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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.

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.

Aggregate and limestone mining in San Antonio’s Bexar and Comal counties in Texas, USA, has caused considerable health concerns as of late. Aggregate mining actions can result in localized air quality issues in any neighborhood. Furthermore, heavy truck traffic, hauling, and transportation of the mined material contribute to pollution. In this research, PM species were sampled at four locations north of the San Antonio city limits. The data were collected using a TSI Air Quality Sampler that sampled PM1, PM2.5, PM4, PM10, wind speed, wind direction, temperature, and relative humidity. Continuous data with 1 min averages were recorded during the study period from August to September 2019. The instrument was stationed at every location for a period of 7 days each. The four locations were a ranch, an open field, a residential compound, and an elementary school. PM1 and PM2.5 concentration levels were lower compared to PM10 concentrations at all four studied sites. Our results suggest that PM concentrations are primarily impacted by mining activities. PM species were highest at the residential compound due to its proximity to an active mining area, resulting in deleterious health effects for neighbors living in the vicinity of the sampled site.

Abstract For 118 million residential housing units in the U.S., there is currently a gap between the potential energy savings that can be achieved through the use of existing energy efficiency technologies, and the actual level of energy savings realized, particularly for the 37% of housing units that are considered residential rental properties. Additional quantifiable benefits are needed beyond energy savings to help further motivate residential property owners to invest in energy efficiency upgrades. This research focuses on assessing the adoption of energy efficient upgrades in U.S. residential housing and the impact on rental prices. Ten U.S. cities are chosen for analysis; these cities vary in size across multiple climate zones, and represent a diverse set of housing market conditions. Data was collected for over 159,000 rental property listings, their characteristics, and their energy efficiency measures listed in rental housing postings across each city. Following an extensive data quality control process, over thirty different types energy efficient features were identified. The level of adoption was determined for each city, ranging from 5.3% to 21.6%. Efficient lighting and appliances were among the most common, with many features doubling as energy efficient and other desirable aesthetic or comfort improvements. Then using propensity score matching and conditional mean comparison methods, the relative impact on rent charged in each city was calculated, which ranged from a 6% to 14.1% increase in rent for properties with energy efficient features, demonstrating a positive economic impact of these features, particularly for property owners. This was further subdivided into five types of energy efficiency upgrade and three housing types. Single family homes generally demanded higher premiums with energy efficient features, however there was not a consistent pattern across the types of efficient upgrades. The results of this work demonstrate that investment in energy efficient technologies has quantifiable benefits for rental property owners in the U.S. beyond just energy savings. This methodology and results can also be used in other cities and by property owners, utility companies, or others, ultimately encouraging further investment and positive economic impact in residential energy efficiency and in turn improving energy and resource conservation in the building sector.

Sustainability of agro-food supply chains has recently become the subject of greater interest from consumers, firms, governmental organizations and academia as the environment continues to deteriorate. One of the most critical factors influencing the sustainability of an agro-food supply chain is its network design. A particularly challenging aspect in this context is the broad range of influencing indicators associated with the Triple Bottom Line (TBL) of sustainability that need to be considered. However, many of these indicators could not be fully integrated or measured by single-step optimization problems. This paper presents a critical literature review of operational research methods for the design of sustainable supply chains. A novel two-stage hybrid solution methodology is proposed. In the first stage, a partner selection is performed using a hybrid multi criteria decision making based on Analytic Hierarchy Process (AHP) method and the Ordered Weighted Averaging (OWA) aggregation method. The result obtained in the first stage is used in the second stage to develop a multi-objective mathematical model to optimize the design of the supply chain network. This approach allows the simultaneous consideration of all three dimensions of sustainability including carbon footprint, water footprint, number of jobs created and the total cost of the supply chain design. The proposed approach generates a Pareto frontier to aid users in making decisions. Numerical experiments are completed utilizing data from an agro-food company to demonstrate the efficiency and effectiveness of the proposed solution methodology. The analyzes of the numerical results provide important organizational, practical and policy insights on (1) the impact of financial and environmental sustainability on supply chain network design (2) the tradeoff analysis between environmental emission, water footprint, societal implications and associated cost for making informed decision on supply chain investment.

The Houston-Dallas (I-45) corridor is the busiest route among 18 traffic corridors in Texas, USA. The expected population growth and the surge in passenger mobility may result in a significant impact on the regional environment. This study uses a life cycle framework to predict and evaluate the net changes of environmental impact associated with the potential development of a high-speed rail (HSR) System along the I-45 corridor through its life cycle. The environmental impact is estimated in terms of CO2 and greenhouse gas (GHG) emissions per vehicle/passenger-kilometers traveled (V/PKT) using life cycle assessment. The analyses are performed referring to the Ecoinvent 3.4 inventory database through the phases: material extraction and processing, infrastructure construction, vehicle manufacturing, system operation, and end of life. The environmental benefit is evaluated by comparing the potential development of the HSR system with those of the existing transportation systems. The vehicle component, especially operation and maintenance of vehicles, is the primary contributor to the total global warming potential with about 93% of the life cycle GHG emissions. For the infrastructure component, 56.76% of GHG emissions result from the material extraction and processing phase (23.75 kgCO2eq/VKT). Various life cycle emissions of HSR except PM are significantly lower than for passenger cars.