Publication: Uncertainty models for stochastic optimization in renewable energy applications
Date
2020
Authors
Zakaria A.
Ismail F.B.
Lipu M.S.H.
Hannan M.A.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
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
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