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IEEE Transactions on Smart Grid

Home / Publications / IEEE Transactions on Smart Grid

Impact Factor: 9.6

The IEEE Transactions on Smart Grid is a cross disciplinary journal aimed at disseminating results of research on and development of the smart grid, which encompasses energy networks where prosumers, electric transportation, distributed energy resources, and communications are integral and interactive components, as in the case of microgrids and active distribution networks interfaced with transmission systems. The journal publishes original research on theories and principles of smart grid technologies and systems, used in demand response, Advance Metering Infrastructure, cyber-physical systems, multi-energy systems, transactive energy, data analytics, and EV integration. Surveys of existing work on the smart grid may also be considered for publication when they propose a new viewpoint on history and a challenging perspective on the future of intelligent and active grids.

Paper Categories

Topics within the journal scope:.

• Ac/dc microgrids
• Ac/dc Active Distribution Networks (ADNs)
• Multi-energy systems
• Demand Response (DR) and Demand Side Management (DSM)
• Distributed Energy Resources (DER) interactions and integration with power grids
• Smart sensors, meters, and Advance Metering Infrastructure (AMI)
• PMU hardware/software and applications for distribution systems
• EV power grid integration and impact
• Peer-to-peer, transactive energy, blockchain power grid applications
• Cyber-physical and cybersecurity power grid applications
• Data analytics and big data applications to microgrids and ADNs
• Application of telecommunication technologies to power systems

Examples of topics out-of-scope:

• Transmission system Renewable Energy Sources (RES)
• DER hardware and internal controls
• Non-active distribution networks
• Transmission system load and price forecast, markets, and AI applications
• Transmission system protections
• Power Line Carrier (PLC) communications
• DC transmission systems
• PMU hardware/software and applications for transmission systems such as WAMS and WACS
• Economic, pricing, and market framework issues of DR/DSM, ADNs, microgrids, DERs, EVs, and multi-energy systems

Types of Papers Published

Research Papers  are expected to present innovative solutions, novel concepts, or creative ideas that can help to address existing or emerging technical challenges in the field of power engineering. Papers that are visionary and promise significant advances in the coming years are welcome.

Application Papers  share valuable industry experiences on dealing with challenging technical issues, developing/adopting new standards, applying new technologies or solving complex problems. Papers that can have a significant impact on industry practices in the coming years are welcome.  These kinds of papers should be led by industry experts and/or have co-authors with industry affiliations.

Review Papers  are expected to provide insightful and expert reviews, tutorials, or study cases on an important, timely and widely-interested topic in power engineering. Papers whose analysis, insights and recommendations are original and may have a significant impact on the research and/or application activities in the subject area are welcome.  These types of papers should be authored/co-authored by widely recognized experts in the topic presented.  If the paper exceeds the min. number of pages for the first submission, pre-approval before submission is required by the EIC, who will assess the content, the topic relevance, and the authors reputation in the field.

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The articles in this journal are peer reviewed in accordance with the requirements set forth in the IEEE Publication Services and Products Board Operations Manual . Each published article was reviewed by a minimum of two independent reviewers using a single-anonymous peer review process, where the identities of the reviewers are not known to the authors, but the reviewers know the identities of the authors. Articles will be screened for plagiarism before acceptance.

About resubmission of a rejected paper Rejected papers should not be resubmitted without being substantially revised. After revision, rejected papers can be resubmitted no sooner than 3 months after the date of the rejection. A resubmitted paper will be treated as new submission and must adhere to the same page limit as any other newly submitted paper. If the paper is rejected for the second time, the paper cannot be resubmitted any more even if further modified. The authors must also indicate in the letter to the editor how the paper has been modified. Since the resubmitted paper is not treated as a revision, the authors should not include a separate document in which they address the comments of the reviewers. All modifications should be briefly stated in the letter to the editor. If the paper was rejected from one of the PES journals and the authors have revised the paper and resubmit it to another PES journal they must also indicate in the letter to the editor which journal the paper was submitted to originally and what was the reference number of the rejected paper.

Policy on papers published at PES financially sponsored conferences: The Authors who have presented the paper(s) at the IEEE PES financially sponsored conference including PES General Meeting, PES Innovative Smart Grid Technologies (USA, Latin America, Europe, Asia), PES PowerTech and PES PowerAfrica are eligible to submit the extended version of their conference paper(s) to one of IEEE PES Transactions for consideration of publication. The extended paper requires at least 60% additional new material relative to the original conference paper and is subject to a regular review process of the respective PES Transactions. The original conference paper should be referenced in the extended journal paper and new contributions with respect to the conference paper clearly identified. The final decision for publication on the extended paper is made by the Editor-in-Chief of the respective journal.

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Table of Contents & Abstracts

Volume 15, Number 2, March 2024

Volume 15, Number 1, January 2024

Volume 14, Number 5, September 2023

Volume 14, Number 4, July 2023

Volume 14, Number 3, May 2023

Volume 14, Number 2, March 2023

Volume 14, Number 1, January 2023

Volume 13, Number 6, November 2022

Volume 13, Number 5, September 2022

Volume 13, Number 4, July 2022

Volume 13, Number 3, May 2022

Volume 13, Number 2, March 2022

Volume 13, Number 1, January 2022

Editorial Board

Smart Grid Associate Editors: Jamshid Aghaei, Central Queensland University, Australia Tarek Alskaif, Wageningen University and Research, Netherlands Jose Manuel Arroyo, University Castilla la Mancha, Spain Alberto Berizzi, Politecnico Di Milano, Italy Bishnu Bhattarai, Pacific Northwest National Laboratory, USA Florin Capitanescu, LIST, Luxembourg Saikat Chakrabarti, Indian Institute of Technology Kanpur, India Bo Chen, Argonne National Laboratory, USA Guo Chen, University of New South Wales, Australia Yue Chen, Chinese University of Hong Kong, Hong Kong Ruilong Deng, Zhejiang University, China Fei Ding, NREL, USA Zhaohao Ding, North China Elec Power University, China Wei Du, PNNL, USA Yuhua Du, Northwestern Polytechnical University, China Yury Dvorkin, New York University, USA Izudin Dzafic, University of Sarajevo, Bosnia Mohamed El Moursi, Khalifa University Science and Technology, USA Mostafa Farrokhabadi, BluWave-ai, Canada Claudio Fuerte-Esquivel, University of Michoacan, Mexico Murat Göl, Middle East Technical University, Turkey Sayyed M. Hashemi, University of Toronto, Canada Ali Hooshyar, University of Toronto, Canada Can Huang, PG&E, USA Herbert Iu, The University of Western Australia, Australia Kumarsinh Jhala, Argonne National Lab, USA Youngjin Kim, Pohang University of Science and Technology, Korea Shunbo Lei, Chinese University of Hong Kong-Shenzhen, Hong Kong Ioannis Lestas, University of Cambridge, UK Jie Li, Rowan University, USA Feng Liu, Tsinghua University, China Hui Liu, Guangxi University, China Nian Liu, North China Electric Power University, China Xiaonqing Lu, Wuhan University, China Patricio Mendoza, University of Chile, Chile Wenchao Meng, Zhejiang University, China Sumit Paudyal, Florida International University, USA Ferdinanda Ponci, RWTH Aachen University, Germany Feng Qiu, Argonne National Laboratory, USA Rodrigo Ramos, University of São Paulo at São Carlos, Brazil Majid Sanaye-Pasand, University of Tehran, Iran John Simpson-Porco, University of Toronto, Canada Zhouyang Ren, Chongqing University, China Pirathayini Srikantha, York University, Canada Dipti Srinivasan, National University of Singapore, Singapore Wencong Su, University of Michigan-Dearborn, USA Qun Sun, University of Central Florida, USA Alfredo Vaccaro, University of Sannio, Italy Gregor Verbic, University of Sydney, Australia Jose Carlos Vieira, University of Sao Paulo, Brazil Maria Vrakopoulou, University of Melbourne, Australia Meng Wang, Rensselaer Polytechnic Institute, USA Yan-Wu Wang, Huazhong University of Science and Technology, China Yi Wang, University of Hong Kong, Hong Kong Wei Wei, Tsinghua University, China Dan Wu, Siemens Gamesa Renewable Energy, Brazil Hongyu Wu, Kansas State University, USA Yingmeng Xiang, Geirina, USA Jingrui Xie, National Grid, USA Qianwen Xu, KTH Royal Institute of Technology, Sweden Yan Xu, Nanyang Technological University, Singapore Rul Yang, NREL, USA Yujian Ye, Southeast University, Bangladesh Liang Yu, Nanjing University of Posts and Telecommunications, China Nanpeng Yu, University of California-Riverside, USA Quan Zhou, Hunan University, China Hao Zhu, University Texas at Austin, USA Lantao Xing, Shangdon University, China

How to become an Associate Editor for a PES Transactions

Reviewers Recognition

Transactions on Smart Grid Recognition Process

Transactions on Smart Grid Recognitions 2022 [PDF 113MB]  

Transactions on Smart Grid Recognitions 2021

2023 Outstanding Papers, Reviewers, and Associate Editors [PDF 108KB]

Recent Improvements

• Scope revision and update in 2020 to reduce overlap with other PES Transaction 
• Significant revamp to the TSG Editorial Board in 2020 to increase diversity (gender, region, affiliation) and better reflect PES membership composition and TSG authorship
• Implemented new editorial policies in 2020 explained  here [PDF 149KB]
• Implemented PES Publications Board changes  here [PDF 149KB]

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A novel function of a research process based on a power internet of things architecture intended for smart grid demand schemes.

1. Introduction

• Energy Efficiency and Optimization: The increasing demand for electricity, coupled with the need to reduce energy consumption and carbon emissions, motivates the integration of IoT technology. Smart grid IoT architecture allows for real-time monitoring and analysis of energy consumption patterns, enabling more effective load management and energy optimization.
• Dynamic Demand Management: Traditional electricity grids struggle to manage fluctuating demand and supply. IoT-enabled smart grids provide real-time data on energy usage, allowing utilities to implement demand-response strategies and balance supply and demand efficiently.
• Grid Reliability and Resilience: Aging grid infrastructure and the growing complexity of power distribution increase the risk of outages and disruptions. IoT-based smart grid architecture facilitates predictive maintenance, fault detection, and quick response, enhancing grid reliability and minimizing downtime.
• Integration of Renewable Energy: The transition to renewable energy sources introduces intermittency challenges. IoT technology enables better integration and control of distributed energy resources like solar panels and wind turbines, allowing for optimal utilization of renewable energy.
• Consumer Empowerment: Smart meters and IoT devices provide consumers with real-time data about their energy consumption, helping them make informed decisions to reduce usage and save costs. This empowerment encourages energy-efficient behaviors.
• Real-time Monitoring and Control: IoT-based smart grid architecture offers real-time monitoring and control capabilities that enhance grid visibility. Operators can respond faster to grid events, isolate faults, and manage peak demand effectively.
• Demand Response and Load Management: With IoT devices, utilities can implement demand-response programs that incentivize consumers to reduce energy consumption during peak hours, alleviating strain on the grid and avoiding blackouts.
• Data-Driven Decision-Making: The architecture’s data analytics capabilities provide valuable insights into grid performance, load patterns, and equipment health. This information supports data-driven decision-making for optimal grid operations.
• Environmental Sustainability: The ability to manage energy more efficiently contributes to reducing carbon emissions and supporting sustainable energy initiatives. Smart grid IoT technology aids in achieving environmental goals.
• Technological Advancements: The IoT ecosystem has rapidly evolved, offering cost-effective sensors, communication protocols, and data processing tools. Leveraging these advancements in the context of the smart grid offers a more modern and efficient solution.
• Regulatory Compliance: Regulatory bodies are increasingly encouraging or mandating the adoption of smart grid technologies to enhance energy efficiency, grid stability, and customer satisfaction.
• Future-Proofing Infrastructure: As energy needs continue to evolve, the smart grid IoT architecture provides a flexible and adaptable framework that can accommodate changes in energy generation, consumption patterns, and technological advancements.

1.1. Problem Statement

• Inadequate demand management schemes: Current demand management schemes in smart grid systems fail to provide efficient energy consumption control and optimization, leading to wasted resources and poor system performance.
• Limited use of PIoT capabilities: Existing research processes have not fully explored the capabilities of PIoT technology in the context of smart grid systems, which hinders the development of effective and innovative demand management solutions.
• Integration challenges: Integrating PIoT technology with smart grid systems poses numerous challenges, such as data security, privacy concerns, and interoperability issues, which need to be addressed to ensure seamless and efficient implementation.
• System reliability: Ensuring the reliability of the PIoT-based smart grid system is critical for maintaining a consistent power supply and minimizing the risk of blackouts or other disruptions.
• Demand-side management: Developing effective strategies for demand-side management is essential for balancing energy consumption and generation, reducing peak demand, and promoting energy efficiency.

1.2. Research Objectives

1.3. aim of study.

• Optimizing Energy Utilization: The study aims to design an architecture that enables real-time monitoring and analysis of electricity consumption patterns. By utilizing IoT-enabled sensors and devices, the architecture seeks to identify opportunities for optimizing energy utilization, reducing waste, and enhancing overall efficiency.
• Enabling Demand-Response Strategies: The architecture aims to facilitate effective demand response strategies by providing real-time data on energy demand and consumption. This enables utilities and grid operators to dynamically manage peak loads, balance demand, and minimize strain on the grid during periods of high electricity usage.
• Enhancing Grid Reliability: Through continuous monitoring and data analysis, the architecture seeks to enhance the reliability and stability of the power grid. By identifying potential faults, outages, or irregularities in the grid’s performance, the study aims to enable proactive maintenance and prevent disruptions.
• Integrating Renewable Energy: The study aims to integrate renewable energy sources, such as solar panels and wind turbines, into the grid fabric. By leveraging IoT capabilities, the architecture can manage the variability of these energy sources, optimize their contribution to the grid, and promote sustainable energy generation.
• Real-Time Decision-Making: The architecture intends to empower grid operators, energy managers, and consumers with real-time insights into grid conditions and energy consumption patterns. This information enables informed decision-making, allowing stakeholders to respond promptly to changing circumstances and make data-driven choices.
• Enhancing Consumer Engagement: Through IoT-enabled devices and interfaces, the architecture aims to engage consumers in their energy usage. By providing them with detailed information about their energy consumption and costs, consumers can adjust their behavior to align with energy-saving practices.
• Environmental Sustainability: The study seeks to contribute to environmental sustainability by reducing overall energy wastage, promoting the use of renewable energy, and ultimately lowering carbon emissions. The IoT-enabled architecture can play a vital role in supporting green energy initiatives and advancing the transition to a more sustainable energy future.
• Scalability and Flexibility: The architecture is designed to be scalable and adaptable to future technological advancements. As IoT technologies continue to evolve, the study aims to create a framework that can accommodate new devices, sensors, and communication protocols, ensuring its longevity and relevance.

2. Background

• Smart Grid: PLC is used for smart metering, enabling two-way communication between utility companies and consumers for remote meter reading and demand response, as shown in Table 2 .
• Home Automation: PLC can be used to control smart devices within homes, enabling features like lighting control, security systems, and energy management.
• Industrial Automation: PLC can provide communication between industrial equipment and systems, aiding in process control and monitoring.
• In-Home Networking: PLC can be used for networking devices within a home, providing an alternative to Wi-Fi or Ethernet.

3. Methodology

3.1. dataset description, 3.2. pre-processing and designing the smart grid, 3.3. distribution of smart grid infrastructure.

• Smart Meters: Smart meters are electronic devices installed at consumer premises to measure and record electricity consumption at more frequent intervals compared to traditional meters. They enable two-way communication between consumers and utilities, providing real-time consumption data for billing accuracy, load profiling, and demand response programs.
• Remote Terminal Units (RTUs) and Intelligent Electronic Devices (IEDs): RTUs and IEDs are deployed in substations to gather data from sensors, protection relays, and other devices. RTUs facilitate remote monitoring and control of substations, while IEDs perform specific functions like fault detection, protection, and automation within the substation.
• Distribution Management System (DMS): The DMS is a software platform that integrates data from various sources to monitor, control, and optimize distribution grid operations. It provides tools for real-time situational awareness, outage management, and load balancing.
• Supervisory Control and Data Acquisition (SCADA): SCADA systems allow operators to remotely monitor and control grid equipment in real-time. They collect data from various devices, visualize it on control center screens, and enable operators to take corrective actions.
• Advanced Metering Infrastructure (AMI): AMI comprises smart meters, communication networks, and data management systems. It facilitates bi-directional communication between consumers and utilities, enabling remote meter reading, real-time pricing, and demand management programs.
• Demand Response (DR) Controllers: DR controllers manage electricity demand by adjusting consumption in response to grid conditions or pricing signals. They help prevent grid overload during peak demand periods and encourage energy conservation.
• Energy Storage Systems: Energy storage systems, such as batteries, store excess energy generated during low-demand periods and release it during high-demand times. They improve grid stability and enable efficient integration of renewable energy sources.
• Distribution Automation (DA) Equipment: DA equipment includes switches, reclosers, and capacitors that can be remotely controlled and automated to reroute power, isolate faults, and optimize grid performance.
• Grid Sensors: Grid sensors monitor various parameters, including voltage, current, temperature, and fault conditions. They provide real-time data to enable predictive maintenance and rapid fault detection.
• Communication Infrastructure: Communication networks, such as fiber optics, wireless technologies, and the internet, enable devices to exchange data with each other and the central control systems.
• Power Quality Monitoring Devices: These devices monitor power quality parameters like voltage fluctuations, harmonics, and disruptions, helping maintain stable and high-quality power supply.
• Fault Detection and Location Systems: These systems automatically detect and pinpoint faults in the distribution grid, helping utilities restore power faster and minimize outage durations.
• Renewable Energy Interfaces: Interfaces and devices enable the integration of renewable energy sources like solar and wind into the distribution grid, contributing to greener energy generation.

3.4. Efficient Power Transmission Mechanism

3.5. implementing power line communication technique for smart grid, 5. discussion, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Al-Mashhadani, S.W.T.; Kurnaz, S. A Novel Function of a Research Process Based on a Power Internet of Things Architecture Intended for Smart Grid Demand Schemes. Appl. Sci. 2024 , 14 , 5799. https://doi.org/10.3390/app14135799

Al-Mashhadani SWT, Kurnaz S. A Novel Function of a Research Process Based on a Power Internet of Things Architecture Intended for Smart Grid Demand Schemes. Applied Sciences . 2024; 14(13):5799. https://doi.org/10.3390/app14135799

Al-Mashhadani, Sarmad Waleed Taha, and Sefer Kurnaz. 2024. "A Novel Function of a Research Process Based on a Power Internet of Things Architecture Intended for Smart Grid Demand Schemes" Applied Sciences 14, no. 13: 5799. https://doi.org/10.3390/app14135799

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