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Research Papers

Optimizing the Design and Deployment of Stationary Combined Heat and Power Fuel Cell Systems for Minimum Costs and Emissions—Part I: Model Design

[+] Author and Article Information
Whitney G. Colella

Energy Resources and Systems Analysis, Sandia National Laboratories, P.O. Box 5800, MS 1108, Albuquerque, NM 87185wgcolel@sandia.gov

Stephen H. Schneider

Center for Environmental Science and Policy Environment and Energy Building, Stanford University, MC4205, 473 Via Ortega, Stanford, CA 94305shs@stanford.edu

Daniel M. Kammen

Energy and Resources Group, University of California, Berkeley, Berkeley, CA 94720kammen@berkeley.edu

Aditya Jhunjhunwala

Management Science and Engineering, Terman Engineering Center, Stanford University, 380 Panama Way, Stanford, CA 94305aditya11@stanfordalumni.org

Nigel Teo

Management Science and Engineering, Terman Engineering Center, Stanford University, 380 Panama Way, Stanford, CA 94305nigelteo@gmail.com

Economic growth as defined by the Solow Growth model; for example see Solow, R. M., “Technical Change and the Aggregate Production Function,” Review of Economics and Statistics, August 1997.

See http://www.pge.com/selfgen/ for restrictions. If the new plant replaces existing CHP, the incentive may not apply.

For tax paying entities, see the U.S. Energy Policy Act of 2005.

On top of a carbon tax, the model does not also financially credit generators for avoided emissions through an emission trading system. Most regions that try to internalize the external costs of GHG emissions choose between either a carbon tax or an emission trading system, not both. Although an emission trading system does not preclude the use of carbon taxes, the two are often seen as competing policy instruments aimed at the same goal of GHG emission reductions.

J. Fuel Cell Sci. Technol 8(2), 021001 (Nov 24, 2010) (13 pages) doi:10.1115/1.4001756 History: Received July 07, 2008; Revised March 25, 2010; Published November 24, 2010; Online November 24, 2010

Stationary combined heat and power (CHP) fuel cell systems (FCSs) can provide electricity and heat for buildings and can reduce greenhouse gas (GHG) emissions significantly if they are configured with an appropriate installation and operating strategy. The maximizing emission reduction and economic saving simulator (MERESS) is an optimization tool that was developed to evaluate novel strategies for installing and operating CHP FCSs in buildings. These novel strategies include networking, load following, and the use of variable heat-to-power ratios, all of which industry typically has not implemented. A primary goal of models like MERESS is to use relatively inexpensive simulation studies to identify more financially and environmentally effective ways to design and install FCSs. Models like MERESS can incorporate the pivotal choices that FCS manufacturers, building owners, emission regulators, competing generators, and policy makers make, and empower them to evaluate the effect of their choices directly. MERESS directly evaluates trade-offs among three key goals: GHG reductions, energy cost savings for building owners, and high sales revenue for FCS manufacturers. MERESS allows one to evaluate these design trade-offs and to identify the optimal control strategies and building load curves for installation based on either (1) maximum GHG emission reductions or (2) maximum cost savings to building owners. Part I discusses the motivation and key assumptions behind MERESS model development. Part II discusses run results from MERESS for a California town and makes recommendations for further FCS installments (Colella, 2011, “Optimizing the Design and Deployment of Stationary Combined Heat and Power Fuel Cell Systems for Minimum Costs and Emissions—Part II: Model Results,” ASME J. Fuel Cell Sci. Technol., 8(2), p. 021002).

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Copyright © 2011 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

U.S. Urban Dynamometer Driving Cycle, a simulation of a vehicle’s speed over time in a U.S. city (6)

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Figure 2

Real-time electricity demand from a university campus building (7)

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Figure 3

Comparison of stand alone and networked operating strategies

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Figure 4

Comparison of load following operating strategies

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Figure 5

Comparison of fixed versus variable heat-to-power operating strategies

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Figure 6

Sample measured input data for building load curves showing electricity demand from five different building types over one representative week during winter

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Figure 7

Sample measured input data for building load curves showing heating demand from five different building types over one representative week during winter

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