In this dissertation, an optimal framework is introduced to find out the best PEV charging/discharging strategy using microgrids that includes all the Distributed Energy Resources present in a typical modern building microgrid.
This project aimed to complement The California Air Resource Board’s on-going work on technology assessment by exploring how the near-term development of natural gas infrastructure, in the heavy-duty transportation sector, can be implemented to include technology that can best facilitate the long-term conversion to near-zero technology.
Cal EPA Headquarters, 1001 I Street, Sacramento, CA
This paper: (1) uses empirical data and conducts descriptive and comparative analysis using a spatial lag model to analyze the factors influencing average cruising time (ACT) related to parking search, and (2) proposes a novel framework to predict grid-based ACT and to estimate average emission metrics (AEM).
This project will improve assessment of both overall probability and short-term forecasts for specific locations in the San Francisco Bay that are vulnerable to flooding associated with sea level rise.
Concrete is also responsible for over 8% of annual anthropogenic greenhouse gas (GHG) emissions globally. As population and urbanization increase and existing infrastructure deteriorates, demand for production of concrete will increase, and with it, the environmental burdens from its production. The models used to determine environmental impacts of producing concrete have considerable uncertainty and variability. This makes it challenging to identify the most effective means of mitigating these burdens.
This project involves expanding prior analysis of the Georgia I-75/I-575 Northwest Corridor Express Lanes by performing an energy use and emission assessment for these Express Lanes against the GP lanes on a per VMT basis. The tools and results from this project will support assessment and public outreach efforts related to expansion of Georgia’s Express Lanes systems.
This project is aimed to develop a deep reinforcement learning based smart charging technology for multiple chargers at the University of California, Riverside.
