As photovoltaic (PV) generation has been one of the major renewable energy sources around the world, its PV capacity has also increased. When the large-scale PV systems are integrated into the distribution network, the complexity of the assessment process of the distribution network reliability will increase hazardously. In order to accurately assess this reliability in the distribution network combined with the PV generation, a reliability assessment procedure is proposed. In order to accurately evaluate the impact of the failure of conventional power equipment on reliability, the time-varying failure rate of conventional power equipment is modeled, taking into account the aging period. Then, in order to accurately evaluate the reliability improvement with PV systems integration, the new procedure is proposed highlighting the following contributions: 1) five new indices are added. 2) PV output is modeled so that not only the radiation intensity but also the failure and degradation of PV modules are represented. 3) time-varying islanding operation is considered and integrated. A case study using real-life distribution network topology and data in China is applied to verify that the newly proposed reliability indices display more sensitivity, and the proposed procedure significantly improves the accuracy of the reliability assessment.

The randomness of electric vehicle (EV) charging has negative impacts on three‐phase imbalance and peak–valley difference in electric energy distribution systems. Traditional EV charging strategies have shortcomings: the performance of three‐phase imbalance mitigation may be limited if the grid‐connected EVs are extremely imbalanced on three phases; in addition, the comprehensive regulation of peak–valley difference and three‐phase imbalance is not developed, and the three‐phase imbalance of reactive power is ignored. Therefore, a real‐time multilevel energy management strategy (RMEMS) for EV charging is proposed. A tri‐level optimization model (TOM) is designed as the central system. In upper‐level optimization, the three‐phase selection (TPS) of EVs is optimized to balance active or reactive power consumption on three phases. Based on the results from upper‐level optimization, the charging active power is regulated in middle‐level optimization to reduce the peak–valley difference on each phase. In lower‐level optimization, the reactive power compensated by EV chargers is optimized based on the results from upper‐level and middle‐level optimization to balance the reactive power on three phases. Case studies show that the proposed RMEMS performs well for balancing active and reactive power consumption on three phases, and the peak–valley differences of active power consumption on each phase are all mitigated.

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