Design and analysis of an LVDC offshore microgrid for improving the electrical infrastructure of the multi-use platform

Student thesis: Doctoral Thesis

Abstract

Hybrid and interconnected energy sources are becoming the norm for the low carbon energy sector. This implies parallel connection of different sources to a common platform, as it is seen in wind farm connection, self-contained microgrids, offshore Multi-Use Platforms (MUPs), etc. MUPs and their associated marine activities draw the attention of many countries worldwide to exploit resources of the oceans which cover over 68% of the surface of the earth. This helps to cut greenhouse gas emissions and also facilitate provision of food in a sustainable way. There are many challenges relating to developing the microgrid of the MUPs. These include space limitation, high costs of system components, installations related issues, unavailability of backup options, critical loads concerns such as aquaculture or isolated communities, power quality issues and turbine failure. To tackle the aforementioned problems, this research focuses on improving the electric infrastructure of MUPs by considering Direct Current (DC) systems for the offshore microgrid of these platforms. Integrating various energy resources, such as wind, tidal and solar, in a floating structure is proposed under this research. A new graphical concept for the optimal sizing of the MUPs’ microgrid is proposed which is verified by the Matlab/Linprog tool. In addition, a new methodology is proposed to determine the optimal number of parallel DC-DC converters connected to wind/tidal turbines for conversion efficiency maximization, and for increasing turbines’ reliability and availability. A case study for an MUP at the North Sea is discussed to demonstrate the validity of the proposed optimal sizing concept and the proposed methodology for efficiency maximisation. A new adaptive Instantaneous Average Current Sharing (IACS) controller is proposed for minimizing the circulating current among parallel DC-DC converters. A generalized model of n-parallel-connected DC-DC converters with the improved IACS controller is derived for stability analysis purposes. A New coordinated controller is proposed for the Low Voltage Direct Current (LVDC) microgrid of the MUPs with n-parallel floating structures. A Model of a floating structure is derived which comprises wind and tidal turbines, solar array and energy storage, with boost and bidirectional converters. The outcomes of this research show that wind/tidal turbine failures could be reduced while increasing the turbines’ efficiency and availability by determining the optimal number of parallel DC-DC converters connected to the turbine. Based on the new developed technique of determining optimal number of parallel converters instead of a single rated converter, the annual average efficiency of a wind turbine would be increased from 57 to 75 % under the case study considered in this research. Also, the novel adaptive IACS controller reduced the circulating current among parallel-connected DC-DC converters faster than the conventional controller. In addition, the load voltage is accurately regulated to the nominal value with applying this novel controller, while a steady error is recorded for the load voltage with applying the conventional IACS controller. Modeling and stability analysis were conducted for the DC wind/tidal turbines, which has never been done in the literature, showed that the turbine-based surface-mounted permanent magnet synchronous generator is stable, but two conditions should be fulfilled for a turbine-based interior-mounted permanent magnet synchronous generator to ensure turbine stability. Lastly, based on the modelling and stability analysis held for the LVDC microgrid of the MUPs, the new idea of considering DC system for MUPs is a viable solution that ensures stable and more efficient operation.
Date of Award29 Sep 2022
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SponsorsUniversity of Strathclyde
SupervisorOlimpo Anaya-Lara (Supervisor) & David McMillan (Supervisor)

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