Trends in prices to commercial energy consumers in the competitive Texas electricity market

To date, the price of electricity to commercial or business energy consumers has generally increased at greater rates in the areas of Texas where retail competition has been introduced than in areas that do not enjoy competition. Trends in commercial competitive prices have largely mirrored trends in residential prices. Market restructuring has tended to increase the sensitivity of retail electricity prices to changes in the price of natural gas, the marginal fuel used for generation in Texas. Consequently, the rapid increases in the commodity price of natural gas following restructuring led to increases in competitive electric rates which exceeded the increases in areas not exposed to restructuring, where the fuel component of electric rates tend to reflect a weighted average of the utilities’ fuel costs. There is some evidence that pricing behavior by competitive retailers changed when the retailers affiliated with the incumbent utilities were permitted some pricing flexibility, resulting in a reduction in prices.     

Application of a Novel Method for Machine Performance Degradation Assessment Based on Gaussian Mixture Model and Logistic Regression

The currently prevalent machine performance degradation assessment techniques involve estimating a machine’s current condition based upon the recognition of indications of failure features,which entail complete data collected in different conditions.However,failure data are always hard to acquire,thus making those techniques hard to be applied.In this paper,a novel method which does not need failure history data is introduced.Wavelet packet decomposition(WPD) is used to extract features from raw signals,principal component analysis(PCA) is utilized to reduce feature dimensions,and Gaussian mixture model(GMM) is then applied to approximate the feature space distributions.Single-channel confidence value(SCV) is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of single-channel.Furthermore,multi-channel confidence value(MCV),which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression(LR) and that the task of decision-level sensor fusion is also completed.Both SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine breakdown.The method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this task.The proposed method has engineering significance for machine performance degradation assessment.     

Performance evaluation of canine-associated Bacteroidales assays in a multi-laboratory comparison study

The contribution of fecal pollution from dogs in urbanized areas can be significant and is an often underestimated problem. Microbial source tracking methods (MST) utilizing quantitative PCR of dog-associated gene sequences encoding 16S rRNA of Bacteroidales are a useful tool to estimate these contributions. However, data about the performance of available assays are scarce. The results of a multi-laboratory study testing two assays for the determination of dog-associated Bacteroidales (DogBact and BacCan-UCD) on 64 single and mixed fecal source samples created from pooled fecal samples collected in California are presented here. Standardization of qPCR data treatment lowered inter-laboratory variability of sensitivity and specificity results. Both assays exhibited 100% sensitivity. Normalization methods are presented that eliminated random and confirmed non-target responses. The combination of standardized qPCR data treatment, use of normalization via a non-target specific Bacteroidales assay (GenBac3), and application of threshold criteria improved the calculated specificity significantly for both assays. Such measures would reasonably improve MST data interpretation not only for canine-associated assays, but for all qPCR assays used in identifying and monitoring fecal pollution in the environment.     

有限区间内设备顺序预防性维护策略研究

为了克服等周期维护模型忽略设备可靠性要求,以及长期运行期望维护周期模型忽略设备特定阶段维护需求的缺点,通过引入基于役龄递减因子和故障率递增因子的混合式故障率演化规则,建立了有限区间内基于设备可靠性的顺序预防性维护优化模型,并以威布尔分布为例,利用仿真方法对其进行了优化。仿真结果显示,该模型可使设备的维护周期呈递减之势,反映了设备在某一特定阶段的可靠性需求。     

Application and validation of incrementally complex models for wind turbine aerodynamics,isolated wind turbine in uniform inflow conditions

A comparison of several incrementally complex methods for predicting wind turbine performance, aeroelastic behavior, and wakes is provided. Depending on a wind farm's design, wake interference can cause large power losses and increased turbulence levels within the farm. The goal is to employ modeling methods to reach an improved understanding of wake effects and to use this information to better optimize the layout of new wind farms. A critical decision faced by modelers is the fidelity of the model that is selected to perform simulations. The choice of model fidelity can affect the accuracy, but will also greatly impact the computational time and resource requirements for simulations. To help address this critical question, three modeling methods of varying fidelity have been developed side by side and are compared in this article. The models from low to high complexity are as follows: a blade element‐based method with a free‐vortex wake, an actuator disc‐based method, and a full rotor‐based method. Fluid/structure interfaces are developed for the aerodynamic modeling approaches that allow modeling of discrete blades and are then coupled with a multibody structural dynamics solver in order to perform an aeroelastic analysis. Similar methods have individually been tested by researchers, but we suggest that by developing a suite of models, they can be cross‐compared to grasp the subtleties of each method. The modeling methods are applied to the National Renewable Energy Laboratory Phase VI rotor to predict the turbine aerodynamic and structural loads and then also the wind velocities in the wake. The full rotor method provides the most accurate predictions at the turbine and the use of adaptive mesh refinement to capture the wake to 20 radii downstream is proven particularly successful. Though the full rotor method is unmatched by the lower fidelity methods in stalled conditions and detailed prediction of the downstream wake, there are other less complex conditions where these methods perform as accurately as the full rotor method. Copyright © 2014 John Wiley & Sons, Ltd.     

Aggregate industrial energy consumer response to wholesale prices in the restructured Texas electricity market

The aggregate response of consumers to wholesale price signals is very limited in the restructured Electric Reliability Council of Texas (ERCOT) market. An overall average own-price elasticity of demand of − 0.000008 for industrial energy consumers served at transmission voltage is estimated using a Symmetric Generalized McFadden cost function model. To date, ERCOT has sought to promote demand response to price signals without reliance on “stand alone” demand response programs, but with a market structure that is designed to facilitate economic demand response. This very limited responsiveness to wholesale price signals may prove problematic in light of policy decisions to pursue an “energy only” resource adequacy mechanism for ERCOT.     

Evaluation of haptic guidance virtual fixtures and 3D visualization methods in telemanipulation—a user study

Intelligent Service Robotics - This work presents a user-study evaluation of various visual and haptic feedback modes on a real telemanipulation platform. Of particular interest is the potential...     

Outlier accommodation in moving-horizon state estimation: A risk-averse performance-specified approach

This paper presents a novel state estimation approach for linear dynamic systems when measurements are corrupted by outliers. Since outliers can degrade the performance of state estimation, outlier accommodation is critical. The standard approach combines outlier detection utilizing Neyman-Pearson (NP) type tests with a Kalman filter (KF). This approach ignores all residuals greater than a designer-specified threshold. When measurements with outliers are used (ie, missed detections), both the state estimate and the error covariance matrix become corrupted. This corrupted state and covariance estimate are then the basis for all subsequent outlier decisions. When valid measurements are rejected (ie, false alarms), potentially using the corrupted state estimate and error covariance, measurement information is lost. Either using invalid information or discarding too much valid information can result in divergence of the KF. An alternative approach is moving-horizon (MH) state estimation, which maintains all recent measurement data within a moving window with a time horizon of length L. In MH approaches, the number of measurements available for state estimation is affected by both the number of measurements per time step and the number of time steps L over which measurements are retained. Risk-averse performance-specified (RAPS) state estimation works within an optimization setting to choose a set of measurements that achieves a performance specification with minimum risk of outlier inclusion. This paper derives and formulates the MH-RAPS solution for outlier accommodation. The paper also presents implementation results. The MH-RAPS application uses Global Navigation Satellite Systems measurements to estimate the state of a moving platform using a position, velocity, and acceleration model. In this application, MH-RAPS performance is compared with MH-NP state estimation.