A Robust Model for Daily Operation of Grid-connected Microgrids During Normal Conditions

Document Type : Research Note


Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran


Microgrids (MGs) are designed to be able to serve hosting critical load in island-mode during major events. However, during normal condition when they are in grid-connected mode, MGs may have opportunity to achieve monetary profits through optimizing operation of energy resources and their participation in wholesale markets. This paper proposes a model to optimize MGs participation in the markets and operation of energy resources. Since MGs usually host renewable energy resources, making decision without considering the uncertainties may prone MGs to risk. So, the model considers uncertainties associated with generation of renewable DGs, demand, and market prices via robust optimization technique. The model is a max-min problem which is modelled as a bi-level optimization problem. The problem is solved in two iterative steps. In the first step, a genetic algorithm (GA) is applied to obtain the worst case wherein uncertain parameters are determined such that MG profit is minimized. Then, a mixed-integer linear problem is solved to maximize the profit over MG decision variables considering the values determined in the first step. The steps are iterated to converge to the best solution. To verify performance of the approach, it is applied to a typical MG and the results are reported.


1. Department of Energy, Summary report: 2012 doe
microgrid workshop", Chicago, Illinois, Jul (2012).
2. Parhizi, S., Lot , H., Khodaei, A., et al. State of the
art in research on microgrids: A review", IEEE Access,
3, pp. 890{925 (2015).
3. Ferruzzi, G., Cervone, G., Delle Monache, L., et
al. Optimal bidding in a day-ahead energy market
for micro grid under uncertainty in renewable energy
production", Energy, 106, pp. 194{202 (2016).
4. Mayhorn, E., Kalsi, K., Elizondo, M., and Butler-
Purry, K. Optimal control of distributed energy
resources using model predictive control", IEEE Power
and Energy Society General Meeting, pp. 1{8 (2012).
5. Hamidi, A., Nazarpour, D., and Golshannavaz, S.
Multiobjective scheduling of microgrids to harvest
higher photovoltaic energy", IEEE Trans. Indust. Inform.,
14(1), pp. 47{57 (2018).
6. Soroudi, A. and Amraee, T. Decision making under
uncertainty in energy systems: state of the art",
Renewable and Sustainable Energy Reviews, 28, pp.
376{384 (2013).
7. Bagherian, A. and Tafreshi, S.M.M. A developed
energy management system for a microgrid in the
competitive electricity market", IEEE Bucharest PowerTech,
pp. 1{6 (2009).
8. Hemmati, M., Mohammadi-Ivatloo, B., Ghasemzadeh,
S., et al. Risk-based optimal scheduling of recon gurable
smart renewable energy based microgrids" , Int.
J. Elect. Power Energy Syst., 101, pp. 415{428 (2018).
9. Shi, L., Luo, Y., and Tu, G. Bidding strategy of
microgrid with consideration of uncertainty for participating
in power market", Int. J. Elect. Power Energy
Syst., 59, pp. 1{13 (2014).
10. Nguyen, D.T. and Le, L.B. Optimal bidding strategy
for microgrids considering renewable energy and building
thermal dynamics", IEEE Trans. Smart Grid, 5(4),
pp. 1608{1620 (2014).
11. Ranjbar, H., Hosseini, S.H., and Kasebahadi, M.
Robust transmission expansion planning considering
private investments maximization", IEEE Int. Conf.
Power Syst. Tech. (POWERCON), pp. 1{6 (2016).
3490 H. Ranjbar and A. Safdarian/Scientia Iranica, Transactions D: Computer Science & ... 28 (2021) 3480{3491
12. Street, A., Oliveira, F., and Arroyo, J.M.
Contingency-constrained unit commitment with n-k
security criterion: A robust optimization approach",
IEEE Trans. Power Syst., 26(3), pp. 1581{1590
13. Bertsimas, D., Litvinov, E., Sun, X.A., et al. Adaptive
robust optimization for the security constrained
unit commitment problem", IEEE Trans. Power Syst.,
28(1), pp. 52{63 (2013).
14. Jiang, R., Wang, J., and Guan, Y. Robust unit
commitment with wind power and pumped storage
hydro", IEEE Trans. Power Syst., 27(2), pp. 800{810
15. Zhao, L. and Zeng, B. Robust unit commitment
problem with demand response and wind energy",
IEEE Power and Energy Society General Meeting, pp.
1{8 (2012).
16. Alizadeh, B., Dehghan, S., Amjady, N., et al. Robust
transmission system expansion considering planning
uncertainties", IET Gener. Transm. Distrib., 7(11),
pp. 1318{1331 (2013).
17. Yu, H., Chung, C.Y., and Wong, K.P. Robust
transmission network expansion planning method with
Taguchi's orthogonal array testing", IEEE Trans.
Power Syst., 26(3), pp. 1573{1580 (2011).
18. Jabr, R.A. Robust transmission network expansion
planning with uncertain renewable generation and
loads", IEEE Trans. Power Syst., 28(4), pp. 4558{
4567 (2013).
19. Chen, B., Wang, J., Wang, L., et al. Robust optimization
for transmission expansion planning: Minimax
cost vs. minimax regret", IEEE Trans. Power Syst.,
29(6), pp. 3069{3077 (2014).
20. Nojavan, S., Mohammadi-Ivatloo, B., and Zare, K.
Optimal bidding strategy of electricity retailers using
robust optimisation approach considering time-of-use
rate demand response programs under market price
uncertainties", IET Gener. Transm. Distrib., 9(4), pp.
328{338 (2015).
21. Thatte, A.A., Xie, L., Viassolo, D.E., et al. Risk
measure based robust bidding strategy for arbitrage
using a wind farm and energy storage", IEEE Trans.
Smart Grid, 4(4), pp. 2191{2199 (2013).
22. Liu, G., Xu, Y., and Tomsovic, K. Bidding strategy
for microgrid in day-ahead market based on hybrid
stochastic/robust optimization", IEEE Trans. Smart
Grid, 7(1), pp. 227{237 (2016).
23. Nojavan, S., Mohammadi-Ivatloo, B., and Zare, K.
Robust optimization based price-taker retailer bidding
strategy under pool market price uncertainty",
Int. J. Elect. Power Energy Syst., 73, pp. 955{963
24. Soroudi, A. Robust optimization based self scheduling
of hydro-thermal genco in smart grids", Energy, 61,
pp. 262{271 (2013).
25. Wang, L., Li, Q., Ding, R., et al. Integrated scheduling
of energy supply and demand in microgrids under
uncertainty: A robust multi-objective optimization
approach", Energy, 130, pp. 1{14 (2017).
26. Ji, L., Huang, G., Xie, Y., et al. Robust cost-risk
tradeo  for day-ahead schedule optimization in residential
microgrid system under worst-case conditional
value-at-risk consideration", Energy, 153, pp. 324{337
27. Mehdizadeh, A. and Taghizadegan, N. Robust optimisation
approach for bidding strategy of renewable
generation-based microgrid under demand side management",
IET Renewable Power Generation, 11(11),
pp. 1446{1455 (2017).
28. Attarha, A., Amjady, N., Dehghan, S., et al. Adaptive
robust self-scheduling for a wind producer with compressed
air energy storage", IEEE Trans. Sustainable
Energy, 9(4), pp. 1659{1671 (2018).
29. Heidarabadi, H., Hosseini, S.H., and Ranjbar, H. A
robust approach to schedule 
exible ramp in Realtime
electricity market considering demand response",
25th Iranian Conf. Elect. Eng. (ICEE), pp. 1159{1164
30. Mashhour, E. and Moghaddas-Tafreshi, S.M. Mathematical
modeling of electrochemical storage for incorporation
in methods to optimize the operational
planning of an interconnected micro grid", Journal of
Zhejiang University SCIENCE C, 11(9), pp. 737{750
31. Algarni, A. and Bhattacharya, K. A generic operations
framework for discos in retail electricity markets",
IEEE Trans. Power Syst., 24(1), pp. 356{367
32. Habibifar, R., Karimi, M.R., Ranjbar, H., et al. Economically
based distributed battery energy storage
systems planning in microgrids", 26th Iranian Conf.
Elect. Eng. (ICEE), pp. 1257{1263 (2018).
33. Kazemi, M., Zareipour, H., Ehsan, M., et al. A robust
linear approach for o ering strategy of a hybrid electric
energy company", IEEE Trans. Power Syst, 32(3), pp.
1949{1959 (2017).
34. Bertsimas, D. and Sim, M. Robust discrete optimization
and network 
ows", Mathematical Programming,
98(1), pp. 49{71 (2003).
35. Tsikalakis, A.G. and Hatziargyriou, N.D. Centralized
control for optimizing microgrids operation", IEEE
Trans. Energy Conv., 23(1), pp. 241{248 (2008).