Chance-constrained optimal control for multireservoir system optimization and risk analysis

A vast array of techniques have been developed and applied to optimal opera tion of large-scale multireservoir systems. Researchers continue to be chal lenged by the highly complex, stochastic, nonlinear, and high dimensional n ature of this dynamic optimization problem. An optimal control model is pre sented which incorporates chance-constraints on system state variables that assure satisfaction of operational restrictions under specified levels of reliability. The chance-constrained optimal control (CCOC) model is tested on a four-reservoir case study, and its performance assessed based on vario us quantitative and qualitative criteria, including maintenance of acceptab le levels of risk and provision of risk-benefit trade-off information. The concepts of reliability, resiliency and vulnerability are utilized to chara cterize operating policies generated by the algorithm. CCOC is recommended for operational guidance of large-scale multireservoir systems due to its r obustness, flexibility, modest computational requirements, and ability to i nclude risk considerations directly impacting the choice of operational sch emes.