CN112926185A - Power economy emission scheduling method based on improved kernel search optimization - Google Patents

Power economy emission scheduling method based on improved kernel search optimization Download PDF

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CN112926185A
CN112926185A CN202110100175.XA CN202110100175A CN112926185A CN 112926185 A CN112926185 A CN 112926185A CN 202110100175 A CN202110100175 A CN 202110100175A CN 112926185 A CN112926185 A CN 112926185A
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董如意
高兴泉
朱建军
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Abstract

The invention relates to an electric power economic emission scheduling method for improving nuclear search optimization, which comprises the following steps: s1, converting fuel cost and pollution emission into a single objective function by adopting a weight summation method, and establishing an electric power economic emission scheduling model; s2, randomly initializing an active power output scheduling matrix of the generator set; s3, randomly initializing another scheduling scheme, and sequentially taking out the output of a certain generator to replace the active output of the corresponding generator set in the scheduling matrix to form a new scheduling matrix; s4, solving the active power output of the last group of generators by a power flow equation according to a Newton iteration method; s5, calculating a penalty function value of the final group of generators with the active power output exceeding the constraint; s6, calculating the sum of fuel cost and pollution emission of the generator set in the scheduling scheme, and normalizing the sum and the penalty function value together; s7, executing an improved kernel search optimization algorithm to update the active power output of the generator set; s8, outputting the output of the optimal generator set if the maximum iteration times is reached; otherwise go to S3.

Description

Power economy emission scheduling method based on improved kernel search optimization
Technical Field
The invention relates to the field of power system economic emission scheduling, in particular to a power economic emission scheduling method for improving core search optimization.
Background
With the increasing emphasis on environmental pollution, the problem of economic emission scheduling becomes one of the most realistic tasks in the management of power systems. The economic emission scheduling problem needs to reasonably arrange and schedule the active power output of each power plant, so that the fuel cost and the pollution emission are minimized on the basis of meeting the user load requirements and the power plant output limit. The economic emission scheduling problem is a multi-objective optimization problem with two contradictory targets in nature, and has a very important significance for stable economic operation of a power system and reduction of environmental pollution.
Many studies propose the use of group intelligence optimization methods to solve the economic emissions scheduling problem. However, for the multi-objective economic emission scheduling problem, firstly, the problem needs to be converted into a single-objective optimization problem through a weight summation method, and then the problem is solved by using a group intelligent optimization algorithm. Common group intelligence algorithms such as genetic algorithm, particle swarm optimization algorithm, pollen pollination algorithm, differential evolution algorithm and the like. However, these algorithms need to carefully adjust a large number of hyper-parameters, and are prone to fall into local optimality, and the scheduling schemes obtained by searching are not necessarily optimal.
Therefore, developing an electric power economic emission scheduling method which does not need to set any hyper-parameter and has more global search capability is a difficult point of electric power scheduling research.
The invention content is as follows:
the invention aims to provide an electric power economic emission scheduling method which can simultaneously optimize fuel cost and pollution emission and has strong global convergence capability.
In order to achieve the above object, an embodiment of the present invention provides an electric power economic emission scheduling method based on improved kernel search optimization, including the following steps:
s1, converting two objective functions of fuel cost and pollution emission into a single objective optimization function by adopting a weight summation method, and establishing an economic emission scheduling model of the power system by combining power flow equation constraint of the power system and active power output constraint of a generator set;
s2, setting N groups of generator set active power output to be scheduled, randomly initializing M scheduling schemes, and calculating N-1 groups of active power output P according to a formula (1)i1,2, … N-1, together forming a generator set active power output scheme matrix A;
Pi＝Pmin+rand*(Pmax-Pmin),
Figure RE-GDA0003040665800000011
wherein P isiminAnd PimaxRespectively the upper and lower limits of the active power output of the ith group of generator set, and rand is [0, 1%]The random number of (2);
s3, randomly initializing another 1 scheduling scheme, wherein N-1 groups of active power outputs are P', and sequentially taking out the active power outputs P of a certain group of generators from the scheduling schemei' replace A corresponding generator set active output P of each scheduling schemeiForming new M × N-1 scheduling scheme matrixes A' as shown in a formula (2);
Figure RE-GDA0003040665800000021
s4, solving active power output P of the Nth group of generators A and A' by the constraint of a power flow equation according to a Newton iteration methodN；
S5, calculating the active power output P of the Nth group of generatorsNPenalty function values outside the generator set constraint range;
s6, calculating the sum of fuel cost and pollution emission of N groups of generator sets in the scheduling schemes A and A', and forming a fitness function value y together with the penalty function valueiAnd y'iThen normalized to [0,1]]；
S7, performing an improved kernel search optimization algorithm updateActive power output P of motor seti；
S8, if the maximum iteration times are reached, outputting the output P of the optimal generator setgbest(ii) a Otherwise, turning to S3;
preferably, in step S7, the performing the improved kernel search optimization algorithm specifically includes:
step S7.1: calculating a kernel vector a as shown in formula (3);
Figure RE-GDA0003040665800000022
step S7.2: computing a near-optimal scheduling plan PbestAs shown in formula (4);
Figure RE-GDA0003040665800000023
step S7.3: calculating new active power output P of generator setnewAs shown in formula (5)
Figure RE-GDA0003040665800000024
Wherein rand is a random number of [0,1 ].
Compared with the prior art, the invention has the beneficial effects that:
the method improves the original kernel search optimization algorithm, simplifies the calculation process of kernel parameters, optimizes the iterative update equation, avoids trapping in a local optimal solution, and can solve a more economic and less-pollution power economy scheduling scheme.
Description of the drawings:
FIG. 1 is a schematic flow diagram of a method framework;
the specific implementation mode is as follows:
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, in an embodiment of the present invention, a proposed power economy emission scheduling method based on improved kernel search optimization includes the steps of:
s1, converting two objective functions of fuel cost and pollution emission into a single objective optimization function by adopting a weight summation method, and establishing an economic emission scheduling model of the power system by combining power flow equation constraint and generator set active power output constraint, wherein the specific form is as follows:
the specific form of fuel cost is:
Figure RE-GDA0003040665800000031
wherein N is the number of generator sets in the system; piThe active output of the ith group of generating sets; a isi，biAnd ciThe fuel cost coefficient of the ith group of generator sets; e.g. of the typeiAnd fiIs the valve point effect coefficient.
The specific form of pollutant emission is as follows:
Figure RE-GDA0003040665800000032
wherein alpha isi，βi，γi，ηiAnd deltaiIs a pollution discharge coefficient.
The fuel cost and the pollution emission are converted into a single objective function of
F＝wC+γ(1-w)E
Where w is the summing weight factor and γ is the scaling factor.
The power system flow equation is constrained to
Figure RE-GDA0003040665800000033
Wherein, PDFor the user load demand, PLIn order to transmit the loss of the network,Bijis the loss factor.
The active output constraint of the generator set is as follows:
Pi min≤Pi≤Pi max
wherein P isi maxAnd Pi minThe upper limit and the lower limit of the active output of the ith generating set are shown.
S2, setting N groups of generator set active power output to be scheduled, randomly initializing M scheduling schemes, and initializing and calculating N-1 groups of active power output P according to a formula (1)i1,2, … N-1, together forming a generator set active power output scheme matrix A;
Pi＝Pmin+rand*(Pmax-Pmin)，
Figure RE-GDA0003040665800000041
wherein P isiminAnd PimaxRespectively the upper and lower limits of the active power output of the ith group of generator set, and rand is [0, 1%]The random number of (2);
s3, randomly initializing another 1 scheduling scheme, wherein the active power output of the N-1 groups is Pi', i is 1,2, … N-1, and active output P of a certain group of generators is taken out from the scheduling scheme in turni' replace the corresponding active power output P of each scheduling scheme in AiForming new M × N-1 scheduling scheme matrixes A' as shown in a formula (2);
Figure RE-GDA0003040665800000042
s4, solving active power output P of the Nth group of generators A and A' by the constraint of a power flow equation according to a Newton iteration methodN. The iterative solution step is as follows:
s4.1 generating set active output P according to N-1iAnd calculating the initial active output of the Nth generator set:
Figure RE-GDA0003040665800000043
wherein
Figure RE-GDA0003040665800000044
The initial active output of the Nth generating set.
S4.2 according to the active power output P of the N generator setsiAnd calculating the active loss of the system:
Figure RE-GDA0003040665800000045
wherein
Figure RE-GDA0003040665800000046
S4.3 according to user load PDN-1 generator set active power output PiActive loss of the system
Figure RE-GDA0003040665800000047
Calculating new active output of Nth generator set
Figure RE-GDA0003040665800000048
S4.4 calculation error
Figure RE-GDA0003040665800000049
If epsilon>If the error is allowed, returning to the step S4.2; otherwise, save
Figure RE-GDA00030406658000000410
And exit.
S5, calculating the active power output P of the Nth group of generatorsNAnd the penalty function value is beyond the constraint range of the generator set. The concrete form of the penalty function is as follows:
Figure RE-GDA00030406658000000411
where λ is a penalty factor.
S6, calculating the sum of fuel cost and pollution emission of N groups of generator sets in the scheduling schemes A and A', and forming a fitness function value y together with the penalty function valueiAnd y'iThen normalized to [0,1]]；
The specific process is
yi＝Fi+Fai，
Figure RE-GDA0003040665800000051
S7, performing an improved kernel search optimization algorithm to update the active power output P of the generator seti；
Step S7.1: calculating a kernel vector a as shown in formula (3);
Figure RE-GDA0003040665800000052
step S7.2: computing a near-optimal scheduling plan PbestAs shown in equation (4);
Figure RE-GDA0003040665800000053
step S7.3: calculating new active power output P of generator setnewAs shown in formula (5)
Figure RE-GDA0003040665800000054
S7, if the maximum iteration times are reached, outputting the output P of the optimal generator setgbest(ii) a Otherwise, turning to S3;
optimal generator set output PgbestNamely the optimal power economy discharge scheduling scheme.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (2)

1. An electric power economic emission scheduling method based on improved kernel search optimization is characterized by comprising the following steps:
s1, converting two objective functions of fuel cost and pollution emission into a single objective optimization function by adopting a weight summation method, and establishing an economic emission scheduling model of the power system by combining power flow equation constraint of the power system and active power output constraint of a generator set;
s2, setting N groups of generator set active power output to be scheduled, randomly initializing M scheduling schemes, and calculating N-1 groups of active power output P according to a formula (1)i1,2, … N-1, together forming a generator set active power output scheme matrix A;
Figure RE-FDA0003040665790000011
wherein P isminAnd PmaxRespectively the upper and lower limits of the active power output of the ith group of generator set, and rand is [0, 1%]The random number of (2);
s3, randomly initializing another 1 scheduling scheme, wherein N-1 groups of active power outputs are P', and sequentially taking out the active power outputs P of a certain group of generators from the scheduling schemei' replace A corresponding generator set active output P of each scheduling schemeiForming new M × N-1 scheduling scheme matrixes A' as shown in a formula (2);
Figure RE-FDA0003040665790000012
s4, solving active power output P of the Nth group of generators A and A' by the constraint of a power flow equation according to a Newton iteration methodN；
S5, calculating the Nth groupActive power output P of generatorNPenalty function values outside the generator set constraint range;
s6, calculating the sum of fuel cost and pollution emission of N groups of generator sets in the scheduling schemes A and A', and forming a fitness function value y together with the penalty function valueiAnd y'iThen normalized to [0,1]]；
S7, performing an improved kernel search optimization algorithm to update the active power output P of the generator seti；
S8, if the maximum iteration times are reached, outputting the output P of the optimal generator setgbest(ii) a Otherwise go to S3.
2. The method according to claim 1, wherein the step S7 specifically includes:
step S7.1: calculating a kernel vector a, wherein a specific formula is shown in (3);
Figure RE-FDA0003040665790000021
step S7.2: computing a near-optimal scheduling plan PbestThe concrete formula is shown as (4);
Figure RE-FDA0003040665790000022
step S7.3: calculating new active power output P of generator setnewThe concrete formula is shown as (5);
Figure RE-FDA0003040665790000023
wherein rand is a random number of [0,1 ].
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CN114066282A (en) * 2021-11-23 2022-02-18 吉林化工学院 Power economic emission scheduling method based on hybrid nuclear search and slime bacteria optimization

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CN114066280A (en) * 2021-11-23 2022-02-18 吉林化工学院 Electric power economic emission scheduling method based on improved optimization of badgers
CN114066281A (en) * 2021-11-23 2022-02-18 吉林化工学院 Electric power economic discharge scheduling method based on hybrid optimization of cuckoos and bats
CN114066282A (en) * 2021-11-23 2022-02-18 吉林化工学院 Power economic emission scheduling method based on hybrid nuclear search and slime bacteria optimization

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