Open AccessElectrical power generation by an optimised autonomous PV/wind/tidal/battery system
Author(s) -
Askarzadeh Alireza
Publication year - 2017
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2016.0194
Subject(s) - particle swarm optimization , wind power , battery (electricity) , tidal power , genetic algorithm , renewable energy , metaheuristic , computer science , electric power system , power (physics) , wind speed , automotive engineering , mathematical optimization , engineering , meteorology , algorithm , electrical engineering , mathematics , geography , physics , quantum mechanics , machine learning
The main contributions of this study are to (i) incorporate tidal power into a hybrid PV/wind/battery renewable energy system and (ii) introduce a new metaheuristic technique named crow search algorithm (CSA) for optimisation of the PV/wind/tidal/battery system. For this aim, power equations of the different components are introduced and an objective function is defined based on the economic analysis of the system. The proposed CSA is then used to optimally size the PV/wind/tidal/battery system. On the case study, simulation results show that using tidal energy decreases the total cost of the system. Moreover, the proposed CSA produces better results in comparison with two well‐known metaheuristic methods, namely, particle swarm optimisation and genetic algorithm in terms of accuracy and run time.