GIS-Integrated Agent-Based Modeling of Residential Solar PV Diffusion
Energy Systems transformation
Scott A. Robinson, Matt Stringer, Varun Rai, &
Abhishek Tondon
Motivation
Agent Based Modeling
-> Time
Follow decision rules (functions)
Have memory
Perceive their environment
Are heterogeneous
Are autonomous
Agents:
From: Deffuant, 2002.
Agent Attribute Example: Wealth
PV Adoption by Quartile Average Income by Quartile
Agent Attribute: Wealth
Environment Example: Tree Cover
> 60% Tree cover
< 15% Tree cover
Yes
No
Are there PV owners in my
network?
RA: select one network
connection. Is connection credible?
No further activity
Agent Initialization: Small World Network of n% Locals, 1-n% Non-locals. Assign initial Attitude
Modify SIA. Is SIA >= threshold?
ADOPT
Financially capable? Wealth +
NPV + PP (Control)
Behavioral Model
Attitude becomes socially
informed: SIA
From: Watts, 1998.
Implementation
Focus Test Site: One zip code in Austin, TX
7692 households
146 PV Adopters (1.9%) as of Q2 2012
City of Austin had approx. 1750 PV Adopters
Time Period:Q1 2008 – Q2 2012
Methods: Multiple runs in each batch to allow
for inherent randomness in network initialization and interaction effects
Runs in a batch have identical parameters
Validation: Batches test different parameters against real test site
data.
Temporal Validation
Empirical
Many strong interactions, radial
neighborhoods, 90% local
connections. Adopters are EOHs.
Few weak interactions, no
EOHs
Weak interactions
More non-local connections
Weak interactions, contiguous
neighborhoods
Spatial Validation
Current Work
Agent Class: Installers
-> Time
Summary
ABMs are virtual laboratories
PV diffusion is a complex process with rich interaction effects:
Agent behavior: theory of planned behaviorAgent networks: small world networksAgent interaction: relative agreement algorithm
Multidimensional validation (space and time) allows the robustness of the ABM to be tested against “ground truth” events.
Early testing: Strong, monthly interactions 90% geographic locals.2000ft radial neighborhoodsExisting adopters with low uncertainty in attitude.Low RMSE (3.6), and accurate clustering (1 false
positive).
Q & A
Robinson, S.A., Stringer, M, Rai, V., Tondon, A., "GIS-Integrated Agent-Based Modeling of Residential Solar PV Diffusion,“ USAEE North America Conference Proceedings 2013, Anchorage, AK.
Rai, V. and Robinson, S. A. "Effective Information Channels for Reducing Costs of Environmentally-Friendly Technologies: Evidence from Residential PV Markets," Environmental Research Letters 8(1), 014044, 2013
Rai, V. and Sigrin, B. "Diffusion of Environmentally-friendly Energy Technologies: Buy vs. Lease Differences in Residential PV Markets," Environmental Research Letters , 8(1), 014022, 2013.
Rai, V., and McAndrews, K. “Decision-making and behavior change in residential adopters of solar PV,” World Renewable Energy Forum, 2012, Denver, CO.
Selected References:
Appendix: TPB
• Theory of Reasoned Action• Rational Choice• Continuous opinions, discrete actions
(CODA)• Consumat Framework• Stages of Change• …and many more
Other options:
Energy Systems transformation
From Deffuant et al. 2012.
Appendix: Relative Agreement Algorithm
AE Program Data+ App. Status+ Address+ Date+ System Specs
COA Parcel Data+ Home value+ Address+ Land Use+ Sq. footage
GIS of Parcels+ Coordinates+ DEM+ Geometry+ Tree cover
Financial Model+ Cash flows+ Discount Rates
Appendix: Data Streams
UT Solar Survey+ Sources of Info.+ Decision-making
Agent:•Attitude•Uncertainty•Wealth•Home sq. footage•Age of home•Network•PP•Discount rate
Environment:•Tree Cover•Shade•Electricity Price
Appendix: Model Design
Appendix: Seasonal Effects
Energy Systems transformation
Batch mu EOHs LocalsRelative
AgreementPercent Locals
AUC
mu
2 0.5 No Radial 1x 90% 0.693
10 0.5 Yes Contiguous 4x 90% 0.687
18 0.7 Yes Radial 4x 90% 0.680
19 0.7 Yes Radial 3x 90% 0.686
20 0.5 Yes Radial 3x 90% 0.679
22 0.5 Yes Radial 3x 80% 0.682
Appendix: Key Batch Parameters
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