Microgrids/Nanogrids Implementation, Planning, and Operation

Edited by
October 2022
180 pages
  • ISBN978-3-0365-5651-2 (Hardback)
  • ISBN978-3-0365-5652-9 (PDF)

This book is a reprint of the Special Issue Microgrids/Nanogrids Implementation, Planning, and Operation that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Environmental & Earth Sciences
Physical Sciences

Today’s power system is facing the challenges of increasing global demand for electricity, high-reliability requirements, the need for clean energy and environmental protection, and planning restrictions. To move towards a green and smart electric power system, centralized generation facilities are being transformed into smaller and more distributed ones. As a result, the microgrid concept is emerging, where a microgrid can operate as a single controllable system and can be viewed as a group of distributed energy loads and resources, which can include many renewable energy sources and energy storage systems. The energy management of a large number of distributed energy resources is required for the reliable operation of the microgrid. Microgrids and nanogrids can allow for better integration of distributed energy storage capacity and renewable energy sources into the power grid, therefore increasing its efficiency and resilience to natural and technical disruptive events. Microgrid networking with optimal energy management will lead to a sort of smart grid with numerous benefits such as reduced cost and enhanced reliability and resiliency. They include small-scale renewable energy harvesters and fixed energy storage units typically installed in commercial and residential buildings. In this challenging context, the objective of this book is to address and disseminate state-of-the-art research and development results on the implementation, planning, and operation of microgrids/nanogrids, where energy management is one of the core issues.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
energy demand; long-term forecasting; machine learning; R programming; solar power generation; support vector regression; economic energy; Bonobo Optimizer; hybrid renewable energy system; microgrid; PV panels; wind turbine; energy storage; microgrid; hierarchical control; primary; secondary control; droop control; frequency restoration; voltage restoration; grid synchronization; proportional resonant controller; robust control; distributed generation, microgrid; LMI; microgrid; optimization; lightning attachment procedure optimization (LAPO) algorithm; photovoltaic panel; uncertainty; microgrid; cooperation; energy; reliability; Monte Carlo; smart energy control; grid-synchronization; dynamic voltage restorer; converter control system; sliding mode control; microgrid; economic scheduling; clean energy; quantum mayfly algorithm (QMA); n/a