Dynamic rating monitoring is a solution to face challenges in both the operation and the planning of power systems. With thermal monitoring, dynamic overloads of transformers are allowed, however, the thermal model used to predict hot-spot must be capable of ensuring the assessment of insulation life expectancy. Hence, this paper presents an online algorithm to estimate and predict transformers' rating in order to assess the overload capability during short and long times. The overload factors are computed using estimations of top-oil temperature. The main characteristic of this proposal is that the environmental cooling conditions are considered by the fitting of the parameters of a simple equivalent thermal circuit using an Extended Kalman Filter during the transformer operation. To validate the implementation of the algorithm, the information provided by typical transformer rating monitoring systems is used. Thus, the performance and effectiveness of the algorithm are verified using field measurement data during the normal operation of two power transformers.

The traditional methodology for defining the ampacity of overhead lines is based on conservative criteria regarding the operating conditions of the line, leading to the so-called static line rating. Although this procedure has been considered satisfactory for decades, it is nowadays sensible to account for more realistic line operating conditions when calculating its dynamic ampacity. Dynamic line rating is a technology used to improve the ampacity of overhead transmission lines based on the assumption that ampacity is not a static value but a function of weather and line's operating conditions. This paper presents an algorithm to estimate and predict the temperature in overhead line conductors using an Extended Kalman Filter. The proposed algorithm assumes both actual weather and current intensity flowing along the conductor as control variables. The temperature of the conductor, mechanical tension and sag of the catenary are used as measurements because the common practice is to measure these values with dynamic line rating hardware. The algorithm has been validated by both simulations and measurements. The results of this study conclude that it is possible to implement the algorithm into Dynamic Line Rating systems, leading to a more accurate estimation and prediction of temperature.

Increased use of renewable energies in smart microgrids (SMGs) present new technical challenges to system operation. SMGs must be self-sufficient and operate independently; however, when more elements are integrated into SMGs, as distributed energy resources (DER), traditional explicit mathematical formulations will demand too much data from the network and become intractable. In contrast, tools based on optimization with metaheuristics can provide near optimal solutions in acceptable times. Considering this, this paper presents the variable neighborhood search differential evolutionary particle swarm optimization (VNS-DEEPSO) algorithm to solve multi-objective stochastic control models, as SMGs system operation. The goal is to control DER while maximizing profit. In this work, DER considered the bidirectional communication between energy storage systems (ESS) and electric vehicles (EVs). They can charge/discharge power and buy/sell energy in the electricity markets. Also, they have elements such as traditional generators (e.g., reciprocating engines) and loads, with demand response/control capability. Sources of uncertainty are associated with weather conditions, planned EV trips, load forecasting and the market prices. The VNS-DEEPSO algorithm was the winner of the IEEE Congress on Evolutionary Computation/The Genetic and Evolutionary Computation Conference (IEEE-CEC/GECCO 2019) smart grid competition (with encrypted code) and also won the IEEE World Congress on Computational Intelligence (IEEE-WCCI) 2018 smart grid competition (these competitions were developed by the group GECAD, based at the Polytechnic Institute of Porto, in collaboration with Delft University and Adelaide University). In the IEEE-CEC/GECCO 2019, the relative error improved between 32% and 152% in comparison with other algorithms.

Power systems are susceptible to disturbances due to their nature. These disturbances can cause overloads or even contingencies of greater impact. In case of an extreme situation, load curtailment is considered the last resort for reducing the contingency impact, its activation being necessary to avoid the collapse of the system. However, load shedding systems seldom work optimally and cause either excessive or insufficient reduction of the load. To resolve this issue, the present paper proposes a methodology to enhance the load curtailment management in medium voltage distribution systems using Particle Swarm Optimization (PSO). This optimization seeks to minimize the amount of load to be cut off. Restrictions on the optimization problem consist of the security operation margins of both loading and voltage of the system elements. Heuristic optimization algorithms were chosen, since they are considered an online basis (allowing a short processing time) to solve the formulated load curtailment optimization problem. Best performances regarding optimal value and processing time were obtained using a PSO algorithm, qualifying the technique as the most appropriate for this study. To assess the methodology, the CIGRE MV distribution network benchmark was used, assuming dynamic load profiles during an entire week. Results show that it is possible to determine the optimal unattended power of the system. This way, improvements in the minimization of the expected energy not supplied (ENS) as well as the System Average Interruption Frequency Index (SAIDI) at specific hours of the day were made.

In this study we demonstrate the capabilities of scanning spreading resistance microscopy (SSRM), which is an atomic force microscope based technique, for two-dimensional (2D) carrier profiling with nanometer spatial resolution, high quantification accuracy, and high concentration sensitivity. As a test vehicle we discuss the application of SSRM on devices obtained from a 90 nm node complementary metal–oxide–semiconductor technology. Transistors processed in this technology, and with poly-gate lengths down to 70 nm have been analyzed using SSRM. Dedicated quantification software has been used to calculate carrier concentration profiles. The source/drain implants and the halo- and threshold voltage adjustment implants have been analyzed for different gate sizes in order to understand their impact on the 2D channel carrier profile. The SSRM results have been compared to the results of calibrated process simulators.

The power system expansion and the integration of technologies, such as renewable generation, distributed generation, high voltage direct current, and energy storage, have made power system simulation challenging in multiple applications. The current computing platforms employed for planning, operation, studies, visualization, and the analysis of power systems are reaching their operational limit since the complexity and size of modern power systems results in long simulation times and high computational demand. Time reductions in simulation and analysis lead to the better and further optimized performance of power systems. Heterogeneous computing—where different processing units interact—has shown that power system applications can take advantage of the unique strengths of each type of processing unit, such as central processing units, graphics processing units, and field-programmable gate arrays interacting in on-premise or cloud environments. Parallel Heterogeneous Computing appears as an alternative to reduce simulation times by optimizing multitask execution in parallel computing architectures with different processing units working together. This paper presents a review of Parallel Heterogeneous Computing techniques, how these techniques have been applied in a wide variety of power system applications, how they help reduce the computational time of modern power system simulation and analysis, and the current tendency regarding each application. We present a wide variety of approaches classified by technique and application.