Simulation Studies to Quantify the Impact of Demand Side Management on Environmental Footprint
The increased use of energy leads to increased energy-related emissions. Demand side management (DSM) is a potential means of mitigating these emissions from electric utility generating units. DSM can significantly reduce emissions and provide economic and reliability benefits. This work presents so...
Gespeichert in:
Veröffentlicht in: | Sustainability 2021-09, Vol.13 (17), p.9504 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 17 |
container_start_page | 9504 |
container_title | Sustainability |
container_volume | 13 |
creator | Almohaimeed, Sulaiman A. Suryanarayanan, Siddharth O’Neill, Peter |
description | The increased use of energy leads to increased energy-related emissions. Demand side management (DSM) is a potential means of mitigating these emissions from electric utility generating units. DSM can significantly reduce emissions and provide economic and reliability benefits. This work presents some DSM techniques, such as load shifting, energy conservation, and valley filling. Furthermore, this work explains the most common DSM programs. To quantify the effect of DSM in diminishing carbon footprint, this paper performs power flow analysis on a yearly load profile corresponding to Fort Collins, Colorado, U.S. This work used the IEEE 13-node test system to simulate several scenarios from the multi-criteria decision-making (MCDM) alternatives, both individually and integrated. For the base case, emissions decrease by 16% from the 2005 level. The “energy conservation” option achieved a 20% reduction in emissions, integrating both alternatives increased the emissions mitigation up to 22%. Simulation of the residential sector shows the “communication and intelligence” option reduces emissions about 14% from the 2005 level. A scenario that combines “electric stationary storage” with “communication and intelligence” diminishes the emissions by more than 15%. The last scenario examined all MCDM alternatives combined into one option, resulting in a 20% emissions reduction. We also conducted a cost benefit analysis (CBA) to investigate economic, technical, and environmental costs and benefits associated with each alternative. The economic evaluation shows that “electric stationary storage” is the best option since it charges during lower electricity prices and discharges during peaking demand. The economic analysis presents a trade-off chart, so the decision maker can select the alternative based on their preference. |
doi_str_mv | 10.3390/su13179504 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2571539301</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2571539301</sourcerecordid><originalsourceid>FETCH-LOGICAL-c254t-212cd12056a1c409f0697d396eee480e757f3baf80702d9c3970fd183ffe9fae3</originalsourceid><addsrcrecordid>eNpNUNFKAzEQDKJgqX3xCwK-Caeb5HJpHqW2WqiIVF894mWjV3pJTXJC_96TCrovu8MMs8MQcs7gSggN16lngiktoTwiIw6KFQwkHP-7T8kkpQ0MIwTTrBqR13Xb9VuT2-DpOve2xURzoE-98bl1e5o_kC67nWkyDY7eYme8pevWIn0w3rxjh35gPJ37rzYG_wPNli5CyLvY-nxGTpzZJpz87jF5WcyfZ_fF6vFuObtZFQ2XZS44441lHGRlWFOCdlBpZYWuELGcAiqpnHgzbgoKuNWN0AqcZVPhHGpnUIzJxcF3F8NnjynXm9BHP7ysuVRMCi2ADarLg6qJIaWIrh5Cdibuawb1T4X1X4XiG7dQY3w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2571539301</pqid></control><display><type>article</type><title>Simulation Studies to Quantify the Impact of Demand Side Management on Environmental Footprint</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Almohaimeed, Sulaiman A. ; Suryanarayanan, Siddharth ; O’Neill, Peter</creator><creatorcontrib>Almohaimeed, Sulaiman A. ; Suryanarayanan, Siddharth ; O’Neill, Peter</creatorcontrib><description>The increased use of energy leads to increased energy-related emissions. Demand side management (DSM) is a potential means of mitigating these emissions from electric utility generating units. DSM can significantly reduce emissions and provide economic and reliability benefits. This work presents some DSM techniques, such as load shifting, energy conservation, and valley filling. Furthermore, this work explains the most common DSM programs. To quantify the effect of DSM in diminishing carbon footprint, this paper performs power flow analysis on a yearly load profile corresponding to Fort Collins, Colorado, U.S. This work used the IEEE 13-node test system to simulate several scenarios from the multi-criteria decision-making (MCDM) alternatives, both individually and integrated. For the base case, emissions decrease by 16% from the 2005 level. The “energy conservation” option achieved a 20% reduction in emissions, integrating both alternatives increased the emissions mitigation up to 22%. Simulation of the residential sector shows the “communication and intelligence” option reduces emissions about 14% from the 2005 level. A scenario that combines “electric stationary storage” with “communication and intelligence” diminishes the emissions by more than 15%. The last scenario examined all MCDM alternatives combined into one option, resulting in a 20% emissions reduction. We also conducted a cost benefit analysis (CBA) to investigate economic, technical, and environmental costs and benefits associated with each alternative. The economic evaluation shows that “electric stationary storage” is the best option since it charges during lower electricity prices and discharges during peaking demand. The economic analysis presents a trade-off chart, so the decision maker can select the alternative based on their preference.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su13179504</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Alternatives ; Coal ; Decision making ; Demand curves ; Demand side management ; Economic analysis ; Electric power demand ; Electric utilities ; Electrical loads ; Electricity pricing ; Emissions ; Energy conservation ; Energy consumption ; Energy management ; Energy storage ; Environmental impact ; Environmental management ; Footprint analysis ; Intelligence ; Load ; Load shifting ; Mitigation ; Multiple criterion ; Power flow ; Residential areas ; Simulation ; Test systems</subject><ispartof>Sustainability, 2021-09, Vol.13 (17), p.9504</ispartof><rights>2021 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><cites>FETCH-LOGICAL-c254t-212cd12056a1c409f0697d396eee480e757f3baf80702d9c3970fd183ffe9fae3</cites><orcidid>0000-0002-9509-6927 ; 0000-0002-9442-0086</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Almohaimeed, Sulaiman A.</creatorcontrib><creatorcontrib>Suryanarayanan, Siddharth</creatorcontrib><creatorcontrib>O’Neill, Peter</creatorcontrib><title>Simulation Studies to Quantify the Impact of Demand Side Management on Environmental Footprint</title><title>Sustainability</title><description>The increased use of energy leads to increased energy-related emissions. Demand side management (DSM) is a potential means of mitigating these emissions from electric utility generating units. DSM can significantly reduce emissions and provide economic and reliability benefits. This work presents some DSM techniques, such as load shifting, energy conservation, and valley filling. Furthermore, this work explains the most common DSM programs. To quantify the effect of DSM in diminishing carbon footprint, this paper performs power flow analysis on a yearly load profile corresponding to Fort Collins, Colorado, U.S. This work used the IEEE 13-node test system to simulate several scenarios from the multi-criteria decision-making (MCDM) alternatives, both individually and integrated. For the base case, emissions decrease by 16% from the 2005 level. The “energy conservation” option achieved a 20% reduction in emissions, integrating both alternatives increased the emissions mitigation up to 22%. Simulation of the residential sector shows the “communication and intelligence” option reduces emissions about 14% from the 2005 level. A scenario that combines “electric stationary storage” with “communication and intelligence” diminishes the emissions by more than 15%. The last scenario examined all MCDM alternatives combined into one option, resulting in a 20% emissions reduction. We also conducted a cost benefit analysis (CBA) to investigate economic, technical, and environmental costs and benefits associated with each alternative. The economic evaluation shows that “electric stationary storage” is the best option since it charges during lower electricity prices and discharges during peaking demand. The economic analysis presents a trade-off chart, so the decision maker can select the alternative based on their preference.</description><subject>Algorithms</subject><subject>Alternatives</subject><subject>Coal</subject><subject>Decision making</subject><subject>Demand curves</subject><subject>Demand side management</subject><subject>Economic analysis</subject><subject>Electric power demand</subject><subject>Electric utilities</subject><subject>Electrical loads</subject><subject>Electricity pricing</subject><subject>Emissions</subject><subject>Energy conservation</subject><subject>Energy consumption</subject><subject>Energy management</subject><subject>Energy storage</subject><subject>Environmental impact</subject><subject>Environmental management</subject><subject>Footprint analysis</subject><subject>Intelligence</subject><subject>Load</subject><subject>Load shifting</subject><subject>Mitigation</subject><subject>Multiple criterion</subject><subject>Power flow</subject><subject>Residential areas</subject><subject>Simulation</subject><subject>Test systems</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNUNFKAzEQDKJgqX3xCwK-Caeb5HJpHqW2WqiIVF894mWjV3pJTXJC_96TCrovu8MMs8MQcs7gSggN16lngiktoTwiIw6KFQwkHP-7T8kkpQ0MIwTTrBqR13Xb9VuT2-DpOve2xURzoE-98bl1e5o_kC67nWkyDY7eYme8pevWIn0w3rxjh35gPJ37rzYG_wPNli5CyLvY-nxGTpzZJpz87jF5WcyfZ_fF6vFuObtZFQ2XZS44441lHGRlWFOCdlBpZYWuELGcAiqpnHgzbgoKuNWN0AqcZVPhHGpnUIzJxcF3F8NnjynXm9BHP7ysuVRMCi2ADarLg6qJIaWIrh5Cdibuawb1T4X1X4XiG7dQY3w</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Almohaimeed, Sulaiman A.</creator><creator>Suryanarayanan, Siddharth</creator><creator>O’Neill, Peter</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</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/0000-0002-9509-6927</orcidid><orcidid>https://orcid.org/0000-0002-9442-0086</orcidid></search><sort><creationdate>20210901</creationdate><title>Simulation Studies to Quantify the Impact of Demand Side Management on Environmental Footprint</title><author>Almohaimeed, Sulaiman A. ; Suryanarayanan, Siddharth ; O’Neill, Peter</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c254t-212cd12056a1c409f0697d396eee480e757f3baf80702d9c3970fd183ffe9fae3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Alternatives</topic><topic>Coal</topic><topic>Decision making</topic><topic>Demand curves</topic><topic>Demand side management</topic><topic>Economic analysis</topic><topic>Electric power demand</topic><topic>Electric utilities</topic><topic>Electrical loads</topic><topic>Electricity pricing</topic><topic>Emissions</topic><topic>Energy conservation</topic><topic>Energy consumption</topic><topic>Energy management</topic><topic>Energy storage</topic><topic>Environmental impact</topic><topic>Environmental management</topic><topic>Footprint analysis</topic><topic>Intelligence</topic><topic>Load</topic><topic>Load shifting</topic><topic>Mitigation</topic><topic>Multiple criterion</topic><topic>Power flow</topic><topic>Residential areas</topic><topic>Simulation</topic><topic>Test systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Almohaimeed, Sulaiman A.</creatorcontrib><creatorcontrib>Suryanarayanan, Siddharth</creatorcontrib><creatorcontrib>O’Neill, Peter</creatorcontrib><collection>CrossRef</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>Almohaimeed, Sulaiman A.</au><au>Suryanarayanan, Siddharth</au><au>O’Neill, Peter</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Simulation Studies to Quantify the Impact of Demand Side Management on Environmental Footprint</atitle><jtitle>Sustainability</jtitle><date>2021-09-01</date><risdate>2021</risdate><volume>13</volume><issue>17</issue><spage>9504</spage><pages>9504-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>The increased use of energy leads to increased energy-related emissions. Demand side management (DSM) is a potential means of mitigating these emissions from electric utility generating units. DSM can significantly reduce emissions and provide economic and reliability benefits. This work presents some DSM techniques, such as load shifting, energy conservation, and valley filling. Furthermore, this work explains the most common DSM programs. To quantify the effect of DSM in diminishing carbon footprint, this paper performs power flow analysis on a yearly load profile corresponding to Fort Collins, Colorado, U.S. This work used the IEEE 13-node test system to simulate several scenarios from the multi-criteria decision-making (MCDM) alternatives, both individually and integrated. For the base case, emissions decrease by 16% from the 2005 level. The “energy conservation” option achieved a 20% reduction in emissions, integrating both alternatives increased the emissions mitigation up to 22%. Simulation of the residential sector shows the “communication and intelligence” option reduces emissions about 14% from the 2005 level. A scenario that combines “electric stationary storage” with “communication and intelligence” diminishes the emissions by more than 15%. The last scenario examined all MCDM alternatives combined into one option, resulting in a 20% emissions reduction. We also conducted a cost benefit analysis (CBA) to investigate economic, technical, and environmental costs and benefits associated with each alternative. The economic evaluation shows that “electric stationary storage” is the best option since it charges during lower electricity prices and discharges during peaking demand. The economic analysis presents a trade-off chart, so the decision maker can select the alternative based on their preference.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su13179504</doi><orcidid>https://orcid.org/0000-0002-9509-6927</orcidid><orcidid>https://orcid.org/0000-0002-9442-0086</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2071-1050 |
ispartof | Sustainability, 2021-09, Vol.13 (17), p.9504 |
issn | 2071-1050 2071-1050 |
language | eng |
recordid | cdi_proquest_journals_2571539301 |
source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
subjects | Algorithms Alternatives Coal Decision making Demand curves Demand side management Economic analysis Electric power demand Electric utilities Electrical loads Electricity pricing Emissions Energy conservation Energy consumption Energy management Energy storage Environmental impact Environmental management Footprint analysis Intelligence Load Load shifting Mitigation Multiple criterion Power flow Residential areas Simulation Test systems |
title | Simulation Studies to Quantify the Impact of Demand Side Management on Environmental Footprint |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T14%3A09%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Simulation%20Studies%20to%20Quantify%20the%20Impact%20of%20Demand%20Side%20Management%20on%20Environmental%20Footprint&rft.jtitle=Sustainability&rft.au=Almohaimeed,%20Sulaiman%20A.&rft.date=2021-09-01&rft.volume=13&rft.issue=17&rft.spage=9504&rft.pages=9504-&rft.issn=2071-1050&rft.eissn=2071-1050&rft_id=info:doi/10.3390/su13179504&rft_dat=%3Cproquest_cross%3E2571539301%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2571539301&rft_id=info:pmid/&rfr_iscdi=true |