GIS Data Is Actually Important!

Transcription

1 1 1. Context 2. The Requirements 3. PSNH Context 4. PSNH Challenges 5. Solutions 6. Opportunities 7. Discussion March 5, 2013 Robert Sarfi Thomas Mitchell (720) (603) GIS Data Is Actually Important V0.2

2 2 1. Context Smart Grid Requires Improved Data! Monetizing GIS: The Value of GIS Data Quality for Electric Utilities An EPRI Smart Grid Assessment Report. EPRI, Palo Alto, CA:

3 Respondents 3 Utility Data Self-Assessment 70% 60% 50% 40% 30% 20% 10% 0% Worse than 50% 50% - 75% Accuracy and Completeness 75% - 90% Better than 90% Data Accuracy Data Completeness 75% accuracy may not be good enough Monetizing GIS: The Value of GIS Data Quality for Electric Utilities An EPRI Smart Grid Assessment Report. EPRI, Palo Alto, CA:

4 4 2. The Problems OMS requires the following mandatory data: A complete connectivity model (100% connectivity) No parallel circuits, unless a network is supported Customer to transformer matching (in excess of 70%) Phasing is consistent with fundamental engineering practices, e.g. few phase mismatches Substation data is not mandatory nor is substation modeling possible in all GIS, but the breaker configuration at the substation can be useful to avoid false outage predictions Customer geocoding (in excess of 80%) Customer phone numbers (in excess of 70%) Nightly update or better system and customer data Engineering Analysis requires the following mandatory data: A complete connectivity model (100% connectivity) Mapping of transformers, conductors, capacitors, regulators to their associated electrical parameters Mapping of protective devices to their protective characteristics Substation data is not mandatory nor is substation modeling possible in all GIS, but the breaker configuration at the substation can be useful to avoid false outage predictions The real-time nature can ne daunting! CIS requires alignment of the addresses with the GIS. This is typically a complicated process but is necessary, as this will enable more accurate outage predictions. ADMS functionality typically requires OMS and Engineering Analysis minimum data levels, in addition to better customer loading

5 3. PSNH Context 5

6 6 4. PSNH Challenges Customer to transformer connectivity field verification Transformer to electrical service point Proper customer to ESP Transformers without customers Field Verification of customer to transformer connectivity Transformer to ESP Proper customer associated to ESP (variable completeness in C2 data) Phase validation Landbase New streets Cleanup of existing streets Update of CIS to reflect a corporate street database Validation and identification of critical customers, e.g. Medical alert, Schools, Fire, Police, Water / Sewer, Hospitals, Radio Towers Non or incorrectly geocoded customers Sufficient staffing for ongoing update Self-actualization and integrity is the key!

7 7 Integration of People, Process, Technology Process 1. Governance 2. Improvement 3. Enablement/Automation Technology 1. Culture 2. Organization 3. Skills 4. Change 1. Communications 2. Architecture 3. Integration 4. Applications People

8 8 5. Solutions Understand the cost of bad data Rationalize where data is stored and who updates it Conceptual common data model Plan cleanup Programmatic, e.g. cust-xfmr proximity Office inspection, e.g. Google Earth, Bing Field inspection, e.g. random, retirees, students, contractors Statistical analysis, e,g. AMI to identify phasing Reconsider maintenance approach People, process technology Ownership is critical factor

9 9 6. Industry Opportunities Better use of 3 rd party data, e.g. streets maintenance, customer connectivity Improved statistical analysis e.g. phasing, customer connectivity User empowerment through field access to GIS Commonly accepted industry data requirements and definitions More intuitive and streamlined process through standards based integration Industry accepted integration architecture Published definition of minimal acceptable GIS quality and service levels Standardized quality assessment tools

10 10 6. Discussion 1. We need to work together as an industry to improve data quality. 2. Field surveys aren t the only option we need to consider.