Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources

Nikita Tomin; Alexey Zhukov; Alexander Domyshev

doi:10.1051/epjconf/201921701016

All issues

Volume 217 (2019)

EPJ Web Conf., 217 (2019) 01016

References

Open Access

Issue		EPJ Web Conf. Volume 217, 2019 International Workshop on Flexibility and Resiliency Problems of Electric Power Systems (FREPS 2019)


Article Number		01016
Number of page(s)		9
DOI		https://doi.org/10.1051/epjconf/201921701016
Published online		15 October 2019

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R. Aboli, M. Ramezani, H. Falaghi, Joint optimization of day-ahead and uncertain near real-time operation of microgrids, Int. J. Electr. Power Energy Syst., 107, 34-46, (2019) [CrossRef] [Google Scholar]
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T. Logenthiran, D. Srinivasan, A.M. Khambadkone, Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system, Electr. Power Syst. Res., 81, 138-148, (2011) [CrossRef] [Google Scholar]
B. Li, R, Roche, D. Paire, A. Miraoui, A price decision approach for multiple multi-energy-supply microgrids considering demand response, Energy, 167, 117-135, (2019) [CrossRef] [Google Scholar]
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[1] H. Shayeghi, E. Shahryari, M. Moradzadeh, P. Siano, A Survey on Microgrid Energy Management Considering Flexible Energy Sources, Energies, 12, 2156 (2019) [Google Scholar]

[2] V. Francois-Lavet et al., Deep Reinforcement Learning Solutions for Energy Microgrids Management, in European Workshop on Reinforcement Learning, (2016) [Google Scholar]

[3] R. Aboli, M. Ramezani, H. Falaghi, Joint optimization of day-ahead and uncertain near real-time operation of microgrids, Int. J. Electr. Power Energy Syst., 107, 34-46, (2019) [CrossRef] [Google Scholar]

[4] M. Sedighizadeh, M. Esmaili, A. Jamshidi, M.-H. Ghaderi, Stochastic multi-objective economic-environmental energy and reserve scheduling of microgrids considering battery energy storage system, Int. J. Electr. Power Energy Syst., 2019, 106, 1-16, (2019) [CrossRef] [Google Scholar]

[5] C.-S. Karavas, G. Kyriakarakos, K.G. Arvanitis, G. Papadakis, A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids, Energy Convers. Manag. 103, 166-179, (2015) [Google Scholar]

[6] E.E. Sfikas, Y.A. Katsigiannis, P.S. Georgilakis, Simultaneous capacity optimization of distributed generation and storage in medium voltage microgrids. Int. J. Electr. Power Energy Syst., 2015, 67, 101-113, (2015) [CrossRef] [Google Scholar]

[7] T. Logenthiran, D. Srinivasan, A.M. Khambadkone, Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system, Electr. Power Syst. Res., 81, 138-148, (2011) [CrossRef] [Google Scholar]

[8] B. Li, R, Roche, D. Paire, A. Miraoui, A price decision approach for multiple multi-energy-supply microgrids considering demand response, Energy, 167, 117-135, (2019) [CrossRef] [Google Scholar]

[9] C. Dou, et al., Decentralised coordinated control of microgrid based on multi-agent system. IET Gener. Transm. Distrib., 9, 2474-2484, (2015) [CrossRef] [Google Scholar]

[10] F. Katiraei, R. Iravani, N. Hatziargyriou, A. Dimeas, A. Microgrids management. IEEE Power Energy Mag., 6, 54-65, (2008) [CrossRef] [Google Scholar]

[11] W.L. Theo, et al., Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods. Renew. Sustain. Energy Rev., 67, 531-573, (2017) [CrossRef] [Google Scholar]

[12] R.S. Sutton, A.G. Barto, Introduction to Reinforcement Learning (MA: MIT Press, Cambridge, 2018). [Google Scholar]

[13] V. Francois-Lavet, P. Henderson, R. Islam, M.G. Bellemare, J. Pineau, An Introduction to Deep Reinforcement Learning, Foundations and Trends in Machine Learning, 11(3-4), (2018) [CrossRef] [Google Scholar]

[14] V. Mnih, K. Kavukcuoglu, D. Silver, et al. Human-level control through deep reinforcement learning, Nature, 518(7540), 529-533, (2015) [CrossRef] [PubMed] [Google Scholar]

[15] Q. Gemine, V. François-Lavet, D. Ernst, R. Fonteneau. Towards the minimization of the levelized energy costs of microgrids using both long-term and short-term storage devices, Smart Grid: Networking, Data Management, and Business Models, 295-319, (2016). [Google Scholar]

[16] D. Sidorov, I. Muftahov, N. Tomin et al., A Dynamic Analysis of Energy Storage with Renewable and Diesel Generation Using Volterra Equations, IEEE Trans. on Industrial Informatics, 14(8), (2019) [Google Scholar]

[17] R. Dufo-Lopez, J.L. Bernal-Agustin, Multi-objective design of PV–wind–diesel–hydrogen–battery systems, Renewable Energy, 33(12), 2559-2572, (2008) [Google Scholar]