Utilizing Highway Rest Areas for Electric Vehicle Charging: Economics and Impacts on Renewable Energy Penetration in California

California policy is incentivizing rapid adoption of zero emission electric vehicles for light-duty and freight applications. This project explored how locating charging facilities at California’s highway rest stops might impact electricity demand, grid operation, and integration of renewables like solar and wind into California’s energy mix. Assuming a growing population of electric vehicles to meet state goals, state-wide growth of electricity demand was estimated, and the most attractive rest stop locations for siting chargers identified. Using a California-specific electricity dispatch model developed at UC Davis, the project estimated how charging vehicles at these stations would impact renewable energy curtailment in California. It estimated the impacts of charging infrastructures on California’s electricity system and how they can be utilized to decrease the duck curve effect resulting from a large amount of solar energy penetration by 2050.View the NCST Project Webpag

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19’s spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications

California Hydrogen Infrastructure and ZEV Adoption Towards a Carbon Free Grid in 2045

The transportation sector is a major source of California’s greenhouse gas emissions, contributing 41% of the state total[1]. California policy is moving rapidly toward Zero Emission battery electric vehicles (BEV) and hydrogen fuel cell vehicles (FCV). Governor Newsom has issued an executive order that all new in-state sales of passenger vehicles should be Zero Emission Vehicles (ZEV) by 2035. Further, the California Air Resources Board has approved rulemaking requiring that more than half of trucks sold in the state must be zero-emissions by 2035, and all of them by 2045 [1a].California has the ambitious goal of achieving a 60% renewable electricity grid by 2030 and 100% carbon free grid by 2045. High penetration of variable renewable energy (VRE) requires seasonal storage to match supply and demand and hydrogen could be a possible candidate for this purpose [1b]. The author has developed the CALZEEV energy-economic model to study possible roles for hydrogen in a VRE intensive future grid with a large Zero Emission Vehicle fleet, comprised of both BEVs and FCVs. In particular, we study whether we can provide sufficient seasonal storage for a 100% zero carbon electricity grid and the potential role of H2 infrastructure in a BEV/FCEV combination for a sustainable path towards a zero-emission energy system. The role of hydrogen infrastructure in seasonal storage for balancing VRE generation while meeting demand for hydrogen vehicles year around has been studied, including economic impacts

A system dynamic model for production and consumption policy in Iran oil and gas sector

A system dynamic model is presented, which considers the feedback between supply and demand and oil revenue of the existing system in Iran considering different sectors of the economy. Also the export of the oil surplus and the injection of the gas surplus into the oil reservoirs are seen in the model by establishing a balance between supply and demand. In this model the counter-effects and existing system feedbacks between supply and demand and oil revenue can be seen considering different sectors of the economy. As a result, the effects of oil and gas policies in different scenarios for different sectors of Iran's economy together with the counter-effects of energy consumption and oil revenue are examined. Three scenarios, which show the worst, base and ideal cases, are considered to find future trends of major variables such as seasonal gas consumption in power plants, seasonal injected gas in oil reservoirs, economic growth in the industrial sector, oil consumption in the transportation sector, industrial gas consumption and exported gas. For example, it is shown that the exported gas will reach between 500 and 620 million cubic-meter per day in different scenarios and export revenues can reach up to $500 billion by 2025.System dynamics model Oil and gas sector Policy implications

Future Electric Vehicle Charging Demand at Highway Rest Areas and Implications for Renewable Energy Penetration in California

California has goals to rapidly expand electric vehicle adoption, with executive orders calling for 1.5 million electric vehicles on the roads by 2025 and 5 million by 2030. Significant charging infrastructure will be needed to support these new vehicles. While many urban areas in California have prioritized construction of charging stations, most rural areas lack charging infrastructure. This deficit hinders electric vehicle adoption in rural areas and makes long distance electric vehicle travel difficult.To address this issue, Caltrans has begun investing in charging infrastructure in rural and underserved areas around the state, particularly at highway rest areas. However, an understanding of potential future intercity charging demand will be needed to inform continued investments in support of a growing electric vehicle fleet.This policy brief summarizes findings from researchers at the University of California, Davis, who collected state travel data and electricity demand data to run a model that identified optimal highway rest areas for electric vehicle charger installation and calculated how an increase in charging demand would affect the California electricity grid at selected highway locations. The project aimed to maximize the use and generation of solar and wind energy, while also increasing electric vehicle adoption and mobility in the state.View the NCST Project Webpag

Future Electric Vehicle Charging Demand at Highway Rest Areas and Implications for Renewable Energy Penetration in California

California has goals to rapidly expand electric vehicle adoption, with executive orders calling for 1.5 million electric vehicles on the roads by 2025 and 5 million by 2030. Significant charging infrastructure will be needed to support these new vehicles. While many urban areas in California have prioritized construction of charging stations, most rural areas lack charging infrastructure. This deficit hinders electric vehicle adoption in rural areas and makes long distance electric vehicle travel difficult.To address this issue, Caltrans has begun investing in charging infrastructure in rural and underserved areas around the state, particularly at highway rest areas. However, an understanding of potential future intercity charging demand will be needed to inform continued investments in support of a growing electric vehicle fleet.This policy brief summarizes findings from researchers at the University of California, Davis, who collected state travel data and electricity demand data to run a model that identified optimal highway rest areas for electric vehicle charger installation and calculated how an increase in charging demand would affect the California electricity grid at selected highway locations. The project aimed to maximize the use and generation of solar and wind energy, while also increasing electric vehicle adoption and mobility in the state.View the NCST Project Webpag