Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives

Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to increasingly complex and data-driven power systems with distributed energy resources to enable evidence-driven decision-making and mitigate catastrophic power shortages. The book reviews foundations towards the integration of machine learning and smart power systems before addressing key challenges and issues. The work then explores AI- and ML-informed techniques to rebalancing of supply and demand. Methods discussed include distributed energy resources and prosumer markets, electricity demand prediction, component fault detection, and load balancing. Security solutions are introduced, along with potential solutions to cyberattacks, security data detection and critical loads in power systems. The work closes with a lengthy discussion, informed by case studies, on integrating AI and ML into the modern energy sector. Helps improve the prediction capability of AI algorithms to make evidence-based decisions in the smart supply of electricity, including load shedding Focuses on how to integrate AI and ML into the energy sector in the real-world, with many chapters accompanied by case studies Addresses a number of proven AI and ML- informed techniques in rebalancing supply and demand INDICE: 1. Introduction: Artificial intelligence and Smart Power Systems2. Integrated Architecture of Machine Learning and Smart Power System3. Challenges and issues in Power Systems4. Load shedding and related techniques to solve the power crisis5. ML in distributed energy resources and prosumers market6. ML-based electricity demand prediction7. Applying ML to determine the power outage8. Predictive and Prescriptive analytics for component fault detection9. Balancing demand and supply of electricity with machine learning10. Preventive care of grid hardware with anomaly detection11. AI-based Smart feeder monitoring system12. Algorithms for buss loss and reliability indices calculations13. ML-based security solutions to protect smart power systems14. Cyber-attacks ,security data detection, and critical loads in the power systems15. Integration of AI/ML into the energy sector: Case Studies

• ISBN: 978-0-323-91664-6
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 296
• Fecha Publicación: 01/09/2022
• Nº Volúmenes: 1
• Idioma: Inglés