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http://dx.doi.org/10.3741/JKWRA.2022.55.7.515

Evaluation of hydropower dam water supply capacity (II): estimation of water supply yield range of hydropower dams considering probabilistic inflow  

Jeong, Gimoon (Korea Rural Community Corporation, Rural Research Institute)
Kang, Doosun (Department of Civil Engineering, Kyung Hee University)
Kim, Dong Hyun (Department of Civil Engineering, Hongik University)
Lee, Seung Oh (Department of Civil Engineering, Hongik University)
Kim, Taesoon (Hangang Hydro Power Site, Korea Hydro & Nuclear Power Co., LTD.)
Publication Information
Journal of Korea Water Resources Association / v.55, no.7, 2022 , pp. 515-529 More about this Journal
Abstract
Identifying the available water resources amount is an essential process in establishing a sustainable water resources management plan. Dam facility is a major infrastructure storing and supplying water during the dry season, and the water supply yield of the dam varies depending on dam inflow conditions or operation rule. In South Korea, water supply yield of dam is calculated by reservoir simulation based on observed historical dam inflow data. However, the water supply capacity of a dam can be underestimated or overestimated depending on the existence of historical drought events during the simulation period. In this study, probabilistic inflow data was generated and used to estimate the appropriate range of the water supply yield of hydropower dams. That is, a method for estimating the probabilistic dam inflow that fluctuates according to climatic and socio-economic conditions and the range of water supply yield for hydropower dams was presented, and applied to hydropower dams located in the Han river in South Korea. It is expected that the understanding water supply yield of the hydropower dams will become more important to respond to climate change in the future, and this study will contribute to national water resources management planning by providing potential range of water supply yield of hydropower dams.
Keywords
Climate change; Hydropower dam; Probabilistic dam inflow; Reservoir simulation; Water supply yield;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Choi, J., Jeong, G., Kang, D., Ahn, J., and Kim, T. (2021). "Classification of hydropower dam in North-han River based on water storage characteristics." Journal of Korea Water Resources Association, Vol. 54, No. 8, pp. 567-576.   DOI
2 Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp. 1735-1780.   DOI
3 Luo, B., Fang, Y., Wang, H., and Zang, D. (2020). "Reservoir inflow prediction using a hybrid model based on deep learning." IOP Conference Series: Materials Science and Engineering, IOP Publishing, Shanghai, China, Vol. 715, No. 1, 012044.
4 Ministry of Construction and Traffic (MOCT) and K-water (1997). Reevaluation of existing dams (Han river basin).
5 Ministry of Land, Infrastructure and Transport (MOLIT) and K-water (2016). Efficient utilization of existing water resources.
6 Tran, T.D., Tran, V.N., and Kim, J. (2021). "Improving the accuracy of dam inflow predictions using a long short-term memory network coupled with wavelet transform and predictor selection." Mathematics, Vol. 9, No. 5, 551.   DOI
7 US. Army Corps of Engineers (USACE) (2021). HEC-ResSim: Reservoir system simulation user's manual version 3.3. US Army Corps of Engineers Institute for Water Resources Hydrologic Engineering Center (HEC). CPD-82, Davis, CA, U.S.
8 Zhang, Z., Zhu, Y., Zhang, X., Ye, M., and Yang, J. (2018). "Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural area." Journal of Hydrology, Vol. 561, pp. 918-929.   DOI
9 Ministry of Construction and Traffic (MOCT) and K-water (2011). The national river basin investigation 2011.
10 Chen, W.B., Liu, W.C., and Hsu, M.H. (2012). "Comparison of ANN approach with 2D and 3D hydrodynamic models for simulating estuary water stage." Advances in Engineering Software, Vol. 45, No. 1, pp. 69-79.   DOI
11 Tran, Q.K., and Song, S.K., (2017). "Water level forecasting based on deep learning: A use case of Trinity River-Texas-The United States." Journal of Korean Institute of Information Scientists and Engineers, Vol. 44, No. 6, pp. 607-612.
12 Ivanov, V.Y., Bras, R.L., and Curtis, D.C. (2007). "A weather generator for hydrological, ecological, and agricultural applications." Water Resources Research, Vol. 43, No. 10, W10406.   DOI
13 Ministry of Construction and Traffic (MOCT) and K-water (2010). Reevaluation of existing dams and optimum allocation of the capacity.
14 Yeo, W.K, Seo, Y.M, Lee, S.Y, and Jee, H.K (2010) "Study on water stage prediction using hybrid model of artificial neural network and genetic algorithm." Journal of Korea Water Resources Association, Vol. 43, No. 8, pp. 721-731.   DOI