Microgrid intelligent control with optimized power-energy management | Multi-layer energy management strategy towards smart grid interaction. Este artículo no está disponible.Microgrid intelligent control with optimized power-energy management | Multi-layer energy management strategy towards smart grid interaction

By grouping renewable sources, traditional sources, storage and local consumption as a microgird, it is possible to handle the difficulty of large scale renewable energy penetration due to the issues of intermittent and random production. Based on a representative microgrid in urban area and integrated in buildings, a multi-layer supervision is proposed, in order to realise a systemic study while particularly attempting to cover the research gap of implementing optimisation in realtime operation. The supervision handles together power balancing, optimisation, metadata and information from both users and the grid. The supervision has been validated by experimental tests. The feasibility of implementing optimisation in real-time operation is validated even with uncertainties. The supervision is able to manage efficiently the power flow while maintaining power balancing in any case. Nevertheless, optimization effect relies on prediction precision. This problem can be improved in future works by updating optimization in real-time.

By grouping renewable sources, traditional sources, storage and local consumption as a microgird, it is possible to handle the difficulty of large scale renewable energy penetration due to the issues of intermittent and random production. Based on a representative microgrid in urban area and integrated in buildings, a multi-layer supervision is proposed, in order to realise a systemic study while particularly attempting to cover the research gap of implementing optimisation in realtime operation. The supervision handles together power balancing, optimisation, metadata and information from both users and the grid. The supervision has been validated by experimental tests. The feasibility of implementing optimisation in real-time operation is validated even with uncertainties. The supervision is able to manage efficiently the power flow while maintaining power balancing in any case. Nevertheless, optimization effect relies on prediction precision. This problem can be improved in future works by updating optimization in real-time.