Design optimization and two-stage control strategy on combined cooling, heating and power system
•Design optimization and two-stage control strategy of the CCHP system were conducted.•CCHP system performance was investigated in two different climatic regions.•The performance of different control strategies was compared. Combined cooling, heating and power (CCHP) is a promising energy supply tec...
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Veröffentlicht in: | Energy conversion and management 2019-11, Vol.199, p.111869, Article 111869 |
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creator | Zhu, Guangya Chow, Tin-Tai |
description | •Design optimization and two-stage control strategy of the CCHP system were conducted.•CCHP system performance was investigated in two different climatic regions.•The performance of different control strategies was compared.
Combined cooling, heating and power (CCHP) is a promising energy supply technology to improve the overall energy utilization efficiency. It provides an opportunity to achieve low or zero carbon energy supply with tactful control strategies. In this study, the two-stage model predictive control (MPC) strategy with combined rolling optimization and real-time adjustment is demonstrated to be highly effective for CCHP system application. In most previous studies, the MPC strategy was performed under a given design configuration, which was not optimally constructed. In real practice, the system configuration, the installed equipment capacity, and the control strategy altogether affect the CCHP system performance. Both the system design optimization and the two-stage MPC strategy were conducted in our numerical analysis through a hypothetical case study. Our simulation results illustrated that the equipment selection affects the running cost of the CCHP system, and the desirable solution is well linked to the building load profile. The effect of forecasting error on the two-stage MPC was also investigated. The results showed that although the two-stage MPC strategy is able to give better performance than the traditional strategies in most cases, poor performance may still exist when the load forecasting error is more than 8.8%. |
doi_str_mv | 10.1016/j.enconman.2019.111869 |
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Combined cooling, heating and power (CCHP) is a promising energy supply technology to improve the overall energy utilization efficiency. It provides an opportunity to achieve low or zero carbon energy supply with tactful control strategies. In this study, the two-stage model predictive control (MPC) strategy with combined rolling optimization and real-time adjustment is demonstrated to be highly effective for CCHP system application. In most previous studies, the MPC strategy was performed under a given design configuration, which was not optimally constructed. In real practice, the system configuration, the installed equipment capacity, and the control strategy altogether affect the CCHP system performance. Both the system design optimization and the two-stage MPC strategy were conducted in our numerical analysis through a hypothetical case study. Our simulation results illustrated that the equipment selection affects the running cost of the CCHP system, and the desirable solution is well linked to the building load profile. The effect of forecasting error on the two-stage MPC was also investigated. The results showed that although the two-stage MPC strategy is able to give better performance than the traditional strategies in most cases, poor performance may still exist when the load forecasting error is more than 8.8%.</description><identifier>ISSN: 0196-8904</identifier><identifier>EISSN: 1879-2227</identifier><identifier>DOI: 10.1016/j.enconman.2019.111869</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Computer simulation ; Configuration management ; Configurations ; Control equipment ; Cooling ; Cooling systems ; Design ; Design optimization ; Energy utilization ; Equipment costs ; Forecasting ; Heating ; Numerical analysis ; Predictive control ; Strategy ; Systems design</subject><ispartof>Energy conversion and management, 2019-11, Vol.199, p.111869, Article 111869</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Nov 1, 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c388t-c336eb8b8ecad4e9aacc244dc91b28aba9ecca2f43c99c86ccf1753602959ece3</citedby><cites>FETCH-LOGICAL-c388t-c336eb8b8ecad4e9aacc244dc91b28aba9ecca2f43c99c86ccf1753602959ece3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.enconman.2019.111869$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Zhu, Guangya</creatorcontrib><creatorcontrib>Chow, Tin-Tai</creatorcontrib><title>Design optimization and two-stage control strategy on combined cooling, heating and power system</title><title>Energy conversion and management</title><description>•Design optimization and two-stage control strategy of the CCHP system were conducted.•CCHP system performance was investigated in two different climatic regions.•The performance of different control strategies was compared.
Combined cooling, heating and power (CCHP) is a promising energy supply technology to improve the overall energy utilization efficiency. It provides an opportunity to achieve low or zero carbon energy supply with tactful control strategies. In this study, the two-stage model predictive control (MPC) strategy with combined rolling optimization and real-time adjustment is demonstrated to be highly effective for CCHP system application. In most previous studies, the MPC strategy was performed under a given design configuration, which was not optimally constructed. In real practice, the system configuration, the installed equipment capacity, and the control strategy altogether affect the CCHP system performance. Both the system design optimization and the two-stage MPC strategy were conducted in our numerical analysis through a hypothetical case study. Our simulation results illustrated that the equipment selection affects the running cost of the CCHP system, and the desirable solution is well linked to the building load profile. The effect of forecasting error on the two-stage MPC was also investigated. The results showed that although the two-stage MPC strategy is able to give better performance than the traditional strategies in most cases, poor performance may still exist when the load forecasting error is more than 8.8%.</description><subject>Computer simulation</subject><subject>Configuration management</subject><subject>Configurations</subject><subject>Control equipment</subject><subject>Cooling</subject><subject>Cooling systems</subject><subject>Design</subject><subject>Design optimization</subject><subject>Energy utilization</subject><subject>Equipment costs</subject><subject>Forecasting</subject><subject>Heating</subject><subject>Numerical analysis</subject><subject>Predictive control</subject><subject>Strategy</subject><subject>Systems design</subject><issn>0196-8904</issn><issn>1879-2227</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNqFkM1OwzAQhC0EEqXwCigSVxJsJ3XtG6j8SpW4wNk4ziY4auxiu1Tl6XEJnLnsrrQzs9oPoXOCC4IJu-oLsNrZQdmCYiIKQghn4gBNCJ-LnFI6P0STtGA5F7g6Rich9BjjcobZBL3dQjCdzdw6msF8qWiczZRtsrh1eYiqgyxlR-9WWYheReh2WVJoN9TGQpMGtzK2u8zeIXlt9-Nduy34LOxChOEUHbVqFeDst0_R6_3dy-IxXz4_PC1ulrkuOY-plgxqXnPQqqlAKKU1rapGC1JTrmolQGtF26rUQmjOtG7JfFYyTMUsraCcoosxd-3dxwZClL3beJtOSloSTDnhFUsqNqq0dyF4aOXam0H5nSRY7mnKXv7RlHuacqSZjNejEdIPnwa8DNokJTTGg46ycea_iG-XjYP1</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>Zhu, Guangya</creator><creator>Chow, Tin-Tai</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope><scope>SOI</scope></search><sort><creationdate>20191101</creationdate><title>Design optimization and two-stage control strategy on combined cooling, heating and power system</title><author>Zhu, Guangya ; Chow, Tin-Tai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c388t-c336eb8b8ecad4e9aacc244dc91b28aba9ecca2f43c99c86ccf1753602959ece3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer simulation</topic><topic>Configuration management</topic><topic>Configurations</topic><topic>Control equipment</topic><topic>Cooling</topic><topic>Cooling systems</topic><topic>Design</topic><topic>Design optimization</topic><topic>Energy utilization</topic><topic>Equipment costs</topic><topic>Forecasting</topic><topic>Heating</topic><topic>Numerical analysis</topic><topic>Predictive control</topic><topic>Strategy</topic><topic>Systems design</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Guangya</creatorcontrib><creatorcontrib>Chow, Tin-Tai</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>Energy conversion and management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhu, Guangya</au><au>Chow, Tin-Tai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Design optimization and two-stage control strategy on combined cooling, heating and power system</atitle><jtitle>Energy conversion and management</jtitle><date>2019-11-01</date><risdate>2019</risdate><volume>199</volume><spage>111869</spage><pages>111869-</pages><artnum>111869</artnum><issn>0196-8904</issn><eissn>1879-2227</eissn><abstract>•Design optimization and two-stage control strategy of the CCHP system were conducted.•CCHP system performance was investigated in two different climatic regions.•The performance of different control strategies was compared.
Combined cooling, heating and power (CCHP) is a promising energy supply technology to improve the overall energy utilization efficiency. It provides an opportunity to achieve low or zero carbon energy supply with tactful control strategies. In this study, the two-stage model predictive control (MPC) strategy with combined rolling optimization and real-time adjustment is demonstrated to be highly effective for CCHP system application. In most previous studies, the MPC strategy was performed under a given design configuration, which was not optimally constructed. In real practice, the system configuration, the installed equipment capacity, and the control strategy altogether affect the CCHP system performance. Both the system design optimization and the two-stage MPC strategy were conducted in our numerical analysis through a hypothetical case study. Our simulation results illustrated that the equipment selection affects the running cost of the CCHP system, and the desirable solution is well linked to the building load profile. The effect of forecasting error on the two-stage MPC was also investigated. The results showed that although the two-stage MPC strategy is able to give better performance than the traditional strategies in most cases, poor performance may still exist when the load forecasting error is more than 8.8%.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.enconman.2019.111869</doi></addata></record> |
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source | ScienceDirect Journals (5 years ago - present) |
subjects | Computer simulation Configuration management Configurations Control equipment Cooling Cooling systems Design Design optimization Energy utilization Equipment costs Forecasting Heating Numerical analysis Predictive control Strategy Systems design |
title | Design optimization and two-stage control strategy on combined cooling, heating and power system |
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