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GIS-Integrated Agent-Based Modeling of Residential Solar PV Diffusion

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

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GIS-Integrated Agent-Based Modeling of Residential Solar PV Diffusion

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  1. GIS-Integrated Agent-Based Modeling of Residential Solar PV Diffusion Scott A. Robinson, Matt Stringer, Varun Rai, & Abhishek Tondon Energy Systems transformation

  2. Motivation

  3. Agent Based Modeling -> Time Agents: Follow decision rules (functions) Have memory Perceive their environment Areheterogeneous Are autonomous From: Deffuant, 2002.

  4. Agent Attribute Example: Wealth PV Adoption by Quartile Average Income by Quartile

  5. Agent Attribute: Wealth

  6. Environment Example: Tree Cover > 60% Tree cover < 15% Tree cover

  7. Agent Initialization: Small World Network of n% Locals, 1-n% Non-locals. Assign initial Attitude Behavioral Model No further activity Are there PV owners in my network? From: Watts, 1998. ADOPT Attitudebecomes socially informed: SIA Modify SIA. Is SIA>= threshold? RA: select one network connection. Is connection credible? Financially capable? Wealth + NPV + PP (Control) Yes No

  8. Implementation Focus Test Site: One zip code in Austin, TX 7692 households 146 PV Adopters (1.9%) as of Q2 2012City 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.

  9. 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

  10. Spatial Validation

  11. Current Work -> Time Agent Class: Installers

  12. 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. 2000ft radial neighborhoods Existing adopters with low uncertainty in attitude. Low RMSE (3.6), and accurate clustering (1 false positive).

  13. 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 McAndrews, K. “Decision-making and behavior change in residential adopters of solar PV,” World Renewable Energy Forum, 2012, Denver, CO.

  14. Appendix: TPB Other options: • Theory of Reasoned Action • Rational Choice • Continuous opinions, discrete actions (CODA) • Consumat Framework • Stages of Change • …and many more

  15. Appendix: Relative Agreement Algorithm From Deffuant et al. 2012. Energy Systems transformation

  16. Appendix: Data Streams 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 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

  17. Appendix: Model Design

  18. Appendix: Seasonal Effects

  19. Appendix: Key Batch Parameters Energy Systems transformation

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