HECO Companies Experience with Distributed PVLisa DangelmaierApril 11 2012

PJM/EPRI/NREL Inverter-based Gen Interconnection Workshop – Distribution Reliability Needs/Studies

DG Growth from 2009 to 2011

2Summary of Interconnected Distribution Level Penetration on Major Island Grids* DG Penetration = Installed DG MW / Max Feeder Load 2010 data

*

Tracking Tremendous Growth in Distributed PV

3

Red areas indicate circuits with > 15% PV penetration

Local Circuit Impact – light load, or large projects lead to export

Concerns:1. The above graphs illustrate the need to assess the feeder loading not only at peak periods but also on the days

where the loads are not as high (light load Sundays) – Rule 14H 2. Circuit peaks often not coincident with System peaks 4

Improved Models to Account for PV as Generation vs Negative Load

Enables more accurate modeling of DG resources for planning

Consistent distribution system model expedites modeling and analysis process

Allows for “what-if” analysis to stay ahead of system change and minimize risks of stranded assets

5

Translate feeder level impacts to system level

Interface Between T&D Models

6

SynerGEEUnbalanced Distribution

Aggregated BEW PV Sites

SynerGEE Balanced System

46 kV/12 kV

Aggregated BEW System/PV Sites

PSLF Models

PowerWorldBalanced

138 kV/46 kV Model

PSS/E138 kV Model

PPLUS

Generic PV/Inverter User- Defined

Model

PSS/E400+Sites

PV Data

Individual12 kV Feeder

46 kV System 138 kV System Generation

Reso

urce

s

7000

+Si

tes

900Sites

Data Transfer Point

Distribution Planning

ResourcePlanning

Transmission Planning

Note: Line ratings shown for Oahu system for illustration

Industry Partners: SMUD/HECO High Penetration PV Initiative, BEW Engineering, GL Noble Denton, GIS & EMS providers

Modeled Variability : 30% PV Penetration Island Wide (light load)

7

Color-coded impacts at distribution substations

Simulated results at 30% PV penetration across the islands and impacted areas

Impact of Variability On LTC Operations

Highlighted % of total days with tap changer count above n > 5 for months of January to June

Increase in count correspond with highest variability months- May and April (light load months)

8

Impact of Increasing Distributed PV on Apparent Demand

9

MW

Midnight Midnight12 noon

Min-Max output of dispatchable and base generating units

Excess Energy –curtailed

Energy Shortfall must be met by fast starting units

SCADA, feeder caps or new monitoring to account for DG contributions

Typical Hawaii load profile –Evening Peaking

Output of intermittent units

Supply exceeds Demand at Lower Demand Periods –Encroaching into Daytime

Supply does not meet demand at maximum load

DG (PV) changing system daytime load shape

Curtailment ReliabilityMasked Loads

2008 difference

2011 difference

Comparison of 2008 and 2011 Weekend Actual Loads (HELCO)

• Weekday comparison: evening peak has not significantly changed between 2008 and 2011 but daytime demand has.

• Weekend comparison (light load day): difference between daytime peak and evening peak has increased. 10

Source: HELCO

Monitoring solar availability and variability at 2 sec SCADA rate. Operators observe correlation of solar irradiance and demand change.

LM-1 solar availability sensors

11

Hawaii

Piloting Control Room Situational Awareness Tools to "See" Variability

12

59.6

59.7

59.8

59.9

60

60.1

60.2

0

0.5

1

1.5

2

2.5

3

13:20:00 13:21:00 13:22:00 13:23:00 13:24:00 13:25:00 13:26:00 13:27:00 13:28:00 13:29:00 13:30:00

Freq

uenc

y (H

z)

Momentary Fault, Auto-Reclose 6500 Line 2/7/2012 13:20Load Response based on Per Unit Pre-fault Load

FEEDER 11 MW

FEEDER 12 MW

Frequency (Hz)

PPA, SIA, larger FIT Interconnection Requirements

13

•Wind/solar forecasting data•Off-normal frequency ride-through•SCADA telemetry and control (500/250 KVA)•Does not apply to small projects (NEM, smaller FIT)•Working on voltage-ride-through requirements

Questions/Comments??

14

Mahalo

Dora Nakafuji, PhDdora.nakafuji@heco.comDirector of Renewable Energy PlanningSystem Integration DepartmentHawaiian Electric Co

Lisa DangelmaierLisa.dangelmaier@helcohi.comOperations Superintendent, Production Dept., Hawaii Electric Light Co

HECO MECO HELCO

Family of HEI Utilities

For more information please contact:

mailto:dora.nakafuji@heco.com�mailto:Lisa.dangelmaier@helcohi.com�

BACKUP MATERIALS

15

Recommendations: Identified Gaps & Enhancements to Interconnection Process

Analysis Type Normal Detail Level EnhancementSTEADY STATE STUDIES

Load Flow - Back-feed potential

Peak Load ConditionsComment on equipment setting

(LTC and LDC)

Minimum Daytime LoadInvestigate equipment settings and

impact of changingIrradiance data for capacity vs.

generated power

Tap Changer CyclingStep Maximum output to Minimum

Output at Peak Load, 1% limit in voltage change specified to impact LTC

Time sequential analysis with measured irradiance data over seconds and time delay of LTC

Peak and Minimum daytime load conditions

16

Analysis Type Normal Detail Level EnhancementDYNAMIC STUDIES

Dynamic/Stability Studies - All PV trip

Not normally completedMultiple sites/nodal/cluster studies,

PV is dynamic Inverter

Dynamic/Stability Studies - N-1

Not normally completedFull dynamic analysis on range of

site sizes and configurations

Field Validation

17

Ramp tracking through afternoon

Cloud variability during day peak

Morning load risetracked

Source: HELCO17

Visibility to DG (Data to Output Probability)

18

Capturing statistical variation of PV with respect to time and by geographic location.

Nodal Estimation Approach to Expedite PV Integration Studies

19

• Characterize discrete and aggregated circuit load profiles at 12 kV substation (residential, industrial, commercial)

• Expedite circuit evaluations and aggregate DG impacts for planning

• Use reference LM-1 sensors to estimate solar resource output at location

• Extend solar forecasting to account for aggregated impact of behind-the-meter generationHiP-PV Industry Partners: SMUD, BEW

Engineering, GL Noble Denton, NREL, EPRI

Growth of Variable Distributed Gen

20

21

Excess Energy Curtailment (Feb 2011)

0.000

20.000

40.000

60.000

80.000

100.000

120.000

140.000

160.000

180.000

200.000M

W

TIME

Wind

Geothermal

Hydro

Diesels

CT

Combined CycleSteam

SYSTEM LOAD

Curtailment of wind and geothermal

22

59.6

59.7

59.8

59.9

60

60.1

60.2

60.3

0

0.5

1

1.5

2

2.5

3:03:18 3:04:18 3:05:18 3:06:18 3:07:18

Freq

uenc

y (H

z)

MW

/ M

VA

R

Thursday Dec 22 2011

Momentary Fault, Auto-Reclose 6500 Line 12/22/2011 03:03Load Response based on Per Unit Pre-fault Load

FEEDER 11 MW

FEEDER 12 MW

Frequency (Hz)

Situational Awareness & Decision Support for Operations

23

• Improve operational awareness of ramp (0-2hr) conditions and impacts

• Animated images of global horizontal irradiance

• Visual to measured data• NWP forecast for next 6 hours

• Animated visible satellite image

• Actual satellite image for last 6 hours• Simulated forecast of visible image for next 6

hours

Forecasting Industry Partners: EPRI, AWS TruePower, US DOE WFIP, SCE, SMUD, CaISO, UH Manoa, UCSD

SolarSENSE

������HECO Companies Experience with Distributed PV�Lisa Dangelmaier�April 11 2012DG Growth from 2009 to 2011Tracking Tremendous Growth in Distributed PVLocal Circuit Impact – light load, or large projects lead to export Improved Models to Account for PV as Generation vs Negative LoadInterface Between T&D ModelsModeled Variability : 30% PV Penetration Island Wide (light load)Impact of Variability On LTC OperationsImpact of Increasing Distributed PV on Apparent Demand Comparison of 2008 and 2011 Weekend Actual Loads (HELCO)Piloting Control Room Situational Awareness Tools to "See" Variability Slide Number 12PPA, SIA, larger FIT Interconnection RequirementsSlide Number 14Slide Number 15Recommendations: Identified Gaps & Enhancements to Interconnection ProcessField ValidationVisibility to DG �(Data to Output Probability)Nodal Estimation Approach to Expedite PV Integration StudiesGrowth of Variable Distributed GenExcess Energy Curtailment �(Feb 2011)Slide Number 22Situational Awareness & Decision Support for Operations