Distributionally Robust Optimization And Its Applications In Power System Energy Storage Sizing
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Distributionally Robust Optimization and its Applications in Power System Energy Storage Sizing
This book introduces the mathematical foundations of distributionally robust optimization (DRO) for decision-making problems with ambiguous uncertainties and applies them to tackle the critical challenge of energy storage sizing in renewable-integrated power systems, providing readers with an efficient and reliable approach to analyze and design real-world energy systems with uncertainties. Covering a diverse range of topics, this book starts by exploring the necessity for energy storage in evolving power systems and examining the benefits of employing distributionally robust optimization. Subsequently, the cutting-edge mathematical theory of distributionally robust optimization is presented, including both the general theory and moment-based, KL-divergence, and Wasserstein-metric distributionally robust optimization theories. The techniques are then applied to various practical energy storage sizing scenarios, such as stand-alone microgrids, large-scale renewable power plants, bulk power grids, and multi-carrier energy networks. This book offers clear explanations and accessible guidance to bridge the gap between advanced optimization methods and industrial applications. Its interdisciplinary scope makes the book appealing to researchers, graduate students, and industry professionals working in electrical engineering and operations research, catering to both beginners and experts.
Smart Applications and Sustainability in the AIoT Era
This book gathers recent research work on emerging Artificial Intelligence (AI) methods for processing and storing data generated by smart infrastructures. Smart infrastructures gather Terabytes of data nowadays with no need for traditional control. The data automatically uploads to the cloud computing platform. The cloud analyses the data and generates the required output in visualization, graph, and action. A remote access network can be constructed dependent on either low-elevation or high-altitude stages. When associated with satellite and earthly frameworks, these stages empower a far-reaching access network with worldwide inclusion and diverse administration provisioning. Data analytics are used in agriculture, mining, waste management, energy, and military defenses. Major topics covered include the analysis and development of AI-powered mechanisms in future IoT and smart infrastructures applications. Further, the book addresses new technological developments, current research trends, and industry needs. Presenting case studies, experience and evaluation reports, and best practices in utilizing AI applications in IoT networks, it strikes a good balance between theoretical and practical issues. It also provides technical/scientific information on various aspects of AI technologies, ranging from basic concepts to research grade material, including future directions. The book is intended for researchers, practitioners, engineers and scientists involved in the design and development of protocols and AI applications for smart and sustainable infrastructure-related devices.
Advanced Anomaly Detection Technologies and Applications in Energy Systems
Anomaly detection is an important topic which has been well‐studied in diverse research areas and application domains. It generally involves detection of abnormal data, unhealthy status, fault diagnosis, and can be helpful to guarantee industrial systems’ stability, security, and economy. As development of intelligent industries and sensor systems grows, large amounts of data become easily available, and challenges arise in industrial systems’ anomaly detection. One typical case is the study within energy‐related systems, like thermal energy, renewable energy study (e.g., wind energy, photovoltaic), electric vehicles, and so on. These systems can involve various data formats and more complex data structures making anomaly data detection a challenge. Currently, under the development of deep learning and big data analytics, many promising results have been achieved in energy systems’ anomaly data detection. However, many challenging problems remain unsolved due to the complex nature of energy industries. New techniques and advanced engineering applications on anomaly detection in energy systems still appeal to a wide range of scholars and industries.