Economic Feasibility of Implementation of Load Curtailment and Critical Peak Pricing Schemes as Demand Response Programs to Network Consumption Management (Case study: Azerbaijan Regional Electrical Company)

Document Type : Original Article

Authors

1 M.Sc. student, Department of Energy Engineering, Sharif University of Technology

2 Assistant Professor, Department of Energy Engineering, Sharif University of Technology

3 Department of Electrical Engineering, Sharif University of Technology

4 Azerbaijan Regional Electrical Company

5 Ph.D. Candidate, Faculty of Energy Engineering and Sustainable Resources, University of Tehran

10.22059/ses.2023.366788.1041

Abstract

With increased energy consumption, the need to find new ways and methods to supply electricity has become more pressing than ever, and the feasibility of growing generation capacity in the power system is limited due to some economic, political, and environmental constraints. As a result, in recent years, demand optimization has been studied, with an emphasis on the use of programs such as demand response programs. In this study, a proposed approach for assessing the real option of demand response programs for electric companies is offered to supply the best possible electricity to consumers. The typical load profile of the industrial pilot introduced by the Azerbaijan Regional Electrical Company, as well as the energy pricing in the electrical market, are analyzed, and the parameters needed to value the option of demand response programs are extracted before calculating the option value of the plans.

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References

[1] A. N. Shiryaev, Essentials of stochastic finance: facts, models, theory, vol. 3. World scientific, 1999.
[2] J. C. Cox, S. A. Ross, and M. Rubinstein, “Option pricing: A simplified approach,” J. financ. econ., vol. 7, no. 3, pp. 229–263, 1979.
[3] A. Azar and M. Momeni, “Statistics and its application in management,” Samt Publ. Second Vol. tenth Publ., 2006.
[4] G. B. Folland, Real analysis: modern techniques and their applications, vol. 40. John Wiley & Sons, 1999.
[5] “P. Plotter, ‘Stochastic integration and differential equation,’ Stochastic Modeling and Applied Probability, vol. 21, 2005.”
[6] N. Ikeda and S. Watanabe, Stochastic differential equations and diffusion processes. Elsevier, 2014.
[7] J. L. Devore, Probability and Statistics for Engineering and the Sciences. Cengage Learning, 2015.
[8] E. A. Martinez-Cesena and J. Mutale, “Wind power projects planning considering real options for the wind resource assessment,” IEEE Trans. Sustain. Energy, vol. 3, no. 1, pp. 158–166, 2011.
[9] D. V. Widder, The heat equation, vol. 67. Academic Press, 1976.
[10]         L. Liu, M. Zhang, and Z. Zhao, “The application of real option to renewable energy investment: a review,” Energy Procedia, vol. 158, pp. 3494–3499, 2019.
[11]         B. Lin and Z. Tan, “How much impact will low oil price and carbon trading mechanism have on the value of carbon capture utilization and storage (CCUS) project? Analysis based on real option method,” J. Clean. Prod., vol. 298, p. 126768, 2021.
[12]         H.-L. Zhou, B.-J. Tang, and H. Cao, “Abandonment decision-making of overseas oilfield project coping with low oil price,” Comput. Econ., vol. 55, pp. 1171–1184, 2020.
[13]         H. Zhao, S. Song, Y. Zhang, Y. Liao, and F. Yue, “Optimal decisions in supply chains with a call option contract under the carbon emissions tax regulation,” J. Clean. Prod., vol. 271, p. 122199, 2020.
[14]         L. Jiang, “Monte Carlo pricing of Bermudan-style derivatives with lower and upper bound methods.” University of Twente, 2012.
[15]         R. Korn, E. Korn, and G. Kroisandt, Monte Carlo methods and models in finance and insurance. CRC press, 2010.
[16]         F. A. Longstaff and E. S. Schwartz, “Valuing American options by simulation: a simple least-squares approach,” Rev. Financ. Stud., vol. 14, no. 1, pp. 113–147, 2001.
Journal of sustainable Energy Systems
Volume 2, Issue 2
April 2023
Pages 123-142

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