The increase in the photovoltaic (PV) generation on distribution grids may cause reverse power flows and challenges such as service voltage violations and transformer overloading. To resolve these issues, utilities need situational awareness, e.g., PV-feeder mapping to identify the potential back-feeding feeders and meter-transformer mapping for transformer overloading. As circuit schematics are outdated, this work relies on data. In cases where the advanced metering infrastructure (AMI) data is unavailable, e.g., analog meters or bandwidth limitation, the dissertation proposes to use feeder measurements from utilities and solar panel measurements from solar companies to identify PV-feeder mapping. Several sequentially improved methods based on quantitative association rule mining (QARM) are proposed, where a lower bound for performance guarantee is also provided. However, binning data in QARM leads to information loss. So, bands are designed to replace bins for increased robustness. For cases where AMI data is available but solar PV data is unavailable, the AMI voltage data and location data are used for situational awareness, i.e., meter-transformer mapping, to resolve voltage violation and transformer overloading. A density-based clustering method is proposed that leverages AMI voltage data and geographical information to efficiently segment utility meters such that the segments comprise meters of few transformers only. Although it is helpful for utilities, it may not directly recover the meter-transformer connectivity, which requires transformer-wise segmentation. The proposed density-based method and other past methods ignore two common scenarios, e.g., having large distance between a meter and parent transformer or high similarity of a meter's consumption pattern to a non-parent transformer's meters. However, going from meter-meter can lead to the parent transformer group meters due to the usual observation that the similarity of intra-cluster meter voltages is usually stronger than the similarity of inter-cluster meter voltages. Therefore, performance guarantee is provided via spectral embedding with voltage data under reasonable assumption. Moreover, the assumption is partially relaxed using location data. It will benefit the utility in many ways, e.g., mitigating voltage violations by transformer tap settings and identifying overloaded transformers.

Climate change is one of the most pressing issues facing humanity, and cities are likely to experience many of the most dangerous effects of climate change. One way that cities aim to adapt to become more resilient to climate change is through the provision of locally produced ecosystem services: the benefits that people get from nature. In cities, these ecosystem services are provided by diverse forms of urban ecological infrastructure (UEI): all parts of a city that include ecological structure and function. While there is a growing body of research touting the multifunctionality of UEI and an increasing number of cities implementing UEI plans, there remain important gaps in understanding how UEI features perform at providing ecosystem services and how the local social-ecological-technological context affects the efficacy of UEI solutions. Inspired by the need for cities to adapt to become more resilient to climate change, this dissertation takes an interdisciplinary approach to understand how diverse UEI features and their ecosystem services are perceived, provided, and prioritized for current and future climate resilience. The second chapter explores how a diverse group of local actors in Valdivia, Chile perceives the city’s urban wetlands and identifies common trade-offs in the perceived importance of different ecosystem services from the wetlands. The third chapter demonstrates species-level differences and trade-offs between common street trees in Phoenix, Arizona in their ability to provide the ecosystem services of both local climate regulation and stormwater regulation. The fourth chapter compares how participatory scenarios from nine cities across the United States and Latin America vary in the degree to which they incorporate UEI and ecosystem services into future visions. The fifth chapter returns focus to Phoenix and illustrates dominant perspectives on the prioritization of ecosystem services for achieving climate resilience and how those priorities change across temporal scales. The dissertation concludes with a synthesis of the previous chapters and suggestions for future urban ecosystem services research. Combined, this dissertation advances understanding of ecosystem services from UEI and highlights the importance of considering trade-offs among UEI features in order help achieve more just, verdant, and resilient urban futures.

Nowadays, the widespread use of distributed generators (DGs) raises significant challenges for the design, planning, and operation of power systems. To avoid the harm caused by excessive DGs, evaluating the reliability and sustainability of the system with high penetration of DGs is essential. The concept of hosting capacity (HC) is used to achieve this purpose. It is to assess the capability of a distribution grid to accommodate DGs without causing damage or updating facilities. To obtain the HC value, traditional HC analysis methods face many problems, including the computational difficulties caused by the large-scale simulations and calculations, lacking the considering temporal correlation from data to data, and the inefficient on real-time analysis. This paper proposes a machine learning-based method, the Spatial-Temporal Long Short-Term Memory (ST-LSTM), to overcome these drawbacks using the traditional HC analysis method. This method will significantly reduce the requirement of calculations and simulations, and obtain HC results in real-time. Using the time-series load profiles and the longest path method, ST-LSTMs can capture the temporal information and spatial information respectively. Moreover, compared with the basic Long Short-Term Memory (LSTM) model, this modified model will improve the performance in the HC analysis by some specific designs, which are the sensitivity gate to consider voltage sensitivity information, the dual forget gates to build spatial and temporal correlation.

Water is the main driver of net primary productivity (NPP) in arid ecosystems, followed by nitrogen and phosphorous. Precipitation is the primary factor in determining water availability to plants, but other factors such as surface rocks could also have an impact. Surface rocks may positively affect water availability by preventing evaporation from soil, but at higher densities, surface rocks may also have a negative impact on water availability by limiting water infiltration or light availability. However, the direct relationship between rock cover and aboveground net primary productivity (ANPP), a proxy for NPP, is not well understood. In this research we explore the relationship between rock cover, ANPP, and soil nutrient availability. We conducted a rock cover survey on long-term fertilized plots at fifteen sites in the Sonoran Desert and used 4 years of data from annual plant biomass surveys to determine the relationship between peak plant biomass and surface rock cover. We performed factorial ANCOVA to assess the relationship among annual plant biomass, surface rocks, precipitation, and fertilization treatment. Overall we found that precipitation, nutrients, and rock cover influence growth of Sonoran Desert annual plants. Rock cover had an overall negative relationship with annual plant biomass, but did not show a consistent pattern of significance over four years of study and with varying average winter precipitation.