Cost-effective Distribution Reliability Improvement Using Predictive Models

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Cost-effective Distribution

Reliability Improvement Using

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1. Reliability Modeling

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1. Reliability Modeling

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1. Reliability Modeling

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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

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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

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3. Reliability Targets

 Define reliability targets

y = -19.037Ln(x) + 234.79 R2 = 0.2563 0 50 100 150 200 250 300 1 10 100 1,000 10,000

Customers per Square Mile

SAIDI (min /yr) C9 C7 C6 C8 C1 C2 50% 25% 75% SYSTEM C5 C4 C3

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4. Historical outage analysis

 Analyze historical outage data to identify the main causes of outages and the most efficient alternatives for improving reliability

0 1000 2000 3000 4000 5000 6000 7000 AO BA CP CR DU EF EO EQ FI LI NW OD OE PO SO TF TO UN VA 2003 2004 2005 2006 2007 Equipment Failure Trees Birds and Animals

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© 2014 Quanta Technology LLC 0 5 10 15 20 25 30 35 40 45 0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1 2.3 2.5 2.7 2.9 3.1 3.3 3.5 3.7 3.9 Mor e

Failure Rate OH (f/yr/mi)

F req u en cy 0% 20% 40% 60% 80% 100% 120%

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4. Historical outage analysis

0 5 10 15 20 25 30 35 40 45 0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1 2.3 2.5 2.7 2.9 3.1 3.3 3.5 3.7 3.9 Mor e

Failure Rate UG (f/yr/mi)

F req u en cy 0% 20% 40% 60% 80% 100% 120%

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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

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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)

0.0 0.5 1.0 1.5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Feeders CMI r edu ctio n/$

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© 2014 Quanta Technology LLC 0% 20% 40% 60% 80% 100% 0 20 40 60 80 100 120 140 160 180 0 100,000 200,000 300,000 400,000 500,000 600,000 ENS (%) ENS (MWh /yr) Cumulative cost ($)

6. Cost-benefit analysis

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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)

3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4.0 0 400,000 800,000 1,200,000 1,600,000 2,000,000 Cumulative cost ($) S AIDI (hr/c ust-yr)

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 This figure shows the corresponding system SAIDI versus

cumulative cost curve (%) for the proposed portfolio of projects. Each dot represents a project

70% 75% 80% 85% 90% 95% 100% 105% - 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 ($) SA ID I (%)

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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)

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 0 10 20 30 40 50 60 70 80 90

Reliabilty Roadmap Spending ($M)

S A ID I (m in /y r)

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 Spatial distribution of expected SAIDI before (hr/cust-yr)

SAIDI > SAIDI < Color

0 0.5 0.5 1 1 1.5 1.5 2 2 2.5 2.5 3 3 3.5 3.5 4 4

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 Spatial distribution of expected SAIDI after (hr/cust-yr)

SAIDI > SAIDI < Color

0 0.5 0.5 1 1 1.5 1.5 2 2 2.5 2.5 3 3 3.5 3.5 4 4

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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 $ 158.5 M over a period of 10 years

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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

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11. References

[1] Predictive Reliability Task Force, L. Xu, 2013 IEEE PES

GM,

http://grouper.ieee.org/groups/td/dist/sd/doc/2013-07%20Predictive%20Reliability%20Task%20Force.pdf

[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

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Cost-effective Distribution Reliability Improvement Using Predictive Models