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

• 作者: Ming Zhao Shengyuan Chen
• 来源期刊: 美国运筹学期刊(英文)
• 学科: 医学
• 关键词: Carbon TAX GENERATOR PORTFOLIO MARKOV REGIME Switching Model Stochastic Programming Unit COMMITMENT Simulation
• 年，卷（期）: mgycxqkyw_2011,（4）
• 页码范围: 268-276
• 页数: 9页
• 分类号: R73

摘要

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.