A Computational Approach to Sequential Decision Optimization in Energy Storage and Trading

Falbo, P; Hinz, J; Leelasilapasart, P; Pelizzari, C

A Computational Approach to Sequential Decision Optimization in Energy Storage and Trading

Falbo, P Hinz, J

Leelasilapasart, P Pelizzari, C

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Journal of Risk and Financial Management, 2021, 14, (6), pp. 1-22
Issue Date:: 2021-05-24

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Field	Value	Language
dc.contributor.author	Falbo, P
dc.contributor.author	Hinz, J https://orcid.org/0000-0003-4076-8818
dc.contributor.author	Leelasilapasart, P
dc.contributor.author	Pelizzari, C
dc.date.accessioned	2021-11-04T20:19:47Z
dc.date.available	2021-11-04T20:19:47Z
dc.date.issued	2021-05-24
dc.identifier.citation	Journal of Risk and Financial Management, 2021, 14, (6), pp. 1-22
dc.identifier.issn	1911-8066
dc.identifier.issn	1911-8074
dc.identifier.uri	http://hdl.handle.net/10453/151342
dc.description.abstract	Due to recent technical progress, battery energy storages are becoming a viable option in the power sector. Their optimal operational management focuses on load shift and shaving of price spikes. However, this requires optimally responding to electricity demand, intermittent generation, and volatile electricity prices. More importantly, such optimization must take into account the so-called deep discharge costs, which have a significant impact on battery lifespan. We present a solution to a class of stochastic optimal control problems associated with these applications. Our numerical techniques are based on efficient algorithms which deliver a guaranteed accuracy
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Journal of Risk and Financial Management
dc.relation.isbasedon	10.3390/jrfm14060235
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Computational Approach to Sequential Decision Optimization in Energy Storage and Trading
dc.type	Journal Article
utslib.citation.volume	14
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-11-04T20:19:46Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	6

Abstract:

Due to recent technical progress, battery energy storages are becoming a viable option in the power sector. Their optimal operational management focuses on load shift and shaving of price spikes. However, this requires optimally responding to electricity demand, intermittent generation, and volatile electricity prices. More importantly, such optimization must take into account the so-called deep discharge costs, which have a significant impact on battery lifespan. We present a solution to a class of stochastic optimal control problems associated with these applications. Our numerical techniques are based on efficient algorithms which deliver a guaranteed accuracy

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http://hdl.handle.net/10453/151342