Demand response (DR) involves the controlled adjustment of electric load consumption to better align power demand with supply. This approach supports higher integration of intermittent renewable energy sources, such as solar and wind, by maximizing the utilization of generated clean power and reducing reliance on storage.
This book provides a comprehensive overview of DR principles, implementation, and applications. Chapters address industrial DR strategies, cybersecurity, DR for industrial customers, price-based demand response, electric vehicles, transactive energy, residential appliance DR, machine learning and neural network applications, measurement and verification, and case studies including the Aran Islands. An illustrative use case demonstrates the application of AI and neural networks in energy consumption markets.
Written by an international team of experts from academia and industry, the book offers both theoretical insights and practical guidance. Industrial Demand Response: Methods, Best Practices, Case Studies, and Applications is a valuable reference for researchers, power system engineers, grid operators, and advanced students aiming to develop and implement effective DR strategies.
