Sparsity-Based Error Detection in DC Power Flow State Estimation

This paper presents a new approach for identifying the measurement error in the DC power flow state estimation problem. The proposed algorithm exploits the singularity of the impedance matrix and the sparsity of the error vector by posing the DC power flow problem as a sparse vector recovery problem that leverages the structure of the power system and uses $l_1$-norm minimization for state estimation. This approach can provably compute the measurement errors exactly, and its performance is robust to the arbitrary magnitudes of the measurement errors. Hence, the proposed approach can detect the noisy elements if the measurements are contaminated with additive white Gaussian noise plus sparse noise with large magnitude. The effectiveness of the proposed sparsity-based decomposition-DC power flow approach is demonstrated on the IEEE 118-bus and 300-bus test systems

Optimization of plasmonic nano-antennas

The interaction of light with plasmonic nano-antennas is investigated. First, an extensive parametric study is performed on the material and geometrical effects on dipole and bow-tie nano-antennas. The transmission efficiency is studied for various parameters including length, thickness, width, and composition of the antenna as well as the wavelength of incident light. The modeling and simulation of these structures is done using 3-D finite element method based full-wave solutions of Maxwell’s equations. Next, a modeling-based automated design optimization framework is developed to optimize nano-antennas. The electromagnetic model is integrated with optimization solvers such as gradient-based optimization tools and genetic algorithms

Comparative analysis of approximation methods in electromagnetic design

Design optimization of complex electromagnetic devices such as antennas with volumetric material and conductor variations require high computational resources. This burden can be reduced by introducing cost-effective surrogate models into the design optimization framework. Here we present a comparative analysis of various surrogate modeling techniques for material design studies of electromagnetic devices. Also a new polynomial based approximation technique is proposed for modeling frequency based responses of complex electromagnetic devices especially exhibiting multi-resonance behavior

Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks

Electrified transportation and power systems are mutually coupled networks. In this paper, a novel framework is developed for interdependent power and transportation networks. Our approach constitutes solving an iterative least cost vehicle routing process, which utilizes the communication of electrified vehicles (EVs) with competing charging stations, to exchange data such as electricity price, energy demand, and time of arrival. The EV routing problem is solved to minimize the total cost of travel using the Dijkstra algorithm with the input from EVs battery management system, electricity price from charging stations, powertrain component efficiencies and transportation network traffic conditions. Through the bidirectional communication of EVs with competing charging stations, EVs’ charging demand estimation is done much more accurately. Then the optimal power flow problem is solved for the power system, to find the locational marginal price at load buses where charging stations are connected. Finally, the electricity prices were communicated from the charging stations to the EVs, and the loop is closed. Locational electricity price acts as the shared parameter between the two optimization problems, i.e., optimal power flow and optimal routing problem. Electricity price depends on the power demand, which is affected by the charging of EVs. On the other hand, location of EV charging stations and their different pricing strategies might affect the routing decisions of the EVs. Our novel approach that combines the electrified transportation with power system operation, holds tremendous potential for solving electrified transportation issues and reducing energy costs. The effectiveness of the proposed approach is demonstrated using Shanghai transportation network and IEEE 9-bus test system. The results verify the cost-savings for both power system and transportation networks

Smart vehicle monitoring and assistance using cloud computing in vehicular Ad Hoc networks

The increasing number of on road vehicles has become a major cause for congestion, accidents and pollution. Intelligent Transportation Systems (ITS) might be the key to achieve solutions that help in reducing these problems significantly. The connected vehicular networks stream is a rapidly growing field for research and development of various real-time applications. In this paper, novel techniques have been proposed to serve the speed based lane changing, collision avoidance and time of arrival (TOA) based localization in Vehicular Ad Hoc Networks (VANETs). As GPS requires clear line-of-sight for accurate services of positioning and localization applications, we designed a Time of Arrival (ToA) based algorithm for areas where strong GPS signals are unavailable. Collision avoidance using automatic braking and camera-based surveillance are a few other applications that we addressed. The feasibility and the viability of the algorithms were demonstrated through simulations in Simulation of Urban Mobility (SUMO) and Network Simulator-2 (NS-2). We prototyped a working hardware and tested it on actual vehicles to assess the effectiveness of the proposed system. We designed a mobile app interface for the on-board unit for smart, efficient and remote traffic monitoring. The integrated VANET Cloud Computing architecture acts as the platform for the proposed applications

ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation

Large-scale utilization of electric vehicles (EVs) affects the total electricity demand considerably. Demand forecast is usually designed for the seasonally changing load patterns. However, with the high penetration of EVs, daily charging demand makes traditional forecasting methods less accurate. This paper presents an autoregressive integrated moving average (ARIMA) method for demand forecasting of conventional electrical load (CEL) and charging demand of EV (CDE) parking lots simultaneously. Our EV charging demand prediction model takes daily driving patterns and distances as an input to determine the expected charging load profiles. The parameters of the ARIMA model are tuned so that the mean square error (MSE) of the forecaster is minimized. We improve the accuracy of ARIMA forecaster by optimizing the integrated and auto-regressive order parameters. Furthermore, due to the different seasonal and daily pattern of CEL and CDE, the proposed decoupled demand forecasting method provides significant improvement in terms of error reduction. The impact of EV charging demand on the accuracy of the proposed load forecaster is also analyzed in two approaches: (1) integrated forecaster for CEL + CDE, and (2) decoupled forecaster that targets CEL and CDE independently. The forecaster outputs are used to formulate a chance-constrained day-ahead scheduling problem. The numerical results show the effectiveness of the proposed forecaster and its influence on the stochastic power system operation

ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation

Large-scale utilization of electric vehicles (EVs) affects the total electricity demand considerably. Demand forecast is usually designed for the seasonally changing load patterns. However, with the high penetration of EVs, daily charging demand makes traditional forecasting methods less accurate. This paper presents an autoregressive integrated moving average (ARIMA) method for demand forecasting of conventional electrical load (CEL) and charging demand of EV (CDE) parking lots simultaneously. Our EV charging demand prediction model takes daily driving patterns and distances as an input to determine the expected charging load profiles. The parameters of the ARIMA model are tuned so that the mean square error (MSE) of the forecaster is minimized. We improve the accuracy of ARIMA forecaster by optimizing the integrated and auto-regressive order parameters. Furthermore, due to the different seasonal and daily pattern of CEL and CDE, the proposed decoupled demand forecasting method provides significant improvement in terms of error reduction. The impact of EV charging demand on the accuracy of the proposed load forecaster is also analyzed in two approaches: (1) integrated forecaster for CEL + CDE, and (2) decoupled forecaster that targets CEL and CDE independently. The forecaster outputs are used to formulate a chance-constrained day-ahead scheduling problem. The numerical results show the effectiveness of the proposed forecaster and its influence on the stochastic power system operation