Smart Grid Big Data Analytics: Applications...2019/06/04 · M. Kezunovic, T. Dokic, "Predictive...
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Smart Grid Big Data Analytics: Applications
Mladen Kezunovic, Ph.D., P.E.Regents Professor
Texas A&M UniversityJune 4, 2019
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OutlineBackground: - The evolving Grid- ResilienceThe Grid Edge:- Application centric view- Data centric viewBig Challenges- Big Data Properties- ExpectationsExample: Predicting outages/failures- Transmission- DistributionTakeaways
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Outline
Background: - The evolving Grid- Resilience
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The evolving grid
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The evolving grid
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IntroductionLayered Architecture
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IntroductionControl/application layer
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Interoperability layer
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Resilience
• degrades gradually, and not abruptly, when it experiences stressed conditions and it is able to restore back into its normal state thereafter
• learns from its previous lessons and experiences under major disturbances and uses this knowledge to adapt and fortify itselftoprevent or mitigate the consequences of a similar event in the future.
• minimizes interruptions of service during an extraordinary and hazardous event• anticipates, absorbs, adapts to and/or rapidly recovers from a disruptive event• plans and prepares for a disruptive event, absorbs it and is able to recover from it
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Resilience
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T. Mc. Junkin, C.G. Rieger, “Electricity distribution system resilient control system metrics.” 2017 Resilience Week, Workshop Proceedings, Idaho National Laboratory, September 2017
K. Eshghi, B.K. Johnson, C. G Rieger“PowerSystem Protection and Resiliency Metrics.” 2015 Resilience Week, Workshop Proceedings, Idaho National Laboratory, August 2015
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Design Requirements: 3RAP• Robustness (withstand low probability but high consequence events),• Resourcefulness (effectively manage a disturbance as it unfolds),• Rapid recovery (get things back to normal as fast as possible after the disturbance),• Adaptability (absorb new lessons from a catastrophe).• Predictability (learns from the past and anticipates future disturbances)
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Figure: Resilient Control System Framework (Wikipedia)
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Manifestation
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Source: Annual Eaton Investigation 2013
Source: Alaska Electric light and Power CompanySource: We Energies
Causes
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OutlineThe Grid Edge:- Application centric view- Data centric view
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Application centric viewUtility CentricThe “Edge” Centric
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The Grid View
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The Edge View
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Data Centric view
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SynchrophasorData
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Feeder Data
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Smart Meter Data
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Industrial Plant Data
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Renewables Data
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Asset Condition Data
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Oil and Electricity Monthly Average Price
Jan-
05
Mar
-05
May
-05
Jul-0
5
Sep
-05
Nov
-05
Jan-
06
Mar
-06
May
-06
Jul-0
6
Sep
-06
30,00
40,00
50,00
60,00
70,00
80,00
90,00
IPEX baseload (euro/MWh) Brent (euro/bbl) WTI (euro/bbl)
Market Data
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Cyber Security Data
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Data from other sources
Weather Forecast
Vegetation Indices
Lightning Data GIS
UAS
Animals Data
Satellite data
Radar data
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Outline
Big Challenges- Big Data Properties- Expectations
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Big Data Properties
10-9 10-3 100 103 106 109
0.1° 1° 1 cycle 12cycles
clockaccuracy
Accuracy of GPStime stamp
differential absolute
256 samplesper cycle
High-frequency,switching devices,inverters
Synchro-phasors
Protective relayoperations
Dynamic systemresponse (stability)
Demandresponse
Wind and solaroutput variation
Servicerestoration
Day-aheadscheduling
Life spanof anassets
T&D planning
Hour-ahead schedulingand resolution of most renewablesintegration studies
seconds
Weather
NANOSECOND MICROSECOND MILLISECOND SECOND MINUTE HOUR DAY MONTH YEAR
10-6
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Expectations
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Data Science& Processing Infrastructure
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Outline
Example: Predicting outages/failures- Transmission- Distribution
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Cause of outages
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Impact
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BD Framework
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BD Data Properties
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• Preprocessing: extract the data for the full graph of the network, and provide precise location of outages
• Spatiotemporal Correlation: correlate every network component with weather parameters
• Prediction Algorithm: graph based – combination of GCRF and logistic regression
The Processing Steps
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383838
Data Integration
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Predictive Data Analytics
M. Kezunovic, Z. Obradovic, T. Dokic, B. Zhang, J. Stojanovic, P. Dehghanian, and P. -C. Chen, "Predicating Spatiotemporal Impacts of Weather on Power Systemsusing Big Data Science," Springer Verlag, Data Science and Big Data: An Environment of Computational Intelligence, Pedrycz, Witold, Chen, Shyi-Ming (Eds.), ISBN978-3-319-53474-9, 2017.
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Risk = Hazard x Vulnerability x Economic Impact
• Probability of hazardous weather conditions
• Depends on Weather Forecast
• Pick a moment in time (or a period of time) and estimate probability of hazardous conditions
• Probability that hazardous conditions will cause an event in the network
• Depends on Historical Weather and Outage Data
• Learn from the historical data what may happen if hazardous conditions occur
• Expected economic impact in case of an event
• Depends on the type of economic loss that the user wants to consider
• Identify type of economic loss that is of interest for the study and calculate it
Weather Driven Risk Analysis
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Example : Vegetation Risk Model
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T. Dokic, P.-C. Chen, M. Kezunovic, “Risk Analysis for Assessment of Vegetation Impact on Outages in Electric Power Systems“, CIGRE US National Committee 2016 Grid of the Future Symposium, Philadelphia, PA, October-November 2016.
P. C. Chen and M. Kezunovic, “Fuzzy Logic Approach to Predictive Risk Analysis in Distribution Outage Management”, IEEE Transactions on Smart Grid, vol. 7, no. 6, pp. 2827-2836, November 2016.
Risk
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Results – Risk Maps
Weather Hazard
Network Vulnerability
Risk Map
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BD Analytics Outcomes
Probabilities of outages for vegetation Probabilities of outages for ice
Probabilities of outages for no outage Probabilities of outages for lightning
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Takeaways• Extensive research is needed to bring BD Analytics into utility practice:
- Data analytics has been used in the power system domain for over 50 years, but Big Data Analytics is in its infancy
- The Big Data Applications require intensive and costly effort to prepare the data (ingestion, cleansing, curation)
- The gap between the Big Data platforms and utility legacy software (EMS, DMS, MMS) uses is huge, and costly
- Utility predictive methods do not explore data sciences advances (Deep learning, spatiotemporal scaling, etc.)
• Lessons learned:
- Assessment of risk is not meaningful without clear mitigation steps (design, component health, operating steps)
- Big Data Predictive Analytics is cost effective and feasible if Big Data is readily available
- Acceptance of Big Data Analytics depends on whether it is able to solve problems that otherwise are not solved
- The target need to be great challenges with high returns if solved to justify the cost of implementation
- The solutions are not necessarily intuitive, so extensive training and mind set change may be needed
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T. Dokic, M. Kezunovic, “Predictive Risk Management for Dynamic Tree Trimming Scheduling for Distribution Networks,” IEEE Transactions on Smart Grid, (Accepted, In print).
T. Dokic, M. Pavlovski, Dj. Gligorijevic, M. Kezunovic, Z. Obradovic, "Spatially Aware Ensemble-Based Learning to Predict Weather-Related Outages in Transmission," The Hawaii International Conference on System Sciences - HICSS, Maui, Hawaii, January 2019.
M. Kezunovic, T. Dokic, R. Said, “Optimal Placement of Line Surge Arresters based on Predictive Risk Framework using Spatio-Temporally Correlated Big Data,” CIGRE Paris, August 2018.
M. Kezunovic, T. Dokic, "Predictive Asset Management Under Weather Impacts Using Big Data, Spatiotemporal Data Analytics and Risk Based Decision-Making," 10th Bulk Power Systems Dynamics and Control Symposium – IREP’2017, Espinho, Portugal, August 2017.
http://smartgridcenter.tamu.edu/resume/long_resume/Html/index.html#publ
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Publications
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