Optimal Reservoir Operation Using MOPSO with Time Variant Inertia and Acceleration Coefficients

Abstract

Recently application of optimization techniques has been suggested to derive reservoir operation policies for multi-objective reservoir systems. Water use involves a large number of stakeholders with different objectives. In reservoir operation some of objectives often are conflicting objectives, hence Optimization techniques are expected to provide balanced their solutions. This paper presents an efficient and reliable swarm intelligence-based approach, namely a novel Particle Swarm Optimization (PSO) approach to Multi-Objective optimization Problems (MOP), called Time Variant Multi-Objective Particle Swarm Optimization (TV-MOPSO) technique, to derive a set of optimal operation policies for a multi-objective reservoir system. Classical optimization methods are often unable to attain a good Pareto front. To overcome this problem for Multi-Objective optimization Problem, this study employs a heuristic algorithm, Time Variant Multi-Objective Particle Swarm Optimization to generate a Pareto optimal set. To show practical utility, TV-MOPSO is then applied to a realistic case study, namely the Doroodzan reservoir system in Iran. The reservoir serves multiple objectives comprise of minimizing domestic supply (industry) deficits, minimizing irrigation deficits and maximizing hydropower production in that order of priority. The results obtained demonstrate that Time Variant Multi-Objective Particle Swarm Optimization is consistently performing better than the standard Particle Swarm Optimization. This study demonstrates the usefulness of Time Variant Multi-Objective Particle Swarm Optimization for water resource management problem.