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Equilibrium Modeling of Combined Heat and Power Deployment

This study examines the economic potential for Combined Heat and Power (CHP) systems within an electricity market equilibrium framework, considering their impact on electricity pricing. Despite the low current utilization of CHP in the U.S., the technique promises significant efficiency improvements. By analyzing Philadelphia as a case study with high technical potential and electricity costs, the research identifies threshold points for technology shifts and supply curve dynamics. The findings suggest that higher natural gas prices may present new economic opportunities for CHP adoption.

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Equilibrium Modeling of Combined Heat and Power Deployment

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  1. Equilibrium Modeling of Combined Heat and Power Deployment AnandGovindarajan Seth Blumsack Pennsylvania State University USAEE Conference, Anchorage, 29 July 2013

  2. Problem Statement • Assess the economic potential for Combined Heat and Power (CHP) in electricity-market equilibrium framework, accounting for the impact that CHP adoption will have on electricity prices

  3. Some Motivation • U.S. utilization of CHP is low but technical potential is vast • Utilization pathway for shale-gas supplies Current CHP capacity Technical potential for additional CHP

  4. Basic CHP Economics • Increased efficiency of heat + electricity (adsorptive chiller can add cooling) • Avoided electricity purchases • Other benefits : reduced emissions, reliability benefits

  5. Technical vs Economic potential • CHP investment reduces demand for grid provided power, lowering market price • At some point, incremental CHP units become uneconomical • The economic potential maybe different(less) than the technical potential

  6. Equilibrium CHP Modeling

  7. Philadelphia Case Study • We use Philadelphia, PA as a case study for our equilibrium modeling • High technical potential, high electricity prices • Transmission constrained

  8. Supply curve modeling (Sahraei-Ardakani et al 2012) We want to identify: Thresholds where the marginal technology changes; The slope of each portion of the locational dispatch curve.

  9. CHP Load Profiles Building-integrated CHP (BCHP) tool used to generate profiles for eight building types Electric load-following (FEL) and thermal load-following (FTL)

  10. Method

  11. Energy Savings from Incremental CHP Investment in Philadelphia Assumes $4/mmBTU natural gas price

  12. Energy Savings from Incremental CHP Investment in Philadelphia Assumes $8/mmBTU natural gas price

  13. NPV of Incremental CHP ($4 gas)

  14. NPV of Incremental CHP ($8 gas)

  15. Conclusion: Are High Gas Prices Good for CHP? • Higher gas prices may mean more economic opportunities for CHP, otherwise economic potential is perhaps 1/3 of technical potential. • Disproportionate impacts on electricity prices relative to operational costs • FTL maybe a more economical operational strategy when fuel prices are low $4/mmBTU Gas $8/mmBTU Gas

  16. Thank You! AnandGovindarajan axg5179@psu.edu

  17. Locational Marginal Cost Curves

  18. Life is Heaven When Gas is $7 Price separation between fuels (on $/MBTU basis) means that thresholds are easy to identify. Note: Other fuel prices – Coal $2/mmBTU; Oil $20/mmBTU

  19. Life Ain’t a Breeze When Gas is $3 When relative fuel price differences are small, a mix of fuels/technologies can effectively be “on the margin.” Note: Other fuel prices – Coal $2/mmBTU; Oil $20/mmBTU

  20. Estimation Procedure Classification parameters Generation i CMA-ES OLS Regression Regression Parameters / SSE Generation i-1 We want to minimize the SSE of: Choose initial parameters φ Find associated slope parameters ω using least squares Given estimates for ω and the regression SSE, choose a new set of threshold parameters φ* Repeat until convergence.

  21. Marginal Fuel Results

  22. Estimating Threshold Functions Thresholds are estimated using a fuzzy logic approach to capture multiple marginal fuels: Relative fuel price threshold for having the fuzzy gap Fuzzy gap width coefficient

  23. Example Result Wide band where gas/coal are jointly setting prices. More defined threshold between gas and oil.

  24. Supply Curve Modeling • Philadelphia is transmission-constrained, so the available capacity of a generator is not relevant – only the amount of electricity that is deliverable to a location in the network. • Power Transfer Distribution Factor (PTDF): G2 G1 k

  25. Piecewise Supply Curve Estimation Slope of the relevant portion of the supply curve Threshold indicator function

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