Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5252
Título: A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles
Autor: Sousa, Tiago
Vale, Zita
Carvalho, João
Pinto, Tiago
Morais, Hugo
Palavras-chave: Ant colony optimization
Energy resource management
Electric vehicle
Hybridization
Simulated annealing
Virtual power player
Data: 2014
Editora: Elsevier
Relatório da Série N.º: Energy;Vol. 67
Resumo: The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.
Peer review: yes
URI: http://hdl.handle.net/10400.22/5252
DOI: 10.1016/j.energy.2014.02.025
ISSN: 0360-5442
Versão do Editor: http://www.sciencedirect.com/science/article/pii/S0360544214001595
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