NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Hawaii Solar Integration Study: 

Solar Modeling Developments 

and Study Results

Preprint

R. Piwko

GE Energy

L. Roose

Hawaii Natural Energy Institute

K. Orwig

National Renewable Energy Laboratory

M. Matsuura

Hawaiian Electric Company

D. Corbus

National Renewable Energy Laboratory

M. Schuerger

Energy Systems Consulting Services

Presented at the 2nd Annual International Workshop on Integration of Solar 

Power into Power Systems Conference 

Lisbon, Portugal

November 12–13, 2012

This research work is jointly sponsored and funded by the U.S. Department of 

Energy, the Hawaii Natural Energy Institute, and the Hawaiian Electric Company.

Conference Paper

NREL/CP-5500-56311

December 2012
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1

Hawaii Solar Integration Study:

Solar Modeling Developments and Study Results

Richard Piwko, Leon Roose, Kirsten Orwig, Marc Matsuura, David Corbus, Matt Schuerger

Abstract—The Hawaii Solar Integration Study (HSIS) is a 

follow up to the Oahu Wind Integration and Transmission 

Study (OWITS) completed in 2010. HSIS examines the impacts 

of higher penetrations of solar energy on the electrical grid, 

focusing on impacts to the operation of the bulk power

transmission system and other interconnected generation 

resources. Issues specific to generation resource 

interconnection (normally the subject of a generator 

interconnection requirements study) and distribution system 

impacts of high distributed solar penetration scenarios were not 

the focus of the study. HSIS goes beyond the island of Oahu 

and investigates Maui as well. The study examines reserve 

strategies, impacts on thermal unit commitment and dispatch, 

utilization of energy storage, renewable energy curtailment, and 

other aspects of grid reliability and operation. For the study, 

high-resolution (2-second) solar power profiles were generated 

using a new combined Numerical Weather Prediction (NWP) 

model / stochastic-kinematic cloud model approach, which 

represents the “sharp-edge” effects of clouds passing over solar 

facilities. As part of the validation process, the solar data was 

evaluated using a variety of techniques including: wavelets, 

power spectral densities, ramp distributions, extreme values, 

and cross correlations. This paper provides an overview of the 

study objectives, results of the solar profile validation, and 

results for the Oahu portion of the study.

Keywords- Wind, solar, integration studies, reserves, 

renewable energy.

I. INTRODUCTION

The Hawaii Solar Integration Study (HSIS), a follow up to 

the Oahu Wind Integration and Transmission Study (OWITS) 

completed in 2010, focuses on the operating impacts of higher 

penetrations of solar energy on the Oahu and Maui bulk 

power systems. The studies support the Hawaii Clean Energy 

Initiative (HCEI), announced in January 2008, which includes 

a commitment to achieve 40% renewable generation from 

clean energy by 2030, expanded distributed generation, and 

load reduction. The HSIS study objectives are:

1. Analyze the Oahu and Maui bulk power systems with high 

penetrations of solar (and wind) generation and a) assess 

the levels of solar/wind energy delivered for a range of 

distributed/centralized photovoltaic (PV) plant scenarios, 

b) assess curtailment impacts, c) identify the operating 

characteristics (commitment, dispatch), d) assess the 

dynamic performance, e) identify the operational and 

reliability challenges across the range of operational 

timeframes.

2. Identify operational/mitigation strategies and new 

technologies that could help enable high penetrations of 

wind and solar power.

3. Assess the impact of each mitigation approach across the 

range of operational timescales; and 

4. Recommend mitigation strategies and requirements to 

increase wind and solar energy delivered, reduce adverse 

impacts on the thermal units, and ensure reliable and cost 

conscious system operation.

Figure 1. Organization of the HSIS.

The HSIS was jointly sponsored by the Hawaii Natural 

Energy Institute (HNEI), the U.S. Department of Energy 

(DOE), and the Hawaiian Electric Company (HECO); see 

Figure 1. The National Renewable Energy Laboratory 

(NREL) convened and facilitated a Technical Review 

Committee (TRC), comprised of a diverse group of technical 

experts, for the study. The primary focus of the TRC was 

technical review of HSIS study work including methods, 

assumptions, and preliminary results.

II. OVERVIEW OF OAHU GRID

HECO is a vertically integrated investor-owned electric 

utility with two wholly-owned subsidiary utilities, Maui 

Electric Company, Ltd. (MECO), and Hawaii Electric Light 

Company, Inc. (HELCO). HECO’s service territory is the 

island of O`ahu, with MECO serving the county of Maui 

(Maui, Lana`i and Moloka`i islands), and HELCO serving 

Hawai`i Island. Together, HECO and its subsidiaries serve 

95% of the State of Hawaii’s 1.2 million residents. There are 

no transmission interconnections between the islands so each 

island’s generating system must stand alone without backup 

from its neighbor island utility grids. Further, without 

indigenous conventional fuel resources such as oil, coal and 

natural gas, Hawaii imports most of its energy resources, the 
2

vast majority of which is oil. Not surprisingly, Hawaii has

among the highest electricity rates in the United States.

The island of Oahu measures 44 miles long and 30 miles 

across and is the most populated of the 7 main Hawaiian 

Islands. The capital of Hawaii, Honolulu, is located on the 

southeast shore of Oahu as is the popular vacation destination 

of Waikiki. Today, system peak power demand on the island 

of Oahu is between 1100 MW to 1200 MW, with a system 

minimum demand between 550 and 700 MW. For the HSIS 

study year of 2015, Figure 2 shows the projected average 

daily load profile assumed for Oahu.

Figure 2. Projected average daily load profile for Oahu in year 2015.

The majority of the firm capacity generation on the island 

is located on the west side, and includes the utility owned 

Kahe and CIP power stations as well as plants owned and 

operated by independent power producers AES, Kalaeloa and 

HPower. Two additional utility owned power stations, Waiau 

and Honolulu, are located near Pearl Harbor and downtown 

Honolulu respectively.

Oahu is presently served by a firm capacity generating 

fleet that totals 1,817 MW, about 81% of which is oil-fired. 

A summary of the generation resources on the island is 

provided below.

Firm generating capability (as of August, 2012):

HECO power plants

Honolulu (Steam, Cycling, Oil).......................113 MW

Waiau (Steam, Baseload, Oil)..........................186 MW

Waiau (Steam, Cycling, Oil)............................211 MW

Waiau (CT, Peaking, Oil).................................103 MW

Kahe (Steam, Baseload, Oil)............................650 MW

CIP (CT, Peaking, Biodiesel)...........................120 MW

Independent power producers

HPower (Steam, Baseload, Waste-to-energy)...46 MW

Kalaeloa (Combined-cycle, Baseload, Oil)......208 MW

AES (Steam, Baseload, Coal)...........................185 MW

Total firm generating capability...........1,817 MW

Non-firm generation (as of August, 2012):

Kahuku Wind Farm.............................................30 MW

Distributed PV........................................80 MW

Utility-scale PV........................................1 MW

Total non-firm generation .......................111 MW

Moreover, in addition to the continued strong growth in 

new distributed PV installations on Oahu, the following new 

utility-scale renewable energy generating plants totaling more 

than 100 MW nameplate are in progress to be commissioned 

on the HECO grid by year-end 2012.

HPower Expansion ..................................27 MW

Kawailoa Wind Farm ..........................................69 MW

Utility-scale PV ................................................5 MW

Energy produced by firm capacity generating resources 

located in the west is transmitted to load centers in the east 

via 138kV transmission lines. The 138kV transmission 

circuits deliver energy to 46kV sub-transmission circuits, 

which in turn are tied to distribution circuits at 25kV, 12kV 

and 4kV. The majority of the distribution circuits on Oahu 

operate at 12 kV. Figure 3 is an island map showing the 

general locations of:

• Utility scale generation sites

• 138kV transmission corridors

• High load density areas

• Transmission areas nearing capacity limits per HECO 

planning criteria

Figure 3. HECO generation sites and 138kV transmission corridors.

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Figure 4. HECO 46kV subtransmission circuits and customer load areas.
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Figure 4 shows the general locations of HECO’s 46 KV 

sub-transmission circuits and the load areas served on Oahu. 

The blue shaded area indicates that more than one-half (56%) 

of HECO’s total system load is located on the eastern portion 

of the island. Generally, customer loads located in the 

northern part of the island and along the coastal regions are 

served by HECO’s 46 KV sub-transmission circuits which 

have a significantly lower power flow capability compared to 

138 KV transmission circuits.

III. OAHU STUDY SCENARIOS

The HSIS Oahu system study scenarios focus on the 

operating impacts of higher penetrations of solar energy on 

the Oahu bulk power systems, building upon high wind 

penetration scenarios evaluated in the 2010 OWITS analyses. 

The HSIS analyzed and assessed the Oahu grid operations 

and the levels of PV/wind energy delivered for a range of 

both distributed and centralized PV plant scenarios. The 

scenarios evaluated are identified in Table 1 below.

TABLE I. STUDY SCENARIOS FOR OAHU POWER GRID

In each of the scenarios, 100 MW of wind energy located 

in the northern region of Oahu was included to represent the 

wind plants presently in service and scheduled for

commercial operation by year-end 2012. A scenario 

(Scenario 4B) evaluating additional wind energy produced by 

a proposed 200 MW wind plant located on a neighbor island 

and transmitted via submarine cable to the Oahu grid was also 

included.

Scenarios of high penetration distributed and central PV 

were then layered on these high wind scenarios. Four basic 

scenarios were developed. Scenario 1 included a total of 

100MW of additional distributed and central station PV. 

Scenario 2 included a total of 200MW of additional 

distributed and central station PV. In Scenarios 3A and 3B, a 

total of 360 MW of total PV generation was evaluated in both 

cases, with the respective levels of distributed/central PV 

varied to distinguish the impact to grid operation of more or 

less PV of distributed versus central plant characteristics tied 

to the system. In Scenario 4A, the level of total PV was 

increased substantially to 760 MW to evaluate grid operation 

and cost impacts at a very high PV penetration level, with a 

fairly even split of distributed and central plant PV modeled. 

Scenario 4B has the same level of annual wind+solar energy 

as 4A, but the generation portfolio has 400 MW less solar 

resources and 200 MW more wind resources. Upon 

completing analysis of the Baseline case, Scenario 3A was 

analyzed next and based on the results, the project sponsors 

determined that an analysis of lower PV penetration levels in 

Scenarios 1 and 2 were not of high value interest and further 

analyses proceeded directly to Scenarios 3B, 4A and 4B.

IV. SOLAR DATA DEVELOPMENT AND VALIDATION

Solar power production profiles are modeled to represent 

future potential installations and penetrations. For integration 

studies, there are several key characteristics of the solar 

power production profiles that are essential to simulate 

realistic production profiles, as detailed in Orwig et al. [1].

These characteristics include:

• Representative variability at all timescales, i.e. ramp 

behavior

• Spatial correlation, both intra-plant and plant-to-plant

• Temporal correlation

• Capacity factor

Synthetic solar power forecasts are also generated to 

replicate real-time operational solar power forecasts that grid 

operators might use for unit commitment, dispatch, reserves 

allocations, transmission congestion, etc. The error 

distributions of the synthetic forecasts should be 

representative of those of the real-time operational forecasts.

Misrepresentations of any of these characteristics could 

cause unrealistic simulations of power production operations, 

grid impacts, and associated costs. Therefore, the data 

generated for this study went through a rigorous development 

and validation process to minimize the uncertainty.

AWS Truepower generated the solar power production 

profiles using a Numerical Weather Prediction (NWP) model 

coupled with a stochastic-kinematic cloud model. The model 

is able to generate and dissipate clouds over time, and advects 

them through time and space at a 1-s time resolution. The 

NWP model drives the cloud movement, while the cloud 

model drives the evolution. More details of this coupled 

model can be found in M. Brower et al. [2].

The data created consisted of 2-s solar power production 

profiles for central and distributed PV systems for 2007-2008, 

the penetrations of which are described in Section III. The 

data was validated by verifying the ramp characteristics (also 

discussed in Section V), correlations, distributions, and 

extreme values. The validation process is shown in Figure 5.

The details of these results are pending publication as an 

NREL technical report.

Figure 5. Schematic of data validation process.

An example of the validation is the distribution of the 

clearness index between measured and modeled irradiance, as 

shown in Figure 6. The verification was done for May 2010, 

when recorded observations were available. The figure 
4

demonstrates that the distributions are very similar, indicating 

that the proportional amount of cloud cover to clear sky of the 

modeled data is realistic.

Figure 6. Frequency distribution of measured (top) and modeled (bottom) 

clearness index on Oahu. Blue horizontal line is for scale reference.

V. SOLAR INTEGRATION STUDY RESULTS - OAHU

A. Variability in Wind/Solar Power Generation

The output of an individual solar generation facility is 

highly variable. Output can change from 100% to 20% 

within 1-2 minutes when a cloud passes over. Variations in 

power output of a single PV facility can cause troublesome 

local voltage variations on its interconnected feeder. 

Furthermore, the aggregate variability of all PV facilities 

connected to the Oahu grid introduce new operational 

challenges for grid operators and the thermal generation fleet. 

Given that generation and load must always be balanced, the 

thermal fleet must ramp up and down to follow all variations 

in wind, solar, and load. In addition, the thermal fleet (and 

possibly storage systems or demand response) must provide 

sufficient reserves to cover sustained drops in wind and solar 

power output so that grid frequency is maintained at 60 Hz.

Figure 7 shows the variability in output of aggregate wind 

and solar resources for Scenarios 4A (dominated by solar PV) 

and 4B (mix of solar and wind). Variability is shown for time 

periods of 5, 10, 30 and 60 minutes. The horizontal axis is 

scaled in per-unit of the aggregate wind+solar resource rating 

(860 MW for 4A and 660 MW for 4B). Table II summarizes 

the worst-case drops in aggregate wind and solar power 

output over the four different time periods. This data 

indicates that Scenario 4B has significantly lower aggregate 

variability than Scenario 4A, which implies a lower 

requirement for system operating reserves.

TABLE II. WORST-CASE VARIABILITY FOR SCENARIOS 4A AND 4B

Scenario 4A Scenario 4B

Worst 5-min drop -186 MW -132 MW

Worst 10-min drop -183 MW -143 MW

Worst 30-min drop -308 MW -219 MW

Worst 60-min drop -399 MW -324 MW

Figure 7. Variability in aggregated wind and solar power output for 

Scenarios 4A and 4B

B. Spinning Reserve Requirements

The vast majority of Oahu’s electrical energy is generated 

by a group of thermal power plants fueled predominantly by 

oil and coal. Historically their operation was dominated by 
5

following the daily load pattern and providing contingency 

reserves.

Being an island system with no interties to neighboring 

power grids, Oahu’s planning and operating criteria is to 

always carry sufficient contingency reserves to completely 

cover loss of the largest single generating unit on-line

(typically the 185 MW coal plant). Spinning reserves to 

cover variability in wind and solar generation must be 

separate from contingency reserves, so that down-ramps in 

wind and solar generation do not compromise the grid’s 

ability to survive loss of a large thermal generator.

To accomplish this, the study assumes the total reserves 

are separated into two categories; contingency reserves (to 

cover loss of generating unit) and operating reserves (to cover 

variability in wind and solar output). Contingency reserves 

are all spinning reserves, as they must respond immediately to 

a loss of generation event. Operating reserves are comprised 

of both spinning and non-spinning reserves, considering that 

wind and solar variations occur over multiple timescales. The 

following discussion focuses on the spinning portion of 

operating reserves.

A criteria was developed so that the grid would have 

sufficient operating reserves to cover wind and solar 

variability 99.99% of time periods without dipping into 

contingency reserves. During daytime hours, operating 

reserves need to cover variability from both wind and solar 

resources. During nighttime hours solar generation is offline 

so only wind variability is relevant.

Figure 8 shows the spinning portion of operating reserves 

for the study scenarios during daytime hours, as a function of 

how much wind and solar power is being delivered to the 

grid. Scenario 4A, with 760 MW of PV generation capacity, 

requires the most spinning reserves (240 MW when wind and 

solar generation exceeds 300 MW). The figure shows that 

other scenarios require significantly less operating reserves 

during daytime hours,

Figure 8. Operating reserve requirements, spinning, daytime hours.

Figure 9 is a similar plot for nighttime hours, when solar

generation is offline. Scenario 4B has 300 MW of wind 

generation and requires approximately 105 MW of spinning 

reserves when wind generation exceeds 120 MW. The other 

scenarios have only 100 MW of wind generation and 

nighttime spinning reserve requirements are below 35 MW.

These reserve requirements are based solely on capacity 

reserve requirements and will need to be verified by the 

dynamic response and frequency impact assessment to be 

done later in the study.

Figure 9. Operating reserve requirements, spinning, nighttime hours.

C. Impacts on Generation Commitment and Dispatch

Annual hourly operation of the Oahu power grid was 

simulated using the GE-MAPS production simulation 

program. Input data accounted for thermal generation 

operating characteristics (fuel costs, start time, ramp rate, 

etc.), hourly system load profiles, wind/solar generation 

profiles, 4-hour-ahead wind/solar forecasts, and 

operating/contingency reserves as described previously. 

Table III shows the contributions of thermal, wind, and solar

generation resources to serving Oahu’s annual load energy. 

Scenarios 3A and 3B both have 360 MW of solar generation, 

but 3B has a much high percentage of solar in central plants, 

including two plants rated 100 MW each. The central solar 

plants are assumed to use single-axis tracking and therefore 

have higher capacity factors (22%) than distributed rooftop 

solar resources (18%). Scenarios 4A and 4B have roughly the 

same available energy from wind and solar resources (about 

1640 GWh, or 20.3% of load energy). However, not all of the 

wind and solar can be accommodated by the grid and 

curtailment is required, reducing the load-serving contribution 

of wind+solar to 19.2% in Scenario 4A and 19.5% in 

Scenario 4B.

TABLE III. GENERATION ENERGY BY TYPE FOR STUDY SCENARIOS

Base 3A 3B 4A 4B

Thermal 95.1% 88.8% 88.5% 80.8% 80.5%

Wind 3.7% 3.7% 3.7% 3.7% 11.7%

Solar 1.2% 7.5% 7.8% 15.5% 7.8%

Wind+Solar 4.9% 11.2% 11.5% 19.2% 19.5%

Note: Annual load energy is 8084 GWh for all Scenarios.

Figure 10 shows duration curves of hourly wind and solar 

energy penetration for the study scenarios. Scenarios 3A and 
6

3B with 360 MW solar and 100 MW wind generation have 

maximum hourly energy penetrations near 30%; i.e., 30% of 

load energy is served by wind+solar generation in some hours 

of the year. Scenario 4A (760 MW solar & 100 MW wind) 

has a maximum hourly wind+solar penetration of 50%. 

Scenario 4B reaches 45% hourly penetration.

One significant observation is the distinct bimodal nature 

of the duration curve for Scenario 4A. This scenario is 

dominated by solar generation, so there is a distinct difference 

in the magnitude of wind+solar energy penetration for day 

and night.

Figure 10. Duration curves of hourly wind+solar energy penetration.

D. Wind and Solar Utilization and Curtailment

One objective of the study was to find “pain points” in PV 

penetration, where the amount of PV generation would 

challenge the existing operational capabilities of the Oahu 

power grid. One measure of “pain” is curtailment, which 

occurs when the grid is not able to accept all the wind and 

solar energy that is available during some hours of the year. 

Figure 11 shows annual wind and solar energy delivered and

curtailed for each study scenario. In scenarios 3A and 3B 

(460 MW wind+solar), all the available wind and solar energy 

is accommodated by the grid. In Scenarios 4A and 4B, 

however, there are time periods when some of the wind and 

solar energy must be curtailed. Both scenarios have about 

1640 GWh of total wind+solar energy available in a year. In 

Scenario 4B 85 GWh is curtailed and in Scenario 4B 70 GWh 

is curtailed.

Figure 11. Wind+solar energy delivered and curtailed.

Figure 12 shows the annual curtailed wind+solar energy 

by hour of day. Scenario 4A is dominated by 760 MW of 

solar generation. Curtailment occurs during daytime hours 

when solar generation is high and spinning reserve 

requirements are also high (185 MW contingency reserve plus 

240 MW operating reserve). Scenario 4B has a balance of 

wind and solar generation. Overall curtailment is lower (70 

GWh vs. 85 GWh), and curtailment occurs primarily during 

nighttime hours when load is low and thermal generating 

plants are operating near minimum power limits holding 

adequate down-reserves.

These levels of wind and solar curtailment are based on 

the assumption that Oahu thermal generation carries 90 MW 

of down-reserves to respond to loss-of-load events. Further 

investigation of Oahu grid operations indicated that 140 MW 

of down-reserves would likely be required during daytime 

hours, which would approximately double the amount of 

wind and solar energy curtailed in Scenario 4A. Curtailment 

in Scenario 4B would not be significantly affected with the 

higher level of daytime down-reserves.

Figure 12. Wind+solar curtailment by hour of day.

E. Impacts on Operation of Baseload Generation

As penetration of wind and solar power increase, thermal 

generating resources are operated at reduced power levels. 

During periods of high wind and solar output, some thermal 

generating units will need to operate at minimum power. 

Although some units can be decommitted, others must remain 

online to provide spinning reserves and/or other ancillary 

services.

Table IV shows the number of hours in a year that the 

Oahu baseload generating units operate at minimum load for 

each of the study scenarios. As the penetration of wind and 

solar generation increases, the number of hours at minimum 

load for Oahu baseload units increases dramatically. In 

Scenarios 4A and 4B, several units operate at minimum load 

for more than 6000 hours in a year. The operations and 

maintenance impact of operating baseload generation at this 

low level for the number of hours shown will need to be 

assessed and could impact the results, but is not within the 

scope of this study.
7

TABLE IV. ANNUAL HOURS AT MINIMUM LOAD FOR OAHU BASELOAD 

GENERATING UNITS

F. Non-Synchronous Generation Sources

With increasing penetration of wind and solar generation, 

thermal generation is displaced during many hours of the 

year. When thermal generators are decommitted, the system 

loses the contributions of their synchronous machines to 

voltage support and maintaining the short-circuit level of the 

grid. One way to quantify this effect is to calculate the 

percentage of non-synchronous generation that is connected 

to the grid in each hour of the year. Duration curves showing 

the percentage of non-synchronous generation on the Oahu 

grid for the study scenarios is shown in Figure 13. All curves 

have a distinct bimodal shape, where the left portion is 

dominated by daytime hours when solar PV generation is 

online, and the right portion is dominated by wind generation 

alone during nighttime hours. Scenario 4A (760 MW PV and 

100 MW wind) exceeds 50% non-synchronous generation 

during a few hours of the year.

Figure 13. Duration curve of percent nonsyschronous generation.

G. Challenging Periods for Grid Operations

The results of the annual hourly production cost 

simulations were screened to identify challenging time 

periods for grid operation. The screening criteria are shown 

below:

• Wind and solar power can drop in a sustained fashion in 

10-20 minutes. This challenges ramp rate capability of 

thermal units.

• Wind and solar power can drop in a sustained fashion in 

30-60 minutes. This consumes up-reserve and requires 

use of quick-start units.

• Wind and solar power can rise when thermal units are 

near min power. This consumes down-reserves.

• Wind and solar power can vary its production rapidly

within an hour. This challenges the ramp rate and 

maneuvering capability of thermal units.

• Load rejection event could occur when thermal units are 

backed down. Transient response could reduce generator 

power below stable minimum point.

• A large thermal generator or HVDC converter can trip 

when the system is low on reserves. This triggers underfrequency events and could cause distributed rooftop PV 

to trip and possibly load-shedding.

Each of these screening criteria identified specific hours 

when the Oahu grid would be in unfavorable operating 

conditions to survive each type of event. For worst-case 

conditions, dynamic simulations were performed to quantify 

the overall system response and to determine if the system 

could survive with adequate reliability margins. A 

representative simulation for the most severe loss of 

generation event is shown below.

H. Frequency Response for Generation Trip Event

System risk for a loss of generation event is most severe 

when system up-reserves are low, the percentage on nonsynchronous generation is high (i.e, system inertia is low), 

and distribution-connected rooftop PV output is high. An 

hour was identified to meet this criteria and loss of a 185 MW 

coal unit was simulated using PSLF. Before the disturbance, 

system load was 1176 MW, total wind and solar generation 

was 358 MW of which 178 MW was from distributionconnected PV. The system frequency response is plotted in 

Figure 14. Note that this simulation did not take into account

the automatic under frequency load shed protection that is 

currently in place.

Figure 14. System frequency following trip of 185 MW thermal generator, 

without underfrequency load shedding.

If all the wind and solar generation stays on line after the 

event (i.e., is equipped with frequency ride-through 

capability), the system frequency dips to 58.5 Hz in about 4 
8

seconds and then begins to recover, per the blue trace. If the 

distribution-connected PV is assumed to be compliant with 

IEEE standard 1547, it would trip off line when the frequency 

falls to 59.3 Hz. The red trace shows that if the distributionconnected PV trips, another 178 MW of generation is lost and 

system frequency declines to 56.3 Hz. This illustrates that 

island systems like Oahu would face significant system 

impacts if the penetration of 1547-compliant PV resources 

becomes too large.

VI. CONCLUSIONS

Although this study is still under way, results to date have 

substantiated several significant conclusions.

The Oahu bulk power system can accommodate the 100 

MW wind and 360 MW PV solar generation modeled in the 

study without significant modifications to existing systemlevel operating practices beyond carrying additional spinning 

reserves. The level of wind and solar resources are capable of 

serving a little more than 11% of Oahu’s annual load energy. 

The majority of impacts with this penetration level are 

expected to be at the individual feeder level, where voltage 

variations and reactive power management will need to be 

studied and engineering solutions developed to address any 

issues that are identified.

Two study scenarios evaluated grid operation with wind 

and solar resources capable of supplying approximately 20% 

of Oahu’s annual load energy (Scenario 4A: 760 MW PV and 

100 MW wind; Scenario 4B: 360 MW PV and 300 MW 

wind). With this level of wind and solar penetration, high 

levels of operating reserves are required to cover the 

variability/uncertainty of wind/solar power output. Up to 

10% of the wind and solar energy would need to be curtailed 

assuming grid operating practices and thermal plant 

capabilities remain the same as they are now. Curtailment 

can potentially be decreased, emissions can be reduced, and 

overall system operating efficiency can be increased by the 

following measures, however these mitigation measures have 

not been fully assessed in the study to date:

• Modifying operating practices (e.g., changing must-run 

schedules for baseload thermal units);

• Improving the operational flexibility of selected thermal 

plants (e.g., reducing minimum power limits and 

increasing ramp rates);

• Reducing spinning reserves on thermal units by using 

other sources of reserves (e.g., battery energy storage 

systems, load control); and

• Having wind/solar resources participate in grid dispatch 

and ancillary services (e.g., down-reserves from 

wind/solar plants).

The Oahu grid is capable of surviving the most severe loss 

of generation contingency, even with high penetrations of 

wind and solar resources. However, this assumes that the 

wind and solar resources are capable of riding through 

transient low-frequency events. If a significant portion of the 

solar resources are distribution-connected rooftop systems 

that are compliant with IEEE Standard 1547, then there is 

increasing likelihood that loss of a large thermal plant would

trigger underfrequency tripping of rooftop solar resources, 

compounding the severity of the contingency. This could, in 

turn, trigger increased levels of underfrequency loadshedding 

to arrest the decline in frequency – a critical issue for island 

systems where frequency is inherently more variable than in 

larger interconnected grids. This analysis has highlighted 

1547-compliant solar PV as a potential risk to the system, 

should the penetration of such resources become sufficiently 

large. Subsequent analysis is required to better quantify the 

impacts, including coordination with loadshedding schemes. 

IEEE Standards Coordinating Committee 21 has recognized 

the grid reliability risks associated with underfrequency PV 

tripping and has reconvened to discuss revisions to Standard 

1457 [4].

HECO has implemented rules that require distributionconnected PV inverters to have adjustable underfrequency 

ride-through capability, set to withstand underfrequency 

events down to 57 Hz for 300 cycles. For frequency 

excursions below 57 Hz or above 60.5 Hz, the inverters must 

disconnect in 10 cycles.

There is still more work to be done as the penetration 

renewable resources in Hawaii continues to grow and the mix 

of resources continues to evolve. Future work includes more 

analysis of loadshedding performance, dynamic performance 

analysis for challenging time periods, distribution-level 

interconnection issues, and the impacts of running thermal 

generation at low power levels for extended periods.

AUTHORS AND AFFILIAIONS

Richard Piwko, GE Energy Consulting, Schenectady, New 

York USA.

Kirsten Orwig & David Corbus, National Renewable 

Energy Laboratory, Golden, Colorado, USA.

Matt Schuerger, Energy Systems Consulting Services LLC, 

St. Paul Minnesota, USA.

Marc Matsuura, Hawaiian Electric Company, Honolulu, 

Hawaii, USA.

Leon Roose, Hawaii Natural Energy Institute, Honolulu, 

Hawaii, USA.

REFERENCES

[1] K. Orwig, M. Hummon, B-M Hodge, D. Lew, "Solar Data Inputs for 

Integration and Transmission Planning Studies," in 1st Int. Workshop 

on the Integration of Solar Power into Power Systems, Aarhus, 

Denmark, 2011.

[2] M. Brower, P. Beaucage, J. Frank, “Development of a Cloud Model to 

Generate High-Frequency Solar Irradiance and Power Data”, 2nd 

International Workshop on Integration of Solar Power into Power 

Systems, Paper SIW12-48, Lisbon, Portugal, November 12-13, 2012.

[3] Hawaii Natural Energy Institute, Hawaiian Electric Company, General 

Electric Compaly, “Oahu Wind Integration Study”, pdf available at 

www.hnei.hawaii.edu, February 2011.

[4] SCC21 Home Page, http://grouper.ieee.org/groups/scc21/dr_shared/