Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals.

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Institutions

Publication Details

Journal
Energies
Published
2024-09-02
DOI
https://doi.org/10.3390/en17174399
Citations
8
Primary Topic
Electric Power System Optimization
Type
article
Field-Weighted Citation Impact
1.67

Funders

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article

Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges

Siniša Djurović, Judith Apsley, Jamal Aldahmashi, Mohamed Benbouzid et al.
8 citations
Energies
Electric Power System Optimization
1.67
article

Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges

Siniša Djurović, Judith Apsley, Jamal Aldahmashi, Mohamed Benbouzid, Xiandong Ma, Aras Ghafoor
article en
8 citations

Abstract

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals.

EnergiesVol. 17(17)
Northern Border University (SA), Centre National de la Recherche Scientifique (FR), Université de Bretagne Occidentale (FR), University of Manchester (GB), Institut de Recherche Dupuy de Lôme (FR), Lancaster University (GB)
UK Research and Innovation, Northern Border University, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 16%
Electric Power System Optimization
1.67
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Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges — Siniša Djurović, Judith Apsley, et al. · Energies (2024) | TGRS Research Map | TGRS