Geographic information system algorithms to locate prospective sites for pumped hydro energy storage
dc.contributor.author | Lu, Bin | en_AU |
dc.contributor.author | Stocks, Matthew | en_AU |
dc.contributor.author | Anderson, Kirsten | en_AU |
dc.contributor.author | Blakers, Andrew | en_AU |
dc.date.accessioned | 2018-04-12T05:20:51Z | |
dc.date.available | 2018-04-12T05:20:51Z | |
dc.date.created | 15/07/2018 | en_AU |
dc.description.abstract | Pumped hydro energy storage is capable of large-scale energy time shifting and a range of ancillary services, which can facilitate high levels of photovoltaics and wind integration in electricity grids. This study aims to develop a series of advanced Geographic Information System algorithms to locate prospective sites for off-river pumped hydro across a large land area such as a state or a country. Two typical types of sites, dry-gully and turkey’s nest, are modelled and a sequence of Geographic Information System-based procedures are developed for an automated site search. A case study is conducted for South Australia, where 168 dry-gully sites and 22 turkey’s nest sites have been identified with a total water storage capacity of 441 gigalitres, equivalent to 276 gigawatt-hours of energy storage. This demonstrates the site searching algorithms can work efficiently in the identification of off-river pumped hydro sites, allowing high-resolution assessments of pumped hydro energy storage to be quickly conducted on a broad scale. The sensitivity analysis shows the significant influences of maximum dam wall heights on the number of sites and the total storage capacity. It is noted that the novel models developed in this study are also applicable to the deployments of other types of pumped hydro such as the locations of dry-gully and turkey’s nest sites adjacent to existing water bodies, old mining pits and oceans. | en_AU |
dc.format.extent | 13 pages | en_AU |
dc.format.mimetype | application/pdf | en_AU |
dc.identifier.issn | 3062619 | en_AU |
dc.identifier.uri | http://hdl.handle.net/1885/142560 | en_AU |
dc.language.iso | en_AU | en_AU |
dc.publisher | Elsevier | en_AU |
dc.rights | Author/s retain copyright | en_AU |
dc.rights.license | Under a Creative Commons license | en_AU |
dc.source | Applied Energy | en_AU |
dc.subject | Geographic information system; Energy storage; Pumped hydro | en_AU |
dc.title | Geographic information system algorithms to locate prospective sites for pumped hydro energy storage | en_AU |
dc.type | Journal article | en_AU |
dcterms.accessRights | Open Access | en_AU |
local.bibliographicCitation.lastpage | 312 | en_AU |
local.bibliographicCitation.startpage | 300 | en_AU |
local.contributor.affiliation | College of Engineering & Computer Science, The Australian National University | en_AU |
local.contributor.authoremail | bin.lu@anu.edu.au | en_AU |
local.identifier.ariespublication | u4485658xPUB1831 | |
local.identifier.citationvolume | 222 | en_AU |
local.identifier.doi | 10.1016/j.apenergy.2018.03.177 | en_AU |
local.identifier.uidSubmittedBy | u1027010 | en_AU |
local.type.status | Published Version | en_AU |