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Optimal Placement and Sizing of Distributed Generation in Radial Distribution System Using Differential Evolution Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7677))

Abstract

Integration of renewable energy based distributed generation (DG) units provides potential benefits to conventional distribution systems. The power injections from renewable DG units located close to the load centers provide an opportunity for system voltage support, reduction in energy losses and emissions, and reliability improvement. Therefore, the allocation of DG units should be carefully determined with the consideration of different planning incentives. Optimal placement and sizing of DG in distribution network is an optimization problem with continuous and discrete variables. This paper proposes a Differential Evolution Algorithm (DEA) for optimal placement and sizing of distributed generation (DG) in radial distribution system to minimize the total real power loss and improve the voltage profile within the frame work of system operation and security constraints. The proposed DE algorithm is also used to determine optimal sizes and locations of multi-DGs. The proposed method is tested on standard IEEE 69-bus test system and the results are presented and compared with different approaches available in the literature. The proposed method has outperformed than the other methods in terms of the quality of solution and computational efficiency.

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© 2012 Springer-Verlag Berlin Heidelberg

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Nayak, M.R., Dash, S.K., Rout, P.K. (2012). Optimal Placement and Sizing of Distributed Generation in Radial Distribution System Using Differential Evolution Algorithm. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_17

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  • DOI: https://doi.org/10.1007/978-3-642-35380-2_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35379-6

  • Online ISBN: 978-3-642-35380-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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