Using Predictive Models to Estimate Distribution Reliability Benefits of Modern Reclosing and Distribution Automation Schemes

Transcription

1 Using Predictive Models to Estimate Distribution Reliability Benefits of Modern Reclosing and Distribution Automation Schemes July 19, 2016 Boston, MA Dr. Julio Romero Agüero Vice President Strategy & Business Innovation Houston, TX

2 Distribution System Modeling Source: IEEE Predictive Reliability Task Force Page 2

3 Reliability Modeling Reliability models are analogous to (and as good as) power flow models Inputs Power Flow System features Topology Line and component data Transformer loads Reliability System features Topology Line and component data Transformer loads Protective/switching device locations Reliability parameters (failure rates, repair times, etc.) Customer counts Operating practices (fuse saving/fuse clearing, etc.) Outputs Voltages Currents Power flows (P, Q) Frequency & duration of momentary & sustained interruptions Reliability indices Reliability models perform analytical simulations for expected value or stochastic analyses Page 3

4 Reliability Modeling Objectives Common objectives Estimate existing and predict future reliability of distribution system Improve reliability of area or service territory to meet utility s goals (e.g., comply with regulatory requirements) o o o o Must be done in cost-effective way Identify and prioritize portfolio of solutions with highest benefit/cost ratio Outline deployment roadmap Take advantage of existing utility tools to increase efficiency, quality and productivity Page 4

5 Reliability Modeling Objectives Additional objectives Develop/update distribution protection and automation planning guidelines by incorporating reliability improvement criteria o o o Location of protective/switching devices Overcurrent protection philosophy (fuse saving vs. fuse clearing) Location of distribution automation schemes Evaluate impacts and benefits of emerging technologies, e.g., Distributed Energy Resources (DER), microgrids o Asses how technologies can impact or improve reliability Page 5

6 Reliability Modeling Steps Existing Engineering, Operations and Safety Procedures Reliability Improvement Targets Power Flow Models (Synergi) Existing T&D System Features Clustering and Representative Feeder Selection Development of Predictive Reliability Models Selection of Solution Portfolio Simulations & Analysis Benefit-Cost Analyses for Representative Feeders Project Prioritization and Expected Improvement Estimation Extrapolation to Feeder Clusters Historical Data (Load, Interruptions, Reliability Indices, etc.) Existing Communications Systems & Infrastructure Root-Cause Analyses Development of Protection & Distribution Automation Planning Guidelines Protection & Distribution Automation Planning Guidelines Page 6

7 Reliability Modeling Steps (2) Protection & Distribution Automation Planning Guidelines Extrapolation to Feeder Clusters System Aggregation Overall Cost-Benefit Analyses Preparation of Deployment Roadmap Deployment Roadmap Optional steps needed for large scale (e.g., system-wide) studies: Clustering and representative feeder selection Extrapolation to feeder clusters System aggregation Page 7

8 Reliability Modeling Steps (3) Inputs Existing engineering, operations and safety procedures Reliability improvement targets Power flow models Existing T&D features Historical data (load, interruptions, reliability indices, etc.) Existing communications system & infrastructure Outputs Expected reliability Prioritized cost-effective project portfolio for reliability improvement Impacts and benefits of emerging technologies Distribution protection and automation planning guidelines Deployment roadmap Page 8

9 Key Steps Reliability Targets Define reliability targets % C4 y = Ln(x) R 2 = C5 SAIDI (min/yr) % C7 75% SYSTEM C2 C1 C8 C3 C9 C ,000 10,000 Customers per Square Mile Page 9

10 Key Steps Historical Outage Analysis Analyze historical outage data to identify the main causes of outages and the most efficient alternatives for improving reliability Equipment Failure 5000 Birds and Animals Trees AO BA CP CR DU EF EO EQ FI LI NW OD OE PO SO TF TO UN VA Page 10

11 Key Steps Historical Outage Analysis % % 30 80% Frequency Failure Rate OH (f/yr/mi) More 60% 40% 20% 0% Page 11

12 Key Steps Historical Outage Analysis % % 30 80% Frequency Failure Rate UG (f/yr/mi) More 60% 40% 20% 0% Page 12

13 Key Steps - 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 13

14 CMI REDUCTION/$ CUMULATIVE CMI REDUCTION/$ Key Steps Benefit-Cost 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, $/CI, ENS, etc.) ,000 1,000,000 1,500,000 2,000,000 2,500,000 CUMULATIVE COST ($) CMI reduction/$ Cummulative CMI reduction/$ Page 14

15 Key Steps Project Prioritization Estimate the expected reliability of the study area due to the progressive implementation of the optimal mix of projects (prioritization) Cost vs. reliability (SAIDI) 4.0 SAIDI (hr/cust-yr) High Priority Projects Low Priority Projects , ,000 1,200,000 1,600,000 2,000,000 Cumulative cost ($) Page 15

16 Key Steps 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% SAIDI (%) 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

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

18 Examples Feeder (Improvement Projects) Substation Existing Reclosers New DA Scheme N.C. Recloser N.O. Recloser Page 18

19 Examples SAIDI (Before/After) Colors indicate relative magnitude of SAIDI values, areas colored in red have higher SAIDI values than those colored in green Page 19

20 Examples Area (SAIDI Before) Spatial distribution of expected SAIDI before (hr/cust-yr) SAIDI > SAIDI < Color Page 20

21 Examples Area (SAIDI After) Spatial distribution of expected SAIDI after (hr/cust-yr) SAIDI > SAIDI < Color Page 21

22 Examples Area (SAIFI Before) Page 22

23 Examples Area (SAIFI After) Page 23

24 Examples MAIFI Before (Fuse Clearing) Page 24

25 Examples MAIFI After (Fuse Clearing) Page 25

26 Examples MAIFI After (Fuse Saving) Page 26

27 Examples Single Phase Tripping Theoretical additional reliability improvement (SAIFI & SAIDI reduction) for different recloser locations Benefits are a function of number of downstream customers Page 27

28 Key Steps Benefit-Cost Analysis Fuse Clearing Difference in SAIDI reduction achieved via single-phase tripping (1P) and three-phase tripping (3P) Greater reduction when majority of faults is single-phase Fuse Saving Percentage of faults that are single-phase Page 28

29 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 Prioritizing projects and build cost-effective portfolios Developing protection and distribution automation planning guidelines that incorporate reliability improvement criteria Evaluating effectiveness of overcurrent protection philosophies (fuse saving, fuse clearing), and advanced reclosing practices such as single-phase tripping Evaluating the potential impacts and benefits of emerging technologies such as Distributed Energy Resources, microgrids, etc. Page 29

30 Conclusions Most distribution system analysis software include predictive reliability modeling and simulation capabilities that can model typical improvement projects Additional capabilities are needed to model more complex applications without the need of using modeling simplifications, this is still an area of improvement for software developers Page 30

31