E3S Web Conf.
Volume 239, 2021International Conference on Renewable Energy (ICREN 2020)
|Number of page(s)||14|
|Published online||10 February 2021|
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- EUROPEAN COMMISSION, “EU Energy, Transport And GHG Emissions Trends to 2050, ” (2013) [Google Scholar]
- “REN SIMEE Preços Mercado Spot Portugal e Espanha.” [Online]. Available:http://www.mercado.ren.pt/PT/Electr/InfoMercado/InfOp/MercOmel/Paginas/Precos.aspx. [Google Scholar]
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