Optimal reactive power dispatch (ORPD) is a particular case of the optimal power flow (OPF) which consists in determining the state of an electric power system by optimizing a specific objective function and satisfying a set of some operating constraints. In this paper, the purpose is to solve deterministic and stochastic multi‐objective ORPD (MO‐ORPD) problem under load and renewable energy sources (RES) uncertainties. The uncertainty is modelled using stochastic scenario‐based approach (SSBA). The objectives to be minimized are active power loss and cumulative voltage deviation from their corresponding nominal values. The MO‐ORPD is solved using sum weighed method, and fuzzy satisfying method is used to select the best compromise solution among Pareto front of non‐dominated solutions. In this paper, quantum‐behaved particle swarm optimization differential mutation (QPSODM) algorithm is proposed to solve the ORPD problem. The proposed methodology has been examined and confirmed on the IEEE 14‐bus and the practical Adrar's isolated power system. The performance of the proposed methodology is compared with recent algorithms. Simulation results show that the proposed methodology can solve the MO‐ORPD including RES effectively and can give best and logic results. Furthermore, a sensitivity analysis is carried out to show the performance of the proposed algorithm comparing to own developed algorithms particle swarm optimization (PSO) and quantum PSO (QPSO).

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Due to the deregulation of the electricity market, power systems are showing increased uncertainties related to loads and renewable sources. Such uncertainties will involve the assessment of the impact of uncertain variables on the control and monitoring of the future power systems. Identification and classification of influential uncertain variables are imperative for power system stability assessment in future smart grid because they provide guidance for handling uncertainty for electrical system operators for management and securing the power grid operations with efficient control and monitoring. In this paper, a probabilistic analysis methodology is proposed for the assessment of the impact of critical uncertain variables on voltage and small-signal stability using quantitative sensitivity technique. The proposed methodology is carried out with different level of penetration of renewable sources and the impacts of influential variables are treated and discussed. The proposed methodology is performed using the modified IEEE 14-bus benchmark. The performances of the probability density function graphs for stability indexes are used for assessing their sensitivities.