TITLE:
Optimal Generator Portfolio in Day-Ahead Market under Uncertain Carbon Tax Policy
AUTHORS:
Shengyuan Chen, Ming Zhao
KEYWORDS:
Carbon Tax, Generator Portfolio, Markov Regime Switching Model, Stochastic Programming, Unit Commitment, Simulation
JOURNAL NAME:
American Journal of Operations Research,
Vol.1 No.4,
December
5,
2011
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.