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PROJECT HIGH WINDS:

Small-Scale Urban Wind Study

5.92J Projects in Energy

May 13, 2010

Cat Thu Nguyen Huu, Alex Teuffer, Xiaoran Xu, Minshu Zhan

Nguyen Huu, Teuffer, Xu, Zhan 2

TABLE OF CONTENTSA. Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

B. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

C. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

D. Background Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

E. Report Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

F. Project Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

I. Micro Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1. Turbine Model Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.1 Models Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.2 Turbine Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2. Site Selection and Measurement . . . . . . . . . . . . . . . . . . . . . . . 15

2.1 Site Selection and Data Collection . . . . . . . . . . . . . . . . . . . . 15

2.2 Correlation and Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3. Wind Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.1 Average Wind Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2 Weibull Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.3 Discussion of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4. Turbine Cost Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.1 Turbine Power Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2 Cost Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.3 Discussion of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

II. Macro Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5. Support Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.1 Policies and Incentives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.2 Other Support Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . 30


5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

6. Urban Wind Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

6.1 Existing Installations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

6.2 Turbine Performance and Economic Feasibility . . . . . . . . . 34

6.3 Application Feasibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

6.4 Permitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

G. Major Findings and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

H. Limits and Areas for Further Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

I. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

J. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

K. Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

1. Appendix I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

2. Appendix II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3. Appendix III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4. Appendix IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5. Appendix V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51


Part A

EXECUTIVE SUMMARY

The purpose of our project is to assess the technical and economic feasibility of

installing a building-mounted wind turbine on the campus of MIT and to evaluate the

development of urban wind in the broader context of the surrounding areas off-campus.

In the micro component of our project, we collected and studied wind data from six

locations on campus along with five selected turbine models and expect that our optimal site-

turbine combination – the Sailing Pavilion and Bergey model – would generate electricity at a

rate of 13.9 cents per kWh, which is within the bounds of the 13 to 16 cents per kWh that MIT

has historically paid for electricity from NSTAR. Our study and previous wind studies at MIT

indicate that a number of sites on campus possess the wind resources necessary to produce

electricity at such a reduced rate.

On a broader level, many changes have occurred in the surrounding Cambridge and

Boston areas regarding urban wind turbine regulations. As cities are becoming more interested

in wind power, various support mechanisms have been put into practice. In the macro

component of our project, we discuss incentives such as federal tax credits (ITC/PTC), Regional

Greenhouse Gas Initiative (RGGI), Renewable Portfolio Standard (RPS), and Renewable Energy

Certificates (RECs). These incentives indicate that now is the prime time to expand the

utilization of small-scale wind energy.

As the demand for small-scale urban wind energy rises, more research and collaboration

in this new but high potential field will be needed. For the second part of the macro component


of this project, we reached out to other wind turbine owners and researchers in the area –

such as the wind laboratory at the Boston Museum of Science, the Medford Clean

Energy Committee, and the Woods Hole Research Center – and discussed collaboration

opportunities. To facilitate future research, our anemometers will remain on site and continue

recording data after the end of the spring 2010 semester. The open sharing of such data and

other wind expertise, not only within MIT but also in the surrounding areas, will help spread

understanding of and encourage further research of urban winds.

Part B

ACKNOWLEDGEMENTS

We are grateful to the many people who have helped us along the way. Kathy Araujo,

PhD student in the MIT Urban Studies and Planning Department and our TA for this project,

provided us with helpful guidance throughout the semester. Stephen Connors from MIT's Lab

for Energy and Environment was our main methodology advisor. We also consulted with

Samantha Fox and Richard Bates, former members of the 2007 wind study group, about site

selection and data analysis. Professor Heidi Nepf from MIT's Environmental Engineering

Department helped us to determine the air flow pattern around campus and made some useful

recommendations on testing sites. A visit to the wind exhibition at the Boston Museum of

Science and a talk with Marian Tomusiak, Museum of Science Wind Turbine laboratory analyst,

provided us with useful information on different kinds of wind turbines and how to select the

turbines to study in our project. Alex Kalmikov, PhD student in mechanical engineering and

member of project Full Breeze, gave insightful advice on our data collection process. Kevin


Connolly, interim manager of MIT Repair and Maintenance, helped our project greatly by taking

the time to escort us to the studied roofs weekly during our data collection. Cy Chan, PhD

student in the MIT Electrical Engineering and Computer Science Department; Paul O'Gorman,

Victor P. Starr Assistant Professor of Atmospheric Science at MIT; Alvar Saenz-Otero, research

scientist in the MIT Aeronautics and Astronautics Department; and Patrick Barry and Bob Paine,

members of the Medford Clean Energy Committee, also enhanced our research by providing us

with existing wind data. Additional assistance and guidance was provided to us by Phil

Thompson, Network Administrator from the MIT Urban Studies and Planning Department; and

City of Cambridge's Iram Farooq, Senior Project Manager; John Bolduc, Environmental Planner;

Rosalie Anders, Project Administrator. Also, thanks to Rui Jin, undergraduate in electrical

engineering, who helped with data analysis and power calculations.

Part C

INTRODUCTION

Human society has entered a unique period in Earth’s history. Industrialization has

brought about dramatic reformation not only within human society but also to the Earth’s

climate. Atmospheric carbon dioxide concentration levels, which have remained stable for the

last 800,000 years, have recently shown unprecedented growth. The turning point of this

dramatic shift was the industrial revolution, during which fossil fuels, such as coal, oil, natural

gas, began to enter the ecosystem and reform the environment in which we are living.

Confronted with this crisis today, we have been pressed to develop sustainable energy

supplies that rely on renewable resources. Wind power, one type of renewable energy, holds


especially strong promise. According to the 2008 United States Department of Energy's report,

it is possible for the nation to derive 20% of its electricity need from wind power by 2030 (U.S.

Department of Energy). This is an ambitious goal, but achieving it is not impossible. In the wind

energy department, many possibilities have not been fully explored, such as the utilization of

small-scale wind. Until recently, wind turbines had only been used in large-scale wind farms

that often entail large, open spaces, but notably in the last decade, as concerns over climate

change have become increasingly evident, small wind power has attracted greater public

attention. Different from large-scale wind farms which often involve governmental planning

and complicated construction, small wind systems are easily deployable and adaptable to built

environments, making them favorable choices for private sectors and city planners. If proven

cost-efficient, small wind turbines could become viable commercial products in today’s market.

The MIT Energy Initiative (MITEI), established in September 2006, is an Institute-wide

initiative designed to help transform the global energy system to meet the needs of the future

and to help build a bridge to that future by improving upon today's energy systems. Complying

with the goal of MITEI, Project High Winds explores urban wind by adopting two distinct

approaches: a micro-level feasibility study for on-campus building-integrated small turbines and

a macro-level investigation of the current urban development and learning curve for small

turbines. These approaches are purposed to both reform MIT's own energy system and identify

tasks to further small-scale urban wind utilization.


Part D

BACKGROUND INFORMATION

Due to the rising energy demands of our society and the slowly dwindling supply of

fossil fuels, the world has recently turned to renewable sources, like sunlight, wind, tides, and

geothermal heat, for energy. The combination of increased costs of oil extraction, high

projected costs of oil, and environmental hazards associated with fossil fuels have made

renewable energy the fastest-growing and most attractive source of energy in society today.

Wind energy, in particular, is one such source of renewable energy. Currently, in the

wind turbine market, there are two main categories of turbine models based on the orientation

of the main rotor shaft that includes the hub and blades of the turbine. Horizontal axis wind

turbines (HAWTs) are usually tower-mounted with a propeller-type rotor and can be further

classified into downwind or upwind machines. Upwind HAWTs are usually preferred because

downwind HAWTs experience fatigue failures as a result of cyclic wind turbulence from the

towers. In general, HAWTs are considered to be reliable in terms of harnessing strong prevailing

wind, and thus have been the standard for wind farms worldwide.

Vertical axis wind turbines (VAWTs) have the rotor shafts arranged perpendicular to the

wind direction, and their gearbox and generator can usually be placed close to the ground,

making the turbine more accessible for maintenance. VAWTs are generally more conducive to

rooftop installation. In urban environments, winds often hit the sides of buildings and are

redirected over the rooftops. Effective urban wind turbines can leverage this condition by

making use of the effects of this updraft and the resulting wind acceleration.


Part E

REPORT OUTLINE

Figure E.1 outlines the areas focused on in this study:

Figure E.1: Report structure


This report is structured as follows:

In the micro study:

Section 1 contains a detailed analysis of five small-scale turbine models: UrbanGreenEnergy

(UGE-1K), Bergey Windpower (BWC XL.1), Proven Energy (WT2500), ReDriven Power (3kW),

and Endurance Windpower (S-343).

Section 2 outlines the six campus sites studied - Sloan Building E-52 (three sites), Building 2, the

Green Building, and the Sailing Pavilion - as well as the procedures used to install the

instruments and obtain data from on-site anemometers. This section also includes an overview

of the measure-correlate-predict (MCP) method we used to extrapolate our short-term data to

year-long data.

Section 3 provides analysis of the collected wind data. The extrapolated data were evaluated

for average wind speeds and Weibull distribution parameters. Cost analysis specific to each

location and turbine immediately follows.

In the macro study:

Section 4 summarizes current policies and measures relevant to wind energy in the

Cambridge/Boston area, including the Production Tax Credit (PTC), the Investment Tax Credit

(ITC), the Massachusetts Renewable Energy Portfolio Standard, the Regional Greenhouse Gas

Initiative (RGGI), net metering, and other economic incentives.

Section 5 provides an overview of current wind turbines projects and local zoning rules in

Cambridge, Boston, Allston, Quincy, and Medford. In particular, the zoning policies and local

progress of Boston and Cambridge are compared.


Part F

PROJECT REPORT

I. Micro Study

1. Turbine Model Comparison

This section gives an overview of some of the available options in small-scale wind

turbine technology.

1.1 Models Overview

In the interest of exploring multiple wind turbine configurations for future wind energy

projects, five turbine models possessing varying specifications were selected for investigation in

this study. The manufacturers were chosen based on four specific criteria: power output, cut-in

wind speed, cut-out speed, and rated wind speed. Specifically, the models in question all have

rated power outputs of less than 10 kW to accommodate the pre-condition of being conducive

to building-integrated installation. More powerful and, in turn, larger and heavier turbines were

not considered.


1.1.1 Urban Green Energy (UGE-1K)

The UrbanGreenEnergy UGE-1K is a vertical-axis wind turbine

intended primarily for rooftop applications. One key advantage to

its vertically oriented turbine rotor shafts is that the arrangement of

the turbine blades eliminates the need for the turbine to be pointed

in the direction of the prevailing wind to be effective. Instead, it can

harness winds that are highly variable in direction, such as the interrupted wind flows most

commonly found in urban environments. Furthermore, the compact size and low noise level of

the UGE-1K makes it ideal for inconspicuous integration into an urban area (Urban Green

Energy).

1.1.2. Bergey Windpower (BWC XL.1)

The Bergey XL.1 is a horizontal-axis, three-bladed turbine with a

power output of 1 kW. It has a mobile tail capable of orienting the

turbine so that it faces the wind, which is required because of the

turbine’s up-wind operating design. The BWC XL.1 also possesses a

passive sideways furling system and an extra-stiff blade option,

useful for protecting the turbine blades from strong, turbulent winds. This model was

considered because of the recent attention garnered from the installation of two 10 kW Bergey

turbines in Cambridge in the fall of 2009. Since then, the city of Cambridge has expressed an

interest in the continued investigation of the Bergey turbines (Bergey Windpower Co.).

Figure F.I.1.1.2: BWC XL.1

Figure F.I.1.1.1: UGE-1K


1.1.3. Proven Energy (WT2500)

The Proven WT2500 is also a non-tailed horizontal-axis turbine with

a power output of 2.5kW. A different Proven Energy turbine model

was adopted by the Museum of Science in Boston as a part of their

wind study in 2009. Compared to the other turbines installed at the

Museum, the Proven model consistently generated the most power

throughout four months of data collection. A unique aspect of Proven Energy turbines is the

ability of their blades to bend and pitch away from the wind at high velocities. While protecting

the equipment from damage, the turbine is also able to preserve power output (Proven Energy

Wind Turbine).

1.1.4. ReDriven Power (3 kW)

The ReDriven 3 kW turbine has a non-tailed, horizontal-axis, sleek

and slim design in the style of large-scale tower-mounted wind

turbines; however, the ReDriven is made specifically for low to

medium winds, as it possesses the lowest cut in and rated wind

speeds among the five models. Lower cut-in and rated wind speeds

generally translate to longer periods of time during which the turbine is actually generating

usable voltage. One unique aspect of this turbine is that its controller possesses an internet

communication interface that allows for real-time monitoring of the turbine at all times

(ReDriven Power Inc.).

Figure F.I.1.1.3: WT2500

Figure F.I.1.1.4: RP 3 kW


1.1.5. Endurance Wind Power (S-343)

The Endurance S-343 is a horizontal-axis turbine that was originally a

project taken on by Windward Engineering in response to a grant

awarded by the U.S. Department of Energy to develop a wind

turbine with an overspeed control system. As a result, the turbine

employs specially designed overspeed protection techniques. Stall

control allows for power regulation by stalling the blades of the turbine after rated speed is

achieved, making the system aerodynamically more efficient while also increasing the systems

operational reliability. In addition to this, there are also rapid, dual redundant brakes and an

automatic shutdown system that is triggered by excessive wind speeds (Endurance Wind

Power).

1.2. Turbine Specifications

Table F.I.1.2 contains detailed technical specifications for the turbines investigated:

Table F.I.1.2: Turbine model specifications

Figure F.I.1.1.5: S-343


2. Site Selection and Measurement

This section outlines site selection, instrument installation, and data collection

processes of the current study.

2.1. Site Selection and Data Collection

Sites were selected based on criteria which factored in prior and ongoing studies,

anticipated wind resources, public perception, and accessibility. Six sites were chosen on the

east side of campus since a companion study is currently underway on the west side of campus

with Project Full Breeze. Campus map with site locations is located in Appendix I.

Anemometers were installed on the roofs of Building 2 (one unit) and the Sloan Building

(three units) in late March/early April. Data from pre-existing anemometers located on the

Sailing Pavilion and the Green Building are also considered in this study. The Sloan Building E52

was chosen for its satisfaction of the criteria mentioned previously as well as its proximity to

Eastgate graduate housing, the optimal site on campus identified in a 2007 wind study (Bates et

al. 2007). In terms of rooftop configuration, two Sloan Building anemometers were installed in

late March - one on the southeast Memorial Drive edge of the building and a second in the

center of the western edge of the upper roof. Both were chosen for the unobstructed wind flow

they would likely receive from the river. Questions over the long-term feasibility of upper roof

access led to a site shift to the southeast mid-section of the lower roof during the first week of

April. The Building 2 test site was chosen based on its satisfaction of the above criteria, in

particular its proximity to the river and its visibility to the public, enabling a turbine installed

there to be a symbol of MITs commitment to clean energy.


Anemometer installation consisted of attaching the anemometer to a pole of

approximately 2.5 meters, which was then held in place by a second stationary pole. The

anemometers were programmed with Hoboware micrologger stations to collect data every 5

seconds continuously. Data was retrieved from the logger stations every 1-2 weeks. Retrieval

was done using Hoboware software and a laptop. The data were then converted into Microsoft

Excel files for analysis. Following each retrieval, the anemometer was reprogrammed to erase

the previous data and begin collecting anew. Global positioning estimates were also made to

ascertain site coordinates and elevations. Multiple readings were taken at each site and

averages were derived, which are shown in Figure F.I.2.1.

SITE COORDINATES

Sloan 1 42.36072 N -71.0833 W

Sloan 2 42.36064 N -71.0837 W

Building 2 42.35901 N -71.0901 W

Table F.I.2.1: Site coordinates


2.2. Correlation and Prediction

Based on recommendations made by Steve Connors and Richard Bates, both of whom

have conducted wind analyses in the past, we used a well-known method called Measure-

Correlate-Predict (MCP) that was developed by Dr. Jim Manwell, wind energy researcher at the

University of Massachusetts (Bates et al. 2007). This method correlates collected data with

year-long reference site data in order to extrapolate the collected data, providing a prediction

of year-round wind resources (Rogers et al. 2005).

= − +In which:

is the predicted wind speed

and are the mean wind speeds at the local and reference locations, respectively

and are the standard deviations at the local and reference location, respectively

is the wind speed at the reference location

This formula was used in the Hull Wind II case study (Manwell 2003) and proved to be a

reliable method for data extrapolation. For our reference data, we used data collected at Logan

Airport in Boston. Notably, the 2007 study used data from Beverly Airport instead of Logan

Airport because of the latter site's special location on a peninsula. We considered both data

sources and, after consulting meteorologist Alex Kalmikov, chose Logan Airport as a more

suitable reference because of its proximity to MIT. We suggest that future studies test

correlations between the data from both airports.


3. Wind Data Analysis

This section evaluates the wind conditions at the studied locations through the data

collected by the procedure described in Section 2.

3.1. Average Wind Speed

Average wind speed provides a rough estimation of the quality of wind resources at

each site. Figure F.I.3.1 on the following page is a summary of the recorded and correlated

wind speeds at our tested site: the blue columns are the averages of the recorded wind speeds,

which were recorded on site by anemometers; the red columns are the averages of the

correlated wind speeds, which were calculated after our recorded data were correlated with

Logan Airport's wind data.

Average wind speeds at most of our studied sites are below 4.4 m/s, which is the

division between class 1 and class 2 wind resource. The correlated average at Sloan 1 and the

averages at the Sailing Pavilion, however, are within the 4.4 m/s to 4.1 m/s boundary and

qualify at class 2 wind. Notably, the recorded wind speed for the sailing pavilion (recorded

during 7 months as noted in Appendix II) is on the wind class 3 and 4 boundary of 5.6 m/s

(AWEA). Collectively, the range of our wind data agrees with that of the data collected by the

2007 wind project.


Figure F.I.3.1: Average wind speeds

Error evaluation for this data is discussed in Appendix III.

3.2. Weibull Distribution

Since a turbine’s power output is not linear functions of wind speed, average wind

speed is not an accurate evaluation of local wind resources. A more complete measure

frequently used in the industry is the Weibull distribution, which is a probability density curve

that shows the probability at which each wind speed will occur. The Weibull distribution curves

based on the correlated data at each site are plotted in Figure F.I.3.2 on the following page.

Sloan 1Sloan 2

Sloan 3Building 2

Green buildingSailing pavilion

0

1

2

3

4

5

6

Average wind speeds

RecordedCorrelated

Site

Win

d s

pee

d (

m/s

)


Figure F.I.3.2: Weibull distributions

Each Weibull curve is defined by a shape parameter k and a scale parameter λ. A list of k

and λ parameters for each site is provided in Appendix IV.

Weibull distribution is often the preferred method in the industry because, qualitatively,

it provides a good understanding of the range of wind speeds at which our turbine with mostly

operates. The k and λ parameters also allow easy calculation of the statistical characteristics

such as the mean, median, mode, and standard deviation of each data set, as well as easy

approximation of the annual power output in kWh.

3.3. Discussion of Results

The optimal sites out of the six that we assessed are the Sailing Pavilion, Sloan 1, and

the Green Building. As seen in Figure F.I.3.1, the average wind speeds at these three sites

exceed the remaining three by notable amounts. Among them, however, there are interesting


and remarkable differences between recorded and correlated wind speeds. The Sailing Pavilion

has the highest average recorded wind speed, followed by the Green Building and then Sloan 1.

However, in terms of average correlated wind speeds – which our power calculation is based

upon – Sloan 1 is the highest, followed by the Sailing Pavilion and then the Green Building. As

later discussed in power and cost analysis section, the Sailing Pavilion provides the location for

the best turbine-site calculation despite having a lower average correlated wind speed and

demonstrates our previous point about the short-coming of using average wind speeds to

evaluate local wind resource.

The Sailing Pavilion and Green Building data, notably, were collected by pre-existing

anemometers installed to serve other purposes. These anemometers were not likely placed at

an optimal position or height on the roof for a wind turbine; therefore if a turbine is to actually

be installed, the wind input is expected to be even more favorable than that seen in our data.


4. Turbine Cost Analysis

This section provides individual cost analyses of each of the five turbine models to

evaluate their projected cost-effectiveness on campus.

4.1. Turbine Power Curves

The relationship between wind speed and wind power is expressed by the following

equation (Bates et al., 2007):

= 12In which:

is the power generated by the turbine

is the air density

is the blade area of the turbine

is the velocity of the wind

It is important to note that while power is proportional in a linear manner to the swept

area of the turbine and the air density, it is a cubic function of wind speed. This means that a

slight variation in wind speed can greatly affect the amount of power that can be converted

into mechanical energy by a generator.

Site-specific attributes are thus very important. It is worth underscoring that the

equation expresses the power that is available in a free-owing stream of wind; however, it is

impossible to extract 100% of the power contained within the wind since the rotor is unable to

convert all of the kinetic energy into electrical energy.


In order to account for this less-than-ideal efficiency, Betz’s Law states that the

theoretical efficiency of a wind turbine is limited to 59.3% (“Betz Limit”). The experimental

equivalent of this efficiency is shown through power curves provided by turbine manufacturers

which estimate the energy production for specific wind turbine models. The power curves for

the five investigated turbine models can be found in the appendix.

4.2. Cost Analysis

To calculate the expected annual power output for each turbine model, the raw

experimental data collected in this study were correlated with 14 years of data collected by

Logan Airport in Boston to extrapolate a set of annual wind speed data based on the collected

data. Then, this wind speed data were used to determine the expected power outputs for each

of the turbine models based on their power curves. A full chart of levelized costs for each

site/model combination can be found in Appendix V.

Note, the cost associated with the turbines include turbine and installation pricing as

well as maintenance costs. All models were assumed to have a product lifetime of 20 years and

a capacity factor of 20%. The total operating and maintenance costs were estimated to amount

to 20% of the turbines initial installed cost, which represents a 1% annual allocation over a

lifetime of 20 years.

After calculating the levelized costs of electricity without incentives, we calculated these

with the addition of Renewable Energy Credits (RECs) and either the Production Tax Credit

(PTC) or the Investment Tax Credit (ITC). Figures F.I.4.2.1, F.I.4.2.2, F.I.4.2.3, and F.I.4.2.4 show

the costs results, including incentives, for the top three sites and top three models.


Levelized Costs of Bergey XL.1 at Top 3 Sites (including RECs and PTC):

Table F.I.4.2.1: Levelized costs of Bergey XL.1 at top 3 sites (RECs and PTC)

Levelized Costs of Bergey XL.1 at Top 3 Sites (including RECs and ITC):

Table F.I.4.2.2: Levelized costs of Bergey XL.1 at top 3 sites (RECs and ITC)

Levelized Costs of Top 3 Models at Sailing Pavilion (including RECs and PTC):

Table F.I.4.2.3: Levelized costs of top 3 models at Sailing Pavilion (RECs and PTC)

Levelized Costs of Top 3 Models at Sailing Pavilion (including RECs and ITC):

Table F.I.4.2.4: Levelized costs of top 3 models at Sailing Pavilion (RECs and ITC)


It is important to note that there is a discrepancy between the levelized cost calculated

using the 30% Investment Tax Credit* (ITC) and the levelized cost calculated using the 2.1

¢/kWh Production Tax Credit (PTC). The Investment Tax Credit is an upfront discount from the

total installed cost of the turbine while the Production Tax Credit is a discount applied

depending on the annual power generation of the turbine. From the calculations, the

Investment Tax Credit results in a levelized cost that is slightly lower than the levelized cost

calculated using the Production Tax Credit. This discrepancy could be reduced if better wind

resources were available at the installed turbine site and thus the turbine would be able to

generate more power annually and result in a greater discount.

*only applicable for projects reaching commercial operation by Dec. 31, 2012

4.3. Discussion of Results

As shown by the charts, the most cost-efficient turbine models is the Bergey XL.1, with

the ReDriven 3 kW and the Proven WT2500 following closely behind. The predicted levelized

cost of the Bergey XL.1 on the Sailing Pavilion, including the ITC and Renewable Energy

Certificates (RECs), would be 13.9 ¢/kWh, compared to 16.6 ¢/kWh and 21.2 ¢/kWh for the

ReDriven 3 kW and the Proven WT2500 models, respectively. Among all the sitting and model

combinations we investigated, the Bergey XL.1 installed on top of the Sailing Pavilion proved to

be the only economically feasible option. This scenario would produce a levelized cost of

electricity that is within the range of what MIT has historically paid for electricity, which is

between 13 ¢ and 16 ¢/kWh. This range is expected to rise to 18 ¢ and 20 ¢/kWh in the future


due to high energy demands, so the levelized cost of 13.9 ¢/kWh estimated by this scenario

would be cost-beneficial for the institution.

The three best-performing turbines were all horizontal-axis turbines that had rated

power outputs of less than 5 kW. Based on the results, it is clear that the vertical-axis design

UrbanGreenEnergy turbine is not a model that would be recommended for further

consideration, as it produced some of the highest levelized cost estimates. This indicates that

though the variable wind capture capability of the vertical-axis design may seem attractive in

theory, it does not make enough of a difference in power generation to offset the high cost of

the turbine. In addition to the UrbanGreenEnergy, the Endurance S-343 model also did not

come close to being economically feasible. This may be due to the electrical size of this turbine,

as it has the greatest rated power output out of the five models studied. The wind speeds in

Cambridge may not necessarily accommodate the conditions required by a turbine of such high

rated power output. We investigated turbines that generated less than 10 kW of power, but the

results of the Endurance model lead us to propose the recommendation that only turbines that

generate less than 5 kW of power be considered in the future.


II. Macro Study

5. Support Mechanisms

This section discusses key policies and other factors which affect integration of urban

wind development in the Cambridge area.

5.1. Policies and Incentives

As shown in the previous section, the production and investment tax credits have a

positive impact on the overall cost of a turbine. Considering the big picture of the last few

years, the city of Cambridge has developed complementary policies and incentives to

encourage the utilization of wind energy. Understanding these relevant policies and measures

will enable better optimization of wind power opportunities.

The focus in this study will be placed upon the following incentive options: Production

Tax Credits (PTC), Investment Tax Credit (ITC), the Regional Greenhouse Gas Initiative (RGGI),

the Massachusetts Renewable Portfolio Standard (RPS), Renewable Energy Credits (RECs), net

metering, and laws which govern grid interconnection.

Federal level:

The Production Tax Credit (PTC), enacted as part of the Energy Policy Act of 1992 and

recently extended through the end of 2012, provides owners of wind generators with a credit

for each kilowatt-hour of electricity produced during the first ten years of operation for wind,

solar, bioenergy, and other installations of eligible clean energy technologies. This corporate tax

credit is currently 2.1 cents/kWh for wind power. In addition, wind developers have the option


of receiving a 30% investment tax credit (ITC) up front or post installation in lieu of the PTC if

the construction of the wind facility begins in 2010. This 30% covers the hard costs – equipment

and construction – of a wind turbine project. ("Production Tax Credit").

Note that as a tax-exempt educational entity, MIT does not pay income tax on its

research and education revenue; so tax credits such as the federal production tax credit,

investment tax credit and grants in lieu of tax credits in the American Recovery and

Reinvestment Act of 2009 (ARRA), may not be utilized. However, such credits may be applied to

unrelated business income (UBI), as described under Internal Revenue Code §§ 511-514 and the

Treasury Regulations promulgated there under. If current UBI is a loss, tax credits may be

carried forward for a period, until UBI is reported as a gain. This study assumes that the

investment tax credit will be carried forward and used within the 20 year lifetime of the

project. Additional incentives like the Federal Modified Accelerated Cost-Recovery System

(MACRS) 26 USC § 48(a)(3)(A) may also be utilized, depending on the terms of equipment

acquisition and UBI.

Regional level:

The Regional Greenhouse Gas Initiative (RGGI) is the first mandatory, market-based

effort in the United States to reduce greenhouse gas emissions. With it, 10 Northeastern and

Mid-Atlantic states have capped and will reduce C02 emissions from the power sector 10% by

2018 ("RGGI."). Under the RGGI model, wind power generators do not require allowances to

operate and do not receive any allowances or direct financial benefit; however, as a result of

RGGI, the cost of electricity generated by fossil-fuel plants will increase, as the plants will need


to buy C02 allowances in order to operate. Given this arrangement, the relative value of

emission-free wind electricity will increase compared to fossil fuel-based power under RGGI.

State level:

A Renewable Portfolio Standard (RPS) ensures that a minimum amount of energy is

included in the portfolio of the electricity resources serving a state. Currently, under the

Massachusetts Class I RPS, all retail electricity suppliers must provide a minimum of 5.0% of

sales of kilowatt-hours (kWh) to end-use customers by 12/31/2010 from eligible renewable

energy resources, like wind installed after December 31, 1997. It is important to note that there

is some overlap between RPS policies and the RGGI model because both may create

overlapping CO2 emissions reductions

Notably, RPS compliance is often met by Renewable Energy Certificates or RECs, which

are tradable, non-tangible energy commodities that certify a certain amount of electricity was

generated from an eligible renewable energy resource. They are a form of subsidy for electricity

generated from renewable resources. In Massachusetts, certain non-profit organizations such

as Mass Energy Consumers Alliance purchase RECs from renewable energy producers; the

current rate for wind power RECs is $0.03/kWh. Currently, the REC market appears to be

temporarily saturated due to a shortage of entities like Mass Energy Consumers Alliance that

enable trading of RECs. The Massachusetts net metering program allows an owner of a

renewable energy system to deduct any energy surplus from its metered energy inflows with a

retail credit for at least a portion of the electricity they generate. Amendments made in 1997 to

the net metering program have increased the allowable capacity from 30 to 60 kW. These


amendments also state that any net energy generated by such a renewable energy system will

be credited at the average monthly market rate to the following month’s power bill. The

purpose of this policy is to encourage small power production facilities. In addition, the Federal

Energy Regulatory Commission (FERC) makes it easier for small power generation systems, such

as wind turbines, to connect to the power grid managed by local companies. Provided wind

generators meet specified standards, they must be allowed to connect to the grid.

5.2. Other Support Mechanisms

The incentives mentioned in the previous section are not the only ones available for

wind turbine owners. Table F.II.5.2 on the following page is a chart detailing various other

support mechanisms that are applicable to wind energy.


Incentive Type Amount Maximum Eligible System Size Authority

Micro Wind Initiative

Initial incentive: $1,000 + $1.25/W based on rated capacity of system at 11 m/s Production incentive: $2/kWh based on actual total production during Y1

$4/W ($40,000)

1 to less than 100 kW Massachusetts Clean Energy Center (MassCEC)

Community-Scale Wind Initiative

Varies depending on type of project funded (feasibility vs. construction and design) and type of entity funded (private vs. public) and number of turbines. Minimum project size is 100 kW (DC).

$260,000 for private and $400,000 for public entities (this is subject to change)

Wind turbine models must be either 3rd-party certified as meeting IEC WT 01 or have a 3rd-party power curve certification, 2-year manufacturer warranty, adequate technical documentation, fleet wide history of retrofits made for proposed model, and a fleet wide turbine availability history. All equipment must be new, have UL listing and be compliant with IEEE standards, comply with NEC provisions, and include lightning protection and surge suppression.

Massachusetts Technology Collaborative

State Residential Renewable Energy Income Tax Credit

15% $1000 Not Specified M.G.L. Ch. 62, § 6(d)

830 CMR 62.61

Renewable Energy Property Tax Exemption

100% exemption for 20 years

Not Specified Not Specified M.G.L. Ch. 59 § 5 (45, 45A)

Informational Guideline 84-209

Table F.II.5.2: Summary chart of wind energy incentives


5.3. Summary

The array of policies and other factors related to urban wind indicates that it is a good

time for the installation of wind turbines capable of directing energy into the New England

power grid. Furthermore, the city of Cambridge currently has zoning rules, discussed in the

following section, that allow for the installation of wind turbines anywhere in the city after the

granting of a special permit. However, it is also possible for the wind turbine installation to be

permitted as of-right if a turbine is installed on campus and does not extend more than 40 feet

from the top of any building because the Institute is considered a special zoning district. The

city has adopted a more clean energy resource focus, since joining the Cities for Climate

Protection (CCP), a campaign of ICLEI-Local Governments for Sustainability, in 1999. The

resolution committed the city to prepare a greenhouse gas emissions inventory, which involved

setting a target to reduce emissions, developing and implementing a plan, and monitoring the

results. Cambridge is one of over 600 local governments around the world (147 in the United

States and 20 in Massachusetts) that have joined the CCP effort.


6. Urban Wind Development

This section is devoted to investigating the current status of urban wind turbine projects

in the local area and examining what can be done to improve the facilitation of wind turbine

projects in the future.

6.1. Existing Installations

Table F.II.6.1 summarizes the existing small wind projects in Cambridge, Boston, and

select neighboring cities. One commonality among these projects is the construction time;

nearly all constructions occurred within the past one or two years.

Wind Turbine User Permitting City

Installation Time Model

Boston Museum of Science

Cambridge/Boston 2009 April

Mariah Power WindspireSouthwest WindpowerSkystream 3.7Cascade Engineering SwiftAeroVironment AVX1000 (5 units) Proven 6

Harvard Business School

Allston, Boston 2009 October Bergey Excel (2 units)

Harvard University Holyoke Center Cambridge 2009 December AVX 1000 (6 units)

IBEW 103 Local Boston 2005 May Fuhrlaender FL 100

Logan Airport Boston 2008 May AVX 1000 (20 units)

McGlynn Middle School Medford 2009 January Northwind 100

Woods Hole Research Center Falmouth 2009 May Northwind 100

Table F.II.6.1: Existing wind projects


6.2. Turbine Performance and Economic Feasibility

Wind turbine performance is essential to a wind project’s economic feasibility. Primary

prediction of economic feasibility is based on a turbine’s manufacturer-supplied power curve as

well as on-site monitored wind data, with additional considerations for surrounding

obstructions. The performance and economic status of local projects at McGlynn Middle School

in Medford, Woods Hole Research Center, and the wind laboratory at the Boston Museum of

Science were assessed in this study.

At McGlynn Middle School the annual electricity output of their Northwind 100, a 100

kW ground-mounted horizontal-axis small turbine, is predicted to be around 170,000 kWh,

which translates to 10% of the school's electricity need (Medford Wind Turbine Project). This

offsets an estimated 133 tons of greenhouse gas emissions annually and saves the school

$25,000 each year on electricity bills. According to Northwind's live data feed website, from

January 2009 to May 2010, only 114,456 kWh of electricity was produced by the school’s

turbine, which is less than half of the predicted output (Medford Wind Turbine Project).

However, for Woods Hole Research Center, located on the southern coast of Cape Cod,

the same model over-performed during roughly the same time range. This turbine produced

124,000 kWh of electricity from November 2009 to April 2010 (Hackler 2010); in other words,

72.9% of the predicted annual 170,000 kWh of electricity was reached within only a half year.

The Boston Museum of Science Wind Turbine Laboratory began monitoring six different

roof-top turbine models in April 2009. The 1 kW model AVX 1000 reached approximately 60%


of its estimated power output and the 6 kW Proven model reached 75%, while other models

did not perform as well due to sitting problems (Boston Museum of Science).

Such discrepancies between projected outputs and actual outputs have also been seen

in other cases. Many factors can contribute to this discrepancy, such as sitting accuracy,

technical issues with the model, and most importantly, variability of wind resources. For

example, since the Boston area experienced its lowest annual wind speed in 80 years in 2009,

many sites were very likely affected.

From the owners that we interviewed from Medford, Woods Hole Research Center, and

the Museum of Science, we can also see that small wind is capable of providing a meaningful

amount of energy even if the wind turbine does not perform up to par. From here, further

research must be conducted on how to predict wind turbine performance more accurately so

that consumers can plan accordingly without being disappointed.

6.3. Application Feasibility

Whether a wind project can proceed does not solely depend on its economic feasibility

but also on whether it receives the appropriate approval from authorities and the community.

From interviews with city planners and a survey of existing wind zoning ordinances, we

examined the following major concerns of planners and the public regarding small wind turbine

projects.


1. Location

Small wind turbines are only allowed in designated zones. Specifically, in Cambridge,

projects with educational purposes are granted with a permit as-of-right in zones Residence C-

3, C-3A, C-3B or Special District 6 (Cambridge City Council).

2. Visual Impact

Understandably, most cities have zoning ordinances that clearly state constraints on

turbine height and other possible setbacks to minimize unpleasant visual impacts. Cambridge,

for example, specifically emphasizes the preservation of the city's historic characteristics. In the

wind zoning ordinances of Cambridge, Boston, and Quincy, numeric limits are set for a turbine's

dimension, and the turbines are also required to be colored to minimize visibility. All these

efforts are made in order to ensure that the turbine visually conforms to the built environment.

3. Sound/Noise

Noise is a major concern for both city planners and the local community. Complaints of

noise are rare but not non-existent. Although noise standards are well established among local

cities, evaluation of the noise of small wind turbines is not. John Bolduc, the environmental

planner of Cambridge mentioned that the city would like to see quantified evaluations of small

wind turbine noise levels (Bolduc 2010).

4. Shadow/Flicker

Though purely determined by fixed physical conditions, such as the size and location of

the turbine, quantitative assessments for shadow and flicker effect have not yet been

established in the local areas. Such evaluation is also desired by authorities.


The receptiveness of the local public to small wind varies from city to city; however, city

planners from Cambridge, Medford, Boston, Quincy, and Falmouth have described the public

response as generally positively. Patrick Barry, former Environmental Director of Medford, said

that "the public welcomes wind turbines with open arms” (Barry 2010).

6.4. Permitting

The complexity of permitting for a small wind projects varies depending on the nature of

project and the planner’s experience. In general, it is often easier for municipal and educational

projects to pass permitting processes. Sites adjacent to bodies of water are often favorable

sites for wind turbines and thus are more likely to involve environmental agencies intervention.

While permitting complications are sometimes unavoidable since the optimal turbine

sites are often in proximity of such natural resorts, they can be anticipated and reduced by

increasing the experience of city planners and introducing streamlined permitting processes. As

pointed out by Bob Mitchell, from the American Planning Association, "Towns are struggling

with their existing zoning as to how to handle individual requests for such [small wind turbine]

devices. So zoning is probably one of the biggest issues for towns both for their own devices

and for individual devices" (Mitchell 2010). For example, in 2007, when the Museum of Science

proposed its Small Wind Turbine Laboratory project, it took the city of Cambridge nearly six

months to determine whether a variance was needed to approve the project (Cambridge City

Council). The city gained valuable insights and experiences through the permitting of the

Museum of Science project and new zoning ordinances were established in 2009 (Rabkin 2010).

Thus, when MIT sought approval for its proposal to install a ground-mounted turbine on

campus, it took the city only three months to grant the permitting.


Boston also has newly established specific wind zoning ordinances due to several

requests for turbine installations. Currently, in cooperation with Quincy, Boston is developing a

municipal wind project at Moon Island in Boston Harbor. Larry Chretien, former Quincy city

councilor who helped write Quincy's zoning ordinance comments, "Without a wind ordinance,

we may see good projects go before the zoning board of appeals and be denied or we may see

bad projects be approved. This is not a matter to be put off any longer” (Chretien 2010).

Brian Currie, regional representative of the American Planning Association and the town

planner of Falmouth, a city that owns five major wind projects and at least ten individual wind

projects, provided concise advice on how planners new to small wind should start planning:

“Albeit obvious and important, the first step is to clarify the goal of developing wind. Then, the

land and wind resources of the city need to be identified. Third, early zoning rules need to be

established. Fourth, cities need to consider how to make political sense - or recognizing their

clients and the local community – and economic sense of wind projects” (Currie 2010). Now

that the American Wind Energy Association and the state of Massachusetts have both

published sample zoning ordinances, and frontier cites have accumulated knowledge in small

wind planning, it is the perfect time for cities that have begun to welcome small wind to

establish their own preliminary zoning by-laws.

6.5. Summary

Currently, there is high demand for first-hand information on urban small turbine

performance in built environments. As the majority of local projects are newly constructed, it is

the opportune time for these project developers to begin monitoring turbine performance as


well as wind resources in their area. Open sharing of data collected concerning the

performance of certain turbine models and the insights gained through owning and operating

wind turbines in the area would help move the urban wind revolution forward. Representatives

from the Boston Museum of Science, Woods Hole Research Center, and Logan Airport have all

expressed interests in collaborating with MIT in the future.

In addition, urban wind project developers are in need of wind resource information

and would specifically benefit from the development of a higher resolution wind map than the

2.5 km one that is currently in place (Department of Energy). A wind map of this kind would be

helpful for city planners who wish to reserve wind resources by pre-zoning and for interested

parties who wish to discover proper sites for small wind projects on their property.

On the regulatory level, more standardized, streamlined regulating systems are essential

to the permitting process so that good wind projects will not be unnecessarily impeded.

Part G

MAJOR FINDINGS AND RECOMMENDATIONS

Integrated analysis of site-specific wind resource with cost assessments of five turbine

models indicates that the optimal site-model combination for the study would be the

installation of a Bergey XL.1 turbine on the roof of the Sailing Pavilion. This scenario could

produce electricity at a levelized cost of 13.9 cents per kWh after factoring in maintenance

costs and wind energy incentives like the investment tax credit and renewable energy

certificates, which lies within the historical range of what MIT pays for electricity, 13 to 16 cents

per kWh. Due to increasing energy demands, this range is expected to rise to 18 to 20 cents per


kWh, which is more expensive than the estimated electricity cost for our proposed scenario. In

addition to being economically feasible, this scenario would also be environmentally beneficial,

as it would displace current cogeneration fuel consumption and reduce greenhouse gas

production by an estimated amount of 1,600 pounds of CO2 per year. This reduction of CO2 is a

fraction of the 600 million pounds of CO2 emissions that MIT is responsible for each year; yet it

is important to recognize this is an initial step that may be replicated with additional turbine

units (Bates et al. 2007). Perhaps more importantly, it can serve as an important vehicle for

learning and leadership in an economically and environmentally favorable manner.

Under substantial supporting policies, small-scale urban wind power is now

experiencing its initialization period during which understanding and experience in its

application and regulation are accumulating. Studies on turbine performance and local wind

resource, which are the technical determinants of small turbines' economic values, need to be

conducted in larger quantity and higher specificity. Quantified assessment on non-economic

influences of small turbines needs to be made available to small wind consumers and regulating

authorities. Zoning by-laws need to reviewed or created, if not initially in place, in order to

assure generally streamlined, standardized permitting for basic small wind projects. Networking

among concerned parties will expedite the progress of small wind development. Educational

institutions, such as MIT and the Museum of Science, play an especially important role in

technological advancement and network coordination. Several current small wind project

developers have shown interest in partnership with MIT on future wind energy projects.


Part H

LIMITS AND AREAS FOR FURTHER RESEARCH

Due to the limits and time constraints of this study, more extensive research should be

conducted to explore local wind energy more fully. The availability of equipment bounded the

quantity of sites to six locations. Additional sites, such as MacGregor undergraduate housing,

could certainly be tested. The Sloan 1 site could also be more fully tested, if access constraints

are addressed. Acknowledging that this study and that of the 2007 team can serve as

preliminary pre-screening, we suggest that future wind studies should conduct simultaneous

monitoring of the Sailing Pavilion, the best wind resource site found in this study, and Eastgate,

the best wind resource site found in the 2007 MIT wind study (Bates et al. 2007). Because of

the short time period during which this study was conducted, there is a margin of error

associated with the data presented in this report. Year-long collection of wind speed data

would work toward reducing these inaccuracies. Also, for more accurate data correlation, the

wind speed data from Logan Airport and Beverly Airport, reference sites for this study and the

2007 study, respectively, can be compared (Bates et al. 2007). In addition, more research

should be done on air flow dynamics and turbulence effects in order to gain a better

understanding of wind flow as it relates to campus.


Part I

CONCLUSION

The potential and demand for wind energy is immense: experts suggest that wind

power is capable of generating 20% of U.S. electricity (Department of Energy 2008). Though less

than 2% of the United States’ electricity is currently being harnessed from the wind, the wind

market has been steadily increasing (Great Plains Windustry Project). In 2008, the U.S. market

for small wind turbines grew nearly 78%, more than five times the increase of 14% in 2007

(Stimmel 2009). The reason for this sudden spurt is most likely in response to the rising

residential electricity demands and the public’s heightened awareness of the global energy

crisis. This accelerated growth in the turbine market shows promise for the growth and

expansion of wind energy in the future so that it may become a truly viable and reliable source

of energy rather than merely an energy alternative.

In 2005, President Susan Hockfield set MIT on a course to tackle the complex energy

problems of our time. Our consideration of wind power at MIT and select areas goes straight to

the heart of this. MIT and area partners, like the Museum of Science and Woods Hole Research

Center, can work to further the knowledge of wind technology by serving as central partners for

data sharing and collaboration. As an educational hub focused on advancing energy solutions in

the largest energy consuming nation in the world, MIT is particularly well-suited to lead and

build networks to address our energy systems in concrete ways. By developing an interactive

network of research and community partners, which contributes to existing studies and

strengthens the broad database of wind knowledge, an effective living, learning laboratory can


be established at MIT that can further wind energy exploration and increase public awareness

of the strong potential of wind energy.


Part J

REFERENCES

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Bates, Richard et al., "Wind Study: Feasibility Study and Recommendations for Implementing Wind Power on MIT’s Campus." 16 May, 2007.

Bergey, Michael. "Small Wind Turbines: Primer." altestore. Available from http://www.altestore.com/store/. Internet; accessed 13 May 2010.

Bergey Windpower Co.. Available from http://www.bergey.com/. Internet; accessed 25 April 2010.

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"Betz Limit." Renewable Energy UK. Available from http://www.reuk.co.uk/Betz-Limit.htm. Internet; accessed 25 April 2010.

Boston City Council. Zoning Ordinance Article 88. Feb 26, 2009.

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Great Plains Windustry Project. "Why Wind Energy?." Windustry. Available from http://www.windustry.org/wind-basics/learn-about-wind-energy/wind-basics-why-wind-energy/why-wind-energy. Internet; accessed 25 April 2010.

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"Medford Wind Turbine Project." Medford Clean Energy Committee. Available from http://www.medfordcleanenergy.org/MEIPoverview.html. Internet; accessed 13 May 2010.

Northern Power Systems, "McGlynn Middle School, Medford, MA." Available from https://smartview.northernpower.com/public/medford/. Internet; accessed 13 May 2010.

"Production Tax Credit." AWEA. Available from http://www.awea.org/policy/ptc.html. Internet; accessed 25 April 2010.

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Rabkin, David. Museum of Science Wind Turbine Lab Project Report. Jan 2010.

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Contacts/Interviewees:

Currie, Brian. Interview. May 2010

Barry, Patrick. Email. April 2010.

Buldoc, John. Interview. April 2010.

Chretien, Larry. Interview. May 2010.

Civic, Teresa. Interview. May 2010.

Hackler, Joseph. Email. May 2010.

Mitchell, Bob. Email. April 2010.


Part K

APPENDICES

1. Appendix I

Site locations:

Figure K.1: Campus site locations


2. Appendix II

Average wind speeds:

Table K.2.1: Average wind speeds for our installed sites

Table K.2.2: Average wind speeds for additional sites


3. Appendix III

Wind speed error:

To determine how good our short term to long term correlations are, we used the

Pearson product-moment correlation coefficient. The Pearson's sample correlation coefficient

between two samples is sum of the products of the standard scores of the two measures

divided by the degrees of freedom.

= ∑ ( − )( − )∑ ( − ) ∑ ( − )

Statistically, r is the percentage of the variation in the data predicted by the correlation.

The Pearson coefficient for each of our correlation is presented in the table below.

Correlated data set Pearson coefficient

Sloan 1 0.8202

Sloan 2 0.7338

Sloan 3 0.8382

Building 2 0.7515

Green Building 0.2721

Sailing Pavilion 0.3532

Table K.3: Pearson coefficients

As expected the r-values for the Green Building and the Sailing Pavilion are much lower

than that of the remaining sites because of the longer data collection time and, consequently,

larger variation in the recorded data.


4. Appendix IV

Weibull distribution parameters:

The recorded parameters are calculated based on the data we collected, whereas the

historical parameters are calculated based on our correlated data.

Site Recorded shape factor k

Historical shape factor k

Recorded scale factor λ

Historical scale factor λ

Sloan 1 5.0654 10.2654 3.6668 4.4480

Sloan 2 0.8171 2.4439 3.4344 1.9956

Sloan 3 1.6050 2.4835 2.8155 2.5343

Building 2 1.6193 2.2008 1.5747 1.4935

Green building 2.1338 2.9795 4.2611 3.8619

Sailing Pavilion 2.1193 1.7331 7.1323 5.1692

Table K.4: Weibull parameters


5. Appendix V

Levelized costs of electricity (without added incentives):

Table K.5: Levelized costs of electricity (without added incentives)

project high winds - mitweb.mit.edu/windenergy/campuswind/reports/2010highwinds.pdf ·...

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