Risk-Aware Planning of Power Distribution Systems Using Scalable Cloud Technologies Article Swipe
Shiva Poudel
,
Poorva Sharma
,
Abhineet Parchure
,
Daniel Olsen
,
Suryasnata Bhowmik
,
Torge Martin
,
Dylan Locsin
,
Andrew P. Reiman
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2503.14730
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2503.14730
The uncertainty in distribution grid planning is driven by the unpredictable spatial and temporal patterns in adopting electric vehicles (EVs) and solar photovoltaic (PV) systems. This complexity, stemming from interactions among EVs, PV systems, customer behavior, and weather conditions, calls for a scalable framework to capture a full range of possible scenarios and analyze grid responses to factor in compound uncertainty. Although this process is challenging for many utilities today, the need to model numerous grid parameters as random variables and evaluate the impact on the system from many different perspectives will become increasingly essential to facilitate more strategic and well-informed planning investments. We present a scalable, stochastic-aware distribution system planning application that addresses these uncertainties by capturing spatial and temporal variability through a Markov model and conducting Monte Carlo simulations leveraging modular cloud-based architecture. The results demonstrate that 15,000 power flow scenarios generated from the Markov model are completed on the modified IEEE 123-bus test feeder, with each simulation representing an 8,760-hour time series run, all in under an hour. The grid impact extracted from this huge volume of simulated data provides insights into the spatial and temporal effects of adopted technology, highlighting that planning solely for average conditions is inadequate, while worst-case scenario planning may lead to prohibitive expenses.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2503.14730
- https://arxiv.org/pdf/2503.14730
- OA Status
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4414901578Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2503.14730Digital Object Identifier
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Risk-Aware Planning of Power Distribution Systems Using Scalable Cloud TechnologiesWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-03-18Full publication date if available
- Authors
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Shiva Poudel, Poorva Sharma, Abhineet Parchure, Daniel Olsen, Suryasnata Bhowmik, Torge Martin, Dylan Locsin, Andrew P. ReimanList of authors in order
- Landing page
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https://arxiv.org/abs/2503.14730Publisher landing page
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https://arxiv.org/pdf/2503.14730Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2503.14730Direct OA link when available
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0Total citation count in OpenAlex
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