# Risk-Based Energy Procurement via IGDT

## 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%.

## Keywords

Information gap decision theory Uncertainty modeling Large consumer Power procurement Power price uncertainty## References

- 1.Y. Ben-Haim, Y. Ben-Haim,
*Info-Gap Decision Theory: Decisions Under Severe Uncertainty*(Academic Press, New York, 2006)zbMATHGoogle Scholar - 2.A.J. Conejo, M. Carrión, J.M. Morales,
*Decision Making Under Uncertainty in Electricity Markets*, vol 153 (Springer, Boston, 2010)zbMATHGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)MathSciNetCrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle Scholar