Integration and Control of Distributed Renewable Energy Resources
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...
| Format: | eBook |
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| Language: | English |
| Published: |
Basel
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Subjects: | |
| Online Access: | Open Access: DOAB: description of the publication Open Access: DOAB, download the publication |
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| 245 | 0 | 0 | |a Integration and Control of Distributed Renewable Energy Resources |
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| 520 | |a 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. | ||
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| 650 | 7 | |a History of engineering & technology |2 bicssc | |
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 653 | |a composite control strategy | ||
| 653 | |a DG | ||
| 653 | |a diesel generator | ||
| 653 | |a different PV technologies | ||
| 653 | |a distributed generation | ||
| 653 | |a distribution networks | ||
| 653 | |a distribution system | ||
| 653 | |a energy independence | ||
| 653 | |a energy storage system (ESS) | ||
| 653 | |a Gaussian process regression | ||
| 653 | |a green communities | ||
| 653 | |a HOMER | ||
| 653 | |a intraday forecasting | ||
| 653 | |a machine learning | ||
| 653 | |a microgrids | ||
| 653 | |a min-max optimisation | ||
| 653 | |a model-based predictive control | ||
| 653 | |a Monte Carlo simulations | ||
| 653 | |a n/a | ||
| 653 | |a off-grid system | ||
| 653 | |a permanent magnet brushless DC machine (PMBLDCM) | ||
| 653 | |a photovoltaics | ||
| 653 | |a PLO's profit | ||
| 653 | |a power losses | ||
| 653 | |a power quality | ||
| 653 | |a power quality improvement | ||
| 653 | |a power system management | ||
| 653 | |a power system optimization | ||
| 653 | |a power system reliability | ||
| 653 | |a PV curves | ||
| 653 | |a PV hosting capacity | ||
| 653 | |a robustness | ||
| 653 | |a smart grid paradigm | ||
| 653 | |a smart grids | ||
| 653 | |a solar photovoltaic panel | ||
| 653 | |a solar-powered electric vehicle parking lots | ||
| 653 | |a synchronous machine (SM) | ||
| 653 | |a TSA/SCA | ||
| 653 | |a uncertainties | ||
| 653 | |a wind turbine | ||
| 653 | |a wind turbines | ||
| 653 | |a worst-case scenario | ||
| 793 | 0 | |a DOAB Library. | |
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| 856 | 4 | 0 | |u https://mdpi.com/books/pdfview/book/5311 |7 0 |z Open Access: DOAB, download the publication |