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Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems.

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Title: Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems.
Authors: Javed, Muhammad Shahzad1 (AUTHOR) shahzad.sjtu@yahoo.com, Ma, Tao1 (AUTHOR) tao.ma@connect.polyu.hk, Jurasz, Jakub2,3 (AUTHOR), Ahmed, Salman1 (AUTHOR), Mikulik, Jerzy3 (AUTHOR)
Source: Energy. Nov2020, Vol. 210, pN.PAG-N.PAG. 1p.
Subjects: Heuristic algorithms, Bees algorithm, Mathematical optimization, Standard deviations, Particle swarm optimization, Algorithms, Rural electrification
Abstract: Hybrid renewable energy systems have been widely acknowledged as a clean, affordable and reliable mechanism to generate electricity and to accomplish global sustainable development goals. In this study, first, an operating strategy and an optimization problem are developed for a hybrid, off-grid, solar-wind system based on pumped hydro battery storage, and then a non-linear optimization problem is described for the considered system. To solve the optimization problem, four different optimization techniques are employed i.e. ant colony (ACO), firefly algorithm (FA), particle swarm optimization (PSO) and genetic algorithm (GA) and their performance is compared using statistical parameters like relative error, mean absolute error and root mean square error. Each optimization technique's working principle is discussed in detail and formulated considering the proposed optimization problem. The exploration and exploitation behavior of each algorithm is comprehensively analyzed explaining that ACO and FA have higher exploitation behavior, while GA and PSO have more exploration behavior, revealing that these behavior depend on the range of operator controlling parameters, type of optimization problem and formulation structure of the optimizers. The reference controlling parameters of each optimizer (which are operator dependent) are defined for the proposed optimization problem. The results reveal that FA performs better – i.e. with the least relative error (0.126) – while PSO outperforms best in terms of least objective function value (0.2435 $/kWh). The mean efficiency of each algorithm in terms of repeated executions (30 times) is ACO = 95.94%, FA = 96.20%, GA = 93.93%, PSO = 96.20%. The proposed study could help decision-makers to choose an optimization method to solve non-linear problems in the context of storage-based, off-grid systems under different scenarios. • Energy management strategy for hybrid pumped and battery storage is proposed. • Four well-regraded heuristic algorithms performance are statistically compared. • User defined parameters of the employed algorithms are presented. • Firefly algorithm has least relative error while particle swarm optimizer has least objective value. • Facilitates the process of optimal algorithm selection for non-linear problems. [ABSTRACT FROM AUTHOR]
Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 146147197
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems.
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  Data: <searchLink fieldCode="AR" term="%22Javed%2C+Muhammad+Shahzad%22">Javed, Muhammad Shahzad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shahzad.sjtu@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+Tao%22">Ma, Tao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tao.ma@connect.polyu.hk</i><br /><searchLink fieldCode="AR" term="%22Jurasz%2C+Jakub%22">Jurasz, Jakub</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ahmed%2C+Salman%22">Ahmed, Salman</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mikulik%2C+Jerzy%22">Mikulik, Jerzy</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energy%22">Energy</searchLink>. Nov2020, Vol. 210, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Bees+algorithm%22">Bees algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Rural+electrification%22">Rural electrification</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Hybrid renewable energy systems have been widely acknowledged as a clean, affordable and reliable mechanism to generate electricity and to accomplish global sustainable development goals. In this study, first, an operating strategy and an optimization problem are developed for a hybrid, off-grid, solar-wind system based on pumped hydro battery storage, and then a non-linear optimization problem is described for the considered system. To solve the optimization problem, four different optimization techniques are employed i.e. ant colony (ACO), firefly algorithm (FA), particle swarm optimization (PSO) and genetic algorithm (GA) and their performance is compared using statistical parameters like relative error, mean absolute error and root mean square error. Each optimization technique's working principle is discussed in detail and formulated considering the proposed optimization problem. The exploration and exploitation behavior of each algorithm is comprehensively analyzed explaining that ACO and FA have higher exploitation behavior, while GA and PSO have more exploration behavior, revealing that these behavior depend on the range of operator controlling parameters, type of optimization problem and formulation structure of the optimizers. The reference controlling parameters of each optimizer (which are operator dependent) are defined for the proposed optimization problem. The results reveal that FA performs better – i.e. with the least relative error (0.126) – while PSO outperforms best in terms of least objective function value (0.2435 $/kWh). The mean efficiency of each algorithm in terms of repeated executions (30 times) is ACO = 95.94%, FA = 96.20%, GA = 93.93%, PSO = 96.20%. The proposed study could help decision-makers to choose an optimization method to solve non-linear problems in the context of storage-based, off-grid systems under different scenarios. • Energy management strategy for hybrid pumped and battery storage is proposed. • Four well-regraded heuristic algorithms performance are statistically compared. • User defined parameters of the employed algorithms are presented. • Firefly algorithm has least relative error while particle swarm optimizer has least objective value. • Facilitates the process of optimal algorithm selection for non-linear problems. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.energy.2020.118599
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Heuristic algorithms
        Type: general
      – SubjectFull: Bees algorithm
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Standard deviations
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Rural electrification
        Type: general
    Titles:
      – TitleFull: Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems.
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            NameFull: Javed, Muhammad Shahzad
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            NameFull: Ma, Tao
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            NameFull: Jurasz, Jakub
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            NameFull: Ahmed, Salman
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            – D: 01
              M: 11
              Text: Nov2020
              Type: published
              Y: 2020
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              Value: 210
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