ProgramMPhil Scientific Computing & Industrial Modeling
Graduating Class of2018
Research InterestsComputational fluid mechanics
Affiliate InstitutionUniversity of Cape Coast
Degree ObtainedBsc. Mathematics
Abubakar Ali has a BSc Mathematics degree from the University of Cape Coast, Ghana, obtained in 2015. He was a Teaching Assistant at the Department of Mathematics and Statistics, of the University of Cape Coast, for his mandatory one year National Service. He is currently enrolled in the 'Scientific Computing and Industrial Modelling' postgraduate programme of the National Institute for Mathematical Sciences, Ghana.
He intends to focus his research on Computational Fluid Mechanics.
The generations of Renewable Energy Sources (RES) are purely stochastic in nature, that is, the RES completely depend on climatic conditions which make them very complicated to be predicted accurately and this might result in load failure or blackouts at some points. This issue could be addressed by mixing two or more energy sources to form a Hybrid Energy System (HES). Designing the HES is extremely complicated due to the variabilities in the RES and power consumption.
Moreover, the design involves multiple conflicting objectives. In this study, a practical optimization tool has been built to optimally size the components (PV panel, battery) involve in designing a HES comprising Solar, Grid and batteries. This tool would help Decision Makers (DM) and engineers in building a much more cost effective and reliable HES. Monte Carlo simulation was employed to handle randomness effect in the design and a simulation-based Non-dominated Sorting Algorithm -II was applied to the multi-objective problem consisting of the Net Present Cost (C) and the total Energy. The proposed tool was tested on a Household in KNUST. Both stochastic and deterministic designs were considered and the results demonstrated that the stochastic approach could improve the performance of the HES. A Post-Pareto Analysis was carried out to aid the DM to select the most preferred Pareto optimal solution and lastly, through sensitivity analysis, the most sensitive parameter was found to be the Capital cost of the PV.