Open AccessHybrid energy system design using greedy particle swarm and biogeography‐based optimisation
Author(s) -
Abuelrub Ahmad,
Khamees Mohammad,
Ababneh Jehad,
AlMasri Hussein
Publication year - 2020
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.2019.0858
Subject(s) - particle swarm optimization , sorting , mathematical optimization , sizing , computer science , greedy algorithm , genetic algorithm , renewable energy , wind power , grid , swarm behaviour , engineering , algorithm , mathematics , art , geometry , electrical engineering , visual arts
Renewable energy systems (RESs) are affordable, clean and sustainable. However, their output power is intermittent. Therefore, RESs are usually combined with an energy storage system or conventional sources to make the overall operation uninterruptable. Optimal sizing of hybrid energy system components is imperative to be financially and technically feasible. In this study, a multi‐objective optimisation based on a hybrid optimisation procedure, which combines the exploitation ability of the biogeography‐based optimisation (BBO) with the exploration ability of the particle swarm optimisation (PSO), is used to handle the system design. This algorithm is known as greedy particle swarm and BBO algorithm (GPSBBO). Weighted sum method is added to the GPSBBO to handle the multi‐objective nature of the design problem. A case study for a hybrid wind‐PV energy system design in the standalone and grid‐connected configurations is presented to illustrate the proposed method. Coverage of two sets, hypervolume and diversity performance indices are used to compare results of the proposed method to non‐dominated sorting genetic algorithm and the multi‐objective PSO. These indices show an improved performance of the suggested method in finding the optimal system design.