Using Willingness to Pay to Forecast the Adoption of Solar Photovoltaics: A ''Parameterization + Calibration'' Approach

Dong, Changgui; Sigrin, Benjamin

doi:10.1016/j.enpol.2019.02.017

Title: Using Willingness to Pay to Forecast the Adoption of Solar Photovoltaics: A ''Parameterization + Calibration'' Approach

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Abstract

Distributed energy resources, such as rooftop solar photovoltaics (PV), are likely to comprise a substantial fraction of new generation capacity in the United States. However, forecasting technology adoption based on people's willingness to pay (WTP) faces two major challenges: the stated-intention and omitted-variable biases. Previous solar adoption literature has neglected to address these two biases altogether. Here, we adopt a 'parameterization + calibration' approach to address both biases and estimate customers' WTP for PV. After collecting survey data on respondents' WTP for adopting PV, we characterize its empirical cumulative density function using a gamma distribution. We further calibrate the gamma distribution parameters using a national distributed PV adoption simulation model, finding the parameters that produce the best fit between simulated and historic solar adoption. We then show that the calibrated gamma distribution improves the raw WTP data after correcting for the two biases. Finally, we use our optimally-calibrated WTP to forecast market demand for residential PV at the county-level of the United States in 2020. Improving estimates of customer willingness to pay has significant implications for policy directly, e.g. estimating the effect of a proposed policy on technology adoption, and other regulatory processes that use forecasting, e.g. integrated resource planning.

Authors:

Dong, Changgui ^[1]; Sigrin, Benjamin ^[2]

Renmin Univ. of China, Beijing (China)
National Renewable Energy Lab. (NREL), Golden, CO (United States)

Publication Date:: Tue Feb 12 00:00:00 EST 2019

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

OSTI Identifier:: 1494980

Report Number(s):: NREL/JA-6A20-66020
Journal ID: ISSN 0301-4215

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: Energy Policy

Additional Journal Information:: Journal Volume: 129; Journal Issue: C; Journal ID: ISSN 0301-4215

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 14 SOLAR ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; solar photovoltaics; willingness to pay; adoption forecasting; parameterization; calibration

Citation Formats


                    Dong, Changgui, and Sigrin, Benjamin. Using Willingness to Pay to Forecast the Adoption of Solar Photovoltaics: A ''Parameterization + Calibration'' Approach.  United States: N. p., 2019. 
Web.  doi:10.1016/j.enpol.2019.02.017.

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                    Dong, Changgui, & Sigrin, Benjamin. Using Willingness to Pay to Forecast the Adoption of Solar Photovoltaics: A ''Parameterization + Calibration'' Approach.  United States.  https://doi.org/10.1016/j.enpol.2019.02.017

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                    Dong, Changgui, and Sigrin, Benjamin. Tue .  
"Using Willingness to Pay to Forecast the Adoption of Solar Photovoltaics: A ''Parameterization + Calibration'' Approach".  United States.  https://doi.org/10.1016/j.enpol.2019.02.017.  https://www.osti.gov/servlets/purl/1494980.

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@article{osti_1494980,

  title        = {Using Willingness to Pay to Forecast the Adoption of Solar Photovoltaics: A ''Parameterization + Calibration'' Approach},

  author       = {Dong, Changgui and Sigrin, Benjamin},

  abstractNote = {Distributed energy resources, such as rooftop solar photovoltaics (PV), are likely to comprise a substantial fraction of new generation capacity in the United States. However, forecasting technology adoption based on people's willingness to pay (WTP) faces two major challenges: the stated-intention and omitted-variable biases. Previous solar adoption literature has neglected to address these two biases altogether. Here, we adopt a 'parameterization + calibration' approach to address both biases and estimate customers' WTP for PV. After collecting survey data on respondents' WTP for adopting PV, we characterize its empirical cumulative density function using a gamma distribution. We further calibrate the gamma distribution parameters using a national distributed PV adoption simulation model, finding the parameters that produce the best fit between simulated and historic solar adoption. We then show that the calibrated gamma distribution improves the raw WTP data after correcting for the two biases. Finally, we use our optimally-calibrated WTP to forecast market demand for residential PV at the county-level of the United States in 2020. Improving estimates of customer willingness to pay has significant implications for policy directly, e.g. estimating the effect of a proposed policy on technology adoption, and other regulatory processes that use forecasting, e.g. integrated resource planning.},

  doi          = {10.1016/j.enpol.2019.02.017},

  journal      = {Energy Policy},

  number       = C,

  volume       = 129,

  place        = {United States},

  year         = {Tue Feb 12 00:00:00 EST 2019},

  month        = {Tue Feb 12 00:00:00 EST 2019}

}

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