Publications

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Battery storage systems are increasingly being installed at photovoltaic (PV) sites to address supply-demand balancing needs. Although there is some understanding of costs associated with PV operations and maintenance (O&M), costs associated with emerging technologies such as PV plus storage lack details about the specific systems and/or activities that contribute to the cost values. This study aims to address this gap by exploring the specific factors and drivers contributing to utility-scale PV plus storage systems (UPVS) O&M activities costs, including how technology selection, data collection, and related and ongoing challenges. Specifically, we used semi-structured interviews and questionnaires to collect information and insights from utility-scale owners and operators. Data was collected from 14 semi-structured interviews and questionnaires representing 51.1 MW with 64.1 MWh of installed battery storage capacity within the United States (U.S.). Differences in degradation rate, expected life cycle, and capital costs are observed across different storage technologies. Most O&M activities at UPVS related to correcting under-performance. Fires and venting issues are leading safety concerns, and owner operators have installed additional systems to mitigate these issues. There are ongoing O&M challenges due the lack of storage-specific performance metrics as well as poor vendor reliability and parts availability. Insights from this work will improve our understanding of O&M consideration at PV plus storage sites.

The Grey Zone Test Range (GZTR) social model operates as a piece of the overall GZTR modeling effort. It works in conjunction with supply models for resources, an electric grid model for power availability, and a traffic model for road congestion, as well as a general controller framework that allows external system effects. The social model functions as an aggregate model where the entire population of the city is divided into groups based on the Transportation Analysis Zones (TAZs), a common geospatial boundary present in all GZTR models. These groups will act as a singular community; each time step the state of the system around them will be assessed and then community will come up with a general plan of action that they will attempt to follow for the day. Additionally, they will track values for their general emotional state and memory about negative impacts in the near past.

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Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV.

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A proof-of-concept tool, the Produced Water-Economic, Socio, Environmental Simulation model (PW-ESESim), was developed to support ease of analysis. The tool was designed to facilitate head-to-head comparison of alternative produced water source, treatment, and reuse water management strategies. A graphical user interface (GUI) guides the user through the selection and design of alternative produced water treatment and reuse strategies and the associated health and safety risk and economic benefits. At the highest conceptual level, alternative water strategies include the selection of a source water (locally or regionally available produced water), treatment strategy (pre-treatment, physical, chemical, biological, desalination, and post-treatment processes) and product water purpose (e.g., irrigation, industrial processing, environmental). After selection of these details, the PW-ESESim output a number of key economic, societal, environmental, public/ecological health and safety metrics to support user decision-making; specific examples include, cost of treatment, improvements in freshwater availability, human and ecologic health impacts and growth in local jobs and the economy. Through the simulation of different produced water treatment and management strategies, tradeoffs are identified and used to inform fit-for-purpose produced water treatment and reuse management decisions. While the tool was initially designed using Southeastern New Mexico (Permian Basin) as a case study, the general design of the PW-ESESim model can be extended to support other oil and gas regions of the U.S.

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Drinking water has and will continue to be at the foundation of our nation’s well-being and there is a growing interest in United States (US) drinking water quality. Nearly 30% of the United States population obtained their water from community water systems that did not meet federal regulations in 2019. Given the heavy interactions between society and drinking water quality, this study integrates social constructionism, environmental injustice, and sociohydrological systems to evaluate local awareness of drinking water quality issues. By employing text analytics, we explore potential drivers of regional water quality narratives within 25 local news sources across the United States. Specifically, we assess the relationship between printed local newspapers and water quality violations in communities as well as the influence of social, political, and economic factors on the coverage of drinking water quality issues. Results suggest that the volume and/or frequency of local drinking water violations is not directly reflected in local news coverage. Additionally, news coverage varied across sociodemographic features, with a negative relationship between Hispanic populations and news coverage of Lead and Copper Rule, and a positive relationship among non-Hispanic white populations. These findings extend current understanding of variations in local narratives to consider nuances of water quality issues and indicate opportunities for increasing equity in environmental risk communication.

There has been ever-growing interest and engagement regarding net-zero and carbon neutrality goals, with many nations committing to steep emissions reductions by mid-century. Although water plays critical roles in various sectors, there has been a distinct gap in discussions to date about the role of water in the transition to a carbon neutral future. To address this need, a webinar was convened in April 2022 to gain insights into how water can support or influence active strategies for addressing emissions activities across energy, industrial, and carbon sectors. The webinar presentations and discussions highlighted various nuances of direct and indirect water use both within and across technology sectors (Figure ES-1). For example, hydrogen and concrete production, water for mining, and inland waterways transportation are all heavily influenced by the energy sources used (fossil fuels vs. renewable sources) as well as local resource availabilities. Algal biomass, on the other hand, can be produced across diverse geographies (terrestrial to sea) in a range of source water qualities, including wastewater and could also support pollution remediation through nutrient and metals recovery. Finally, water also influences carbon dynamics and cycling within natural systems across terrestrial, aquatic, and geologic systems. These dynamics underscore not only the critical role of water within the energy-water nexus, but also the extension into the energy-watercarbon nexus.

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Although unique expected energy models can be generated for a given photovoltaic (PV) site, a standardized model is also needed to facilitate performance comparisons across fleets. Current standardized expected energy models for PV work well with sparse data, but they have demonstrated significant over-estimations, which impacts accurate diagnoses of field operations and maintenance issues. This research addresses this issue by using machine learning to develop a data-driven expected energy model that can more accurately generate inferences for energy production of PV systems. Irradiance and system capacity information was used from 172 sites across the United States to train a series of models using Lasso linear regression. The trained models generally perform better than the commonly used expected energy model from international standard (IEC 61724-1), with the two highest performing models ranging in model complexity from a third-order polynomial with 10 parameters (R2adj= 0.994) to a simpler, second-order polynomial with 4 parameters (R2adj= 0.993), the latter of which is subject to further evaluation. Subsequently, the trained models provide a more robust basis for identifying potential energy anomalies for operations and maintenance activities as well as informing planning-related financial assessments. We conclude with directions for future research, such as using splines to improve model continuity and better capture systems with low (≤1000 kW DC) capacity.