With construction of new large-scale water storage projects on the wane in the U.S. and other developed countries, attention must focus on improving the operational effectiveness and efficiency of existing reservoir systems for maximizing the beneficial uses of these projects. Optimal coordination of the many facets of reservoir systems requires the assistance of computer modeling tools to provide information for rational management and operational decisions. The purpose of this review is to assess the state-of-the-art in optimization of reservoir system management and operations and consider future directions for additional research and application. Optimization methods designed to prevail over the high-dimensional, dynamic, nonlinear, and stochastic characteristics of reservoir systems are scrutinized, as well as extensions into multiobjective optimization. Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rule-based systems for inferring reservoir system operating rules.

Optimal Operation of Multireservoir Systems: State-of-the-Art Review

This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulation–optimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, such as, evolutionary computations, fuzzy set theory and artificial neural networks, in reservoir system operation studies are reviewed. Conclusions and suggestive remarks based on this survey are outlined, which could be helpful for future research and for system managers to decide appropriate methodology for application to their systems.

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Simulation-optimization modeling: a survey and potential application in reservoir systems operation.

An interval-parameter multi-stage stochastic programming model for water resources management under uncertainty

The method of nearest-neighbor resampling is extended to simultaneous simulation of daily precipitation and temperature at multiple locations over a large area (25 stations in the German part of the Rhine basin). Nearest neighbors refer here to historical days for which the observed weather is closest to that of the simulated weather for a given day. Resampling is done from these nearest neighbors to obtain the weather variables for the next day. The nearest neighbors are defined in terms of a weighted Euclidean distance to a feature vector containing summary statistics of the daily precipitation and temperature fields (spatial averages, fraction of stations with precipitation, and principal components). The inclusion of atmospheric circulation variables in the feature vector is also studied. There is a weak tendency to underestimate the standard deviations and autocorrelation coefficients of daily precipitation and temperature and the standard deviations of the monthly precipitation totals and monthly mean temperatures. However, the underprediction of these second-order moment statistics is not statistically significant if the number k of nearest neighbors in the resampling procedure is small (k ≈ 5) and the dimension q of the feature vector is low (q ≈ 3). A small systematic underprediction is also observed for the quantiles of the distributions of the N-day winter maximum precipitation amounts. The spatial dependence of these extremes and the distributions of N-day maximum snowmelt are adequately reproduced. Long-duration simulations show that realistic unprecedented multiday precipitation amounts can be generated.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2001WR000291

Multisite simulation of daily precipitation and temperature in the Rhine Basin by nearest‐neighbor resampling

Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts

Most models for reservoir operation optimization have employed either deterministic optimization or stochastic dynamic programming algorithms. This paper develops sampling stochastic dynamic programming (SSDP), a technique that captures the complex temporal and spatial structure of the streamflow process by using a large number of sample streamflow sequences. The best inflow forecast can be included as a hydrologic state variable to improve the reservoir operating policy. A case study using the hydroelectric system on the North Fork of the Feather River in California illustrates the SSDP approach and its performance.

Sampling stochastic dynamic programming applied to reservoir operation

The role of mathematical simulation and optimization models in hydropower planning at Pacific Gas and Electric Company is discussed, and some of the models are described. Applications of sampling stochastic dynamic programming to develop reservoir release rules for the simulation models are presented. Probabilistic constraints on the minimum reservoir storage value are explicitly considered. The models for PG&E's North Fork Feather River hydroelectric system are used as an example.

Eric Hsu

Papers

Sampling stochastic dynamic programming applied to reservoir operation

Use of Probabilistic Constraints in Reservoir Operation Policies With Sampling Stochastic Dynamic Programming