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Energy Hub optimal sizing in the smart grid; Machine learning approach

Sheikhi, A ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1109/ISGT.2015.7131796
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2015
  4. Abstract:
  5. The interests in 'Energy Hub' (EH) and 'Smart Grid' (SG) concepts have been increasing, in recent years. The synergy effect of the coupling between electricity and natural gas grids and utilizing intelligent technologies for communicating, may change energy management in the future. A new solution entitling 'Smart Energy Hub' (S. E. Hub) that models a multi-carrier energy system in a SG environment studied in this paper. Moreover, the optimal size of CHP, auxiliary boiler, absorption chiller, and also transformer unit as main elements of a S. E. Hub is determined. Authors proposed a comprehensive cost and benefit analysis to optimize these elements and apply Reinforcement Learning (RL) algorithm for solving the optimization problem. To confirm the proposed method, a residential customer has been investigated as an S. E. Hub in a dynamic electricity pricing market
  6. Keywords:
  7. Reinforcement Learning (RL) ; Smart Energy Hub (S. E. Hub) ; Artificial intelligence ; Cost benefit analysis ; Electric power transmission networks ; Energy management ; Energy management systems ; Learning systems ; Optimization ; Financial analysis ; Intelligent technology ; Machine learning approaches ; Optimal size ; Optimization problems ; Residential customers ; Smart energies ; Smart grid ; Smart power grids
  8. Source: 2015 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2015, 18 February 2015 through 20 February 2015 ; Feb , 2015 ; 9781479917853 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/7131796/?reload=true&arnumber=7131796&filter%3DAND(p_IS_Number:7131775)