Subjects -> COMMUNICATIONS (Total: 518 journals)
    - COMMUNICATIONS (446 journals)
    - DIGITAL AND WIRELESS COMMUNICATION (31 journals)
    - HUMAN COMMUNICATION (19 journals)
    - MEETINGS AND CONGRESSES (7 journals)
    - RADIO, TELEVISION AND CABLE (15 journals)

COMMUNICATIONS (446 journals)                  1 2 3 | Last

Showing 1 - 200 of 480 Journals sorted alphabetically
3C TIC     Open Access   (Followers: 1)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
Acta Universitatis Danubius. Communicatio     Open Access  
Acta Universitatis Sapientiae Communicatio     Open Access  
Advances in Image and Video Processing     Open Access   (Followers: 20)
Advances in Journalism and Communication     Open Access   (Followers: 26)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Information Systems     Open Access   (Followers: 6)
African Journal of Rhetoric     Full-text available via subscription   (Followers: 3)
African Yearbook of Rhetoric     Full-text available via subscription   (Followers: 3)
Ambitos     Open Access   (Followers: 1)
American Journal of Semiotics     Full-text available via subscription   (Followers: 4)
Anagrama     Open Access  
Anagramas : Rumbos y Sentidos de la Comunicación     Open Access   (Followers: 2)
Anàlisi : Quaderns de Comunicació i Cultura     Open Access  
Âncora : Revista Latino-Americana de Jornalismo     Open Access  
Andharupa : Journal of Visual Communication Design & Multimedia     Open Access   (Followers: 3)
Annales Universitatis Paedagogicae Cracoviensis / Studia de Cultura     Open Access  
Annals of Telecommunications     Hybrid Journal   (Followers: 6)
Annals of the International Communication Association     Hybrid Journal   (Followers: 4)
Anuario electrónico de estudios en Comunicación Social "Disertaciones"     Open Access   (Followers: 1)
Apparatus. Film, Media and Digital Cultures of Central and Eastern Europe     Open Access   (Followers: 5)
Área Abierta     Open Access   (Followers: 2)
Art Design & Communication in Higher Education     Hybrid Journal   (Followers: 22)
At-Tabsyir : Jurnal Komunikasi Penyiaran Islam     Open Access  
Atatürk İletişim Dergisi     Open Access  
Aturá : Revista Pan-Amazônica de Comunicação     Open Access  
Auditory Perception & Cognition     Hybrid Journal  
Augmentative and Alternative Communication     Hybrid Journal   (Followers: 46)
Avatares de la Comunicación y la Cultura     Open Access  
Baltic International Yearbook of Cognition, Logic and Communication     Open Access   (Followers: 2)
Baltic Screen Media Review     Open Access   (Followers: 1)
Bioelectromagnetics     Hybrid Journal   (Followers: 1)
Black Camera     Full-text available via subscription   (Followers: 8)
Borderlands Journal : Culture, Politics, Law and Earth     Open Access   (Followers: 1)
C&SC - Communication & Social Change     Open Access   (Followers: 7)
Caderno de Letras     Open Access  
Canadian Journal of Communication     Partially Free   (Followers: 24)
Catalan Journal of Communication & Cultural Studies     Hybrid Journal   (Followers: 2)
Celebrity Studies     Hybrid Journal   (Followers: 13)
Chasqui. Revista Latinoamericana de Comunicación     Open Access   (Followers: 1)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Communication     Hybrid Journal   (Followers: 12)
Church, Communication and Culture     Open Access   (Followers: 1)
CIC. Cuadernos de Informacion y Comunicacion     Open Access   (Followers: 4)
Comedy Studies     Hybrid Journal   (Followers: 10)
Commons. Revista de Comunicación y Ciudadanía Digital     Open Access  
Communicatio : South African Journal for Communication Theory and Research     Hybrid Journal   (Followers: 3)
Communication     Open Access   (Followers: 28)
Communication & Language at Work     Open Access   (Followers: 10)
Communication & Society     Open Access   (Followers: 6)
Communication & Sport     Hybrid Journal   (Followers: 7)
communication +1     Open Access   (Followers: 2)
Communication and Culture Online / Komunikacija i kultura     Open Access   (Followers: 1)
Communication and Media in Asia Pacific (CMAP)     Open Access   (Followers: 2)
Communication and the Public     Hybrid Journal   (Followers: 2)
Communication Booknotes Quarterly     Hybrid Journal   (Followers: 15)
Communication Cultures in Africa     Open Access   (Followers: 7)
Communication et organisation     Open Access  
Communication et Professionnalisation     Open Access  
Communication Papers : Media Literacy & Gender Studies     Open Access   (Followers: 20)
Communication Research and Practice     Hybrid Journal  
Communication Theory     Hybrid Journal   (Followers: 29)
Communication, Culture & Critique     Hybrid Journal   (Followers: 28)
Communication, technologies et développement     Open Access  
Communications in Mobile Computing     Open Access   (Followers: 14)
Communications of the Association for Information Systems     Open Access   (Followers: 15)
Communiquer : Revue de communication sociale et publique     Open Access  
Computational Communication Research     Open Access   (Followers: 1)
Comuni@cción     Open Access  
Comunicação & Educação     Open Access  
Comunicação & Sociedade     Open Access   (Followers: 1)
Comunicação e Sociedade     Open Access  
Comunicació. Revista de recerca i d'anàlisi     Open Access  
Comunicación     Open Access  
Comunicación y Ciudadanía     Open Access  
Comunicación y Género     Open Access  
Comunicación y Medios     Open Access   (Followers: 2)
Comunicación y sociedad     Open Access   (Followers: 2)
Comunicar     Open Access   (Followers: 1)
Conexión     Open Access  
Connections     Open Access  
Connections : A Journal of Language, Media and Culture     Open Access   (Followers: 3)
Contratexto     Open Access  
Convergence The International Journal of Research into New Media Technologies     Hybrid Journal   (Followers: 48)
Creative Artist : A Journal of Theatre and Media Studies     Open Access   (Followers: 11)
Cross-cultural Communication     Open Access   (Followers: 8)
Cryptography     Open Access   (Followers: 1)
Cuadernos de H Ideas     Open Access  
Cuadernos de Informacion     Open Access  
Cuadernos.info     Open Access  
De Signos y Sentidos     Open Access  
Democratic Communiqué     Open Access   (Followers: 1)
Design Ecologies     Hybrid Journal   (Followers: 13)
Digithum     Open Access   (Followers: 2)
Discourse, Context & Media     Open Access   (Followers: 23)
Distúrbios da Comunicação     Open Access  
Dixit     Open Access  
Documentación de las Ciencias de la Información     Open Access  
Doxa Comunicación : Revista interdisciplinar de estudios de Comunicación y Ciencias Sociales     Open Access  
e-Journal of Linguistics     Open Access   (Followers: 3)
e-learning and education (eleed)     Open Access   (Followers: 39)
Electronic Journal of Knowledge Management     Open Access   (Followers: 25)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Empedocles : European Journal for the Philosophy of Communication     Hybrid Journal   (Followers: 2)
Entreculturas : Revista de Traducción y Comunicación Intercultural     Open Access  
ESSACHESS : Journal for Communication Studies     Open Access   (Followers: 2)
Etudes de communication     Open Access   (Followers: 3)
Evidence Based Library and Information Practice     Open Access   (Followers: 386)
Explorations in Media Ecology     Hybrid Journal   (Followers: 5)
Fibreculture Journal     Open Access   (Followers: 9)
FLEKS : Scandinavian Journal of Intercultural Theory and Practice     Open Access   (Followers: 1)
Folia Toruniensia     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Framework : The Journal of Cinema and Media     Full-text available via subscription   (Followers: 19)
Fronteiras - estudos midiáticos     Open Access  
Frontiers in Communication     Open Access   (Followers: 1)
Frontiers in Human Dynamics     Open Access  
Genre en séries. Cinéma, télévision, médias     Open Access  
Gesture     Hybrid Journal   (Followers: 4)
Global Advances in Business Communication     Open Access   (Followers: 5)
Global Media and China     Open Access  
Global Media and Communication     Hybrid Journal   (Followers: 17)
Global Media Journal     Open Access   (Followers: 11)
Globe : A Journal of Language, Culture and Communication     Open Access   (Followers: 3)
Green Letters : Studies in Ecocriticism     Hybrid Journal   (Followers: 1)
GSI Journals Serie C : Advancements in Information Sciences and Technologies     Open Access   (Followers: 1)
GSTF Journal on Media & Communications     Open Access   (Followers: 2)
H-ermes. Journal of Communication     Open Access   (Followers: 3)
Health Information Management Journal     Hybrid Journal   (Followers: 23)
HERMES - Journal of Language and Communication in Business     Open Access   (Followers: 3)
Hipertext.net : Anuario Académico sobre Documentación Digital y Comunicación Interactiva     Open Access  
Historia y Comunicación Social     Open Access  
Human Communication Research     Hybrid Journal   (Followers: 17)
Ibérica     Open Access   (Followers: 2)
Ícone     Open Access  
ICSES Transactions on Computer Networks and Communications     Full-text available via subscription  
IEEE Communications Standards Magazine     Full-text available via subscription   (Followers: 2)
IEEE Open Journal of the Communications Society     Open Access  
IEEE Transactions on Cognitive Communications and Networking     Hybrid Journal   (Followers: 4)
IEEE Transactions on Smart Grid     Hybrid Journal   (Followers: 18)
IEICE - Transactions on Fundamentals of Electronics, Communications and Computer Sciences     Full-text available via subscription   (Followers: 9)
IET Communications     Open Access   (Followers: 11)
İletişim Kuram ve Araştırma Dergisi     Open Access  
Imaging Decisions MRI     Hybrid Journal   (Followers: 2)
Improntas     Open Access  
index.comunicación     Open Access  
Informacijos mokslai     Open Access  
Informal Logic     Open Access   (Followers: 9)
Informatics     Open Access   (Followers: 3)
Informatio. Revista del Instituto de Información de la Facultad de Información y Comunicación     Open Access  
Information & Communications Technology Law     Hybrid Journal   (Followers: 22)
Information Design Journal     Hybrid Journal   (Followers: 7)
Information Technologies & International Development     Open Access   (Followers: 81)
Information, Communication & Society     Hybrid Journal   (Followers: 70)
Inquiry : Critical Thinking Across the Disciplines     Full-text available via subscription   (Followers: 2)
Intelligent Information Management     Open Access   (Followers: 7)
Interaction Studies     Hybrid Journal   (Followers: 9)
Interactions : Studies in Communication & Culture     Hybrid Journal   (Followers: 3)
International Journal of Ad Hoc and Ubiquitous Computing     Hybrid Journal   (Followers: 8)
International Journal of Advanced Media and Communication     Hybrid Journal   (Followers: 22)
International Journal of Autonomous and Adaptive Communications Systems     Hybrid Journal   (Followers: 3)
International Journal of Broadband Cellular Communication     Full-text available via subscription  
International Journal of Business Communication     Hybrid Journal   (Followers: 10)
International Journal of Communication     Open Access   (Followers: 15)
International Journal of Community Development and Management Studies (IJCDMS)     Open Access  
International Journal of Computer Science and Telecommunications     Open Access   (Followers: 13)
International Journal of Cooperative Information Systems     Hybrid Journal   (Followers: 4)
International Journal of Critical Media Literacy     Hybrid Journal  
International Journal of Electronics and Telecommunications     Open Access   (Followers: 8)
International Journal of Entertainment Technology and Management     Hybrid Journal   (Followers: 1)
International Journal of Information and Communication Technology Education     Full-text available via subscription   (Followers: 13)
International Journal of Information Communication Technologies and Human Development     Full-text available via subscription   (Followers: 4)
International Journal of Information Science and Technology     Open Access  
International Journal of Information Technology, Communications and Convergence     Hybrid Journal   (Followers: 14)
International Journal of Intelligence Science     Open Access   (Followers: 3)
International Journal of Interdisciplinary Telecommunications and Networking     Full-text available via subscription   (Followers: 3)
International Journal of Knowledge and Systems Science     Full-text available via subscription   (Followers: 1)
International Journal of Monitoring and Surveillance Technologies Research     Full-text available via subscription   (Followers: 3)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 2)
International Journal of Society, Culture & Language     Open Access   (Followers: 15)
International Journal of Telecommunications & Emerging Technologies     Full-text available via subscription   (Followers: 1)
International Journal of Telework and Telecommuting Technologies     Full-text available via subscription  
International Journal of Trust Management in Computing and Communications     Hybrid Journal   (Followers: 1)
International Journals Digital Communication and Analog Signals     Full-text available via subscription   (Followers: 6)
International Review of Communication and Marketing Mix : IROCAMM     Open Access   (Followers: 1)
International Review of Pragmatics     Hybrid Journal   (Followers: 4)
Investigative Radiology     Hybrid Journal   (Followers: 7)
IRIS - Revista de Informação, Memória e Tecnologia     Open Access  
Izvestia Ural Federal University Journal. Series 1. Issues in Education, Science and Culture     Open Access  
Javnost - The Public     Hybrid Journal  
Język. Komunikacja. Informacja     Open Access  
Journal for the History of Rhetoric     Hybrid Journal   (Followers: 16)
Journal of Advertising Education     Hybrid Journal  
Journal of African Media Studies     Hybrid Journal   (Followers: 2)
Journal of Applied Communications     Open Access  
Journal of Applied Journalism & Media Studies     Hybrid Journal   (Followers: 14)
Journal of Argumentation in Context     Hybrid Journal   (Followers: 2)
Journal of Arts & Communities     Hybrid Journal   (Followers: 4)

        1 2 3 | Last

Similar Journals
Journal Cover
IEEE Transactions on Smart Grid
Journal Prestige (SJR): 2.854
Citation Impact (citeScore): 9
Number of Followers: 18  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1949-3053
Published by IEEE Homepage  [228 journals]
  • IEEE Transactions on Smart Grid Publication Information

    • Free pre-print version: Loading...

      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • IEEE Power Engineering Society Information for Authors

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      Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
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      Abstract: This page or pages intentionally left blank.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Stability Result for Network Reduced Power Systems Using Virtual
           Friction and Inertia

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      Authors: Florian Reissner;Hang Yin;George Weiss;
      Pages: 1668 - 1678
      Abstract: We prove that virtual friction can stabilize a power grid containing several virtual synchronous machines (VSMs), connecting line impedances and loads. Virtual friction is a torque added to the swing equation of each VSM, proportional to the deviation of its frequency from the overall center of inertia (COI) frequency. Our analysis is based on the network reduced power system (NRPS) model. We support our results with simulations for a two-area network of four VSMs, looking at the transients induced by a change of tie-line impedance and an asymmetric load change. We compare the results for the NRPS model with the corresponding results using detailed models of synchronverters and line impedances. We find that virtual friction has a strong stabilizing effect both for the NRPS model and for the detailed model. Furthermore we show simulation results on the influence of time delays in the communication between the inverters. This communication is used to compute the COI frequency. It turns out that communication delays of less than 100ms have practically no effect on the overall system.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Holomorphic Embedding Power Flow Algorithm for Isolated AC Microgrids With
           Hierarchical Control

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      Authors: Ying Huang;Xiaomeng Ai;Jiakun Fang;Shichang Cui;Runfeng Zhong;Wei Yao;Jinyu Wen;
      Pages: 1679 - 1690
      Abstract: Power flow (PF) calculation plays a fundamental role in the steady-state analysis of isolated microgrids (MGs). In this paper, a novel analytical PF algorithm based on holomorphic embedding (HE) is proposed for isolated AC MGs considering hierarchical control (HC). With a deliberate design in embedding technique, the presented algorithm not only inherits the deterministic property of the canonical embedding that enables to derive the upper-branch (operable) solution explicitly, but also is compatible with the HC characteristic of isolated MGs. Furthermore, the embedded PF model features a general structure with a constant recursive matrix, which can accommodate various bus types and network re-configurations (including both radial and meshed networks), as well as non-nonlinearities of voltage-dependent load models. Case studies are carried out on 3 test systems: a 12-bus MG and modified IEEE 33-bus and 123-bus distribution systems. Numerical results reveal the applicability and efficacy of the proposed algorithm.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Distributed Three-Phase Power Flow for AC/DC Hybrid Networked Microgrids
           Considering Converter Limiting Constraints

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      Authors: Yuntao Ju;Wenwu Liu;Zifeng Zhang;Ruosi Zhang;
      Pages: 1691 - 1708
      Abstract: In three-phase AC/DC hybrid networked microgrids (NMGs), the operational limits of AC/DC interconnected converters and distributed generator (DG) interface inverters increase the non-convexity of the power flow model, and conventional distributed power flow (DPF) algorithms based on heuristic rule may encounter convergence problems when processing limit. This paper proposed a fully DPF calculation method that can robustly handle the non-smooth reactive power limits of converters and non-smooth voltage regulation of step voltage regulators, also reducing the model dependence on the initial values. In this algorithm, the non-smooth constraints were converted into smooth functions, and based on a bi-level augmented Lagrangian alternating direction inexact Newton (ALADIN) method with the second-order convergence rate the original DPF problem was transformed into the problem of distributed step increment optimization. Accurate power flow results can be obtained by exchanging boundary information between microgrids, and the proposed algorithm can converge rapidly with step increment optimization at the second level. Numerical experiments demonstrated the accuracy, convergence, and efficiency of the proposed method.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Accurate Distributed Secondary Control for DC Microgrids Considering
           Communication Delays: A Surplus Consensus-Based Approach

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      Authors: Yuhua Du;Xiaonan Lu;Wenyuan Tang;
      Pages: 1709 - 1719
      Abstract: The state-of-the-art dynamic consensus-based microgrid (MG) secondary controls suffer from the communication delay effect. Specifically, the system could not converge to the desired operating points with time-delayed communications. Such deviations are hard to detect in a decentralized manner and could destabilize the system. This paper proposes an accurate distributed secondary controller for DC MGs based on the surplus consensus algorithm. The proposed controller achieves accurate proportional power sharing and average voltage regulation among distributed generators (DGs) with the presence of variable and bounded communication delays. A surplus consensus-based observer is developed. The developed observer is proved robust against variable and bounded communication delays; it tracks the average of a group of dynamic states with zero steady-state deviations, which cannot be done using the conventional dynamic consensus-based observer. The convergence speed of the developed observer is analyzed and a parameter design procedure is presented. Moreover, the delay-dependent stability analysis of DC MG operation with the proposed secondary controller is derived. The marginal delay that leads the system to instability is calculated. At last, the performance of the proposed secondary controller and the developed stability analysis are validated under various scenarios using MATLAB/Simulink.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Energy Management Strategy of a Reconfigurable Grid-Tied Hybrid AC/DC
           Microgrid for Commercial Building Applications

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      Authors: Kannan Thirugnanam;Mohamed Shawky El Moursi;Vinod Khadkikar;Hatem H. Zeineldin;Mohamed Al Hosani;
      Pages: 1720 - 1738
      Abstract: This paper proposes an energy management strategy (EMS) of a reconfigurable grid-tied hybrid ac/dc microgrid (HMG) architecture for commercial building (CB) applications. This HMG architecture consists of a multi-mode configuration (MMC) with renewable-based distributed generators (DGs), energy storages (ESs), and genset. The main advantage of this architecture is its capability to reconfigure its structure based on the predicted building load power (BLP) and renewable-based DGs power. However, minimizing the building electricity cost (BEC) and maximizing the reliability index (RI) of a reconfigurable grid-tied HMG architecture is a challenging task due to the stochastic behaviors of BLP and renewable-based DGs power. In this context, the BLP and renewable-based DGs behaviors are modeled using an artificial neural network approach to predict future time slot values. Then, system level models are developed for HMG energy sources and grid-tied converters/switches. Furthermore, BEC and RI models are developed based on the dynamic pricing of grid/HMG electricity cost and supplied energy. Then, an EMS is proposed for the developed reconfigurable grid-tied HMG architecture, which consists of a two-stage control strategy, i.e., stage I and stage II. Stage I control strategy minimizes BEC and maximizes RI using multi-objective particle swarm optimization (MOPSO) and MMC. Stage II control strategy generates control signals for HMG energy sources, converters, and grid-tied converters/switches based on stage I reference signals. Historical data is used to demonstrate the effectiveness of the proposed EMS for a reconfigurable grid-tied HMG architecture. Through numerical simulation studies, it is shown that the proposed EMS is capable of reducing BEC and increasing RI by concurrently enabling MMC of a reconfigurable grid-tied HMG architecture.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Multi-Agent Reinforcement Learning for Decentralized Resilient Secondary
           Control of Energy Storage Systems Against DoS Attacks

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      Authors: Pengcheng Chen;Shichao Liu;Bo Chen;Li Yu;
      Pages: 1739 - 1750
      Abstract: While distributed secondary controllers have been studied for multiple energy storage systems in islanded microgrids, information infrastructure has to be added for the extensive information transmission among these secondary controllers and the additional communication among distributed controllers is costly and increases the vulnerability surface to cyberattacks. In this work, a data-driven decentralized secondary control scheme is proposed for multiple heterogeneous battery energy storage systems (BESSs). The proposed secondary control scheme can achieve frequency regulation and the state-of-charge (SoC) balancing simultaneously for BESSs without requiring accurate BESS models. This scheme leverages an asynchronous advantage actor-critic (A3C) based multi-agent deep reinforcement learning (MA-DRL) algorithm where the centralized off-line learning with shared convolutional neural networks (CNN) is designed to maximize global rewards and ensure the performance of the entire system and a decentralized online execution mechanism is applied to each BESS. Furthermore, in view of possible denial-of-service (DoS) attack on local communication networks used for signal transfer between secondary controllers and remote sensors, a signal-to-interference-plus-noise ratio (SINR)-based dynamic and proactive event-triggered communication mechanism is proposed to alleviate the impact of DoS attacks and reduce the occupation of communication resources. Simulation results on a four-bus multiple BESS system show that the proposed decentralized secondary controller can achieve simultaneous frequency regulation and SoC balancing. Comparison results with other event-triggered mechanisms and MA-DRL algorithms show the A3C based MA-DRL algorithm with CNN can obtain a comparatively optimal policy through training and the designed event-triggered strategy can dynamically adapt the release frequency based on real-time SINR and significantly reduce the occupied network bandwidth and packet -oss rate (PER) induced by DoS attacks.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Decentralized Coordination and Stabilization of Hybrid Energy Storage
           Systems in DC Microgrids

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      Authors: Mengfan Zhang;Qianwen Xu;Chuanlin Zhang;Lars Nordström;Frede Blaabjerg;
      Pages: 1751 - 1761
      Abstract: Hybrid energy storage system (HESS) is an attractive solution to compensate power balance issues caused by intermittent renewable generations and pulsed power load in DC microgrids. The purpose of HESS is to ensure optimal usage of heterogeneous storage systems with different characteristics. In this context, power allocation for different energy storage units is a major concern. At the same time, the wide integration of power electronic converters in DC microgrids would possibly cause the constant power load instability issue. This paper proposes a composite model predictive control based decentralized dynamic power sharing strategy for HESS. First, a composite model predictive controller (MPC) is proposed for a system with a single ESS and constant power loads (CPLs). It consists of a baseline MPC for optimized transient performance and a sliding mode observer to estimate system disturbances. Then, a coordinated scheme is developed for HESS by using the proposed composite MPC with a virtual resistance droop controller for the battery system and with a virtual capacitance droop controller for the supercapacitor (SC) system. With the proposed scheme, the battery only supplies smooth power at steady state, while the SC compensates all the fast fluctuations. The proposed scheme achieves a decentralized dynamic power sharing and optimized transient performance under large variation of sources and loads. The proposed approach is verified by simulations and experiments.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Complex Power Sharing Is Not Complex

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      Authors: Manel Velasco;Carlos Alfaro;Antonio Camacho;Ángel Borrell;Pau Martí;
      Pages: 1762 - 1773
      Abstract: This paper presents a distributed complex power sharing approach that solves the problem of active and reactive power sharing in inverter-based islanded microgrids. Previous state-of-the-art strategies based on droop controls with possibly virtual impedance methods and supplemented with hierarchical schemes may provide poor power sharing due to the R/X ratio and mismatched line impedances, even compromising microgrids stability if droop control parameters are not properly set. The novel control approach, that uses a communication network to exchange data among all inverters to fairly inject power, provides a set of appealing properties. First, it achieves accurate active and reactive power sharing for both resistive and inductive power lines with no additional control cost (avoiding unnecessary power injections), and regardless of load changes and connections and disconnections of inverters. Moreover, it ensures an exponential convergence rate, and the tuning of the control parameters can not compromise microgrid stability. The theoretical development relies on a change of variables that linearizes the complex power dynamics, thus easing the control law design task and providing the means for computing the appropriate amplitude and phase for each inverter output voltage. Selected experimental results on a laboratory microgrid certify the applicability and performance of the proposed control scheme.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Stochastic-Robust Approach for Resilient Microgrid Investment Planning
           Under Static and Transient Islanding Security Constraints

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      Authors: Agnes Marjorie Nakiganda;Shahab Dehghan;Uros Markovic;Gabriela Hug;Petros Aristidou;
      Pages: 1774 - 1788
      Abstract: When planning the investment in Microgrids (MGs), usually static security constraints are included to ensure their resilience and ability to operate in islanded mode. However, unscheduled islanding events may trigger cascading disconnections of Distributed Energy Resources (DERs) inside the MG due to the transient response, leading to a partial or full loss of load. In this paper, a min-max-min, hybrid, stochastic-robust investment planning model is proposed to obtain a resilient MG considering both High-Impact-Low-Frequency (HILF) and Low-Impact-High-Frequency (LIHF) uncertainties. The HILF uncertainty pertains to the unscheduled islanding of the MG after a disastrous event, and the LIHF uncertainty relates to correlated loads and DER generation, characterized by a set of scenarios. The MG resilience under both types of uncertainty is ensured by incorporating static and transient islanding constraints into the proposed investment model. The inclusion of transient response constraints leads to a min-max-min problem with a non-linear dynamic frequency response model that cannot be solved directly by available optimization tools. Thus, in this paper, a three-stage solution approach is proposed to find the optimal investment plan. The performance of the proposed algorithm is tested on the CIGRE 18-node distribution network.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Optimal Distributed Control of AC Microgrids With Coordinated Voltage
           Regulation and Reactive Power Sharing

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      Authors: Sheik M. Mohiuddin;Junjian Qi;
      Pages: 1789 - 1800
      Abstract: In this paper, we propose an optimal distributed voltage control for grid-forming (GFM) inverters in islanded AC microgrids. An optimization problem is formulated where the distributed generator (DG) output voltage is considered as the control variable with technical constraints on voltage and reactive power output capacity and an objective function that makes a trade-off between voltage regulation and reactive power sharing. A distributed primal-dual gradient based algorithm is developed to solve the formulated optimization problem to address the challenges due to non-separable objective function, unavailable global average voltage, and globally coupled reactive power constraints. The effectiveness of the proposed optimal distributed control is validated through simulations on the 4-DG test microgrid and the modified IEEE 34-bus distribution test system, and the advantages of the proposed control over existing controls are demonstrated.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Novel Adaptive Model Predictive Control for Proton Exchange Membrane
           Fuel Cell in DC Microgrids

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      Authors: Yulin Liu;Yingjie Hu;Yuxuan Wang;Tat Kei Chau;Xinan Zhang;Herbert H. C. Iu;Tyrone Fernando;
      Pages: 1801 - 1812
      Abstract: In this paper, a novel adaptive model predictive control (AMPC) algorithm is proposed for proton exchange membrane fuel cells (PEMFCs) to improve its output power tracking performance in DC microgrids. The proposed AMPC algorithm is capable of producing superior PEMFC control performance over the conventional MPC and PI controllers. Compared with PI control, AMPC is able to handle physical constraints, which ensures the safe operation of PEMFC under different conditions. Furthermore, it overcomes the common problem of system model dependence that is shared by nearly all the model-based control methods. The convergence of parameter estimation in the proposed AMPC is rigorously proved. The effectiveness of the proposed algorithm under various operating conditions and system parameter variations are verified by simulation results.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Holomorphic Embedding Power Flow Algorithm for Islanded Hybrid AC/DC
           Microgrids

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      Authors: Mohammed Y. Morgan;Mostafa F. Shaaban;Hatem F. Sindi;Hatem H. Zeineldin;
      Pages: 1813 - 1825
      Abstract: This paper proposes a novel power flow algorithm for islanded hybrid AC/DC microgrids. The algorithm is based on a recursive (non-iterative) method called the holomorphic embedding load flow method. The convergence properties of this method are more favorable than other iterative-based techniques such as Newton Raphson. The proposed power flow algorithm accounts for the special characteristics of hybrid microgrids by developing new holomorphically embedded versions for the DG droop characteristics on both the AC and DC microgrid as well as the interlinking AC/DC converter droop characteristics. The proposed approach is tested on two systems and in the presence of multiple interlinking systems. For each case, the results from the proposed algorithm are compared to detailed time-domain simulations performed using PSCAD/EMTDC to validate the accuracy of the proposed algorithm.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Hybrid Power Sharing Control to Enhance the Small Signal Stability in DC
           Microgrids

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      Authors: Abd Alelah Derbas;Morteza Kheradmandi;Mohsen Hamzeh;Nikos D. Hatziargyriou;
      Pages: 1826 - 1837
      Abstract: Low-frequency power oscillations pose serious problems to the operation of Distributed Generations (DGs) equipped with droop control. This paper presents a new hybrid power-sharing control for damping low-frequency power and current oscillations in DC microgrids. The proposed control consists of a decentralized droop control together with a centralized average current sharing control. A small-signal equivalent model is analytically derived for the proposed hybrid control and is incorporated into the DC microgrid model. The parameters of the PI controllers involved in the average current sharing control units are tuned individually to achieve a satisfactory performance in a stand-alone operation of DGs with a local load. The contributions of control signals contained in the hybrid control signal to the damping characteristic are analyzed. The dynamic response of the DC microgrid under a variety of disturbance scenarios is investigated. The power-sharing accuracy of the proposed control is also examined under the investigated scenarios. The method performance is also investigated for the oscillations caused by constant-power loads. Sensitivity analyses are conducted to evaluate the impact of the loading level and line parameters on the damping ratio associated with the dominant low-frequency modes. The effectiveness of the control method is examined in case of losing a number of communication links. The impact of the delay in the communication links on the method performance is also examined. The simulations are conducted on a multi-source DC microgrid, and the results are verified in the OPAL-RT real-time simulator.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A TOU-IBT Pricing Strategy to Manage the Cryptocurrency Micro-Miners

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      Authors: Mehran Hajiaghapour-Moghimi;Kamyar Azimi Hosseini;Ehsan Hajipour;Mehdi Vakilian;
      Pages: 1838 - 1848
      Abstract: Cryptocurrency mining device (CMD) is a plug-and-play device installed by any low-voltage customer to convert electrical energy to digital money. Recently, the significant increase in the price of digital currencies has persuaded residential customers in many countries to employ CMDs to obtain quick and easy profit. However, these rapidly evolving loads can cause a substantial increase in the coincident peak load of distribution networks. Therefore, it can threaten the safe and reliable operation of the network. A straightforward way to restrict the penetration rate of these home-based miners is to increase the electricity price such that this business becomes unprofitable; however, this solution affects the national regulations and is not practical in many countries. This paper proposes a practical electricity pricing strategy to manage the operation of cryptocurrency mining loads appropriately. This pricing strategy can change the miner’ threat to an opportunity to sell more electrical energy during the off-peak hours to these loads and to maximize the profit of the utility. The proposed strategy is studied using the load profile dataset obtained from the measurements of Tehran Electricity Distribution Company, and its effectiveness is demonstrated.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • An Analytical Zero Sequence Method to Locate Fault in Distribution Systems
           Rich in DG

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      Authors: Débora Rosana Ribeiro Penido;Leandro Ramos de Araujo;Victor T. S. Rodrigues;Kelvin Bryan do Nascimento;
      Pages: 1849 - 1859
      Abstract: Locating high impedance faults (HIF) in a distribution system increases the system’s reliability and improves the service’s quality for customers. However, this type of fault produces currents of low magnitude, making it difficult to locate using conventional techniques. Therefore, this paper proposes a method for the location of single-phase high impedance faults in distribution systems with photovoltaic cells in low voltage. The method is based on the use of the sequence component of the current circulating in the medium voltage feeders of the distribution system, which are obtained from synchronized phasorial meters positioned along the feeder. The method considers nonsymmetrical lines, unbalanced loads, capacitors banks, photovoltaic DGs, and load variation. The performance of the proposed method was evaluated and tested on several distribution systems using ATP or PSCAD® and the results show that the method can locate HIFs with high precision. Also, a comparison was made with recently published methods to show the efficiency of the proposed method.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Learning to Operate Distribution Networks With Safe Deep Reinforcement
           Learning

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      Authors: Hepeng Li;Haibo He;
      Pages: 1860 - 1872
      Abstract: In this paper, we propose a safe deep reinforcement learning (SDRL) based method to solve the problem of optimal operation of distribution networks (OODN). We formulate OODN as a constrained Markov decision process (CMDP). The objective is to achieve adaptive voltage regulation and energy cost minimization considering the uncertainty of renewable resources (RSs), nodal loads and energy prices. The control actions include the number of in-operation units of the switchable capacitor banks (SCBs), the tap position of the on-load tap-changers (OLTCs) and voltage regulators (VRs), the active and reactive power of distributed generators (DGs), and the charging and discharging power of battery storage systems (BSSs). To optimize the discrete and continuous actions simultaneously, a stochastic policy built upon a joint distribution of mixed random variables is designed and learned through a neural network approximator. To guarantee that safety constraints are satisfied, constrained policy optimization (CPO) is employed to train the neural network. The proposed approach enables the agent to learn a cost-effective operating strategy through exploring safe scheduling actions. Compared to traditional deep reinforcement learning (DRL) methods that allow agents to freely explore any behaviors during training, the proposed approach is more practical to be applied in a real system. Simulation results on a modified IEEE-34 node system and a modified IEEE-123 node system demonstrate the effectiveness of the proposed method.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Optimal Power Quality Compensation of Energy Storage System in
           Distribution Networks Based on Unified Multi-Phase OPF Model

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      Authors: Xianyong Xiao;Zhuocheng Li;Yang Wang;Yaxiang Zhou;Ke Liu;
      Pages: 1873 - 1887
      Abstract: Energy Storage System (ESS) is a promising solution to suppress the peak-valley difference of residential distribution networks (RDN) with high penetration of distributed photovoltaic generations. Meanwhile, it can also provide certain power quality compensations to RDN due to the flexible adjusting ability of its converter interface. To make full use of this feature, this paper investigates the optimal power quality compensation of the ESS. This is achieved by formulating and solving an optimal power flow (OPF) problem, the objectives of which are to minimize the power loss, harmonic distortion, and voltage unbalance of the network. Noticeably, the established model includes the neutral line and inter-phase loads; thus, it is unified and can be easily extended to networks with different structures. However, the coupling between different phases and the inclusion of harmonic variables significantly increase the complexity of the OPF problem. To address this issue, the optimization model is further transformed into a convex quadratic constrained quadratic program (QCQP) by replacing power flow equations with current injection equations. As a result, the problem can be solved by the primal-dual interior-point method (PDIPM) with guaranteed convergence and computational efficiency. An iterative solution method is further proposed to improve the accuracy of the solution. Finally, the proposed method is verified using a three-phase three-wire 25-node distribution network and a three-phase four-wire 162-node distribution network in North America.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Nonlinear Modeling Analysis and Arc High-Impedance Faults Detection in
           Active Distribution Networks With Neutral Grounding via Petersen Coil

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      Authors: Bin Wang;Xin Cui;
      Pages: 1888 - 1898
      Abstract: For Arc High-Impedance Faults (AHIFs) in Active Distribution Networks (ADNs) with neutral grounding via Petersen coil, both nonlinear characteristics caused by arc and the influence of zero-input response have been not considered in previous research. Quantitative analysis methods based on the threshold setting comparison is mostly adopted in current field applications, which cannot balance the detection reliability and sensitivity simultaneously. In response to this problem, a higher precision nonlinear AHIF equivalent model is established based on the logarithmic arc model. State equations describing the occurrences of AHIFs are solved by using piecewise linear fitting technique, and analytical expressions of zero-sequence voltage and currents are present in paper. Furthermore, the regular volt-ampere characteristic dynamic trajectory between the zero-sequence voltage and current in characteristic frequency band is analyzed, and a tangent-inverse method is proposed to extract the direction of dynamic trajectory, then an effective fault detection algorithm is designed. The validity of the proposed nonlinear equivalent model and detection algorithm are verified by various cases’ simulations and field test data.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • An Overview of Soft Open Points in Electricity Distribution Networks

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      Authors: Xun Jiang;Yue Zhou;Wenlong Ming;Peng Yang;Jianzhong Wu;
      Pages: 1899 - 1910
      Abstract: Soft open points (SOPs) are power electronic devices that are usually placed at normally open points of electricity distribution networks to provide flexible power control to the networks. This paper gives a comprehensive overview of both academic research and industrial practice on SOPs in electricity distribution networks. The topologies of SOPs as multi-functional power electronic devices are identified and compared, which include back-to-back voltage source converters, multi-terminal voltage source converters, unified power flow controllers, and direct AC-to-AC modular multilevel converters. The academic research is reviewed in three aspects, i.e., benefit quantification, control, and optimal siting and sizing of SOPs. The benefit quantification indices are categorized into feeder load balancing, voltage profile improvement, power losses reduction, three-phase balancing and DG hosting capacity enhancement. The control of SOPs is summarized as a three-level control structure, where the system-level and converter-level control are further discussed. For optimal siting and sizing of SOPs, problem formulation and solution methods are analyzed. Besides the academic research, practical industrial projects of SOPs worldwide are also summarized. Finally, opportunities of research and industrial application of SOPs are discussed.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Towards Optimal and Executable Distribution Grid Restoration Planning With
           a Fine-Grained Power-Communication Interdependency Model

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      Authors: Xin Liu;Bo Zhang;Bo Chen;Alex Aved;Dong Jin;
      Pages: 1911 - 1922
      Abstract: Distribution service restoration (DSR) under natural disasters is always a critical and challenging problem for utility companies. An effective solution must not ignore the power-communication interdependency as various systems are getting increasingly connected in the Smart Grid era. In this paper, we propose a two-layer distribution system model with both power and communication components. Based on this model, we formulate the restoration process as a routing problem that schedules the path and action sequence of utility crews that involves repairing damaged components, closing power switches, and enabling communication paths between the control center and remote field devices. We develop a simulation-based method to quantitatively evaluate the restoration process with public reference models of large-scale power systems. The experimental results show that our method improves the total restored energy up to 57.6% and reduces the recovery time up to 63% by considering the power-communication interdependency.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Self-Organizing Map-Based Resilience Quantification and Resilient Control
           of Distribution Systems Under Extreme Events

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      Authors: Kumar Utkarsh;Fei Ding;
      Pages: 1923 - 1937
      Abstract: Due to climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution system operators (DSO) to ensure that there is uninterrupted power supply to critical loads in their networks. To embed resilience into DSO’s decision-making, resilience needs to be first quantified and then integrated into the system-level optimization. Therefore, this paper first develops a novel self-organizing map (SOM) based method (called SomRes) to quantify the time-varying resilience index of a system that can leverage the powerful classification property of SOMs and removes some of the disadvantages of subjective weight assignment methods. Using SomRes, a resilient resource allocation and operational dispatch algorithm is further developed to enhance system resilience against extreme events by considering the SomRes resilience index directly as the feedback. The proposed resilience quantification approach is benchmarked with a state-of-the-art approach and the efficacy of the proposed resilient dispatch algorithm is demonstrated through several deterministic and statistical case studies on the IEEE 123-bus distribution system. Simulation studies show that the proposed SomRes quantification method is an appropriate indicator of system resilience, and the resilient resource allocation and dispatch strategy can significantly reduce critical load shedding under varying event propagation scenarios.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Online Coordination of LNG Tube Trailer Dispatch and Resilience
           Restoration of Integrated Power-Gas Distribution Systems

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      Authors: Boda Li;Ying Chen;Wei Wei;Zhaojian Wang;Shengwei Mei;
      Pages: 1938 - 1951
      Abstract: Severe winter storms can damage critical components of integrated power-gas distribution systems (IPGS), such as pulling down distribution lines and freezing gas pipelines/sources, causing electricity and gas supply disruptions. Liquefied natural gas (LNG) has a high energy density and can be transported by trailers, hence has great potential in multi-energy restoration. This paper studies the application of LNG trailers in the IPGS restoration. The process that the post-storm restoration coordinated with the trailer dispatch and the emergency operation is fully explored. The trailer dispatch is modeled as a finite-state machine; An efficient policy is proposed to dynamically update the real-time dispatch strategy without future knowledge of uncertainties. For the emergency operation of IPGS, storm-induced damages are considered in the modeling. Then, an online framework is proposed to satisfy the need for IPGS restoration with trailers. Compared with the offline method, the proposed framework does not require future information, so is adaptive to environmental uncertainty and easy to implement. Also, the framework solves a dedicated problem involving the observed information in a single period, hence enjoys a high computational efficiency. The simulation results demonstrate the potential of LNG trailers in resilience enhancement and validate the efficiency of the proposed method.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Demand Response Application as a Service: An SDN-Based Management
           Framework

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      Authors: Ahmadreza Montazerolghaem;Mohammad Hossein Yaghmaee;
      Pages: 1952 - 1966
      Abstract: With an increase in the utilization of appliances, meeting the energy demand of consumers by traditional power grids is an important issue. The success of Demand Response (DR) depends conclusively on real-time data communication between the consumers and the suppliers. Hence, a scalable and programmable communication network is required to handle the data generated. We prove that the problem of DR global load balancing includes energy and data constraints is NP-hard. So, a dynamic and self-configurable network technology known as Software-defined Networking (SDN) can be an efficient solution. In order to handle DR communication challenges, an SDN-enabled framework for DR flow management is designed in this paper. This framework is based on two-tier cloud computing and manages energy and data traffic seamlessly. We also equip this framework with Network Functions Virtualization (NFV) technology. The proposed framework is implemented on a practical testbed, which includes Open vSwitch, Floodlight controller, and OpenStack. Its performance is appraised by comprehensive experiments and scenarios. Based on the results, it achieves low delay, a high throughput, and improves Peak to Average Ratio (PAR) by balancing the energy and data on the entire DR network.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Spectrum Allocation and Computing Resources Optimization for Demand-Side
           Cooperative Communications in Smart Grid

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      Authors: Mingyue Sun;Yazhou Yuan;Kai Ma;Pei Liu;Xinbin Li;Xiaoyuan Luo;
      Pages: 1967 - 1975
      Abstract: In this paper, we introduce edge computing technologies (ECT) to improve the quality of smart grid demand-side communications and simultaneously optimize bandwidth along with computing power resources, further reducing the costs to the utility company (UC). First, we employ the edge node (EN) with data computing capability as relay, meanwhile, constructing a analytical model of computing resources and bandwidth. Second, the electricity costs is built based on the packet loss model and the regulation errors in direct load control. Then the UC decides the computing resources and the bandwidth for relaying to minimize its total costs, and the EN sets the base price to maximize its profit. Furthermore, we consider the interactions between the UCs and the ENs as a Stackelberg game. Simulation results demonstrate a significant reduction in costs to the utility company and an exaltation in profits of the relays.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Two-Stage Reinforcement Learning Policy Search for Grid-Interactive
           Building Control

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      Authors: Xiangyu Zhang;Yue Chen;Andrey Bernstein;Rohit Chintala;Peter Graf;Xin Jin;David Biagioni;
      Pages: 1976 - 1987
      Abstract: This paper develops an intelligent grid-interactive building controller, which optimizes building operation during both normal hours and demand response (DR) events. To avoid costly on-demand computation and to adapt to non-linear building models, the controller utilizes reinforcement learning (RL) and makes real-time decisions based on a near-optimal control policy. Learning such a policy typically amounts to solving a hard non-convex optimization problem. We propose to address this problem with a novel global-local policy search method. In the first stage, an RL algorithm based on zero-order gradient estimation is leveraged to search for the optimal policy globally, due to its scalability and the potential to escape some poor performing local optima. The obtained policy is then fine-tuned locally to bring the first-stage solution closer to that of the original unsmoothed problem. Experiments on a simulated five-zone commercial building demonstrate the advantages of the proposed method over existing learning approaches. They also show that the learned control policy outperforms a pragmatic linear model predictive controller (MPC) and approaches the performance of an oracle MPC in testing scenarios. Using a state-of-the-art advanced computing system, we demonstrate that the controller can be learned and deployed within hours of training.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • An Occupancy-Informed Customized Price Design for Consumers: A Stackelberg
           Game Approach

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      Authors: Li He;Yuanzhi Liu;Jie Zhang;
      Pages: 1988 - 1999
      Abstract: Residential occupancy patterns are closely tied with consumers’ activities and can potentially be leveraged to improve demand response by identifying the periods when high demand occurs. Occupancy detection can be generally divided into two categories in existing studies: intrusive and non-intrusive detection. However, both methods are still challenging to implement in practice, due to privacy concerns raised by intrusive sensors and the lack of labeled data required by non-intrusive methods. This paper seeks to design customized prices based on different consumers’ occupancy status and consumption patterns to enhance both retailer and consumers’ benefits using smart meter data. The interaction between the retailer and consumers is modeled as a one-leader-N-follower Stackelberg game. A two-stage decision-making framework, including day-ahead and real-time, is proposed for revenue maximization. The ECO (Electricity Consumption and Occupancy) Swiss households dataset is adopted to evaluate the effectiveness of the proposed occupancy-informed demand response program design.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • MPC Coordinated Primary Frequency Support of Small- and Large-Scale Heat
           Pumps

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      Authors: Theis Bo Harild Rasmussen;Qiuwei Wu;Jakob Glarbo Møller;Menglin Zhang;
      Pages: 2000 - 2010
      Abstract: Heat pumps (HPs) can provide flexibility for different power system services, including frequency support. The modern power system integrates various sized HPs as part of individual or district heating systems. While the basic principles and layout are similar for differently sized HPs, differences in dynamic response characteristics entail different limitations on their participation in grid service provision. Small-scale HPs (S-HPs) have a fast response and are widely distributed, while large-scale HPs (L-HPs) are more centralized with slow dynamics. This paper propose an innovative and coordinated control scheme for primary frequency response from S-HPs and L-HPs. The method configures the control of S-HPs based on an observed homogeneous S-HP population response to control actions. Furthermore, a centralized optimization of L-HPs based on model predictive control (MPC), balances the S-HP population’s response. Analysis of simulation results evaluates and compares the proposed methodology against exiting methods that consider only S-HP units. Results show the significant improvement of the frequency nadir with the support from the heating system through the proposed method.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Demand Response Aggregation With Operating Envelope Based on Data-Driven
           State Estimation and Sensitivity Function Signals

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      Authors: Shuying Lai;Jing Qiu;Yuechuan Tao;Xianzhuo Sun;
      Pages: 2011 - 2025
      Abstract: With the increasing penetration of renewable energy sources (RES), the value of the demand response (DR) draws wide attention. In order to realize the coordinated dispatch of widely spread resources, the aggregation of the controllable residential loads is managed by a single entity, namely the DR aggregator. Under the price-based DR programs, the DR aggregators actively respond to the market signals to reach maximum welfare. To avoid the quality of electricity services being jeopardized, the operational constraints of the network should be considered by the DR aggregators. However, DR aggregators are not expected to have access to the monitoring equipment and have limited knowledge of the network states. Hence, in this paper, we proposed a DR aggregation with the operating envelope framework based on the representative signals produced by the distributed network operator (DNO) in the context of big data era. The DNO provides representative signals, including real-time state estimation and sensitivity functions, to the DR aggregators based on the proposed Semi-supervised Coupled Generative Adversarial Imputation Network (SC-GAIN) and big data analysis. The DR aggregators can realize the secure and efficient real-time dispatch of the controllable loads based on the received signals. The proposed framework was verified on the IEEE 33-bus and 123-bus systems. The case studies show that the proposed SC-GAIN algorithm can better deal with the missing data, and the learned sensitivity functions can effectively avoid the overestimation of the true DR potential.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Comparison of Priority Service With Multilevel Demand Subscription

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      Authors: Céline Gérard;Daniel Ávila;Yuting Mou;Anthony Papavasiliou;Philippe Chevalier;
      Pages: 2026 - 2037
      Abstract: Priority service and multilevel demand subscription have been proposed as two alternative methods for the mobilization of residential demand response. Whereas priority service relies on the differentiation of electricity service according to reliability, multilevel demand subscription further differentiates electricity service according to duration. Despite its increased complexity, multilevel demand subscription promises increased operational efficiency, as it permits a finer differentiation of consumer classes by the utility. It also allows households to reduce their electricity bills relative to priority service. This paper proposes a framework for quantifying these effects. We design a modeling approach for evaluating the performance of these different aggregator service offerings in a system with utility-scale renewable supply, residential renewable supply, and residential storage. We compare priority service to multilevel demand subscription, and discuss the implications of these different residential demand response options on operational efficiency and consumer expenditures for electricity service on a realistic model of the Belgian power market. We show how the comparison between the two schemes is affected by the adoption of a different time resolution in a detailed case study.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Distributed Stochastic Model Predictive Control for Peak Load Limiting in
           Networked Microgrids With Building Thermal Dynamics

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      Authors: Ehsan Rezaei;Hanane Dagdougui;Mahdi Rezaei;
      Pages: 2038 - 2049
      Abstract: Today’s increasing in demand for renewable energy resources has attracted a lot of attention towards Microgrids (MGs). Due to their reliability, computation power and performance, a distributed network of MGs seems to be a promising approach to integrate a large number of distributed energy resources, energy storage systems and loads. However, the local privacy, the consensus process and the stochastic behaviors of renewable energy resources are some of the challenges which should be addressed before a wide deployment of such networks. In this paper, we introduce a distributed MGs integrated with buildings, focusing on their ability to provide peak load limiting. The algorithm is formalized as a two-stage stochastic problem, where the first stage variables determine the next time-step buildings’ temperatures setpoints in each microgrid, while the second-stage variables define the power exchange decisions for the purpose of limiting peak load in the network of microgrids. Leveraging by alternating direction method of multipliers (ADMM), the proposed framework can work in a real-time environment as a supervisory controller to coordinate MGs.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Network-Secure Envelopes Enabling Reliable DER Bidding in Energy and
           Reserve Markets

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      Authors: Ahmad Attarha;S. Mahdi Noori R. A.;Paul Scott;Sylvie Thiébaux;
      Pages: 2050 - 2062
      Abstract: As our power system modernises to embrace consumer-owned distributed energy resources (DER), network operators must ensure a safe and reliable operation while enabling consumers to trade their flexibility in the wholesale market. To enable this, we obtain network-secure operating envelopes that facilitate participation of consumers in energy and reserve markets. Given the distributed nature of the real-world problem, we use the alternating direction method of multipliers (ADMM) to optimise for operating envelopes such that any market action of consumers within these envelopes satisfies the distribution network constraints. To guarantee that the uncertainty realisation in live operation neither leads to network infeasibilities (due to exceeding the operating envelope) nor penalises DER-owners (due to market bid violation), we introduce a piecewise affinely adjustable robust bidding approach that can compensate for uncertainty variations in real-time. We also open up network capacity and minimise its losses, by proposing an additional piecewise affine Q-P controller that exchanges inverter reactive power with the grid. Our results on a 69-bus distribution network highlight the effectiveness of our proposal compared to alternative approaches.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Three-Phase Unbalanced Distribution Network Dynamic Reconfiguration: A
           Distributionally Robust Approach

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      Authors: Anping Zhou;Hefeng Zhai;Ming Yang;You Lin;
      Pages: 2063 - 2074
      Abstract: The active distribution network has witnessed an increasing penetration of distributed generation (DG) while the stochasticity and variability arising from DGs also impose significant challenges on system operation. To mightily accommodate the uncertainty of DG, we introduce a distributionally robust chance-constrained dynamic reconfiguration approach for a three-phase unbalanced distribution network. The proposed framework optimizes the switching cost and the expected power supply cost from upstream grid, and stipulates that the chance constraints hold under the worst-case distribution within a novel ambiguity set, which incorporates the Wasserstein distance and the first-order moment. Then we develop tractable and scalable solution methods to tackle the expected objective function and chance constraints. As a result, the proposed model is reduced to a mixed-integer linear programming problem that can readily be implemented. Numerical experiments are carried out on the IEEE 34-bus and 123-bus test systems to demonstrate the effectiveness and efficiency of the suggested approach.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Adaptive Power Control Strategy for Smart Droop-Based Grid-Connected
           Inverters

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      Authors: Nabil Mohammed;Mihai Ciobotaru;
      Pages: 2075 - 2085
      Abstract: Grid-connected inverters play an important role in the integration of renewable energy sources such as solar and wind. However, due to the unneglectable grid impedance value seen by the inverters at the point of common coupling (PCC), especially in the weaks and resistive low voltage distribution networks, there is an inherent strong coupling between active and reactive power flow. This power coupling causes significant power quality problems including 1) voltage fluctuation of the common AC bus resulted from high penetration of intermittent renewable generation systems, 2) non-optimal control of neither power flow nor power factor of the delivered power by inverters to the common AC bus, and 3) unintended/uncompensated transmission losses, where the flow of active power through the transmission/distribution lines will cause unintended reverse reactive power flow from the grid-side and vice versa with the reactive power flow. To solve these issues, this paper proposes an adaptive mechanism for droop-based grid-connected inverters to decouple the power flow by compensating the associated unintended active and reactive power losses flowing through the transmission line (or any desired segment of it). This control strategy relies on modifying the power command provided to the frequency and voltage droop loops by considering the effects of both the transmission line resistance and inductance components on the power flow between the inverter and the grid. It uses only the local current and voltage measurements to first perform an online estimation of the transmission line resistance and inductance and then to calculate the proposed adaptive power terms. The performance of the proposed control is validated in MATLAB/Simulink and HIL experiment for a 350 kW droop-based grid-connected inverter system. The proposed control strategy can be utilized to provide ancillary services to the grid such as accurate frequency and voltage support at the location of interest.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Variable Step Size Robust Least Mean Logarithmic Square-Based Control
           Scheme for Improved Power Quality of Grid-Interfaced PV System

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      Authors: Abhisek Parida;Bidyadhar Subudhi;
      Pages: 2086 - 2093
      Abstract: Power Quality (PQ) issue has always been a challenge for Grid-Interfaced PV Systems (GIPS). A novel Variable Step Size Robust Least Mean Logarithmic Square (VSS-RLMLS)-based control scheme is thus presented for a GIPS. In the proposed control scheme, the VSS-RLMLS algorithm extracts the fundamental components of the nonlinear load currents accurately even during dynamic conditions which results in generation of accurate reference grid current thus allowing efficient functioning of the Voltage Source Converter (VSC) and delivering high quality power to the grid by mitigating the harmonics of the loads. The performance of the proposed control scheme is validated both in simulation and laboratory developed prototype system with different load and environmental conditions. This VSS-RLMLS control scheme is compared with LMF and variable leaky LMS. It is found that the proposed control exhibits superior PQ performance with least grid current THD.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Automated Event Region Identification and Its Data-Driven Applications in
           Behind-the-Meter Solar Farms Based on Micro-PMU Measurements

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      Authors: Parviz Khaledian;Hamed Mohsenian-Rad;
      Pages: 2094 - 2106
      Abstract: This paper is motivated by the fact that behind-the-meter solar farms are being increasingly deployed in California and elsewhere in recent years. The objective is to use real-world micro-PMU measurements at a 4.3 MW behind-the-meter solar Photovoltaic (PV) farm to build a foundation for event-based situational awareness and its data-driven application. Two essential tasks are conducted. First, through developing an automated event region identification mechanism, we identify whether an event at a behind-the-meter solar farm is “locally-induced”, i.e., it is caused by the solar farm, thus potentially indicating internal issues in the solar farm, or “grid-induced”, i.e., it is caused by something else on the grid, thus revealing how the solar farm responded to external disturbances. We show that this is a highly challenging task in practice: the conventional impedance-based method is ineffective, the statistical method and the machine learning method each has its weaknesses. Accordingly, a novel mixed-integrated method is proposed and tested that can achieve very high performance metrics. The proposed mixed-integrated method also closes the gap between the accuracies in identifying grid-induced events versus locally-induced events. Second, the outcome of automated event region identification is used to unmask the constructive use of the proposed analysis. Practical use cases are proposed to take advantage of the situational awareness that we gain from analyzing both types of events to provide critical reporting, unmask trends and relationships, adjust control parameters, or take remedial actions when needed.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Distributed Robust Model Predictive Control-Based Energy Management
           Strategy for Islanded Multi-Microgrids Considering Uncertainty

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      Authors: Zhuoli Zhao;Juntao Guo;Xi Luo;Chun Sing Lai;Ping Yang;Loi Lei Lai;Peng Li;Josep M. Guerrero;Mohammad Shahidehpour;
      Pages: 2107 - 2120
      Abstract: A microgrid is considered to be a smart power system that can integrate local renewable energy effectively. However, the intermittent nature of renewable energy causes operating pressure and additional expense in maintaining the stable operation by the energy management system in a microgrid. The structure of multi-microgrids provides the possibility to construct flexible and various energy trading framework. In this paper, in order to reduce the adverse effects of uncertain renewable energy output, a distributed robust model predictive control (DRMPC)-based energy management strategy is proposed for islanded multi-microgrids. This strategy balances the robustness and economy of single-microgrid system operation by combining the advantages of robust optimization and model predictive control, while coping with the uncertainty of renewable energy sources. Furthermore, a dynamic energy trading market is formed among microgrids, which can enhance the overall economy of the multi-microgrids system. Simulation results verify the feasibility of the proposed DRMPC strategy.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Employing Machine Learning for Enhancing Transient Stability of Power
           Synchronization Control During Fault Conditions in Weak Grids

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      Authors: Amir Sepehr;Oriol Gomis-Bellmunt;Edris Pouresmaeil;
      Pages: 2121 - 2131
      Abstract: Grid-connected converters are exposed to the loss of synchronisation with the grid during severe voltage sags particularly when operating under weak grid condition which introduces voltage and frequency volatility. This paper presents employing machine learning methods besides modifying the converter control scheme to enhance the transient stability of power synchronization control (PSC). For early detection of synchronization instability of PSC to provide adequate time for taking correcting control actions, an encoder stacked classifier is proposed which is trained to be robust against data corruption and added noise. Then, by integrating the proposed instability detection scheme to the synchronization loop of PSC, a phase freezing mode is introduced to avoid losing synchronism during grid faults. It is disclosed that the frozen synchronization loop, which is activated by the proposed instability detection scheme, can ensure synchronization stability of PSC. Time-domain simulations are conducted to confirm the presented findings.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Distributionally Robust Joint Chance-Constrained Dispatch for Integrated
           Transmission-Distribution Systems via Distributed Optimization

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      Authors: Junyi Zhai;Yuning Jiang;Yuanming Shi;Colin N. Jones;Xiao-Ping Zhang;
      Pages: 2132 - 2147
      Abstract: This paper focuses on the distributionally robust dispatch for integrated transmission-distribution (ITD) systems via distributed optimization. Existing distributed algorithms usually require synchronization of all subproblems, which could be hard to scale, resulting in the under-utilization of computation resources due to the subsystem heterogeneity in ITD systems. Moreover, the most commonly used distributionally robust individual chance-constrained dispatch models cannot systematically and robustly ensure simultaneous security constraint satisfaction. To address these limitations, this paper presents a novel distributionally robust joint chance-constrained (DRJCC) dispatch model for ITD systems via asynchronous decentralized optimization. Using the Wasserstein-metric based ambiguity set, we propose data-driven DRJCC models for transmission and distribution systems, respectively. Furthermore, a combined Bonferroni and conditional value-at-risk approximation for the joint chance constraints is adopted to transform the DRJCC model into a tractable conic formulation. Meanwhile, considering the different grid scales and complexity of subsystems, a tailored asynchronous alternating direction method of multipliers (ADMM) algorithm that better adapts to the star topological ITD systems is proposed. This asynchronous scheme only requires local communications and allows each subsystem operator to perform local updates with information from a subset of, but not all, neighbors. Numerical results illustrate the effectiveness and scalability of the proposed model.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • An Effective CPT-Based Nonintrusive Load Monitoring for Cognitive Meters

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      Authors: Wesley Angelino de Souza;Tiago Agostinho de Almeida;
      Pages: 2148 - 2157
      Abstract: Nonintrusive Load Monitoring (NILM) is the process to disaggregate the power consumption in appliances using a single meter installed on the residence electric entrance. We introduce a novel load modeling approach for power consumption disaggregation using the equivalent circuit concept from the Conservative Power Theory (CPT). CPT can represent an appliance or an operation stage using an equivalent circuit based on load parameters. This method allows obtaining load disaggregation models efficiently, easily, and fast, without requiring to collect and label plenty of samples. To validate it, we have (i) used a meter to capture signals from the aggregate consumption; (ii) extracted the features from the CPT equivalent circuit parameters for appliance recognition in a load event; (iii) created and validated a classifier constructed through equivalent circuits of the appliances, and (iv) tested the classifier in NILM real-world case studies. Experiments from different appliances achieved promising results, indicating effective load disaggregation capacity and reinforcing CPT as groundwork for load characterization towards an efficient load disaggregation.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Low-Voltage Distribution Grid Topology Identification With Latent Tree
           Model

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      Authors: Haipeng Zhang;Jian Zhao;Xiaoyu Wang;Yi Xuan;
      Pages: 2158 - 2169
      Abstract: Distribution grid topology information is the basis of grid analysis functions such as power flow and state estimation. Due to lack of monitoring and measurement devices in low-voltage distribution grids (LVDGs), the intermediate nodes connecting LVDG transformers to end-users cannot upload the nodal operation status. The existence of these latent nodes poses a huge challenge to LVDG topology identification. This paper proposes a LVDG latent node and topology identification method based on end-user data from smart meters. Specifically, a special latent-node embedded Bayesian network, defined as latent tree model, is proposed to provide probabilistic representation for all possible LVDG topologies. A search-based algorithm is proposed to generate candidate topologies and then a Bayesian information criterion is proposed to describe the accuracy of the candidate topologies. Meanwhile, the Expectation-Maximization algorithm is introduced to complement measurement lacking latent nodes. Test results in different simulation scenarios and practical LVDGs demonstrate the effectiveness and robustness of the proposed method.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Synchronized Lissajous-Based Method to Detect and Classify Events in
           Synchro-Waveform Measurements in Power Distribution Networks

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      Authors: Milad Izadi;Hamed Mohsenian-Rad;
      Pages: 2170 - 2184
      Abstract: Waveform measurement units (WMUs) are a new class of smart grid sensors. They capture synchro-waveforms, i.e., time-synchronized high-resolution voltage waveform and current waveform measurements. In this paper, we propose new methods to detect and classify power quality events in power distribution systems by using synchro-waveform measurements. The methods are built upon a novel graphical concept, called synchronized Lissajous curve. The proposed event detection and event classification methods work by analyzing the shape of the synchronized Lissajous curves during disturbances and events. The impact of challenging factors, such as the angle, the location, and other parameters of the event are discussed. We show that these challenges can be addressed if we treat the synchronized Lissajous curves as images, instead of as time series as in the raw synchronized waveform measurements. Hence, we can take advantage of the recent advancements in the field of image processing so as to capture the overall characterizing patterns in the shapes of the synchronized Lissajous curves. We develop a Convolutional Neural Network (CNN) method to classify the events, where the input is the synchronized Lissajous images. The effectiveness of the proposed event detection and classification methods is demonstrated through computer simulations, including hardware-in-the-loop simulations, and real-world field data. Multiple case studies verify the performance of the proposed methods. The proposed event detection method can accurately detect events, and identify the start time and the end time of each event. The proposed event classification method can classify power quality events with high accuracy. The proposed detection and classification methods do not require any prior knowledge about the network. They use data from as few as only two WMUs.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Anomaly-Aware Adaptive Sampling for Electrical Signal Compression

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      Authors: K. R. Sahasranand;Francis C. Joseph;Himanshu Tyagi;Gurunath Gurrala;Ashish Joglekar;
      Pages: 2185 - 2196
      Abstract: Intelligent electronic devices for power systems often entail high frequency sampling of electric signals, enabled to capture anomalous signal behavior. However, in normal operation this oversampling is redundant and leads to excessive data being stored or transmitted. This gives rise to a new compression problem where the collected samples should be further subsampled and quantized based on the presence of an anomaly in the underlying signal. We propose an Anomaly-aware Compressive Sampler (ACS) which tests the signal for the presence of an anomaly in a block of samples, and subsamples in a hierarchical manner to retain the desired sampling rate. ACS has been designed keeping hardware constraints in mind, using integer operations, an appropriate bit-packing, a simple iterated delta filter, and a streaming data pipeline. We present a mathematical formulation of the problem and analyze the performance of ACS, establishing theoretically its ability to identify anomalies in the signal and adapt the sampling rate. ACS competes with the state-of-the-art algorithm for the better-behaved transmission system data from DOE/EPRI, and outperforms it significantly on real-time distribution system data recorded in our laboratory. Finally, ACS is lightweight and was implemented on an ARM processor.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Distributed Measurement-Based Optimal DER Dispatch With Estimated
           Sensitivity Models

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      Authors: Severin Nowak;Yu Christine Chen;Liwei Wang;
      Pages: 2197 - 2208
      Abstract: This paper presents a distributed measurement-based method to determine distributed energy resource (DER) active- and reactive-power setpoints that minimize bus voltage deviations from prescribed references, bus active- and reactive-power deviations from desired values, as well as cost of DER outputs. The proposed method partitions the system into multiple areas and performs per-area computations in parallel, thus mitigating scalability concerns of centralized implementations. A linear sensitivity model for each area is first estimated from measurements via the recursive weighted partial least-squares algorithm. The estimated sensitivity models are then embedded in an optimization problem, the structure of which is amenable to decomposition into per-area subproblems. The subproblems are solved in parallel using consensus-based alternating direction method of multipliers to obtain optimal DER setpoints. Both the estimation and optimization tasks require only the exchange of information at boundary buses among adjacent areas. Numerical simulations involving the IEEE 33-bus distribution test system illustrate the ability of the proposed method to determine optimal DER setpoints that adapt to operating-point changes in a distributed fashion. Additional numerical simulations involving the IEEE 123-bus system demonstrate computational scalability and the application to multi-phase systems in a practical scenario.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Smoothed Least-Laxity-First Algorithm for Electric Vehicle Charging:
           Online Decision and Performance Analysis With Resource Augmentation

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      Authors: Niangjun Chen;Christian Kurniawan;Yorie Nakahira;Lijun Chen;Steven H. Low;
      Pages: 2209 - 2217
      Abstract: Adaptive charging can charge electric vehicles (EVs) at scale cost effectively, despite of the uncertainty in EV arrivals. We formulate adaptive EV charging as a feasibility problem that meets all EVs’ energy demands before their deadlines while satisfying constraints in charging rate and total charging power. We propose an online algorithm, smoothed least-laxity-first (sLLF), that decides the current charging rates without the knowledge of future arrivals and demands. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has a significantly higher rate of feasible EV charging than several other existing EV charging algorithms. Resource augmentation framework is employed to assess the feasibility condition of the algorithm. The assessment shows that the sLLF algorithm achieves perfect feasibility with only a 7% increase in the maximal power supply of the charging station.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Cooperative Hierarchical Multi-Agent System for EV Charging Scheduling
           in Presence of Multiple Charging Stations

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      Authors: Can Berk Saner;Anupam Trivedi;Dipti Srinivasan;
      Pages: 2218 - 2233
      Abstract: The increasing penetration of plug-in electric vehicles (EVs) to the electrical grid raises concerns over secure and economic operation of the system. A coordination mechanism between system operator and EV aggregators is necessary to ensure that the system is operated within the security limits, and to reduce the charging costs while satisfying EV users’ energy needs. In this work, we present a cooperative hierarchical multi-agent system and propose an EV charging scheduling strategy in order to minimize the demand and energy charges while meeting the EV users’ energy requirements and satisfying the system security constraints. Within the designed framework, the higher-level agents calculate a set of proposed control signals by solving the designated optimization problems, and send them to the lower-level agents to facilitate an optimal scheduling in line with the aforementioned objectives. Through this hierarchically distributed approach, it is possible to effectively coordinate multiple EV charging stations without the need of direct communication or any prior information related to EV arrivals. The computational complexity of the problem is reduced by distributing the work among agents, and the privacy of sensitive data, such as system topology, load profiles, and EV parameters, is preserved. Moreover, unlike the traditional distributed solution methods that converge iteratively, the proposed approach calculates the optimal charging schedule after a single round of communication. The efficacy of the proposed methodology is demonstrated by a series of case studies on 33-bus and 118-bus distribution test feeders.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Centralized and Decentralized Pricing Strategies for Optimal Scheduling of
           Electric Vehicles

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      Authors: Aastha Kapoor;Viresh S. Patel;Ankush Sharma;Abheejeet Mohapatra;
      Pages: 2234 - 2244
      Abstract: Three novel pricing schemes based on the centralized and decentralized architectures for day-ahead optimal scheduling of Electric Vehicles (EVs) are proposed in this paper. A novel $psi $ -Iterative Pricing Scheme ( $psi $ -IPS) is proposed for the decentralized model. In contrast, the Bi-level Pricing Scheme (BPS) and Multi-objective Pricing Scheme (MPS) are proposed for the centralized model. The issues of load valley-filling and rebound peak occurrence are addressed effectively while fulfilling the objectives of multiple players, i.e., the EV owners, the aggregator, and the Distribution System Operator (DSO). The aggregated load, i.e., predicted non-EV load and EV charging requests, served by the distribution transformer, is used for the scheduling process. Solar PV integrated residential load (non-EV load) is predicted using the Random Forest technique. The input load and weather data are utilized after data cleaning and features selection. The $psi $ -IPS and BPS are implemented using game-theoretic approaches, while MPS utilizes a multi-objective formulation. The three schemes based upon the two architectures, i.e., centralized and decentralized, are compared, and the key benefits of the schemes are highlighted.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • An Efficient and Incentive-Compatible Mechanism for Energy Storage Markets

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      Authors: Bharadwaj Satchidanandan;Munther A. Dahleh;
      Pages: 2245 - 2258
      Abstract: A key obstacle to increasing renewable energy penetration in the power grid is the lack of utility-scale storage capacity. Transportation electrification has the potential to overcome this obstacle since Electric Vehicles (EVs) that are not in transit can provide battery storage as a service to the grid. This is referred to as EV-Power grid integration, and could potentially be a key milestone in the pathway to decarbonize the electricity and the transportation sectors. We first show that if EV-Power grid integration is not done carefully, then contrary to improving the cost efficiency of operating the grid, it could in fact be counterproductive to it. This occurs due to two phenomena operating in tandem — the randomness of EV usage patterns and the possibility of strategic behavior by EV users. We present a market-based solution to address this issue. Specifically, we develop a mechanism for energy storage markets using which the system operator can efficiently integrate a fleet of strategic EVs with random usage patterns into the grid, utilize them for storage, and satisfy the demand at the minimum possible cost.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Knowledge-Based Systematic Feature Extraction for Identifying Households
           With Plug-in Electric Vehicles

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      Authors: Md Fazla Elahe;Min Jin;Pan Zeng;
      Pages: 2259 - 2268
      Abstract: Hassle-free in-house charging of plug-in electric vehicles (PEVs) is getting popular. Due to high power consumption, in-house PEV charging has a significant impact on the distribution network, and the utility companies are facing challenges in balancing the power demand. The problem could be solved by increasing distribution network capacity, designing an effective demand response program aimed at PEV charging, implementing vehicle to grid (V2G) or vehicle to house (V2H), and so on. One of the fundamental parameters for the mentioned solutions is to identify households with PEVs. Multi-level charging, power consumption similar to other home appliances, and absence of submeter for charging outlets make identification difficult. In this paper, a new feature extraction technique called knowledge-based systematic feature extraction is proposed to identify households with PEVs. The extracted features are easily interpretable and validated using two datasets from different regions. Keeping real scenarios in mind, the study examines scenario-based results and finds that the accuracy using extracted features ranges from 80.20% to 100% depending on classifier, number of vehicles, and level of charging. Moreover, results show improved performance compared to existing methods for identifying households with PEVs and other state-of-the-art feature extraction techniques.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Peer-to-Peer Energy Trading With Energy Path Conflict Management in Energy
           Local Area Network

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      Authors: Xingyue Jiang;Chuan Sun;Lingling Cao;Law Ngai-Fong;K. H. Loo;
      Pages: 2269 - 2278
      Abstract: The increasing penetration of distributed renewable generations has given rise to a novel energy management mechanism, peer-to-peer (P2P) energy trading. The concept of Energy Internet (EI) is proposed as a new energy system framework to facilitate P2P energy trading where all distributed electrical devices are interconnected via energy routers (ERs). A proper market clearing approach is required to coordinate decentralized decision making in P2P market for demand-supply balance and feasible end-to-end energy delivery. In this paper, a decentralized market clearing mechanism considering energy path conflict management is proposed for P2P energy trading in energy local area network (e-LAN). An adjusted conflict-based search (ACBS) algorithm is proposed to deal with the non-convexity of the social welfare maximization problem where the nonconvex problem is transformed into multiple independent convex problems. Considering the privacy protection of participants, a dual decomposition based approach is proposed to solve these convex problems in a decentralized manner where self rationality of each participant can be satisfied by maximizing its personal welfare. In addition, energy path conflicts caused by decentralized optimization is resolved by imposing penalty fee and observing the rule of energy sharing maximization. Numerical simulations are presented to verify the effectiveness of the proposed market clearing approach.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Transactive Energy Sharing in a Microgrid via an Enhanced Distributed
           Adaptive Robust Optimization Approach

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      Authors: Bo Wang;Cuo Zhang;Chaojie Li;Guangya Yang;Zhao Yang Dong;
      Pages: 2279 - 2293
      Abstract: The rapid growth of microgrids with various distributed energy resources (DERs) brings new opportunities for local energy sharing in the microgrids. However, the uncertainties of renewable distributed generation and loads pose a great technical challenge for a microgrid operator (MGO). Thus, this paper proposes a transactive energy sharing (TES) approach for the MGO and DER aggregators to minimize the total social cost, considering network operating constraints. Accordingly, a two-settlement transactive energy (TE) market with an incentive energy pricing scheme is developed to encourage the participation in the energy sharing. Besides, the real-time energy transactions of the aggregators are considered in a day-ahead optimization stage to address their negative impacts on microgrid operation. To solve the proposed TES problem, an alternating direction method of multipliers (ADMM) is applied. The uncertainties in each ADMM local problem are further addressed by an adaptive robust optimization (ARO) method solved by a column-and-constraint generation (C&CG) algorithm. The updated dual and coupling variables at each ADMM iteration could interact with the C&CG algorithm, impairing ADMM convergence. To solve this issue, an alternating uncertainty-update procedure is developed. The simulation results verify the high efficiency and solution robustness of the proposed TES method.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • PF-DA: Pairing Free and Secure Data Aggregation for Energy Internet-Based
           Smart Meter-to-Grid Communication

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      Authors: Girraj Kumar Verma;Prosanta Gope;Neeraj Kumar;
      Pages: 2294 - 2304
      Abstract: The amalgamation of energy, economics and information technology has evolved the paradigm of Energy Internet (EI). This evolution of EI has transformed houses or buildings into home networks (HN) to ease communication between consumer-to-grid. Smart meters deployed at various sites (of HNs) send load, consumption and related information to the control center of the distribution utility. However, data sharing using wireless channels creates associated privacy and security issues and challenges. For example, adversary can capture family life-style from consumption data and may try to commit crime. Besides, data received at control center from various sites must be processed in an efficient manner. Therefore, an efficient protocol for such sharing and processing of data is aggregation scheme. Even though there is a considerable number of data aggregation schemes relying on public-key cryptography have been proposed in the literature. However, most of them are based on the expensive pairing computation and hence are not efficient. To address this issue, in this article we present an efficient and new pairing free and highly secure data aggregation protocol in certificate-based infrastructure. Due to pairing free design, the method’s computational cost is adequately efficient for any resource-constrained devices such as smart appliances or smart meters. The security and performance analysis has been presented to support the deployability of the scheme.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Multi-Stage Information Protection Scheme for CDA-Based Energy Trading
           Market in Smart Grids

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      Authors: Yuan Ma;Jing Qiu;Xianzhuo Sun;Yuechuan Tao;
      Pages: 2305 - 2317
      Abstract: Building a reliable information exchange scheme is the prerequisite for Peer-to-Peer (P2P) energy trading in the smart grid. This paper proposes a multi-stage information protection scheme to meet the data security requirements including personal privacy-preserving, message authentication and confidentiality. In this study, the adversary is supposed to attack the continuous double auction (CDA)-based energy trading system in both the bidding period and implementation period. To prevent the smart grid from all potential cyber-attacks, our protection scheme is proposed to deal with personal privacy leakage, malicious data injection and impersonation attack. Before the trading period, in the home energy data collection stage, we propose a dual privacy-preserving scheme based on the short key homomorphic encryption and the decentralized framework. In the trading period, an encryption-signature mixed (E-S) communication model is proposed to achieve the secure transmission of trading messages (e.g., energy bidding from prosumer). At last, in the implementation period, a decentralized field measurement monitoring scheme is proposed to detect the node compromise attack to the power measurements. In addition, the computational feasibility, time cost and communication cost are evaluated with the home-use PC. The anti-attack performance of the proposed scheme is verified on the IEEE 39-bus distribution network with the CDA-based P2P energy trading market.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Highly Discriminative Detector Against False Data Injection Attacks in
           AC State Estimation

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      Authors: Gang Cheng;Yuzhang Lin;Junbo Zhao;Jun Yan;
      Pages: 2318 - 2330
      Abstract: False data injection attacks (FDIAs) can bypass conventional bad data detection methods. Recently developed FDIA detection methods based on statistical consistency of measurement values may not work effectively when false data do not significantly deviate from historical trends. They may also mistakenly treat actual power grid events as FDIAs. In this paper, a highly discriminative FDIA detector named the ${k}$ -smallest residual similarity ( ${k}$ SRS) test is proposed. The method is based on the rationale that perfect FDIAs can hardly be achieved in AC state estimation, and real-world imperfect FDIAs always lead to subtle changes in the probability distributions of measurement residuals. Therefore, the statistical consistency of measurement residuals can be carefully portrayed to detect practical FDIAs in AC state estimation. Herein, the Jensen-Shannon distance (JSD) is used to precisely quantify the similarity of measurement residual distributions. Simulations on the IEEE 30-bus system demonstrate that the proposed method can achieve high detection rates and low false alarm rates under a variety of conditions where existing methods do not yield satisfactory results.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Quantifying Resilience of Wide-Area Damping Control Against Cyber Attack
           Based on Switching System Theory

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      Authors: Yifan Zhao;Wei Yao;Chuan-Ke Zhang;Xing-Chen Shangguan;Lin Jiang;Jinyu Wen;
      Pages: 2331 - 2343
      Abstract: Since the cyber attack on the communication network will deteriorate the performance of wide-area damping controllers (WADCs) or even cause instability, many resilient WADCs are developed to mitigate the adverse influence of cyber attacks recently. However, there is a lack of quantitative indexes to guide the controller design in order to achieve the trade-off between attack resilience and damping performance. To address this problem, an index is proposed to quantify the strongest attack that the power system with a given WADC can tolerate, which is called as resilience margin. Firstly, the power system with a WADC subjected to cyber attack is modeled as a switching system consisting of stable and unstable subsystems. Then, based on switching system theory, the definition of resilience margin is presented. To calculate the resilience margin, the Lyapunov stability analysis is implemented on the switching power system to derive a practical calculation algorithm, which combines the bisection method and the linear matrix inequalities (LMIs) technology. The case study on the 16-machine 68-bus system with a voltage source converter based high voltage direct current system is performed. Simulation results demonstrate the effectiveness of the calculation algorithm and the significance of the resilience margin in the design of WADC.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Distributionally Robust Scheme for Critical Component Identification to
           Bolster Cyber-Physical Resilience of Power Systems

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      Authors: Zhaoxi Liu;Lingfeng Wang;
      Pages: 2344 - 2356
      Abstract: With the increasing risks of malicious cyber-physical attacks and natural disasters, it is urged for power grids to improve the resilience performance against such high-impact, low-frequency events. The critical component identification is an important problem for the effective planning, operation and asset management of power systems. Thus, it is highly necessary and beneficial to develop a resilience oriented critical component identification method for defending power systems against extreme events. In this paper, a tri-level optimization model is developed to identify the critical components while the coordinated cyber-physical attacks on power systems are formulated considering the resources of attackers, the optimal attack strategy, and the actions of the system operator to enhance the system resilience against potential attacks. Meanwhile, a distributionally robust optimization (DRO) based model is proposed to consider the impacts of distribution uncertainty of the resource or ability of the malicious attackers in the criticality identification framework. A constraint-generation based algorithm is developed to solve the overall multi-level DRO model with a master and sub-problem scheme. Further, a criticality index is proposed to quantify the importance of key components to the system resilience against malicious cyber-physical attacks. In order to validate the proposed method, case studies were conducted on the IEEE reliability test system (RTS-79). The obtained results show that the proposed critical component identification method can provide effective and robust importance evaluation of system components to bolster the grid resilience against potential cyber-physical threats.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Co-Estimation of State and FDI Attacks and Attack Compensation Control for
           Multi-Area Load Frequency Control Systems Under FDI and DoS Attacks

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      Authors: Xiaoli Chen;Songlin Hu;Yu Li;Dong Yue;Chunxia Dou;Lei Ding;
      Pages: 2357 - 2368
      Abstract: This paper proposes a co-estimation and resilient compensation control framework for multi-area load frequency control (LFC) systems in the presence of false data injection (FDI) attacks and denial-of-service (DoS) attacks. To relax the requirement on existing DoS attack models, which are usually considered under the assumption that both its frequency and duration are constrained, a new DoS model within a known bounds of the sleeping and active period of the attack signal is proposed. The injected FDI attack signal is generated by an external dynamic systems. A switching impulsive observer and a switching state observer are constructed to estimate the unknown FDI attack signal and the system state, respectively. Moreover, a new exponential stability criteria of the output feedback multi-area LFC systems under DoS and FDI attacks is first derived by using attack parameter dependent time varying Lyapunov function method. Then, a co-design method of a resilient attack compensation controller, state estimator, and attack estimator is developed. Finally, case studies are considered for verification of the effectiveness of the proposed theoretical results.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Adaptive Hierarchical Cyber Attack Detection and Localization in Active
           Distribution Systems

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      Authors: Qi Li;Jinan Zhang;Junbo Zhao;Jin Ye;Wenzhan Song;Fangyu Li;
      Pages: 2369 - 2380
      Abstract: Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. This paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated sub-region. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack ‘coarse’ localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a ‘fine’ cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Fraud Detection on Power Grids While Transitioning to Smart Meters by
           Leveraging Multi-Resolution Consumption Data

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      Authors: Pablo Massaferro;J. Matías Di Martino;Alicia Fernández;
      Pages: 2381 - 2389
      Abstract: The technological upgrade of power utilities to smart metering is a process that can take several years. Meanwhile, smart meters coexist with previous generations of digital and electromechanical power meters. While the smart meters provide high-resolution power measurements, electromechanical meters are typically read by an operator once a month. The coexistence of these two technologies poses the challenge of monitoring non-technical losses (NTL) and fraud where some customers’ consumption is sampled every 15 minutes, while others are sampled once a month. In addition, since companies already have years of monthly historical consumption, it is natural to reflect how the past data can be leveraged to predict and improve NTL on smart grids. This work addresses both problems by proposing a multi-resolution deep learning architecture capable of simultaneously training and predicting input consumption curves sampled 1 a month or every 15 minutes. The proposed algorithms are tested on an extensive data set of users with and without fraudulent behaviors collected from the Uruguayan utility company UTE and on a public access data set with synthetic fraud. Results show that the multi-resolution architecture performs better than algorithms trained for a specific type of meters (i.e., for a particular resolution).
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Practical Adoption of Cloud Computing in Power Systems—Drivers,
           Challenges, Guidance, and Real-World Use Cases

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      Authors: Song Zhang;Amritanshu Pandey;Xiaochuan Luo;Maggy Powell;Ranjan Banerji;Lei Fan;Abhineet Parchure;Edgardo Luzcando;
      Pages: 2390 - 2411
      Abstract: Motivated by the Federal Energy Regulatory Commission’s (FERC) recent direction and ever-growing interest in cloud adoption by power utilities, a Task Force was established to assist power system practitioners with secure, reliable and cost-effective adoption of cloud technology to meet various business needs. This paper summarizes the business drivers, challenges, guidance, and best practices for cloud adoption in power systems from the Task Force’s perspective, after extensive review and deliberation by its members, including grid operators, utility companies, software vendors, and cloud providers. The paper begins by enumerating various business drivers for cloud adoption in the power industry. It follows with the discussion of the challenges and risks of migrating power grid utility workloads to the cloud. Next, for each corresponding challenge or risk, the paper provides appropriate guidance. Notably, the guidance is directed toward power industry professionals who are considering cloud solutions and are yet hesitant about the practical execution. Finally, to tie all the sections together, the paper documents various real-world use cases of cloud technology in the power system domain, which both the power industry practitioners and software vendors can look toward to design and select their own future cloud solutions. We hope that the information in this paper will serve as helpful guidance for the development of NERC guidelines and standards relevant to cloud adoption in the industry.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • SAMNet: Toward Latency-Free Non-Intrusive Load Monitoring via Multi-Task
           Deep Learning

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      Authors: Yinyan Liu;Jing Qiu;Jin Ma;
      Pages: 2412 - 2424
      Abstract: Non-intrusive load monitoring (NILM), including state detection and energy disaggregation, aims to identify the on/off state and energy consumption from the aggregate load of a building. By monitoring the electrical behavior of consumers, smart grid applications such as demand response and recommendation services can also be realized for saving energy bills, environmental effectiveness, assisted living, and fault diagnosis. To achieve latency-free monitoring, this paper presents a Scale- and Attention-experts based Multi-task neural network (SAMNet) with a large enough context of the view to make full use of the correlation between the tasks of the NILM. A shared expert learner is designed to learn a good summary of the features. Self-attention mechanism is creatively adopted to realize the weighted fusion of different experts. To address the problem of manually setting different threshold values for different appliances to decide the on/off states, we designed the network automatically labeling the on/off states. Extensive experimental results with the public datasets demonstrate the effectiveness and superiority of our proposed model.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Federated Clustering for Electricity Consumption Pattern Extraction

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      Authors: Yi Wang;Mengshuo Jia;Ning Gao;Leandro Von Krannichfeldt;Mingyang Sun;Gabriela Hug;
      Pages: 2425 - 2439
      Abstract: The wide popularity of smart meters enables the collection of massive amounts of fine-grained electricity consumption data. Extracting typical electricity consumption patterns from these data supports the retailers in their understanding of consumer behaviors. In this way, diversified services such as personalized price design and demand response targeting can be provided. Various clustering algorithms have been studied for electricity consumption pattern extraction. These methods have to be implemented in a centralized way, assuming that all smart meter data can be accessed. However, smart meter data may belong to different retailers or even consumers themselves who are not willing to share their data. In order to better protect the privacy of the smart meter data owners, this paper proposes two federated learning approaches for electricity consumption pattern extraction, where the k-means clustering algorithm can be trained in a distributed way based on two frequently used strategies, namely model-averaging and gradient-sharing. Numerical experiments on two real-world smart meter datasets are conducted to verify the effectiveness of the proposed method.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • FPSeq2Q: Fully Parameterized Sequence to Quantile Regression for Net-Load
           Forecasting With Uncertainty Estimates

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      Authors: Anthony Faustine;Lucas Pereira;
      Pages: 2440 - 2451
      Abstract: The increased penetration of Renewable Energy Sources (RES) as part of a decentralized and distributed power system makes net-load forecasting a critical component in the planning and operation of power systems. However, compared to the transmission level, producing accurate short-term net-load forecasts at the distribution level is complex due to the small number of consumers. Moreover, owing to the stochastic nature of RES, it is necessary to quantify the uncertainty of the forecasted net-load at any given time, which is critical for the real-world decision process. This work presents parameterized deep quantile regression for short-term probabilistic net-load forecasting at the distribution level. To be precise, we use a Deep Neural Network (DNN) to learn both the quantile fractions and quantile values of the quantile function. Furthermore, we propose a scoring metric that reflects the trade-off between predictive uncertainty performance and forecast accuracy. We evaluate the proposed techniques on historical real-world data from a low-voltage distribution substation and further assess its robustness when applied in real-time. The experiment’s outcomes show that the resulting forecasts from our approach are well-calibrated and provide a desirable trade-off between forecasting accuracies and predictive uncertainty performance that are very robust even when applied in real-time.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Electric Load Profile of 5G Base Station in Distribution Systems Based on
           Data Flow Analysis

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      Authors: Yao Zou;Qianggang Wang;Yuan Chi;Jian Wang;Chao Lei;Niancheng Zhou;Qinqin Xia;
      Pages: 2452 - 2466
      Abstract: This paper proposes an electric load demand model of the 5th generation (5G) base station (BS) in a distribution system based on data flow analysis. First, the electric load model of a 5G BS is developed according to its components and their characteristics. Second, critical factors of the power consumption of 5G BS, including area, data flow with uncertainty number of activated terminals and data usage of the terminal, are discussed. A statistical model of the number of activated terminals and data usage of the terminal are established using a probability distribution, and Monte Carlo sampling is used to handle the uncertainties. Then, the model is extended for multiple terminal connections. A case study is conducted to analyze the impact of the critical factors on the load of 5G BS and the influence of 5G BSs load on the other loads in three typical areas. Numerical results demonstrate that the proposed model is effective and can be employed as an accurate representation of the 5G BS load profile for the analysis of load characteristics. Case studies also show that the 5G BS loads have diverse impact on different typical areas in a distribution system.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Distributionally Robust Joint Chance-Constrained Optimization for
           Networked Microgrids Considering Contingencies and Renewable Uncertainty

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      Authors: Yifu Ding;Thomas Morstyn;Malcolm D. McCulloch;
      Pages: 2467 - 2478
      Abstract: In light of a reliable and resilient power system under extreme weather and natural disasters, networked microgrids integrating local renewable resources have been adopted extensively to supply demands when the main utility experiences blackouts. However, the stochastic nature of renewables and unpredictable contingencies are difficult to address with the deterministic energy management framework. The paper proposes a comprehensive distributionally robust joint chance-constrained (DR-JCC) framework that incorporates microgrid island, power flow, distributed batteries and voltage control constraints. All chance constraints are solved jointly and each one is assigned to an optimized violation rate. To highlight, the JCC problem with the optimized violation rates has been recognized as NP-hard and challenging to solve. This paper proposes a novel evolutionary algorithm that successfully solves this problem and reduces the solution conservativeness (i.e., operation cost) by around 50% compared with the baseline Bonferroni Approximation. We construct three data-driven ambiguity sets to model uncertain solar forecast error distributions. The solution is thus robust for any distribution in sets with the shared moments and shape assumptions. The proposed method is validated by robustness tests based on these sets and firmly secures the solution robustness.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Data-Driven False Data Injection Attack: A Low-Rank Approach

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      Authors: Debottam Mukherjee;
      Pages: 2479 - 2482
      Abstract: False data injection attack on the state estimation algorithms have already showcased its detrimental effects for modern grid operation. This letter promotes a novel attack vector formulation policy for the linear state estimation algorithm by exploring the low-rank subspace of the mapping matrix. An extensive analysis on the IEEE 14 bus test bench promotes the aforementioned propositions.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Resilience Metrics for Integrated Power and Natural Gas Systems

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      Authors: Haipeng Xie;Xiaotian Sun;Chen Chen;Zhaohong Bie;João P. S. Catalão;
      Pages: 2483 - 2486
      Abstract: The integrated power and natural gas system (IPGS) is a promising technique against extreme weather. In this letter, a valley-shaped resilience model is proposed to evaluate the performance of IPGSs under extreme weather and inter-energy assistances between the electric power sector and the natural gas sector. Furthermore, a quantification metrics framework for IPGS resilience evaluation is raised. Novel metrics are proposed, especially the distortion rate ( $varTheta $ ) and linepack effect ( $varDelta t^{*}$ ) for quantifying coupling tightness and assistance from natural gas transient in resilience perspective respectively. Evaluation on different restoration strategies is conducted to validate the effectiveness of the proposed metrics framework.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • A Novel Bilevel False Data Injection Attack Model Based on Pre- and Post-
           Dispatch

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      Authors: Shibin Gao;Jieyu Lei;Xiaoguang Wei;Yigu Liu;Tao Wang;
      Pages: 2487 - 2490
      Abstract: This letter develops a new bilevel optimization model to construct false data injection attack based on pre- and post- dispatch. In order to enhance the attack concealment, the proposed bilevel model can minimize the variation of uploaded measurements between pre- and post-attack before dispatching, after which the attack can lead the system to an uneconomic and insecure operating state after dispatching. Simulation results validate the effectiveness of the proposed bilevel model in term of operating cost and network overloads.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Evaluating the Impact of Connectivity on Transactive Energy in Smart Grid

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      Authors: Reza Zamani;Mohsen Parsa Moghaddam;Mahmoud-Reza Haghifam;
      Pages: 2491 - 2494
      Abstract: In future power systems, local energy networks (LENs) as one of the main infrastructures will have a key role in the realization of transactive energy (TE) exchange between involved players. In order to adopt this environment fully accessible to trade energy competitively between participants, the connectivity level of the network should be always retained. The connectivity is an attribute of the LENs which determines local network transitive capability (LNTC). This letter investigates the ways to widely implement the TE framework using the connectivity strength among prosumers, customers, and local networks. A methodology to derive the connectivity index for LENs is proposed based on the graph theory and algebraic approach in this letter. The proposed method could be considered as a supporting tool for TE management in future power systems. It is found that the strength of connectivity between the participants has direct influence on the quantity as well as quality of their energy transactions. It is considered that $mu $ PMU data be used for on-line evaluation of the connectivity.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • Erratum to “Resilience-Motivated Distribution System Restoration
           Considering Electricity-Water-Gas Interdependency”

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      Authors: Jiaxu Li;Yin Xu;Ying Wang;Meng Li;Jinghan He;Chen-Ching Liu;Kevin P. Schneider;
      Pages: 2495 - 2495
      Abstract: In [1], during typesetting process, some mistakes were made in symbols. The errors and corrections are listed below. The errors are highlighted in blue. The corrections are highlighted in red.
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
  • TechRxiv: Share Your Preprint Research with the World

    • Free pre-print version: Loading...

      Pages: 2496 - 2496
      Abstract: Advertisement: TechRxiv is a free preprint server for unpublished research in electrical engineering, computer science, and related technology. TechRxiv provides researchers the opportunity to share early results of their work ahead of formal peer review and publication. Benefits: Rapidly disseminate your research findings; Gather feedback from fellow researchers; Find potential collaborators in the scientific community; Establish the precedence of a discovery; and Document research results in advance of publication. Upload your unpublished research today!
      PubDate: May 2022
      Issue No: Vol. 13, No. 3 (2022)
       
 
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