Orkun Karabasoglu

Distributed security constrained economic dispatch

In this paper, we investigate two decomposition methods for their convergence rate which are used to solve security constrained economic dispatch (SCED): 1) Lagrangian Relaxation (LR), and 2) Augmented Lagrangian Relaxation (ALR). First, the centralized SCED problem is posed for a 6-bus test network and then it is decomposed into subproblems using both of the methods. In order to model the tie-line between decomposed areas of the test network, a novel method is proposed. The advantages and drawbacks of each method are discussed in terms of accuracy and information privacy. We show that there is a tradeoff between the information privacy and the convergence rate. It has been found that ALR converges faster compared to LR, due to the large amount of shared data.

DC power flow estimation utilizing bayesian-based LMMSE estimator

In recent years, Smart Grid was introduced to achieve an environmentally-friendly, adequate, secure and fossil fuel-independent power system. The large scale smart grid studies require accurate state estimation to obtain an acceptable adequacy level. There exist some challenges regarding anomalous power flow studies which motivate grid operators to utilize robust and accurate estimation methods. Therefore, power system state estimators play a pivotal role in real-time grid management. In this paper, a sequential linear minimum mean square error (LMMSE) estimator is utilized to solve the DC power flow problem. First, we introduce the classic linear estimator model which assumes that to-be-estimated parameter values are unknown but deterministic. The LMMSE estimator will be discussed which treats the to-be-estimated parameter as a random variable with a known prior probability density function (pdf). We evaluate the accuracy of the LMMSE estimator by comparing it with maximum likelihood estimator (MLE). Finally, the effect of covariance matrix topology will be studied by defining three scenarios with different noise covariance matrices.

Optimal Autonomous Charging of Electric Vehicles with Stochastic Driver Behavior

This paper proposes the application of the Markov decision problem (MDP) framework for optimizing the autonomous charging of individual plug-in electric vehicles (EVs). Two infinite horizon average cost MDP formulations are described, one for plug-in hybrid electric vehicles (PHEVs) and one for battery only electric vehicles (BEVs). In both formulations, we assume no direct input from the driver to the smart charger about the drivers travel schedule. Instead, we use stochastic models of plug-in and unplug behaviors as well as energy required for transportation to represent a drivers charging requirements. We also assume that electric energy prices follow a Markov random process. These stochastic models can be built from historical data on vehicle usage. The objective of the MDPs is to minimize the sum of electric energy charging costs, driving costs, and the cost of any driver inconvenience. We demonstrate the solution of the MDPs with assumed parameter values and analyze the results. This work presents a new approach to minimizing EV charging costs while reducing the need for trip planning by a driver.

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 l1-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, which could be caused by data injection attacks. The effectiveness of the proposed sparsity-based decomposition-DC power flow approach is demonstrated on the IEEE 118-bus and 300-bus test systems.

Hierarchical Electric Vehicle Charging Aggregator Strategy Using Dantzig-Wolfe Decomposition

This article focuses on reducing a charging cost for electric vehicles (EVs). A charging strategy is proposed to minimize the charging cost of EVs within the charging station constraints.<italic>—Zili Shao</italic>

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.

Finding Risk-Averse Shortest Path with Time-Dependent Stochastic Costs

In this paper, we tackle the problem of risk-averse route planning in a transportation network with time-dependent and stochastic costs. To solve this problem, we propose an adaptation of the A* algorithm that accommodates any risk measure or decision criterion that is monotonic with first-order stochastic dominance. We also present a case study of our algorithm on the Manhattan, NYC, transportation network.

Turning conventional vehicles in secured areas into connected vehicles for safety applications

Remote and mobile vehicular monitoring has become a very important need since over speeding vehicles or reckless driving can be a threat to life and property. In this work, we propose a framework to convert the conventional vehicles into connected vehicles in secured areas such as educational institutions, residential societies, hospitals and etc. where the entry and exit points are secured by gates. We designed a GPS based wireless hardware system to monitor the speed and location of a moving vehicle. As the conventional vehicle enters through the gate, the security guard gives this device to the driver and removes it when the vehicle exits. Once activated inside the region, the system monitors the speed of the vehicle and communicates any rule violations to the security station in the premises and also to the driver. If the speed of the vehicle increases beyond the threshold limit, the vehicle driver is alerted and a warning message is communicated to the system. A record of the driving patterns is separately maintained at the receiver unit so that the penalizing action can be taken against the defaulters. The effectiveness of the proposed system is validated with the results of a field trial with different drivers, with and without our hardware prototype installed in the vehicle for a period of two days on a pre-defined route. The average number of over speeds from day 1 to day 2 was reduced by 63%. Our system is promising to increase safety in secured residential areas.

Economic Optimization of Indirect Sewage Sludge Heat Dryer Unit for Sewage Sludge Incineration Plants

This paper presents the economic optimization of indirect sewage sludge heat dryer for sewage sludge incineration plants. The objective function based on two-phase heat transfer, and economic relations is provided to demonstrate the optimum size for the minimum investment cost. De-watered sludge is fed into the dryer with a mass flow rate of 165 tons per day and consists of 27% dry matter. After the sludge drying process, the dryness of sludge increases up to 40%. In the indirect sludge dryer unit, thermal oil is used to heat the dryer wall and to prevent heat loss. Thermal oil is circulated in a closed cycle and gathered into an oil tank. Total cost of the sludge dryer unit changes proportional to the dryer area. The optimum dryer area is found as 32.54 m2. The corresponding minimum cost is found as

Regenerative Braking Control Strategy for Hybrid and Electric Vehicles Using Artificial Neural Networks

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