from smartgrid to softgrid - 15, 2019 · market studies say that the roi for energy data analytics...
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DNV GL © 2013 SAFER, SMARTER, GREENER DNV GL © 2015
From Smartgrid to Softgrid
Adding data analytics capabilities to the operational landscape
Theo Borst
DNV GL - Energy
DNV GL © 2013
We are a global classification, certification, technical assurance and advisory company
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Global reach – local competence
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400 offices
100 countries
16,000 employees
150 years
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Industry consolidation
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The DNV GL organisation
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Headquartered in
Hamburg, Germany
Headquartered in
Høvik, Norway
Headquartered in
Arnhem, Netherlands
Headquartered in
Milan, Italy
Maritime Oil & Gas Energy Business Assurance
DNV GL Group Headquarter: Oslo, Norway
DNV GL © 2013
Agenda
The ‘Data Tsunami’ in the power industry
Introducing Big Data / Energy Data Analytics
Best practices and Examples
Where to begin?
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The electricity infrastructure is changing…
Yesterdays infrastructure: simple and straight forward
Smart Grids: Bi-directional power- and information flows Intelligent and integrated devices and infrastructures.
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Utilities are facing a ‘Data Tsunami’
Utilities will need to find out how to harness and effectively utilize the exponentially
growing amount of data emerging from increasing number of digital components in
the Smart Grid.
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Big Data!
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Emerging Data Sources in the Operational landscape…
System Operation data:
Enhanced EMS/SCADA
Outage Management System
PMUs
Forecasting
Fault Location
GIS related Applications
Asset condition into control center
Dynamic rating
Digital fault recorders
Social media
Planning data:
Load modeling and forecasting
Business related applications
Reliability data
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Weather Data:
Weather data for early detection of storms
Daily Wind speeds
Daily overcast conditions
Solar data
Smart Meter data:
Voltage from AMI meters
Complex event processing for AMI and Outage data
Asset Management data
Asset sensors for knowledge management
Local substation information to support asset management
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… consisting of both New and Existing data
New data
Customer data
Operational data from grid sensors
Event data (Call center, Social Media)
Third parties
Existing data (just lots more of it and more often)
Smart Metering:
From 12 monthly meter reads to 8760 hourly reads (=800 times more data)
SCADA data
Now available at the sub-second level, down from every minute (=3600 times more data)
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to From:
DNV GL © 2013
Agenda
The ‘Data Tsunami’ in the power industry
Introducing Big Data / Energy Data Analytics
Best practices and Examples
Where to begin?
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DNV GL © 2013
Big Data - definition
In short: Too big, too complex, and too fast
“big data” typically applies when technical limitations of currently used computer systems and
software are encountered and when current (standard) toolset limits what you can do with the
data. Use of new hardware, software, and/or algorithms is required to solve the problem.
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Data Analytics
Data analytics is about connecting data from different sources to make predictions
on unknown quantities
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Big DataToo Big, too complex, too fast
BenefitsInsight, optimization, automated decision making.
Data AnalyticsData management, stream computing, generic statistical algorithms
The Problem The Process The Result
“To extract meaningful value from big data, you need optimal processing power, analytics capabilities and skills.” (IBM 2014)
Leveraging data in the core utility process to gain efficiency, reduce costs and improve performance.
“Big Data will underpin new waves of productivity growth and consumer surplus.” (McKinsey 2011)
Secure data access! Develop smart tools!Integrate analytics in the core processes
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Types of Data Analytics
Diagnostic Analytics
Determine why something has happened, using content analytics and natural language processing to
harvest insights found in documents, email, websites, social media and so on.
Descriptive Analytics (Visual Analytics)
Know what is happening now by gaining a context-relevant view of your business through exploration-and-
discovery and visualization-and-interaction capabilities. See historical trends and patterns related to your
current business situation through dashboards and business intelligence reports.
Predictive analytics
Assess what could happen next, by using predictive models, data-and-text mining, statistical analysis.
Discover patterns and trends from all types of data.
Prescriptive Analytics (Decision Support)
Recommends one or more courses of action based on predictive modeling, localized rules, scoring and
optimization techniques. Shows expected outcome of each. Enables decisions based on real-time data
instead of on gut instinct.
Cognitive Analytics
Systems that learn from every interaction and outcome in a naturally human-like way through the integration
of all types of analytics to adapt your processes and engagements. Allows to find correlations, create
hypotheses and learn from the outcomes.
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The three types of Energy Data Analytics
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Enterprise Analytics
Grid Analytics
Consumer Analytics
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Applications of Grid Analytics and Customer Analytics
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Grid Analytics Customer Analytics
TSO • Dynamic Stability Assessment & Control
• Transformer load management. • Asset use for predictive line/asset
maintenance. • Near real-time renewable and
microgrid monitoring. • Outage management (OMS).
• Load forecasting. • Load research and pricing. • Load disaggregation.
DSO • Voltage optimization • Outage management (OMS) • Augmented distribution
management system (DMS) in near real time
• Distribution Planning (Targeted EE/DR, DER, Renewables)
• Pre-pay tracking. • Revenue protection and reporting • Meter asset management. • New Rate Design & Analysis • Demand response monitoring and evaluation. • Theft Detection • Customer Engagement • Customer segmentation • Energy efficiency advice
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The seven highest valued Energy Data analytics applications
1. Outage management
2. Voltage optimization
3. Asset management
4. Revenue protection
5. Load forecasting
6. Detailed customer segmentation
7. Energy efficiency advice
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Grid analytics
Consumer analytics
Most immediate growth in grid analytics expected due to clear ROI
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Increased complexity in the control room
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Controlling and operating the power grid becomes increasingly complex
Training will enhance the performance of operators
Analytics and Decision Support systems will help reduce complexity in operation
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In the next decade, the Smartgrid will tranform into a ‘Softgrid’
What is Soft Grid ?
The Softgrid adds energy data analytics, scalable software platforms and cloud
services to the SmartGrid.
Where Smartgrid was about new and digital hardware, the Softgrid will add
layers of software and applications.
The Softgrid will transform the
power industry allowing Utilities
to provide added
value, differentiated services
and growth!
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Agenda
The ‘Data Tsunami’ in the power industry
Introducing Big Data / Energy Data Analytics
Best practices and Examples
Where to begin?
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SAS, OSISoft, IBM, HP
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AppOrchid – Cognitive Analytics & Natural Language Processing
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Optique – Predictive analytics based on Ontologies (such as CIM)
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Investing in Energy Data Analytics
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0%
50%
100%
150%
200%
250%
300%
350%
400%
450%
500%
550%
600%
650%
700%
750%
2012 2013 2014 2015 2016 2017 2018 2019 2020
Utility Analytics Spending Weighted ROI by Type
Automated Reported (188%) Tactical Analysis (389%) Strategic Phase (969%)
Predictive Phase (1,290%) Weighted ROI Per Year
Market studies say that the ROI for Energy Data Analytics is anywhere from
2 to 12 times the investment
Market analyst GTM Research predicts global utility company expenditure on data analytics will grow from $700m in 2012 to $3.8bn in 2020
DNV GL © 2013
Agenda
The ‘Data Tsunami’ in the power industry
Introducing Big Data / Energy Data Analytics
Best practices and Examples
Where to begin?
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DNV GL © 2013
6 Critical success factors for Energy Data Analytics applications
1. Define good business questions
2. Acquire the skills to build knowledge into models, to visualise and communicate
the insights.
3. Invest in powerful tools for analytics and visualization
4. The solution should be scalable
5. The solution should be flexible
6. Pay attention to data governance
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DNV GL Energy Data Analytics Roadmapping service
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Goal: To help clients get and start extracting value from the new
available grid data and enable realizing operational benefits.
It’s about getting ahead and beyond the ‘hype curve’.
Deliverables:
• Requirement definition
• Use cases
• Roadmap with a prioritized list of projects
• Business plan with clear ROI
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DNV GL Energy Data Analytics Maturity Evaluation
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Stage 1: Limited use of energy data analytics Stage 2: Dispersed usage of analytics. Stage 3: Looking at a more integrated process, metrics, and ways to improve. Stage 4: Adoptation of an enterprise-wide analytics perspective.
Where is your organisation?
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Energy Data Analytics Roadmap
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SAFER, SMARTER, GREENER
www.dnvgl.com
Questions & Discussion
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DNV GL – Energy, Intelligent Networks & Communication (INC) Theo Borst
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