The growth of advanced metering infrastructure, enhanced communication infrastructure in power grids, and the ability of end-user consumer to actively participate helps in realizing vision of ...sustainable energy systems. Demand response (DR) programs are developed in order to deploy this ability and make power grids more efficient, environmental friendly, and reliable. This paper presents a review of DR, existing application and a possible implementation strategy in a smart grid environment. Furthermore, classification and status of DR programs in different U.S. electricity markets have been also discussed.
This paper presents a method for quantifying and enabling the resiliency of a power distribution system using analytical hierarchical process and percolation theory. Using this metric, quantitative ...analysis can be done to analyze the impact of possible control decisions to pro-actively enable the resilient operation of distribution system with multiple microgrids and other resources. Developed resiliency metric can also be used in short term distribution system planning. The benefits of being able to quantify resiliency can help distribution system planning engineers and operators to justify control actions, compare different reconfiguration algorithms, and develop proactive control actions to avert power system outage due to impending catastrophic weather situations or other adverse events. Validation of the proposed method is done using modified CERTS microgrids and a modified industrial distribution system. Simulation results show topological and composite metric considering power system characteristics to quantify the resiliency of a distribution system with the proposed methodology, and improvements in resiliency using two-stage reconfiguration algorithm and multiple microgrids.
The interest on microgrid has increased significantly triggered by the increasing demand of reliable, secure, efficient, clean, and sustainable electricity. More research and implementation of ...microgrid will be conducted in order to improve the maturity of microgrid technology. Among different aspects of microgrid, this paper focuses on controls of microgrid with energy storage. A comprehensive review on current control technology is given with a discussion on challenges of microgrid controls. Basic simulation results are also presented to enhance and support the analysis. Finally, research needs and roadmap for microgrid control are also described.
Sumoylation is one of the post translational modifications, which affects cellular processes in plants through conjugation of small ubiquitin like modifier (SUMO) to target substrate proteins. ...Response to various abiotic environmental stresses is one of the major cellular functions regulated by SUMO conjugation. SIZ1 is a SUMO E3 ligase, facilitating a vital step in the sumoylation pathway. In this report, it is demonstrated that over-expression of the rice gene OsSIZ1 in Arabidopsis leads to increased tolerance to multiple abiotic stresses. For example, OsSIZ1-overexpressing plants exhibited enhanced tolerance to salt, drought, and heat stresses, and generated greater seed yields under a variety of stress conditions. Furthermore, OsSIZ1-overexpressing plants were able to exclude sodium ions more efficiently when grown in saline soils and accumulate higher potassium ions as compared to wild-type plants. Further analysis revealed that OsSIZ1-overexpressing plants expressed higher transcript levels of P5CS, a gene involved in the biosynthesis of proline, under both salt and drought stress conditions. Therefore, proline here is acting as an osmoprotectant to alleviate damages caused by drought and salt stresses. These results demonstrate that the rice gene OsSIZ1 has a great potential to be used for improving crop's tolerance to several abiotic stresses.
With the advent of phasor measurement units (PMUs), high resolution synchronized phasor measurements enables real time system monitoring and control. PMUs transmit data to local controllers in ...substations and phasor data concentrators for system wide monitoring and control application in the control center. They provide real-time phasor data for critical power system applications such as remedial action schemes, oscillation detection, and state estimation. The quality of phasor data from PMUs is critical for smart grid applications. Several methods are developed to detect anomalies in time series data, tailored for PMU data analysis. Nevertheless, applying stand alone methods (with fixed parameters) takes great tuning effort, and does not always achieve high accuracy. In this paper, we adopt an unsupervised ensemble learning approach to develop fast, scalable bad data/event detection for PMU data. The ensemble method invokes a set of base detectors to generate anomaly scores of the PMU data, and makes decisions by aggregating the scores from each detector. We develop two algorithms: 1) a learning algorithm that trains the ensemble model and 2) an online algorithm that infers the anomaly scores with the ensemble model over PMU data streams. The proposed method provides flag for data anomalies and triggers further analysis to differentiate between events and bad data. Using both simulated and real-world PMU data, we show that our ensemble model can be efficiently trained, can achieve high accuracy in detecting diversified errors/events, and outperforms its counterparts that use single standalone detection method.
With an increasing number of extreme events, grid components and complexity, more alarms are being observed in the power grid control centers. Operators in the control center need to monitor and ...analyze these alarms to take suitable control actions, if needed, to ensure the system's reliability, stability, security, and resiliency. Although existing alarm and event processing tools help in monitoring and decision making, synchrophasor data along with the topology and component location information can be used in detecting, classifying and locating the event, which is the focus of this work. Phasor Measurement Unit's (PMU's) data quality issue is also addressed before using data for event analysis. The developed algorithms include statistic, clustering, and Maximum Likelihood Criterion (MLE) based anomaly detection, Density-based spatial clustering of applications with noise (DBSCAN) for event detection and physics-based rule/ decision tree for event classification. Further, topology information, statistical techniques, and graph search algorithms are used for event localization. Developed algorithms have been validated with satisfactory results for IEEE 14 bus and 39 Bus as well as with real PMU data from the western US interconnection (WECC).
Defects are often symbolized as deformity in material that deteriorate its performance. However, in nanoscale regime, defects lead to generate a useful and novel material for device applications. In ...the present report, the vacancy defects, i.e., single vacancy and double vacancy defects (with different symmetry) on graphene sheet have been analyzed to understand the electronic as well as transport properties using density functional theory and NEGF approach. Conductance, current-voltage, and sensitivity analysis of these defected graphene sheets have been examined for its suitability for gas sensing application particularly for ammonia gas. The study observes that a single vacancy defected graphene is a good candidate for ammonia molecule sensing in comparison to double vacancy.
This article addresses a voltage control and energy management strategy of active distribution systems with a grid-connected dc microgrid as well as for an islanded dc microgrid with hybrid energy ...resources. In the islanded mode, a control and management strategy using a backup diesel generator (DG), a renewable energy source (RES), and an energy storage system plays a vital role in maintaining the microgrid bus voltages within the limits. However, operating backup diesel generator (DG) has its own challenges including startup delay, frequent switching, and uneven loading when operated along with a RES. Additionally, fuel efficiency and emission characteristics vary with loading since most of DGs are driven by constant-speed diesel engines. Hence, an exhaustive power management scheme (PMS) is proposed by utilizing the hybrid energy storage system. Real-time simulation and experimental validation of the proposed scheme are provided using a real-time digital simulator (RTDS) and a laboratory-scale prototype, respectively. Extreme scenarios including DG failure/scheduled maintenance, low power generation, and battery charge are analyzed in the islanded mode. Furthermore, a dc microgrid is connected to an IEEE active distribution system feeder to analyze control and management challenges for the grid connected mode with contribution from a microgrid and with no contribution from a microgrid. These scenarios resemble more realistic unbalanced utility grid conditions. A centralized optimization problem is formulated at an advanced distribution management system level to maintain all the node voltages within limits in the IEEE test system. RTDS is used to simulate dc microgrid connected with the IEEE test system and an optimization algorithm is implemented in MATLAB. Superior performance of the developed algorithms are demonstrated and validated for coordination between centralized optimization at ADMS and the microgrid energy management system.
Growing cybersecurity risks in the power grid require that utilities implement a variety of security mechanisms (SMs), including VPNs, firewalls, authentication, and access control mechanisms. While ...these mechanisms provide some level of defense, they also may contain software vulnerabilities which allow an attack to bypass their protection. Because the same SM type is often used to protect a large number of substations, a single vulnerability could enable a coordinated attack by simultaneously targeting many substations. To protect against such an attack, utilities can adopt a strategy to use a diverse set of SMs, such that the impact from a vulnerability in any SM is minimized. This paper introduces a game-theoretic graph coloring technique to determine the optimal allocation of SM diversity that minimizes the impact of security vulnerabilities to the grid. This paper demonstrates that the proposed approach provides a Nash equilibrium solution. Furthermore, the technique is demonstrated against cyber-physical models for both IEEE-14 and IEEE-118 bus systems, and compared with other non-strategic diversity allocation methods to demonstrate its effectiveness.
Distribution-level phasor measurement units (D-PMU) data are prone to different types of anomalies given complex data flow and processing infrastructure in an active power distribution system with ...enhanced digital automation. It is essential to preprocess the data before being used by critical applications for situational awareness and control. In this work, two approaches for detection of data anomalies are introduced for offline (larger data processing window) and online (shorter data processing window) applications. A smoothing wavelet denoising method is used to remove high-frequency noises. An ensemble approach built upon the margin-based maximum likelihood estimator (MB-MLE) method is developed to detect anomalies in denoised data by integration of the results from different base detectors including Hampel filter, Quartile detector, and DBSCAN. The processed data with offline analysis is used to fit a model to the underlying dynamics of synchrophasor data using Koopman mode analysis, which is subsequently employed for online denoising and bad data detection using Kalman filter (KF). The parameters of the KF are adjusted adaptively based on similarity to the training dataset for model fitting purposes. Developed techniques have been validated for the modified IEEE test system with multiple D-PMUs, modeled and simulated in real-time for different case scenarios using the OPAL-RT hardware-in-the-loop simulator.
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