Life Cycle Assessment of Closed-Loop Pumped Storage Hydropower in the United States.

The United States has begun unprecedented efforts to decarbonize all sectors of the economy by 2050, requiring rapid deployment of variable renewable energy technologies and grid-scale energy storage. Pumped storage hydropower (PSH) is an established technology capable of providing grid-scale energy storage and grid resilience. There is limited information about the life cycle of greenhouse gas emissions associated with state-of-the-industry PSH technologies. The objective of this study is to perform a full life cycle assessment of new closed-loop PSH in the United States and assess the global warming potential (GWP) attributed to 1 kWh of stored electricity delivered to the nearest grid substation connection point. For this study, we use publicly available data from PSH facilities that are in the preliminary permitting phase. The modeling boundary is from facility construction to decommissioning. Our results estimate that the GWP of closed-loop PSH in the United States ranges from 58 to 530 g CO2e kWh-1, with the stored electricity grid mix having the largest impact, followed by concrete used in facility construction. Additionally, PSH site characteristics can have a substantive impact on GWP, with brownfield sites resulting in a 20% lower GWP compared to greenfield sites. Our results suggest that closed-loop PSH offers climate benefits over other energy storage technologies.

Adversarial super-resolution of climatological wind and solar data.

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a [Formula: see text] resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change's Fifth Assessment Report.