A model-less control algorithm of DC microgrids based on feedback optimization
This work addresses the problem of the optimal real-time control of a DC microgrid without relying on its corresponding network model. The main goal of such a controller is to keep the nodal network voltages within the regulatory limits while offering current sharing capability between the different...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2022 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositório: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/135107 |
| Acesso em linha: | https://hdl.handle.net/11441/135107 https://doi.org/10.1016/j.ijepes.2022.108087 |
| Access Level: | Acceso aberto |
| Palavra-chave: | DC microgrids Distributed control Feedback optimization Load sharing control Secondary voltage control |
| Resumo: | This work addresses the problem of the optimal real-time control of a DC microgrid without relying on its corresponding network model. The main goal of such a controller is to keep the nodal network voltages within the regulatory limits while offering current sharing capability between the different controllable generators powering the DC microgrid. The proposed model-less methodology is based on feedback optimization, which takes advantage of the available real-time measurements to update the setpoints of the DC generation assets. The optimal control variables are determined in an iterative manner by applying a primal–dual saddle-point method, which guarantees appropriate convergence features. The paper details both centralized and distributed implementations which are compared through simulations. The results evidence a good dynamic performance and an optimal steady-state operation as the proposed control algorithm converges to the solution provided by a conventional model-based Optimal Power Flow. |
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