Integration and Control of Distributed Renewable Energy Resources

Edited by
April 2022
148 pages
  • ISBN978-3-0365-3689-7 (Hardback)
  • ISBN978-3-0365-3690-3 (PDF)

This book is a reprint of the Special Issue Integration and Control of Distributed Renewable Energy Resources that was published in

Business & Economics
Chemistry & Materials Science
Environmental & Earth Sciences

The deployment of distributed renewable energy resources (DRERs) has accelerated globally due to environmental concerns and an increasing demand for electricity. DRERs are considered to be solutions to some of the current challenges related to power grids, such as reliability, resilience, efficiency, and flexibility. However, there are still several technical and non-technical challenges regarding the deployment of distributed renewable energy resources. Technical concerns associated with the integration and control of DRERs include, but are not limited, to optimal sizing and placement, optimal operation in grid-connected and islanded modes, as well as the impact of these resources on power quality, power system security, stability, and protection systems. On the other hand, non-technical challenges can be classified into three categories—regulatory issues, social issues, and economic issues.

This Special Issue will address all aspects related to the integration and control of distributed renewable energy resources. It aims to understand the existing challenges and explore new solutions and practices for use in overcoming technical challenges.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
distribution system; microgrids; power quality; power system management; power system reliability; smart grids; distribution networks; Monte Carlo simulations; PV hosting capacity; photovoltaics; green communities; energy independence; HOMER; wind turbines; power losses; power system optimization; PV curves; DG; TSA/SCA; solar-powered electric vehicle parking lots; different PV technologies; PLO’s profit; uncertainties; smart grid paradigm; distributed generation; model-based predictive control; robustness; worst-case scenario; min–max optimisation; intraday forecasting; Gaussian process regression; machine learning; off-grid system; composite control strategy; solar photovoltaic panel; wind turbine; diesel generator; energy storage system (ESS); synchronous machine (SM); permanent magnet brushless DC machine (PMBLDCM); power quality improvement; n/a