Open Access
E3S Web Conf.
Volume 239, 2021
International Conference on Renewable Energy (ICREN 2020)
Article Number 00010
Number of page(s) 14
Published online 10 February 2021
  1. M. Ampatzis, P. H. Nguyen, and W. Kling, “Local electricity market design for the coordination of distributed energy resources at district level, ” IEEE PES Innov. Smart Grid Technol. Conf. Eur., Vol. 2015-Janua, no. January, pp. 1–6, (2015) [Google Scholar]
  2. I. Ilieva, B. Bremdal, S. Ø. Ottesen, J. Rajasekharan, and P. Olivella-rosell, “Design characteristics of a smart grid dominated local market, ” no. 646476, pp. 2–5, (2017) [Google Scholar]
  3. I. S. Bayram, M. Z. Shakir, M. Abdallah, and K. Qaraqe, “A survey on energy trading in smart grid, ” 2014 IEEE Glob. Conf. Signal Inf. Process. Glob. 2014, pp. 258–262, (2014) [Google Scholar]
  4. E. Mengelkamp, P. Staudt, J. Garttner, and C. Weinhardt, “Trading on local energy markets: A comparison of market designs and bidding strategies, ” Int. Conf. Eur. Energy Mark. EEM, (2017) [Google Scholar]
  5. L. Zhang, Z. Li, and C. Wu, “Randomized auction design for electricity markets between grids and microgrids, ” ACM SIGMETRICS Perform. Eval. Rev., Vol. 42, no. 1, pp. 99–110, (2014) [Google Scholar]
  6. P. Olivella-Rosell et al., “Local flexibility market design for aggregators providing multiple flexibility services at distribution network level, ” Energies, Vol. 11, no. 4, pp. 1–19, (2018) [Google Scholar]
  7. S. Kahrobaee, R. A. Rajabzadeh, L. K. Soh, and S. Asgarpoor, “Multiagent study of smart grid customers with neighborhood electricity trading, ” Electr. Power Syst. Res., Vol. 111, pp. 123–132, (2014) [Google Scholar]
  8. A. J. D. Rathnayaka, V. M. Potdar, T. Dillon, O. Hussain, and S. Kuruppu, “GoalOriented Prosumer Community Groups for the Smart Grid, ” IEEE Technol. Soc. Mag., Vol. 33, no. 1, pp. 41–48, (2014) [Google Scholar]
  9. E. Mengelkamp, S. Bose, E. Kremers, J. Eberbach, B. Hoffmann, and C. Weinhardt, “Increasing the efficiency of local energy markets through residential demand response, ” Energy Informatics, Vol. 1, no. 1, pp. 1–18, (2018) [Google Scholar]
  10. C. Rosen and R. Madlener, “An auction design for local reserve energy markets, ” Decis. Support Syst., Vol. 56, no. 1, pp. 168–179, (2013) [Google Scholar]
  11. J. Horta, D. Kofman, D. Menga, and A. Silva, “Novel market approach for locally balancing renewable energy production and flexible demand, ” 2017 IEEE Int. Conf. Smart Grid Commun. SmartGridComm 2017, Vol. 2018-Janua, pp. 533–539, (2018) [Google Scholar]
  12. C. Zhang, J. Wu, Y. Zhou, M. Cheng, and C. Long, “Peer-to-Peer energy trading in a Microgrid, ” Appl. Energy, Vol. 220, no. February, pp. 1–12, (2018) [Google Scholar]
  13. J. Gärttner, E. Mengelkamp, and C. Weinhardt, “Decentralizing Energy Systems Through Local Energy Markets: The LAMP-Project, ” Multikonferenz Wirtschaftsinformatik (MKWI), pp. 924–930, (2018) [Google Scholar]
  14. T. Logenthiran, D. Srinivasan, A. M. Khambadkone, and H. N. Aung, “Multiagent system for real-time operation of a microgrid in real-time digital simulator, ” IEEE Trans. Smart Grid, Vol. 3, no. 2, pp. 925–933, (2012) [Google Scholar]
  15. H. T. Nguyen and L. B. Le, “Optimal Energy Management for Cooperative Microgrids With Renewable Energy Resources, ” 2014 IEEE Int. Conf. Smart Grid Commun. SmartGridComm 2014, pp. 133–138, (2015) [Google Scholar]
  16. H.S.V.S. Kumar Nunna; Doolla Suryanarayana, “Energy Management in Microgrids Using Demand Response and Distributed Storage A Multiagent Approach, ” IEEE Trans. power Deliv., pp. 1–9, (2014) [Google Scholar]
  17. G. Santos, T. Pinto, Z. Vale, H. Morais, and I. Praca, “Balancing market integration in MASCEM electricity market simulator, ” IEEE Power Energy Soc. Gen. Meet., pp. 1–8, (2012) [Google Scholar]
  18. I. Praça, et al., “MASCEM: A Multi-Agent System that Simulates Competitive Electricity Markets”. IEEE Intelligent Systems, Vol. 18, no. 6, pp. 54-60, Special Issue on Agents and Markets, (2003) [Google Scholar]
  19. G. Santos, T. Pinto, I. Praça, and Z. Vale, “MASCEM: Optimizing the performance of a multi-agent system” Energy, Vol. 111, pp. 513–524, (2016) [Google Scholar]
  20. T. Pinto and Z. Vale, “AiD-EM: Adaptive Decision Support for Electricity Markets Negotiations, ” proceeding 28th Int. Jt. Conf. Artif. Intell. (IJCAI 2019), (2019) [Google Scholar]
  21. T. Pinto, Z. Vale, T. M. Sousa, I. Praça, G. Santos, “Adaptive Learning in Agents Behaviour: A Framework for Electricity Markets Simulation, ” Integr. Comput. Eng., (2014) [Google Scholar]
  22. P. Oliveira, T. Pinto, H. Morais, Z. Vale, and S. Member, “MASGriP – A Multi-Agent Smart Grid Simulation Platform, ” IEEE Power Energy Soc. Gen. Meet., pp. 1–8, (2012) [Google Scholar]
  23. J. Soares, C. Lobo, Z. Vale, and P. B. De Moura Oliveira, “Realistic traffic scenarios using a census methodology: Vila real case study, ” IEEE Power Energy Soc. Gen. Meet., Vol. 2014-Octob, no. October, (2014) [Google Scholar]
  24. EUROPEAN COMMISSION, “EU Energy, Transport And GHG Emissions Trends to 2050, ” (2013) [Google Scholar]
  25. “REN SIMEE Preços Mercado Spot Portugal e Espanha.” [Online]. Available: [Google Scholar]

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