Showing papers in "International Journal of Electrical Power & Energy Systems in 2022"

TL;DR: In this article, the authors reviewed the history, state-of-the-art and the future of the DL's application in power system frequency analysis and control, and the application status of DL in frequency situation awareness, frequency security and stability assessment, and frequency regulation and control were summarized.

TL;DR: In this article , the authors reviewed the history, state-of-the-art and the future of the DL's application in power system frequency analysis and control, and the application status of DL in frequency situation awareness, frequency security and stability assessment, and frequency regulation and control.

TL;DR: The proposed technique produces a comprehensive generator cost reduction of the microgrid system and the statistical analysis endorses the improvements of GWOSCACSA over other algorithms presented in the state-of-art-literature.

TL;DR: In this paper, a modified structure of the tilted integral derivative (TID) controller is developed for the load frequency control issue of a multi-area interconnected multi-source power system and a new optimization algorithm known as Archimedes optimization algorithm (AOA) is used to fine-tune the proposed ID-T controller parameters.

TL;DR: In this paper , the authors proposed a hybrid model for short-term load forecasting, namely the RF-MGF-RSM model, that hybridizes random forest model and the mean generating function (MGF) model.

TL;DR: In this article , a dual-stage attention-based LSTM network is proposed for short-term zonal load probabilistic forecasting, where a feature attention based encoder is built to calculate the correlation of input features with electricity load at each time step, and a temporal attention based decoder is developed to mine the time dependencies.

TL;DR: It is argued that blockchain is not a panacea for energy systems because blockchain’s component technologies have their own generic issues, and that the application of energy blockchain should be accompanied with improvement measures that conform to practical requirements of energy systems.

TL;DR: In this article, a hybrid forecasting model is developed by using the decomposition strategy, nonlinear weighted combination, and two deep learning models to overcome the drawbacks of the linear weighted combination and further enhance wind power forecasting accuracy and stability.

TL;DR: In this article, a virtual synchronous generator (VSG) control for photovoltaic (PV) generation was introduced to provide frequency support without energy storage, where PV generation reserve a part of the active power in accordance with the pre-defined power versus voltage curve.

TL;DR: This paper provides a comprehensive summary of inertia definitions for both synchronous generators and CIGs as well as their corresponding estimation methods and offers for the first time a framework to quantify the virtual inertia of Cigs at the component and aggregation levels.

TL;DR: This model combines the principle of deep learning algorithm and the improved BP neural network to deal with nonlinear wind speed prediction and shows that this model can achieve the best performance and obtain higher prediction accuracy.

TL;DR: In this paper , a combination forecasting model based on Long Short-Term Memory (LSTM) network and BP neural network is designed to deal with nonlinear wind speed prediction.

TL;DR: In this article , the authors proposed a two-stage optimization model for the optimal sizing and siting of electrical energy storage devices (EESDs) in Smart Grids, accounting for the presence of time-varying electricity tariffs due to Demand Response Program (DRP) participation.

TL;DR: In this article , a robust modified perturb and observe (MPO) maximum power point tracking (MPPT) algorithm is proposed to improve the tracking speed of the PV system by dividing the solar module/cell P-V curve into four operating areas depending on the open circuit voltage estimation method.

TL;DR: In this article, the echo state network is used for the classification of the insulators based on the ultrasound signal, which achieves 87.36 % accuracy for the multiclassification and 99.99 % for the specific classification of drilling.

TL;DR: In this article, an ensemble learning model that combines bagging and stacking methods applied to time series forecasting with very short-term (10 and 30-minutes) and shortterm (60 and 120 minutes) evaluations of wind power generation is evaluated.

TL;DR: The present work addresses the need to reduce the operating cost of multi-microgrids and improve the convergence performance of the solution algorithms applied for their optimized electric power dispatch when considering the uncertainties associated with existing loads, renewable energy sources, and electric vehicle usage by proposing a novel double-layer robust optimization dispatch model.

TL;DR: In this paper , the authors proposed federated learning for load forecasting with smart meter data, which enables training a single model with all participating smart meters without the need to share local data.

TL;DR: Simulation results in EMTP-RV indicate that the proposed adaptive droop control scheme is beneficial to improving the frequency nadir, preventing DFIGs from stalling, and minimizing the second drop in the frequency under various conditions.

TL;DR: A systematic overview of the positive roles of buildings and interactive transaction behaviors of electric vehicles in establishing sustainable transactive energy community from energy physical space, data cyber space, and human social space is provided.

TL;DR: In this paper , the authors present an overview of the latest research of EV charging stations and highlight some important issues and challenges in power architectures design, energy storage techniques, control strategies of micro-grid, and energy management optimization.

TL;DR: Simulation and experimental results show that the FTFOSMC can achieve superior dynamic performances than the PI control with shorter convergence time, less vibration, higher tracking accuracy and stronger robustness, thus improving power quality of the PMSG wind turbine.

TL;DR: This paper proposes a novel multi-task load forecasting method for predicting both active and reactive power simultaneously, using the long short-term memory (LSTM) architecture in the backbone prediction model, supported by an attention mechanism to prevent performance deterioration.

TL;DR: In this paper , a multinode charging load joint adversarial generation interval-forecasting method considering the spatial correlation of the charging load between nodes is proposed to effectively forecast the spatial-temporal distribution of EV charging load.

TL;DR: Evaluations demonstrate the capability of the C-LSTM followed by SVM, DT, k-NN, CNN, and LSTM in categorizing the type and location of transmission line faults.

TL;DR: In this article , the authors provide a comprehensive summary of inertia definitions for both synchronous generators and converter-interfaced generation (CIGs) as well as their corresponding estimation methods.

TL;DR: In this paper, a stochastic many-objective solution framework based on mixed integer linear programming (MILP) formulation is proposed for home energy management systems, where the energy cost is normally the most important objective while other goals can be considered secondary.

TL;DR: In this article , an efficient but effective stochastic many-objective solution framework based on Mixed Integer Linear Programming (MILLP) is proposed for home energy management systems.

TL;DR: In this paper , a cooperative game theory framework is proposed to expedite stable trading algorithms and incentivize individual users to participate in P2P energy trading, and an economic analysis to distribute the revenue generated from the grand coalition in a fair manner is analyzed in this work.

TL;DR: In this paper, the authors reviewed different interconnection methods and control challenges of AC and DC microgrids in HMGs and then overviewed various control strategies of BILPCs presented in literature, all carried out in a comprehensive manner.