Multi-objective particle swarm optimization with crowding distance in the optimal design of hybrid renewable energy system - Bautista, Izrael Zenar C.; Orillaza, Jordan Rel (Quezon City College of Engineering, University of the Philippines Diliman, 2017)
Optimization techniques mimicking biological behaviours such as Genetic algorithm (GA) and Particle swarm optimization (PSO) has received significant attention and has been applied to solve real-world problems. One application is in the field of renewable energy system sizing which aims to optimize resources to reduce cost, greenhouse gas (GHG) emissions and unreliability of the system. In this paper, a novel modification to PSO known as Multi-Objective Particle Swarm Optimization with crowding distance (MOPSOCD) is applied to a hybrid renewable energy system (HRES) comprised of Solar PV, generator and batteries using R software. A case study involving a base transceiver station load (BTS) load was simulated and sensitivity analysis was done on the MOPSOCD parameters to improve convergence rate. It was concluded that the MOPSOCD output is able to produce comparable results against the commercial software, iHOGA, and help decision makers in the process of choosing the best system to implement based on their preference.
Thesis (M.S. Energy Engineering)--University of the Philippines Diliman
Keywords: Solar energy; Electric generators; Mathematical optimization; Genetic algorithms; iHOGA; Energy system sizing