Distributed Energy Resources In Local Integrated Energy Systems

Download or read book Distributed Energy Resources in Local Integrated Energy Systems written by Giorgio Graditi and published by Elsevier. This book was released on 2021-02-27 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed Energy Resources in Local Integrated Energy Systems: Optimal Operation and Planning reviews research and policy developments surrounding the optimal operation and planning of DER in the context of local integrated energy systems in the presence of multiple energy carriers, vectors and multi-objective requirements. This assessment is carried out by analyzing impacts and benefits at local levels, and in distribution networks and larger systems. These frameworks represent valid tools to provide support in the decision-making process for DER operation and planning. Uncertainties of RES generation and loads in optimal DER scheduling are addressed, along with energy trading and blockchain technologies. Interactions among various energy carriers in local energy systems are investigated in scalable and flexible optimization models for adaptation to a number of real contexts thanks to the wide variety of generation, conversion and storage technologies considered, the exploitation of demand side flexibility, emerging technologies, and through the general mathematical formulations established. - Integrates multi-energy DER, including electrical and thermal distributed generation, demand response, electric vehicles, storage and RES in the context of local integrated energy systems - Fosters the integration of DER in the electricity markets through the concepts of DER aggregation - Addresses the challenges of emerging paradigms as energy communities and energy blockchain applications in the current and future energy landscape - Proposes operation optimization models and methods through multi-objective approaches for fostering short- and long-run sustainability of local energy systems - Assesses and models the uncertainties of renewable resources and intermittent loads in the short-term decision-making process for smart decentralized energy systems