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Show Specifically the features modeled are : ( 1) Operation of all or selected reservoirs for some/ all of the purposes indicated earlier. ( 2) Deterrnining the optimal levels of irrigation and M& I demands with a set of specified reliabilities. ( 3) Production of hydropower as firm energy or as dump energy from surplus water. ( 4) Determination of the conservation, active and flood control storage capacities for each reservoir ( including the option of not building a reservoir). ( 5) Reservoir operation at a monthly level of detail. ( 6) Accommodation of relationships between various levels of yield. It is possible to model a structure where both the magnitudes of excess/ shortages in supply can be modeled with a specified reliability ( relative frequency of occurrence). ( 7) Determination of the optimal size of the hydropower plant at each reservoir. ( 8) Diversion of a fraction of the streamflow from a point on a stream to an off- stream reservoir. ( 9) Generality and flexibility in formulation, and limited data requirement. The model formulation presented differs from those in the literature by not including month by month decision and state variables ( e. g. release and storage), and monthly mass balance constraints for the reservoirs as part of the optimization model. Instead these variables and their associated constraints are implicitly enforced by specifying ' reliable' yields as decision variables, and solving directly for the storage capacity required using a modified sequent peak algorithm. A review of the existing literature on optimization models for reservoir screening and reservoir system capacity expansion indicated that most existing models would lead to an inordinately large number of decision variables and constraints with a practical situation. It was felt that the development of an algorithm that is more compact and computationally efficient was called for. Selecting between candidate reservoir sites is primarily a reconnaissance exercise where the decision maker is concerned with the determination of the optimal combinations and sizes of reservoirs, and the associated yields at levels of reliability that are likely to be required as part of the resulting water supply contracts. Most models reported on in the literature assume that failures of the water supply system may be addressed by considering a loss function associated with the degree of yield failure, and proceed to solve for optimal yield reliabilities. For the situations in Utah, the permissible degree of yield failure is often prescribed in the water supply contracts. The planning exercise is often conducted by the state agency for individual municipal water supply agencies and agrarian water users. The users will often specify their acceptable yield failure from the proposed development through fairly rigid contracts. These specifications ( agrarian) are typically stated as no more than 50% yield failure in the worst year, no more than 75% of annual yield failure in 2 consecutive years, no more than 100% of annual yield failure in 10 consecutive years. This corresponds to a 90% reliable yield, but with additional restrictions on the failure pattern. Similarly, the municipal users state an aversion to any failure of the supply, and associate a very low economic value with surplus or variable yields from the reservoir system. Consequently the formulation developed analyses yield reliabilities by considering the satisfaction of the likely contract structure, rather than through the traditional loss function approach. At the screening stage of the planning process the decision focus is on which reservoirs to build, and what their sizes are likely to be. Insight into this issue is more useful than the specification of the optimal operating rule for the reservoir system. The argument of Klemes ( 1979) that the most stable release from the reservoir system is the most preferred is perhaps an adequate descriptor of the operating rule. A model that focuses directly on reservoir sizing with annual yields as the decision variables, and 103 |