Resilient Operation Scheduling of Microgrid Using Stochastic Programming Considering Demand Response and Electrical Vehicles

Document Type : Research paper

Authors

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

2 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Abstract

Resilient operation of microgrid is an important concept in modern power system. Its goal is to anticipate and limit the risks, and provide appropriate and continuous services under changing conditions. There are many factors that cause the operation mode of micogrid changes between island and grid-connected modes. On the other hand, nowadays, electric vehicles (EVs) are desirable energy storage systems (ESSs) because of clean transportation. Besides, energy storage systems are helpful to decrease power generation fluctuations arising from renewable energy sources (RESs) in new power systems. In addition, both sides (EV and RESs’ owners) can gain a good profit by integrating EVs and RESs. Therefore, in this paper, a resilient operation model for microgrid is presented considering disasters and islands from the grid. In the proposed formulation, microgrid (MG) operator schedules its energy resources, EVs and ESSs in minimum cost considering demand response (DR) program and resiliency of the microgrid to islanding and uncertainties in market price, load, and generation of RESs. The impact of uncertainties is modeled in the scenario based framework as stochastic programming. The efficiency of presented method is validated on IEEE standard test system and discussed in two cases.

Keywords

Main Subjects

References

[1]    L. Che, M. Khodayar, and M. Shahidehpour, “Only connect: Microgrids for distribution system restoration,” IEEE Power Energy Mag., Vol 12, no. 1, pp.70-80, 2014.
[2]    M. E. Khodayar, M. Barati, and M. Shahidehpour, “Integration of high reliability distribution system in microgrid operation,” IEEE Trans. Smart Grid., vol. 3, no. 4, pp. 1997-2006, 2012.
[3]    A. Gholami, F. Aminifar, and M. Shahidehpour, “Front lines against the darkness: Enhancing the resilience of the electricity grid through microgrid facilities,” IEEE Electr. Mag., vol. 4, no. 1, pp. 18-24, 2016.
[4]    A. Khodaei, “Provisional microgrids,” IEEE Trans. Smart Grid, vol. 6, no. 3, pp. 1107-1115, 2015.
[5]    Y. Bian and Z. Bie, “Multi-Microgrids for Enhancing Power System Resilience in Response to the Increasingly Frequent Natural Hazards,” IFAC-PapersOnLine, vol. 51, no. 28, pp. 61-66, 2018.
[6]    M. H. Amirioun, F. Aminifar, M. and Shahidehpour, “Resilience-Promoting Proactive Scheduling against Hurricanes in Multiple Energy Carrier Microgrids,” IEEE Trans. Power Syst., 2018.
[7]    A. S. Siddiqui, and C. Marnay, “Distributed generation investment by a microgrid under uncertainty,” Energy., vol. 33, no. 12, pp. 1729-1737, 2008.
[8]    M. A. Sofla, and G. B. Gharehpetian, “Dynamic performance enhancement of microgrids by advanced sliding mode controller,” International Journal of Electr. Power Energy Syst., vol. 33, no. 1, pp. 1-7, 2011.
[9]    W. Su, and J. Wang, “Energy management systems in microgrid operations,” Electr. J., vol. 25, no. 8, pp. 45-60, 2012.
[10]  M. Alilou, D. Nazarpour, and H. Shayeghi, “Multi-Objective Optimization of Demand Side Management and Multi DG in the Distribution System with Demand Response,” J. Oper. Autom. Power Eng., vol. 6, no.2, pp. 230-242, 2018.
[11]  K. Zhang, L. Xu, M. Ouyang, H. Wang, L. Lu, J. Li, and Z. Li, “Optimal decentralized valley-filling charging strategy for electric vehicles,” Energy Conv. Manag., vol. 78, pp. 537-550, 2014.
[12]  M. A. Hannan, F. A. Azidin, and A. Mohamed, “Multi-sources model and control algorithm of an energy management system for light electric vehicles,” Energy Conv. Manag., vol. 62, pp. 123-130, 2012.
[13]  Q. Zhang, K. N. Ishihara, B. C. Mclellan, and T. Tezuka, “Scenario analysis on future electricity supply and demand in Japan,” Energy., vol. 38, no. 1, pp. 376-385, 2012.
[14]  A. Zakariazadeh, S. Jadid, and P. Siano, “Stochastic multi-objective operational planning of smart distribution systems considering demand response programs,” Electric Power Syst. Res., vol. 111, pp. 156-168, 2014.
[15]  B. S. M. Borba, A. Szklo, and R. Schaeffer, “Plug-in hybrid electric vehicles as a way to maximize the integration of variable renewable energy in power systems: the case of wind generation in northeastern Brazil,” Energy, vol. 37, no. 1, pp. 469-481, 2012.
[16]  H. Lund, and W. Kempton, “Integration of renewable energy into the transport and electricity sectors through V2G,” Energy policy, vol. 36, no. 9, pp. 3578-3587, 2008.
[17]  M. Moradijoz, M. P. Moghaddam, M. R. Haghifam, and E. Alishahi, “A multi-objective optimization problem for allocating parking lots in a distribution network,” Int. J. Elec. Power Energy Syst., vol. 46, pp. 115-122, 2013.
[18]  A. Mohamed, V. Salehi, T. Ma, and O. Mohammed, “Real-time energy management algorithm for plug-in hybrid electric vehicle charging parks involving sustainable energy,” IEEE Trans. Sust. Energy, vol. 5, no. 2, pp. 577-586, 2014.
[19]  H. Rashidizadeh-Kermani, H. R. Najafi, A. Anvari–Moghaddam, and J. M. Guerrero, “Optimal Decision Making Framework of an Electric Vehicle Aggregator in Future and Pool markets,” J. Oper. Autom. Power Eng., pp. 1-19, 2018.
[20]  D. T. Ton, and M. A. Smith, “The US Department of Energy's microgrid initiative,” Electr. J., vol. 25, no. 8, pp. 84-94, 2012.
[21]  J. G. Slootweg, S. W. H. De Haan, H. Polinder, and W. L. Kling, “General model for representing variable speed wind turbines in power system dynamics simulations,” IEEE Trans. Power Syst.., vol. 18, no. 1, pp. 144-151, 2003.
[22]  http://bergey.com/documents/2013/10/excel-10-brochure_2013.pdf
[23]  C. Wang, Y. Liu, X. Li, L. Guo, L. Qiao, and H. Lu, “Energy management system for stand-alone diesel-wind-biomass microgrid with energy storage system,” Energy., vol. 97, pp. 90-104, 2016.
[24]  A. Rabiee, M. Sadeghi, J. Aghaeic, and A. Heidari, “Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV unit’s uncertainties,” Renew. Sust. Energy Rev., vol. 57, pp. 721-739, 2016.
[25]  Y. Xu, C. C. Liu, K. Schneider, F. Tuffner, and D. Ton, “Microgrids for Service Restoration to Critical Load in a Resilient Distribution System,” IEEE Trans. Smart Grid, 2016.
[26]  M. A. Zehir, A. Batman, and M. Bagriyanik, “Review and comparison of demand response options for more effective use of renewable energy at consumer level,” Renew. Sust. Energy Rev., vol. 56, pp. 631-642, 2016.
[27]  A. Ghasemi, S. S. Mortazavi, and E. Mashhour, “Hourly demand response and battery energy storage for imbalance reduction of smart distribution company embedded with electric vehicles and wind farms,” Renew. Energy., vol. 85, pp. 124-136, 2016.
[28]  M. Doostizadeh, and H. Ghasemi, “A day-ahead electricity pricing model based on smart metering and demand-side management,” Energy, vol. 46, no. 1, pp. 221-230, 2012.
[29]  M. Aien, A. Hajebrahimi, and M. Fotuhi-Firuzabad,” A comprehensive review on uncertainty modeling techniques in power system studies,” Renew. Sust. Energy Rev., vol. 57, pp. 1077-1089, 2016.
[30]  P. S. Georgilakis, and N. D. Hatziargyriou, “A review of power distribution planning in the modern power systems era: Models, methods and future research,” Electr. Power Syst. Res., vol. 121, pp. 89-100, 2015.
[31]  http://www.wind-power–program.com/wind_statistics.htm
[32]  S. Afanasyeva, J. Saari, M. Kalkofen, J. Partanen, and O. Pyrhönen, “Technical, economic and uncertainty modelling of a wind farm project,” Energy Conv. Manag., vol. 107, pp. 22-33, 2016.
[33]  Q. Wang, J. Wang, and Y. Guan, “Stochastic unit commitment with uncertain demand response,” IEEE Trans. Power Syst., vol. 28, no. 1, pp. 562-563, 2013.
[34]  W. Gao, R. Zhou, and D. Zhao, “Heuristic failure prediction model of transmission line under natural disasters,” IET Gen. Trans. Dist., vol. 11, no. 4, pp. 935-942, 2017.
[35]  A. Parisio, E. Rikos, and L. Glielmo, “Stochastic model predictive control for economic/ environmental operation management of microgrids: An experimental case study,” J. Proc. Cont., vol. 43, pp. 24-37, 2016.
[36]  S. Mohammadi, S. Soleymani, and B. Mozafari, “Scenario-based stochastic operation management of microgrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices,” International J. Electr. Power Energy Syst., vol. 54, pp. 525-535, 2014.
[37]  M. Shafie–khah et al. , “Optimal behavior of electric vehicle parking lots as demand response aggregation agents,” IEEE Trans. Smart Grid, vol. 7, no.6, pp. 2654-2665, 2016.
[38]  S. Tabatabaee, S. S. Mortazavi, and T. Niknam, “Stochastic scheduling of local distribution systems considering high penetration of plug-in electric vehicles and renewable energy sources,” Energy, vol. 121, pp. 480-490, 2017.
[39]  I. E. Grossmann, J. Viswanathan, A. Vecchietti, R. Raman, and E. Kalvelagen, “GAMS/DICOPT: A discrete continuous optimization package,” GAMS Corporation Inc. 2002.
[40]  W. Rmisch, “Scenario Reduction in Stochastic Programming: An Approach Using Probability Metrics,” 2000.
Journal of Operation and Automation in Power Engineering
Volume 7, Issue 2
October 2019
Pages 157-167

Files

History

  • Receive Date: 19 October 2018
  • Revise Date: 10 March 2019
  • Accept Date: 26 March 2019

Share

How to cite

Statistics