GIS-Integrated Agent-Based Modeling of Residential Solar PV Diffusion Scott A. Robinson, Matt Stringer, Varun Rai, & Abhishek Tondon Energy Systems transformation
Motivation
Agent Based Modeling -> Time Agents: Follow decision rules (functions) Have memory Perceive their environment Are heterogeneous Are autonomous 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
Behavioral Model Agent Initialization: Small World Network of n% Locals, 1 -n% Non-locals. Assign initial Attitude From: Watts, 1998 Are there PV owners in my network? . Yes No further activity No Attitude becomes socially informed: SIA ADOPT RA: select one network connection. Is connection credible? Financially capable? Wealth + NPV + PP (Control) Modify SIA. Is SIA >= threshold?
Implementation Focus Test Site: One zip code in Austin, TX 7692 households 146 PV Adopters (1. 9%) as of Q 2 2012 City of Austin had approx. 1750 PV Adopters Time Period: Q 1 2008 – Q 2 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. Weak interactions, contiguous neighborhoods More non-local connections Weak interactions Few weak interactions, no EOHs
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 behavior Agent networks: small world networks Agent 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. 2000 ft radial neighborhoods Existing adopters with low uncertainty in attitude. Low RMSE (3. 6), and accurate clustering (1 false positive).
Q&A Selected References: 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 Mc. Andrews, K. “Decision-making and behavior change in residential adopters of solar PV, ” World Renewable Energy Forum, 2012, Denver, CO.
Appendix: TPB Other options: • • • Theory of Reasoned Action Rational Choice Continuous opinions, discrete actions (CODA) Consumat Framework Stages of Change …and many more
Appendix: Relative Agreement Algorithm From Deffuant et al. 2012. Energy Systems transformation
Appendix: Data Streams AE Program Data + App. Status + Address + Date + System Specs Financial Model + Cash flows + Discount Rates COA Parcel Data + Home value + Address + Land Use + Sq. footage GIS of Parcels + Coordinates + DEM + Geometry + Tree cover 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
Appendix: Key Batch Parameters Batch mu mu EOHs Relative Agreement Locals Percent Locals AUC 2 0. 5 No Radial 1 x 90% 0. 693 10 0. 5 Yes Contiguous 4 x 90% 0. 687 18 0. 7 Yes Radial 4 x 90% 0. 680 19 0. 7 Yes Radial 3 x 90% 0. 686 20 0. 5 Yes Radial 3 x 90% 0. 679 22 0. 5 Yes Radial 3 x 80% 0. 682 Energy Systems transformation
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