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Smart Grid and Optimization-Based Scheduling of Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 2532

Special Issue Editors


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Guest Editor
Cybersecurity and Cybersecurity for Cyber-Physical Systems (C3i HUB) Indian Institute of Technology Kanpur (IITK), Kanpur, Uttar Pradesh-208016, India
Interests: smart grid; time series analysis; cyber physical security; deep learning; optimization and demand response for smart grid

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Guest Editor
Department of Electrical Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan
Interests: modern power systems and renewable energy; power protection systems; smart grid; load forecasting as well as control system and modelling of energy storage systems; applying emerging technologies such as machine learning and optimization methods for micro and smart grid
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Special Issue Information

Dear Colleagues,

Smart grid technology has emerged as a significant technological advancement in the power sector, one which enables efficient and reliable energy management. With the growing demand for renewable energy and distributed energy resources,  traditional power systems have become more complex and the need for advanced power scheduling algorithms has become inevitable. Optimization-based scheduling techniques have proven effective in achieving energy efficiency, reducing costs, and ensuring grid stability. In this context, integrating smart grids and optimization-based scheduling can significantly improve the performance and resilience of power systems.

Integrating smart grids and optimization-based scheduling requires the development of advanced algorithms that can effectively manage the complex nature of the power grid. Researchers have proposed several methods to achieve the efficient scheduling of power systems, including linear programming, dynamic programming, and the use of metaheuristic algorithms. These techniques aim to minimize energy costs, reduce greenhouse gas emissions, and improve the grids’ overall performance. Moreover, integrating smart grid technologies, such as advanced metering infrastructure, demand response, and energy storage systems, can further enhance the effectiveness of these scheduling algorithms.

Despite the potential benefits of smart grids and optimization-based scheduling, several challenges must be addressed to ensure their successful implementation. These challenges include achieving data security, establishing communication protocols, and integrating renewable energy sources. Additionally, the development of standardized protocols and regulatory frameworks is crucial to ensure the compatibility and interoperability of smart grid technologies. Therefore, further research is needed to address these challenges and develop advanced optimization-based scheduling techniques with which to manage the evolving power grid efficiently.

Overall, the integration of smart grids and optimization-based scheduling constitutes an emerging field which has significant potential to revolutionize the power sector. The development of advanced algorithms and the integration of smart grid technologies can lead to the development of efficient, reliable, and sustainable power systems. Therefore, further research in this area is critical to realize the full potential of smart grids and optimization-based scheduling in the power sector.

Dr. Ayush Sinha
Prof. Dr. William Holderbaum
Dr. Feras Alasali
Guest Editors

Manuscript Submission Information

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Keywords

  • smart grids
  • time series
  • cyber physical security
  • deep learning
  • optimization
  • demand response

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Published Papers (1 paper)

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Research

21 pages, 3274 KiB  
Article
FedGrid: A Secure Framework with Federated Learning for Energy Optimization in the Smart Grid
by Harshit Gupta, Piyush Agarwal, Kartik Gupta, Suhana Baliarsingh, O. P. Vyas and Antonio Puliafito
Energies 2023, 16(24), 8097; https://doi.org/10.3390/en16248097 - 16 Dec 2023
Cited by 2 | Viewed by 1918
Abstract
In the contemporary energy landscape, power generation comprises a blend of renewable and non-renewable resources, with the major supply of electrical energy fulfilled by non-renewable sources, including coal and gas, among others. Renewable energy resources are challenged by their dependency on unpredictable weather [...] Read more.
In the contemporary energy landscape, power generation comprises a blend of renewable and non-renewable resources, with the major supply of electrical energy fulfilled by non-renewable sources, including coal and gas, among others. Renewable energy resources are challenged by their dependency on unpredictable weather conditions. For instance, solar energy hinges on clear skies, and wind energy relies on consistent and sufficient wind flow. However, as a consequence of the finite supply and detrimental environmental impact associated with non-renewable energy sources, it is required to reduce dependence on such non-renewable sources. This can be achieved by precisely predicting the generation of renewable energy using a data-driven approach. The prediction accuracy for electric load plays a very significant role in this system. If we have an appropriate estimate of residential and commercial load, then a strategy could be defined for the efficient supply to them by renewable and non-renewable energy sources through a smart grid, which analyzes the demand-supply and devises the supply mechanism accordingly. Predicting all such components, i.e., power generation and load forecasting, involves a data-driven approach where sensitive data (such as user electricity consumption patterns and weather data near power generation setups) is used for model training, raising the issue of data privacy and security concerns. Hence, the work proposes Federated Smart Grid (FedGrid), a secure framework that would be able to predict the generation of renewable energy and forecast electric load in a privacy-oriented approach through federated learning. The framework collectively analyzes all such predictive models for efficient electric supply. Full article
(This article belongs to the Special Issue Smart Grid and Optimization-Based Scheduling of Power Systems)
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