Document Type : Research Articles

Authors

1 Electrical Engineering Department, Engineering Faculty, Razi Univerity, Kermanshah, Iran

2 Electrical Engineering Departments, Engineering Faculty, Razi University, Kermanshah, Iran.

Abstract

The advent of DG and SEGs has led to fundamental changes in various fields of power system operation. The current paper is aimed to investigate the reliability of SEGs considering DGRs based on the self-healing concept. Due to the emergence of new uncertainties in the power system resulted from the presence of DGRs, this paper is dedicated to comparing network reliability indices before and after the entry of DGRs and analyzing their effect on improving network reliability. To do so, improving the indices based on customer satisfaction, such as reducing the SAIFI, and SAIDI, is evaluated. More specifically, the improvement of the most important index based on load and energy, namely energy not supplied (ENS), is investigated. To do this, the MCS method is used given the pdf of the samples due to the presence of uncertainty created by the presence of DGRs, demanded load change and network restoration time after the presence of DG. Also, after providing an appropriate model for problem analysis, results of applying this model to the case study system are investigated using reliability indices. Subsequently, in order to improve performance of the system, impacts of the changes of various parameters on the given indices are reported. One of the most important points in this regard is to investigate the impacts of the changes in the system configuration on the results. It is observed that self-healing positively affects the reduction of the electrical energy restoration time as well as the system reliability.

Keywords

Main Subjects

[1] Ackermann, T., Andersson, G., & Söder, L.
(2001). Distributed generation: a definition.
Electric power systems research, 57(3), 195-204.
[2] Barati, H., & Shahsavari, M. (2018). Simultaneous
Optimal placement and sizing of distributed
generation resources and shunt capacitors in radial
distribution systems using Crow Search
Algorithm. International Journal of Industrial
Electronics, Control and Optimization, 1(1),
27-40.
[3] Pepermans, G., Driesen, J., Haeseldonckx, D.,
Belmans, R., & D’haeseleer, W. (2005).
Distributed generation: definition, benefits and
issues. Energy policy, 33(6), 787-798.
[4] Kundur, P., Paserba, J., & Vitet, S. (2003,
October). Overview on definition and
classification of power system stability. In
CIGRE/IEEE PES International Symposium
Quality and Security of Electric Power Delivery
Systems, 2003. CIGRE/PES 2003. (pp. 1-4).
IEEE.
[5] Alonso, J., Torres, J., Griffith, R., Kaiser, G., &
Silva, L. M. (2009). Towards self-adaptable
monitoring framework for self-healing. In grid
and Services Evolution (pp. 1-9). Springer, Boston,
MA.
[6] Košt’álová, A., & Carvalho, P. M. (2011).
Towards self-healing in distribution networks
operation: Bipartite graph modelling for
automated switching. Electric power systems
research, 81(1), 51-56.
[7] Li, T., & Xu, B. (2010). The self-healing
technologies of smart distribution grid. In CICED
2010 Proceedings (pp. 1-6). IEEE.
[8] Amin, M. (2008, July). Challenges in reliability,
security, efficiency, and resilience of energy
infrastructure: Toward smart self-healing electric
power grid. In 2008 IEEE Power and energy
society general meeting-conversion and delivery
of electrical energy in the 21st century (pp. 1-5).
IEEE.
[9] Butler-Purry, K. L., & Sarma, N. D. R. (2004).
Self-healing reconfiguration for restoration of
naval shipboard power systems. IEEE
Transactions on Power Systems, 19(2), 754-762.
[10] Ray, S., Bhattacharya, A., & Bhattacharjee, S.
(2016). Optimal placement of switches in a radial
distribution network for reliability improvement.
International Journal of Electrical Power &
Energy Systems, 76, 53-68.
[11] Serincan, M. F. (2016). Reliability considerationsof a fuel cell backup power system for telecom
applications. Journal of Power Sources, 309,
66-75.
[12] Li, G., Bie, Z., Xie, H., & Lin, Y. (2016).
Customer satisfaction based reliability evaluation
of active distribution networks. Applied Energy,
162, 1571-1578.
[13] Vahedi, H., Gharehpetian, G. B., & Karrari, M.
(2012). Application of duffing oscillators for
passive islanding detection of inverter-based
distributed generation units. IEEE Transactions on
Power Delivery, 27(4), 1973-1983.
[14] Sedghi, M., Ahmadian, A., & Aliakbar-Golkar, M.
(2016). Optimal storage planning in active
distribution network considering uncertainty of
wind power distributed generation. IEEE
Transactions on Power Systems, 31(1), 304-316.
[15] Cortes, C. A., Contreras, S. F., & Shahidehpour,
M. (2018). Microgrid topology planning for
enhancing the reliability of active distribution
networks. IEEE Transactions on Smart Grid, 9(6),
6369-6377.
[16] Pinto, R. S., Unsihuay-Vila, C., & Fernandes, T. S.
(2018). Multi-objective and multi-period
distribution expansion planning considering
reliability, distributed generation and self-healing.
IET Generation, Transmission & Distribution,
13(2), 219-228.
[17] Mohammadi-Hosseininejad, S. M., Fereidunian,
A., Shahsavari, A., & Lesani, H. (2016). A healer
reinforcement approach to self-healing in smart
grid by PHEVs parking lot allocation. IEEE
Transactions on Industrial Informatics, 12(6),
2020-2030.
[18] Li, C., Liu, X., Zhang, W., Cao, Y., Dong, X.,
Wang, F., & Li, L. (2016). Assessment method
and indexes of operating states classification for
distribution system with distributed generations.
IEEE Transactions on Smart Grid, 7(1), 481-490.
[19] Wang, Z., Chen, B., Wang, J., & Chen, C. (2016).
Networked microgrids for self-healing power
systems. IEEE Transactions on smart grid, 7(1),
310-319.
[20] Torres, B. S., Ferreira, L. R., & Aoki, A. R. (2018).
Distributed Intelligent System for Self-Healing in
Smart Grids. IEEE Transactions on Power
Delivery, 33(5), 2394-2403.
[21] Allahnoori, M., Kazemi, S., Abdi, H., & Keyhani,
R. (2014). Reliability assessment of distribution
systems in presence of microgrids considering
uncertainty in generation and load demand.
Journal of Operation and Automation in Power
Engineering, 2(2), 113-120.