Imperial College London

Tomás Ochoa Abett De La Torre

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Assistant
 
 
 
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Contact

 

t.ochoa

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Summary

Tomás is a Research Assistant for the Leverhulme project 'System Services in 100% Renewable Grids' and a PhD candidate in the Department of Electrical and Electronic Engineering at Imperial College London. His research aims to establish a novel operation paradigm for power systems dominated by Inverter-Based Resources (IBRs).

In 2022, Tomás completed his MSc in Electrical Engineering with a minor in Power Systems at Universidad Técnica Federico Santa María (UTFSM) in Valparaíso, Chile. His master's research topic, titled 'Multi-agent Deep Reinforcement Learning for Efficient Multi-Timescale Bidding of a Hybrid Power Plant in Day-Ahead and Real-Time Markets', resulted in a publication in the Applied Energy journal and a conference article presented at the IEEE–PES General Meeting.

Prior to joining Imperial, Tomás primarily worked as a Consultant Engineer at the Advanced Center for Electrical and Electronic Engineering (AC3E) and the Department of Electrical Engineering at UTFSM. He assisted in the developement of reports for Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), Electricidade de Portugal, and the Chilean Ministry of Energy, particularly in the context of the elaboration of the national accelerated decarbonisation plan.

Publications

Journals

Ochoa T, Gil E, Angulo A, et al., 2022, Multi-agent deep reinforcement learning for efficient multi-timescale bidding of a hybrid power plant in day-ahead and real-time markets, Applied Energy, Vol:317, Pages:119067-119067

Gil E, Morales Y, Ochoa T, 2021, Addressing the effects of climate change on modeling future hydroelectric energy production in Chile, Energies, Vol:14, Pages:241-241

Conference

Ochoa T, Serpell C, Gil E, et al., 2023, A Four Parameter Distribution Family for Probabilistic Load Forecasting and Scenario Generation with Mixture Density Networks, Pages:1-5

Ochoa T, Gil E, Angulo A, 2022, Efficient Bidding of a PV Power Plant with Energy Storage Participating in Day-Ahead and Real-Time Markets Using Artificial Neural Networks, Pages:1-5

More Publications