Artificial neural network and Monte Carlo optimization for reservoir operation

Determining the optimal operation of a reservoir system is frequently hampered by uncertainty associated with future inflows. Generalized operating policies are a potentially fast and easy to use means of real time operation. They use readily available predictors (i.e., current reservoir storage and short term predicted inflow) calibrated against the optimal response of the system. Artificial neural networks represent the most recent attempt to improve generalized reservoir release models. This study builds on prior research in water resources planning by investigating the incorporation of time lagged inputs of inflow and demand to improve the performance of a generalized neural network reservoir release model. This study differs from much of the previous work on generalized operating rules in that Monte Carlo optimization is used. Previous research has relied primarily on deterministic data, but here the Monte Carlo element generates reservoir inflow synthetically. The nonlinear objective function is to minimize the sum of square deficits and maximize hydropower production. Dynamic Programming has been the primary optimization tool in generalized operating rule research. In this study however, a nonlinear programming (NLP) model is developed and applied to a series of hypothetical water resource systems. The reduced gradient algorithm is used for the solution of the NLP. The Monte Carlo optimization methodology allows a virtually unlimited pool of calibration and validation data to be rapidly derived for a variety of reservoir configurations. The performance of the neural network model relative to the nonlinear programming solution is compared over a range of reservoir storage, demand-deficit, and streamflow correlation structures in order to investigate its applicability. The results show that a significant improvement in the performance of generalized reservoir release neural network can be achieved using time lagged inputs of inflow and demand. Furthermore, the ability of a generalized neural network reservoir release model to incorporate time series information is substantiated.