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

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Figure 3: From Watts (2004) [44]. Three conceptions of network connections between nodes. Small worldnetworks are shown in the center.2.3 Size and Scope of the ModelAustin has the highest number of PV installations in Texas, lending greater statistical power to thevalidation of the ABM. Despite this, adoption rates are far below the potential market, which is roughlyestimated to be 25% of residential households [46]. From spring 2005 to summer 2012, 1,750 of the170,000 households in the City of Austin area have installed PV systems, representing a compoundannual growth rate (CAGR) of 35.6%. Even though adoption is increasing, total market penetration isonly 0.9% of all residential households.As the primary goal of this paper is to lay out the development of the integrated modeling framework, forthe empirical tests reported in this paper we chose to keep the spatial scale limited, while still allowingfor a high granularity of agent networks. Specifically, we focused on a single zip code within the AustinEnergy Service Area. This subsample involves the interaction of nearly 7,700 agents (households) out ofthe more than 170,000 households in Austin.2.4 Model StructureThe model was initialized with empirical household-level data from the Austin Energy Solar Rebateprogram from January 1, 2005 to the program data midpoint–January 1, 2008. Then the model resultstest runs from the midpoint to the 2nd quarter 2012 were compared against empirical results.Data streams 1-4 (see Section 3 for details) were incorporated into the ABM according to the modeldiagram in figure B.1. The figure also shows the function hierarchy and relevant variables at the modeland agent level. Broadly, the intention is driven by the relative agreement function within small worldnetworks. After reaching intention to install, adopters will assess payback and wealth to determine theadoption decision.2.5 InitializationThe ABM design is shown in figure B.1. The model was initialized to reflect the “ground truth” asof 1/1/2008 as closely as possible. Small world networks were created for each agent by dividing allhouseholds into neighbor and non-neighbor sets according to their actual location in the study area. 95%6
Figure 4: Flowchart of an agent’s decision process. following the framework from the Theory of PlannedBehavior. The intention threshold is set at 0.667. Physical compatibility is determined randomly, with aprobability of 0.75.1 Financial capability is determined by market value of the house, a proxy for wealth(see text for details).of network contacts were drawn from the neighbors set, and 5% were drawn randomly from the nonneighborsset. Because we did not have comprehensive information about opinion levels regarding solaramong non-adopters, opinion between -1 and 1 and uncertainty between 0 and 2 (x, u, in equation 1) areassigned according to a normal distribution. PV adopters were initialized in the model to reflect actualadoption status on an individual basis as of 1/1/2008. Each household was assigned a threshold paybackperiod, drawn randomly from a distribution of payback periods generated by the “Very optimistic(Scenario 5)” of the financial model described in Rai and Sigrin (2013). This scenario was described bythe authors as the best estimate of PV “buyer” decision-making [37].2.6 Model Quarterly CycleIn the model, each cycle is equivalent to one fiscal quarter, in which each agent follows the process shownin figure 4. Once the household becomes an adopter, x and u are fixed at 1, and 0.001 respectively torepresent the very high general satisfaction PV owners report with their systems [35]. Agent interactionsare represented by small world networks (see Section 2.2). The local set is created as a function of spatialcontiguity or distance, which is calculated in ArcGIS R○ . Household intention to adopt PV is a dynamicvariable, altered by the relative agreement function within the agent’s small world network. In orderto assess “perceived control” construct of TPB, a household compares the empirical payback for thequarter to the household’s payback threshold.Solar PV adopters’ home values are consistently above the aggregate median home value ( $190,000);7
Page 3: Figure 1: Two ABMs used in this stu
Page 9 and 10: adopters for each cell, and S is th
Page 11 and 12: Batch 2 distributed uncertainty (u)
Page 13 and 14: Figure 6: Mapping hot-spots in the
Page 15 and 16: [12] A.T. Crooks and A.J. Heppensta
Page 17 and 18: [43] D. Watts and S. Strogatz. Coll
Page 19: CHot-Spot Mapping of Austin, TXFigu

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

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