Dynamic Rating based Design and Operation of Offshore Windfarm Export Systems

Offshore Windfarms (OWFs) will dominate the energy generation across the globe by 2050. The ten-fold increase in offshore wind over the last decade has primarily been driven by two factors: Firstly, the decrease in its Levelized Cost of Energy (LCOE) from 180 to less than 40 e/MWh in this period, due to optimization of the entire value chain; secondly, the development of large-scale OWFs farther from the shore.

In order to keep up with the targets of 2050, further reduction in LCOE is needed. This can be done by making use of the intermittent nature and low capacity factors of the wind to optimally design and utilize the OWF transmission system. In this thesis, novel methodologies and investment decision tools are developed for operation and planning of the OWF export system and its HV components on the basis of Dynamic Thermal Rating (DTR) concept.

Debottlenecking of the OWF export system has revealed that there are inherent thermal pinch-points that need to be resolved for methodical DTR-based design and operation. For this purpose efficient thermal estimation of critical HV components in real-time is possible using empirically derived, differential equations based Thermo-Electric Equivalent (TEE) models.

In this thesis, state-of-the-art TEE models (particularly for cables and transformers) are investigated and modified for linear optimization. This is complemented by evaluation of their performance under actual operating conditions from test case OWFs around the globe. Further analysis has revealed that the time-variant physical attributes of the HV components, along with the respective environmental conditions dictate their thermodynamic behavior.

Therefore, data analytics and Machine Learning (ML) have been used to develop, test and benchmark statistical tools for thermal estimation and dynamic condition monitoring of these components. The proposed ML-based methods, ranging between semi-physical grey-box and non-physical black-box models, offer unique advantages due to their self-learning nature, which includes identification of information not readily visible to operators due to abundance of data.

Furthermore, these models are found to improve the performance of the conventional TEE models and can potentially be used for real-time condition monitoring in the offshore environment. DTR-based optimal utilization of OWF transformers is given keen attention in this thesis. The analysis of ten test case OWF transformers has revealed that the potential for their optimal utilization is considerable and state-of-the-art thermo-chemical ageing models can be used for this purpose.

Therefore, an enhanced DTR methodology for transformer utilization has been developed for a novel optimization problem that facilitates large-scale integration of offshore windfarms by minimizing the energy dispatch cost in the day-ahead market. By testing the methodology on actual generation and load patterns of West-Denmark, the dynamic lifetime based utilization of transformer is found to delay the grid reinforcement costs by facilitating the integration of large-scale OWFs in wind-dominated power systems.

The proposed framework accounts for common risk-aversion standards without compromising on system reliability demands. Finally, cost-effective DTR-based design optimization of HV components in the OWF export system during the planning phase is addressed in the last part of the thesis. The unique uncertainty challenges due to stochastic nature of wind speed profile, wind turbine availability and contingency of components over the entire OWF lifetime are addressed by probabilistic weighing of operational scenarios.

Novel investment decision support tools, (one iterative and one two-stage stochastic optimization model developed over the course of this project), account for variation in energy losses and possibility of curtailment, while ensuring reliable system operation. By validating the developed methodologies for a test case OWF off the east coast of UK, the potential for improvement in business case, while balancing the threat of poor transmission efficiency, is successfully demonstrated.

All in all, the analysis presented in this thesis shows that DTR can have significant positive impacts on the design and operation of OWF export systems.