Reliability Assessment of Power Systems Using Probabilistic Methods

Abstract

Ensuring high reliability in power systems is crucial for uninterrupted electricity supply, particularly in the context of increasing system complexity due to distributed generation and renewable integration. This paper presents a comprehensive study on probabilistic reliability assessment methods applied to electrical power systems. Traditional deterministic approaches are compared with probabilistic methods, which account for the stochastic nature of component failures, load variations, and generation uncertainty. Techniques such as Monte Carlo simulation, state-space modeling, and Markov processes are evaluated for their effectiveness in quantifying system reliability indices such as Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). The study also explores how probabilistic methods assist in informed decision-making for system planning, preventive maintenance, and investment prioritization. Simulation results from a test power system demonstrate the superiority of probabilistic assessment in accurately capturing system behavior under uncertainty.

KEYWORDS: Reliability Assessment, Probabilistic Methods, Monte Carlo Simulation, Loss of Load Probability, Markov Processes