Distribution market as a ramping aggregator for grid flexibility support

The growing proliferation of microgrids and distributed energy resources in distribution networks has resulted in the development of Distribution Market Operator (DMO). This new entity will facilitate the management of the distributed resources and their interactions with upstream network and the wholesale market. At the same time, DMOs can tap into the flexibility potential of these distributed resources to address many of the challenges that system operators are facing. This paper investigates this opportunity and develops a distribution market scheduling model based on upstream network ramping flexibility requirements. That is, the distribution network will play the role of a flexibility resource in the system, with a relatively large size and potential, to help bulk system operators to address emerging ramping concerns. Numerical simulations demonstrate the effectiveness of the proposed model on when tested on a distribution system with several microgrids.Comment: IEEE PES Transmission and Distribution Conference and Exposition (T&D), Denver, CO, 16-19 Apr. 201

Capturing Distribution Grid-Integrated Solar Variability and Uncertainty Using Microgrids

The variable nature of the solar generation and the inherent uncertainty in solar generation forecasts are two challenging issues for utility grids, especially as the distribution grid integrated solar generation proliferates. This paper offers to utilize microgrids as local solutions for mitigating these negative drawbacks and helping the utility grid in hosting a higher penetration of solar generation. A microgrid optimal scheduling model based on robust optimization is developed to capture solar generation variability and uncertainty. Numerical simulations on a test feeder indicate the effectiveness of the proposed model.Comment: IEEE Power and Energy Society General Meeting, 201

Machine Learning Applications in Estimating Transformer Loss of Life

Transformer life assessment and failure diagnostics have always been important problems for electric utility companies. Ambient temperature and load profile are the main factors which affect aging of the transformer insulation, and consequently, the transformer lifetime. The IEEE Std. C57.911995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem.Comment: IEEE Power and Energy Society General Meeting, 201

Numerical Studies and Optimization of Magnetron with Diffraction Output (MDO) Using Particle-in-Cell Simulations

The first magnetron as a vacuum-tube device, capable of generating microwaves, was invented in 1913. This thesis research focuses on numerical simulation-based analysis of magnetron performance. The particle-in-cell (PIC) based MAGIC software tool has been utilized to study the A6 and the Rising-Sun magnetron structures, and to obtain the optimized geometry for optimizing the device performance. The A6 magnetron is the more traditional structure and has been studied more often. The Rising-Sun geometry, consists of two alternating groups of short and long vanes in angular orientation, and was created to achieve mode stability. The effect of endcaps, changes in lengths of the cathode, the location of cathodes with respect to the anode block, and use of transparent cathodes have been probed to gauge the performance of the A6 magnetron with diffraction output. The simulations have been carried out with different types of endcaps. The results of this thesis research demonstrate peak output power in excess of 1GW, with efficiencies on the order of 66% for magnetic (B)-fields in the range of 0.4T - 0.42T. In addition, particle-in-cell simulations have been performed to provide a numerical evaluation of the efficiency, output power and leakage currents for a 12-cavitiy, Rising-Sun magnetron with diffraction output with transparent cathodes. The results demonstrate peak output power in excess of 2GW, with efficiencies on the order of 68% for B-fields in the 0.42T - 0.46T range. While slightly better performance for longer cathode length has been recorded. The results show the efficiency in excess of 70% and peak output power on the order of 2.1GW for an 18 cm cathode length at 0.45T magnetic field and 400 kV applied voltage. All results of this thesis conform to the definite advantage of having endcaps. Furthermore, the role of secondary electron emission (SEE) on the output performance of the12-cavity, 12-cathodes Rising-Sun magnetron has been probed. The results indicate that the role of secondary emission is not very strong, and leads to a lowering of the device efficiency by only a few percentage points

Leveraging Sensory Data in Estimating Transformer Lifetime

Transformer lifetime assessments plays a vital role in reliable operation of power systems. In this paper, leveraging sensory data, an approach in estimating transformer lifetime is presented. The winding hottest-spot temperature, which is the pivotal driver that impacts transformer aging, is measured hourly via a temperature sensor, then transformer loss of life is calculated based on the IEEE Std. C57.91-2011. A Cumulative Moving Average (CMA) model is subsequently applied to the data stream of the transformer loss of life to provide hourly estimates until convergence. Numerical examples demonstrate the effectiveness of the proposed approach for the transformer lifetime estimation, and explores its efficiency and practical merits.Comment: 2017 North American Power Symposium (NAPS), Morgantown, WV, 17-19 Sep. 201

Analysis of distributed ADMM algorithm for consensus optimization in presence of error

ADMM is a popular algorithm for solving convex optimization problems. Applying this algorithm to distributed consensus optimization problem results in a fully distributed iterative solution which relies on processing at the nodes and communication between neighbors. Local computations usually suffer from different types of errors, due to e.g., observation or quantization noise, which can degrade the performance of the algorithm. In this work, we focus on analyzing the convergence behavior of distributed ADMM for consensus optimization in presence of additive node error. We specifically show that (a noisy) ADMM converges linearly under certain conditions and also examine the associated convergence point. Numerical results are provided which demonstrate the effectiveness of the presented analysis

FPGA-based true random number generation using circuit metastability with adaptive feedback control

13th International Workshop, Nara, Japan, September 28 – October 1, 2011. ProceedingsThe paper presents a novel and efficient method to generate true random numbers on FPGAs by inducing metastability in bi-stable circuit elements, e.g. flip-flops. Metastability is achieved by using precise programmable delay lines (PDL) that accurately equalize the signal arrival times to flip-flops. The PDLs are capable of adjusting signal propagation delays with resolutions higher than fractions of a pico second. In addition, a real time monitoring system is utilized to assure a high degree of randomness in the generated output bits, resilience against fluctuations in environmental conditions, as well as robustness against active adversarial attacks. The monitoring system employs a feedback loop that actively monitors the probability of output bits; as soon as any bias is observed in probabilities, it adjusts the delay through PDLs to return to the metastable operation region. Implementation on Xilinx Virtex 5 FPGAs and results of NIST randomness tests show the effectiveness of our approach

Determination of the constants of damage models

AbstractDamage models are basic elements in numerical simulation materials failure in particular at high strain rates. Many damage models can be found in the literature. However, a few of them such as GNT (Gurson, Tvergaard and Needleman) and Johnson-Cook models have gained wide application in the simulations. The models involve a number of constants to be determined normally by experiment in which void development in the specimen must be considered. This is usually a hard task and the results are always questionable. In this investigation a combined experimental, numerical and optimization technique is employed for identification of the constants of Johnson-Cook material and damage model. The experiments are conducted at low to high strain rate regimes using standard testing devices such Instron and high rate apparatuses such as “Flying Wedge”. The experiments are simulated using the same specimen geometries and the apparatus. The simulations are carried out using the commercial codes, Ls-dyna. The differences between the deformed shapes of the specimens from the experiments and those predicted from the numerical simulations are taken as the objective function for optimization purposes. The optimum constants are obtained using generic algorithm