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Multiagent genetic algorithm: An online probabilistic view on economic dispatch of energy hubs constrained by wind availability

Moeini-Aghtaie, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/TSTE.2013.2271517
  3. Abstract:
  4. Multiple energy carriers (MECs) have been broadly engrossing power system planners and operators toward a modern standpoint in power system studies. Energy hub, though playing an undeniable role as the intermediate in implementing the MECs, still needs to be put under examination in both modeling and operating concerns. Since wind power continues to be one of the fastest-growing energy resources worldwide, its intrinsic challenges should be also treated as an element of crucial role in the vision of future energy networks. In response, this paper aims to concentrate on the online economic dispatch (ED) of MECs for which it provides a probabilistic ED optimization model. The presented model is treated via a robust optimization technique, i.e., multiagent genetic algorithm (MAGA), whose outstanding feature is to find well the global optima of the ED problem. ED once constrained by wind power availability, in the cases of wind power as one of the input energy carriers of the hub, faces an inevitable uncertainty that is also probabilistically overcome in the proposed model. Efficiently approached via MAGA, the presented scheme is applied to test systems equipped with energy hubs and as expected, introduces its applicability and robustness in the ED problems
  5. Keywords:
  6. Probabilistic logics ; Energy resources ; Multiagent genetic algorithm (MAGA) ; Multiple energy carriers (MECs) ; Probabilistic modeling ; Wind power ; Energy hub ; Multi agent systems ; Economic dispatch (ED) ; Scheduling ; Energy carriers ; Genetic algorithms
  7. Source: IEEE Transactions on Sustainable Energy ; Vol. 5, issue. 2 , 2014 , p. 699-708 ; ISSN: 19493029
  8. URL: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6571283&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F5165391%2F5433168%2F06571283.pdf%3Farnumber%3D6571283