Transmission Service Cost Calculation with Power Loss and Congestion Considerations

Author:

Shooshtari Alireza Tavakoli1,Joorabian Mahmood1,Milani Armin Ebrahimi2

Affiliation:

1. Islamic Azad University, Dezful Branch, Iran

2. Islamic Azad University, Iran

Abstract

One of the most challenging issues of transmission grids in restructured power networks is determining the real transmission service cost. In this paper, a method for calculating the transmission service in different grids is presented. In the proposed method, the cost of power loss and congestion of transmission services are considered. Using this integrated model, the strengths and weaknesses of a power grid can be straightforwardly analyzed. All simulations have been done on IEEE nine-bus test system. Computing the transmission service cost, the next step is to optimize power loss and congestion using NSGA-II which is a two objective genetic algorithm method in which optimal power flow is implemented and power loss and congestion are considered as objective functions. Numerical results prove the effectiveness of the proposed optimizing method in which decreasing the cost of power loss and congestion have a direct effect on improving the quality of transmission services.

Publisher

IGI Global

Subject

General Medicine,General Chemistry

Reference22 articles.

1. Ahiakwor, C. O., Chukwu, U. C., & Dike, D. O. (2008). Optimal transmission line pricing algorithm for a restructured power system. In Proceedings of the IEEE/PES Conference and Exposition on Transmission and Distribution (Vol. 1, pp. 1-6).

2. Alomoush, M. (2000). Actionable fixed transmission rights for congestion management. Unpublished doctoral dissertation, Illinois Institute of Technology, Chicago, IL.

3. Analysis Group/Economics. (2001). Potential adverse consequences of poor transmission pricing. Retrieved from http://www.southernco. com

4. Araneda, J. (2002). Foundations of pricing and investment in electricity transmission. Unpublished master’s thesis, University of Manchester Institute of Science and Technology, Manchester, UK.

5. A fast and elitist multiobjective genetic algorithm: NSGA-II

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