Techno-economic evaluation of off-grid hybrid renewable energy system for rural resort electrification in Malaysia / Monowar Hossain

Monowar, Hossain (2017) Techno-economic evaluation of off-grid hybrid renewable energy system for rural resort electrification in Malaysia / Monowar Hossain. Masters thesis, University of Malaya.

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    Energy is considered as a prime driving force for the socio-economic development of a country. Malaysia is one of the developing countries located in Southeast Asia, consisting of two distinct regions namely West Malaysia (Peninsular Malaysia) and East Malaysia (Sabah and Sarawak), divided by the South China Sea, covering an area of 33.27 million hectares. Although the rate of the electrification in urban areas in this country is high, the tourist sites, which are less than 200km2, located in rural and decentralized islands in the South China Sea, Malaysia (SCSM) mostly depend on diesel generators for 24-hour power supply. The emissions from diesel-based power plants are environmentally risky for tourist spots. Moreover, diesel-based power plants are subjected to many technical and economic problems such as the high operation and maintenance cost, the risk of oil spilling and unpredictable diesel fuel price. Besides the aforementioned problems, an extension of the national electrical grid in these islands is not feasible due to complex terrain. In this dissertation, a multi-optimal combination of the stand-alone hybrid renewable energy system (HRES) for a large resort center located in Tioman Island in the SCSM has been proposed with detailed techno-economic performance analysis. Hybrid Optimization Model for Electric Renewable (HOMER) software is used for economic and technical analysis of the proposed hybrid renewable energy system. The estimated peak and average load per day for the resort are 1,185 kW and 13,048 kW respectively. Also in this dissertation, novel soft computing methodologies based on the hybrid adaptive neuro-fuzzy inference system (ANFIS) have been developed to predict monthly wind power density for the nearest coastal city of Tioman Island, and then the capability of the proposed hybrid ANFIS models have been examined to extrapolate the wind speed data for Tioman Island as there is no meteorological station in this Island. For these purposes, the standalone ANFIS, ANFIS-PSO (particle swarm optimization),ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) prediction algorithms have been developed in MATLAB platform. The best optimized stand-alone hybrid energy system comprises the wind, PV, diesel generator, converter, and the battery. The optimized system resulted in the net present cost (NPC) of $17.15 million, cost of energy (COE) of $0.279/kWh, a renewable fraction (RF) of 41.6%, and CO2 of 2,571,131 kg/year. Whereas, the diesel only system takes NPC of $21.09 million, COE of $0.343/kWh and CO2 of 5,432,244 kg/year. The diesel only system has been observed to have the higher NPC, COE and CO2 emission than the optimized HRES. The designed and analyzed HRES model might be applicable to any tourist locations and decentralized places in the SCSM and around the world with similar climate conditions. On the other hand, the performance of the proposed hybrid ANFIS models has been determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The analysis of wind power density prediction result reveals that the ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be recommended for practical application to predict monthly mean wind power density.

    Item Type: Thesis (Masters)
    Additional Information: Dissertation (M.A.) - Faculty of Engineering, University of Malaya, 2017.
    Uncontrolled Keywords: Diesel-based power plants; Environmentally risky; Tourist spots; National electrical grid
    Subjects: T Technology > T Technology (General)
    T Technology > TK Electrical engineering. Electronics Nuclear engineering
    Divisions: Faculty of Engineering
    Depositing User: Mr Prabhakaran Balachandran
    Date Deposited: 12 Mar 2019 02:21
    Last Modified: 22 Jun 2020 02:11

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