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A multi-scale optimization framework for electricity market participation

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  • Dowling, Alexander W.
  • Kumar, Ranjeet
  • Zavala, Victor M.

Abstract

Power grids coordinate a diverse set of energy systems (generators, loads, storage devices) to ensure that supply and demands are matched at multiple timescales (from hours to milliseconds). Such coordination is achieved through hierarchical market transactions. This work presents an optimization framework to evaluate revenue opportunities provided by these multi-scale market hierarchies and to determine optimal participation strategies for individual participants. The proposed framework models day-ahead and real-time transactions of energy, ancillary services, and virtual bidding products provided by independent system operators (ISOs). We apply the framework to a combined heat and power system and a utility-scale battery to determine revenue potential from different market layers and products. Analysis using real price signals for 2015 from the California ISO reveals that 60–90% of the total revenue potential (obtained by participating in all markets) is provided by real-time markets alone (which operate at fast timescales). Our studies also indicate that providing ancillary services (in addition to day-ahead and real-time energy products) increases revenue potential by 40–100%, depending on the physical flexibility of the technology. The proposed framework can be used to identify which market layers and products offer the greatest economic potential for different energy technologies. Our results also highlight that existing techno-economic studies that focus exclusively on day-ahead energy markets (operating at slower timescales) can dramatically undervalue dynamic flexibility.

Suggested Citation

  • Dowling, Alexander W. & Kumar, Ranjeet & Zavala, Victor M., 2017. "A multi-scale optimization framework for electricity market participation," Applied Energy, Elsevier, vol. 190(C), pages 147-164.
    • Handle: RePEc:eee:appene:v:190:y:2017:i:c:p:147-164
    DOI: 10.1016/j.apenergy.2016.12.081
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    15. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2022. "Economic model predictive control of integrated energy systems: A multi-time-scale framework," Applied Energy, Elsevier, vol. 328(C).
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