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

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posted on 2023-02-08, 03:41 authored by Soheil Mohseni, Alan BrentAlan Brent, Daniel BurmesterDaniel Burmester, WN Browne, S Kelly
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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Preferred citation

Mohseni, S., Brent, A. C., Burmester, D., Browne, W. N. & Kelly, S. (2021, January). Adding a Computationally-Tractable Probabilistic Dimension to Meta-Heuristic-Based Microgrid Sizing. In IEEE Region 10 Annual International Conference, Proceedings/TENCON TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) (2021-December pp. 464-469). IEEE. https://doi.org/10.1109/TENCON54134.2021.9707310

Conference name

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)

Conference start date

2021-12-07

Conference finish date

2021-12-10

Title of proceedings

IEEE Region 10 Annual International Conference, Proceedings/TENCON

Volume

2021-December

Publication or Presentation Year

2021-01-01

Pagination

464-469

Publisher

IEEE

Publication status

Published

ISSN

2159-3442

eISSN

2159-3450

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