Key Takeaways & Executive Findings
- •• • Hybrid automata and hybrid Petri net models are structurally too broad for practical system analysis; switching and piecewise affine models are currently the most widely applied in power systems, as evidenced by their use in low-voltage ride-through switching sequences. • • A second-order trajectory sensitivity-based parameter identification method for switching models is proposed, addressing the discontinuity in objective functions caused by discrete event triggering, which renders traditional gradient-based search ineffective. • • For piecewise affine hybrid models, three parameter identification methods—algebraic geometry, data aggregation, and Bayesian—are presented, with their respective advantages and applicable scenarios discussed, enabling robust parameter estimation under discrete state changes. • • A unit grouping method considering switching sequence similarity and continuous system dynamic similarity is proposed for hybrid equivalent modeling of renewable energy stations, along with discrete state and continuous system aggregation strategies, facilitating accurate station-level equivalents despite dispersed units and diverse discrete event states.
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Abstract
The increasing penetration of renewable energy sources has introduced novel instability phenomena in power grids, such as sustained and repeated low-voltage ride-through events, which existing models fail to analyze or explain. This paper addresses the typical continuous-discrete hybrid characteristics of renewable energy generation systems at both the unit and station levels. It discusses the interwoven discrete multi-mode switching and continuous state evolution during faults, emphasizing the necessity of hybrid models. Four classes of hybrid models are compared, including hybrid automata, hybrid Petri nets, switching models, and piecewise affine models, with their applicable scenarios. For switching models and piecewise affine models, parameter identification methods are proposed, and their applicable scenarios are discussed. To tackle the challenge of aggregating dispersed renewable units with diverse discrete event states, a mechanism-data fusion hybrid model aggregation method is proposed. Future research directions for hybrid characteristic modeling of renewable energy generation systems are outlined. The review highlights that current hybrid models remain in an early stage, and mechanism-data fusion modeling is a promising supplement. Key challenges include balancing model complexity and accuracy, and addressing the contradiction between diverse transient behaviors and model universality.
1. Introduction
Existing commercial simulation models for renewable energy generation systems, such as electromechanical transient models, electromagnetic transient models, and impedance models, are predominantly time-driven and fail to capture the state-switching conditions and processes during transients. This limitation has led to inaccurate representation of dynamic reactive power characteristics, as evidenced by the 2023 Brazil blackout where a 500 kV line protection misoperation triggered cascading failures, resulting in a load loss of 23,368 MW. The root cause was inaccurate simulation modeling of renewable dynamic reactive characteristics, which prevented proactive deployment of safety defenses. Furthermore, these models cannot explain novel instability phenomena like sustained and repeated low-voltage ride-through, highlighting a critical gap in modeling capabilities.
To address this bottleneck, this review systematically examines the continuous-discrete hybrid characteristics inherent in renewable energy generation systems, focusing on wind turbines, MPPT control, drivetrains, generators, photovoltaic cells, power electronic converters, and their control systems. The study proposes a comprehensive hybrid modeling framework at both unit and station levels, including parameter identification methods for switching and piecewise affine models, and a mechanism-data fusion aggregation method for dispersed units. By capturing the interwoven discrete events and continuous dynamics, this approach aims to enhance the accuracy of stability analysis and control strategy formulation for high-penetration renewable power systems, ultimately improving grid resilience and renewable accommodation capacity.
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PAN Xueping, WEI Yongkai, LIANG Wei, HAN Jun, GUO Jinpeng, SUN Xiaorong, JU Ping (2026). Review of Hybrid Characteristic Modeling for Renewable Energy Power Generation Systems. Power Automation Equipment. https://doi.org/10.16081/j.epae.202606001
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Frequently Asked Questions
What are the primary failure mechanisms that necessitate hybrid modeling in renewable energy systems?
The failure mechanisms stem from the interaction between discrete events (e.g., control mode switching, safety limiting, protection activation) and continuous state evolution (e.g., current and voltage dynamics). For instance, during low-voltage ride-through, the sequential actions of control and protection, such as Crowbar activation and high/low voltage ride-through switching, are closely tied to the unit's operating state and disturbance severity. These discrete events alter the continuous system's trajectory, leading to complex dynamics that time-driven models cannot capture. The 2023 Brazil blackout exemplifies how inaccurate modeling of dynamic reactive characteristics during such events can result in cascading failures and massive load loss (23,368 MW).
How do the proposed parameter identification methods improve upon traditional approaches for hybrid models?
Traditional gradient-based parameter search strategies fail when discrete event triggering introduces discontinuities in the objective function. The proposed second-order trajectory sensitivity method for switching models addresses this by accounting for the sensitivity of trajectories to parameter variations across discrete transitions. For piecewise affine models, algebraic geometry, data aggregation, and Bayesian methods offer robust alternatives: algebraic geometry provides exact solutions under certain conditions, data aggregation handles large datasets with noise, and Bayesian inference quantifies uncertainty. These methods enable accurate parameter estimation despite the hybrid nature, enhancing model fidelity for stability analysis.
What are the scalability bottlenecks for aggregating hybrid models of renewable energy stations, and how does the proposed method mitigate them?
The scalability bottleneck arises from the large number of dispersed renewable units with diverse discrete event states, which are influenced by initial operating points, disturbances, unit types, control modes, and parameters. Traditional aggregation methods cannot handle such heterogeneity. The proposed mechanism-data fusion aggregation method groups units based on switching sequence similarity and continuous dynamic similarity, then applies discrete state and continuous system aggregation strategies. This reduces computational complexity while preserving dynamic characteristics, enabling efficient station-level equivalent modeling. However, the method is still in early stages and requires further validation in engineering cases.
What are the key contradictions and challenges in hybrid modeling of renewable energy systems?
The main contradictions are between model complexity and accuracy, and between diverse transient behaviors and model universality. High-fidelity hybrid models are computationally intensive, while simplified models may miss critical dynamics. Additionally, renewable units exhibit varied transient responses due to differences in types, control strategies, and operating conditions, making a one-size-fits-all model impractical. The review notes that mechanism-data fusion modeling can supplement existing methods, but current research is in its infancy. Future directions include self-evolving data-driven models as historical data accumulates and AI methods advance, yet engineering application remains limited.
How do the proposed hybrid models compare to existing impedance models for stability analysis?
Impedance models linearize the system around an operating point and assume small disturbances without entering nonlinearities such as limiting, switching, or protection. Thus, they are only valid for small-signal stability analysis and cannot capture state-switching or dynamic mode changes during large disturbances. In contrast, hybrid models explicitly represent discrete events and continuous dynamics, enabling analysis of large-signal stability and novel instability phenomena like repeated low-voltage ride-through. For example, switching models can replicate the sequence of control mode transitions during faults, providing accurate stability margins. However, hybrid models are more complex and require parameter identification, which impedance models avoid.
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