This work uses an agent-based model to examine how installers of photovoltaic (PV) panels influence panel design and the success of residential solar energy. It provides a novel approach to modeling intermediary stakeholder influence on product design, focusing on installer decisions instead of the typical foci of the final customer (homeowners) and the designer/manufacturer. Installers restrict homeowner choice to a subset of all panel options available, and, consequentially, determine medium-term market dynamics in terms of quantity and design specifications of panel installations. This model investigates installer profit-maximization strategies of exploring new panel designs offered by manufacturers (a risk-seeking strategy) versus exploiting market-tested technology (a risk-averse strategy). Manufacturer design decisions and homeowner purchase decisions are modeled. Realistic details provided from installer and homeowner interviews are included. For example, installers must estimate panel reliability instead of trusting manufacturer statistics, and homeowners make purchase decisions based in part on installer reputation. We find that installers pursue new and more-efficient panels over sticking-with market-tested technology under a variety of panel-reliability scenarios and two different state scenarios (California and Massachusetts). Results indicate that it does not matter if installers are predisposed to an exploration or exploitation strategy—both types choose to explore new panels that have higher efficiency.
Issue Section:
Design for Manufacture and the Life Cycle
References
1.
Millstein
, D.
, Cates
, S.
, Disanti
, N.
, Widiss
, R.
, Barbose
, G.
, and Darghouth
, N.
, 2016
, “Tracking the Sun IX the Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States
,” Berkeley Lab, Berkeley, CA, Report No. LBNL-1006036
.https://www.osti.gov/biblio/13451942.
U.S. Department of Energy, 2017, “
SunShot Initiative
,” U.S. Department of Energy, Washington, DC, accessed Feb. 10, 2017, https://energy.gov/eere/sunshot/sunshot-initiative3.
“
SolarCity Laying Off Workers, Restructuring
,” accessed Feb. 10, 2017, http://www.mercurynews.com/2016/08/17/solarcity-laying-off-workers-restructuring/4.
Karakaya
, E.
, and Sriwannawit
, P.
, 2015
, “Barriers to the Adoption of Photovoltaic Systems: The State of the Art
,” Renewable Sustainable Energy Rev.
, 49
, pp. 60
–66
.5.
Islam
, T.
, and Meade
, N.
, 2013
, “The Impact of Attribute Preferences on Adoption Timing: The Case of Photo-Voltaic (PV) Solar Cells for Household Electricity Generation
,” Energy Policy
, 55
, pp. 521
–530
.6.
Woodhouse
, M.
, Jones-Albertus
, R.
, Feldman
, D.
, Fu
, R.
, Horowitz
, K.
, Chung
, D.
, Jordan
, D.
, and Kurtz
, S.
, 2016
, On the Path to SunShot: The Role of Advancements in Solar Photovoltaic Efficiency, Reliability, and Costs
, National Renewable Energy Laboratory
, Golden, CO
.7.
Chung
, D.
, Horowitz
, K.
, and Kurup
, P.
, 2016
, On the Path to SunShot: Emerging Opportunities and Challenges in U.S. Solar Manufacturing
, National Renewable Energy Laboratory
, Golden, CO
.8.
Chen
, H. Q.
, Honda
, T.
, and Yang
, M. C.
, 2013
, “Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels
,” ASME J. Mech. Des.
, 135
(6
), p. 61007
.9.
Gomez
, K.
, Sinitskaya
, E.
, Bao
, Q.
, MacDonald
, E.
, and Yang
, M.
, 2017
, “Designing Linked Journey Maps to Understand the Complexities of the Residential Solar Energy Market
,” accessed Jan. 1, 2019, http://erinmacd.stanford.edu/10.
Wu
, J.
, Botterud
, A.
, Mills
, A.
, Zhou
, Z.
, Hodge
, B. M.
, and Heaney
, M.
, 2015
, “Integrating Solar PV (Photovoltaics) in Utility System Operations: Analytical Framework and Arizona Case Study
,” Energy
, 85
, pp. 1
–9
.11.
Wilkerson
, J. T.
, Cullenward
, D.
, Davidian
, D.
, and Weyant
, J. P.
, 2013
, “End Use Technology Choice in the National Energy Modeling System (NEMS): An Analysis of the Residential and Commercial Building Sectors
,” Energy Econ.
, 40
, pp. 773
–784
.12.
Janko
, S. A.
, Gorman
, B. T.
, Singh
, U. P.
, and Johnson
, N. G.
, 2015
, “High Penetration Residential Solar Photovoltaics and the Effects of Dust Storms on System Net Load
,” ASME
Paper No. DETC2015-48030.13.
Frischknecht
, B. D.
, and Whitefoot
, K. S.
, 2014
, “Market Simulation Based Sensitivity Analysis as a Means to Inform Design Effort as Applied to Photovoltaic Panels
,” ASME J. Mech. Des.
, 136
, p. 54501
.14.
Islam
, T.
, 2014
, “Household Level Innovation Diffusion Model of Photo-Voltaic (PV) Solar Cells From Stated Preference Data
,” Energy Policy
, 65
, pp. 340
–350
.15.
Rai
, V.
, and Henry
, A. D.
, 2016
, “Agent-Based Modelling of Consumer Energy Choices
,” Nat. Clim. Change
, 6
, pp. 556
–562
.16.
Zhang
, H.
, and Vorobeychik
, Y.
, 2017
, “Empirically Grounded Agent-Based Models of Innovation Diffusion: A Critical Review
,” e-print arXiv:1608.08517
https://arxiv.org/abs/1608.0851717.
Nasrinpour
, H. R.
, Friesen
, M. R.
, and (Bob)McLeod, D. R., 2016
, “An Agent-Based Model of Message Propagation in the Facebook Electronic Social Network
,” e-print arXiv: 1611.07454
.https://arxiv.org/abs/1611.0745418.
Tesfatsion
, L.
, 2006
, Handbook of Computational Economics
, Elsevier, Amsterdam, The Netherlands, pp. 831
–880
.19.
Henry
, A. D.
, and Vollan
, B.
, 2014
, “Networks and the Challenge of Sustainable Development
,” Annu. Rev. Environ. Resour.
, 39
(1
), pp. 583
–610
.20.
Zhao
, J.
, Mazhari
, E.
, Celik
, N.
, and Son
, Y.-J.
, 2011
, “Hybrid Agent-Based Simulation for Policy Evaluation of Solar Power Generation Systems
,” Simul. Model. Pr. Th.
, 19
(10
), pp. 2189
–2205
.21.
Robinson
, S. A.
, and Rai
, V.
, 2015
, “Determinants of Spatio-Temporal Patterns of Energy Technology Adoption: An Agent-Based Modeling Approach
,” Appl. Energy
, 151
, pp. 273
–284
.22.
Zhang
, H.
, Vorobeychik
, Y.
, Letchford
, J.
, and Lakkaraju
, K.
, 2015
, “Data-Driven Agent-Based Modeling, With Application to Rooftop Solar Adoption
,” International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
, Istanbul, Turkey, May 4–8, pp. 513
–521
.23.
Palmer
, J.
, Sorda
, G.
, and Madlener
, R.
, 2015
, “Modeling the Diffusion of Residential Photovoltaic Systems in Italy: An Agent-Based Simulation
,” Technol. Forecast. Soc. Change
, 99
, pp. 106
–131
.24.
Mashhadi
, A. R.
, Esmaeilian
, B.
, and Behdad
, S.
, 2016
, “Simulation Modeling of Consumers' Participation in Product Take-Back Systems
,” ASME J. Mech. Des.
, 138
(5
), pp. 51403
–51411
.25.
Fudenberg
, D.
, and Tirole
, J.
, 1991
, Game Theory
, The MIT Press
, Cambridge, MA.26.
International Energy Agency
, 2014
, “Review of Failures Photovoltaic Modules Final
,” International Energy Agency, Paris, France, Report No. IEA-PVPS T13-01.27.
Feldman
, D.
, and Bolinger
, M.
, 2016
, “On the Path to SunShot: Emerging Opportunities and Challenges in Financing Solar
,” National Renewable Energy Laboratory, Golden, CO, Report No. NREL/TP-6A20-65638
.https://www.nrel.gov/docs/fy16osti/65638.pdf28.
NREL Transforming Energy, 2017, “
U.S. Solar Resource Maps
,” National Renewable Energy Laboratory, Golden, CO, accessed Feb. 10, 2017, http://www.nrel.gov/gis/solar.html 29.
Wilson
, C.
, and Dowlatabadi
, H.
, 2007
, “Models of Decision Making and Residential Energy Use
,” Annu. Rev. Environ. Resour.
, 32
(1
), pp. 169
–203
.30.
Sinitskaya
, E.
, and Tesfatsion
, L.
, 2015
, “Macroeconomies as Constructively Rational Games
,” J. Econ. Dyn. Control
, 61
, pp. 152
–182
.31.
Foster
, A. D.
, and Rosenzweig
, M. R.
, 2010
, “Microeconomics of Technology Adoption
,” Annu. Rev. Econom.
, 2
(1
), pp. 395
–424
.32.
Jara-Ettinger
, J.
, Gweon
, H.
, Schulz
, L. E.
, and Tenenbaum
, J. B.
, 2016
, “The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology
,” Trends Cogn. Sci.
, 20
(8
), pp. 589
–604
.33.
Gill
, J.
, 2014
, Bayesian Methods: A Social and Behavioral Sciences Approach
, CRC Press
, Boca Raton, FL.34.
Bubeck
, S.
, and Cesa-Bianchi
, N.
, 2012
, “Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems
,” eprint arXiv: 1204.5721
.https://arxiv.org/abs/1204.572135.
Szepesvári
, C.
, 2010
, “Algorithms for Reinforcement Learning
,” Synth. Lect. Artif. Intell. Mach. Learn.
, 4
(1
), pp. 1
–103
.36.
Meyer
, R. J.
, and Hutchinson
, J. W.
, 2016
, “(When) Are We Dynamically Optimal? A Psychological Field Guide for Marketing Modelers
,” J. Mark.
, 80
(5
), pp. 20
–33
.37.
Mas-Colell
, A.
, Whinston
, M. D.
, and Green
, J. R.
, 1995
, Microeconomic Theory
, Oxford University Press
, New York
.38.
Gelman
, A.
, Carlin
, J. B.
, Stern
, H. S.
, Dunson
, D. B.
, Vehtari
, A.
, and Rubin
, D. B.
, 2014
, Bayesian Data Analysis
, CRC Press
, Boca Raton, FL
.39.
Sigrin
, B.
, Gleason
, M.
, Preus
, R.
, Baring-Gould
, I.
, and Margolis
, R.
, 2016
, “Distributed Generation Market Demand Model (dGen): Documentation
,” National Renewable Energy Laboratory, Golden, CO, Report No. NREL/TP-6A20-65231
.https://www.nrel.gov/docs/fy16osti/65231.pdf40.
Russell, S., and Norvig, P., 2009, Artificial Intelligence: A Modern Approach, 3rd ed., Prentice Hall, Upper Saddle River, NJ, p. 1152
41.
International Energy Agency
, 2014
, “Review of Failures of Photovoltaic Modules Final
,” International Energy Agency, Paris, France, Report No. IEA-PVPS T13-01
.http://iea-pvps.org/fileadmin/dam/intranet/ExCo/IEA-PVPS_T13-01_2014_Review_of_Failures_of_Photovoltaic_Modules_Final.pdf42.
Paulson
, E. J.
, and Starkey
, R. P.
, 2013
, “Development of a Multistage Reliability-Based Design Optimization Method
,” ASME J. Mech. Des.
, 136
(1
), p. 11007
.43.
Smith
, W. F.
, Millisavijevic
, J.
, Sabeghi
, M.
, Allen
, J. K.
, and Mistree
, F.
, 2015
, “The Realization of Engineered Systems With Considerations of Complexity
,”44.
Zhang
, H.
, Vorobeychik
, Y.
, Letchford
, J.
, and Lakkaraju
, K.
, 2016
, “Data-Driven Agent-Based Modeling, With Application to Rooftop Solar Adoption
,” Auton. Agent. Multi. Agent. Syst.
, 30
(6
), pp. 1023
–1049
.45.
Fu
, R.
, Feldman
, D.
, Margolis
, R.
, Woodhouse
, M.
, and Ardani
, K.
, 2017
, U.S. Solar Photovoltaic System Cost Benchmark: Q1 2017
, National Renewable Energy Laboratory
, Golden, CO
.46.
U.S. Energy Information Administration
, 2009
, “Residential Energy Consumption Survey
,” U.S. Energy Information Administration, Washington, DC, accessed Feb. 10, 2017, http://www.eia.gov/consumption/residential/47.
California Energy Commission & California Public Utilities Commission
, 2010
, “California Solar Statistics
,” California Energy Commission & California Public Utilities Commission, Sacramento, CA, accessed May, 24, 2017, https://www.californiasolarstatistics.ca.gov/#48.
Feldman
, D.
, Boff
, D.
, and Margolis
, R.
, 2016
, Q2/Q3 2016 Solar Industry Update
, SunShot, Pimpri-Chinchwad
, India
.49.
Rai
, V.
, Reeves
, D. C.
, and Margolis
, R.
, 2016
, “Overcoming Barriers and Uncertainties in the Adoption of Residential Solar PV
,” Renewable Energy
, 89
, pp. 498
–505
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