support system residential wind turbine design decision · 2017-05-02 · jedi tax & local...
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By:Mohammed Almutairi
Anutaj ChahalJames FritzLuis Soto
Faculty Advisor:Dr. Lance Sherry
Residential Wind Turbine Design Decision Support System
$54 Variance
Rising Utility Bills Microclimate Rooftop & Venturi Effect
http://inhabitat.com
http://thebritishgeographer.weebly.com
AgendaContext Analysis
Problem Statement
Stakeholder Analysis
Requirements
Decision Support System
Case Study Results
Business Case
2
VA Electricity Prices
● The average homeowner in Virginia pays $130 every month and this price continues to increase. [1]
● Virginia homeowners pay 14% more than the national average.● The variance in monthly bills is about $54, causing an uncertainty
for homeowners. [1]● This variance can be reduced by supplementing energy generated
by wind turbines
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Virginia Electricity Price Variability
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Stable Weather
Variable Weather
Wind vs Solar● Ability to generate energy when direct sunlight is not available.● Round the clock energy generation● Can be installed above or around obstructions● Residential wind turbines offer the ability to save money, decrease
pollution and to increase the use of alternate renewable sources.
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Energy Source Solar Panels Wind Turbine
Rate of Power Generation 2 kW 2 kW
Time period of power generation
Average over 12 hours[2]
Average over 24 hours
Power Output 9.22 kWh 12.24kWh
Horizontal Axis Wind Turbine (HAWT)
Vertical Axis Wind Turbine (VAWT)
Wind Turbine Design
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Direct Industry
Propulsion generated through drag [4]
quietrevolution
Propulsion generated through lift [3]
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Wind
Electricity generated by rotation of the
turbine
Electricity sent from turbine to the the
inverter
Electricity stored in battery bank
Electricity distributiononsite use or net
metering
Small Wind Implementation
en.openei.org
AgendaContext Analysis
Problem Statement
Stakeholder Analysis
Requirements
Decision Support System
Case Study Results
Business Case
8
Problem Statement● Residential electricity costs are continuously increasing. There exists a variability in
the monthly utility bills because of high peaks of demand of electricity during the summer and winter months with a variance cost of $54.
● According to Energy Information Administration (EIA) prices will increase by 18% in the next 23 years. [7]
● The use of fossil fuels which include coal, natural gas and nuclear energy cause climate change. An alternative method of generating energy is through renewable sources (e.g. wind).
● Currently, the residential wind turbine technology market is developing and has improved throughout the years. [7]
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Need Statement● Reduce cost and variability of utility bills for residential owners.
● Protect the environment by utilizing alternate renewable sources of energy to
further reduce the carbon footprint.
● Take advantage of microclimate fixed locations with potential wind power and
exploit Venturi effect, if present. Taking these factors into consideration, a set
of best turbine configurations (based on cost & power output) will be
recommended to homeowners in order to install at that specific location.
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AgendaContext Analysis
Problem Statement
Stakeholder Analysis
Requirements
Decision Support System
Case Study Results
Business Case
11
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Stakeholder Objective Tension
Residents - Reduce utility bill prices- Reduce their carbon footprint- Reduce price variability
- Excessive net metering requirements- NIMBY
Utility Companies - Provide reliable safe electricity generation.- Minimize environmental impacts.
- An end of a monopoly- Tough energy regulations from Public Utility Regulation
VA – Division of Public Utility Regulation - Regulate just rates.- Ensure consumers receive adequate utility services
- Pressure from utility companies to block sales of renewable sources.
Federal Regulators (EPA, OSHA, FERC) - Overall Safety - Uncertainty of residential safety
Renewable Energy Manufacturers - Increase profit- Lower technology costs
- Uncertain medium- to long-term demand- Strict federal and state laws- Availability of federal and state incentives
Neighborhood Associations - Save on wind turbine installation costs by sharing
- Strict regulation from Public Utility Regulation
County Zoning Departments - Regulating installation heights - Increased workload with more turbines
Stakeholders Interaction Diagram
PUR board
State
Regulators Renewable Energy Manufacturers
FERC
EPA
OSHA
Federal
Utility-Scale Residential-Scale
UtilityResidents State Representatives
Pay Bills
Provide Power
Vote
Political Donations
Appoint members
Regulations
Compliance
Provide Sources
Purchase Sources
High Tension
13Tension: Allowing residential homeowners to have energy independence from utility providers
AgendaContext Analysis
Problem Statement
Stakeholder Analysis
Requirements
Decision Support System
Case Study Results
Business Case
14
CONOPSThe DSS will:
● Use Microclimate data for specific locations● Offer rooftop mounted solutions● Take advantage of Venturi effect to increase power output
To:
1. Reduce variability of utility bills 2. Generate clean energy3. Reduce utility bills
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Mission Requirements● MR1. Decision Support System (DSS) shall accept input data based on a
specific location.
● MR2. DSS shall analyze input data to determine the best existing wind
turbine configurations (based on cost & power output).
● MR3. DSS shall recommend the best wind turbine configurations based on the analysis of input data and existing wind turbine configurations.
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Functional Requirements● FR1: DSS shall accept at least one year’s worth of wind speed data.
● FR2: DSS shall provide an output of the best wind turbine configuration for
a given site.
● FR2.1: The report shall consist of the specifications of the best wind turbine.
(Dimensions, power coefficient, cost)
● FR2.2: DSS shall produce an estimated annual energy output [kWh]
● FR2.3: DSS shall produce financial report consisting time of return on
investment, net present value, and total cost of the system.
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Design Requirements● DR1. DSS shall require input wind data for specific site.
● DR2. DSS shall consist of an inventory with the capacity
up to 100 turbines.
● DR3. Each inventory slot will consist of the specifications regarding the dimensions, efficiency, and cost.
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AgendaContext Analysis
Problem Statement
Stakeholder Analysis
Requirements
Decision Support System
Case Study Results
Business Case
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Residential Wind Turbine Design Decision Support System (RWTDDSS)Purpose of the RWTDDSS is to use microclimate data to find optimal wind turbines for specific sites
● Inputs1. Wind speed collected from through AWS TruePower Merra 2 data2. Existing wind turbines design and performance characteristics
● Function:3. Wind Turbine Power Model computes annual power output 4. Cost Model and Financial Analysis Model calculates breakeven point and time
of return on investment● Output
○ Best Turbine Configurations○ Annual Energy Output○ Return on Investment
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Power Model
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Swept AreaCp
Annual Hourly Wind Speed
Financial Analysis Model
Annual Power Output
Wind Turbine InventoryDatabase
Cost ModelAcquisitionInstallation
MaintenanceInsurance
Turbine CostTower Cost
JEDI Data-base(NREL)
Venturi Coefficient
Residential Wind Turbine Design Decision Support System
Local Zoning Laws, Building/Labor Cost & Taxes
Costs
Outputs:Total Cost
NPVBreak-EvenAnnual Cost
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2
4
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Wind Turbine Inventory Database● General database characteristics:
○ Swept area (m²): Refers to the area of the circle created by the blades as they sweep through the air.
○ Rotor diameter: Is the diameter of the area covered by the blades.○ Rated power (kW @ m/s): The maximum limit that the electrical generator
is capable of at a certain speed.○ Cut in speed (m/s): The speed at which the wind turbine starts to generate
power.● Statistics:
○ The database has 40+ wind turbine models and 15+ wind turbine manufacturers.
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Image source: mmpa.org
#1
Inventory Design Space
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#1
Power calculated using power model (see slide 27)
Wind Speed Model● Assumption: Wind is blowing at observed rate for one hour.● Weather data collected for a time period of 1 year at a rate of once per
hour (8760 data points) and stored in a text file.● Collected via MERRA 2 Nasa Satellite data from the AWS True Power
software.● Wind speed data is isolated from irrelevant weather data and then used as
an input into the RWTDDSS .
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#2
Wind Data (Annapolis, MD)
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#2
Annapolis, MD location used for wind source
Power Model● Assumptions: All wind turbines have fixed pitch blade designs● Takes in swept area and Cp from the Wind Turbine Inventory● Outputs Annual Power Output in kW
Power output:
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#3
Power Output
Power Output =
Air Density = 1.225 kg/m3
A Swept area = * Blade radius^2 m2
k kiloWatt coefficient kW
v wind speed m/s
Cp coefficient of power
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#3
[12]
Coefficient of Power (CP)
● Coefficient of power: Measure of wind turbine efficiency [12]
● CP=
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Actual Energy ProducedWind Energy into Turbine
#3
Cut-In Speed: speed at which the wind turbine will begin to generate electricityRated Speed: maximum speed at which the turbine will rotate. Limited for efficiency and safetyCut-Out Speed: speed at which the wind turbine will stop rotatingSurvival Speed: Speed at which the turbines structural integrity will start to fail (63-67m/s)(140-150mph)
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#3
Power ModelDefinition:
“The Venturi effect, published in 1797 by Giovanni Venturi … As the fluid flows through the constriction, the fluid molecules speed up.” sciphile.org
Why is it important?
The force of funneling wind between two objects causings and increase in the wind's velocity therefore generating more energy.
We will be looking to exploit this effect to produce higher outputs of energy
Image source: sciphile.org/ [10]
Propertycasualty360.com [11]
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#3
Cost ModelAssumptions:● Maintenance cost of 0.07 ¢/kW produced [9].
● Roof mounted will be for tower option
● Insurance cost of 1% of total investment (estimated by the JEDI model)
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Component Description Cost
Turbine Aeolos 10 kW $22,920
Tower Roof Mounted $1,500
Battery Tesla Powerwall 2 $7000
Total Cost: $31,420
#4
Cost ModelBattery (Tesla Powerwall 2)Provides storage of energy produced so it may be used on demand
Cost:
One 14 kWh Powerwall battery: $5,500
Installation and supporting hardware starts at: $1,500
Total estimate: $7,000
Performance:
Usable Capacity: 13.5 kWh
Source: tesla.com/powerwall
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#4
Cost ModelJEDI Tax & Local Construction Cost Data-base
The Jobs and Economic Development Impacts (JEDI) is a model developed by the National Renewable Energy Laboratory (NREL) to help in estimating the following costs of a wind project:
● Construction materials and labor costs● Turbine, tower, blade costs, and local content information● Utility interconnection, engineering, land easements, and permitting costs● Annual operating and maintenance costs (personnel, materials, and services)● Tax, land lease, and financing parameters
nrel.gov/analysis/jedi/about_jedi_wind.html
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#4
Cost ModelSample Calculations from JEDI Data-base
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#4
Financial ModelAssumptions:● A price of electricity starting at $0.09 per kilowatt and growing at growing
at a rate of $0.0078 per year [7]
● Monthly consumption of energy was assumed to be 901 KiloWatts (kW)
(Yearly total of 10812 kW)
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#5
Financial ModelNet Present Value
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● Used to evaluate the profitability of an investment.
C_t: Net Cash inflow During period tC_0: Total investment costsr: Discount ratet: Number of time periods
#5
Financial ModelNet Present Value Results
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#5
AgendaContext Analysis
Problem Statement
Stakeholder Analysis
Requirements
Decision Support System
Case Study Results
Business Case
39
Case Study Description● Wind data collected for
○ Naval Academy in Annapolis, Maryland○ Spotsylvania, Virginia○ Amarillo, Texas
● Wind data collected from AWS from January 1st, 2016 to December 31st, 2016
● Monte Carlo Simulation performed varying:○ Wind (stochastic)○ Swept Area○ Rated Power○ Cut-In Speeds
● Existing turbines will be used for parameters for the varied turbine design data
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Results24 hours of data collected from weather stations
1 year for each location
8,760 data points
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ResultsWind Speed
Power Output
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Annapolis, Maryland
Spotsylvania, Virginia
Amarillo,Texas
Average Wind Speed
5.45 m/s 4.58 m/s 7.23 m/s
Percentage of Power Generation
of 1 Year32% 26% 90%
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● As swept area increase, power output increases
● As price increases, power output increases
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Aeolos 10kW Wind Turbine installed at Annapolis, MD Roof Mounted and with Federal Tax Credit of 30%
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Aeolos 10kW Wind Turbine installed at Amarillo, Tx Roof Mounted and with Federal Tax Credit of 30%
Break-Even Summary
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Location MD, with monopole
MD, roof mounted with
tax rebate
TX, with monopole
TX, roof mounted with
tax rebate
Cost $31,420 $18,424 $31,420 $18,424
Break-Even 39 8 16 5
AgendaContext Analysis
Problem Statement
Stakeholder Analysis
Requirements
Decision Support System
Case Study Results
Business Case
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Business Plan● No system exists that uses micro-climate data to make decisions on the best turbine for a
specific location.● Customers include green builders and members and business owners located in
residential areas who have an invested interest in renewable technologies.● Users will access the system through the company’s website.● Upon completion of the required information for the RWTDDSS, users will receive their
Optimal Wind Turbine Package.● Package consists of:
○ Expected Annual Wind Profile○ List of Existing turbines that with design characteristics(swept area, coefficient of power,
cut-in speed) that will capture the the greatest amount of wind energy available○ Each turbine will have total cost installed, expected annual power output and financial
breakdown of savings per year and expected break-even point
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Business Model● The method of profit will be through commission on the sale of the wind turbines in the Wind
Turbine Inventory.● In addition the system will be paired with green builders in the Mid-Atlantic region to offer a
one stop solution for both purchase and installation.● Green builders have built tens of thousands homes in the past ten years in the Mid-Atlantic.● Wind turbine manufactures will be allowed to have their wind turbines as part of the
inventory of the RWTDDSS with the agreement that 10% of sales through the system will be kept by the RWTDDSS Company.
● Wind turbine manufacturers will save money and generate an increase in revenue by using the RWTDDSS by eliminating need for person to be paid for on-site surveys.
● An increase in revenue will be seen through growth in a market that is currently not be exploited on a large scale.
● NREL estimates that by 2020 that small wind turbines have the potential to contribute to 8% of the U.S. electrical demand.
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Operational Costs
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Projected Revenue
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- Market: 10,000 homes [8]- Avg. wind turbine price based on Wind Turbine Inventory:$24,000- 10% commision- 3% Goes to builder - 10% increase every year
- Market penetration● Optimistic 5%● Expected 3% ● Pessimistic 1%
Projected Profit
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Return on investment
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Residential Wind Turbine Design Decision Support System
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References [1]https://www.eia.gov/electricity/data/browser/#/topic/5?agg=0,1&geo=00000001&endsec=8&freq=M&start=200101&end=201510&ctype=linechart
[2] https://www.energysage.com/solar/101/impact-of-roof-angle/
[3]http://www.directindustry.com/prod/eveready-diversified-products/product-39096-1553744.html#product-item_520428
[4] https://solarconduit.com/shop/wind/urban-green-energy-vawt-uge5mc10m.html
[5] http://www.seia.org/state-solar-policy/virginia-solar
[6]http://en.openei.org/w/images/thumb/5/57/GridConnectedSystems.png/520px-GridConnectedSystems.png
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References[7] https://www.eia.gov/outlooks/aeo/pdf/0383(2017).pdf
[8] http://resilientvirginia.org/resources/case-studies
[9] Textbook: Wind Turbine technology: principles and design by Adaramola and Acho. Pg 251
[10] http://sciphile.org/lessons/bernoullis-principle-and-venturi-tube
[11] http://www.propertycasualty360.com/2014/02/19/technical-notebook-the-venturi-effect
[12]http://www.windpowerengineering.com/construction/calculate-wind-power-output/
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