Alcigeimes B. Celeste

Improving Implicit Stochastic Reservoir Optimization Models with Long-Term Mean Inflow Forecast

This technical note introduces a reservoir operation model based on implicit stochastic optimization (ISO) in which the release policy is guided by the forecast of the mean inflow for a given future horizon rather than by the prediction of the current-month inflow, such as in typical ISO models. The model also does not require the forecast of all inflows for the future horizon and shows to be more efficient in finding less vulnerable release policies when compared to several other explicit and implicit stochastic procedures.

Implicit Stochastic Optimization for deriving reservoir operating rules in semiarid Brazil

This paper deals with the application of Implicit Stochastic Optimization (ISO) to determine monthly operating rules for a reservoir system located in the semiarid Northeast of Brazil. ISO employs a deterministic optimization model to find optimal reservoir allocations under several possible inflow scenarios and later constructs the rules by analyzing the ensemble of these optimal releases. The operating policies provide the monthly reservoir release conditioned on the storage at the beginning of the month and the inflow predicted for the month. In addition to the classical regression analysis, this study establishes the rules by a two-dimensional interpolation strategy. After the rules are identified, they are applied to operate the system under new inflow realizations and show ability to produce policies similar to those obtained by deterministic optimization taking the same inflows as perfect forecasts.

Performance evaluation of implicit stochastic reservoir operation optimization supported by long-term mean inflow forecast

This study applies implicit stochastic optimization (ISO) to develop monthly operating rules for a reservoir located in Northeast Brazil. The proposed model differs from typical ISO applications as it uses the forecast of the mean inflow for a future horizon instead of the current-month inflow. Initially, a hundred different 100-year monthly inflow scenarios are synthetically generated and employed as input to a deterministic operation optimization model in order to build a database of optimal operating data. Later, such database is used to fit monthly reservoir rule curves by means of nonlinear regression analysis. Finally, the established rule curves are validated by operating the system under 100 new inflow ensembles. The performance of the proposed technique is compared with those provided by the standard reservoir operating policy (SOP), stochastic dynamic programming (SDP) and perfect-forecast deterministic optimization (PFDO). Different forecasting horizons are tested. For all of them, the results indicate the feasibility of using ISO in view of its lower vulnerability in contrast to the SOP as well as the proximity of its operations with those by PFDO. The results also reveal that there is an optimal choice for the forecasting horizon. The comparison between ISO and SDP shows small differences between both, justifying the adoption of ISO for its simplified mathematics as opposed to SDP.

Assessment of Stochastic Operation Optimization for Reservoirs of Contrasting Scales

Deriving optimal release policies for dams and corresponding reservoirs is crucial for the sustainable water resources management of a region as they directly control the distribution of water to several users. Mathematical optimization algorithms can help in finding efficient reservoir operating strategies taking into account complex system constraints and hydrologic uncertainty. The robustness of operation optimization models may be influenced by physical reservoir characteristics such as size and scale and the effectiveness of a model for a particular case study does not always guarantee the same level of success for another application. This research focused on assessing the applicability of an implicit stochastic optimization (ISO) procedure to derive rule curves for two different dams of contrasting reservoir scales in terms of physical and operational characteristics. The results demonstrated the feasibility of the proposed technique for both small- and large-scale systems in view of the lower vulnerability provided by the ISO-derived policies in contrast to operations carried out by the standard reservoir operating policy as well as the proximity of the ISO operations with those by perfect-forecast deterministic optimization. The ISO procedure also provided operating rules similar to, and even less vulnerable than, those derived by stochastic dynamic programming.

Rescaled range analysis of streamflow records in the São Francisco River Basin, Brazil

Hydrological time series are sometimes found to have a distinctive behavior known as long-term persistence, in which subsequent values depend on each other even under very large time scales. This implies multiyear consecutive droughts or floods. Typical models used to generate synthetic hydrological scenarios, widely used in the planning and management of water resources, fail to preserve this kind of persistence in the generated data and therefore may have a major impact on projects whose design lives span for long periods of time. This study deals with the evaluation of long-term persistence in streamflow records by means of the rescaled range analysis proposed by British engineer Harold E. Hurst, who first observed the phenomenon in the mid-twentieth century. In this paper, Hurst’s procedure is enhanced by a strategy based on statistical hypothesis testing. The case study comprises the six main hydroelectric power plants located in the São Francisco River Basin, part of the Brazilian National Grid. Historical time series of inflows to the major reservoirs of the system are investigated and 5/6 sites show significant persistence, with values for the so-called Hurst exponent near or greater than 0.7, i.e., around 40% above the value 0.5 that represents a white noise process, suggesting that decision makers should take long-term persistence into consideration when conducting water resources planning and management studies in the region.

DYNAMIC AND IMPLICIT STOCHASTIC OPTIMIZATION FOR RESERVOIR OPERATION: CASE STUDY IN SERGIPE

Water supply reservoirs are usually operated in accordance with so-called rule curves, which decide the ratio of demand to be met based on current conditions. This paper assesses the suitability of implicit stochastic optimization (ISO) against stochastic dynamic programming (SDP) to set up reservoir release policies. The case study is the largest reservoir in the state of Sergipe, Brazil. The models are applied to operate the reservoir under several possible 100-year inflow scenarios. Operations are also carried out by the standard operating policy (SOP) of reservoirs and by deterministic optimization based on perfect inflow forecast (PFDO), the latter used as a benchmark. In all of the 100 scenarios utilized, the performance of both SPD and ISO is superior to that of SOP and close to that of PFDO. Furthermore, the simple ISO shows to perform similarly to the more complex SDP.

Reservoir Design Optimization Incorporating Performance Indices

This paper introduces a reservoir design optimization model adapted to incorporate performance norms so that the active storage capacity can be determined assuming that failures may occur during the reservoir operation. The model is able to find the optimal reservoir capacity admitting either a predefined minimum number of failure periods or a maximum failure magnitude, or both. It is formulated as a mixed integer linear program that properly manages reservoir spills and includes evaporation losses. The procedure performs effectively for an example problem and Monte Carlo simulations and shows that lower accepted reliabilities and higher accepted vulnerabilities require less active storage.

AVALIAÇÃO DE ALGORITMOS DE OTIMIZAÇÃO E FUNÇÕES OBJETIVO PARA CALIBRAÇÃO AUTOMÁTICA DO MODELO CHUVA-VAZÃO TANK MODEL

Este trabalho aplica combinacoes de sete algoritmos de otimizacao matematica e de oito funcoes objetivo (que guiam a otimizacao) para a calibracao automatica dos parâmetros do modelo chuva-vazao Tank Model. A area de estudo e a bacia hidrografica do rio Japaratuba, localizada no estado de Sergipe. Os resultados sao comparados com os de pesquisa anterior que utilizou uma unica combinacao de otimizador e funcao objetivo para calibrar o modelo. O desempenho do Tank Model e confrontado tambem com o do modelo conceitual MODHAC, aplicado nos estudos de elaboracao do Plano Estadual de Recursos Hidricos do estado de Sergipe. Os resultados encontrados indicam ajustes bastante satisfatorios entre vazoes observadas e simuladas.