Document Type : Research Articles


Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran


This paper deals with the optimal scheduling of a microgrid (MG) equipped with dispatchable distributed generators (DGs), renewable generators and electrical storages (batteries). A chance-constrained model is developed to handle normal operation and emergency conditions of MG including DG outage and unwanted islanding. Purchasing reserve from the upstream grid is also considered. Moreover, the uncertainties of loads and renewable resources are incorporated into the model. Furthermore, a novel probabilistic formulation is presented to determine the amount of required reserve in different conditions of MG by introducing separate probability distribution functions (PDFs) for each condition. Accordingly, an index named as the probability of reserve sufficiency (PRS) is introduced. The presented model keeps a given value of PRS in normal and emergency conditions of MG operation. In addition, some controllable variables are added to the chance constraints as an innovative technique to reduce the complexity of the model. Finally, a test microgrid is studied in different case studies and the results are evaluated.


Main Subjects

[1] Schwaegerl C, Tao L, The microgrids concept
microgrids architectures and control. United
Kingdom: John Wiley & Sons2013; 1-24.
[2] Aboli R, Ramezani M, Falaghi H. Joint optimization
of day-ahead and uncertain near real-time operation
of microgrids. International Journal of Electrical
Power & Energy Systems 2019; 107: 34-46.
[3] Kumar K P, Saravanan B. Day ahead scheduling of
generation and storage in a microgrid considering
demand Side management. Journal of Energy
Storage 2019; 21: 78-86.
[4] Ortega-Vazquez M, Kirschen D. Estimating the
spinning reserve requirements in systems with
significant wind power generation penetration. IEEE
Trans. Power Syst. 2009; 24: 114–123.
[5] Zakariazadeh A, Jadid S, Siano P. Stochastic
operational scheduling of smart distribution system
considering wind generation and demand response
programs. Int J Electr Power Energy Syst 2014;218–
[6] Zakariazadeh A, Jadid S, Siano P. Smart microgrid
energy and reserve scheduling with demand response
using stochastic optimization. Int. J. Electr. Power
Energy Syst. 2014; 63: 523-533.
[7] Bouffard F, Galiana F D. Stochastic security for
operations planning with significant wind power
generation. IEEE Trans. Power Syst. 2008;306–316.
[8] Bouffard F G F, Conejo AJ. Market-clearing with
stochastic security-Part II: case studies. IEEE Trans.
Power Syst. 2005; 20: 1827–35.
[9] SoltaniNejad Farsangi A, Hadayeghparast S,
Mehdinejad M, et al. A novel stochastic energy
management of a microgrid with various types of
distributed energy resources in presence of demand
response programs. Energy 2018; 160: 257-274.
[10] Tabar V S, Jirdehi M A, Hemmati R. Energy
management in microgrid based on the multi
objective stochastic programming incorporating
portable renewable energy resource as demand
response option. Energy 2017; 118: 827-839.
[11] Bouffard F, Galiana F D. An electricity market with
a probabilistic spinning reserve criterion. IEEE
Trans. Power Sys. 2004; 19: 300–307.
[12] Simopoulos D N, Kavatza S D, Vournas C D.
Reliability constrained unit commitment using
simulated annealing. IEEE Trans. Power Syst. 2006;
21: 1699–1706.
[13] M. Q. Wang, Gooi H B. Spinning reserve estimation
in microgrids. IEEE transaction on power systems
2011; 26: 1164-1174.
[14] Young Lee S, Gyu Jin Y, Tae Yoon Y. Determining
the Optimal Reserve Capacity in aMicrogrid With
Islanded Operation. IEEE Trans Power Sys. 2016;
31: 1369-76.
[15] Ahn S-J, Nam S-R, Choi J-H, et al. Power
scheduling of distributed generators for economic
and stable operation of a microgrid. IEEE
Transactions on Smart Grid 2013; 4: 398-405.
[16] Khodaei A. Microgrid optimal scheduling with
multi-period islanding constraints. IEEE Trans.
power syst. 2014; 29: 1383-1392.
[17] Liu G, Starke M, Xiao B, et al. Microgrid optimal
scheduling with chance-constrained islanding
capability. Electric Power Systems Research 2017;
145: 197-206.
[18] Aminifar F, Fotuhi-Firuzabad M, Shahidehpour M.
Unit commitment with probabilistic spinning
reserve and interruptible load considerations. IEEE
Trans. Power Syst. 2009; 24: 388–397.
[19] Moghaddam I G, Saniei M, Mashhour E. A
comprehensive model for self-scheduling an energyhub to supply cooling, heating and electrical
demands of a building. Energy 2016; 94: 157-170.
[20] Xiaobo Z, Baohui Z, Qifei H, et al., Microgrid
optimal operation considering virtual reserve
capacity trading of storage battery. in 2016 IEEE
16th International Conference on Environment and
Electrical Engineering (EEEIC), 2016, pp. 1-5.
[21] Boroojeni K, Amini M, Bahrami S, et al. A novel
multi-time-scale modeling for electric powerdemand forecasting: from short-term to medium-
term horizon. Electric Power Systems Research2017; 142: 58–73.