References
- R. Aggarwal,Y. Song: Artificial Neural Networks in Power Systems, Part I: General introduction to neural computing, Power Engineering Journal, Volume: 11, Issue: 3, June 1997 , Page(s): 129 – 134.
- R. Aggarwal, Y. Song: Artificial Neural Networks in Power Systems, Part II: Types of artificial neural networks Power Engineering Journal Volume 12, Issue 1, February 1998, p. 41 – 47.
- S. Khaitan: A Survey Of Techniques for using Neural Networks in Power Systems, https://hal.archives-ouvertes.fr/hal-01631454, 2017.
- Sutton, Barto: Reinforcement learning: an introduction, Second ed. Cambridge, MA, 2018.
- A. Bernadić, G. Kujundžić, I. Primorac: „Primjena algoritama podržanog učenja u upravljanju elektroenergetskog sustava “, 3. Savjetovanje BH CIRED, Mostar, 2022.
- A. Bernadić: „Deep and Reinforcement learning, and Computer Vision Methods in power systems – practical examples in Python ecosystem “, Znanstveno-stručna konferencija: Umjetna inteligencija u BiH/istraživanje, primjena i perspektive razvoja Konferencija / Scientific conference: AI in Bosnia Herzegovina, Intera technological park, Mostar, April 2022., Zbornik radova ISBN 978-9958-11-165-5, Ministarstvo znanosti FBiH.
- S. Duque, J. Giraldo, P. Vergara, P. Nguyen, A. van der Molen, H. Slootweg: „Community energy storage operation via reinforcement learning with eligibility traces “, in Electric Power Systems, Research, Volume 212, 2022, ISSN 0378-7796.
- Y. Liu et al.: „A Reinforcement Learning-Based Energy Management System for a Hybrid Power System with Renewable Energy Sources “, in International Conference on Power Electronics, Control and Automation (ICPECA), New Delhi, India, 2019, pp. 1-5, doi: 10.1109/ICPECA47973.2019.8975505.
- Zang, H.; Kim, J. „Reinforcement Learning Based Peer-to-Peer Energy Trade Management Using Community Energy Storage in Local Energy Market“, Energies 2021, 14, 4131. https://doi.org/10.3390/en14144131
- S. Kim, H. Lim: „Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings“, in Energies 2018, 11, 2010. https://doi.org/10.3390/en11082010
- K. Mason, S. Grijalva: „A Review of Reinforcement Learning for Autonomous Building Energy Management”, 2019, doi: https://arxiv.org/abs/1903.05196
- N. Taha, T. Pekka: „Deep RL for Energy Management in a Microgrid with Flexible Demand “, 2020, doi: 10.20944/preprints202010.0156.v1.
- M. Li, H. Zhang, T. Ji and Q. H. Wu: “Fault Identification in Power Network Based on Deep Reinforcement Learning,” in CSEE Journal of Power and Energy Systems, vol. 8, no. 3, pp. 721-731, May 2022, doi: 10.17775/CSEEJPES.2020.04520.
- M. Ibrahim, A. Alsheikh, R. Elhafiz: „Resiliency Assessment of Power Systems Using Deep Reinforcement Learning “, Volume 2022, Article ID 2017366, https://doi.org/10.1155/2022/2017366
- M. Glavić, (Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives, Annual Reviews in Control, Volume 48, 2019, Pages 22-35, https://doi.org/10.1016/j.arcontrol.2019.09.008.
- Y. Zhu: „Power Grid Cascading Failure Mitigation by Reinforcement Learning“, 2021, https://arxiv.org/abs/2108.10424
- Ungureanu, S.; Topa, V.; Cziker, A.: „Deep Learning for Short-Term Load Forecasting “, Industrial Consumer Case Study, Appl. Sci. 2021, 11, 10126.
- Yujie Gao et al: „Reinforcement Learning Based Short-Term Load Forecasting with Dynamic Features Selection “, 2021.
- Daniel Carlos do Vale Ramos: „Reinforcement Learning of a Multi-Agent System for the Forecasting of Electricity Consumption, Dissertation/project report/internship report, University of Porto 2020/2021., available on https://repositorio-berto.up.pt/bitstream/10216/138254/2/519034.pdf, last accessed 16/03/2023.
- Lehna, Hoppmann, Heinrich, Scholz: „A Reinforcement Learning Approach for the Continuous Electricity Market of Germany: Trading from the Perspective of a Wind Park Operator “, Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), 2021.
- D. Perera, P. Kamalaruban: „Applications of reinforcement learning in energy systems “, Renewable and Sustainable Energy Reviews 137, 2021.
- Z. Yu, G. Ruan, X. Wang, G. Zhang, Y. He, H. Zhong: „Evaluation of Look-ahead Economic Dispatch Using Reinforcement Learning“, 2022, doi: https://arxiv.org/pdf/2209.10207.pdf
- A. Ajagekar, F. You: „Scheduling of Electrical Power Systems under Uncertainty using Deep Reinforcement Learning“, Computer Aided Chemical Engineering,Elsevier, Volume 49, 2022, Pages 463- 468,ISBN 9780323851596
- V. Solberg: “Reinforcement learning for grid control in an electric distribution system”, Master thesis, NMBU University, Norway, 2019.
- S. Ravichandiran: Deep Reinforcement Learning with Python, Second Edition, Packt Publishing, 2020., ISBN 9781839210686.
- S. Chowdhury, S. P. Chowdhury, and P. Crossley: Microgrids and active distribution networks, Institution of Engineering and Technology, 2009.
- Pandapower, power system simulation tool (2022), Available: http://www.pandapower.org/,
- L. Thurner; A. Scheidler; F. Schäfer: “Pandapower—An Open-Source Python Tool for Convenient Modeling, Analysis, and Optimization of Electric Power Systems”, IEEE Transactions on Power Systems, Volume: 33, Issue: 6, Nov. 2018.
- Anaconda Python distribution (2022), Available: www.anaconda.com
- Gym reinforcement learning library, (2022), Available: https://www.gymlibrary.dev/
- Stable baselines Python RL library (2022), Available: https://stable-baselines.readthedocs.io/en/master/
- K.Eljand: „Training an Energy Decision Agent With Reinforcement Learning“,2022., Available: https://towardsdatascience.com/training-an-energy-decision-agent-with-reinforcement-learning-a7567b61d0aa, last accessed 15.4.2023.
- EU Energy prices data and visualisation tool, (2023), Available: https://ec.europa.eu, last accessed 11.4.2023.
- Geramifar, H., Shahabi, M. and Barforoshi, T. „Coordination of energy storage systems and DR resources for optimal scheduling of microgrids under uncertainties “, IET Renewable Power Generation, (2017), 11: 378-388. https://doi.org/10.1049/iet-rpg.2016.0094