Reservoir operation rules are intended to help an operator so that water releases and storage capacities are in the best interests of the system objectives. In multi-reservoir systems, a large number of feasible operation policies may exist. System engineering and optimization techniques can assist in identifying the most desirable of those feasible operation policies. This paper presents and tests a set of operation rules for a multi-reservoir system, employing a multi-swarm version of particle swarm optimization (MSPSO) in connection with the well-known HEC-ResPRM simulation model in a parameterizationsimulation-optimization (parameterization SO) approach. To improve the performance of the standard particle swarm optimization algorithm, this paper incorporates a new strategic mechanism called multi-swarm into the algorithm. Parameters of the rule are estimated by employing a parameterization-simulation-optimization approach, in which a full-scale simulation model evaluates the objective function value for each trial set of parameter values proposed with an efficient version of the particle swarm optimization algorithm. The usefulness of the MSPSO in developing reservoir operation policies is examined by using the existing three-reservoir system of Mica, Libby, and Grand Coulee as part of the Columbia River Basin development. Results of the rule-based reservoir operation are compared with those of HEC-ResPRM. It is shown that the real-time operation of the three reservoir system with the proposed approach may significantly outperform the common implicit stochastic optimization approach.

Cyclic storage system (CSS) refers to the joint development and operation of surface impoundment and subsurface subsystems with natural and physical interaction and a prespecified operating rule which manages the inter-relation between different components of the system. This paper presents a lumped modeling approach to a generalized large scale cyclic storage system. The model is capable to optimally design and operate a cyclic system in an irrigable area. The excitation units with pronounced impact and a complexity of the semi-distributed model are replaced with approximated lumped functions, while maintaining a desirable level of accuracy in the system's performance in a long-term planning horizon. The proposed model has a MINLP structure, which is solved using powerful well-known LINGO solver. Design capacities of different components of the system and the associated operating rules parameters are considered as decision variables that minimize total cost of operating costs over the planning horizon. Extensive simulation runs show that the derived operating rules perform quite satisfactory with nonsignificant deficits over the entire horizon.

This paper describes the development and application of a reservoir management decision support system for evaluating floodplain benefits and socioeconomic trade-offs of reservoir management alternatives in the Connecticut River watershed. The decision support system is composed of a reservoir system simulation model, an ecological model, and two river hydraulics models. The reservoir model simulated current operations at 73 reservoirs and flows at locations of interest in the Connecticut River watershed. Regulated flows from the reservoir model were compared with unregulated flows, both statistically and spatially, for a suite of environmental flow metrics based on inundation patterns related to floodplain vegetation communities. Analyses demonstrate use of the decision support system and show how its use illuminates (1) trends in existing hydrologic alteration for the Connecticut River mainstem and one of its tributaries, the Farmington River, and (2) management scenarios that might have ecological benefits for floodplain plant communities. The decision support system was used to test two management scenarios to assess potential floodplain benefits and associated trade-offs in hydropower generation and flood risk. The process described shows the usefulness of large-scale reservoir management decision support systems that incorporate environmental considerations in assisting with watershed planning and environmental flow implementation.

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