The solar energy consumer agent decision (SECAD) model : addressing complexity through GIS-integrated agent-based modeling
| dc.contributor.advisor | Rai, Varun | en |
| dc.contributor.committeeMember | Arima, Eugenio | en |
| dc.contributor.committeeMember | Zarnikau, Jay | en |
| dc.creator | Robinson, Scott Austen | en |
| dc.date.accessioned | 2015-11-17T21:08:00Z | en |
| dc.date.accessioned | 2018-01-22T22:29:13Z | |
| dc.date.available | 2015-11-17T21:08:00Z | en |
| dc.date.available | 2018-01-22T22:29:13Z | |
| dc.date.issued | 2014-05 | en |
| dc.date.submitted | May 2014 | en |
| dc.date.updated | 2015-11-17T21:08:00Z | en |
| dc.description | text | en |
| dc.description.abstract | This thesis presents a step-by-step implementation of the Solar Energy Consumer Agent Decision (SECAD) model: an empirically-grounded multi-agent model of residential solar photovoltaic (PV) adoption with an integrated geospatial topology. Solar PV diffusion is a complex system with geographic heterogeneity, uncertain information, high financial risk, and important social interaction and feedback effects between consumers. A key limitation for agentbased models in human socio-technical systems is the integration of empirical patterns in the model structure, initialization, and validation efforts. This limitation is addressed though highly granular and interlocking data-streams from the geographic, social network, financial, demographic, and decision-making process of real households in the study. The fitted and validation model is used to simulate implementation of potential policies to inform decision-makers: i) Targeted informational dissemination campaigns, ii) Tiered rebates, iii) Locational pricing, and iv) Alternative rebate schedules. Informational campaigns can increase cumulative installations by as much as 12%, but vary greatly in their effectiveness based on which agents are targeted. Simulations suggest that by lowering the cost barrier to lower wealth households through a slightly higher rebate (+$0.25/Watt), the mean difference in wealth between solar adopters and non adopters could be reduced by 22.6%. Locational pricing can allow the utility more control over diffusion patterns with regard to load pockets--a $0.25 higher offering increased the percentage of adopters in the target area from less than 1% to over 10%. Relative to flatter rebate schedules, sharply decreasing schedules are effective in terms of motivating adoption but inefficient in small markets. It is our hope that this work will provide a working example for other agent-based models of human socio-technical systems as well as provide insight into the likely outcomes of novel policy-levers such as those described above. | en |
| dc.description.department | Energy and Earth Resources | en |
| dc.description.department | Public Affairs | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier | doi:10.15781/T28H0Q | en |
| dc.identifier.uri | http://hdl.handle.net/2152/32555 | en |
| dc.language.iso | en | en |
| dc.subject | Agent-based modelling | en |
| dc.subject | Solar | en |
| dc.subject | Diffusion | en |
| dc.subject | Innovation | en |
| dc.subject | Electricity | en |
| dc.subject | Energy | en |
| dc.subject | Decision | en |
| dc.subject | Economics | en |
| dc.subject | Policy | en |
| dc.subject | Simulation | en |
| dc.title | The solar energy consumer agent decision (SECAD) model : addressing complexity through GIS-integrated agent-based modeling | en |
| dc.type | Thesis | en |