Two-Stage Optimal Economic Scheduling for Commercial Building Multi-Energy System Through Internet of Things

In this study, an advanced Internet of Things (IoT) technology is applied to the energy management of an intelligent combined cooling, heating, and power (CCHP) commercial building system. Based on the framework of a smart energy management system (SEMS) using IoT technology, a two-stage optimal sch...
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Gespeichert in:

Autor*in:	Chunming Liu [verfasserIn] Dingjun Wang [verfasserIn] Yujun Yin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Internet of things (IoT) combined cooling heating and power (CCHP) renewable energy resource (RES) two-stage optimal dispatch

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 174562-174572
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:174562-174572

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2957267

Katalog-ID:	DOAJ04734024X

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spelling	10.1109/ACCESS.2019.2957267 doi (DE-627)DOAJ04734024X (DE-599)DOAJac3b53dd837746e09bfbe614f40b3f10 DE-627 ger DE-627 rakwb eng TK1-9971 Chunming Liu verfasserin aut Two-Stage Optimal Economic Scheduling for Commercial Building Multi-Energy System Through Internet of Things 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, an advanced Internet of Things (IoT) technology is applied to the energy management of an intelligent combined cooling, heating, and power (CCHP) commercial building system. Based on the framework of a smart energy management system (SEMS) using IoT technology, a two-stage optimal scheduling model is proposed to determine the most economic CCHP commercial building system integrated with a three-way valve. In the day-ahead scheduling stage, the schedule is planned using the lowest operating costs of the system. In the real-time correction stage, the correction strategy employs minimum adjustment of the output of each unit. Moreover, the schedule plan is corrected to smooth out fluctuations in the loads and renewable energy resources (RES) in a timely manner to better absorb green energy. The day-ahead scheduling model is a large-scale mixed integer nonlinear programming (MINLP) problem solved through a linearization method proposed in this study and the mixed integer linear programming method. The real-time correction optimization model is a nonlinear programming problem solved by the quantum genetic algorithm (QGA). A case study is employed to demonstrate that the IoT-based SEMS improves the system automation energy management level and user comfort. Furthermore, the proposed system structure can significantly reduce system operating costs and improve the utilization of waste heat from the internal combustion engine. In conclusion, the economic and environmental superiority of the two-stage optimal dispatch model is verified. Internet of things (IoT) combined cooling heating and power (CCHP) renewable energy resource (RES) two-stage optimal dispatch Electrical engineering. Electronics. Nuclear engineering Dingjun Wang verfasserin aut Yujun Yin verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 174562-174572 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:174562-174572 https://doi.org/10.1109/ACCESS.2019.2957267 kostenfrei https://doaj.org/article/ac3b53dd837746e09bfbe614f40b3f10 kostenfrei https://ieeexplore.ieee.org/document/8920058/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 174562-174572
allfields_unstemmed	10.1109/ACCESS.2019.2957267 doi (DE-627)DOAJ04734024X (DE-599)DOAJac3b53dd837746e09bfbe614f40b3f10 DE-627 ger DE-627 rakwb eng TK1-9971 Chunming Liu verfasserin aut Two-Stage Optimal Economic Scheduling for Commercial Building Multi-Energy System Through Internet of Things 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this study, an advanced Internet of Things (IoT) technology is applied to the energy management of an intelligent combined cooling, heating, and power (CCHP) commercial building system. Based on the framework of a smart energy management system (SEMS) using IoT technology, a two-stage optimal scheduling model is proposed to determine the most economic CCHP commercial building system integrated with a three-way valve. In the day-ahead scheduling stage, the schedule is planned using the lowest operating costs of the system. In the real-time correction stage, the correction strategy employs minimum adjustment of the output of each unit. Moreover, the schedule plan is corrected to smooth out fluctuations in the loads and renewable energy resources (RES) in a timely manner to better absorb green energy. The day-ahead scheduling model is a large-scale mixed integer nonlinear programming (MINLP) problem solved through a linearization method proposed in this study and the mixed integer linear programming method. The real-time correction optimization model is a nonlinear programming problem solved by the quantum genetic algorithm (QGA). A case study is employed to demonstrate that the IoT-based SEMS improves the system automation energy management level and user comfort. Furthermore, the proposed system structure can significantly reduce system operating costs and improve the utilization of waste heat from the internal combustion engine. In conclusion, the economic and environmental superiority of the two-stage optimal dispatch model is verified. Internet of things (IoT) combined cooling heating and power (CCHP) renewable energy resource (RES) two-stage optimal dispatch Electrical engineering. Electronics. Nuclear engineering Dingjun Wang verfasserin aut Yujun Yin verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 174562-174572 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:174562-174572 https://doi.org/10.1109/ACCESS.2019.2957267 kostenfrei https://doaj.org/article/ac3b53dd837746e09bfbe614f40b3f10 kostenfrei https://ieeexplore.ieee.org/document/8920058/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 174562-174572
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callnumber-first	T - Technology
author	Chunming Liu
spellingShingle	Chunming Liu misc TK1-9971 misc Internet of things (IoT) misc combined cooling misc heating misc and power (CCHP) misc renewable energy resource (RES) misc two-stage optimal dispatch misc Electrical engineering. Electronics. Nuclear engineering Two-Stage Optimal Economic Scheduling for Commercial Building Multi-Energy System Through Internet of Things
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topic_title	TK1-9971 Two-Stage Optimal Economic Scheduling for Commercial Building Multi-Energy System Through Internet of Things Internet of things (IoT) combined cooling heating and power (CCHP) renewable energy resource (RES) two-stage optimal dispatch
topic	misc TK1-9971 misc Internet of things (IoT) misc combined cooling misc heating misc and power (CCHP) misc renewable energy resource (RES) misc two-stage optimal dispatch misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Internet of things (IoT) misc combined cooling misc heating misc and power (CCHP) misc renewable energy resource (RES) misc two-stage optimal dispatch misc Electrical engineering. Electronics. Nuclear engineering
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title	Two-Stage Optimal Economic Scheduling for Commercial Building Multi-Energy System Through Internet of Things
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title_sort	two-stage optimal economic scheduling for commercial building multi-energy system through internet of things
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title_auth	Two-Stage Optimal Economic Scheduling for Commercial Building Multi-Energy System Through Internet of Things
abstract	In this study, an advanced Internet of Things (IoT) technology is applied to the energy management of an intelligent combined cooling, heating, and power (CCHP) commercial building system. Based on the framework of a smart energy management system (SEMS) using IoT technology, a two-stage optimal scheduling model is proposed to determine the most economic CCHP commercial building system integrated with a three-way valve. In the day-ahead scheduling stage, the schedule is planned using the lowest operating costs of the system. In the real-time correction stage, the correction strategy employs minimum adjustment of the output of each unit. Moreover, the schedule plan is corrected to smooth out fluctuations in the loads and renewable energy resources (RES) in a timely manner to better absorb green energy. The day-ahead scheduling model is a large-scale mixed integer nonlinear programming (MINLP) problem solved through a linearization method proposed in this study and the mixed integer linear programming method. The real-time correction optimization model is a nonlinear programming problem solved by the quantum genetic algorithm (QGA). A case study is employed to demonstrate that the IoT-based SEMS improves the system automation energy management level and user comfort. Furthermore, the proposed system structure can significantly reduce system operating costs and improve the utilization of waste heat from the internal combustion engine. In conclusion, the economic and environmental superiority of the two-stage optimal dispatch model is verified.
abstractGer	In this study, an advanced Internet of Things (IoT) technology is applied to the energy management of an intelligent combined cooling, heating, and power (CCHP) commercial building system. Based on the framework of a smart energy management system (SEMS) using IoT technology, a two-stage optimal scheduling model is proposed to determine the most economic CCHP commercial building system integrated with a three-way valve. In the day-ahead scheduling stage, the schedule is planned using the lowest operating costs of the system. In the real-time correction stage, the correction strategy employs minimum adjustment of the output of each unit. Moreover, the schedule plan is corrected to smooth out fluctuations in the loads and renewable energy resources (RES) in a timely manner to better absorb green energy. The day-ahead scheduling model is a large-scale mixed integer nonlinear programming (MINLP) problem solved through a linearization method proposed in this study and the mixed integer linear programming method. The real-time correction optimization model is a nonlinear programming problem solved by the quantum genetic algorithm (QGA). A case study is employed to demonstrate that the IoT-based SEMS improves the system automation energy management level and user comfort. Furthermore, the proposed system structure can significantly reduce system operating costs and improve the utilization of waste heat from the internal combustion engine. In conclusion, the economic and environmental superiority of the two-stage optimal dispatch model is verified.
abstract_unstemmed	In this study, an advanced Internet of Things (IoT) technology is applied to the energy management of an intelligent combined cooling, heating, and power (CCHP) commercial building system. Based on the framework of a smart energy management system (SEMS) using IoT technology, a two-stage optimal scheduling model is proposed to determine the most economic CCHP commercial building system integrated with a three-way valve. In the day-ahead scheduling stage, the schedule is planned using the lowest operating costs of the system. In the real-time correction stage, the correction strategy employs minimum adjustment of the output of each unit. Moreover, the schedule plan is corrected to smooth out fluctuations in the loads and renewable energy resources (RES) in a timely manner to better absorb green energy. The day-ahead scheduling model is a large-scale mixed integer nonlinear programming (MINLP) problem solved through a linearization method proposed in this study and the mixed integer linear programming method. The real-time correction optimization model is a nonlinear programming problem solved by the quantum genetic algorithm (QGA). A case study is employed to demonstrate that the IoT-based SEMS improves the system automation energy management level and user comfort. Furthermore, the proposed system structure can significantly reduce system operating costs and improve the utilization of waste heat from the internal combustion engine. In conclusion, the economic and environmental superiority of the two-stage optimal dispatch model is verified.
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title_short	Two-Stage Optimal Economic Scheduling for Commercial Building Multi-Energy System Through Internet of Things
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