A Distortion-Based Potential Game for Secondary Voltage Control in Micro-Grids

David Alejandro Martinez, Eduardo Mojica-Nava, Ameena Saad Al-Sumaiti, Sergio Rivera

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


In this work, we present a multi-agent learning model based on the maximum entropy (MAXEnt) and the rate distortion function to define, respectively, the environment of the agents and their understanding about it. The avoidance of redundant information under distortion conditions is used to define a distortion-based potential function that is minimized in order to find an equilibrium point in a potential game setting, in which the Lagrange multiplier beta , used as input in the Blahut-Arimoto algorithm, determines the rationality in the learning process. The model performance is evaluated in a secondary voltage controller in order to achieve reactive power sharing between distributed generators (DGs) in a micro-grid. Simulation results demonstrate a good response in terms of reactive power distribution when the load is increased in a DG without considerable affectations in the voltage stability.

Original languageBritish English
Article number9117110
Pages (from-to)110611-110622
Number of pages12
JournalIEEE Access
StatePublished - 2020


  • Maximum entropy
  • micro-grids
  • potential games
  • rate distortion function


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