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Optimising a Microgrid System by Deep Reinforcement Learning Techniques

Author

Listed:
  • David Domínguez-Barbero

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Javier García-González

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Miguel A. Sanz-Bobi

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Eugenio F. Sánchez-Úbeda

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

Abstract

The deployment of microgrids could be fostered by control systems that do not require very complex modelling, calibration, prediction and/or optimisation processes. This paper explores the application of Reinforcement Learning (RL) techniques for the operation of a microgrid. The implemented Deep Q-Network (DQN) can learn an optimal policy for the operation of the elements of an isolated microgrid, based on the interaction agent-environment when particular operation actions are taken in the microgrid components. In order to facilitate the scaling-up of this solution, the algorithm relies exclusively on historical data from past events, and therefore it does not require forecasts of the demand or the renewable generation. The objective is to minimise the cost of operating the microgrid, including the penalty of non-served power. This paper analyses the effect of considering different definitions for the state of the system by expanding the set of variables that define it. The obtained results are very satisfactory as it can be concluded by their comparison with the perfect-information optimal operation computed with a traditional optimisation model, and with a Naive model.

Suggested Citation

  • David Domínguez-Barbero & Javier García-González & Miguel A. Sanz-Bobi & Eugenio F. Sánchez-Úbeda, 2020. "Optimising a Microgrid System by Deep Reinforcement Learning Techniques," Energies, MDPI, vol. 13(11), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2830-:d:366492
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    2. Brida V. Mbuwir & Frederik Ruelens & Fred Spiessens & Geert Deconinck, 2017. "Battery Energy Management in a Microgrid Using Batch Reinforcement Learning," Energies, MDPI, vol. 10(11), pages 1-19, November.
    3. Amjad Ali & Wuhua Li & Rashid Hussain & Xiangning He & Barry W. Williams & Abdul Hameed Memon, 2017. "Overview of Current Microgrid Policies, Incentives and Barriers in the European Union, United States and China," Sustainability, MDPI, vol. 9(7), pages 1-28, June.
    4. Ying Ji & Jianhui Wang & Jiacan Xu & Xiaoke Fang & Huaguang Zhang, 2019. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning," Energies, MDPI, vol. 12(12), pages 1-21, June.
    5. Rae-Kyun Kim & Mark B. Glick & Keith R. Olson & Yun-Su Kim, 2020. "MILP-PSO Combined Optimization Algorithm for an Islanded Microgrid Scheduling with Detailed Battery ESS Efficiency Model and Policy Considerations," Energies, MDPI, vol. 13(8), pages 1-17, April.
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    Cited by:

    1. Domínguez-Barbero, David & García-González, Javier & Sanz-Bobi, Miguel Á. & García-Cerrada, Aurelio, 2024. "Energy management of a microgrid considering nonlinear losses in batteries through Deep Reinforcement Learning," Applied Energy, Elsevier, vol. 368(C).
    2. Tovar Rosas, Mario A. & Pérez, Miguel Robles & Martínez Pérez, E. Rafael, 2022. "Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico," Renewable Energy, Elsevier, vol. 188(C), pages 1141-1165.
    3. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    4. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).

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