Considering Practical Constraint's Effect in Power Station Problems for Optimizing Power Generation by comparison NSGA-II and Nested PSO

Year 2016, Volume: 16 Issue: 2, 3017 - 3023, 23.09.2016

Murtaza Farsadi Hadi Hosseinnejad Tohid Sattarpour Dizaji

https://izlik.org/JA42KX57LY

Abstract

After solving problems like Unit Commitment and Economic Dispatch it is important to consider some special constraint which come directly from nature of generators. These constraints which will mention are some related to temperate limits and other are related to dynamic of turbines. In this paper after solving the unit commitment problem and economic dispatch simultaneously, the main effect of this constraints and method for skip them will be tried. In the next part the main cost function will be detailed this kind of functions on problems which consider two cost function instead of one The main algorithm that used is nested PSO. The nested PSO can optimize two function which one is in the inner layer of other one.The second algorithm which will try the results is NSGA-II.

Keywords

nested PSO , constraints , Power system , NSGA-II

References

• Z. L. Gaing, “Particle swarm optimization to solving the economic dispatch considering the generator constraints,” IEEE Trans. Power Syst., vol. 18, no. 3, pp. 1187–1195, 2003.
• D. C. Walters and G. B. Sheble, “Genetic algorithm solution of economic dispatch with valve point loading.pdf,” IEEE Trans. Power Syst., vol. 8, no. 3, pp. 1325–1332, 1993.
• J. Zaborszky, G. Huang, B. Zheng, and T. C. Leung, “on the Phase Portrait of a Class of Large Nonlinear Dynamic Systems Such As the Power System.,” IEEE Trans. Automat. Contr., vol. 33, no. 1, pp. 4–15, 1988.
• S. Sayah and A. Hamouda, “A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems,” Appl. Soft Comput., vol. 13, no. 4, pp. 1608–1619, 2013.
• S. Jiang, Z. Ji, and Y. Shen, “A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints,” Int. J. Electr. Power Energy Syst., vol. 55, pp. 628–644, 2014.
• D. Bertsimas, E. Litvinov, X. A. Sun, J. Zhao, and T. Zheng, “Adaptive robust optimization for the security constrained unit commitment problem,” Power Syst. IEEE Trans., vol. 28, no. 1, pp. 52–63, 2013.
• M. Basu, “Artificial bee colony optimization for multi-area economic dispatch,” Int. J. Electr. Power Energy Syst., vol. 49, pp. 181–187, 2013.
• T. Niknam, R. Azizipanah-Abarghooee, and J. Aghaei, “A new modified teaching-learning algorithm for reserve constrained dynamic economic dispatch,” Power Syst. IEEE Trans., vol. 28, no. 2, pp. 749–763, 2013.
• P. K. Roy and S. Bhui, “Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem,” Int. J. Electr. Power Energy Syst., vol. 53, pp. 937–948, 2013.
• B. Bahmani-Firouzi, E. Farjah, and A. Seifi, “A new algorithm for combined heat and power dynamic economic dispatch considering valve-point effects,” Energy, vol. 52, pp. 320– 332, 2013.
• Q. Wang, J.-P. Watson, and Y. Guan, “Two-stage robust optimization for Nk contingency-constrained unit commitment,” Power Syst. IEEE Trans., vol. 28, no. 3, pp. 2366–2375, 2013.
• Z.-S. Zhang, Y.-Z. Sun, D. W. Gao, J. Lin, and L. Cheng, “A versatile probability distribution model for wind power forecast errors and its application in economic dispatch,” Power Syst. IEEE Trans., vol. 28, no. 3, pp. 3114–3125, 2013.
• C. von Lücken, B. Barán, and C. Brizuela, “A survey on multi-objective evolutionary algorithms for many-objective problems,” Comput. Optim. Appl., vol. 58, no. 3, pp. 707– 756, 2014.
• J. Kennedy, “Particle swarm optimization,” in Encyclopedia of machine learning, Springer, 2011, pp. 760–766.
• L. Davis, “Handbook of genetic algorithms,” 1991.
• D. B. Fogel, Evolutionary computation: toward a new philosophy of machine intelligence, vol. 1. John Wiley & Sons, 2006.
• R. C. Eberhart and Y. Shi, “Comparison between genetic algorithms and particle swarm optimization,” in Evolutionary Programming VII, 1998, pp. 611–616.
• M. Farsadi, H. Hosseinnejad, and T. S. Dizaji, “Solving Unit Commitment and Economic Dispatch Simultaneously Considering Generator Constraints by Using Nested PSO,” 2015, pp. 493–499.
• H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi, “A particle swarm optimization for reactive power and voltage control considering voltage security assessment,” Power Syst. IEEE Trans., vol. 15, no. 4, pp. 1232–1239, 2000.