Data for: Electric sector capacity planning under uncertainty: Climate policy and natural gas in the US

Abstract of associated article: This research investigates the dynamics of capacity planning and dispatch in the US electric power sector under a range of technological, economic, and policy-related uncertainties. Using a two-stage stochastic programming approach, model results suggest that the two most critical risks in the near-term planning process of the uncertainties considered here are natural gas prices and the stringency of climate policy. Stochastic strategies indicate that some near-term hedging from lower-cost wind and nuclear may occur but robustly demonstrate that delaying investment and waiting for more information can be optimal to avoid stranding capital-intensive assets. Hedging strategies protect against downside losses while retaining the option value of deferring irreversible commitments until more information is available about potentially lucrative market opportunities. These results are explained in terms of the optionality of investments in the electric power sector, leading to more general insights about uncertainty, learning, and irreversibility. The stochastic solution is especially valuable if decision-makers do not sufficiently account for the potential of climate constraints in future decades or if fuel price projections are outdated.关联论文摘要：本研究考察了美国电力部门在一系列技术、经济与政策相关不确定性下的容量规划与调度动态。本研究采用两阶段随机规划（two-stage stochastic programming）方法，模型结果表明，在本次研究考量的各类不确定性中，近中期规划流程里最关键的两大风险为天然气价格与气候政策严格程度。随机策略显示，电力行业或会通过低成本风电与核电开展部分近中期对冲操作，但稳健性结果证实，延迟投资、等待更多信息可避免资本密集型资产搁浅，该策略具备最优性。对冲策略可防范下行损失，同时保留不可逆承诺的期权价值，直至获取关于潜在盈利市场机遇的更多信息后再作出决策。上述结论可通过电力部门投资的期权性加以阐释，并衍生出关于不确定性、学习与不可逆性的更一般性学术洞见。若决策者未充分考虑未来数十年气候约束的潜在影响，或燃料价格预测已过时，则随机规划解的应用价值将尤为显著。