Model Predictive Control of Dual-Mode Energy-Stored Quasi-Z-Source Photovoltaic System

Yushan Liu, Xuyang Liu, Xiao Li, Haiwen Yuan, Yaosuo Xue

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The energy-stored quasi-Z-source inverter (ES-qZSI) has attracted much attention for photovoltaic (PV) power generations, due to its capability to stabilize the PV power fluctuations and other advantages of Z-source inverter. Traditional ES-qZSI suffers from poor operation at night or cloudy days when PV power is extremely low. In order to overcome that, this paper proposes a model predictive control (MPC) strategy for an improved dual-mode ES-qZSI, which is designed to support all-weather operation of PV power generation system. The system predictive models in both day and night operating modes are derived. And the control strategy based on MPC is disclosed to ensure the high-performance operation of the system. Simulation is carried out on PV power fluctuation in daytime and switching between the two modes, to verify the effectiveness of proposed control strategy.

Original languageEnglish
Title of host publication3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665479080
DOIs
StatePublished - 2022
Externally publishedYes
Event3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 - Doha, Qatar
Duration: Mar 20 2022Mar 22 2022

Publication series

Name3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022 - Proceedings

Conference

Conference3rd International Conference on Smart Grid and Renewable Energy, SGRE 2022
Country/TerritoryQatar
CityDoha
Period03/20/2203/22/22

Funding

ACKNOWLEDGMENT This work was supported in part by the National Natural Science Foundation of China under Grant 52107175, in part by the Beijing Nova Program under grant Z211100002121080, and in part by the Fundamental Research Funds for the Central Universities under Grant KG16076201, China.

FundersFunder number
National Natural Science Foundation of China52107175
Beijing Nova ProgramZ211100002121080
Fundamental Research Funds for the Central UniversitiesKG16076201

    Keywords

    • Energy storage
    • Model predictive control
    • Photovoltaic power systems
    • quasi-Z-source inverter

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