An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Assessment team report on flight-critical systems research at NASA Langley Research Center

The quality, coverage, and distribution of effort of the flight-critical systems research program at NASA Langley Research Center was assessed. Within the scope of the Assessment Team's review, the research program was found to be very sound. All tasks under the current research program were at least partially addressing the industry needs. General recommendations made were to expand the program resources to provide additional coverage of high priority industry needs, including operations and maintenance, and to focus the program on an actual hardware and software system that is under development

Pipe failure prediction and impacts assessment in a water distribution network

Abstract Water distribution networks (WDNs) aim to provide water with desirable quantity, quality and pressure to the consumers. However, in case of pipe failure, which is the cumulative effect of physical, operational and weather-related factors, the WDN might fail to meet these objectives. Rehabilitation and replacement of some components of WDNs, such as pipes, is a common practice to improve the condition of the network to provide an acceptable level of service. The overall aim of this thesis is to predict—long-term, annually and short-term—the pipe failure propensity and assess the impacts of a single pipe failure on the level of service. The long-term and annual predictions facilitate the need for effective capital investment, whereas the short-term predictions have an operational use, enabling the water utilities to adjust the daily allocation and planning of resources to accommodate possible increase in pipe failure. The proposed methodology was implemented to the cast iron (CI) pipes in a UK WDN. The long-term and annual predictions are made using a novel combination of Evolutionary Polynomial Regression (EPR) and K-means clustering. The inclusion of K-means improves the predictions’ accuracy by using a set of models instead of a single model. The long-term predictive models consider physical factors, while the annual predictions also include weather-related factors. The analysis is conducted on a group level assuming that pipes with similar properties have similar breakage patterns. Soil type is another aggregation criterion since soil properties are associated with the corrosion of metallic pipes. The short-term predictions are based on a novel Artificial Neural Network (ANN) model that predicts the variations above a predefined threshold in the number of failures in the following days. The ANN model uses only existing weather data to make predictions reducing their uncertainty. The cross-validation technique is used to derive an accurate estimate of accuracy of EPR and ANN models by guaranteeing that all observations are used for both training and testing, and each observation is used for testing only once. The impact of pipe failure is assessed considering its duration, the topology of the network, the geographic location of the failed pipe and the time. The performance indicators used are the ratio of unsupplied demand and the number of customers with partial or no supply. Two scenarios are examined assuming that the failure occurs when there is a peak in either pressure or demand. The pressure-deficient conditions are simulated by introducing a sequence of artificial elements to all the demand nodes with pressure less than the required. This thesis proposes a new combination of a group-based method for deriving the failure rate and an individual-pipe method for evaluating the impacts on the level of service. Their conjunction indicates the most critical pipes. The long-term approach improves the accuracy of predictions, particularly for the groups with very low or very high failure frequency, considering diameter, age and length. The annual predictions accurately predict the fluctuation of failure frequency and its peak during the examined period. The EPR models indicate a strong direct relationship between low temperatures and failure frequency. The short-term predictions interpret the intra-year variation of failure frequency, with most failures occurring during the coldest months. The exhaustive trials led to the conclusion that the use of four consecutive days as input and the following two days as output results in the highest accuracy. The analysis of the relative significance of each input variable indicates that the variables that capture the intensity of low temperatures are the most influential. The outputs of the impact assessment indicate that the failure of most of the pipes in both scenarios (i.e. peak in pressure and demand) would have low impacts (i.e. low ratio of unsupplied demand and small number of affected nodes). This can be explained by the fact that the examined network is a large real-life network, and a single failure of a distribution pipe is likely to cause pressure-deficient conditions in a small part of it, whereas performance elsewhere is mostly satisfactory. Furthermore, the complex structure of the WDN allows them to recover from local pipe failures, exploiting the topological redundancy provided by closed loops, so that the flow could reach a given demand node through alternative paths

Deep Learning Based Reliability Models For High Dimensional Data

The reliability estimation of products has crucial applications in various industries, particularly in current competitive markets, as it has high economic impacts. Hence, reliability analysis and failure prediction are receiving increasing attention. Reliability models based on lifetime data have been developed for different modern applications. These models are able to predict failure by incorporating the influence of covariates on time-to-failure. The covariates are factors that affect the subjects’ lifetime. Modern technologies generate covariates which can be utilized to improve failure time prediction. However, there are several challenges to incorporate the covariates into reliability models. First, the covariates generally are high dimensional and topologically complex. Second, the existing reliability models are not efficient in modeling the effect on the complex covariates on failure time. Third, failure time information may not be available for all covariates, as collecting such information is a costly and time-consuming process. To overcome the first challenge, we propose a statistical approach to model the complex data. The proposed model generalizes penalized logistic regression to capture the spatial properties of the data. An efficient parameter estimation method is developed to make the model practical in case of large sample sizes. To tackle the second challenge, a deep learning-based reliability model is proposed. The model can capture the complex effect of the data on failure time. A novel loss function based on the partial likelihood function is developed to train the deep learning model. Furthermore, to overcome the third difficulty, we proposed a transfer learning-based reliability model to estimate failure time based on the failure time of similar covariates. The proposed model is based on a two-level autoencoder to minimize the distribution distance of covariates. A new parameter estimation method is developed to estimate the parameter of the proposed two-level autoencoder model. Various simulation studies are conducted to demonstrate the proposed models. The results show that the proposed models outperformed the traditional statistical and reliability models. Moreover, physical experiments on advanced high strength steel are designed to demonstrate the proposed model. As microstructure images of the steels affect the failure time of the steel, the images are considered as covariates. The results show that the proposed models predict the failure time and hazard function of the materials more accurately than existing reliability models

Voices from the Source: Struggles with Local Water Security in Ethiopia

This report explores local water security in two different sites in Ethiopia, Shinile and Konso. This issue cannot be reduced to a single diagnostic such as measures of water use or presence of an improved source. The pressures of water security on livelihoods and household-level responses are discussed and local and national government responses are examined

BUDGET-CONSTRAINED POWER SYSTEM RELIABILITY OPTIMIZATION

Electric utilities face constant pressure from regulators to defer rate increases while simultaneously maintaining or improving levels of service. Capital spending projects are coming under increasing scrutiny as the costs of the projects can no longer be assumed to be rolled into projected rate increases. At the same time, utility customers expect near uninterrupted service to their homes and businesses. Electric lines and components that make up the power system are getting older and are reaching or have exceeded an assumed end-of-life. Planners at these utilities need a way to prioritize constrained budget dollars across seemingly disparate transmission, substation, and distribution work areas. This thesis provides a framework for quantifying the reliability impacts of these different projects as measured by Customer-Minutes of Interruption (CMI) avoided. Transmission lines are modeled as continuous time Markov processes with common-cause failure modes. The parallel-series configuration of the substation and failure modes of its components is evaluated using Failure Mode and Effects Analysis (FMEA). Circuit breaker operational failures are evaluated and Poisson process models are fitted by interrupting medium. The transmission and substation systems are then evaluated as a decoupled equivalent source per feeder in series with the distribution system. Competing projects impacts are evaluated and prioritized based upon dollars spent per CMI avoided ($/CMI)

Using a systems approach to analyze the operational safety of dams

Dam systems are arrangements of interacting components that store and convey water for beneficial purposes. Dam failures are associated with extreme consequences to human life, the environment and the economy. Existing techniques for dam safety analysis tend to focus on verifying system performance at the edge of the design envelope. In analyzing the events which occur within the design envelope, linear chain-of-events models are often used to analyze the potential outcomes for the system. These chain-of-events models require that combinations of conditions are identified at the outset of the analysis, which can be very cumbersome given the number of physically possible combinations. Additional complications arising from feedback behaviour and time are not easily overcome using existing tools. Recent work in the industry has begun to focus on systems approaches to the problem, especially stochastic simulation. Given current computational abilities, stochastic simulation may not be capable of analyzing combinations of events that have a low combined probability but potentially extreme consequences. This research focuses on developing and implementing a methodology that dynamically characterizes combinations of component operating states and their potential impacts on dam safety. Automated generation of scenarios is achieved through the use of a component operating states database that defines all possible combinations of component states (scenarios) using combinatorics. A Deterministic Monte Carlo simulation framework systematically characterizes each scenario through a number of iterations that vary adverse operating state timing, impacts and inflows. Component interactions and feedbacks are represented within the system dynamics simulation model. Simulation outcomes provide useful indicators for dam operators including conditional failure rates, times to failure, failure inflow thresholds, and reservoir level exceedance frequencies. Dynamic system response can be assessed directly from the simulation outcomes. The scenario results may be useful to dam owners in emergency decision-making to inform response timelines and to justify the allocation of resources. Results may also help inform the development of improved operating strategies or upgrade alternatives that can reduce the impacts of these extreme events. This work offers a significant improvement in the ability to systematically characterize the potential combinations of events and their consequences

Developing Leading Indicators Framework for Predicting Kicks and Preventing Blowouts

Due to the operational complexities of drilling, completion and well intervention activities, it is often quite challenging to predict a potential blowout scenario timely and efficiently. In drilling operations, blowouts are usually preceded by kicks and predicting kicks early is crucial for regaining control of the well and preventing major incident. Kicks and blowouts happen due to failure of well control barriers and leading indicators could be very effective in identify vulnerabilities in such systems. For assessing integrity of well control barriers with appropriate sets of leading indicators, a robust framework was proposed and sets of probabilistic models were developed in this work. By following a systematic cause-based methodology proposed in this work, sets of leading indicators were identified for monitoring barrier performances while drilling, completion and well intervention activities. Analyses of Montara and Deepwater Horizon blowout incidents demonstrated applicability of leading indicators framework in revealing system weaknesses prior to major incidents. Using the real-time kick indicators, decision support algorithms were developed in this work which would help to understand a kick progression scenario and actions required to confirm a kick. Leading indicators-based probabilistic models were developed for evaluating the relative importance of different organizational and operational factors, and assessing their impacts on the key causal factors of well control barrier failure events. These models were constructed for hydrostatic head failure events which can be caused by abnormal pore pressure and swabbing, and cementing failure during drilling and completion activities. An integrated iii model for assessing well control failure events during wireline operations was also constructed. These models represent realistic scenario of barrier health and could be very useful for determining barrier failure probabilities from observed data. Addition to these, efficiencies of kick detection parameters to detect potential influxes and factors impacting their performances can also be assessed with the developed models. These functions enable informed decision-making for preventing kicks and blowouts while drilling or intervening a well, by providing real-time status of the well control system

Market Structure and Energy Efficiency: The Case of New Commercial Buildings

This is a report on why commercial office buildings aren’t more energy efficient. Several decades of energy efficiency programs have resulted in some gains, but overall increases in the energy efficiency of buildings have fallen far short of the 30 to 50 percent improvement that many efficiency advocates believe is possible. The purpose of this study is to consider the “why” question by empirically examining the dynamics of new commercial building markets. To do so, the authors used multiple research techniques, including qualitative field observation and interview methods that allow for a more in-depth understanding of complicated market processes. Their research focused primarily on new office buildings and centered in four regional markets: Sacramento, San Francisco, Seattle, and Portland. The authors identify key dynamics of commercial office building markets, describe how change and innovation occurs in commercial development, discuss the implications for energy efficiency, and suggest next steps