October 2013

There have been many significant advances in software for the wind energy industry over the past decade. A search of the Internet reveals hundreds of different software products for the wind industry, covering specific topics such as: site selection, wind forecasting, turbine design, wind data analysis, wind farm modeling and visualization, and flow modeling, among others. These are primarily engineering and design tools. While they are important in their own right, they do not help in the day-to-day operation of the business.

You can also find standalone products from other industries for applications such as: project management, maintenance & spares inventory, health and safety, land lease and royalty payments, and contract management, as well as standard enterprise resource planning (ERP) solutions for accounting and finance, which can be applied to any industry.

However, this same search does not return a single result for an integrated solution for the overall management of wind farm operations—a solution that would bundle together all of the required operations management and intelligence applications into a single, integrated solution for running the business.

This situation has also existed—and has been successfully addressed—in other industries. Initial solutions tended to be “point solutions,” which were focused on a specific topic. Over time, problems developed with duplication of data and processes, and there were difficulties in integrating the different “islands of automation.”

Further solutions were later developed to address these problems, initially in discrete manufacturing, and subsequently in process industries. In those industries today, we see two very important developments which have resulted in the elimination of the problems:

• The creation of standards for deploying information systems
• The development of integrated solution maps for the required applications.

As a result, software companies started building integrated solutions, with industry-standard interfaces. In manufacturing today, there is a wide selection of integrated solutions available to the benefit of all prospective customers.

These same developments need to be brought into the Wind Industry.

Develoment of Standards
A key example of standards for manufacturing systems is ISA 95, as seen in Figure 1.

These same levels of automation exists in the wind industry. While significant progress has been made at the lowest level by integrating sensors and SCADA systems, as well as standard ERP solutions for accounting and finance, little has been done in developing integrated solutions for the middle layer—operations management and intelligence.

Solution Map
The time has come to specify a similar solution map for the wind industry, in which:

• all of the functions required for operations management (running the business) are addressed;
• the solution provides the operational intelligence for performance management;
• the system is built around a common, shared database;
• the solution provides seamless integration of related systems and data from operations (SCADA, data historians), with the business systems (ERP, financials), as well as the Internet (forecasting and pricing).

The following represents an initial proposal for the desired content of a solution map for an operations management and intelligence system for the wind energy industry. Figure 2

Just like the ISA 95 standard for the manufacturing industry, the standard model for the wind industry should consist of five layers, as outlined in Figure 4. Here, the solutions in Levels 1, 2 & 4 are well developed. The balance of this section will focus on the application needs for Level 3 – Operations Management & Intelligence. The individual modules are grouped into subsystems under the headings of “Generation,” “Maintenance,” “Health & Safety,” “Accounting & Reporting,” “Performance Management,” and “Technology.” The proposed solution map calls for a common data structure, and for integration between all of the modules. This is accomplished through the use of a common set of foundation modules.

Foundation Modules
The foundation modules include the basic building blocks to configure the system for use, and include: Asset Hierarchy; Event Manager; Performance Criteria; Electronic Logs; Alarms and Notifications; and KPI (key performance indicator) Manager.

• Asset Hierarchy­—This module allows the user to manage overall asset hierarchy, from organizational units (company, geographical unit, division, plant, etc.) down to equipment, sub-assemblies, and components. There is no limit to the number of levels the user can create in the hierarchy. The asset hierarchy module is the backbone of the software solution—against which all performance criteria, events, and activities are structured. This solution seamlessly integrates with other standard industry solutions [ERP, computerized maintenance management systems (CMMS) and data historians] to provide the information on asset availability to all foundation modules and application modules
• Event Manager—Operating and non-operating events within the organization are detected, recorded, and communicated through the Event Manager Module. This module (Figure 10) is suited for monitoring operating events that cannot be tracked manually or with traditional system alarms. It can also capture events either electronically or manually. This module automates and highlights unusual conditions, and notifies the user when these events occur. As a result, the user is able to reduce operating costs and improve yields with fast, effective follow-up, while improving regulatory compliance, reducing incidents, and analyzing events.
• Performance Criteria—Once the Asset Hierarchy has been established and attention switches to collecting data for analyzing performance, the targets, standards, or metrics for the different events (performance criteria), must be established. Examples include: the rated output of the wind turbine for different wind speeds; expected operating temperature for wind turbine gearbox oil; and the schedule for preventive maintenance tasks. Performance Criteria can also be established for the business—for expected daily generation; target for real-time energy prices; and other daily, weekly, monthly, or annual statistics.
• Electronic Logs—This module provides an electronic log framework (Figure 3) to allow users to make log entries under predefined categories such as safety, environment, and operating procedures, among others. The log entries should be supportable with any digital media (voice, pictures, videos).
• Alarms and Notifications—The Alarms and Notifications engine is intended to notify users that any predefined situation may be developing or occurring. Notifications can be sent via email or text message. The software includes an escalation function should there be no response within a predefined time frame.
• KPI Manager—This module provides a real-time solution framework for the collection, analysis, and calculation of key performance indicators. The KPI Manager delivers accurate and timely information the user needs in order to enhance visibility into operations and increase the efficiency and effectiveness of resources. It provides accurate key performance indicator data throughout the organization, allowing everyone to drive performance improvement initiatives. The KPI Manager rolls up low-level performance indicators (such as unit-specific production generation) to high-level business objectives (e.g. site productivity or asset utilization). Users can quickly drill down to view actual performance measures and respond appropriately.

Generation Management Modules
The modules in Generation Management are related to tracking electricity generation and downtime and include: Generation, Downtime, Outage Management, Generation Forecast, and Lost Generation Analysis.

• Generation—Generation data—collected from SCADA systems through the data historian or other central collective—is tracked by individual asset, summarized, and presented in real-time display (Figure 4). This data can be used for comparisons to specifications, or comparisons between equivalent units.
• Downtime—This module provides tracks all downtime events—planned or unplanned, imposed or self-induced. These events are captured either manually or automatically, and include the capability to code events by different fault codes (e.g. manufacturer, O&M, NERC, PPM). The user should be able to review and modify all events captured in the system. Displays (Figure 5) to review event logs, as well as charts to analyze downtime by turbine, component, fault code, etc., should be included.
• Outage Management—This application supports logging and tracking of site outages—planned and unplanned—and captures generation and resource availability at the time of the outage, while triggering required notifications or work orders. This increases visibility of outage information across the organization; ensures that key personnel are notified of outages; captures critical information associated with an outage; and ensures the required procedures are followed for bringing a unit back online.
• Generation Forecast—An environmental forecast is brought into the system from a third-party provider. This information is used to calculate the expected generation from each asset, considering known outages for maintenance or curtailment. The generation forecast can also be converted to a financial forecast based on market or contact prices.
• Lost Generation Analysis—This module allows the user to analyze and categorize all lost generation, both in terms of lost production and lost revenue. Standard reporting categories in the industry are: generation, gross availability, contract availability, project availability, and commercial availability. The solution must also consider downtime related to curtailment or maintenance. The loss analysis should use pricing from contract actual agreements, or real-time pricing from the Web. (Figure 9)

Maintenance Modules
The modules for maintenance functionality are those related to planning, coordinating, and tracking maintenance activity, and include: Equipment Records, Maintenance Information, Work Orders, Inspection Tours, Condition Monitoring, and Spares Inventory.

• Equipment Records—This module allows the user to maintain a complete hierarchical record of all operating equipment. There should be no limit to the number of levels in the hierarchy, and the asset hierarchy should include all sub-components (e.g. sub-assemblies and parts). Ideally, the Equipment Records module also points to digital records of the equipment such as engineering drawings, pictures, and operating manuals.
• Maintenance Information—Detailed information should be associated with the equipment records, specifying the maintenance work to be done, the interval between service, the job skill requirements, and the spare parts and tooling required for the work.
• Work Orders—The system should support work orders for different types of activity, such as inspection tours, corrective maintenance, and preventive maintenance. The work orders should clearly identify the following: equipment involved; safety procedures to be followed; repairs to be made; parts and tools required; and the expected start and completion date of the work.
• Inspection Tours—This function (Figure 6) allows the user to use printed reports or mobile devices to specify safety or maintenance inspection requirements to field personnel. With mobile devices, the user can easily communicate maintenance tasks (safety procedures, job steps, drawings, etc) to field personnel, who in turn can use the device to indicate task completion, record verbal comments, or take photographs and videos of the situation.
• Condition Monitoring—This module (Figure 7) allows the user to collect condition data from the operating units, comparing this data standard or expected values. It also triggers any necessary alarms, alerts, notifications, or work orders. The system should handle any type of condition measures, including vibration analysis, oil sample analysis, bore hole photography, and dynamic loads, while coordinating this information with other system data (e.g. weather conditions, generation data).
• Spares Inventory—Here, the user can track the quantity and location of all spare parts, sub-assemblies, or tools, along with the associated information for procurement. The module should also allow for tracking equivalent parts or allowable substitutions. In the case of repairable or re-usable parts, the module should also allow for tracking usage history of such parts.

Health and Safety Modules
• Incident Tracking—Our incident tracking application allows you to enter new incidents and assign them to the right personnel for investigation and resolution. It classifies incidents in a variety of categories enabling powerful search capabilities and flexible reporting.
• Work Place Hazards—This application assists workplace safety professionals in documenting workplace and material hazards, and in recording a complete history of the hazard investigations and assessments. It covers job safety analysis, ergonomic analysis, lockout/tagout, and risk assessment. It also allows the user to record any events and analyze them using the root cause analysis application.
• Compliance Management—Provides the user the ability to make compliance programs repeatable, sustainable, and cost-effective. Compliance Management provides a common framework and an integrated approach to manage all compliance requirements faced by an organization. It enables companies to manage cross-industry mandates and regulations such as SOX, OSHA, EH&S, and FCPA, as well as industry-focused regulatory guidelines from FDA, FERC, FAA, HACCP, AML, Basel II, and Data Retention laws.
• OSHA Reporting—This application allows the user to track all health and safety issues, material and workplace hazards, and risk assessments, and has the capability to generate all OSHA standard reports. The module can easily track safety investigations and audits, while also providing the ability to record the costs associated with safety incidents. In addition to standard reports, this module lets the user create personalized dashboards and analytical reports to optimize reporting.
• Environmental Issues—This module provides users a means to track all issues related to the environment in one central system. It allows you issues related to land use, track oil spills and waste disposal, and track issues related to wildlife and birdlife. The software can also track the noise created by wind turbines operating in different wind conditions.

Accounting and Reporting Modules
The modules in Accounting and Reporting are those related to executing accounting tasks, as well as providing in-house and statutory reporting about operations, above and beyond the functionality ERP solutions provide, and include: Energy Settlements, Lease and Royalty Payments, NERC GADS Reporting, Revenue Forecasting, Day Ahead Training, and Warranty Management. Figure 11

• Energy Settlements—All Generation and downtime information is paired with metered data and pricing information to calculate Energy Settlements. Pricing data can be taken from contracts or from real-time pricing on the Web, and can also be used to value losses from downtime events.
• Land Lease & Royalty Payments—This application tracks all of the agreements for the user’s operating assets. It manages contracts for land lease and royalty payments, automatically triggering payments for any period and/or frequency. It also allows the user to preview the complete output to ensure accuracy and completeness prior to transferring the data into an accounting system.
• NERC GADS Reporting—The application should be programmed to generate the reporting requirements specified by the North American Electric Reliability Corporation (NERC) Generating Availability Data System (GADS). It allows users to gather and verify event and performance data, as well as analyze (Figure 8) current and historical event and performance data, including energy availability, outages, and demand factors.
• Revenue Forecasting—The revenue forecast application allows the user to import weather condition forecasts, and then calculate a generation forecast using the historical performance of each asset (taking into consideration all planned maintenance outages, curtailment, or other planned outages). It also translates data into revenue forecast by accessing contract pricing stored in the system, or by accessing real-time pricing data on the web.
• Day-Ahead Trading—Comparing forecasting module information with customer power commitments, the software can predict anticipated surplus generation for a future time period. This surplus can then be offered on the day-ahead trading market at asking price.
• Warranty Management—This module allows the user to monitor all assets under warranty, tracking work performed and replacements made during the warranty period. This includes tracking parts or sub-assemblies that have been removed, repaired and put back into service as warrantied parts on other turbines. This system should be put in place well before the equipment comes out of warranty, so equipment records are established and the maintenance tracking procedures are in place.

Performance Management
The modules for Performance Management are those related to collecting, analyzing, and reporting on all data across the organization, and include: Operational Planning, Capacity Analysis, Performance Analytics, Comparative Reporting, Root Cause Analysis, and Activity Tracking.

• Operational Planning—This enables improvement of operational planning capability through increased visibility of key site-related data. It consolidates critical operations data into a single, centralized location for visualization and analysis. Planned and unplanned downtime events, scheduled and unscheduled outages, and weather forecast data are all centralized into a single, consolidated system that can provide the insight needed to enhance operational planning.
• Capacity Analysis—Gaining insight into project capacity can dramatically increase the profitability of renewable asset(s). Whether it is individual wind turbine power curve or an entire wind farm’s peak power, the capacity analysis application provides the operator with the ability to understand how well assets are performing in relation to their rated capacity.
• Performance Analytics—Allows the user to create performance summaries for all operating assets, including measures such as gross, contract, and commercial availability, performance to manufacturer’s standards, and comparative performance. The software should support hundreds of thousands of data calculations, generating actionable intelligence to support performance improvement initiatives and the pursuit of increased ROI.
• Comparative Reporting—One of the key benefits of an information system independent from a particular vendor’s SCADA system is that it allows the user to consolidate and compare data from different units. This can be accomplished by establishing codes for normalizing data from different sources. Examples include using fault codes from multiple vendors, and comparing performance of similar turbines in different areas versus the prevailing wind speeds in those areas.
• Root Cause Analysis—The root cause application should serve an accountability function. It enables the user to analyze structures, prioritize corrective actions, and track progress. This ensures that resources are focused, to avoid performance losses. Root Cause Analysis improves problem resolution with automatic and precise downtime logging, tracks implementation of recommendations, and allows the user to assesses effectiveness of recommendations.
• Activity Tracking—Projects are defined and broken down into specific tasks, including responsibility, start/end dates, priority, targeted results, and risk evaluation. Daily updates can be viewed both on a per project basis as well as in schedule performance charts. Hierarchical management of data allows authorized viewing up the organizational ladder. Impact of change activities can be measured over time. Software supports drill down to source data.

Additional Features
• Design Studio—The Design Studio allows for creation and/or modification of displays and reports within the system. The user can also configure customized KPI’s, as well as create custom modules within the software.
• Visualization—The software should provide tools for visualization (Figure 12), including map overlays, a dashboard object library, dashboard/report templates, and a library of pre-defined KPI’s.
• Import/Export—The software should allow the user to import data from external systems (e.g. bulk loading specific turbine fault codes, or importing regional wind forecasts). The software should also allow exporting of data to related systems, as well as the export of on-screen content to Excel or PDF formats.
• Interfaces—This includes the software required for interfacing with other related business systems, SCADA systems, data historians, or the Web.
• Mobile Technology—With the exponential growth of mobility and due to the distributed nature of the wind energy assets, it is imperative that the system solution utilize mobile technology. Alerts and notifications should be transmitted to mobile devices. Dashboards, performance summaries, and KPI’s should be visible on smartphones and tablet computers.
• Messaging Network—This module allows users to have better interaction with the platform’s data flow. By providing a push-and-pull mechanism, users are able to experience enhanced interaction with module applications and faster notifications from any computer, or device connected to the Messaging Network.
• Data Mining & Analysis—The software should allow for ad hoc data mining and querying of the data, making selections by asset, event type, tag status, or tag information. Results should be presentable in tabular form or in charts (Figure 13), and the user should be able to perform “what if” analyses or causal analyses on the resultant data.

The End Product
If we summarize all of the preceding information into a single model, we end up with the solution map for Operations Management and Intelligence for wind farm management, outlined in Table 1 and Table 2.

Hopefully this will be viewed as a viable first step in the creation of an overall model for Wind Farm Information Systems. Ekho for Wind, from Ekhosoft, has been developed in accordance with this proposed standard model. The author welcomes feedback and suggestions on the proposed model, and is open to inquiries about Ekho for Wind. 

Abstract
The Virginia Center for Wind Energy at James Madison University, supported by the National Renewable Energy Laboratory (NREL) and the U.S. Department of Energy (DOE), performed a study that applied the new offshore Jobs and Economic Development Impacts (JEDI) model to estimate the economic impacts associated with potential offshore wind power development off the coasts of Virginia, North Carolina, South Carolina, and Georgia. The Southeast region presents an ample wind resource in waters beyond 12 miles from the coast. According to the American Wind Energy Association, the region currently employs an estimated 11 percent of the total U.S. wind workforce. This analysis finds that construction costs for offshore wind within the region are among the lowest in the nation, suggesting a competitive advantage for this industry.

The major attributes associated with the region were identified and analyzed in order to define likely scenarios for offshore wind development in the region. Relevant data and justifiable assumptions were made to develop five scenarios for JEDI analysis.

1. Introduction
The offshore wind industry represents a major opportunity to provide clean, stable-priced energy using a domestic renewable resource, while promoting significant job growth and economic development. The industry is currently being driven by individual state policies, with Mid-Atlantic and Northeastern states all competing to become the “hub” of this new industry. While state-by-state competition can drive down costs, a regional approach would realize the full potential of the industry. Some of the benefits of a coordinated regional approach include:

• reduction of ratepayer impacts by spreading costs over a wider base
• the ability to spread costs and share lessons learned
• coordination of research, resource assessment and environmental studies
• expansion of the scope of transmission integration analyses
• allocation of economic development resources based on comparative strengths
• aggregated or collaborative procurement could result in lower energy costs

In this study, a regional approach was adopted in the development of scenarios for application to the Jobs and Economic Development Impacts (JEDI) model, to investigate the potential economic impacts of offshore wind in the Southeast. A regional overview highlighting the comparative strengths of the Southeast region—Virginia, North Carolina, South Carolina and Georgia—is provided in Section 2. The JEDI model is described in Section 3. In Section 4, the development of three distinct, justifiable offshore wind energy scenarios for the Southeast is discussed. Finally, in Sections 5 and 6, the results of JEDI model runs are presented followed by conclusions.

2. Regional Overview
In order to be able to develop reasonable justifiable assumptions for the offshore wind industry in the Southeast—defined in this study to be Virginia (1) (2) (3) (4), North Carolina (5), South Carolina (6) (7) and Georgia (8) (9), the major characteristics of the region must be understood. These include Federal and State activities, the wind resource, transmission infrastructure, ports and the existing supply chain in the region. Each state was researched thoroughly, through reliable sources as well as collaboration with local and regional experts in the region.

Federal and State Activities
The Bureau of Ocean Energy Management (BOEM) manages the exploration and development of the nation’s offshore resources. BOEM runs a number of offshore Renewable Energy Programs and it grants leases, easements, and rights-of-way for orderly, safe, and environmentally responsible renewable energy development activities.

To assist the development of offshore wind energy in the region, BOEM established Renewable Energy Task Forces in Virginia, North Carolina and South Carolina, to facilitate intergovernmental communications regarding outer continental shelf renewable energy activities.

Wind Resource
According to the National Renewable Energy Laboratory (NREL), the Southeast region represent 45 percent of the total East Coast offshore wind resource and 82 percent of the resource in shallow water and more than 12 miles offshore, as shown in Figure 1. (10) NREL resource maps show that average wind speeds are slightly lower in the southern states. However, the most important metric is ultimately the Levelized Cost of Energy (LCOE), and the Southeast has numerous advantages that should result in lower LCOE in the region.

Market Size
The Southeastern states represent five of the six largest electricity markets on the East Coast with high per-capita electricity consumption and five of the six fastest growing populations (see Figure 2) (11). The low electricity rates in the region attract energy-intensive industries, which points to a high demand growth rate and the ability to accommodate long-term, large-scale offshore wind energy development.

Cost
The U.S. Energy Information Administration (EIA) estimates that the Southeast region offers the lowest construction costs for offshore wind energy among East Coast states, as shown in Figure 3 (12). This advantage would result in lower capital and expenditures (CAPEX) and energy costs from offshore wind and a competitive advantage for manufacturers that locate facilities in the region.

Currently, electricity supplied in the Southeast primarily comes from coal, nuclear and natural gas (11)—all technologies that are susceptible to fuel price volatility and large-scale outages. Dispatchable generating technologies, such as coal, gas-combined-cycle and nuclear can be controlled by the systems operator and can be switched on and off based on their economic attractiveness to supply electricity and to supply network reliability services. Electricity rates can change quickly as the demand for electricity changes throughout the day, while power generation must continuously be adjusted to match electrical load to avoid outages. Non-dispatchable generating technologies such as wind energy would diversify the region’s electricity supply and provide long-term, stable-priced energy by putting less demand on conventional technologies to match the electrical load.

Infrastructure and Workforce
The Southeast is home to some of the largest and industrious ports and logistics infrastructure in the United States, including ports at Norfolk Harbor (VA), Newport News (VA), Morehead City (NC), Wilmington (NC), Charleston (SC), and Savannah (GA) (13). The region has a highly skilled manufacturing and maritime workforce and employs thousands of people in the land-based wind industry, despite having no large-scale wind plants. (14) (15)

3. The Offshore Wind Jedi Model
The Offshore Wind Jobs and Economic Development Impact Model (JEDI) was developed by the National Renewable Energy Laboratory (NREL) in order to demonstrate the magnitude of economic impacts associated with developing and operating offshore wind power plants in the United States. (16)

The JEDI model uses input-output analysis to estimate the number of jobs, income (wages and salary), and economic activity that may be supported in the state (or region) from the project (1). Three separate impacts are examined,

• Project Development and Onsite Labor Impacts
• Turbine and Supply Chain Impacts
• Induced Impacts

In order to accomplish this analysis, multipliers and expenditure patterns were used to derive these results. These regional multipliers for employment, earnings, and output and personal expenditure patterns were derived from the Impact Analysis for Planning (IMPLAN) model 3.0 (17).

Model Input
JEDI utilizes either default or user-supplied construction cost data, operating cost data as well as data pertaining to the percentage of goods and services acquired in the region to produce outputs. From a broad perspective, JEDI input variables can be classified into three main categories

• Market and Deployment—The number and size of wind turbines deployed each year.
• Regional Investment—The percentage, for each component or service, which is being acquired or produced regionally.
• Cost—The cost (per MW capacity) of an offshore wind project.

Model Output
JEDI provides information to understand the magnitude of the gross economic impacts within the region being analyzed, including construction-related spending and operations and maintenance, as well as the portion of the spending that could occur regionally. JEDI reports the local jobs in Full-Time Equivalents (FTEs), earnings and output supported as a result of the project for the construction phase and for the ongoing operations phase. Construction phase impacts are assumed to occur of the equivalent of one year, while O&M impacts are assumed to be ongoing for the life of the facility.

Caveats
First, the offshore wind JEDI model is intended to construct a reasonable profile of expenditures and demonstrates the magnitude of gross economic impacts, and is an estimate, not a prediction.

Second, the JEDI is a static model that relies on inter-industry relationships and personal consumption patterns and does not account for supply-side changes such as inflation, changes in technology, taxes, or subsidies. Additionally, the model does not consider constraints on labor, goods or money.

Third, the model was not designed to provide cash flow projections or for use as a cash flow analysis tool and results do not measure of project viability or profitability.

Finally, the analysis assumes that sufficient revenues are generated for equity and debt repayment and annual operating expenditures. (1)
 
4. Scenario Development
As discussed in Section 3, the JEDI model is built around three major variables – Market and Deployment, Regional Investment, and Cost, based on information gathered and other similar studies. (18) For each, three distinct ‘paths’ with varying rates for how these variables change over time were developed. Three distinct scenarios, running from 2020-2030 for offshore wind energy in the Southeast were generated, and JEDI was run for each year.

Market and Deployment
For Market and Deployment, a conservative, a moderate and an aggressive approach to the deployment of offshore wind turbines in the Southeast region were created. An analysis of the historical growth rates for electricity capacity, as shown in Table 1, indicates that this is around 2.2GW/year. It was assumed that the Southeast could support a maximum build-in rate of around 1.1GW/year.

For Low Market and Deployment, as shown in Table 2, investment in the offshore wind industry was assumed to be very conservative, defined by pilot projects and small wind farms. For Medium Market and Deployment, as in Table 3, a moderate level of investment in the industry was assumed. Initially, this path is similar to that of the low growth path, with a more consistent level of growth being observed in the later years of the model run. For High Market and Deployment, as in Table 4, an aggressive level of offshore wind turbine deployment was assumed, and assumed a large percentage or new power-generating plants derived from offshore wind facilities.

Regional Investment
As in the case of Market and Deployment, three different paths for how the regional supply chain could develop were built. The higher the regional share percentage in a specific line item, say wind turbine blades, the more money is being circulated into the regional economy, thereby creating more regional jobs.

Each individual component was examined separately when determining its potential for regional sourcing. The regional share of many of these components and services were not expected to change over time, called static components and services. A list of these, with regional share percentages and justifications are given in Figure 5. Components and services of which the regional share is expected to vary over time are called dynamic components and services, and are discussed in the next section. Table 5

Dynamic Components and Services
For the Low Regional Investment path, it was assumed that the development of the regional supply chain is minimal due to uncertainties in the industry. However, due to the presence of manufacturers and developers already in the region, some regional contributions are expected, but development of supply chain is slow.

Initially, the Medium Regional Investment path was assumed to be similar to the low path, but higher growth rates are applied, as more of the larger components are manufactured regionally, and as expertise is gained. Approximately half of manufacturing and services are assumed to be regional by 2030. Table 6

The High Regional Investment path assumed immediate and significant regional investment into the offshore wind industry, resulting in a rapid development of the supply chain. Nearly all components and services are regionally sourced by 2030. A summary of all three Regional Investment paths are given is Figure 6.

Cost
Three simple Cost reduction models were established for application to the JEDI model, which may occur due to technological advancements, economies of scale, and other factors. These paths, establish upper and lower bounds for cost reduction in the Southeast. A baseline Cost of $5,600/MW in 2015 was established based on 2010 Energy Information Administration (EIA) estimates for the industry in the region (12).

For the High Cost path, there is limited development in offshore wind energy technologies, and a cost reduction of 3.5% every 5 years was applied, resulting in an overall cost reduction of around 10%. For the Medium Cost path, a more aggressive cost reduction model was applied, representing more significant technological advances. A cost reduction of 7.2% every 5 years was applied, resulting in an overall cost reduction of around 20%. For the Low Cost path, the most aggressive cost reduction model was applied, representing optimal improvements in the technology. The average cost of offshore wind is assumed to decrease by 11.2% every 5 years, for an overall cost reduction of around 30%. A summary of the cost reduction models are given in Table 7.

Scenario Compilation
Using this method, three combinations of these variables were combined that best represented all the combinations and to reduce redundancy. Scenario A, shown in Table 8, is the most conservative of the three scenarios, representing a small industry with limited regional investment due to uncertainties. As such, much of the labor and capital is outsourced and the high cost reduction model was adapted. Scenario B, as shown in Table 9, represents an ‘average case’ marked by moderate and steady growth in both Market and Deployment and Regional Investment. This growth helps spurn advancements and efficiencies and the Medium Cost reduction model was applied. The details of Scenario C are given in Table 10 and represent the ‘best case’ scenario for the offshore wind industry. Therefore, a high Market and Deployment and Regional Investment paths were selected, presenting a situation where the industry grows very quickly, resulting in a Low Cost reduction model being selected for this scenario. Table 10

5. Results
Jobs–Construction
Construction is highly labor intensive, requiring a large number of workers to complete a project, supporting thousands of jobs. However, unless the offshore wind market is robust with multiple projects in the pipeline, these jobs may cease to exist after construction.

For Scenario A, the offshore wind industry is projected to support around 1,000 FTEs, increasing to over 4,000 FTEs in 2030, as shown in Figure 4. In other words, the industry is expected to require four times more labor after 10 years, despite conservative increases in Market and Deployment and Regional Investment. Many of the total jobs created are from supply chain and induced impacts.

Scenario B, as shown in Figure 5, shows significantly higher projected jobs throughout the modeling period, increasing from under 4,000 FTEs in 2020 to over 18,000 FTEs in 2030. Scenario C, as shown in Figure 6, projects the most FTEs by a significant margin, with the 2020 estimate of 15,000 FTEs being very close to the 2030 value of Scenario B. By 2030, if all the assumptions made hold, this Scenario projects over 40,000 FTEs.

An important metric to consider in order to be able to directly compare jobs supported by each scenario is the normalized FTEs per MW. As seen in Figure 7, this will show how more Regional Investment into the offshore wind supply chain will support more jobs per MW installed than if the supply chain were developed outside the region. For instance, in 2020, where the percentage investment during the construction phase is comparable for all three scenarios, the normalized FTEs/MW values are similar, ranging from around 14 FTEs/MW in Scenario A to around 19 FTEs/MW in Scenario C.

Since the three scenarios follow different regional investment growth patterns, the rate at which the normalized FTEs/MW grow accordingly. Scenario A assumes marginal increases in regional investment – therefore the model suggests that around 19 FTEs/MW would be supported by 2030. On the other hand, Scenario C assumed an aggressive investment growth pattern, and this is reflected in the FTEs/MW value over time, which increases very sharply to over 35 FTEs/MW by 2025. During the next five years, the rate of growth tapers off because much of the supply chain is already regional, with a normalized value of around 39 FTEs/MW by 2030.

Jobs–Operations and Maintenance
For operations and maintenance, the total number of FTEs projected is significantly less than for construction. However, these jobs last for 20 to 25 years, the typical lifetime of an offshore wind project, and are therefore permanent, career-length opportunities. Construction jobs, as reported by JEDI, are the equivalent of one year (job-years or person-years).

The results for FTEs supported under Scenarios A, B, and C are given in Figure 8, Figure 9 and Figure 10 respectively and show very similar patterns as for construction. The majority of jobs would be in supply chain and induced impacts – this trend is even more prevalent during the operations and maintenance phase.

Similarly to the analysis in the previous section, Scenario A projected the least FTEs, with around 2,700 FTEs over 1,695MW total generating capacity by 2030. On the other hand, Scenario B supports around 7,000 FTEs over 4,027MW generating capacity, and the expected total for Scenario C is over 16,000 FTEs over 9,760MW capacity by 2030.

For operations and maintenance, the normalized FTEs/MW increases at a much slower rate from around 1.64 FTE/MW to around 1.67 FTEs/MW for all three scenarios. This is because it is assumed that the majority of services and materials would already be regionally sourced for this phase of a project, therefore this metric stays relatively consistent from 2020 through 2030.

Earnings and Output
As explained previously, earnings refer to wages and salaries paid to workers and employer-provided supplements, while output refers to the total economic activity supported by the scenario of analysis. As expected, as the industry grows the model projects higher earnings and outputs for all three scenarios. The earnings and output charts for Scenarios A, B and C can be seen in Figure 11 and Figure 12 respectively. All three sets of charts show similarities – (1) the growth patterns for earnings/outputs are very similar for each scenario; (2) Turbine and Supply Chain has the highest portion of earnings and output across all scenarios, typically over half the total; (3) conversely, Project Development and Onsite Labor has by far the smallest portion of earnings and output, particularly for scenario A; and (4) there is a greater discrepancy in these proportions for the output rather than the earnings.

6. Conclusions
The Southeast has the capacity to become a long-term leader in offshore wind energy, with ample resources for the industry to thrive. A very good shallow wind resource, low manufacturing costs, manufacturing expertise and access to some of the largest and most industrious ports on the East Coast are all very attractive features of this region.

The JEDI model was used to provide estimates of the magnitude of economic impacts for the region using three distinct scenarios. Scenario A projected the least economic activity and is considered to be too small to encourage industry growth.  Scenario C projects the largest gross economic impacts, but it requires the regional supply chain to develop at a extremely fast rate. Finally, Scenario B is a moderate and offers sufficient economic returns to encourage growth.

As described previously, the results of this study are only estimates intended to provide a reasonable profile of what the offshore wind industry could look like in the Southeast region. Since a mature offshore wind industry does not currently exist in the United States, revisions of this model would be required in the future, as the impacts of the industry are better understood. 

Keywords
Economic impacts; supply chain; Southeast region; JEDI; offshore wind

Acknowledgements
The U.S. D.O.E. Wind & Water Power Technologies Office funded James Madison University and the National Renewable Energy Laboratory to perform this work under Contract No. DE-AC36-08GO28308. Addtionally, the authors would like to acknowledge Simon Mahan, Renewable Energy Manager, Southern Alliance for Clean Energy and Brian O’Hara; President, Southeastern Coastal Wind Coalition.

Footnotes
1. See www.secoastalwind.org for more information on how a regional approach can benefit the Southeast
2. www.boem.gov/Renewable-Energy-Program/index.aspx
3. http://www.boem.gov/Renewable-Energy-Program/State-Activities/Virginia.aspx
4. http://www.boem.gov/Renewable-Energy-Program/State-Activities/North-Carolina.aspx
5. http://www.boem.gov/Renewable-Energy-Program/State-Activities/South-Carolina.aspx
6. Input-output analysis is a method of evaluating and summing the impacts of a series of effects generated by expenditure.
7. A ‘User Add-in Location’ feature was added to allow users to derive the necessary data to complete analysis for specific regions. The necessary inputs include direct, indirect, and induced multipliers for employment, earnings and output, and personal consumption expenditure patterns – calculated as a percentage for each industry, for the 14 aggregated industries and the IMPLAN 432 industry sectors
8. A FTE is equivalent to 2,080 hours of work, and could also mean 2 part-time jobs of 1,040 hours each.
9. Economic activity in the region
10. Note that nearly half of this capacity growth occurred between 2000 and 2004.
11. Generally, these are components and services which are easily produced regionally, such as concrete and legal services
12. These are components and services which require expertise in offshore wind industry, such as foundations and project financing.
13. A linear scaling system was applied for Regional Investment percentages between 2021 and 2024, and 2026 to 2029.
14. $100 million against $200 million
15. $450 million against $1.4 billion
16. $300 million against $1 billion
17. $1.8 billion against $7 billion
18. $1.25 billion vs. $3.75 billion
19. $4.25 billion vs. $11 billion

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