Bonneville Power Administration

ABSTRACT Restoration of floodplain habitats is one of the most common approaches to restoring ecological processes, ecosystem services, increasing floodplain capacity, and improving habitat for endangered salmon and trout. Restoring floodplains at a reach or valley segment scale to an anastomosing or other multi‐thread channel patterns, often called “Stage 0” or valley bottom restoration, has become common in the United States. However, additional information is needed to ensure these projects are successful, implemented in the right locations, and not the latest watershed restoration technique applied inappropriately. Using lessons learned from floodplain restoration in the Grande Ronde River Basin in Oregon, we outline key considerations for planning, designing, and implementing valley bottom restoration projects. We first crosswalk the stages of channel evolution in the stream evolution model, commonly used for valley bottom restoration, with channel types in popular stream classification schemes, and then identify natural anastomosing and meandering channels in the Grande Ronde Basin to assist with designing valley bottom restoration projects. Next, we discuss how to determine whether a reach or valley segment is appropriate for valley bottom restoration, including examining the current, historic, and potential for a site to support anastomosing or other channel types. We then discuss considerations for project design and implementation including: goals, hydraulic modeling, habitat suitability modeling, and other tools to develop and evaluate design alternatives. Valley bottom restoration projects present unique challenges for monitoring, and we close with guidelines for monitoring that rely on remote sensing to map the entire valley bottom before and after restoration and that are linked directly to project objectives. This article is categorized under: Water and Life > Nature of Freshwater Ecosystems

ABSTRACT: Rock surface roughness influences strength, stiffness, and fluid flow dynamics. Roughness algorithms, classified as either Euclidian or fractal, have been used to quantify roughness through computing roughness indices. Because of the complexity of the microtopography of the rock surface, it is difficult to put into context the meaning of a roughness index. The purpose of this study is to visualize roughness indices of simple simulated rough surfaces in amplitude-frequency space. We used a 1000 mm sine wave with varying amplitudes and frequencies to simulate the rough surface. Five Euclidian roughness algorithms were used to quantify the roughness. To visualize these methods, simulated wavy profiles were created using sine waves with specific bounds to capture both the waviness and unevenness of a profile. The findings highlight the superior performance of the Z2 and Mean Absolute Angle algorithms while indicating that care should be taken when using the Sinuosity to assess low frequency (<2 Hz) and low amplitude (<2 mm) profiles, and Root Mean Square, and Energy algorithms when assessing high frequency (>1 Hz) profiles. 1. INTRODUCTION Roughness is a parameter that is ubiquitous across many science and engineering fields. Surface roughness is a controlling factor in the strength, stiffness, and fluid flow properties and an assessment tool for weathering. The separate works of Patton and Barton are fundamental to understanding and quantifying rock roughness. Patton (1966) introduced the concept of two length-based roughness scales of rock joints. Waviness or large-scale roughness occurs on a decimeter-to-meter scale, and unevenness or small-scale roughness occurs on a millimeter-to-centimeter scale. Waviness is low-frequency and high amplitude, whereas unevenness is high-frequency and low amplitude. Barton (1973) and Barton and Choubey (1977) introduced the joint roughness coefficient (JRC) parameter. The later reference provided the first visualization of a rock profile linked to a roughness index (JRC), which prompted new research efforts to quantify roughness. Contact and non-contact methods were developed to measure surface microtopography. Existing roughness algorithms were applied to quantify the JRC profiles and natural rock surfaces. More importantly, new roughness algorithms were and are continuously being developed. Methods to quantify rock surface roughness are categorized into Euclidean geometry methods, fractal geometry methods, and empirical methods. The Euclidean geometry methods, 2D and 3D, are further classified as statistical, amplitude-based, slope-based, and amplitude-and-slope-based methods (Kulatilake and Ankah, 2023). These authors state Euclidian geometry methods are ill-suited to accurately represent the highly erratic rock joint surfaces. These methods are still extensively used because they are less complex than fractal geometry. Artificial intelligence, specifically deep learning, has recently been used to assess rock joint roughness (Zhang et al., 2023).

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This research accomplishes these aims. The models built in this paper identify wind forecast, sunrise/sunset and the hour of day as primary predictors of net load imbalance, among other variables, and show that the average size of the INC and DEC capacity requirements can be reduced by over 25% with the margin of error currently used in the industry while also significantly improving closeness and exceedance metrics. The reduction in INC and DEC capacity requirements would yield an approximate cost savings of $4 million annually for one of nineteen Western Energy Imbalance market participants. Reconstructability Analysis performs the best among the machine learning methods tested.

The investment in solar and wind power plants continues to accelerate given the economical and policy incentives provided. This paper studies the integration of these resources and displacement of synchronous generators in the Western Interconnection. It specifically assesses the potential impacts that significant shares of inverter-based resources (IBR) could have on the frequency response of this system operating with high percentages of renewable energy. To this end, it replaces the extant synchronous generators with IBRs in four phases, and closely emphasizes whether the underfrequency load shedding (UFLS) plan is instigated. The results suggest that operation under high shares of IBR could activate the UFLS plan. They also show that IBR power plants with headroom reserve could enhance frequency even under very low inertia conditions because of their ability to respond quickly.

In this column, we look back at the content and imagery found in the Journal ofthe Biological Photographic Association (JBPA, later renamed the Journal ofBiological Photography (JBP). This column explores important articles from 25years ago. In doing so, we gain some insight into those legacy photographytechniques of that time.

Abstract This paper shows how to bootstrap hypothesis tests in the context of the Parks’s (1967) Feasible Generalized Least Squares estimator. It then demonstrates that the bootstrap outperforms FGLS(Parks)’s top competitor. The FGLS(Parks) estimator has been a workhorse for the analysis of panel data and seemingly unrelated regression equation systems because it allows the incorporation of cross-sectional correlation together with heteroskedasticity and serial correlation. Unfortunately, the associated, asymptotic standard error estimates are biased downward, often severely. To address this problem, Beck and Katz (1995) developed an approach that uses the Prais-Winsten estimator together with “panel corrected standard errors” (PCSE). While PCSE produces standard error estimates that are less biased than FGLS(Parks), it forces the user to sacrifice efficiency for accuracy in hypothesis testing. The PCSE approach has been, and continues to be, widely used. This paper develops an alternative: a nonparametric bootstrapping procedure to be used in conjunction with the FGLS(Parks) estimator. We demonstrate its effectiveness using an experimental approach that creates artificial panel datasets modelled after actual panel datasets. Our approach provides a superior alternative to existing estimation options by allowing researchers to retain the efficiency of the FGLS(Parks) estimator while producing more accurate hypothesis test results than the PCSE.

The transmission system in the Pacific Northwest is highly coupled among utilities. To meet NERC MOD-033 requirements for validating dynamic performance in this region, a validation process focusing on large disturbances' system-wide response was designed to take into account the interaction of all dynamic components in a wide area. Unlike plant level or local disturbance validation studies, such a system level validation process brings new challenges that require a collaborative approach to evaluate factors including power flow conditions, event sequence, and model limitations. The principles, methodology and performance criteria for this process are discussed in this paper, and supported by a validation study for a disturbance having both large frequency and voltage impact.

Interconnection reliability operating limits are system operating limits that, if exceeded, could result in instability, uncontrolled separation, or cascading if the critical contingency were to occur, and are operated within to ensure reliable operation of the bulk power system in North America. This paper presents an analytical framework for establishing IROLs, and recommended modeling and simulation techniques for instability assessment and cascading analysis. The recommended practices are being adopted and used in North America for consistency in study approaches for establishing IROLs.

Over the past several years, large magnitude earthquakes have inflicted significant damage on lifeline infrastructure, including electric power systems. These events serve as reminders of the importance of implementing good seismic design practices in order to deploy resilient systems that sustain limited damage and can be restored within reasonable time frames. IEEE Std. 693, Recommended Practice for the Seismic Design of Substations is the standard of choice for the seismic qualification and design of substation equipment and their supports. At the time of this writing, the new edition of the standard was in the final stages of approval. This paper discusses basic concepts of IEEE Std. 693 and describes the most significant changes expected in the approved version. These changes include guidance for the use of seismic protective systems (e.g., base isolators and dampers), qualification of DC equipment, insulator seismic strength criteria, the explicit consideration of conductor load effects in equipment qualification procedures, guidance for nonlinear dynamic analysis of equipment/supports, revised procedures for developing input time histories for testing and analysis, and changes to transformer bushing shake table test protocols. Improved consistency and coordination with IEEE Std. 1527 (Design of Bus Work in Seismically Active Areas) and ASCE Manual of Practice 113 (Design of Substation Structures) have also been built into the revised IEEE Std. 693.