Abstract

Power utilities are continuously under high pressure to ensure the best performance of their grid. Nevertheless, power outages continue to be periodically observed. This paper assesses the applicability and implications of the Three-Phases method for optimized dataset selection in dynamic risk analysis, through a case study focusing on vegetation along power lines—a major hazard in power grid management. The case study comprises 17 different real-world datasets originating from 12 different types of data sources. We estimate how these datasets can inform eight parameters related to the physical configuration—one of the three dimensions impacting the probability of tree falls on power lines. The results provide two main take-aways: (1) datasets initially considered as less valuable for risk analysis can end up being the most relevant ones; (2) the potential of knowledge of a dataset needs to be assessed parameter per parameter. The results demonstrate that the Three-Phases method is a step toward traceable, data-driven, and dynamic risk analyses of power grids, resulting in a more reliable management of those large-scale infrastructures.

References

1.
Vaiman
,
Bell
,
Chen
,
Chowdhury
,
Dobson
,
Hines
,
Papic
,
Miller
, and
Zhang
,
2012
, “
Risk Assessment of Cascading Outages: Methodologies and Challenges
,”
IEEE Trans. Power Syst.
,
27
(
2
), pp.
631
641
.10.1109/TPWRS.2011.2177868
2.
Ciapessoni
,
E.
,
Cirio
,
D.
,
Member
,
S.
,
Kjølle
,
G.
,
Member
,
S.
,
Massucco
,
S.
,
Member
,
S.
,
Pitto
,
A.
, and
Sforna
,
M.
,
2016
, “
Probabilistic Risk-Based Security Assessment of Power Systems Considering Incumbent Threats and Uncertainties
,”
IEEE Trans. Smart Grid
,
7
(
6
), pp.
2890
2903
.10.1109/TSG.2016.2519239
3.
Doostan
,
M.
,
Sohrabi
,
R.
,
Chowdhury
,
B.
, and
Francisco
,
S.
,
2019
, “
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems
,”
Int. Trans. Electr. Energy Syst.
,
30
(
1
), pp.
1
13
.10.1002/2050-7038.12154
4.
Guikema
,
S. D.
,
Davidson
,
R. A.
, and
Liu
,
H.
,
2006
, “
Statistical Models of the Effects of Tree Trimming on Power System Outages
,”
IEEE Trans. Power Delivery
,
21
(
3
), pp.
1549
1557
.10.1109/TPWRD.2005.860238
5.
Johansson
,
E.
,
Uhlen
,
K.
,
Kjølle
,
G.
, and
Toftevaag
,
T.
,
2011
, “
Reliability Evaluation of Wide Area Monitoring Applications and Extreme Contingencies
,”
Proceedings of the 17th Power Systems Computation Conference PSCC 2011
, Stockholm, Sweden, Aug. 22–26, pp.
1297
1309
.https://www.sintef.no/globalassets/project/vulnerability-andsecurity/publications/papers/pscc_2011_ej_reliability-evaluation.pdf
6.
Radmer
,
D. T.
,
Kuntz
,
P. A.
,
Christie
,
R. D.
,
Venkata
,
S. S.
, and
Fletcher
,
R. H.
,
2002
, “
Predicting Vegetation-Related Failure Rates for Overhead Distribution Feeders
,”
IEEE Trans. Power Delivery
,
17
(
4
), pp.
1170
1175
.10.1109/TPWRD.2002.804006
7.
Sand
,
K.
,
Kjolle
,
G.
, and
Bilberg
,
J.
,
1989
, “
Reliability Aspects Concerning Distribution System Expansion Planning
,”
10th International Conference on Electricity Distribution,
Brighton, UK, May 8–12, pp.
530
534
.https://ieeexplore.ieee.org/document/206137
8.
Vefsnmo
,
H.
,
Kjølle
,
G.
,
Jakobsen
,
S. H.
,
Ciapessoni
,
E.
,
Cirio
,
D.
, and
Pitto
,
A.
,
2015
, “
Risk Assessment Tool for Operation: From Threat Models to Risk Indicators
,”
IEEE Eindhoven PowerTech, Eindhoven
,
Netherlands
, June 29–July 2, pp. 1–6.10.1109/PTC.2015.7232557
9.
Wanik
,
D. W.
,
Parent
,
J. R.
,
Anagnostou
,
E. N.
, and
Hartman
,
B. M.
,
2017
, “
Using Vegetation Management and LiDAR-Derived Tree Height Data to Improve Outage Predictions for Electric Utilities
,”
Electr. Power Syst. Res.
,
146
, pp.
236
245
.10.1016/j.epsr.2017.01.039
10.
Wanik
,
D. W.
,
Anagnostou
,
E. N.
,
Hartman
,
B. M.
,
Frediani
,
M. E. B.
, and
Astitha
,
M.
,
2015
, “
Storm Outage Modeling for an Electric Distribution Network in Northeastern USA
,”
Nat. Hazards
,
79
(
2
), pp.
1359
1384
.10.1007/s11069-015-1908-2
11.
European Commission
,
2014
, “
After—A Framework for Electrical Power SysTems Vulnerability Identification, DEfense and Restoration
,” Community Research and Development Information Service, Brussels, Belgium, accessed July 20, 2022, https://cordis.europa.eu/project/id/261788
12.
European Commission
,
2015
, “
UMBRELLA—Toolbox for Common Forecasting, Risk Assessment, and Operational Optimisation in Grid Security Cooperations of Transmission System Operators (TSOs)
,” Community Research and Development Information Service, Brussels, Belgium, accessed July 20, 2022, https://cordis.europa.eu/project/id/282775/de
13.
European Commission
,
2016
, “
ITesla—Innovative Tools for Electrical System Security Within Large Areas
,” Community Research and Development Information Service, Brussels, Belgium, accessedJuly 20, 2022, https://cordis.europa.eu/about/en
14.
European Commission
,
2017
, “
HyRiM—Hybrid Risk Management for Utility Networks
,” Community Research and Development Information Service, Brussels, Belgium, accessedJuly 20, 2022, https://cordis.europa.eu/project/id/608090
15.
European Commission
,
2017
, “
GARPUR—Generally Accepted Reliability Principle With Uncertainty Modelling and Through Probabilistic Risk Assessment
,” Community Research and Development Information Service, Brussels, Belgium, accessedJuly 20, 2022, https://cordis.europa.eu/project/id/608540
16.
Perkin
,
S.
,
2018
, “
Real-Time Weather-Dependent Probabilistic Reliability Assessment of the Icelandic Power System
,”
Reykjavík University
,
Reykjavík, Iceland
.
17.
Nordgård
,
D. E.
,
2010
, “
Risk Analysis for Decision Support in Electricity Distribution System Asset Management
,”
Ph.D. thesis
,
Norwegian University of Science and Technology (
NTNU),
Trondheim, Norway
.https://ntnuopen.ntnu.no/ntnuxmlui/bitstream/handle/11250/256437/321282_FULLTEXT02.pdf?sequence=2&isAllowed=y
18.
Pacevicius
,
M.
,
Ramos
,
M. A.
, and
Paltrinieri
,
N.
,
2020
, “
Optimizing Technology-Based Decision-Support for Management of Infrastructures Under Risk: The Case of Power Grids
,”
Proceedings of the 30th ESREL-15th PSAM
, Venice, Italy, Nov. 1–6, p.
8
.https://rpsonline.com.sg/proceedings/9789811485930/pdf/4552.pdf
19.
ISO,
2018
, “
ISO 31000:2018—Risk Management—Guidelines
,”
International Standardization Organization
,
Geneva, Switzerland
.
20.
Fitch
,
P. J. R.
,
Lovell
,
M. A.
,
Davies
,
S. J.
,
Pritchard
,
T.
, and
Harvey
,
P. K.
,
2015
, “
An Integrated and Quantitative Approach to Petrophysical Heterogeneity
,”
Mar. Pet. Geol.
,
63
, pp.
82
96
.10.1016/j.marpetgeo.2015.02.014
21.
Ali
,
N.
,
Neagu
,
D.
, and
Trundle
,
P.
,
2019
, “
Evaluation of K-Nearest Neighbour Classifier Performance for Heterogeneous Data Sets
,”
SN Appl. Sci.
,
1
(
12
), pp.
1
15
.10.1007/s42452-019-1356-9
22.
Batini
,
C.
,
Cappiello
,
C.
,
Francalanci
,
C.
, and
Maurino
,
A.
,
2009
, “
Methodologies for Data Quality Assessment and Improvement
,”
ACM Comput. Surv. (CSUR)
,
41
(
3
), pp.
1
52
.10.1145/1541880.1541883
23.
Byabazaire
,
J.
,
O'Hare
,
G.
, and
Delaney
,
D.
,
2020
, “
Data Quality and Trust: A Perception From Shared Data in IoT
,” IEEE International Conference on Communications Workshops (
ICC Workshops
),
Dublin, Ireland
,
June 7–11
, pp.
1
6
.10.1109/ICCWorkshops49005.2020.9145071
24.
Borgonovo
,
E.
, and
Cillo
,
A.
,
2017
, “
Deciding With Thresholds: Importance Measures and Value of Information
,”
Risk Anal.
,
37
(
10
), pp.
1828
1848
.10.1111/risa.12732
25.
Pacevicius
,
M. F.
,
Ramos
,
M.
,
Roverso
,
D.
,
Eriksen
,
C. T.
, and
Paltrinieri
,
N.
,
2022
, “
Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures
,”
Energies
,
15
(
9
), p.
3161
.10.3390/en15093161
26.
European Network of Transmission System Operators for Electricity (ENTSO-E)
,
2010
, “
Operational Handbook—Policy 5: Emergency Operations
,” European Network of Transmission System Operators for Electricity (ENTSO-E), Brussels, Belgium, accessed Sept. 4, 2024, https://eepublicdownloads.entsoe.eu/clean-documents/Publications/SOC/Continental_Europe/oh/20150916_Policy_5_Approved_by_ENTSO-E_RG_CE_Plenary.pdf
27.
Hoffman
,
P.
, and
Bryan
,
W.
,
2013
, “
Comparing the Impacts of Northeast Hurricanes on Energy Infrastructure
,” Office of Electricity Delivery and Energy Reliability, United States Department of Energy, Washington, DC, p.
50
.
28.
NERC
,
2018
, “
Hurricane Irma Event Analysis Report
,” NERC, Atlanta, GA, pp.
1
33
.
29.
ISAIAS Task Force
,
2020
, “
Tropical Storm ISAIAS 30-Day Report
,” ISAIAS Task Force, Long Island, NY.
30.
Sullivan
,
B. K.
, and
Malik
,
N. S.
,
2021
, “
5 Million Americans Have Lost Power From Texas to North Dakota After Devastating Winter Storm
,” Time, New York, accessed July 20, 2022, https://time.com/5939633/texas-power-outage-blackouts/#:∼:text=5 Million Americans Have Lost,on Feb. 15%2C 2021
31.
Task Force on Power Outages—Eurelectric,
2006
, “
Impacts of Severe Storms on Electric Grids
,” Task Force on Power Outages—Eurelectric, Brussels, Belgium.
32.
U.S.-Canada Power System Outage Task Force
,
2004
, “
Final Report on the August 14, 2003 Blackoutin the United States and Canada: Causes and Recommendations
,” U.S.-Canada Power System Outage Task Force, Ottawa, ON, Canada, accessed Sept. 4, 2024, https://www3.epa.gov/region1/npdes/merrimackstation/pdfs/ar/AR-1165.pdf
33.
Sforna
,
M.
, and
Delfanti
,
M.
,
2006
, “
Overview of the Events and Causes of the 2003 Italian Blackout
,”
IEEE PES Power Systems Conference and Exposition,
Atlanta, GA
,
Oct. 29–Nov. 1
, pp.
301
308
.10.1109/PSCE.2006.296323
34.
Alhelou
,
H. H.
,
Hamedani-Golshan
,
M. E.
,
Njenda
,
T. C.
, and
Siano
,
P.
,
2019
, “
A Survey on Power System Blackout and Cascading Events Research: Motivations and Challenges
,”
Energies
,
12
(
4
), pp.
1
28
.10.3390/en12040682
35.
Yu
,
W.
, and
Pollitt
,
M. G.
,
2009
, “
Does Liberalisation Cause More Electricity Blackouts? Evidence From a Global Study of Newspaper Reports Study of Newspaper Reports
,”
EPRG Working Paper 0902, Cambridge Working Paper in Economics 0911.
University of Cambridge. Cambridge, UK.https://www.jstor.org/stable/resrep44820?seq=1
36.
Masson-Delmotte
,
V.
,
Pörtner
,
H.-O.
,
Skea
,
J.
,
Zhai
,
P.
,
Roberts
,
D.
,
Shukla
,
P. R.
,
Pirani
,
A.
,
et al.
,
2018
, “
Global Warming of 1.5 °C: Summary for Policymakers
,” Cambridge University Press. Cambridge, UK.
37.
Smith
,
A. B.
,
2021
, “
2020 U.S. Billion-Dollar Weather and Climate Disasters in Historical Context
,” Climate, Washington, DC, accessed July 20, 2022, https://www.climate.gov/news-features/blogs/beyond-data/2020-us-billion-dollar-weather-and-climate-disasters-historical
38.
FERC and NERC,
2011
, “
Report on Outages and Curtailments During the Southwest Cold Weather Event of February 1–5, 2011
,”
FERC and NERC
, Washington, DC.https://www.ferc.gov/sites/default/files/2020-04/08-16-11-report.pdf
39.
Pacevicius
,
M.
,
Paltrinieri
,
N.
,
Thieme
,
C. A.
, and
Rossi
,
P. S.
,
2021
, “
Addressing the Importance of Data Veracity During Data Acquisition for Risk Assessment Processes
,” 67th Annual Reliability and Maintainability Symposium (
RAMS
), Orlando, FL, May 24–27, pp. 1–7.10.1109/RAMS48097.2021.9605737
40.
Pacevicius
,
M.
,
Dammann
,
D. O.
,
Gazzea
,
M.
, and
Sapronova
,
A.
,
2021
, “
Heterogeneous Data-Merging Platform for Improved Risk Management in Power Grids
,” 67th Annual Reliability and Maintainability Symposium (
RAMS
), Orlando, FL, May 24–27, pp. 1–
7
.10.1109/RAMS48097.2021.9605796
41.
Ni, M., McCalley
,
J. D.
,
Vittal
,
V.
, and
Tayyib
,
T.
,
2003
, “
Online Risk-Based Security Assessment
,”
IEEE Trans. Power Syst.
,
18
(
1
), pp.
258
265
.10.1109/TPWRS.2002.807091
42.
Catrinu
,
M. D.
, and
Nordgard
,
D. E.
,
2011
, “
Integrating Risk Analysis and Multi-Criteria Decision Support Under Uncertainty in Electricity Distribution System Asset Management
,”
Reliab. Eng. Syst. Saf.
,
96
(
6
), pp.
663
670
.10.1016/j.ress.2010.12.028
43.
Pacevicius
,
M.
,
Roverso
,
D.
,
Salvo
,
P.
, and
Paltrinieri
,
N.
,
2018
, “
Smart Grids: Challenges of Processing Heterogeneous Data for Risk Assessment
,”
Proceedings of the 14th International Conference on Probabilistic Safety Assessment and Management
,
Los Angeles, CA
, Sept. 16–21, pp. 1–11.https://folk.ntnu.no/salvoros/pubpdf/2018%20psam.pdf
44.
Gazzea
,
M.
,
Pacevicius
,
M.
,
Dammann
,
D. O.
,
Sapronova
,
A.
,
Lunde
,
T. M.
,
Arghandeh
,
R.
, and
Member
,
S.
,
2021
, “
Automated Power Lines Vegetation Monitoring Using High-Resolution Satellite Imagery
,”
IEEE Trans. Power Delivery
,
8977
(
1
), pp.
1
10
.10.1109/TPWRD.2021.3059307
45.
Eggum
,
E.
,
2019
, “
Rapport Nr. 29-2019—Avbrotsstatistikk 2018
,” Norges vassdrags- og energidirektorat. Oslo, Norway.
46.
Hansen
,
H.
,
2018
, “
Rapport Nr. 64-2018—Avbrotsstatistikk 2017
,” Norges vassdrags- og energidirektorat. Oslo, Norway.
47.
Kumagai
,
Y.
,
Bliss
,
J. C.
,
Daniels
,
S. E.
, and
Carroll
,
M. S.
,
2004
, “
Research on Causal Attribution of Wildfire: An Exploratory Multiple-Methods Approach
,”
Soc. Nat. Resour.
,
17
(
2
), pp.
113
127
.10.1080/08941920490261249
48.
Matikainen
,
L.
,
Lehtomäki
,
M.
,
Ahokas
,
E.
,
Hyyppä
,
J.
,
Karjalainen
,
M.
,
Jaakkola
,
A.
,
Kukko
,
A.
, and
Heinonen
,
T.
,
2016
, “
Remote Sensing Methods for Power Line Corridor Surveys
,”
ISPRS J. Photogramm. Remote Sens.
,
119
, pp.
10
31
.10.1016/j.isprsjprs.2016.04.011
49.
Jenssen
,
R.
, and
Roverso
,
D.
,
2018
, “
Automatic Autonomous Vision-Based Power Line Inspection: A Review of Current Status and the Potential Role of Deep Learning
,”
Int. J. Electr. Power Energy Syst.
,
99
, pp.
107
120
.10.1016/j.ijepes.2017.12.016
50.
Kobayashi
,
Y.
,
Karady
,
G. G.
,
Heydt
,
G. T.
, and
Olsen
,
R. G.
,
2009
, “
The Utilization of Satellite Images to Identify Trees Endangering Transmission Lines
,”
IEEE Trans. Power Delivery
,
24
(
3
), pp.
1703
1709
.10.1109/TPWRD.2009.2022664
51.
Electric Power Research Institute (EPRI)
,
2019
, “
Eyes in the Sky: Satellite Remote Sensing and Data Analytics for Electric Utilities
,” EPRI, Washington DC.
52.
Pacevicius
,
M.
,
Haskins
,
C.
, and
Paltrinieri
,
N.
,
2020
, “
Supporting the Application of Dynamic Risk Analysis to Real-World Situations Using Systems Engineering: A Focus on the Norwegian Power Grid Management
,”
18th Annual Conference on Systems Engineering Research—Recent Trends and Advances in Model-Based Systems Engineering
, Redondo Beach, CA, Mar. 19–21, pp.
675
685
.10.1007/978-3-030-82083-1_57
53.
Iwanaga
,
T.
,
Wang
,
H. H.
,
Hamilton
,
S. H.
,
Grimm
,
V.
,
Koralewski
,
T. E.
,
Salado
,
A.
,
Elsawah
,
S.
,
et al.
,
2021
, “
Socio-Technical Scales in Socio-Environmental Modeling: Managing a System-of-Systems Modeling Approach
,”
Environ. Modell. Software
,
135
, p.
104885
.10.1016/j.envsoft.2020.104885
54.
Stefana
,
E.
,
Cocca
,
P.
,
Marciano
,
F.
,
Rossi
,
D.
, and
Tomasoni
,
G.
,
2019
, “
A Review of Energy and Environmental Management Practices in Cast Iron Foundries to Increase Sustainability
,”
Sustainability
,
11
(
24
), p.
7245
.10.3390/su11247245
55.
Paté-Cornell
,
M. E.
,
1996
, “
Uncertainties in Risk Analysis: Six Levels of Treatment
,”
Reliab. Eng. Syst. Saf.
,
54
(
2–3
), pp.
95
111
.10.1016/S0951-8320(96)00067-1
You do not currently have access to this content.