Reliability modeling for multi-state power systems with decentralized renewable energy integration

The overall reliability of a microgrid hinges on the performance of its individual components. This paper proposes a probabilistic model based on Markov chains to evaluate the reliability of a microgrid embedded within a multi- state power system(MSS). The microgrid comprises a wind turbine, photovoltaic panels, and a battery-based energy storage system, interconnected with the main grid. We develop a methodology integrating reliability analysis into power systems incorporating decentralized renewable generation via microgrids. This assessment serves both operational and economic objectives by quantifying the impact of distributed renewable units on distribution net- works and mitigating outage costs from main grid failures. Reliability is modeled through Markov state-space dia- grams that capture stochastic transitions via component- specific failure rates (λ) and repair rates (μ). Applied to an adapted IEEE 34-node test feeder, our results extend beyond conventional reliability indices. An importance analysis quantifies each component’s contribution to system availability and expected energy shortfall, complemented by sensitivity analysis revealing their impact on overall performance. These findings equip network planners and operators with a decision-support tool for investment prioritization and grid resilience enhancement. Notably, estimating production capacity from stochastic renewable sources proves meaningful only when supported by highly reliable storage and power conversion systems.