Abstract

The network reliability is one of significant characteristics of operation of the power system that plays an important role in designing a standard electricity market especially in case of unit commitment. In the present article, a power network with maximum load 2700 MW is considered. For calculation of produced energy of each power plant and cost of production for 1 year, this network is examined in two modes with and without considering emergency exit of production units. Regarding the characteristics of production units like number, capacity and cost of production with zero probability of emergency exit for each of them and considering network peak load that is determined by the user. For computation cost of production of each power plant in the network and calculation of its cost of production, a graphical program is provided by Graphical User Interface (GUI) environment and MATLAB software, which computes the energy and costs of production of the system as well as rate of reliability indexes LOLP and EENS. According to such results, it can be concluded that the charged cost to the system for mode A that all production costs are entered the circuit without considering probability of their emergency exit (FOR = 0) and only based on their own production cost (or their biding prices in the market of electricity) will be much lesser than mode B, which units participate in supplying network load in the electricity market on the basis of their probability of presence (FOR ≠ 0).

Keywords:

Different constraints, graphical user interface, MATLAB, powerhouse,

References

1. Billinton, R. and R. Karki, 1999. Capacity reserve assessment using system well-being analysis. IEEE T. Power Syst., 14(2): 433- 438.
   CrossRef    
2. Bouffard, F. and F.D. Galiana, 2004. An electricity market with a probabilistic spinning reserve criteria. IEEE T. Power Syst., 19(1): 300-306.
   CrossRef    
3. Chattopadhyay, D. and R. Balclick, 2002. Unit commitment with probabilistic reserve. Proceeding of the IEEE Power Engineering Society Winter Meeting, pp: 280-285.
   CrossRef    
4. Cheng, C.P., C.W. Liu and G.C. Liu, 2000. Unit commitment by Lagrangian relaxation and genetic algorithm. IEEE T. Power Syst., 15: 707-714.
   CrossRef    
5. Damousis, I.G., A.G. Bakirtzis and P.S. Dokopoulos, 2004. A solution to the unit commitment problem using integer-coded genetic algorithm. IEEE T. Power Syst., 19(2): 1165-1172.
   CrossRef    
6. Ghanbarzadeh, T., S. Goleijani and M.P. Moghadam, 2011. Reliability constrained unit commitment with electric vehicle to grid using hybrid particle swarm optimization and ant colony optimization. Proceeding of the 2011 IEEE Power and Energy Society General Meeting. San Diego, CA, pp: 1-7.
   CrossRef    
7. Kazarlis, S.A., A.G. Bakirtzis and V. Petridis, 1996. A genetic algorithm solution to the unit commitment problem. IEEE T. Power Syst., 11: 83-92.
   CrossRef    
8. Keyhani, A. and M. Marwali, 2011. Smart Power Grid. Springer, Berlin, New York.
   
9. Ouyang, Z. and S.M. Shahidehpour, 1992. A multi-stage intelligence system for unit commitment. IEEE T. Power Syst., 7: 639-646.
   CrossRef    
10. Purushothama, G.K. and L. Jenkins, 2003. Simulated annealing with local search: A hybrid algorithm for unit commitment. IEEE T. Power Syst., 18(1): 273-278.
    CrossRef    
11. Saber, A.Y. and G. Kumar, 2009a. Unit commitment with vehicle-to-grid using particle swarm optimization. Proceeding of the IEEE Bucharest Power Technology Conference.
    CrossRef    
12. Saber, A.Y. and G.K. Venayagamoorthy, 2009b. Intelligent unit commitment with vehicle-to-grid a-cost emission optimization. J. Power Source., 195(3): 898-911.
    CrossRef    
13. Sasaki, H., M. Watanabe and R. Yokoyama, 1992. A solution method of unit commitment by artificial neural networks. IEEE T. Power Syst., 7: 974-981.
    CrossRef    
14. Simopoulos, D.N., S.D. Kavatza and C.D. Vournas, 2006. Reliability constrained unit commitment using simulated annealing. IEEE T. Power Syst., 21(4): 1699- 1706.
    CrossRef    
15. Wang, C. and S.M. Shahidehpour, 1992. A decomposition approach to non-linear multi-area generation scheduling with tie-line constraints using expert systems. IEEE T. Power Syst., 7: 1409-1418.
    CrossRef    
16. Wang, S.J., S.M. Shahidehpour, D.S. Kirschen, S. Mokhtari and G.D. Irisarri, 1995. Short term generation scheduling with transmission and environmental constraints using an augmented Lagrangian relaxation. IEEE T. Power Syst., 10(3): 1294-301.
    CrossRef    
17. Wong, Y.W., 1998. An enhanced simulated annealing approach to unit commitment. Int. J. Electr. Power Energy Syst., 20: 359-368.
    CrossRef    
18. Wood, A.J. and B.F. Wollenberg, 1996. Power Generation Operation and Control. 2nd Edn., Wiley, New York.
    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.