Optimizing Economic Load Dispatch with Renewable Energy Sources via Differential Evolution Immunized Ant Colony Optimization Technique

N. A. Rahmat (1), N. F. A. Aziz (2), M. H. Mansor (3), Ismail Musirin (4)
(1) Department of Electrical Power Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, Selangor, Malaysia
(2) Department of Electrical Power Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, Selangor, Malaysia
(3) Department of Electrical Power Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, Selangor, Malaysia
(4) Universiti Teknologi MARA Shah Alam, Selangor
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How to cite (IJASEIT) :
Rahmat, N. A., et al. “Optimizing Economic Load Dispatch With Renewable Energy Sources via Differential Evolution Immunized Ant Colony Optimization Technique”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 6, Dec. 2017, pp. 2012-7, doi:10.18517/ijaseit.7.6.2328.
Recently, renewable energy (RE) has become a trend in power generation. It is slowly evolving from an alternative energy source into the main energy source. The technology is currently working as an auxiliary to the existing generators. Demands for electricity is expanding rapidly nowadays, which require generators to run near its operation limit. This activity put grieve risk to the generators. Nonetheless, the extensive analysis should be conducted upon RE integration into the existing power system. This paper assesses its economic impact on the power system. Setting up RE technology such as photovoltaic and wind turbine are costly, yet may reduce generator’s fuel cost in the long run. Thus, economic load dispatch (ELD) is conducted to compute the operating cost of power system with the integration of RE system. In this study, the operating cost represents the fuel cost of conventional fossil-fuel generators. Furthermore, a novel optimization technique namely Differential Evolution Immunized Ant Colony Optimization is proposed as the optimization engine. Comparative studies are conducted to assess the performance of the proposed approach.

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