• Energy Research
• 2016-2025close
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Abstract Levee systems are an important part of California’s water infrastructure, engineered to provide resilience against flooding and reduce flood losses. The growth in California is partly associated with costly infrastructure developments that led to population expansion in the levee protected areas. Therefore, potential changes in the flood hazard could have significant socioeconomic consequences over levee protected areas, especially in the face of a changing climate. In this study, we examine the possible impacts of a warming climate on flood hazard over levee protected land in California. We use gridded maximum daily runoff from global circulation models (GCMs) that represent a wide range of variability among the climate projections, and are recommended by the California’s Fourth Climate Change Assessment Report, to investigate possible climate-induced changes. We also quantify the exposure of several critical infrastructure protected by the levee systems (e.g. roads, electric power transmission lines, natural gas pipelines, petroleum pipelines, and railroads) to flooding. Our results provide a detailed picture of change in flood risk for different levees and the potential societal consequences (e.g. exposure of people and critical infrastructure). Levee systems in the northern part of the Central Valley and coastal counties of Southern California are likely to observe the highest increase in flood hazard relative to the past. The most evident change is projected for the northern region of the Central Valley, including Butte, Glenn, Yuba, Sutter, Sacramento, and San Joaquin counties. In the leveed regions of these counties, based on the model simulations of the future, the historical 100-year runoff can potentially increase up to threefold under RCP8.5. We argue that levee operation and maintenance along with emergency preparation plans should take into account the changes in frequencies and intensities of flood hazard in a changing climate to ensure safety of levee systems and their protected infrastructure.

In this study, levulinic acid (LA) was produced from rice straw biomass in co-solvent biphasic reactor system consisting of hydrochloric acid and dichloromethane organic solvent. The modified protocol achieved a 15% wt LA yield through the synergistic effect of acid and acidic products (auto-catalysis) and the designed system allowed facile recovery of LA to the organic phase. Further purification of the resulting extractant was achieved through traditional column chromatography, which yielded a high purity LA product while recovering ∼85% wt. Upon charcoal treatment of the resultant fraction generated an industrial grade target molecule of ∼99% purity with ∼95% wt recovery. The system allows the solvent to be easily recovered, in excess of 90%, which was shown to be able to be recycled up to 5 runs without significant loss of final product concentrations. Overall, this system points to a method to significantly reduce manufacturing cost during large-scale LA preparation.

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

As the concern with global warming increases causing the need for CO2 reduction, renewable energy is of great interest as it has lower carbon footprint when compared to conventional sources (natural gas, coal, oil and nuclear). Solar energy has been drawing worldwide attention since it can transform sunlight directly into electricity with the use of photovoltaic (PV) cells. However, this technology has some drawbacks that need to be addressed including dust deposition on solar panels, also known as soiling. Soiling can decrease PV panel's efficiency thereby resulting in less energy production. The soiling rates are very site specific and depend on the geographic location of the panels and the climate in that area. The solar panels can be cleaned naturally (by rainfall, snow or wind) or mechanically washed. This thesis addresses the impact of solar panel soiling and washing on the energy production of solar PV plants located at the UNLV campus. The objectives of this project were (a) to evaluate whether rainfall alone, in the desert environment with low rainfall, is sufficient to clean up the solar panels, and, if possible, determine the minimum amount of rainfall necessary to clean up panels.; (b) to examine the efficiency loss caused by soiling using different methods of analyses and (c) to evaluate if panel washing is worthwhile given the cost and the efficiency gain that is obtained by washing. To calculate the efficiency of the panels, a model was developed to generate parameters that were not measured at the site. Panel efficiencies before and after rainfall events were compared to determine the minimum amount of rain necessary to clean the panels. It was found that at least 0.2 inches of rain was needed to partially restore clean-panel efficiency. In Las Vegas, the recurrence periods of different depths rainfall were calculated using data from the past 29 years. It was observed that the 50th percentile recurrence period of a rainfall event with depth of 0.2 inches or higher was approximately 52 days. Student Union: -0.0044%/day, CBC-C: -0.00099%/day, and Dayton Hall: -0.0034%/day The amount of efficiency lost during the dry intervals (periods between rainfall events) was analyzed in three different ways. The average efficiency loss per day during the dry periods varied from -0.000171 % to -0.00533 %, depending on the method used and the building where the panels were located. However, there were some limitations to the calculations. It was not possible to completely isolate the effects of only soiling on the efficiency of the panels. The rate of decline seemed to be also impacted by seasonal effects. To better evaluate the effect of washing, a professional company was hired to wash a set of solar panels located on UNLV's Student Union building. The panels were washed with water with a low concentration of TDS. The power output and the efficiency of those panels were analyzed from before and after the washing. There was a very small efficiency and power increase due to the washing. Therefore, it was concluded that washing in this area is not worthwhile, and that rainfall events in excess of 0.2 inches can adequately restore the efficiency of the panels. If there is a change in cost of energy, washing, water or a great increase in the efficiency of the solar panels, it would be necessary to reevaluate the analysis.

Abstract. Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities. In this study, river discharge data from the Global Flood Awareness System (GloFAS 2.1) and observed data at 58 gauging stations are used to examine whether positive or negative phases of several Pacific and Atlantic indices significantly alter the characteristics of river flows throughout the Amazon basin (1979–2015). Results show significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative El Niño–Southern Oscillation (ENSO) phases when the sea surface temperature (SST) anomaly is positioned in the central tropical Pacific. This response is not identified for the eastern Pacific index, highlighting how the response can differ between ENSO types. Although flood magnitude and duration were found to be highly correlated, the impacts of large-scale climate variability on these characteristics are non-linear; some increases in annual flood maxima coincide with decreases in flood duration. The impact of flood timing, however, does not follow any notable pattern for all indices analysed. Finally, observed and simulated changes are found to be much more highly correlated for negative ENSO phases compared to the positive phase, meaning that GloFAS struggles to accurately simulate the differences in flood characteristics between El Niño and neutral years. These results have important implications for both the social and physical sectors working towards the improvement of early warning action systems for floods.

AbstractClimate change poses new and unique challenges that threaten lives and livelihoods. Given the increasing risks and looming uncertainty of climate change, increasing attention has been directed towards adaptation, or the strategies that enable humanity to persist and thrive through climate change the best it can. Though climate change is a global problem often discussed at the national scale, urban areas are increasingly seen as having a distinct role, and distinctive motivation and capacity, for adaptation. The 12 articles in this special issue explore ways of understanding and addressing climate change impacts on urban areas. Together they reveal young but rapidly growing scholarship on how to measure, and then overcome, challenges of climate change. Two key themes emerge in this issue: 1) that we must identify and then overcome current barriers to urban adaptation and 2) frameworks/metrics are necessary to identify and track adaptation progress in urban settings. Both of these themes point to the power of indicators and other quantitative information to inform priorities and illuminate the pathway forward for adaptation. As climate change is an entirely new challenge, careful measurement that enables investment by private and public parties is necessary to provide efficient outcomes that benefit the greatest number of people.

Abstract With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.