Optimal power flow (OPF) has gained a growing attention from electrical power researchers since it is a significant tool that assists utilities of power system to determine the optimal economic and secure operation of the electric grid. The key OPF objective is to optimize a certain objective function such as: minimization of total fuel cost, emission, real power transmission loss, voltage deviation, etc. while fulfilling certain operation constraints like bus voltage, line capacity, generator capability and power flow balance. Optimal reactive power dispatch (ORPD) is a sub-problem of optimal power flow. ORPD has a considerable impact on the economic and the security of the electric power system operation and control. ORPD is considered a mixed nonlinear problem because it contains continuous and discrete control variables. Another sub-problem of OPF is Economic dispatch (ED) which one of the complex problems in the power system which its purposes is to determine the optimal allocation output of generator unit to satisfy the load demand at the minimum economic cost of generation while meeting the equality and inequality constraints. In this thesis, a recent metaheuristic nature-inspired optimization algorithm namely: Moth-Flame Optimizer (MFO) is applied to solve the two subproblems of Optimal power flow (OPF) namely: Economic dispatch (ED) and Optimal reactive power dispatch (ORPD) simultaneously. Three objective functions will be considered: generation cost minimization, transmission power loss minimization, and voltage deviation minimization using a weighted factor. The IEEE 30-bus test system and IEEE 57-bus test system will be employed, to investigate the effectiveness of the proposed MFO in solving the above-mentioned problems. Then the obtained MFO methods results is compared with other reported well-known methods. The comparison proves that MFO offers a better result compared to the other selected methods. In IEEE 30-bus test system, MFO outperform the other optimization methods with 967.589961/hcomparedto971.411400/h, 983.738069/h,975.346233/h, 985.198050/h,1035.537820/h for Improved Grey Wolf Optimizer (IGWO), Grey Wolf Optimizer (GWO), Ant Loin Optimizer (ALO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA) respectively. In IEEE 57-bus test system, MFO offers a minimization of 19.16% compared to 19.03% and 18.98% for Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) respectively. Moreover, the MFO have speedy convergence rate and smooth curves more than the other algorithms
