Abstract
Determination of optimal placement and sizing of Distributed Generations (DGs) is one of the important tasks in power system operation. Several conventional as well as heuristics techniques like particle swarm optimization, differential evolution etc. have been applied to solve the problem. But one of the major drawback of these techniques are the improper selection of user defined parameters for optimal solution. Improper selection of the parameters may even lead to premature convergence. A new modified differential evolution technique based algorithm is proposed in this paper for the solution of optimal sizing and location of distributed generation to avoid premature convergence. The proposed algorithm is applied on IEEE 14 and 30 bus systems to verify its effectiveness. The results obtained by the proposed method are compared with other methods. It is found that the results obtained by the proposed algorithm are superior in terms of cost and losses.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Electric Power Research Institute, http://www.epri.com/gg/newgen/disgen/index.html
CIGRE, “Impact of increasing contribution of dispersed generation on the power system”, CIGRE Study Committee no 37, Final Report (September 1998)
Distributed Generation in Liberalized Electricity Markets, International Energy Agency/OECD. IEA, Paris, p. 19 (2002)
Ackermann, T., Andersson, G., Sodder, L.: Distributed generation: a definition. Elect. Power Syst. Res. 57, 195–204 (2001)
Ghosh, S., Ghoshal, S.P., Ghosh, S.: Optimal sizing and placement of distributed generation in a network system. Electrical Power and Energy Systems 32, 849–856 (2010)
Al Abri, R.S., El-Saadany, E.F., Atwa, Y.M.: Optimal Placement and Sizing Method to Improve the Voltage Stability Margin in a Distribution System Using Distributed Generation. IEEE Trans. on Power Systems 28(1) (February 2013)
Abdi, S., Afshar, K.: Application of IPSO-Monte Carlo for optimal distributed generation allocation and sizing. Electrical Power and Energy Systems 44, 786–797 (2013)
Prenc, R., Škrlec, D., Komen, V.: Distributed generation allocation based on average daily load and power production curves. Electrical Power and Energy Systems 53, 612–622 (2013)
García, J.A.M., Mena, A.J.G.: Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm. Electrical Power and Energy Systems 50, 65–75 (2013)
Nayak, M.R., Dash, S.K., Rout, P.K.: 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.) SEMCCO 2012. LNCS, vol. 7677, pp. 133–142. Springer, Heidelberg (2012)
Vijayakumar, K., Jegatheesan, R.: Optimal Location and Sizing of DG for Congestion Management in Deregulated Power Systems. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds.) SEMCCO 2012. LNCS, vol. 7677, pp. 679–686. Springer, Heidelberg (2012)
Storn, R., Price, K.: Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. Berkeley (CA): International Computer Science Institute. Technical report TR-95-012 (1995)
Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)
Kaelo, P., Ali, M.M.: A numerical study of some modified differential evolution algorithms. European Journal of Operational Research 169(3), 1176–1184 (2006)
Amjady, N., Sharifzadeh, H.: Solution of non-convex economic dispatch problem considering valve loading effect by a new Modified Differential Evolution algorithm. Electrical Power and Energy Systems 32, 893–903 (2010)
El Ela, A.A.A., Abido, M.A., Spea, S.R.: Differential evolution algorithm for optimal reactive power dispatch. Electric Power Systems Research 81, 458–464 (2011)
Pai, M.A.: Techniques in power system analysis. Tata McGraw-Hill Publishing Company Limited, New Delhi (2006)
Wallach, Y.: Calculations & Programs for Power System Networks. Prentice-Hall, Inc., Englewood Cliffs
Yokoyama, R., Bae, S.H., Morita, T., Sasaki, H.: Multiobjective optimal generation dispatch based on probability security criteria. IEEE Trans. on Power Systems 3, 317–324 (1988)
Durga, G., Nadarajah, M.: Optimal DG placement in deregulated electricity market. Electric. Power Syst. Res. 77, 1627–1636 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Kumar, S., Pal, D., Mandal, K.K., Chakraborty, N. (2013). Performance Study of a New Modified Differential Evolution Technique Applied for Optimal Placement and Sizing of Distributed Generation. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-03753-0_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03752-3
Online ISBN: 978-3-319-03753-0
eBook Packages: Computer ScienceComputer Science (R0)