THE VALUE OF HYDROLOGIC INFORMATION IN STOCHASTIC DYNAMIC-PROGRAMMINGMODELS OF A MULTIRESERVOIR SYSTEM

Reservoir operating policies can be derived using stochastic dynamic p rogramming (SDP) with different hydrologic state variables. This paper considers several choices for such hydrologic state variables for SDP models of the Shasta-Trinity system in northern California, for three different benefit functions. We compare how well SDP models predict t heir policies will perform, as well as how well these policies perform ed when simulated. For a benefit function stressing energy maximizatio n, all policies did nearly as well, and the choice of the hydrologic s tate variable mattered very little. For a benefit function with larger water and firm power targets and severe penalties on corresponding sh ortages, predicted performance significantly overestimated simulated p erformance, and policies that employed more complete hydrologic inform ation performed significantly better.