A Novel Sequential Sampling Algorithm for Reliability Assessment of Microgrids

Abstract

Due to the polymorphic uncertainties in microgrids (MGs), prohibitive computational burden is produced in reliability assessment. In this work, a novel sequential sampling algorithm (NSSA) compatible with sequential Monte Carlo (SMC) simulation is developed to overcome the computational burden. First, optimal probability density functions (PDFs) of random variables are worked out based on variation method. Then, optimal PDFs are employed to chronologically simulate the random states of microturbine (MT), photovoltaics (PV) and time varying load with improved computational efficiency. Therefore, the convergence of reliability assessment is accelerated accordingly. A series of case studies have been conducted, and the computational results show that NSSA provides a favorable sampling efficiency and adaptability to system conditions in reliability assessment of MGs. At last, based on optimal PDFs produced by NSSA, dominant joint PDF (DJ-PDF) is defined and employed to quantify the contributions of different scenarios to the reliability indices. Case studies have confirmed that DJ-PDF can provide detailed information for scenario-based reliability analysis. INDEX TERMSMicrogrid, sequential sampling, reliability assessment, computational efficiency, coefficient of variation NOMENCLATURE Random Variables u random variable sampled for random operation hours v random variable sampled for random repairing hours q random variable defined to model time varying load p random variable defined to model intermittency of PV Indices and Sets f p estimate of fp * (p) on the wp-th subinterval of [0, 1] FR(•) reliability test function in terms of u, v, q and p with uniform PDFs FR ' (•) reliability test function in terms of u, v, q and p with varying PDFs GR(•) reliability test function in terms of the hourly states of MT, PV and load FL[•] Functional in terms of fu(u), fv(v), fq(q) and fp(p) I. INTRODUCTION

Reliability evaluation of the renewable energy‐based microgrids considering resource variation

“…In this part, the equivalent failure rate of a wind turbine based on the PMSG technology is determined. As can be shown in FIGURE 5 The two-step Markov model for the system components Figure 2, the failure of the main components of a wind generation unit covering gearbox, PMSG, electrical converters, control system, transformer and cable leads the transmission of the produced power of the wind turbine to the microgrid stops and the output power would be zero. Thus, according to the reliability concept, since all of this equipment is located in series in the system, the equivalent failure rate of the wind generation unit and repair rate can be calculated as [25]:…”

“…However, in the proposed reliability assessment method, the impact of the variation in the renewable resources and the components failure rate are not taken into account. In [5], a novel sequential sampling algorithm is proposed to evaluate the reliability of the microgrids including microturbines and PV systems. This paper uses the variation method to determine the optimal probability density function of the random variables.…”