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Predictive Verification of Reliability Evaluation for Multiple Interconnected Microgrids

delete2026-05-28
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PRE
AI
M
Mohamed Abdelghany
A
Adnan Rashid
S
Sofiène Tahar
DOI：10.1109/jsyst.2026.3681298delete
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Abstract

Abstract

En 中文
Modern power systems increasingly rely on interconnected microgrids (MGs) integrating a high proportion of renewable energy sources (RES). However, the intermittent nature of RES introduces significant reliability challenges, particularly in island-mode operation. Traditional Monte Carlo (MC) sampling-based simulations, while widely used, suffer from randomness-induced variability and lack provable guarantees of accuracy. Recent Artificial Intelligence (AI)-based approaches have shown potential in handling complex operational data but often rely on large training datasets and lack mathematical interpretability. This article introduces a novel formal verification-based approach that enables a more precise and comprehensive reliability assessment of interconnected smart MGs at the subsystem level. We illustrate the capability of our proposed formal analysis method by evaluating important MG reliability and energy indices, such as average service availability index, average service unavailability index, average system curtailment index, energy not supplied index, loss of energy expectation, loss of load expectation, and energy index of reliability. Subsequently, we compare our obtained results with those of MATLAB-based MC simulations and mathematical manual paper-and-pencil analysis.
Keywords:
Average service availability index (ASAI)
average system curtailment index (ASCI)
average service unavailability index (ASUI)
energy index of reliability (EIR)
energy not supplied (ENS)
formal methods
interconnected microgrids (MGs)
loss of energy expectation (LOEE)
loss of load expectation (LOLE)
Monte Carlo (MC)
power system reliability
renewable resources
smart grids (SGs)

Journal

I
IEEE Systems Journal
IF:
4.4
Papers:
106
Citations:
0

Organization

No organization information available