Least Squares Monte Carlo and Pathwise Optimization for Merchant Energy Production

Abstract

Modeling as real options the operations of energy production companies that operate in wholesale markets gives rise to a challenging Markov decision process. In “Least Squares Monte Carlo and Pathwise Optimization for Merchant Energy Production,” Yang, Nadarajah, and Secomandi study the performance of two reinforcement learning techniques that can be used to determine feasible operating policies and optimality bounds for this model, namely least squares Monte Carlo and pathwise optimization, extending the applicability of the latter method beyond optimal stopping by using principal component analysis and block coordinate descent. They find that both approaches lead to near optimal policies, but pathwise optimization outperforms least squares Monte Carlo in terms of dual bounds at the expense of more sizable computational requirements. These findings have potential relevance for managers of energy production assets that use analytics to optimize their operations and researchers interested in broadening the scope of pathwise optimization.