Showing papers in "Electric Power Systems Research in 2023"

TL;DR: In this paper , the authors provide an overview of existing methods for modeling and optimization of problems affected by uncertainty, targeted at researchers with a familiarity with power systems and optimization, and provide an outlook to future directions of research.

TL;DR: In this article , the authors present the main barriers, research gaps, gains and suggestions for applying deep learning to power quality, including lack of novelty, low transparency of deep learning methods and lack of benchmark databases.

TL;DR: In this paper , a multi-objective whale optimization (MOWOA) algorithm was proposed to solve the problem of optimal sizing and placement of distributed generation (DG) units.

TL;DR: A comprehensive review of existing research and pilot projects on P2P energy trading is provided in this article , where the authors provide a detailed analysis of the existing research, implementation methodologies, and demonstration projects.

TL;DR: In this article , the authors used deep learning techniques and regression were used to identify patterns in the environmental data and forecasted electric power transmission line ampacity at different lead times and evaluated them with point and probabilistic error metrics.

TL;DR: In this article , a model predictive controller aided with Leader Harris Hawks Optimization (MPC-LHHO) algorithm is proposed for the regulation of frequency and voltage in renewable penetrated power systems.

TL;DR: In this paper , the secondary frequency control problem defending DoS attacks in multi-terminal high voltage direct current (MTDC) systems is addressed, and two strategies respectively using the reference value and the data of the previous time interval are proposed to compensate for the lost data due to DoS attack.

TL;DR: In this paper , a new voltage compensation mechanism is presented to resolve the control issues of DC microgrid in a distributed manner, where a fractional-order voltage compensation term is used in the outer controller loop which eliminates the voltage deviation in the steady-state condition.

TL;DR: In this article , the authors proposed a stochastic multi-objective framework considering demand-side flexibility to enhance the performance of demand response programs in distribution networks, and the proposed model remains the operating cost of multi-microgrid systems at its optimal.

TL;DR: Wang et al. as discussed by the authors combined variational mode decomposition (VMD), the federated k-means clustering algorithm (FK), and SecureBoost into a single algorithm, called VMD-FK-SecureBoost.

TL;DR: In this paper , a new algorithm for energy storage system (ESS) scheduling has been suggested in order to manage MG in a reliable manner, because reliability considering and cost minimization are conflicting objectives in ESS scheduling, the multiobjective optimization problem should be solved for optimal scheduling of ESS.

TL;DR: In this article , the optimal operation of a microgrid with several distributed generation (DG) units and uncertain behavior of RESs is suggested in this research using a stochastic optimization approach.

TL;DR: In this article , the relay curve types of the first and second parts of the DS relay models and breakpoints (BPs) were optimized to improve the speed of the protection scheme based on independent changes in the setting groups of DOCRs.

TL;DR: In this article , the authors investigated the use of advanced signal processing and deep learning for pattern recognition and classification of signals with power quality disturbances, where the continuous wavelet transform is used to generate 2D images with the time-frequency representation from signals with voltage disturbances.

TL;DR: In this paper , the relay coordination problem is formulated and solved using hybrid GA-NLP approach. And the proposed relay coordination strategy is validated on the meshed distribution system with synchronous-based DGs.

TL;DR: In this article , the authors proposed a taxonomy of voltage control approaches for real-world comparative performance analysis and deployability of these controllers, considering three inter-disciplinary domains: power system, optimization and decision-making, and networking and cyber-security.

TL;DR: In this paper , the authors proposed an ensemble framework for short-term load forecasting based on parallel convolutional neural network (CNN) and gated recurrent unit (GRU) with improved ResNet (iResNet).

TL;DR: In this article , a two-level structure of optimal planning and operation of the energy hub (EH) based on demand uncertainty and renewable energy resources (RES) is presented, where the optimal planning based on stochastic-probability models is presented at the primary and optimal operation based on Stochastic-Probability Models (SPM) at the secondary level.

TL;DR: In this paper , the authors provided a more accurate estimation of operating costs for the distribution system operator (DSO) under considering the effects of upstream market uncertainty, which encourages the DSO to trade energy with MEMG and improve the energy trading of the local market.

TL;DR: In this paper , the authors proposed a new application of Pelican Optimization Algorithm (POA) for optimal Energy Management (EM) in microgrid (MG) considering Demand Response program (DRP).

TL;DR: In this article , the benefits and drawbacks of several control architectures that function as distributed, centralized, and decentralized controls have been discussed, and the principle of operation and effectiveness of each microgrid architecture control method has been discussed.

TL;DR: In this paper , a hybrid model incorporating CEEMDAN, SE-TR, and TR model is proposed for short-term load forecasting (STLF) in New York city.

TL;DR: In this paper , an Optimized Fractional Overhead Power Term Polynomial Grey Model (OFOPGM) with L-SHADE algorithm is developed for market clearing price prediction.

TL;DR: In this article , a comprehensive review of the state-of-the-art techniques for alleviation of the unbalanced conditions in both three-wire and four-wire LV three-phase distribution networks is presented.

TL;DR: In this article , a deep learning assisted adaptive nonlinear deloading (DL-AND) methodology based on a Newtonian interpolated polynomial for WTG integrated with an interconnected power system to provide effective load frequency control (LFC) is proposed.

TL;DR: In this paper , a new model is proposed for transformer-integrated passive power filter (TIPPF) to cancel the effects of not complete elimination of mutual-inductances and improve the filter performance.

TL;DR: In this paper , a flexible multi-objective optimization approach was proposed to evaluate and deploy vehicle-to-grid and grid-tovehicle technologies considering techno-economical and environmental factors.

TL;DR: In this article , the effect of residual mineral oil on the AC breakdown strength of vegetable oil is widely studied to understand vegetable oil's reliability and dielectric integrity in retrofitting a mineral oil-filled transformer.

TL;DR: In this article , an optimal V2G control strategy using Deep Reinforcement Learning (DRL) is proposed to simultaneously maximise the benefits of EV owners and aggregators while fulfilling the driving needs of EVs owners.