from smartgrid to softgrid - 15, 2019 · market studies say that the roi for energy data analytics...

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

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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

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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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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:

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Agenda




Where to begin?

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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




Where to begin?

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Investing in Energy Data Analytics

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0%

50%

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150%

200%

250%

300%

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400%

450%

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550%

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650%

700%

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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

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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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SAFER, SMARTER, GREENER

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Questions & Discussion

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DNV GL – Energy, Intelligent Networks & Communication (INC) Theo Borst

from smartgrid to softgrid - 15, 2019 · market studies say that the roi for energy data analytics...

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