Degree of Optimality as a Measure of Distance of Power System Operation from Optimal Operation

Document Type : Research paper

Authors

1 University of Tuzla

2 University of Tuzla, Faculty of Electrical Engineering, Department for Power and Energy Engineering

Abstract

This paper presents an algorithm based on inter-solutions of having scheduled electricity generation resources and the fuzzy logic as a sublimation tool of outcomes obtained from the schedule inter-solutions. The goal of the algorithm is to bridge the conflicts between minimal cost and other aspects of generation. In the past, the optimal scheduling of electricity generation resources has been based on the optimal activation levels of power plants over time to meet demand for the lowest cost over several time periods. At the same time, the result of that type of optimization is single-dimensional and constrained by numerous limitations. To avoid an apparently optimal solution, a new concept of optimality is presented in this paper. This concept and the associated algorithm enable one to calculate the measure of a system’s state with respect to its optimal state. The optimal system state here means that the fuzzy membership functions of the considered attributes (the characteristics of the system) have the value of one. That particular measure is called the “degree of optimality” (DOsystem). The DOsystem can be based on any of the system's attributes (economy, security, environment, etc.) that take into consideration the current and/or future state of the system. The calculation platform for the chosen electric power test system is based on one of the unit commitment solvers (in this paper, it is the genetic algorithm) and fuzzy logic as a cohesion tool of the outcomes obtained by means of the unit commitment solver. The DO-based algorithm offers the best solutions in which the attributes should not to distort each other, as is the case in a strictly deterministic nature of the Pareto optimal solution.

Keywords

Main Subjects

References

[1]     G. Eichfelder, Adaptive Scalarization methods in Multiobjective Optimization, Springer, 2008, pp. 55.
[2]     V. Chankong, and Y. Y. Haimes, Multiobjective Decision Making: Theory and Methodology, Dover Publications, Incorporated, 2008, pp. 121.
[3]     B. Basturk, and D. Karaboga, “An artificial bee colony (ABC) algorithm for numeric function optimization,” IEEE Swarm Intelligence Symposium, 12-14, 2006, Indianapolis, Indiana, USA.
[4]     D. C. Karia, and V. V. Godbole, “New approach for routing in mobile ad-hoc networks based on ant colony optimisation with global positioning system,” IET Networks, vol.2, no.3, pp. 171-180, 2013.
[5]     P. M. Pardalos, D. Z. Du, and R. L. Graham, Handbook of Combinatorial Optimization, 2nd ed., Springer, 2013, pp. 89.
[6]     J. Momoh, Electric power system applications of optimization, Marcel Dekker Inc., New York – Basel, 2001, pp. 139.
[7]     A. Tuohy, P. Meibom, E. Denny, and M. O'Malley, “Unit commitment for systems with significant wind penetration,” IEEE Trans. Power Syst., vol. 24, pp. 592-601, 2009.
[8]     P. A. Ruiz, C. R. Philbrick, E. Zak, K. W. Cheung, and P. W. Sauer, “Uncertainty management in the unit commitment problem,” IEEE Trans. Power Syst., vol. 24, pp. 642- 651, 2009.
[9]     H. Shayeghi, M. Ghasemi, “FACTS devices allocation using a novel dedicated improved PSO for optimal operation of power system,” J. Oper. Autom. Power Eng., vol. 1, no. 2, pp. 124-135, 2013.
[10]  N. Ghorbani, E. Babaei, “Combined economic dispatch and reliability in power system by using PSO-SIF algorithm,” J. Oper. Autom. Power Eng., vol. 3, no. 1, pp. 23-33, 2015.
[11]  S. M. Mohseni-Bonab, A. Rabiee, S. Jalilzadeh, B. Mohammadi-Ivatloo, S.               Nojavan, “Probabilistic multi objective optimal reactive power dispatch considering load uncertainties using monte carlo simulations” J. Oper. Autom. Power Eng., vol. 3, no. 1, pp. 83-93, 2015.
[12]  I. G. Damousis, A. G. Bakirtzis, and P. S. Dokopoulos, “A solution to the unit-commitment problem using integer-coded genetic algorithm,” IEEE Trans. Power Syst., vol. 19, pp. 1165-1172, 2004.
[13]  A. Viana, J. P. Pedroso, “A new MILP-based approach for unit commitment in power production planning,” Int. J. Electr. Power Energy Syst., pp. 997-1005, 2013.
[14]  LINGO User’s guide, LINDO Systems Inc., 2011.      
[15]  J. Bracken, J. McGill, “Mathematical programs with optimization problems in the constraints,” Oper. Res., vol. 21, pp. 37-44, 1973.
[16]  B. Colson, P. Marcotte, G. Savard, “An overview of bi-level optimization,” Ann. Oper. Res., 153:235-256, 2007.
[17]  A. Sinha, P. Malo, and K. Deb, “Tutorial on bi-level optimization,” In Proc. Genetic Evolution. Comput. Conf., Amsterdam, Netherlands, 2013.
[18]  D. P. Kothari, and J. Nagrath, Power System Engineering,Tata McGraw-Hill Publication, 2nd ed., 2008, pp. 124.
[19]  S. N. Pant, and K. E. Holbert, “Fuzzy logic in decision making and signal processing,” online database, http://enpub.fulton.asu.edu/powerzone/fuzzylogic
[20]  V. Shanthi, A. E. Jeyakumar, “Unit commitment by genetic algorithms,” Proc. IEEE PES Power Syst. Conf. Expos., 2004, vol.3, 2004, pp. 1329-1334.
[21]  S. Halilčević, “Procedures for definition of generation ready-reserve capacity,”          IEEE Trans. Power Syst., vol. 13, pp. 649-655, 1998.
[22]  S. A. Kazarlis, A. G. Bakirtzis, and V. Petridis, “A genetic algorithm solution to the unit commitment problem,” IEEE Trans. Power Syst., vol.11, pp. 83-92, 1996.
[23]  K. Iba, “Reactive power optimization by genetic algorithm,” IEEE Trans.  Power Syst., vol. 9, pp. 685-692, 1994.
[24]  J. Varela, N. Hatziargyriou, L.J. Puglisi, M. Rossi, A. Abart, and B. Bletterie,            “The IGREEN grid project,” IEEE Power Energy Mag., vol. 15, pp. 30-40, 2017.
Journal of Operation and Automation in Power Engineering
Volume 6, Issue 1
June 2018
Pages 69-79

Files

History

  • Receive Date: 02 March 2017
  • Revise Date: 22 June 2017
  • Accept Date: 19 October 2017
  • First Publish Date: 01 June 2018

Share

How to cite

Statistics