Capacity optimization of hydropower storage projects using particle swarm optimization algorithm

S. Jamshid Mousavi (corresponding author) M. Shourian Department of Civil Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran E-mail: [email protected] A mixed integer optimization model is formulated for capacity optimization of a hydropower storage project with control on reliability of meeting the project’s firm energy yield. Particle swarm optimization (PSO) is used as the optimization algorithm, in which the method of sequential streamflow routing is called for objective function evaluations. Two types of problems are studied. The first one is an optimal design problem, in which the reservoir’s normal and minimum operating levels as well as the powerplant’s production capacity are optimized while reservoir releases are determined using a predefined operating policy. Two models are presented for this problem. In the first one (model A1), the normal and minimum operating levels are considered as the PSO decision variables, whereas the production capacity is iteratively adjusted in the simulation model. In the second model (model A2), the production capacity is also searched for by the PSO and the reliability constraint on meeting the system’s energy yield is satisfied using a penalty approach. In the second problem, reservoir releases as operational variables are also optimized by considering either the unknown parameters of linear release rules (model B1) or reservoir releases (model B2) as the PSO decision variables. The proposed models are employed for optimal design and operation of the Bakhtiari Hydropower Dam project in Iran. Results indicate that the PSO algorithm is capable of finding good solutions for the models explained while LINGO software employing gradient-based optimization techniques fails to solve the problems. Moreover, the system’s performance is much more affected by optimizing the design variables than the operational ones, unless greater penalties are assigned to severe energy deficits.