用于动态稳定性分析的并网光伏-风力涡轮机-电池储能混合系统的新型降序建模方法

IF 5 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE open journal of power electronics Pub Date : 2024-09-06 DOI:10.1109/OJPEL.2024.3455933
Mohammad Adnan K. Magableh;Amr Ahmed A. Radwan;Yasser Abdel-Rady I. Mohamed;Ehab Fahmy El-Saadany
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引用次数: 0

摘要

本文提出了一种新颖的降阶建模方法,用于对由光伏阵列(PV)、风力涡轮机(WT)、电池储能系统(BESS)以及相关电力电子变流器和控制系统组成的公用事业级混合并网系统进行高效建模和动态稳定性分析。利用奇异扰动分析法,将并网混合光伏-风力涡轮机-电池储能系统的时域非线性模型(TDNLM)线性化,从而构建线性化状态空间全阶模型(LSSFOM)。利用参与因子分析和基于残差的方法,根据加权动态将 LSSFOM 的动态分为快态和慢态,然后将模型进一步简化为线性化状态空间降阶模型 (LSSROM),重点关注表征整个系统动态的主要慢态。LSSROM 用于研究各种运行条件下的直流和交流动态相互作用,包括所有光伏、风电和 BESS 运行区域以及电网刚度条件。所提出的缩减方法简单高效,减轻了计算负担,有助于开发可靠的缩减阶次模型,捕捉到原始详细全阶次模型的基本特征,具有可接受的高精度,可用于不同运行条件下的动态和稳定性分析,同时确保通用性。详细的离线和实时仿真结果验证了分析结果,证明了所提出的方法在不同运行情况下的效率。
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A Novel Reduced-Order Modeling Approach of a Grid-Tied Hybrid Photovoltaic–Wind Turbine–Battery Energy Storage System for Dynamic Stability Analysis
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.
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