Document Type : Research Articles


1 Department of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

2 Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran


Emergency demand response (EDR) and under frequency load shedding (UFLS) are used as two separate methods for frequency restoration of power system after the usual methods of frequency control are not able to maintain the frequency stability of the system. This paper proposes the optimized emergency demand side management (OEDSM) method which improves the performance of previous methods by integrating UFLS and EDR methods along with introducing new critical status detection and optimization modules. The advantages of the proposed method are simultaneous operation of EDR and UFLS processes, the high speed of critical condition detection using the proposed emergency index, increasing the speed of the algorithm with parallel operation of modules, and optimal load shedding by providing a separate optimization module. In order to validate and evaluate the performance of the proposed method, the power system was tested under different scenarios using DIgSILENT software, which the extracted results indicate better performance of the proposed method in frequency restoration, as well as improvement of utilization and power quality of the system compared to previous methods.


[1] Y. Y. Hong, M. C. Hsiao, Y. R. Chang, Y. D. Lee, and H. C.
Huang, “Multiscenario underfrequency load shedding in a
microgrid consisting of intermittent renewables,” IEEE
Trans. Power Deliv., Vol. 28, No. 3, pp. 1610-1617, July

[2] A. Ketabi, M. Hajiakbari. Fini, “An underfrequency load
shedding scheme for islanded microgrids,” Int. J. Electr.
Power Energy Syst., Vol. 62, No. 1, pp. 599-607, Nov.

[3] A. J. Wood and B. F. Wollenberg, Power generation,
operation, and control, Wiley, Chap. 3, Dec. 2013.

[4] J. Laghari, H. Mokhlis, A. Bakar, and H. Mohamad,
“Application of computational intelligence techniques for
load shedding in power systems: A review,” Energy
Convers. Manag.., Vol. 75, No. 1, pp. 130-140, Nov. 2013.

[5] G.S. Grewal, J.W. Konowalec and M. Hakim, “A new
centralized adaptive underfrequency load shedding
controller for microgrids based on a distribution state
estimator,” IEEE Trans. Power Deliv., Vol. 32, No. 1, pp.
370-380, Feb. 2017.

[6] V. Terzija, “Case study: Adaptive underfrequency load
shedding based on the magnitude of the disturbance
estimation,” IEEE Trans. Power Syst., Vol. 21, No. 3, pp.
1260-1266, July 2006.

[7] A. Chandra and A. K. Pradhan, “An Adaptive
Underfrequency Load Shedding Scheme in the Presence of
Solar Photovoltaic Plants,” IEEE Syst. J., Vol. 15, No. 1,
pp. 1235-1244, May 2020.
[8] T. Shekari, F. Aminifar, and M. Sanaye-Pasand, “An
analytical adaptive load shedding sheme againts severe
combinational disturbances,” IEEE Trans. Power Syst., Vol.
31, No. 5, pp. 4135-4143, Sept. 2016.

[9] J. Tang, J. Liu, F. Ponci, and A. Monti, “Adaptive load
shedding based on combined frequency and voltage
stability assessment using synchrophasor measurements,”
IEEE Trans. Power Syst., Vol. 28, No. 2, pp. 2035-2047,
May 2013.

[10] S.S. Banijamali and T. Amraee, “Semi-adaptive setting of
under frequency load shedding relays considering credible
generation outage scenarios,” IEEE Trans. Power Deliv.,
Vol. 34, No. 3, pp. 1098-1108, June 2019.

[11] Ch. Li, Y. Wu, Y. Sun, H. Zhang and Y. Liu, “Continuous under-frequency load shedding sheme for power system
adaptive frequency control,” IEEE Trans. Power Syst., Vol.
35, No. 2, pp. 950-961, Mar. 2020.

[12] J.A. Laghari, H. Mokhlis, M. Karimi, A.H. Abu Bakar and
H. Mohamad, “A new under-frequency load shedding
technique based on combination of fixed and random
priority of loads for smart grid applications,” IEEE Trans.
Power Syst., Vol. 30, No. 5, pp. 2507-2515, Sept. 2015.

[13] L. Zhang, S. Zhou, J. An and Q. Kang, “Demand-side
management optimization in electric vehicles battery
swapping service,” IEEE Access, Vol. 7, pp. 95224-95232,
July 2019.

[14] P. Herath, V. Fusco and M. Navarro, “Computational
Intelligence-Based Demand Response Management in a
Microgrid,” IEEE Trans. Ind. Appl., Vol. 55, No. 1, pp.
732-740, Jan. 2019.

[15] L. R. Chang-Chien, L. N. An, T. W. Lin, W. J. Lee,
“Incorporating demand response with spinning reserve to
realize an adaptive frequency restoration plan for system
contingencies,” IEEE Trans. Smart Grid, Vol. 3, No. 3, pp.
1145-1153, Sept. 2012.

[16] M. Collotta and G. Pau, “An Innovative Approach for
Forecasting of Energy Requirements to Improve a Smart
Home Management System Based on BLE,” IEEE Trans.
Green Commun. Netw., Vol. 1, No. 1, pp. 112-120, Feb.

[17] S. A. Pourmousavi and M. H. Nehrir, “Real-time central
demand response for primary frequency regulation in
microgrids,” IEEE Trans. Smart Grid, Vol. 3, No. 4, pp.
1988-1996, Mar. 2012.

[18] K. M. Rogers, R. Klump, H. Khurana, A. A. Aquino-Lugo,
and T. J. Overbye, “An authenticated control framework
for distributed voltage support on the smart grid,” IEEE
Trans. Smart Grid, Vol. 1, No. 1, pp. 40-47, June 2010.

[19] M. Sankur, D. Arnold, and D. Auslander, “Dynamic
programming for optimal load-shedding of office scale
battery storage and plug-loads,” IEEE Power and Energy
Society General Meeting, pp. 1-5, 2015.

[20] IEEE Guide for the Application of Protective Relays Used
for Abnormal Frequency Load Shedding and Restoration,
IEEE Std C37.117, 2007.