EPJ Web Conf.
Volume 217, 2019International Workshop on Flexibility and Resiliency Problems of Electric Power Systems (FREPS 2019)
|Number of page(s)||9|
|Published online||15 October 2019|
- H. Shayeghi, E. Shahryari, M. Moradzadeh, P. Siano, A Survey on Microgrid Energy Management Considering Flexible Energy Sources, Energies, 12, 2156 (2019) [Google Scholar]
- V. Francois-Lavet et al., Deep Reinforcement Learning Solutions for Energy Microgrids Management, in European Workshop on Reinforcement Learning, (2016) [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- F. Katiraei, R. Iravani, N. Hatziargyriou, A. Dimeas, A. Microgrids management. IEEE Power Energy Mag., 6, 54-65, (2008) [CrossRef] [Google Scholar]
- 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]
- R.S. Sutton, A.G. Barto, Introduction to Reinforcement Learning (MA: MIT Press, Cambridge, 2018). [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- 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]
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