Reliability Analysis for Optimization of Galma Dam Reservoir Operations Using an Explicit Stochastic Model
Downloads
Effective handling of water resources in dams is important for guaranteeing consistent water supply, flood management, and food safety. This research introduces a method for assessing the dependability of Galma's dam reservoir operations by utilizing a specific stochastic modeling technique. The stochastic optimization model includes the natural uncertainties in hydrological inflows, water demands, and operating conditions. The stochastic optimization model is created by selecting the probability distribution of two parameters from the Weilbull distribution, with a scale factor of 25.3522 and a shape factor of 0.55190. This model is converted into a chance-constrained programming problem to determine the yield for a specified demand. The input discharge levels were optimized to achieve the Maximum-Minimum yields at reliability levels of 25%, 50%, 75% and 85%. The highest amount of water produced was 669.890 Mega Cubic Meters (MCM) with an 85% assurance level, while the lowest amount was 334.2666 MCM at a 25% confidence level. This demonstrates the need to find the best operational strategies that consider trade-offs between different goals and guarantee a specific level of system dependability. The results of this research offer important information for water resource managers and decision-makers to create resilient and sustainable reservoir management plans, especially amidst growing uncertainties from climate change and other environmental influences.
Aghakouchak, A. (2015). Recognize anthropogenic drought. Nature; Vol. 524(766), pp: 409-411.
Al-Aghbari, M., & Gujarathi, A. M. (2022). Stochastic multi-objective optimization approaches in a real-world oil field waterflood management. Journal of Petroleum Science and Engineering, 218(July), 110920.
https://doi.org/10.1016/j.petrol.2022.110920
Benson, R. (2016). Reviewing Reservoir operations: Can Federal water projects adapt to change. Columbia Journal of Environmental Law; Vol. 42, pp: 353.
Bertoni, F., Castelleti, A., Giuliani, M., and Reed, P. M. (2019). Discovering dependencies, trade-offs, and robustness in joint dam design and operations: An ex-post assessment of the Kariba Dam. Earth’s Future; Vol. 7(12), pp: 1367-1390.
Billington, D., and Jackson, D. (2017). Big dams of the new deal era: confluence of engineering and politics. University of Oklahoma Press.
Celeste, A. B., & Billib, M. (2009). Evaluation of stochastic reservoir operation optimization models. Advances in Water Resources, 32(9), 1429–1443. https://doi.org/10.1016/j.advwatres.2009.06.008
Chen, C., Feng, S., Liu, S., Zheng, H., Zhang, H., & Wang, J. (2023). A stochastic linear programming model for maximizing generation and firm output at a reliability in long-term hydropower reservoir operation. Journal of Hydrology, 618(June 2021), 129185. https://doi.org/10.1016/j.jhydrol.2023.129185
De Queiroz, A. R. (2016). Stochastic hydro-thermal scheduling optimization: An overview. Renewable and Sustainable Energy Reviews, 62, 382–395. https://doi.org/10.1016/j.rser.2016.04.065
Farias, C. A. S., Machado, E. C. M., and Brasiliano, L. N. (2016). Monthly reservoir operating rules generated by implicit stochastic optimization and self-organizing maps. Sustainable Hydraulics in the Era of Global Change: Proc. of the 4th IAHR Europe Congress. CRC Press; pp. 138-144.
Gauvin, C., Delage, E., & Gendreau, M. (2018). A stochastic program with time series and affine decision rules for the reservoir management problem. European Journal of Operational Research, 267(2), 716–732.
https://doi.org/10.1016/j.ejor.2017.12.007
Gomes, M. G., Maia, A. G., and Medeiros, J. D. F. (2022). Reservoir operation rule in semiarid areas: The quantity-quality approach. Journal of Hydrology; Vol. 610(n), pp: 127944.
Karamouz, M., Ahmadi, A., & Moridi, A. (2009). Probabilistic reservoir operation using Bayesian stochastic model and support vector machine. Advances in Water Resources, 32(11), 1588–1600. https://doi.org/10.1016/j.advwatres.2009.08.003
Loucks, D. P., & van Beek, E. (2005). Optimal operation of multi-reservoir systems using dynamic programming. Water Resources Research, 41(6).
Lund, J. R. (2008). Optimization modelling in water Resource Systems and Markets. Expo Zara Goza; Water Economics and Financing. pp: 2-11.
Macian-Sorribes, H. (2017). Design of Optimal Reservoir Operating Rules in Large Water Resources Systems Combining Stochastic Programming, Fuzzy Logic and Expert Criteria. PhD Thesis (Published) Submitted to the Universitat Poletecnica De Valencia, Italy; May, 2017.
Mallakpour, I., Aghakouchak, A., and Sadegh, M. (2019). Climate induced changes in the risk of hydrological failure of dams in California. Geophysical Research Letters; Vol. 46(4), pp: 2130-2139.
Moeini, R., & Hadiyan, P. P. (2022). Hybrid methods for reservoir operation rule curve determination considering uncertain future condition. Sustainable Computing: Informatics and Systems, 35(November 2020), 100727. https://doi.org/10.1016/j.suscom.2022.100727
Mora, C., Spirandelli, D., Franklin, E., Lynham, J., Kantar, M., and Miles, W. (2018). Broad threat to humanity from cumulative climate hazards intensified by greenhouse emissions. Nature climate change; Vol. 8(12), pp: 1062-1071.
Nandalal, K., and Bogardi, J. (2007). Dynamic programming based operation of reservoirs: Applicability and limits. Cambridge University Press. National Research Council. (2005). Getting up to speed: The future of supercomputing. National Academies Press. https://doi.org/10.17226/11148
Padowski, J., Gorelick, S., Thompson, B., Rozelle, S., and Fendor, S. (2015). Assessment of human-natural system characteristics influencing global freshwater supply vulnerability, Environmental Research Letters. Vol. 10(10), 104014, pp: 1-26.
Pinheiro, A. R.., Farias, C. A., and Guimaraes Santos, C. A. (2022). Implicit Stochastic Optimization and Random Forest for Monthly Reservoir Operation. XXX Congreso Latino Americano De Hidraulica, 07, November, 2022.
Poff, N. L., Brown, C. M., Graham, T. E., Matthews, J. H., Palmer, M. A., and Spence, C. M. (2015). Sustainable water management under future uncertainty with eco-engineering decision scaling. Nature climate change; https/doi.org/10.1038/nclimate 2765.
Wada, Y., Bierkens, M., De Roo, A., Dirmeyer, P., Famiglietti, J., and Hanasaki, N. (2017). Human water interface in hydrological modelling: current and future directions. Hydrology and Earth Sciences, Discussions; pp: 1-39.
Wild, T., Reed, P., Loucks, D., Mallen-Cooper, M., and Jensen, E. (2019). Balancing Hydropower Development and Ecological Impacts in the Mekong: Trade-offs for Samber Mega Dam. Journal of Water Resources Planning and Management; Vol. 145(2), 05018019, https://doi.org/10.1061/(asce)wr. 1943-5452.0001036.
Yang, S., Yang, D., Chen, J., & Zhao, B. (2019). Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model. Journal of Hydrology, 579(September), 124229.
https://doi.org/10.1016/j.jhydrol.2019.124229
Zarfl. C., Lumsdon, A., Berllekamp, J., Tydecks, L., and Tochner, K. (2015). A global boom in hydropower dam construction. Aquatic Sciences; Vol. 77(1), pp: 161-170.
Zeinabady, D., Tabasinejad, F., & Clarkson, C. R. (2023). A stochastic method to optimize flowback DFIT (“DFIT-FBA”) test design in tight reservoirs. Gas Science and Engineering, 110(July 2022), 204874. https://doi.org/10.1016/j.jgsce.2023.204874