The complete schedule for the semester is online at

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Webviewing at UC Davis: Academic Surge 2050

Webviewing at UC Merced: SE1 100

Webviewing at UC Santa Cruz: SOE E2 Building, Room 506

Abstract:

The transition to a more robust and sustainable energy system faces enormous challenges and requires effective strategies, policies, and planning for future expansion of renewable technologies and resources. Opportunities exist to improve overall sustainability and optimize system performance in redesigning energy infrastructure to accommodate greater use of renewables.

For example, in attempting to meet the federal renewable fuel standard (RFS), an essentially new sustainable biorefining industry must be created to supply the required amounts of biofuels. To better understand how such an industry might be designed, a geospatial bioenergy systems model (GBSM) has been developed to investigate the full fuel supply chain optimization. The GBSM combines geographic information system (GIS) models with optimization algorithms to identify potential preferred sites, resource demand, technology types, and facility capacities.

The GBSM has been applied to a variety of system analyses at the state, regional, and national levels including scenario analysis using the federal Billion Ton study results as well as independent assessments of feedstock supplies. The model optimizes across the entire supply chain from biomass production to delivery of finished product into final demand. The model is also capable of estimating greenhouse gas and other sustainability effects to evaluate potential system level carbon and other environmental impacts.

The GBSM is also being integrated with other models such as the bioenergy crop adoption model (BCAM) being developed at UC Davis to better address higher resolution effects at the farm level. Models of this type address a continuing need for spatially explicit assessments of likely impacts from plans and policies relating to sustainable energy development.