NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by 

the Alliance for Sustainable Energy, LLC.

HAWAII SOLAR 

INTEGRATION STUDY:

Executive Summary

A summary of research funded by:

U.S. Department of Energy

Hawaii Natural Energy Institute

Hawaiian Electric Co. 

iii

HAWAII SOLAR 

INTEGRATION STUDY:

Executive Summary

Kevin Eber and David Corbus

National Renewable Energy Laboratory

Prepared under Task No. SS13.7010

Technical Report

NREL/TP-5500-57215

June 2013
iv

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v

Acknowledgements 

This project involved the collaborative efforts of a large interdisciplinary team representing members of several organizations. We 

gratefully acknowledge their contributions. In addition, we would like to thank Kevin Lynn, Bill Parks, and Steve Lindenberg from 

the U.S. Department of Energy for their leadership and for sponsoring this work.

Project Managers

Richard Piwko GE Energy

Dave Corbus National Renewable Energy Laboratory

Marc DeNarie Hawaiian Electric Co.

Rick Rocheleau Hawaii Natural Energy Institute 

Mat McNeff Maui Electric Co.

Leon Roose Hawaii Natural Energy Institute, Hawaiian Electric Co. 

Technical Review Committee Leader

Matt Schuerger Energy Systems Consulting Services, LLC

Lead Project Engineers

Gene Hinkle Maui Solar Integration Study

Harjeet Johal Oahu Solar Integration Study

Study Team

GE Energy Consulting Sebastian Achilles, Gene Hinkle, Shakeer Meeran, Robert Woodfield, 

Gary Jordan, Wei Ren, Durga Gautam, Ryan Konopinski, Mark McDonald, 

Mark Walling, Denise Benoit

GE Global Research Wei Ren, Harjeet Johal, Yan Pan

Hawaiian Electric Co. Marc DeNarie, Marc Matsuura, Robert Young, Ron Bushner, Dora Nakafuji, 

Dean Arakawa, Dean Oshiro, Ross Sakuda, Robert Uyeunten

Maui Electric Co. Mat McNeff, Steve Rymsha

Hawaii Natural Energy Institute Rick Rocheleau, Larry Cutshaw, Jay Griffin

National Renewable Energy Laboratory Dave Corbus, Kirsten Orwig

AWS Truepower Michael Brower, Jackie Frank

Energy Systems Consulting Services, LLC Matt Schuerger
vi

Technical Review Committee

Mike Behnke, Ron Davis BEW Engineering/DNV

Dave Corbus National Renewable Energy Laboratory

Abe Ellis, Ross Guttromson, James Ellison Sandia National Laboratory

Brendan Kirby Kirby Consulting

Michael Kintner-Meyer, Frank Tuffner Pacific Northwest National Laboratory

Kevin Lynn, Venkat Banunarayanan U.S. Department of Energy Solar Energy Technologies Office SunShot Initiative

Russ Neal Southern California Edison

Steve Pullins Horizon Energy Group

Rick Rocheleau, Leon Roose, Jay Griffin Hawaii Natural Energy Institute 

John Shindo U.S. Navy 

Charlie Smith Utility Wind Integration Group

Jim Spaeth U.S. Department of Energy

Chifong Thomas BrightSource Energy

Aidan Touhy Electric Power Research Institute

Keith Yamanaka U.S. Army

Hawaii Solar Integration Study Technical Review Committee Observers

Mike Champley, Rich VanDrunen Hawaii Public Utilities Commission

Alison Silverstein Hawaii Reliability Standards Work Group
vii

Table of Contents

1 Introduction 

2 Key Insights 

4 How Power Grids Work 

5 Study Methodology

6 Oahu

8 Required Reserves for Oahu

8 Oahu Grid Operating Characteristics

11 Operational Challenges for the Oahu Grid

15 Mitigation Measures

19 Maui

20 Required Reserves for Maui

21 Maui Grid Operating Characteristics

22 Operational Challenges for the Maui Grid

23 Mitigation Measures

27 Conclusions

28 References

1

Introduction 

The Hawaii Solar Integration Study is a detailed technical examination of the effects of high penetrations of solar and wind 

energy on the operations of the electric grids of two Hawaiian Islands: Maui and Oahu. Carried out under the auspices of 

the Hawaii Clean Energy Initiative (see below), the study was jointly sponsored by the Hawaii Natural Energy Institute, 

the U.S. Department of Energy, and the Hawaiian Electric Co. (HECO).

Unlike mainland power grids, island power grids are self-contained and isolated, so they have no neighboring grids to 

turn to for support if they are pushed beyond their normal operating limits. The Hawaiian Islands also rely heavily on 

oil and oil products to fuel their power plants, which results in high electricity costs that help make renewable energy 

economically competitive.

Maui and Oahu already have significant wind and solar power feeding their electric grids, but the utilities on each island 

wanted to know how their grids will operate with more renewable energy. Some of this is will be under utility control in 

the form of centralized, utility-owned solar power plants, but much of the solar power will be distributed throughout the 

island on homes and businesses—outside of the utility’s control.

The study included detailed computer modeling and simulations of the generation and transmission systems on each 

island to examine how future scenarios of high penetrations of solar and wind power will affect generator operations 

under normal system configurations. The distribution-level impacts were not assessed in this study. For cases in which the 

generator deviated from its preferred operating parameters, potential mitigation strategies were proposed and modeled.

About the Hawaii Clean Energy Initiative

The Hawaii Clean Energy Initiative is an unprecedented collaboration between the State of Hawaii and 

the U.S. Department of Energy that was initiated in January 2008. The purpose of the Hawaii Clean Energy 

Initiative is to transform Hawaii into a world model for energy independence and sustainability. The state’s 

goal is to meet 70% of its energy needs with energy efficiency and renewable energy by 2030 (meeting 40% 

with renewable energy while eliminating 30% through energy efficiency). In October 2008, with the Hawaii 

Clean Energy Initiative as a foundation, the State of Hawaii, the Hawaiian Electric companies, and the State 

Consumer Advocate signed an energy agreement—the first of its kind in the United States—that set specific 

commitments and timetables to accomplish this transformation.

The Department of Energy and its national laboratories have been working with its partners in Hawaii 

to provide technical expertise and support across key energy segments, including renewable electricity 

generation and transmission, end-use energy efficiency, transportation, and renewable fuels. An important 

aspect of the Department of Energy’s technical contribution has been in the area of grid integration of 

renewables, including the Hawaii Solar Integration Study and a prior study, the Oahu Wind Integration and 

Transmission Study.
2

Key Insights

The Hawaii Solar Integration Study found that adding large amounts of new solar power to the electric grids on Maui 

and Oahu—enough to achieve roughly 20% renewable energy penetration—will create operational challenges that could 

affect grid reliability, but it also recommended a variety of mitigation strategies that could address those challenges while 

optimizing the use of renewable energy. The study recommends specific mitigation strategies for further study and details 

the reduction in renewable energy curtailment for those strategies. 

The Maui and Oahu power grids present contrasting examples of small power grids, although they also have similarities. 

At the time of the Hawaii Solar Integration Study in 2012, the Oahu grid was much larger, with firm generation sources at 

nearly seven times the capacity of the Maui grid (1,822 MW versus 264 MW). Both islands featured wind power plants 

and distributed solar power, but Oahu included a 1-MW central-station photovoltaic (PV) facility, while Maui had a much 

higher percentage of wind and solar power. Maui also featured a 10-MW battery energy storage system (BESS). 

The scenarios examined for both islands involved increases of both 

centralized and distributed PV capacity, while an additional wind 

plant was included in one of the Oahu scenarios. Key insights from 

the study included the following:

• There is less variability with distributed PV systems than with 

central-station PV plants because one dark cloud can affect an 

entire central-station facility quickly, while only a large storm 

system can affect all distributed PV generators—and this takes 

place over a longer time. 

• Variability is lower for a mix of wind and solar power rather 

than all-solar power because wind and solar resources generally 

don’t move in lockstep and are sometimes complementary. An 

example is a cold front that brings cloudy and windy conditions, 

which decreases solar power production but boosts wind 

power production. 

• Distributed PV contributes less variability to the grid, but 

it presents a challenge in high-penetration scenarios (in the 

absence of a smart grid) because of the inability of the utility to 

curtail its power production, which results in less flexibility for 

grid operators.

• Centralized PV plants and wind plants can be controlled 

and curtailed. In addition, both centralized PV systems and 

wind plants can potentially serve a grid-support function. For 

example, the study found that adding governor controls to the 

wind and central-station PV plants on Oahu would allow them to 

respond to loss-of-load events.

The study also looked at the value of several mitigation strategies that consisted of changes to the operational practices of 

conventional generators. 

Key Terms

Ancillary services: Services that assist the 

grid operator in maintaining system 

balance. 

Curtailment: A reduction in the scheduled 

capacity or energy delivery of a generator.

Frequency response: The ability of a 

generator (or responsive load) to increase 

output (or reduce consumption) in 

response to a decline in system frequency 

and decrease output (or increase 

consumption) in response to an increase 

in system frequency. For generators, this 

requires governor response.

Minimum power: The minimum output 

that can be provided from a generator. 

Different generators have different 

minimum levels based on fuel source, 

plant design, and common use.

Operating reserves: That capability above 

firm system demand required to provide 

for regulation, load forecasting error, 

equipment forced and scheduled 

outages, and local area protection. Up 

reserves can inject additional power into 

the grid when needed; down reserves can 

reduce the power fed into the grid.
3

• HECO identified several units that could potentially reduce their minimum power levels by about half by investing in 

unit modifications (the cost of which was not determined in this study), thereby significantly reducing wind and solar 

power curtailment and creating more operational flexibility for handling challenges to the grid. 

• Other potential Oahu mitigation measures include relaxing the operating schedule for certain baseload units and 

providing reserves from alternate resources, such as demand response or a BESS. 

• The top Maui mitigation measure is to upgrade the utility’s combined-cycle units to enable them to switch between 

single- and dual-train operation as needed to reduce renewable energy curtailment. 

• Other Maui potential mitigation strategies include relaxed operating schedules for the utility’s four oil-fired units and a 

change in the commitment process to increase the priority of operating reserves. 

Maui has an additional tool for responding to grid events: a 10-MW BESS, which is capable of delivering 10 MW of up 

reserve continuously for 45 minutes, after which it will ramp down linearly for 45 minutes until it reaches zero. The BESS 

can also reduce down reserves by charging at its full capacity following loss-of-load events. The study found that the BESS 

helped the Maui grid restrict frequency excursions to a band of ±0.1 Hz. 

In evaluating the potential benefits of adding another 10-MW BESS with 20 MWh of storage, two operating strategies 

were considered: using the BESS to provide an additional 10 MW of operating reserves or using it to “time-shift” energy—

storing the wind and solar power that would otherwise be curtailed and delivering it to the grid at the first opportunity. The 

study found that using the BESS to provide operating reserves resulted in greater benefits by reducing curtailment and 

thereby reducing operating costs. Using a larger, 100-MWh BESS for time-shifting energy yielded results similar to using 

the 10-MW BESS for reserves but at a higher capital cost. 

In summary, the Hawaii Solar Integration Study provided valuable insights into the integration of solar and wind power 

into two very different utility grids. The results provide a deeper understanding of the differences between distributed PV 

systems, centralized PV power plants, and wind power in terms of variability, ability to curtail power output, grid support 

function, and characteristics relative to the load. Although reliability challenges increase with increasing levels of variable 

renewable generation, this study found those challenges are manageable from the standpoint of the bulk power system for 

the scenarios studied with the mitigation approaches recommended. Note that this study did not look at the capital costs 

for the higher renewable energy scenarios or the mitigation strategies. It also did not assess the integration issues at the 

distribution level of the power systems.

Ultimately, the Hawaii Solar Integration Study found that higher levels of variable renewables can be reliably 

accommodated by the bulk power system in a collaborative fashion (i.e., by changes made to both utility equipment 

and operating practices and variable generation equipment capabilities) and noted the importance of asking for the right 

capabilities (e.g., inertial and frequency response, voltage and frequency ridethrough, and provision of ancillary services) 

from variable generating plants in grid codes. The study did not look at incentives, and they may not be necessary for the 

mitigation measures that were considered. The importance and value of reviewing existing operating practices and adjusting 

certain assumptions (e.g., minimum power generation levels, certain must-run generator rules) was modeled and the impact 

on solar and wind curtailment shown. Finally, the use and value of storage in a high-penetration system was modeled and 

discussed. The insights from the Hawaii Solar Integration Study form a large body of knowledge for future grid integration 

studies, and the results can be used to further our understanding of grid integration in other island systems as well as in 

mainland U.S. systems with high regional solar and wind penetrations.
4

How Power Grids Work 

Like all complex machines, electric grids have a special nomenclature and operate in a unique way. Knowing the ins and outs 

of electric grids is critical to understanding the Hawaii Solar Integration Study.

An essential task of system operators is to match the amount of electricity being generated with the amount being consumed. 

In grid parlance, that means matching generation with load, where the load is the amount of power being drawn from the 

system. Most grids, including the Hawaii grids, deliver alternating current, so grid operators also need to keep the frequency 

of the current at a constant level. In the United States, that level is 60 cycles per second, or 60 Hz. Electrical devices are often 

designed to operate at 60 Hz, and deviations from this standard can damage equipment.

Electric grids are branching networks, with the main branches being feeder lines, which feed electricity to smaller 

distribution lines that run house-to-house and business-to-business. Circuit breakers protect the lines from short circuits or 

“ground faults”—unintentional connections of power lines to ground—by opening, or “tripping,” thereby taking the power 

lines out of service.

If a grid is balanced so generation equals load, a loss of load, perhaps caused by a feeder line tripping offline, will cause 

generation to exceed load. The excess generation in the system will increase the frequency in what is called an “overfrequency event.” Likewise, a power plant tripping offline can cause load to exceed generation, causing an “underfrequency event.”

To avoid such excursions from normal operating parameters, grid operators maintain power reserves that can be drawn 

on during emergencies. Contingency reserves (often called “spinning reserves”) are idled but operating plants, or plants 

operating at less than full power that can quickly be brought up to full power. A BESS can also serve as spinning reserve. 

Contingency reserves are meant to cover the single largest generation outage that could occur on a system, such as the 

largest power plant tripping offline. Because wind and solar units are not large, they do not typically affect the contingency 

reserve needed.

Operating reserves are meant to cover the net variability of the power system. Wind resources, solar resources, and 

continuously changing loads all contribute to net system variability. Operating reserves can be a mixture of spinning reserves 

and non-spinning reserves, such as quick-start diesel generators and combustion turbines. Total reserves are the sum of 

contingency and operating reserves.

There are also two types of operating reserves. “Down reserves” are used when solar and wind generation suddenly increase 

and are often power plants that can quickly ramp down power production to balance the system. Likewise, “up reserves” are 

used when solar and wind power suddenly drop and are often power plants that are either idled or operating at less than full 

power and have the ability to ramp up power production rapidly.

Up reserves can also include demand response, which typically involves a large industrial user that agrees to reduce its 

power use when needed in exchange for favorable electricity rates. More sophisticated demand-response technologies 

use the Internet or smart grid technologies to control user devices. In the summer, for instance, demand response could 

involve delaying the on-and-off cycling of a large number of customer air conditioners or domestic water heaters, which can 

effectively remove a large load from the power system.

Electric utilities rely on a variety of power generation sources. On Maui and Oahu, large, centrally located coal- and oilfired power plants often run 24 hours a day to provide a steady source of power throughout the day. These types of plants 
5

are called “baseload” units because they provide a base that can be added to with other types of power. Other plants 

can be “cycling” units that ramp up and down quickly to match the variation in system load, or they can be “peaking” 

units that are normally idled but can ramp up quickly to help meet peak power demands. The efficiency and operating 

characteristics of each power plant contribute to the determination of how it is used by the utility.

All power plants have limits on how quickly they can ramp up and down—called a “ramp rate”—and a minimum power 

level below which they cannot operate reliably. Operating units below their minimum power levels can result in unstable 

operating conditions and unanticipated unit outages that lead to system instabilities and even blackouts. But as we’ll see, 

in some cases, minimum power levels can be relaxed with additional support from batteries and renewable resources.

Study Methodology 

Modeling the behavior of an electric grid with significant solar and wind power required an excellent weather model to 

allow researchers to examine how solar and wind power behave over time in real-world situations. For the Hawaii Solar 

Integration Study, AWS Truepower generated solar power production profiles using a numerical weather prediction 

model coupled with a stochastic-kinematic cloud model. The weather prediction model drove the cloud movements, 

while the cloud model determined when and where clouds were generated and how they dissipated over time.

The weather model was run for a historical year, and the weather was “re-created” so historical weather inputs could 

drive the model and continually update it. Re-creating weather is more accurate than predicting weather, and it allows 

the weather to be correlated to the actual loads of the historical year. The loads can then be adjusted for load growth for 

the future study year. Capturing the synchronization between the weather and loads is important because the loads, the 

solar output, and the wind output are all driven by the weather and interconnected.

The weather model yielded a set of solar power production profiles that showed the power produced over time, at 

2-second intervals, for both distributed and central-station solar power facilities. These profiles provided critical 

information about how quickly the solar power was likely to ramp up and down with changing weather—providing 

realistic “worst case” scenarios that were used to test the robustness of each island’s power grid for an entire year of data. 

The weather predictions and power profiles were also compared with real-world data to verify their accuracy.

The weather prediction model was also used to create solar power forecasts, which simulated actual forecasts that grid 

operators use to plan each day’s operations. These modeled forecasts were used to schedule resources for each day, as 

is done with actual load forecasts, and then the grid simulation model was used to see how the system performed. But 

just like real weather forecasts, the modeled forecasts would occasionally miss significant weather events that adversely 

affected the power grid. Performing these simulations allowed the researchers to determine realistic operating parameters 

for each of the island power grids and single out the worst-case operating conditions of the year.

This study examined the behavior of each grid with increasing amounts of solar and wind power. For each scenario 

examined, the required contingency and operating reserves were calculated, and, once these parameters were set, the 

operation of the grid was simulated at hourly intervals for a full year using the Multi-Area Production Simulation 

Software (MAPS) developed by General Electric Co. (GE). This simulation quantified how the conventional (thermal) 

power plants would be dispatched over the course of a year and showed if wind and solar energy resources would be 

curtailed, allowing an hourly calculation of the variable operating costs and the resultant power-plant emissions.

The variable operating costs are dominated by fuel costs but also include operating costs for the plants. They do 

not include the capital costs of wind or solar plants, so the study is an annual operational impact study that details 

production costs, including those from the additional operating reserves needed, and not a life-cycle cost study that 

includes capital costs.
6

The hourly operation results from the simulation were then analyzed to quantify the impact of wind and solar variability. This 

analysis examined wind and solar power production at subhourly intervals to determine how well the conventional power plants 

kept up with variations in renewable power production, within the constraints of each power plant’s ramp rate and power limits. 

Worst-case conditions were examined to evaluate dynamic effects, such as a power plant’s control system overshooting the 

desired power level, using GE’s Positive Sequence Load Flow software. As a result, the study identified the most challenging 

events that each grid was likely to face and then examined potential mitigations to improve grid performance.

Oahu 

The Oahu study looked at increasing penetrations of wind and solar power on the island, with different distributions of central 

The Oahu Electric Grid

Oahu’s power supply includes one baseload coal plant, one baseload waste-to-energy plant, three 

baseload oil-fired plants, two cycling oil-fired plants, and one peaking oil-fired plant. Together, these 

plants provide 1,822 MW of “firm” generation resources—power plants that the utility can count on to 

produce power when they are in service. In addition, the island draws on two wind farms with a total 

capacity of 99 MW, a 1-MW solar PV power facility, and about 80 MW of distributed, grid-connected PV 

systems located at homes and businesses throughout the island.

For the Oahu grid, the operating criterion is to always carry sufficient contingency reserves to 

completely make up for the loss of the largest single generating unit on line (usually the 

185-MW coal plant).

Firm Generation Resources Capacity 

(Gross MW)

Honolulu (steam, cycling, oil) - HECO 108 

Waiau (steam, baseload, oil) - HECO 169

Waiau (steam, cycling, oil) - HECO 201

Waiau (combustion turbine, peaking, 

oil) - HECO

103

Kahe (steam, baseload, oil) - HECO 604

CIP (combustion turbine, peaking, 

biodiesel) - HECO

113

HPower (steam, baseload, waste-toenergy) - IPP46

Kalaeloa (combined-cycle, baseload, 

oil) - IPP

208

AES (steam, baseload, coal) - IPP 185

Total firm generating capability 1,737

Non-Firm Generation Capacity 

(Net MW)

Kahuku wind farm 30

Distributed PV 80

Utility-scale PV 1

Total non-firm generation 111

Table 1. Oahu Generation Resources as of August 2012
7

The Oahu study looked at increasing penetrations of wind and solar power on the island, with different distributions of central 

PV power plants versus distributed PV systems. Starting with a baseline of 60 MW of distributed PV power, scenarios 3A 

and 3B examined the impact of adding another 300 MW of PV power and 100 MW of wind (30 MW existing plus 70 MW 

scheduled to be in service at year-end 2012). Scenario 3A added a 100-MW central PV plant and 200 MW of distributed PV 

systems, while 3B added a 200-MW central PV plant and 100 MW of distributed PV systems.

Scenarios 4A and 4B examined the impact of adding 500–700 MW of renewable power; 4A added a 400-MW central PV plant 

and 300 MW of distributed PV systems, while 4B added a 200-MW central PV plant, 100 MW of distributed PV systems, 

and 200 MW of wind capacity from a proposed wind plant on a neighboring island, sent by submarine cable (see Table 2). 

Although scenarios 4A and 4B differ by 200 MW of renewable generating capacity, the annual power generation of the solar 

and wind resources results in essentially the same level of renewable energy. (Scenarios 1 and 2, meant to examine lower PV

penetrations, were dropped because the results were expected to result in small operating impacts to the system and, hence, less 

information on mitigation options.)

Each of these scenarios was then modeled to determine the worst-case drop in power from the aggregate wind and solar 

resources, as shown in Table 3. A noteworthy fact is that a central PV power plant can experience larger power drops than the 

same capacity of PV systems distributed across the island. This is because a single dark cloud can cause a power drop at the 

centralized plants, while even a massive storm takes some time to sweep over the island and affect the power production of 

all distributed PV systems. Likewise, wind and solar resources are geographically dispersed and rarely change in lockstep, so 

Scenario 4B, with a mix of wind and solar power, has lower variability than Scenario 4A, which relies entirely on solar power.

The study set a criterion that the grid must have sufficient operating reserves to cover wind and solar variability 99.99% of the 

time without dipping into contingency reserves. Contingency reserves assure that the grid continues to function normally if a 

major power plant shuts down, so this criterion effectively means that it is an acceptable risk to expose the grid to that threat 

0.01% of the time, or slightly less than 1 hour per year. This is similar to accepting the threat of other low-probability events 

such as multiple plants shutting down simultaneously or multiple feeders tripping off line at the same time.

Table 2. Study Scenarios for the Oahu Power Grid

Scenario Distributed 

PV

Central 

Plant PV

On-Island 

Wind

Off-Island 

Wind Total

Baseline 60 MW – 100 MW – 160 MW

3A 260 MW 100 MW 100 MW – 460 MW

3B 160 MW 200 MW 100 MW – 460 MW

4A 360 MW 400 MW 100 MW – 860 MW

4B 160 MW 200 MW 100 MW 200 MW 660 MW

Same 

Solar MW

Same Wind + 

Solar Energy

Table 3. Worst-Case Variability for Oahu Scenarios

Scenario 3A Scenario 3B Scenario 4A Scenario 4B

Worst 5-minute drop (MW) -64 -129 -186 -132

Worst 10-minute drop (MW) -88 -136 -183 -143

Worst 30-minute drop (MW) -151 -168 -308 -219

Worst 60-minute drop (MW) -201 -217 -399 -324
8

Required Reserves for Oahu 

Figure 1 shows the minimum operating reserves required for each of the scenarios under existing utility operating practices. 

During daytime hours, operating reserves need to cover the variability from both wind and solar energy resources, so the 

reserve requirements are significantly different for each scenario. During nighttime hours, operating reserves need to cover 

only the variability of wind power resources, so only Scenario 4B, which draws on an off-island wind plant, has greater 

operating reserve requirements at night.

The Oahu grid also needs to carry sufficient down reserves to accommodate a sudden loss-of-load event. HECO has 

historically allocated a minimum of 40 MW of down reserves for this purpose, but a recent HECO analysis found that 140 

MW of load could be lost during the daytime, while 90 MW of load could be lost during the night. These levels of down 

reserves were assumed for scenarios 4A and 4B, while scenarios 3A and 3B used the 40-MW target. However, the available 

down reserves for scenarios 3A and 3B met the more stringent requirements for almost all hours of the year.

Oahu Grid Operating Characteristics

The MAPS simulations were used to examine how often the solar and wind resources would be curtailed—required to reduce 

their power production—to maintain grid control parameters. Curtailment is to be avoided as much as possible because it 

wastes renewable energy that carries no fuel charge and thereby hurts the economics of the generation source and increases 

the utility’s emissions.

For this study, simulations assumed curtailment is prioritized on a “last installed, first curtailed” basis, which is consistent 

with HECO’s power purchase agreements. This prioritization results in curtailment of the central-station PV system first, then 

the off-island wind plant, then the 70-MW on-island wind plant, and finally the 30-MW on-island wind plant. The distributed 

PV systems cannot be curtailed by the utility.

The study found that the Oahu bulk power grid is able to absorb all the wind and solar energy in scenarios 3A and 3B and 

variable operating costs for both scenarios are the same, at 90.8% of the baseline cost (see figures 2 and 3). The lower costs 

reflect the fuel savings from the displaced fossil-fueled generators. Renewable energy penetration for these two scenarios is 

11.8% and 12.2%, respectively. See Figure 4.

Figure 1. Oahu spinning operating reserve requirements 

for day (left) and night (right) 
9

 Figure 2. Annual wind and solar energy delivered and curtailed for study scenarios

Figure 3. Annual variable operating costs for study scenarios

Figure 4. Renewable energy penetration for study scenarios

$400

$200

0

$600

$800

$1,000

$1,200

100%

90.8% 90.8%

85.3% 84.2%

Base Sc. 3A Sc. 3B Sc. 4A Sc. 4B

Total Variable Cost (M$/yr)

CT1, Waiau CTs,

Diesel

Honolulu cycling

Waiau cycling

Waiau base

Kahe base

AES

Kalaeloa

5%

0

10%

15%

25%

Renewable Energy Penetration (percent)

Base Sc. 3A Sc. 3B Sc. 4A Sc. 4B

400

200

0

600

800

1,000

1,200

1,400

1,600

1,800

Base Sc. 3A Sc. 3B Sc. 4A Sc. 4B

Wind & Solar GWh

Curtailed

Delivered

Note: Does not include capital costs of wind or solar plants
10

For Scenario 4B, curtailment is 4.3% of the available wind and solar energy, resulting in a variable cost that is 84.2% of the 

baseline, with a renewable energy penetration of 20.2%. For Scenario 4A, however, curtailment is much higher, at 8.6% of the 

available wind and solar energy, resulting in a slightly higher variable cost, at 85.3% of baseline, as well as a slightly lower 

renewable energy penetration of 19.1%. Scenario 4B employs more wind power, which has a different generation profile 

from solar power, so it acts to complement the solar resource. In contrast, Scenario 4A relies entirely on solar power. Those 

resources tend to reach peak power generation at the same time, which results in more curtailment. Average daily curtailment 

patterns for the two scenarios are shown in Figure 5. 

When wind and solar power production is high, grid operators need to reduce power production from fossil-fueled generating 

stations. In many cases, the baseload fossil-fueled power plants will need to run at their minimum rated power. As shown 

in Table 4, the hours the baseload power plants run at minimum power increases dramatically as wind and solar penetration 

increases. In scenarios 4A and 4B, several units operate at minimum load for more than 6,000 hours per year, and some 

units operate more than 90% of the time at minimum load. The operations and maintenance impact of operating baseload 

generation at this low level for the hours shown needs to be assessed, but doing so was outside the scope of the study.

0

5

10

15

20

25

30

35

1 3 5 7 9 11 13 15 17 19 21 23

Curtailment (GWHr)

Hour of Day

0

5

10

15

20

25

30

35

1 3 5 7 9 11 13 15 17 19 21 23

Curtailment (GWHr)

Hour of Day

Scenario 4A Scenario 4B

73 GWh total 

141 GWh total 

Figure 5. Average daily wind and solar curtailment patterns for scenarios 4A and 4B

Table 4. Annual Hours at or Near Minimum Load for Oahu Baseload Generating Units

Facility

Name

Unit

Name

Scenario

Baseline 3A 3B 4A 4B

AES Coal AES 442 446 448 2,089 1,806

Kalaeloa

Combined Cycle KALAELOA 291 292 291 1,514 2,305

Kahe Base

KAHK1 4,108 5,934 6,091 7,308 7,675

KAHK2 3,648 5,539 5,874 7,075 7,467

KAHK3 1,108 1,649 1,764 3,733 3,821

KAHK4 1,414 2,134 2,323 4,927 5,148

KAHK5 585 846 906 2,668 2,690

KAHK6 2,625 4,593 4,911 4,898 6,031

Waiau Base

WAIW7 2,648 3,591 3,856 7,686 7,913

WAIW8 789 976 1,052 3,981 4,622
11

Another concern with high penetrations of renewable energy is that a large percentage of the island’s power comes from socalled “nonsynchronous” generation. Conventional steam-driven power plants use a synchronous generator that literally spins 

in synchronicity with the frequency of the power supply; the generator’s rotation period is exactly equal to an integral number 

of alternating current cycles. This helps the grid maintain its normal operating parameters, including controlling voltage. 

Nonsynchronous generators, such as wind turbines and PV systems, do not typically provide this grid support. The fraction of the 

island’s power supply that comes from synchronous generators is an indication of the relative strength and stability of the grid.

The study found that the percentage of synchronous generation is lowest in Scenario 4A, dropping below 50% for a few hours 

each year. Scenario 4A falls short of reaching unstable levels of nonsynchronous generation, but much higher levels of renewable 

energy than reflected in Scenario 4A could cause grid control challenges.

Operational Challenges for the Oahu Grid

Challenges to the Oahu grid with increasing penetrations of wind and solar energy are mainly related to rapid drops or rises 

in wind and solar power production. Wind and solar power drops of a sustained fashion over the course of 5–10 minutes can 

challenge the ability of the conventional (thermal) power plants to compensate by ramping up in power; dropping in a sustained 

fashion for 30–60 minutes consumes up-reserve resources and requires quick-start units. Wind and solar power can also rise 

when thermal power plants are near their minimum power level, consuming down reserves, and renewable power can vary 

rapidly within an hour, challenging the ramp rate and maneuvering ability of thermal power plants. Other concerns are the 

transient responses to a loss of load or the trip of a large thermal power plant.

Impacts of Wind and Solar Variability

The screening analysis showed there were adequate reserves to cover sustained drops of wind and solar power in periods of 5–60 

minutes in all hours for all scenarios. In all cases, the thermal generation was able to compensate for the change in wind and 

solar power output without excessive deviation from nominal system frequency. There were, however, some instances in which 

20–60 MW of contingency reserves were used to compensate for large reductions in wind and solar power output, and Scenario 

4B experienced one event in which 128 MW of contingency reserves were consumed by a wind and solar power reduction. 

However, these were infrequent events, and all scenarios met the criterion for operating reserves to cover 99.99% of wind and 

solar variability.

Transient analysis of the grid’s response to the worst-case hourly variability of wind and solar power found it was able to 

maintain proper frequency levels under all scenarios. The worst-case variability was under Scenario 4A, and the worst-case 

frequency excursion in this scenario was within ±0.2 Hz, which was deemed acceptable. However, this scenario resulted in 

considerable maneuvering of thermal units. It should be noted that the HECO units were assumed to be more responsive to 

system events than they are today and not all aspects of the generator systems are modeled. The ability of the HECO units to 

achieve this level of performance will need to be verified. 

The situation is trickier when wind and solar power output is high and the thermal power plants are operating at or near 

minimum load to maximize the use of wind and solar power. If the wind and solar power output experiences a sustained increase 

for 5–30 minutes, the thermal power plants cannot reduce their power levels further to compensate. As a result, down reserves 

are consumed, and the study found that down reserves fall below their minimum levels for the high-penetration scenarios. 

Specifically, the analysis found about 2,000 hours of down-reserve violations for Scenario 4A and 950 hours for Scenario 4B, 

meaning grid reliability is at risk during these periods.
12

300

250

200

350

400

500

450

0 10 20 30 40 50 60 0 10 20 30 40 50 60

Power (MW)

Time (min)

Wind and Solar Power Down Reserves

-50

-100

-150

0

50

150

100 Power (MW)

Time (min)

154 MW

 in 5 min

Centralized PV

increases by

150 MW in 5 min

126 MW

0 MW

-82 MW

Centralized PV

Distributed PV

Wind

0 10 20 30 40 50 60

300

250

200

150

100

50

0

Figure 6. Down reserves consumed by an increase in wind and solar power 

under Scenario 4A during the worst-case 5-minute increase in wind and solar power

Figure 6 shows the down-reserve violation for the worst-case 5-minute increase in wind and solar power under Scenario 4A, 

caused by a rapid power increase at the central PV power plant. These results demonstrate the need to continuously manage 

down reserves with high penetrations of wind and solar power. One practical solution is to automatically curtail wind and central 

PV power plant resources when the down reserves fall below the minimum level required for loss-of-load contingencies.

Worst-Case Loss-of-Load Event

Transient simulations of the worst-case loss-of-load event on the Oahu grid showed that even though adequate down reserves 

were allocated to the thermal power plants, transient overshoot caused some thermal units to momentarily fall below their 

minimum power limits. As shown in Figure 7, under Scenario 4A, a 140-MW loss-of-load event caused two thermal units to 

experience transient power swings 16%–17% below their minimum power limits. Further analysis is required to quantify the risk 

of unit trips due to such transient events. However, if the wind and central-station PV resources were equipped with governor 

functions, they could participate in the response to loss-of-load events and reduce the reliability risk for thermal plants.
13

Figure 7. Transient response to a 140-MW loss-of-load event under Scenario 4A

Worst-Case Generating Plant Trips With Low System Reserves

The worst-case trip of a generating plant on Oahu is the loss of the 185-MW coal plant when system reserves are low. Figure 8 

shows the system response to that event for the baseline case and Scenario 4A. In the baseline case, the frequency nadir (a direct 

measure of how close a system has come to interrupting the delivery of electricity to customers) is 58.65 Hz, and 95 MW of load 

is shed in two stages. Under Scenario 4A, the frequency nadir is 58.8 Hz, and 47 MW of load is shed in one stage. This indicates 

the Oahu grid handles the plant trip better with a high penetration of wind and solar energy, apparently because of the greater 

diversity of generating sources.

In 2012, HECO began requiring its distributed PV systems to ride through frequency excursions down to 57 Hz without tripping. 

Without that requirement, PV systems operating to conventional standards would trip offline at 59.3 Hz, exacerbating the 

frequency excursion. In that case, the frequency nadir would be 58.2 Hz under Scenario 4A, and the system would have to shed 

190 MW of load. However, about 40 MW of distributed PV currently interconnected with the Oahu system were installed (prior 

to 2012) without ridethrough capability. Thus, the worst-case loss of generation for Oahu would be 225 MW, corresponding to 

the combined loss of the 185-MW plant and 40 MW of distributed PV. Further analysis will better establish the worst-case lossof-generation events and the system reserves needed to deal with those events.

350

300

250

400

450

0 5 10

Thermal Power (MW)

Time (seconds)

ΔMW Thermal

= 127MW

Governor

overshoot 

Pelectrical Pmechanical

61

60.5

60

61.5

Frequency (Hz)

Units

Transient

Minimum

Mechanical

Power (MW) 

Unit

Minimum

Rating

(MW)

Turn Down

Below the

Rating 

KAHE-1 31.4 34.7 -10%

KAHE-3 34.7 35.0 -1%

KAHE-4 33.0 35.2 -6%

KAHE-6 46.3 55.1 -16%

WAI-7 24.2 25.7 -6%

WAI-8 20.9 25.1 -17%

AES 70.2 76.0 -8%
14

Figure 8. Response to a trip of the 185-MW coal plant 

under the baseline scenario and Scenario 4A

95 MW in baseline scenario

47 MW in Scenario 4A

58.8 Hz

58.65 Hz

59.3 Hz: Distributed PV would trip if not subject to HECO’s

 under-frequency ridethrough requirement 

4A

Base

4A

Base

40

20

0

60

80

100

Load Shed (MW)

Time (seconds)

4A – Pelectrical

4A – Pmechanical

Base – Pelectrical

Base – Pmechanical

900

800

700

600

1,000

1,100

1,200

0 5 10 15 20 25 30

0 5 10 15 20 25 30

P Thermal (MW)

58.5

59

59.5

60

0 5 10 15 20 25 30

Frequency (Hz)
15

Table 5. Summary of Oahu Operational Challenges and Possible Mitigation Measures

Mitigation Measures

A summary of operational challenges and potential mitigation measures associated with increased wind and solar energy 

penetration on the Oahu grid is presented in Table 5.

Operational Challenges

of Increased Wind and

Solar Penetration

Mitigation Measures

Significant curtailment of 

wind and solar energy

Lower the minimum power level of thermal generation resources. This 

reduces curtailment from 8.6% to 2.7% in Scenario 4A and from 4.4% to 0.5% 

in Scenario 4B. This is by far the most effective mitigation measure.

Relax fixed operating schedules for Oahu baseload generation. This reduces 

curtailment from 8.6% to 6.4% in Scenario 4A and from 4.4% to 1.2% in 

Scenario 4B.

Provide a portion of spinning reserves from demand response or a BESS. 

Providing 50 MW of reserves from nonthermal resources reduces curtailment 

from 8.6% to 7.1% in Scenario 4A and from 4.4% to 3.9% in Scenario 4B.

Provide a portion of down reserves from wind or central solar PV plants. This 

reduces curtailment from 8.6% to 5.5% in Scenario 4A and from 4.4% to 2.5% 

in Scenario 4B.

Frequency response of 

thermal unit operation 

during loss-of-load events

Equip wind and solar plants with governor controls that reduce power 

output in response to over-frequency events. This reduces the governing 

duty on thermal plants and prevents them from transiently falling below 

their minimum power limits.

Frequency response 

of thermal fleet during 

generator trip events

Provide a portion of spinning reserves from a BESS with a governor or from 

frequency-responsive demand response. This increases the system’s overall 

governing response and shares the governing duty over more resources. It 

also helps avoid protective load-shedding actions.

Apply synthetic inertia control functions on wind plants.

Wind and solar generation 

consumption of down 

reserves intended for 

loss-of-load events

Automate curtailment of wind and central PV plants to continuously

maintain required levels of down reserves on thermal plants.

Increased variability 

in system frequency 

and regulation duty 

on thermal generation 

caused by wind and

solar power variation

Study results show this is not expected to be a significant problem, but if 

better performance is desired:

• Limit ramp rates on individual central solar PV plants (requires a BESS to 

limit down-ramps)

• Apply a BESS as a grid-level frequency-regulating resource (would need to 

be about one-third the total rating of BESS applied to limit ramp rates on 

individual PV plants).
16

Curtailment Reduction

The study found the best way to reduce the curtailment of wind and solar power was to lower the minimum power limit on 

select thermal power units. HECO has identified several units that could potentially reduce minimum power levels by about 

50%. Other mitigating measures include relaxing the operating schedules of certain baseload units, procuring additional 

spinning reserves through demand response or a BESS, and procuring additional down reserves from wind and solar energy 

resources. Combining all these mitigation measures yields significant improvements. Under Scenario 4A, curtailments 

can be lowered from 8.6% to only 0.9%. These mitigation measures will require investment in utility-owned generation or 

collaboration from the wind and solar power producers.

Improving Grid Performance Under Loss-of-Load Events

Under high penetrations of solar and wind power, thermal generators can fall below their minimum power levels during 

transient responses to severe loss-of-load events. The solution to this problem is to include the wind and central-station PV 

plants in the response by equipping them with governor controls. The responsiveness of governor controls is indicated by 

the “droop,” which is the percentage difference in speed with no load on the generator and with full load on the generator. A 

governor with a lower-percentage droop results in a more responsive system.

North American power plants use governors with a droop of 5%. A standard droop helps ensure that the synchronous 

generating sources share the load equally in proportion to their generating capacity. To evaluate the benefit of adding 

governors to wind and central-station PV plants, the study examined adding governors with 5% droop—as responsive as the 

synchronous generators—and with 2% droop, which makes them more responsive.

The study found the response of the thermal units was reduced from 127 MW with no wind and solar governors to 80 MW 

using governors with 5% droop and to 50 MW using governors with 2% droop. As shown in Table 6, with no wind and solar 

governors, all five thermal units have transient power excursions that fall below their minimum power limit by 20%–30%. 

With 5% droop governors on wind and solar plants, four units remain above their minimum power levels, and one unit falls 

below by only 1%. With 2% droop governors on wind and solar plants, all the thermal units stay well above their minimum 

power levels for the loss-of-load event. In this case, it might be possible to reduce the minimum dispatch limits of some 

thermal units, thereby further reducing the curtailment of wind and solar energy.
17

Improving Grid Performance During Thermal Plant Trips

The loss of the 185-MW coal plant requires the Oahu grid to shed 95 MW of load with a frequency nadir of 58.65 Hz under 

the baseline case and 47 MW of load with a frequency nadir of 58.8 Hz under Scenario 4A. Although the Oahu grid performs 

better with high penetrations of wind and solar power and higher generation reserves on the system, it would be preferable to 

reduce the load shed and minimize the deviation from standard operating frequency.

Proposed mitigation measures include using demand response to shed 75 MW of load when system frequency falls below 

59.5 Hz and using “synthetic inertia control functions” on the wind plants to momentarily boost wind power output when 

system frequency falls below 59.85 Hz. These synthetic inertia control functions draw on the kinetic energy of the spinning 

blades to generate extra power, thereby slowing the blades. After a power boost of a few seconds, the wind plant takes about 

30 seconds to return to speed, so its power output dips during that period.

The study found that under Scenario 4B, drawing on 75 MW of frequency-responsive demand response would raise the 

frequency nadir from 58.6 Hz to 58.9 Hz and reduce load shedding to 30 MW. Using synthetic inertia controls on the wind 

plants would boost wind plant output by 35 MW but have little impact on the system, improving the frequency nadir by only 

0.1 Hz without changing the amount of load shed. However, combining both mitigation measures would limit the frequency 

nadir to 59 Hz and eliminate the need to shed load.

No Governors on

Wind + Solar Plants

Governors With

5% Droop on

Wind + Solar Plants

Governors With

2% Droop on

Wind + Solar Plants

Units

Unit Pmin 

Limit 

(MW)

Transient 

Min Mech 

Power (MW)

Turn Down 

Relative to 

Pmin Limit

Transient 

Min Mech 

Power (MW)

Turn Down 

Relative to 

Pmin Limit

Transient 

Min Mech 

Power (MW)

Turn Down 

Relative to 

Pmin Limit

KAHE-1 17.0 12.4 -27% 18.5 9% 21.2 25%

KAHE-2 17.2 12.9 -25% 18.4 7% 21.2 23%

KAHE-6 55.1 38.5 -30% 54.8 -1% 61.0 11%

WAIAU-8 17.0 12.0 -29% 18.4 8% 21.0 23%

AES 76.0 59.8 -21% 80.5 6% 89.2 17%

Table 6. Thermal Power Plant Excursions Following a 140-MW Loss-of-Load Event
18

Conclusions for the Oahu Electric Grid

The Oahu electric grid can accommodate 360 MW of solar PV generation and 100 MW of wind generation 

with additional spinning reserves, investments in generating units to reduce their minimums, and improved 

ramp rates. This mix of wind and solar generation supplies 11% of Oahu’s annual electric load. If the solar PV 

resources are concentrated into larger plants with single-axis tracking rather than geographically dispersed 

with no tracking, the system will require more operating reserves to respond to the additional short-term 

variability in power output.

The Oahu electric grid with system modifications and additional reserves can accommodate additional 

wind and solar resources that result in renewable energy providing roughly 20% of the island’s annual 

electricity needs. Additional operating reserves are required under the 20% renewable energy scenarios. 

Daytime operating reserves are higher for the PV-dominated scenario, and nighttime operating reserves 

are required for scenarios with wind. Centralized PV plants have more variability and, hence, need more 

operating reserves than distributed PV.

These high-penetration scenarios resulted in some curtailment of wind and solar power. Under current 

operating practices, the wind and solar mix resulted in curtailment of 4.3% of the renewable energy, while 

the solar-dominant mix resulted in curtailment of 8.6%. Reducing the minimum power level of the thermal 

power plants is the most effective method of reducing curtailment. Other effective methods include:

 • Allocating down reserves to wind and solar plants

 • Relaxing fixed operating schedules for a few thermal units

 • Providing reserves from alternate resources such as a BESS or demand response.

Combining these mitigation measures can reduce curtailment to less than 1%, which is very acceptable.

The study also found that, under the scenarios with high renewable energy penetration, the modified grid 

controls are capable of maintaining adequate frequency control for subhourly variations of wind and solar 

power. However, certain operating conditions and grid stresses could degrade the safety margins of the grid 

and make it less reliable. For example, when thermal generation is backed down to minimum dispatch limits, 

increased wind and solar generation will consume down reserves intended for loss-of-load contingencies. 

This can be resolved with an automated scheme to curtail wind and solar plant output and maintain 

required down reserves on the thermal units.

Likewise, system over-frequency responses for loss-of-load events can be improved if wind and solar 

plants are equipped with over-frequency governor controls. This reduces the risk of thermal unit trips 

because of transient excursions below minimum power levels. In addition, system under-frequency 

responses to generator trip events can be improved by a combination of synthetic inertia on wind plants 

and frequency-sensitive demand response.
19

Maui

The Maui Power Grid

Maui’s power supply includes four oil-fired power plants, a number of small diesel-fueled generators, 

two large diesel-fueled units, and a biomass-fueled power plant. Together, these power plants provide 

264 MW of “firm” generation resources—power plants that the utility can count on to produce power 

when they are in service. For this study, the 16-MW biomass plant, which uses coal as a secondary fuel, is 

assumed to provide 13 MW of power during daylight hours and 9 MW at night. In addition, Maui has three 

wind farms that provide a total of 72 MW of power and 20 MW of grid-connected PV systems distributed 

around the island at homes and businesses. Finally, the Maui grid has a 10-MW BESS installed at one of 

the wind power plants. It provides up to 10 MW of up reserves for the Maui grid. A detailed listing of Maui 

generation resources is presented in Table 7.

Firm Generation Resources Capacity (MW)

Maalaea, combined cycle, distilate, 

2×58 MW

116

Maalaea, diesels, distilate 96

Kahului, oil steam, four units 34

Hana, diesels, distilate 2

HC&S Sugar Mill, IPP, biomass 16

Total firm generating capability 264

Non-Firm Generation Capacity (MW)

KWP1 wind farm 30

Auwahi wind farm 21

KWP2 wind farm 21

Distributed PV 20

Utility-scale PV 0

Total non-firm generation 92

Table 7. Maui Generation Resources as of November 2012

The Maui study examined only two scenarios: a baseline scenario that reflects the system as it is today and Scenario 3, which 

adds 15 MW of centralized PV and 15 MW of distributed PV. (Scenario 2, meant to examine lower PV penetrations, was 

dropped because the results were expected to be trivial.) See Table 8.

Table 8. Study Scenarios for the Maui Power Grid

Scenario Distributed PV (MW) Centralized PV (MW) Wind (MW) Total (MW)

Baseline 15 0 72 87

Scenario 3 30 15 72 117
20

Each of these scenarios was modeled to determine the worst-case drop and rise in power from the aggregate wind and solar 

resources. The results are shown in Table 9. The study found renewable resource variability on Maui is dominated by the 

wind power plants, and, as a result, there is little difference in variability between the baseline scenario and Scenario 3.

Baseline Scenario

72 MW Wind + 15 MW Solar

Scenario 3

72 MW Wind + 45 MW Solar

Time Interval Max ∆P Drop (MW) Max ∆P Rise (MW) Max ∆P Drop (MW) Max ∆P Rise (MW)

30 seconds 7.0 6.8 8.1 7.1

1 minute 10.4 9.9 10.7 11.2

5 minutes 18.2 17.7 19.1 19.7

10 minutes 29.9 26.7 30.0 28.2

20 minutes 38.3 45.8 38.3 45.8

60 minutes 50.8 56.5 52.3 56.5

Table 9. Worst-Case Variability for Maui Scenarios

Required Reserves for Maui

On Maui, contingency and operating reserves are pooled together, with a minimum reserve level of 6 MW. This study 

focused on integrating more solar resources into the Maui grid. The technical analysis assumed that operating reserves would 

be allocated to cover variability of both wind and solar resources. A new method for calculating reserve requirements was 

developed, and that method produced reserve requirement curves (see Figure 9) that are significantly different from those 

presently used for Maui grid operations.

The reserve requirement developed for this study would generally require more reserves for lower levels of wind and solar 

contributions and less reserves for higher levels of wind and solar contributions, as compared with the existing reserve 

requirements for the system. However, it is difficult to compare the two methods because the new reserve requirement also 

adds reserves for solar power in addition to the wind power, where the existing reserve method only carries reserves for the 

wind power added to the system. 

Further technical analysis is recommended to quantify curtailment impacts to the existing wind plants and to explore if and 

how the existing reserve practices should be changed to cover both wind and solar variability.

Figure 9. Maui operating reserve requirements, including BESS contribution, for day (left) and night (right)

0

10

20

30

40

50

0 20 40 60 80 100 120

Min Operating Reserves, Spin (MW)

Min Operating Reserves, Spin (MW)

Wind + Solar Generation (MW)

0

10

20

30

40

50

0 20 40 60 80 100 120

Wind + Solar Generation (MW)

Base Base

Sc 3 Sc 3

Existing

(for wind only)

Existing

(for wind only)

6-MW minimum for

contingency reserve

6-MW minimum for

contingency reserve
21

Table 10. Annual Wind and Solar Energy Use, Curtailment, and Penetration

The existing reserve requirements include the contributions of the BESS, which is capable of delivering 10 MW of up reserve 

continuously for 45 minutes, after which it will ramp down linearly for 45 minutes until it reaches zero. The BESS can also 

contribute 10 MW to down reserves by charging at its full capacity.

Maui Grid Operating Characteristics

In a similar fashion to the Oahu analysis, MAPS simulations examined how often solar and wind resources would be 

curtailed to maintain grid control parameters. On Maui, the central PV power plant was curtailed first, followed by the wind 

plants. The results, shown in Table 10, are that wind and solar penetration increases from 21% in the baseline scenario to 

23.5% under Scenario 3, while wind and solar curtailment increases from 19.9% to 23.1%.

So although Scenario 3 features 34.5% more wind and solar capacity than the baseline scenario, it delivers only 11.7% more 

renewable energy to the grid because only 57% of the additional renewable power is delivered to the grid. Nearly half of the 

central PV resource is curtailed (the additional distributed PV cannot be curtailed); curtailment of one of the wind plants also 

increases. This happens because Maui has a high wind penetration in the baseline scenario and already has significant wind 

curtailment, so the bulk of the additional renewable energy contributions in Scenario 3 are curtailed.

Maui Wind + Solar Resources Renewable 

Utilization

Comparison

to Baseline

30 MW 

KWP1

21 MW 

Auwahi

21 MW 

KWP2

Central PV

Dist PV

Total (GWh)

Wind + Solar 

Curtailment

Wind + Solar 

Penetration

Add’l RE 

Available

(MWh)

Add’l RE 

Delivered

(MWh)

% Add’l RE 

Delivered

Baseline

Available 

GWh 129 88 90 0 25 332

19.9% 21.0%

Curtailed 

GWh 3 18 46 0 0 66

Delivered 

GWh 126 70 45 0 25 266

CF

(Delivered) 48% 38% 24% 19%

Scenario 3

Available 

GWh 129 88 90 30 49 386

23.1% 23.5% 54 31 57%

Curtailed 

GWh 2 18 54 14 0 89

Delivered 

GWh 126 69 36 15 49 297

CF

(Delivered) 48% 38% 20% 12% 19%
22

Like Oahu, Maui draws on nonsynchronous power sources for a large percentage of its power supply. Under the baseline 

scenario, the grid approaches 50% nonsynchronous generation for a few hours of the year, while under Scenario 3, that 

percentage approaches 60%. Scenario 3 exceeds 50% nonsynchronous generation for about 100 hours per year.

Operational Challenges for the Maui Grid

As on Oahu, the challenges of increasing penetrations of wind and solar energy to the operation of the Maui grid are 

primarily related to rapid drops or rises in wind and solar power production. There is also a potential vulnerability to loss-ofload events.

Impacts of Wind and Solar Variability

The screening analysis confirmed there are adequate reserves in all hours to cover sustained drops of wind and solar power in 

periods of 5–60 minutes. The analysis exhausted the thermal reserves first and then drew on the BESS, and, in most cases, the 

thermal reserves were sufficient to compensate for the drop. Under the baseline scenario, there are 10 hours per year when 

the reserve margin falls below 5 MW, and during the worst-case event, the reserve margin falls to 1 MW. Under Scenario 3, 

there are only 7 hours per year when the reserve margin falls to 5 MW, which is the lower limit on the reserve margins during 

the worst-case event.

Transient analysis of the grid’s response to the worst-case hourly variability of wind and solar power found the grid was able 

to maintain proper frequency under all scenarios. In the simulations, the thermal units handled most of the regulation duty, 

but the BESS contributed briefly when the frequency excursion exceeded ±0.1 Hz. As a result, all the frequency excursions 

were held within that ±0.1-Hz band.

Worst-Case Loss-of-Load Event

The largest anticipated loss-of-load event on the Maui grid is 20 MW. Per Maui Electric Co. operating practices, the system 

carries 5 MW of down reserves on its thermal resources during the day and 3 MW at night, and the BESS contributes another 

10 MW to down reserves. Analysis shows there are 3,530 hours per year when the thermal units and BESS could not cover a 

20-MW loss-of-load event. Transient analysis shows that four diesel-fueled combustion turbines at Maalaea fall below their 

minimum power limits during the worst-case loss-of-load event, and two of these units settle at a final power level that is 1.5 

MW below their minimum power level.

However, the wind plants and the central PV plant can contribute to down reserves by curtailing output. The study 

determined these three sources of down reserves are sufficient to compensate for the largest loss-of-load event. Another 

alternative is to automatically curtail wind and solar power to preserve down reserves on thermal plants. During the 3,530 

hours when the thermal units and BESS have inadequate reserves, the wind and central solar plants could be automatically 

curtailed to maintain a thermal plant down reserve of at least 10 MW, but doing so would significantly increase wind and 

solar curtailment.
23

Operational Problems With 

Increased Wind/Solar Penetration Mitigation Measures

Significant curtailment of wind and 

solar energy

Combined-cycle units switch between single- and dual-train 

operation as needed.

Relax or eliminate fixed operating schedules for Kahului 1-4.

Increase the priority of reserves in the commitment process.

Install an additional 10-MW BESS for reserves.

Install an additional 10-MW BESS for time-shifting energy.

Wind and solar generation consumption of down reserves intended for 

loss-of-load events

Automate curtailment of wind and central PV plants to

continuously maintain required levels of down reserves on 

thermal plants.

Thermal unit operation during 

loss-of-load events

Equip wind and solar plants with governor controls that will 

reduce power output in response to over-frequency events.

This reduces the governing duty on thermal plants and prevents 

them from transiently falling below their minimum power limits.

Install an additional 10-MW BESS with a frequency-responsive 

governor.

Wind and solar generation consumption of down reserves because of 

rapid and/or sustained decreases of 

wind and solar power

Analytical results demonstrate the assumed criteria for operating 

reserves (both spinning and non-spinning) was sufficient to 

cover all subhourly down-ramps in wind and solar. No additional 

mitigations are required.

Increased variability in system 

frequency and regulation duty on 

thermal generation caused by wind 

and solar power variation

Study results show that frequency regulation during the most 

volatile hour is within 0.1 Hz, as the BESS responds rapidly for 

excursions beyond its 0.1-Hz governor deadband.

Ramping duty on thermal units and frequency variations can

be reduced by reducing or eliminating the 0.1-Hz deadband on 

the BESS.

Table 11. Annual Wind and Solar Energy Use, Curtailment, and Penetration

Mitigation Measures

A summary of operational challenges and potential mitigation measures associated with increased wind and solar energy penetration 

on the Maui grid is presented in Table 11. Note that the reductions in variable cost discussed below do not account for the cost of 

implementing the mitigation action assumed or any cost of the additional renewable energy accepted by the system.

Curtailment Reduction

A screening analysis assessed the ideas for reducing curtailment. The analysis found that enabling the combined-cycle units to 

switch between single-and dual-train operation increased wind and solar penetration from 23.5% to 25.1%, reduced curtailment 

from 23.1% to 17.9%, and increased the percentage of additional renewable energy that was actually delivered to 94%. 

However, adding in relaxed operating schedules for the four oil-fired units and a change in the commitment process to increase the 

priority of reserves improved grid performance, raised wind and solar penetration to 25.7%, lowered curtailment to 15.8%, and 

actually delivered more renewable energy than the additional renewable energy that was added. This reflects a more efficient use 

of the existing wind and solar resources.
24

The study also evaluated the benefits of adding another 10-MW, 20-MWh BESS, similar to the existing one, to the Maui 

grid. Two BESS operating strategies were considered: using the second BESS to provide an additional 10 MW of operating 

reserves or using it to “time-shift” energy—storing the wind and solar energy that would otherwise be curtailed and 

delivering it to the grid at the first opportunity. These two strategies were examined independent of the other proposed 

mitigation methods.

When providing reserves, the BESS raised wind and solar penetration to 17.6%, reduced curtailment to 25.2%, and delivered 

96% of the available added renewable energy to the grid. It also reduced variable operating costs by $5 million per year. 

When used to time-shift energy, the BESS reduced curtailment to 19% and dropped operating costs by $1.8 million per year, 

so it is clearly preferable to use the BESS to provide operating reserves.

However, the study also examined time-shifting energy with a BESS that has more hours of storage. Using a 100-MWh 

BESS instead of a 20-MWh BESS produced a result similar to using a 20-MWh BESS to provide operating reserves, with 

curtailment lowered to 17.6%. However, the 20-MWh BESS used for operating reserves results in lower operating costs than 

the 100-MWh BESS used for time-shifting energy, so it is still preferable to employ the smaller BESS to provide reserves. 

Note that the cost reductions stated show the relative benefits provided by an additional BESS but do not include the cost of 

the BESS or the additional renewable energy accepted by the system.

Improving Grid Performance for Loss-of-Load Events

A loss-of-load event could cause four combustion turbines at Maalaea to drop below their minimum power levels. To mitigate 

this problem, the study examined the use of over-frequency governor controls on the central-station PV plant and two of 

the three wind farms, representing 42 MW of capacity. (The third wind farm is under a power purchase agreement that does 

not allow a governor function. However, the BESS does have an aggressive fequency response.) This resulted in a modest 

reduction in the thermal plant response. Only three of the four combustion turbines went below their minimum power levels 

because of overshoot on their transient responses, and two of the four ended up operating below their minimum power levels 

after the event.

Other possible mitigation approaches include modifying the Maalaea combined-cycle units to reduce their minimum power 

levels and installing over-frequency relays with low settings on selected wind-plant feeders to trip a portion of the wind 

generation during loss-of-load events. These approaches were not investigated in the study.

Improving Grid Frequency Regulation for High-Variability Periods

Maui’s thermal units can adjust their output for most periods of high variability of solar and wind resources, but, occasionally, 

the BESS kicks in to keep the frequency range within a ±0.1-Hz band around the nominal 60-Hz frequency. To see if 

the BESS could provide even better frequency regulation, the study examined whether system performance under highvariability periods could be improved by removing the 0.1-Hz deadband in the BESS control scheme. The analysis found that 

operating the BESS with no deadband resulted in greater cycling of the BESS but decreased the power maneuvering of the 

thermal plants and improved frequency control. The results are shown in Figure 10.
25

-2

-4

-6

0

2

4

6

BESS Output (MW)

Time (seconds)

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000

Without BESS

BESS with deadband

BESS without deadband

Without BESS

BESS with deadband

BESS without deadband

60

59.9

59.8

60.1

60.2

Frequency (Hz)

Time (seconds)

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000

Figure 10. Regulation performance for the most volatile hour under Scenario 3, 

with and without the 0.1-Hz deadband in the BESS control scheme
26

Conclusions for the Maui Electric Grid

Compared with the baseline scenario, Scenario 3 has 54 MWh of additional solar energy available from 

30 MW of new solar PV resources. However, under existing operating practices, only 57% of this solar energy 

can be used to serve load; the rest must be curtailed. Despite the curtailment, wind and solar resources 

provide 23.5% of Maui’s power needs under Scenario 3. The reserves required for the 72 MW of wind 

resources in the baseline scenario were found to be sufficient to cover the additional 30 MW of PV resources 

in Scenario 3.

Several methods for reducing curtailment were explored. The two mitigation methods that performed 

well were enabling the single- or dual-train operation of the combined-cycle units and adding a 10-MW, 

20-MWh BESS. The flexible operation of the combined-cycle units enabled 94% of the additional renewable 

energy in Scenario 3 to be delivered to the grid, with only 6% curtailed, while the BESS enabled 96% of the 

additional renewable energy to be delivered to the grid, with only 4% curtailed. Using the BESS to time-shift 

energy was not as effective for reducing curtailment as using the BESS to provide reserves.

The study also found that some thermal units fell below their minimum operating power levels during the 

worst-case loss-of-load event with high wind and solar power production. The addition of frequency droop 

response in the new wind power plants resulted in a modest improvement, but the study recommended 

that the utility review the modeled power excursions at the thermal units to evaluate the risk of them 

tripping offline under these conditions.

However, the system has enough maneuvering capability in the thermal plants and the BESS to 

counteract the most volatile periods of wind and solar power production. With the existing BESS deadband, 

the thermal units do most of the maneuvering in response to short-term variations, but with the deadband 

removed, the BESS shares in the system frequency regulation and significantly reduces maneuvering duty 

on the thermal units.
27

Conclusions

In summary, the Hawaii Solar Integration Study provided valuable insights into the integration of solar and wind power into 

two very different utility grids. The results provide a deeper understanding of the differences among distributed PV systems, 

centralized PV power plants, and wind power plants in terms of variability, ability to curtail power output, grid support, and 

characteristics relative to the load. Although reliability challenges increase with increasing variable renewable generation, this 

study found that those challenges are manageable for the scenarios studied with the mitigation approaches recommended.

This study found that higher levels of variable renewables can be reliably accommodated in a collaborative fashion (i.e., by 

changes to both utility equipment and operating practices and variable generation equipment) and noted the importance of asking 

for the right capabilities (e.g., inertial and frequency response, voltage and frequency ridethrough, and ancillary services) from 

variable generating plants. The importance and value of reviewing existing operating practices and adjusting certain assumptions 

(e.g., minimum power generation levels and must-run generator rules) was modeled and the impact on solar and wind 

curtailment shown. Finally, the use and value of storage in a high-penetration system was modeled and discussed. The insights 

from the Hawaii Solar Integration Study form a large body of knowledge for future grid integration studies, and the results can be 

used to further our understanding of grid integration in other island systems as well as mainland U.S. systems with high regional 

solar and wind penetrations. 
28

References

AWS Truepower. (2013). Synthesis of PV Data for the Hawaii Solar Integration Study. NREL/SR-5500-57844. Work performed 

by AWS Truepower, LLC, Albany, NY. Golden, CO: National Renewable Energy Laboratory. 

Corbus, D.; Schuerger, M.; Roose, L.; Strickler, J.; Surles, T.; Manz, D.; Burlingame, D.; Woodford, D. (2010). Oahu Wind 

Integration and Transmission Study: Summary Report. NREL/TP-5500-48632. Golden, CO: National Renewable Energy 

Laboratory. http://www.nrel.gov/docs/fy11osti/48632.pdf. 

GE Energy Consulting. (March 2013). Hawaii Solar Integration Study – Final Technical Report for Maui. Schenectady, NY: 

GE Energy.

GE Energy Consulting. (April 2013). Hawaii Solar Integration Study – Final Technical Report for Oahu. Schenectady, NY: 

GE Energy.

GE Energy Consulting. (March 2013). Hawaii Solar Integration Study – Summary Technical Report. Schenectady, NY: 

GE Energy. 

Woodford, D. (2011). Oahu Wind Integration and Transmission Study (OWITS): Hawaiian Islands Transmission Interconnection 

Project. NREL/SR-5500-50411. Work performed by Electranix Corp., Winnipeg, Manitoba, Canada. Golden, CO: National 

Renewable Energy Laboratory. Accessed May 9, 2013: http://www.nrel.gov/docs/fy11osti/50411.pdf.

Zack, J.; Young, S.; Natenberg, E. (2013). Observation Targeting for Wind Power Production Forecasting for Wind Farm Sites 

on Maui, Lanai, and Molokai. NREL/SR-5500-57845. Work performed by AWS Truepower, LLC, Albany, NY. Golden, CO: 

National Renewable Energy Laboratory.

Zack, J.; Young, S.; Natenberg, E.; Pennock, K. (2013). Wind Forecast System for Hawaii Grid Management Applications. 

NREL/SR-5500-57846. Work performed by AWS Truepower, LLC, Albany, NY. Golden, CO: 

National Renewable Energy Laboratory.