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Full Description
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.
Contents
Chapter 1: A comprehensive review on industrial demand response strategies and applications
Chapter 2: Demand response cybersecurity for power systems with high renewable power share
Chapter 3: Recurrent neural networks for electrical load forecasting to use in demand response
Chapter 4: Optimal demand response strategy of an industrial customer
Chapter 5: Price-based demand response for thermostatically controlled loads
Chapter 6: Electric vehicle massive resources mining and demand response application
Chapter 7: Demand response measurement and verification approaches: analyses and guidelines
Chapter 8: Transactive energy industry demand response management market
Chapter 9: Industrial demand response opportunities with residential appliances in smart grids
Chapter 10: Modelling and optimal scheduling of flexibility in energy-intensive industry
Chapter 11: Industrial demand response: coordination with asset management
Chapter 12: A machine learning-based approach for industrial demand response
Chapter 13: Feasibility assessment of industrial demand response
Chapter 14: Measurement and verification of demand response: the customer load baseline
Chapter 15: Modeling and optimizing the value of flexible industrial processes in the UK electricity market
Chapter 16: Case study of Aran Islands: optimal demand response control of heat pumps and appliances
Chapter 17: Use case of artificial intelligence, and neural networks in energy consumption markets, and industrial demand response
