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Robust Optimization Method for Obtaining Optimal Scheduling of Active Distribution Systems Considering Uncertain Power Market Price

  • Morteza Nazari-Heris
  • Saeed Abapour
  • Behnam Mohammadi-ivatloo
Chapter

Abstract

Active network management (ANM) is responsible for real-time controlling of active distribution systems based on real-time measurements of the network parameters. The power systems problems with uncertainty parameters have been investigated using different uncertainty handling models. This chapter aims to study the effect of uncertain power market price on optimal scheduling of distribution systems. The robust optimization (RO) method is applied to deal with the uncertainty associated with power market price. The proposed robust optimal scheduling of active distribution systems is studied based on a multi-objective scheme to obtain maximum benefit of distribution company (DisCo) and maximum benefit of distributed generation owner (DGO). Accordingly, the obtained optimal solution by RO method for the scheduling of distribution network prevents the DisCo and DGO from being exposed to low benefit taking undesired deviation of market power prices into account. ε-constraint is implemented on the problem to deal with the multi-objectives, and the best compromise solution is selected using a fuzzy satisfying method. The proposed model has been implemented on a test system to evaluate the performance and verify the practicality of the model.

Keywords

Active distribution network Optimal scheduling Uncertain power market price Robust optimization method Benefit maximization 

References

  1. 1.
    Saint-Pierre, A., & Mancarella, P. (2017). Active distribution system management: A dual-horizon scheduling framework for DSO/TSO interface under uncertainty. IEEE Transactions on Smart Grid, 8(5), 2186–2197.CrossRefGoogle Scholar
  2. 2.
    Wang, F., Xu, H., Xu, T., Li, K., Shafie-Khah, M., & Catalão, J. P. (2017). The values of market-based demand response on improving power system reliability under extreme circumstances. Applied Energy, 193, 220–231.CrossRefGoogle Scholar
  3. 3.
    Abapour, S., Nojavan, S., & Abapour, M. (2018). Multi-objective short-term scheduling of active distribution networks for benefit maximization of DisCos and DG owners considering demand response programs and energy storage system. Journal of Modern Power Systems and Clean Energy, 6(1), 95–106.CrossRefGoogle Scholar
  4. 4.
    Nazari-Heris, M., Abapour, S., & Mohammadi-Ivatloo, B. (2017). Optimal economic dispatch of FC-CHP based heat and power micro-grids. Applied Thermal Engineering, 114, 756–769.CrossRefGoogle Scholar
  5. 5.
    Cho, J., Jeong, S., & Kim, Y. (2015). Commercial and research battery technologies for electrical energy storage applications. Progress in Energy and Combustion Science, 48, 84–101.CrossRefGoogle Scholar
  6. 6.
    Wu, Y., Liu, L., Gao, J., Chu, H., & Xu, C. (2017). An extended VIKOR-based approach for pumped hydro energy storage plant site selection with heterogeneous information. Information, 8(3), 106.CrossRefGoogle Scholar
  7. 7.
    Nazari-Heris, M., & Kalavani, F. (2017). Evaluation of peak shifting and energy saving potential of ice storage based air conditioning systems in Iran. Journal of Operation and Automation in Power Engineering, 5(2), 163–170.Google Scholar
  8. 8.
    Budt, M., Wolf, D., Span, R., & Yan, J. (2016). A review on compressed air energy storage: Basic principles, past milestones and recent developments. Applied Energy, 170, 250–268.CrossRefGoogle Scholar
  9. 9.
    Siano, P. (2014). Demand response and smart grids—A survey. Renewable and Sustainable Energy Reviews, 30, 461–478.CrossRefGoogle Scholar
  10. 10.
    Nojavan, S., Mohammadi-Ivatloo, B., & Zare, K. (2015). Optimal bidding strategy of electricity retailers using robust optimisation approach considering time-of-use rate demand response programs under market price uncertainties. IET Generation, Transmission & Distribution, 9(4), 328–338.CrossRefGoogle Scholar
  11. 11.
    Neves, D., Pina, A., & Silva, C. A. (2015). Demand response modeling: A comparison between tools. Applied Energy, 146, 288–297.CrossRefGoogle Scholar
  12. 12.
    Zhou, Y., Mancarella, P., & Mutale, J. (2015). Modelling and assessment of the contribution of demand response and electrical energy storage to adequacy of supply. Sustainable Energy, Grids and Networks, 3, 12–23.CrossRefGoogle Scholar
  13. 13.
    Luo, F., Meng, K., Dong, Z. Y., Zheng, Y., Chen, Y., & Wong, K. P. (2015). Coordinated operational planning for wind farm with battery energy storage system. IEEE Transactions on Sustainable Energy, 6(1), 253–262.CrossRefGoogle Scholar
  14. 14.
    Palizban, O., Kauhaniemi, K., & Guerrero, J. M. (2014). Microgrids in active network management—Part I: Hierarchical control, energy storage, virtual power plants, and market participation. Renewable and Sustainable Energy Reviews, 36, 428–439.CrossRefGoogle Scholar
  15. 15.
    Parastegari, M., Hooshmand, R. A., Khodabakhshian, A., & Zare, A. H. (2015). Joint operation of wind farm, photovoltaic, pump-storage and energy storage devices in energy and reserve markets. International Journal of Electrical Power & Energy Systems, 64, 275–284.CrossRefGoogle Scholar
  16. 16.
    Rahbar, K., Xu, J., & Zhang, R. (2015). Real-time energy storage management for renewable integration in microgrid: An off-line optimization approach. IEEE Transactions on Smart Grid, 6(1), 124–134.CrossRefGoogle Scholar
  17. 17.
    Abapour, S., Zare, K., & Mohammadi-Ivatloo, B. (2015). Dynamic planning of distributed generation units in active distribution network. IET Generation, Transmission & Distribution, 9(12), 1455–1463.CrossRefGoogle Scholar
  18. 18.
    Nazari-Heris, M., Mohammadi-Ivatloo, B., Gharehpetian, G. B., & Shahidehpour, M. (2018). Robust short-term scheduling of integrated heat and power microgrids. IEEE Systems Journal, PP(99), 1–9.CrossRefGoogle Scholar
  19. 19.
    Nazari-Heris, M., & Mohammadi-Ivatloo, B. (2018). Application of robust optimization method to power system problems. In Classical and recent aspects of power system optimization (pp. 19–32). Elsevier PublisherGoogle Scholar
  20. 20.
    Soroudi, A., Mohammadi-Ivatloo, B., & Rabiee, A. (2014). Energy hub management with intermittent wind power. In Large scale renewable power generation (pp. 413–438). Singapore: Springer.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Morteza Nazari-Heris
    • 1
  • Saeed Abapour
    • 1
  • Behnam Mohammadi-ivatloo
    • 1
  1. 1.Faculty of Electrical and Computer EngineeringUniversity of TabrizTabrizIran

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