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Cited by 160

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Intelligent energy resource management considering vehicle-to-grid: A Simulated Annealing approach

Sousa, T., Morais, H., Vale, Z., Faria, P., Soares, J.·2012·2012 IEEE Power and Energy Society General Meeting

Abstract

Summary form only given. This paper proposes a Simulated Annealing (SA) approach to address energy resources management from the point of view of a Virtual Power Player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multi-period basis according to V2G users' profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer non-linear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

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Metadata

Authors
Sousa, T., Morais, H., Vale, Z., Faria, P., & Soares, J.
Venue
2012 IEEE Power and Energy Society General Meeting
Publisher
IEEE
Published
2012-07
Pages
1-1
OpenAlex
W2037422242
Cited by
160
References
1
Type
conference abstract
Language
en
Open access
closed

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CoChat. (2026). Citation check: Intelligent energy resource management considering vehicle-to-grid: A Simulated Annealing approach. https://app.cochat.ai/papers/10.1109/pesgm.2012.6344637
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Sousa, T., Morais, H., Vale, Z., Faria, P., & Soares, J. (2012). Intelligent energy resource management considering vehicle-to-grid: A Simulated Annealing approach. 2012 IEEE Power and Energy Society General Meeting. https://doi.org/10.1109/pesgm.2012.6344637

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.