Optimum Size of Hybrid Renewable Energy System to Supply the Electrical Loads of the Northeastern Sector in the Kingdom of Saudi Arabia

Due to the unpredictable nature of renewable sources such as sun and wind, the integration of such sources to a grid is complicated. However, a hybrid renewable energy system (HRES) can solve this problem. Constructing a reliable HRES in remote areas is essential. Therefore, this paper proposes a ne...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Sustainability 2022-10, Vol.14 (20), p.13274
Hauptverfasser: Alshammari, Sulaiman, Fathy, Ahmed
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 20
container_start_page 13274
container_title Sustainability
container_volume 14
creator Alshammari, Sulaiman
Fathy, Ahmed
description Due to the unpredictable nature of renewable sources such as sun and wind, the integration of such sources to a grid is complicated. However, a hybrid renewable energy system (HRES) can solve this problem. Constructing a reliable HRES in remote areas is essential. Therefore, this paper proposes a new methodology incorporating a crow search algorithm (CSA) for optimizing the scale of an HRES installed in a remote area. The constructed system comprises photovoltaic (PV) panels, wind turbines (WTs), batteries, and diesel generators (DGs). The target is to achieve the most economical and efficient use of renewable energy sources (RESs). The CSA is used as it is simple in implementation, it only requires a few parameters, and it has a high flexibility. The designed system is constructed to serve an electrical load installed in the northeastern region of the Kingdom of Saudi Arabia. The load data are provided by the Saudi Electricity Company, including those of the Aljouf region (Sakaka, Alqurayyat, Tabarjal, Dumat Aljandal, and its villages) and the northern border region (Arar, Tarif, Rafha, and its affiliated villages). The temperature, irradiance, and wind speed of the Aljouf region (latitude 29.764° and longitude 40.01°) are collected from the National Aeronautics and Space Administration (NASA) from 1 January to 31 December 2020. Three design factors are considered: the PV number, the WT number, and the number of days of battery autonomy (AD). We compared our results to the reported approaches of an elephant herding optimizer (EHO), a grasshopper optimization algorithm (GOA), a Harris hawks optimizer (HHO), a seagull optimization algorithm (SOA), and a spotted hyena optimizer (SHO). Moreover, the loss of power supply probability (LPSP) is calculated to assess the constructed system’s reliability. The proposed COA succeeded in achieving the best fitness values of 0.03883 USD/kWh, 0.03863 USD/kWh, and 0.04585 USD/kWh for PV/WT/battery, PV/battery, and WT/battery systems, respectively. The obtained results confirmed the superiority of the proposed approach in providing the best configuration of an HRES compared to the others.
doi_str_mv 10.3390/su142013274
format Article
fullrecord <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2728549074</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A747158528</galeid><sourcerecordid>A747158528</sourcerecordid><originalsourceid>FETCH-LOGICAL-c371t-b32c920cd0ad8b6db1dc92fac2d8e2d51c620f0d9305b06f0f43eaa4756d03453</originalsourceid><addsrcrecordid>eNpVkclOwzAQhiMEEhVw4gUscUKoMLazHquqLKKiUgPnyPFSjJI42I4gvACvjUs50JnDbN8_c5goOsdwTWkBN27AMQFMSRYfRBMCGZ5iSODwX34cnTn3BsEoxQVOJ9H3qve6HVpU6i-JjEL3Y221QGvZyQ9WNxItOmk3IypH52WLvEHl0PfNiPxrmDWSe6s5a9DSMOG2C7b9J2NDYEFhO1QGxliku9_Ro-42wrRbsmSD0GhmWa3ZaXSkWOPk2V88iV5uF8_z--lydfcwny2nnGbYT2tKeEGAC2Air1NRYxFqxTgRuSQiwTwloEAUFJIaUgUqppKxOEtSATRO6El0sdvbW_M-SOerNzPYLpysSEbyJC4giwN1vaM2rJGV7pTxlvHgQraam04qHfqzLM5wkickD4LLPUFgvPz0GzY4Vz2U6332asdya5yzUlW91S2zY4Wh2n6y-vdJ-gP944_u</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2728549074</pqid></control><display><type>article</type><title>Optimum Size of Hybrid Renewable Energy System to Supply the Electrical Loads of the Northeastern Sector in the Kingdom of Saudi Arabia</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Alshammari, Sulaiman ; Fathy, Ahmed</creator><creatorcontrib>Alshammari, Sulaiman ; Fathy, Ahmed</creatorcontrib><description>Due to the unpredictable nature of renewable sources such as sun and wind, the integration of such sources to a grid is complicated. However, a hybrid renewable energy system (HRES) can solve this problem. Constructing a reliable HRES in remote areas is essential. Therefore, this paper proposes a new methodology incorporating a crow search algorithm (CSA) for optimizing the scale of an HRES installed in a remote area. The constructed system comprises photovoltaic (PV) panels, wind turbines (WTs), batteries, and diesel generators (DGs). The target is to achieve the most economical and efficient use of renewable energy sources (RESs). The CSA is used as it is simple in implementation, it only requires a few parameters, and it has a high flexibility. The designed system is constructed to serve an electrical load installed in the northeastern region of the Kingdom of Saudi Arabia. The load data are provided by the Saudi Electricity Company, including those of the Aljouf region (Sakaka, Alqurayyat, Tabarjal, Dumat Aljandal, and its villages) and the northern border region (Arar, Tarif, Rafha, and its affiliated villages). The temperature, irradiance, and wind speed of the Aljouf region (latitude 29.764° and longitude 40.01°) are collected from the National Aeronautics and Space Administration (NASA) from 1 January to 31 December 2020. Three design factors are considered: the PV number, the WT number, and the number of days of battery autonomy (AD). We compared our results to the reported approaches of an elephant herding optimizer (EHO), a grasshopper optimization algorithm (GOA), a Harris hawks optimizer (HHO), a seagull optimization algorithm (SOA), and a spotted hyena optimizer (SHO). Moreover, the loss of power supply probability (LPSP) is calculated to assess the constructed system’s reliability. The proposed COA succeeded in achieving the best fitness values of 0.03883 USD/kWh, 0.03863 USD/kWh, and 0.04585 USD/kWh for PV/WT/battery, PV/battery, and WT/battery systems, respectively. The obtained results confirmed the superiority of the proposed approach in providing the best configuration of an HRES compared to the others.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su142013274</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Aeronautics ; Algorithms ; Alliances ; Alternative energy sources ; Batteries ; Design factors ; Diesel generators ; Electrical loads ; Electricity ; Energy ; Energy sources ; Genetic algorithms ; Herding ; Irradiance ; Methods ; Optimization ; Photovoltaics ; Reliability analysis ; Remote regions ; Renewable energy sources ; Renewable resources ; Search algorithms ; Solar energy ; Sustainability ; Turbines ; Villages ; Wind power ; Wind speed</subject><ispartof>Sustainability, 2022-10, Vol.14 (20), p.13274</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c371t-b32c920cd0ad8b6db1dc92fac2d8e2d51c620f0d9305b06f0f43eaa4756d03453</citedby><cites>FETCH-LOGICAL-c371t-b32c920cd0ad8b6db1dc92fac2d8e2d51c620f0d9305b06f0f43eaa4756d03453</cites><orcidid>0000-0002-8340-4056</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Alshammari, Sulaiman</creatorcontrib><creatorcontrib>Fathy, Ahmed</creatorcontrib><title>Optimum Size of Hybrid Renewable Energy System to Supply the Electrical Loads of the Northeastern Sector in the Kingdom of Saudi Arabia</title><title>Sustainability</title><description>Due to the unpredictable nature of renewable sources such as sun and wind, the integration of such sources to a grid is complicated. However, a hybrid renewable energy system (HRES) can solve this problem. Constructing a reliable HRES in remote areas is essential. Therefore, this paper proposes a new methodology incorporating a crow search algorithm (CSA) for optimizing the scale of an HRES installed in a remote area. The constructed system comprises photovoltaic (PV) panels, wind turbines (WTs), batteries, and diesel generators (DGs). The target is to achieve the most economical and efficient use of renewable energy sources (RESs). The CSA is used as it is simple in implementation, it only requires a few parameters, and it has a high flexibility. The designed system is constructed to serve an electrical load installed in the northeastern region of the Kingdom of Saudi Arabia. The load data are provided by the Saudi Electricity Company, including those of the Aljouf region (Sakaka, Alqurayyat, Tabarjal, Dumat Aljandal, and its villages) and the northern border region (Arar, Tarif, Rafha, and its affiliated villages). The temperature, irradiance, and wind speed of the Aljouf region (latitude 29.764° and longitude 40.01°) are collected from the National Aeronautics and Space Administration (NASA) from 1 January to 31 December 2020. Three design factors are considered: the PV number, the WT number, and the number of days of battery autonomy (AD). We compared our results to the reported approaches of an elephant herding optimizer (EHO), a grasshopper optimization algorithm (GOA), a Harris hawks optimizer (HHO), a seagull optimization algorithm (SOA), and a spotted hyena optimizer (SHO). Moreover, the loss of power supply probability (LPSP) is calculated to assess the constructed system’s reliability. The proposed COA succeeded in achieving the best fitness values of 0.03883 USD/kWh, 0.03863 USD/kWh, and 0.04585 USD/kWh for PV/WT/battery, PV/battery, and WT/battery systems, respectively. The obtained results confirmed the superiority of the proposed approach in providing the best configuration of an HRES compared to the others.</description><subject>Aeronautics</subject><subject>Algorithms</subject><subject>Alliances</subject><subject>Alternative energy sources</subject><subject>Batteries</subject><subject>Design factors</subject><subject>Diesel generators</subject><subject>Electrical loads</subject><subject>Electricity</subject><subject>Energy</subject><subject>Energy sources</subject><subject>Genetic algorithms</subject><subject>Herding</subject><subject>Irradiance</subject><subject>Methods</subject><subject>Optimization</subject><subject>Photovoltaics</subject><subject>Reliability analysis</subject><subject>Remote regions</subject><subject>Renewable energy sources</subject><subject>Renewable resources</subject><subject>Search algorithms</subject><subject>Solar energy</subject><subject>Sustainability</subject><subject>Turbines</subject><subject>Villages</subject><subject>Wind power</subject><subject>Wind speed</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpVkclOwzAQhiMEEhVw4gUscUKoMLazHquqLKKiUgPnyPFSjJI42I4gvACvjUs50JnDbN8_c5goOsdwTWkBN27AMQFMSRYfRBMCGZ5iSODwX34cnTn3BsEoxQVOJ9H3qve6HVpU6i-JjEL3Y221QGvZyQ9WNxItOmk3IypH52WLvEHl0PfNiPxrmDWSe6s5a9DSMOG2C7b9J2NDYEFhO1QGxliku9_Ro-42wrRbsmSD0GhmWa3ZaXSkWOPk2V88iV5uF8_z--lydfcwny2nnGbYT2tKeEGAC2Air1NRYxFqxTgRuSQiwTwloEAUFJIaUgUqppKxOEtSATRO6El0sdvbW_M-SOerNzPYLpysSEbyJC4giwN1vaM2rJGV7pTxlvHgQraam04qHfqzLM5wkickD4LLPUFgvPz0GzY4Vz2U6332asdya5yzUlW91S2zY4Wh2n6y-vdJ-gP944_u</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Alshammari, Sulaiman</creator><creator>Fathy, Ahmed</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-8340-4056</orcidid></search><sort><creationdate>20221001</creationdate><title>Optimum Size of Hybrid Renewable Energy System to Supply the Electrical Loads of the Northeastern Sector in the Kingdom of Saudi Arabia</title><author>Alshammari, Sulaiman ; Fathy, Ahmed</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c371t-b32c920cd0ad8b6db1dc92fac2d8e2d51c620f0d9305b06f0f43eaa4756d03453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aeronautics</topic><topic>Algorithms</topic><topic>Alliances</topic><topic>Alternative energy sources</topic><topic>Batteries</topic><topic>Design factors</topic><topic>Diesel generators</topic><topic>Electrical loads</topic><topic>Electricity</topic><topic>Energy</topic><topic>Energy sources</topic><topic>Genetic algorithms</topic><topic>Herding</topic><topic>Irradiance</topic><topic>Methods</topic><topic>Optimization</topic><topic>Photovoltaics</topic><topic>Reliability analysis</topic><topic>Remote regions</topic><topic>Renewable energy sources</topic><topic>Renewable resources</topic><topic>Search algorithms</topic><topic>Solar energy</topic><topic>Sustainability</topic><topic>Turbines</topic><topic>Villages</topic><topic>Wind power</topic><topic>Wind speed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alshammari, Sulaiman</creatorcontrib><creatorcontrib>Fathy, Ahmed</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alshammari, Sulaiman</au><au>Fathy, Ahmed</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimum Size of Hybrid Renewable Energy System to Supply the Electrical Loads of the Northeastern Sector in the Kingdom of Saudi Arabia</atitle><jtitle>Sustainability</jtitle><date>2022-10-01</date><risdate>2022</risdate><volume>14</volume><issue>20</issue><spage>13274</spage><pages>13274-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Due to the unpredictable nature of renewable sources such as sun and wind, the integration of such sources to a grid is complicated. However, a hybrid renewable energy system (HRES) can solve this problem. Constructing a reliable HRES in remote areas is essential. Therefore, this paper proposes a new methodology incorporating a crow search algorithm (CSA) for optimizing the scale of an HRES installed in a remote area. The constructed system comprises photovoltaic (PV) panels, wind turbines (WTs), batteries, and diesel generators (DGs). The target is to achieve the most economical and efficient use of renewable energy sources (RESs). The CSA is used as it is simple in implementation, it only requires a few parameters, and it has a high flexibility. The designed system is constructed to serve an electrical load installed in the northeastern region of the Kingdom of Saudi Arabia. The load data are provided by the Saudi Electricity Company, including those of the Aljouf region (Sakaka, Alqurayyat, Tabarjal, Dumat Aljandal, and its villages) and the northern border region (Arar, Tarif, Rafha, and its affiliated villages). The temperature, irradiance, and wind speed of the Aljouf region (latitude 29.764° and longitude 40.01°) are collected from the National Aeronautics and Space Administration (NASA) from 1 January to 31 December 2020. Three design factors are considered: the PV number, the WT number, and the number of days of battery autonomy (AD). We compared our results to the reported approaches of an elephant herding optimizer (EHO), a grasshopper optimization algorithm (GOA), a Harris hawks optimizer (HHO), a seagull optimization algorithm (SOA), and a spotted hyena optimizer (SHO). Moreover, the loss of power supply probability (LPSP) is calculated to assess the constructed system’s reliability. The proposed COA succeeded in achieving the best fitness values of 0.03883 USD/kWh, 0.03863 USD/kWh, and 0.04585 USD/kWh for PV/WT/battery, PV/battery, and WT/battery systems, respectively. The obtained results confirmed the superiority of the proposed approach in providing the best configuration of an HRES compared to the others.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su142013274</doi><orcidid>https://orcid.org/0000-0002-8340-4056</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2071-1050
ispartof Sustainability, 2022-10, Vol.14 (20), p.13274
issn 2071-1050
2071-1050
language eng
recordid cdi_proquest_journals_2728549074
source MDPI - Multidisciplinary Digital Publishing Institute; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Aeronautics
Algorithms
Alliances
Alternative energy sources
Batteries
Design factors
Diesel generators
Electrical loads
Electricity
Energy
Energy sources
Genetic algorithms
Herding
Irradiance
Methods
Optimization
Photovoltaics
Reliability analysis
Remote regions
Renewable energy sources
Renewable resources
Search algorithms
Solar energy
Sustainability
Turbines
Villages
Wind power
Wind speed
title Optimum Size of Hybrid Renewable Energy System to Supply the Electrical Loads of the Northeastern Sector in the Kingdom of Saudi Arabia
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T14%3A51%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Optimum%20Size%20of%20Hybrid%20Renewable%20Energy%20System%20to%20Supply%20the%20Electrical%20Loads%20of%20the%20Northeastern%20Sector%20in%20the%20Kingdom%20of%20Saudi%20Arabia&rft.jtitle=Sustainability&rft.au=Alshammari,%20Sulaiman&rft.date=2022-10-01&rft.volume=14&rft.issue=20&rft.spage=13274&rft.pages=13274-&rft.issn=2071-1050&rft.eissn=2071-1050&rft_id=info:doi/10.3390/su142013274&rft_dat=%3Cgale_proqu%3EA747158528%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2728549074&rft_id=info:pmid/&rft_galeid=A747158528&rfr_iscdi=true