A Multi-Stage Stochastic Risk Assessment With Markovian Representation of Renewable Power

Jose Lara, Oscar Dowson, Kate Doubleday, Bri-Mathias Hodge, Duncan Callaway

Research output: Contribution to journalArticlepeer-review

5 Scopus Citations


Probabilistic forecasts provide a distribution of possible outputs and so can capture the uncertainty and variability of Variable Renewable Energy (VRE). However, taking advantage of uncertainty information has practical challenges that make it difficult to integrate probabilistic forecasting into control room decision-making. This paper proposes a novel use-case for probabilistic forecasts by incorporating them into the hour-ahead operations for situational awareness via a risk-averse multi-stage stochastic program. We employ a Markovian representation of the probabilistic forecasts that enables the formulation of the multi-stage problem and avoids a scenario generation phase. We test the model on a realistically sized system to assess risk and showcase the capability of using probabilistic renewable forecast as input to produce probabilistic output forecasts of future system states. The results show that the model can capture time consistency in the reserves and Area Control Error (ACE) forecast. The solution times are adequate for risk profiling in hour-ahead timescales.

Original languageAmerican English
Pages (from-to)414-426
Number of pages13
JournalIEEE Transactions on Sustainable Energy
Issue number1
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

NREL Publication Number

  • NREL/JA-6A40-82849


  • forecasting
  • power generation dispatch
  • power generation dispatcholar power
  • risk
  • Solar power


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