Article
Energy & Fuels
Marcos Tostado-Veliz, Salah Kamel, Hany M. Hasanien, Rania A. Turky, Francisco Jurado
Summary: This paper presents an uncertainty-aware day-ahead optimal scheduling tool for grid-connected microgrids, which incorporates additional constraints to bound the duration of demand response signals and lessens the harmful effects caused by response fatigue. The effectiveness of the developed methodology in managing uncertainties and limiting the impact of response fatigue is demonstrated through a benchmark case study.
Article
Energy & Fuels
Marcos Tostado-Veliz, Salah Kamel, Flah Aymen, Ahmad Rezaee Jordehi, Francisco Jurado
Summary: With the increasing frequency of contingencies and outages in power systems due to climate changing effects and equipment aging, this paper proposes a novel energy management tool for isolated microgrids that is robust against failures. A novel stochastic-IGDT formulation is developed to handle uncertainties and component failures, and the operation cost is also considered to achieve useful results with limited reliability cost. Simulation results validate the developed model and highlight the positive effect of demand response programs in reducing operation costs and improving robustness against failures.
Article
Green & Sustainable Science & Technology
Ghasem Ansari, Reza Keypour
Summary: A new structure is proposed for the participation of the commercial sector in the electricity market, enhancing responsiveness through the use of a commercial aggregator. The study utilizes mathematical models and information-gap decision theory for time-based commercial demand response planning. A multi-layered structure integrates the flexibility of shopping centers from the demand side to the supply side through a commercial demand response aggregator. Implementation in the New York electricity market demonstrates demand flexibility of up to 18% compared to the traditional system. The study aims to address the challenges of integrating renewable energies with the electricity system by presenting a responsive structure for commercial systems.
Article
Energy & Fuels
Yuli Astriani, G. M. Shafiullah, Farhad Shahnia
Summary: The return on investment for a microgrid can be accelerated through maximizing profits, which can be achieved by implementing demand response. This study aims at determining demand response incentives for customers feasible for both participants and the operator, aiming to increase profits while minimizing customer discomfort. The results show that the proposed demand response program can increase profits for the microgrid, part of which is allocated to consumers as incentives for participation.
Article
Green & Sustainable Science & Technology
Daniel J. Sambor, Samantha C. M. Bishop, Aaron Dotson, Srijan Aggarwal, Mark Z. Jacobson
Summary: Reliance on imported diesel fuel and high transportation costs have made power and water treatment expensive in remote diesel microgrids in the Arctic; attempts to implement piped water in these areas have proven difficult; a modular Water Reuse system provides affordable, distributed water service but still consumes substantial electricity.
JOURNAL OF CLEANER PRODUCTION
(2022)
Review
Energy & Fuels
Maysam Abbasi, Ehsan Abbasi, Li Li, Ricardo P. P. Aguilera, Dylan Lu, Fei Wang
Summary: This paper provides a comprehensive overview of the microgrid concept, including definitions, challenges, advantages, components, structures, communication systems, and control methods, with a focus on low-bandwidth, wireless, and wired control approaches. Microgrids are crucial for current and future electricity network development, offering benefits such as enhanced stability, increased efficiency, integration of clean energies, and improved power quality. Recent research has investigated efficient control systems for different types of microgrids, particularly those based on low-bandwidth communication.
Article
Energy & Fuels
Marcos Tostado-Veliz, Ahmad Rezaee Jordehi, Lazuli Fernandez-Lobato, Francisco Jurado
Summary: This paper proposes a robust energy management methodology for isolated microgrids considering hydrogen storage and demand response. The problem is solved using a nested max-min optimization framework and a master-slave scheme. The approach is applied to a benchmark microgrid, and the results show that flexible demand has a greater impact on monetary savings than hydrogen storage, reducing the total cost by 6%.
Article
Computer Science, Information Systems
Md Noor-A-Rahim, Mohammad Omar Khyam, Md Apel Mahmud, Md Tanvir Ishtaique ul Huque, Xinde Li, Dirk Pesch, Amanullah M. T. Oo
Summary: This article proposes a robust communication framework for state estimation/tracking of unstable microgrids in a smart grid, demonstrating its ability to closely track microgrid states and outperform traditional schemes.
IEEE SYSTEMS JOURNAL
(2021)
Article
Green & Sustainable Science & Technology
Monir Sadat AlDavood, Abolfazl Mehbodniya, Julian L. Webber, Mohammad Ensaf, Mahdi Azimian
Summary: This paper presents a new robust scheduling model for an islanded microgrid considering demand response, which can minimize the total costs of the microgrid under uncertainties.
Article
Computer Science, Information Systems
Babak Arbab-Zavar, Emilio J. Palacios-Garcia, Juan C. Vasquez, Josep M. Guerrero
Summary: This study explores the integration of LPWAN technology, particularly LoRaWAN, into smart inverter control architectures for remote microgrids. A point-by-point LoRa architecture is proposed and experimentally evaluated for effectiveness in grid-feeding control. The study offers insights into a hybrid communication system that can effectively manage remote residential microgrids.
Article
Computer Science, Information Systems
Hao Zhou, Atakan Aral, Ivona Brandic, Melike Erol-Kantarci
Summary: The article introduces a method for energy management in microgrids that is resilient to communication failures, utilizing multiagent Bayesian deep reinforcement learning (BA-DRL) and double deep Q-learning (DDQN) architecture to enhance rewards and achieve collaborative decision-making among multiple agents.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Nehmedo Alamir, Salah Kamel, Mohamed H. Hassan, Sobhy M. Abdelkader
Summary: In this paper, an improved algorithm called LINFO is proposed for solving the energy management problem of microgrids and applied to demand response programs. The efficiency of the algorithm is confirmed by comparing its results with other optimization techniques.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Materials Science, Multidisciplinary
Xiang Wan, Zi Ai Huang, Jia Wei Wang, Wen Hao Wang, Bai Yang Li, Qiang Xiao, Xu Jie Wang, Jia Chen Wan, Tie Jun Cui
Summary: This article proposes the idea of using a single information metasurface to achieve electromagnetic sensing and wireless communication. The metasurface can function as a remote sensor for imaging and locating targets, as well as transmitting sensing information to local users by modulating the radiation phase of directional beams. This concept has significant implications for the future information society.
ADVANCED MATERIALS TECHNOLOGIES
(2023)
Article
Energy & Fuels
Pudong Ge, Fei Teng, Charalambos Konstantinou, Shiyan Hu
Summary: This paper proposes a cyber-physical cooperative mitigation framework to enhance power systems resilience against power outages caused by extreme events. It utilizes microgrids for physical-layer response and wireless networks for regional emergency communication, ensuring power supply for critical loads.
Article
Energy & Fuels
Krishna Mohan Reddy Pothireddy, Sandeep Vuddanti, Surender Reddy Salkuti
Summary: Due to increased load demand and concerns about climate change, distributed energy resources have become alternatives to large conventional power generation. However, the installation of distributed generation technology has led to increased variability, volatility, and poor power quality in microgrids. Energy management strategies and load participation in energy management are necessary to reduce peak demand and avoid power outages in the distribution system.
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.