The quality of modern life is intrinsically tied to the development and maintenance of infrastructure systems. Modern energy and electricity infrastructure systems have high-reliability requirements, with people expecting power at the flip of a switch. The complex market structure and public-private partnerships at multiple levels in power generation and transmission systems make ensuring high reliability even more difficult. The 21st century brings with it multiple challenges and opportunities within these sectors. A large portion of the infrastructure fleet, like dams and fossil fuel generation plants, is old and needs replacement. Further, the decarbonization of the power sector is poised to result in the inclusion of large amounts of variable renewable energy sources, thereby introducing stochasticity in supply. The research presented in this dissertation seeks to assess and improve energy infrastructure resilience against regional spatiotemporal climate risk in the face of the upcoming decarbonization of the power sector. This dissertation seeks to develop our understanding of climate risk to energy infrastructure systems at a regional level. The analysis will be focused on the identification of organized modes of climate variability that lead to space-time clustering of risk.These investigations are accompanied by specific case studies in the contiguous United States and are applicable to electricity grids and river basins. Overall, I will focus on the ability to simulate and predict extreme climate events which pose reliability and failure risks to energy infrastructure systems. Since such events are rare, I propose methods that establish event excedance probabilities accounting for their underlying uncertainties. In chapter I, I present a novel statistical simulation model that can produce realistic, synthetic realizations of hydroclimatic fields across a large region. This k-nearest neighbor-based space-time simulator can be applied to single or multiple hydroclimatic fields across a large domain. The algorithm facilitates the estimation of the probability of extreme events that are not well represented in relatively short observational records. I apply this algorithm to wind and solar fields across the Texas Interconnection. Many regions plan to integrate more wind and solar generation into the energy grid, increasing power supply variability that can pose risks of under-supply. This simulation tool facilitates the estimation of the probability of regional wind and solar energy “droughts” and hence allows for the estimation of the storage needed to achieve desired supply-side reliability. In chapter II, I present a clustering based variant of the simulator developed in chapter I. I show how the algorithm developed in chapter I is a special case of a general class of algorithms. In Chapter II, I generalize the algorithm by introducing clustering on the neighbor likelihoods, thereby allowing for the identification of sub-regions with different state-space evolution characteristics. This allows for the application of the generalized algorithm to cases with greater heterogeneity, for example, increased temporal resolution. The clustering based k-nearest neighbor space-time simulator was developed to generate synthetic simulations of wind-solar data at an hourly timescale. I present an application of this algorithm to hourly wind-solar data across the Texas Interconnection. The application of this algorithm to estimate the underlying uncertainty and risk faced by power producers in entering short-term power supply contracts is demonstrated. In chapter III, I present a retrospective analysis of inferred energy demand trends across the contiguous United States. Future net zero scenarios generally require replacing all fossil-fuel heating with electric heat, thereby precipitating higher electricity peak loads during winter. Assuming 100% penetration of efficient electric space-heating and cooling, this chapter carries out a spatially explicit trend analysis of temperature-based proxies of electricity demand over the past 70 years. As expected, annual mean heating and cooling demand decreases and increases over most of the contiguous US, respectively. Peak thermal load is generally dominated by heating, showing large inter-annual and decadal variability, thus far not displaying statistically significant decreasing trends. In the south, the peak cooling demand has started to dominate the peak demand, but the possibility of an occasional high peak heating demand can not be discounted. Conversely, in the north, the average thermal loads are declining while the peak thermal loads are not. This points to the need for an improved pre-season forecast of peak winter heating loads. In chapter IV, I present a method for the diagnosis of low-frequency climate variability from multi-site data, which leads to spatiotemporal clustering of flooding risk at a regional level. Disruptions to energy infrastructure systems are often caused due to flooding, and the characterization of climate risk to energy infrastructure due to flooding is explored in this context. The approach is demonstrated using the Ohio River Basin as a case study. I show that the dominant timescales of flood risk within the Ohio River Basin are in the interannual (6-7 years), decadal (11-12 years), and long-term (secular) scales, with different sub-regions responding to different climate forcings. These leading modes are associated with El-Nino Southern Oscillation and secular trends. Further, the secular trend points to an east-to-west shift in flood incidence and changes in the storm track, which are consistent with certain climate change projections. Overall, the results point to the presence of compound climate risk inherent at regional levels, with the low-frequency climate variability translating into periods of increased and decreased flood risk, which all the stakeholders should consider.

Wind energy is a promising alternative energy source due to its environmental, economic, and social benefits and, as such, has garnered public support and government incentives for its development and implementation. With the growing number of wind parks in Oregon, a life cycle assessment (LCA) study for a representative new wind park is needed to investigate the potential impacts on the environment. One of the major drawbacks of wind energy generation is its variability due to the stochastic nature of wind. To make wind energy a more reliable source, wind energy generation should be supplemented with controllable energy generation or storage. Thus, the aim of this research is to improve the understanding of the effects of supplemental energy systems on the environmental impacts of wind energy systems. First, the environmental impact of a single wind turbine is examined from raw material extraction to the end of life stage. Research needs are identified to support the assessment of the environmental impacts of wind energy and supplemental energy systems from a life cycle perspective. Next, supplemental electricity generation systems investigated are biomass, hydro, and natural gas electricity generation, and zinc-bromine battery storage. Finally, the results for each system are compared to coal energy generation. It appears that the wind park has lower environmental impact than coal energy generation when paired with any of the complimentary systems investigated. Overall, hydropower appears to be the best option to supplement wind power from an environmental perspective for a potential wind park site in northern Oregon. / Graduation date: 2013

Capes / Os indicadores energéticos são instrumentos de apoio a processos decisórios, sobre energia, e com a crescente discussão sobre desenvolvimento sustentável estes instrumentos passaram a incorporar informações socioambientais, além dos tradicionais fatores econômicos. Sendo assim, na busca pelo desenvolvimento sustentável, torna-se relevante conhecer quais são as contribuições e limitações destes instrumentos. Com este fim, o objetivo geral é analisar as contribuições e limitações dos indicadores energéticos como instrumentos de apoio ao desenvolvimento sustentável. Esta pesquisa é classificada como descritiva, utilizando levantamento bibliográfico e documental. Como resultado da análise documental foram selecionados 55 indicadores energéticos para o desenvolvimento sustentável (Energy Indicator Sustainable Development – EISD), sendo estes identificados a partir das instituições International Atomic Energy Agency (IAEA), Helio International e World Energy Council (WEC), dentre 19 instituições ligadas à pesquisa sobre energia identificadas na pesquisa. Durante a análise, percebeu-se que a maioria dos indicadores selecionados, 19 EISDs (34,54%), se concentra na dimensão econômica, seguidos de 10 EISDs (18,18%) na dimensão ambiental, 9 EISDs (16,36%) na dimensão social, 7 EISDs (12,45%) são classificados em resiliência, 4 EISDs (7,27%) em governança, 3 EISDs (5,45%) em vulnerabilidade e 3 EISDs (5,45%) em política. Apesar da inclusão de indicadores ligados a outras dimensões, além da econômica, a qualidade da informação gerada pelos indicadores surge como uma limitação destes, pois, identificou-se que, em casos recorrentes, as informações geradas pelos EISDs podem ser interpretadas tanto de forma a favorecer o desenvolvimento sustentável quanto a levar a ações opostas a este objetivo. Ainda, foram identificados EISDs cujos componentes não foram especificados, o que pode possibilitar a geração de informações afastadas do cenário real, caso sejam utilizados componentes que não possuem relação com o EISD, ou mesmo a não utilização de componentes relevantes. Ainda assim, apesar das limitações, a existência de conjuntos de EISDs para auxiliar os tomadores de decisão é um fato que contribui na busca por desenvolvimento sustentável, e que deve ser aprimorado, pois a disponibilidade de informações envolvendo questões socioambientais, como emissão de poluentes atmosféricos, de solo e de água, resultantes de fontes energética, possibilita identificar quais fontes são mais, ou menos, prejudiciais ao desenvolvimento sustentável. Contudo, a dificuldade na coleta de dados, na identificação dos componentes para o cálculo de cada indicador e mesmo na interpretação deste, como destacado, pode não só deixar de contribuir com o desenvolvimento sustentável, como pode protelar a tomada de decisões corretivas ou preventivas. / Energy indicators are tools to support decision-making on energy. The growing debate on sustainable development, contributed to the energy indicators began to incorporate, besides the traditional economic, social and environmental information. Therefore, taking sustainable development into account, it is important to know contributions and limitations of these tools. The overall goal of this study is to analyze the contributions and limitations of the energy indicators as assets to support sustainable development.This study can be classified as descriptive because it relies on bibliographical and documental material. As a result of documental analysis, 55 energy indicators for sustainable development (EISD) were selected. The selection took place by identification of those indicators through the institutions International Atomic Energy Agency (IAEA), Helio International and World Energy Council (WEC), among 19 institutions involved in research on energy identified in the survey. The study stresses that most of the selected indicators focuses on the economic dimension, 19 EISDs (34.54%), followed by 10 EISDs (18.18%) focused on the environmental dimension, 9 EISDs (16.36%) focused on the social issues, 7 EISDs (12.45%) are classified as resilience, 4 EISDs (7.27%) is about governance, 3 EISDs (5.45%) focused on vulnerability and 3 EISDs (5.45%) is about policy. Despite the inclusion of indicators associated with other dimensions than economy, information provided by those indicators emerges as their own limitation. Because, recently, indicators’ information were used to promote sustainable development as well as the opposite. Additionally, the study identified EISDs whose components were not specified. They may enable generation of information far from the real scenario, if components dissociated EISD would be taking into consideration or even the non-consideration of relevant components. Despite limitations, EISDs assisting decision-makers contributes to the pursuit of sustainable development. But they may be improved through information about environmental issues, such as emission of atmospheric pollutants, soil and water, resulting from energy sources, helps identifying which sources are more or less harmful for sustainable development. However, difficulty in collecting data, identifying the components for calculation of each indicator and even interpretation of this, as analyzed, may not only fail to contribute to sustainable development, as can delay taking corrective or preventive decisions.