Meghdad Tourandaz Kenari

When a natural disaster occurs in a distribution network, a widespread power interruption may occur for a few days or weeks. This study presents a bi‐level optimisation‐based model for reconfiguration of the distribution network to improve the resilience of electricity distribution network against severe weather events such as storm and hurricane with the aim of minimising the cost of load outage. To achieve this, a model is first presented for evaluating the vulnerability of distribution network poles to estimate the damages imposed by the threat. Then, in the first level, according to the forecasting of possible failed lines and based on the predicted wind speed before the storm, a network reconfiguration strategy is employed to minimise the expected cost of load outage. In the second level, a new reconfiguration is carried out to restore the system loads and minimise the cost of load outage after the storm. The proposed model is then applied to a standard 33‐bus radial distribution system using the GAMS software. The simulation results demonstrate the effectiveness of the proposed model in increasing network resilience and highlight the importance of network reconfiguration in the face of extreme natural disasters.

With increasing penetration of distributed generations, the importance of the voltage stability assessment of distribution networks has been increased. Considering this situation, a probabilistic model is proposed to evaluate the voltage stability of distribution network integrating wind turbine (WT) units. With this intention, a probabilistic voltage stability index (PVSI) of a radial distribution system with wind power generation is presented. This index investigates the probabilistic risk of voltage collapse for all the buses of system considering uncertainty of WTs. Therefore, the most sensitive bus to the voltage collapse can be identified. The problem model is based on the catastrophe theory that can find the bifurcation point of system. Three‐point estimation method is employed to calculate the statistical moments of voltage and PVSI of nodes. Moreover, to estimate the cumulative distribution function of output random variables, the Cornish−Fisher series are used. The performance of the probabilistic index is tested on the IEEE 69‐bus radial distribution system where different load models are considered. The results demonstrate that the PVSI can accurately predict the voltage instability condition of the system.

The simultaneity of power systems development and uncertainty of system elements has promoted the importance of probabilistic load flow (PLF) in the operating and planning studies of the system. This clarifies that the use of the fast and accurate approaches for PLF computation is necessary. To achieve this objective, this study presents an analytical technique, based on the properties of Laplace transform (LT). The suggested methodology is applicable for every continuous probability distribution function as the input random variable. The proposed procedure is applied to the MATPOWER 9‐ and 118‐bus test systems. To validate the combined cumulants and LT (CCLT) technique, the results are compared with the Monte Carlo simulation and the cumulants method combined with the maximum entropy (CCME) principle. The test results show that the proposed approach gives accurate results, with the lower computational burden comparing CCME. Furthermore, the method formulation and case study results demonstrate that the CCLT method is mathematically straightforward and computationally efficient.