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Swarm-based intelligent strategies for charging plug-in hybrid electric vehicles
(2022)
Vasant, Pandian
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Banik, Anirban
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Thomas, J. Joshua
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Marmolejo Saucedo, José Antonio
;
Ganesan, Timothy
Presently, there is a significant emphasis on green technology in order to increase the usage of clean energy sources in the transportation sector while also reducing emissions. At this phase, a sufficient charge allocation strategy is needed to use plug-in hybrid power vehicles (PHEVs), including the implementation of smart charging infrastructure and intelligent grid networks. Daytime charging stations are needed for PHEV regular use, and at this stage, only adequate charging control and infrastructure management will contribute to broader PHEV adoption. The researchers are attempting to establish an effective control system for filling as well as promoting the penetration of upcoming PHEVs on highways. In this case, intelligent energy management necessitates the creation of statistical models over optimization strategy focused on computer intelligence. The state of charge of PHEVs was optimized employing particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization (APSO), and a combined form of PSO and GSA (PSOGSA). In this perspective, the individual and comparative performance of four techniques was defined in terms of convergence speed, computation time, and best fitness. © 2022 Elsevier Inc. All rights reserved.
Scopus© Citations 4 9 1
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Review on recent implementations of multiobjective and multilevel optimization in sustainable energy economics
(2022)
Ganesan, Timothy
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Litvinchev, Igor
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Marmolejo Saucedo, José Antonio
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Thomas, J. Joshua
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Vasant, Pandian
Rapid progress is currently being made globally in the sustainable energy industry. This trend has been seen to concentrate on specific focus areas in the global energy ecosystem. The integration of sustainability ideas into the existing energy ecosystem has given rise to various complexities, e.g., multilevel and multiobjective (MO) scenarios. This in return has generated various avenues for the implementation of mathematical optimization as well as state-of-the-art operations research methodologies on such real-world systems. This chapter aims to provide a concise review on recent implementations of MO and multilevel optimization on sustainable energy economic systems. Three key industrial areas are given emphasis—economic load/emission dispatch, bioenergy supply chains, and sustainable capacity planning. © 2022 Elsevier Inc. All rights reserved.
Scopus© Citations 1 18 1
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Correction to: Preface: Special Issue on Modeling, Simulation, and Optimization in Operational Research
(2022)
Weber, Gerhard-Wilhelm
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Thomas, J. Joshua
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Marmolejo Saucedo, José Antonio
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Fiore, Ugo
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Litvinchev, Igor
The original online version of this article was revised: Modifications have been made to the number of articles mentioned in the preface. © Springer Nature
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Preface: Special Issue on Modeling, Simulation, and Optimization in Operational Research
(2021)
Weber, Gerhard-Wilhelm
;
Thomas, J. Joshua
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Marmolejo Saucedo, José Antonio
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Fiore, Ugo
;
Litvinchev, Igor
“Operational Research” (OR) is “the discipline of applying advanced analytical methods to help make better decisions”. By using techniques such as problem structuring methods and mathematical modeling to analyze nontrivial situations, OR gives executives the power to make more effective decisions and build more productive systems. The host journal of this special issue, Journal of the Operations Research Society of China (JORSC), is the flagship journal of the Operations Research Society of China. Its primary goal is to promote researches and applications of all aspects of OR. JORSC provides a forum for practioners, academics and researchers in OR and related fields. It reflects the rapid social and economic development of China and leads to new problems and challenges which require new OR methodology and techniques. For closer information about JORSC, we refer to https://www.springer.com/journal/40305/aims-and-scope. © Springer Nature
Scopus© Citations 1 31 2
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Bio-inspired approaches for a combined economic emission dispatch problem
(2023)
Vasant, Pandian
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Banik, Anirban
;
Thomas, J. Joshua
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Marmolejo Saucedo, José Antonio
;
Fiore, Ugo
In chapter 3, economic load dispatch (ELD) and emission dispatch problems are optimized separately using particle swarm optimization, quantum-inspired particle swarm optimization, and quantum-inspired bat algorithm for the various numbers of units. Later, both objectives are assumed simultaneously as an optimization problem with multiple objectives. The emission dispatch problem is divided into three independent objectives to minimize SO2, NO X and CO2 emissions. Thus, the combined economic emission dispatch (CEED) problem is an optimization problem with four objectives. A unit-wise price penalty factor was assumed to change over all the targets into a single target. The idea is to attain a balanced trade-off between secured and profitable energy choices while maintaining a healthy and sound environment. The quantum computing phenomenon was integrated with swarm intelligence-based particle swarm optimization (PSO) and bat algorithm (BA) to make them computationally more powerful and robust. The results obtained from quantum-inspired bat algorithm (QBA) and quantum particle swarm optimization (QPSO) to solve the CEED problem contrasted with other existing techniques such as Lagrangian relaxation, PSO, and simulated annealing. © Copyright 2024 IOP Publishing
Scopus© Citations 2 10 1

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