Skip to main content

Risk-Based Energy Procurement via IGDT

  • Chapter
  • First Online:
Robust Energy Procurement of Large Electricity Consumers
  • 243 Accesses

Abstract

In this chapter, the power procurement problem of a large consumer under power price uncertainty is solved using information gap decision theory (IGDT). In contrast to other uncertainty modeling methods, the IGDT method makes it possible to model the positive and negative impacts of uncertainty through different strategies. To do so, the risk of the power procurement process of a large consumer is assessed considering the opportunity and robustness functions of the IGDT method, and three different strategies, risk-averse, risk-neutral, and risk-taker, are derived. It should be noted that the problem is formulated as mixed integer nonlinear programming and solved using GAMS programming software.

Based on the obtained results, in the risk-averse strategy, the large consumer is 12.16% robust against the power price uncertainty without implementing demand response program (DRP) by paying $42,000 to meet electricity demand. In addition, considering $40,000 as the procurement cost, 11.4% robustness is obtained against the power price increase in the pool market due to implement TOU-DRP. Also, in the risk-taker strategy, for a 15% power price reduction in the market, the large consumer’s costs will be $37,250 and $35,700 in cases without and with DRP, respectively, which means DRP will reduce procurement costs by about 4.2%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Y. Ben-Haim, Y. Ben-Haim, Info-Gap Decision Theory: Decisions Under Severe Uncertainty (Academic Press, New York, 2006)

    MATH  Google Scholar 

  2. A.J. Conejo, M. Carrión, J.M. Morales, Decision Making Under Uncertainty in Electricity Markets, vol 153 (Springer, Boston, 2010)

    MATH  Google Scholar 

  3. I. Ahmadian, O. Abedinia, N. Ghadimi, Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization. Front. Energy 8(4), 412–425 (2014)

    Article  Google Scholar 

  4. Z. Chen, L. Wu, Y. Fu, Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Trans. Smart Grid 3(4), 1822–1831 (2012)

    Article  Google Scholar 

  5. S. Nojavan, H. Ghesmati, K. Zare, Robust optimal offering strategy of large consumer using IGDT considering demand response programs. Electr. Power Syst. Res. 130, 46–58 (2016)

    Article  Google Scholar 

  6. A. Najafi-Ghalelou, S. Nojavan, K. Zare, Heating and power hub models for robust performance of smart building using information gap decision theory. Int. J. Electr. Power Energy Syst. 98, 23–35 (2018)

    Article  Google Scholar 

  7. S. Nojavan, K. Zare, M.A. Ashpazi, A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market. Int. J. Electr. Power Energy Syst. 69, 335–343 (2015)

    Article  Google Scholar 

  8. S. Nojavan, K. Zare, M.R. Feyzi, Optimal bidding strategy of generation station in power market using information gap decision theory (IGDT). Electr. Power Syst. Res. 96, 56–63 (2013)

    Article  Google Scholar 

  9. S. Nojavan, K. Zare, Risk-based optimal bidding strategy of generation company in day-ahead electricity market using information gap decision theory. Int. J. Electr. Power Energy Syst. 48, 83–92 (2013)

    Article  Google Scholar 

  10. K. Zare, M.P. Moghaddam, M.K. Sheikh El Eslami, Electricity procurement for large consumers based on information gap decision theory. Energy Policy 38(1), 234–242 (2010)

    Article  Google Scholar 

  11. K. Zare, M.P. Moghaddam, M.K. Sheikh El Eslami, Demand bidding construction for a large consumer through a hybrid IGDT-probability methodology. Energy 35(7), 2999–3007 (2010)

    Article  Google Scholar 

  12. M. Alipour, K. Zare, B. Mohammadi-Ivatloo, Optimal risk-constrained participation of industrial cogeneration systems in the day-ahead energy markets. Renew. Sust. Energ. Rev. 60, 421–432 (2016)

    Article  Google Scholar 

  13. S. Shafiee, H. Zareipour, A.M. Knight, N. Amjady, B. Mohammadi-Ivatloo, Risk-constrained bidding and offering strategy for a merchant compressed air energy storage plant. IEEE Trans. Power Syst. 32, 1–1 (2016)

    Article  Google Scholar 

  14. A. Najafi-Ghalelou, S. Nojavan, K. Zare, Information gap decision theory-based risk-constrained scheduling of smart home energy consumption in the presence of solar thermal storage system. Sol. Energy 163, 271–287 (2018)

    Article  Google Scholar 

  15. J. Zhao, C. Wan, Z. Xu, J. Wang, Risk-based day-ahead scheduling of electric vehicle aggregator using information gap decision theory. IEEE Trans. Smart Grid 8(4), 1609–1618 (2017)

    Article  Google Scholar 

  16. A. Rabiee, S. Nikkhah, A. Soroudi, E. Hooshmand, Information gap decision theory for voltage stability constrained OPF considering the uncertainty of multiple wind farms. IET Renew. Power Gen. 11(5), 585–592 (2017)

    Article  Google Scholar 

  17. A. Kazemi, S. Dehghan, N. Amjady, Multi-objective robust transmission expansion planning using information-gap decision theory and augmented ɛ-constraint method. IET Gener. Transm. Distrib. 8(5), 828–840 (2014)

    Article  Google Scholar 

  18. X. Cao, J. Wang, B. Zeng, A chance constrained information-gap decision model for multi-period microgrid planning. IEEE Trans. Power Syst. 33(3), 2684–2695 (2018)

    Article  Google Scholar 

  19. D. Ke, F. Shen, C.Y. Chung, C. Zhang, J. Xu, Y. Sun, Application of information gap decision theory to the design of robust wide-area power system stabilizers considering uncertainties of wind power. IEEE Trans. Sustain. Energy 9(2), 805–817 (2018)

    Article  Google Scholar 

  20. M. Eslami, H.A. Moghadam, H. Zayandehroodi, N. Ghadimi, A new formulation to reduce the number of variables and constraints to expedite scuc in bulky power systems. Proc. Natl. Acad. Sci. India Sect. A: Phys. Sci., 1–11 (2018). https://doi.org/10.1007/s40010-017-0475-1

  21. O. Abedinia, M. Bekravi, N. Ghadimi, Intelligent controller based wide-area control in power system. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 25(01), 1–30 (2017)

    Article  Google Scholar 

  22. A. Jalili, N. Ghadimi, Hybrid harmony search algorithm and fuzzy mechanism for solving congestion management problem in an electricity market. Complexity 21(S1), 90–98 (2016)

    Article  MathSciNet  Google Scholar 

  23. F. Jabari, S. Nojavan, B. Mohammadi-ivatloo, H. Ghaebi, H. Mehrjerdi, Risk-constrained scheduling of solar Stirling engine based industrial continuous heat treatment furnace. Appl. Therm. Eng. 128, 940–955 (2018)

    Article  Google Scholar 

  24. M. Charwand, Z. Moshavash, Midterm decision-making framework for an electricity retailer based on information gap decision theory. Int. J. Electr. Power Energy Syst. 63, 185–195 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Farashah, M.D. (2019). Risk-Based Energy Procurement via IGDT. In: Nojavan, S., Shafieezadeh, M., Ghadimi, N. (eds) Robust Energy Procurement of Large Electricity Consumers . Springer, Cham. https://doi.org/10.1007/978-3-030-03229-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03229-6_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03228-9

  • Online ISBN: 978-3-030-03229-6

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics