| description abstract | Reservoirs are infrastructures that play an important role in supplying water. Hydrological and meteorological parameters, as the most important elements in reservoir systems’ simulations, are uncertain. Thus, such uncertainties must be considered in integrated water resource management. This paper includes (1) generation of artificial time series based on probabilistic statistical models; (2) operation policies of hydropower reservoir systems; (3) analysis of risk and uncertainty of a reservoir system; and (4) development of performance indices for evaluation of hydropower reservoir system efficiency. To evaluate the effects of inflow uncertainty on the performance of a hydropower multireservoir system, this paper uses Monte Carlo simulation (MCS). Periodic stochastic models are used to generate different inflow time series. The generated time series are then used to run simulation and optimization models considering five scenarios. Subsequently, probability distributions of performance indices (volumetric reliability, time-based reliability, vulnerability, and resiliency) are extracted for each scenario. Moreover, the uncertainty of each parameter is measured by the coefficient of variation. Results show that the use of an optimization method to increase hydropower multireservoir system performance that considers inflow uncertainty yields the best volumetric reliability and vulnerability of management scenarios considered. The best values of temporal reliability and resiliency are obtained in single hydropower reservoir simulation and hydropower multireservoir simulation, respectively. In contrast, inflow uncertainty has the least effect on volumetric reliability (about 2%) and the most effect on resiliency (about 18%). | |
