Deep reinforcement learning-based secondary control for microgrids in islanded mode
Microgrids are generally low-inertia systems with a high penetration of renewable energy sources. The design of advanced control structures is required to keep these grids’ electrical variables within an acceptable range. In this context, the present article proposes an intelligent secondary control...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/383524 |
| Acceso en línea: | https://hdl.handle.net/2117/383524 https://dx.doi.org/10.1016/j.epsr.2022.108315 |
| Access Level: | acceso abierto |
| Palabra clave: | Microgrids (Smart power grids) Microxarxes (Xarxes elèctriques intel·ligents) Àrees temàtiques de la UPC::Enginyeria elèctrica |
| Sumario: | Microgrids are generally low-inertia systems with a high penetration of renewable energy sources. The design of advanced control structures is required to keep these grids’ electrical variables within an acceptable range. In this context, the present article proposes an intelligent secondary controller for islanded microgrids using the Deep Deterministic Policy Gradient (DDPG). The DDPG controller changes the output power of the storage elements to secure the voltage and frequency stability. This work tested the designed controller for a microgrid that comprises a synchronous generator, two battery energy storage systems and one photovoltaic generator. The controller performance was compared to droop controllers, considering a short-circuit event, feeder and load disconnections. Results showed a consistent reduction of the microgrid’s voltage and frequency deviations with the DDPG algorithm. |
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