4.8 Article

Demand response modeling: A comparison between tools

Journal

APPLIED ENERGY
Volume 146, Issue -, Pages 288-297

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2015.02.057

Keywords

Economic dispatch; HOMER; EnergyPLAN; Renewable energy; Demand response; Flexible load

Funding

  1. Portuguese Foundation of Science and Technology [SFRH/BD/62446/2009]
  2. Fundação para a Ciência e a Tecnologia [SFRH/BD/62446/2009] Funding Source: FCT

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The potential to reschedule part of the electricity demand in energy systems is seen as a significant opportunity to improve the efficiency of the systems, especially on remote and isolated systems. From the supply point of view, that flexibility might bring significant improvements to the generation dispatch, especially when in the presence of renewable resources; from the demand point of view, that flexibility could allow customers to benefit from reducing their energy bills. To study these types of implications, modeling tools have been introducing the possibility to include flexible loads on the optimization process, although some use very simplified methodologies to do it. This study compares how different modeling tools consider fixed and flexible loads in the dispatch optimization, analyzing their different strategies. Three different scenarios were simulated in HOMER, EnergyPLAN and an economic dispatch self-built model in Matlab, using as case study the Corvo Island, Portugal. The comparison results indicate that HOMER and EnergyPLAN still assume that flexible loads are a second priority load that are met in off-peak hours or in the presence of excess electricity from renewable sources, not taking directly into account the economic impact of such decision. On the other hand, the self-built model that is more flexible on the optimization approach is the more close to the actual operation and presents the best savings when using demand response strategies, albeit representing only a 0.3% decrease in the operation costs. We conclude that the modeling tools should evolve and refine their optimization strategies to capture the total benefits of using demand response to improve energy systems performance. (c) 2015 Elsevier Ltd. All rights reserved.

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