An improved hybrid method combined with a cloud-based supervisory control to facilitate smooth coordination under low-inertia grids
Over recent years, a transformation driven by the adoption of smart grid technologies has posed significant challenges to the traditional power grid. While this evolution promises greater efficiency, sustainability, and transparency for utility providers and consumers, it also introduces new complex...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Elsevier B.V.
2025
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/47963/3/An%20improved%20hybrid%20method%20combined%20-%20Copy.pdf http://ir.unimas.my/id/eprint/47963/ https://www.sciencedirect.com/science/article/abs/pii/S2950425225000246 https://doi.org/10.1016/j.pes.2025.100072 |
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| Summary: | Over recent years, a transformation driven by the adoption of smart grid technologies has posed significant challenges to the traditional power grid. While this evolution promises greater efficiency, sustainability, and transparency for utility providers and consumers, it also introduces new complexities. Conventionally, inertia provided by synchronous generators plays a significant role in maintaining power grid stability by resisting large frequency deviations arising from sudden changes in generation or demand. With the increasing penetration of inverter-based resources such as solar and energy storage systems, the grid’s overall inertia can significantly be impacted. Despite various research on smart grid integration technologies, there is still a lack of rigorous studies focusing on grid performance during low inertia. For this reason, the paper proposed an optimized hybrid generalized droop method combined with a cloud-based supervisory control using the Internet of Things (IoT) to facilitate smooth transitions and maintain system stability. The presented approach synthesizes the traditional droop control and the generalized cloud-based algorithm to address challenges related to dynamic load variations and intermittent renewable energy sources. The framework has been validated on interconnected Simulink models running in a real-time cloud platform with data collected from local systems, interfaced in a closed-loop test environment. The proposed algorithm demonstrates outstanding performance in maintaining system stability under various operating conditions. Specifically, it limits frequency deviations to 0.01 %, significantly outperforming the traditional droop control algorithm, which exhibits deviations of 0.8 %. Similarly, voltage fluctuations are effectively minimized to 0.02 %, ensuring a stable operating voltage around 240 V. Furthermore, integrated with IoT-based control, the optimized hybrid generalized droop method mitigates transient instabilities, as evidenced by a 29 % reduction in voltage overshoot during fault clearance. These results highlight the effectiveness of the presented control strategy in enhancing microgrid resilience, particularly under high renewable energy penetration. |
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