Mujumdar, PP and Nirmala, B (2007) Bayesian Stochastic Optimization Model for a Multi Reservoir Hydropower System. In: Water Resources Management, 21 (9). pp. 1465-1485.
This paper presents the development of an operating policy model for a multireservoir system for hydropower generation by addressing forecast uncertainty along with inflow uncertainty. The stochastic optimization tool adopted is the Bayesian Stochastic Dynamic Programming (BSDP), which incorporates a Bayesian approach within the classical Stochastic Dynamic Programming (SDP) formulation. The BSDP model developed in this study considers, the storages of individual reservoirs at the beginning of period t, aggregate inflow to the system during period t and forecast for aggregate inflow to the system for the next time period t+1, as state variables. The randomness of the inflow is addressed through a posterior flow transition probability, and the uncertainty in flow forecasts is addressed through both the posterior flow transition probability and the predictive probability of forecasts. The system performance measure used in the BSDP model is the square of the deviation of the total power generated from the total firm power committed and the objective function is to minimize the expected value of the system performance measure. The model application is demonstrated through a case study of the Kalinadi Hydroelectric Project (KHEP) Stage I, in Karnataka state, India.
|Item Type:||Journal Article|
|Additional Information:||The copyright belongs to Springer Science|
|Keywords:||bayesian stochastic dynamic programming;reservoir operation;power generation;transition probability|
|Department/Centre:||Division of Mechanical Sciences > Civil Engineering|
|Date Deposited:||02 Nov 2007|
|Last Modified:||19 Sep 2010 04:40|
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