OPTIMAL ECONOMIC AND ENVIRONMENTAL OPERATION OF ELECTRIC POWER SYSTEMS VIA MODERN META-HEURISTIC OPTIMIZATION ALGORITHMS
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Due to world-wide escalating fuel costs, increasing demand for electricity, and growing concern for the environment, power utilities strive for optimal economic operation of their electric networks. Striking a balance between profitable energy choices and environmentally-friendly practices is the main goal of this thesis. The dynamic economic dispatch (DED) occupies a prominent place in power system operation and control. However, there is a paucity of studies of the DED problem, as it has not been as thoroughly investigated as other electric power system optimization areas. The nonlinear and non-convex characteristics are more prevalent in the DED problem. Therefore, it is possible that computational methods may not yield a global extrema as many local extrema may be encountered, and, in this case, obtaining a truly optimal solution presents a challenge. Two modern meta-heuristic optimization algorithms are utilized to solve the DED problem. The artificial bee colony (ABC) algorithm is a recently introduced population-based algorithm motivated by the intelligent foraging behaviour of the honeybee swarm. This thesis proposes a novel meta-heuristic optimization algorithm inspired by the intelligent behaviour or survival instincts of a sensory-deprived human being. The sensory-deprived optimization algorithm (SDOA) uses the exploration and exploitation processes simultaneously and distinctly from other algorithms. After solving different benchmark optimization functions, the SDOA efficiency is evident with results that outperform or match those attained by other well-known methods. To enhance the utilized algorithms’ performances in solving the dynamic economic and emission dispatch problems, a new constrained search-tactic is offered. Two groups of test cases are used to validate the effectiveness of the proposed algorithms. The first one is designated to solve the single objective function scenario and to verify the presented constrained search-tactic. The second group focuses on the multiple objective functions’ scenario as well as an attempt to integrate a renewable source and analyze its impact. The outcomes of ABC and SDOA algorithms are compared with those of other older and known methods. The promising results in both utilized algorithms show great potential that can be employed in several electric power system optimization areas.