<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Reinforcement learning (RL) is area of Machine Learning (ML) and part of wide-range portfolio of the Artificial intelligence (AI) methods. Besides the explanations of the concepts and principles of RL, in the paper are presented practical RL models for control and optimizing operation of power system – controlling tap-changers for maintain voltage levels and model for techno-economical optimizing operation of energy storages of households in microgrid. Trained RL agent in the practical example synchronizes operation of tap-changers to maintain satisfactory voltage level for the consumers, even in the network with distributed generation. Energy storages are in wide use in households, especially in the combination with PV. In the second example, microgrid’s energy management system (EMS) RL agent after learning process act in the simulated environment with variable electrical energy prices, variable load profiles and efficiency of PV modules of households to maximize profit for the houseowners in the microgrid. Agent controls charging and discharging of energy storages and obtain maximal benefit in randomly determined conditions of microgrid operation and different tariff situations. Models are implemented in the Python programming environment Python with specialized power system simulation software (Pandapower) and RL libraries (RLib, OpenAI).</p>
</abstract>

Reinforcement learning (RL) is area of Machine Learning (ML) and part of wide-range portfolio of the Artificial intelligence (AI) methods. Besides the explanations of the concepts and principles of RL, in the paper are presented practical RL models for control and optimizing operation of power system – controlling tap-changers for maintain voltage levels and model for techno-economical optimizing operation of energy storages of households in microgrid. Trained RL agent in the practical example synchronizes operation of tap-changers to maintain satisfactory voltage level for the consumers, even in the network with distributed generation. Energy storages are in wide use in households, especially in the combination with PV. In the second example, microgrid’s energy management system (EMS) RL agent after learning process act in the simulated environment with variable electrical energy prices, variable load profiles and efficiency of PV modules of households to maximize profit for the houseowners in the microgrid. Agent controls charging and discharging of energy storages and obtain maximal benefit in randomly determined conditions of microgrid operation and different tariff situations. Models are implemented in the Python programming environment Python with specialized power system simulation software (Pandapower) and RL libraries (RLib, OpenAI).

Reinforcement Learning in Power System Control and Optimization

B&H Electrical Engineering

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Reinforcement Learning in Power System Control and Optimization"}

Reinforcement learning (RL) is area of Machine Learning (ML) and part of wide-range portfolio of the Artificial intelligence (AI) methods. Besides the...

Reinforcement Learning in Power System Control and Optimization

Article Category: Review scientific paper

Pubblicato online: 04 lug 2023

Pagine: 26 - 34

Ricevuto: 27 mar 2023

Accettato: 16 mag 2023

DOI: https://doi.org/10.2478/bhee-2023-0004

Parole chiave
Artificial Intelligence, Reinforcement learning, power system, microgrid, Python

© 2023 Alen Bernadić et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Reinforcement Learning in Power System Control and Optimization

Article Category: Review scientific paper

Pubblicato online: 04 lug 2023

Pagine: 26 - 34

Ricevuto: 27 mar 2023

Accettato: 16 mag 2023

DOI: https://doi.org/10.2478/bhee-2023-0004

Parole chiaveArtificial Intelligence, Reinforcement learning, power system, microgrid, Python

© 2023 Alen Bernadić et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Artificial Intelligence, Reinforcement learning, power system, microgrid, Python