Reliability Evaluation of Distribution Systems under μ Grid-Tied and Islanded μ Grid Modes Using Monte Carlo Simulation


Reliability evaluation of distribution networks under grid-tied and islanded μ grid modes is presented. The Monte Carlo simulation (MCS) algorithm is applied to a modified RBTS Bus 2 distribution network. The network includes three types of distributed energy resources, namely, solar photovoltaic (PV), wind turbine (WT), and diesel turbine generator (DTG). These distributed generators contribute to supply part of the load during grid-connected mode, but supply 100% of the load in the islanded μ grid mode. A storage system is included to decrease the peak load since the peak of the output power of the PV’s and the peak load do not match time wise in most load profiles. The impact of implementing renewable distributed generation, storage systems, and conventional generation on the reliability of distribution network is studied. This study shows that the penetration of distributed generations can improve the reliability indices of the distribution network.

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Abul’Wafa, A. and Taha, A. (2014) Reliability Evaluation of Distribution Systems under μ Grid-Tied and Islanded μ Grid Modes Using Monte Carlo Simulation. Smart Grid and Renewable Energy, 5, 52-62. doi: 10.4236/sgre.2014.53006.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Bae, I. and Kim, J. (2008) Reliability Evaluation of Customers in a Microgrid. IEEE Transactions on Power Systems, 23, 1416-1422.
[2] Li, Z., Xu, Q. and Li, Z. (2011) Reliability Analysis of Distributed System with DGs. 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), China, 14-17.
[3] Atwa, Y. and El-Saadany, E. (2009) Reliability Evaluation for Distribution System with Renewable Distributed Generation during Islanded Mode of Operation. IEEE Transactions on Power Systems, 24, 572-581.
[4] Chowdhury, A., Agarwal S. and Koval D. (2002) Reliability Modeling of Distributed Generation in Conventional Distribution System Planning and Analysis. 37th IEEE Industry Application Conference, Davenport, 13-18 October 2002, 1089-1094.
[5] Heydt, G. (2010) The Next Generation of Power Distribution Systems. IEEE Transactions on Smart Grid, 1, 225-235.
[6] Cha, S., Jeon, D., Bea, I., Il-Ryon, L. and Kim J. (2004) Reliability Evaluation of Distribution System Connected Photovoltaic Generation Considering Weather Effects. 8th International Conference on Probabilistic Methods Applied to Power Systems, Iowa State University, Ames, 12-16 September 2004, 451-456.
[7] Khallat, M. and Rahman, S. (1986) A Probabilistic Approach to Photovoltaic Generator Performance Prediction. IEEE Transactions on Energy Conversion, 1, 34-40.
[8] Lin, S., Han, M., Fan, R. and Hu, X. (2011) Configuration of Energy Storage System for Distribution Network with High Penetration of PV. IET Renewable Power Generation Conference (RPG 2011), Edinburgh, September 2011, 1-6.
[9] Liang, H.S., Su, J. and Liu, S.G. (2010) Reliability Evaluation of Distribution System Containing Microgrid. China International Conference of Electricity Distribution (CICED), China, September 2010, 1-6.
[10] Giorestto, P. and Utsurgoi, K. (1983) Development of a New Procedure for Reliability Modeling of Wind Turbine Generators. IEEE Transactions on Power Apparatus and Systems, 102, 134-143.
[11] Jangamehetti, S. and Rau, V. (2001) Optimum Sitting of Wind Turbine Generators. IEEE Transactions on Energy Conversion, 16, 8-13.
[12] Liu, S.G., Zhou, X.X., Fan, M.T. and Zhang, Z.P. (2006) Probabilistic Power Flow Calculation Using Sigma-Point Transform Algorithm. International Conference on Power System Technology, Chongqing, 22-26 October 2006, 1-5.
[13] Wu, Y.-C. and Chang, W.-F. (2002) Run-Time Reduction in Schur Complement Method Based Transmission Constrained Dispatch for Monte Carlo Production Cost. International Conference on Power System Technology, Kunming, 13-17 October 2002, 204-210.
[14] Billinton, R. and Wang, P. (1999) Teaching Distribution System Reliability Evaluation Using Monte Carlo Simulation. IEEE Transactions on Power Systems, 14, 397-403.
[15] Talari, S. and Haghifam, M.R. (2013) The Impact of Load and Distributed Energy Resources Management on Microgrid Reliability. 22nd International Conference on Electricity Distribution, Stockholm, 10-13 June 2013.
[16] Qiao, L. (2013) A Summary of Optimal Methods for the Planning of Stand-Alone Microgrid System. Energy and Power Engineering, 5, 992-998.
[17] Abul’Wafa, A.R. (2011) Reliability/Cost Evaluation of a Wind Power Delivery System. Electric Power Systems Research, 81, 873-879.
[18] Li, J., Zhang, B., Wang, J., Mao, C., Liu, Y., Wang, K., Duan, Y., Zheng, X. and Wan, L. (2010) Steady Security Risk Assessment Considering Uncertainties of Wind Power and Fault in Smart Grid. International Conference on Modeling, Identification and Control, Okayama, July 2010, 600-605.
[20] Allan, R.N., Billinton, R., Sjarief, I., Goel, L. and So, K.S. (1991) A Reliability Test System for Educational Purposes Basic Distribution System Data and Results. IEEE Transactions on Power Systems, 6, 813-820.
[21] Billinton, R. and Allan, R. (1996) Reliability Evaluation of Power Systems. Longmans, London Press, New York.

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