Design P2P Energy Trading

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

Microgrid Exchange System

After 100%, energy goes into the ground.

Excess Renewable Solar Excess Renewable Wasted Solar Energy Energy Solar Energy Area =2503 kWh Area =520 kWh

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

2 Context Analysis-Cheap Solar • Installed Solar: Price of Solar Energy Trend is projected to drop Opportunity: Lower upfront solar costs goes down to below $50 per mWh in 2024 from $350 per mWh in 2009 . 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.

Wasted Solar Energy 1200.00

1000.00

) 800.00

W n Residential

k (

Demand

y 600.00

g r

e n Residential n “The greatest challenge that solar power faces is energy storage. E 400.00 Solar Generated GMU ENGR Solar arrays can only generate power while the sun is out, so they 200.00 Demand can only be used as a sole source of electricity if they can produce 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 and store enough excess power to cover the times when the sun is Hours hidden.” Source: The Energy Collective 3 Source: Bloomberg News: Solar Energy Source: Green Technical3 Media Source: NIST: Metrology for Distributed Smart Grid Storage Systems utilizing Advanced Battery Technology Context Analysis-Rising Energy Demand

GMU Engineering Load Duration Diagram Rising Energy Demand: Poses potentially

higher costs during peak demand, lower 400 capacity, higher levels of dependency from 350 300

regional energy providers, greater risk to the e

u 250

l a

V 200

traditional grid, and higher risk of power h

150 W outages. k 100 100% of the Day GMU 50 0 Engineering is at a minimum 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

of 250 kWh per hour per day % of Time Load Exceeds kWh Value annually.

GMU Engineering Daily Yearly Load/Demand Profile of an Energy System/Utility We found this energy demand signature consistent in analyzing over 8600 lines of 400

) 350 W

historical data in a 12 month period. k 300

(

d 250 n

a 200 m

e 150 D

Opportunity: Unutilized residential renewable solar /

d 100 a

o 50

energy could lower the energy demand from utility L 0

providers, lower costs for industrial users, and serve a M M M M M M M M M M M M A A A A A A P P P P P P 1 1 3 5 7 9 1 as a revenue stream for residents. 1 3 5 7 9 1 1 4 Hours of a day 4 Context Analysis-Rising Energy Costs Potential Savings Potential Savings of George Mason University Monthly Energy Costs $64,079.39 of $102,271.81 12000000 $600,000.00 y = 10.933x R² = 0.0005 Energy Cost Snapshot 10000000 $500,000.00 August 2016-

8000000 $400,000.00 a

l Sepetember 2017

W y = 184.35x

i k Average

R² = 0.00051 s

6000000 $300,000.00 s

g 262,866 kWh per day

E t over 14 months

4000000 $200,000.00 o

M Spring Summer 2000000 Fall Winter $100,000.00

0 $0.00 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

kWh Monthly Costs Linear (kWh) Linear (Monthly Costs) Linear (Monthly Costs)

Opportunity: Potential Unused Renewable Solar Energy could levelized swings in monthly energy bills and utilized as a revenue with energy trading for residential 5 users. 5 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.

6 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 MEX 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. 7 Source: “Deploying Solar-Plus-Storage Microgrids” by Colavito and Michael 7 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.

8 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.

9 9 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. Homeowners Association in the US 400,000

350,000

300,000

250,000

200,000

150,000 NumberofHOAs The trend is expected to continue with the growth of HOAs to 100,000

capitalize on opportunities to expand community solar 50,000

systems. 0 1970 1980 1990 2000 2002 2004 2006 2008 2010 2011 2012 2013 2014 2015 2016 10 Years Stakeholder’s Diagram 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. 11 11 “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.

Renewable Solar Energy Going to Waste

Wasted Solar Energy Residential User Demand 1200.00

1000.00

800.00 Industrial User Demand n Residential Demand 600.00 n Residential

Energy Energy (kW) 400.00 Solar Generated GMU ENGR 200.00 Demand

0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hours 12 MEX Concept of Operations (Current Usage and Billing) 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.

13 13 Source: Dominion Virginia Power MEX Concept of Operations (Proposed System) Wasted Solar Energy HOA Energy Usage and Billing Process: 1200.00 • Residents pay a flat rate for energy use each month 1000.00 over a 25 year period. ) 800.00

W n Residential

• HOA would use excess solar energy to trade with GMU ( Demand y 600.00

or an industrial user. e n Residential

E 400.00 Solar Generated • HOA would potential earn revenue with energy trading GMU ENGR though a trading platform graphical user interface. 200.00 Demand 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hours

14 14 Source: Dominion Virginia Power 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 MEX Microgrid Design Cloud Computing and Storage MEX Residential Solar PV System Subsystem 1 MW Battery Energy Storage Energy System Trading Platform

Subsystem

Shared Residential Battery System Microgrid Battery Storage System

Point of Common Coupling 16 Cloud Computing and Storage 16 MEX System Diagram

17 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.

18 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.

19 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.

20 Simulation Inputs, Outputs and Parameters Purpose: To record energy production from residential solar PV systems, and determine if excess energy is produced.

Inputs Parameters Outputs System Losses Appliance List Watt Array tilt Degree Excess Energy kWh Losses Percent Soiling 2.0% Shading 3.0% Probability of Appliance usage % Array Azimuth Degree Hourly Total Energy Consumed kWh Snow 0.0% Mismatch 2.0% Hourly temperature Degree Invertor Efficiency % Hourly Total Energy Generated kWh Wiring 2.0% Connections 0.5% Annual Avg solar irradiation kWh/m^2 Panel Area m^2 Light-Induced Degration 1.5% History of Energy Usage kWh Panel Yield % Nameplate Rating 1.0% System Losses % Age 0.0% Availability 3.0% Time of Day hr Overall System Loss 14.08%

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%) 21 Other Losses 21 Time of Day MEX Model Appliance Probability usage of Residential Usage per hour for a day appliances per hour Energy Demand Simulation Hourly temp for year

Excess energy +/- Array Tilt, Array Azimuth, System Losses Invertor Efficiency, Panel Area, Panel Yield

Residential Solar level Solar Total Excess Hourly solar generated +/- Energy Generation E = A * r * H *PR Time of Day

Industrial Usage per hour for a day History of Energy Usage Energy Demand (GMU Engineering Building)

Purpose: To record energy production from residential solar PV systems, and determine if excess energy is produced. 22 MEX Solar PV System Equations & Simulation Setup

Formula: PR includes: E = A * r * H *PR • Inverter losses (6% to 15%) • E: Energy (kWh) • Temperature losses (5% to 15%) • A: Total solar panel area (m^2) • DC cables losses (1% to 3%) • r: solar panel yield (%) [16.0% - 19.6%] • AC cable losses (1% to 3%) • H: Annual average irradiation (shadings not included) • Shadings (0 to 40%) • PR: Performance Ratio, coefficient for losses (ranges • Losses (3% to 7%) between 0.9 and 0.5) • Losses due to dust, snow (0 to 2%) • Other misc. losses

= (1-PR_1) * (1-PR_2) * … * (1-PR_n) 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.

Source: http://photovoltaic-software.com/PV-solar-energy-calculation.php 23 ftp://ftp.ncdc.noaa.gov/pub/data/nsrdb-solar/documentation-2010/NSRDB_UserManual_r20120906.pdf 23

Single Residential Energy Demand (Month of July) for one day 4 MEX Model

3 2 kWh 1 0 Simulation 1 2 3 4 5 6 7 8 9 101112131415161718192021222324 Hours Residential Excess Energy +/- Single Residential Solar Generation (Month of July) in one day 5.000

4.000

3.000 Total 2.000 kWh +/- Excess 1.000 Energy 0.000 1 3 5 7 9 11 13 15 17 19 21 23 Hours

GMU ENGR Energy Demand (Month of July) for one day 500.000

400.000

300.000

200.000 kWh 100.000 0.000 1 3 5 7 9 11 13 15 17 19 21 23 Hours 24 MEX System Results-Excess Energy Best Case Worst Case

July Generation vs Demand Dec Generation vs Demand 8.000 4.500 4.000 7.000 3.500 3.000

6.000 2.500

kWh 2.000 5.000

1.500

4.000 1.000 kWh 0.500 3.000 0.000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 2.000 hour

1.000 Average daily demand: 49.73 kW 0.000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Average daily solar generation: 16.65 kW hour Shaded Area Under the Curve: 2.34 kW

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 MEX System Benefits-Total Excess Energy Total GMU Average: 7583.3 kW GMU Demand vs GMU Demand Reduced When n = 250, 450.00 250 Residents generate excess 400.00 energy: 350.00 3972.14 kW on average

300.00 54.1% Total GMU demand if traded: 250.00 7583.30 – 3972.14 = kWh 200.00 3611.16 kW

150.00 Overall 54.1% decrease. 100.00 50.00 Cost perspective: 0.00 FFX GMU pays $0.06 / kWh, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Original cost: Hours $454.99 GMU ENGR GMU ENGR Reduced cost: Demand Reduced Demand $216.67 26 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

27 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

28 MEX System Energy Platform

29 MEX System Graphical User Interface

30 30 MEX Business Case

MEX Services MEx Business Case Projections: HOA Energy Platform Sales • Data Analytics on Microgrid Distribution $4,500,000.00 Management $4,000,000.00 • Access to Energy Exchange Platform $3,500,000.00

• Predictive Analytical Tools $3,000,000.00 Profit

• Application Programming Interfaces through AWS $2,500,000.00 Accum. Revenue Revenue Accum. Costs $2,000,000.00 Accum. Profit

MEX Gets $1,500,000.00

• Collect Energy Platform Monthly Fees: $5000.00 $1,000,000.00

• Per Trade Service Fee: 2 cent per kWh per trade $500,000.00

$0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 MEX Pays Initial Startup Investment: $30K • Cloud Platform Hosting Monthly Fees: $600.00 Annual Operating Costs: $80K (Labor, Overhead, AWS/IBM Cloud) • Labor and Overhead: $5,000 Five Yr. Profit Projection: $720K • Advertising/Marketing: $1,000 Break-Even Point: Year One ROI: 64.56% Ten Yr. Profit Projection: $1.5M Fifteen Yr. Profit Projection: $2.4M 31 31 Source: Department of Energy Study of AC and DC Microgrids HOA Business Case HOA Business Case Projections Builder/Developer Provides $55,000,000.00 • Offer Residents: • Zero Energy House $45,000,000.00 • Solar PV System (Utility Analysis) $35,000,000.00 • Energy Star Appliances Revenue • Flat Monthly Fee: Predictive Analytical Tools $25,000,000.00 Accum. Revenue Accum. Costs • Return on Initial Investment: Year 5 Total Costs $15,000,000.00 Accum. Profit Profit

$5,000,000.00 HOA Pays 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 • Energy Platform Monthly Fees: $5000.00 -$5,000,000.00 • Per Trade Service Fee: 2 cent per kWh per trade • Microgrid Monthly Services: $8,000.00 -$15,000,000.00 (Third Party Vendor) DC Microgrid: $6.3M (Microgrid Type from a Sensitivity Analysis) HOA Gets Housing Subdivision: 250 Houses • Collect Flat Fees per Resident: $225.00 Annual Microgrid Operating Costs: $160,000 • Per Trade Service Fee: 3 cent per kWh per trade Five Yr. Accumulated Profit Projection: $8M Break Even Point: at Year 5 at $8M ROI: 27.9% Ten Yr. Accumulated Profit Projection: $39.5M 32 Fifteen Yr. Profit Projection: $88.9M 32 Source: Department of Energy Study of AC and DC Microgrids MEX Business Plan Solar Market Penetration MEX Services • Solar Market: Virginia Forecast 10% Growth (5 Years) • Energy Trading Platform • 50K in Jobs and Manufacturing Projections • Data Analytics for Microgrid Management

Distribution Strategy • Distribution: Mobile Sales Teams, Targeted Ad Buys in Customer Value Chain Senior/Elderly Markets, Social Media Ads • HOA Conferences and Conventions • Advertising with Zero Energy Realtors and Developers

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.

Source: VA Solar Energy Development and Energy Storage Report Source: EnergySage.com Source: National HOA Website Source: NAHB Website 33 Source: SolarCity Survey Reports (2014-2015) 33 Source: Pew Research Survey: The Politics of Climate MEX Business Application

Projected Revenue to Trade Per Day: $3,053.65

Projected Revenue to Trade Per Day: $1,747.77

MEX System The data suggest connecting industrial size users with residential solar PV systems to lower energy costs and capitalize on wasted solar energy.

34 Residential Yearly Energy Bill Savings Over 25-year Span

Residential Cost n number of Residential Yearly Energy Bill Savings per kWh $600,000.00 $ 0.12 For n residents: $500,000.00 Cost Savings using MEX (Cum): $400,000.00 $ 4,169,790.72

Do Nothing: $300,000.00

Cost $ 13,226,713.68

$200,000.00 Bill Reduced 68.47%

$100,000.00 For single resident: Cost Savings using $- MEX (Cum): 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 Year $ 15,145.21 Do Nothing: Cost Savings With MEX Do Nothing $ 51,521.51 35 GMU Engineering with MEX System

Industrial Yearly Energy Bill Savings over 25 years $180,000.00 $160,000.00 $140,000.00

$120,000.00

$100,000.00

$80,000.00 Cost $60,000.00 $40,000.00 $20,000.00 $- 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 Year

Cost Savings With MEX Do Nothing

Industrial Savings

GMU ENGR Residential kWh Years Cost Savings With MEX Do Nothing Consumption Generation total after saving

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%

36 36 Conclusion and Results 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.

Future Research Recommendations: • Battery Energy Storage Systems are one of the primary research areas that would benefit the expansion of renewable solar energy. 37 37 Backup Slides

38 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.

Project Plan: Work Breakdown Structure

Design P2P Energy Trading

1.0 2.0 3.0 4.0 5.0 7.0 8.0 6.0 Analysis Context Analysis Requirements Project Plan Simulation Testing Project Results Presentations

7.1 8.1 1.1 2.1 3.1 4.1 5.1 6.1 Utility Analysis Project Result Faculty Project Scope CONOPS Budget Plan Objectives Testing Plans Conclusion Presentation

1.2 2.2 3.2 4.2 5.2 6.2 Sensitivity 8.2 Stakeholder System 7.2 Documentation Schedule Plan Requirements Conduct Testing Analysis Final Report Analysis Requirements

1.3 2.3 5.3 8.3 3.3 4.3 Problem/Need Functional Simulation Final Report Project Plan Framework Statement Requirements Modification Presentation Slides

2.4 4.3.1 8.4 1.4 3.4 5.4 Design Solar Generation Conference Project CORE Resources Statement of Work Output Testing Requirements Model Paper

4.3.2 8.5 Energy Trading Video Presentation Platform

4.4 Data Collection

4.5 Simulation Enhancement 41 Project Plan: Project Schedule

M 8/28/17 M 5/7/18

Past Due Tasks

42 42 Project Plan: Earned Value

43 43 Project Plan: Earned Value

44 44 Risks/Mitigation to Project Plan 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 7 4 3 84 Understand the needs and requirements tensions 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

• 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. 45 Context Analysis

• 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.

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.”

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.

46 Source: SolarCity Survey Reports (2014-2015) 46 Source: Pew Research Survey: The Politics of Climate IDEF0 Diagram

47 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

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 Source: Department of Energy 48 Source: Zero Energy Project Zero Energy Homebuilders

49 Local Utility Costs

50 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.

51 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 52 Microgrid

Microgrid includes: • Individual residential housing • Commercial buildings • INOVA • FFX GMU • Restaurants • FFX Fire Department • Malls

53 Appliance Table

54 FFBD

55 Input: Weather data every 4 hours for the 15th of each month( Meter )

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 56 Input : Energy Users(Appliances) & Energy Profile(Probability) for every 4 hours

Hourly Energy Consumption Appliance (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 57 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 58 MEx Comparison of Design Alternatives

59 MEx Sensitivity Analysis

60 MEx Sensitivity Analysis

61 MEx Sensitivity Analysis

Percent of Weight on Energy Costs Per Year Percent of Weight on Converter Losses Per Year

62 MEx Sensitivity Analysis

Percent of Weight on Network Losses Per Year Percent of Weight on Total System Costs

63 MEx Matrix and Results

Result: DC Microgrid: Higher Utility

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

64 MEx Multi-Attribute Utility Analysis Solar Modules Type Astronergy CHSM6610P-270 Solar Panel Polycrystalline 1. Multi-Attribute Utility Analysis LG LG335N1C-A5 Neon2 335W Blk Solar Panel Monocrystalline used to handle tradeoffs with Grape Solar 265-Watt Solar Panel Polycrystalline decision makers. Hanwha Q.Peak-G4.1, 300W MC4, Korea Monocrystalline 2. Solar PV System Analysis consisted Silfab Solar 350 Watt Solar Panel Monocrystalline of three major components: Solar Solar Battery Systems Type Panel Modules, Solar Batteries, LG Chem RESU10H 9.8kWh 400V Battery Energy Storage SLyitshtieumm-Ion and Inverters. The weights were Crown 2CRP3690, 2550Ah 2V Battery (100hours) Flooded Lean Acid selected based on the Tesla Powerwall Solar Battery (2 of each) Lithium-Ion manufacturers specifications. The Outback Power 4000 W FPR-4048A- Renewable EVnaelrvgey- RSyesgtuelmated Lead-Acid components were assembled into Yeti 1400 Lithum Potable Power Station Lithium-Ion five groups or systems. 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

65 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

66 Microgrids-Worst Case Scenario (Costs)

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)

67 Microgrid Worst Case Scenario (Costs)

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 68 Project Budget

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

69 Residential Solar PV System (Highest Utility)

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

70 Microgrid Cost Estimates (Labor)

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 Project Assumption Marketing/Advertising Costs Annual Costs Eq+Markup Borrow-Loan for Initial Microgrid Costs 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%

71 MEx Energy Trading Utility Functions

U(D1) = [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)] [Astronergy(AVIE)+Yeti(AVIE)+SMA(AIEC)]*(Weight=0.1) +[Astronergy(KwH)+Yeti (Kwh)+SMA(Kwh)]*(Weight U(D2) = =0.5)+[Astronergy(Variability)+Yeti(Variability)+SMA(Variability)]*(Weight=0.3)+[Astronergy(Independence)+Yeti(Independence)+L G(Independence)]*(Weight=0.1)] [Silab(AVIE)+LG(AVIE)+Schneider(AIEC)]*(Weight=0.1) +[Silab(KwH)+LG(Kwh)+Schneider(Kwh)]*(Weight U(D3) = =0.5)+[Silab(Variability)+LG(Variability)+Schneider(Variability)]*(Weight=0.3)+[Hanwha(Independence)+LG(Independence)+Schneid er(Independence)]*(Weight=0.1)] U(D4) = [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(Independ ence)+NaturePower(Independence)]*(Weight=0.1)]

U(D5) = [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)]

72 MEx Multi-Attribute Utility Analysis

MEx Solar Battery Costs Inverters 1.0 1.0 0.9 0.9 SMA 0.8 0.8 0.7 0.7 Schneider

SolarEdge 0.6 Outback Victron LG 0.6 0.5

Utility 0.4 0.5

Utility X 0.4 SolarEdge

0.3 X Tesla 0.2 Yeti 0.3 Crown 0.1 0.2 - 0.1 $1,195.00 $1,799.00 $6,300.00 $10,620.00 $11,700.00 Cost in Dollars ($) - $1,541.05 $1,595.00 $1,650.00 $1,655.29 $1,862.99 Cost in Dollars ($)

MEx Solar Panels

1 0.9 0.8

0.7

0.6

0.5 Silfab Solar Utility

X 0.4 0.3 Grape Solar LG 0.2 Astronergy 0.1 Hanwha 0 73 15.6 16.2 17.9 18.3 19.6 Panel Efficiency (%) Solar PV Multi-Attribute Utility Analysis

Less Dollars per kWh Revenue from Solar PV Production Highest Utility: D5 Solar PV System Panels Batteries Inverters Panels Batteries Inverters D1 Hanwha Crown Vicron D1 Hanwha Crown Vicron 80 90 70 70 50 80 U(D1)= 220 D2 Astronergy Yeti SMA D2 Astronergy Yeti SMA U(D2)= 195 50 50 80 60 90 60 D3 Silab LG Schneider D3 Silab LG Schneider U(D3)= 215 70 80 60 90 80 50 U(D4)= 180 D4 Grape Outback NaturePower D4 Grape Outback NaturePower 60 70 50 50 60 70 U(D5)= 240 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

U(D1) = [Hanwha(Less kwh)+Crown(Less kWh)+Vicron(Less kWh)]*(Weight=0.5) +[Hanwha(SolarPV)+Crown (SolarPV)+Vicron(SolarPV)]*(Weight =0.5) U(D2) = [Astronergy(Less kWh)+Yeti(Less kWh) + SMA (Less kWh)*(Weight=0.5) +[Astronergy(SolarPV)+Yeti (SolarPV)+SMA(SolarPV)]*(Weight =0.5) U(D3) = [Silab(Less kWh)+LG(Less kWh)+Schneider(Less kWh)]*(Weight=0.5) +[Silab(SolarPV)+LG(SolarPV)+Schneider(SolarPV)]*(Weight =0.5) U(D4) = [Grape(Less kWh)+Outback(Less kWh)+NaturePower(Less kWh)]*(Weight=0.5) +[Grape(SolarPV)+Outback(SolarPV)+NaturePower(SolarPV)]*(Weight =0.5) U(D5) = [LG(Less kWh)+Tesla(Less kWh)+SolarEdge(Less kWh]*(Weight=0.5) +[LG(SolarPV)+Tesla(SolarPV)+SolarEdge(SolarPV)]*(Weight =0.5) 74 MEx System Utility Function

MEx Utility System Function Do Nothing vs MEx 1 Implementation: 0.9 1. Design and Architecture MEx 0.8 0.8 2. Initial Investment Costs

0.7

0.6

0.5

Utility X 0.4

0.3 0.2 Do Nothing 0.1 0.05 0 $9,245,724.24 $13,029,356.24 Costs in Dollars (25 Years)

75 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.

76 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:77 “40% of US Homeowners Have Considered Solar PV” Source: Pew Research Center: Politics of Climate (2016) MEx Business Case: Survey Findings • 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.

Commonwealth of Virginia • Potential MEx Consumer Market State Demographics: 8,470,020 residents • Middle Aged and Elderly VA Residents Target Market • Other Typical Residential Solar Demographics Persons Aged 65 or older: 14.6% or 1.23M • 70% of Solar Household have annual income residents range of $45,00 - $150,000. Housing Units: 3,491,054 (2016) • Most Overrepresented Income Demographic: • Source: US Census Bureau $100,000-$150,000. • Source: GTM Research 78

Battery Energy Storage System Black Start Service by Storage

Schematic of Battery Energy Storage System

Electric Supply Resource Stack

79 Source: Sandia National Laboratories Battery Energy Storage System

80 Source: AMDC Energy Limited Retail Energy Time-Shift

Time of Use Summer Energy Prices for Small Industrial Users

81 Source: Sandia National Laboratories Demand Charge Management

On-Peak Demand Reduction Using Energy Storage

82 Source: Sandia National Laboratories