Technology Selection of High-Voltage Offshore Substations Based on Artificial Intelligence

Tiago A. Antunes, Rui Castro, Paulo J. Santos and Armando J. Pires ()
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Tiago A. Antunes: Electrical Engineering Department, Instituto Superior Técnico (IST), Alameda Campus, University of Lisbon, 1049-001 Lisbon, Portugal
Rui Castro: Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC-ID) Co-Owned by Instituto Superior Técnico (IST), University of Lisbon, 1000-029 Lisbon, Portugal
Paulo J. Santos: MARE-URI IPS & Escola Superior Tecnologia (EST) Setúbal, Polytechnic Institute of Setúbal, 2910-761 Setúbal, Portugal
Armando J. Pires: CTS-UNINOVA, LASI & Escola Superior Tecnologia (EST) Setúbal, Polytechnic Institute of Setúbal, 2910-761 Setúbal, Portugal

Energies, 2024, vol. 17, issue 17, 1-22

Abstract: This paper proposes an automated approach to the technology selection of High-Voltage Alternating Current (HVAC) Offshore Substations (OHVS) for the integration of Oil & Gas (O&G) production and Offshore Wind Farms (OWF) based on Artificial Intelligence (AI) techniques. Due to the complex regulatory landscape and project diversity, this is enacted via a cost decision-model which was developed based on Knowledge-Based Systems (KBS) and incorporated into an optioneering software named Transmission Optioneering Model (TOM). Equipped with an interactive dashboard, it uses detailed transmission and cost models, as well as a technological and commercial benchmarking of offshore projects to provide a standardized selection approach to OHVS design. By automating this process, the deployment of a technically sound and cost-effective connection in an interactive sandbox environment is streamlined. The decision-model takes as primary inputs the power rating requirements and the distance of the offshore target site and tests multiple voltage/rating configurations and associated costs. The output is then the most technically and economically efficient interconnection setup. Since the TOM process relies on equivalent models and on a broad range of different projects, it is manufacturer-agnostic and can be used for virtually any site as a method that ensures both energy transmission and economic efficiency.

Keywords: offshore transmission optimization; applied artificial intelligence; HVAC & HVDC; oil & gas production; wind energy; efficiency (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
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