Browsing by Author "Stauffer, Yves"

Now showing items 1-4 of 4

    • Adaptive Volt-Var Control Algorithm to Grid Strength and PV Inverter Characteristics 

      Cantero, Toni; Colet Subirachs, Alba; Canals Casals, Lluc; Corchero García, Cristina; Domínguez García, José Luis; Álvarez de Sotomayor, Amelia; Martin, Williams; Stauffer, Yves; Alet, Pìère-Jean (2021-04-16)
      Article
      Open Access
      The high-penetration of Distributed Energy Resources (DER) in low voltage distribution grids, mainly photovoltaics (PV), might lead to overvoltage in the point of common coupling, thus, limiting the entrance of renewable ...

    • How building and district algorithms enhance renewable energy integration in energy markets 

      Canals Casals, Lluc; Corchero García, Cristina; Ortiz, Joana; Salom, Jaume; Cardoner, David; Igualada González, Lucía; Carrillo, Rafael E.; Stauffer, Yves (2019)
      Conference report
      Open Access
      This study shows the results of the SABINA H2020 project, which analyzes the effect of two level optimization algorithms to increase the consumption of renewable power sources and reduce greenhouse gas emissions. First, ...

    • Management and activation of energy flexibility at building and market level: a residential case study 

      Carrillo, Rafael E.; Canals Casals, Lluc; Stauffer, Yves; Corchero García, Cristina; Salom, Jaume (2020-03-01)
      Article
      Open Access
      The electricity sector foresees a significant change in the way energy is generated and distributed in the coming years. With the increasing penetration of renewable energy sources, smart algorithms can determine the ...

    • State-space models for building control: how deep should you go? 

      Schubnel, Baptiste; Carrillo, Rafael E.; Taddeo, Paolo; Canals Casals, Lluc; Salom, Jaume; Stauffer, Yves (2020-09-14)
      Article
      Open Access
      Power consumption in buildings show nonlinear behaviours that linear models cannot capture, whereas recurrent neural networks (RNNs) can. This ability makes RNNs attractive alternatives for the model-predictive control ...