Hybrid renewable energy systems: the value of storage as a function of PV-wind variability

As shares of variable renewable energy (VRE) on the electric grid increase, sources of grid flexibility will become increasingly important for maintaining the reliability and affordability of electricity supply. Lithium-ion battery energy storage has been identified as an important and cost-effective source of flexibility, both by itself and when coupled with VRE technologies like solar photovoltaics (PV) and wind. In this study, we explored the current and future value of utility-scale hybrid energy systems comprising PV, wind, and lithium-ion battery technologies (PV-wind-battery systems). Using a price-taker model with simulated hourly energy and capacity prices, we simulated the revenue-maximizing dispatch of a range of PV-wind-battery configurations across Texas, from the present through 2050. Holding PV capacity and point-of-interconnection capacity constant, we modeled configurations with varying wind-to-PV capacity ratios and battery-to-PV capacity ratios. We found that coupling PV, wind, and battery technologies allows for more effective utilization of interconnection capacity by increasing capacity factors to 60%–80%+ and capacity credits to close to 100%, depending on battery capacity. We also compared the energy and capacity values of PV-wind and PV-wind-battery systems to the corresponding stability coefficient metric, which describes the location-and configuration-specific complementarity of PV and wind resources. Our results show that the stability coefficient effectively predicts the configuration-location combinations in which a smaller battery component can provide comparable economic performance in a PV-wind-battery system (compared to a PV-battery system). These PV-wind-battery hybrids can help integrate more VRE by providing smoother, more predictable generation and greater flexibility

Vulnerability of cold-water and cool-water fishes to climate change within an anthropogenic context using boosted regression trees, decision scaling, and ecosystem services

Includes bibliographical references.2016 Fall.Across the globe, environmental changes are occurring in ways that are profoundly important for freshwater ecosystems with implications for the occurrence of species. Typically, ecologists have sought to understand the distribution of freshwater species using natural environmental gradients. However, because rivers and streams embody a wide range of conditions due to human activity, adequately characterizing modern day drivers of species occurrence requires assessing both natural and anthropogenic influences within the context of global change. In recent decades, growing concerns over climate change have further contributed to the need to assess contemporary drivers of species occurrence. Despite this urgency, forecasting ecological responses to climate change remains a key conservation challenge. The aims of my research were to: a) investigate the drivers of western US riverine fish species occurrence within the context of global change; and b) project range-wide and site-level vulnerability of cold-water fish species to climate-induced changes in stream temperature and streamflow and to alternative land use trajectories. In my assessment of contemporary drivers of cold-water and cool-water fish species distribution, I found that primary determinants of fish occurrence included human influences that accounted for a substantial portion of modeled outcomes among species. Sedimentation and nutrient enrichment were the two primary disturbance pathways by which human activities influence aspects of stream condition that drive patterns of species occurrence. I also found that species had variable responses across anthropogenic gradients, suggesting that future efforts to characterize species-environment relations consider approaches that can capture nonlinear and threshold responses that occur along continuous gradients. In a second analysis, I evaluated the range-wide vulnerability of cold-water fish species to projected climate change in the western United States and assessed site-level vulnerability to varying degrees of exposure to climate change and additional environmental stressors. I focused on rainbow trout (Oncorhynchus mykiss sp.) and cutthroat trout (Oncorhynchus clarkii sp.) -- two wide-ranging salmonids of significant conservation and economic importance. Using high resolution data on future stream temperature and mean annual flow, I projected climate-induced changes in suitable habitat across the historic native ranges of both species within the western United States. Projected declines in suitable habitat for cutthroat trout were substantial by 2080 and exceeded those of rainbow trout. A sensitivity analysis revealed that stream temperature warming was the primary driver of habitat loss for both species. Both cutthroat trout and rainbow trout exhibited regional variability in habitat loss that was consistent with the magnitude of projected warming for summer stream temperature. Cutthroat trout distributions are expected to shift upwards along an elevational gradient with warming causing fragmentation of contiguous habitat that will likely expose them to additional environmental disturbances. I conducted a complementary set of analyses using a decision-scaling approach to explore site-level vulnerability as a function of feasible climate futures and human-influenced environmental factors that have previously been implicated as key components of suitable habitat for cutthroat and rainbow trout. I uncovered important insights into species vulnerability including differential sensitivity to stream temperature warming among cutthroat trout and rainbow trout as well as predominant influences of land use on species vulnerability independent of climate. Under a hypothetical climate adaptation scenario, I found that increased riparian cover shifted the distribution of vulnerability of cutthroat trout towards less frequent extirpations and that these benefits were achieved throughout feasible climate space. My findings suggest that augmentation of riparian vegetation is likely to be a robust climate adaptation strategy in an uncertain future. I conclude by offering two complementary approaches for advancing climate adaptation for freshwater systems in the face of uncertainty. I also conducted a systematic review of hydrologic ecosystem services (HES) studies published within the past decade, finding compelling evidence that variability in methods used to quantify HES reflects an orientation towards decision making. I discuss implications of my findings on climate change vulnerability and consider ways to integrate an ecosystem services approach into the management and conservation of freshwater fish

Spatially-Explicit Prediction of Capacity Density Advances Geographic Characterization of Wind Power Technical Potential

Mounting interest in ambitious clean energy goals is exposing critical gaps in our understanding of onshore wind power potential. Conventional approaches to evaluating wind power technical potential at the national scale rely on coarse geographic representations of land area requirements for wind power. These methods overlook sizable spatial variation in real-world capacity densities (i.e., nameplate power capacity per unit area) and assume that potential installation densities are uniform across space. Here, we propose a data-driven approach to overcome persistent challenges in characterizing localized deployment potentials over broad extents. We use machine learning to develop predictive relationships between observed capacity densities and geospatial variables. The model is validated against a comprehensive data set of United States (U.S.) wind facilities and subjected to interrogation techniques to reveal that key explanatory features behind geographic variation of capacity density are related to wind resource as well as urban accessibility and forest cover. We demonstrate application of the model by producing a high-resolution (2 km × 2 km) national map of capacity density for use in technical potential assessments for the United States. Our findings illustrate that this methodology offers meaningful improvements in the characterization of spatial aspects of technical potential, which are increasingly critical to draw reliable and actionable planning and research insights from renewable energy scenarios

A systematic review of approaches to quantify hydrologic ecosystem services to inform decision-making

<p>Global threats to freshwater resources are prompting widespread concern about their management and implications for well-being. In recent decades, hydrologic ecosystem services (HES) have emerged as an innovative concept to evaluate freshwater resources, providing opportunity for researchers to engage in decision-relevant science. We conducted a systematic review of studies published within the last decade, documenting approaches for mapping and quantifying HES and classifying the decision context. To gauge the relevance of HES science, we evaluated 49 case studies using multiple criteria for credibility, legitimacy, and saliency. We found compelling evidence that much of the variability in the quantification of HES can be explained by research motivations and scoping, reflecting the decision-oriented framing of the ecosystem services concept. Our review highlights key knowledge gaps in the state of the science including the need to articulate beneficiaries and to make connections to policy and management more explicit. To strengthen the potential for impact of HES science, we provide recommendations to assist researchers, practitioners, and decision-makers in identifying goals, formulating relevant questions, and selecting informative approaches for quantifying HES. We argue that sustained progress in applying HES requires critical evaluation and careful framing to link science and practice.</p

Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning.

Key to understanding the implications of climate and land use change on biodiversity and natural resources is to incorporate the physiographic platform on which changes in ecological systems unfold. Here, we advance a detailed classification and high-resolution map of physiography, built by combining landforms and lithology (soil parent material) at multiple spatial scales. We used only relatively static abiotic variables (i.e., excluded climatic and biotic factors) to prevent confounding current ecological patterns and processes with enduring landscape features, and to make the physiographic classification more interpretable for climate adaptation planning. We generated novel spatial databases for 15 landform and 269 physiographic types across the conterminous United States of America. We examined their potential use by natural resource managers by placing them within a contemporary climate change adaptation framework, and found our physiographic databases could play key roles in four of seven general adaptation strategies. We also calculated correlations with common empirical measures of biodiversity to examine the degree to which the physiographic setting explains various aspects of current biodiversity patterns. Additionally, we evaluated the relationship between landform diversity and measures of climate change to explore how changes may unfold across a geophysical template. We found landform types are particularly sensitive to spatial scale, and so we recommend using high-resolution datasets when possible, as well as generating metrics using multiple neighborhood sizes to both minimize and characterize potential unknown biases. We illustrate how our work can inform current strategies for climate change adaptation. The analytical framework and classification of landforms and parent material are easily extendable to other geographies and may be used to promote climate change adaptation in other settings

Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis

High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, precise information about energy systems is often of limited availability, incomplete, or only accessible for a substantial fee or through a non-disclosure agreement. Recently, remotely sensed data (e.g., satellite imagery, aerial photography) have emerged as a potentially rich source of energy systems information. However, the use of these data is frequently challenged by its sheer volume and complexity, precluding manual analysis. Recent breakthroughs in machine learning have enabled automated and rapid extraction of useful information from remotely sensed data, facilitating large-scale acquisition of critical energy system variables. Here we present a systematic review of the literature on this emerging topic, providing an in-depth survey and review of papers published within the past two decades. We first taxonomize the existing literature into ten major areas, spanning the energy value chain. Within each research area, we distill and critically discuss major features that are relevant to energy researchers, including, for example, key challenges regarding the accessibility and reliability of the methods. We then synthesize our findings to identify limitations and trends in the literature as a whole, and discuss opportunities for innovation

Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning

<div><p>Key to understanding the implications of climate and land use change on biodiversity and natural resources is to incorporate the physiographic platform on which changes in ecological systems unfold. Here, we advance a detailed classification and high-resolution map of physiography, built by combining landforms and lithology (soil parent material) at multiple spatial scales. We used only relatively static abiotic variables (i.e., excluded climatic and biotic factors) to prevent confounding current ecological patterns and processes with enduring landscape features, and to make the physiographic classification more interpretable for climate adaptation planning. We generated novel spatial databases for 15 landform and 269 physiographic types across the conterminous United States of America. We examined their potential use by natural resource managers by placing them within a contemporary climate change adaptation framework, and found our physiographic databases could play key roles in four of seven general adaptation strategies. We also calculated correlations with common empirical measures of biodiversity to examine the degree to which the physiographic setting explains various aspects of current biodiversity patterns. Additionally, we evaluated the relationship between landform diversity and measures of climate change to explore how changes may unfold across a geophysical template. We found landform types are particularly sensitive to spatial scale, and so we recommend using high-resolution datasets when possible, as well as generating metrics using multiple neighborhood sizes to both minimize and characterize potential unknown biases. We illustrate how our work can inform current strategies for climate change adaptation. The analytical framework and classification of landforms and parent material are easily extendable to other geographies and may be used to promote climate change adaptation in other settings.</p></div

Physiographic diversity in the conterminous USA.

<p>Multi-scale physiographic diversity, calculated using the Shannon-Weaver index. Labels are mean diversity by Landscape Conservation Cooperatives, with standard deviations in parentheses.</p

Landform classes in the conterminous USA at different resolutions.

<p>Landform classes in the conterminous USA at different resolutions.</p

Landforms of the conterminous USA.

<p>(A) A landform map of the USA, with Landscape Conservation Cooperatives used by the Department of Interior to guide climate change adaptation. Labels a-h refer to inset examples and legend for class types. (B) Examples of landform classes, zoomed in to illustrate different patterns: (a) the Pacific Northwest around Mount St. Helens (1:175,000); (b) along the Missouri River at the boundary of Montana and North Dakota (1:200,000); (c) near Milton, Pennsylvania (1:500,000); (d) in the Sky Islands of southern Arizona (1:500,000); (e) Estes Park, Colorado (1:175,000); (f) near Smithfield, North Carolina (1:400,000); (g) along the Ogeechee River near Statesboro, Georgia (1:300,000); and (h) south Texas tablelands (1:200,000).</p