Optimal forward trading and battery control under renewable electricity generation

Hinz, Juri; Yee, Jeremy

doi:10.1016/j.jbankfin.2017.06.006

Cited by 11 publications

(13 citation statements)

References 33 publications

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“…For instance, approximating a compact action set by a sufficiently dense finite grid of representative actions, an approximate solution with excellent precision and numerical performance can be obtained. This technique is illustrated by battery storage control (Hinz and Yee (2017a) in the present contribution. Furthermore, various extension to non-linear state dynamics are possible.…”

Section: Motivationmentioning

confidence: 96%

“…This paper points any interested readers to the technical details contained in Hinz (2014). The package rcss represents the first software implementation of these algorithms and has already been used to address problems such as pricing financial options (Hinz and Yap (2015)), natural resource extraction (Hinz et al (In Press)), battery management (Hinz and Yee (2017a)), and optimal asset allocation under hidden state dynamics (Hinz and Yee (2017b)). One of the major benefits of implementing these methods within R (R Core Team (2013)) is that the results can be analysed using the vast number of statistical tools available in this language.…”

Section: Motivationmentioning

confidence: 99%

“…This section examines optimal energy storage for an energy retailer in the presence of a battery. The model is beyond the scope of this paper and this paper points any interested readers to Hinz and Yee (2017a) for more details. The model is represented by the above figure and the basic gist is as follows:…”

Section: Example: Optimal Energy Storagementioning

confidence: 99%

“…This gives the expected cumulative cost (and its 99% confidence interval) faced by the retailer depending on the current amount of stored energy in the battery. If interested, we point the reader to Hinz and Yee (2017a)…”

Section: ## Grid N_gridmentioning

confidence: 99%

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rcss: R package for optimal convex stochastic switching

Hinz¹,

Yee²

2019

The R Journal

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The R package rcss provides users with a tool to approximate the value functions in the Bellman recursion under certain assumptions that guarantee desirable convergence properties. This R package represents the first software implementation of these methods using matrices and nearest neighbors. This package also employs a pathwise dynamic method to gauge the quality of these value function approximations. Statistical analysis can be performed on the results to obtain other useful practical insights. This paper describes rcss version 1.6.

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Section: Motivationmentioning

confidence: 96%

Section: Motivationmentioning

confidence: 99%

Section: Example: Optimal Energy Storagementioning

confidence: 99%

Section: ## Grid N_gridmentioning

confidence: 99%

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rcss: R package for optimal convex stochastic switching

Hinz¹,

Yee²

2019

The R Journal

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“…Our research is potentially relevant for other commodity merchant operations contexts and related real option models (Secomandi andSeppi 2014, 2016), including oil and natural gas extraction fields, liquefied natural gas facilities, copper mines, and renewable energy plant (Brennan and Schwartz 1985, Smith and McCardle 1998, 1999, Smith 2005, Cortazar et al 2008, Rømo et al 2009, Enders et al 2010, Lai et al 2011, Arvesen et al 2013, Denault et al 2013, Hinz and Yee 2018, Zhou et al 2019. The use of PCA as a preconditioning technique may have applicability in mathematical programming beyond our specific application.…”

Section: Introductionmentioning

confidence: 99%

Pathwise Optimization for Merchant Energy Production

2019

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We study merchant energy production modeled as a compound switching and timing option. The resulting Markov decision process is intractable. State-of-the-art approximate dynamic programming methods applied to realistic instances of this model yield policies with large optimality gaps that are attributed to a weak upper (dual) bound on the optimal policy value. We extend pathwise optimization from stopping models to merchant energy production to investigate this issue. We apply principal component analysis and block coordinate descent in novel ways to respectively precondition and solve the ensuing ill conditioned and large scale linear program, which even a cutting-edge commercial solver is unable to handle directly. Compared to standard methods, our approach leads to substantially tighter dual bounds and smaller optimality gaps at the expense of considerably larger computational effort. Specifically, we provide numerical evidence for the near optimality of the operating policies based on least squares Monte Carlo and compute slightly better ones using our approach on a set of existing benchmark ethanol production instances. These findings suggest that both these policies are effective for the class of models we investigate. Our research has potential relevance for other commodity merchant operations settings.

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