From Smartgrid to Softgrid

Transcription

1 From Smartgrid to Softgrid Adding data analytics capabilities to the operational landscape Theo Borst DNV GL - Energy 2015 SAFER, SMARTER, GREENER

2 We are a global classification, certification, technical assurance and advisory company 2

3 Global reach local competence ,000 years offices countries employees 3

4 Industry consolidation 4

5 The DNV GL organisation DNV GL Group Headquarter: Oslo, Norway Maritime Oil & Gas Energy Business Assurance Headquartered in Headquartered in Headquartered in Headquartered in Hamburg, Germany Høvik, Norway Arnhem, Netherlands Milan, Italy 5 5

6 Agenda The Data Tsunami in the power industry Introducing Big Data / Energy Data Analytics Best practices and Examples Where to begin? 7

7 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.

8 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. Big Data! 9

9 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 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 10

10 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) From: to 11

11 Agenda The Data Tsunami in the power industry Introducing Big Data / Energy Data Analytics Best practices and Examples Where to begin? 12

12 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. 13

13 Data Analytics Data analytics is about connecting data from different sources to make predictions on unknown quantities The Problem The Process The Result Big Data Too Big, too complex, too fast Data Analytics Data management, stream computing, generic statistical algorithms Benefits Insight, optimization, automated decision making. Big Data will underpin new waves of productivity growth and consumer surplus. (McKinsey 2011) 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. Secure data access! Develop smart tools! Integrate analytics in the core processes 14

14 Types of Data Analytics Diagnostic Analytics Determine why something has happened, using content analytics and natural language processing to harvest insights found in documents, , 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-anddiscovery 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. 15

15 The three types of Energy Data Analytics Enterprise Analytics Grid Analytics Consumer Analytics 16

16 Applications of Grid Analytics and Customer Analytics Grid 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). Customer Analytics 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 17

17 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 Grid analytics Consumer analytics Most immediate growth in grid analytics expected due to clear ROI 18

18 Increased complexity in the control room 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 19

19 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! 20

20 Agenda The Data Tsunami in the power industry Introducing Big Data / Energy Data Analytics Best practices and Examples Where to begin? 21

21 SAS, OSISoft, IBM, HP 22

22 AppOrchid Cognitive Analytics & Natural Language Processing 23

23 Optique Predictive analytics based on Ontologies (such as CIM) 24

24 Investing in Energy Data Analytics Market analyst GTM Research predicts global utility company expenditure on data analytics will grow from $700m in 2012 to $3.8bn in 2020 Utility Analytics Spending Weighted ROI by Type 750% 700% 650% 600% 550% 500% 450% 400% 350% 300% 250% 200% 150% 100% 50% 0% 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 25

25 Agenda The Data Tsunami in the power industry Introducing Big Data / Energy Data Analytics Best practices and Examples Where to begin? 27

26 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 28

27 DNV GL Energy Data Analytics Roadmapping service 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 29

28 DNV GL Energy Data Analytics Maturity Evaluation 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? 30

29 Energy Data Analytics Roadmap 31

30 Questions & Discussion DNV GL Energy, Intelligent Networks & Communication (INC) Theo Borst SAFER, SMARTER, GREENER 32