Reservoir Scheduling Research Articles

Modeling Evaporation-Seepage Losses for Reservoir Water Balance in Semi-arid Regions

In the water balance of reservoir system, evaporation plays a crucial role particularly so for the reservoir systems of smaller size located in the semi-arid or arid regions. Such regions are most often characterized by significant seepage losses from reservoirs, besides evaporation losses. Usually, in the optimization of a reservoir system, it is a common practice to assume evaporation loss either as some constant value or as negligible. Such assumptions, however, may affect the results of reservoir optimization. This is demonstrated in this study by a case study in the optimal scheduling of Pilavakkal reservoir system in Vaipar basin of Tamilnadu, India. For modeling reservoir losses, many models are available, of which, Penman combination model is most commonly used. In this study, an alternative approach based on Genetic Programming (GP) is proposed. The results of GP and Penman model for both evaporation loss estimation and reservoir scheduling are compared. It is found that while GP and Penman combination model performs equally well for estimating evaporation losses, GP is also able to model seepage losses (or other losses from reservoir) to a much better degree. It is also shown the reservoir scheduling does get influenced based on how the reservoir losses are modeled in the reservoir water balance equation.

Development of a stochastic optimization for multireservoir scheduling

Seasonal reservoir scheduling for multireservoir hydrothermal power systems is a practical, nonlinear, stochastic problem of high dimension. Extension of an existing deterministic algorithm to handle stochastic aspects is desirable. A linear feedback rule, but with chance constraints on lower limits only, gives some promising results. Exact solution by stochastic dynamic programming is possible only for a one-reservoir model. This result is compared to the deterministic and linear feedback solutions.

A decomposition method for the long‐term scheduling of reservoirs in series

This paper presents a method for determining the weekly operating policy of a power system of n reservoirs in series; the method takes into account the stochasticity of the river flows. The method consists of rewriting the stochastic nonlinear optimization problem of n state variables as n −1 problems of two state variables which are solved by dynamic programing. The release policy obtained with this method for reservoir i is a function of the water content of that reservoir and of the total amount of potential energy stored in the downstream reservoirs. The method is applied to a power system of four reservoirs, and the results obtained are compared to the true optimum.

Application of Dynamic Team Theory to the Scheduling of Reservoirs in the Presence of Random Inflows

The management of water in a multiple reservoir system constitutes an important element of the operational scheduling problem in electric power systems. The presence of random inflows and system nonlinearities would require stochastic dynamic programming to provide an optimal feedback solution. However, the dimensionality of the hydro-scheduling problem rules out the direct use of stochastic dynamic programming, and leads to the consideration of decomposition methods requiring less computation. This paper uses concepts from dynamic team theory to characterize and develop stochastic spatial decomposition methods to systems of (a) parallel reservoirs, and (b) series reservoirs. These methods can be combined to generate the stochastic spatial decomposition algorithm for the general reservoir configuration. The computational features of these algorithms are discussed and numerical results will be presented at the conference.