A Novel Reduced-Order Modeling Approach of a Grid-Tied Hybrid Photovoltaic-Wind Turbine-Battery Energy Storage System for Dynamic Stability Analysis

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Abstract

This paper presents a novel reduced-order modeling approach for efficient modeling and dynamic stability analysis of a utility-scale hybrid grid-tied system comprising a photovoltaic (PV) array, wind turbine (WT), battery energy storage system (BESS) and the associated power electronic converters and control systems. Utilizing the singular perturbation analysis, the time-domain nonlinear model (TDNLM) of the grid-tied hybrid PV-WT-BESS system is linearized to construct the linearized state-space full-order model (LSSFOM). Categorizing the dynamics of the LSSFOM into fast and slow states based on their weighted dynamics utilizing the participation factor analysis and the residue-based method, the model is further reduced to the linearized state-space reduced-order model (LSSROM), focusing on dominant slow-dynamic states that characterize the overall system dynamics. The LSSROM is employed to investigate dc and ac dynamic interactions under various operational conditions, including all PV, WT, and BESS operating regions and grid stiffness conditions. The proposed reduction approach reduces the computational burden with simplicity and efficiency, facilitating the development of reliable reduced-order models capturing the essential features of the original detailed full-order model with a high degree of acceptable accuracy for dynamic and stability analyses across diverse operating conditions while ensuring versatility. Detailed offline and real-time simulation results validate the analytical results, demonstrating the efficiency of the proposed approach across different operational scenarios.

Original languageBritish English
Pages (from-to)1459-1483
Number of pages25
JournalIEEE Open Journal of Power Electronics
Volume5
DOIs
StatePublished - 2024

Keywords

  • Grid-tied hybrid PV-wind-battery generation
  • interaction dynamics
  • participation factor analysis
  • reduced-order modeling
  • residues-based method
  • singular perturbation analysis
  • small-signal analysis
  • stability analysis
  • state-space modeling

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