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Reinforcement learning algorithms for optimizing real-time purchase and sale of energy produced by a renewable energy plant

Fabio Grillo

Reinforcement learning algorithms for optimizing real-time purchase and sale of energy produced by a renewable energy plant.

Rel. Giuseppe Bruno Averta, Tullio Re. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2023

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Abstract:

This thesis investigates the use of reinforcement learning algorithms for optimizing the purchase and sale of energy produced by a renewable energy plant in real-time. The study explores the effectiveness of different reinforcement learning algorithms in achieving optimal energy trading strategies, considering different environmental and economic factors. The goal is to build an environment as close to reality as possible and investigate the use of algorithms that can process and refine trading techniques. The study is expected to provide valuable insights into the potential of using intelligent algorithms to optimize trading in intraday electricity markets.

Relators: Giuseppe Bruno Averta, Tullio Re
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 57
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Aziende collaboratrici: eVISO spa
URI: http://webthesis.biblio.polito.it/id/eprint/27694
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