CN112926185B - Power economy emission scheduling method based on improved kernel search optimization - Google Patents
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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 active output of a certain generator to replace a corresponding generator set in a 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 when the active power output exceeds 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, turning to S3.
Description
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 nuclear 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, the problem needs to be converted into a single-objective optimization problem through a weight summation method, and then the single-objective optimization 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 be trapped in local optimization, and the searched scheduling scheme is 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 capacity.
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 outputs P according to a formula (1) i I =1,2, \ 8230, N-1, together forming a generator set active power output scheme matrix A;
P i ＝P min +rand*(P max -P min ),
wherein P is imin And P imax Respectively 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 scheme i ' replace A corresponding generator set active output P of each scheduling scheme i Forming new M (N-1) scheduling scheme matrixes A' as shown in formula (2);
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 method N ；
S5, calculating the active power output P of the Nth group of generators N Penalty function values outside the generator set constraints;
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 value i And y' i Then normalized to [0,1]]；
S7, performing an improved kernel search optimization algorithm to update the active power output P of the generator set i ；
S8, if the maximum iteration times are reached, outputting the output P of the optimal generator set gbest (ii) a Otherwise, turning to S3;
preferably, in step S7, executing an improved kernel search optimization algorithm specifically includes:
step S7.1: calculating a kernel vector a as shown in formula (3);
step S7.2: computing a near-optimal scheduling plan P best As shown in formula (4);
step S7.3: calculating new active power output P of generator set new As shown in formula (5)
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 of the power system and active power output constraint of a generator set, wherein the specific form is as follows:
the specific form of fuel cost is:
wherein N is the number of generator sets in the system; p is i The active output of the ith group of generating sets; a is i ，b i And c i The fuel cost coefficient of the ith group of generator sets; e.g. of the type i And f i Is the valve point effect coefficient.
The specific form of pollutant emission is as follows:
wherein alpha is i ，β i ，γ i ，η i And delta i Is 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 additive weight factor and gamma is the scaling factor.
The power system flow equation is constrained to
Wherein, P D For the user load demand, P L For transmission network loss, B ij Is the loss coefficient.
The active output constraint of the generator set is as follows:
P i min ≤P i ≤P i max
wherein P is i max And P i min The 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 outputs P according to a formula (1) i I =1,2, \ 8230, N-1, together forming a generator set active power output scheme matrix A;
P i ＝P min +rand*(P max -P min )，
wherein P is imin And P imax Respectively the upper and lower limits of the active power output of the ith group of generator sets, 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 P i ', i =1,2, \ 8230, N-1, and taking out the active power output P of a certain group of generators from the scheduling scheme in turn i ' replace the corresponding active power output P of each scheduling scheme in A i Forming new M (N-1) scheduling scheme matrixes A' as shown in formula (2);
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 method N . The iterative solution step is as follows:
s4.1 generating set active output P according to N-1 i And calculating the initial active power output of the Nth generator set:
wherein
The initial active output of the Nth generating set.
S4.2 according to the active power output P of the N generator sets i And calculating the active loss of the system:
S4.3 according to user load P D N-1 generator set active power output P i Active loss of the system
Calculating new active output of Nth generator set
S4.4 calculation error
If epsilon>If the error is allowed, returning to the step S4.2; otherwise, save
And exit.
S5, calculating the active power output P of the Nth group of generators N And the penalty function value is beyond the constraint range of the generator set. The penalty function is embodied as follows:
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 together with the penalty function valuey i And y' i Then normalized to [0,1]]；
The specific process is
y i ＝F i +Fa i ，
S7, performing an improved kernel search optimization algorithm to update the active output P of the generator set i ；
Step S7.1: calculating a kernel vector a as shown in formula (3);
step S7.2: calculating a near-optimal scheduling scheme P best As shown in equation (4);
step S7.3: calculating new active output P of generator set new As shown in formula (5)
S7, if the maximum iteration times are reached, outputting the output P of the optimal generator set gbest (ii) a Otherwise, turning to S3;
optimal generator set output P gbest Namely 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 (1)
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) i I =1,2, \8230, N-1, together forming a generator set active power output scheme matrix A;
wherein P is min And P max Respectively 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 output P of a certain group of generators from the scheduling scheme i ' replace A corresponding generator set active output P of each scheduling scheme i Forming new M (N-1) scheduling scheme matrixes A' as shown in formula (2);
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 method N ；
S5, calculating the active power output P of the Nth group of generators N Penalty 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 value i And y' i Then normalized to [0,1]]；
S7, performing an improved kernel search optimization algorithm to update the active power output P of the generator set i The method comprises the following substeps:
step S7.1: calculating a kernel vector a, wherein a specific formula is shown in (3);
step S7.2: computing a near-optimal scheduling plan P best The concrete formula is shown as (4);
step S7.3: calculating new active output P of generator set new The concrete formula is shown as (5);
wherein rand is a random number of [0,1]
S8, if the maximum iteration times are reached, outputting the output P of the optimal generator set gbest (ii) a Otherwise, turning to S3.
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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 |
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