A New Fast Deterministic Economic Dispatch Method and Statistical Performance Evaluation for the Cascaded Short-Term Hydrothermal Scheduling Problem

Muhammad Ahmad Iqbal, Muhammad Salman Fakhar, Noor Ul Ain, Ahsen Tahir, Irfan Ahmad Khan (), Ghulam Abbas () and Syed Abdul Rahman Kashif
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Muhammad Ahmad Iqbal: Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Muhammad Salman Fakhar: Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Noor Ul Ain: Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Ahsen Tahir: Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Irfan Ahmad Khan: Clean and Resilient Energy Systems (CARES) Lab, Electrical and Computer Engineering Department, Texas A & M University, Galveston, TX 77553, USA
Ghulam Abbas: Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan
Syed Abdul Rahman Kashif: Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan

Sustainability, 2023, vol. 15, issue 2, 1-23

Abstract: The Cascaded Short-Term Hydrothermal Scheduling (CSTHTS) problem is a highly non-linear, multi-modal, non-convex, and NP-hard optimization problem that has been solved by conventional and metaheuristic algorithms in the past. As the CSTHTS problem falls under the category of applied operational research, therefore, the work is still on-going to find new algorithms and variants of the existing algorithms that would better approximate the optimal global solution in a shorter computational time. This article proposes a novel deterministic thermal economic dispatch method embedded with the improved Accelerated Particle Swarm Optimization (APSO) algorithm to infinitesimally reduce the Big O time complexity for the standard benchmark test case of the CSTHTS optimization problem. Then, it discusses and presents the importance of performing standard statistical tests to establish the supremacy of one metaheuristic algorithm over the other one in solving the CSTHTS problem. The results obtained are better than the results of the many state-of-the-art algorithms applied to solve the considered test case of the CSTHTS problem in the literature, and the superiority of the improved APSO algorithm has been established statistically using the parametric independent samples t -test and the non-parametric Mann–Whitney U-test over the other metaheuristic algorithms such as particle swarm optimization in solving the chosen test case of the CSTHTS problem.

Keywords: economic dispatch; hydrothermal scheduling; optimization; statistics; swarm intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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