IEEE Alt Energy 05-03-2012 Presentation

Report
IEEE Alternate Energy Presentation
May 3, 2012
URS Corp., Southfield, MI
Michelle Rogers & Ian Hutt
Team Background
 Michelle Rogers
 Master’s student at Wayne State studying Civil &
Environmental Engineering
 B.S. Chemical Engineering from Michigan State
 Ian Hutt
 Electric Engineer at Commonwealth Associates, Inc
 Expertise in electrical power systems & power marketing
Team Background
 Other Team Members:
 Wayne State: Dr. Carol Miller, Dr. Caisheng Wang, Dr.
McElmurry, Tim Carter
 Commonwealth Associates: Stephen Miller
 TYJT: Awni Qaqish, Steve Jin, Carrie Smalley
Outline
 Introduction to the project
 How project was started
 Purposes of development
 How it works
 LMP
 Marginal Generating Unit
 Emissions
 Application for water distribution systems
 Wider applications: household electricity use
 HERO smartphone App
Introduction
 Algorithm estimates real-time emissions based on
locational marginal price (LMP)
 Started as a project for sustainable water delivery
 Also has wider implications / uses
Why was this project started?
 GLPF, Great Lakes Protection Fund grant
 Grant title: “Real-Time System Optimization for
Sustainable Water Transmission and Distribution”
 Purpose: minimize environmental impacts to the Great
Lakes
 Optimize energy use in water system distribution
(pumping)
Why was this project started?
 GLPF, Great Lakes Protection Fund grant
 Became clear that emissions, not just energy use, was
the key in minimizing environmental impact
 Not all energy use is equal (from emissions
standpoint)
 Emissions vary with type of generation fuel
 Depends on time and location
Applications
 Not all energy use is equal (from emissions
standpoint)
 Any power user that has ability to vary timing of energy
use could save emissions
 Timing does not affect economics, but could still affect
emissions
 Industrial or commercial users that have storage
capacity (like compressed air or pumps)
Methodology
 Use LMP to predict the marginal fuel type
 Calculate emissions associated with that fuel type for a
specific area
Locational Marginal Prices
 LMPs available from MISO
 (Midwest Independent System Operator)
 LMPs for select Commercial Pricing Nodes (CPNs)
available every 5 minutes
Locational Marginal Prices
 LMPs based on marginal cost of supplying the next
increment of electric demand at a specific location
 LMP Accounts for:
 generation marginal cost (fuel cost)
 physical aspects of transmission system (constraint in
transmission lines)
 Cost of marginal power losses
Locational Marginal Prices
Locational Marginal Prices
Locational Marginal Prices
 Key Assumptions:
 Any change in electricity use is small enough to not
affect generation mix
 LMP cost takes into account electrical transmission
constraint
 Model predicts the marginal unit type
Locational Marginal Prices
 LMP Accounts for:
 physical aspects of transmission system (constraint in
transmission lines)
 Within a small focus area, can assume constraint in the
physical transmission system = ~ zero
 Cost of marginal power losses
 Assume marginal power losses = ~ zero
 Generation marginal cost (fuel cost)
 Left with LMP = ~ fuel cost
Locational Marginal Prices
Price ($/MWh)
 LMP = ~ fuel cost
LMP at time ti
Hydro & Nuclear
Coal
Natural Gas
Oil
Fuel Prices
 LMP = ~fuel cost
 Find fuel price data (EIA – public sources)

Heat Rate (efficiency) of each plant:

Weighted average of monthly fuel price calculated from plant
fuel purchases

Cost of electric generation computed:
Fuel Prices
 Get price ranges for Fuel types
 For Example: DTE Power plants in SE Michigan
Marginal Fuel Type
Min LMP
Max LMP
Nuclear/Renewable
< $10
$10
Coal
$10
$50
Nat. Gas & RFO
$50
$180
Dist. Fuel Oil
$180
> $180
 LMP  Marginal Generator Type  Air Emissions
Emission Rates
 LMP  Marginal Generator Type  Air Emissions
 Measured Air Emissions Data from EPA’s eGRID
 (Emissions & Generation Resource Integrated Database)
 Data on thousands of power plants in the US
 Sort by EGCL code (Electric Generating Company,
Location-Based)
 i.e., all of DTE-operated plants in SE Michigan
Emission Rates
 Calculate average emission rate for entire area for each
fuel type
 Example, Detroit Edison: (2008 data)
Air Emissions in pounds pollutant per MWhr generated (lb/MWh)
Pollutant
Nuclear
Coal
Natural Gas Distilled Fuel Oil
SO2
0
10.54
1.65
2.3445
NOX
0
3.05
1.57
21.73
CO2 equiv
0
2071
2292
1862
Hg
0
5.26E-05
3.62E-06
5.81E-06
Pb
1.09E-07
3.10E-05
1.66E-06
3.65E-05
 LMP  Marginal Generator Type  Air Emissions
Application for water
distribution systems
 GLPF Grant: “Real-Time System Optimization for
Sustainable Water Transmission and Distribution”
 Emissions estimation algorithm used in optimization
program for pumping stations.
 Two pilot water systems:
Hydraulic Model
 Use EPANet hydraulic models
 Input:
 Pipe length
 Pipe diameter
 Demand at each node
 Diurnal demand pattern
 Pump power
 Pump efficiency curves
 Elevation
 Tanks and reservoirs
Hydraulic Model
 City of Monroe
Hydraulic Model
 DWSD
Sustainable Water Transmission
 Need to combine:
 Hydraulic Model + Emissions Estimation Model
 PEPSO: Pollutant Emissions Pump Station
Optimization
 Uses hydraulic model to output optimized pumping
schedule
 Optimization based on:



Emissions
Energy Cost
Pressure constraints in system
PEPSO Input: Load Hydraulic Model
PEPSO Input:
Load
Pressure
Monitoring
Nodes
PEPSO Input:
Select
Commercial
Pricing Nodes
(CPNs)
PEPSO Input:
Select
Pollutants of
Interest
PEPSO Output
 Energy use per hour for each
pump station.
 Pounds of pollutant
emissions per hour for
optimized operation of each
pump station.
 Pressure violations, if any.
PEPSO Output
Sustainable Water Transmission
 PEPSO will be used to evaluate many scenarios
 High/low demand
 Different pollutants
 Availability of raised storage
 Optimization based on cost vs. emissions
 Use as a tool to make policy and operational
recommendations
Reaching a broader audience:
the HERO app
 HERO = Home Emissions Read-Out
 (LMP  Marginal Generator Type  Air Emissions)
 Applying this concept to household energy use
 App for smart phones
HERO
 Uses location to determine marginal emissions
in real-time
 Knowledge of current emissions empowers
consumers to reduce emissions just by
changing the timing of electricity use
HERO Input
 HERO can automatically find
nearest CPN based on
phone’s GPS
 User also has choice to pick
location from map
HERO Output
 Current, Past, and Projected
Future emissions
 CO2, NOX, SOX, Mercury,
Lead
HERO Output
 User can view more to see
background information on
CO2, NOX, SOX, Mercury,
Lead
 Environmental Effects,
Human Health Effects
 Example: NOX & SOX
HERO Status
 Still under development
 Preliminary version should be finished in Fall
 After small test audience makes
recommendations, fix all bugs, then beta
version release in Google Play App Store
Questions?

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