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.
Highlights
Reliability assessment of microgrids (MGs) has attracted more and more interests, since a growing number of customers are powered by MGs in a more flexible and environment-friendly way [1]–[3]
It can be seen from (5) that the intermittent nature of PV is determined by CP(rV) and CP(sV), and the stochastic nature of PV is determined by the probability density functions (PDFs) of PaPvVailable, which can be specified by Beta function [25], for example
The results in Tab. 2 show that an unbiased loss of load probability (LOLP) and significant improvement in computational efficiency are provided by novel sequential sampling algorithm (NSSA) under a system condition different from that in Case 1
Summary
Reliability assessment of microgrids (MGs) has attracted more and more interests, since a growing number of customers are powered by MGs in a more flexible and environment-friendly way [1]–[3]. The ubiquitous issues with polymorphic uncertainties, such as stochastic operation cycles of MG components, intermittent output of distributed generators (DGs) and time varying load, etc., impose heavy computational burden on the reliability assessment of MGs [4]. Efficient sampling techniques are required more urgently when SMC simulation is applied, since the ‘crude’ SMC converges more slowly than NSMC [21] To fill these gaps, within the context of standalone MG, an novel sequential sampling algorithm (NSSA) with high computational efficiency and compatibility with chronological issues has been developed. (b) The variance-reducing mechanisms are thoroughly analyzed, followed with analytical solutions of optimal PDFs. NSSA is thereafter developed to deal with the prohibitive computational burden produced by the polymorphic uncertainties of MGs. Fu(u) and fv(v) will be optimized to improve the simulation efficiency
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