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Uncertainty models for stochastic optimization in renewable energy applications

Decision making; Optimization; Renewable energy resources; Stochastic systems; Uncertainty analysis; Deterministic optimization method; Renewable energy applications; Renewable energy integrations; Renewable energy systems; Scenario generation; Stochastic optimization methods; Stochastic optimizatio...

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Main Authors: Zakaria A., Ismail F.B., Lipu M.S.H., Hannan M.A.
Other Authors: 36070214400
Format: Review
Published: Elsevier Ltd 2023
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spelling my.uniten.dspace-258402023-05-29T16:15:05Z Uncertainty models for stochastic optimization in renewable energy applications Zakaria A. Ismail F.B. Lipu M.S.H. Hannan M.A. 36070214400 58027086700 36518949700 7103014445 Decision making; Optimization; Renewable energy resources; Stochastic systems; Uncertainty analysis; Deterministic optimization method; Renewable energy applications; Renewable energy integrations; Renewable energy systems; Scenario generation; Stochastic optimization methods; Stochastic optimizations; Uncertainty modeling; Stochastic models; alternative energy; integrated approach; model; optimization; power generation; sampling; stochasticity With the rapid surge of renewable energy integrations into the electrical grid, the main questions remain; how do we manage and operate optimally these surges of fluctuating resources? However, vast optimization approaches in renewable energy applications have been widely used hitherto to aid decision-makings in mitigating the limitations of computations. This paper comprehensively reviews the generic steps of stochastic optimizations in renewable energy applications, from the modelling of the uncertainties and sampling of relevant information, respectively. Furthermore, the benefits and drawbacks of the stochastic optimization methods are highlighted. Moreover, notable optimization methods pertaining to the steps of stochastic optimizations are highlighted. The aim of the paper is to introduce the recent advancements and notable stochastic methods and trending of the methods going into the future of renewable energy applications. Relevant future research areas are identified to support the transition of stochastic optimizations from the traditional deterministic approaches. We concluded based on the surveyed literatures that the stochastic optimization methods almost always outperform the deterministic optimization methods in terms of social, technical, and economic aspects of renewable energy systems. Thus, this review will catalyse the effort in advancing the research of stochastic optimization methods within the scopes of renewable energy applications. � 2019 Elsevier Ltd Final 2023-05-29T08:15:05Z 2023-05-29T08:15:05Z 2020 Review 10.1016/j.renene.2019.07.081 2-s2.0-85070731538 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070731538&doi=10.1016%2fj.renene.2019.07.081&partnerID=40&md5=76ec8bfa400c4075e20e4bfd899d4319 https://irepository.uniten.edu.my/handle/123456789/25840 145 1543 1571 Elsevier Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Decision making; Optimization; Renewable energy resources; Stochastic systems; Uncertainty analysis; Deterministic optimization method; Renewable energy applications; Renewable energy integrations; Renewable energy systems; Scenario generation; Stochastic optimization methods; Stochastic optimizations; Uncertainty modeling; Stochastic models; alternative energy; integrated approach; model; optimization; power generation; sampling; stochasticity
author2 36070214400
author_facet 36070214400
Zakaria A.
Ismail F.B.
Lipu M.S.H.
Hannan M.A.
format Review
author Zakaria A.
Ismail F.B.
Lipu M.S.H.
Hannan M.A.
spellingShingle Zakaria A.
Ismail F.B.
Lipu M.S.H.
Hannan M.A.
Uncertainty models for stochastic optimization in renewable energy applications
author_sort Zakaria A.
title Uncertainty models for stochastic optimization in renewable energy applications
title_short Uncertainty models for stochastic optimization in renewable energy applications
title_full Uncertainty models for stochastic optimization in renewable energy applications
title_fullStr Uncertainty models for stochastic optimization in renewable energy applications
title_full_unstemmed Uncertainty models for stochastic optimization in renewable energy applications
title_sort uncertainty models for stochastic optimization in renewable energy applications
publisher Elsevier Ltd
publishDate 2023
_version_ 1806428366571569152
score 13.222552

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