Value of Data Quality for Asset Management Decisions Western Energy Institute April 17-20, 2017

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2 Value of Data Quality for Asset Management Decisions Western Energy Institute April 17-20, 2017

3 Introduction Value of Data Quality for AM Decisions

4 .04 Good data is required to make good decisions Enterprise Systems ERP OMS GIS AMI Harmonized Data Storage Electronic Data Files Insp. SLD Data Asset EOL Profile 11% 10% Risk Profile 8% 7% 9% Paper Records Insp. Forms Numerous utility data sources are analyzed to create useful engineering decisions, including Reliability projections, asset economic EOL results, system risk metrics, and asset condition assessment 79% 76% Asset Condition Profile UG TX NTK TX UG SW Pole System 0% 20% 40% 60% 80% 100%

5 .05 What is a good decision in AM? Discount and compare A good decision is one which optimizes capital spending versus operating risk costs Leads to a minimization of total cost of ownership (TCO) throughout the system

6 SAIDI / SAIFI Risk Cost ($) Total Costs ($) Total Cost of Ownership ($M) Probability of Failures (%) (within sample size) Total Spending ($M).06 Overall Framework for Asset Management Asset Failure Probability Long-Term Plan Short-Term Projects Year Age (years) Safety Environmental An nualized Ris k Cost Life-Cycle Analysis An nualized Capital Cost L ife Cycle Cos t Eq uivalent Annualized Cost (EAC) Asset Risk Business Case Customer Financial Age (years) EXISTING NEW Relative Probability: 88% Impact: $5,566 Inspection- Based Normal Relative Probability: 3% Impact: $5,566 Safety Environmental Customer Financial $34,524 Catastrophic Pole Fire Relative Probability: 1% Impact: $1,451,645 Asset Failure Impact Relative Probability: 8% Impact: $186,781 Reliability Forecast

7 SAIDI / SAIFI Risk Cost ($) Total Costs ($) Total Cost of Ownership ($M) Probability of Failures (%) (within sample size) Total Spending ($M).07 The Unattainable Race for Data Perfection Asset Failure Probability Asset Risk Relative Probability: 88% Impact: $5,566 Relative Probability: 1% Impact: $1,451, Age (years) Inspection- Based Catastrophic Safety Environmental Customer Financial Normal $34,524 Pole Fire Asset Failure Impact Relative Probability: 3% Impact: $5,566 Relative Probability: 8% Impact: $186,781 An nualized Ris k Cost Life-Cycle Analysis An nualized Capital Cost Long-Term Plan L ife Cycle Cos t Eq uivalent Annualized Cost (EAC) Age (years) Data Requirements Year Reliability Forecast Short-Term Projects Business Case EXISTING Safety Environmental Customer Financial NEW AM approach has to be developed in advance of any data improvements Only after the data requirements are clearly established (data collections is costly!) Application of current (unperfected) data allows for results to be tested for accuracy and data gaps to be identified However, better quality data improves the decisions within the AM framework and help to minimize TCO of the system Nameplate information, asset classifications, asset demographics, inspection data, connectivity and geospatial data Asset installation costs, customer interruption costs, asset failure mode identification, environmental cleanup costs, revenue statistics, employee and public injury data

8 SAIDI / SAIFI Risk Cost ($) Total Costs ($) Total Cost of Ownership ($M) Probability of Failures (%) (within sample size) Total Spending ($M).08 Asset Model Data required to make a decision Nameplate information (GIS) Inspection fields Business values (ERP) Business constraints (workflow) Asset Failure Probability Long-Term Plan Short-Term Projects Connectivity data (load flow) Customer loading (AMI, ADMS) Customer impacts Revenue statistics Environmental cleanup costs Employee and public injury data Asset Risk Relative Probability: 88% Impact: $5, Age (years) Inspection- Based Safety Environmental Customer Financial Normal Relative Probability: 3% Impact: $5,566 An nualized Ris k Cost Life-Cycle Analysis An nualized Capital Cost L ife Cycle Cos t Year Eq uivalent Annualized Cost (EAC) Age (years) Business Case EXISTING NEW Safety Environmental Customer Financial Asset installation costs (ERP) Financial parameters (ERP) Relative Probability: 1% Impact: $1,451,645 Catastrophic $34,524 Pole Fire Asset Failure Impact Relative Probability: 8% Impact: $186,781 Reliability Forecast Historical outage data (AIM) Existing asset demographics (GIS)

9 .09 AM Data Life-Cycle Process Data Packaging Consolidating data from various sources. Data Optimization Reformatting data to meet application requirements. Actions to improve data following validation of results. Data Validation Applying data to AM analysis to deliver results. Data Application AM Data Life-Cycle

10 .10 Imperfect data: only asset count is known Asset Management Approach: Run to Failure (RTF) Failure Projections Reliability Metrics - SAIFI Asset count: RTF RTF Total Cost of Ownership Investments: Proactive and Reactive Total Annual Costs $350M $300M $250M $200M $150M $100M $50M $0M RTF Age based Condition based Risk based $10M $9M $8M $7M $6M $5M $4M $3M $2M $1M $0M RTF $20M $18M $16M $14M $12M $10M $8M $6M $4M $2M $0M RTF

11 .11 Imperfect data: age is known Asset Management Approach: Age based replacement at 50 years Age Demographics Failure Projections Reliability Metrics - SAIFI < > RTF >54 RTF Age based Total Cost of Ownership Investments: Proactive and Reactive Total Annual Costs $350M $300M $250M $200M $150M $100M $50M $0M RTF Age based Condition based Risk based $10M $9M $8M $7M $6M $5M $4M $3M $2M $1M $0M RTF Age based $20M $18M $16M $14M $12M $10M $8M $6M $4M $2M $0M RTF Age absed

12 .12 Imperfect data: condition is known Asset Management Approach: Condition based replacement of Poor and Very Poor condition Condition Demographics Failure Projections Reliability Metrics - SAIFI Very Poor Poor Fair Good Very Good RTF >54 VP_P RTF Age based Condition based Total Cost of Ownership Investments: Proactive and Reactive Total Annual Costs $350M $300M $250M $200M $150M $100M $50M $0M RTF Age based Condition based Risk based $10M $9M $8M $7M $6M $5M $4M $3M $2M $1M $0M RTF Age based Condition based $20M $18M $16M $14M $12M $10M $8M $6M $4M $2M $0M RTF Age absed Condition based

13 .13 Imperfect data: risk information is known Asset Management Approach: Risk based replacement based on optimized life-cycle cost analysis Risk Demographics Failure Projections Reliability Metrics - SAIFI Immediate intervention Within 10 years Beyond 10 years RTF >54 VP_P OIT RTF Age based Condition based Risk based Total Cost of Ownership Investments: Proactive and Reactive Total Annual Costs $350M $300M $250M $200M $150M $100M $50M $0M RTF Age based Condition based Risk based $10M $9M $8M $7M $6M $5M $4M $3M $2M $1M $0M RTF Age based Condition based Risk based $20M $18M $16M $14M $12M $10M $8M $6M $4M $2M $0M RTF Age absed Condition based Risk based

14 .14 Risk approach prioritize high-risk assets while low-risk assets are RTF Risk analysis diversifies replacement in the system Immediate replacement required for high-risk assets, while low-risk assets are run-to-failure 2/3 of total risk is represented only by about 1/4 of the assets Assets that a set run-to-failure account only for 12% of total risk 100% 50% of assets are replaced 80% proactively 60% Replacement Methods 50% Replacement Methods Asset Risks 50% of assets 40% are best run-to-failure 20% 50% 0%

15 .15 Data Quality is Critical to Making Prudent and Accurate AM Decision Making! Data Quality represents the single most important element that will influence AM decision-making: Poor data will result in inaccurate decision making. It is critical for utilities to execute continuous improvement procedures through the AM Data Life-Cycle, such that data gaps and inaccuracies can be addressed and mitigated. At the same time, data quality can only be addressed in a cost efficient once a single data lifecycle has been completed: Without knowing the possible data applications, it is impossible to predict what types of data or data granularity will be required Often data gaps and inaccuracies will not be identified until the results are produced from the data applications.

16 Thor Hjartarson, METSCO Robert Otal, METSCO THANK YOU.

17 Beyond Data: Asset Management Decisions How to Overcome a Lack of Data City of Medicine Hat Electric Distribution 4/17/2017-4/20/2017

18 Agenda How to plan and make optimal asset management decisions with: No data Incomplete data As it relates to: Assets, asset metadata, and operating characteristics System connectivity Other model inputs Beyond Data: Asset Management Decisions City of Medicine Hat Electric Distribution City of Medicine Hat 4/17/2017-4/20/2017

19 The Ideal World Img. Source: Ameren Corp. Beyond Data: Asset Management Decisions City of Medicine Hat Electric Distribution City of Medicine Hat 4/17/2017-4/20/2017

20 No Data - General belief is we don t have any data or we don t have enough data - Intellectual capital is an invaluable resource - Engineering and operating philosophies - Widely available industry models - Lack of data doesn t mean you cant apply sound AM practices - it means you have to be more creative in your approach - improve as it becomes available Beyond Data: Asset Management Decisions City of Medicine Hat Electric Distribution City of Medicine Hat 4/17/2017-4/20/2017

21 Case Study: With Inaccurate Data We Applied Machine Learning to Estimate Lacking Demographics Data To Enrich Risk Analysis Example: U/G Switches: 20% of the asset population was installed in 1900 Accurate Data Device ID Install_Year Feeder x y 92-S S S S S S MS S MS S S S S S S S Training Data 80% of U/G switches U/G Transformers Two-thirds of accurate dataset (randomly selected) Testing Data One-third of accurate dataset (randomly selected) Using spatial data to build and train algorithms Accuracy prediction: Mean Squared Deviation (MSD) 1 of an estimator measures the average of the squares of the "errors" Beyond Data: Asset Management Decisions City of Medicine Hat Electric Distribution City of Medicine Hat 4/17/2017-4/20/2017

22 Case Study: With Inaccurate Data We Applied Machine Learning to Optimize Demographics Data To Enrich Risk Analysis Example: Underground Primary Cable: Over 90% of underground primary cable segments had erroneous installation years UNDERGROUND PRIMARY CABLE INSTALLATION YEAR DISTRIBUTION (ORIGINAL) UNDERGROUND PRIMARY CABLE INSTALLATION YEAR DISTRIBUTION (OPTIMIZED) Beyond Data: Asset Management Decisions City of Medicine Hat Electric Distribution City of Medicine Hat 4/17/2017-4/20/2017

23 Case Study: With Inaccurate Data We Applied System Principles To Optimize Customer Disruption Results For Risk Analysis Example: Customer Loading and Restoration Times: Customer information was required on an asset-byasset basis, separated by customer class. This information was only available on a feeder level. Load restoration times were determined from installed switching capabilities. Beyond Data: Asset Management Decisions City of Medicine Hat Electric Distribution City of Medicine Hat 4/17/2017-4/20/2017

24 Case Study: All Optimized Data Was Used to Produce Investment Recommendations Based On Risk Analysis Example: Risk Analysis Outputs: All optimized data sets were combined to produce comprehensive risk analysis results for each system asset. Results were amalgamated to produce an optimal system-wide investment program. Similar results were produced for each substation to assess full renewal feasibility. Beyond Data: Asset Management Decisions City of Medicine Hat Electric Distribution City of Medicine Hat 4/17/2017-4/20/2017

25 Ted Zalucki Project Engineer and Design Supervisor City of Medicine Hat Electric Distribution Beyond Data: Asset Management Decisions City of Medicine Hat Electric Distribution City of Medicine Hat 4/17/2017-4/20/2017

26 EPCOR Asset Condition Assessment Study WEI Operations Conference Beyond Data: Asset Management Decisions Natalia Kazakova April 17 th - April 20 th, \

27 Agenda (7-10 min) Introduction Asset Management Studies Asset Data requirements, challenges and benefits Risk based asset management framework implementation transformational phase Conclusion 2 \

28 Introduction Wholly-owned subsidiaries build, own and operate electrical transmission and distribution networks, water and wastewater treatment facilities and infrastructure Selected as Alberta s Best Overall Workplace for companies with more than 750 employees Headquartered in Edmonton, Alberta, Canada Serving the City Of Edmonton: Population 900,000 Residential Services - 353,000 Commercial Services 37,000 Distribution system Poles 52,000 Aerial Transf s 12,000 Pads 20,000 UG Transf s 19,000 Cabinets 3,000 UG Cables 4,000 3

29 EDTI s Asset Management Studies EDTI s Asset Management studies completed in : Asset Risk Framework (ARF) - foundation for sustainability, optimization and prioritization Condition based asset management Risk based short-term and long-term planning Reliability Projection Model (RPM) foundation for performance Defective equipment is the highest contributor to unreliability Asset management mid / long- term planning Adequate asset and system investment levels to maintain performance Both studies and models are integrated single version of truth Currently EDTI is implementing ARF; testing it in front of the regulator 4

30 Asset Risk Framework Study Scope The Asset Risk Framework study scope: the configuration of a Health Index, Failure Curves, Impact Quantifications and the production of life-cycle costing results to be used as part of long-term and short-term planning functions for 6 models. Asset Condition Assessment Failure Curve Calibration Derivation of Impact Quantification Results and Associated Frameworks Risk-Based Life-Cycle Calculations Capital Program- Long Term Planning Results Field Inspection Recommendations Asset Life Cycle Management Planning tool Poles O/H & U/G TX U/G Cables Switching Cubicles Network TX 5

31 Reliability Projection Study Scope Long-term projections for system reliability performance metrics as they relate to: Run-to-failure approach Proactive investments Linkage between investments and reliability impacts Provides long-term recommendations for future reliability data improvements Reliability Planning tool, which utilizes the established asset models 6 Poles Poles O/H & U/G TX O/H & U/G TX U/G Cables U/G Cables Switching Switching Cubicles Cubicles Network TX Network TX

32 Asset Data Requirements, Challenges and Benefits Extremely Data intensive undertake Risk based: Reliability, Financial, Environmental, Collateral Damage/Safety - data types Asset Data Requirements: Data sources: IVARA as the asset registry, EO and GIS; Asset data: installation/ decommission dates, asset age and history, asset failure and removal reasons, outage data, etc.; Asset condition within specified criteria, inspections content; Costs: Reactive vs. Proactive costs, associated labour (internal and external) and material costs; Asset connectivity, data for asset attributes; 7

33 Asset Data Requirements, Challenges and Benefits Challenges with mining data for the studies: Data was not consolidated in one place / system Asset data gaps (age, installation, condition, inspections etc.) Associating different data types with each other as studies required operational and nonoperational asset related data Data integrity varied from system to system for the same type of data/ or the same data Linear assets (as non-discrete assets) are the most difficult to model Solutions: Defined and validated assumptions (non-elastic) Models are based on the EDTI s data, but in its absence or partial availability used extrapolations or the industry data Used statistical algorithms to scrub and validate data Used fuzzy logic to recognise particular assets Increased system integrations creating single version of truth 8

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35 Old Accounting Assets Substation Circuit Asset Group 10

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37 Inspection Templates are Defined in AssetWise APM 12

38 Inspection Data is Collected in FieldSmart

39 Asset Data Requirements, Challenges and Benefits Current phase: framework implementation in PDCA cycle Revised and standardized asset inspections and redefined asset condition data Revamped planning processes Studies and our data mining process improved our asset data related strategy, process, structure etc. So, what is really beyond data? Foundational systematic asset management approach consistent with ISO55K and PAS55 that ensures asset sustainability and performance Consistent asset management long and mid-term plans that link performance, risks and costs Ability to optimize and prioritize required investments in a constrained environment adaptable to a higher level of integrated planning Better data- better decisions - OMS, DMS and AMI implementations have positive impact 14

40 Asset Risk Framework Toolkit 15

41 Where are We Heading SAIDI 16 % Confidence Interval Regulatory Threshold 1.15 Target

42 Questions? 17 \