Technical Brief

Market Simulation Based Sensitivity Analysis as a Means to Inform Design Effort as Applied to Photovoltaic Panels

[+] Author and Article Information
Bart D. Frischknecht

Centre for the Study of Choice,
Business School,
University of Technology Sydney,
Sydney 2007, Australia
e-mail: bart.frischknecht@uts.edu.au

Kate S. Whitefoot

National Academy of Engineering,
Washington, DC 20001
e-mail: kwhitefoot@nae.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received October 4, 2012; final manuscript received February 22, 2014; published online March 19, 2014. Editor: Shapour Azarm.

J. Mech. Des 136(5), 054501 (Mar 19, 2014) (8 pages) Paper No: MD-12-1492; doi: 10.1115/1.4026991 History: Received October 04, 2012; Revised February 22, 2014

Product design success depends on the engineering performance of the product and also on the reaction of external stakeholders such as customers, retailers, and policymakers. This article illustrates how an early-stage engineering design performance model can be incorporated into a decision framework representing customers, retailers, and policymakers to assess the revenue potential for different technologies. Sensitivity analysis is performed for revenue and other stakeholder decision criteria with respect to the design performance measures. We illustrate our approach for photovoltaic panels in the context of the residential solar electricity generation system market in New South Wales, Australia that experienced a variety of federal and state government incentive programs between 2010 and 2012. The analysis is based on engineering performance modeling, discrete choice demand modeling, and cost modeling all with simplifying assumptions.

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Hazelrigg, G., 1998, “Framework for Decision-Based Engineering Design,” ASME J. Mech. Des., 120(4), pp. 653–658. [CrossRef]
Wassenaar, H. J., and Chen, W., 2003, “An Approach to Decision-Based Design With Discrete Choice Analysis for Demand Modeling,” ASME J. Mech. Des., 125(3), pp. 490–497. [CrossRef]
Shiau, C. S., and Michalek, J. J., 2009, “Optimal Product Design Under Price Competition,” ASME J. Mech. Des., 131(7), p. 071003. [CrossRef]
Georgiopoulos, P., Jonsson, M., and Papalambros, P. Y., 2005, “Linking Optimal Design Decisions to the Theory of the Firm: The Case of Resource Allocation,” ASME J. Mech. Des., 127(3), pp. 358–366. [CrossRef]
Shiau, C. S., and Michalek, J., 2007, “A Game-Theoretic Approach to Finding Market Equilibria for Automotive Design under Environmental Regulation,” Proceedings of the ASME 2007 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Las Vegas, NV, Sept. 4–7, ASME Paper No. DETC2007-34884, pp. 187–196. [CrossRef]
Intlekofer, K., Bras, B., and Ferguson, M., 2010, “Energy Implications of Product Leasing,” Environ. Sci. Technol., 44(12), pp. 4409–4415. [CrossRef] [PubMed]
Shiau, C. S. N., Michalek, J. J., and Hendrickson, C. T., 2009, “A Structural Analysis of Vehicle Design Responses to Corporate Average Fuel Economy Policy,” Transp. Res. A43(9), pp. 814–828.
Williams, N., Azarm, S., and Kannan, P. K., 2008, “Engineering Product Design Optimization for Retail Channel Acceptance,” ASME J. Mech. Des., 130(6), p. 061402. [CrossRef]
Shiau, C. S. N., and Michalek, J. J., 2009, “Should Designers Worry About Market Systems?,” ASME J. Mech. Des., 131(1), p. 011011. [CrossRef]
Michalek, J. J., Papalambros, P. Y., and Skerlos, S. J., 2004, “A Study of Fuel Efficiency and Emission Policy Impact on Optimal Vehicle Design Decisions,” ASME J. Mech. Des., 126(6), pp. 1062–1070. [CrossRef]
Whitefoot, K., Fowlie, M., and Skerlos, S., 2011, “Product Design Responses to Industrial Policy: Evaluating Fuel Economy Standards Using an Engineering Model of Endogenous Product Design,” Energy Institute at Haas Working Paper No. WP-214.
Fischer, C., and Newell, R., 2008, “Environmental and Technology Policies for Climate Change and Renewable Energy,” J. Environ. Econ. Manage., 55(2), pp. 142–162. [CrossRef]
McGinty, M., and de Vries, F. P., 2009, “Technology Diffusion, Product Differentiation and Environmental Subsidies,” B.E. J. Econ. Anal. Policy, 9(1). [CrossRef]
Shiau, C. S. N., Samaras, C., Hauffe, R., and Michalek, J. J., 2009, “Impact of Battery Weight and Charging Patterns on the Economic and Environmental Benefits of Plug-In Hybrid Vehicles,” Energy Policy, 37(7), pp. 2653–2663. [CrossRef]
Bass, F., 1969, “A New Product Growth Model for Consumer Durables,” Manage. Sci., 15(5), pp. 215–227. [CrossRef]
Fisher, J. C., and Pry, R. H., 1971, “A Simple Substitution Model for Technological Change,” Technol. Forecast. Soc. Change, 3, pp. 75–88. [CrossRef]
Sterman, J., 2000, Business Dynamics: Systems Thinking and Modeling for a Complex World, McGraw Hill, Boston, MA.
Jaffe, A., and Stavins, R., 1995, “Dynamic Incentives of Environmental Regulations: The Effects of Alternative Policy Instruments on Technology Diffusion,” J. Environ. Econ. Manage., 29(3), pp. S43–S63. [CrossRef]
Jaffe, A. B., Newell, R. G., and Stavins, R. N., 2002, “Environmental Policy and Technological Change,” Environ. Resource Econ., 22(1), pp. 41–70. [CrossRef]
Train, K., 2003, Discrete Choice Methods With Simulation, Cambridge University Press, Cambridge, UK.
Louviere, J. J., Hensher, D. A., and Swait, J. D., 2000, Stated Choice Methods: Analysis and Applications, Cambridge University Press, Cambridge, UK.
Scarpa, R., and Willis, K., 2009, “Willingness-to-Pay for Renewable Energy: Primary and Discretionary Choice of British Households' for Micro-Generation Technologies,” Energy Econ., 32(1), pp. 129–136. [CrossRef]
Boyd, M. T., Klein, S. A., Reindl, D. T., and Dougherty, B. P., 2011, “Evaluation and Validation of Equivalent Circuit Photovoltaic Solar Cell Performance Models,” ASME J. Sol. Energy Eng., 133(2), p. 021005. [CrossRef]
SolarQuotes.com.au, 2010, “Choosing Your Solar Panels-What Are Your Choices?,” last accessed Jan. 27, 2010, http://www.solarquotes.com.au/types-of-solar-panel.html.
Duffie, J. A., and Beckman, W. A., 2006, Solar Engineering of Thermal Processes, 3rd ed., John Wiley & Sons, Hoboken, NJ.
National Oceanic and Atmospheric Administration, 2011, “NOAA Solar Calculator,” last accessed July 20, 2011, http:// www.esrl.noaa.gov/gmd/grad/solcalc/.
Bureau of Meteorology, Commonwealth of Australia, 2011, “Monthly Weather Review,” last accessed July 20, 2011, www.bom.gov.au/climate/mwr/.
Hottel, H. C., 1976, “A Simple Model for Estimating the Transmittance of Direct Solar Radiation Through Clear Atmospheres,” Sol. Energy, 18(2), pp. 129–134. [CrossRef]
Liu, B. Y. H., and Jordan, R. C., 1960, “The Interrelationship and Characteristic Distribution of Direct, Diffuse and Total Solar Radiation,” Sol. Energy, 4(3), pp. 1–19. [CrossRef]
Erbs, D., Klein, S., and Duffie, J., 1982, “Estimation of the Diffuse Radiation Fraction for Hourly, Daily and Monthly-Average Global Radiation,” Sol. Energy, 28(4), pp. 293–302. [CrossRef]
Environmental Energy Technologies Division, E. O. Lawrence Berkeley National Laboratory, Roofing Tile, last accessed July 20, 2011, http://eetd.lbl.gov/coolroof/tile.htm.
Uryas'ev, S., and Rubinstein, R. Y., 1994, “On Relaxation Algorithms in Computation of Noncooperative Equilibria,” IEEE Trans. Autom. Control, 39(6), pp. 1263–1267. [CrossRef]
Feldman, D., Barbose, G., Margolis, R. R., Wiser, N. D., and Goodrich, A., 2012, “Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections,” Technical Report No. DOE/GO-102012-3839, last accessed Feb. 17, 2014, http:// www.osti.gov/scitech.
Department of Climate Change and Energy Efficiency, 2011, “Fact Sheet: Solar Credits for Small Generation Units,” Commonwealth of Australia, last accessed Feb. 11, 2011, www.climatechange.gov.au/en/government/initiatives/renewable-target/fs-solar-credits-small-scale.aspx.
Train, K., 2009, “Kenneth Train's Home Page,” University of California, Berkeley, last accessed Feb. 17, 2014, http://elsa.berkeley.edu/∼train/software.html.
Australian Bureau of Statistics, 2010, “Australian Social Trends, Data Cube - Housing,” Commonwealth of Australia, Catalogue No. 4102.0, last accessed Feb. 11, 2011, www.abs.gov.au/AUSSTATS/abs@.nsf/mf/4102.0?opendocument#from-banner=LN.
NSW State Government, 2011, “Solar Bonus Scheme for NSW,” last accessed Feb. 11, 2011, www.industry.nsw.gov.au/energy/sustainable/renewable/solar/solar-scheme.
Frischknecht, B., and Whitefoot, K., 2011, “Defining Technology-Adoption Indifference Curves for Residential Solar Electricity Generation Using Stated Preference Experiments,” Proceedings of the ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Washington, DC, Aug. 28–31, ASME Paper No. DETC2011-48007, pp. 395–404. [CrossRef]


Grahic Jump Location
Fig. 1

Distribution of posterior parameter estimates

Grahic Jump Location
Fig. 2

Schematic of solution process for market simulations

Grahic Jump Location
Fig. 3

Elasticity of producer revenue with respect to changes in the engineering parameters of 1%, 5%, 10%, 15%, or 20%



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