Enhanced OPF for DG Penetrated Power System Network under Variable Load Conditions

Abstract:

In modern power systems, distributed generation turns out to be progressively significant. Conversely, the growing utilize of distributed generators origins the concerns on the growing system hazard owing to their probable breakdown or unruly power productivity based on such renewable energy sources as wind and the sun. Power contribution at the required proportion by the grids is the chief performance consideration which depends upon the penetration of distributed generation and the accessibility of conventional sources during the load transform. In this paper, the projected approach is that the essential load power is divided evenly between the grids composed of Distributed Generation (DG) units and the utility based on the PSO algorithm during the load transform. A case study is carried out based on the New England test system (10-Generator-39-Bus) as a standard by using Particle swarm optimization (PSO) algorithm.

• Periodical: Advanced Materials Research (Volumes 984-985)
• Pages: 1301-1305
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.984-985.1301
• Online since: July 2014
• Authors: P. Sivakumar*, Arumugam Rajapandiyan
• Keywords: Distributed Generation (DG), Optimal Power Flow (OPF), Particle Swarm Optimization (PSO), Power System

References

[1] Yang Ping, Yang Jin-ming, Zhang Hao, Development of new hybrid (solar-wind) power system, North China Electric Power, vol. 34, no. 1, pp.12-15.

[2] P. Chiradeja and R. Ramakumar, Voltage profile improvement with distributed wind turbine generation—a case study, in Proc. 2003 IEEE PES General Meeting, vol. 4, p.2331–2336.

DOI: 10.1109/pes.2003.1270993

[3] C. Wang and M.H. Nehrir, Analytical approaches for optimal placement of distributed generation sources in power systems, IEEE Trans. Power Syst., vol. 19, no. 4, p.2068–(2076).

DOI: 10.1109/tpwrs.2004.836189

[4] L. Lai and J. Ma, Improved genetic algorithms for optimal power flow under both normal and contingent operation states, Int. J. Elect. Power Energy Syst., vol. 19, no. 5, p.287–92, (1997).

DOI: 10.1016/s0142-0615(96)00051-8

[5] S. H. Karaki, R. B. Chedid, and R. Ramadan, Probabilistic performance assessment of autonomous solar-wind energy conversion systems, IEEE Trans. Energy Convers., vol. 14, no. 3, p.766–772, Sep. (1999).

DOI: 10.1109/60.790949

[6] E. Koutroulis, D. Kolokotsa, A. Potirakis, and K. Kalaitzakis, Methodology for optimal sizing of stand-alone photovoltaic/windgenerator systems using genetic algorithms, Solar Energy, vol. 80, p.1072–1088, Sep. (2006).

DOI: 10.1016/j.solener.2005.11.002

[7] C. Wang, H. Yuan, Z. Huang, J. Zhang, and C. Sun, A modified particle swarm optimization algorithm and its application in optimal power flow problem, in Proc. Int. Conf. Machine Learning and Cybernetics, Guangzhou, China, 2005, vol. 5, p.2885–2889.

DOI: 10.1109/icmlc.2005.1527435