Abstract
The integration of renewable generation and electric vehicles (EVs) into smart grids poses an additional challenge to the stochastic energy resource management problem due to the uncertainty related to weather forecast and EVs user-behavior. Moreover, when electricity markets are considered, market price variations cannot be disregarded. In this paper, a two-stage stochastic programming approach to schedule the day-ahead operation of energy resources in smart grids under uncertainty is presented. A realistic case study is performed using a large-scale scenario with nearly 4 million variables with the goal to minimize expected operation cost of energy aggregators. Three scenarios are analyzed to understand the effect of market transactions and external suppliers on the aggregator model. The results suggest that the market transactions can reduce expected cost, while the external supplier offers risk-free price. In addition, the performance metric shows the superiority of the stochastic approach over an equivalent deterministic model. © 2018 Power Systems Computation Conference.

Abstract

Abstract
Writing mixed-precision kernels allows to achieve higher throughput together with outputs whose precision remain within given limits. The recent introduction of native half-precision arithmetic capabilities in several GPUs, such as NVIDIA P100 and AMD Vega 10, contributes to make precision-tuning even more relevant as of late. However, it is not trivial to manually find which variables are to be represented as half-precision instead of single- or double-precision. Although the use of half-precision arithmetic can speed up kernel execution considerably, it can also result in providing non-usable kernel outputs, whenever the wrong variables are declared using the half-precision data-type. In this paper we present an automatic approach for precision tuning. Given an OpenCL kernel with a set of inputs declared by a user (i.e., the person responsible for programming and/or tuning the kernel), our approach is capable of deriving the mixed-precision versions of the kernel that are better improve upon the original with respect to a given metric (e.g., time-to-solution, energy-to-solution). We allow the user to declare and/or select a metric to measure and to filter solutions based on the quality of the output. We implement a proof-of-concept of our approach using an aspect-oriented programming language called LARA. It is capable of generating mixed-precision kernels that result in considerably higher performance when compared with the original single-precision floating-point versions, while generating outputs that can be acceptable in some scenarios.

Abstract
This paper presents a novel high-gain transimpedance amplifier for flexible radiation sensing systems that can be used as large-area dosimeters. The circuit is implemented with indium-gallium-zinc-oxide thin-film-transistors and uses two stages for the amplification of the sensor signal (current). The first stage consists of cascode current mirrors with a diode connected load that performs current amplification and voltage conversion. Then, the first stage is followed by a voltage amplifier based on a positive feedback topology for gain enhancement. The proposed circuit converts nano-ampere (10 nA) currents into hundreds of millivolts (280 mV), showing a gain around 149 dB and a power consumption of 0.45 mW. The sensed radiation dose level, in voltage terms, can drive the next stages in the radiation sensing system, such as analog to digital converters. These radiation sensing devices can find potential applications in real-time, large area, flexible health, and security systems.

Abstract
This paper proposes a new optimization tool based on the cross-entropy (CE) method to assess security-constrained optimal power flow (SCOPF) solutions. First, the corresponding SCOPF stochastic problem is defined so that the optimum solution is interpreted as a rare event to be reached by a random search. Second, the CE method solves this new problem efficiently by making adaptive changes to the probability density function according to the Kullback-Leibler distance, creating a sequence of density functions that guides the search in the direction of the theoretically degenerate density at the optimal point. Different types of density functions are tested in order to cope with discrete variables present in the SCOPF problem. Two test systems, namely the IEEE 57 bus and the IEEE 300 bus, are used to evaluate the effectiveness of the proposed method in terms of solution quality and computational effort. Comparisons carried out with reference algorithms in the literature demonstrate that the CE method is capable of finding better solutions for the SCOPF problem with fewer evaluations.

Abstract
This paper presents a high speed digitally programmable Ring Oscillator (RO) using Indium-galliumzinc oxide thin-film transistors (IGZO TFTs). Proposed circuit ensures high speed compared to the conventional ROs using negative skewed scheme, in which each inverter delay is reduced by pre-maturely switching on/off the transistors. In addition, by controlling the load capacitance of each inverter through digital control bits, a programmable frequency of oscillation was attained. Proposed RO performance is compared with two conventional designs under same conditions. From simulation, it has been observed that the proposed circuit has shown a higher frequency of oscillations (283 KHz) compared to the conventional designs (76.52 KHz and 144.9 KHz) under same conditions. Due to the programmable feature, the circuit is able to generate 8 different linearly spaced frequencies ranging from 241.2 KHz to 283 KHz depending upon three digital control bits with almost rail-to-rail voltage swing. The circuit is a potential on-chip clock generator in many real-world flexible systems, such as, smart packaging, wearable devices, RFIDs and displays that need multi frequencies.