As the number and capacity of photovoltaic (PV) power stations increase, it is of great significance to evaluate the PV-connected power systems in an effective, reasonable, and quick way. In order to overcome the challenge of PV’s time-sequential characteristic and improve upon the computational efficiency, this paper presents a new methodology to evaluate the reliability of the power system with photovoltaic power stations, which combines intelligent state space reduction and a pseudo-sequential Monte Carlo simulation (PMCS). First, a non-aggregate Markov model of photovoltaic output is established, which effectively retains some time-sequential representation of the PV output. Then, the differential evolution algorithm (DE) is introduced into the sampling stage of PMCS to carry out an intelligent state space reduction (ISSR). By using the DE algorithm, success states are searched out and removed, thus the state space is reduced and formed with a high density of loss-of-load. Hence, unnecessary samplings are avoided, which optimizes the PMCS sampling mechanism and improves the computational efficiency. Finally, the proposed method is tested in the modified IEEE RTS-79 system. The results indicate that this new method has a better computational efficiency than the time-sequential Monte Carlo simulation method (TMCS) and pure PMCS. In addition, the effectiveness and feasibility of this method are also verified.

Flexible interconnection switch (FIS) is a power electronic device applied to electrical distribution system to realise the flexible control of power flow. Due to the fact that the reliability of FIS tends to be influenced by its operating conditions and dynamic characteristics during its actual operation, it is of significance to characterise the effects of loadings on reliability of FIS. Therefore, an improved reliability model of FIS is proposed, considering the state uncertainty of current and voltage loadings. First, the structural reliability model of modular multilevel converter (MMC) is established. Next, in view of the uncertainty of source and load in distribution network, Monte Carlo simulation is used to deal with the random current loadings, thus the equivalent reliability model of insulated gate bipolar transistor module is built. Then, considering the arm's voltage loading sharing mechanism, a state‐dependent arm reliability model is built. Finally, an eight‐state reliability model for the entire FIS is developed, and Markov‐based analytical method is used to solve it. With the standard test system as well as the actual project in Hangzhou are taken as the testing systems, the reliability indices of FIS are calculated and compared with the field data. The numerical results validate the validity of the proposed model.

With large-scale wind farms integrated into power systems, the computational requirements for reliability evaluation of composite power systems are increasing. This paper introduces a new concept of dynamic orthogonal list, which is a form of multiple failure retrievals to avoid repeatedly calling the optimal power flow (OPF) program in the stage of state evaluation. An existing algorithm of cross-entropy (CE) method was developed to deal with the rare events in sampling. The major contribution of CE is to achieve convergence faster and reduce the number of samples, thus shortening the consuming time in the stage of sampling. This paper proposes a fast reliability evaluation process which combines the CE method with dynamic orthogonal list (CE-DOL), comprehensively improving the computational efficiency of Monte Carlo simulation (MCS). The proposed method is tested on a modified IEEE-RTS 79 system. In addition, for comparison purposes, the efficiencies of MCS, CE, and CE-DOL are estimated in the cases of different system adequacies. It is shown that, in range of certain reliability level, the proposed method is much more efficient compared with the CE method. Finally, the applicability of different methods is analyzed under different system reliability levels.