Cost-effective Distribution Reliability Improvement Using Predictive Models

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1 Cost-effective Distribution Reliability Improvement Using Predictive Models Dr. Julio Romero Agüero Sr. Director, Distribution Quanta Technology Raleigh, NC

2 Table of contents 1. Reliability modeling 2. Objectives 3. Reliability targets 4. Historical outage analysis 5. Predictive reliability modeling 6. Cost-benefit analysis 7. Expected reliability estimation 8. Develop reliability roadmap 9. Examples 10. Conclusions 11. References Page 2

Predictive reliability modeling 6. Cost-benefit analysis 7.

3 1. Reliability Modeling Source: IEEE Predictive Reliability Task Force Page 3

4 1. Reliability Modeling Source: IEEE Predictive Reliability Task Force Page 4

5 1. Reliability Modeling Source: IEEE Predictive Reliability Task Force Page 5

6 1. Reliability Modeling Reliability models are analogous to power flow models and usually perform analytical simulation for expected value analysis Inputs System topology and device locations Reliability parameters (failure rates, repair times, etc) Customer counts Operating practices (fuse saving/fuse clearing, etc) Outputs Momentary & sustained interruptions, outage duration Reliability indices Page 6

Reliability parameters (failure rates, repair times, etc) Customer counts Operating practices (fuse

7 2. Objectives To improve the reliability of a specific area or service territory to meet utility s goals (e.g., to comply with regulatory requirements). This most be done in the most cost-effective way, i.e., by identifying the projects that represent more bang for the buck Taking advantage of existing utility tools, to increase efficiency, quality and productivity Page 7

8 SAIDI (min/yr) 3. Reliability Targets Define reliability targets % C4 y = Ln(x) R 2 = C % 75% C7 C2 C3 C9 100 SYSTEM C1 C8 C ,000 10,000 Customers per Square Mile Page 8

25% C4 y = -19.037Ln(x) + 234.79 R 2 = 0.

9 4. Historical outage analysis Analyze historical outage data to identify the main causes of outages and the most efficient alternatives for improving reliability Equipment Failure Birds and Animals Trees AO BA CP CR DU EF EO EQ FI LI NW OD OE PO SO TF TO UN VA Page 9

7000 6000 Equipment Failure 5000 4000 Birds and Animals 3000 2000 Trees 1000 0 AO

10 Frequency 4. Historical outage analysis % % 80% % 40% Failure Rate OH (f/yr/mi) More 20% 0% Page 10

25 20 15 60% 40% 10 5 0 0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.

11 Frequency 4. Historical outage analysis % % 80% % 40% Failure Rate UG (f/yr/mi) More 20% 0% Page 11

12 5. Predictive reliability modeling Develop a predictive reliability model of the study area using distribution analysis software. The model is calibrated to represent the area s existing reliability Page 12

13 CMI reduction/$ 6. Cost-benefit analysis Evaluate the impact of a comprehensive set of projects and select the most cost-effective alternatives for improving the reliability of the study area ($/CMI, $/CI, ENS, etc) Feeders Page 13

and select the most cost-effective alternatives for improving the reliability

14 ENS (MWh/yr) ENS (%) 6. Cost-benefit analysis % 80% 60% % 20% 0% 0 100, , , , , ,000 Cumulative cost ($) Page 14

15 SAIDI (hr/cust-yr) 7. Expected reliability estimation Estimate the expected reliability of the study area due to the progressive implementation of the optimal mix of projects (prioritization) Cost vs. reliability (SAIDI) , ,000 1,200,000 1,600,000 2,000,000 Cumulative cost ($) Page 15

due to the progressive implementation of the optimal mix of projects

16 SAIDI (%) 7. Expected reliability estimation This figure shows the corresponding system SAIDI versus cumulative cost curve (%) for the proposed portfolio of projects. Each dot represents a project 105% 100% 95% 90% 85% 80% 75% 70% - 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 8,000,000 Cost ($) Page 16

versus cumulative cost curve (%) for the proposed portfolio of projects.

17 SAIDI (min/yr) 8. Develop reliability roadmap Extrapolate the study area results to the utility s service territory, considering the different features of each feeder (length, overhead exposure, voltage, etc) Reliabilty Roadmap Spending ($M) Page 17

service territory, considering the different features of each feeder (length,

18 9. Examples feeder (improvement projects) Page 18

19 9. Examples SAIDI (before/after) Page 19

20 9. Examples area (SAIDI before) Spatial distribution of expected SAIDI before (hr/cust-yr) SAIDI > SAIDI < Color Page 20

(hr/cust-yr) SAIDI > SAIDI < Color 0 0.5 0.

21 9. Examples area (SAIDI after) Spatial distribution of expected SAIDI after (hr/cust-yr) SAIDI > SAIDI < Color Page 21

22 9. Examples area (SAIFI before) Page 22

23 9. Examples area (SAIFI after) Page 23

24 9. Examples MAIFI before (fuse clearing) Page 24

25 9. Examples MAIFI after (fuse clearing) Page 25

26 9. Examples MAIFI after (fuse saving) Page 26

27 9. Examples This approach was used to estimate the following reliability improvements: Utility 1 (North East): SAIDI reduction of approximately 30% for a pilot area with a total of 61,000 customers by investing about $ 2.5 M Utility 2 (Midwest): SAIFI reduction of approximately 20% for a pilot area with a total of 35,000 customers by investing roughly $ 1.9 M Utility 3 (North West): SAIDI reduction of approximately 50% for the overall service territory (approximately 1 million customers) by investing approximately $ M over a period of 10 years Page 27

28 10. Conclusions Predictive reliability modeling using computational tools is becoming a standard distribution planning practice Predictive models allow improving and maintaining reliability in a systematic and cost-effective manner Estimated reliability improvements can be used to prioritize iprojects and build cost-effective portfolios Most distribution system analysis software include predictive reliability modeling and simulation capabilities that can model typical improvement projects Next steps are adding more complex capabilities such as modeling advanced distribution automation schemes, distributed generation and microgrids, and probabilistic modeling and analysis Page 28

29 11. References [1] Predictive Reliability Task Force, L. Xu, 2013 IEEE PES GM, [2] Distribution System Reliability Improvement Using Predictive Models, J. R. Aguero et. al., 2009 IEEE PES General Meeting [3] A Reliability Improvement Roadmap Based on a Predictive Model and Extrapolation Technique, J. R. Aguero et. al., 2009 IEEE PSCE [4] Improving the Reliability of Power Distribution Systems Through Single-Phase Tripping, J. R. Aguero et. al., 2010 IEEE PES T&D Conference and Exposition Page 29

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DISTRIBUTION RELIABILITY USING RECLOSERS AND SECTIONALISERS

DISTRIBUTION RELIABILITY USING RECLOSERS AND SECTIONALISERS ABB DISTIBUTION ELIABILITY USING ECLOSES AND SECTIONALISES obert E. Goodin Chief Engineer ABB Inc. Lake Mary, FL Timothy S. Fahey, PE Sr. Application Engineer ABB Inc. aleigh, NC Andrew Hanson, PE Executive