Open AccessOptimal Techno-Economic Design of Standalone Hybrid Renewable Energy System Using Genetic Algorithm
Author(s) -
Mohamad Izdin.Hlal,
Vigna K. Ramachandaramurthy,
Farrukh Nagi,
Tuan Ab Rashid Bin Tuan Abdullah
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/268/1/012012
Subject(s) - sizing , renewable energy , genetic algorithm , wind power , turbine , automotive engineering , sorting , photovoltaic system , wind speed , rural electrification , electrification , computer science , engineering , simulation , environmental science , electricity , electrical engineering , algorithm , meteorology , mechanical engineering , art , physics , machine learning , visual arts
This paper presents a methodology to size Standalone Hybrid Renewable Energy System (SHRES) which combines solar PV, wind turbine (WT) and battery energy storage (BES) for application in rural areas. These sources are integrated via an AC bus to support the load demand. SHRES is simulated under varying load demand, solar radiation, temperature and wind speed obtained from the Malaysian Meteorological Department. A Multi-objective Optimization using Non-dominate Sorting Genetic Algorithm (NSGA-II) was utilized to determine the best sizing of PV / wind turbine / battery, and minimize Cost of Energy (COE) and Loss of Power Supply Probability (LPSP). The results show that the NSGAII optimization of the model is able to determine the best techno-economic sizing for the suggested location. For the case study, the optimum COE was 0.1099 (USD/kWh) and LPSP was 0.0865. The proposed tool can be used to size the SHRES for rural electrification and enhance energy access within remote locations.