It has been 32 years since the Brundtland Report was published. That was the first time that the term Sustainable Development (SD) was coined. In this context, renewable sources of energy play an important role on not to deplete our natural resources in order to meet our need without compromising future generations. Smart Grids are on the pathway to achieve the SD goals. This Thesis focuses on the integration of renewables, specifically Solar PV panels and inverters and its interactions with the distribution grid. Since the power injection caused by the PV inverter can alter the voltage range, Reinforcement Learning (RL) is applied as method for voltage regulation. This research aims to integrate all these elements in a Co-simulation Real-Time system. To achieve complexity and reality to this cosimulation frame, an external load is aggregated from an external source. Methodology and results are described, and conclusions and future work suggested.
reinforcement learning distribution feeder reactive power control simulation external aggregated load
Level of Degree
Electrical and Computer Engineering
First Committee Member (Chair)
Second Committee Member
Third Committee Member
Acosta Molina, Ivonne D.. "INTEGRATION OF MACHINE LEARNING FOR REACTIVE POWER CONTROL FOR A DISTRIBUTION FEEDER SIMULATION WITH EXTERNAL LOAD." (2020). https://digitalrepository.unm.edu/ece_etds/520