A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand

Unit commitment problem (UCP) is classified as a mixed-integer, large combinatorial, high-dimensional and nonlinear optimization problem. This paper suggests solving the UCP under deterministic and stochastic load demand using a hybrid technique that includes the modified particle swarm optimization...

Full description

Saved in:
Bibliographic Details
Published in:Energies (Basel) 2021-12, Vol.14 (23), p.8014
Main Authors: Sayed, Aml, Ebeed, Mohamed, Ali, Ziad M., Abdel-Rahman, Adel Bedair, Ahmed, Mahrous, Abdel Aleem, Shady H. E., El-Shahat, Adel, Rihan, Mahmoud
Format: Article
Language:English
Subjects:
Algorithms
Combinatorial analysis
Demand
equilibrium optimizer
Fish migration
Generators
Genetic algorithms
Heuristic
Hybridization
Integer programming
Literature reviews
Mathematical problems
Monte Carlo simulation
Mutation
Operating costs
Optimization
Optimization algorithms
Optimization techniques
particle swarm optimization
Stochasticity
uncertainty
Unit commitment
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Unit commitment problem (UCP) is classified as a mixed-integer, large combinatorial, high-dimensional and nonlinear optimization problem. This paper suggests solving the UCP under deterministic and stochastic load demand using a hybrid technique that includes the modified particle swarm optimization (MPSO) along with equilibrium optimizer (EO), termed as MPSO-EO. The proposed approach is tested firstly on 15 benchmark test functions, and then it is implemented to solve the UCP under two test systems. The results are basically compared to that of standard EO and previously applied optimization techniques in solving the UCP. In test system 1, the load demand is deterministic. The proposed technique is in the best three solutions for the 10-unit system with cost savings of 309.95 USD over standard EO and for the 20-unit system it shows the best results over all algorithms in comparison with cost savings of 1951.5 USD over standard EO. In test system 2, the load demand is considered stochastic, and only the 10-unit system is studied. The proposed technique outperforms the standard EO with cost savings of 40.93 USD. The simulation results demonstrate that MPSO-EO has fairly good performance for solving the UCP with significant total operating cost savings compared to standard EO compared with other reported techniques.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14238014