A robust optimization approach to energy and reserve dispatch in electricity markets

Marco Zugno, Antonio J. Conejo

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

To a large extent, electricity markets worldwide still rely on deterministic procedures for clearing energy and reserve auctions. However, increasing shares of the production mix consist of renewable sources whose nature is stochastic and non-dispatchable, as their output is uncertain and cannot be controlled by the operators of the production units. Stochastic programming models allow the joint determination of the day-ahead energy and reserve dispatch accounting for the uncertainty in the output from these sources. However, the size of these models gets quickly out of hand as a large number of scenarios are needed to properly represent the uncertainty. In this work, we take an alternative approach and cast the problem as an adaptive robust optimization problem. The resulting day-ahead energy and reserve schedules yield the minimum system cost, accounting for the cost of the redispatch decisions at the balancing (real-time) stage, in the worst-case realization of the stochastic production within a specified uncertainty set. We propose a novel reformulation of the problem that allows considering general polyhedral uncertainty sets. In a case-study, we show that, in comparison to a risk-averse stochastic programming model, the robust optimization approach progressively trades off optimality in expectation with improved performance in terms of risk. These differences, however, gradually taper off as the level of risk-aversion increases for the stochastic programming approach. Computational studies show that the robust optimization model scales well with the size of the power system, which is promising in view of real-world applications of this approach.
Original languageEnglish
JournalEuropean Journal of Operational Research
Volume247
Issue number2
Pages (from-to)659-671
ISSN0377-2217
DOIs
Publication statusPublished - 2015

Keywords

  • Decomposition
  • Electricity market
  • OR in energy
  • Renewable energy
  • Robust optimization
  • Commerce
  • Electric load dispatching
  • Optimization
  • Power markets
  • Stochastic programming
  • Stochastic systems
  • Computational studies
  • Renewable energies
  • Robust optimization models
  • Stochastic production
  • Stochastic programming model
  • Stochastic models

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