Management Optimization of Saguling Reservoir with Bellman Dynamic Programming and “Du Couloir” Iterative Method

Mariana Marselina, Arwin Sabar, Indah R S Salami, Dyah Marganingrum



The increasingly growth of population and industry sector have lead to an enhanced demand for electrical energy. One of the electricity providers in the area of Java-Madura Bali (Jamali) is Saguling Reservoir. Saguling Reservoir is one of the three reservoirs that stem the flow of Citarum River in advance of to Jatiluhur and Cirata Reservoir. The average electricity production of Saguling Reservoir was 2,334,318.138 MWh/year in the period of 1986-2014. The water intake of Saguling Reservoir is the upstream Citarum Watershed with an area of 2340.88 km2 which also serves as the irrigation, inland fisheries, recreation, and other activities. An effort to improve the function of Saguling Reservoir in producing electrical energy is by optimizing the reservoir management. The optimization of Saguling Reservoir management in this study refers to Government Regulation No. 37/2010 on Dam/Reservoir Article 44 which states that the system of reservoir management consisting of the operation system in dry years, normal years, and wet years. In this research, the determination of the trajectory guideline in Saguling operation was divided in dry, normal and wet years. Trajectory guideline was conducted based on the electricity price of turbine inflow that various in every month. The determination of the trajectory guideline in various electricity price was done by using Program Dynamic Bellman (PD Bellman) and “Du Couloir” iterative method which the objective to optimize the gain from electricity production. and “Du Couloir” iterative method was development of PD Bellman that can calculate the value of gain with a smaller discretization until 0,1 juta m3 effectively where PD Bellman just calculate until 10 million m3.  Smaller discretization can give maximum benefit from electricity production and the trajectory guideline will be closer to trajectory actual so optimization of Saguling operation will be achieved.


Bellman; volume discretization; time discretization; Du Couloir; reservoir optimization

Full Text:



Arwin. 1992. Modelisation des Resources en Eau et Leur Exploitation Energetique sur L’exemple du Bassin Superieur du Citarum en Indonesie. Dissertation, INPT Toulouse, France.

Thirrot., Arwin. 1991. Detailed Critical Numerical Study of Discretizing Effects in Optimizing Using Bellman’s Dynamic Programming. Method Computer Aided Engineering in Water Resources. 3:67-81.

Fayaed, Sabah S. 2013. Reservoir-System Simulation and Optimization Techniques. Stoch Environ Res Risk Assess, 27:1751-1772.

Heidari M, Chow VT, Kokotovic PV, Meredith DD. 1971. Discrete differential Dynamic Programming Approach to Water Resources Systems Optimization. Water Resour Res.7(2):273–82

Larson RE. 1968. State increment dynamic programming. New York: Elsevier Science.

Larson RE, Korsak AJ. 1970. Dynamic Programming Successive Approximations Technique with Convergence Proofs. Automatica. 6(2):245–52.

Muchlis. 2003. Analisis Kebutuhan Listrik di Indonesia 2003-2020. Perusahaan Listrik Negara Indonesia.

Santosa, P. B., Mitani, Y.. 2009. Geospatial Analysis And Turbidity Measurement For Monitoring Suspended Solid Of Hitotsuse Dam In Miyazaki Prefecture, Kyushu, Japan. Forum Geografi. 29(2), pp: 153 - 164.

Trott WJ, Yeh WWG. 1973. Optimization of Multiple Reservoir System. J Hydraul Eng Div;99(10):1865–84.

Article Level Metrics


  • There are currently no refbacks.