Management of a River Reservoir Using Multi-Objective Particle Swarm Optimization Technique

Water is elixir of Life is a notion which is accepted without exception. With increasing human population and consequent increasing human activities, coupled with decreasing natural resources, the need of water for survival has taken on added dimensions in recent years. Progressive demand of freshwater supplies by accelerated human developments has made an already critical problem, even more acute. To combat this problem, techniques are being developed to ensure economic and optimal usage.In case of a dam constructed across a river, the river reservoir management during monsoon period is complicated due to conflicting objectives like flood control, irrigation, hydro power generation and conservation, which is relatively simple during non-monsoon period.River reservoir management is a complex problem that involves many decision variables, multiple objectives as well as considerable risk and uncertainty. In addition, the conflicting objectives lead to significant challenges for the managers while making operational decisions.Traditionally, reservoir management is based on heuristic procedures, embracing rule curves and subjective judgements by the operator. This provides general operation strategies for reservoir releases according to the current reservoir level, hydrological conditions, water demands and the time of the year. Established rule curves, however, do not allow a fine-tuning of the operations in response to changes in the prevailing conditions. Traditional optimization techniques have failed to take care of non linearity and uncertainties. Emerging soft computing heuristic techniques such as Artificial Neural Network (ANN), Fuzzy logic, Neuro-Fuzzy (ANFIS), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimsation (PSO), Multi-objective Particle Swarm Optimization (MOPSO), etc. can be gainfully employed to handle such problems when conditions of the systems are uncertain.Application of optimization techniques to reservoir operation has become a major focus of water resources planning and management. Water use involves a large number of stakeholders with different objectives, and optimization technique like MOPSO is expected to provide balanced solutions between often conflicting objectives. This paper proposes an avenue for changing traditional reservoir operation into optimized strategies, taking advantage of the rapid development in artificial intelligence techniques.

Keywords:

Reservoir Management , Optimization , Soft computing , Water Management