Showing papers in "Electric Power Systems Research in 2021"

TL;DR: A new hybrid forecast model for short-term electricity load and price prediction has been developed that consists of a deep learning algorithm with LSTM networks which improves the accuracy of predictions.

TL;DR: An extensive analysis based on a practical dataset of 5000 customers reveals that bagging models outperform other algorithms and the precision analysis shows that the proposed bagging methods perform better.

TL;DR: The results of experiments show the superiority of the proposed method compared to some recent works in the field of short-term load forecasting.

TL;DR: An updated review of most important frequency stability concerns, applied modern control strategies, and existing challenges for the integration of renewable energy sources is provided.

TL;DR: A novel interval prediction model based on temporal convolutional networks to forecast wind speed has a significant performance improvement on both prediction interval coverage probability and prediction interval width criteria and thus can be a practical tool for wind speed forecasting.

TL;DR: This paper presents a deep learning algorithm i.e. Convolutional Neural Network customized for fault classification in the distributed networks integrated with DGs using CNN for fault detection using raw and sampled-data of three-phase voltage and current signals of various fault classes and no-fault class.

TL;DR: This paper explores and analyses various microgrid protection techniques to find out their shortcomings and seek a viable solution for future smart microgrid.

TL;DR: The problems of the current industrialized APF have been analyzed by simulation, and other existing problems also have been summarized simply, such as supra-harmonics, background harmonics, multi-function power quality Controller.

TL;DR: A comprehensive flexibility definition and unified characterizing terms for flexibility resources are proposed and a taxonomy method which is applied to classify flexibility resources is presented, clearing the confusion on "what-is-what" under the concept of flexibility.

TL;DR: A novel set of formulations to determine the optimal BES size, technology, depth of discharge (DOD), and replacement year considering its technical characteristics, service life, and capacity degradation to minimize the MG scheduling total cost and improve the precision and economic feasibility of the BES sizing method are proposed.

TL;DR: A new methodology for optimal planning of charging stations (CS) along with capacitors (CAP) using quantum-behaved and Gaussian mutation strategies on the performance of DA is presented.

TL;DR: In the present work, an ODGA and NR processes have been incorporated to improve the voltage stability and loss profile of the distribution system considering probabilistic loads and DGs which are operated at varying pfs.

TL;DR: In this paper, an optimal framework for the resilience-oriented design (ROD) in distribution networks to protect these grids against extreme weather events such as earthquakes and floods is presented.

TL;DR: A bilevel stochastic optimization model for generating the optimal joint demand and virtual bidding strategy for a strategic retailer in the short-term electricity market, where virtual bidding is used to improve the retailer's market power in the day-ahead electricity market.

TL;DR: A multi-objective decision-making framework is proposed to determine the optimal scheduling of EHs and simulation results show that the proposed model improves the reserve capacity, emission, and system losses.

TL;DR: Generation cost model includes the no-load cost and nonlinear behavior of losses inside the DG, and the capability of an energy storage system (ESS) to compensate generation shortage and minimize the emission is investigated.

TL;DR: A comprehensive review of 42 NILM datasets aided by comparison tables, generated to elaborate on the diverse features of existing datasets and to help the researchers to evaluate the performance of new NilM algorithms.

TL;DR: An overview of mathematical modeling and optimization of demand response (DR) algorithms reported in the literature is presented, providing detailed information on modeling, implementation, uncertainty handling, and future research directions in the field of DR.

TL;DR: A stacked recurrent neural network with parametric sine activation function (PSAF) algorithm for wind power forecasting, whose parametrization can be tuned adaptively and iteratively during prediction, to illustrate high capability in retrieving manifold features in wind power sources.

TL;DR: This study presents a novel method for classifying faults which compares transfer function indices called statistical control chart and shows that visual fault detection is increased and classification accuracy is improved verifying high performance of this method in detecting and determining various faults.

TL;DR: The issues such as trend of papers during recent years, common test systems and the main gaps in the literature are addressed and the comprehensive comparison tables are introduced to show the additional information such as test systems, response times and required infrastructures.

TL;DR: The proposed SCGTEP is tested on the 6-bus and 118-bus IEEE networks in the GAMS software and can be simultaneously improved operation and security indices about 34.5% and 100%, respectively, compared to the power flow analysis based on the optimal location of generation units and transmission lines.

TL;DR: In this paper, the authors aim to calculate and assess the electricity load trends for Brazil and its geographic regions, considering the changes due to the COVID-19 pandemic, and the analysis comprehended the period between January 1 and September 29, 2020.

TL;DR: A computationally-efficient algorithm is proposed to solve the formulated optimal control problem and show the effectiveness of the employed control framework on a commercial utility-scale 720 kVA/560 kWh BESS.

TL;DR: The results of the example show that the optimization model can effectively realize the orderly charging of Electric Vehicles, release the peak shaving capacity, absorb the excess wind and photovoltaic power, and reduce costs of LAs and users.

TL;DR: This work proposes a methodology to define the optimal location of EV semi-fast charging stations (CS) at a neighborhood level, through a multi-objective approach that applies a hierarchical clustering method to define CS service zones, considering both technical and mobility aspects.

TL;DR: This study proposes an optimal location and reference set point for static synchronous series compensator (SSSC) for maximizing the system predictability, minimizing the total active power loss, and increasing the reliability of the system, simultaneously.

TL;DR: An intelligent anomaly identification (IAI) technique for such systems is presented utilizing data driven artificial intelligence tools that employ multi class support vector machines (MSVM) for anomaly classification and localization.

TL;DR: A review and comparison of the active power control and inertial capabilities of ten VSG solutions available in the literature and a common tuning procedure is proposed to obtain a fair performance comparison.

TL;DR: A two-layer energy management model (EMM) in the smart distribution network (SDN) considering flexi-renewable virtual power plants (FRVPPs) that participate in the day-ahead energy and reserve markets is presented.