Assessment of Ancillary Service Demand Response and Time of Use in a Market-Based Power System Through a Stochastic Security Constrained Unit Commitment

  • Saber Talari
  • Miadreza Shafie-khah
  • Neda Hajibandeh
  • João P. S. CatalãoEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 499)


In this paper, the impacts of an incentive-based Demand Response, i.e., Ancillary Service DR (ASDR), and a price-based DR, i.e., Time of Use (ToU), are revealed in a restructured power system which has some wind farms. This network is designed based on the pre-emptive market which is a day-ahead market with a balancing market prognosis. It is a proper mechanism to deal with the stochastic nature of non-dispatchable and outage of all units of the network. With Monte Carlo Simulation (MCS) method, several scenarios are generated in order to tackle the variability and uncertainties of the wind farms generation. The impacts of merging ASDR and ToU are investigated through running a two-stage stochastic security constrained unit commitment (SCUC), separately .


Ancillary service demand response Security constrained unit commitment Time of Use Two-stage stochastic programming 



This work was supported by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under Projects SAICT-PAC/0004/2015 - POCI-01-0145-FEDER-016434, POCI-01-0145-FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, and UID/EMS/00151/2013. Also, the research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007-2013 under grant agreement no. 309048.


  1. 1.
    Jannati, M., Hosseinian, S.H., Vahidi, B., Li, G.J.: A survey on energy storage resources configurations in order to propose an optimum configuration for smoothing fluctuations of future large wind power plants. Renew. Sustain. Energy Rev. 29, 158–172 (2014)CrossRefGoogle Scholar
  2. 2.
    Zafirakis, D., Kaldellis, J.K.: Autonomous dual-mode CAES systems for maximum wind energy contribution in remote island networks. Energy Convers. Manag. 51, 2150–2161 (2010)CrossRefGoogle Scholar
  3. 3.
    De Vos, K., Petoussis, A.G., Driesen, J., Belmans, R.: Revision of reserve requirements following wind power integration in island power systems. Renew. Energy 50, 268–279 (2013)CrossRefGoogle Scholar
  4. 4.
    Arasteh, H., et al.: Iot-based smart cities: a survey. In: 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, pp. 1–6 (2016). doi: 10.1109/EEEIC.2016.7555867
  5. 5.
    Kamyab, F., Amini, M.H., Sheykhha, S., Hasanpour, M., Jalali, M.M.: Demand response program in smart grid using supply function bidding mechanism. IEEE Trans. Smart Grid 7(2), 1277–1284 (2016)CrossRefGoogle Scholar
  6. 6.
    Bahrami, S., Sheikhi, A.: From demand response in smart grid toward integrated demand response in smart energy hub. IEEE Trans. Smart Grid 7(2), 650–658 (2016)Google Scholar
  7. 7.
    Sioshansi, R., Short, W.: Evaluating the impacts of real-time pricing on the usage of wind generation. IEEE Trans. Power Syst. 24, 516–524 (2010)CrossRefGoogle Scholar
  8. 8.
    Papavasiliou, A., Oren, S.O., Neill, R.P.: Reserve requirements for wind power integration: a scenario based stochastic programming framework. IEEE Trans. Power Syst. 26, 2197–2206 (2011)CrossRefGoogle Scholar
  9. 9.
    Heydarian-Forushani, E., Golshan, M.E.H., Moghaddam, M.P., Shafie-khah, M., Catalão, J.P.S.: Robust scheduling of variable wind generation by coordination of bulk energy storages and demand response. Energy Convers. Manag. 106, 941–950 (2015)CrossRefGoogle Scholar
  10. 10.
    Liu, G., Tomsovic, K.: Quantifying spinning reserve in systems with significant wind power penetration. IEEE Trans. on Power Syst 27, 2385–2393 (2012)CrossRefGoogle Scholar
  11. 11.
    Pina, A., Silva, C., Ferrão, P.: The impact of demand side management strategies in the penetration of renewable electricity. Energy 41, 128–137 (2012)CrossRefGoogle Scholar
  12. 12.
    Heydarian-Forushani, E., Golshan, M.E.H., Shafie-khah, M.: Flexible security-constrained scheduling of wind power enabling time of use pricing scheme. Energy 90, 1887–1900 (2015)CrossRefGoogle Scholar
  13. 13.
    Critz, D.K., Busche, S., Connors, S.: Power systems balancing with high penetration renewables: The potential of demand response in Hawaii. Energy Convers. Manag. 76, 609–619 (2013)CrossRefGoogle Scholar
  14. 14.
    Dietrich, K., Latorre, J.M., Olmos, L., Ramos, A.: Demand response in an isolated system with high wind integration. IEEE Trans. Power Syst. 27, 20–29 (2012)CrossRefGoogle Scholar
  15. 15.
    Zhechong, Z., Wu, L.: Impacts of high penetration wind generation and demand response on LMPs in day-ahead market. IEEE Trans. Smart Grid 5(1), 220–229 (2014)CrossRefGoogle Scholar
  16. 16.
    Boroojeni, K.G., Amini, M.H., Iyengar, S.S.: Overview of the security and privacy issues in smart grids. In: Boroojeni, Kianoosh G., Amini, M.Hadi, Iyengar, S.S. (eds.) Smart Grids: Security and Privacy Issues, pp. 1–16. Springer, Cham (2017). doi: 10.1007/978-3-319-45050-6_1 CrossRefGoogle Scholar
  17. 17.
    Sheikhi, A., et al.: A cloud computing framework on demand side management game in smart energy hubs. Int. J. Electr. Power Energy Syst. 64, 1007–1016 (2015)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Saber Talari
    • 1
  • Miadreza Shafie-khah
    • 1
  • Neda Hajibandeh
    • 1
  • João P. S. Catalão
    • 1
    • 2
    • 3
    Email author
  1. 1.C-MASTUniversity of Beira InteriorCovilhãPortugal
  2. 2.INESC TEC and The Faculty of Engineering of the University of PortoPortoPortugal
  3. 3.INESC-ID, Instituto Superior TécnicoUniversity of LisbonLisbonPortugal

Personalised recommendations