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

Aras Ghafoor, Jamal Aldahmashi, Judith Apsley, Siniša Djurović (), Xiandong Ma and Mohamed Benbouzid
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Aras Ghafoor: Department of Electrical and Electronic Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK
Jamal Aldahmashi: School of Engineering, Lancaster University, Lancaster LA1 4YW, UK
Judith Apsley: Department of Electrical and Electronic Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK
Siniša Djurović: Department of Electrical and Electronic Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK
Xiandong Ma: School of Engineering, Lancaster University, Lancaster LA1 4YW, UK
Mohamed Benbouzid: Institut de Recherche Dupuy de Lôme (UMR CNRS 6027), University of Brest, 29238 Brest, France

Energies, 2024, vol. 17, issue 17, 1-29

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.

Keywords: renewable integration; advanced solutions; thermal margin; fibre optic sensor; power flow; optimisation; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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