  This study explores the ways to introduce physical interpretability into the
process of optimizing operating rules for multireservoir systems with multiple
objectives. Prior studies applied the concept of direct policy search (DPS), in
which the release policy is expressed as a set of parameterized functions
(e.g., neural networks) that are optimized by simulating the performance of
different parameter value combinations over a testing period. The problem with
this approach is that the operators generally avoid adopting such artificial
black-box functions for the direct real-time control of their systems,
preferring simpler tools with a clear connection to the system physics. This
study addresses this mismatch by replacing the black-box functions in DPS with
physically based parameterized operating rules, for example by directly using
target levels in dams as decision variables. This leads to results that are
physically interpretable and may be more acceptable to operators. The
methodology proposed in this work is applied to a network of five reservoirs
and four power plants in the Nechi catchment in Colombia, with four interests
involved: average energy generation, firm energy generation, flood hazard, and
flow regime alteration. The release policy is expressed depending on only 12
parameters, which significantly reduces the computational complexity compared
to existing approaches of multiobjective DPS. The resulting four-dimensional
Pareto-approximate set offers a variety of operational strategies from which
operators may choose one that corresponds best to their preferences. For
demonstration purposes, one particular optimized policy is selected and its
parameter values are analyzed to illustrate how the physically based operating
rules can be directly interpreted by the operators.
