00000ctm a22000004i 4500 UP-99796217612684138 Buklod 20180213152559.0 m |o d | ta 180213s2017 xx d r |||| u| (iLib)UPD-00360913261 DENG rda eng DMLUC LG 995 2017 E65 B38 Bautista, Izrael Zenar C. author. Multi-objective particle swarm optimization with crowding distance in the optimal design of hybrid renewable energy system thesis by Izrael Zenar C. Bautista ; Jordan Rel Orillaza, adviser. Quezon City College of Engineering, University of the Philippines Diliman 2017. vi, 166 leaves color illustrations 28 cm text rdacontent unmediated rdamedia volume rdacarrier Thesis (M.S. Energy Engineering)--University of the Philippines Diliman December 2017. F - no patentable invention or creation, not for personal publication and no confidential information. Yes - available to the general public. 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. Solar energy. Electric generators. Mathematical optimization. Genetic algorithms. iHOGA. Energy system sizing. Orillaza, Jordan Rel adviser. FI UP UPD DENG-II LG 995 2017 E65 B38 Thesis