Abstract
Ph.D.
Desalination is a viable solution to meet water scarcity but is regarded as cost-intensive. The reverse osmosis (RO) technique of desalination is commonly used for its cost-effectiveness as compared to other methods but still considered energy-intensive. Conventional fossil energy sources emit carbon gas, which has environmental and cost implications, while renewable energy sources are limited by intermittency. This study formulates mathematical optimization models used to determine the optimal energy mix to power a reverse osmosis desalination unit. The models are formulated to ensure balance between power supply and demand, water demand and water produced, cost functions, and a time-of-use demand response (TOU DR) program. The cost functions include carbon emission cost, DR cost and components cost. The objective of The optimization models is to minimize the annualized cost of system (ACS), levelized cost of energy (LCOE), unit cost of water (COW) and carbon emissions, while maximizing the quantity of freshwater production subject to different economic and system reliability constraints. The first model has an energy mix of grid power, a diesel generator and a photovoltaic (PV) module to supply an RO desalination unit with the objective of maximizing freshwater production at minimal cost. Also considered is the impact of DR and its cost on the quantity of water produced at different hours of the day, and the unit cost of freshwater. Three cases of optimal sizing approaches were compared. Case 1 is a system with only grid power and a diesel generator as energy sources; Case 2 has PV power incorporated in the energy supply mix, while Case 3 has the three energy sources and a TOU DR program on the demand side. The result shows that Case 3 turns out the highest freshwater production (1 520 m3/day) at low cost of (1.33 $/m3) when compared to Case 1 with daily freshwater production of 1 250 m3/day at a unit cost of 1.54 $/m3 and Case 2 with daily freshwater production of 1 501 m3/day at a unit cost of 1.28 $/m3.