Skip to main content

Demand-side Flexibility in Smart Grid

  • Book
  • © 2020

Overview

  • Highlights recent advances in the identification, prediction, and exploitation of flexibility in demand side management in smart grids
  • Discusses the challenges of integrating renewable energy sources into power systems
  • Presents solutions for effectively estimating and tackling the available flexibility on the demand side (DS)

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

This is a preview of subscription content, log in via an institution to check access.

Access this book

eBook USD 16.99 USD 39.99
Discount applied Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 16.99 USD 54.99
Discount applied Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (5 chapters)

Keywords

About this book

This book highlights recent advances in the identification, prediction and exploitation of demand side (DS) flexibility and investigates new methods of predicting DS flexibility at various different power system (PS) levels. Renewable energy sources (RES) are characterized by volatile, partially unpredictable and mostly non-dispatchable generation. The main challenge in terms of integrating RES into power systems is their intermittency, which negatively affects the power balance. Addressing this challenge requires an increase in the available PS flexibility, which in turn requires accurate estimation of the available flexibility on the DS and aggregation solutions at the system level. This book discusses these issues and presents solutions for effectively tackling them.

Authors and Affiliations

  • Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, Tallinn, Estonia

    Roya Ahmadiahangar, Argo Rosin, Ivo Palu

  • School of Engineering, Computing and Mathematics, Oxford Brookes University, Oxford, UK

    Aydin Azizi

About the authors

Dr. Roya Ahmadiahangar received her M.S. and Ph.D. degrees in power systems engineering from the Babol University of Technology, Babol, Iran, in 2009 and 2014, respectively. She is a Postdoctoral researcher with the Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology. Her research interests Include integration of DER in smart grids, demand response, AI applied to smart grid and planning and management of power systems.

Prof. Argo Rosin received his Dipl. Eng, M. Sc. and Dr. Sc. techn. Degrees in electrical engineering from Tallinn University of Technology, Tallinn, Estonia, in 1996, 1998 and 2005, respectively. He is presently a Professor in the Department of Electrical Power Engineering, Tallinn University of Technology. He has published several books and over 80 scientific papers on smart grids, including energy management, control and diagnostic systems development and is the holder of a Patent in this field. His research interests include modelling and simulation of power management and industrial control systems. He is a Chartered Engineer, and member of Estonian Association of Engineers.

Prof. Ivo Palu received his PhD in electrical engineering from Tallinn University of Technology, Tallinn, Estonia, in 2009. He is presently a Professor and Head of Department of the Department of Electrical Power Engineering, Tallinn University of Technology. His research interests include Integration of renewable energy sources into electrical networks including co-operation of power units, power quality measurements and research.

Prof. Aydin Azizi holds a PhD degree in Mechanical Engineering and currently serves as a Senior Lecturer at the Oxford Brookes University, also he conducts SMSCP certification program as an official Siemens certified mechatronics instructor at German University of Technology in Oman. His current research focuses on investigating and developing novel techniques to model, control andoptimize complex systems. Prof. Azizi’s areas of expertise include Control & Automation, Artificial Intelligence and Simulation Techniques. Prof. Azizi is the recipient of the National Research Award of Oman for his AI-focused research, DELL EMC’s “Envision the Future” completion award in IoT for “Automated Irrigation System”, and ‘Exceptional Talent’ recognition by the British Royal Academy of Engineering.

Bibliographic Information

Publish with us