Merchant Energy Trading in a Network

• 成果类型: Article
• 署名作者: Nadarajah, Selvaprabu; Secomandi, Nicola
• 署名单位: University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Carnegie Mellon University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2018.1732
• 发表日期: 2018
• 页码: 1304-1320
• 关键词: gas-storage valuation; dynamic-programming approach; real option management; squares monte-carlo; commodity storage; stochastic optimization; uncertainty; simulation; regression; price

摘要

We formulate the merchant trading of energy in a network of storage and transport assets as a Markov decision process with uncertain energy prices, generalizing known models. Because of the intractability of our model, we develop heuristics and both lower and dual (upper) bounds on the optimal policy value estimated within Monte Carlo simulation. We achieve tractability using linear optimization, extending near optimal approximate dynamic programming techniques for the case of a single storage asset, versions of two of which are commercially available. We propose (i) a generalization of a deterministic reoptimization heuristic, (ii) an iterative version of the least squares Monte Carlo approach, and (iii) a perfect information dual bound. We apply our methods to a set of realistic natural gas instances. The combination of our reoptimization heuristic and dual bound emerges as a practical approach to nearly optimally solve our model. Our iterative least squares Monte Carlo heuristic is also close to optimal. Compared to our other heuristic, it exhibits slightly larger optimality gaps and requires some tuning, but is faster to execute in some cases. Our methods could enhance single energy storage asset software and have potential relevance beyond our specific application.