Design a Peer to Peer Energy Trading Model: Can Residents Trade Excess Renewable Solar Energy with Industrial Users?
William Jackson, Lara Basyouni, Joseph Kim, Anar Altangerel, Casey Nguyen
Excess Renewable Solar Energy
Area =520 kWh
Excess Renewable Solar Energy
Area =2503 kWh
Microgrid Exchange System
Wasted Solar Energy
After 100%, energy goes into the ground.
1
Overview
• Context Analysis
• Stakeholders
• Problem/Need Statement
• Confluence Interaction Diagram
• Gap Analysis
• Concept of Operations
• IDEF0 Diagram
• Model Simulation
• System Requirements
• Physical Hierarchy
• Model Results
• Model Verification Plan
• Graphical User Interface
• Business Case
• System Applications
• Conclusion
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Context Analysis-Cheap Solar • Installed Solar: Price of Solar Energy Trend is projected to drop
to below $50 per mWh in 2024 from $350 per mWh in 2009 .
Opportunity: Lower upfront solar costs goes down
for residential users.
Challenge: Technology gap still exists with
distribution battery energy storage systems.
Those limitations on storage capacity could result in
excess solar energy production during peak daytime
hours going into the ground.
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Ene
rgy
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)
Hours
Wasted Solar Energy
n ResidentialDemand
n ResidentialSolar Generated
GMU ENGRDemand
Source: NIST: Metrology for Distributed Smart Grid Storage Systems utilizing Advanced Battery Technology Source: Green Technical Media
Source: Bloomberg News: Solar Energy
“The greatest challenge that solar power faces is energy storage. Solar arrays can only generate power while the sun is out, so they can only be used as a sole source of electricity if they can produce and store enough excess power to cover the times when the sun is hidden.” Source: The Energy Collective
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Context Analysis-Rising Energy Demand Rising Energy Demand: Poses potentially
higher costs during peak demand, lower
capacity, higher levels of dependency from
regional energy providers, greater risk to the
traditional grid, and higher risk of power
outages.
Opportunity: Unutilized residential renewable solar
energy could lower the energy demand from utility
providers, lower costs for industrial users, and serve a
as a revenue stream for residents.
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GMU Engineering Daily Yearly Load/Demand Profile of an Energy System/Utility
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% of Time Load Exceeds kWh Value
GMU Engineering Load Duration Diagram
100% of the Day GMU Engineering is at a minimum of 250 kWh per hour per day
annually.
We found this energy demand signature consistent in analyzing over 8600 lines of historical data in a 12 month period.
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Context Analysis-Rising Energy Costs
Opportunity: Potential Unused Renewable Solar Energy could levelized swings in
monthly energy bills and utilized as a revenue with energy trading for residential
users.
y = 184.35xR² = 0.00051
y = 10.933xR² = 0.0005
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Aug-16 Sep-16 Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17
Mo
nth
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ost
s in
Do
llars
Ene
rgy
in k
Wh
George Mason University Monthly Energy Costs
kWh Monthly Costs Linear (kWh) Linear (Monthly Costs) Linear (Monthly Costs)
Potential Savings of $102,271.81
Fall Winter
Potential Savings of $64,079.39
Energy Cost Snapshot
August 2016-Sepetember 2017
Average 262,866 kWh per day
over 14 months
Spring Summer
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Problem and Need Statements
Problem:
• Residents with solar panels generate electricity during daylight hours when the demand for electricity is at its lowest. In locations without net metering, the excess energy is not taken in by the utility and is wasted into the ground.
Need:
• There is a need for a P2P energy trading platform to mitigate exponential peak energy demand, stabilize monthly energy costs, reduce wasted energy, and utilize excess solar PV energy. The system is designed in which residences with available excess solar energy can pool their energy generated in daylight hours and trade at their own discretion.
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What is a Microgrid? • Definition: “ A Microgrid is a group of
interconnected loads and distributed energy
resources within a clearly defined electrical
boundaries that acts as a single controllable
entity with respect to the grid and can connect
and disconnect from the grid to enable it to
operate in both grid-connect or island mode.”
• Nanogrid (Level 1)—serves a single building or
load.
• Campus Microgrid (Level 2) —customer owns
and maintains assets to include distribution
system behind the meters.
• Community Microgrid (Level 3) —integrated
into utility network with same technologies as
campus microgrids but utility controls the
system and distributed energy assets operating
within the regulatory framework.
Source: “Deploying Solar-Plus-Storage Microgrids” by Colavito and Michael
MEX
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Microgrid Energy Exchange System (MEX)
MEX is a system designed to connected wasted renewable solar energy between residential producers and industrial consumers.
The objective to take advantage of solar energy that would otherwise go into the ground when battery systems reaches to its maximum capacity during daylight hours when residential demand is at its lowest point (”Bathtub Effect”) and distribute that energy to an industrial user with a consistent energy demand.
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Confluence Interaction Diagram Opportunity: The intersection of
influencing environment factors
provide the impetus of the
Microgrid Exchange System to
capitalize on unused renewable
solar that would otherwise be
wasted into the ground.
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Context Analysis-Homeowners Associations General Statistics
1. 21.3 percent of the US are in community associations representing a value of $5.545 trillion dollars in value.
2. HOAs collected $88 billion in assessments from homeowners. Assessments include management services, utilities,
security, insurance, common area maintenance, landscaping, capital improvement projects, and amenities.
3. Virginia has a total of 8,600 HOAs representing 1,735,000 residents as of 2016.
4. Virginia ranks 12th in the number of HOAs with Florida ranked as 1st with 47,900 HOAs.
5. The trend is continued strong growth of HOAs in the US and Virginia.
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1970 1980 1990 2000 2002 2004 2006 2008 2010 2011 2012 2013 2014 2015 2016N
um
be
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f H
OA
s Years
Homeowners Association in the US
The trend is expected to continue with the growth of HOAs to
capitalize on opportunities to expand community solar
systems.
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Stakeholder’s Diagram
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Tensions [RED] 1. Regulators may classify the solar HOA
development as a utility substation subjecting them zoning ordinances and restrictions.
2. County would be regulating body for zoning but solar HOA may require legislative approval under Code of Virginia.
3. Energy Companies could raise potential risk of solar HOA connected to the traditional grid network.
Resolution of Tensions Developers and Builders establishing public-private partnerships, power purchase agreements with utility providers, and outline solar PV guidelines in the Articles of Incorporation along with the Covenants, Conditions, and Restrictions (CCR) for the HOA early in the process.
“Gap” Analysis • Gap for Residential and Industrial Users:
• The gap is the ability to use potential excess solar PV energy production and avoid wasted energy produced
during daylight hours from residential users to offset peak demand of industrial users to stabilize cost in
overall electrical energy costs over time.
• Win-Win Analysis: Lower all regulatory restrictions for residential solar PV systems collectively to trade with an
industrial user, favorably renewable energy solar market on peer-to-peer energy trading, and continual use of
passive renewable energy from residential users to eliminate unutilized wasted solar energy.
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erg
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W)
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Wasted Solar Energy
n ResidentialDemand
n ResidentialSolar Generated
GMU ENGRDemand
Residential User Demand
Industrial User Demand
Renewable Solar Energy Going to Waste
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MEX Concept of Operations (Current Usage and Billing)
Source: Dominion Virginia Power
Residential User Current Usage and Billing Process: • Consume energy through appliance loads throughout their
household to include their HVAC. • Local Utility Provider (Dominion Virginia or NVEC) verifies
consumption via metered system. • Local Utility Provider bills the residents for use on a monthly
basis. • Customers are charged higher costs on a two-tiered rate for
peak use (typically between 2pm -10pm daily) and off-peak rates (11pm to 10am daily).
• Energy bills can vary significantly from month to month.
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MEX Concept of Operations (Proposed System)
Source: Dominion Virginia Power
HOA Energy Usage and Billing Process: • Residents pay a flat rate for energy use each month
over a 25 year period. • HOA would use excess solar energy to trade with GMU
or an industrial user. • HOA would potential earn revenue with energy trading
though a trading platform graphical user interface.
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rgy
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Wasted Solar Energy
n ResidentialDemand
n ResidentialSolar Generated
GMU ENGRDemand
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MEX Concept of Operations (Proposed)
Concept of Operations: 1. Resident pays flat rate for
electricity with solar PV for 25 years.
2. GMU establishes a Power Purchase Agreement for power from HOA.
3. HOA verifies energy storage system levels.
4. HOA distributes power to GMU and Residents via Energy Platform.
5. HOA provides a receipt for both energy transactions.
Objective: Move and Distribute Excess Solar Energy to GMU 15
16
MEX Microgrid Design Cloud Computing and Storage
Cloud Computing and Storage
Microgrid Battery Storage System
Residential Solar PV System
Point of Common Coupling
Shared Residential Battery System
Energy Trading
Platform
Subsystem
Subsystem MEX
1 MW Battery Energy Storage
System
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MEX System Diagram
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MEX System and Functional Requirements
MEX System Requirements
• SR1 System shall provide P2P energy trading over the microgrid.
• SR2 System shall provide access to solar-based renewable energy for users.
• SR3 System shall allow users to store excess energy.
MEX Functional Requirements
• FR1 MEX shall allow users to set up online user accounts.
• FR2 MEX shall record renewable solar energy generation, battery storage amount, and distribution of excess energy.
• FR3 MEX shall allow users to set rates for excess energy.
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MEX Model Simulation and Objectives
SO1 Identify overall residential and industrial demand
SO2 Identify residential solar generation.
SO3 Identify when supply and demand are the highest and the lowest.
SO4 Identify average supply and demand.
SO5 Identify average excess energy and wasted energy.
SO6 Identify minimum supply to meet demand requirements.
SO7 Identify residential energy demand hourly, daily, monthly, and yearly.
SO8 Identify industrial energy demand for specific building hourly, daily, monthly, and yearly.
SO9 Identify residential solar generation supply hourly, daily, monthly, and yearly.
SO10 Identify months with the highest solar generation.
Purpose: To record energy production from residential solar PV systems, and determine if excess energy is produced.
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MEX Simulation Requirements
Solar PV Simulation Requirements
• PVR1 Solar PV Simulation shall calculate daily, monthly, and yearly energy generation.
• PVR2 Solar PV Simulation shall calculate excess energy amount.
Residential Demand Simulation Requirements
• RDS1 Residential Demand Simulation shall record daily, monthly, and yearly energy demand.
• RDS2 Residential Demand Simulation shall identify peak hours of energy use.
Industrial Demand Simulation Requirements
• IDSR1 Industrial Demand Simulation shall record daily, monthly, and yearly energy demand for GMU engineering building.
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Simulation Inputs, Outputs and Parameters Purpose: To record energy production from residential solar PV systems, and determine if excess energy is produced.
System Losses Losses Percent
Soiling 2.0%
Shading 3.0%
Snow 0.0%
Mismatch 2.0%
Wiring 2.0%
Connections 0.5%
Light-Induced Degration 1.5%
Nameplate Rating 1.0%
Age 0.0%
Availability 3.0%
Overall System Loss 14.08%
Inputs Parameters Outputs
Appliance List Watt Array tilt Degree Excess Energy kWh
Probability of Appliance usage % Array Azimuth Degree Hourly Total Energy Consumed kWh
Hourly temperature Degree Invertor Efficiency % Hourly Total Energy Generated kWh
Annual Avg solar irradiation kWh/m^2 Panel Area m^2
History of Energy Usage kWh Panel Yield %
System Losses %
Time of Day hr
System Losses Details (depend of site, technology, and sizing of the system)
Inverter losses (6% to 15 %)
Température losses (5% to 15%)
DC cables losses (1 to 3 %)
AC cables losses (1 to 3 %)
Shadings 0 % to 40% (depends of site)
Losses weak irradiation 3% to 7%
Losses due to dust, snow... (2%)
Other Losses
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Residential Energy Demand
Industrial Energy
Demand (GMU Engineering Building)
+/-
+/-
Residential Solar
Generation
Appliance Probability usage of appliances per hour
Hourly temp for year
Array Tilt, Array Azimuth, System Losses Invertor Efficiency, Panel Area, Panel Yield
Solar level Total Excess Energy
History of Energy Usage
Time of Day
Time of Day
Usage per hour for a day
Hourly solar generated
Excess energy
Purpose: To record energy production from residential solar PV systems, and determine if excess energy is produced.
MEX Model Simulation
Usage per hour for a day
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E = A * r * H *PR
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MEX Solar PV System Equations & Simulation Setup Formula:
E = A * r * H *PR • E: Energy (kWh) • A: Total solar panel area (m^2) • r: solar panel yield (%) [16.0% - 19.6%] • H: Annual average irradiation (shadings not included) • PR: Performance Ratio, coefficient for losses (ranges
between 0.9 and 0.5)
PR includes: • Inverter losses (6% to 15%) • Temperature losses (5% to 15%) • DC cables losses (1% to 3%) • AC cable losses (1% to 3%) • Shadings (0 to 40%) • Losses (3% to 7%) • Losses due to dust, snow (0 to 2%) • Other misc. losses
= (1-PR_1) * (1-PR_2) * … * (1-PR_n)
Source: http://photovoltaic-software.com/PV-solar-energy-calculation.php ftp://ftp.ncdc.noaa.gov/pub/data/nsrdb-solar/documentation-2010/NSRDB_UserManual_r20120906.pdf
Settings: • A set to standardized panel area of 55.0 m^2 • r set to 18.5% • H set to historical solar radiation database • PR set in random based on season and time.
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+/-
+/- Total Excess Energy
MEX Model Simulation
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Residential Excess Energy
MEX System Results-Excess Energy
Average daily demand: 47.28 kW Average daily solar generation: 54.73 kW Shaded Area Under the Curve: 30.35 kW
Industrial average daily demand = 7583.3 kW
7583.3 = 30.35 * n n = 249.88 OR 250 Residents homes 25
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July Generation vs Demand
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Dec Generation vs Demand
Best Case Worst Case
Average daily demand: 49.73 kW Average daily solar generation: 16.65 kW Shaded Area Under the Curve: 2.34 kW
MEX System Benefits-Total Excess Energy Total GMU Average:
7583.3 kW When n = 250, 250 Residents generate excess energy:
3972.14 kW on average Total GMU demand if traded:
7583.30 – 3972.14 = 3611.16 kW
Overall 54.1% decrease.
Cost perspective: FFX GMU pays $0.06 / kWh, Original cost:
$454.99 Reduced cost:
$216.67
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GMU Demand vs GMU Demand Reduced
GMU ENGRDemand
GMU ENGRReduced Demand
54.1%
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MEX System Use Cases (Transfer of Energy)
Scenario: MEX users transfers renewable solar energy. • Setting up a user profile • Checking available power for
trading • Transfer of energy
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MEX System Use Cases (Transfer of Funds)
Scenario: MEX User transfers funds from the GUI. • Setting up a user profile • Account balance check • Transfer of funds
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MEX System Energy Platform
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MEX System Graphical User Interface
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MEX Business Case
Initial Startup Investment: $30K Annual Operating Costs: $80K (Labor, Overhead, AWS/IBM Cloud) Five Yr. Profit Projection: $720K Break-Even Point: Year One ROI: 64.56% Ten Yr. Profit Projection: $1.5M Fifteen Yr. Profit Projection: $2.4M
Source: Department of Energy Study of AC and DC Microgrids
MEX Gets • Collect Energy Platform Monthly Fees: $5000.00 • Per Trade Service Fee: 2 cent per kWh per trade
MEX Services • Data Analytics on Microgrid Distribution
Management • Access to Energy Exchange Platform • Predictive Analytical Tools • Application Programming Interfaces through AWS
$0.00
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$1,000,000.00
$1,500,000.00
$2,000,000.00
$2,500,000.00
$3,000,000.00
$3,500,000.00
$4,000,000.00
$4,500,000.00
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MEx Business Case Projections: HOA Energy Platform Sales
Profit
Accum. Revenue
Revenue
Accum. Costs
Accum. Profit
MEX Pays • Cloud Platform Hosting Monthly Fees: $600.00 • Labor and Overhead: $5,000 • Advertising/Marketing: $1,000
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HOA Business Case
DC Microgrid: $6.3M (Microgrid Type from a Sensitivity Analysis) Housing Subdivision: 250 Houses Annual Microgrid Operating Costs: $160,000 Five Yr. Accumulated Profit Projection: $8M Break Even Point: at Year 5 at $8M ROI: 27.9% Ten Yr. Accumulated Profit Projection: $39.5M Fifteen Yr. Profit Projection: $88.9M
Source: Department of Energy Study of AC and DC Microgrids
HOA Pays • Energy Platform Monthly Fees: $5000.00 • Per Trade Service Fee: 2 cent per kWh per trade • Microgrid Monthly Services: $8,000.00 (Third Party Vendor)
Builder/Developer Provides • Offer Residents:
• Zero Energy House • Solar PV System (Utility Analysis) • Energy Star Appliances • Flat Monthly Fee: Predictive Analytical Tools
• Return on Initial Investment: Year 5
HOA Gets • Collect Flat Fees per Resident: $225.00 • Per Trade Service Fee: 3 cent per kWh per trade
-$15,000,000.00
-$5,000,000.00
$5,000,000.00
$15,000,000.00
$25,000,000.00
$35,000,000.00
$45,000,000.00
$55,000,000.00
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HOA Business Case Projections
Revenue
Accum. Revenue
Accum. Costs
Total Costs
Accum. Profit
Profit
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MEX Business Plan Solar Market Penetration
• Solar Market: Virginia Forecast 10% Growth (5 Years) • 50K in Jobs and Manufacturing Projections
Distribution Strategy • Distribution: Mobile Sales Teams, Targeted Ad Buys in
Senior/Elderly Markets, Social Media Ads • HOA Conferences and Conventions • Advertising with Zero Energy Realtors and Developers
Source: VA Solar Energy Development and Energy Storage Report Source: EnergySage.com Source: National HOA Website Source: NAHB Website Source: SolarCity Survey Reports (2014-2015) Source: Pew Research Survey: The Politics of Climate
Solar Market Competitors • No direct market competitors at this time. • Our system is ahead the commercial market and
holds potential in renewable solar energy options.
MEX Services • Energy Trading Platform • Data Analytics for Microgrid Management
Customer Value Chain
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MEX Business Application
MEX System The data suggest connecting industrial size users with residential solar PV systems to lower energy costs and capitalize on wasted solar energy.
Projected Revenue to Trade Per Day: $3,053.65
Projected Revenue to Trade Per Day: $1,747.77
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Residential Yearly Energy Bill Savings Over 25-year Span
$-
$100,000.00
$200,000.00
$300,000.00
$400,000.00
$500,000.00
$600,000.00
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Co
st
Year
n number of Residential Yearly Energy Bill Savings
Cost Savings With MEX Do Nothing
Residential Cost per kWh
$ 0.12
For n residents: Cost Savings using MEX (Cum): $ 4,169,790.72 Do Nothing: $ 13,226,713.68
Bill Reduced
68.47%
For single resident: Cost Savings using MEX (Cum): $ 15,145.21 Do Nothing: $ 51,521.51
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GMU Engineering with MEX System
$-
$20,000.00
$40,000.00
$60,000.00
$80,000.00
$100,000.00
$120,000.00
$140,000.00
$160,000.00
$180,000.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Co
st
Year
Industrial Yearly Energy Bill Savings over 25 years
Cost Savings With MEX Do Nothing
Industrial Savings
Years GMU ENGR
Consumption Residential Generation
kWh total after saving
Cost Savings With MEX Do Nothing
25 2556256.45 kW 2734756.57 kW -178500.12 $ 127,812.82 $ 150,819.13 Bill Reduced
Total 67673526.04 kW 75430959.64 kW -7757433.60 $ 3,383,676.30 $ 3,992,738.04 15.25%
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Conclusion and Results
Future Research Recommendations:
• Battery Energy Storage Systems are one of the primary research areas that would benefit the expansion of renewable
solar energy.
Project Questions:
• Is Renewable Solar Energy Trading Feasible?
• Yes. Energy Trading is feasible between residential users and to industrial users.
• The modeling results suggest that the number of houses to exchange energy is dependent on the energy
consumption levels of both the industrial user and residential users AND based on the simulation 250 residential
homes are sufficient to trade and provide energy to GMU ENGR building.
• How many home with how many solar panels would it take?
• The model results suggests the minimum number of houses is 250 houses based on GMU Volgenau Engineering
Building energy consumption levels. Which equates to 14,000 meters squared of solar panels.
• Which months of the year would it work/not work ?
• The best months for energy trading were in July and the worst month of the year was December when the ceiling
heights are at their lowest and the hours of the day are at their shortest.
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Backup Slides
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MEX Physical Hierarchy
Project Plan: Statement of Work
• Scope • Project completion will be as follows:
Planning, design, validation, verification. • Deliver a proof-of-concept simulation model designed of an energy trading platform
replicating conventional power generation on a local microgrid with battery storage for the integration and distribution of renewable energy sources.
• Deliver a business case analysis on the use of a renewable energy trading platform using blockchain technology.
• Work Requirements • The design of the system will be completed in the span of 180 work days, beginning
August 31st. • Workflow design will follow the systems engineering V-model. • Deadlines will be met and completed within Fairfax/GMU vicinity.
40
Project Plan: Work Breakdown Structure Design P2P Energy
Trading
1.0 Context Analysis
1.1 Project Scope
1.2 Stakeholder
Analysis
1.3 Problem/Need
Statement
1.4 CORE Resources
2.0 Requirements
2.1 CONOPS
2.2 System
Requirements
2.3 Functional
Requirements
2.4 Design
Requirements
3.0 Project Plan
3.1 Budget Plan
3.2 Schedule Plan
3.3 Project Plan
3.4 Statement of Work
4.0 Simulation
4.1 Objectives
4.2 Requirements
4.3 Framework
4.3.1 Solar Generation
Model
4.3.2 Energy Trading
Platform
4.4 Data Collection
4.5 Simulation
Enhancement
5.0 Testing
5.1 Testing Plans
5.2 Conduct Testing
5.3 Simulation
Modification
5.4 Output Testing
6.0 Analysis
6.1 Utility Analysis
6.2 Sensitivity Analysis
7.0 Project Results
7.1 Project Result
Conclusion
7.2 Documentation
8.0 Presentations
8.1 Faculty
Presentation
8.2 Final Report
8.3 Final Report
Presentation Slides
8.4 Conference Project
Paper
8.5 Video Presentation
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M 8/28/17 M 5/7/18
Past Due Tasks
Project Plan: Project Schedule
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Project Plan: Earned Value
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Project Plan: Earned Value
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Risks/Mitigation to Project Plan
• Risks severity was determined based on a 1-10 scale.
• S stands for Severity of failure, the ratings go from 1 being a failure with no risk, to 10 being a very severe and hazardous failure that occurs without warning.
• L stands for the Likelihood of failure, the ratings go from 1 being the failure not likely to occur, and 10 being a failure that is almost certain to occur.
• D stands for the Detectability of failure, with 1 being monitoring and control systems almost certain to detect the failure, and 10 being no chance of detecting failure.
• RPN was the Risk Priority Number and is a result of multiplying the values for S, L, and D. The higher that the RPN numbers are, the more of a focus there should be on lowering the associated risk.
Potential Risk S L D RPN Risk Mitigation Strategy
Failure to properly test 10 4 5 200 Identify and create a testing plan to follow
Failure to create a model 10 3 6 180 Choose a software tool on which we are able
to create simulation models
Failure to collect data 10 3 5 150 Find as many possible sources for data
Personal issues 6 5 4 120 Communicate and work from home, or the
group can fill in for them until they are
available
Stakeholder conflicts and
tensions 7 4 3 84 Understand the needs and requirements
completely and stay in contact
School closing 2 2 2 8 Communicate online or meet up somewhere
else
Weather issues 2 3 1 6 Communicate online
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Context Analysis
Opportunity: Residents are motivated by energy independence to minimize disruptions as a result of power outages.
Renewable solar energy options though a microgrid structure may hold the key to bridge the gap between energy supply
and demand in the event of power loss--while not ideal in all situations.
• Market Research on current solar residential customers mention
power outages as a decisive factor into purchasing solar PV systems.
• Power Outages: Virginia is rated 9th on the number of power
outages since Superstorm Sandy affecting 182,811 customers.
• Energy Independence from utility companies was also mentioned in
survey market research and power outages were a part of the
decision to purchase solar PV residential systems.
Source: SolarCity Survey Reports (2014-2015) Source: Pew Research Survey: The Politics of Climate
Quote: “Four in ten Americans say they have recently experienced power outages with their current utility and that motivates them to get backup power; 50% of homeowners are interested in backup power for their homes.”
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IDEF0 Diagram
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48
Zero Energy Homes
• 2014 Department of Energy Initiative
• Homes with Solar Panels –zero net consumption on an
annual basis.
• Geothermal Energy Systems
• Solar Photovoltaic (PV) Power
• High Efficiency Appliances
• Smart Home Technology
Source: Department of Energy Source: Zero Energy Project
Quote: “…regular grid-tied homes that are so air-tight, well insulated, and energy efficient that they produce as much renewable energy as they consume over the course of a year, leaving the occupants with a net zero energy bill, and a carbon-free home.”
48
Zero Energy Homebuilders
49
Local Utility Costs
50
51
Confluence Interaction Diagram Opportunity: The intersection of
influencing environment factors
provide the impetus of the
Microgrid Exchange System to
capitalize on unused renewable
solar that would otherwise be
wasted into the ground.
52
Project Assumptions 1. Homeowners Owners Association (HOA) is the primary customer.
2. The homes in the neighborhood lead typical lives with highest energy peak demands at 7-9am and 6-
9pm.
3. The new construction would have homes preconfigured with the latest AMI smart meter technology,
energy efficiency technology, and limited standard floor plans.
4. US average size house of 2,100 to 2,500 square feet as basis for solar PV system similar in range to
sample data set.
52
Microgrid
53
Microgrid includes:
• Individual
residential
housing
• Commercial
buildings
• INOVA
• FFX GMU
• Restaurants
• FFX Fire
Department
• Malls
54
Appliance Table
FFBD
55
Input: Weather data every 4 hours for the 15th of each month( Meter )
56
Date 12:52 AM 4:52 AM 8:52 AM 12:52 PM 4:52 PM 8:52 PM
January 15-2-
16 39 32 36 48 45 43
February 19 19.9 21.9 24.1 27 28
March 46.9 46 46 53.1 60.1 51.1
April 46.9 39 50 60.1 63 54
May 50 46.9 48.9 55 55.9 51.1
June 64.9 66 69.1 75.9 79 75.9
July 15- 2016 78.1 70 84 86 88 80.1
August 78.1 73 81 93 91 73
September 73.9 69.1 69.1 73.9 75 70
October 48 39.9 46.9 64 66 55
November 46 45 48.9 62.1 55 43
December 30 30 23 23 19 14
57
Input : Energy Users(Appliances) & Energy Profile(Probability) for every 4 hours
Appliance
Hourly Energy
Consumption
(Watts) 12:00 AM 4:00 AM 8:00 AM 12:00 PM 4:00 PM 8:00 PM
100W light bulb
(Incandescent) 100 0.1 0.001 0.001 0.001 0.001 0.9
Ceiling Fan 50 0.001 0.001 0.001 0.001 0.001 0.001
Clothes Dryer 2500 0.001 0.001 0.001 0.001 0.001 0.1
Dishwasher 1350 0.001 0.001 0.001 0.001 0.001 0.1
Food Blender 350 0.001 0.001 0.1 0.001 0.001 0.1
Fridge / Freezer 275 0.6 0.6 0.6 0.6 0.6 0.6
Hair Blow dryer 2150 0.001 0.001 0.001 0.001 0.001 0.001
Home Internet
Router 10 0.6 0.6 0.6 0.6 0.6 0.6
Inkjet Printer 25 0.001 0.001 0.001 0.001 0.001 0.001
Iron 1000 0.001 0.001 0.001 0.001 0.001 0.001
Laptop Computer 75 0.001 0.001 0.001 0.001 0.001 0.5
LED Light Bulb 8.5 0.001 0.001 0.001 0.001 0.001 0.9
Microwave 1150 0.001 0.001 0.5 0.001 0.001 0.1
Oven 2150 0.001 0.001 0.001 0.001 0.001 0.1
Smart Phone
Charger 6.5 0.6 0.6 0.6 0.001 0.001 0.4
Table Fan 17.5 0.001 0.001 0.001 0.001 0.001 0.1
Tablet Charger 12.5 0.6 0.6 0.6 0.001 0.001 0.4
Tablet Computer 7.5 0.001 0.001 0.001 0.001 0.001 0.5
Toaster 1300 0.001 0.001 0.5 0.001 0.001 0.1
TV (19" colour) 70 0.001 0.001 0.1 0.001 0.001 0.6
Vacuum Cleaner 450 0.001 0.001 0.001 0.001 0.001 0.1
Washing Machine 500 0.001 0.001 0.001 0.001 0.001 0.1
58
Input : Ceiling (ft) for 15th of each month every 4 hours
Date 12:52 AM 4:52:00 AM 8:52:00 AM 12:52:00 PM 4:52:00 PM 8:52:00 PM
1/15/2016 25000 25000 25000 25000 15000 13000
2/15/2016 13000 1900 1900 6000 800 1000
3/15/2016 800 800 900 1100 1700 3200
4/15/2016 23000 23000 23000 23000 25000 25000
5/15/2016 6000 6000 7000 5000 7500 8000
6/15/2016 11000 15000 7000 4500 2900 3600
7/15/2016 6000 25000 25000 9000 4500 5500
8/15/2016 6000 25000 25000 25000 4500 6000
9/15/2016 25000 5000 10000 8000 3500 3900
10/15/2016 25000 22000 25000 15000 3500 25000
11/15/2016 4500 4500 3500 3500 9000 7500
12/15/2016 4700 7500 6500 6500 5000 5500
59
MEx Comparison of Design Alternatives
60
MEx Sensitivity Analysis
61
MEx Sensitivity Analysis
62
MEx Sensitivity Analysis
Percent of Weight on Energy Costs Per Year Percent of Weight on Converter Losses Per Year
63
MEx Sensitivity Analysis
Percent of Weight on Network Losses Per Year Percent of Weight on Total System Costs
64
MEx Matrix and Results
Design P2P Energy Total System Costs Energy Cost Per Yr Network Losses Per Yr Converter Losses Per Yr
DC Microgrid System 0.889 0.927 0.967 0.879 0.676
AC Microgrid System 0.218 0.049 0.299 0.011 0.789
Weight 1.000 0.340 0.270 0.240 0.150
Result: DC Microgrid: Higher Utility
65
MEx Multi-Attribute Utility Analysis
1. Multi-Attribute Utility Analysis used to handle tradeoffs with decision makers.
2. Solar PV System Analysis consisted of three major components: Solar Panel Modules, Solar Batteries, and Inverters. The weights were selected based on the manufacturers specifications. The components were assembled into five groups or systems.
Solar Modules Type
Astronergy CHSM6610P-270 Solar Panel Polycrystalline
LG LG335N1C-A5 Neon2 335W Blk Solar Panel Monocrystalline
Grape Solar 265-Watt Solar Panel Polycrystalline
Hanwha Q.Peak-G4.1, 300W MC4, Korea Monocrystalline
Silfab Solar 350 Watt Solar Panel Monocrystalline
Solar Battery Systems Type
LG Chem RESU10H 9.8kWh 400V Battery Energy Storage SystemLithium-Ion
Crown 2CRP3690, 2550Ah 2V Battery (100hours) Flooded Lean Acid
Tesla Powerwall Solar Battery (2 of each) Lithium-Ion
Outback Power 4000 W FPR-4048A- Renewable Energy SystemValve-Regulated Lead-Acid
Yeti 1400 Lithum Potable Power Station Lithium-Ion
Name of Inverter Type
SMA Sunny Boy 5.0-US Triple Input-MPPT
SolarEdge SE10000A-US-U Inverter 3 Unfused Inputs
Schneider Electric Conext SW 4024 Inveter/Charger Inverter/Charger
Nature Power 3000W Pure Sine Inverter/Charger Inverter/Charger
Victron Energy Multiplus Inverter/Charger Inverter/Charger
66
GMU Energy Usage (Aug 2016 - Sept 2017
GMU Energy Usage History
Month kWh Temp Costs Costs St Dev. KwH StDev Avg KwH Period kWh Costs
Sep-17 8,218,000 69 $487,368.49 $58,485.22 986,177 273,933 1 8218000 $487,368.49
Aug-17 8,218,080 74 $487,373.23 Variance Variance 273,936 2 8218080 $487,373.23
Jul-17 9,611,080 78 $569,985.10 320,369 3 9611080 $569,985.10
Jun-17 7,539,080 73 $447,105.14 251,303 4 7539080 $447,105.14
May-17 7,476,080 63 $443,368.92 249,203 5 7476080 $443,368.92
Apr-17 8,484,080 61 $503,148.36 282,803 6 8484080 $503,148.36
Mar-17 6,419,080 44 $380,683.54 213,969 7 6419080 $380,683.54
Feb-17 6,839,080 45 $405,591.64 227,969 8 6839080 $405,591.64
Jan-17 7,056,080 40 $418,460.82 235,203 9 7056080 $418,460.82
Dec-16 6,335,080 38 $375,701.92 211,169 10 6335080 $375,701.92
Nov-16 8,631,080 48 $511,866.20 287,703 11 8631080 $511,866.20
Oct-16 7,875,080 60 $467,031.62 262,503 12 7875080 $467,031.62
Sep-16 8,967,080 73 $531,792.68 298,903 13 8967080 $531,792.68
Aug-16 8,743,080 80 $518,508.36 291,436 14 8743080 $518,508.36
Microgrids-Worst Case Scenario (Costs)
67
Worst Case Microgrid Costs
Microgrid Engineering $80,000
Permitting and Inspection Fees $20,000
Customer Owned Equipment $600,000
Microgrid Power and Control Wiring to Critical Faciliities $250,000
Testing and Commisssioning $25,000
Emissions Testing $5,000
Office Space and Supplies Fairfax VA( $18-28.50 sf) $38,000
Marketing and Advertising Costs $20,000
Construction Costs $350,000 Total Cost (25 years)
Total $1,388,000 $10,538,000
Solar PV Packages
Annual Operating Costs $2,410,992.50
Operations and Maintenance Fees (Annual) (Routine Maintenance and Consumables $150,000
Labor (2 Engineers and 2 Network Technicians) $216,000 MEx (25 years)
Additional Contingency Costs (Construction Delays,etc) $80,000 $12,948,992.50
Total $366,000 MEx (25 years)
Microgrid Worst Case Scenario (Costs)
68
Assumptions: Fairfax County (Land Site) $1,097,152.00
Typical Single-Family Detached Housing Subdivisions:
Median Size: 26 acrces
Median Area Dedicated to Housing: 17 acres
Median Number of Housing Units: 45 units
Median Net Residential Density: 3.2 units per arce
Includes 3% for retail space
Includes 3% for (non-retail) commercial space
Estimated 60 acres of land in Fairfax County
Source: National Association of Home Builders Website
Cost of 1 acre of land in Fairfax County: $500,000
Source: Landwatch.com
Labor: Power/Mechnical/Stationary Engineers Annual Salary: $60,000==$120,000
Electrical Technicians: $48,000==$96,000
Source: Salary. Com
Average Cost of Residential Microgrids: Range frm $250,00 to $100 million
Source: Microknowledge.com
Project Budget
69
Budget Hours Budget Overhead
Optimistic 2,080 $77,916.80 $118,433.54
Most Likely 3,120 $116,875.20 $177,650.30
Pessimistic 4,160 $155,833.60 $236,867.07
Residential Solar PV System (Highest Utility)
70
MEx Microgrid Exchange, LLC
Equipment
Package 1:
Platinum Package Specs Retail Price
LG Panels 19.6% Efficient $422.10
Tesla Battery 13.5kWh $11,700.00
SolarEdge Inverters 10,800 Watts $1,655.00
Installation/Connections $2,755.42
Operating Costs $7,577.41
Total $24,109.93
Microgrid Cost Estimates (Labor)
71
Labor* Annual Salary Profit (Existing)
2 Electrical Technicians $104,000.00 $1,928.79
1 Electrical Engineer $65,000.00
Office Space and Supplies Annual Costs
Fairfax, Virginia $38,000
Marketing/Advertising Costs Annual Costs Eq+Markup
Northern Virginia $30,000 $92,242.17
Microgrid Installation** Equipment Costs Equipment Markup # of Houses Microgrid Installation
DC Microgrid System $87,630.06 $4,612.11 100 $23,060.54
HOA New Construction Costs Net Profit Annual Inflation
100 Single Family Houses $2,758,683.10 $2,190,377.73 $568,305.38 2.10%
Borrow-Loan for Initial Microgrid Costs
Project Assumption
72
MEx Energy Trading Utility Functions
[Hanwha(AVIE)+Crown(AVIE)+Vicron(AIEC)]*(Weight=0.1) +[Hanwha(KwH)+Crown (Kwh)+Vicron(Kwh)]*(Weight =0.5)+[Hanwha(Variability)+Crown(Variability)+Vicron(Variability)]*(Weight=0.3)+[Hanwha(Independence)+Crown(Independence)+ Vicron(Independence)]*(Weight=0.1)]
U(D1) =
U(D2) =
U(D3) =
U(D4) =
U(D5) =
[Astronergy(AVIE)+Yeti(AVIE)+SMA(AIEC)]*(Weight=0.1) +[Astronergy(KwH)+Yeti (Kwh)+SMA(Kwh)]*(Weight =0.5)+[Astronergy(Variability)+Yeti(Variability)+SMA(Variability)]*(Weight=0.3)+[Astronergy(Independence)+Yeti(Independence)+LG(Independence)]*(Weight=0.1)]
[Silab(AVIE)+LG(AVIE)+Schneider(AIEC)]*(Weight=0.1) +[Silab(KwH)+LG(Kwh)+Schneider(Kwh)]*(Weight =0.5)+[Silab(Variability)+LG(Variability)+Schneider(Variability)]*(Weight=0.3)+[Hanwha(Independence)+LG(Independence)+Schneider(Independence)]*(Weight=0.1)]
[Grape(AVIE)+Outback(AVIE)+NaturePower(AIEC)]*(Weight=0.1) +[Grape(KwH)+Outback(Kwh)+NaturePower(Kwh)]*(Weight =0.5)+[Grape(Variability)+Outback(Variability)+NaturePower(Variability)]*(Weight=0.3)+[Grape(Independence)+Outback(Independence)+NaturePower(Independence)]*(Weight=0.1)]
[LG(AVIE)+Tesla(AVIE)+SolarEdge(AIEC)]*(Weight=0.1) +[LG(KwH)+Tesla(Kwh)+SolarEdge(Kwh)]*(Weight =0.5)+[LG(Variability)+Tesla(Variability)+SolarEdge(Variability)]*(Weight=0.3)+[LG(Independence)+Tesla(Independence)+SolarEdge(Independence)]*(Weight=0.1)]
73
MEx Multi-Attribute Utility Analysis
Crown Yeti
LG Outback
Tesla
-
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
$1,195.00 $1,799.00 $6,300.00 $10,620.00 $11,700.00
XU
tilit
y
Cost in Dollars ($)
MEx Solar Battery Costs
Victron
SMA
Schneider
SolarEdge
SolarEdge
-
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
$1,541.05 $1,595.00 $1,650.00 $1,655.29 $1,862.99
XU
tilit
y
Cost in Dollars ($)
Inverters
Astronergy
Grape Solar
Silfab Solar
Hanwha
LG
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
15.6 16.2 17.9 18.3 19.6
XU
tilit
y
Panel Efficiency (%)
MEx Solar Panels
74
Solar PV Multi-Attribute Utility Analysis
Panels Batteries Inverters Panels Batteries Inverters
D1 Hanwha Crown Vicron D1 Hanwha Crown Vicron
80 90 70 70 50 80
D2 Astronergy Yeti SMA D2 Astronergy Yeti SMA
50 50 80 60 90 60
D3 Silab LG Schneider D3 Silab LG Schneider
70 80 60 90 80 50
D4 Grape Outback NaturePower D4 Grape Outback NaturePower
60 70 50 50 60 70
D5 LG Tesla SolarEdge D5 LG Tesla SolarEdge
90 60 90 80 70 90
Weights 0.5 0.5 0.5 Weights 0.5 0.5 0.5
Less Dollars per kWh Revenue from Solar PV Production
U(D1)= 220
U(D2)= 195
U(D3)= 215
U(D4)= 180
U(D5)= 240
[Hanwha(Less kwh)+Crown(Less kWh)+Vicron(Less kWh)]*(Weight=0.5) +[Hanwha(SolarPV)+Crown (SolarPV)+Vicron(SolarPV)]*(Weight =0.5)
[Astronergy(Less kWh)+Yeti(Less kWh) + SMA (Less kWh)*(Weight=0.5) +[Astronergy(SolarPV)+Yeti (SolarPV)+SMA(SolarPV)]*(Weight =0.5)
[Silab(Less kWh)+LG(Less kWh)+Schneider(Less kWh)]*(Weight=0.5) +[Silab(SolarPV)+LG(SolarPV)+Schneider(SolarPV)]*(Weight =0.5) [Grape(Less kWh)+Outback(Less kWh)+NaturePower(Less kWh)]*(Weight=0.5) +[Grape(SolarPV)+Outback(SolarPV)+NaturePower(SolarPV)]*(Weight =0.5)
[LG(Less kWh)+Tesla(Less kWh)+SolarEdge(Less kWh]*(Weight=0.5) +[LG(SolarPV)+Tesla(SolarPV)+SolarEdge(SolarPV)]*(Weight =0.5)
U(D1) = U(D2) = U(D3) = U(D4) = U(D5) =
Highest Utility: D5 Solar PV System
75
MEx System Utility Function
Do Nothing vs MEx Implementation:
1. Design and Architecture 2. Initial Investment Costs
Do Nothing
MEx
0.05
0.8
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
$9,245,724.24 $13,029,356.24
XU
tilit
y
Costs in Dollars (25 Years)
MEx Utility System Function
76
MEx Business Case: 2015 SolarCity Survey • 2015 US Homeowners on Clean Energy: A National Survey Poll Results and Clean Energy Solar Generation in
Collaboration with NASDAQ
• Number of Survey Participants: 1400
• Number of Customers (2014): 300,000 in 14 States
• Key Results:
• “Saving Money” ranked at 82% on the top of the list as the primary motivator influencing homeowners decisions
to purchase clean-energy products and services.
• “Reducing my environment impact” ranked a distant second at 34% of participants.
• 64% of participants said “saving on monthly electrical bills” would have the greatest impact on the decision to
install solar panels.
• Most planned over the preceding 12 months to purchase clean energy purchase LED bulbs (27%), smart
thermostats (12%), and Energy Star-Rated Hot Water Heaters (9%).
• 74% of homeowners support the continuation of federal tax incentives for solar and wind power of which
breakdown as such by major party affiliation: Democrats (82%), Republicans (67%), and Independents (72%).
• 61% of participants believe utilities should not block the expansion of solar power.
• 79% of Homeowners think is it very important to manufacture solar energy systems and solar panel components
domestically.
77
MEx Business Case: 2016 Pew Climate Study • PV Magazine posted a Pew study on the Politics on Climate and found the following:
• Key Results:
• 40% of homeowners station they have seriously considered install solar PV systems. Interest by region is as
follows: Midwest—42%, Northwest—40%, West—52%, and the South at the lowest at 35%.
• Pew noted their motivations for installing solar: 90% considered or installed solar were motivated by saving
money on utility bills, 87% by helping the environment, 67% said it would improve their health, and 60%
associated interest with the Federal Investment Tax Credit.
• 83% of conservative Republicans favor more solar panel farms and 97% of liberal Democrats favor more solar
panel farms.
• 75% of US adults say they are particularly concerned with the environment and 24% not particularly concerned
with the environment.
• 63% of US adults say they make an effort to live in ways that help protect the environment some of the time and
20% all of the time.
• Environmentally conscious Americans are more bothered than others when they see people waste energy—92%
are either bothered all or some of the time when leaving lights and electronics on.
• Conclusion: The survey data indicates that homeowners are primarily motivated by saving money on monthly utility bills,
have a strong interest in tax incentives, and are very environmentally conscious. This data supports answering our project
question by how we market our system to potential customers and how the system needs to differentiate its from potential
competitors.
Source: PV Magazine: “40% of US Homeowners Have Considered Solar PV” Source: Pew Research Center: Politics of Climate (2016)
78
MEx Business Case: Survey Findings
• Potential MEx Consumer Market
• Middle Aged and Elderly VA Residents
• Other Typical Residential Solar Demographics
• 70% of Solar Household have annual income
range of $45,00 - $150,000.
• Most Overrepresented Income Demographic:
$100,000-$150,000.
• Source: GTM Research
Commonwealth of Virginia
State Demographics: 8,470,020 residents
Target Market
Persons Aged 65 or older: 14.6% or 1.23M
residents
Housing Units: 3,491,054 (2016)
• Source: US Census Bureau
• SolarCity Findings:
• Customers want more residential energy options, tend to be older residents, are price sensitive to
monthly energy utility costs, have a strong interest in renewable solar energy and at least 50% of
homeowners have an interest in energy backup power.
• We also know that homeowners have an interest in home efficiency.
• Pew Research Study
• Residents are motivated to purchase a solar PV system for energy bill savings, environmental
conscious, and tax incentives.
79
Battery Energy Storage System
Source: Sandia National Laboratories
Black Start Service by Storage
Schematic of Battery Energy Storage System
Electric Supply Resource Stack
80
Battery Energy Storage System
Source: AMDC Energy Limited
81
Retail Energy Time-Shift
Time of Use Summer Energy Prices for Small Industrial Users
Source: Sandia National Laboratories
82
Demand Charge Management
On-Peak Demand Reduction Using Energy Storage
Source: Sandia National Laboratories
