Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5242
Título: Day-ahead Resource Scheduling Including Demand Response for Electric Vehicles
Autor: Soares, João
Sousa, Tiago
Morais, Hugo
Vale, Zita
Palavras-chave: Demand response
electric vehicle
Energy resource management
Particle swarm optimization
Data: Mar-2013
Editora: IEEE
Citação: Soares, J.; Morais, H.; Sousa, T.; Vale, Z.; Faria, P., "Day-Ahead Resource Scheduling Including Demand Response for Electric Vehicles," Smart Grid, IEEE Transactions on , vol.4, no.1, pp.596,605, March 2013 doi: 10.1109/TSG.2012.2235865
Relatório da Série N.º: IEEE Transactions on Smart Grid;Vol. 4, Issue 1
Resumo: The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs i n the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Othe r important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.
Peer review: yes
URI: http://hdl.handle.net/10400.22/5242
DOI: 10.1109/TSG.2012.2235865
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6410473&queryText%3DDay-ahead+Resource+Scheduling+Including+Demand+Response+for+Electric+Vehicles
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