Trading in Local Energy Markets and Energy Communities

This book presents trading in local energy markets and communities. It covers electrical, business, economics, telecommunication, information technology (IT), environment, building, industrial, and computer science and examines the intersections of these areas with these markets and communities. Additionally, it delivers an vision for local trading and communities in smart cities. Since it also lays out concepts, structures, and technologies in a variety of applications intertwined with future smart cities, readers running businesses of all types will find material of use in the book. Manufacturing firms, electric generation, transmission and distribution utilities, hardware and software computer companies, automation and control manufacturing firms, and other industries will be able to use this book to enhance their energy operations, improve their comfort and privacy, as well as to increase the benefit from the energy system. This book is also used as a textbook for graduate level courses.

Explains new directions in engineering use of peer-to-peer communication and energy Discusses energy markets information privacy and data security which play a key role in future energy systems Includes an in-depth study on applications of renewables energy storage systems and blockchain in future energy trading

Auteur
Miadreza Shafie-khah received his MSc and first PhD in electrical engineering from Tarbiat Modares University, Tehran, Iran. He received his second PhD in electromechanical engineering and first postdoc from the University of Beira Interior (UBI), Covilha, Portugal. He received his second postdoc from the University of Salerno, Salerno, Italy. Currently, he is a Professor (tenure track) at the University of Vaasa, Vaasa, Finland. He is an Editor of the IEEE Transactions on Sustainable Energy, an Associate Editor of the IEEE Systems Journal, an Associate Editor of the IEEE Transactions on Intelligent Transportation Systems, an Associate Editor of the IEEE Access, an editor of the IEEE Open Access Journal of Power and Energy (OAJPE), an Associate Editor for IET-RPG, the guest Editor-in-Chief of the IEEE OAJPE, the guest editor of IEEE Transactions on Cloud Computing, and the guest editor of more than 14 Special Issues. He has co-authored more than 500 publications including journal articles, book chapters and conference papers. He is also the volume editor of the books "Blockchain-based Smart Grids", Elsevier, 2020; "Flexible Resources for Smart Cities", Springer, 2021; and "Cyberphysical Smart Cities Infrastructures: Optimal Operation and Intelligent Decision Making", Wiley, 2022. He is a Top Scientist in the Research.com Ranking in Engineering and Technology, and he has won five Best Paper Awards at IEEE Conferences. His research interests include electricity market, demand response, electric vehicles, and smart grids.

Amin Shokri Gazafroudi received the B.Sc. and M.Sc. degrees in power electrical engineering, in 2011 and 2013, respectively. He also received the Ph.D. degree in computer science from the University of Salamanca, Salamanca, Spain, in 2019. Afterward, he was a Postdoctoral Researcher involved in CoNDyNet2 project from Oct. 2019 to Dec. 2021 with the Energy System Modeling Research Group, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. He is currently Project Manager at Stromdao GmbH, Germany, to coordinate research and development national and international projects in smart grids, energy communities, local energy and flexibility markets, peer-to-peer (P2P) energy transactions, blockchain technology, and electric vehicle (EV) charging strategies. His research interests include power system and electricity market modeling, power ow and contingency analysis, local energy and exibility markets design, peer-to-peer energy trading in smart grids, market-based coordination mechanisms, decentralized energy management systems, bidding strategies for autonomous home energy management systems, planning and operation of integrated energy systems, and application of machine learning algorithms on price and demand forecasting.

Contenu

Energy communities.- Local energy markets.- Active players in local energy markets.- Community-based local energy markets.- Peer-to-peer local energy trading.- Hybrid structures for local energy trading.-mClustering-based local energy markets.