CRIS

Permanent URI for this communityhttps://scripta.up.edu.mx/handle/20.500.12552/1

Browse

Settings

Author: search.filters.author.Banik, Anirban

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Swarm-based intelligent strategies for charging plug-in hybrid electric vehicles
    (2022)
    Vasant, Pandian
    ;
    Banik, Anirban
    ;
    Thomas, J. Joshua
    ;
    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
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Bio-inspired approaches for a combined economic emission dispatch problem
    (2023)
    Vasant, Pandian
    ;
    Banik, Anirban
    ;
    Thomas, J. Joshua
    ;
    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