Author : Y N Vijaya Kumar 1
Date of Publication :7th March 2017
Abstract: This paper presents a particle swarm optimization (PSO) based fuzzy stochastic long term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level, and reduction in voltage deviation are simultaneously considered as the objective functions. At first these objectives are fuzzified and designed to be comparable with each other, then they are introduced to a PSO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. IEEE 30-bus radial distribution test system is used as an illustrative example to show the effectiveness of the proposed method.
- M. Afkousi-Paqaleh and S. H. Hosseini, “Transmission constrained energy and reserve dispatch by harmony search algorithm,” in Proc. IEEE Gen. Meeting, 2009, pp. 1–8.
- S. Kamel, M. Abdel-Akher, and M. K. El-Nemr, “Implementation of SSSC model in the newtonraphson power flow formulation using current injections,” in Proc. 45th Int. Univ. Power Eng. Conf., Sep. 2010, pp. 1–5.
- K. K. Sen, “SSSC-static synchronous series compensator: Theory, mod-eling, and applications,” IEEE Trans. Power Del., vol. 13, no. 1, pp. 241–246, Jan. 1998.
- J. M. Lopez´-Lezama, A. Padilha-Feltrin, J. Contreras, and J. I. Munoz,˜ “Optimal contract pricing of distributed generation in distribution net-works,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 128–136, Feb. 2011
- H. A. Gil and G. Joos, “Models for quantifying the economic benefits of distributed generation,” IEEE Trans. Power Syst., vol. 23, no. 2, pp. 327–335, May 2008.