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Abstract

Electricity demand in remote and island areas are generally supplied by diesel or other fossil fuel based generation systems. Nevertheless, due to the increasing cost and harmful emissions of fossil fuels there is a growing trend to use standalone hybrid renewable energy systems (HRESs). Due to the complementary characteristics, matured technologies and availability in most areas, hybrid systems with solar and wind energy have become the popular choice in such applications. However, the intermittency and high net present cost are the challenges associated with solar and wind energy systems. In this context, optimal sizing is a key factor to attain a reliable supply at a low cost through these standalone systems. Therefore, there has been a growing interest to develop algorithms for size optimization in standalone HRESs. The optimal sizing methodologies reported so far can be broadly categorized as classical algorithms, modern techniques and software tools. Modern techniques, based on single artificial intelligence (AI) algorithms, are becoming more popular than classical algorithms owing to their capabilities in solving complex optimization problems. Moreover, in recent years, there has been a clear trend to use hybrid algorithms over single algorithms mainly due to their ability to provide more promising optimization results. This paper aims to present a comprehensive review on recent developments in size optimization methodologies, as well as a critical comparison of single algorithms, hybrid algorithms, and software tools used for sizing standalone solar and wind HRES. In addition, an evaluation of all the possible combinations of standalone solar and wind energy systems, including their assessment parameters of economical, reliability, environmental, and social aspects, are also presented.

Item Type:	Article
Authors/Creators:	Al-Falahi, MDA and Jayasinghe, SDG and Enshaei, H
Keywords:	hybrid energy system, off-grid, optimization, renewable energy sources, solar, standalone, wind
Journal or Publication Title:	Energy Conversion and Management
Publisher:	Elsevier Ltd
ISSN:	0196-8904
DOI / ID Number:	https://doi.org/10.1016/j.enconman.2017.04.019
Copyright Information:	Copyright 2017 Elsevier Ltd. All rights reserved.
Related URLs:	Google Scholar: Enshaei, H UTAS Profile: Enshaei, H
Item Statistics:	View statistics for this item

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