Demand response (DR) describes controlled changes in the power consumption of an electric load to better match the power demand with the supply. This helps with increasing the share of intermittent renewables like solar and wind, thus ensuring use of the generated clean power and reducing the need for storage capacity.


This book conveys the principles, implementation and applications of demand response. Chapters cover an overview of industrial DR strategies, cybersecurity, DR of industrial customers, price-based demand response, EV, transactive energy, DR with residential appliances, use of machine learning and neural networks, measurement and verification, and case studies in the Aran Islands, as well as a use case of AI and NN in energy consumption markets.


The chapters have been written by an international team of highly qualified experts from academia as well as industry, ensuring a balanced and practically oriented insight. Readers will be able to develop and apply DR strategies to their respective systems.


Industrial Demand Response: Methods, best practices, case studies, and applications is a valuable resource for researchers involved with regional as well as industrial power systems, power system engineers, experts at grid operators and advanced students.