@Article{ee.2024.055899,
AUTHOR = {Zhi Li, Yufei Zhao, Yueming Ji, Hanwen Gu, Zaibin Jiao},
TITLE = {Rapid Parameter-Optimizing Strategy for Plug-and-Play Devices in DC Distribution Systems under the Background of Digital Transformation},
JOURNAL = {Energy Engineering},
VOLUME = {121},
YEAR = {2024},
NUMBER = {12},
PAGES = {3899--3927},
URL = {http://www.techscience.com/energy/v121n12/58712},
ISSN = {1546-0118},
ABSTRACT = {By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement, information communication, and other fields, the digital DC distribution network can efficiently and reliably access Distributed Generator (DG) and Energy Storage Systems (ESS), exhibiting significant advantages in terms of controllability and meeting requirements of Plug-and-Play (PnP) operations. However, during device plug-in and -out processes, improper system parameters may lead to small-signal stability issues. Therefore, before executing PnP operations, conducting stability analysis and adjusting parameters swiftly is crucial. This study introduces a four-stage strategy for parameter optimization to enhance system stability efficiently. In the first stage, state-of-the-art technologies in measurement and communication are utilized to correct model parameters. Then, a novel indicator is adopted to identify the key parameters that influence stability in the second stage. Moreover, in the third stage, a local-parameter-tuning strategy, which leverages rapid parameter boundary calculations as a more efficient alternative to plotting root loci, is used to tune the selected parameters. Considering that the local-parameter-tuning strategy may fail due to some operating parameters being limited in adjustment, a multi-parameter-tuning strategy based on the particle swarm optimization (PSO) is proposed to comprehensively adjust the dominant parameters to improve the stability margin of the system. Lastly, system stability is reassessed in the fourth stage. The proposed parameter-optimization strategy’s effectiveness has been validated through eigenvalue analysis and nonlinear time-domain simulations.},
DOI = {10.32604/ee.2024.055899}
}