Reserves from Controllable Swimming Pool Pumps: Reliability Assessment and Operational Planning

dc.contributor.authorModarresi, Mohammad Sadegh
dc.contributor.authorXie, Le
dc.contributor.authorSingh, Chanan
dc.date.accessioned2017-12-28T01:03:32Z
dc.date.available2017-12-28T01:03:32Z
dc.date.issued2018-01-03
dc.description.abstractThis paper introduces a conceptual framework, a capacity assessment method, and a data-driven optimization algorithm to aggregate flexible loads such as in-ground swimming pool pumps for reliable provision of spinning reserves. Enabled by Internet of Things (IoT) technologies, many household loads offer tremendous opportunities for aggregated demand response at wholesale level markets. The spinning reserve market is one that fits well in the context of swimming pool pumps in many regions of the U.S. and around the world (e.g. Texas, California, Florida). This paper offers rigorous treatment of the collective reliability of many pool pumps as firm generation capacity. Based on the reliability assessment, an optimal scheduling of pool pumps is formulated and solved using scenario-based approach. The case study is performed using empirical data from Electric Reliability Council of Texas (ERCOT). Cost-benefit analysis based on a city suggests the potential business viability of the proposed framework.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2018.323
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50211
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMarkets, Policy, and Computation
dc.subjectAncillary service, demand response, internet of things (IoT), reliability assessment, scenario based optimization
dc.titleReserves from Controllable Swimming Pool Pumps: Reliability Assessment and Operational Planning
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
paper0324.pdf
Size:
722.51 KB
Format:
Adobe Portable Document Format