Adding a Computationally-Tractable Probabilistic Dimension to Meta-Heuristic-Based Microgrid Sizing

Mohseni, S; Brent, AC; Burmester, D; Browne, WN; Kelly, S

Adding a Computationally-Tractable Probabilistic Dimension to Meta-Heuristic-Based Microgrid Sizing

Mohseni, S Brent, AC Burmester, D Browne, WN Kelly, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2022, 2021-December, pp. 464-469
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Mohseni, S
dc.contributor.author	Brent, AC
dc.contributor.author	Burmester, D
dc.contributor.author	Browne, WN
dc.contributor.author	Kelly, S https://orcid.org/0000-0002-8454-6144
dc.date	2021-12-07
dc.date.accessioned	2022-11-06T22:12:45Z
dc.date.available	2022-11-06T22:12:45Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2022, 2021-December, pp. 464-469
dc.identifier.isbn	9781665495325
dc.identifier.issn	2159-3442
dc.identifier.issn	2159-3450
dc.identifier.uri	http://hdl.handle.net/10453/163246
dc.description.abstract	A robust solution to the optimal micro-grid (MG) sizing problem requires comprehensive quantification of the underlying parametric uncertainties-particularly, the uncertainty in forecasts of meteorological, load demand, and wholesale electricity price time-series data. However, the associated data-driven processes for probabilistic uncertainty quantification are computationally expensive. Accordingly, the mainstream meta-heuristic-based MG sizing literature has failed to concurrently quantify more than four sources of forecast uncertainty. To address this knowledge gap, this paper introduces a novel computationally efficient, probabilistic MG sizing model that enables the simultaneous treatment of any (reasonable) number of data uncertainty. This provides a platform to characterize the uncertainty in ambient temperature and river streamflow for the first time in the MG optimal sizing literature. Importantly, the model supports the associated long-Term strategic MG energy planning optimization processes through in-depth analyses of the worst-case, most likely case, and best-case planning scenarios. To demonstrate the utility of the proposed model for community MG projects, a case study is presented for the town of Ohakune, New Zealand. Notably, the numeric simulation results have shown that the whole-life cost of the conceptualized MG would have been underestimated and overestimated by as much as 17% and 30% respectively in the best-case and worst-case scenarios if the problem-inherent uncertainties were not explicitly factored into the associated techno-economic analyses.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE Region 10 Annual International Conference, Proceedings/TENCON
dc.relation.ispartof	IEEE Region 10 Annual International Conference
dc.relation.isbasedon	10.1109/TENCON54134.2021.9707310
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Adding a Computationally-Tractable Probabilistic Dimension to Meta-Heuristic-Based Microgrid Sizing
dc.type	Conference Proceeding
utslib.citation.volume	2021-December
utslib.location.activity	Auckland, New Zealand
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
pubs.organisational-group	/University of Technology Sydney/Strength - ISF - Institute for Sustainable Futures
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-11-06T22:12:44Z
pubs.finish-date	2021-12-10
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-12-07
pubs.volume	2021-December
dc.location	Piscataway, USA

Abstract:

A robust solution to the optimal micro-grid (MG) sizing problem requires comprehensive quantification of the underlying parametric uncertainties-particularly, the uncertainty in forecasts of meteorological, load demand, and wholesale electricity price time-series data. However, the associated data-driven processes for probabilistic uncertainty quantification are computationally expensive. Accordingly, the mainstream meta-heuristic-based MG sizing literature has failed to concurrently quantify more than four sources of forecast uncertainty. To address this knowledge gap, this paper introduces a novel computationally efficient, probabilistic MG sizing model that enables the simultaneous treatment of any (reasonable) number of data uncertainty. This provides a platform to characterize the uncertainty in ambient temperature and river streamflow for the first time in the MG optimal sizing literature. Importantly, the model supports the associated long-Term strategic MG energy planning optimization processes through in-depth analyses of the worst-case, most likely case, and best-case planning scenarios. To demonstrate the utility of the proposed model for community MG projects, a case study is presented for the town of Ohakune, New Zealand. Notably, the numeric simulation results have shown that the whole-life cost of the conceptualized MG would have been underestimated and overestimated by as much as 17% and 30% respectively in the best-case and worst-case scenarios if the problem-inherent uncertainties were not explicitly factored into the associated techno-economic analyses.

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http://hdl.handle.net/10453/163246