Climate Change and Its Effects on the Energy-Water Nexus
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| Language: | English |
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The Ohio State University / OhioLINK
2018
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=osu1534307556870925 |
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NDLTD |
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English |
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NDLTD |
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Environmental Science precipitation statistical downscaling energy-water nexus electricity load forecasting hydrological modeling coal-fired power plants United States Asia |
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Environmental Science precipitation statistical downscaling energy-water nexus electricity load forecasting hydrological modeling coal-fired power plants United States Asia Wang, Yaoping Climate Change and Its Effects on the Energy-Water Nexus |
| author |
Wang, Yaoping |
| author_facet |
Wang, Yaoping |
| author_sort |
Wang, Yaoping |
| title |
Climate Change and Its Effects on the Energy-Water Nexus |
| title_short |
Climate Change and Its Effects on the Energy-Water Nexus |
| title_full |
Climate Change and Its Effects on the Energy-Water Nexus |
| title_fullStr |
Climate Change and Its Effects on the Energy-Water Nexus |
| title_full_unstemmed |
Climate Change and Its Effects on the Energy-Water Nexus |
| title_sort |
climate change and its effects on the energy-water nexus |
| publisher |
The Ohio State University / OhioLINK |
| publishDate |
2018 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1534307556870925 |
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AT wangyaoping climatechangeanditseffectsontheenergywaternexus |
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1719454518453731328 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-osu15343075568709252021-08-03T07:08:05Z Climate Change and Its Effects on the Energy-Water Nexus Wang, Yaoping Environmental Science precipitation statistical downscaling energy-water nexus electricity load forecasting hydrological modeling coal-fired power plants United States Asia Energy and water are two essential resources that are inter-connected and vulnerable to climate change. The procedure of assessing the impacts of climate change on the Energy-Water nexus usually involves first bias-correcting and downscaling the outputs of global climate models to higher spatio-temporal resolutions, and then using the outputs to drive impact models. Through a combination of data, statistical modeling, and process modeling approaches, this study investigated a few aspects of this impacts assessment procedure, namely the (1) the robustness of statistical downscaling methods that reconcile the scale difference between global climate models and inputs to impact models in application under climate change; (2) the response of electricity demand to climate variables; (3) the impact of climate change on the water availability for electricity generation through hydrological changes. In the first part of this study, we found that two popular statistical downscaling methods, quantile-mapping and the generalized linear model method Rglimclim, violated their stationarity assumption, i.e., are not robust, when they are applied to downscale precipitation in the eastern United States. This violation of the stationarity assumption is best identified when several different sets of cross-validation periods are used, instead of one set. The results highlighted the need to develop statistical downscaling methods that can be reliably applied climate change conditions, and also suggested a need for more research into how to choose cross-validation periods and stationarity metrics in in order to maximize their relevance to the reliability of statistical downscaling methods under future climate change. In the second part of this study, we developed and applied a segmented regression technique to hourly electricity load data to estimate weather-electricity relationships in the eastern United States. The empirical results showed that the reference temperatures for cooling- and heating-degree hours differ from each other for every hour of the day and vary in accordance with the ambient temperature. We also identified the existence of threshold temperatures for the effect of relative humidity. These results suggested that electricity load forecasts and long-term projections should have a ~7oC “comfort zone” in their reference temperatures, and include the effects of relative humidity. In the third part of this study, we applied a hydro-climatic modeling chain to assess the impacts of 1.5oC, 2oC, and 3oC climate change on coal-fired power plants and their capacity expansion in Asia. We found that climate change can be expected to increase the vulnerability of the power plants due to decreased streamflow in southeastern China and Southeast Asia, but decrease that vulnerability elsewhere. The vulnerability should increase with the incorporation of planned coal-fired power plants in most of the study region, because they are planned for locations with high water-scarcity and increased competition for water, with reductions in average potential available capacity factor of 74% in Mongolia, and 20-40% in Vietnam, and parts of India and China. Using cooling systems with higher water-use efficiencies should decrease the vulnerability to climate change, but the energy penalty of dry cooling may prevent its deployment in South and Southeast Asia. These results suggest that reducing planned capacity expansion and retiring existing plants so as to shift the overall fleet to locations with more water availability, and to reduce the competition between the plants, could reduce the vulnerability of coal-fired power plants to climate change in Asia.With these findings, we concluded that climate change and the associated hydrological changes will alter the supply and demand relationship in the electricity system, so that short- and long-term energy planning can improve the performance of the system under climate change by accounting for the linkages between the climate and energy-water nexus in their models. Potential future works include connecting the modeling frameworks and results of this study with existing energy planning models, developing of multivariate bias correction and downscaling methods that are consistent with the physical causes of bias in climate models, and developing performance-based model weighting methods that are suited to the needs of energy planning. 2018 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1534307556870925 http://rave.ohiolink.edu/etdc/view?acc_num=osu1534307556870925 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |