LITERATURE REVIEW
2.2 PREVIOUS WORKS ON RESERVOIR OPERATION
2.2.5 MODEL USED AS COMBINATION OF TWO OR MORE METHODS
associated with firm power and water targets of different magnitudes. The models were applied to Shasta-Trinity system in northern California and performance of each model was studied by simulation. For an objective with only moderate water and power targets, there was little difference among performance of policies that used different hydrologic state variables.
Talukdar (1999) developed a SDP model for optimal operation of multipurpose Sardar Sarovar Reservoir of Narmada River, India.
2.2.5 MODEL USED AS COMBINATION OF TWO OR MORE
waters in the reservoirs resulting from any release policy. In the DP model, the energy constraint parameter was considered as single decision variable, the cumulative sum of the energy constraint parameter as the single state variable and maximization of the weighted sum of the reservoir end of month storages as the objective function.
Takeuchi and Moreau (1974) used a combination of LP with SDP models. In this model the objective function consists of two parts: immediate economic losses within the month and the expected present value of future losses as a function of end-of -storage levels in the reservoirs. The latter function is estimated by embedding the linear programming problem in a SDP problem.
Becker et al. (1976) used the same monthly model of Becker and Yeh (1974) and developed daily and hourly model for the CVP system. The monthly model output was used as an input to the daily model and the output of the daily model was used as an input to the hourly model.
Chaturvedi and Srivastava (1981) analyzed six major reservoirs of Narmada basin in India using deterministic LP with simulation model.
Palmer et al. (1982) developed simulation and LP models to determine the yield of the reservoir system when operated jointly with the Potomac River.
Marino and Mohammadi (1983) developed a methodology for the monthly operation of a system of two parallel multipurpose reservoirs. The model employed linear programming (LP) nested in dynamic programming (DP). At every stage of DP (i.e. months) a series of LP’s are solved. The objective of LP is to minimize the total releases from the reservoir in each month. The objective of DP is to maximize the weighted sum of monthly water and power production.
Mohammadi and Marino (1984) developed one efficient algorithm for the real time monthly operation of a multipurpose reservoir. The model is a combination of LP (used for month by month optimization) and DP (used for annual optimization).
Kuo et al. (1990) developed a model for real time operation of Feitsui and Shihmen Reservoirs in the Tanshui River Basin, Taiwan. The model consists of a 10-day streamflow forecast model, a rule curve based simulation model and a DP optimization model. After getting an initial feasible operating policy by using the simulation model, the DP based optimization model was then used to determine an improved operating policy.
Vedula and Mohan (1990) developed a real time operational methodology for the Bhadra reservoir in the state of Karnataka (India). The algorithm have three phases of operation. The first phase determines the optimal release policy for a given initial storage and inflow using SDP. Second phase constitute the flow forecasting using ARIMA model and in the last phase a real time simulation model was developed. In the SDP model, the inflows were assumed to follow a discrete Markov process.
Jain et al. (1992) developed a model based on SDP formulation, which considers risk explicitly. The objective of the model was to maximize the reservoir storage at the end of flood season while ensuring that the risk of the overflow is within acceptable limit. Current period storage of the reservoir and an information variable, which can be used to determine the probabilistic properties of future inflows, was taken as state variables of the DP formulation. The model was applied to the Dharoi multipurpose reservoir of the Sabarmati River in Gujarat (India) and its performance was tested by simulation.
Vedula and Nagesh Kumar (1996) developed an integrated model to determine the optimal reservoir release policies and irrigation allocation to multiple crops. The model used LP-SDP combination as module 1 and module 2 respectively.
Module 1 was an intraseasonal allocation model to maximize the sum of relative yields of all crops for a given state of the system using LP. Module 2 was a seasonal allocation model to derive operating policy using SDP. Reservoir storage, seasonal inflow, and seasonal rainfall were the three variables, which maximizes the expected sum of relative yield of all crops in a year. Seasonal inflow and seasonal rainfall were each assumed to constitute a stationary Markov process.
Ravi Kumar and Venugopal (1998) developed an integrated real time operation method for a large-scale south Indian irrigation system by using simulation and SDP. The irrigation demand pattern was determined by simulating the command area with historical data. The SDP was used to obtain an optimal release policy. The SDP model considered both the demand and inflow as stochastic and is assumed to follow first order Markov chain model. Finally another simulation model was used to study the degree of failure associated with adoption of the optimal operating policy for different reservoir storages at the start of the crop season.