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Hybridizing genetic algorithm with grey prediction evolution algorithm for solving unit commitment problem

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Abstract

The unit commitment problem (UCP), which includes the unit schedule and power dispatch, is a nonlinear high-dimensional and highly constrained mixed-integer combinatorial optimization problem. One challenge herein is to obtain high-quality solutions considering various constraints. Developing a competitive hybrid method is a mainstream study goal in this field, which has focused on the unit schedule optimization but less on power dispatch. Inspired by the advantage of genetic algorithms (GAs) in solving combinational optimization problems and the characteristic of grey prediction evolution algorithm (GPE) with strong exploration ability, this paper proposes a novel hybrid GA and GPE method, termed hGAGPE, to solve the UCP. In hGAGPE, GPE, as a novel real parameter stochastic search algorithm based on the grey prediction theory for data mining, is first employed to solve the power dispatch of the UCP. Meanwhile, the unit schedule is performed by the popular GA. Additionally, some heuristic repair mechanisms based on the priority list and an elite selection mechanism are incorporated to enhance the performance of hGAGPE. The proposed hGAGPE is evaluated on six test systems with generating units in the range of 10 to 100 during a 24-h scheduling period. The numerical results demonstrate the feasibility and effectiveness of hGAGPE in comparison with other existing approaches.

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Abbreviations

a i,b i,c i :

Fuel cost coefficients of unit i

C S C i :

Cold start-up cost of unit i

f i,t :

Fuel cost of unit i at time t

H S C i :

Hot start-up cost of unit i

i :

Generating unit index

I i,status :

Initial status of unit i

N :

Number of units

\(P_i^{\max \limits }\) :

Maximum generation limit of uniti

\(P_i^{\min \limits }\) :

Minimum generation limit of unit i

P i,t :

Output power of unit i at time t

P D t :

System demand at time t

S R t :

Spinning reserve at time t

S U i,t :

Start-up cost of unit i at time t

T :

Number of scheduled hours

t :

Hourly time index

\(T_i^{cold}\) :

Cold start hour of unit i

\(T_i^{up}\)/\(T_i^{down}\) :

Minimum up/down time of unit i

\(T_{i,t}^{on}\)/\(T_{i,t}^{off}\) :

Continuously on/off time of unit i up to hour t

u i,t :

Unit commitment status of unit i at time t (1=ON, 0=OFF)

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Acknowledgements

This work was supported in part by the State Key Laboratory of Biogeology and Enviromental Geology (China University of Geosciences, No. GBL21801), the National Nature Science Foundation of China (No. 61972136), Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation(No. T201410), Education Bureau of Hunan Province of China (No.15B061).

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Authors and Affiliations

  1. School of Computer Science, Hubei University of Technology, Wuhan, Hubei, China

    Wangyu Tong

  2. School of Information and Mathematics, Yangtze University, Jingzhou, Hubei, China

    Di Liu, Zhongbo Hu & Qinghua Su

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  1. Wangyu Tong
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Correspondence to Di Liu or Zhongbo Hu.

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Tong, W., Liu, D., Hu, Z. et al. Hybridizing genetic algorithm with grey prediction evolution algorithm for solving unit commitment problem. Appl Intell 53, 19922–19939 (2023). https://doi.org/10.1007/s10489-023-04527-2

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