data management & analytics for utilities 2014

8/12/2019 DATA MANAGEMENT & ANALYTICS FOR UTILITIES 2014

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DATA MANAGEMENT & ANALYTICS

FOR UTILITIES 2014

In-depth briefing

AuthorStephen Witt


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DATA MANAGEMENT AND ANALYTICS FOR UTILITIES|2www.smartgridupdate.com

DisclaimerThe information and opinions in this document were prepared by Smart Grid Update (FC

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No part of th is document may be distributed, resold, copied or adapted without

Smart Grid Update's prior written permission.

FC Business Intelligence Ltd 2014

Author

Stephen Witt

Antanina Kapchonava

Research manager

About Smart Grid Update

Smart Grid Update is a research-driven news, market analysis, online networking portal and conference producer.

Our work focuses on three core areas of the smart energy technology sector:

Providing business intelligence in all areas of smart energy.

Building smart energy communities for all key players to enable the exchange and sharing of ideas.

Producing conferences that assemble senior management, decision makers, and innovators to produce results

for the smart energy initiative.

DATAMANAGEMENTFORUTILITIES

DATA MANAGEMENT & ANALYTICS

FOR UTILITIES 2014

In-depth briefing


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Acknowledgments

Smart Grid Update wishes to thank the following people and organizations for their help in compiling this briefing:

ACKNOWLEDGMENTS

John D. McDonald

Director, Technical Strategy and Policy DevelopmentGE Digital Energy

James Horstman

Manager, Enterprise ArchitectureSouthern California Edison

Cheri WarrenVice President Asset ManagementNational Grid

Samuel Harrel

Industry Director, Utilities North AmericaOracle


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1. Big Data Sources

MARKETOVERVIEW

One of the keys to long-term return on investment (ROI)on smart grid deployment is managing the big dataassets associated with the smart grid roll out. This datacomes from an increasing number of new and enhancedinternal enabling technologies including:

Advanced Metering Infrastructure (AMI)

Meter Data Management Systems (MDMS)

Outage Management Systems (OMS)

Distribution Management Systems (DMS)

Enterprise Asset Management Systems (EAS)

Big data assets are also coming from third-party sourcessuch as off-grid data sets coming from various sourcessuch as pricing details for demand response andforecasting information for renewable energy. Other bigdata comes from additional software and servicevendors for asset management of additional smartdevices and associated operating systems.

Source: Oracle Analytics Presentation 2013


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MARKETOVERVIEW

The rise of big data poses the challenge of where to putthe data and what to do with it. The answer is in thecreation of a dedicated data center, of which there areseveral options, all of which have their pros and cons.

Some of these options include:

Data center facilities These facilities are dedicated,custom-built housing and are usuallycustomer-owned or leased from telecommunicationsor IT service providers. While these facilities usuallyhave some form of contingencies for disasterrecovery, they often increase the levels of

capital expenditures.

Storage arrays These provide data storagefunctionality and the current main vendors are EMCCorporation, NetApp and IBM.

Server platforms This is computing hardwareneeded for data, and the Main vendors are HP, IBMand Dell.

Storage area network equipment This connectsserver, storage and external network resources, andthe main vendors are Brocade, Cisco and QLogic.

Database systems This is software systems for datamanagement and analysis. The main vendors areOracle, IBM and Microsoft.

Virtualization systems These systems allow moreefficient use of discrete storage and computingresources. The main vendors are VMware, Citrix andMicrosoft.

Corresponding to the storage of big data are theanalytics options in interpreting the data. Some of theseoptions include:

Develop systems in house The benefit to this isthe system is highly tailored to the utilitys need. Onthe downside there is often a lack of in-housedevelopment skills and resources to create afully-fledged a data analytics system from scratch.

Rely on OT vendor system The advantage ofrelying on OT vendors for data analytics is that thesecompanies are well versed in the issues facing

utilities and can therefore provide products that arehighly relevant to the industry. On the downside theanalytics and IT integration capabilities might notalign well with IT vendors.

Rely on IT vendor systems The advantages are agood integration with IT systems with good designand support. On the downside there may be anincreased cost for unnecessary features and a lack ofalignment with the operational side.

Rely on point products or pure-play vendor

system.This produces close alignment withoperational processes and good integration with OT,but theres a potential lack of IT system integrationand lifetime support concerns.

Rely on third-party data analytics service

provider system. There is a reduced cost andimproved analytics, but it is an evolving concept witha limited track record and data sharing issues.

2. Data Management


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MARKETOVERVIEW

Along with assuring a ROI in smart grid deploymentthrough big data management and analytics systems,this new digital paradigm also requires creating a neworganizational foundation for data and analyticsinitiatives through a workplace culture change.

This starts with defining data ownership andresponsibility. According to the Data GovernanceInstitute, data doesnt belong to individuals, but is anasset to the enterprise. As such some organizationsassign owners to data making individuals or preferablyteams responsible for data subsets utilized foroperational efficiency.

Organizational culture also entails the reduction ofdepartmental segregation and the need to balance outdisconnects between operational and IT departments.

This must be done by the utility organization itself, saysJohn D. McDonald, Director, Technical Strategy andPolicy Development at GE Digital Energy. Onesuggestion is for the operational department to provideseveral of its people to the IT department to work forone to two years. Likewise, IT does the same tooperations. In this way, each department will have abetter understanding of the other department, and haveclose friendships in the other department.

McDonald also recommends the utility should have aknowledge management system in place to capture thisknowledge from workforce retirees to0 allow forincorporation of better standard engineering designs.

3. Changing Organizational Culture ToAccommodate a Data Driven Utility


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MARKETOVERVIEW

In the past, utilities tended to focus business practiceson a single functional area, but in todays integratedsystems, these processes cross several boundaries tocreate synergies. Thus, these synergies challenge theutilitys traditionally siloed functional organization as theline between operations technology (OT) and utilityinformation technology (IT) blurs.McDonald recommends utility management discuss theneed to work holistically across siloed groups. Thisincludes having the CEO strongly encourage ormandate holistic work. Make sure the project managersfor the Smart Grid projects have the responsibility and

multi-discipline authority (across siloed groups). Withthe implementation of new technology there will berevision of existing business processes and changes inorganizational structure and new skill sets needed.

Also crucial to organizational change is the utilitycompanys ability to assess what data to collect. In orderto do this management must first identify the businessgroups (asset management, power quality,maintenance, etc.) within the utility. Second, eachbusiness group must identify their data needs bothoperational and non-operational data. This includesattributes of each data point such as how often thedata point is needed and frequency of update. Thenoperational data levels of importance are assigned todifferent scan groups (different scan rates).

Maximizing the value of smart grid systems involvesbringing disparate data under a unified managementapproach. Operations mangers must determine whatdata to collect, how to manage it, who should haveaccess to specific data and how that data is used andstored.

This kind of organizational change demands jobredesign, training and new skill development.

But just as legacy roles and responsibilities have to bemodified, so do legacy systems. This presents its ownintegration challenges. Many legacy systems were not

designed to manage significant data loads or specificattributes that are needed to gain desired operationalbenefits, while at the same time maintaining ongoingreliable operations.

Many customized systems are no longer supported bythe original vendors. This makes for high risk associatedwith breaking open the applications or systems formodification.

Southern California Edison Manager of EnterpriseArchitecture James Horstman said this has not been amajor issue for the utility. Up until a few years ago the

majority of our business applications were internallydeveloped and supported. While we have gone more toa COTS (commercial off-the-shelf ) approach the vendorswe have worked with are all supported by the vendors,he said.

Horstman did allow there were a few instances ofsmaller applications losing support but those weretypically replaced without significant risks coming tobear. Typically our contracts with vendors includeescrow provisions for providing the software to us in theevent of non support. I can only think of one instancewhere that actually resulted in us taking over thesoftware and it was not a critical application, he said.

Another utility executive said his company looks at thischallenge on a case-to-case basis. You accept the factand either change process to match the new version oraccept the current version for all its issues, he said.

In total, changing organizational culture into a datadriven utility is crucial for utilizing the big data analyticsto make a utility run smoother which translates into alarger ROI.

4. Breaking down organizational silos


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MARKETOVERVIEW

In selecting a vendor, the best place to start, accordingto McDonald, is in evaluating the utility s needs. Identifythe missing technology components needed for anintegrated solution, and consider companies who havethis technology. Frequently, these are start-upcompanies, he said.

Other evaluation methods for selecting vendors include:

Get an overview of the latest big data solutions andplug in how they would work for the utility.

Assess the cost and risk factors associated with smart

grid and big data investments and match them withproposed vendor solutions

Put together an RFP (Request for proposals) to selectvendors to address the scope of the utilities needsand have vendors respond with solution plans andcosts including possible cost overruns.

6. Selecting a vendor


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MARKETOVERVIEW

Utilities are unique in that the nature of harnessing,storing, supplying and distributing electric, gas andwater is critical to the infrastructure of the entire globe.

Therefore, the use of analytics demands levels ofreliability and safety far greater than typical personal,business and industrial requirements.

The three analytic subsets often used within the utilityindustry are:

Descriptive analytics that gauge current performance

Predictive analytics that tell utilities whats about togo wrong

Prescriptive analytics that point to problemprevention.

Analytics in utilities are used to improve reliability inbusiness operations by preventing re-work, improvingoutage statistics like System Average InterruptionFrequency Index (SAIFI), replacing overloadedtransformers before they cause outages and identifyingequipment malfunctions and cost trends.

Some fields where analytics improve reliability include:

Predictive analytics are utilized for the maintenanceand modernization of an aging infrastructure and forimproved visibility across automated systems

Descriptive analytics leverage real time data forsmart control to enable situational awareness inoperations

Prescriptive analytics identify gaps in existing assetsand establish sound asset management practicesand programs.

7. Analytic Applications

Source: Oracle Analytics Presentation 2013

8. Improve Reliability


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MARKETOVERVIEW

Analytics are also used to drive efficiency by mining datafor common indicators of problems like slow meters,high usage, or failing feeders. This includes usingsophisticated algorithms to identify, for instance, thecauses of load inefficiencies or missed opportunities forconservation program outreach. Analytics also createsefficiencies by:

Utilizing predictive customer analytics for successfulrollout of demand/supply programs

Predicting demand and supply, which reducesoutages by geographic area with higher efficiency

Hold down energy costs and offer enhanced servicesthrough smart integrated control systems

Boost consumer engagement through newcommunications initiatives related to Smart Gridprograms

Leverage data to improve relationship withcustomers through direct marketing programs andnew tailored services

National Grid Vice President of Asset Management CheriWarren said the introduction and availability of both real

time and detailed operational performance informationintroduces opportunities to understand the state of theutilitys system and its performance at a lower level ofthe network than they have previously had available.

National Grid will leverage this information in severalways. Through more detailed short term outageinformation, the company can better identify potentialinfrastructure issues or better understand customerexperience and causes. By collecting this big data formultiple periods or seasons, long term assetperformance will greatly improve as the data will informthe performance, health, network design, asset policyand peak needs of the network and identify investments

that will enhance the resiliency and reliability, Warrensaid.

Warren added that National Grid has been exploringintegrating other operational and customer data fromother source systems into a common "data fusion"database. Bringing together information such ascustomer relationship management, asset performance,work management and smart grid data will producenew insights to help drive improved asset managementpolicies, asset planning practices and customerexperience, she said.

Warren said smart grid technology allows utilities to

gather information about system performance at a finerlevel of accuracy. Using communication and locationcapabilities National Grid will be better positioned toensure the accuracy of asset registers and gather verydetailed network operation measurements. Using theseand other pieces of information, National Grid will beable to enhance our network modeling tools andtechniques, moving away from estimates for datamodels, and allowing us to more precisely identify assetunderperformance issues in existing assets to furtherfocus investments. Making smarter investmentdecisions will help drive increased reliability,performance and power quality.

9. Drive Efficiency


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MARKETOVERVIEW

Protecting critical assets and ensuring compliance islong a part of standard operating procedure for utilities.However, new challenges have arisen with the newgame-changing smart technology initiatives andheightened public awareness of privacy concerns. Andas data threats multiply, so do compliance requirements.Ever changing legislative and regulatory activity relatedto data protection is at an all time high from bothfederal and state governments as well as from publicutility commissions about the privacy implications ofsmart meters.

Some key elements of effective privacy are:

Understand your companys compliance and culture

Align and train management and staff on securitypractices

Know your data, where it is, and what must beprotected

Ensure third parties comply with your privacypolicies

Understand your threats and controls

Test and update controls regularly

Be prepared to respond to incidents

Additionally, utilities must keep up with compliancestandards relating to security as set by the NorthAmerican Electric Reliability Corporation (NERC), whichacts at the direction of the Federal Energy RegulatoryCommission (FERC) - a federal organization overseeinginterstate transportation and marketing of energy.

NERC is responsible for establishing reliability standardsfor the Bulk Electric System (aka the power grid), whichhas become an increasingly tempting target for cyberterrorism and nation level threats. NERC's CriticalInfrastructure Protection (CIP) Standards identify theminimum cyber controls and protections which powersuppliers and generators must address or facesignificant penalties and fines.

The NERC CIP compliance standards are organized intonine (9) key areas:

Sabotage Critical Asset Identification

Security Management Controls

Personnel and Training

Electronic Security Perimeter

Physical Security Protection

Systems Security Management

Incident Reporting and Response Planning

Recovery Plans

10. Privacy, fraud protection and Cybersecurity risks


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MARKETOVERVIEW

Smart technology and its associated big datamanagement and analytics have transformed theindustry with many avenues to ensure a growing ROI.

This new technology has streamlined and improvedtechniques for distributing safe and clean energy for theglobal marketplace.

This technology has also created a growing cyberterrorism risk for enemies that understand all too wellthat the world depends and functions on utilityinfrastructure. It has also created challenges for theutility industry to safeguard privacy while at the sametime continuing its mission of providing safe electricity,gas and water.

The complexity of delivering services on a mass scalehas made utilities slow to change in keeping pace withnew technologies, but both consumers in themarketplace and government, through compliancemandates and regulations, dictate that smart grids arenecessary for sustained energy needs. In facing thesechallenges, utilities realize it means not only deployingand integrating smart technology, but changingorganizational structure.

Data analytics has brought a new precision tounderstanding and improving operational efficiencyand reliability. Whether developed in-house orincreasingly through soft grid vendors offering varioussolutions, data analytics has shifted organizationalculture as it continues to create synergies and bringdisparate data under a unified management approach.

The future of data analytics continues to evolve as utilitycompanies explore new uses such as developing acommon data fusion database integrating otheroperational and customer data from other sourcesystems into a common system.

The role of data analytics in the utility industry is nowmoving towards similar roles it plays in thetelecommunications and finance sectors. Due to theindustrys key role in global infrastructure it is critical tocontinue developing new uses and strategies for thiscutting-edge technology.

11. Concluding Remarks


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data management & analytics for utilities 2014

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