Reliability evaluation method for distribution network with distributed generations considering feeder fault recovery and network reconfiguration

Abstract

This paper presents a reliability evaluation method for distribution network with distributed generations considering feeder fault recovery and network reconfiguration, and mainly addresses issues: 1) insufficient consideration of the characteristics for the components and distributed generations (DGs) of distribution network such as time sequences, randomness and intermittency; 2) changes in the feeder area of the distribution network after the access of DGs; and 3) multi‐dimensional reliability index for distribution network with DGs has not been formed perfectly. Firstly, Markov reliability model for distribution network components/DGs is established in our study. Then, considering the fault recovery and network reconfiguration of the feeder area, island partitioning method of distribution network with DGs is also proposed. Moreover, a reliability evaluation model is presented for distribution network with DGs based on sequential Monte Carlo method. Case study is performed on the improved IEEE RBTS BUS6 F4 feeder system, and the research results indicate the validity and effectiveness of the proposed method. In addition, the system reliability indicators, such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), and average system availability index (ASAI), are studied and discussed in detail from three dimensions: the access of DGs, the types of DGs, and the capacity of DGs.

Optimized allocation of microgrids’ distributed generations and electric vehicle charging stations considering system uncertainties by clustering algorithms

“…The MCS is a powerful method for evaluating the SGs' reliability, considering the probabilistic behaviours of sub-systems, e.g. RERs, EVs, and EVCSs [19,20]. In [21], MCS was used to stochastically model the EVs and their charging management.…”

“…In Equation (20), the inputs of clustering for EVCS modelling are shown. These inputs are extracted from the scenario generation by MCS.…”