DG Modeling Challenges & Results. Jens Schoene

Transcription

1 DG Modeling Challenges & Results Jens Schoene December 13, 2013

2 2 Outline PV Caused Issues on Distribution Systems Modeling Challenges Case Study

3 3 PV Caused Issues on Distribution Systems

4 4 Potential PV-caused issues Category Reverse Power Flow Voltage Fluctuation Feeder Section Loading Issue Overcurrent protection gets confused -> false trips, no trips Line regulators get confused -> high/low voltage on DG side Capacitor switching, Load Tap Changer (LTC) operation, and line Voltage Regulator (VR) operation caused by cloud shading. Flicker caused by step voltage change during switching. Capacitor switching transients (synchronous closing, preinsertion impedance, point-on-wave) Low/medium PV penetration -> PV offsets load thereby decreasing section loading High PV penetration -> PV may exceed base load, capacity sufficient to distribute surplus power? Power Losses PV changes loading (see row above). Impact on losses

5 5 More potential PV-caused issues Category Fault Current Unintentional Islanding Ground Fault Overvoltage Harmonics Dynamics Feeder Imbalance Issue PV increases fault current. Impact on relay protection. Utility system reclosing into live island may damage switchgear and loads. Single-phase fault -> TOVs on unfaulted phase. Harmonics caused by PV inverter Effect of fast transients caused by cloud shading and system disturbances. Dynamic interaction of transients with other conventional and non-conventional control devices. Imbalance caused by uneven distribution of PV causing Neutral-to- Earth voltages, Overloaded Neutrals

6 Modeling Challenges 6

7 7 Picking the right tool for the job Load Flow, balanced Load Flow, unbalanced Short Circuit Relay Coordination Arc Flash Harmonics Transient Analysis Dynamic Analysis Quasi Steady- State Analysis ATP, EMTP-RV, Simulink, PSCAD Aspen, Cape DesignBase, PowerFactory, Gridiant NexHarm PSLF, PSS/E OpenDSS GridLAB-D Best choice Can be done, but not preferred choice Cannot be done

8 Large Systems 8

9 9 Lots of variability Monthly variation (season) Net-PV profile of a residential unit Variation over hours and minutes (time of day, clouds)

10 10 Dealing with variability: Easy Way Make simplifying assumptions: All PVs follow the same generation curve. Accurate for a clear day, not accurate for cloudy day. Conservative when looking at overvoltages. Not conservative for voltage regulations. All loads follow the same load curve. Never accurate. Ignores changing nature of loads.

11 Case Studies 11

12 45 PV generators (actual) PV ID Power, kw PV ID Power, kw

13 432 PV generators (future scenario) Two large 1 MW PVs Simulations run with and without large PVs Effect of centralized PV vs distributed PV 13

14 14 Cloud Tracking Impact of High PV Penetration on Distribution Feeders in the USA

15 Accounting for Cloud Movement 15

16 16 Different PV production at different locations during same time

17 Disaggretating Loads Disaggregating 10 loads (shown for illustrative purpose) Disaggregating all residential loads used in our simulation 17

18 Simulation Scenarios 1. Low (actual) penetration of small PV w/ 2 MW PV 2. Low (actual) penetration of small PV w/o 2 MW PV 3. High penetration of small PV w/ 2 MW PV 4. High penetration of small PV w/o 2 MW PV 18

19 19 Simulation Cases: Residential Feeder Case # Load PV Resolution Sky Condition Aggregated Aggregated 0 Yes Yes 1 h Cloudy to Overcast 1 Yes Yes 30 sec Cloudy to Overcast 2 No Yes 30 sec Cloudy to Overcast 3 Yes No 30 sec Cloudy to Overcast 4 No No 30 sec Cloudy to Overcast 5 No No 1 h Cloudy to Overcast 6 No Yes 30 sec Clear

20 Residential PV: Tapchange Operation 20 PV significantly increases tap changing operations. Case 2 (PV aggregated) vs. Case 4 (no PV aggregation). Cases 0 and 5, 1 hour simulation step size (vs. 30 seconds for other cases). Case 6, clear day.

21 21 Residential PV: Voltage Profile 5% voltage limit PV raises voltage over permissible limit (at some locations, at some times).

22 Conclusions 22

23 Selected Conclusions Effect of aggregating PV generation Exaggerates the actual tap changing operations for high-pv penetration scenarios Model predicted tap changes = Actual tap changes Increasing PV on a Feeder Tap changes predicted from models that use aggregated PV generation Actual tap changes 23

24 24 Some Observations Reactive Power Use Tap Changes Real Power Use Increasing PV on a Feeder Line Losses Comprehensive CSI report publicly available here (do not confuse with Final Report of the same name): Improving Economics of Solar Power through Analysis, Forecasting, and Dynamic System Modeling. October Schoene, J. (Director of Research Studies, EnerNex) and J. Kleissl (Assoc. Prof., UC San Diego). University of California, San Diego

25 Discussion 25

26 Backup 26

27 Issues depend on PV penetration 27 level & feeder characteristics

28 28 What type of tools are out there? Operational Tools Online operation Facilitate real-time operational decision regarding voltage regulation, transformer loading, PQ, etc. Gridiant s GRIDview, PowerAnalytic s Paladin Live, Paladin SmartGrid Planning/Analysis Tools Offline simulations Facilitate planning/design decisions Look at what if scenarios

29 We have lots of tools 29

30 30 Automatic System Conversion CYME SynerGEE Electric EMTP-RV Centralized Data Format In MATLAB Based on OpenDSS OpenDSS OpenDSS File

31 31 Use of Solar Irradiance Data Historical irradiance data facilitate realistic simulations to determine overvoltages, equipment wear, protection issues, etc. Forecasting data useful for dispatch (ramp rates). Total generation fleet must have sufficient operational flexibility to reliably integrate variable renewable generation. Forecasting data potentially useful for voltage regulation.

32 32 Conclusions: Residential PV PV caused overvoltage => additional voltage regulators required PV increased tap changing operation of voltage regulators increased => loss-of-life and increased maintenance

33 OpenDSS 33

34 OpenDSS 34

35 OpenDSS 35