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Optimising operational cost of a smart energy hub, the reinforcement learning approach

Rayati, M ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1080/17445760.2014.974600
  3. Publisher: Taylor and Francis Ltd , 2015
  4. Abstract:
  5. The concept of smart grid (SG) has been introduced to improve the operation of the power systems. In modern structures of power systems, different reasons prompt researchers to suggest integrated analysis of multi-carrier energy systems. Considering synergy effects of the couplings between different energy carriers and utilising intelligent technologies for monitoring and controlling of energy flow may change energy system management in the future. In this paper, we propose a new solution which is entitled smart energy hub (SEH) that models a multi-carrier energy system in a SG. SEH solutions allow homeowners to manage their energy consumption to reduce their electricity and gas bill. We present this concept for a residential customer by an energy management system which uses reinforcement learning algorithm and Monte Carlo estimation method for finding a near optimal solution. The simulation results show that by using this concept and then applying the algorithm for a residential customer, running costs are reduced up to 40% while keeping the household owner's desired comfort levels
  6. Keywords:
  7. Smart energy hub ; Energy management systems ; Energy utilization ; Housing ; Monte Carlo methods ; Reinforcement learning ; Energy system management ; Monitoring and controlling ; Monte Carlo estimation method ; Near-optimal solutions ; Optimisations ; Reinforcement learning approach ; Smart energies ; Smart grid ; Smart power grids
  8. Source: International Journal of Parallel, Emergent and Distributed Systems ; Volume 30, Issue 4 , Oct , 2015 , Pages 325-341 ; 17445760 (ISSN)
  9. URL: http://www.tandfonline.com/doi/full/10.1080/17445760.2014.974600