Bessa, Marcelo Rodrigues2006-07-282006-07-2819991999http://hdl.handle.net/10012/357In order to recognize the intrinsic stochastic features of the natural inputs and take them into consideration explicitly, Stochastic Dynamic Programming is employed to generate long-term operation policies. Because of the well-known "curse of dimensinoality", that can affect the optimization of large systems, the technique called Multi-Level Approximate Aggregation/Decomposition - Stochastic Dynamic Programming (MAM-SDP) Methodology is employed. The performance of this technique can be enhanced by using the suggested alternate approximation to the conditional distribution of the releases from the reservoirs. So far, MAM-SDP performs physical diagnosis to determine the aggregation scheme. A means of applying Principal Components Analysis is presented, therefore adding a different perspective to solving the problem, i.e., a statistical decomposition. This work also aims at obtaining the relationship performance of the system versus its respective variance. To this effect, an extension of the Expected Return-Variance of Return Rule was developed, applied to a multistage decision type of problem. This technique was called Two-Pass Mean-Variance Approach. The algorithm for doing so is described. It was possible to show a significant range of performances at the variances associated with them for the operation of reservoir systems. This work ends with the application of the techniques above mentioned to a real case study. In it, an alternate closed-loop type operation policy is presented for the North American Great Lakes. These policies and those from the Two-Pass Mean-Variance Approach are then compared with the ones obtained from a simplified model of the actual operation, based on heuristics. Two sets of synthetic Net Basin Supplies for the five lakes are studied, generating two different sets of release policies.application/pdf15606053 bytesapplication/pdfenCopyright: 1999, Bessa, Marcelo Rodrigues. All rights reserved.Harvested from Collections CanadaDoctoral Thesis