Control tolerante a fallos en comunidades energéticas basado en blockchain
[EN] This work describes a distributed control system that optimizes energy management using model predictive control in an energy community. The system has been extended to provide each agent with a fault-tolerant mechanism capable of detecting, isolating, and reconfiguring agents in case of failur...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/222708 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/222708 |
| Access Level: | acceso abierto |
| Palavra-chave: | Energy systems Predictive control Decentralized control Fault-tolerant FDI for linear systems Sistemas energéticos Control predictivo Control descentralizado Tolerante a fallos Detección y diagnóstico de fallos para sistemas lineales |
| Resumo: | [EN] This work describes a distributed control system that optimizes energy management using model predictive control in an energy community. The system has been extended to provide each agent with a fault-tolerant mechanism capable of detecting, isolating, and reconfiguring agents in case of failures. Fault detection involves the calculation of residual signals and probability-based thresholds that minimize false positives. Once a fault is identified, reconfiguration is performed by adjusting the parameters of the agent s predictive controller to bring the system to an acceptable level of safety. If the reconfiguration affects more than one agent, the information must be shared with the other agents. The control algorithm relies on a smart contract on a blockchain network, enabling the problem to be solved in a distributed manner without a centralized coordinator, while ensuring the security and integrity of the data. The proposed control strategy has been evaluated through various simulations in an energy community. |
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