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Article: Unit Commitment Incorporating Spatial Distribution Control of Air Pollutant Dispersion
Title | Unit Commitment Incorporating Spatial Distribution Control of Air Pollutant Dispersion |
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Authors | |
Keywords | Air pollutant dispersion Gaussian plume model robust optimization unit commitment (UC) wind power |
Issue Date | 2017 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424 |
Citation | IEEE Transactions on Industrial Informatics, 2017, v. 13 n. 3, p. 995-1005 How to Cite? |
Abstract | Air pollution problems are attracting increasing attention, especially among developing countries with frequent haze events. Renewable energy sources such as wind power are expected to help relieve such environmental concerns. However, air pollution issues under such a changing energy structure receive inadequate attention. Mostly, constraints for total pollutant emissions are considered in unit commitment (UC) and economic dispatch (ED) problems. In this paper, we propose a UC model with wind power that considers the dispersion of air pollutants. The dispersion process is described by models involving meteorological conditions and the system’s geographical distribution, to estimate the spatial distribution of air pollutants, i.e. the concentration of ground-level air pollutants at monitored locations such as load centers. A penalty cost is introduced based on this estimation. Particulate matter 2.5 micrometers or less in diameter, the major air pollutant concerning most developing countries, is selected as the focus of this work. To properly estimate and sufficiently utilize the benefits of wind power for air pollutant dispersion control, robust optimization is applied to accommodate wind power uncertainty. Case studies justify this consideration of air pollutant dispersion, and demonstrate the effectiveness of the proposed model for improving load centers’ air pollution control and utilizing wind power benefits. |
Persistent Identifier | http://hdl.handle.net/10722/237017 |
ISSN | 2023 Impact Factor: 11.7 2023 SCImago Journal Rankings: 4.420 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lei, S | - |
dc.contributor.author | Hou, Y | - |
dc.contributor.author | Wang, X | - |
dc.contributor.author | Liu, K | - |
dc.date.accessioned | 2016-12-20T06:14:56Z | - |
dc.date.available | 2016-12-20T06:14:56Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IEEE Transactions on Industrial Informatics, 2017, v. 13 n. 3, p. 995-1005 | - |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | http://hdl.handle.net/10722/237017 | - |
dc.description.abstract | Air pollution problems are attracting increasing attention, especially among developing countries with frequent haze events. Renewable energy sources such as wind power are expected to help relieve such environmental concerns. However, air pollution issues under such a changing energy structure receive inadequate attention. Mostly, constraints for total pollutant emissions are considered in unit commitment (UC) and economic dispatch (ED) problems. In this paper, we propose a UC model with wind power that considers the dispersion of air pollutants. The dispersion process is described by models involving meteorological conditions and the system’s geographical distribution, to estimate the spatial distribution of air pollutants, i.e. the concentration of ground-level air pollutants at monitored locations such as load centers. A penalty cost is introduced based on this estimation. Particulate matter 2.5 micrometers or less in diameter, the major air pollutant concerning most developing countries, is selected as the focus of this work. To properly estimate and sufficiently utilize the benefits of wind power for air pollutant dispersion control, robust optimization is applied to accommodate wind power uncertainty. Case studies justify this consideration of air pollutant dispersion, and demonstrate the effectiveness of the proposed model for improving load centers’ air pollution control and utilizing wind power benefits. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424 | - |
dc.relation.ispartof | IEEE Transactions on Industrial Informatics | - |
dc.rights | ©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Air pollutant dispersion | - |
dc.subject | Gaussian plume model | - |
dc.subject | robust optimization | - |
dc.subject | unit commitment (UC) | - |
dc.subject | wind power | - |
dc.title | Unit Commitment Incorporating Spatial Distribution Control of Air Pollutant Dispersion | - |
dc.type | Article | - |
dc.identifier.email | Hou, Y: yhhou@eee.hku.hk | - |
dc.identifier.email | Wang, X: wx315@hku.hk | - |
dc.identifier.authority | Hou, Y=rp00069 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1109/TII.2016.2631572 | - |
dc.identifier.scopus | eid_2-s2.0-85020670780 | - |
dc.identifier.hkuros | 270772 | - |
dc.identifier.volume | 13 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 995 | - |
dc.identifier.epage | 1005 | - |
dc.identifier.isi | WOS:000402929700007 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1551-3203 | - |