Two dynamic programming models — one deterministic and one stochastic — that may be used to generate reservoir operating rules are compared. The deterministic model (DPR) consists of an algorithm that cycles through three components: a dynamic program, a regression analysis, and a simulation. In this model, the correlation between the general operating rules, defined by the regression analysis and evaluated in the simulation, and the optimal deterministic operation defined by the dynamic program is increased through an iterative process. The stochastic dynamic program (SDP) describes streamflows with a discrete lag‐one Markov process. To test the usefulness of both models in generating reservoir operating rules, real‐time reservoir operation simulation models are constructed for three hydrologically different sites. The rules generated by DPR and SDP are then applied in the operation simulation model and their performance is evaluated. For the test cases, the DPR generated rules are more effective in the operation of medium to very large reservoirs and the SDP generated rules are more effective for the operation of small reservoirs.

The development of general reservoir system operating rules by deterministic optimization is investigated in this paper. An algorithm that cycles through a deterministic dynamic program, a regression analysis, and a simulation model is proposed and tested for 48 cases: annual operating rules are determined for 12 cases, and monthly operating rules are determined for 36 cases. The algorithm is easy to use, and each component of the algorithm is relatively simple. The results of using the algorithm for the 48 cases demonstrate the significant value of the algorithm in selecting reservoir operating rules.

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