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

Aml Sayed, Mohamed Ebeed, Ziad M. Ali, Adel Bedair Abdel-Rahman, Mahrous Ahmed, Shady H. E. Abdel Aleem, Adel El-Shahat and Mahmoud Rihan
Additional contact information
Aml Sayed: Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena 83523, Egypt
Mohamed Ebeed: Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag 82524, Egypt
Ziad M. Ali: Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia
Adel Bedair Abdel-Rahman: Electronics and Communications Engineering Department, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt
Mahrous Ahmed: Department of Electrical, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Shady H. E. Abdel Aleem: Department of Electrical Engineering, Valley High Institute of Engineering and Technology, Science Valley Academy, Qalyubia 44971, Egypt
Adel El-Shahat: Energy Technology Program, School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA
Mahmoud Rihan: Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena 83523, Egypt

Energies, 2021, vol. 14, issue 23, 1-21

Abstract: 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.

Keywords: unit commitment; optimization; equilibrium optimizer; particle swarm optimization; uncertainty (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/23/8014/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/23/8014/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:23:p:8014-:d:692191

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().