Comparison of two approaches for implementing multireservoir operating policies derived using stochastic dynamic programming

Abstract

This paper compares two approaches for implementing reservoir operating policies derived using stochastic dynamic programming (SDP) models. In particular, operating policies for the Shasta‐Trinity system in Northern California are generated using SDP algorithms that employ either multilinear or multidimensional piecewise cubic functions to approximate the cost‐to‐go function. Release decisions in the simulations are then determined by either (1) interpolating in the policy tables or (2) reoptimizing the policy within the simulation, using the cost‐to‐go function generated by the SDP. The impact on simulated system performance of several discretization and interpolation schemes in the SDP is also evaluated. Reoptimizing the policy when a decision is made within the simulation resulted in better system performance, particularly when severe penalties were incurred for water and power shortages and coarse discretizations were employed in the SDP.