Optimal Generator Portfolio in Day-Ahead Market under Uncertain Carbon Tax Policy

Shengyuan Chen, Ming Zhao

.

DOI: 10.4236/ajor.2011.14031                

Abstract

The global liberalization of energy market and the evolving carbon policy have profound implication on a producer’s optimal generator portfolio problem. On one hand, the daily operational flexibility from a well- composed generator portfolio enables the producer to implement a more aggressive bidding strategy in the liberalized day-ahead market on a daily basis; on the other hand, the evolving carbon policy demands the long term robustness of a generator portfolio: it should be able to generate stable cash flow under different stages of the evolving carbon tax policy. It is computationally very challenging to incorporate the daily bidding strategy into such a long term generator portfolio study. We overcome the difficulty by a powerful vertical decomposition. The long term uncertainty of carbon tax policy is simulated by scenarios; while the daily electricity price fluctuation with jumps is modeled by a more complicated Markov Regime Switching model. The proposed model provides the senior executives an efficient quantitative tool to select an optimal generator portfolio in the deregulated market under evolving carbon tax policy.

Keywords

Carbon Tax, Generator Portfolio, Markov Regime Switching Model, Stochastic Programming, Unit Commitment, Simulation

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. M. Arroyo and A. J. Conejo, “Optimal Response of a Thermal Unit to an Electricity Spot Market,” IEEE Transactions on Power Systems, Vol. 15, No. 3, 2000, pp. 1098-1104. doi:10.1109/59.871739
[2]	ISO New England Inc., Technical Report, 2002. http://www.iso-ne.com
[3]	ERCOT, The Electric Reliability Council of Texas, Inc., Technical Report, 2002. http://www.ercot.com
[4]	J. Hinz, “Optimizing a Portfolio of Power-Producing Plants,” Bernoulli, Vol. 9, No. 4, 2003, pp. 659-669. doi:10.3150/bj/1066223273
[5]	N. M. Pindoriya, S. N. Singh and S. K. Singh, “Optimal Generation Portfolio Allocation in Competitive Electricity Market,” Annual IEEE India Conference, Gujarat, 18-20 December 2009, pp. 1-4. doi:10.1109/INDCON.2009.5409351
[6]	X. Yin, Z. Y. Dong and T. K. Shaha, “Optimal Portfolio Selection for Generators in the Electricity Market,” Proceeding of IEEE PES General Meeting, Pittsburgh, 20-24 July 2008, pp. 1-7.
[7]	A. J. Conejo, F. J. Nogales and J. M. Arroyo, “Price- Taker Bidding Strategy under Price Uncertainty,” IEEE Transactions on Power Systems, Vol. 17, No. 4, 2002, pp. 1081-1087. doi:10.1109/TPWRS.2002.804948
[8]	J. Doege, P. Schiltknecht and H.-J. Lu¨thi, “Risk Management of Power Portfolios and Valuation of Flexibility,” OR Spectrum, Vol. 28, No. 2, 2006, pp. 267-287. doi:10.1007/s00291-005-0005-4
[9]	M. Bierbrauer, S. Tru¨ck1, and R. Weron, “Modeling Electricity Prices with Regime Switching Models,” Lecture Notes in Computer Science, Vol. 3039, 2004, pp. 859-867. doi:10.1007/978-3-540-25944-2_111
[10]	M. T. Barlow, “A Diffusion Model for Electricity Prices,” Mathematical Finance, Vol. 12, No. 4, 2002, pp. 287-298. doi:10.1111/j.1467-9965.2002.tb00125.x
[11]	P. Skantze, M. Ilic and J. Chapman, “Stochastic Modeling of Electric Power Prices in a Multi-Market Environment,” Proceeding of Power Engineering Society Winter Meeting, Singapore, 23-27 January 2000, pp. 1109-1114.
[12]	F. Gao, X. Guan, X.-R. Cao and A. Papalexopoulos, “Forecasting Power Market Clearing Price and Quantity Using a Neural Network Method,” Proceeding of Power Engineering Society Summer Meeting, Seattle, 16-20 July 2000, pp. 2183-2188.
[13]	J. J. Lucia and E. S. Schwartz, “Electricity Prices and Power Derivatives: Evidence from the Nordic Power Exchange,” Review of Derivatives Research, Vol. 5, No. 1, 2002, pp. 5-50. doi:10.1023/A:1013846631785
[14]	J. D. Hamilton, “Regime Switching Models,” Palgrave Dictionary of Economics, Palgrave McMillan Ltd., New York, 2005.
[15]	L. J. Hong and B. L. Nelson, “Discrete Optimization via Simulation Using COMPASS,” Operations Research, Vol. 54, No. 1, 2006, pp. 115-129. doi:10.1287/opre.1050.0237
[16]	C. Wang and S. M. Shahidehpour, “Ramprate Limits in Unit Commitment and Economic Dispatch Incorporating Rotor Fatigue Effect,” IEEE Transactions on Power Systems, Vol. 9, No. 3, 1994, pp. 1539-1545. doi:10.1109/59.336106
[17]	P. S. Reinelt and D. W. Keith, “Carbon Capture Retrofits and the Cost of Regulatory Uncertainty,” The Energy Journal, Vol. 28, No. 4, 2007, pp. 101-128. doi:10.5547/ISSN0195-6574-EJ-Vol28-No4-5