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

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.

Accelerated Particle Swarm Optimization Algorithms Coupled with Analysis of Variance for Intelligent Charging of Plug-in Hybrid Electric Vehicles

“…The fixed parameters remain constant throughout the use of the algorithm, as taken from reference [31].…”

“…In 2007, at Cambridge University, to obtain convergence of the algorithm quickly by using the global best only, Yang developed the accelerated particle swarm optimization (APSO) algorithm [28]. This algorithm falls into the metaheuristic algorithms category and has been applied to many problems, as listed in [30][31][32][33][34][35][36][37][38][39][40][41]. One of the advantages of using APSO compared with other metaheuristic algorithms is its simplicity, as it uses only one update per iteration to reach convergence in a relatively short time.…”

“…APSO is a more efficient algorithm with a single update equation, a high-quality solution, and local exploitation capability. Reference [31] also highlights the significance of utilizing the APSO technique, since it infinitesimally reduces the Big O time complexity for the common benchmark test case of the CSTHTS optimization issue. Thus, the accelerated particle swarm optimization (APSO) approach and its five variants are employed in this research project to provide real-time and largescale optimization for intelligently distributing available grid electricity to cars while maintaining the stability of the grid system.…”

On Employing a Constrained Nonlinear Optimizer to Constrained Economic Dispatch Problems

“…One of the most critical and challenging issues that need to be solved in power systems is optimal economic dispatch [1]. The aim of economic dispatch problems (EDPs) is to determine the best power output combination across all generating units in order to reduce overall fuel costs while still meeting load demand and equality and inequality constraints [2].…”