Big Data and Advanced Analytics Technologies for the Smart Grid Arnie de Castro, PhD SAS Institute IEEE PES 2014 General Meeting July 27-31, 2014 Panel Session: Using Smart Grid Data to Improve Planning, Analytics, and Operation of the US Capital region T&D Systems 1
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Big Data and Advanced Analytics Technologies for the Smart Grid
Arnie de Castro, PhDSAS Institute
IEEE PES 2014 General MeetingJuly 27-31, 2014
Panel Session: Using Smart Grid Data to Improve Planning, Analytics, and Operation of the US Capital region T&D Systems
1
BIG DATA
Meter Traditional AMI Meter PMU
Reads/month 1 2,880 77,760,000
Big Data is Relative, not Absolute
When volume, velocity and variety of data exceeds an organization’s storage or compute capacity for accurate and timely decision-making
Analytics Across the Energy Value Chain
Technologies for the Smart Grid
• Enterprise Analytics
– Situational Awareness, Descriptive to Predictive, Visualization
• Grid Operations Analytics
– Predictive Asset Maintenance, Outage Management, PMU Monitoring and Analytics, Smart Meter Analytics, Distribution Optimization
• Consumer Analytics
– Energy Forecasting, Consumption Analysis, Revenue Protection
5
ENTERPRISE ANALYTICS
Innovative
Strategies for
Big Data
Analytics
• A flexible enterprise architecture that supports many data types and usage patterns
• Upstream use of analytics to optimize data relevance
• Real-time visualization and advanced analytics to accelerate understanding and action
• Common analytical framework across the enterprise
GRID OPTIMIZATION ANALYTICS
Predictive Asset MaintenanceIdentify equipment that is likely to fail
• Modify MILP Framework for Customer Restoration Constraints
• MILP Solver to Create Optimal Solutions versus Standard Utility Routing
20
Constrained Customer Restoration Problem
21
PMU MONITORING AND ANALYTICS
Phasor Measurement Units (PMUs)A REAL WORLD EXAMPLE FROM THE POWER GRID
Issue:Latency; a delay of 3 seconds or more may be too late to take action to control system stability, leading to a blackout.
Background:With Phasor Measurement Units (PMUs), measurements taken are precisely time-synchronized and taken many times a second (i.e. 30 to 60 samples/second) offering dynamic visibility into the power system.
Approach:Develop analytics to:
• Understand Steady State operation
• Detect events on the network
• Categorize the event on the network
• Direct appropriate action based on the event
• Capture data for post event analysis
Phasor Measurement UnitsWHAT ARE PHASOR MEASUREMENT UNITS?
Courtesy: US Dept of Energy
• Next-Gen
measurement
devices for power
grid
• Collect
measurements
(frequency, voltage,
current, phase
angle) at 30
meas/second
• Synchronized across
locations by GPS
clock
PMU Analytics Process
Data Quality/Transform
Event Detection
Event Identification
Event Quantification
Notifications
Pi Server
PMU Event AnalysisDETAIL CHARTS FOR EVENT
• Current oscillates
after event, but then
dampens down to
normal
Event IdentificationSIMILARITY ANALYSIS
• Reference time
series for various
events
• Incoming data
stream is compared
to reference time
series
Event IdentificationSIMILARITY ANALYSIS
• Similarity between
incoming stream
and reference time
series is measured
and quantified
SMART METER ANALYTICS
Smart Meter Analytics
30
Customer Analysis
31
Load Analysis
32
DISTRIBUTION OPTIMIZATION
Distribution Optimization
Distribution Network Model
GIS, OMSSCADA/DMS, Meter Data,Sensor Data
Network Operations Model
Distribution Optimization
ConservationVoltageReduction
Loss Minimization
Tap Changing Transformers
Capacitors
Regulators
Direct Load Control
CostOptimization
Distributed Intelligence
Distributed Generation
Energy Storage
Load Forecasts
Load Models
Load Analytics
Measurement and Verification
Connectivity(Static) Data
Operational (Dynamic) Data
Optimization Software
ENERGY FORECASTING
Energy Forecasting
• Spatial load forecasting
• Outlier detection
• Demand response forecasting
• Weather forecasting
• Hydro/wind/solar generation forecasting
• Price forecasting
CONSUMPTION ANALYSIS
Load Profile Comparisons via Segmentation
ENABLING TECHNOLOGIES
HIGH PERFORMANCE ANALYTICS
HIGH-
PERFORMANCE
ANALYTICS
Analytics Server Architecture
Metadata
Mid-Tier
SAS VA Server
Workspace Server
Co-Located Data Storage
SAS® LASR Analytic Server
LASR Cluster
HadoopRDBMS Nonrelational ERP unstructured PC Files
MEMORY
STORAGE
PROCESSING
DATA
SOURCES
Co-Located Data Storage
SAS® LASR Analytic Server
LASR Cluster
Co-Located Data Storage
SAS® LASR Analytic Server
LASR Cluster
Massively Parallel Processing (‘MPP’) in the context of SAS® Visual Analytics…