Cost optimisation in offshore wind through procurement data analytics.

Shittu, Quadri; Ezenkwu, Chinedu Pascal

Abstract

Governments have implemented a variety of national and international efforts to reduce carbon emissions (so as to prevent the damaging effects of climate change on the environment and the global economy) through the execution of several policies, including the Paris Agreement. The Paris Agreement seeks to keep the increase in average global temperature to well below 2 degrees Celsius and, preferably, below 1.5 degrees Celsius higher than pre-industrial levels. Achieving the objectives of the Paris Agreement will need a shift towards renewable energy sources, like solar and wind power. As a result of these efforts, renewable energy sources' capacity is projected to expand in the upcoming years. Offshore wind is the UK's leading renewable energy source for power generation. Recently, cost optimisation efforts in the offshore wind industry have been focused on engineering design, especially with increasing turbine capacities. This research demonstrates how data analytics can achieve cost optimisation in procurement activities of offshore wind projects, which presents opportunities to reduce the Levelized Cost of Energy (LCOE). Ten (10) offshore wind projects have been selected in the UK offshore wind industry by building a workflow and designing an analytic app using Alteryx Designer. The workflow is built on procurement quotations of wind turbines, marine vessels and export cables, to optimise procurement costs and delivery timelines while fulfilling the established constraints for the respective projects. The optimisation results identified three areas of cost-optimisation opportunities: Contracting, Collaboration and Reusing Components.

Citation

SHITTU, Q. and EZENKWU, C.P. 2024. Cost optimisation in offshore wind through procurement data analytics. In Arai, K. (eds.) Intelligent computing: proceedings of the 12th Computing conference 2024 (Computing 2024), 11-12 July 2024, London, UK. Lecture notes in networks and systems, 1019. Cham: Springer [online], volume 4, pages 80-98. Available from: https://doi.org/10.1007/978-3-031-62273-1_6

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 12th Computing conference (Computing 2024)
• Start Date: Jul 11, 2024
• End Date: Jul 12, 2024
• Acceptance Date: Dec 15, 2023
• Online Publication Date: Jun 15, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Jan 4, 2024
• Publicly Available Date: Jun 16, 2025
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 80-98
• Series Title: Lecture notes in networks and systems
• Series Number: 1019
• Series ISSN: 2367-3370; 2367-3389
• Book Title: Intelligent computing: proceedings of the 12th Computing conference (Computing 2024)
• ISBN: 9783031622724; 9783031622755
• DOI: https://doi.org/10.1007/978-3-031-62273-1_6
• Keywords: Data analytics; Offshore wind industry; Cost optimisation; Environmental policy; Energy policy; Project management
• Public URL: https://rgu-repository.worktribe.com/output/2193599