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

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

Contract No. DE-AC36-08GO28308

JEDI Marine and Hydrokinetic 

Model: User Reference Guide 

Marshall Goldberg

MRG & Associates

Nevada City, California

Mirko Previsic

RE Vision Consulting

Sacramento, California

Subcontract Report

NREL/SR-6A20-50402

April 2011
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

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

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

JEDI Marine and Hydrokinetic

Model: User Reference Guide 

Marshall Goldberg

MRG & Associates

Nevada City, California

Mirko Previsic

RE Vision Consulting 

Sacramento, California

NREL Technical Monitor: Suzanne Tegen

Prepared under Subcontract No. LEE-7-77549-01

Subcontract Report

NREL/SR-6A20-50402

April 2011
This publication received minimal editorial review at NREL.

NOTICE

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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 herein to any specific commercial product, process, or service by trade name, 

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 

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iii

Acknowledgments

Funding for the development of the JEDI Marine and Hydrokinetic Model and this companion 

reference guide was provided by the U.S. DOE Water Power Program. Thanks to Alejandro 

Moreno, Mike Reed, and Hoyt Battey for supporting this work throughout. In addition, thanks to 

our reviewers, including Aaron Hernandez (Verdant Power), John Cooper (Ocean Renewable 

Power Company), Suzanne Tegen (NREL), Lynn Billman (NREL), and Eric Lantz (NREL), as 

well as NREL technical editor, Mary Lukkonen. Of course, any remaining errors are the sole 

responsibility of the authors. 
iv

Executive Summary

The Jobs and Economic Development Impact Model (JEDI) for Marine and Hydrokinetics 

(MHK) is a user-friendly spreadsheet-based tool designed to demonstrate the economic impacts 

associated with developing and operating MHK power systems in the United States. The JEDI 

MHK User Reference Guide was developed to assist users in using and understanding the model. 

This guide provides information on the model’s underlying methodology, as well as the sources 

and parameters used to develop the cost data utilized in the model. This guide also provides basic 

instruction on model add-in features, operation of the model, and a discussion of how the results 

should be interpreted. 
v 

Table of Contents

Introduction ................................................................................................................................................. 1

Methodology ................................................................................................................................................ 2

Inputting Data and Running the Model ..................................................................................................... 4

Interpreting the Results.............................................................................................................................. 5

Caveats......................................................................................................................................................... 6

User Add-in Location Feature.................................................................................................................... 7

Accessing and Viewing Model Work Areas.............................................................................................. 9

Data Sources ............................................................................................................................................. 10

Cost Uncertainty Ranges..........................................................................................................10

Pilot Cost Versus Estimated Cost ............................................................................................11

Cumulatively Deployed Capacity—Impacts on Cost Reduction .............................................12

Cost Categories ........................................................................................................................12

References................................................................................................................................................. 13

Publicly Available MHK Cost Studies ..................................................................................................... 14
1 

Introduction

The Marine and Hydrokinetic (MHK) Jobs and Economic Development Impact Model (JEDI) is 

designed to demonstrate the economic impacts associated with developing and operating MHK

power systems in the United States. Hydrokinetic technologies include wave, tidal, river 

hydrokinetic, and ocean current technologies. MHK is an emerging renewable technology sector 

with limited deployed capacity (about 5 MW) to date. Unlike wind and other renewable and 

conventional technologies, there are relatively few examples of commercial applications 

operating today and little publicly available cost data [installation or operation and maintenance 

(O&M)] for these systems. In the absence of historical cost data, engineering cost models were 

used to develop a set of reference cost data. While the data incorporates significant 

uncertainties, we believe the information presented in this model represents the most detailed, 

current, and accurate information available to the public at this time. 

The primary goal in developing this model is to provide a tool for MHK developers, renewable 

energy advocates, government officials, decision makers, and other potential users to easily 

identify the potential local economic impacts associated with constructing and operating MHK

power systems. As an emerging technology with associated installation and operating 

uncertainties, users are advised to use the preliminary results with caution. 

Consistent with these goals, strong emphasis was placed on designing the model in a userfriendly format that can be easily modified—reflecting different levels of project-specific 

information and user skill. This ensures the greatest flexibility for inexperienced spreadsheet 

users, those unfamiliar with economic impact analysis, and more experienced and 

knowledgeable users who have a need for this specific type of analysis. 

The model was designed by Marshall Goldberg of MRG & Associates1 in 2010 under contract to 

the National Renewable Energy Laboratory (NREL). Reference cost input data and technology 

assumptions were developed by Mirko Previsic of RE Vision Consulting under contract with the 

U.S. Department of Energy.2

 

1 Marshall Goldberg can be reached at mrgassociates@earthlink.net. 

2 Mirko Previsic can be reached at mirko@re-vision.net. 
2 

Methodology

The model offers users the capability to analyze four different system types. These include: 

wave, tidal, ocean current, and river hydrokinetic. Given basic information about a marine or 

hydrokinetic project (minimally, the state it is to be located in, the year of construction, and the 

size of the system), users can estimate not only what a project will cost (i.e., specific 

expenditures) but also the number of jobs, income (i.e., wages and salary), and economic activity 

that will accrue to the state (or local region) from the project.3

Input-output models were originally developed to trace supply linkages in the economy. For 

example, they show how purchases of underwater electrical cable not only benefit cable

manufacturers but also the metal industries and other businesses supplying inputs to those 

manufacturers. The benefits that are ultimately generated by expenditures for MHK systems

depend upon the extent to which those expenditures are spent locally and the structure of the 

local economy. Consistent with the spending pattern and state-specific economic structure, 

different expenditures support a different level of employment, income, and output. 

Due to the nature and availability 

of cost and operating data on these emerging technologies, the model has been calibrated to one 

deployment scale (MW) for each of the system types available. To evaluate these impacts, inputoutput or multiplier analysis is used.

Input-output analysis can be thought of as a method of evaluating and summing the impacts of a 

series of effects generated by an expenditure (i.e., input). To determine the total effect of 

developing an MHK system, three separate impacts are examined for each expenditure. Often 

these effects are referred to as direct, indirect, and induced effects. However, to provide results 

that are more intuitive, in NREL’s MHK JEDI model these impacts are labeled as follows:

On-site labor and professional services impacts: the on-site or immediate effects 

created by an expenditure. When installing an MHK system, these impacts refer to the 

on-site jobs of the contractors and crews hired to install the system.

Local revenues and equipment and supply chain impacts: effects that accrue in 

supporting industries. These results are driven by the increase in demand for goods and 

services from direct on-site project spending. Businesses and companies included in the 

second tier of economic activity include construction material and component suppliers, 

analysts and attorneys who assess project feasibility and negotiate contract agreements, 

banks financing the projects, and all equipment and manufacturers of replacement and 

repair parts. 

Induced impacts: effects driven by reinvestment and spending of earnings by direct and 

indirect beneficiaries. Induced results are often associated with increased business at local 

restaurants, hotels, and retail establishments but also include child care providers and any 

other entity affected by increased economic activity and spending occurring at the first 

two tiers.

 

3 Although the JEDI model is designed to provide state-level analysis, the model also includes a “User Add-in 

Location” feature. This feature allows users to import specific county or region-level multipliers and personal 

expenditure patterns to localize the analysis to a smaller or larger region than the state level. For more detail on this 

procedure, refer to the User Add-in Location section description in this document.
3 

The sum of these three sets of impacts yields a total effect resulting from a single expenditure. 

To generate the specific impact at each level, state-specific multipliers and personal expenditure 

patterns are used. These state-by-state multipliers—employment, wage, and salary income and 

output (economic activity)—and personal expenditure patterns are adapted from the IMPLAN 

model Version 3.0 using 2008 state data.4

Consistent with an analysis of this type and scope, the assumptions used play an important role 

in influencing the results. Thus, to accommodate the greatest level of flexibility in user skill 

level and availability of specific detailed project information, the model is designed to 

incorporate model default values or new values entered by the user. The default values represent 

a reasonable expenditure pattern for constructing and operating each of the four types of MHK

systems in the United States and the share of expenditures spent locally. The default expenditure 

pattern is based on an extensive review of MHK data (see Data Sources at the end of this 

document). 

 To calculate the results, the expenditures brought 

about by investments in developing MHK systems are matched with their appropriate sectorspecific multiplier. 

Admittedly, not every project will follow this exact “default” pattern for expenditures. In fact, 

due to the uncertainties associated with these emerging technologies, the default cost pattern is 

only available for the specific system size noted in the model. Users desiring to model 

alternative system sizes must enter the detailed cost data for each category noted. Location, 

financing arrangements, and numerous site-specific factors influence the installation and 

operating costs in addition to project size. Similarly, the availability of local resources, including 

labor and materials, and the availability of locally manufactured components can have a 

significant effect on the costs and the economic impacts that accrue to the state or local region. 

To the extent the user has and can incorporate project-specific data and the share of spending that 

is expected to occur locally, the more localized the impact analysis will be. 

 

4 IMPLAN (Impact Analysis for Planning) is a social accounting and impact analysis tool. Minnesota IMPLAN 

Group (MIG, Inc). www.IMPLAN.com. Accessed January 2011. 
4 

Inputting Data and Running the Model

The JEDI model is designed for all levels of users, requiring minimal experience with 

spreadsheets and no background in economic modeling. The model includes “online” 

instructions explaining how to proceed with entering data for analysis and informative 

“comments” assisting users to understand the type of data required in specific cells. The 

comments are viewed by pointing the cursor to the triangle located in the corner of the cell. 

Please note: The model formulas and default data are protected, and user-modified data is only 

applicable to the specific analysis users are performing while the model is open. If unwanted 

changes are made, click on the “Restore Default Values” button on the ProjectData page or 

merely close the model and reopen it to start over with the initial model default values. 

Users with little or no experience with MHK systems or economic impact analysis need only, at 

a minimum, choose the state in which the system will be installed, enter the year the system will 

be installed, choose a system type, and accept the default reference system size. The user can 

then choose to accept all project defaults (or review and edit the defaults) and go directly to 

Summary Impacts to view the results of the analysis. Please note: To modify the defaults (i.e., 

not utilize the default model values), enter an “N” in the designated cell on the ProjectData 

page to incorporate the new data in the analysis.

Users with more experience with MHK systems or economic impact analysis (e.g., those with 

more project-specific information on costs and expenditures, financing, taxes, and local shares of 

spending) can review and choose to (or choose not to) modify the default values and then go to 

Summary Impacts to view the results of the analysis. The expenditure values are entered in 

purchaser prices. The model then automatically allocates the expenditures to the respective 

producing industries (margins). 

Once the analysis is complete, users have several options for saving the data and results. If a 

hard copy is desired, users can choose to print (from the summary page) by clicking on the 

“Project Data Summary and Summary Results” (the data and results contained on the summary 

page) button and print the “Detailed Project Data” (a detailed version of all cost and expenditure 

data used in the analysis) by clicking on the respective button. Alternately, users can export the 

data and results to a separate Excel file by clicking the “Export” button. If users wish to save the 

entire model (with the user-modified data) for future use or reference, merely click the “Save” 

button on the toolbar, name the model, and choose a directory. Changing the name ensures the 

original model (with model defaults) is kept intact for future analysis. Users always have the 

option to simply “block” and “copy” any desired cells to another spreadsheet or document. 
5 

Interpreting the Results

Regardless of how much project-specific data is entered by the user, JEDI provides sufficient 

information to help users (and others) better understand the magnitude of the economic impacts 

associated with the system being analyzed. 

The model provides basic project information to help users identify the magnitude of the 

construction/installation-related spending and ongoing O&M expenditures. The model also 

identifies the portion of the spending that occurs locally (again, this is determined by the “local 

share” values—default or user-modified—used in the model for each of the expenditures). 

Similarly, the model identifies local spending on debt and equity payments, property taxes, and 

land lease payments, if applicable. 

In addition to the basic system information and costs, the model analyzes and reports the local 

job earnings and output (economic activity) generated as a result of the project for the 

installation phase and for the ongoing operations phase. For the short-term installation phase, the 

impacts are broken out by project development and on-site labor impacts, equipment and supply 

chain impacts, and induced impacts. 

For example, users interested in understanding the economic impacts from installing a 10 MW 

ocean current MHK system in Oregon, to be built in the year 2012 at a cost of $6,777/kW, can 

easily and quickly find the answers by using the JEDI model. By inputting these few pieces of 

information into the model (in project description) and accepting the model defaults, users find 

(by going directly to the summary results) that the construction of the plant will support almost 

158 local jobs (full-time equivalent for a year) and generate over $27 million in local economic 

activity over the 24-month installation period.

5

This same project, once it is up and running, continues to benefit the state. We see a total of 27

jobs (full-time equivalent during each year of operation) supported, with just over a third (9.5 

jobs) of these directly employed by the MHK plant. The total annual local economic activity 

supported by the ongoing operation is $4 million. 

 Of the total jobs, users see that approximately 23 

are directly in the construction sector. 

 

5 Based on the 24-month construction period, the total 158 jobs is the equivalent of an annual average of 79 full-time 

equivalent jobs supported in each of the years.
6 

Caveats 

Several important caveats should be noted at this point. First, the intent of the MHK impact 

model is to construct a reasonable profile of investments (i.e., MHK system 

construction/installation and operating costs) and demonstrate the magnitude of the economic 

impacts that will likely result, assuming a project occurs during the stated period of analysis. 

Given the emerging nature of the industry, expected changes in MHK system costs (as the 

technologies mature) and changes in industry and personal consumption patterns in the economy

are likely—with this in mind the analysis is not intended to provide a precise forecast. Rather, 

the analysis should be viewed as preliminary and as an approximate estimate of the overall 

magnitude of the impacts. Similarly, since the model is designed solely to analyze only the 

specific deployment sizes identified in the model (i.e., unless a user has detailed knowledge of 

alternate systems and specific cost inputs to revise the defaults), users are cautioned against 

extrapolating the impacts to similar but different sized projects. (See the discussion on costs, 

learning curves, and economies of scale later in the document.) 

Second, the analysis is specific to developing MHK systems only and thus is considered a gross 

analysis. That is, it does not reflect net impacts associated with alternate spending (such as 

constructing and operating other types of electricity generating systems or power plants) or 

replacement of existing power generation resources. 

Third, the analysis assumes the output from the MHK system and the specific terms of a power 

purchase agreement generate sufficient revenues to accommodate the equity and debt repayment 

and annual operating expenditures. To the extent additional revenues (i.e., profits and tax 

advantages above actual costs) accrue to the project owner, there will be added benefits. These 

benefits are not included in the analysis. 
7 

User Add-in Location Feature

The initial design for the JEDI model provided for state-level impact analysis. However, it was 

apparent that many potential users might wish to perform a similar level of analysis for a smaller 

or more localized region (such as an individual county or group of counties) or for a larger 

region (such as a group of states) to better capture the regional benefits. Unfortunately, the high 

cost of including multiplier and expenditure data in the model for every county in the United 

States and the complexities associated with designing the model to analyze the endless number 

of possibilities for combining counties and states made this impractical. 

However, to accommodate users who desire to do this level of analysis, a User Add-in Location 

feature is provided in the model. This feature allows users with the capability to derive or obtain 

the necessary data to complete analysis for a specific region of interest other than the state level 

included with the base model. The necessary inputs include direct, indirect, and induced 

multipliers for employment, earnings and output (per million dollars change in final demand), 

and personal consumption expenditure patterns (i.e., average consumer expenditures on goods 

and services—calculated as a percentage, in decimal format, for each industry, totaling 100% 

combined) for the 14 aggregated industries. The aggregated industries include: 

1. Agriculture

2. Construction 

3. Electrical equipment 

4. Fabricated metals

5. Finance, insurance, and real estate

6. Government 

7. Machinery

8. Mining 

9. Other manufacturing 

10. Other services

11. Professional services

12. Retail trade

13. Transportation, communication, and public utilities 

14. Wholesale trade. 
8 

For IMPLAN users, gathering the necessary data will require several steps:

1. Purchase the desired county or state-level data files 

2. Using IMPLAN Version 3 software, create a new model with the desired region (one 

county, group of counties, or group of states) 

3. Construct the model

4. Aggregate the model. This step requires the user to create a new 14-industry aggregation 

scheme to aggregate the new model. Alternatively, the JEDI aggregation template used 

to aggregate the state multipliers into the 14 industries noted above is available by 

request.

5. Reconstruct the model 

6. Export household local commodity demand (personal consumption expenditures) and 

multipliers for employment, employee compensation, and output to spreadsheet files 

7. Format data contained in each of these files to input (i.e., cut and paste) into the 

respective location (MyCounty for a single county or MyRegion for a group of counties 

or states) in the User Add-in Location worksheet in JEDI. 

Once the user data is entered into the JEDI model, the user need only identify the location of the 

MHK system (in the project description section of the ProjectData worksheet) as MyCounty or 

MyRegion, depending upon the type of data and where the data is entered, and proceed with the 

analysis. 

For non-IMPLAN users or those unfamiliar with input-output modeling, there are several options 

for gathering the necessary data to perform specific county or regional analysis. These include:

1. Follow a similar process as that noted above to derive the aggregated multiplier and 

consumer expenditure data from another input-output modeling tool 

2. Purchase the necessary data (aggregated multiplier and consumer commodity demand—

see description above) from someone skilled in input-output modeling

3. Purchase the necessary data (aggregated multiplier and consumer commodity demand—

see description above) from Minnesota IMPLAN Group, Inc. (IMPLAN). 
9 

Accessing and Viewing Model Work Areas

To help ensure the JEDI model is as user-friendly as possible and to adhere to strict licensing 

restrictions on proprietary data contained in the model (IMPLAN), several of the intermediate 

work areas have been hidden from view. These areas include: default data, calculations, 

deflators, household expenditures, and multipliers. If desired, default data, calculations, and 

deflators, can be viewed by users by simply clicking on the respective worksheet. Viewing the 

worksheets will not affect the operation of the model. Please note: The data and formulas 

contained in all work areas are protected (except those specifically designed to accept user 

input) and should not be modified. Modifying any of the data or formulas could seriously impact 

the accuracy or usability of the model. 
10 

Data Sources 

Unlike more mature renewable energy sectors, historical cost data available in this sector is 

limited to a few pilot and demonstration projects worldwide. Prototypes or single device 

deployments tend to be poor indicators of true technology costs because cost profiles tend to be 

dominated by infrastructure, tooling, and mobilization costs. As such, the current state of this 

technology provides unique challenges in the evaluation of installation and operations cost 

profiles and relies heavily on predictive cost models and engineering cost assessments. 

Cost assessments carried out to date have shown that the critical issue with respect to developing 

industry-representative cost profiles is that they are dominated by cost uncertainty (RE Vision 

Consulting 2010). These cost uncertainties are best addressed using a comprehensive technoeconomic approach. Over the last 6 years, a suite of parametrically driven techno-economic 

models have been developed for use in a wide range of siting and economic studies for a variety 

of clients (RE Vision Consulting 2010). 

These techno-economic models were developed in close collaboration with device manufacturers 

that have undergone a substantial amount of technical development and hence have attained a 

reasonable technical maturity. This approach reduces technological uncertainties and 

assumptions to a minimum. However, because many data-points are commercially sensitive and 

were obtained under strict non-disclosure provisions, details on these cost assessments cannot be 

shared with the general public. Resources of publicly available cost studies of wave, tidal, and 

river systems are included at the end of this document. 

In order to provide representative inputs to the JEDI model, techno-economic modeling results 

(e.g., RE Vision Consulting 2010) and publicly available cost studies (see resources list) were revisited, and cost profiles were indexed and normalized to 2010 constant dollars. Where required, 

technical specifications were updated to reflect more recent technological developments by 

device manufacturers. In order to put the baseline reference cost into perspective, a few 

fundamental driving considerations are outlined below. 

Cost Uncertainty Ranges 

Prior studies have indicated that manufacturers typically underestimate costs in the early stages 

of development. As the technologies progress toward commercial maturity, such cost projections 

increase. The actual build and operational cost of a pilot device or a pilot tidal farm reveals a 

complete picture of how different factors affect life cycle cost. 

Once a technology reaches commercial maturity, volume production begins driving down costs. 

Figure 1 shows the typical cost projection as a function of design maturity. 
11 

Figure 1. Cost projection as a function of development status

Based on experience estimating energy project cost, the Electric Power Research Institute (EPRI) 

has developed a cost estimate rating table that assesses the likely range of uncertainty based on 

the technology’s design maturity and the amount of detail going into the cost estimate (see Table 

1). This study uses cost estimates from the simplified level of detail and technology’s stage of 

maturity at the pilot level, thereby yielding a likely cost uncertainty of –30% to +30% on the 

overall life cycle cost. However, cost uncertainties in sub-categories can be much larger and 

some of the uncertainties cancel themselves out at the system level. 

Table 1. EPRI Cost Estimate Rating Table Showing the Predicted Cost Uncertainty Range (in %) as 

a Function of Technology Maturity (Conceptual through Mature) and Level of Detail in the Cost 

Assessment (Actual through Goal)

Pilot Cost Versus Estimated Cost

Life cycle cost of prototypes and demonstration systems tend to be dominated by single unit cost 

effects. These include: tooling cost, grid interconnection cost, and quality control issues leading 

to premature failure. To eliminate these effects on cost that are difficult to quantify, it was 

assumed the technology has failure rates consistent with products that have reached a reasonable 

amount of design maturity. Further, to minimize the impacts on total cost of one-off expenses 

such as tooling and grid interconnection, deployment was assumed to have a reasonable scale of 

10 MW for wave, tidal, and ocean current technologies. River in-stream technology was 

assumed to have an installed capacity of 5 MW because these devices tend to have a smaller 

Cost

Stage of Development

Lab/Idea Prototype Commercial

Volume 

Production

Likely Cost Uncertainty
12 

rated capacity per unit deployed. These deployment scales represent early commercial adopter 

plants. Costs are high because they have not taken advantage of the economies-of-deployment 

scale experienced by other renewable technologies. Wind farms, for example, are deployed 

oftentimes at sizes greater than 100 MW. 

Cumulatively Deployed Capacity—Impacts on Cost Reduction

As with any renewable energy source, costs will decline as the cumulative experience of the 

industry increases. Learning curves have been used extensively to evaluate the future cost of 

emerging energy alternatives. A learning factor predicts what the likely cost will be every time 

installed capacity doubles. Historically, various renewable energy alternatives have shown 

learning rates between 75% (solar) and 85% (wind). These effects can be felt most dominantly 

in the early phases of technology deployment and will result in significant cost reductions in the 

short term. For the purpose of this assessment, it was assumed that no learning curve effects are 

present. Only effects of manufacturing multiple units for the plant itself were considered. 

Cost Categories

In order to quantify the economic and job-creation effects of MHK renewable technologies, all 

major life cycle costs need to be broken out in suitable cost categories. The project-specific data 

includes a bill of goods (costs associated with actual construction/installation of the system and 

facility, as well as costs for equipment and other services and fees required), annual O&M costs 

and data on the portion of expenditures spent locally, financing terms, and tax rates (if 

applicable), among others. More specifically, the model requires the following project inputs: 

• Materials and equipment costs (electrical collector system, transmission cable, landing 

and grid interconnection, and balance of plant) 

• Installation/labor costs (mooring and device, cable, and landing and grid interconnection) 

• Permitting costs (engineering, legal, and permits) 

• Annual O&M costs (personnel, materials, and services)

• Other parameters (e.g., financial—debt and equity, taxes, and land lease)

Unfortunately, as noted previously, many developers consider this type of information 

proprietary due to competitive forces in the marketplace. Similarly, project-specific differences 

can and do significantly impact costs. As a result, it is virtually impossible to identify a oneprice-fits-all situation. Nevertheless, the model provides default values for each of the inputs 

noted above and all those necessary for the analysis. 
13 

References 

RE Vision Consulting. (2010). Internal reports. Sacramento, CA: RE Vision Consulting. 
14 

Publicly Available MHK Cost Studies

The following documents are available at no cost from the EPRI website 

(www.epri.com/oceanenergy). 

Bedard, R.; Previsic, M.; Siddiqui, O.; Hagerman, G.; Robinson, M. (2006). “Survey and 

Characterization: Tidal In Stream Energy Conversion (TISEC) Devices.” EPRI. 

http://oceanenergy.epri.com/attachments/streamenergy/reports/004TISECDeviceReportFinal111

005.pdf. Accessed April 2011. 

Bedard, R.; Siddiqui, O.; Previsic, M.; Polagye, B. (2006). “Economic Assessment Methodology 

for Tidal In-Stream Power Plants.” EPRI. 

http://oceanenergy.epri.com/attachments/streamenergy/reports/002_TP_Econ_Methodology_06-

10-06.pdf. Accessed April 2011. 

Polagye, B.; Previsic, M. (2006). “System Level Design, Performance, Cost and Economic 

Assessment – Knik Arm Alaska Tidal In-Stream Power Plant.” EPRI. 

http://oceanenergy.epri.com/attachments/streamenergy/reports/006-AK_06-10-06.pdf. Accessed 

April 2011. 

Polagye, B.; Previsic, M. (2006). “System Level Design, Performance, Cost and Economic 

Assessment – Tacoma Narrows Washington Tidal In-Stream Power Plant.” EPRI. 

http://oceanenergy.epri.com/attachments/streamenergy/reports/TP-006-

WA_Design_Feasibility_Report_010106.pdf. Accessed April 2011. 

Previsic, M.; Bedard, R. (2005). “California Wave Power Demonstration Project: Bridging the 

Gap Between the Completed Phase 1 Project Definition Study and the Next Phase – Phase 2 

Detailed Design and Permitting.” Electric Power Research Institute (EPRI). 

http://oceanenergy.epri.com/attachments/wave/reports/Phase_15_CA_Report_031407.pdf. 

Accessed April 2011. 

Previsic, M.; Bedard, R. (2006). “Methodology for Conceptual Level Design of Tidal In Stream 

Energy Conversion (TISEC) Power Plants.” EPRI. 

http://oceanenergy.epri.com/attachments/streamenergy/reports/005TISECSystemLevelConceptu

alDesignMethodologyRB08-31-05.pdf. Accessed April 2011. 

Previsic, M.; Bedard, R.; Hagerman, G.; Siddiqui, O. (2005). “System Level Design, 

Performance and Costs for San Francisco California Pelamis Offshore Wave Power Plant.” 

EPRI. 

http://oceanenergy.epri.com/attachments/wave/reports/006_San_Francisco_Pelamis_Conceptual

_Design_12-11-04.pdf. Accessed April 2011. 

Previsic, M.; Bedard, R.; Hagerman, G.; Siddiqui, O. (2005). “System Level Design, 

Performance and Costs – Hawaii State Offshore Wave Power Plant.” EPRI. 

http://oceanenergy.epri.com/attachments/wave/reports/006_Hawaii_System_Level_Conceptual_

Design_RB_01-12-05.pdf. Accessed January 2011.
15 

Previsic, M.; Bedard, R.; Hagerman, G.; Siddiqui, O. (2005). “System Level Design, 

Performance and Costs – Massachusetts State Offshore Wave Power Plant.” EPRI. 

http://oceanenergy.epri.com/attachments/wave/reports/006_Mass_System_Level_l_Design_RB_

11-30-04.doc.pdf. Accessed April 2011. 

Previsic, M.; Bedard, R.; Hagerman, G.; Siddiqui, O. (2005). “System Level Design, 

Performance and Costs – Oregon State Offshore Wave Power Plant.” EPRI. 

http://oceanenergy.epri.com/attachments/wave/reports/006_Oregon_System_Level_Design_RB_

11-29-04.pdf. Accessed April 2011. 

Previsic, M.; Bedard, R.; Hagerman, G.; Siddiqui, O. (2005). “System Level Design, 

Performance and Costs – San Francisco California Energetech Offshore Wave Power Plant.”

EPRI. 

http://oceanenergy.epri.com/attachments/wave/reports/006_San_Francisco_Energetech_Concept

ual_Design_RB_12-20-04.pdf. Accessed April 2011. 

Previsic, M.; Polagye, B.; Bedard, R. (2006). “System Level Design, Performance, Cost and 

Economic Assessment – Maine Western Passage Tidal In-Stream Power Plant.” EPRI. 

http://oceanenergy.epri.com/attachments/streamenergy/reports/006_ME_RB_06-10-06.pdf. 

Accessed April 2011. 

Previsic, M.; Polagye, B.; Bedard, R. (2006). “System Level Design, Performance, Cost and 

Economic Assessment – Massachusetts Muskeget Channel Tidal In-Stream Power Plant.” EPRI. 

http://oceanenergy.epri.com/attachments/streamenergy/reports/006_MA_RB_06-10-06.pdf. 

Accessed April 2011. 

Previsic, M.; Polagye, B.; Bedard, R. (2006). “System Level Design, Performance, Cost and 

Economic Assessment – Minas Passage Nova Scotia Tidal In-Stream Power Plant.” EPRI. 

http://oceanenergy.epri.com/attachments/streamenergy/reports/006_NS_RB_06-10-06.pdf. 

Accessed April 2011. 

Previsic, M.; Polagye, B.; Bedard, R. (2006). “System Level Design, Performance, Cost and 

Economic Assessment – New Brunswick Head Harbour Passage Tidal In-Stream Power Plant.” 

EPRI. http://oceanenergy.epri.com/attachments/streamenergy/reports/006_NB_RB_061006.pdf. 

Accessed April 2011. 

Previsic, M.; Polagye, B.; Bedard, R. (2006). “System Level Design, Performance, Cost and 

Economic Assessment - San Francisco Tidal In-Stream Power Plant.” EPRI. 

http://oceanenergy.epri.com/attachments/streamenergy/reports/006_CA_06-10_-06.pdf. 

Accessed April 2011. 
F1146-E(10/2008) 

REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

April 2011

2. REPORT TYPE

Subcontract Report

3. DATES COVERED (From - To)

 

4. TITLE AND SUBTITLE

JEDI Marine and Hydrokinetic Model: User Reference Guide

5a. CONTRACT NUMBER

DE-AC36-08GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

Marshall Goldberg and Mirko Previsic

5d. PROJECT NUMBER

NREL/SR-6A20-50402

5e. TASK NUMBER

WA09.1101

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

MRG and Associates

17798 Starduster Drive

Nevada City, California 95959

8. PERFORMING ORGANIZATION

REPORT NUMBER

LEE-7-77549-01

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

10. SPONSOR/MONITOR'S ACRONYM(S)

NREL

11. SPONSORING/MONITORING 

AGENCY REPORT NUMBER

NREL/SR-6A20-50402

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfield, VA 22161

13. SUPPLEMENTARY NOTES

NREL Technical Monitor: Suzanne Tegen

14. ABSTRACT (Maximum 200 Words)

The Jobs and Economic Development Impact Model (JEDI) for Marine and Hydrokinetics (MHK) is a user-friendly 

spreadsheet-based tool designed to demonstrate the economic impacts associated with developing and operating 

MHK power systems in the United States. The JEDI MHK User Reference Guide was developed to assist users in 

using and understanding the model. This guide provides information on the model’s underlying methodology, as well

as the sources and parameters used to develop the cost data utilized in the model. This guide also provides basic 

instruction on model add-in features, operation of the model, and a discussion of how the results should be 

interpreted.

15. SUBJECT TERMS

JEDI; marine and hydrokinetic; MHK; user guide; economics; input-output model

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OF ABSTRACT

UL

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OF PAGES

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Unclassified

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Unclassified

c. THIS PAGE

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Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18