Load shedding (LS) is the last firewall and the most expensive control action against power system blackout. In the conventional under frequency LS (UFLS) schemes, the load drop locations are already determined independently of the event location. Furthermore, the frequency thresholds of LS relays are prespecified and constant values which may not be a comprehensive solution for widespread range of possible events. This paper addresses the decentralized LS in which the instantaneous voltage deviation of load buses is used to determine the frequency thresholds of LS relays. The higher frequency thresholds are assigned to the loads with larger voltage decay which are often located in the vicinity of disturbance location. The proposed method simultaneously benefits from individual UFLS and under voltage LS (UVLS) features which operate in the power system without coordination. Numerical simulations in DigSilent PowerFactory software confirm the efficiency of proposed methodology in the stabilization of the power system after various severe contingencies.

The traditional methodology for defining the ampacity of overhead lines is based on conservative criteria regarding the operating conditions of the line, leading to the so-called static line rating. Although this procedure has been considered satisfactory for decades, it is nowadays sensible to account for more realistic line operating conditions when calculating its dynamic ampacity. Dynamic line rating is a technology used to improve the ampacity of overhead transmission lines based on the assumption that ampacity is not a static value but a function of weather and line's operating conditions. This paper presents an algorithm to estimate and predict the temperature in overhead line conductors using an Extended Kalman Filter. The proposed algorithm assumes both actual weather and current intensity flowing along the conductor as control variables. The temperature of the conductor, mechanical tension and sag of the catenary are used as measurements because the common practice is to measure these values with dynamic line rating hardware. The algorithm has been validated by both simulations and measurements. The results of this study conclude that it is possible to implement the algorithm into Dynamic Line Rating systems, leading to a more accurate estimation and prediction of temperature.

SEV, the Faroese Power Company, has a vision to reach a 100% renewable power system by 2030. SEV is committed to achieve this, starting from a 41% share of renewables in 2019. A detailed expansion plan for the generation, storage and transmission is needed to reach this goal. This is the focus of this study. Practical constrains e.g. resource potential and available space must be considered. Balmorel, an optimisation tool, has been used to optimise investments and dispatch. A method to translate optimal results to a realistic RoadMap was developed and applied. The impact of different technologies and costs has been investigated through multiple scenarios. In ratios of average consumption in 2030, installed power will be 224% wind, 105% solar with 8–9 days of pumped hydro storage according to the proposed RoadMap. The plan is economically favorable up to 87% of renewables, but in order to reach a 100% renewable production in an average weather year, the renewable generation capacity has to be increased by 80%. The study also shows that if biofules or tidal technologies become viable, these will be game changers needing a significantly lower total sum of installed renewable power.

Dynamic State Estimation (DSE) is a critical tool for analysis, monitoring and planning of a power system. The concept of DSE involves designing state estimation with Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) methods, which can be used by wide area monitoring to improve the stability of power system. State estimation with EKF and UKF methods can be used for monitoring and estimating the dynamic state variables of multi-machine power systems, which are generator rotor speed and rotor angle. This paper uses Powerfactory to solve power flow analysis of simulations, then a non-linear state estimator is developed in MatLab to solve states by applying the unscented Kalman filter (UKF) and Extended Kalman Filter (EKF) algorithm. Finally, a DSE model is built for a 14 bus power system network to evaluate the proposed algorithm for the networks. This article will focus on comparing and studying the advantages and disadvantages of both methods under transient conditions. It is demonstrated that UKF is easier to implement and accurate in estimation.