Probabilistic Stability Analysis in Power Systems with Renewable Energy Integration
Summary
The increasing penetration of renewable energy sources challenges traditional deterministic stability assessments. Probabilistic stability analysis offers a systematic framework to quantify the impact of uncertainty arising from variable generation, load fluctuations and market-driven dispatch on the small-signal, transient, voltage and frequency stability of large-scale power systems. By representing uncertain quantities as random variables or stochastic processes, and by employing techniques such as Monte Carlo simulation, importance sampling, Latin hypercube sampling and probabilistic collocation, researchers can estimate the likelihood of stability margins being exceeded under a wide range of operating conditions. Advances in copula-based modelling have enabled the capture of complex dependencies among input variables, while global and local sensitivity analyses identify the most influential sources of uncertainty and guide targeted mitigation strategies. Recent trends include the integration of data-driven surrogate models to accelerate online assessments, the use of multi-stability operational boundaries to enhance resilience under high converter-based generation, and hybrid analytical–numerical methods that balance accuracy with computational efficiency. These developments underscore the global significance of probabilistic methods in ensuring the secure and efficient operation of power systems undergoing rapid decarbonisation and digitalisation.
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Probabilistic Stability Analysis in Power Systems with Renewable Energy Integration publication trend
The graph below shows the total number of articles in probabilistic stability analysis in power systems with renewable energy integration across all publications each year (not limited to Nature Index journals).
Technical terms
Probabilistic Stability Analysis: Framework for assessing the likelihood of maintaining system stability in the presence of uncertain inputs such as renewable output and load demand.
Monte Carlo Method: Statistical simulation technique that uses repeated random sampling to estimate the probability of different system states or outcomes.
Copula: Mathematical function that models the joint probability distribution of correlated random variables without assuming normality.
Importance Sampling: Variance reduction technique that biases sample generation towards critical regions of the input space to improve estimation efficiency.
Latin Hypercube Sampling: Stratified sampling method that ensures uniform coverage of each input variable’s distribution by dividing it into intervals of equal probability.
References
- Existing approaches and trends in uncertainty modelling and probabilistic stability analysis of power systems with renewable generation. Renewable and Sustainable Energy Reviews (2019).
- A Review of Uncertainty Modelling Techniques for Probabilistic Stability Analysis of Renewable-Rich Power Systems. Energies (2022).
- State of the Art Monte Carlo Method Applied to Power System Analysis with Distributed Generation. Energies (2022).
- Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems. Energies (2021).
- Probabilistic multi-stability operational boundaries in power systems with high penetration of power electronics. International Journal of Electrical Power & Energy Systems (2022).
- Data‐driven look‐ahead voltage stability assessment of power system with correlated variables. IET Generation Transmission & Distribution (2022).
- Assessing the Applicability of Uncertainty Importance Measures for Power System Studies. IEEE Transactions on Power Systems (2016).
- Influence of Stochastic Dependence on Small-Disturbance Stability and Ranking Uncertainties. IEEE Transactions on Power Systems (2017).
- Probabilistic small signal stability analysis of power system with wind power and photovoltaic power based on probability collocation method. Global Energy Interconnection (2019).
- Latin Hypercube Sampling Method for Location Selection of Multi-Infeed HVDC System Terminal. Energies (2020).
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