Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system
To address the primary energy shortage problem, Japan has implemented a series of policies and measures for residential energy conservation and emission reduction. Among them, the home energy management system (HEMS) as a hub connecting users and power companies to realize energy visualization has b...
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Veröffentlicht in: | Energy (Oxford) 2021-08, Vol.229, p.120538, Article 120538 |
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description | To address the primary energy shortage problem, Japan has implemented a series of policies and measures for residential energy conservation and emission reduction. Among them, the home energy management system (HEMS) as a hub connecting users and power companies to realize energy visualization has been widely studied. The research object of this study is a two-story detached residence integrated with HEMS in the “Jono Zero Carbon Smart Community” in Japan. To predict the energy consumed on the next day based on historical data, a short-term household load forecasting model based on the particle swarm optimization regression vector machine algorithm was developed. Then a dynamic pricing model was developed to guide the users’ electricity consumption behavior and adjust the grid load. According to the prediction results obtained by the load forecasting model, the annual electricity charges of users under the three pricing schemes of multistep electricity pricing (MEP), time-of-use pricing (TOU), and real-time pricing (RTP) were calculated and compared. The result indicated that the annual electricity cost generated by RTP was less than those generated by MTP and TOU. In addition, after adjusting the users’ peak load and combining it with the fluctuating future electricity prices, RTP presented evident economic advantage over MTP and TOU in terms of the annual electricity cost of the users. The study results can provide policy suggestions for the future Japanese government’s promotion of RTP strategy, while acting as a reference for further developing the characteristics of HEMS and optimizing the relation between the supply and demand sides.
•A short-term forecasting model suitable for household load is established based on PSO-RVM algorithm.•A real-time price (RTP) model is proposed to guide the use behaviors and balance the grid load.•RTP has economic advantages over TOU and MTP under different price schemes.•RTP has a great potential to combine with demand side response and future price fluctuation.•Results can promote RTP and application of HEMS to optimize the relationship between supply and demand. |
doi_str_mv | 10.1016/j.energy.2021.120538 |
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•A short-term forecasting model suitable for household load is established based on PSO-RVM algorithm.•A real-time price (RTP) model is proposed to guide the use behaviors and balance the grid load.•RTP has economic advantages over TOU and MTP under different price schemes.•RTP has a great potential to combine with demand side response and future price fluctuation.•Results can promote RTP and application of HEMS to optimize the relationship between supply and demand.</description><identifier>ISSN: 0360-5442</identifier><identifier>EISSN: 1873-6785</identifier><identifier>DOI: 10.1016/j.energy.2021.120538</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Algorithms ; Dynamic pricing model ; Electrical loads ; Electricity ; Electricity consumption ; Emission measurements ; Emissions control ; Energy conservation ; Energy management ; Energy shortages ; Forecasting ; Historical account ; Home energy management system ; Mathematical models ; Particle swarm optimization ; Peak load ; Price scheme selection ; Residential energy ; Short-term load forecasting ; Time of use electricity pricing</subject><ispartof>Energy (Oxford), 2021-08, Vol.229, p.120538, Article 120538</ispartof><rights>2021 Elsevier Ltd</rights><rights>Copyright Elsevier BV Aug 15, 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c446t-b931ee6f94b890dd14c5091b3403d999f19caed4a03b516d376baa783daf714b3</citedby><cites>FETCH-LOGICAL-c446t-b931ee6f94b890dd14c5091b3403d999f19caed4a03b516d376baa783daf714b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.energy.2021.120538$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3549,27923,27924,45994</link.rule.ids></links><search><creatorcontrib>Zhao, Xueyuan</creatorcontrib><creatorcontrib>Gao, Weijun</creatorcontrib><creatorcontrib>Qian, Fanyue</creatorcontrib><creatorcontrib>Ge, Jian</creatorcontrib><title>Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system</title><title>Energy (Oxford)</title><description>To address the primary energy shortage problem, Japan has implemented a series of policies and measures for residential energy conservation and emission reduction. Among them, the home energy management system (HEMS) as a hub connecting users and power companies to realize energy visualization has been widely studied. The research object of this study is a two-story detached residence integrated with HEMS in the “Jono Zero Carbon Smart Community” in Japan. To predict the energy consumed on the next day based on historical data, a short-term household load forecasting model based on the particle swarm optimization regression vector machine algorithm was developed. Then a dynamic pricing model was developed to guide the users’ electricity consumption behavior and adjust the grid load. According to the prediction results obtained by the load forecasting model, the annual electricity charges of users under the three pricing schemes of multistep electricity pricing (MEP), time-of-use pricing (TOU), and real-time pricing (RTP) were calculated and compared. The result indicated that the annual electricity cost generated by RTP was less than those generated by MTP and TOU. In addition, after adjusting the users’ peak load and combining it with the fluctuating future electricity prices, RTP presented evident economic advantage over MTP and TOU in terms of the annual electricity cost of the users. The study results can provide policy suggestions for the future Japanese government’s promotion of RTP strategy, while acting as a reference for further developing the characteristics of HEMS and optimizing the relation between the supply and demand sides.
•A short-term forecasting model suitable for household load is established based on PSO-RVM algorithm.•A real-time price (RTP) model is proposed to guide the use behaviors and balance the grid load.•RTP has economic advantages over TOU and MTP under different price schemes.•RTP has a great potential to combine with demand side response and future price fluctuation.•Results can promote RTP and application of HEMS to optimize the relationship between supply and demand.</description><subject>Algorithms</subject><subject>Dynamic pricing model</subject><subject>Electrical loads</subject><subject>Electricity</subject><subject>Electricity consumption</subject><subject>Emission measurements</subject><subject>Emissions control</subject><subject>Energy conservation</subject><subject>Energy management</subject><subject>Energy shortages</subject><subject>Forecasting</subject><subject>Historical account</subject><subject>Home energy management system</subject><subject>Mathematical models</subject><subject>Particle swarm optimization</subject><subject>Peak load</subject><subject>Price scheme selection</subject><subject>Residential energy</subject><subject>Short-term load forecasting</subject><subject>Time of use electricity pricing</subject><issn>0360-5442</issn><issn>1873-6785</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kE9LxDAQxYMouK5-Aw8Bz12TJm2TiyCL_2DBi55DmkzXlDZZk6zQb2-XevYyc5jfm5n3ELqlZEMJre_7DXiI-2lTkpJuaEkqJs7QioqGFXUjqnO0IqwmRcV5eYmuUuoJIZWQcoXC0wAmR2dcnrAJKc9lPOjoUvA4dNhOXo_O4MMJ8Xs8BgsDbnUCi2diCNriLkQwOuXT3Hn8FUbAy0N41F7vYQSfcZpShvEaXXR6SHDz19fo8_npY_ta7N5f3raPu8JwXueilYwC1J3krZDEWspNRSRtGSfMSik7Ko0GyzVhbUVry5q61boRzOquobxla3S37D3E8H2ElFUfjtHPJ1VZVaQWohFipvhCmRhSitCp2eeo46QoUadoVa8WJ-oUrVqinWUPiwxmBz8OokrGgTdg3ZxEVja4_xf8Aun-hdY</recordid><startdate>20210815</startdate><enddate>20210815</enddate><creator>Zhao, Xueyuan</creator><creator>Gao, Weijun</creator><creator>Qian, Fanyue</creator><creator>Ge, Jian</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>SOI</scope></search><sort><creationdate>20210815</creationdate><title>Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system</title><author>Zhao, Xueyuan ; Gao, Weijun ; Qian, Fanyue ; Ge, Jian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c446t-b931ee6f94b890dd14c5091b3403d999f19caed4a03b516d376baa783daf714b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Dynamic pricing model</topic><topic>Electrical loads</topic><topic>Electricity</topic><topic>Electricity consumption</topic><topic>Emission measurements</topic><topic>Emissions control</topic><topic>Energy conservation</topic><topic>Energy management</topic><topic>Energy shortages</topic><topic>Forecasting</topic><topic>Historical account</topic><topic>Home energy management system</topic><topic>Mathematical models</topic><topic>Particle swarm optimization</topic><topic>Peak load</topic><topic>Price scheme selection</topic><topic>Residential energy</topic><topic>Short-term load forecasting</topic><topic>Time of use electricity pricing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Xueyuan</creatorcontrib><creatorcontrib>Gao, Weijun</creatorcontrib><creatorcontrib>Qian, Fanyue</creatorcontrib><creatorcontrib>Ge, Jian</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>Energy (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Xueyuan</au><au>Gao, Weijun</au><au>Qian, Fanyue</au><au>Ge, Jian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system</atitle><jtitle>Energy (Oxford)</jtitle><date>2021-08-15</date><risdate>2021</risdate><volume>229</volume><spage>120538</spage><pages>120538-</pages><artnum>120538</artnum><issn>0360-5442</issn><eissn>1873-6785</eissn><abstract>To address the primary energy shortage problem, Japan has implemented a series of policies and measures for residential energy conservation and emission reduction. Among them, the home energy management system (HEMS) as a hub connecting users and power companies to realize energy visualization has been widely studied. The research object of this study is a two-story detached residence integrated with HEMS in the “Jono Zero Carbon Smart Community” in Japan. To predict the energy consumed on the next day based on historical data, a short-term household load forecasting model based on the particle swarm optimization regression vector machine algorithm was developed. Then a dynamic pricing model was developed to guide the users’ electricity consumption behavior and adjust the grid load. According to the prediction results obtained by the load forecasting model, the annual electricity charges of users under the three pricing schemes of multistep electricity pricing (MEP), time-of-use pricing (TOU), and real-time pricing (RTP) were calculated and compared. The result indicated that the annual electricity cost generated by RTP was less than those generated by MTP and TOU. In addition, after adjusting the users’ peak load and combining it with the fluctuating future electricity prices, RTP presented evident economic advantage over MTP and TOU in terms of the annual electricity cost of the users. The study results can provide policy suggestions for the future Japanese government’s promotion of RTP strategy, while acting as a reference for further developing the characteristics of HEMS and optimizing the relation between the supply and demand sides.
•A short-term forecasting model suitable for household load is established based on PSO-RVM algorithm.•A real-time price (RTP) model is proposed to guide the use behaviors and balance the grid load.•RTP has economic advantages over TOU and MTP under different price schemes.•RTP has a great potential to combine with demand side response and future price fluctuation.•Results can promote RTP and application of HEMS to optimize the relationship between supply and demand.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.energy.2021.120538</doi><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Dynamic pricing model Electrical loads Electricity Electricity consumption Emission measurements Emissions control Energy conservation Energy management Energy shortages Forecasting Historical account Home energy management system Mathematical models Particle swarm optimization Peak load Price scheme selection Residential energy Short-term load forecasting Time of use electricity pricing |
title | Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system |
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