A tuning method for the supplementary voltage controller of dual-side grid forming converters in distributed storage systems
Utility-scale battery energy storage systems (BESSs) are currently being used to provide auxiliary services, such as frequency regulation, peak shaving, or grid balancing, among others. Hybrid ac/dc distribution grids where the BESS systems are connected in the dc side and the dc/ac interface is imp...
| Autores: | , , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/77302 |
| Acesso em linha: | http://hdl.handle.net/10810/77302 |
| Access Level: | acceso abierto |
| Palavra-chave: | grid forming (GF) virtual synchronous machine (VSM) battery energy storage system (BESS) hybrid ac/dc distribution grids |
| Resumo: | Utility-scale battery energy storage systems (BESSs) are currently being used to provide auxiliary services, such as frequency regulation, peak shaving, or grid balancing, among others. Hybrid ac/dc distribution grids where the BESS systems are connected in the dc side and the dc/ac interface is implemented through a grid forming (GF) converter are currently researched. These solutions combine the benefits given by the dc distribution and the possibility to provide emulated inertia and damping to the system through the use of GF control techniques. This article presents a novel tuning method, based on small signal analysis, for the configuration parameters of a dual-side GF controller. It aims to minimize the dynamic performance difference between the dual-side and ideal GF controllers, thus ensuring that the dual-side GF provides the expected support to the grid in terms of inertia, damping and primary response, while simultaneously controlling the dc voltage. This is achieved through the optimum tuning of the supplementary dc voltage regulator embedded in the dual-side GF controller. Real-time estimation of the optimum controller gains by making use of an artificial neural network is proposed. Simulation and experimental results are presented to validate the method. |
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