Optimization of Electrical Generation Cost Using Differential Evolutionary Algorithm for Large Four Regions Electrical Grid


In this paper, a techno-economic assessment of electrical generation cost optimization for four region large electrical grid is presented. This optimization was attained by using the Differential Evolutionary Algorithm (DEA). The study is the first of its kind as none of the previous studies were conducted in the context of a real fuel value and system constraints. In each of the large grid four regions there is generation fleet with different technology and large load center. The four regions are connected via transmission lines with power flow constraints. The performance the DEA in optimizing the generation cost is bench-marked with business as usual (BAU) case. The problem was articulated as a constrained nonlinear problem. The constraints were all real values reflecting the system equipment and componentsā€™ limitations and operation constraints. The results obtained from the research show the efficiency and prospects of the proposed research in optimizing the generation cost. Also addressed in this study the annual cost avoidance, due to the study objectivesā€™ optimization.

Author Information
Muhammad AlHajri, Saudi Aramco, Saudi Arabia
Mohammed Abido, King Fahd University of Petroleum and Minerals, Saudi Arabia

Paper Information
Conference: ECSEE2017
Stream: Energy: Energy Economics and Ecological Economics

This paper is part of the ECSEE2017 Conference Proceedings (View)
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Posted by James Alexander Gordon