interconnection of grid systems for maui and oahu counties · 2019-11-08 · acknowledgement: this...

Interconnection of Grid Systems for

Maui and Oahu Counties: Different Mixes of Renewable Generation

and Several Possible Future Scenarios

Prepared for the

U.S. Department of Energy

Office of Electricity Delivery and Energy Reliability

Under Cooperative Agreement No. DE-EE0003507

Hawai‘i Energy Sustainability Program

Task 2: Energy Modeling and Scenario Analysis

Prepared by

GE Energy Consulting

Submitted by

Hawai‘i Natural Energy Institute

School of Ocean and Earth Science and Technology

University of Hawai‘i

February 2014

Acknowledgement: This material is based upon work supported by the United States

Department of Energy under Cooperative Agreement Number DE-EE0003507.

Disclaimer: This report was prepared as an account of work sponsored by an agency of the

United States Government. Neither the United States Government nor any agency thereof, nor

any of their employees, makes any warranty, express or implied, or assumes any legal liability

or responsibility for the accuracy, completeness, or usefulness of any information, apparatus,

product, or process disclosed, or represents that its use would not infringe privately owned

rights. Reference here in to any specific commercial product, process, or service by tradename,

trademark, manufacturer, or otherwise does not necessarily constitute or imply its

endorsement, recommendation, or favoring by the United States Government or any agency

thereof. The views and opinions of authors expressed herein do not necessarily state or reflect

those of the United States Government or any agency thereof.

Final Report:

Stage 2 Oahu-Maui

Interconnection Study

Prepared for:

Hawaiian Electric Company

Hawaii Natural Energy Institute

Prepared by:

GE Energy Consulting

May 21, 2013

Hawaii Stage 2 Interconnection Study Legal Notices

GE Energy Consulting ii Final Report 5/21/2013

Legal Notices

This report was prepared by General Electric International, Inc. as an account of work

sponsored by Hawaiian Electric Company and Hawaii Natural Energy Institute. Neither

Hawaiian Electric Company, Hawaii Natural Energy Institute, General Electric International,

Inc., nor any person acting on their behalf:

1. Makes any warranty or representation, expressed or implied, with respect to the use of any information contained in this report, or that the use of any information, apparatus, method, or process disclosed in the report may not infringe privately owned rights.

2. Assumes any liabilities with respect to the use of or for damage resulting from the use of any information, apparatus, method, or process disclosed in this report.

Hawaii Stage 2 Interconnection Study Foreword

GE Energy Consulting iii Final Report 5/21/2013

Foreword

This report was prepared by General Electric International, Inc. (GEII), acting through its

Energy Consulting group (EC) based in Schenectady, NY, and submitted to Hawaiian Electric

Company and Hawaii Natural Energy Institute. Technical and commercial questions and any

correspondence concerning this document should be referred to:

Richard Piwko or Derek Stenclik

GE, Energy Consulting

General Electric International, Inc.

Building 53

One River Road

Schenectady, New York 12345

Phone: 518-385-7610 or 518-385-4998

Hawaii Stage 2 Interconnection Study Project Teams

GE Energy Consulting iv Final Report 5/21/2013

Project Teams

General Electric

Richard Piwko Gary Jordan Derek Stenclik Shakeer Meeran Yan Pan Wei Ren Ryan Konopinski

Hawaiian Electric Company

Dean Arakawa Ron Bushner Marc DeNarie Marc Matsuura Dora Nakafuji Dean Oshiro Ross Sakuda Robert Uyeunten Robert Young

Hawaii Natural Energy Institute

Leon Roose Richard Rocheleau

Hawaii Stage 2 Interconnection Study Table of Contents

GE Energy Consulting v Final Report 5/21/2013

Table of Contents 1 Introduction 1

1.1 Study Objectives and Approach 1

1.2 Study Scope & Limitations 2

2 Inputs & Assumptions 4

2.1 Macroeconomic Assumptions 4

2.1.1 Load 4

2.1.2 Fuel 6

2.1.3 Emissions Prices 6

2.2 Transmission 7

2.3 Existing Generation Mix 8

2.4 Thermal Plant Configurations 9

2.5 Operating Practices 13

2.5.1 Fixed Operating Schedules 13

2.5.2 Planned Outages and Maintenance Schedules 13

2.5.3 Quick-Start Capability 14

2.5.4 Independent Power Producers 15

2.5.5 Combined Cycle Plant Modeling 15

2.6 Wind & Solar Plants 16

2.6.1 Wind & Solar Profiles 16

2.6.2 Forecasting 17

2.6.3 Curtailment 18

2.7 Reserves 19

2.7.1 Up Reserve Requirements 19

2.7.2 Down Reserve Requirements 20

3 Scenario Overview 22

3.1 Renewable Capacity Additions 22

3.2 Available Renewable Energy 24

3.3 DC & AC Interconnection 25

3.4 Reserve Strategy 26

4 Results 30

4.1 Interconnecting Maui, Molokai and Lanai 30

4.2 Base Case and Scenario 1 34

4.2.1 Total System Energy Production 34

4.2.2 Thermal Unit Operation 36

4.2.3 Emissions 39

4.2.4 Curtailment of Renewable Generation 39

4.2.5 Interisland Line Flows 41

4.2.6 Weekly Generation Profiles 43

4.3 Production Cost Analysis 47

4.4 Delivered Renewable Generation 50

4.5 Renewable Curtailment 54

Hawaii Stage 2 Interconnection Study Table of Contents

GE Energy Consulting vi Final Report 5/21/2013

4.6 DC & AC Line Flows 57

4.7 Value of Renewable Energy Resources 63

4.8 Emissions 65

4.9 Thermal Cycling 67

4.10 Down-Regulation and Up-Range 69

4.11 Key Observations 73

5 Changes to Operational Practices 74

5.1 Production Cost Impacts 75

5.2 Curtailment and Renewable Generation 78

5.3 DC Cables Constrained 83

5.4 Value of Renewable Energy Resources 87

5.5 Other Impacts 90

5.6 Key Observations 93

6 Sensitivity Analysis 94

6.1 Load Growth Sensitivities 94

6.2 Fuel Price Sensitivities 96

7 Energy Storage Options 99

7.1 Energy Storage as a Reserve Asset 99

7.2 Energy Storage as an Energy Shifting Asset 104

7.3 Comparison of Energy Storage Results 106

8 Cost-Benefit Analysis 109

8.1 Capital Cost Data 109

8.2 Capital Costs for Study Scenarios 111

8.3 Annual Benefits with Existing Operating Practices 112

8.4 Annual Capital Recovery Required 113

8.5 Cost of Renewable Electricity (COE) 117

8.6 Cost-Benefit Analysis with Modified Operating Practices 118

8.7 Total Cost Including PPA Costs 122

9 Observations & Conclusions 125

10 Future Research 126

11 References 128

11.1 Previous Studies 128

11.2 Capital Cost Assumptions 128

12 Appendix 129

12.1 GE MAPS Description 129

12.2 Maintenance and Outage Schedule 130

12.3 Detailed Capital Cost Assumptions 132

Hawaii Stage 2 Interconnection Study List of Figures

GE Energy Consulting vii Final Report 5/21/2013

List of Figures Figure 2-1: Total System Load and Peak Demand by Month ........................................................................................ 5

Figure 2-2: Average Weekly Load Pattern .............................................................................................................................. 5

Figure 2-3: Base Case Installed Capacity by Island, by Fuel .......................................................................................... 8

Figure 2-4: Weekly Snapshot of Wind & Solar Forecast and Real-Time Profiles .............................................. 17

Figure 2-5: Wind & Solar Forecast Error Duration Curves between Scenario 1 and 7 .................................. 17

Figure 3-1: Renewable Capacity (MW) ................................................................................................................................... 23

Figure 3-2: Scenario Tree ............................................................................................................................................................ 23

Figure 3-3: Available Renewable Energy (GWh) ............................................................................................................... 25

Figure 3-4: System Interconnections with Two HVDC Cables .................................................................................. 25

Figure 3-5: Oahu Base Case Variable Operating Reserve versus Wind and Solar Generation ................ 26

Figure 3-6: Maui County Operating Reserve versus Wind and Solar Generation ........................................... 26

Figure 3-7: Combined System Operating Reserve versus Wind and Solar Generation ............................... 27

Figure 3-8: Combined System Operating Reserve Duration Curves by Scenario ........................................... 28

Figure 3-9: Maximum Operating Reserves by Scenario .............................................................................................. 29

Figure 4-1: Spinning Reserve Requirements for Maui Alone and Maui County Interconnected.............. 32

Figure 4-2: Quick-Start Capability for Maui Alone and Maui Combined with Lanai and Molokai ........... 32

Figure 4-3: Energy Production by Power Plant for Scenario 1 ................................................................................. 36

Figure 4-4: Capacity Factors of Generating Plants for Base Case and Scenario 1 ........................................ 37

Figure 4-5: Average Annual Hours of Operation and Starts for Thermal Generation, by Type ................ 38

Figure 4-6: Annual Emissions for Base Case and Scenario 1 .................................................................................... 39

Figure 4-7: Average Hourly Available and Delivered Wind Power by Time of Day, Scenario 1 ................ 40

Figure 4-8: Duration Curve of Wind and Solar Curtailment, Base Case and Scenario 1 ............................. 41

Figure 4-9: Interisland Line Flow Duration Curves for Scenario 1 .......................................................................... 42

Figure 4-10: Interisland Line Flows for Highest Load Week ...................................................................................... 43

Figure 4-11: Scenario 1 Generation Profiles for Week with Maximum Renewable Energy Penetration

(% of Load) ............................................................................................................................................................................... 44

Figure 4-12: Scenario 1 Generation Profiles for Week with Maximum Renewable Generation (MW) ... 45

Figure 4-13: Scenario 1 Generation Profiles for Week with Worst Wind & Solar Forecast Error ............. 45

Figure 4-14: Scenario 1 Generation Profiles for Week with Peak Demand, Showing Changes from

Base Case ................................................................................................................................................................................. 46


GE Energy Consulting viii Final Report 5/21/2013

Figure 4-15: Production Costs for All Scenarios, by Generation Type ................................................................... 49

Figure 4-16: Generation by Type for All Scenarios ......................................................................................................... 50

Figure 4-17: Wind, Solar, and Firm Renewable Energy for All Scenarios ............................................................ 51

Figure 4-18: Annual Renewable Energy Penetration for All Scenarios ................................................................. 52

Figure 4-19: Duration Curves of Hourly Wind and Solar Penetration Levels as % of Load for All

Scenarios .................................................................................................................................................................................. 53

Figure 4-20: Duration Curves of Wind and Solar Penetration Levels for Different Time Periods,

Scenarios 1 and 8 ................................................................................................................................................................. 54

Figure 4-21: Wind and Solar Curtailment Duration Curves for All Scenarios .................................................... 55

Figure 4-22: Wind, Solar and Firm Renewable Energy Delivered and Curtailed, as % of Load ................ 56

Figure 4-23: Available Wind, Solar and Firm Renewable Energy versus Curtailment ................................... 56

Figure 4-24: Curtailment of the Maui 100 MW Wind Plant for Scenarios 8, 9 and 10. ................................. 57

Figure 4-25: Duration Curves for Maui County to Oahu DC Cable Flows ............................................................ 59

Figure 4-26: Energy Transfer on Maui County to Oahu DC Cables, with and without Power Flow

Constraints ............................................................................................................................................................................... 60

Figure 4-27: Flow Duration Curves for AC Cables Interconnecting Maui, Lanai and Molokai................... 61

Figure 4-28: Total Emissions from Dispatchable Thermal Generation ................................................................. 65

Figure 4-29: Reductions in Emissions from Thermal Generation Relative to Base Case ............................. 66

Figure 4-30: Duration Curve of AES Output for All Scenarios .................................................................................... 68

Figure 4-31: Hourly Generation Profiles and System Down-Regulation Range for One Week, Scenario

1 .................................................................................................................................................................................................... 69

Figure 4-32: Duration Curve of Down-Regulation Range for Scenarios 1 and 10 .......................................... 70

Figure 4-33: Hourly Generation Profiles and System Up-Range for One Week, Scenario 1 ...................... 71

Figure 4-34: Up-Range Duration Curves for Scenarios 1 and 10 (Min & Max Renewables) ....................... 72

Figure 4-35: Reserve Requirements and System Up-Range for Scenarios 1 and 10 .................................... 72

Figure 4-36: Hourly Up-Range and DC Cable Flow for Week with Highest DC Cable in Scenario 10 ... 73

Figure 5-1: Sequential Changes to Operational Practices ........................................................................................... 74

Figure 5-2: Production Cost Savings by Sensitivity for Scenario 1 .......................................................................... 76

Figure 5-3: Impact of Operating Changes on Production cost for Scenarios 1-10 ......................................... 76

Figure 5-4: Production Cost Savings from all Changes ................................................................................................. 78

Figure 5-5: Reduced Curtailment due to Operational Changes for Scenario 1 ................................................. 79

Figure 5-6: Curtailment of Renewable Energy by Sensitivity for Scenarios 1-10 ............................................. 80


GE Energy Consulting ix Final Report 5/21/2013

Figure 5-7: Potential Total Reduction in Curtailment ..................................................................................................... 81

Figure 5-8: Impact on Renewable Penetration of Modified Operational Changes. ......................................... 82

Figure 5-9: Change in Curtailment with Modified Operating Practices by Scenario ...................................... 82

Figure 5-10: DC Cable Flow Duration Curves for Scenario 10 by Operating Sensitivity ................................ 83

Figure 5-11: Maximum DC Cable Flows (MW) by Operating Sensitivity ................................................................ 84

Figure 5-12: Energy Flows (GWh) on DC Cables by Sensitivity ................................................................................... 85

Figure 5-13: DC Cable Flow (MW) Duration Curves for Scenario 1-10 with Operating Changes .............. 86

Figure 5-14: DC Cable Energy Flows (GWh) by Scenario with Operating Changes in Place ....................... 87

Figure 5-15: Comparison of Values of Renewable Resources with Different Operating Assumptions . 89

Figure 5-16: Kahe and AES Plant Operating Impacts for Scenario 10 ................................................................... 90

Figure 5-17: Oahu Baseload Unit Operating Impacts for Scenario 10 .................................................................. 91

Figure 5-18: Maui Baseload Unit Operating Impacts for Scenario 10 ................................................................... 91

Figure 5-19: Oahu Up-Range Duration Curve with Operating Changes .............................................................. 92

Figure 5-20: Oahu Down-Regulation Duration Curves by Sensitivity .................................................................... 92

Figure 6-1: Load Growth Sensitivity Hourly Duration Curves ..................................................................................... 94

Figure 6-2: Change in Production Cost in Load Sensitivities ...................................................................................... 95

Figure 6-3: Wind & Solar Curtailment by Load Growth Sensitivity .......................................................................... 95

Figure 6-4: Fuel Cost as a Component of Overall Production Cost ......................................................................... 97

Figure 6-5: Elasticity of Fuel Prices and Production Cost ............................................................................................. 97

Figure 6-6: Change in Production Cost in Fuel Sensitivities ........................................................................................ 98

Figure 7-1: Operating and Contingency Reserve Duration Curves with Energy Storage .......................... 100

Figure 7-2: Annual Production Cost Savings with Energy Storage as a Reserve Asset .............................. 101

Figure 7-3: Oahu Baseload Unit Operating Impacts with Reduced Reserve Requirement ...................... 103

Figure 7-4: Value of Energy Storage as an Energy Shifting Device ...................................................................... 104

Figure 7-5: Utilization of Energy Storage Device to Shift Energy ........................................................................... 105

Figure 7-6: Utilization of Energy Storage by Time of Day.......................................................................................... 105

Figure 7-7: Impact of 200MW, 6hr Storage Device on Renewable Curtailment and Coal Generation106

Figure 7-8: Comparison of Savings for a 2-hour Storage Device ......................................................................... 107

Figure 7-9: Comparison of Storage Options .................................................................................................................... 108

Figure 7-10: Comparison of Storage Options for a Range of Sizes ...................................................................... 108

Figure 8-1: Capital Cost Assumptions by Technology ................................................................................................ 110


GE Energy Consulting x Final Report 5/21/2013

Figure 8-2: Cost-Benefit Analysis (M$/Year) by Scenario Using Different Capital Cost Assumptions and

Assuming a 14% FCR ....................................................................................................................................................... 114

Figure 8-3: Cost-Benefit Analysis (M$/Year) by Scenario Using Different FCR Assumptions and

Assuming Average Capital Costs ............................................................................................................................... 115

Figure 8-4: Cost Benefit Analysis ($/MWh) Based on Delivered Renewable Energy .................................... 118

Figure 8-5: Cost-Benefit Analysis (M$/Year) with Modified Operating Practices ........................................... 119

Figure 8-6: Cost-Benefit Analysis ($/MWh) Based on Delivered Renewable Energy with Modified

Operating Practices .......................................................................................................................................................... 120

Figure 8-7: Total Operating Cost by Scenario (M$) ....................................................................................................... 123

Figure 8-8: Comparison of Total Cost (M$) with Existing and Modified Operating Practices ................... 124

Figure 10-1: Determining the Incremental Value of Interconnection vs. Modified Operating Practices

.................................................................................................................................................................................................... 126

Hawaii Stage 2 Interconnection Study List of Tables

GE Energy Consulting xi Final Report 5/21/2013

List of Tables Table 2-1: Summary of Load by Island .................................................................................................................................... 4

Table 2-2: Fuel Prices (2015$/MMBtu) ...................................................................................................................................... 6

Table 2-3: Load Flow Assignment by Scenario .................................................................................................................... 7

Table 2-4: Base Case Installed Capacity by Island, by Fuel (MW) ................................................................................ 9

Table 2-5: Oahu Thermal Unit Characteristics .................................................................................................................. 11

Table 2-6: Maui County Thermal Unit Characteristics ................................................................................................... 12

Table 2-7: Planned Maintenance Schedule ........................................................................................................................ 14

Table 2-8: Base Case Wind & Solar Plants ........................................................................................................................... 16

Table 2-9: Wind and Solar Curtailment Order ................................................................................................................... 18

Table 2-10: Down Reserves by Unit ........................................................................................................................................ 20

Table 3-1: Renewable Capacity (MW) .................................................................................................................................... 22

Table 3-2: Available Renewable Energy (GWh) ................................................................................................................ 24

Table 3-3: Maximum Operating Reserves by Scenario. ............................................................................................... 28

Table 4-1: Annual Production Costs and Energy for Maui County System......................................................... 31

Table 4-2: Wind and Solar Energy Delivered and Curtailed for Maui County System ................................... 31

Table 4-3: Annual Production Costs and Energy for Base Cases and Scenario 1 ........................................... 35

Table 4-4: Generation Utilization for Base Case and Scenario 1 ............................................................................. 37

Table 4-5: Annual Renewable Energy Available and Delivered for Base Case and Scenario 1 ............... 40

Table 4-6: Annual Production Costs and Energy for All Scenarios ......................................................................... 48

Table 4-7: Annual Generation in GWh by Type and by Island for All Scenarios ............................................... 49

Table 4-8: Wind, Solar and Firm Renewable Energy Delivered and Curtailed for All Scenarios............... 52

Table 4-9: Maximum and Average Hourly Penetration Levels for Wind and Solar Resources ................. 53

Table 4-10: Wind and Solar Energy Curtailment for All Scenarios ......................................................................... 55

Table 4-11: Maui County to Oahu DC Cable Flows for All Scenarios ..................................................................... 60

Table 4-12: Flows (MW) for AC Cables Interconnecting Maui, Lanai and Molokai by Percentile ............... 62

Table 4-13: Value of Additional Renewable Resources with Existing Thermal Plant Operating

Practices ................................................................................................................................................................................... 63

Table 4-14: Average Annual Starts and Hours of Operation by Unit Type ......................................................... 67

Table 5-1: Reduced Minimum Power Limits on Oahu Units ........................................................................................ 75

Table 5-2: Production Cost Impact (M$) of Operating Changes................................................................................ 77

Hawaii Stage 2 Interconnection Study List of Tables

GE Energy Consulting xii Final Report 5/21/2013

Table 5-3: Incremental Savings (M$) from Operating Changes for Scenario 1-10 .......................................... 77

Table 5-4: Curtailment (GWh) of Renewable Energy by Sensitivity for Scenarios 1-10 ................................. 80

Table 5-5: Reduction in Curtailment by Sensitivity .......................................................................................................... 81

Table 5-6: Energy Flows (GWh) on DC Cables by Sensitivity ....................................................................................... 85

Table 5-7: DC Cable Flow Summary with Operating Changes .................................................................................. 86

Table 5-8: Value of Additional Renewable Resources with Modified Operating Practices .......................... 88

Table 5-9: Comparison of the Value of Renewable Resources Based on Operating Assumptions ......... 88

Table 7-1: Annual Production Cost Savings with Energy Storage as a Reserve Asset ............................... 101

Table 7-2: Delivered Wind and Solar Energy with Energy Storage as a Reserve Asset ............................. 102

Table 7-3: Energy Generation by Island with Energy Storage as a Reserve Asset ....................................... 102

Table 8-1: Capital Cost Data for Wind, Solar and Geothermal from External Sources ($/KW) ............... 109

Table 8-2: Total Estimated Capital Cost by Scenario (M$)......................................................................................... 111

Table 8-3: Annual Capital Recovery Required ($/Year) by Scenario using a 14% FCR................................ 112

Table 8-4: Existing PPA Prices by Plant ............................................................................................................................... 112

Table 8-5: Annual Benefits by Scenario with Existing Operating Practices ...................................................... 113

Table 8-6: Break Even Cost of the AC Cable Network Assuming a 14% FCR ................................................... 116

Table 8-7: Annual Benefits by Scenario with Modified Operating Practices .................................................... 119

Table 8-8: Break Even Cost of the AC Cable Network Assuming a 14% FCR and Modified Operating

Practices ................................................................................................................................................................................ 121

Table 12-1: Maui County Weekly Maintenance and Outage Schedule .............................................................. 130

Table 12-2: Oahu Weekly Maintenance and Outage Schedule ............................................................................. 131

Table 12-3: Capital Cost Calculator using Minimum Cost Assumptions ............................................................ 132

Table 12-4: Capital Cost Calculator using Average Cost Assumptions .............................................................. 132

Table 12-5: Capital Cost Calculator using Maximum Cost Assumptions........................................................... 133

Table 12-6: Capital Cost Assumptions for the DC Cable Interconnections ...................................................... 133

Hawaii Stage 2 Interconnection Study List of Acronyms

GE Energy Consulting xiii Final Report 5/21/2013

List of Acronyms $M Million Dollars AGC Automatic Gain Control BESS Battery Energy Storage System BTU British Thermal Unit CO2 Carbon Dioxide DC Direct Current GE General Electric International, Inc. GE MAPSTM GE Multi Area Production Simulation GE PSLFTM GE Positive Sequence Load Flow GW Gigawatt GWh Gigawatt Hours HC&S Hawaiian Commercial & Sugar HECO Hawaiian Electric Company HNEI Hawaii Natural Energy Institute HPOWER Honolulu Program of Waste Energy Recovery HVDC High-Voltage Direct Current IPP Independent Power Producer KW Kilowatt NOX Nitrogen Oxides MECO Maui Electric Company MMBTU Million British Thermal Units MW Megawatt MWh Megawatt Hours PPA Power Purchase Agreement PV Photovoltaic RE Renewable Energy RT Real Time Dispatch SCUC/EC Security Constrained Unit Commitment / Economic Dispatch SO2 Sulfur Dioxide VOC Variable Operation Cost

Hawaii Stage 2 Interconnection Study Introduction

GE Energy Consulting 1 Final Report 5/21/2013

1 Introduction

1.1 Study Objectives and Approach

This study addresses the following objectives

1. To quantify the value of interconnecting three islands in Maui County (Maui, Lanai, Molokai) and operating them as one combined system.

2. To quantify the value of interconnecting the Oahu and Maui County grids and operating them as one combined system, considering several possible future scenarios with different mixes of renewable generation.

The analysis was performed using production simulation models of the Oahu and Maui power

system originally developed for the Oahu Wind and Transmission Integration Study (OWITS) and the

Hawaii Solar Integration Study (HSIS). Two base cases were developed for this study:

Oahu Base Case: Includes 100 MW wind generation and 100 MW solar PV resources on Oahu as

well as 200 MW wind generation on Lanai connected to Oahu by a dedicated 200 MW DC cable.

Maui County Base Case: Includes 15 MW distributed solar PV and three wind plants with

combined rating of 72 MW on Maui.

Note that between the commencement of this study and the time of publishing, the distributed PV

system on Maui had grown to over 25 MW, with an additional 1 MW on Lanai and 1 MW on Molokai.

Although this will cause absolute values in the report to deviate from reality, the overall findings

concerning comparison of scenarios are unchanged given the 12 MW increase in solar capacity.

Using the GE-MAPS production cost simulation tool, production costs (variable costs) were calculated

for the Oahu and Maui County systems operating separately (i.e., not interconnected). Production

costs were also calculated for the Oahu and Maui County systems operating as one interconnected

system. The study examined 10 scenarios for interconnected operation, with each scenario having a

different combination of wind, solar, and firm renewable energy resources. Four of the scenarios

included one 200 MW DC cable interconnecting Oahu and Maui County; six scenarios included two

200 MW DC cables.

The study also included cost-benefit analysis to assess the relative values of the renewable energy

resources in the scenarios. Benefits were calculated as reductions to system production costs

(primarily fuel and variable operating costs for thermal plants). Costs were calculated in two ways:

1. From estimates of the capital costs for new renewable energy resources and DC cable equipment, and

2. From a combination of actual and estimated PPA costs for renewable energy.

Although this study includes capital cost estimates for renewable capacity additions and DC cable

infrastructure, it does not include estimates for AC cable infrastructure between the Maui County

islands or the necessary capital costs for thermal generating unit or system upgrades required to

achieve the changes to operating practices discussed in Chapter 5.



1.2 Study Scope & Limitations

All modeling and forecasting research is limited by a variety of factors and is often constrained by

the accuracy of study inputs. It is neither possible nor necessary to have perfect economic

representation of the Oahu and Maui County power systems. On the other hand, it is necessary to

understand modeling assumptions and their impact on the study results. In cases where inputs or

assumptions are uncertain and may have a significant impact on results, this study attempted to

conduct sensitivity analyses to properly bound and “bookend” the problem. Throughout the study

period, HECO, HNEI and GE Energy Consulting worked together to formulate study scenarios and

sensitivities that reflect reasonable and rational future scenarios available for an interconnected

Hawaii System. However there are several important research items that are not contained in the

scope of this study and will not be addressed at this time. These include, but are not limited to, the

following items:

1. Not an Integrated Resource Plan (IRP): The objective of this study was to quantify various impacts of interconnecting the Oahu, Maui, Lanai, and Molokai power systems, with a focus on renewable energy penetration. This study is not intended to be an overall integrated resource plan for the islands and does not fully outline necessary plans, financial costs and implications associated with each scenario outlined. Instead, this study attempts to provide a high-level “bookend” analysis to identify potential mitigation strategies and options to be more fully investigated in future study work. The primary goal of this study was to establish how an interconnected system will work within the existing and future infrastructure, and it provides insights into the relative benefits of various mitigating options, including changes in the generation portfolio and operations that could improve system performance and economics.

2. Production Cost as the Critical Economic Metric: Throughout this report, the focus is on “variable operating cost”, also known as “production cost”. Production cost is NOT the total cost incurred to serve load, but rather it is the component of cost that varies with operation, and which can be affected by operating decisions. It includes the cost of fuel, the costs of starting and stopping plants, and the costs of operation and maintenance that vary with energy produced (variable O&M). Other costs are fixed and do not count towards production cost. The cost of capital for all plant and equipment is fixed. The cost of operation and maintenance independent of energy production (i.e. the cost that a plant incurs just to stay able to produce power) is fixed. While these costs may play a role in whether to invest in new plant or keep a plant in service, they play no role in operational decision making. Production cost also does NOT include the PPA price that HECO/MECO must pay to procure wind, solar and firm renewable energy. These energy sources are considered price takers with zero fuel cost and will always be accepted by the grid if possible, regardless of PPA prices. In addition, production cost does NOT include any capital costs associated with plant upgrades necessary to change operational practices.

3. Changes to existing operating routines: This study includes sensitivity analysis involving changes to the existing baseload unit operating schedules currently practiced in HECO’s grid operations. Changes to operating schedules will not be possible without plant and/or transmission modifications. This study does not describe, in detail, the necessary modifications required or the potential costs associated with those modifications. In addition, changes to unit minimums may not be technically feasible or economically justifiable.



4. HVDC Transmission Design: This study considers only the power transfer capability of HVDC

transmission lines. Further analysis will be required to assess HVDC short-circuit requirements, control design, and control stability for scenarios that are considered economically and operationally viable.

Hawaii Stage 2 Interconnection Study Inputs & Assumptions


2 Inputs & Assumptions The Stage 2 Oahu-Maui Interconnection Study builds upon previous production simulation modeling

conducted for the Hawaii Solar Integration Study (HSIS) and the Oahu Wind Integration and

Transmission Study (OWITS). The MAPSTM production simulation models were utilized as the starting

point for the Stage 2 analysis. As a result, the underlying system parameters and unit characteristics

have been validated and benchmarked in great detail over many years and provide an accurate and

reliable simulation of the islands’ electric power grids.

The independent Maui County and Oahu models were merged together to form the interconnected

database. This section describes the many inputs and assumptions used throughout the production

simulation modeling, including system load, fuel prices, transmission, reserve strategy, wind and

solar profiles and unit characteristics.

2.1 Macroeconomic Assumptions

2.1.1 Load

The production simulation modeling performed in this study utilizes a weather normalized hourly

load shape for each area in the study system; Oahu, Maui, Molokai, and Lanai. Table 2-1 shows the

overall system load characteristics for each island. Oahu has the largest electricity load, with a peak

demand nearly six times larger than Maui and 86 percent of total system load. The Maui grid

constitutes much of the remaining load, with less than 1 percent of total system load from the Lanai

and Molokai systems. Table 2-1 shows summary load statistics by island used throughout the study.

Table 2-1: Summary of Load by Island

Oahu Maui Molokai Lanai Total

System

Net Energy for Load (GWh) 8,084 1,264 31 26 9,406

Peak Demand (MW) 1,263 210 5.9 5.0 1,473

Minimum Load (MW) 577 69 0.5 1.7 675

Average Load (MW) 923 144 3.5 3.0 1,074

Load Factor 73% 69% 60% 59% 73%

Percent of Total System Load 86% 13% 0.3% 0.3% 100%

Using hourly load shapes allows for detailed modeling of seasonal, weekly, and daily variation. The

temporal nature of load patterns is an important driver of system operation, especially when

combined with wind and solar profiles. Figure 2-1 shows the total system load, measured in energy,

and the total system peak demand for each month of the year.



Figure 2-1: Total System Load and Peak Demand by Month

Weekly and daily patterns are also important for system operation. Figure 2-2 shows the average

weekly load pattern and highlights the on-peak and off-peak nature of the weekdays and weekends.

Loads tend to be lower on Saturdays and Sundays when commercial and industrial users reduce

consumptions. In addition, the figure highlights the daily load profile as well. In general there is a

large morning load ramp occurring in the early morning hours (5:00 to 9:00 am), peak load occurring

just after 6:00 pm, and minimum loads occurring during the overnight periods. The cycling of load

profiles will be looked at in greater detail throughout the report, highlighting times of low system load

and potential curtailment.

Figure 2-2: Average Weekly Load Pattern



2.1.2 Fuel

Fuel price is one of the most important variables in production cost modeling and fuel consumption

typically accounts for the vast majority of total production cost. Table 2-2 lists the fuel price

assumptions used throughout the study. Fuel assignments to individual generating units can be

found in Section 2.4. The assumed prices represent HECO’s best estimate of fuel prices in 2015,

however fuel prices are particularly volatile and difficult to predict. As a result, several sensitivities

were run using various levels of fuel prices to demonstrate how potential changes to assumed fuel

prices impact system operation, total production cost and overall economic benefits. For more

information on fuel sensitivities, see Section 6.2.

Table 2-2: Fuel Prices (2015$/MMBtu)

The majority of the Oahu and Maui County thermal units use some derivative of fuel oil (distillate,

residual, diesel, etc.). AES is the only coal plant on the system and has a significantly lower fuel price

of $1.87 per MMBtu. This price makes the AES coal plant the most economic unit on the system.

2.1.3 Emissions Prices

Although the MAPSTM model has the ability to include emission prices in the production cost

optimization, this study does not include any emission policy, either through prices or emission caps.

However, this study recognizes the important policy implications of emission reductions and

quantifies the total system emissions of NOX, SOX and CO2 for each scenario. This will help grid

operators and policy makers quantify ancillary benefits associated with different operating regimes.

AES CoalBiodiesel

(CT1, DG)

Honolulu

LSFO

Kahe

LSFO

Kalaeloa

LSFO

Lanai

LSFO

Maui

HSFO

Maui

LSFO

Molokai

LSFO

Waiau

Diesel

Waiau

LSFO

1.87 42.57 17.35 16.82 17.38 27.08 14.81 23.09 24.65 21.88 16.82



2.2 Transmission

The production simulation modeling used throughout this study uses a full transmission, security

constrained economic dispatch model, however transmission outage contingencies were outside the

scope of this study. As a result, the MAPSTM model optimizes the system dispatch while taking into

account hourly limits on existing transmission infrastructure. To do this, the model utilizes a PSLFTM

solved load flow for the transmission topology. The load flows originated from other studies

conducted by GE Energy Consulting on the islands of Oahu, Maui, Lanai, and Molokai, and include all

transmission lines, generator busses, load busses, and significant transmission interfaces. The load

flows used throughout this study originated from two separate load flows from Maui County

(including AC connections between Lanai, Molokai and Maui) and Oahu. The separate load flows were

then connected via 1 or 2 DC cables depending on the scenario. As a result, a total of four PSLFTM

load flows were used throughout this study. The load flow assignment for each scenario is provided

in Table 2-3.

Table 2-3: Load Flow Assignment by Scenario

Maui County

Load Flow Oahu Load

Flow

Interconnect Load Flow

(1 DC Cable)

Interconnect Load Flow

(2 DC Cables)

Maui Only* X Maui County Base X Oahu Base X Scenario 1 X Scenario 1B X Scenario 1C X Scenario 3 X Scenario 2 X Scenario 4A X Scenario 4B X Scenario 4C X Scenario 4D X Scenario 4E X

*Note: The Maui only simulation without the AC connection of Lanai and Molokai was run using the Maui County Load Flow, with no generation or load assigned to the Lanai and Molokai areas.

For the purposes of this study, the DC cable was implemented unconstrained in the direction towards

Oahu. This allows the analysis to calculate the maximum rating necessary for the DC cables in each

of the study scenarios, rather than constraining flow on the lines to 200 MW. However, flow on the

DC cables is constrained to 30 MW in the opposite direction (from Oahu to Maui County). This

constraint is included to ensure that the single largest contingency on Maui County does not become

larger than 30 MW.



2.3 Existing Generation Mix

The baseline scenario used in this study includes the existing and expected future online generating

capacity for both Oahu and Maui County. Overall, the system is comprised of 2,536 MW of installed

capacity; 2,178 MW of which are on Oahu and 358 MW are on Maui County. The Maui County

capacity value includes the 11 MW of capacity on Lanai and 15 MW of capacity on Molokai.

Traditionally, the two systems have relied on thermal generators to provide the majority of all

electricity generation. In particular, generation is predominantly from burning oil fuels (diesel,

distillate oil, residual oil, etc.) due to resource constraints and reliance on imports. The only thermal

units that do not fit into this category are the AES coal plant on Oahu and a few biomass, biodiesel

and waste units throughout the system (Honua Power waste, HPower waste, Honolulu Airport

biodiesel, CIP CT-1 biodiesel, HC&S Sugar biomass plant).

Recent developments and new projects have greatly increased the amount of renewable generating

capacity on both Oahu and Maui. The Base Case in this study includes 100 MW of wind and 100 MW

solar on Oahu and an additional 72 MW of wind and 15 MW of solar on Maui. In addition, the Oahu

Base Case includes 200 MW of wind capacity on Lanai. Note that the 200 MW Lanai wind plant is

included in the Oahu Base case because the existing plan is for the plant to be electrically isolated

from the rest of the Lanai grid, with a direct DC link to Oahu. Also note that the 15 MW of distributed

solar on Maui represents the amount of solar anticipated when this study began. However, solar

installations on Maui have exceeded expectations and are already at 30 MW when this report was

published, with additional fast growth expected.

Figure 2-3 and Table 2-4 provide the capacity resource mix by fuel, in MW ratings and percentages,

for the existing and expected installed capacity included in the study’s Base Cases. “Oil” includes all

fuels derived from oil products (diesel, distillate oil, residual oil, etc.) and “Other” includes all biomass,

biodiesel and waste units.

Figure 2-3: Base Case Installed Capacity by Island, by Fuel



Table 2-4: Base Case Installed Capacity by Island, by Fuel (MW)

2.4 Thermal Plant Configurations

In order to accurately model the detailed system operation of the Oahu and Maui County grids, each

individual thermal generating unit is modeled according to its unique characteristics. Working closely

with HECO and MECO, GE developed individual unit specifications used throughout the study. This

section first defines the many different variables required by the model, and concludes with a table

outlining the characteristics for each unit. For detailed description of operating practices, refer to

Section 2.5. Table 2-5 and Table 2-6 summarize the following unit characteristics:

Unit Name: An eight-character MAPS unit name is assigned to each generating unit included in

the model. This is used throughout the analysis to identify individual units.

Plant Name: In instances where there are multiple generating units at a single plant, this field is

used to aggregate data and tabulate results.

Area: This field represents which system the generating unit is assigned to. In most cases, this is

the physical location and island of the unit. However in the case of the Lanai wind plant, the area

assignment is Oahu because it is electrically integrated with the Oahu grid in the base case. This

field is used for aggregating and summarized data only; it does not impact economic dispatch

because each unit is sited to an individual bus in the load flow.

Fuel: Each unit is assigned to a specific fuel type. Although multiple fuels assignments are

possible, this study only evaluated a single, primary fuel for each unit. The fuel assignment is

used to calculate emissions and total fuel cost. For more details on prices associated with each

fuel, please refer to Section 2.1.2.

Type: Each unit is also assigned to a unit type; Baseload, Cycling, or Peaking. Again, this field does

not impact utilization of the plant throughout the simulations, and is only used to aggregate and

summarize data throughout the report. The assignments were provided by HECO and are based

on historical and expected utilization.

Quick-Start: Quick-start units refer to units that can be started and ramped to full-load 10-

minutes. This feature is technology specific and is most often attributed to combustion turbines

or reciprocating diesel units. There are two stages in the MAPS production simulation model;

commitment and dispatch. The commitment sequence schedules the thermal units and

determines which ones must be available to provide at least their minimum power. The dispatch

sequence then determines how much power each unit will produce for a given hour. Quick-start

Oahu Maui Molokai LanaiTotal

System

Oil 1,393 233 15 11 1,652

Coal 185 0 0 0 185

Other 200 13 0 0 213

Wind 300 71 0 0 371

Solar 100 15 0 0 115

Total Island 2,178 332 15 11 2,536



units are ones that are always committed at 0 MW, and are available to reach full load within the

hour. These are the only units that have the ability to turn on during the dispatch sequence and

are vital to system operation to solve renewable forecast error.

Capacity: Each unit has a maximum capacity rating (MW). This represents the maximum amount

of power a given unit can produce.

Minimum Rating: Minimum rating refers to the minimum stable power output for each unit. The

number of MW between the minimum rating and maximum capacity represents the unit’s

operating range. In addition, once a unit is committed and online, it must operate at least at the

minimum powerpoint. For some units, the values listed in following table include MWs carried for

down-reserve regulation (see Section 2.7.2).

FLHR: Full-Load Heat Rate represents the total fuel consumption (btu) to produce a KWh of

energy while a unit is at its maximum power. Often this represents the most efficient point on the

unit’s average heat rate curve. Although not represented in this section, each unit also has a heat

rate curve allowing for varying levels of fuel consumption at each level of power output.

Start-Up Energy: Start-up energy, in mbtu, is the amount of fuel consumption required to start-

up a unit. If multiplied by the fuel cost, the resulting value represents the total start-cost for the

unit. This cost is applied every time the unit comes online.

Variable O&M: Variable operations and maintenance is also captured during the production cost

optimization. These values are dependent on the unit’s utilization and represent ancillary costs

associated with running a unit that are not fixed. This includes, but is not limited to, things such

as maintenance on turbine parts, water consumption, lubricating oils, etc. Two values are given

to each unit; one in dollars per MWh of energy produced, and the other in dollars per factor-fired

hour.

Min Down Time & Min Run Time: In order to constrain the operational flexibility of a unit, each

generator is given a minimum down time and minimum run time in hours.

Emissions Rates: Each unit is assigned an emission rate curve. The value included in the

following table is the emission rate for a unit operating at full capacity. Emission rates are

included for SOX, NOX and CO2. Although the emissions from each unit are tracked throughout

the study, there is no cost associated with the emissions, so this variable does not impact

utilization or production cost.

Forced Outage Rate: In order to account for unexpected and random generator outages, each

unit is given a forced outage rate dictating the amount of time that the unit is unavailable to

produce energy. For this study, the forced outage rates are a five year average of historical

outages. This outage rate is in addition to any planned or scheduled maintenance or fixed

operating schedules (outlined in Section 2.5.2).



Table 2-5: Oahu Thermal Unit Characteristics

*Note: Units modeled with a minimum rating of 0 MW do not actually have a minimum load of 0 MW. Instead, the units are modeled in this way to act as quick-start

generators, which are “online” at 0 MW and ready to be dispatched to full-load during the dispatch process. For more information, see Section 2.5.3.

**Note: The H Power and Honua Waste unit are modeled on a fixed generation schedule. For more information, see Section 2.5.4.

Unit Name Plant Name Area Fuel TypeQuick

Start

Capacity

(MW)

Minimum

Rating

(MW)

FLHR

(btu/kwh)

Start Up

Energy

(mbtu)

Variable

O&M

($/MWh)

Variable

Cost

($/hr)

Min Down

Time (hrs)

Min Run

Time (hrs)

Forced

Outage

Rate

SOX Rate

(lbs/MWh)

NOX Rate

(lbs/MWh)

CO2 Rate

(lbs/MWh)

Percent Spin

Contribution

HONH8 Honolulu Oahu Honolulu LSFO Cycling N 53.4 22.3 11,264 190.30 0.32 0.00 5 3 15.1% 4.73 5.21 2,074 100%

HONH9 Honolulu Oahu Honolulu LSFO Cycling N 54.4 22.3 11,399 190.30 0.32 0.00 5 3 15.1% 4.51 5.20 1,957 100%

WAIW3 Waiau Oahu Waiau LSFO Cycling N 46.6 22.3 11,903 337.44 0.32 0.00 5 3 15.0% 4.34 5.29 1,575 100%




WAIW7 Waiau Oahu Waiau LSFO Baseload N 82.9 32.0 10,600 5,044.06 0.32 0.00 1 0 5.2% 3.10 4.02 1,740 100%

WAIW8 Waiau Oahu Waiau LSFO Baseload N 86.1 32.0 10,205 5,044.06 0.32 0.00 1 0 5.2% 2.83 4.02 1,724 100%

WAIW9 Waiau Oahu Waiau Diesel Peaking Y 52.9 0.0 13,115 36.04 0.00 99.19 1 0 10.9% 22.51 1.50 2,730 0%

WAIW10 Waiau Oahu Waiau Diesel Peaking Y 49.9 0.0 12,499 32.60 0.00 99.19 1 0 10.9% 21.98 1.44 2,949 0%

KAHK1 Kahe Oahu Kahe LSFO Baseload N 82.1 41.5 10,100 5,044.06 0.32 0.00 1 0 3.6% 4.61 4.21 1,716 100%






KALKAL1 Kalaeloa CC Oahu Kalaeloa LSFO Baseload N 90.0 69.5 8,585 0.00 0.00 0.00 5 0 1.5% 2.84 2.53 996 100%

KALKAL2 Kalaeloa CC Oahu Kalaeloa LSFO Baseload N 90.0 69.5 8,585 0.00 0.00 0.00 5 0 1.5% 2.84 2.53 996 100%

KALKAL3 Kalaeloa GT Oahu Kalaeloa LSFO Baseload N 28.0 0.1 8,651 0.00 0.00 0.00 1 0 1.5% 4.05 3.62 1,422 0%

AES AES Coal Oahu AES Coal Baseload N 185.0 72.0 17,313 0.00 0.00 0.00 1 0 1.5% 1.84 1.24 3,482 100%

HPOWER H Power Oahu Waste Baseload N 73.0 46.0 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 0%

CIPCT1 CIP CT Biodiesel Oahu Biodiesel Peaking Y 113.0 0.0 11,688 126.00 40.76 0.00 1 0 15.0% 1.51 3.63 1,417 0%

AIRDSG8 Airport DSG Oahu Biodiesel Peaking Y 8.0 0.0 10,205 126.00 37.46 0.00 1 0 0.0% 1.51 3.63 1,417 0%

HONUA Honua Waste Unit Oahu Waste Baseload N 6.0 6.0 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 0%



Table 2-6: Maui County Thermal Unit Characteristics

*Note: Units modeled with a minimum rating of 0 MW do not actually have a minimum load of 0 MW. Instead, the units are modeled in this way to act as quick-start

generators, which are “online” at 0 MW and ready to be dispatched to full-load during the dispatch process. For more information, see Section 2.5.3.

Unit Name Plant Name Area Fuel TypeQuick

Start

Capacity

(MW)

Minimum

Rating

(MW)

FLHR

(btu/kwh)

Start Up

Energy

(mbtu)

Variable

O&M

($/MWh)

Variable

Cost

($/hr)

Min Down

Time (hrs)

Min Run

Time (hrs)

Forced

Outage

Rate

SOX Rate

(lbs/MWh)

NOX Rate

(lbs/MWh)

CO2 Rate

(lbs/MWh)

Percent Spin

Contribution

X1 Maalaea X Maui Maui LSFO Peaking Y 2.5 0.0 10,288 0.80 0.51 28.49 1 1 8.5% 1.87 34.22 1,729 0%

X2 Maalaea X Maui Maui LSFO Peaking Y 2.5 0.0 10,288 0.80 0.51 28.49 1 1 8.5% 1.90 34.37 1,740 0%

M1 Maalaea Maui Maui LSFO Peaking Y 2.5 0.0 10,288 0.80 0.51 28.49 1 1 8.5% 1.89 20.87 1,754 0%



M4 Maalaea Maui Maui LSFO Cycling N 5.5 0.0 10,149 5.05 1.57 21.59 1 1 1.8% 1.93 34.74 1,758 0%



M7 Maalaea Maui Maui LSFO Peaking N 5.5 1.9 10,149 5.05 1.57 21.59 1 1 1.8% 1.89 64.01 1,816 66%







M141516 Maalaea CC Maui Maui LSFO Baseload N 53.0 35.0 8,494 233.00 0.35 116.46 13 1 0.3% 1.24 0.91 1,222 20%



K1 Kahului Maui Maui HSFO Baseload N 4.7 2.3 15,445 36.29 1.41 0.14 7 1 0.0% 31.35 6.27 2,822 0%




HC&S Hawaii Commercial and Sugar Maui Biomass Baseload N 13.0 9.0 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 0%

GEN_LAN1 Lanai Generation 1 Lanai Lanai LSFO Peaking Y 1.0 0.0 13,469 0.80 4.00 0.00 1 0 3.0% 1.51 3.63 1,417 0%








GEN_LANM Lanai Monele Bay CHP Lanai Lanai LSFO Peaking Y 0.8 0.0 9,877 0.80 4.00 0.00 1 0 3.0% 1.51 3.63 1,417 0%

GEN_MOL1 Molokai Generation P1 Molokai Molokai LSFO Peaking Y 1.3 0.0 11,579 0.80 4.00 0.00 1 0 3.0% 1.51 3.63 1,417 0%









GEN_MOLS Molokai Generation Solar CT Molokai Molokai LSFO Peaking Y 2.2 0.0 17,890 0.80 4.00 0.00 1 0 3.0% 1.51 3.63 1,417 0%


GE Energy Consulting 13 Final Report 5/21/2013 Final Report 4/30/2013

2.5 Operating Practices

2.5.1 Fixed Operating Schedules

Oahu and Maui systems have several baseload power plants. In this report these units are also

referred to, interchangeably, as must-run and fixed operating schedule units. The baseload units are

generally the largest and most efficient units to operate and are economically dispatched to meet

system load. These plants were not designed to be cycled on and off on a daily basis, and have

therefore been historically operated 24 hours per day. In the model simulations, the units are always

running at least at their minimum output plus the required down reserve and are unable to be turned

off except for planned or unplanned outages. This is required to ensure appropriate down-reserves,

voltage support and frequency, which cannot be modeled directly using the production simulation

software.

On Oahu, the baseload units include AES, Kalaeloa CC, and Kahe 1-6. In addition, Waiau 7 and 8 are

required to support the Oahu transmission system voltage between the generation area in the

southwest and the load are in the southeast. In addition, several units followed fixed daily or weekly

schedules of availability;

Kalaeloa CC: The Unit 1 GT (KALKAL1) is taken down every week from 9pm on Friday to 9am on Saturday for wash and maintenance and Unit 2 GT (KALKAL2) is taken down from 9pm on Saturday to 9am on Sunday.

The Airport Biodiesel unit (AIRDSG8) is taken down and is unavailable all day Saturday and Sunday each week.

Honolulu 8 & 9 are unavailable each night during off-peak hours from 11pm to 8am.

On Maui the fixed operating schedules include M141516 or M1718 or M1819, K3 and K4. In addition,

K1 and K2 were assumed to be must-run during the hours of 2pm and 11pm daily and alternate

operation daily. Additional operating constraints for the Maalaea plant were also captured. M4 – M9

were assumed to be unavailable nightly from 11pm to 7am. These fixed operating schedules are only

an approximation of the actual operating schedules used by grid operators, which typically vary day-

by-day depending on real-time system loads and other needs.

In future operating scenarios with high levels of renewable penetration, conventional baseload units

may need to be cycled more than historical operations. As a result, investing in system or unit

modifications to remove the operating constraints highlighted above may lead to reduced renewable

curtailment, and lower operating costs. However, the capital costs associated with the required

modifications need to be weighed against the benefits from reduced production costs. In order to

simulate modified operating schemes, sensitivity analyses were conducted by modifying the fixed

operating schedules. For more information on these sensitivities, see Chapter 5.

2.5.2 Planned Outages and Maintenance Schedules

In addition to the random and forced outages highlighted in Section 2.4 and the weekly availability

schedules highlighted in Section 2.5.1, the production simulation modeling includes extended

outages for planned unit maintenance and overhaul. Typically these extended outages take multiple

weeks to complete and are important assumptions when modeling grid operations. The planned

maintenance schedules can be found in the Table 2-7. In addition, Table 12-1 and Table 12-2 in the



appendix provide a complete weekly outage schedule for the system, including all planned, forced,

and random outages.

Table 2-7: Planned Maintenance Schedule

2.5.3 Quick-Start Capability

In the production simulation modeling, a unit must be committed in the commitment process in

order to be online and available to serve load during the real-time (RT) dispatch process. Once

committed the units must be dispatched at least at their minimum load level. An exception to this

rule is made for quick-start capable plants. In the production simulation modeling, these units (also

referred to as peakers) are ones that can be started and brought to full load in at least 10 minutes.

The peaking units are available during the RT dispatch by committing them each hour and assuming

a minimum load of zero for these units, even if not actually committed during the scheduling process.

This modeling allows the peaking units to alleviate problems associated with forecast error from

renewables. For example, if 200 MW of wind energy is expected to show up during an hour, the

commitment process will take that forecast into account. However, if only 100 MW of wind shows up

during the RT dispatch process, then the system is short by 100 MW. The peaking units are the only

ones available to make up that discrepancy in real-time. As a result, the peaking units are likely to

operate a very few number of hours, but are crucial to accurately modeling high renewable

scenarios. For a list of units that are quick-start capable, see Table 2-5 and Table 2-6.

UnitNumber of

WeeksDates Unit

Number of

WeeksDates

CIPCT1 0.7 April 26th to May 1st M1 2.6 January 1st to January 19th

HONH8 7.6 May 3rd to June 26th M2 2.6 February 1st to February 19th

HONH9 1.6 March 29th to April 10th M3 7.9 February 1st to March 26th

KAHK1 1.7 February 15th to February 27th M10 7.6 September 1st to October 24

KAHK2 1.7 January 18th to January 30th M11 7.4 July 1st to August 23rd

KAHK3 14.4 August 16th to November 27th M141516 3.7 April 1st to April 27th

KAHK4 3.6 July 12th to August 7th M171819 3.6 May 1st to May 26th

KAHK5 17.4 March 8th to July 10th M1718 1.6 May 1st to May 12th

KAHK6 1.4 January 4th to January 14th K1 3.6 November 1st November 26th

KALKAL1 1.9 February 1st to February 14th K2 3.6 January 1st to January 26th

KALKAL2 4.1 February 8th to March 7th K3 3.6 June 1st to June 26th

KALKAL3 5.1 February 1st to March 7th K4 3.6 March 1st to March 26th

WAIW10 6.6 March 8th to April 24th

WAIW3 1.7 March 15th to March 27th

WAIW4 1.7 May 3rd to May 15th

WAIW5 2.4 August 2nd to August 19th

WAIW6 2.4 September 6th to September 23rd

WAIW7 1.7 January 18th to January 30th

WAIW8 5.7 November 1st to December 10th

WAIW9 0.6 October 26th to October 30th

Oahu Unit Planned Maintenance Maui Unit Planned Maintenance



2.5.4 Independent Power Producers

On the Oahu and Maui County systems a variety of Independent Power Producers (IPPs) provide

energy to the grid based on existing operating agreements, independent of economic dispatch.

These units also do not add to total production costs.

HC&S: Between the hours of 7am to 9pm Monday through Saturday, HC&S provides a constant 13 MW of energy. During the hours of 9pm and 7am, and all day Sunday, HC&S provides 9 MW of energy.

HPower: Modeled to follow a fixed dispatch schedule of 73 MW every hour.

Honua Waste: modeled to follow a fixed dispatch schedule of 6 MW every hour.

In addition, the AES and Kalaeloa plants are IPPs, but their cost structure is included in the economic

dispatch. Therefore, the AES and Kalaeloa plant’s utilization is determined based on the model’s

production cost optimization.

2.5.5 Combined Cycle Plant Modeling

The two combined cycle plants on the system, Kalaeloa CC on Oahu and Maalaea CC on Maui, are

modeled with unique configurations in order to simulate actual operation. Kalaeloa was modeled as

three separate units; Unit 1 (CT + ½ ST) rated for 67-90 MW, Unit 2 (CT + ½ ST) rated for 67-90 MW,

and Unit 3 rated for 28 MW and only available when Units 1 and 2 are operating at max capacity. In

addition, Kalaeloa operates in single train mode (67-90 MW) for at least five hours before starting

dual train mode (134-180 MW). The heat rate curves for Units 1 and 2 were based on the dual-train

configuration efficiency. This was necessary to accurately capture the heat rate of the plant when

the two units are both in operation (i.e. dual-train combined cycle configuration). As a result, when

the individual units are operated independently, actual heat rate is expected to be higher (less

efficient operation) than the model results may suggest.

On Maui, the Maalaea CC units are also modeled with a unique configuration. The combined cycle

plant was broken into three sets of units; M141516, M1718 and M171819. M141516 is modeled as a

single 53 MW unit, available all of the time. The rest of the combined cycle plant is modeled as two

separate groups of units. M171819 is modeled as a 52.8 MW unit that is available only during on-

peak hours. During off-peak hours the unit is composed only of M1718, with an available capacity of

27.1 MW. This allows the model to accurately simulate the cycling utilization of the CC plant.



2.6 Wind & Solar Plants

Included in the Oahu base case is 100 MW of wind, 100 MW of solar, plus the 200 MW of wind

located on Lanai. On Maui there is 72 MW of wind and 15 MW of solar. Table 2-8 shows the installed

wind and solar capacity in the Base Case. See Chapter 3 for a discussion about renewable additions

in future scenarios. The second column shows the plants total installed capacity, the third column

shows the available capacity factor, while the fourth column shows the annual available energy.

Available energy represents the maximum potential energy the plant can produce in a single year,

assuming no curtailment. The annual energy was developed using hourly wind and solar profiles

discussed in the following section. Centralized solar represents solar units that can be controlled by

the grid operators whereas distributed solar cannot be dispatched or curtailed.

Table 2-8: Base Case Wind & Solar Plants

2.6.1 Wind & Solar Profiles

Unlike thermal plants, wind and solar plants are modeled as load modifiers. This means that each

wind and solar plant is given an “8760” hourly profile based on resource availability. The wind and

solar profiles were provided by AWS Truepower and used in previous modeling work for Hawaii

including the Hawaii Solar Integration Study (HSIS) and the Oahu Wind Integration and Transmission

Study (OWITS). AWST provided 10-minute and 2-second datasets for modeled wind and solar plants

based on observed weather patterns in 2007 and 2008. Both the 10-minute and 2-second datasets

were used for developing the operating reserve requirements necessary to cover expected wind and

solar variability (see the following section for more information on the reserve analysis). After the

reserve analysis was completed, data was then aggregated into hourly data for use in the production

simulation model. Each wind and solar plant was given a unique operating profile, which was held

constant throughout the study period.

Capacity

(MW)

Available

Capacity

Factor

Available

Energy

(GWh)

Capacity

(MW)

Available

Capacity

Factor

Available

Energy

(GWh)

Kawailoa Wind 70 42% 255 KWP I Wind 30 49% 129

Kahuku Wind 30 39% 103 KWP II Wind 21 49% 90

Lanai Wind Addition 200 52% 902 Auwahi Wind 21 48% 88

Oahu Centralized Solar 60 22% 117 Maui Distributed Solar 15 19% 25

Oahu Distributed Solar 40 18% 63

Total Wind 300 48% 1260 Total Wind 72 49% 307

Total Solar 100 21% 180 Total Solar 15 19% 25

Oahu Wind & Solar Maui County Wind & Solar



2.6.2 Forecasting

Each wind and solar plant is also given a 4-hour forecast profile for each hour of the year

representing the expected wind and solar resource availability in MW. The forecast is used during the

commitment process to schedule the thermal fleet. A 4-hour forecast was used for wind and solar

plants because the commitment of cycling units requires about a 4-hour advance notice.

Figure 2-4 shows the aggregated hourly forecast and real-time energy profiles of each wind and

solar plant for a week with high wind penetration. These represent the input shapes only, not actual

dispatched energy, which may vary due to curtailment.

Figure 2-4: Weekly Snapshot of Wind & Solar Forecast and Real-Time Profiles

During the dispatch process separate real-time wind and solar profiles are used. Due to the

variability in wind and solar forecast error, the thermal units are committed against a net load profile

that is determined by the system load profile and the forecasted wind and solar resource profiles.

Figure 2-5 shows an annual duration curve of forecast error in Scenario 1 and Scenario 7. Positive

forecast errors represent hours where the actual, real-time, wind and solar available energy is

greater than the forecasted energy. Negative forecast errors represent hours where the actual, real-

time, wind and solar available energy is less than the forecast energy.

Figure 2-5: Wind & Solar Forecast Error Duration Curves between Scenario 1 and 7



Positive forecast error is less problematic from a grid stability perspective because a worst-case

scenario only causes additional curtailment due to over-commitment. The result of this is captured

as lost economic value, but the grid remains stable. The negative forecast error is potentially more

problematic because it could represent time periods of under-commitment. In this scenario, thermal

units must make up the shortfall in wind and solar generation. As a result, there must be enough up-

range on the operating units, or additional quick-start capacity for the grid to operate.

2.6.3 Curtailment

As zero variable cost resources, wind and solar energy is always utilized if possible. However, there

are times when accepting all of the available wind and solar energy is not possible due to constraints

on the system. These constraints could include fixed operating schedules, operating reserve

requirements, down-regulation requirements, transmission limits, and excess commitment due to

forecast error. When all available wind and solar energy for a given hour cannot be accepted by the

grid, the system will curtail (spill) excess wind and solar energy. From a total curtailment standpoint, it

does not matter which individual wind or solar plant is curtailed first, but for the purposes of this

study a predefined curtailment order is used in order to assess the impact of curtailment on each

project.

Table 2-1 shows the curtailment order for all scenarios analyzed. When curtailment is required, units

with a lower curtailment order will be curtailed first. If curtailment is required from more than one

plant, the model will select the next wind or solar plant with available energy. The curtailment order

listed in Table 2-9 is based on current contractual terms or assumptions made for future resources.

Note that the Lanai and Molokai wind units have the same curtailment order, which will result in

equal curtailment across the wind plants. In addition, distributed, roof-top solar and the firm

renewable energy cannot be curtailed. Curtailment is discussed in detail throughout this report;

please see following chapters for a more detailed discussion.

Table 2-9: Wind and Solar Curtailment Order

Note: Small hydro and FIT projects were not modeled in this study.

Unit ScenarioCurtailment

Order

Maui 100 MW Wind Addition Scen 8 - 10 1

Molokai 200 MW Wind Addition Scen 5 - 10 2

Lanai 200 MW Wind Addition Scen 1 - 10 2

Oahu Centralized Solar Scen 1 - 10 3

KWP II Wind Scen 1 - 10 4

Kawailoa Wind Scen 1 - 10 5

Auwahi Wind Scen 1 - 10 6

KWP I Wind Scen 1 - 10 7

Kahuku Wind Scen 1 - 10 8

Oahu Distributed Solar Scen 1 - 10 N/A

Maui Distributed Solar Scen 1 - 10 N/A

Maui Firm Renewable Addition Scen 2,3,6,7,9,10 N/A



2.7 Reserves

2.7.1 Up Reserve Requirements

The production simulation modeling takes into account operating reserve constraints to simulate the

precautions a grid operator takes to prepare for unexpected events such as a generator trip or

variation in wind and solar resource availability. Current practice requires that all operating reserves

on the Hawaii system be covered by traditional thermal sources. The production cost simulations

ensure that for any given hour the required operating reserves are being carried by the system’s

thermal fleet based on the wind and solar forecast. The total reserve scheme is defined by several

different components:

1. Contingency Reserve: MWs to cover for the largest system contingency 2. Operating Reserve: MWs to cover for the variability of wind and solar power, including both

spinning and non-spinning reserves, a. Spinning Reserve: Available headroom (MWs) from committed thermal units, which

are considered available immediately when needed and automatically controlled by governor and AGC.

b. Non-spinning Reserve: Available MWs capacity from quick-start units; the availability is dependent on the reaction time of operator to physically start the units and is limited by start-up time and ramp rate of the units.

Based on the above definitions, two important equations can be formulated:

(2-1)

(2-2)

The contingency reserves outlined above are fairly straightforward and practiced across the industry.

For the Oahu system, the single largest contingency is the loss of AES generator at 185 MW. In

scenarios with the DC cable, the largest contingency may change to the flow on the DC cable.

However, since the flows on the DC cables are less than 200 MW during most hours of the year, the

185 MW contingency was deemed an appropriate approximation to continue throughout this study.

On the Maui County system the single largest contingency is currently NOT covered with contingency

reserves due to the additional costs required to do so. Instead, the Maui County system uses a load-

shedding scheme to cover large contingencies.

Operating reserves represent a much more complicated form of reserves and one that is less

consistently applied across the industry. As seen in Equation 2-2, operating reserves are required to

be larger than the expected renewable variation. This variation is a function of power output and is

dependent on the time interval of consideration. This study developed operating reserve

requirements based on the drops in wind and solar output over multiple time windows up to one

hour. The operating reserves assumed in this study are designed to capture 99.9% of the sub-hourly

drops in wind and solar output.

The reserve strategy is dependent on the hourly wind and solar variability. As a result, each scenario

has a specific reserve requirement based on the scenario’s installed renewable capacity. For more

discussion on the reserve strategy for each scenario, see Section 3.4.



2.7.2 Down Reserve Requirements

Adequate down reserves are required to provide the necessary operating range on thermal units in

the event that they must decrease their output in response to a loss of load contingency event. These

reserves are classified as contingency reserves for load rejection on the system. HECO performed an

analysis of Oahu feeder loading and previous load rejection events and determined that up to 140

MW could be lost during a single daytime event and 90 MW could be lost as night. Currently, all

down-reserves are carried by traditional thermal generators. On Oahu, the operating practice

modeled in this study requires that in hours when the thermal units may be backed down (especially

during off-peak periods), the system operators will always ensure that the total dispatched output is

at least 90 MW greater than their minimum power level. For Maui County, the total down reserves

carried is 7 MW during all hours.

In order to capture the down regulation requirements of the HECO and MECO systems, generating

unit dispatch minimums were increased above the minimum power limit. On Oahu, the 90 MW down

reserve requirement is distributed equally across nine units (AES, Kahe #1-6, Waiau 7&8, and

Kalaeloa). For example, the Kahe 1 unit on Oahu is able to turn down to 32.5 MW, however the

minimum dispatch power the generator can produce in the simulations 41.5 MW, thus ensuring that

when the unit is online, it is providing at least 9 MW of down reserves. For Maui County the down

reserves are carried at the Maalaea combined cycle plant. The total amount of down reserves carried

by unit is listed in Table 2-10 for Oahu and Maui County.

Table 2-10: Down Reserves by Unit

In current grid operations on Oahu, the amount of down-reserves is typically not problematic

because thermal units rarely sit at, or close to, their minimum power level. However, in a future

scenario with high wind or solar penetration, carrying adequate down-reserves is more important

because thermal generators will be backed down in an effort to accept more renewable generation.

The impact of operating the thermal generating fleet at their minimum levels during most hours of

the year was not assessed in this study. This could result in more O&M expense as well as changes in

operating practice such as ramping baseload units to their maximum levels occasionally to avoid

clogging of exhaust systems. It is likely that in a high renewable scenario, the down reserve

requirement may be a binding constraint during many hours of the year. Investing in system or unit

modifications to remove this constraint could result in potentially lower costs associated with system

Unit Name

Technical

Minimum

Power

Down

Regulation

Min Capacity

with Down

Regulation

Unit Name

Technical

Minimum

Power

Down

Regulation

Min Capacity

with Down

Regulation

AES 63.0 9.0 72.0 M14, M15, M16 34.0 1.0 35.0

Kahe 1 32.5 9.0 41.5 M17, M18 16.5 2.0 18.5

Kahe 2 32.7 9.0 41.7 M17, M18, M19 37.8 4.0 41.8

Kahe 3 32.3 9.0 41.3

Kahe 4 32.3 9.0 41.3

Kahe 5 50.7 9.0 59.7

Kahe 6 50.0 9.0 59.0

Waiau 7 23.0 9.0 32.0

Waiau 8 23.0 9.0 32.0

Kalaeloa 1 65.0 4.5 69.5

Kalaeloa 2 65.0 4.5 69.5

Oahu Down Reserves (Total = 90 MW) Maui County Down Reserves



operation, however the cost of the system or unit modifications must be weighed against the

benefits associated with reduced operating cost. To account for this, sensitivity analyses were

conducted in order to demonstrate the economic benefits associated with lower minimum turndown

levels. For more information on relaxing the down reserve requirements, refer to Chapter 5.

Hawaii Stage 2 Interconnection Study Scenario Overview


3 Scenario Overview The objective of this study is to quantify and compare the operational performance and costs of the

combined Oahu and Maui County system under a variety of possible future expansion scenarios.

Renewable energy alternatives include Wind, Solar and Firm Renewables (i.e. Geothermal) in various

combinations. As initial results were analyzed new combinations were considered. The final list of

scenarios was then reorganized and is presented here in a more logical, orderly fashion.

3.1 Renewable Capacity Additions

Scenario 1 closes the interconnection between the two Base Cases (Oahu and Maui County) to

determine the impact of the combined operation of the two systems. All other scenarios build onto

this initial interconnected system. The base operating assumptions are held through the initial

analysis presented in Chapter 4. Chapter 5 then examines the impact of changes to some of the

operational practices.

Table 3-1 shows the list of renewable capacity additions for the ten scenarios developed. Scenarios

1 through 4 have a single HVDC connection between Oahu and Lanai, nominally 200 MW, along with

the 200 MW wind plant on Lanai. Scenarios 2 and 3 add 50 and 100 MW respectively of firm

renewable energy in Maui to the initial Scenario 1. Scenario 4 adds 260 MW of solar capacity on

Oahu to Scenario 1.

Table 3-1: Renewable Capacity (MW)

Scenarios 5 through 10 have two HVDC connections between Oahu and Maui County along with 200

MW of wind generation on both Lanai and Molokai. Scenario 8 adds an additional 100 MW of wind

generation on Maui. The remaining scenarios add the 50 MW and 100 MW firm renewable to

Scenarios 5 and 8 respectively. Figure 3-1 and Figure 3-2 show the capacity additions graphically.

Lanai Molokai

ScenarioOahu-

Lanai

Oahu-

Molokai

Wind

(MW)

Solar

(MW)

Firm RE

(MW)

Wind

(MW)

Solar

(MW)

Firm RE

(MW)

Wind

(MW)

Wind

(MW)

Wind

(MW)

Solar

(MW)

Firm RE

(MW)

Scenario 1 200 100 100 79 72 15 13 200 372 115 92

Scenario 2 200 100 100 79 72 15 63 200 372 115 142

Scenario 3 200 100 100 79 72 15 113 200 372 115 192

Scenario 4 200 100 360 79 72 15 13 200 372 375 92

Scenario 5 200 200 100 100 79 72 15 13 200 200 572 115 92

Scenario 6 200 200 100 100 79 72 15 63 200 200 572 115 142

Scenario 7 200 200 100 100 79 72 15 113 200 200 572 115 192

Scenario 8 200 200 100 100 79 172 15 13 200 200 672 115 92

Scenario 9 200 200 100 100 79 172 15 63 200 200 672 115 142

Scenario 10 200 200 100 100 79 172 15 113 200 200 672 115 192

HVDC Maui Renewables Total SystemOahu Renewables



Figure 3-1: Renewable Capacity (MW)

Figure 3-2: Scenario Tree



3.2 Available Renewable Energy

The previous section considered the capacity of the renewable resource additions. Table 3-2 and

Figure 3-3 show the corresponding renewable energy that is available under each scenario. The

total energy available from wind, solar and firm renewables ranges from 19% to 41% of the total

system load for the ten scenarios. It is important to note that this is the available energy and not

necessarily the delivered energy. System operating constraints generally cause some curtailment to

occur. The results presented in Chapters 4 and 5 will quantify the curtailment in each scenario and

how that can be effected by changes in operating practices.

Another aspect that can be seen is that the capacity factors are not the same for the different types

of renewable generation. Even though Scenario 4 has more renewable capacity than Scenario 3 it

has less renewable energy. This is because Scenario 3 had 100 MW of firm renewable energy with

an assumed 100% capacity factor while Scenario 4 had 260 MW of solar capacity with roughly a

20% capacity factor. The wind capacity factors ranged from 43% to 51% depending on the location.

Table 3-2: Available Renewable Energy (GWh)

Lanai Molokai

ScenarioWind

(GWh)

Solar

(GWh)

Firm RE

(GWh)

Wind

(GWh)

Solar

(GWh)

Firm RE

(GWh)

Wind

(GWh)

Wind

(GWh)

Wind

(GWh)

Solar

(GWh)

Firm RE

(GWh)

Total

(GWh)

Total

(%)

Scenario 1 358 180 647 307 25 96 902 1,568 205 743 2,516 27%

Scenario 2 358 180 647 307 25 534 902 1,568 205 1,181 2,953 31%

Scenario 3 358 180 647 307 25 970 902 1,568 205 1,617 3,390 36%

Scenario 4 358 636 647 307 25 96 902 1,568 661 743 2,971 32%

Scenario 5 358 180 647 307 25 96 902 754 2,322 205 743 3,270 35%

Scenario 6 358 180 647 307 25 534 902 754 2,322 205 1,181 3,707 39%

Scenario 7 358 180 647 307 25 970 902 754 2,322 205 1,617 4,144 44%

Scenario 8 358 180 647 734 25 96 902 754 2,749 205 743 3,696 39%

Scenario 9 358 180 647 734 25 534 902 754 2,749 205 1,181 4,134 44%

Scenario 10 358 180 647 734 25 970 902 754 2,749 205 1,617 4,571 49%

Maui Renewables Total SystemOahu Renewables



Figure 3-3: Available Renewable Energy (GWh)

3.3 DC & AC Interconnection

Figure 3-4 shows the HVDC and AC interconnections between the islands for Scenarios 5 through 10.

The first four scenarios are similar but do not include the HVDC connection between Molokai and

Oahu. All of the scenarios include the AC interconnections between Molokai, Lanai and Maui which

were added as part of the Maui County Base Case.

Figure 3-4: System Interconnections with Two HVDC Cables

Although the HVDC cables were assumed to be nominally 200 MW, no limits were assumed in the

modeling. Similarly, the AC cables connecting the islands in Maui County were assumed to have

similar impedance characteristics but no power transfer limits were enforced.

Oahu-Maui Interconnection MapScenarios with 2 HVDC Cables

Maui

Molokai

Oahu

Maui

County

Lahaina

69 kV

Lanai

Iwilei

138 kV



3.4 Reserve Strategy

As described in Chapter 2 the total operating reserves are a function of the contingency reserves and

the variability of the wind and solar generation. Figure 3-5 shows the variable operating reserve

component for the Oahu Base Case as a function of total wind and solar generation. These curves

were developed for both day and night since the variability is different in those two time frames. Also

shown on these curves is the operating reserve for the Oahu Only case which does not include the

200 MW of wind generation on Lanai. Figure 3-6 shows a similar curve for the Maui County Base

Case. The upper curve represents the original operating reserve requirements and the lower curve is

the final requirements after taking credit for the Maui, Lanai and Molokai quick start generation and

the KWP2 Battery Energy Storage System (BESS).

Note: This chart includes operating reserves only. In addition 185 MW of contingency reserve is carried during all hours.

Figure 3-5: Oahu Base Case Variable Operating Reserve versus Wind and Solar Generation

Note: Note: This chart includes operating reserves only. In addition 185 MW of contingency reserve is carried during all hours.

Figure 3-6: Maui County Operating Reserve versus Wind and Solar Generation



Interconnecting the Oahu and Maui County systems has two primary impacts on operating reserves:

1. The reserves are combined into one pool, so the total reserve requirement is smaller than the sum of the individual reserve requirements for Oahu and Maui.

2. The system operator can take advantage of the increased diversity and availability of the combined system resources.

The resulting combined system operating reserves as a function of the wind and solar generation

(excluding contingency reserves) is shown in Figure 3-7. Note that many of the scenarios have the

same operating reserve characteristics since the Firm Renewables are considered dispatchable and

do not contribute to the variability. These curves are then combined with the corresponding

chronological wind and solar generation to develop the chronological operating reserve

requirements for each scenario. Figure 3-8 shows annual duration curves of the hourly operating

reserve requirements for the ten scenarios examined.


Figure 3-7: Combined System Operating Reserve versus Wind and Solar Generation




Figure 3-8: Combined System Operating Reserve Duration Curves by Scenario

Table 3-3 shows the maximum operating reserves for each scenario. The data is shown graphically

in Figure 3-9.

Table 3-3: Maximum Operating Reserves by Scenario.

ScenarioContingency

(MW)

Operating

(MW)

Contingency

(MW)

Operating

(MW)

Contingency

(MW)

Operating

(MW)Total (MW)

Scenario 1 185 98 0 6 185 104 289

Scenario 2 185 98 0 6 185 104 289

Scenario 3 185 98 0 6 185 104 289

Scenario 4 185 146 0 6 185 152 337

Scenario 5 185 174 0 6 185 180 365

Scenario 6 185 174 0 6 185 180 365

Scenario 7 185 174 0 6 185 180 365

Scenario 8 185 208 0 6 185 214 399

Scenario 9 185 208 0 6 185 214 399

Scenario 10 185 208 0 6 185 214 399

Oahu Maui County Total System



Figure 3-9: Maximum Operating Reserves by Scenario

Hawaii Stage 2 Interconnection Study Results


4 Results This chapter presents results of technical analysis for all 10 study scenarios, assuming the existing

operating practices for Oahu and Maui baseload units as described in Chapters 2 and 3. In

particular:

Maui baseload units are assumed to follow the existing operating schedules (also referred to as Maui Must-Run Rules in the MAPS production cost simulations)

Oahu baseload units are assumed to have their existing minimum power limits, Pmin. Reduced Pmin limits on selected Oahu units were also considered, and those results are presented in Chapter 5

Oahu baseload units are assumed to follow existing operating schedules (also referred to as Oahu Must-Run Rules in the MAPS production cost simulations)

Most of the analysis in this chapter is derived from production cost simulations using GE-MAPS. In

this analysis, production costs include system operating costs for thermal power plants (fuel, start-up

costs, variable O&M). Production costs do not include the PPA costs for wind, solar, or firm renewable

energy. Chapter 8 presents the results of cost-benefit analysis, where capital costs for wind, solar

and firm renewable resources are included.

This chapter is organized as follows:

Section 4.1 examines the operational and economic impacts of interconnecting Maui, Molokai and

Lanai. Section 0 describes the analytical results for the Base Case and Scenario 1 in detail,

comparing isolated versus interconnected operation of the Oahu and Maui County systems. The

analytical methods are also described. The subsequent sections show analytical results for all 10

study scenarios together so they can be compared to each other as well as to the Base Case.

4.1 Interconnecting Maui, Molokai and Lanai

The Maui power system is served by a diverse mix of generating resources, including combined cycle

(baseload), diesel, biomass, oil-fired steam, wind, and solar. Lanai and Molokai are each served by a

fleet of diesel generators. Table 4-1 shows production cost simulation results for Lanai, Molokai and

Maui operating as separate power grids and operation together as one interconnected power grid.

The total annual production cost for Lanai, Molokai and Maui is $7.1M +$7.4M +$185M = $199.5M. If

the islands are interconnected and operated as one integrated system, the annual production cost is

$188M, which is $11.5M lower than for separate operation. Some of this reduction in production cost

is due to an 18 GWh decrease in curtailment. However, the additional accepted wind and solar

generation must be paid for by the utility according to existing PPA structures on Maui. As a result,

the additional wind and solar generation costs $3.5 million (see Chapter 8 for more discussion on PPA

prices). Therefore, the total annual savings when interconnecting the Maui County system is $8

million per year. Assuming a fixed charge rate (FCR) of 14%, the break-even cost of the AC cable

infrastructure would be $57 million.

Table 4-2 shows delivered wind and solar energy for both separate and interconnected operation.

Delivered wind and solar energy increases from 84% to 89% with interconnected operation.



Table 4-1: Annual Production Costs and Energy for Maui County System

Table 4-2: Wind and Solar Energy Delivered and Curtailed for Maui County System

Maui

OnlyLanai Only

Molokai

Only

Sum of

Maui, Lanai,

Molokai

Maui

County

Base

210 5 6 221 219

233 11 15 259 259

15 15 15

72 72 72

13 13 13

18 18 8

332 332 332

1,271 26 31 1,328 1,324

278 278 296

97 97 97

896 26 31 953 931

Thermal 896 896 929

Wind & Solar 278 278 296

Firm Renew 97 97 97

26.0 26.0 0.0

30.9 30.9 1.5

185 185 188

7.1 7.1 0.01

7.4 7.4 0.37

185 7 7 200 188

11.5

48.9 48.9 52.4

3.5

8.0

Available Wind & Solar Energy (GWh)

Peak Load (MW)

Thermal Capacity (MW)

Solar Capacity (MW)

Wind Capacity (MW)

Firm Renewable Capacity (MW)

Max Reserve Requirement (MW)

Total Generation (GWh)

Wind & Solar Generation (GWh)

Firm Renewable Generation (GWh)

Thermal Generation (GWh)

Maui

Lanai

Molokai

Generation

by Island

(GWh)

Maui

Lanai

Molokai

Production

Cost by Island

(M$)

Total Annual Production Cost (M$)

Annual Production Cost Savings (M$)

Annual PPA Cost (M$)

Annual Change in PPA Cost (M$)

Total Annual Savings (M$)

Maui

Only

Maui

CountyDelta

Maui

Only

Maui

CountyDelta

KWP I Wind 30 129 127 128 1 99% 100% 1%

KWP II Wind 21 90 50 62 12 56% 69% 13%

Auwahi Wind 21 88 75 81 6 86% 92% 6%

Maui Distributed Solar 15 25 25 25 0 100% 100% 0%

Total Wind & Solar 87 332 277 296 19 84% 89% 5%

Capacity

(MW)

Available

Energy

(GWh)

Delivered Energy (GWh) Delivered Energy (%)



Figure 4-1: Spinning Reserve Requirements for Maui Alone and Maui County Interconnected

Figure 4-2: Quick-Start Capability for Maui Alone and Maui Combined with Lanai and Molokai



Reserve requirements change when the systems are interconnected, and this also contributes to the

reduction in operating costs. Reserves are provided by a combination of spinning reserves (from

committed/running units) and non-spinning reserves (from quick-start units). Figure 4-1 shows that

spinning reserves for the interconnected system are considerably lower than for the Maui system

alone. This is because Maui has a limited quick-start capacity and the quick-start units require more

than 10 minutes to start and deliver power. The diesel units on Lanai and Molokai are capable of

starting faster than the units on Maui (see Figure 4-2). In an isolated system, Maui requires 20

minutes to turn on a quick-start unit. This includes 10 minutes allocated to operator decision-making

and an addition 10 minutes for the unit to be brought up to speed. On Lanai and Molokai, the diesel

units start faster and can begin producing some power shortly after the operator’s decision period.

The Lanai diesel units become the interconnected system’s first resource during events that require

flexible, quick-start generation, followed by the Molokai diesels. As a result, when the Lanai and

Molokai diesel units are added to the Maui generation fleet for interconnected operation, the need for

spinning reserves declines because more of the total reserve requirement can be satisfied by non-

spinning reserves from quick-start units.



4.2 Base Case and Scenario 1

4.2.1 Total System Energy Production

Table 4-3 shows production cost (fuel cost + VOM + start cost) simulation results for the Base Cases

and Scenario 1. The second column shows production costs for the Maui County Base Case (Maui

and Lanai and Molokai operating together as one system, but not connected to Oahu). The third

column shows production costs for the Oahu Base Case (Oahu system with 200 MW of wind capacity

on Lanai, but not interconnected with the Maui County system). The fourth column shows the sum of

the production costs for the Maui County and Oahu Base Case systems. The last column shows

production costs for Scenario 1, where the Maui County and Oahu systems are interconnected and

operating as one combined system. Comparing the two right-hand columns yields the following

observations for interconnected operation versus separate operation:

Thermal generation on Oahu increases by 166 GWh while thermal generation in Maui County decreases by 173 GWh. The AES, Kalaeloa and Kahe units on Oahu are more economical than the baseload units on Maui.

Wind and solar generation on Oahu decreases by 13 GWh while wind and solar generation in Maui County increases by 30 GWh. (Note that the 200 MW wind plant on Lanai is part of the Oahu system and in Scenario 1 that wind plant is first in the curtailment order.)

Molokai and Lanai generation increases. Some of the Lanai and Molokai diesel units have lower operation costs than the cycling and peaking units on Maui.

The annual production cost for operating the Maui County and Oahu systems separately is $1,043M. The production cost for Scenario 1 (interconnected operation) is $1,035, a difference of $8M per year.



Table 4-3: Annual Production Costs and Energy for Base Cases and Scenario 1

Note: Production costs do not include PPA costs for wind, solar, or firm renewable energy.

Maui

County

Base

Oahu

Base

Sum of

Maui

County &

Oahu

Scenario

1

219 1,263 1,482 1,473

259 1,699 1,958 1,958

15 100 115 115

72 300 372 372

13 79 92 92

8 308 316 289

332 1,441 1,773 1,773

1,324 8,019 9,343 9,358

296 1,315 1,611 1,628

97 647 744 744

931 6,056 6,987 6,986

Thermal 6,056 6,056 6,222

Wind & Solar 1,315 1,315 1,302

Firm Renew 647 647 647

Thermal 929 929 756

Wind & Solar 296 296 326

Firm Renew 97 97 97

0.0 0.0 2.3

1.5 1.5 5.9

855 855 878

188 188 155

0.01 0.01 0.7

0.37 0.37 1.2

188 855 1,043 1,035

8.1

Available Wind & Solar Energy (GWh)

Peak Load (MW)

Thermal Capacity (MW)

Solar Capacity (MW)

Wind Capacity (MW)

Firm Renewable Capacity (MW)

Max Reserve Requirement (MW)

Total Generation (GWh)

Wind & Solar Generation (GWh)

Firm Renewable Generation (GWh)

Thermal Generation (GWh)

Generation

by Island

(GWh)Maui

Lanai

Molokai

Oahu

Production

Cost by Island

(M$)

Oahu

Maui

Lanai

Molokai

Total Annual Production Cost (M$)

Annual Production Cost Savings (M$)



Figure 4-3: Energy Production by Power Plant for Scenario 1

Figure 4-3 shows energy by power plant for Scenario 1. This provides a total overview of how the

energy resources on each island are contributing to the operation of the entire interconnected

system.

4.2.2 Thermal Unit Operation

Figure 4-4 shows how the capacity factors of the thermal plants change from the Base Case (isolated

operation) to Scenario 1 (interconnected operation). Table 4-4 summarizes the annual energy output

for the various types of generation resources. On Oahu, energy from baseload and cycling units

increases while energy from peaking units decreases. In Maui County, energy from baseload and

cycling units decreases while energy from peaking units increases. Energy from Lanai and Molokai

Total Generation: 8,171 GWhOahu

Total Generation: 1,178 GWhMaui County



thermal units also increases, as they are more economically attractive for peaking in comparison to

other units on Maui and Oahu.

Figure 4-4: Capacity Factors of Generating Plants for Base Case and Scenario 1

Table 4-4: Generation Utilization for Base Case and Scenario 1

Baseload

Cycling

Peaking

GWh CF GWh CF GWh CF

Oahu Baseload 6,394 59% 6,543 60% 149 1.4%

Oahu Cycling 266 10% 304 11% 38 1.4%

Oahu Peaking 44 2% 22 1% -22 -1.1%

Oahu Wind & Solar 1,315 38% 1,302 37% -14 -0.4%

Oahu Total 8,019 42% 8,170 43% 152 0.8%

Maui Baseload 821 57% 730 50% -91 -6.3%

Maui Cycling 104 17% 18 3% -86 -13.8%

Maui Peaking 2 1% 7 4% 5 2.6%

Maui Wind & Solar 392 35% 423 37% 31 2.7%

Maui Total 1,319 39% 1,178 35% -141 -4.1%

Lanai Thermal 0 0% 2 2% 2 2.4%

Molokai Thermal 1 1% 6 4% 4 3.3%

Maui County Total 1,321 36% 1,186 33% -134 -3.7%

Base Case Scenario 1 Delta



Figure 4-5: Average Annual Hours of Operation and Starts for Thermal Generation, by Type

Figure 4-5 shows the average annual hours of operation and starts for thermal generators for the

Base Case (isolated) and Scenario 1 (interconnected). Operating hours and starts for baseload units

are unchanged, since most of these units are operated on fixed schedules. Operating hours and

starts for Maui cycling and Oahu peaking units decline in Scenario 1, while hours and starts for Oahu

cycling and Maui County peaking units increase.

Base Case Scenario 1 Base Case Scenario 1 Base Case Scenario 1

Oahu Baseload 6,558 6,558 9 9 765 765

Oahu Cycling 1,974 2,243 207 234 10 10

Oahu Peaking 411 179 165 64 2 3

Maui Baseload 4,518 4,518 130 130 35 35

Maui Cycling 2,412 533 281 123 9 4

Maui Peaking 82 434 37 157 2 3

Lanai Peaking 2 184 2 64 1 3

Molokai Peaking 87 328 60 121 1 3

Average Hours Average Starts Average Starts



4.2.3 Emissions

Figure 4-6: Annual Emissions for Base Case and Scenario 1

Figure 4-6 summarizes total NOx, SOx and CO2 emissions for the Base Case and Scenario 1. NOx

declines, SOx increases slightly (likely due to an increase in energy production from the AES coal

plant), and CO2 remains the same.

4.2.4 Curtailment of Renewable Generation

Table 4-5 summarizes the annual available, delivered and curtailed renewable energy for the Base

Case (isolated) and Scenario 1 (interconnected). Curtailment of wind resources on Maui is 30 GWh

lower in Scenario 1. With the interconnection to Oahu, more of that energy is transferred to Oahu.

Curtailment of the Lanai 200 MW wind plant increases by 13 GWh in Scenario 1. This is because of

the assumption that in the interconnected system, the Lanai wind plant would be curtailed before

the existing wind plants on Maui. Overall, curtailment of wind and solar generation is 17 GWh lower

in Scenario 1 than in the Base case. This accounts for most of the reduction in production costs for

Scenario 1 (see Table 4-3), as this this renewable energy displaces thermal generation.

Figure 4-7 shows the average hourly available and delivered wind power by time of day for Scenario

1. Nearly all of the curtailment occurs during the nighttime hours, between 10 pm and 6 am. Figure

4-8 is a duration curve of wind and solar curtailment for the Base Case and Scenario 1. There is not

much difference in the total curtailment. This is primarily due to the assumptions that:

Oahu and Maui baseload units are operated on fixed schedules, per existing practices

Oahu baseload units have existing minimum power limits.

These baseload units cannot be turned off at night. Wind generation forces baseload units on both

Oahu and Maui to operate at minimum power limits at night. Excess wind generation must be

curtailed. The interconnection between the islands does not help reduce curtailment if all units on

both islands are operating at minimum power and cannot be decommitted.

Base Case Scenario 1

NOX Emissions (Tons) 12,469 11,208

SOX Emissions (Tons) 12,142 12,316

CO2 Emissions (K Tons) 6,989 6,997



Table 4-5: Annual Renewable Energy Available and Delivered for Base Case and Scenario 1

Figure 4-7: Average Hourly Available and Delivered Wind Power by Time of Day, Scenario 1

Base

Case

Scenario

1Delta

Base

Case

Scenario

1Delta

Maui County 87 332 296 326 30 36 6 -30

KWP I Wind 30 129 126 129 2 2 0 -2

KWP II Wind 21 90 64 86 22 27 4 -22

Auwahi Wind 21 88 81 87 6 7 1 -6

Maui Distributed Solar 15 25 25 25 0 0 0 0

Oahu 400 1,441 1,315 1,302 -14 125 139 14

Kahuku Wind 30 103 102 103 0 1 1 0

Kawailoa Wind 70 255 248 247 -1 7 8 1

Lanai Wind Addition 200 902 785 772 -13 118 131 13

Oahu Centralized Solar 60 117 117 117 0 0 0 0

Oahu Distributed Solar 40 63 63 63 0 0 0 0

Total Wind & Solar 487 1,773 1,611 1,628 17 161 145 -17

Delivered Energy (GWh) Curtailment (GWh)Available

Energy

(GWh)

Capacity

(MW)



Figure 4-8: Duration Curve of Wind and Solar Curtailment, Base Case and Scenario 1

4.2.5 Interisland Line Flows

Figure 4-9 show annual duration curves of interisland line flows for Scenario 1. The top plot shows

the flow on the DC cable from Lanai to Oahu. The shaded red area represents the power output of

the Lanai wind plant. If the two systems are isolated (Base Case), all of this wind power flows on the

DC cable to Oahu. With the two systems interconnected (Scenario 1), the flow is somewhat lower,

because thermal generation on Maui is partially displaced by more economical thermal generation

on Oahu. The flow from Oahu to Lanai was intentionally limited to 30 MW in this analysis, so that loss

of the DC cable would not create a contingency larger than the loss of a single generating unit on

Maui.

The bottom plot in Figure 4-9 shows flows on the three ac cables interconnecting Maui, Lanai and

Molokai. These traces give an indication of the expected loading on the cables. Note that if one of

the cables experiences an outage, the flows on the other two cables would increase. However,

contingency analysis to quantify contingency flow levels was not in the scope of this study.

Figure 4-10 is a chronological plot of interisland line flows for the week with maximum system load.

The red trace is the power flow from Lanai to Oahu. The shaded area represents the output of the

Lanai wind plant. The profile of the line flow from Lanai to Oahu is similar to the output of the Lanai

200 MW wind plant, but somewhat lower in magnitude. For a period on August 12, the wind plant

output is zero and the DC cable flow changes direction (from Oahu to Lanai). The green trace

represents the flow from Lanai to Maui which changes direction several times but is from Lanai to

Maui for most hours of the week.



Figure 4-9: Interisland Line Flow Duration Curves for Scenario 1

Total Flow

(GWh)

Max Flow

(MW)

Min Flow

(MW)

Avg Flow

(MW)

DC: Lanai to Oahu 665 203 -30 76

AC: Lanai to Maui 139 66 -32 11

AC: Molokai to Maui 64 31 -17 4

AC: Lanai to Molokai 77 36 -17 7



Figure 4-10: Interisland Line Flows for Highest Load Week

4.2.6 Weekly Generation Profiles

The figures in this section illustrate how different types of generation resources work together to

serve load, hour-by-hour, for several selected weeks. Figure 4-11 shows generation profiles for the

week with the highest hourly % penetration of wind and solar generation (on Sunday morning,

delivered wind and solar energy is 40% of total load energy). The lower blue areas represent

baseload generation, the violet bands above represent cycling units, light blue represents peaking

units, green represents wind, and orange (at the top) represents solar. The solid and dotted green

lines near the bottom of the plot represent available and forecasted wind and solar power. The light

blue trace at the bottom represents curtailment, which occurs at night when baseload units are

operating at minimum power and there is more wind energy available than can be absorbed by the

load.

Figure 4-12 shows a similar plot for the week with the highest wind and solar generation, in MW. On

Monday through Thursday, the wind and solar generation is always above 200 MW, and as high as

400 MW for several hours. At noon on Tuesday, total wind and solar generation reaches 433 MW.

Figure 4-13 shows a similar plot for the week with the highest wind and solar forecast error. At mid-

day on Monday, the forecast predicted that wind and solar power would decline. But wind and solar

generation declined faster than the forecast predicted (a forecast error of 219 MW at 3 pm). Peaking

units were committed to make up for the shortfall. The forecast error on Saturday night through

Sunday was over 100 MW. Again, peaking generation was used to help fill the shortfall.



Figure 4-14 shows generation profiles for the week with the highest system load (demand) for

Scenario 1. The bottom of this figure shows the differences in generation output as compared to the

Base Case (isolated operation). The traces show that:

Wind generation in Maui increases and wind generation in the Oahu system decreases (Lanai 200 MW wind plant, due to curtailment order)

Baseload generation on Oahu increases

Use of peakers in Oahu decreases

Solar generation does not change (note that distribution-connected solar cannot be curtailed)

This section has summarized Scenario 1 and compared it to the Base Case (isolated operation). The

following sections of this chapter show analytical results for all 10 scenarios and compare those

scenarios to each other.

Figure 4-11: Scenario 1 Generation Profiles for Week with Maximum Renewable Energy

Penetration (% of Load)



Figure 4-12: Scenario 1 Generation Profiles for Week with Maximum Renewable Generation (MW)

Figure 4-13: Scenario 1 Generation Profiles for Week with Worst Wind & Solar Forecast Error



Figure 4-14: Scenario 1 Generation Profiles for Week with Peak Demand, Showing Changes from

Base Case



4.3 Production Cost Analysis

Table 4-6 shows the annual production costs (fuel cost + VOM + start cost) for the Base Case and all

10 study scenarios. The top section of the table (blue) shows the total system peak load as well as

the MW capacity of installed generation resources for each scenario. The scenarios are arranged so

that renewable generation capacity increases from Scenario 1 to 10. Scenarios 1-4 have one 200

MW DC cable interconnecting Oahu with Maui County; Scenarios 5-10 have two 200 MW cables.

The next section of the table (light blue) shows how much energy from wind, solar, firm renewable

and thermal generation was delivered in each scenario. The middle section of the table (blue)

summarizes energy broken down by island. The section below that (light blue) summarizes

production costs for the thermal generation resources on each island.

The bottom two rows of the table show total annual production cost for each scenario, as well as the

production cost savings relative to the Base Case.

The Base Case and Scenario 1 have the same generation resources. In the Base Case, Oahu and

Maui County are isolated and therefore have separate reserve requirements (higher total reserves,

see 6th row). In Scenario 1, the Oahu and Maui County systems are interconnected and operated as

one system with one common pool of reserves (lower total reserves than the Base case). The total

annual production cost for Scenario 1 is $8M lower than the Base Case. These savings are due to:

Interconnected grid operation which enables dispatch of generation resources on all islands to serve total system load in the most economical manner.

Reduced total system reserve requirements, and

Reduced curtailment of wind and solar resources, primarily on Maui.

Baseload units on Oahu are more economical to operate than the baseload and cycling units on

Maui, so the interconnected scenarios dispatch Oahu resources more and Maui resources less. Table

4-7 shows generation by type and by island for all the scenarios. The differences between separate

and interconnected operation can be seen by comparing data in the columns for the Base Case and

Scenario 1.

Production costs for all scenarios are plotted in Figure 4-15. The height of each bar represents the

total production costs, and the colored segments show the breakdown by generation type. In the

scenarios with higher renewable energy penetration, the renewable generation primarily displaces

Oahu thermal generation. Also, the scenarios with increased levels of firm renewable generation on

Maui (Scenarios 3, 7, 10) show lower utilization of cycling units. Maui baseload generation is nearly

constant across the scenarios, primarily because of the fixed operating schedules of those units.

Figure 4-16 shows delivered energy by type of resource for all the scenarios. The orange, green and

gray segments at the top of each bar represent the annual energy delivered from solar, wind and

firm renewable resources. Total energy from these resources ranges from 2,372 GWh in Scenario 1

to 3,624 GWh in Scenario 10. Significant amounts of renewable energy are also curtailed, especially

in the higher penetration scenarios. The next two sections discuss delivered and curtailed renewable

energy.



Table 4-6: Annual Production Costs and Energy for All Scenarios



Table 4-7: Annual Generation in GWh by Type and by Island for All Scenarios

Figure 4-15: Production Costs for All Scenarios, by Generation Type

One 200 MW DC Cable Two 200 MW DC Cables

Scenario

ScenarioDC

MW

Wind

MW

Solar

MW

Firm RE

MW

1 200 200 0 0

2 50

3 100

4 260 0

5 400 400 0 0

6 50

7 100

8 500 0

9 50

10 100



Figure 4-16: Generation by Type for All Scenarios

4.4 Delivered Renewable Generation

This section describes how much new renewable energy is available in each scenario, how much is

used to serve load, and how much must be curtailed. Note that these scenarios assume existing

baseload thermal unit operating practices.

Figure 4-17 illustrates the total wind, solar and firm renewable energy available and delivered for all

scenarios. The green portion of each bar is delivered wind energy, the orange portion is delivered

solar energy, the dark blue portion is delivered firm renewable energy, and the light blue portion on

top is curtailed energy. The total height of each bar represents the total available wind, solar and

firm renewable energy.

In this analysis, wind energy is curtailed before firm renewables. Scenario 2 adds a 50 MW firm

renewable energy resource to Scenario 1. As a result, delivered wind energy declines and

curtailment increases. Scenario 3 adds another 50 MW firm renewable resource (100 MW total).

Delivered wind energy declines further and curtailment increases. Similar trends are evident in

Scenarios 5-6-7 and Scenarios 8-9-10.



Figure 4-17: Wind, Solar, and Firm Renewable Energy for All Scenarios

Table 4-8 summarizes wind, solar, and firm renewable energy delivered and curtailed for all

scenarios. The right-hand column shows delivered wind, solar and firm renewable energy as a

percent of load.

Figure 4-18 is illustrates total renewable energy penetration for all scenarios. The green portion of

each bar represents wind, solar and firm renewable resources. This includes all existing wind, solar

and firm renewable plants (HC&S biomass, H-Power, Honua) as well as new renewable additions. In

Scenario 1, 25% of load energy is served by renewable energy resources. In Scenario 10, 39% of load

energy is served by renewables.



Table 4-8: Wind, Solar and Firm Renewable Energy Delivered and Curtailed for All Scenarios

Figure 4-18: Annual Renewable Energy Penetration for All Scenarios

Scenario

Renewable

Capacity

(MW)

Available

Energy

(GWh)

Delivered

Energy

(GWh)

Curtailment

(GWh)

Percent

Delivered

Percent

Curtailed

Percent of

Load

Base 578 2,516 2,354 162 94% 6% 25%

1 578 2,516 2,371 145 94% 6% 25%

2 629 2,953 2,730 223 92% 8% 29%

3 679 3,387 3,066 321 91% 9% 33%

4 839 2,971 2,804 167 94% 6% 30%

5 778 3,270 2,896 373 89% 11% 31%

6 828 3,707 3,198 509 86% 14% 34%

7 878 4,141 3,471 671 84% 16% 37%

8 878 3,697 3,118 579 84% 16% 33%

9 928 4,133 3,383 751 82% 18% 36%

10 978 4,568 3,623 945 79% 21% 39%



Figure 4-19 is an hourly duration curve showing wind and solar energy penetration for all scenarios.

Table 4-9 summarizes for useful statistics from the duration curves, including maximum hourly

percent penetration and average annual energy penetration. In Scenario 1, the average annual

energy penetration for wind and solar resources is 17%. However, for one hour the penetration

reaches 40%. The average annual penetration of wind and solar energy for Scenario 8 is 24% and

the maximum hourly penetration is 54%.

Figure 4-19: Duration Curves of Hourly Wind and Solar Penetration Levels as % of Load for All

Scenarios

Table 4-9: Maximum and Average Hourly Penetration Levels for Wind and Solar Resources

Max Hourly

Penetration (%)

Average Annual

Penetration (%)Hours > 40%

Base Case 39% 17% 0

Scenario 1 40% 17% 0



Scenario 4 50% 21% 467

Scenario 5 50% 22% 642

Scenario 6 48% 20% 392

Scenario 7 46% 18% 193

Scenario 8 54% 24% 1,336

Scenario 9 51% 21% 891

Scenario 10 48% 19% 515



Figure 4-20 provides some insights into how wind and solar energy penetration varies as a function

of the time period considered. In Scenario 1, the total annual energy from wind and solar resources

is 17% of the annual load energy. The dotted horizontal line represents annual average penetration.

The purple line shows wind and solar energy penetration calculated on a monthly basis, which varies

from 12% to 21%. The green trace shows weekly penetration, which ranges from 5% to 28%. Daily

penetration (blue) ranges from 2% to 32% and hourly penetration (red) ranges from zero to 40%. A

similar plot is shown for Scenario 8, which has the highest penetrations of wind and solar energy.

Figure 4-20: Duration Curves of Wind and Solar Penetration Levels for Different Time Periods,

Scenarios 1 and 8

4.5 Renewable Curtailment

This section presents wind and solar curtailment information for the study scenarios in several

different forms:

Figure 4-21 shows duration curves of hourly wind and solar curtailment.

Table 4-10 summarizes a few statistics for curtailment, including total wind and solar energy available, total curtailed energy, maximum hourly curtailment in MW, and number of hours of curtailment in a year.

Figure 4-22 illustrates total wind, solar and firm renewable energy delivered and total curtailment as % of load.

Figure 4-23 is a scatter plot showing curtailment as a function of wind, solar and firm renewable energy available in each scenario.

Figure 4-24 illustrates curtailment of the 100 MW wind plant on Maui that is included in Scenarios 8, 9 and 10.

Scenario 1: 17% Annual Penetration Scenario 8: 25% Annual Penetration



Figure 4-21: Wind and Solar Curtailment Duration Curves for All Scenarios

Table 4-10: Wind and Solar Energy Curtailment for All Scenarios

Total

Available

(GWh)

Total

Curtailment

(GWh)

Max

Curtailment

(MW)

Hours of

Curtailment

Scenario 1 1,773 145 313 1,386

Scenario 2 2,210 223 348 1,889

Scenario 3 2,647 321 351 2,452

Scenario 4 2,228 167 313 1,703

Scenario 5 2,527 373 496 2,511

Scenario 6 2,964 509 527 3,118

Scenario 7 3,401 671 527 3,775

Scenario 8 2,953 579 588 3,263

Scenario 9 3,391 751 617 3,841

Scenario 10 3,827 945 617 4,438



Figure 4-22: Wind, Solar and Firm Renewable Energy Delivered and Curtailed, as % of Load

Figure 4-23: Available Wind, Solar and Firm Renewable Energy versus Curtailment



Figure 4-24: Curtailment of the Maui 100 MW Wind Plant for Scenarios 8, 9 and 10.

Scenarios 8, 9 and 10 include an additional 100 MW wind plant on Maui (as compared to Scenario 5).

This plant is assumed to be the last addition to the system and therefore first in the curtailment

order. Figure 4-24 shows how much of the available energy from that plant is delivered and

curtailed. In Scenario 8, where this 100 MW wind plant is the only addition relative to Scenario 5, only

211 GWh of the plant’s 425 GWh of available energy can be delivered (about 50% curtailment). In

Scenario 10, where there is also a 100 MW firm renewable energy resource on Maui, about 67% of

the wind plant’s available energy must be curtailed. This high level of curtailment is mostly driven by

the fact that with this plant, total wind generation on the interconnected system is 672 MW (100 MW

existing on Oahu, 72 MW existing on Maui, and 200 MW new on Lanai, 200 MW new on Molokai, and

100 MW new on Maui). Wind patterns are similar and correlation is high. There are many periods

where there is just too much wind energy available. Chapter 5 examines modified practices to reduce

curtailment.

4.6 DC & AC Line Flows

The production simulations assumed that the DC cable flows were not constrained to their nominal

ratings. As a result, the analysis shows the flow levels that would maximize economic operation of

the interconnected power system (i.e., dispatch lowest cost generation, minimize renewable energy

curtailment).

Figure 4-25 shows duration curves of DC cable flows from Maui County to Oahu. Table 4-11

summarizes some statistics related to the cable flows.

The Base Case and Scenarios 1-4 have one 200 MW DC cable.

In the Base Case the maximum flow is 192 MW and the average flow is 89 MW (roughly a 45% capacity factor). 780 GWh of energy from the Lanai wind plant is transferred to Oahu on the DC cable.

ScenarioDC

MW

Wind

MW

Solar

MW

Firm RE

MW

1 200 200 0 0

2 50

3 100

4 260 0

5 400 400 0 0

6 50

7 100

8 500 0

9 50

10 100



In Scenario 1, Oahu and Maui County systems are interconnected. The peak flow (205 MW) on the DC cable is higher than the Base Case but the average flow (76 MW) is lower. The cable capacity factor is 38%. 665 GWh of energy is transferred from Maui County to Oahu.

In Scenario 2, a 50 MW firm renewable energy resource is added on Maui. With this resource, the DC cable flow increases by about 40 MW for many hours of the year. Total energy flow is 892 GWh (51% capacity factor) versus 665 GWh for Scenario 1 (see Table 4-11). There are 820 hours when flow exceeds 200 MW, representing 11 GWh of energy or 1.3% of the total cable flow.

In Scenario 3, a 100 MW firm renewable energy resource is added on Maui (relative to Scenario 1). With this resource, the DC cable flow increases by about 80 MW for many hours of the year. Total energy flow is 1194 GWh (68% capacity factor) versus 665 GWh for Scenario 1. There are 2301 hours when flow exceeds 200 MW, representing 76 GWh of energy or 6.4% of the total cable flow.

In Scenario 4, 260 MW of solar generation is added on Oahu. Given that this resource is on Oahu, cable flow from Maui County to Oahu is nearly the same as Scenario 1.

Scenarios 5-10 have two 200 MW DC cables interconnecting Maui County and Oahu.

In Scenario 5, there is a 200 MW wind plant on Lanai and another 200 MW wind plant on Molokai. The DC cables transfer most of the energy from those plants to Oahu. Total energy flow is 1147 GWh (33% capacity factor) and power flows are below 400 MW for all hours.

In Scenario 6, a 50 MW firm renewable energy resource is added on Maui (relative to Scenario 5). Total energy flow on the DC cables is 1352 GWh (39% capacity factor). There are 4 hours when flow exceeds 400 MW, but the energy in the excess flow is insignificant (less than 0.5 GWh).

In Scenario 7, a 100 MW firm renewable energy resource is added on Maui (relative to Scenario 5). Total energy flow on the DC cables is 1603 GWh (46% capacity factor). There are 117 hours when flow exceeds 400 MW, but the energy in the excess flow is only 1 GWh (0.06% of total cable flow).

In Scenario 8, a 100 MW wind plant is added on Maui (relative to Scenario 5). Total energy flow on the DC cables is 1329 GWh (38% capacity factor). There are 48 hours when flow exceeds 400 MW, but the energy in the excess flow is insignificant (less than 0.5 GWh).

In Scenario 9, a 100 MW wind plant and a 50 MW firm renewable energy resource are added on Maui (relative to Scenario 5). Total energy flow on the DC cables is 1511 GWh (43% capacity factor). There are 301 hours when flow exceeds 400 MW, but the energy in the excess flow is only 6 GWh (0.4% of total cable flow).

In Scenario 10, a 100 MW wind plant and a 100 MW firm renewable energy resource are added on Maui (relative to Scenario 5). Total energy flow on the DC cables is 1732 GWh (49% capacity factor). There are 589 hours when flow exceeds 400 MW, but the energy in the excess flow is only 18 GWh (1% of total cable flow).

Figure 4-26 compares energy transfer on the DC cables to Oahu for all study scenarios. The blue

portion of each bar represents energy transfer within the 200 MW rating of each DC cable. The red

portion of the bars indicates energy that could not be transferred if the DC cables are constrained to

200 MW each. The only significant congestion is in Scenario 3, where 6.4% of the possible flow falls

above the assumed cable ratings.



In general, the output of additional renewable energy resources considered in these scenarios is not

restricted by the nominal 200 MW transfer capacity of the DC cables. However, a large amount of

renewable energy is curtailed due to assumed fixed operating schedules and existing minimum

power limits for baseload generation. Chapter 5 discusses the impacts of changing these

assumptions.

Figure 4-27 shows flows on the three ac cables interconnecting Maui, Lanai and Molokai. These

traces give an indication of the expected loading on the cables. Note that if one of the cables

experiences an outage, the flows on the other two cables would increase. Contingency analysis was

not in the scope of this study. This data is represented in tabular form in Table 4-12.

Figure 4-25: Duration Curves for Maui County to Oahu DC Cable Flows

One 200 MW

DC Cable

Two 200 MW DC Cables



Table 4-11: Maui County to Oahu DC Cable Flows for All Scenarios

Figure 4-26: Energy Transfer on Maui County to Oahu DC Cables, with and without Power Flow

Constraints



Figure 4-27: Flow Duration Curves for AC Cables Interconnecting Maui, Lanai and Molokai

Lanai to Maui

Molokai to Maui

Lanai to Molokai



Table 4-12: Flows (MW) for AC Cables Interconnecting Maui, Lanai and Molokai by Percentile

20% 40% 60% 80% 100%

Base Case -2.2 -3.0 -3.0 -3.7 -4.9

Scenario 1 23.8 16.9 7.6 -4.3 -32.0

Scenario 2 -3.3 -13.8 -23.5 -35.3 -69.5

Scenario 3 -33.9 -45.2 -54.5 -65.6 -96.4

Scenario 4 24.3 17.4 8.2 -3.9 -41.0

Scenario 5 19.9 12.1 5.3 -3.9 -38.4

Scenario 6 -3.4 -11.7 -18.6 -27.9 -63.4

Scenario 7 -26.3 -35.0 -41.7 -50.8 -87.7

Scenario 8 11.9 2.5 -7.2 -19.7 -65.7

Scenario 9 -10.8 -19.6 -28.9 -40.2 -87.3

Scenario 10 -32.8 -41.4 -49.8 -61.3 -112.2

20% 40% 60% 80% 100%

Base Case -2.6 -3.0 -3.1 -4.2 -5.2

Scenario 1 10.4 6.9 2.5 -3.3 -17.2

Scenario 2 -3.3 -8.5 -13.2 -19.0 -34.8

Scenario 3 -18.7 -24.2 -28.8 -34.2 -48.6

Scenario 4 10.7 7.2 2.7 -3.3 -21.3

Scenario 5 18.0 9.3 1.1 -8.5 -41.4

Scenario 6 -5.3 -14.4 -21.9 -31.4 -67.6

Scenario 7 -27.6 -37.2 -44.3 -53.4 -88.2

Scenario 8 11.5 -0.2 -11.6 -25.7 -75.9

Scenario 9 -11.1 -21.3 -32.1 -45.7 -89.1

Scenario 10 -32.8 -42.3 -52.0 -66.2 -111.0

20% 40% 60% 80% 100%

Base Case 0.4 0.2 0.1 -0.1 -2.5

Scenario 1 13.5 9.8 5.2 -1.0 -17.4

Scenario 2 0.1 -5.3 -10.1 -16.3 -34.6

Scenario 3 -15.1 -20.9 -25.6 -31.2 -47.4

Scenario 4 13.9 10.0 5.5 -0.6 -22.0

Scenario 5 12.9 4.3 0.1 -4.4 -46.9

Scenario 6 11.9 3.3 0.1 -4.3 -47.8

Scenario 7 11.2 1.8 0.1 -4.2 -46.6

Scenario 8 12.9 4.3 0.1 -4.4 -46.9

Scenario 9 11.9 3.3 0.1 -4.3 -47.8

Scenario 10 11.2 1.8 0.1 -4.2 -46.6

Lanai to Maui

Molokai to Maui

Lanai to Molokai



4.7 Value of Renewable Energy Resources

In this section, production cost results are used to calculate the value, in $/kW, for the renewable

resources that are added to Scenarios 1, 5 and 8.

Scenario 2 is built upon Scenario 1 with the addition of a 50 MW firm renewable resource on Maui.

As shown in Table 4-13, the total production cost is $1,035M/yr for Scenario 1 and $982M/yr for

Scenario 2. The reduction in thermal plant production costs is $53M/yr. In other words, the addition

of a 50 MW firm resource with zero fuel cost reduced total system production costs by $53M/yr. By

dividing the change in production cost by the rating of the firm renewable resource, the value of the

resource is calculated to be $1,051/kW/yr. If the fixed charge rate (cost of capital) is assumed to be

14%, then the value of the firm renewable resource in Scenario 2 is $7,505/kW.

The top four rows in Table 4-13 show the values of adding zero variable cost firm renewable

resources or solar PV resources to Scenario 1. Adding a 50 MW zero variable cost firm renewable

resource on Maui is worth $7,505/kw, a 100 MW firm renewable resource is worth $6,899/kW, and a

260 MW Solar PV resource on Oahu is worth $1,730/kW.

Note that production costs are reduced by delivered renewable energy, not curtailed energy.

Therefore, the values presented here are based only on renewable energy that is delivered. A

resource that causes more curtailment would have a lower value than and resource that causes less

curtailment.

Table 4-13: Value of Additional Renewable Resources with Existing Thermal Plant Operating

Practices

The middle four rows in Table 4-13 show the values of adding firm renewable resources or wind

resources to Scenario 5. Adding a 50 MW firm renewable resource on Maui is worth $6,037/kw, a

100 MW firm renewable resource is worth $5,493/kW, and a 100 MW wind resource on Maui is worth

$1,616/kW.

ScenarioAdditional

Renewable Resource

Total

Production

Cost (M$)

Change in

Production

Cost (M$)

Rating of

Additional

Resource

(MW)

Resource

Value

($/kw/yr)

Value

Assuming

14% FCR

($/kw)

1 -- $1,035 -- -- -- --

2 50 MW Firm RE $982 $53 50 $1,051 $7,505

3 100 MW Firm RE $938 $97 100 $966 $6,899

4 260 MW Solar PV $972 $63 260 $242 $1,730

5 -- $970 -- -- -- --

6 50 MW Firm RE $928 $42 50 $845 $6,037

7 100 MW Firm RE $893 $77 100 $769 $5,493

8 100 MW Wind $948 $23 100 $226 $1,616

8 -- $948 -- -- -- --

9 50 MW Firm RE $910 $37 50 $743 $5,308

10 100 MW Firm RE $880 $67 100 $674 $4,816



The bottom three rows in Table 4-13 (highlighted in blue) show the values of adding firm renewable

resources to Scenario 8. Adding a 50 MW firm renewable resource on Maui is worth $5,308/kw and a

100 MW firm renewable resource is worth $4,816/kW.

Note that the value of a given resource declines as the renewable energy penetration level increases.

In Scenario 2, a 50 MW firm renewable resource on Maui is worth $7,505/kW. In Scenario 5, which

has 200 MW more wind resources than Scenario 1, a 50 MW firm renewable resource is worth

$6,037/kW. The diminishing returns are because the 50 MW firm renewable resource causes more

wind energy curtailment in Scenario 6 than in Scenario 2.

The production simulations that provide the basis for this analysis assumed existing operating

practices for Oahu and Maui thermal plants (fixed operating schedules, existing minimum power

limits). In Chapter 5, the resource values presented here are recalculated assuming modified

operating practices. The modified operating practices reduce curtailment and increase the values of

the renewable resources.



4.8 Emissions

Figure 4-28 shows total CO2, SOx and NOx emissions for the study scenarios. Figure 4-29 illustrates

the reductions in total emissions relative to the Base Case, assuming zero emissions from the new

firm renewable plants.

The tabulated data in Figure 4-29 shows that SOx and CO2 emissions in Scenario 1 are higher than in

the Base Case (negative values for reduction). This is because the AES coal plant produces more

energy in Scenario 1. Given that AES is the most economical unit on the system, it displaces some

energy from the Maui baseload units when the islands are interconnected.

Note: Assumes zero emissions from biomass and new firm generation additions

Figure 4-28: Total Emissions from Dispatchable Thermal Generation

ScenarioDC

MW

Wind

MW

Solar

MW

Firm RE

MW

1 200 200 0 0

2 50

3 100

4 260 0

5 400 400 0 0

6 50

7 100

8 500 0

9 50

10 100



Note: Assumes zero emissions from biomass and new firm generation additions

Figure 4-29: Reductions in Emissions from Thermal Generation Relative to Base Case

CO2

(KTons)

SOX

(Tons)

NOX

(Tons)

Scenario 1 -8 -173 1,261

Scenario 2 338 444 2,081

Scenario 3 661 987 2,697

Scenario 4 401 619 2,063

Scenario 5 623 834 2,341

Scenario 6 922 1,310 2,935

Scenario 7 1,188 1,713 3,402

Scenario 8 845 1,119 2,615

Scenario 9 1,110 1,532 3,152

Scenario 10 1,344 1,881 3,574



4.9 Thermal Cycling

Table 4-14 summarizes the annual hours of operation and starts for thermal baseload, cycling and

peaking units. This data is on a per unit basis. For example, the number of starts for a particular unit

type is determined by dividing the total number of starts for all units of that type by the number of

individual generating units of that type.

The hours of operation and number of starts for baseload units are the same for all scenarios. This is

because of the assumption that existing baseload unit operating practices would remain the same

for all scenarios (i.e., baseload units are operated on fixed schedules and are never decommitted).

Duty on cycling units decreases in nearly all interconnected scenarios as compared to the Base Case.

The exceptions are:

Scenario 1, where Oahu cycling unit hours and starts are slightly higher, and

Scenario 4, where Oahu cycling starts are higher.

Duty on Oahu peaking units decreases substantially in all interconnected scenarios. Instead, peaking

services are provided by increased utilization of peaking units on Maui, Lanai and Molokai.

Table 4-14: Average Annual Starts and Hours of Operation by Unit Type

Note: Baseload unit operation is dependent on fixed operating schedules. For example, starts are dictated by fixed daily

schedules and CC plants cycled individual turbines regularly. For more information, see Section 2.5.



Of particular importance for thermal cycling is the AES coal plant because it represents the lowest

variable cost of any thermal resource on the system. Because of this, AES will be the last unit backed

down to its minimum power during periods of high renewable penetration. This makes AES an

interesting plant to look at to observe cycling trends across the scenarios.

Figure 4-30 shows the annual duration curves for the AES coal plant for each scenario. From this

figure it can be observed that the number of partload hours increases dramatically as renewable

penetration increases. In the base case, AES runs about 33% of the time at minimum load (72 MW). In

Scenario 10 this ratio increases to around 55%.

Figure 4-30: Duration Curve of AES Output for All Scenarios



4.10 Down-Regulation and Up-Range

Figure 4-31 shows hourly generation profiles and system down-regulation range for the Scenario 1

week with the most renewable energy. During nighttime hours when load is low and wind

generation is high, some of the available wind generation is curtailed because the thermal units

cannot be decommitted and they cannot be dispatched below the level required to maintain

required down-regulation reserves (90 MW at night). This simulation result assumes that thermal

units are operated on fixed schedules, per existing operating practices.

Figure 4-31: Hourly Generation Profiles and System Down-Regulation Range for One Week,

Scenario 1

Thermal fleet operating at minimum power,

which forces curtailment of wind and solar generationDown-Regulation



Figure 4-32: Duration Curve of Down-Regulation Range for Scenarios 1 and 10

Figure 4-32 is an annual duration curve showing hourly down-regulation range for Scenario 1 (least

renewable energy resources) and Scenario 10 (most renewable energy resources). In Scenario 1, the

thermal fleet is operating at its minimum down-regulation limit for about 8% of the time. In Scenario

10, the thermal fleet is at its minimum down-regulation limit for more than 50% of the time. Two

options for reducing hours at minimum power and thereby reducing curtailment are:

Relax the existing practice of fixed operating schedules for all baseload units

Reduce the minimum power limits on some of the baseload units

Both of these options are examined in Chapter 5 to determine the benefits these options can provide

to the system. However, the costs for these options and extent to which these options can be

implemented are not currently known and not considered in this study.

90 MW = Min Required Down-Reg at Night



Figure 4-33: Hourly Generation Profiles and System Up-Range for One Week, Scenario 1

Figure 4-33 shows hourly generation profiles and system up-range for the Scenario 1 week with the

most renewable energy. Up-range is at its highest level (about 500 MW) during periods when the

thermal fleet is at its minimum power limit, which is also when wind generation is curtailed.

Figure 4-34 is an annual duration curve showing hourly up-range for Scenarios 1 and 10 (least and

most renewable energy resources). The plot illustrates that as renewable energy resources increase,

the thermal fleet is dispatched to lower power levels, thereby increasing up-range significantly for

nearly all hours of the year. Figure 4-35 shows these same two traces together with the hourly total

spinning reserve requirements for Scenarios 1 and 10. The system commitment and dispatch are

dictated by reserve requirement about 16% of the time in Scenario 1 and 21% of the time in Scenario

10. The rest of the time, the system has a significant up-range surplus, primarily due to the existing

practice of operating baseload units on fixed schedules.



Figure 4-34: Up-Range Duration Curves for Scenarios 1 and 10 (Min & Max Renewables)

Figure 4-35: Reserve Requirements and System Up-Range for Scenarios 1 and 10

Un

it c

om

mit

me

nt

dic

tate

d b

y r

es

erv

es

Unit commitment dictated by

fixed operating schedules

Un

it c

om

mit

me

nt

dic

tate

d b

y r

es

erv

es

Unit commitment dictated by

fixed operating schedules



Figure 4-36: Hourly Up-Range and DC Cable Flow for Week with Highest DC Cable in Scenario 10

Figure 4-36 shows the hourly up-range (blue shaded area) for the week with the highest flow on the

two DC cables from Maui County to Oahu. The black dotted trace shows the total flow on both DC

cables. The red regions indicate a few time periods when DC cable flow exceeds the up-range of the

thermal generation on Oahu. This plot shows that with existing operating practices, the Oahu

thermal fleet has adequate up-range to fully cover the double-contingency loss of both DC cables

most of the time. The lower dotted trace shows the flow on one DC cable. The up-range is more

than adequate to cover the loss of one DC cable in all hours.

4.11 Key Observations

This chapter presented scenario analysis assuming existing operating practices and minimum power

limits for baseload generation. The results show that:

Interconnecting Maui County and Oahu reduces thermal plant production cost relative to the base case, lowers overall reserve requirements and adds flexibility in the combined generation fleet.

Much more renewable energy can be delivered to serve load, however there is curtailment of renewable energy in each case, which increases with increasing levels of renewable energy added to the system.

DC cables with 200 MW nominal ratings are able to transfer the all the additional renewable energy from Maui County to Oahu most of the time.

Curtailment occurs mostly at night when baseload units are running at minimum power and cannot

be decommitted given existing operating practices. Results suggest that curtailment could be

significantly reduced if baseload unit operating schedules are relaxed and minimum power limits are

reduced. Chapter 5 examines the impact of mitigation measures on curtailment.

Week of July 6-12: High DC Line Flow

MW

Hawaii Stage 2 Interconnection Study Changes to Operational Practices


5 Changes to Operational Practices One of the key results from Chapter 4 was that curtailment of renewable energy would occur in all

scenarios studies, and at higher penetrations the curtailment could be as much as 25% of available

wind and solar energy. Any modifications that could be made to the operating practices that would

reduce curtailment would reduce the production cost of energy by delivering more of the renewable

energy without having to install any additional capacity. To investigate this, three levels of sensitivity

cases were run on each of the scenarios, each building upon the results of the prior analysis. These

sensitivities are shown pictorially in Figure 5-1 and explained in more detail below.

Figure 5-1: Sequential Changes to Operational Practices

Step “A” was to remove the fixed operating schedules on the Maui baseload generation. These are

units which have historically been operated on fixed daily schedules but which may not be needed to

operate at all times with the addition of new renewable generation. The Maui units that were

affected are the Maalaea Combined Cycle units (M141516 and M171819) and the Kahului generators

(K1-K4). Allowing these units to shut down during periods of low load and/or high renewables could

help reduce total renewable curtailment and improve the operating efficiency of the remaining

thermal generation.

Step “B” was to reduce the minimum power limits on some of the Oahu units. The minimum limits on

some units were assumed to be reduced by 50% from their initial values. For example, the Kahe 1

unit, with a full load capacity of 82.1 MW, had its operating minimum reduced from 32.5 MW to 16.25

MW. In both cases the unit was required to hold 9.0 MW of Down Regulation so that the minimums

used in the modeling were 41.5 MW and 25.25 MW respectively. In this example the reduced

minimum on the Kahe 1 unit could allow the renewable curtailment to be reduced by 16.25 MW in an

hour. Table 5-1 shows the modifications for all of the units that were affected.

The final sensitivity, Step “C”, was to remove the fixed operating schedules on the Oahu generation.

The units affected were Waiau 7&8 and Kahe 1-4.

This section investigates possible future modifications to grid operations. However, the Oahu and

Maui County systems cannot currently operate according to the assumptions outlined above and the

modified units cannot currently run as assumed in this chapter. In addition, the feasibility and costs

required for these modifications is unknown and not considered in the cost-benefit analysis found in

Chapter 8.



It is important to note that the modifications are incremental to one another and that the order of

the modifications matter relative to the overall benefits accrued to each step. For example, if the

Oahu fixed operating schedules were removed prior to the Maui fixed operating schedules, the

incremental value associated with that change in isolation would be different. However, regardless of

the order, the final “modified operating practices” of all three steps would be unchanged.

Table 5-1: Reduced Minimum Power Limits on Oahu Units

5.1 Production Cost Impacts

The production cost (fuel cost + VOM + start cost) impacts for Scenario 1 are shown in the waterfall

chart in Figure 5-2. Eliminating the fixed operating schedules for Maui baseload units reduced the

production costs by $17.4M. Reducing the Oahu minimum power limits saved an additional $23.1M

and eliminating the Oahu must-run requirements further reduced costs by $10.4M. Overall the three

changes reduced the annual system production costs by $51 million, or approximately 5%. This is

also six times the value seen from interconnecting the two independent systems when going from

the two Base Cases to Scenario 1.

Unit Name

Technical

Minimum

Power

Decreased

Minimums

Down

Regulation

Min Capacity

with Down

Regulation

Kahe 1 32.5 16.3 9.0 25.3

Kahe 2 32.7 16.4 9.0 25.4

Kahe 3 32.3 16.2 9.0 25.2

Kahe 4 32.3 16.2 9.0 25.2

Kahe 5 50.7 25.4 9.0 34.4

Waiau 7 23.0 11.5 9.0 20.5

Waiau 8 23.0 11.5 9.0 20.5

AES 63.0 N/A 9.0 72.0

Kalaeloa 1 65.0 N/A 4.5 69.5

Kalaeloa 2 65.0 N/A 4.5 69.5

Oahu Down Reserves (Total = 90 MW)



Figure 5-2: Production Cost Savings by Sensitivity for Scenario 1

Figure 5-3 and Table 5-2 show graphically and in tabular form the production cost impacts of each

of the sensitivities for Scenarios 1 through 10.

Figure 5-3: Impact of Operating Changes on Production cost for Scenarios 1-10



Table 5-2: Production Cost Impact (M$) of Operating Changes

Table 5-3 shows the incremental changes in production costs for each of the sensitivities for the ten

scenarios. As the renewable zero cost generation increases, the value of these operational changes

triples from roughly $50 million in Scenario 1 to nearly $150 million in Scenario 10. This is a

significant impact on the total system production costs and curtailment. Figure 5-4 shows the total

savings impact of all three operating changes as a portion of the original production cost or each

scenario.

Table 5-3: Incremental Savings (M$) from Operating Changes for Scenario 1-10

Existing Operating

Practices

Step A: Remove Maui

Fixed Operating

Schedules

Step B: Reduced Oahu

Minimums

Step C: Remove Oahu

Fixed Operating

Schedules

Scenario 1 1,035 1,017 994 984

Scenario 2 982 957 927 912

Scenario 3 938 906 868 847

Scenario 4 972 952 921 910

Scenario 5 970 943 903 885

Scenario 6 928 894 845 822

Scenario 7 893 853 796 765

Scenario 8 948 916 865 844

Scenario 9 910 861 808 785

Scenario 10 880 821 761 733

Step A: Remove Maui

Fixed Operating

Schedules


Minimums

Step C: Remove Oahu

Fixed Operating

Schedules

Total Production

Cost Savings

Scenario 1 17.4 23.1 10.4 51.0

Scenario 2 25.5 29.8 15.2 70.4

Scenario 3 32.7 37.9 21.1 91.7

Scenario 4 20.3 30.4 11.2 61.9

Scenario 5 26.7 40.3 17.7 84.7

Scenario 6 34.2 48.4 23.4 106.0

Scenario 7 40.1 57.3 30.3 127.7

Scenario 8 31.9 50.6 21.3 103.9

Scenario 9 49.1 53.1 22.3 124.5

Scenario 10 58.7 60.3 27.9 146.9



Figure 5-4: Production Cost Savings from all Changes

5.2 Curtailment and Renewable Generation

Although some of the production cost savings shown in the previous section were due to the

remaining thermal fleet operating more efficiently, the bulk of the savings were simply due to the

reduced curtailment of zero cost renewable energy displacing high cost thermal generation. This

section examines the impact on curtailment directly. Figure 5-5 shows a waterfall of the total

renewable curtailment for Scenario 1 under existing operating practices and each of the three

assumed changes. The existing practices resulted in 145 GWh of curtailment. Removing the Maui

fixed operating schedules reduced curtailment by 36 GWh. Reducing the minimum ratings on the

Oahu units reduced curtailment by an additional 78 GWh, and elimination of the Oahu fixed

operating schedules provided a savings of 25 GWh. Overall the curtailment dropped from 145 GWh

to only 6 GWh, a reduction of over 95%. Also note that adding the assumed modifications in a

different order would produce different incremental results, although the final “modified operating

practices” would be unchanged.



Figure 5-5: Reduced Curtailment due to Operational Changes for Scenario 1

Figure 5-6 and Table 5-4 show the total curtailment for each of the three operating sensitivities for all

ten scenarios. As the renewable penetration increases so does the impact of these operating

changes. Figure 5-7 and Table 5-5 show the corresponding reductions in curtailment.



Figure 5-6: Curtailment of Renewable Energy by Sensitivity for Scenarios 1-10

Table 5-4: Curtailment (GWh) of Renewable Energy by Sensitivity for Scenarios 1-10

Existing Operating

Practices

Step A: Remove Maui

Fixed Operating

Schedules


Minimums

Step C: Remove Oahu

Fixed Operating

Schedules

Scenario 1 145 108 31 6

Scenario 2 223 170 59 13

Scenario 3 321 254 105 31

Scenario 4 167 122 33 6

Scenario 5 373 301 143 71

Scenario 6 509 413 208 109

Scenario 7 671 554 299 168

Scenario 8 579 481 257 156

Scenario 9 751 565 322 218

Scenario 10 945 723 431 303



Figure 5-7: Potential Total Reduction in Curtailment

Table 5-5: Reduction in Curtailment by Sensitivity

Figure 5-8 revisits the renewable penetration by scenario. The dark blue portion of each bar

represents the amount of renewable energy delivered to the system under existing operating

practices. The medium blue segment shows the additional percent of load that can be served by the

renewable generating units when the operating practices are relaxed. Finally, the light blue piece

shows the remaining curtailed energy in each scenario. In all cases the curtailment has been

significantly reduced and in some instances all but eliminated. Figure 5-9 compares curtailment

across scenarios with existing and modified operating practices.

Step A: Remove Maui

Fixed Operating

Schedules


Minimums

Step C: Remove Oahu

Fixed Operating

Schedules

Total Reduction

Scenario 1 36.2 77.7 25.1 139.0

Scenario 2 52.8 111.4 45.6 209.8

Scenario 3 67.1 149.0 73.5 289.6

Scenario 4 44.3 89.6 26.8 160.7

Scenario 5 72.5 158.2 72.0 302.7

Scenario 6 96.6 204.2 98.9 399.7

Scenario 7 116.8 254.6 130.8 502.3

Scenario 8 98.3 223.5 101.5 423.3

Scenario 9 185.2 243.7 104.0 532.8

Scenario 10 221.8 291.7 128.2 641.7



Figure 5-8: Impact on Renewable Penetration of Modified Operational Changes.

Figure 5-9: Change in Curtailment with Modified Operating Practices by Scenario

Existing Operating Practices Modified Operating Practices



5.3 DC Cables Constrained

Figure 5-10 shows duration curves of the flow on the DC cables for Scenario 10 under existing

operating practices as well as the three operating sensitivities. The initial sensitivity, removing the

Maui fixed operating schedules, reduces the flow on the lines because more of the renewable energy

can now displace higher cost energy in Maui County. The other two sensitivities, which affect the

operation of the thermal units on Oahu, then increase the flows on the lines due largely to reduced

curtailment. The maximum flow on the line, however, never exceeds the value found under the

existing practices.

Figure 5-10: DC Cable Flow Duration Curves for Scenario 10 by Operating Sensitivity



Figure 5-11 shows the maximum flow on the DC cables for current operating practices and the three

operating sensitivities for all ten scenarios. The maximum flow for all of the sensitivity cases is

always less than the original flow for each of the scenarios.

Figure 5-11: Maximum DC Cable Flows (MW) by Operating Sensitivity



Figure 5-12 and Table 5-6 show the resulting energy flows for each scenario and operating

sensitivity. In some of the cases the energy flow has increased above that seen in the corresponding

base scenario.

Figure 5-12: Energy Flows (GWh) on DC Cables by Sensitivity

Table 5-6: Energy Flows (GWh) on DC Cables by Sensitivity



Figure 5-13 shows the flow duration curves on the DC cables for all ten scenarios. Remember, the

first four scenarios have a single DC cable with a nominal rating of 200 MW. The remaining scenarios

have two cables with a combined nominal rating of 400 MW, although these ratings were not

imposed in the analysis.

Figure 5-13: DC Cable Flow (MW) Duration Curves for Scenario 1-10 with Operating Changes

Table 5-7 includes various operating statistics for the DC cables for each of the scenarios with all of

the suggested operating changes in place. Included in the statistics is the number of hours with

flows exceeding the nominal ratings and the total energy flows that would have occurred above

those ratings. Figure 5-14 shows the total energy flows below and above the nominal ratings.

Table 5-7: DC Cable Flow Summary with Operating Changes

Total Flow

(GWh)

Max Flow

(MW)

Min Flow

(MW)

Avg Flow

(MW)

Hours of

Flow from

Lanai to

Oahu

Hours

>200 MW

Total

Energy

>200 MW

Scenario 1 625 190 -30 71 70% 0 0

Scenario 2 837 228 -30 96 83% 218 2

Scenario 3 1,143 263 -30 131 97% 1,978 41

Scenario 4 618 188 -30 71 68% 0 0

Scenario 5 1,229 334 -60 140 80% 0 0

Scenario 6 1,455 381 -60 166 89% 0 0

Scenario 7 1,747 417 -51 199 99% 51 0

Scenario 8 1,511 412 -60 172 82% 16 0

Scenario 9 1,727 457 -60 197 91% 366 6

Scenario 10 1,994 487 -47 228 99% 1,009 30



Figure 5-14: DC Cable Energy Flows (GWh) by Scenario with Operating Changes in Place

5.4 Value of Renewable Energy Resources

Section 4.7 examined the value (in $/KW), based on reductions in annual production cost, of the

various renewable energy resources assuming existing operating practices. This value was

determined by looking at the change in production costs between the various scenarios. As the

previous sections have shown, relaxing some of the operating constraints will make the total

production costs drop. This section will re-examine the value of the resources based on the new

operating costs. Table 5-8 shows the updated values of the renewable resources using the

production costs from Table 5-2 based on the modified operating practices. Table 5-9 compares

these results to those shown previously in Table 4-13. This comparison is shown graphically in Figure

5-15. In all cases the resources are more valuable with the relaxed operating constraints. The

impact of diminishing returns is still evident in that in all cases the 100 MW of firm renewable did not

cause as much of a reduction in total production costs, in average $/KW, as the initial 50 MW. Also,

the impact of the firm renewable generation decreases as it is added into base systems with

increasing levels of wind resources.



Table 5-8: Value of Additional Renewable Resources with Modified Operating Practices

Table 5-9: Comparison of the Value of Renewable Resources Based on Operating Assumptions

ScenarioAdditional

Renewable Resource

Total

Production

Cost (M$)

Change in

Production

Cost (M$)

Rating of

Additional

Resource

(MW)

Resource

Value

($/kw/yr)

Value

Assuming

14% FCR

($/kw)

1 -- $984 -- -- -- --

2 50 MW Firm RE $912 $72 50 $1,440 $10,284

3 100 MW Firm RE $847 $137 100 $1,373 $9,807

4 260 MW Solar PV $910 $74 260 $284 $2,030

5 -- $885 -- -- -- --

6 50 MW Firm RE $822 $63 50 $1,266 $9,046

7 100 MW Firm RE $765 $120 100 $1,201 $8,576

8 100 MW Wind $844 $41 100 $412 $2,944

8 -- $844 -- -- -- --

9 50 MW Firm RE $785 $59 50 $1,173 $8,377

10 100 MW Firm RE $733 $111 100 $1,110 $7,928

Existing

Operating

Pracitices

Modified

Operating

Practices

1 -- -- --

2 50 MW Firm RE $7,505 $10,284

3 100 MW Firm RE $6,899 $9,807

4 260 MW Solar PV $1,730 $2,030

5 -- -- --

6 50 MW Firm RE $6,037 $9,046

7 100 MW Firm RE $5,493 $8,576

8 100 MW Wind $1,616 $2,944

8 -- -- --

9 50 MW Firm RE $5,308 $8,377

10 100 MW Firm RE $4,816 $7,928

Value Assuming 14% FCR

ScenarioAdditional

Renewable Resource



Figure 5-15: Comparison of Values of Renewable Resources with Different Operating

Assumptions



5.5 Other Impacts

Other operating impacts were examined as well. Figure 5-16 examines the effect on unit operations

for Scenario 10. The curve on the left shows the total output from Kahe 1-5 for the changes in

operating practices. The lowering of the minimum power limit and the elimination of fixed operating

schedules allow this plant to cycle down significantly from its initial operation. Contrastingly, the

curve on the right shows how the AES coal plant can increase its utilization when other operating

practices are relaxed.

Figure 5-16: Kahe and AES Plant Operating Impacts for Scenario 10

Figure 5-17 shows the combined duration curves of the Oahu baseload unit dispatch for each hour

of the year under the existing operating practices and the modified operating practices discussed

previously in this chapter. In addition, it shows the number of Oahu baseload units online under the

two sets of assumptions. Scenario 10 was selected for this comparison because it represents the

highest penetration of renewable energy and therefore the largest potential reduction in baseload

unit utilization. Under the modified operating practices there is a concern that too many units will be

turned off and the system may have less thermal resources available during times of stress

(contingency events or high renewable variability). However, these charts highlight that the value

from reduced production costs can be captured with only a small change in the minimum number of

baseload units online. Figure 5-18 shows the similar chart for Maui baseload units.



Figure 5-17: Oahu Baseload Unit Operating Impacts for Scenario 10

Figure 5-18: Maui Baseload Unit Operating Impacts for Scenario 10

Figure 5-19 and Figure 5-20 examine the up-range and down regulation duration curves for Oahu for

Scenario 10. The excess up-range is tightened up significantly in the first figure. The second shows

that the down regulation is generally larger, although removing the fixed operating schedules

allowed the down regulation to drop slightly below the requirement about 25% of the time because

units were decommitted. Due to the way that the MAPS model allocated the down regulation to

individual units, their portion was lost when the unit was decommitted. In practice the down

regulation that those units were carrying would be picked up by other units but that should not

significantly affect the overall results shown here.



Figure 5-19: Oahu Up-Range Duration Curve with Operating Changes

Figure 5-20: Oahu Down-Regulation Duration Curves by Sensitivity



5.6 Key Observations

This chapter presented the scenario analysis assuming modified operating practices and reduced

minimum power limits for baseload generation. The results show that:

Significant reductions in production costs can be achieved compared to existing practices. The benefits of reduced production costs increase with increasing renewable penetration. However, the thermal units cannot currently run as assumed in this analysis and the feasibility and costs required to do so is currently unknown. These costs, in addition to the cost of the additional renewable energy provided, would need to be taken into account to determine the value of these modifications.

The benefits from reduced production cost occur largely due to the reduction in renewable curtailment and subsequent reduced utilization of the dispatchable thermal units. In some cases the curtailment is all but eliminated.

The maximum flows on the DC cables are lower than those seen under existing practices. The

energy flows above the nominal ratings (200 MW per cable) have also been reduced.

Hawaii Stage 2 Interconnection Study Sensitivity Analysis


6 Sensitivity Analysis Chapter 2 covered the standard set of assumptions that were used throughout the analysis. In order

to isolate the impact of increased renewables on the interconnected system, load and fuel prices

were held constant throughout each scenario. This section provides load and fuel sensitivities to

understand how the results would be impacted, given a change to the load and fuel price

assumptions.

6.1 Load Growth Sensitivities

Sensitivity analysis was conducted by increasing and decreasing the hourly load patterns by a

constant 5%. No changes were made to the overall annual load shape; rather each hour was

changed by a constant percentage. Figure 6-1 shows the annual hourly duration curves for the base

case and the sensitivities used in this study. The load sensitivities were conducted on two scenarios,

Scenario 1 and Scenario 7, because those scenarios have the largest difference wind and solar

generation. The results discussed in this section look at the change in production cost (fuel cost +

VOM + start cost) and annual wind and solar curtailment.

Figure 6-1: Load Growth Sensitivity Hourly Duration Curves

Figure 6-2 shows the change in production cost between the different load scenarios. The x-axis is

the amount of annual load on the system. On the left plot, the y-axis is total annual production cost

(M$) and the right plot shows the change in production cost relative to the base case.

Figure 6-3 shows the change in wind and solar curtailment under different load sensitivities for

Scenario 1 and Scenario 7. The x-axis represents the amount of load on the system, and the y-axes

represent the amount of curtailment. The left axis represents curtailment in GWh and the right axis

as a percent of total available wind and solar energy.



Figure 6-2: Change in Production Cost in Load Sensitivities

Figure 6-3: Wind & Solar Curtailment by Load Growth Sensitivity



From these two plots it can be determined that;

The total production costs increase in the sensitivities with increased load, and decrease in the sensitivities with decreased load. This is not surprising because increased demand for electricity must be served, at least in part, by increased thermal generation. For example, in Scenario 1 a 5% increase in load will ultimately lead to a $72 million increase in production cost (7%).

Although the trends in Scenario 1 and Scenario 7 are similar, the rate of change (slope) of production cost change is smaller for Scenario 7. For example, overall production cost only increases by $42 million (4.8%) for the same change in load. This is because in a high renewables scenario, an increased share of the additional load will be served by wind and solar energy. In the high load growth scenario, additional load is served first with wind and solar energy that was curtailed in the base case. On the other hand, lower than expected load will mean relatively more curtailment and less than expected production cost savings.

Scenarios with increased renewable energy will experience larger changes of overall curtailment given an equal change in load.

Overall, a high renewable scenario will mean less volatility in production cost given equal changes in system load.

6.2 Fuel Price Sensitivities

Another uncertainty in modeling future scenarios is the fuel price forecast. Historical experience has

shown significant volatility in fuel prices, which leads to volatility in production cost (fuel cost + VOM +

start cost) and electricity prices. This is especially true for Hawaii due to reliance on imported fuel and

the dominance of oil-fired generation resources. To account for this forecast uncertainty, sensitivity

analysis was performed by using a 10% and 20% increase and decrease on the assumed oil price

inputs. For this analysis, “oil price” includes all petroleum derivative products including residual oil,

distillate oil and diesel fuels.

Figure 6-4 shows the breakdown of total production cost by oil consumption, coal consumption,

VOM, and start costs for Scenario 1. Overall, 95% of the total system production cost is directly

related to oil consumption. The VOM and start cost component of production cost make up around

1% of the total. Because such a significant portion of overall production cost relies on the oil price, it

is likely that any increase or decrease in the oil price will lead to an equal percent change in the total

system production cost. For example, a 10% increase in fuel price will lead to a near 10% increase in

total production cost.

Figure 6-5 shows the elasticity of fuel price and production cost. This sensitivity analysis confirms

that fuel price and production cost are near unit elastic – a 1% change in fuel price will lead to a 1%

change in production cost.



Figure 6-4: Fuel Cost as a Component of Overall Production Cost

Figure 6-5: Elasticity of Fuel Prices and Production Cost



Figure 6-6 shows the change in production cost by sensitivity in Scenario 1 and Scenario 7 under

different fuel price assumptions. From the figure it can be determined that;

The change in production cost under different fuel sensitivities is less for the high renewable scenario (Scenario 7) than a scenario with less renewable energy. This is simply due to less overall fuel consumption as renewable penetration increases.

Larger amounts of wind, solar and firm renewable energy will decrease volatility in production cost when fuel prices change assuming the price for the renewable resources is not tied to the price of oil.

Figure 6-6: Change in Production Cost in Fuel Sensitivities

Hawaii Stage 2 Interconnection Study Energy Storage


7 Energy Storage Options Whenever renewable energy is being evaluated the question always arises as to the efficacy of

Energy Storage devices to help mitigate the variability and minimize the amount of curtailment

required. In this analysis Energy Storage is evaluated over a range of sizes for two distinct operating

modes. In section 7.1 Energy Storage is examined as purely a reserve asset. Section 7.2 considers its

ability to shift energy from times of curtailment and low costs in order to offset oil fired thermal

generation at a later period. Section 7.3 compares the results of these two operating strategies.

The analysis in this section was based on the results for Scenario 7 which included 200 MW of wind

generation on Lanai, 200 MW of wind generation on Molokai and 100 MW of firm renewable energy

on Maui in addition to the existing resources. All recommended operating changes were also

incorporated. This included removing the fixed operating schedules on the Maui and Oahu baseload

generation and reducing the minimum power limits on the Oahu base load generation. In addition,

the analysis was considered to be technology neutral and demonstrate the benefits associated with

a generic energy storage device, however battery energy storage was assumed for the cost/benefit

analysis in Section 7.3. In both sets of analysis storage sizes from 50 MW to 200 MW were

considered.

7.1 Energy Storage as a Reserve Asset

The first analysis considered Energy Storage as an asset capable of providing contingency and

operating reserves. As has been described in previous sections the variability of the wind and solar

generation requires additional operating reserves to be carried. When thermal resources are

committed to provide these reserves they may not be able to shut down during low load or high

renewable periods thereby increasing the amount of renewable curtailment that must take place. An

energy storage device, such as a battery, could provide some of the necessary reserves without

introducing additional minimum generating requirements.

Figure 7-1 shows the operating and contingency reserve duration curves for Scenario 7 and the four

different levels of energy storage considered. The top curve is the same as was shown in Figure 3-8

with the addition of the 185 MW of contingency reserves. The storage device allows the reserves

from thermal generation to be reduced in all hours of the year. In order to do this the device can only

be used as a reserve asset and cannot simultaneously be used as an energy shifting device.



Figure 7-1: Operating and Contingency Reserve Duration Curves with Energy Storage

The annual operation of the system was simulated for these four new sensitivities. Figure 7-2 shows

the total production cost (fuel cost + VOM + start cost) and the average benefits of the storage

devices in $/KW. These values, and the total savings, are also shown in Table 7-1. While the overall

benefits continue to increase with increasing amounts of storage it is with diminishing returns. The

maximum value was with 50 MW of storage and was worth roughly $250/KW/yr.

Reserv

e R

eq

uir

em

en

t Scenario 7

50mw Storage

100mw Storage

150mw Storage

200mw Storage



Figure 7-2: Annual Production Cost Savings with Energy Storage as a Reserve Asset

Table 7-1: Annual Production Cost Savings with Energy Storage as a Reserve Asset

The source of this value can be seen in Table 7-2. As energy storage provides increasing amounts of

reserves the amount of wind and solar curtailment drops from 192 GWh to only 133 GWh. With

relaxed operating reserves and 200 MW of energy storage reducing the reserves, nearly 95% of the

available wind and solar generation is captured.

Scenario

Total

Production

Cost (M$)

Savings

(M$)

Average

Benefits

($/KW)

Scenario 7* 751.5

50mw Storage 739.0 12.5 250.00

100mw Storage 730.0 21.5 215.00

150mw Storage 722.7 28.9 192.67

200mw Storage 715.9 35.6 178.00

*Assumes Modified Operating Practices



Table 7-2: Delivered Wind and Solar Energy with Energy Storage as a Reserve Asset

Table 7-3 shows how the energy generation shifted by generation type by island. Not surprisingly

the generation in Maui County did not change significantly. In Oahu, however, the base load thermal

generation increased along with the delivered wind energy while the cycling units backed down by

almost an order of magnitude.

Table 7-3: Energy Generation by Island with Energy Storage as a Reserve Asset

Reducing reserve requirements while also modifying operating practices will ultimately decrease the number of baseload units online during each hour of the year. With 200 MW less reserve requirement on the system, less baseload units will be committed to serve load and provide reserves. Figure 7-3 shows the impact of reduced reserves on the operation of Oahu’s baseload units.

ScenarioTotal Wind &

Solar (GWh)

Additional

Wind (GWh)

Curtailment

(GWh)

Delivered

Energy (%)

Scenario 7* 2,335 - 192.4 92%

50mw Storage 2,365 30.0 162.4 94%

100mw Storage 2,379 44.6 147.8 94%

150mw Storage 2,386 51.4 141.0 94%

200mw Storage 2,394 59.5 132.9 95%

*Assumes modified operating practices

Base50mw

Storage

100mw

Storage

150mw

Storage

200mw

Storage

OAHU 7,589 7,597 7,602 7,604 7,604

BASELOAD 5,364 5,413 5,458 5,494 5,512

CYCLING 210 144 90 50 25

PEAKING 11 7 6 5 5

SOLAR 180 180 180 180 180

WIND 1,823 1,852 1,867 1,874 1,882

MAUI 1,674 1,671 1,671 1,671 1,672

BASELOAD 1,333 1,332 1,332 1,332 1,333

CYCLING 4.6 4.2 4.2 4.5 5.1

PEAKING 4.7 3.6 3.0 2.7 2.3

SOLAR 25 25 25 25 25

WIND 307 307 307 307 307

LANAI 0.6 0.4 0.3 0.3 0.3

MOLOKAI 2.7 1.9 1.7 1.4 1.2



Figure 7-3: Oahu Baseload Unit Operating Impacts with Reduced Reserve Requirement



7.2 Energy Storage as an Energy Shifting Asset

When the energy storage device is modeled to shift energy it is assumed that charging will take

place, storage levels allowing, whenever renewable energy is being curtailed or the AES coal plant is

being operated below its maximum rating. First preference is given to charging with curtailed

energy. Further, it is assumed that the storage device will generate energy whenever there is an

opportunity to displace oil-fired thermal generation and there is available down-range on the system.

In this way the energy is used as soon as it is economically attractive in order to make room for

additional charging as soon as possible. In some instances it may be possible to displace slightly

higher cost generation later in the day. However, there is no significant variation in the marginal cost

among the various oil-fired generators so the incentive to delay usage is small. The storage device is

assumed to be located on Oahu for the purpose of calculating DC cable flows.

In addition to varying the capacity of the storage device from 50 MW to 200 MW this analysis also

varied the size of the “reservoir” from 2 hours up to 100 hours. When the energy storage device is

being used to shift energy the storage size in MWh is a critical parameter. The value of the

generation provided is based on the spot price of the generation displaced minus the cost of

charging from the AES plant. Curtailed wind energy is assumed to be available at no charge. Round

trip efficiency of the storage is 80%.

Figure 7-4 shows the value of an energy storage device that is used to shift zero cost curtailed or low

cost thermal energy to a more desirable time frame.

Figure 7-4: Value of Energy Storage as an Energy Shifting Device

The most significant reductions to production cost are achieved with storage capabilities up to 12

hours. Adding storage levels beyond that increases the value only slightly. Increased capacities

beyond 50 MW provide slightly diminished returns. Figure 7-5 shows the utilization of the energy

storage for a 200 MW device with 6 hours of storage. Using the AES plant to supplement the

charging increased its value by 66%, as compared to charging only with curtailed energy.



Figure 7-5: Utilization of Energy Storage Device to Shift Energy

Figure 7-6 shows the average utilization by time of day for the same example. Almost all of the

charging is done overnight and the bulk of the generation (discharging) is done during the morning

load rise, although some generation is done later in the day.

Figure 7-6: Utilization of Energy Storage by Time of Day

Note: assumes 200MW asset with 6 hours of storage

Total Value: $23.9 million Total Value: $14.4 million



The graph on the left of Figure 7-7 shows the reduction in the renewable curtailment duration curve

for the same 200 MW asset. The curve on the right illustrates the increased utilization of the AES

plant.

Figure 7-7: Impact of 200MW, 6hr Storage Device on Renewable Curtailment and Coal Generation

7.3 Comparison of Energy Storage Results

Figure 7-8 shows the relative value (reduction in dispatchable thermal generation and production

cost) of a 2-hour storage device used for either energy shifting or reserve displacement as a function

of the storage charge/discharge rating. Regardless of the size, the reserve storage function provides

greater value. But while the value of the energy storage when used as a reserve asset will not

change as more hours of storage are added the previous charts have shown that the value of energy

shifting will increase.



Figure 7-8: Comparison of Savings for a 2-hour Storage Device

Figure 7-9 examines the value (reduction in dispatchable thermal generation and production cost) of

a 200 MW energy storage device. The diamond and horizontal dotted line show the value of the

device when used to provide spinning reserve. Increasing the hours of storage beyond one hour

adds no value to a device operated in this mode. The solid line shows the value when the device is

used for shifting energy within the week, and this does increase with increasing hours of storage.

This would seem to imply that a device with over 3 hours of storage would provide greater value

when used for energy shifting. However, the increased hours of storage come at an increased cost.

For a one-hour battery storage system roughly 50% of the cost is for the power conversion

equipment and the other half is for the storage. A two-hour battery will double the storage cost but

have no impact on the power conversion costs. In fact, the storage costs will continue to increase

linearly with the number of hours. In this way an Incremental Benefit can be calculated as:

(7-1)

As an example consider 3 hours of storage. The value when used for reserves is $12.4 M. When used

for energy shifting the value is $18.4 M. However, the relative cost for the 3-hour device is:

0.5 + 3(0.5) = 2 per unit

So that the incremental benefit = $18.4M / 2 = $9.2M. In this way it can be seen that the device used

for reserves, which only requires a single hour of storage, is still the better value. If this is continued

for all hours of storage the dotted curve labeled “Incremental Benefit of Energy Shifting” in Figure 7-9

is obtained. It can now be seen that no matter how many hours of storage are added it will never be

more economic than a one hour battery used to provide spinning reserve.



Figure 7-9: Comparison of Storage Options

Figure 7-10 extends this analysis across all four values of capacity (50MW, 100MW, 150MW and

200MW) for a range of storage sizes from 1 to 15 hours. Regardless of the capacity, no matter how

many hours of storage are added the energy shifting operation does not provide as much value as a

single hour of storage used to provide spinning reserve.

Figure 7-10: Comparison of Storage Options for a Range of Sizes

Assuming a 200MW asset, with different amounts of storage (MWh)

Hawaii Stage 2 Interconnection Study Cost-Benefit Analysis


8 Cost-Benefit Analysis Prior to this section, much of the analysis has concentrated on the benefits (production cost savings)

of an interconnected Oahu and Maui County electricity grid with increased renewable penetration.

The production cost analysis includes all of the variable costs of generating electricity (fuel, variable

operations and maintenance, and start costs). It does not capture the fixed or capital costs necessary

for operating a power system or constructing new renewable energy sources. In order to adequately

plan for future scenarios and an interconnected system, it is important to also analyze the capital

costs required to achieve the production cost savings. This section compares the study scenarios

using a traditional cost-benefit analysis comprised of four steps;

1. Develop capital cost assumptions for each technology from external sources, 2. Apply capital cost assumptions to the renewable capacity and transmission additions and

calculate an estimated range of total capital costs for each scenario, 3. Calculate benefits (production cost savings) for each scenario. 4. Compare costs and benefits across the scenarios.

8.1 Capital Cost Data

Total plant capital cost depends on several variables, including equipment and site characteristics

which are unknown at this stage in the planning process. Rather than use a single capital cost

assumption, this analysis uses a potential range of assumptions for each technology. The range of

capital cost assumptions was developed using a sample of third-party, publicly available reports

deemed by the project team as reasonably accurate and current. Table 8-1 shows the capital cost

assumptions ($/KW) for onshore wind, photovoltaic solar, and geothermal resources across six

different sources (See Chapter 11 for references). For the purposes of the cost-benefit analysis, the

firm renewable capacity discussed throughout the previous sections is considered here to be a

geothermal resource. This was done to allow a cost-benefit analysis similar to other resources (wind

and solar) where the variable fuel cost is zero. All costs in Table 8-1 represent total plant installation

costs for utility-scale applications.

Table 8-1: Capital Cost Data for Wind, Solar and Geothermal from External Sources ($/KW)

In order to simplify the range of capital costs, this analysis uses the minimum, maximum and

average capital costs for each technology, shown at the bottom of Table 8-1. Figure 8-1 shows a

box-plot of the range of capital costs by technology to allow for direct comparison across

Source Wind (Onshore) Solar PV Geothermal

EIA $2,438 $4,755 $4,141 - $5,578

NREL $1,762 $6,051 $3,661

Black & Veatch $1,980 $2,357 $5,940

Big Wind $2,600 $7,100

EPRI Low $2,120 $3,725

EPRI High $2,825 $5,050

Melbourne ERI $1,925 $3,408

Minimum $1,762 $2,357 $3,661

Average $2,236 $4,635 $4,830

Maximum $2,825 $7,100 $5,940



technologies. The capital cost assumptions are lowest for wind and, on average, highest for

geothermal. However, solar PV has the largest range of cost estimates. This reflects the uncertainty

surrounding prices of future solar installations across the industry.

Figure 8-1: Capital Cost Assumptions by Technology

The cost-benefit analysis also includes the cost of the DC cables with a 1x200 MW configuration for

Scenarios 1-4 and a 2x200 MW configuration for Scenarios 5-10. The price for the transmission

upgrades is assumed to be $1,200/KW and is based on previous analysis in the Economics of Big

Wind study conducted for proposed wind projects on Lanai and Molokai. It includes the costs of the

DC cables and any infrastructure required to interconnect the systems. As a result, the total cost of

transmission interconnection is $480 million for scenarios with a single DC cable and $960 million for

two DC cables. This cost is added to the total cost of each scenario and is not assigned to the cost of

an individual project.

An estimate of the AC cable infrastructure capital cost is also included. Initial estimates from the

Interisland Transmission Interconnection Capital Cost Estimate1 suggest additional capital costs

between $601 million and $830 million for three undersea AC cables to interconnect Maui, Molokai,

and Lanai. For the purpose of this study, a central estimate of $715 million was assumed for the tri-

cable configuration. However, Scenarios 1-4 may not require all three cables and it might be possible

to implement some scenarios with fewer AC cables. Scenarios 1-4 are assumed in this analysis to

only require a single AC link between Lanai and Maui. This is because there is only a single DC link

between Lanai and Oahu and the quick-start capabilities are only needed from either Lanai or

Molokai, but not both. As a result, the assumed capital cost for the single AC cable in Scenarios 1-4 is

assumed to be 33% of the full estimate, or $238 million.

1 Interisland Transmission Interconnection Capital Cost Estimate.” Prepared for IRP 2013. HECO Transmission Planning

Division, revised March 20, 2013. Reference: TPD 2013-05-Rev1.



8.2 Capital Costs for Study Scenarios

Using the minimum, average and maximum capital cost data from Section 8.1, a total capital cost is

calculated for each scenario and shown in Table 8-2. The total capital cost (in millions of dollars)

includes the cost of all wind, solar and geothermal additions along with the necessary AC and DC

transmission upgrades to interconnect the island systems.

For example, Scenario 1 includes the 200 MW Lanai wind addition, along with a single DC cable

configuration and a single AC cable configuration. Therefore, the minimum capital cost estimate is

equal to the wind capacity addition (200,000 KW) multiplied by the minimum wind cost assumption

from Table 8-1 (1,762 $/KW). As a result, the cost of the wind addition (using the minimum cost data)

is $352.4 million, plus the cost of the DC transmission upgrades ($480 million) and the AC

transmission upgrades ($239 million), for a total capital cost of $1,071 million. This process was

replicated for each scenario using the minimum, average and maximum capital cost data. Estimated

capital costs range from a low of $1.1 billion to a high of $3.7 billion depending on the capital cost

assumptions used and the scenario selected. For a more complete breakdown of each scenario’s

capital cost assumptions, see Section 12.3.

Table 8-2: Total Estimated Capital Cost by Scenario (M$)

In order to compare these costs with production cost savings (benefits) discussed throughout the

report, the total capital costs must be annualized. The annualized value represents the annual capital

recovery required ($/year) for a given year. In order to calculate this, the total capital cost is

multiplied by the fixed charge rate (FCR) per Equation 8-1. The FCR captures the expected debt

service, equity and income tax associated with an investment. The FCR, which is defined in Equation

8-2, was not calculated specifically for the purposes of this study. Instead, a range of potential FCRs

was used between 12% and 16%, representing typical rates for an investor owned utility. Owners

with easier access to capital will be able to borrow money and fund the project with less return on

investment required, translating to a lower FCR. In turn, this will drive down overall costs associated

with funding a new renewable energy project.

(8-1)

(8-2)

Table 8-3 shows the total annual capital recovery required for each scenario, assuming a 14% FCR

and the total capital cost assumptions in Table 8-2. Each scenario’s annual capital recovery required

Minimum Average Maximum

Scenario 1 1,071 1,166 1,284

Scenario 2 1,254 1,407 1,581

Scenario 3 1,437 1,649 1,878

Scenario 4 1,684 2,371 3,130

Scenario 5 2,381 2,570 2,806

Scenario 6 2,564 2,812 3,103

Scenario 7 2,747 3,053 3,400

Scenario 8 2,557 2,794 3,089

Scenario 9 2,740 3,035 3,386

Scenario 10 2,923 3,277 3,683

Total Capital Cost (M$)



was also analyzed using a 12% and 16% FCR, the results of which will be shown later in this chapter.

These values now represent annualized costs associated with renewable capacity additions and can

be directly compared to the benefits (production cost savings) attributed to each scenario.

Table 8-3: Annual Capital Recovery Required ($/Year) by Scenario using a 14% FCR

Note: Annual capital costs represent the cost of the Scenario’s “bundle” of new renewable additions, AC and DC cables.

8.3 Annual Benefits with Existing Operating Practices

The annual benefits used in the cost-benefit-analysis are the total operating cost savings associated

with each scenario. The total operating cost includes the production costs from dispatchable thermal

units as well as the existing PPA costs from renewable electricity sources. It does NOT include any

cost from new renewable sources, which is addressed later in Section 0. Table 8-4 outlines the

existing PPA prices assumed in this analysis.

Table 8-4: Existing PPA Prices by Plant

All scenarios are compared back to a non-interconnected system without any additional renewable

energy or DC cables. This represents an additional MAPS sensitivity on the Base Cases without the

Lanai wind plant (referred to as the “Existing System”). The additional sensitivity was required to

estimate the change in production costs from the installation of the Lanai wind plant. The results of

Minimum Average Maximum

Scenario 1 150 163 180

Scenario 2 176 197 221

Scenario 3 201 231 263

Scenario 4 236 332 438

Scenario 5 333 360 393

Scenario 6 359 394 434

Scenario 7 385 427 476

Scenario 8 358 391 432

Scenario 9 384 425 474

Scenario 10 409 459 516

Annual Capital Cost (M$)

Plant Name PPA Assumptions

On Peak: $130.72/MWh

Off Peak: $123.08/MWh

Tier 1 (up to 42 GWh): $241.99/MWh

Tier 2 (42 - 62 GWh): $186.80/MWh

Tier 3 (over 62 GWh): $53.07/MWh

Tier 1 (up to 83 GWh): $209.14/MWh

Tier 2 (over 83 GWh): $53.07/MWh

Kawailoa Wind (Oahu 70 MW Wind) $212.65/MWh

Kahuku Wind (Oahu 30 MW Wind) $220.87/MWh

Maui 15 MW Distributed Solar $210.00/MWh

Oahu 60 MW Central Solar $210.00/MWh

Oahu 40 MW Distributed Solar $210.00/MWh

On Peak: $166.1/MWh for the first 28.23 GWh per month, $105.8/MWh after

Off Peak:$161.4/MWh for the first 7.62 GWh per month, $63.5/MWh after

Auwahi Wind (Maui 21 MW Wind)

H Power

Kaheawa Pastures Wind #1 (Maui 30 MW Wind)

Kaheawa Pastures Wind #2 (Maui 21 MW Wind)



this additional sensitivity included a total production cost of $1,161 million. All of the future scenarios

were then compared back to this sensitivity to calculate both a total production cost savings (M$)

and additional delivered wind, solar and geothermal energy (GWh).

Table 8-5 shows the annual benefits by scenario, represented in two ways;

1. Total Reduction in Operating Cost (M$/Year) in Column 7. This is the change in total operating cost (thermal production cost plus existing PPA cost) between each scenario and the “Existing System.”

2. Reduction in Operating Cost ($/MWh) in Column 8. This is the change in total operating cost between each scenario and the “Existing System,” divided by the increase in wind, solar and geothermal energy delivered.

Table 8-5: Annual Benefits by Scenario with Existing Operating Practices

8.4 Annual Capital Recovery Required

Comparing the costs with the benefits illustrates the overall economics associated with each

scenario. Although each scenario has different levels of wind, solar, and geothermal capacity, this

analysis allows for direct comparison to one another. Figure 8-2 provides a visual representation of

the cost and benefits associated with each scenario. The grey bar on the bottom represents the AC

cable cost with a single cable for Scenarios 1-4 and three cables for Scenarios 5-10. The tan bar

represents the DC cable cost with a single cable for Scenarios 1-4 and two cables for Scenarios 5-10.

The blue bars represent the annual capital cost requirements for the renewable energy additions

using the minimum, average and maximum capital cost assumptions presented in Section 8.1. The

annual benefits are represented by the yellow markers. For a scenario to be economically attractive,

the benefits (yellow marker) must exceed the cost (blue bars).

For example, in Scenario 2, the benefit is about the same as the cost when using the minimum

capital cost assumptions. However, costs exceed benefits when using the average or high cost

assumptions. Therefore, Scenario 2 is economically viable if the additional renewable energy is built

at or below the minimum cost assumptions, but unattractive otherwise.

Delivered

Renewable

Energy

(GWh)

Total

Production

Cost

(M$/yr)

Existing PPA

Cost

(M$/yr)

Operating Cost

of Existing

Resources

(M$/yr)

Additional

Renewable

Energy

Delivered

(GWh)

Reduction in

Operating Cost

(M$/yr)

Reduction in

Operating Cost

($/MWh)

Existing System* 1,553 $1,161 $243 $1,404

Scenario 1 2,371 $1,035 $248 $1,283 818 $120 $147.06

Scenario 2 2,730 $982 $245 $1,228 1,177 $176 $149.35

Scenario 3 3,066 $938 $239 $1,178 1,513 $226 $149.24

Scenario 4 2,804 $972 $248 $1,220 1,251 $183 $146.51

Scenario 5 2,896 $970 $248 $1,219 1,343 $185 $137.71

Scenario 6 3,198 $928 $245 $1,173 1,645 $230 $139.94

Scenario 7 3,471 $893 $239 $1,133 1,918 $271 $141.30

Scenario 8 3,118 $948 $248 $1,196 1,565 $208 $132.62

Scenario 9 3,383 $910 $245 $1,156 1,830 $248 $135.35

Scenario 10 3,623 $880 $239 $1,119 2,070 $284 $137.26

*Existing System represents an additional MAPS sensitivity. It includes the isolated system cases, but does not include the Lanai wind plant.

**Operating cost includes production cost from thermal generating units, plus PPA cost for existing renewable energy plants



Figure 8-2: Cost-Benefit Analysis (M$/Year) by Scenario Using Different Capital Cost Assumptions

and Assuming a 14% FCR

Using the results of Figure 8-2, the following conclusions can be made;

When comparing the ten scenarios to one another, it becomes evident that the scenarios with three AC cables and two DC cables (Scenarios 5 – 10) are less economically favorable than the scenarios with single cables due to the increased capital cost associated with the additional cables and the increased level of curtailment. The increased curtailment in the later scenarios weakens the economic attractiveness of the renewable additions due to decreasing marginal benefits associated with the new capacity additions.

The most economically attractive scenarios are Scenarios 1 – 3 because there is limited curtailment and no additional cost associated with a second DC cable or the full tri-island interconnection in Maui County. When the firm renewable generation (geothermal) is added to Scenario 1, the economics become even more favorable. Under these scenarios, the economic benefits associated with the geothermal additions outweigh to capital costs using all but the highest capital cost assumptions.

Assuming minimum capital costs and existing operating practices, only Scenarios 2 and 3 are economic. All other scenarios do not cover the capital cost with the production cost savings.



In addition to using a range of capital cost assumptions, it is also illustrative to analyze a range of

different FCRs. Figure 8-3 shows the same cost-benefit analysis as before, but under a range of FCRs.

In this analysis, the average capital cost assumptions are held constant and the FCR is varied to

include 12%, 14% and 16%. The capital costs of the AC and DC cables are still included, but are not

colored separately. The annual benefits do not change as they are not affected by the FCR.

Interestingly, the range of FCR assumptions from 12% to 16% is similar to the range of different

underlying capital costs. Because the differences are minimal between these two approaches, the

underlying conclusions do not change significantly.

Figure 8-3: Cost-Benefit Analysis (M$/Year) by Scenario Using Different FCR Assumptions and

Assuming Average Capital Costs



Because the specifications and costs of the AC cable system are not fully understood at this point in

time, the break-even cost of the AC cable was also calculated. For this part of the analysis, the AC

cable cost is not included in the overall capital costs for each scenario. Instead, the surplus benefit

(total operating cost minus the capital cost of the DC cables and renewable additions) is calculated

for each scenario under a high, medium and low capital cost assumption. Columns 2 – 4 of Table 8-6

show the surplus benefit for all scenarios. A negative number represents a scenario where total

annualized costs exceed annual benefits (reduction in operating costs). A positive number represents

a scenario where the annual benefits exceed the costs.

In scenarios where surplus benefits are positive, a break-even cost can be calculated for the AC

cable network by taking the annual surplus benefit and dividing by a 14% FCR. This cost represents

the price that the AC cable network would need to be built in order for the scenario’s annual benefits

to cover the annualized costs.

Table 8-6: Break Even Cost of the AC Cable Network Assuming a 14% FCR

For example, results from Section 4.1 show that the AC cable interconnection between Maui, Lanai

and Molokai will reduce total operating cost by $8 million per year with no additional renewable

capacity or DC cables. Assuming a 14% FCR, the AC cable cost would need to be built for $57 million

in order to break even in the Maui County Base Case. The only other scenarios to have a positive

surplus benefit are Scenarios 2 and 3 under the minimum and average capital cost assumptions. For

these scenarios, the break even costs for the AC cables are between $30 million and $357 million

depending on the scenario and capital cost assumption.

In general, the break-even costs shown in Table 8-6 are significantly lower than the initial estimates

from the Interisland Transmission Interconnection Capital Cost Estimate. However, there are

significant benefits associated with the AC interconnection beyond the economic benefits from

reduced operating costs. These ancillary benefits include increased reliability and the ability to build

additional renewable capacity on Maui to be used for the Maui, Oahu, Lanai and Molokai grids.

Surplus

Benefit M$

(Min)

Surplus

Benefit M$

(Avg)

Surplus

Benefit M$

(Max)

Break Even

Cost M$

(Min)

Break Even

Cost M$

(Avg)

Break Even

Cost M$

(Max)

Maui County Base Case 8 8 8 57 57 57

Scenario 1 -4 -18 -34 N/A N/A N/A

Scenario 2 26 4 -20 183 30 N/A

Scenario 3 50 20 -12 357 146 N/A

Scenario 4 -27 -123 -229 N/A N/A N/A






Scenario 10 -33 -82 -139 N/A N/A N/A



8.5 Cost of Renewable Electricity (COE)

Another method of comparing costs and benefits across scenarios is conducting a cost of electricity

(COE) analysis. To do this, all of the costs and benefits are levelized based on the amount of additional

renewable energy delivered. This provides values in terms of dollars per MWh of energy delivered,

rather than total annual capital cost (Equation 8-3). Results of this calculation can be found in the last

column of Table 8-5.

(8-3)

Figure 8-4 shows the costs and benefits on a $/MWh basis under different capital cost assumptions

and a 14% FCR. Using these results, a variety of conclusions can be made;

Benefits in each scenario do not vary significantly and remain close to $150/MWh. This is because the benefits associated with additional renewable energy are equal to the amount of thermal energy it is displacing. Although the actual unit displacement varies across the scenarios, in general the displaced energy is oil or diesel fired generation. As a result the overall benefits of displaced thermal energy do not vary significantly.

When additional amounts of geothermal resources are added, the overall cost in $/MWh decreases.

When incremental geothermal capacity is added, there is no change in the number of DC cables, but the overall delivered renewable energy increases dramatically due the resource’s high capacity factor.

Overall, the cost of electricity method highlights similar findings as the annual capital recovery

requirement method. Scenarios 1 – 3, which contain only a single AC & DC cable configuration, are

more economically viable than the later scenarios which include a second DC cable and experience

increased curtailment. In addition, the COE methodology favors technologies with higher capacity

factors. For example, scenarios which add the geothermal and firm capacity are more attractive

relative to the cases without them.



Figure 8-4: Cost Benefit Analysis ($/MWh) Based on Delivered Renewable Energy

8.6 Cost-Benefit Analysis with Modified Operating Practices

As described in Chapter 5, operating practices have a significant impact on variable operating cost.

Although the capital costs in this analysis don’t change, the annual benefits (production cost savings)

change dramatically when operating constraints are relaxed. This is because curtailment is reduced

and delivered renewable energy increases. Total capital costs do not increase in this analysis

because the incremental capital costs associated with required upgrades to modify operating

practices (retrofits on existing thermal units, synchronous condensers, etc.) are not included in this

study. The feasibility and cost to achieve these modified operating practices need to be evaluated

and capital costs added to the analysis in the future.

Table 8-7 shows the annual benefits (product cost savings) for each scenario, this time with modified

operating practices. The new benefits values are plotted against the same capital cost assumptions

in Figure 8-5. In addition, the benefits are also plotted against the capital costs on a $/MWh basis,

using the increased delivered renewable energy. Note that the existing PPA costs are the same in all

scenarios. This is because modified operating practices allow the system to take all available energy

from the existing renewable plants listed in Table 8-4.



Table 8-7: Annual Benefits by Scenario with Modified Operating Practices

Figure 8-5: Cost-Benefit Analysis (M$/Year) with Modified Operating Practices

Delivered

Renewable

Energy

(GWh)

Total

Production

Cost

(M$/yr)

Existing PPA

Cost

(M$/yr)

Operating Cost

of Existing

Resources

(M$/yr)

Additional

Renewable

Energy

Delivered

(GWh)

Reduction in

Operating Cost

(M$/yr)

Reduction in

Operating Cost

($/MWh)

Existing System* 1,553 $1,161 $243 $1,404

Scenario 1 2,511 $984 $250 $1,234 958 $169 $176.60

Scenario 2 2,939 $912 $250 $1,162 1,386 $241 $174.01

Scenario 3 3,359 $847 $250 $1,097 1,806 $306 $169.70

Scenario 4 2,966 $910 $250 $1,160 1,413 $243 $172.02

Scenario 5 3,200 $885 $250 $1,136 1,647 $268 $162.56

Scenario 6 3,597 $822 $250 $1,072 2,044 $331 $161.96

Scenario 7 3,977 $765 $250 $1,016 2,424 $388 $159.98

Scenario 8 3,541 $844 $250 $1,095 1,988 $309 $155.40

Scenario 9 3,915 $785 $250 $1,036 2,362 $368 $155.62

Scenario 10 4,267 $733 $250 $984 2,714 $420 $154.73

**Operating cost includes production cost from thermal generating units, plus PPA cost for existing renewable energy plants

*Existing System represents an additional MAPS sensitivity. It includes the Oahu and Maui County base cases, but does not include the Lanai wind plant.



Figure 8-6: Cost-Benefit Analysis ($/MWh) Based on Delivered Renewable Energy with Modified

Operating Practices

Figure 8-5 and Figure 8-6 show the added value associated with changes to the operating practices.

These practices include;

Relaxed fixed operating schedules for existing Oahu and Maui baseload units

Reduced minimum power limits on Oahu baseload units

Assuming the modified operating practices are achievable, the additional benefits captured through

the changes to operating practices are large enough to reverse some of the findings from the

previous cost-benefit analysis under the base assumptions. As a result, the following conclusions can

be drawn;

When operating practices are modified, the annual benefits exceed the costs in 3 of the 10 scenarios, even under the highest capital cost assumptions.

The solar scenario (Scenario 4) begins to be cost competitive if solar equipment costs are near the low end of assumed values.

Modifications to the existing thermal fleet will increase utilization of the wind and solar assets, improving their economic value.

The added benefits arise from reduced curtailment when baseload units can be turned down lower

or turned off all together. This demonstrates that the importance of operating practices can be equal

to, or greater, than the overall capital cost assumptions associated with new renewable capacity

additions. However, the costs and feasibility of achieving the operational modifications need to be

assessed to fully capture the cost-benefit analysis.



Similar to the results shown in Section 8.3, the break even costs are also provided for each scenario

with Modified Operating Practices. Table 8-8 shows the surplus benefits and break even costs for

Scenarios 1 – 10 under minimum, average and maximum capital cost assumptions. With modified

operating practices most scenarios are economic and therefore more break even costs can be

computed for the AC cable network. In addition, the break even cost for the AC cable cost now

exceed $600 million and are closer to the initial estimates provided in the Interisland Transmission

Interconnection Capital Cost Estimate report.

Table 8-8: Break Even Cost of the AC Cable Network Assuming a 14% FCR and Modified

Operating Practices

Surplus

Benefit M$

(Min)

Surplus

Benefit M$

(Avg)

Surplus

Benefit M$

(Max)

Break Even

Cost M$

(Min)

Break Even

Cost M$

(Avg)

Break Even

Cost M$

(Max)

Scenario 1 49 35 19 347 252 134

Scenario 2 95 73 49 678 525 352

Scenario 3 135 105 73 962 750 521

Scenario 4 37 -60 -166 262 N/A N/A

Scenario 5 31 4 -29 219 29 N/A

Scenario 6 68 34 -7 488 240 N/A

Scenario 7 99 57 8 710 404 57

Scenario 8 47 14 -27 337 100 N/A

Scenario 9 80 39 -10 573 277 N/A

Scenario 10 107 57 1 764 410 4



8.7 Total Cost Including PPA Costs

Another method for analyzing total system costs is to use estimated PPA costs for delivered

renewable energy rather than using the estimated capital costs of new construction. Publicly

available PPA costs were used as available for existing facilities, and assumptions were made for

future capacity additions throughout the later scenarios.

Table 8-4 shows the PPA assumptions for each existing plant on the interconnected system. For

some of the existing PPA structures, a tiered approach was used where energy is priced differently

depending on the overall extent of annual generation. Other plants assume an on-peak and off-peak

PPA structure where prices are dependent on time of generation.

The existing PPA prices found in Table 8-4 do not include an estimate for new or proposed renewable

resources evaluated across the different scenarios in this study. Instead, this section will assume a

range of potential prices per MWh of renewable electricity. The low price assumption is $110/MWh,

the medium price assumption is $165/MWh and the high price assumption is $220/MWh. The price

assumptions are technology neutral and applied to each of the new renewable sources evaluated in

the future scenarios. Including rough estimates of the future renewable generation, while applying

the cost of existing PPA resources, provides a high-level perspective of total operating costs for an

interconnected Oahu and Maui County grid.

Figure 8-7 shows the total system cost under existing operating practices, including PPA payments,

for each scenario. Using the PPA pricing assumptions highlighted in Table 8-4, a total operating cost

was calculated for each scenario. The total operating cost is equal to the production cost for thermal

resources (grey bars) plus the exisiting PPA cost of the renewable generation (red bars), plus the PPA

cost of new construction renewable generation (range of green bars). The individual cost (M$) of each

component is listed at the production cost and existing PPA cost segments. The total operating cost,

assuming high future PPA costs is highlighted on top of each bar. The PPA cost is included for

renewable assets only. It does not capture the PPA cost for thermal IPP units which are already

accounted for in the production cost.



Figure 8-7: Total Operating Cost by Scenario (M$)

The following conclusions can be drawn from Figure 8-7;

Total production cost (mostly fuel costs) decreases throughout the scenarios as renewable energy increases.

In the Base Case, 24-29% of total operating cost is paid to external PPAs. In the highest renewable scenario (Scenario 10), this share of renewable PPA cost increases to 35-44%.

If future renewable energy is purchased at the higher end of the range considered here, ($165 - $220/MWh), total system operating costs increase as renewable penetration increases. This occurs when PPAs are more expensive than the variable operating cost of existing thermal assets which they displace. As a result, future high penetration renewable scenarios and changes to existing operating practices will actually increase total system operating cost. However, the cost of future renewable resources will not be tied to the price of oil and will therefore be less variable and not expected to increase as quickly as oil prices.

If future renewable energy is purchased at the low end of the range ($110/MWh) total system operating cost is reduced with increased renewable penetration.



Figure 8-8 shows the total operating cost by scenario, comparing each scenario with existing

operating practices and modified operating practices. By comparing the total system cost between

different operating practices, additional conclusions can be drawn;

The modified operating practices leads to decreased utilization of the thermal fleet due to decreased minimum power limits and reducing the fixed operating schedule. This causes production cost for the thermal units to decrease in each scenario.

Total PPA cost increases because the reduced thermal output is replaced by previously curtailed, higher cost, renewable energy.

Future PPAs should continue to follow a tiered structure or share benefits of reduced curtailment

similar to existing PPAs for the KWP, Auwahi and H Power units. Without the tiered structures,

changes made to operating practices in order to reduce curtailment could actually increase total

system costs if the PPA prices were higher than $165/MWh. This increase to total system cost will

occur even though there is no increase to variable cost for the additional renewable energy.

Figure 8-8: Comparison of Total Cost (M$) with Existing and Modified Operating Practices

Hawaii Stage 2 Interconnection Study Observations & Conclusions


9 Observations & Conclusions This analysis investigated the interconnected operation of the combined Oahu and Maui County

power grids with a variety of renewable generation additions. The primary conclusions are:

Interconnection offers a variety of benefits. It enables sharing of reserves and more efficient operation of the existing thermal fleets. In addition, it positions the system to accept more renewable generation and access to better sites for wind and geothermal generation.

Operating changes and reductions in the minimum power levels of baseload generation are very beneficial with the interconnected system and become more valuable as renewable penetration increases.

Energy storage is more attractive as a reserve asset than as an energy shifting device.

PPAs for renewable energy, especially wind and central solar resources, should have a tiered structure so that reducing curtailment benefits the utility and rate payer as well as the developer. Other structures are also possible, but the key aspect is that benefits of reduced curtailment should be shared in a manner that benefits all participants in a fair manner. In addition, the procurement of the renewables should be separate from the access to the DC cables.

The DC cables should be a system asset, not tied to any single renewable asset. This improves overall grid efficiency and available capacity on the cables can be used for additional future renewable energy sources. The nominal 200 MW rating of the cables was not found to be limiting in most cases, even with additional renewable sources.

Hawaii Stage 2 Interconnection Study Future Research


10 Future Research When the study scope was developed, it was decided to calculate the value of the interconnection

with existing operating practices. The additional value of modified operating practices were then

determined on the interconnected system only, as part of a sensitivity and mitigation strategy

analysis.

However, the modified operating practices add value to the scenarios, even if the systems are not

interconnected. In fact, it is likely that much of the value of the modifications is a direct result of the

interconnection and some of the benefits would not be captured if the systems remained isolated. So

there is an open question; what is the value of the interconnection (grid-tie vs. gen-tie) if the

operating practices are modified before the interconnection is implemented? It is likely that the

modified operating practices will increase the value of the interconnection, and thus provide a

greater incentive to choose a grid-tie transmission plan rather than a gen-tie configuration for new

renewable capacity.

In order to appropriately assign value to the interconnect versus the modified operating practices,

several new simulations should be run. These include the following cases;

1. Base Cases with modified operating practices 2. Future scenarios with modified operating practices in a grid-tie only transmission

configuration.

Figure 10-1: Determining the Incremental Value of Interconnection vs. Modified Operating

Practices

Hawaii Stage 2 Interconnection Study Future Research


In addition, there is an open question about the order in which the modified operating practices take

place. In the original project scope system modifications were made in the following steps:

1. Remove Maui fixed operating schedules 2. Step 1 + Reduce Oahu minimums 3. Step 1 + Step 2 + Remove Oahu fixed operating schedules

Although the final end-point will remains unchanged with all three modifications in place, the

economic benefits for each incremental step will change if the order of its implementation changes.

For example, the original Stage 2 analysis shows that reduced Oahu minimums provides the most

incremental value, even though it is implemented after the removal of the Maui fixed operating

schedules. It is possible that if the Oahu minimums were reduced first, the economic value would be

even greater. By running additional simulations with different implementation schedules, HECO will

have a better understanding about the relative benefits associated with each modification in

isolation. This will aid in decision making about which modifications provide the largest economic

benefit.

The following additional simulations are suggested in order to appropriately assign value to the

system modifications in isolation;

1. Remove fixed operating schedules on all Maui units (already run in Stage 2 study) 2. Remove fixed operating schedules on individual Maui units, one at a time, to determine which

units will provide the most economic benefit. 3. Reduce minimum power limits on all Oahu units 4. Reduce minimum power limits on individual Oahu units, one at a time, to determine which

units will provide the most economic benefit 5. Remove fixed operating schedules on all Oahu units 6. Remove fixed operating schedules on individual Oahu units, one at a time, to determine

which units will provide the most economic benefit.

HNEI and GE Energy Consulting are preparing to conduct this analysis. When it is completed, the

results will be documented in a new report.

Hawaii Stage 2 Interconnection Study References


11 References

11.1 Previous Studies

[1] “Hawaii Solar Integration Study; Summary Technical Report”, GE Energy Consulting, December

2012

[2] “Hawaii Solar Integration Study; Final Technical Report for Oahu”, GE Energy Consulting,

December 2012.

[3] “Hawaii Solar Integration Study; Final Technical Report for Maui”, GE Energy Consulting,

December 2012.

[4] 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.

[5] “KWP2 Wind Integration Study - Final Report”, GE Energy, June 2010.

[6] “Oahu Wind Integration Study – Final Report”, GE Energy Consulting, December 2010. Available at:

http://www.hnei.hawaii.edu/sites/web41.its.hawaii.edu.www.hnei.hawaii.edu/files/story/2011/03/

Oahu_Wind_Integration_Study.pdf

11.2 Capital Cost Assumptions

[7] U.S. Energy Information Agency. Updated Capital Cost Estimates for Electricity Generation Plants.

Washington, D.C. November 2010.

http://www.eia.gov/oiaf/beck_plantcosts/pdf/updatedplantcosts.pdf

[8] National Renewable Energy Laboratory. Energy Technology Cost and Performance Data. July

2010. http://www.nrel.gov/analysis/costs.html

[9] Black & Veatch. Cost and Performance Data for Power Generation Technologies, Prepared for the

National Renewable Energy Laboratory. February 2012. http://bv.com/docs/reports-studies/nrel-

costreport.pdf

[10] National Renewable Energy Laboratory. Energy Technology Cost and Performance Data. July

2010. http://www.nrel.gov/analysis/costs.html

[11] Electric Power Research Institute. Generation Technology Options in a Carbon-Constrained World.

EPRI Report 1022782. 2011.

http://globalclimate.epri.com/doc/Generation_Technology_Options_in_a_Carbon-

Constrained_World.pdf

[12] Hearps, Patrick and Dylan McConnell. Renewable Energy Technology Cost Review. University of

Melbourne, Energy Research Institute. March 2011. http://www.garnautreview.org.au/update-

2011/commissioned-work/renewable-energy-technology-cost-review.pdf

Hawaii Stage 2 Interconnection Study Appendix


12 Appendix

12.1 GE MAPS Description

Production cost modeling of the NSPI system was performed with the GE’s Multi Area Production

Simulation (MAPSTM) software program. This commercially available modeling tool has a long history

of governmental, regulatory, independent system operator and investor-owned utility applications.

The production cost model provides the unit-by-unit production output (MW) on an hourly basis for

an entire year of production (GWh of electricity production by each unit). The results also provide

information about the variable cost of electricity production, emissions, fuel consumption, etc.

The overall simulation algorithm is based on standard least marginal cost operating practice. That is,

generating units that can supply power at lower marginal cost of production are committed and

dispatched before units with higher marginal cost of generation. Commitment and dispatch are

constrained by physical limitations of the system, such as transmission thermal limits, minimum

spinning reserve, as well as the physical limitations and characteristics of the power plants.

The primary source of model uncertainty and error for production cost simulations, based on the

model, consist of:

o Some of the constraints in the model may be somewhat simpler than the precise situation dependent rules used by HECO.

o Marginal production-cost models consider heat rate and a variable O&M cost. However, the models do not include an explicit heat-rate penalty or an O&M penalty for increased maneuvering that may be a result of incremental system variability due to as-available renewable resources (in future scenarios).

o The production cost model requires input assumptions like forecasted fuel price, forecasted system load, estimated unit heat rates, maintenance and forced outage rates, etc. Variations from these assumptions could significantly alter the results of the study.

o Prices that HECO pays to IPPs for energy are not, in general, equal to the variable cost of production for the individual unit, nor are they equal to the systemic marginal cost of production. Rather, they are governed by PPAs. The price that HECO pays to third parties is reflected in the simulation results insofar as the conditions can be reproduced.

The simulation results provide insight into hour-to-hour operations, and how the commitment and

dispatch may change subject to various changes, including equipment or operating practices. Since

the production cost model depends on fuel price as an input, relative costs and change in costs

between alternative scenarios tend to produce better and more useful information than absolute

costs. The results from the model approximate system dispatch and production, but do not

necessarily identically match system behavior. The results do not necessarily reproduce accurate

production costs on a unit-by-unit basis and do not accurately reproduce every aspect of system

operation. However, the model reasonably quantifies the incremental changes in marginal cost,

emissions, fossil fuel consumption, and other operations metrics due to changes, such as higher

levels of wind power.



12.2 Maintenance and Outage Schedule

Table 12-1: Maui County Weekly Maintenance and Outage Schedule

UNIT NAME 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

X1 RO RO RO RO PS PR

X2 RO PS RO PR RO RO

M1 FD FD FD PF RO PR RO RO RO

M2 RO PF FD FD PF RO RO PR RO

M3 PF FD FD FD FD FD FD FD PF RO PR RO RO

M4 PR PS

M5 PS PR

M6 PS PR

M7 PR PS

M8 PS PR

M9 PR PS

M10 PF FD FD FD FD FD FD PF PR

M11 PR PF FD FD FD FD FD FD FD

M12 PR PS

M13 PR PS

M141516 PF FD PF FD PF PR

M1718 PR PF FD PF

M171819 PR PF FD PF FD PF

K1 PR PF FD FD FD PF

K2 FD FD FD FD PF PR

K3 FD FD FD PF PR

K4 PF FD FD FD PF PR

GEN_LAN1 PR PS RO

GEN_LAN2 RO PS PR

GEN_LAN3 RO PR PS

GEN_LAN4 RO PS PR

GEN_LAN5 PS PR RO

GEN_LAN6 PS PR RO

GEN_LAN7 PR PS RO

GEN_LAN8 PS PR RO

GEN_LANM RO PR PS

GEN_MOL1 PR PS RO

GEN_MOL2 PS PR RO

GEN_MOL3 RO PS PR

GEN_MOL4 PS PR RO

GEN_MOL5 PS PR RO

GEN_MOL6 PS RO PR

GEN_MOL7 RO PR PS

GEN_MOL8 PS RO PR

GEN_MOL9 PS RO PR

GEN_MOLS PR PS RO

Key: RO = Random Outage PR = Partial Random Outage FD = Fixed Daily Maintenance PF = Partial Fixed Maintenance PS = Partial Scheduled Maintenance

Week of Year -->



Table 12-2: Oahu Weekly Maintenance and Outage Schedule

UNIT NAME 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

HONH8 PR PF FD FD FD FD FD FD FD PF RO RO RO RO RO RO

HONH9 PR PF FD PF RO RO RO RO RO RO RO

WAIW3 PR RO RO PF FD PF RO RO RO RO RO

WAIW4 PR RO RO PF FD PF RO RO RO RO

WAIW5 RO PF FD FD PF PR RO

WAIW6 RO PR RO PF FD FD PF

WAIW7 PF FD PF PR RO RO

WAIW8 RO PR RO PF FD FD FD FD FD PF

WAIW9 RO RO RO PR RO RO PF RO

WAIW10 RO RO PF FD FD FD FD FD FD PF PR RO RO RO

KAHK1 PF FD PF RO PR


KAHK3 RO PR PF FD FD FD FD FD FD FD FD FD FD FD FD FD FD PF

KAHK4 PR RO PF FD FD FD PF RO

KAHK5 PF FD FD FD FD FD FD FD FD FD FD FD FD FD FD FD FD FD PF PR RO


KALKAL1 PF FD PF PR

KALKAL2 PF FD FD FD PF PR

KALKAL3 PF FD FD FD FD PF PR

AES PR

CIPCT1 RO RO RO PF PF RO RO RO RO PR

AIRDSG8

Key: RO = Random Outage PR = Partial Random Outage FD = Fixed Daily Maintenance PF = Partial Fixed Maintenance PS = Partial Scheduled Maintenance

Week of Year -->



12.3 Detailed Capital Cost Assumptions

Table 12-3: Capital Cost Calculator using Minimum Cost Assumptions

Table 12-4: Capital Cost Calculator using Average Cost Assumptions

Capital Cost Assumption Used: Min

Include AC Cable Cost (Y/N)? Yes

RE & Transmission Resources - Capital Cost

1,762 2,357 3,661 2,400 716 0.14

$/kW $/kW $/kW $/kW $/M FCR

Scenario

RE

Delivered

(GWh)

RE

Delivered

(%)

VOC

($M/yr)

Existing

PPA Cost

($M/yr)

Total Cost

($M/yr)

Wind

(MW)

Solar

(MW)

Geother

mal

(MW)

HVDC

(MW)

Wind

($M)

Solar

($M)

Geothermal

($M)

HVDC &

Oahu Grid

Upgrades

($M)

AC Maui

County

Upgrades

($M)

Total Cost

($M)

Add'l RE

Delivered

(GWh)

Add'l Capital

Cost ($M)

Annual Capital

Recovery Req

($M)

COE ($/MWh)

Change in

Total Cost

($M/yr)

Reduction in

VOC ($/MWh)

BASE 1553 17% 1161 243 1404

1 2371 26% 1035 248 1283 200 200 352 0 0 480 239 1071 818 1071 150 $183 -120.3 $147

2 2730 30% 982 245 1228 200 50 200 352 0 183 480 239 1254 1177 1254 176 $149 -175.8 $149

3 3066 33% 938 239 1178 200 100 200 352 0 366 480 239 1437 1513 1437 201 $133 -225.8 $149

4 2804 30% 972 248 1220 200 260 200 352 613 0 480 239 1684 1251 1684 236 $188 -183.3 $147

5 2896 31% 970 248 1219 400 400 705 0 0 960 716 2381 1343 2381 333 $248 -184.9 $138

6 3198 35% 928 245 1173 400 50 400 705 0 183 960 716 2564 1645 2564 359 $218 -230.2 $140

7 3471 38% 893 239 1133 400 100 400 705 0 366 960 716 2747 1918 2747 385 $200 -271.0 $141

8 3118 34% 948 248 1196 500 400 881 0 0 960 716 2557 1565 2557 358 $229 -207.6 $133

9 3383 37% 910 245 1156 500 50 400 881 0 183 960 716 2740 1830 2740 384 $210 -247.7 $135

10 3623 39% 880 239 1119 500 100 400 881 0 366 960 716 2923 2070 2923 409 $198 -284.1 $137

MAPS Simulation Results Comparison to Base Case (Not Interconnected)Additional Capital CostsPPA Costs Capacity Additions

Capital Cost Assumption Used: Average



2,236 4,635 4,830 2,400 716 0.14


Scenario

RE

Delivered

(GWh)

RE

Delivered

(%)

VOC

($M/yr)

Existing

PPA Cost

($M/yr)

Total Cost

($M/yr)

Wind

(MW)

Solar

(MW)

Geother

mal

(MW)

HVDC

(MW)

Wind

($M)

Solar

($M)

Geothermal

($M)

HVDC &

Oahu Grid

Upgrades

($M)

AC Maui

County

Upgrades

($M)

Total Cost

($M)

Add'l RE

Delivered

(GWh)

Add'l Capital

Cost ($M)

Annual Capital

Recovery Req

($M)

COE ($/MWh)

Change in

Total Cost

($M/yr)

Reduction in

VOC ($/MWh)

BASE 1553 17% 1161 243 1404

1 2371 26% 1035 248 1283 200 200 447 0 0 480 239 1166 818 1166 163 $200 -120.3 $147

2 2730 30% 982 245 1228 200 50 200 447 0 241 480 239 1407 1177 1407 197 $167 -175.8 $149

3 3066 33% 938 239 1178 200 100 200 447 0 483 480 239 1649 1513 1649 231 $153 -225.8 $149

4 2804 30% 972 248 1220 200 260 200 447 1205 0 480 239 2371 1251 2371 332 $265 -183.3 $147

5 2896 31% 970 248 1219 400 400 894 0 0 960 716 2570 1343 2570 360 $268 -184.9 $138

6 3198 35% 928 245 1173 400 50 400 894 0 241 960 716 2812 1645 2812 394 $239 -230.2 $140

7 3471 38% 893 239 1133 400 100 400 894 0 483 960 716 3053 1918 3053 427 $223 -271.0 $141

8 3118 34% 948 248 1196 500 400 1118 0 0 960 716 2794 1565 2794 391 $250 -207.6 $133

9 3383 37% 910 245 1156 500 50 400 1118 0 241 960 716 3035 1830 3035 425 $232 -247.7 $135

10 3623 39% 880 239 1119 500 100 400 1118 0 483 960 716 3277 2070 3277 459 $222 -284.1 $137




Table 12-5: Capital Cost Calculator using Maximum Cost Assumptions

Table 12-6: Capital Cost Assumptions for the DC Cable Interconnections

Capital Cost Assumption Used: Max



2,825 7,100 5,940 2,400 716 0.14


Scenario

RE

Delivered

(GWh)

RE

Delivered

(%)

VOC

($M/yr)

Existing

PPA Cost

($M/yr)

Total Cost

($M/yr)

Wind

(MW)

Solar

(MW)

Geother

mal

(MW)

HVDC

(MW)

Wind

($M)

Solar

($M)

Geothermal

($M)

HVDC &

Oahu Grid

Upgrades

($M)

AC Maui

County

Upgrades

($M)

Total Cost

($M)

Add'l RE

Delivered

(GWh)

Add'l Capital

Cost ($M)

Annual Capital

Recovery Req

($M)

COE ($/MWh)

Change in

Total Cost

($M/yr)

Reduction in

VOC ($/MWh)

BASE 1553 17% 1161 243 1404

1 2371 26% 1035 248 1283 200 200 565 0 0 480 239 1284 818 1284 180 $220 -120.3 $147

2 2730 30% 982 245 1228 200 50 200 565 0 297 480 239 1581 1177 1581 221 $188 -175.8 $149

3 3066 33% 938 239 1178 200 100 200 565 0 594 480 239 1878 1513 1878 263 $174 -225.8 $149

4 2804 30% 972 248 1220 200 260 200 565 1846 0 480 239 3130 1251 3130 438 $350 -183.3 $147

5 2896 31% 970 248 1219 400 400 1130 0 0 960 716 2806 1343 2806 393 $293 -184.9 $138

6 3198 35% 928 245 1173 400 50 400 1130 0 297 960 716 3103 1645 3103 434 $264 -230.2 $140

7 3471 38% 893 239 1133 400 100 400 1130 0 594 960 716 3400 1918 3400 476 $248 -271.0 $141

8 3118 34% 948 248 1196 500 400 1413 0 0 960 716 3089 1565 3089 432 $276 -207.6 $133

9 3383 37% 910 245 1156 500 50 400 1413 0 297 960 716 3386 1830 3386 474 $259 -247.7 $135

10 3623 39% 880 239 1119 500 100 400 1413 0 594 960 716 3683 2070 3683 516 $249 -284.1 $137


Electranix Report: HVDC cost is $533M ($1,333/kW) for 2x200MW HVDC lines, Oahu-Molokai & Oahu-Lanai (OWITS config B1-2); no data for ac upgrades

Docket 2009-0162 HVDC and AC upgrade costs itemized as follows:

$533 M HVDC cable cost, 2x200MW rating

$95 M Lanai & Molokai infrastructure

$188 M Project management, engineering, contingency

$142 M Oahu transmission infrastructure

$958 M Total installed cost for 400 MW HVDC system to Lanai & Molokai wind plants, rounded to $1B in the docket ($2,400/kW)

interconnection of grid systems for maui and oahu counties · 2019-11-08 · acknowledgement: this...

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