Authors:
MUQTHIAR ALI SHAIK,M. PADMA LALITHA,N. VISHALI3,DOI NO:
https://doi.org/10.26782/jmcms.2020.03.00007Keywords:
Transmission Cost Allocation,Lightning Search Algorithm,Particle Swarm Optimization,Distributed Energy Resources,Economic Power Generation,Abstract
In the present world scenario, the Distributed Energy Resources (DERs) were getting importance because of their vital importance to plan out a well-defined scheme of Transmission Cost Allocation to the power system. This study focuses on the allocation of optimal and reliable costs for each generating unit for IEEE 30-bus system. This results in economic power generation in all the units of the distributed Energy Resources (DER). To obtain optimal and reliable cost, a cascaded algorithm combining Lightning Search Algorithm (LSA) and Particle Swarm Optimization (PSO) is employed. The LSA obtains the optimal generation unit whereas the PSO determines the optimal cost of generation. Analysis of the power flow was done using the method of Newton Raphson’s method. Line Outage Distribution Factor, Transmission Reliability Margin, Generation cost and load cost are calculated before and after the line outage. The costvalues obtained for the proposed approach of Transmission Cost Allocation are validated with the existing work of Transmission Cost Allocation. The proposed system results in optimal and reliable cost, with economic power generation when compared to the existing method.Refference:
I. Bando, S., Watanabe, H., Asano, H., &Tsujita, S. (2009). Impact of various characteristics of electricity and heat demand on the optimal configuration of a microgrid. Electrical Engineering in Japan, 169(2), 6-13.
II. Bando, S., Watanabe, H., Asano, H., &Tsujita, S. (2009). Impact of various characteristics of electricity and heat demand on the optimal configuration of a microgrid. Electrical Engineering in Japan, 169(2), 6-13
III. Basu, A. K., Bhattacharya, A., Chowdhury, S., &Chowdhury, S. P. (2011). Planned scheduling for economic power sharing in a CHP-based micro-grid. IEEE Transactions on Power systems, 27(1), 30-38
IV. Basu, M. (2011). Economic environmental dispatch using multi- objective differential evolution. Applied soft computing, 11(2), 2845- 2853.
V. Duman, S., Güvenç, U., Sönmez, Y., &Yörükeren, N. (2012). Optimal power flow using a gravitational search algorithm. Energy Conversion and Management, 59, 86-95
VI. El Ela, A. A., Abido, M. A., &Spea, S. R. (2010). Optimal power flow using differential evolution algorithm. Electric Power System Research, 80(7), 878- 885.
VII. Galiana, F. D., Kockar, I., & Franco, P. C. (2002). Combined pool/bilateral dispatch. I. Performance of trading strategies. IEEE Transactions on Power Systems, 17(1), 92-99.
VIII. Justo, J. J., Mwasilu, F., Lee, J., & Jung, J. W. (2013). AC-microgrids versus DC-microgrids with distributed energy resources: A review. Renewable and sustainable energy reviews, 24, 387-405.
IX. Li, F., &Tolley, D. L. (2007). Long-run incremental cost pricing based on unused capacity. IEEE Transactions on Power Systems, 22(4), 1683-1689.
X. Monsef, H., &Jaefari, M. (2009). Transmission cost allocation based on the use of reliability margin under contingency conditions. IET generation, transmission & distribution, 3(6), 574-585.
XI. Naderi, E., Seifi, H., &Sepasian, M. S. (2012). A dynamic approach for distribution system planning considering distributed generation. IEEE Transactions on Power Delivery, 27(3), 1313-1322.
XII. Pan, J., Teklu, Y., Rahman, S., & Jun, K. (2000). Review of usage-based transmission cost allocation methods under open access. IEEE transactions on power systems, 15(4), 1218-1224.
XIII. Padhy, N. P., &Kumari, L. (2004, April). Evolutionary programming based economic power dispatch solutions with independent power producers. In 2004 IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies. Proceedings (Vol. 1, pp. 172-177). IEEE.
XIV. Pipattanasomporn, M., Willingham, M., &Rahman, S. (2005). Implications of on-site distributed generation for commercial/industrial facilities. IEEE Transactions on Power Systems, 20(1), 206-212.
XV. S. G. Smitha, P. V. Satyaramesh, P. Sujatha. “Usage Based Transmission Cost Allocation to Wheeling Transactions in Bilateral Markets”, Journal of The Institution of Engineers (India): Series B, 28th July 2018
XVI. Soares, T., Pereira, F., Morais, H., & Vale, Z. (2015). Cost allocation model for distribution networks considering high penetration of distributed energy resources. Electric Power Systems Research, 124, 120- 132.
XVII. Wang, J., Zhong, H., Xia, Q., & Kang, C. (2017). Optimal planning strategy for distributed energy resources considering structural transmission cost allocation. IEEE Transactions on Smart Grid, 9(5), 5236-5248.
XVIII. Wang, J., Zhong, H., Tang, W., Rajagopal, R., Xia, Q., & Kang, C. (2018). Tri-level expansion planning for transmission networks and distributed energy resources considering transmission cost allocation. IEEE Transactions on Sustainable Energy, 9(4), 1844-1856.
XIX. Yang, Z., Zhong, H., Xia, Q., Kang, C., Chen, T., & Li, Y. (2015). structural transmission cost allocation scheme based on capacity usage identification. IEEE Transactions on Power Systems, 31(4), 2876-2884