A Review of Uncertainties in Power Systems - Modelling, Impact, and Mitigation Article Swipe
Hongji Hu
,
Samson S. Yu
,
Hieu Trinh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202312.1585.v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202312.1585.v1
A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand and manage the challenges faced by the electric grid. Uncertainties in power systems can arise from various sources and can have significant implications for grid reliability, stability, and economic efficiency. Australia, susceptible to extreme weather such as wildfires and heavy rainfall, faces vulnerabilities in its power network assets. The decentralized distribution of population centers poses economic challenges in supplying power to remote areas, a crucial consideration for emerging technologies emphasized in this paper. In addition, the evolution of modern power grids, facilitated by deploying the advanced metering infrastructure (AMI), has also brought new challenges to the system due to the risk of cyber-attacks via communication link. However, the existing literature lacks a comprehensive review and analysis of uncertainties in modern power systems, encompassing uncertainties related to weather events, cyberattacks, and asset management, as well as the advantages and limitations of various mitigation approaches. To fill this void, this review covers a broad spectrum of uncertainties considering their impacts on the power system and explores conventional robust control, and modern probabilistic and data-driven approaches for modelling and correlating the uncertainty events to the state of the grid for optimal decision making. This article also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary service (D-FCAS) and reserve provision for frequency regulation to ensure a design of uncertainty-tolerance power system. This review delves into the trade-offs linked with the implementation of mitigation strategies, such as reliability, computational speed, and economic efficiency. It also explores how these strategies may influence the planning and operation of future power grids.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202312.1585.v1
- https://www.preprints.org/manuscript/202312.1585/v1/download
- OA Status
- green
- Cited By
- 1
- References
- 101
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390080745
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390080745Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202312.1585.v1Digital Object Identifier
- Title
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A Review of Uncertainties in Power Systems - Modelling, Impact, and MitigationWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-12-21Full publication date if available
- Authors
-
Hongji Hu, Samson S. Yu, Hieu TrinhList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202312.1585.v1Publisher landing page
- PDF URL
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https://www.preprints.org/manuscript/202312.1585/v1/downloadDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.preprints.org/manuscript/202312.1585/v1/downloadDirect OA link when available
- Concepts
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Electric power system, Risk analysis (engineering), Probabilistic logic, Grid, Computer science, Reliability engineering, Reliability (semiconductor), Cascading failure, Systems engineering, Engineering, Power (physics), Business, Physics, Geometry, Quantum mechanics, Mathematics, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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101Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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