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Energy Management of Price-maker Community Energy Storage by Stochastic Dynamic Programming

CSEE Journal of Power and Energy systems 2024年第2期10卷 492-503页

作者： Lirong Deng Xuan Zhang Tianshu Yang Hongbin Sun Yang Fu Qinglai Guo Shmuel S.Oren Department of Electrical Engineering Shanghai University of Electric PowerShanghai 200000China Tsinghua-Berkeley Shenzhen Institute Tsinghua UniversityShenzhen 518055China Department of Industrial Engineering and Operations Research University of California BerkeleyBerkeleyCAUSA Risk Analytics and Optimization Chair EPFLSwitzerlandand also with Power Systems LaboratoryETH Zurich8092 ZurichSwitzerland State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

In this paper,we propose an analytical stochastic dynamic programming(SDP)algorithm to address the optimal management problem of price-maker community energy *** a price-maker,energy storage smooths price differences,thus decreasing energy arbitrage ***,this price-smoothing effect can result in significant external welfare changes by reduc-ing consumer costs and producer revenues,which is not negligible for the community with energy storage *** such,we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community *** incorporate market interaction into the SDP format,we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market *** we present an analytical SDP algorithm that does not require state *** from computational efficiency,another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal *** studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only *** proposed algorithm ensures optimality and largely reduces the computational complexity of the standard *** Terms-Analytical stochastic dynamic programming,energy management,energy storage,price-maker,social welfare.

关键词： Analytical stochastic dynamic programming energy management energy storage price-maker social welfare

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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Natural Language to Verilog: Design of a Recurrent Spiking Neural Network using Large Language Models and ChatGPT

Natural Language to Verilog: Design of a Recurrent Spiking N...

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2024 International Conference on Neuromorphic systems, ICONS 2024

作者： Vitolo, Paola Psaltakis, George Tomlinson, Michael Licciardo, Gian Domenico Andreou, Andreas G. Johns Hopkins University Department of Electrical & Computer Engineering BaltimoreMD United States University of Salerno Department of Industrial Engineering IT Fisciano Italy Hellenic Mediterranean University Department of Electrical & Computer Engineering Heraklion Greece

ISBN: (纸本)9798350368659

This paper investigates the use of Large Language Models (LLMs) and natural language prompts to generate hardware description code, namely Verilog. Building on our prior work, we employ employ OpenAI's ChatGPT4 and natural language prompts to synthesize an RTL Verilog module of a programmable recurrent spiking neural network, while also generating test benches to assess the system's correctness. The resultant design was validated in three simple machine learning tasks, the exclusive OR, the IRIS flower classification and the MNIST hand-written digit classification. Furthermore, the design validated on on a Field-Programmable Gate Array (FPGA) and subsequently synthesized in the SkyWater 130 nm technology by using an open-source electronic design automation flow. The design was submitted to Efabless Tiny Tapeout 6. © 2024 IEEE.

关键词： Recurrent neural networks

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Mitigating Overfitting in Bimodal Data Integration with GLOW for Large and Sparse Datasets 5th

Mitigating Overfitting in Bimodal Data Integration with GL...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Yu, Wentao Kolossa, Dorothea Nickel, Robert Electronic Systems of Medical Engineering TU Berlin Berlin Germany Electrical and Computer Engineering Bucknell University Lewisburg United States

ISBN: (纸本)9789819747108

The end-to-end training of neural networks with multimodal data poses challenges beyond those observed for the training with unimodal data. The difficulty lies frequently in a network’s capacity to overfit and generalize at different rates in each modality, resulting networks may thus perform worse than networks trained on unimodal data alone. In the present study, we introduce a late fusion methodology designed for the bimodal binary classification task under the assumption that the logit spaces of both modalities can be modeled by a joint Gaussian distribution. On the one hand, it is suitable when ample bimodal data is available, avoiding the effect of catastrophic fusion, where unimodal models may perform better than the bimodal systems. On the other hand, the proposed technique is particularly well suited for situations in which significant amounts of unimodal data are available to train separate networks for each modality, but where only a limited amount of joint, bimodal data is available to train the fusion mechanism. The proposed method is assessed over the PAN 2018 gender identification dataset, which involves the identification of an authors’ (binary) gender label through their tweets and posted images. The cross-validation results demonstrate that our proposed hybrid model-based method exhibits superior performance and robustness compared to competing state-of-the-art neural-network-based approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gaussian distribution

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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Decentralized control in active distribution grids via supervised and reinforcement learning

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Energy and AI 2024年第2期16卷 137-149页

作者： Stavros Karagiannopoulos Petros Aristidou Gabriela Hug Audun Botterud Laboratory for Information and Decision Systems(LIDS) Massachusetts Institute of TechnologyCambridgeUSA EEH-Power Systems Laboratory ETH Zurichhysikstrasse 38092 ZurichSwitzerland Department of Electrical Engineering Computer Engineering&InformaticsCyprus

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

关键词： Supervised learning Reinforcement learning Deep deterministic policy gradient Decentralized control Active distribution systems

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Application of Permanent Magnets with Different Magnetization in Magnetic Gear Considering Gear Ratio Conversion

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Transactions of the Korean Institute of electrical Engineers 2023年第11期72卷 1366-1371页

作者： Byeon, Beom-Seok Park, Eui-Jong Jung, Sang-Yong Kim, Yong-Jae Dept. of Electrical Engineering Chosun University Korea Republic of Dept. of Engineering for Smart Mobility Convergence Systems Chosun University Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

A permanent magnet was used to improve the torque density of the magnetic gear for gear ratio conversion, and a study was conducted to attach the permanent magnet to the tooth tip of the rotor. Through this, it was found that attaching a permanent magnet to the outer rotor is effective. In addition, in the case of gear ratio conversion, in this method, there is a part where the magnetization direction of the electromagnet and the permanent magnet are opposite, resulting in poor results. Therefore, when applied to teeth with coincident magnetization directions, good results were obtained in all cases even when the gear ratio was converted. Copyright © The Korean Institute of electrical Engineers.

关键词： Permanent magnets

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Grid-Forming PV System for Frequency Ancillary Services Provision 3

Grid-Forming PV System for Frequency Ancillary Services Prov...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Barac, Bojana Capuder, Tomislav Panteli, Mathaios University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Croatia University of Cyprus Department of Electrical and Computer Engineering Cyprus

ISBN: (纸本)9798350375923

With the increasing penetration of photovoltaic (PV) systems posing challenges to power system stability, this paper investigates the effectiveness of grid-forming controlled PV systems in providing frequency ancillary services. The study implements the Virtual Synchronous Machine strategy, utilizing DC-link capacitor energy for inertial response provision (DCVQVSM), integrated within a grid-forming topology. To ensure power-frequency droop control, an additional control loop is introduced within the DCVQ-VSM topology. The efficacy of this control system is proved through simulations conducted on a simple two-bus system and a fully-electrified active distribution network (ADN). Both case studies demonstrate the successful operation of grid-forming controlled PV systems, highlighting their capability to influence power system frequency dynamics. The analysis underscores the significance of deloading PV operation in facilitating flexibility services provision, thereby enhancing the reliability and resilience of system operation. © 2024 IEEE.

关键词： Synchronous machinery

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