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IEEE Alternate Energy Presentation May 3, 2012 URS Corp., Southfield, MI Michelle Rogers & Ian Hutt

Using locational marginal prices to estimate real-time emissions from electricity use. IEEE Alternate Energy Presentation May 3, 2012 URS Corp., Southfield, MI Michelle Rogers & Ian Hutt. Team Background. Michelle Rogers

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IEEE Alternate Energy Presentation May 3, 2012 URS Corp., Southfield, MI Michelle Rogers & Ian Hutt

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  1. Using locational marginal prices to estimate real-time emissions from electricity use IEEE Alternate Energy Presentation May 3, 2012 URS Corp., Southfield, MI Michelle Rogers & Ian Hutt

  2. 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

  3. Team Background • Other Team Members: • Wayne State: Dr. Carol Miller, Dr. Caisheng Wang, Dr. McElmurry, Tim Carter • Commonwealth Associates: Stephen Miller • TYJT: AwniQaqish, Steve Jin, Carrie Smalley

  4. 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

  5. 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

  6. 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)

  7. 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

  8. 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)

  9. Methodology • Use LMP to predict the marginal fuel type • Calculate emissions associated with that fuel type for a specific area

  10. Locational Marginal Prices • LMPs available from MISO • (Midwest Independent System Operator) • LMPs for select Commercial Pricing Nodes (CPNs) available every 5 minutes

  11. 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

  12. Locational Marginal Prices

  13. Locational Marginal Prices

  14. 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

  15. 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

  16. Locational Marginal Prices • LMP = ~ fuel cost Price ($/MWh) LMP at time ti Hydro & Nuclear Coal Natural Gas Oil

  17. 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:

  18. Fuel Prices • Get price ranges for Fuel types • For Example: DTE Power plants in SE Michigan • LMP  Marginal Generator Type  Air Emissions

  19. 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

  20. Emission Rates • Calculate average emission rate for entire area for each fuel type • Example, Detroit Edison: (2008 data) • LMP  Marginal Generator Type  Air Emissions

  21. 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:

  22. 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

  23. Hydraulic Model • City of Monroe

  24. Hydraulic Model • DWSD

  25. 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

  26. PEPSO Input: Load Hydraulic Model

  27. PEPSO Input: Load Pressure Monitoring Nodes

  28. PEPSO Input: Select Commercial Pricing Nodes (CPNs)

  29. PEPSO Input: Select Pollutants of Interest

  30. 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.

  31. PEPSO Output

  32. 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

  33. 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

  34. 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

  35. HERO Input • HERO can automatically find nearest CPN based on phone’s GPS • User also has choice to pick location from map

  36. HERO Output • Current, Past, and Projected Future emissions • CO2, NOX, SOX, Mercury, Lead

  37. HERO Output • User can view more to see background information on CO2, NOX, SOX, Mercury, Lead • Environmental Effects, Human Health Effects • Example: NOX & SOX

  38. 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

  39. Questions?

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