Commutation failure (CF) is a frequent dynamic event at inverter of LCC-HVDC systems caused by AC side faults which can lead to inverter blocking, interruption of active power transfer, and even system blackout. To eliminate CFs and improve system performance, new Flexible LCC-HVDC topologies have been proposed in previous research but with limited analysis on its economic performance. Therefore, to further validate the applicability of Flexible LCC-HVDC topologies, this paper utilizes Life-Cycle Cost Analysis model to analyze the life-cycle cost of inverter stations for conventional LCC-HVDC, Capacitor Commutated Converter based HVDC (CCC-HVDC) topology and Flexible LCC-HVDC topologies including Controllable Capacitor based Flexible LCC-HVDC, AC Filterless Controllable Capacitor based Flexible LCC-HVDC and improved Flexible LCC-HVDC. Through a case study based on a 500 kV, 1000 MW LCC-HVDC scheme, comparison results show that the AC Filterless Controllable Capacitor based Flexible LCC-HVDC topology and the improved Flexible LCC-HVDC topology have lower cost than the conventional LCC-HVDC and CCC-HVDC topologies, which proves that the elimination of CFs can be achieved with reduced cost.

Climate and weather-propelled wind power is characterized by significant spatial and temporal variability. It has been substantiated that the variability of wind power, in addition to contributing hugely to the instability of power grids, can also send the balancing costs of electricity markets soaring. Existing studies on the same establish that curtailment of such variability can be achieved through the geographic aggregation of various widespread production sites; however, there exists a dearth of comprehensive evaluation concerning different levels/scales of such aggregation, especially from a global perspective. This paper primarily offers a fundamental understanding of the relationship between the wind power variations and aggregations from a systematic viewpoint based on extensive wind power data, thereby enabling the benefits of these aggregations to be quantified from a state scale ranging up to a global scale. Firstly, a meticulous analysis of the wind power variations is undertaken at 6 different levels by converting the 7-year hourly meteorological re-analysis data with a high spatial resolution of 0.25 ${}^{\circ }$ $\times$ 0.25 ${}^{\circ }$ (approximate 28 km $\times$ 28 km) into a wind power series globally. Subsequently, the proposed assessment framework employs a coefficient of variation of wind power as well as a standard deviation of wind power ramping rate to quantify the variations of wind power and wind power ramping rate to exhibit the characteristics and benefits yielded by the wind power aggregation at 6 different levels. A system planning example is adopted to illustrate the correlation between the coefficient of variation reduction of wind power and investment reduction, thereby emphasizing the benefits pertaining to significant investment reduction via aggregation. Furthermore, a wind power duration curve is used to exemplify the availability of wind power aggregated at different levels. Finally, the results provide insights into devising a universal approach towards the deployment of wind power, principally along the lines of Net-Zero.