Optimal Techno-Economic Design of Standalone Hybrid Renewable Energy System Using Genetic Algorithm
Energy conservation; Environmental technology; Multiobjective optimization; Renewable energy resources; Rural areas; Wind; Wind turbines; Battery energy storages (BES); Cost of energies; Hybrid renewable energy systems; Loss of power supply probability; Remote location; Rural electrification; Sortin...
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2023
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my.uniten.dspace-245852023-05-29T15:24:46Z Optimal Techno-Economic Design of Standalone Hybrid Renewable Energy System Using Genetic Algorithm Hlal I.M. Ramachandaramurthy V.K. Hafiz Nagi F. Bin Tuan Abdullah T.A.R. 57205344223 6602912020 56373028000 57200039159 Energy conservation; Environmental technology; Multiobjective optimization; Renewable energy resources; Rural areas; Wind; Wind turbines; Battery energy storages (BES); Cost of energies; Hybrid renewable energy systems; Loss of power supply probability; Remote location; Rural electrification; Sorting genetic algorithm; Techno-economics; Genetic algorithms 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. � Published under licence by IOP Publishing Ltd. Final 2023-05-29T07:24:46Z 2023-05-29T07:24:46Z 2019 Conference Paper 10.1088/1755-1315/268/1/012012 2-s2.0-85068688425 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068688425&doi=10.1088%2f1755-1315%2f268%2f1%2f012012&partnerID=40&md5=5b43f8d7495004cfa3b0ed8ac502d652 https://irepository.uniten.edu.my/handle/123456789/24585 268 1 12012 All Open Access, Bronze Institute of Physics Publishing Scopus |
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| description |
Energy conservation; Environmental technology; Multiobjective optimization; Renewable energy resources; Rural areas; Wind; Wind turbines; Battery energy storages (BES); Cost of energies; Hybrid renewable energy systems; Loss of power supply probability; Remote location; Rural electrification; Sorting genetic algorithm; Techno-economics; Genetic algorithms |
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57205344223 |
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57205344223 Hlal I.M. Ramachandaramurthy V.K. Hafiz Nagi F. Bin Tuan Abdullah T.A.R. |
| format |
Conference Paper |
| author |
Hlal I.M. Ramachandaramurthy V.K. Hafiz Nagi F. Bin Tuan Abdullah T.A.R. |
| spellingShingle |
Hlal I.M. Ramachandaramurthy V.K. Hafiz Nagi F. Bin Tuan Abdullah T.A.R. Optimal Techno-Economic Design of Standalone Hybrid Renewable Energy System Using Genetic Algorithm |
| author_sort |
Hlal I.M. |
| title |
Optimal Techno-Economic Design of Standalone Hybrid Renewable Energy System Using Genetic Algorithm |
| title_short |
Optimal Techno-Economic Design of Standalone Hybrid Renewable Energy System Using Genetic Algorithm |
| title_full |
Optimal Techno-Economic Design of Standalone Hybrid Renewable Energy System Using Genetic Algorithm |
| title_fullStr |
Optimal Techno-Economic Design of Standalone Hybrid Renewable Energy System Using Genetic Algorithm |
| title_full_unstemmed |
Optimal Techno-Economic Design of Standalone Hybrid Renewable Energy System Using Genetic Algorithm |
| title_sort |
optimal techno-economic design of standalone hybrid renewable energy system using genetic algorithm |
| publisher |
Institute of Physics Publishing |
| publishDate |
2023 |
| _version_ |
1806427863152328704 |
| score |
13.214268 |