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• Proposed controller controls multiple variables simultaneously in VSC HVDC grids. • Proposed controller provides an improved response during disturbances. • Power-sharing strategy realises fast power balancing between converter stations. • Proposed controller operates steadily under communication delays and outage. High voltage direct current (HVDC) networks are transforming ac power grids to hybrid ac/dc power grids; hence hybrid ac/dc power grids are facing new stability challenges. In this paper, a decentralised control scheme based on model predictive control (MPC) is proposed to enhance the stability of hybrid ac/dc power grids under disturbances. The proposed scheme controls the dc voltage, frequency, active and reactive power simultaneously with minimum deviations from steady-state values. A communication network is used to share the sampled grid data with the local MPC of each voltage source converter (VSC). Hence, the central controller can be avoided. Moreover, the active power-sharing between converters can be pre-designed. Thus, fast and accurate active power control can be realised. Finally, the proposed control scheme is validated using a five-terminal VSC HVDC test system with MATLAB. The VSC HVDC network with the proposed control scheme shows a smaller deviation and a faster response compared with the conventional droop control scheme and centralised MPC scheme under small disturbances. Under large power variations, the proposed control scheme assists to reach the steady-state rapidly with a minimum impact on the entire power grid. The proposed control scheme has also proved its robustness under communication delays and outages. [ABSTRACT FROM AUTHOR]

• Proposed a probabilistic framework to accurately model the independent and correlated uncertainties for generating realistic system data in power system studies. • Represented the actual system response by modeling the system uncertainties and their possible correlation in the power network by employing nine alternative sampling techniques. • Assessed nine uncertainty modeling techniques for probabilistic voltage measures (voltage profile, static voltage stability (based on P-V and Q-V curves), and short-term dynamic voltage stability). • Identified the effect of wind speed and wind power and their natural fluctuation on different probabilistic voltage stability indicators (voltage profile, system loadability, reactive power margin, and pre-fault short-term system voltage). As correlated uncertainties in power grids become more prevalent, modeling of stochastic dependencies is becoming increasingly necessary. Independent probability distributions disregard these correlated uncertainties, potentially leading to significant errors, underscoring the necessity of dependence structures. This paper presents a probabilistic approach for modeling uncertain system parameters with their inherent correlations. The proposed approach is validated through various aspects of system voltage measures, including voltage profile and static and dynamic voltage stability. In total, nine alternative sampling generation techniques are employed, and their accuracy is measured based on real-world data using root mean square error (RMSE) and coefficient of determination (R2) criteria. The result suggests that multivariate (Gaussian and Student's) copula techniques accurately represent the real system data, consistently achieving high accuracy rates of 98 % for voltage profiles, 97 % for static voltage stability, and 93 % for dynamic voltage stability. In contrast, the independent sampling technique failed to follow the real-world system data for the different aspects of system voltage measures. [ABSTRACT FROM AUTHOR]

• Eight probability distributions have been implemented in this study to accurately model wind speed datasets with different characteristics. • An approach to identify the best PDF for a wind speed dataset considering its characteristics (i.e. high or low wind speed). • Verification of the best-suited PDFs (Gamma and Weibull distributions for low and high wind speed, respectively) via voltage profile analysis of a test power network, • P-V curve and Q-V curve analysis to determine how the system loadability and distribution of the voltage collapse points are affected by the characteristic (and subsequent PDFs) of the wind speed dataset. Wind speed characteristics may have a significant impact on the power system operation, stability and dynamics. Hence, appropriate modelling and quantification of wind probability distribution function (PDF) are critical for probabilistic power system analysis. Weibull distribution is the most widely used PDF for power system (particularly power system stability) studies, which might not be a valid PDF for different wind regimes having high, average, and low wind speed data. In this study, twenty different wind speed datasets (each dataset is different based on the location, duration, and resolution) have been collected and analysed with eight PDFs to model the wind speed to replicate different wind speed characteristics. The PDFs include Rayleigh, Weibull, Gamma, Lognormal, Normal, Inverse Gaussian, Generalized Extreme Value, and Exponential distributions. Root mean square error (RMSE) and the coefficient of determination (R2) are applied as the measure of accuracy. The obtained results show that the locations, duration, and resolution have a notable impact on the selection of suitable PDFs. It was found that the Gamma distribution is the best-suited PDF for representing low wind speed data. In comparison, the Weibull distribution represents the best PDF for high wind speed data. Also, the selection of the Gamma PDF for low wind speed data and Weibull distribution for high wind speed data is further validated by presenting voltage profile and voltage stability analysis. Study outcomes would assist power system planners to select the appropriate PDF for power system stability studies based on the characteristics of the available wind speed dataset. [ABSTRACT FROM AUTHOR]