Stochastic analysis for prediction of future performance of Mosul storage

Main Article Content

Nassrin J.H. Al-Mansori
Thair J.M. Al-Fatlawi
Nariman Y. Othman


Keywords : reservoir system, simulation, hydropower generation, reliability
Abstract
An investigation of the Mosul reservoir system within the Tigris river basin in Iraq was conducted to determine the ability of the system to generate hydroelectric power. A reproduction model utilizing the Simulink environment on the MATLAB platform was used to imitate the Mosul reservoir system. The reliability of the system under various future scenarios of data sources was also examined by employing a stochastic model used to create an inflow time series. The Thomas–Fiering model was chosen for this reason, which provided a wide range of data sources (inflows) to generate hydropower from the reservoir system under examination. Generally, the annual potential capacity of the Mosul basin for energy generation reaches 20,000 GW·h–1. Realizing that Iraq’s energy requirements are approximately 12 GW of power, and the integrating power production of the basin under examination is about 1.5 GW, this would cover around 12% of the total demand, which is significant.

Article Details

How to Cite
Al-Mansori, N. J., Al-Fatlawi, T. J., & Othman, N. Y. (2020). Stochastic analysis for prediction of future performance of Mosul storage. Scientific Review Engineering and Environmental Sciences (SREES), 29(2), 145–154. https://doi.org/10.22630/PNIKS.2020.29.2.13
References

Abdel-Hameed, M. (2003). Optimal control of dams using Pλ,τM policies and penalty cost. Mathematical and Computer Modeling, 38(11-13), 1119-1123. https://doi.org/10.1016/s0895-7177(03)90112-9

Al-Ageli, Y.H. (2009). A simulation model for a storage system on the river Dijma (master’s degree, Water Resources Division, Quantity of Engineering, University of Mosul, Mosul, Iraq).

Al-Gazzal, A.M.Y. (2002). Optimal water utilization from Tigris basin reservoirs north of Fatha for hydroelectric power generation (PhD thesis, University of Mosul, Mosul, Iraq).

Al-Mamousi, F. (2007). Evaluation of some disease developmental outcomes in generating expenses for seasonal and permanent rivers (master’s thesis, University of Mosul, Mosul, Iraq).

Al-Mohseen, K.A. (2008). A Mathematical Model for Azizya Evaporating Pond Operation Using SIMULINK Techniques. Al-Rafidain Engineering Journal, 16(3) 14-24. https://doi.org/10.33899/rengj.2008.44692

Anagnostopoulos, J.S. & Papantonis, D.E. (2008). Simulation and size optimization of a pumped–storage power plant for the recovery of wind-farms rejected energy. Renewable Energy, 33(7), 1685-1694. https://doi.org/10.1016/j.renene.2007.08.001

Bormann, H., Ahlhorn, F. & Klenke, T. (2012). Adaptation of water management to regional climate change in a coastal region - hydrological change vs. community perception and strategies. Journal of Hydrology, 454-455, 64-75. https://doi.org/10.1016/j.jhydrol.2012.05.063

Bormann, H. & Martinez, I.M.A. (2014). Towards an indicator based framework analyzing the suitability of existing dams for energy storage. Water Resource Management, 28(6), 1613-1630. https://doi.org/10.1007/s11269-014-0569-3

Buenoa, C. & Carta, J.A. (2006). Wind powered pumped hydro storage systems, a means of increasing the penetration of renewable Energy in the Canary Islands. Renewable and Sustainable Energy Reviews, 10(4), 312-340. https://doi.org/10.1016/j.rser.2004.09.005

Clarke, R.T. (1984). Mathematical models in hydrology. FAO Irrigation and Drainage Paper, 19, 54-59.

Ding, H., Hu Z. & Song, H. (2012). Stochastic optimization of the daily operation of wind farm and pumped-hydro-storage plant. Renewable Energy, 48, 571-578. https://doi.org/10.1016/j.renene.2012.06.008

Heuts, R.M.J. & Anderson, O.D. (1976). Time series analysis and forecasting: the Box-Jenkins approach. Journal of the Royal Statistical Society. Series D (The Statistician), 25(4), 310-314. https://doi.org/10.2307/2988092

Howlett, P., Piantadosi J. & Pearce, C. (2005). Analysis of a practical control policy for water storage in two connected dams. In V. Jeyakumar, A. Rubinov (ed.), Continuous optimization. Applied optimization. Vol. 99 (pp. 435-450). Boston, MA: Springer. https://doi.org/10.1007/0-387-26771-9_16

Karapetyan, S.S. & Mamikonyan, B.M. (2005). The measuring of dynamic parameters of functioning water storage dams. In Proceedings of 2005 International Conference Physics and Control. Saint Petersburg, Russia, 24-26.08.2005 (pp. 853-858). IEEE. https://doi.org/10.1109/phycon.2005.1514108

Kim, T. & Heo, J.H. Application of implicit stochastic optimization in the Han river basin. In Proceedings of the 4th International Conference on Hydro-Science & Engineering, Seoul, South Korea, 26-29.09.2000. Seoul: Korea Water Resources Associates.

Kottagoda, N.T. (1980). Linear stochastic models. In Stochastic water resources technology (pp. 111-127). New York: John Wiley and Sons. https://doi.org/10.1007/978-1-349-03467-3_4

MATLAB (2004). MATLAB manual. Natick, MA: MathWorks.

Naggar, O.M. (1999). Development of decision support systems in water resources (PhD thesis, University of Baghdad, Baghdad, Iraq).

Pereira, M.V.F., Oliveira, G.C., Costa, C.C.G. & Kelman, J. (1984). Stochastic streamflow models for hydroelectric systems. Water Resources Research, 20(3), 379-390. https://doi.org/10.1029/wr020i003p00379

Thomas, H.A. & Feiring, M.B. (1962). Mathematical synthesis of streamflow sequences for the analysis of river basins by simulation In A. Mass (ed.), Design of water resources systems (pp. 459-493). Cambridge: Harvard University Press. https://doi.org/10.4159/harvard.9780674421042.c15

Woodman, S., Hiden, H. & Watson, P. (2017). Applications of provenance in performance prediction and data storage optimization. Future Generation Computer Systems, 75, 299-309. https://doi.org/10.1016/j.future.2017.01.003

Statistics

Downloads

Download data is not yet available.
Recommend Articles
Most read articles by the same author(s)