Combined Optimal Placement of Solar; Wind and Fuel cell Based DGs Using AHP

A. K. Singh
Department of Electrical Engineering, Indian Institute of Technology, Patna, India

S. K. Parida
Department of Electrical Engineering, Indian Institute of Technology, Patna, India

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp110573113

Ingår i: World Renewable Energy Congress - Sweden; 8-13 May; 2011; Linköping; Sweden

Linköping Electronic Conference Proceedings 57:15, s. 3113-3120

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Publicerad: 2011-11-03

ISBN: 978-91-7393-070-3

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


The integration of distributed generations (DGs) into grid has a great importance in improving system reliability. Many methods were proposed in the literature for finding best locations for DG placement considering various criteria. Sometime; it becomes difficult for combined placement of different kinds of renewable based DGs; such as solar; wind and fuel cell. The criterion of minimizing total system cost was used previously by many researchers for locating the optimal sites for DGs using OPF formulations. In this case; three different cost functions are formulated for different kinds of renewable energy sources (RESs). By taking combined cost function of all the RESs in the OPF to identify location for each different kind of sources becomes very cumbersome task. It would be difficult to find the exact locations for various kinds of RESs that is where to place which type of RESs. In order to solve this difficulty; three different objectives have been considered separately for determining the optimal locations for each kind of RESs using mixed integer nonlinear programming (MINLP) method. Having many alternatives with these three objectives; analytic hierarchy process (AHP) has been used to make a decision over getting the optimal locations for these different kinds of RESs. The proposed method for finding the optimal locations of solar; wind and fuel cell based DG placement has been demonstrated on 15 node distribution systems.


Analytic hierarchical process; Distributed generation; Mixed-integer non-linear programming; Optimal power flow; Renewable energy sources


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