Open AccessMulti‐objective optimization of photovoltaic/wind/biomass/battery‐based grid‐integrated hybrid renewable energy system
Author(s) -
Pavankumar Yadala,
Kollu Ravindra,
Debnath Sudipta
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12131
Subject(s) - photovoltaic system , renewable energy , computer science , reliability engineering , backup , grid , probabilistic logic , wind power , energy storage , mathematical optimization , battery (electricity) , automotive engineering , power (physics) , engineering , electrical engineering , mathematics , database , physics , geometry , quantum mechanics , artificial intelligence
Abstract The variable nature of the renewable energy resources (RES) complicates their modelling, operation, and integration to the grid. Therefore, it is difficult to choose optimal RES with a proper energy storage system (ESS) for the economic and reliable operation of the grid‐integrated hybrid renewable energy system (HRES). There is a need to solve this optimal HRES problem using efficient algorithms due to the high cost and model complexity involved. In this study, optimal photovoltaic, wind, biomass, and battery‐based grid‐integrated HRES is proposed using a multi‐objective artificial cooperative search algorithm (MOACS) to minimise annual life cycle costing and loss of power supply probability. ESS is chosen to provide a backup power supply for at least 30 min during peak load condition. A probabilistic approach is used to consider the time‐varying nature of the RES and load while solving optimal HRES design problem by employing MOACS. A comparative analysis is provided at the end, which shows that MOACS can provide a better optimal design of HRES.