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dc.contributor.advisorBruce, Tomen
dc.contributor.advisorHarrison, Garethen
dc.contributor.advisorGreated, Cliveen
dc.contributor.authorWalker, Antony Daviden
dc.date.accessioned2018-11-01T13:45:38Z
dc.date.available2018-11-01T13:45:38Z
dc.date.issued2018-11-29
dc.identifier.urihttp://hdl.handle.net/1842/33199
dc.description.abstractThere is overwhelming scientific evidence that shows the temperature of the Earth’s atmosphere is rising at an unprecedented rate. This is attributed to increased levels of greenhouse gas emissions, a large proportion of which originates from anthropogenic combustion of carbon-based fossil fuels for energy. There is therefore a strong argument for the increased role of less environmentally damaging, low carbon energy sources including renewable energy technologies. Run-of-river hydropower is one such renewable energy option, considered more environmentally benign than traditional hydropower which requires the construction of large dams to create a reservoir. The aim of this study was to develop a model to search for, and map, economically viable run-of-river hydropower resource that can function on any global catchment of any size. Development and testing of the model was conducted on China’s 2 million km2 Yangtze River drainage basin, the third longest river in the world and a rich landscape for hydropower. A gridded, distributed hydrological model was developed integrating high-resolution meteorological datasets and a digital elevation model (DEM). Using the model, the surface hydrology of the Yangtze catchment was simulated at a timestep of 6 minutes to obtain the mean daily surface runoff for every day from the beginning of 1979 to the end of 2007. Observed river flow data from sub-catchments of the Yangtze were used to calibrate the model by differential optimisation, an evolutionary computation technique. Validation was carried out on a 1.6 million km2 sub-catchment resulting in a mean objective function of 0.95 (where a perfect fit would be 1.0) across 8 objective functions commonly used in hydrology. Catchment wide mean daily runoff data was used to develop flow duration curves across the catchment river network. Virtual power stations were constructed at each river cell, iteratively testing differing scheme configurations, and costed using the RETScreen methodology. A best performing hydropower network was determined by a conflict algorithm, designed to prioritise high profit schemes and to remove lower performing and conflicting schemes. This resulted in a potential run-of-river installed capacity across the Yangtze catchment of 103GW (at 10% discount rate), generating 394TWh per annum. This model would be a valuable tool in finding optimal locations for future hydropower resource.en
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.subjectrun-of-river hydropoweren
dc.subjectrenewable energyen
dc.subjectYangtze Riveren
dc.subjecthydrological modelen
dc.subjectdigital elevation modelen
dc.subjectRETScreen methodologyen
dc.titleMapping run-of-river hydropower resource of large catchmentsen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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