Stochastic And Modified Sequent Peak Algorithm For Reservoir Planning Analysis Considering Performance Indices

This study is on modeling the critical period and total storage capacity of reservoir systems employing performance criteria and synthetic data generation technique. Three sites in the Southern part of Peninsular Malaysia are selected as conceptual reservoirs to be the case studies: Johor at Rantau Panjang; Melaka at Pantai Belimbing and Muar at Buluh Kasap gauging stations. Statistical data analysis of both annual and monthly streamflow data of the study sites is carried out prior to the time series analysis. The tests are implemented for testing consistency, stationarity, randomness and determining the most appropriate probability distribution function of the historical data. Subsequently, Auto-regressive lag one, AR(1), coupled with Valencia-Schaake (V-S) disaggregation model are applied to generate synthetic streamflow data. In the next stage, the modified Sequent Peak Algorithm (SPA) is employed for the Storage-yield planning analysis of reservoir systems at different demands, reliability and vulnerability performance metrics employing the synthetic streamflow data. The results show that the reliability and vulnerability metrics are significant in critical period and storage capacity modeling. Subsequently, using the simulation results, new regression equations are developed to model the critical period and total storage capacity of study systems individually and three systems together applying standard demand parameter, reliability and vulnerability performance measures and coefficient of variation and skewness of annual flows. The R2 obtained over the complete range of the critical period and storage capacity prediction is high, being 0.9810 and 0.9856, respectively for the three systems together. Hence, the obtained equations could reproduce the simulated critical period and storage capacity for different demands, reliability and vulnerability indices efficiently