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Win-win possibilities through capacity tariffs and battery storage in microgrids

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  • Milis, Kevin
  • Peremans, Herbert
  • Springael, Johan
  • Van Passel, Steven

Abstract

This paper investigates the impact of capacity tariff design on microgrids. While the possible benefits for utilities of capacity tariffs are well researched, comparatively little work has been done investigating the effects of capacity pricing on prosumers. Through simulating a grid connected microgrid and solving the day-ahead dispatch problem for a calendar year, we show that a well-designed capacity tariff will not only smooth out demand profiles, but could also lead to less erratic charge/discharge cycles in a real-time pricing scenario, lessening battery degradation. These results show that a properly designed capacity tariff has the potential to be beneficial for both the utilities as well as the battery-owning prosumer. Furthermore, we propose a new, heuristic approach to solve the day-ahead economic dispatch problem, which we prove to be effective and efficient. Additionally, we demonstrate that our novel approach does not impose mathematical restrictions such as continuous differentiability of the objective function. We show that the proposed capacity tariff achieves the stated aim of promoting battery storage uptake and that our novel method allows for compression and shorter run times.

Suggested Citation

  • Milis, Kevin & Peremans, Herbert & Springael, Johan & Van Passel, Steven, 2019. "Win-win possibilities through capacity tariffs and battery storage in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    • Handle: RePEc:eee:rensus:v:113:y:2019:i:c:33
    DOI: 10.1016/j.rser.2019.06.045
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    Cited by:

    1. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).

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