Flood Management in Hirakud Reservoir using Particle Swarm Optimization (original) (raw)

This work proposed particle swarm optimization (PSO) a meta-heuristic technique to develop operating policies for flood control in Hirakud multipurpose reservoir, Sambalpur, Odisha, India. The flood control reservoir is designed to moderate flood and not to conserve water. Real time reservoir operation is a challenging task. The reservoir releases are influenced by the inflows and the reservoir storage levels. The present research develops effective reservoir operation policies by using optimization technique and demonstrates the utility for practical applications. The inflows into the Hirakud reservoir are highly uncertain, which often induce lot of uncertainty in release policies. Therefore, it requires developing operation policies at different exceedance probabilities of inflows to assess the associated risks and developing real time reservoir operation models to facilitate easiness in reservoir operation system. Mostly such problems involve non-linear optimization to find a feasible solution as it involves good quantities of equality and inequality constraints. In this study, it is intended to test the usefulness of PSO in solving such type of problems. To formulate the PSO model for reservoir operation, the problem is approached by considering a finite time series of inflows, classifying the reservoir volume into several class intervals and determining the reservoir release for each period with respect to a predefined optimality criterion. The multiple objectives comprise of minimizing flood risks, minimizing irrigation deficits and maximizing hydropower production in that order of priority. The developed model is applied for monthly operation. The results of the models indicate that PSO model performs better in flood control restrictions.