Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques

Kozlov, Alexander N.; Tomin, Nikita V.; Sidorov, Denis N.; Lora, Electo E. S.; Kurbatsky, Victor G.

doi:10.3390/en13102632

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Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques

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Energy Systems Institute of Siberian Branch of Russian Academy of Sciences, 664033 Irkutsk, Russia

Baikal School of BRICS, Irkutsk National Research Technical University, 664074 Irkutsk, Russia

Mechanical Engineering Institute, Federal University of Itajuba, Itajuba 37500-103, Brazil

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Energies 2020, 13(10), 2632; https://doi.org/10.3390/en13102632

Received: 21 April 2020 / Revised: 18 May 2020 / Accepted: 18 May 2020 / Published: 21 May 2020

(This article belongs to the Section A: Electrical Engineering)

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Abstract

The importance of efficient utilization of biomass as renewable energy in terms of global warming and resource shortages are well known and documented. Biomass gasification is a promising power technology especially for decentralized energy systems. Decisive progress has been made in the gasification technologies development during the last decade. This paper deals with the control and optimization problems for an isolated microgrid combining the renewable energy sources (solar energy and biomass gasification) with a diesel power plant. The control problem of an isolated microgrid is formulated as a Markov decision process and we studied how reinforcement learning can be employed to address this problem to minimize the total system cost. The most economic microgrid configuration was found, and it uses biomass gasification units with an internal combustion engine operating both in single-fuel mode (producer gas) and in dual-fuel mode (diesel fuel and producer gas). View Full-Text

Keywords: biomass; operations research; machine learning; microgrids; optimization; CO₂ reduction; mixed integer linear programming; reinforcement learning

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

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MDPI and ACS Style

Kozlov, A.N.; Tomin, N.V.; Sidorov, D.N.; Lora, E.E.S.; Kurbatsky, V.G. Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques. Energies 2020, 13, 2632. https://doi.org/10.3390/en13102632

AMA Style

Kozlov AN, Tomin NV, Sidorov DN, Lora EES, Kurbatsky VG. Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques. Energies. 2020; 13(10):2632. https://doi.org/10.3390/en13102632

Chicago/Turabian Style

Kozlov, Alexander N., Nikita V. Tomin, Denis N. Sidorov, Electo E.S. Lora, and Victor G. Kurbatsky. 2020. "Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques" Energies 13, no. 10: 2632. https://doi.org/10.3390/en13102632

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Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques

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