Date of Award

2019

Publication Type

Doctoral Thesis

Degree Name

Ph.D.

Department

Civil and Environmental Engineering

First Advisor

Tirupati Bolisetti

Second Advisor

Ram Balachandar

Keywords

Climate change adaptation, Climate change impacts, Hydrological modeling, Hydropower, Multi-objective optimization, Uncertainty assessment

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

The climate change resulting from anthropogenic factors is driving governments and policy-makers to provide additional thrust on renewable energy. Hydropower, which is the dominant renewable component of the energy-mix, is also under threat due to the changing climate conditions. The present study aims to quantify the impact of climate change on hydropower generation, the associated revenues and subsequently suggest the adaptation measures through adaptive reservoir management. A modeling chain consisting of hydrologic and hydropower simulation models is adopted to evaluate the impacts of projected climate change on hydropower generation. Calibrated hydrologic models forced with the climate data from various climate models have been widely employed for future streamflow projection. A reliable modelling framework should ensure the simulation of reality with limited uncertainty, thus enhancing its predictive ability. In the literature, the hydrologic model assessment is reported to be inadequate when carried out based on only statistical objectives or limited number of evaluation metrics. In the present research, the thrust is given on improving the hydrologic model simulation through model diagnostic assessment, incorporating hydrologic signatures and multi-objective model calibration. Multi-objective evolutionary algorithm (MOEA) is coupled with the hydrologic model, Soil and Water Assessment Tool (SWAT), to perform model calibration. The methodology was first tested for Saugeen River watershed in Southern Ontario and then applied to the Magpie River watershed model located in Northern Ontario. The uncertainties contributed by the hydrologic models have generally been given a lesser focus in climate change impact analysis. In the present research, the uncertainty emanating from model parameters was investigated and found to dominate during some periods. The accounting of hydrologic model uncertainty is found to be vital for providing an improved assessment. Steephill Falls hydroelectric project located on Magpie River in Northern Ontario is considered as a case study for assessing climate change impacts on hydropower. The results show that the annual generation is not considerably affected but there is a significant seasonal redistribution on energy production. The changes in the hydropower revenues compared to the present level for the four seasons viz., winter, spring, summer and autumn are estimated to be 21.1%, 18.4%, -13.4% and -15.9%, respectively, for mid-century and 23.1%, 19.5%, -20.1% and -22.9% for end-century scenarios. In order to reduce the vulnerability of hydropower system to climate change and consequently mitigate the impacts, it will be profitable for the project owners to provide suitable adaptation measures. Adaptive reservoir management through multi-objective optimization of reservoir level was found to be an effective approach to develop adaptation measures provided additional live storage is made available. It also reduced the vulnerability of the system to climate change by 24%. The seasonal alteration in the energy production will require the project owners to arrange modification in power purchase/sharing agreement with the buyers.

Share

COinS