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.
Highlights
The proposed work depends on Monte Carlo simulation (MCS) and scenario-based reduction techniques to deal with load demand uncertainty
To overcome the defects of standard versions of equilibrium optimizer (EO) and particle swarm optimizer (PSO), this paper proposes a hybridization combined between the two optimizers so that EO’s population diversity increases and the ability of PSO to escape from the local minima increases, but the convergence rate of the hybrid algorithm slows down
The convergence characteristics’ curves comparison between the proposed technique and previously mentioned techniques is given in Figure 2 As shown from the results in Table 1, modified particle swarm optimization (MPSO)-EO achieved the best performance for unimodal functions (F1 -F7 ), except for function (F6 ), in which it achieved the second-best performance after the original EO
Summary
Aml Sayed 1 , Mohamed Ebeed 2 , Ziad M. Ali 3,4 , Adel Bedair Abdel-Rahman 5,6 , Mahrous Ahmed 7 , Shady H. E. Abdel Aleem 8 , Adel El-Shahat 9, * and Mahmoud Rihan 1. Electrical Engineering Department, Aswan Faculty of Engineering, Aswan University, Aswan 81542, Egypt. Electronics and Communications Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt. Energy Technology Program, School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA.
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