In the environment of energy systems, the effective utilization of both conventional and renewable sources poses a major challenge. The integration of microgrid systems, crucial for harnessing energy from distributed sources, demands intricate solutions due to the inherent intermittency of these sources. Academic scholars engaged in power system research find themselves at the forefront of addressing issues such as energy source estimation, coordination in dynamic environments, and the effective utilization of artificial intelligence (AI) techniques. Intelligent Solutions for Sustainable Power Grids focuses on emerging research areas, this book addresses the uncertainty of renewable energy sources, employs state-of-the-art forecasting techniques, and explores the application of AI techniques for enhanced power system operations. From economic aspects to the digitalization of power systems, the book provides a holistic approach. Tailored for undergraduate and postgraduate students as well as seasoned researchers, it offers a roadmap to navigate the intricate landscape of modern power systems. Dive into a wealth of knowledge encompassing smart energy systems, renewable energy integration, stability analysis of microgrids, power quality enhancement, and much more. This book is not just a guide; it is the solution to the pressing challenges in the dynamic field of energy systems.

Decentralized Frameworks for Future Power Systems: Operation, Planning and Control Perspectives is the first book to consider the principles and applications of decentralized decision-making in future power networks. The work opens by defining the emerging power system network as a system-of-systems (SoS), exploring the guiding principles behind optimal solutions for operation and planning problems. Chapters emphasize the role of regulations, prosumption behaviors, and the implementation of transactive energy processes as key components in decentralizing power systems. Contributors explore local markets, distribution system operation and proactive load management. The role of cryptocurrencies in smoothing transactive distributional challenges are presented. Final sections cover energy system planning, particularly in terms of consumer smart meter technologies and distributed optimization methods, including artificial intelligence, meta-heuristic, heuristic, mathematical and hybrid approaches. The work closes by considering decentralization across the cybersecurity, distributed control, market design and power quality optimization vertices. - Develops a novel framework for transactive energy management to enhance flexibility in future power systems - Explores interactions between multiple entities in local power markets based on a distributed optimization approach - Focuses on practical optimization, planning and control of smart grid systems towards decentralized decision-making

Prosumers, such as energy storage, smart home, and microgrids, are the consumers who also produce and share surplus energy with other users. With capabilities of flexibly managing the generation, storage and consumption of energy in a simultaneous manner, prosumers can help improve the operation efficiency of smart grid. Due to the rapid expansion of prosumer clusters, the planning and operation issues of prosumer energy systems have been increasingly raised. Aspects including energy infrastructure design, energy management, system stability, etc., are urgently required to be addressed while taking full advantage of prosumers' capabilities. However, up to date, the research on prosumers has not drawn sufficient attention. This proposal presents the need to introduce a Research Topic on prosumer energy systems in Frontiers in Energy Research. We believe this Research Topic can promote the research on advanced planning and operation technologies of prosumer energy systems and contribute to the carbon neutrality for a sustainable society.

Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today's challenges in this rapidly accelerating area of power engineering. Divided into three parts, this book begins by breaking down the big picture for electric utilities, before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes. Including five brand new chapters on emerging technological solutions, Big Data Application in Power Systems, Second Edition remains an essential resource for the reader aiming to utilize the potential of big data in the power systems of the future. - Provides a total refresh to include the most up-to-date research, developments, and challenges - Focuses on practical techniques, including rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches for processing high dimensional, heterogeneous, and spatiotemporal data - Engages with cross-disciplinary lessons, drawing on the impact of intersectional technology including statistics, computer science, and bioinformatics - Includes five brand new chapters on hot topics, ranging from uncertainty decision-making to features, selection methods, and the opportunities provided by social network data

The advancement of sustainable energy is becoming an important concern for many countries. The traditional electrical grid supports only one-way interaction of power being delivered to the consumers. The emergence of improved sensors, actuators, and automation technologies has consequently improved the control, monitoring and communication techniques within the energy sector, including the Smart Grid system. With the support of the aforementioned modern technologies, the information flows in two-ways between the consumer and supplier. This data communication helps the supplier in overcoming challenges like integration of renewable technologies, management of energy demand, load automation and control. Renewable energy (RE) is intermittent in nature and therefore difficult to predict. The accurate RE forecasting is very essential to improve the power system operations. The forecasting models are based on complex function combinations that include seasonality, fluctuation, and dynamic nonlinearity. The advanced intelligent computing algorithms for forecasting should consider the proper parameter determinations for achieving optimization. For this we need, new generation research areas like Machine learning (ML), and Artificial Intelligence (AI) to enable the efficient integration of distributed and renewable generation at large scale and at all voltage levels. The modern research in the above areas will improve the efficiency, reliability and sustainability in the Smart grid.

As more and more communities around the world are turning to electric vehicles (EVs) to help the environment and save energy, we face a big challenge. The systems that deliver power to our homes and businesses are having a tough time keeping up, especially with the increasing use of EVs. This challenge is a major issue for the experts in the energy field who are working hard to figure out how to make sure our power systems stay reliable. The main goal for these experts right now is to create a strong, flexible system that can smoothly handle the integration of EVs, making sure the power flows well, the grid stays stable, and the systems remain eco-friendly. E-Mobility in Electrical Energy Systems for Sustainability is a comprehensive guide to navigating the complexities of e-mobility integration. Delving into crucial aspects such as architectural reconfiguration, restoration strategies, power quality control, and regulatory frameworks, the book provides solutions on how to address the challenges posed by the integration of EVs into distribution systems. Its examination of advanced technologies, including communication-enabled EV charging systems, battery management systems, and power grid cybersecurity measures, equips readers with the knowledge needed to start the transformative journey towards sustainable electric transportation. This book is a great resource for those seeking to understand, engage with, and contribute to the landscape of e-mobility integration.

This book covers cutting-edge and advanced research on data processing techniques and applications for cyber-physical systems, gathering the proceedings of the International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2020), held in Laibin City, Guangxi Province, China, on December 11–12, 2020. It examines a wide range of topics, including distributed processing for sensor data in CPS networks; approximate reasoning and pattern recognition for CPS networks; data platforms for efficient integration with CPS networks; machine learning algorithms for CPS networks; and data security and privacy in CPS networks. Outlining promising future research directions, the book offers a valuable resource for students, researchers, and professionals alike, while also providing a useful reference guide for newcomers to the field.