Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.egypro.2018.04.060
DC Field | Value | |
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dc.title | Local control of fuel cell systems within hybrid renewable energy generation using model predictive control | |
dc.contributor.author | Chen, S. | |
dc.contributor.author | Chiu, M.-S. | |
dc.contributor.author | Wang, X. | |
dc.contributor.editor | Jia, H. | |
dc.contributor.editor | Wu, J. | |
dc.contributor.editor | Wang, C. | |
dc.contributor.editor | Yu, J. | |
dc.contributor.editor | Zhang, Y. | |
dc.contributor.editor | Xu, T. | |
dc.contributor.editor | Yan, J. | |
dc.date.accessioned | 2022-01-07T03:53:45Z | |
dc.date.available | 2022-01-07T03:53:45Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Chen, S., Chiu, M.-S., Wang, X. (2018). Local control of fuel cell systems within hybrid renewable energy generation using model predictive control. Energy Procedia 145 : 333-338. ScholarBank@NUS Repository. https://doi.org/10.1016/j.egypro.2018.04.060 | |
dc.identifier.issn | 1876-6102 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/213291 | |
dc.description.abstract | This paper presents model predictive control (MPC) strategies to tracking local control systems when subjected to supervisory optimal trajectories. The supervisory trajectories are generated using economic receding horizon optimization based on energy management in the energy-intensive industry (e.g., chlor-alkali process) with a hybrid renewable energy system (HRES), including solar, wind, and fuel cell sub-systems. A planer solid oxide fuel cell system is adopted in this study, and its power output is regulated using a constrained MPC controller. The feasibility of MPC control algorithm in regulating energy sub-systems to meet supervisory commands will be studied and evaluated at different parameters and against traditional PI controller. © 2018 The Authors. Published by Elsevier Ltd. | |
dc.publisher | Elsevier Ltd | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.source | Scopus OA2018 | |
dc.subject | constrained model predictive control | |
dc.subject | economic optimization | |
dc.subject | fuel cell | |
dc.subject | hybrid renewable energy system | |
dc.type | Conference Paper | |
dc.contributor.department | CHEMICAL & BIOMOLECULAR ENGINEERING | |
dc.description.doi | 10.1016/j.egypro.2018.04.060 | |
dc.description.sourcetitle | Energy Procedia | |
dc.description.volume | 145 | |
dc.description.page | 333-338 | |
dc.published.state | Published | |
Appears in Collections: | Staff Publications Elements |
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