Inertia analysis and adaptive inertia control of the diesel-PV-energy storage hybrid system

In traditional solar energy storage systems, there is insufficient synergy between photovoltaics and energy storage devices, as well as between solar energy storage systems and the power grid, and there is a lack of clear basis for the configuration requirements of energy storage capacity. This article proposes an adaptive inertia control strategy based on the original synchronous machine configuration of the system, aiming to enhance the inertia frequency regulation capability of the isolated diesel solar energy storage microgrid and reduce the cost of energy storage configuration. The article first analyzes in depth the inertia frequency regulation requirements of microgrids in islanding mode, clarifying the response characteristics and operational limitations of photovoltaic cells and energy storage systems. An adaptive inertia control strategy utilizing variable power point tracking and coordinated battery charging and discharging states has been proposed. Based on load changes and the state of charge of the energy storage system, dynamically adjust the photovoltaic output power and the inertia and damping control parameters of the virtual synchronous machine, optimizing the power allocation process with the core objective of maximizing the inertia frequency modulation capability of the photovoltaic storage system. Through MATLAB/Simulink simulation verification, this scheme not only ensures system stability, but also fully enhances inertia frequency regulation capability, clarifies the configuration requirements of energy storage capacity, and provides an efficient solution for diesel solar energy storage island microgrids.

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Publication Details

Journal
IET conference proceedings.
Published
2025-10-01
DOI
https://doi.org/10.1049/icp.2025.3230
Primary Topic
Microgrid Control and Optimization
Type
article
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article

Inertia analysis and adaptive inertia control of the diesel-PV-energy storage hybrid system

IET conference proceedings.
Microgrid Control and Optimization
article

Inertia analysis and adaptive inertia control of the diesel-PV-energy storage hybrid system

article en

Abstract

In traditional solar energy storage systems, there is insufficient synergy between photovoltaics and energy storage devices, as well as between solar energy storage systems and the power grid, and there is a lack of clear basis for the configuration requirements of energy storage capacity. This article proposes an adaptive inertia control strategy based on the original synchronous machine configuration of the system, aiming to enhance the inertia frequency regulation capability of the isolated diesel solar energy storage microgrid and reduce the cost of energy storage configuration. The article first analyzes in depth the inertia frequency regulation requirements of microgrids in islanding mode, clarifying the response characteristics and operational limitations of photovoltaic cells and energy storage systems. An adaptive inertia control strategy utilizing variable power point tracking and coordinated battery charging and discharging states has been proposed. Based on load changes and the state of charge of the energy storage system, dynamically adjust the photovoltaic output power and the inertia and damping control parameters of the virtual synchronous machine, optimizing the power allocation process with the core objective of maximizing the inertia frequency modulation capability of the photovoltaic storage system. Through MATLAB/Simulink simulation verification, this scheme not only ensures system stability, but also fully enhances inertia frequency regulation capability, clarifies the configuration requirements of energy storage capacity, and provides an efficient solution for diesel solar energy storage island microgrids.

IET conference proceedings.Vol. 2025(26)
Shanghai Jiao Tong University (CN), Shanghai University of Electric Power (CN), Shanghai Electric (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 43%
Microgrid Control and Optimization
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