Electrical power energy optimization at hydrocarbon industrial plant using intelligent algorithms

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.In this work, the potential of intelligent algorithms for optimizing the real power loss and enhancing the grid connection power factor in a real hydrocarbon facility electrical system is assessed. Namely, genetic algorithm (GA), improve strength Pareto evolutionary algorithm (SPEA2) and differential evolutionary algorithm (DEA) are developed and implemented. The economic impact associated with these objectives optimization is highlighted. The optimization of the subject objectives is addressed as single and multi-objective constrained nonlinear problems. Different generation modes and system injected reactive power cases are evaluated. The studied electrical system constraints and parameters are all real values. The uniqueness of this thesis is that none of the previous literature studies addressed the technical and economic impacts of optimizing the aforementioned objectives for real hydrocarbon facility electrical system. All the economic analyses in this thesis are performed based on real subsidized cost of energy for the kingdom of Saudi Arabia. The obtained results demonstrate the high potential of optimizing the studied system objectives and enhancing the economics of the utilized generation fuel via the application of intelligent algorithms