expressed in the form:
Figure BDA0001734235910000061
wherein,k、
Figure BDA0001734235910000062
β、
Figure BDA0001734235910000063
are the coefficients of the thermocouple relationship, respectively.
Figure BDA0001734235910000064
Respectively the upper limit and the lower limit of the heating output of the first area.
For all njFor the sub-regions, the output characteristics of the cogeneration unit j are expressed in the form:
Figure BDA0001734235910000065
step (2): and converting the segmented constraint of each cogeneration unit in each subregion into the continuous linear constraint of the region according to the output characteristic of each cogeneration unit in each subregion.
In this step, based on the large M method, the piecewise linear constraint in expression (4) is further converted into a continuous linear constraint:
Figure BDA0001734235910000066
wherein M is a large positive number, and takes the value of
Figure BDA0001734235910000067
xj,lIs a binary variable from 0 to 1.
And (3): and establishing an active economic dispatching model of the non-protrusion force characteristic of the cogeneration unit based on the converted continuous linear constraint.
In the step, considering the power generation load balance constraint, the heat supply load balance constraint, the generator output upper and lower limit constraint, the system section tidal current safety constraint and the cogeneration unit output characteristic constraint described in the expression (5), and meanwhile, taking the minimum system operation cost as a target function, establishing an active economic dispatching model considering the non-salient force characteristic of the cogeneration unit, wherein the model type is a mixed integer nonlinear programming model:
Figure BDA0001734235910000071
wherein,
Figure BDA0001734235910000072
in order to reduce the operating cost of the thermal power generating unit,
Figure BDA0001734235910000073
the running cost of the cogeneration unit; p is a radical ofiThe power output is the active power output of the thermal power generating unit i; p is a radical ofj、hjRespectively the active output and the heat supply output of the cogeneration unit j; n isM represents the total number of the thermal power generating units and the cogeneration units respectively; a isi,bi,ciThe cost coefficient is the cost coefficient of the thermal power generating unit i; a isj,bj,cj,dj,ej,fj,gjThe cost coefficient of the cogeneration unit j; d is the system load requirement; s is the heat supply requirement of the system;
Figure BDA0001734235910000074
pirespectively representing the upper limit and the lower limit of the active output of the thermal power generating unit i;
Figure BDA0001734235910000075
TLαrespectively the upper and lower limits of the alpha transmission capacity of the transmission section; l is the total number of the transmission sections; k is a radical ofαiThe sensitivity coefficient of the thermal power generating unit i to the power transmission section alpha is obtained; k is a radical ofαjThe sensitivity coefficient of the cogeneration unit j to the transmission section alpha is shown; the expression (6.1) represents that the active economic dispatching model aims at minimizing the sum of the operating costs of a thermal power unit and a cogeneration unit in the system, and the expression (6.2) represents the system power generation load balance constraint; expression (6.3) represents the system heating balance constraint; an expression (6.4) represents the upper and lower output limit constraints of the generator set; the expression (6.5) represents transmission capacity constraint of a power transmission section; expression (6.6) represents the constraint of the output characteristics of the cogeneration unit, i.e., expression (5).
And (4): and converting the active economic dispatching model into the sum of the single dispatching models of all the cogeneration units by adopting a Lagrange relaxation method.
In this step, the lagrangian dual expression of expression (6) is established as follows:
Figure BDA0001734235910000076
wherein inf { } is the infimum boundary of the expression; deRepresents stand-alone constraints, including constraints (6.4) and (6.6);
Figure BDA0001734235910000077
wαv, η are lagrangian relaxation factors.
Expression (7) is further expressed in the form:
Figure BDA0001734235910000081
wherein C is a constant. The right side of the expression (8) is a form of the sum of the optimization objective functions of all the combined heat and power units. And finally, decomposing an active economic dispatching model of the non-protrusion force characteristic of the cogeneration unit into optimal dispatching of a plurality of cogeneration units.
And (5): and decomposing each single machine scheduling model into a heat supply optimization scheduling model and an active optimization scheduling model, solving by respectively adopting a mixed integer quadratic programming method and a quadratic programming method, and obtaining an optimal heat supply output value and an optimal active output value through an iteration process.
In this step, the optimal scheduling models of the thermal power generating unit and the cogeneration unit are respectively expressed as expressions (9) and (10):
Figure BDA0001734235910000082
Figure BDA0001734235910000083
and (3) directly solving the optimal solution of the expression (9) by utilizing a quadratic programming method and comparing the target function symmetry axis with the upper limit and the lower limit of the active output of the unit, namely:
Figure BDA0001734235910000084
f (p) of expression (10)j,hj) The function containing bilinear terms pjhjIn the present step, the existing optimization algorithm cannot be directly solved, and is decomposed into two sub-optimization models of expressions (12) and (13):
Figure BDA0001734235910000085
Figure BDA0001734235910000086
the expression (12) is a heat supply optimization scheduling submodel, and the expression (13) is an active optimization scheduling submodel; solving the expressions (12) and (13) by adopting the following alternative iteration method:
in the k iteration process, the active output of the unit j is set as the k-1 suboptimal result
Figure BDA0001734235910000091
The optimization model expression (12) is changed into a mixed integer quadratic optimization model, and the optimal heat output of the unit j is solved and obtained by adopting a mixed integer quadratic programming method
Figure BDA0001734235910000092
And variable xj,lValue of (A)
Figure BDA0001734235910000093
Then, the optimal heat supply output of the unit and the binary variable xj,lAre respectively set as
Figure BDA0001734235910000094
The optimization model expression (13) is changed into a quadratic programming model, an optimal solution is directly solved by comparing the symmetry axis of the objective function with the upper limit and the lower limit of the active output of the unit, and the iterative process of solving the optimal heat supply output and the optimal active output is repeatedly carried out until the optimization objective function is not reduced any more.
And (6): and controlling each cogeneration unit to carry out heat supply output and active output work by using the optimal heat supply output value and the optimal active output value to obtain an optimal active economic dispatching curve of the cogeneration units.
In the step, after the solution is completed according to the method provided by the invention, each cogeneration unit is controlled to carry out scheduling work according to the optimization result, and on the premise of meeting the requirements of heat supply output and active output, energy and equipment are reasonably utilized, so that the lowest cost is realized.
The optimization effect of the method provided by the invention is verified as follows:
the verification system comprises a thermal power generating unit, two cogeneration units and a pure heat supply unit. The conditions required to be met in the scheduling process are that the system load requirement is 200MW, the heat supply requirement is 115MWth, and the value of M is 106. The calculation results of the present invention are shown in table 1.
TABLE 1 comparison of the method of the invention with the results of the genetic algorithm
Figure BDA0001734235910000095
As can be seen from the above table, compared with the genetic algorithm, the method of the invention has lower system operation cost; meanwhile, the method is obviously faster than the genetic algorithm in the aspect of calculation time, the effectiveness of the method is proved, and the method can realize online real-time scheduling.
The foregoing is a preferred embodiment of the present application, and it should be noted that those skilled in the art can make several improvements and modifications without departing from the technical principle, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims (5)

1. An alternating iteration optimization scheduling method based on the non-protrusion force characteristic of a cogeneration unit is characterized by comprising the following steps:
s1, dividing a non-protrusion force area in the output characteristic curve into a plurality of sub-areas according to the output characteristic curve of each cogeneration unit;
s2, converting the segmented constraint of each cogeneration unit in each subregion into continuous linear constraint of the region according to the output characteristic of each cogeneration unit in each subregion;
s3, establishing an active economic dispatching model of the non-protrusion force characteristic of the cogeneration unit based on the converted continuous linear constraint;
Figure FDA0003160443900000011
wherein,
Figure FDA0003160443900000012
in order to reduce the operating cost of the thermal power generating unit,
Figure FDA0003160443900000013
the running cost of the cogeneration unit; p is a radical ofiThe power output is the active power output of the thermal power generating unit i; p is a radical ofj、hjRespectively the active output and the heat supply output of the cogeneration unit j; n and m respectively represent the total number of the thermal power generating units and the cogeneration units; a isi,bi,ciThe cost coefficient is the cost coefficient of the thermal power generating unit i; a isj,bj,cj,dj,ej,fj,gjThe cost coefficient of the cogeneration unit j; d is the system load requirement; s is the heat supply requirement of the system;
Figure FDA0003160443900000014
p irespectively representing the upper limit and the lower limit of the active output of the thermal power generating unit i;
Figure FDA0003160443900000015
αTLrespectively the upper and lower limits of the alpha transmission capacity of the transmission section; l is the total number of the transmission sections; k is a radical ofαiThe sensitivity coefficient of the thermal power generating unit i to the power transmission section alpha is obtained; k is a radical ofαjThe sensitivity coefficient of the cogeneration unit j to the transmission section alpha is shown; the expression (6.1) represents that the active economic dispatching model aims at minimizing the sum of the operating costs of a thermal power unit and a cogeneration unit in the system, and the expression (6.2) represents the system power generation load balance constraint; expression (6.3) tableShowing the heat supply balance constraint of the system; an expression (6.4) represents the upper and lower output limit constraints of the generator set; the expression (6.5) represents transmission capacity constraint of a power transmission section; the expression (6.6) represents the output characteristic constraint of the cogeneration unit;
s4, converting the active economic dispatching model into the sum of the single dispatching models of all the cogeneration units by adopting a Lagrange relaxation method;
s5, decomposing each single machine scheduling model into a heat supply optimization scheduling model and an active optimization scheduling model, respectively solving by adopting a mixed integer quadratic programming method and a quadratic programming method, and obtaining an optimal heat supply output value and an optimal active output value through an iteration process;
the optimal scheduling models of the thermal power generating unit and the cogeneration unit are respectively expressed as expressions (9) and (10):
Figure FDA0003160443900000021
Figure FDA0003160443900000022
and (3) directly solving the optimal solution of the expression (9) by utilizing a quadratic programming method and comparing the target function symmetry axis with the upper limit and the lower limit of the active output of the unit, namely:
Figure FDA0003160443900000023
f (p) in expression (10)j,hj) The function containing bilinear terms pjhjIt is decomposed into two sub-optimization models of expressions (12) and (13):
Figure FDA0003160443900000024
Figure FDA0003160443900000025
the expression (12) is a heat supply optimization scheduling submodel, and the expression (13) is an active optimization scheduling submodel; solving the expressions (12) and (13) by adopting the following alternative iteration method:
in the k iteration process, the active output of the unit j is set as the k-1 suboptimal result
Figure FDA0003160443900000026
Solving an optimized model expression (12) by adopting a mixed integer quadratic programming method to obtain the optimal heat supply output of the unit j
Figure FDA0003160443900000027
And variable xj,lValue of (A)
Figure FDA0003160443900000028
Then, the optimal heat supply output of the unit and the binary variable xj,lAre respectively set as
Figure FDA0003160443900000029
For the optimization model expression (13), a quadratic programming method is adopted, an optimal solution is directly solved by comparing the symmetry axis of the objective function with the upper limit and the lower limit of the active output of the unit, and the iterative process of solving the optimal heat supply output and the optimal active output is repeatedly carried out until the optimization objective function is not reduced any more;
and S6, controlling each cogeneration unit to perform heating output and active output work by using the optimal heating output value and the optimal active output value, and obtaining an optimal active economic dispatching curve of the cogeneration unit.
2. The alternating iterative optimization scheduling method based on the non-protrusion force characteristic of the cogeneration unit according to claim 1, wherein the step S1 specifically comprises:
s11, establishing a non-convex non-linear region expression of the cogeneration unit:
Figure FDA0003160443900000031
Figure FDA0003160443900000032
wherein,
Figure FDA0003160443900000033
respectively the upper and lower limits of the active output of the cogeneration unit j;
Figure FDA0003160443900000034
respectively the upper and lower limits of the heat output of the cogeneration unit j;
s12, dividing the output area of the cogeneration unit j into njA sub-region; wherein each subregion is regarded as a convex region; for the l-th zone, the output characteristics of the cogeneration unit are expressed in the form:
Figure FDA0003160443900000035
wherein,k、
Figure FDA0003160443900000036
β、
Figure FDA0003160443900000037
are the coefficients of the thermoelectric coupling relationship, respectively;
Figure FDA0003160443900000038
the upper limit and the lower limit of the heating output of the first area are respectively set;
for all njOutput characteristic table of cogeneration unit j for sub-regionShown in the following form:
Figure FDA0003160443900000039
3. the alternating iterative optimization scheduling method based on the non-protrusion force characteristic of the cogeneration unit according to claim 1, wherein the step S2 specifically comprises:
the piecewise linear constraint shown in expression (4) is further converted into a continuous linear constraint:
Figure FDA00031604439000000310
wherein M is a large positive number, and takes the value of
Figure FDA00031604439000000311
xj,lIs a binary variable from 0 to 1.
4. The alternating iterative optimization scheduling method based on the non-protrusion force characteristic of the cogeneration unit according to claim 1, wherein the step S4 specifically comprises:
expression (6) is simplified:
Figure FDA0003160443900000041
wherein inf { } is the infimum boundary of the expression; deRepresenting a standalone constraint, including constraint expressions (6.4) and (6.6);
Figure FDA0003160443900000042
αwv, η are lagrangian relaxation factors; c is a constant;
the right side of the expression (8) is a form of the sum of the optimization objective functions of all the combined heat and power units, and the active economic dispatching model of the combined heat and power units is finally equivalent to the optimal dispatching of a plurality of combined heat and power units.
5. The alternating iterative optimization scheduling method based on the non-protrusion force characteristic of the cogeneration unit according to claim 1, wherein the step S6 specifically comprises:
and controlling each cogeneration unit to work according to the final iterative optimization result of the active economic scheduling model, so that the active economic scheduling of the cogeneration units is optimal.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106815661A (en) * 2017-02-22 2017-06-09 清华大学 A kind of composition decomposition dispatching method of CHP system
CN107067116A (en) * 2017-04-26 2017-08-18 燕山大学 A kind of multizone electric heating integrated system economic environment combined dispatching method for solving

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9058443B2 (en) * 2012-07-17 2015-06-16 International Business Machines Corporation Planning economic energy dispatch in electrical grid under uncertainty
US9705336B2 (en) * 2013-11-14 2017-07-11 Abb Research Ltd. Method and apparatus for security constrained economic dispatch in hybrid power systems

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106815661A (en) * 2017-02-22 2017-06-09 清华大学 A kind of composition decomposition dispatching method of CHP system
CN107067116A (en) * 2017-04-26 2017-08-18 燕山大学 A kind of multizone electric heating integrated system economic environment combined dispatching method for solving

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A new intelligence solution for power system economic load dispatch;Shaik Affijulla 等;《2011 10th International Conference on Environment and Electrical Engineering》;20110613;第1-5页 *
含禁止区间的电力***经济调度求解方法;丁涛等;《清华大学学报(自然科学版)》;20130415;第53卷(第04期);第447-452页 *

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