AS IT TURNS OUT, THE DATA REALLY DOES MATTER (A LOT) Mike Smith Utility Analytics Institute

Transcription

1 AS IT TURNS OUT, THE DATA REALLY DOES MATTER (A LOT) Mike Smith Utility Analytics Institute

2 WHAT WE LL COVER TODAY Who & What is the Utility Analytics Institute? What is Big Data? What is Analytics? Some macro-perspectives and examples What does this mean for Utilities? Conclusions and Q & A

3 WHO & WHAT IS THE UTILITY ANALYTICS INSTITUTE? Membership-based organization "The mission of the Utility Analytics Institute is to accelerate the adoption, advancement, and utilization of analytics, enabling utilities to operate more, safely, reliably, and efficiently. Building the Community Research-based approach The Institute s many activities Events Utility Analytics Summit Utility Analytics Week Quarterly Webcasts Online Engagement Executive Advisory Council Working Groups Get Involved!

4 Utility Members The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com 4

5 Solution Provider Members The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com The Place Analytics Leaders Turn to for Answers Member.UtilityAnalytics.com 5

6 BIG DATA Big Data is all about seeing and understanding the relations within and among pieces of information that, until very recently, we struggled to fully grasp. Data that is so big and complex in its nature and in its intended use that conventional data management and applications are insufficient. ANALYTICS WHAT IS BIG DATA? WHAT IS ANALYTICS? Analytics are technologies and applications that enable utilities to transform massive, complex data into predictive actionable insights. This process includes three key areas: Collecting, managing, cleaning and storing data Extracting and analyzing data Reporting analysis results

7 RECOMMENDED READING BIG DATA: A Revolution That Will Transform How We Live, Work, and Think By Viktor Mayer- Schonberger & Kenneth Cukier

8 HIGH-VISIBILITY ANALYTICS April 17, USA Today

9 HIGH-VISIBILITY ANALYTICS

10 NON-UTILITY EXAMPLE 1 UPS Predictive maintenance on fleet of 60,000 trucks Monitor individual parts and replace BEFORE they fail Savings in millions of dollars annually Predictive Asset Management in an Asset- Intensive Business

11 NON-UTILITY EXAMPLE 2 State & Local Government Deploying Sensors across roads and bridges Data from sensors indicates when to perform preventive maintenance, saving $millions, millions of personhours, AND lives Sensors deployed across an intelligent network to transform the business

12 UTILITY EXAMPLE 1 Large Investor-Owned Electric Utility in a Large City Fires start in underground infrastructure, often resulting in 300 lb. manhole covers blowing several stories in the air This is not an isolated occurrence Complex, big data problem: 94K+ of underground cables; 50K+ of manhole covers and service boxes Very messy data: hand-written field notes, paper trouble tickets, over 50 years of messy data How messy was it? How many ways can Service Box be recorded by a dispatcher or a field crew? 38 ways! SB, S, S/B, S.B., SBX, S/BX, S/BOX, SERV- BOX, SERV/BOX, SERVICE-BOX,

13 UTILITY EXAMPLE 1, CONT. Result #1: Identification of 106 predictors of major manhole disasters Result #2: Top 10% of manholes accounted for 44% of the severe accidents Result #3: ID d biggest factors in accidents: age of cable and trouble history for that particular manhole

14 UTILITY EXAMPLE 2 Large Water Wastewater Utility Writing off $ millions annually in bad debt This had become an unfortunate, but accepted practice What if bad debt could be reduced by as much as 50%? Customer segmentation Billing & Payment history Demographic data

15 UTILITY EXAMPLE 2 Result 1: More granular targeting of customer types/segments Result 2: Over 100% improvement in bad debt collection

16 NOT TOO LONG AGO devices to collect data communic ation networks data! Applications business value from actionable insights hardware and software development and maintenance RTUs IEDs hardware and software development and maintenance 1-way communications technologies data cleaning data extraction data architecture data management data research DB Arch. hardware and software development and maintenance decision support expertise subject matter expertise

17 TODAY devices to collect data communic ation networks data! analytics applicatio ns business value from actionable insights hardware and software development and maintenance RTUs IEDs smart meters smart grid hardware and software development and maintenance 2-way communications technologies smart grid data cleaning data extraction data architecture data management data research database administration The Four Vs hardware and software development and maintenance business-side understanding of analytics needs decision support expertise analysis to interpret data business process change expertise subject matter expertise analytics strategy

18 Analytics value curve Today, many utilities moving along foundational phase of value curve In 2012 to 2014, utilities will move aggressively into advanced phase of value curve 4 6 Value 1 OMG! Geez we have a lot of data! 2 Data fortress Data stored, secured and available 3 Basic reporting Answer what happened? not intuitive, limited presentation BI Dashboards, dynamic data use, answers what happened? but more intuitive Execution Leveraging real and near real-time data 5 Predictive Modeling and planning based on historic data 7 Business transformation Business process change initiated by analytics-derived information Time 18

19 Key analytics areas UtilityAnalytics.com The Place Analytics Leaders Turn to for Answers GRID ANALYTICS Asset optimization Transformer management Substation equip management Overall T&D asset mgmt Grid optimization Outage management System modeling Power quality optimization Advanced distribution management Analytics for real-time network operations Customer operations Meter data analytics Credit and collections Fraud detection Campaign management Customer segmentation Pricing optimization Customer engagement Demand response programs Energy efficiency programs Distributed gen. programs CUSTOMER ANALYTICS Foundational technology and business processes ANALYTICS BUSINESS INFRASTRUCTURE 19

20 NORTH AMERICAN UTILITY ANALYTICS MARKET, $2,500.0 $2,000.0 $1,500.0 $1,000.0 $500.0 $

21 KEY CHALLENGES Other 16% Budget availability 30% Lack of necessary skills/staff 23% Data availability 14% Lack of business support Lack of IT 10% support 7%

22 UTILITIES MOVING FORWARD NOW, BUT HOW? Analytics 67% 33% Grid analytics 47% 53% Yes No Customer analytics 73% 27% 0% 20% 40% 60% 80% 100%

23 CUSTOMER ANALYTICS Customer analytics are the hardware, software, professional services, business processes and people that enable utilities to analyze data to better serve the utility and its customers. Customer analytics include two key areas: customer operations and customer engagement.

24 CUSTOMER ANALYTICS: EMERGING BEST PRACTICES» Unstructured Data: Improve CSR & Billing Operations» Social Media: Engage customers Gauge customer sentiment» Customer Segmentation Import & Integrate Third-party Data Credit & Collections Special Customer Programs» Revenue Protection

25 GRID ANALYTICS Grid analytics are the hardware, software, professional services, business processes and people that enable utilities to analyze data to ensure better planning, design, construction, operation and maintenance of utility transmission and distribution networks. Grid analytics include two key areas: asset optimization and grid optimization.

26 GRID ANALYTICS: EMERGING BEST PRACTICES» Conservation Voltage Reduction Potential to Save $Millions» Preventative Asset Management Change out that Transformer Before it Blows (with a Business Case to Support it!)» AMI/OMS Integration Identify and Repair Outages Before Customers Know About Them» Improved System Modeling Drawing the curve with a Crayon Vs. a Very Sharp Pencil» FDIR» Deeper Penetration of Intelligence Across the Grid!

27 WHAT MIGHT THIS LOOK LIKE IN YOUR ORGANIZATION IN THE FUTURE? Excerpts from some recently published Industry Briefs

28 Enterprise Analytics, UtilityAnalytics.com Part 1 The Place Analytics Leaders Turn to for Answers The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com IT department [ BEFORE ] Customer marketing IT group 1 IT group 2 Asset optimization (Excel sheets) IT group 3 IT group 4 Customer operations

29 Enterprise Analytics, UtilityAnalytics.com Part 2 The Place Analytics Leaders Turn to for Answers [ AFTER ] IT department Analytics center of excellence (coordinating group) Business IT group 1 IT group 2 Customer operations Customer marketing IT group 3 Increase coordination between business groups Asset optimization 29

30 Analytics value curve Today, many utilities moving along foundational phase of value curve In 2012 to 2014, utilities will move aggressively into advanced phase of value curve 4 6 Value 1 OMG! Geez we have a lot of data! 2 Data fortress Data stored, secured and available 3 Basic reporting Answer what happened? not intuitive, limited presentation BI Dashboards, dynamic data use, answers what happened? but more intuitive Execution Leveraging real and near real-time data 5 Predictive Modeling and planning based on historic data 7 Business transformation Business process change initiated by analytics-derived information Time 30

31 Enterprise Analytics The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com Characteristics Breaking Down Silos Embracing data integration Supporting technology integration Developing analytics excellence Challenges Cross-Departmental Coordination Actually Doing Integration It Will Be Messy! Finding Funding Building the Enterprise Define Key Business Goals Start with Targeted Quick-Win Projects, but Be Ready to Expand Data Governance! Executive Support is Critical for Enterprise Efforts 31

32 Meter Data Analytics The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com 80% 70% 60% 50% 40% 30% 20% 10% 0% Top reasons for deploying smart meters 32

33 Meter Data Analytics The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com Electric Gas/Water Recurring meter data KWH, KW, Power Factor, Time of use, Sag/Swell counts Quantity, Rate of use Meter events Alarms, Alerts, Sags/Swells, Outages, Restorations, Tampers Alarms, Alerts, Outages, Restorations, Leaks Network operation Reliability, Performance, Battery condition Reliability, Performance, Battery condition Meter data analytics focus areas 33

34 Meter Data Analytics The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com 60% 50% 40% 30% 20% 10% 0% Top focus areas for meter data analytics 34

35 ENERGY EFFICIENCY ANALYTICS APPLICATIONS Analytics to understand utility, regulatory, partner and community needs and identify most cost-effective program options Analytics to segment customers based on energy usage, demographics, lifestyles and so forth Analytics available to customers/program participants; real time analysis and automation Analytics to evaluate the effectiveness and value of programs Program development Participant recruitment Program participation Program results Analytics to simulate program impacts on communities and the utility company Analytics to determine which customers will respond best to different programs, and measure enrollment rates Real-time analytics and coordination of program events and participants

36 POTENTIAL TOUCH POINTS FOR ENERGY EFFICIENCY ANALYTICS Business Function Application Possibilities Application possibilities Integrated resource Load planning Generation planning Trading & risk Energy supply management procurement Transmission & distribution Customer operations & engagement Corporate functions Conservation voltage reduction Operational planning & management Load control Product/program management Partnership management Customer segmentation Customer targeting Market research Market planning & analysis Internal communications Regulatory management Water leak detection Water pressure management Community outreach Customer service Channel management Field sales Measurement & verification Rates, tariffs & pricing Human resources Change management

37 Demand Response The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com 2012 breakdown in terms of total reported potential peak reduction: Load as a capacity resource: 29 percent Interruptible load: 24 percent Direct load control: 15 percent Time-of-use:12 percent Source: FERC s 2012 Assessment of Demand Response and Advanced Metering 37

38 Demand Response Challenges The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com Will customers behave? Low enrollment and participation rates Unintended creation of a new peak Efforts to shift peak usage can sometimes cause the creation of a new peak that eliminates the benefits of demand response, increasing costs or even worse, it could create grid instability. When a peak time reduction program begins, customers will shut off load. This can cause a load imbalance in the grid if too much load is shed. At the end of the cycle, customers will turn air conditioners back on or start dishwashers resulting in a new peak. This outcome can be minimized by staggering in pricing programs and staggering in usage via pricing mechanisms or incentives. Inflexible and changing regulatory climate Regulations impact almost every aspect of demand response programs. Rules dictate how incentives are to be provided to customers. Tariff filings must address how customers are to be compensated, impacts on general rate payers, and must be approved by commissions. Revenue Erosion The objective of demand response is to shift the load versus reduce the load. Careful planning and execution is needed to avoid revenue erosion. 38

39 DR & Analytics Program Example 1 The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com A large southwestern IOU is collecting 15-minute residential interval data, integrating that data with internal and external data, and using analytics to: Dispatch DR events and reduce peak load on hot summer days Identify devices able (and unable) to respond to DR calls Determine how long to cycle air conditioning on and off Maximize customer comfort during DR events Improve load forecasting and DR program load reduction estimates Support settlement (rebates or incentives owed to participants) Support M&V of DR program results Maximize DR program management efficiency and minimize cost Sell excess generation capacity when market prices are high Automate previously manual and resource-intensive processes Improve existing DR programs and guide new program design 39

40 DR & Analytics Program Example 2 The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com A small southwestern utility cooperative has implemented a fully integrated intelligent load management solution that combines AMI data, a two-way communications cellular network, and a virtual peak plant created by cutting-edge real-time resource management applications. Members control heating and cooling systems, water heaters, and pool pumps via the web, smart phones or tablet computers and the utility uses the information to call DR events, forecast load and manage capacity. 40

41 DR & Analytics Lingering Questions The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com When (and how) will customer engagement reach critical mass? When will virtual plants become a mainstream and significant component of a utility s or a state s RPS? To what degree will state regulators push the envelope on DR (and EE) requirements? Any predications on the degree to which DR and EE will comprise utility or state RPSs? 41

42 The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com The 4 Vs Volume Velocity Variety Veracity The Enterprise (Near) Real-Time The Roles of the CIO/IT & the Business Units Continuing Emphasis on Energy Efficiency & Demand Response 42

43 THANK YOU Mike Smith Vice President, Utility Analytics Institute