Citation
Jennifer Marley, Maria Vrakopoulou, Ian Hiskens. "An AC-QP Optimal Power Flow Algorithm Considering Wind Forecast Uncertainty". IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), 317-323, November, 2016.

Abstract
While renewable generation sources provide many economic and environmental benefits for the operation of power systems, their inherent stochastic nature introduces challenges from the perspective of reliability. Existing optimal power flow (OPF) methods must therefore be extended to consider forecast errors to mitigate in an economic manner the uncertainty that renewable generation introduces. This paper presents an AC-QP OPF solution algorithm that has been modified to include wind generation uncertainty. We solve the resulting stochastic optimization problem using a scenario based algorithm that is based on randomized methods that provide probabilistic guarantees of the solution. The proposed method produces an AC-feasible solution while satisfying reasonable reliability criteria. Test cases are included for the IEEE 14-bus network that has been augmented with 2 wind generators. The scalability, optimality and reliability achieved by the proposed method are then assessed.

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  Jennifer Marley, Maria Vrakopoulou, Ian Hiskens. <a
  href="http://www.cps-forces.org/pubs/231.html"
  >An AC-QP Optimal Power Flow Algorithm Considering Wind
  Forecast Uncertainty</a>, IEEE Innovative Smart Grid
  Technologies - Asia (ISGT-Asia), 317-323, November, 2016.

• Plain text

  Jennifer Marley, Maria Vrakopoulou, Ian Hiskens. "An
  AC-QP Optimal Power Flow Algorithm Considering Wind Forecast
  Uncertainty". IEEE Innovative Smart Grid Technologies -
  Asia (ISGT-Asia), 317-323, November, 2016.

• BibTeX

  @inproceedings{MarleyVrakopoulouHiskens16_ACQPOptimalPowerFlowAlgorithmConsideringWindForecast,
      author = {Jennifer Marley and Maria Vrakopoulou and Ian
                Hiskens},
      title = {An AC-QP Optimal Power Flow Algorithm Considering
                Wind Forecast Uncertainty},
      booktitle = {IEEE Innovative Smart Grid Technologies - Asia
                (ISGT-Asia)},
      pages = {317-323},
      month = {November},
      year = {2016},
      abstract = {While renewable generation sources provide many
                economic and environmental benefits for the
                operation of power systems, their inherent
                stochastic nature introduces challenges from the
                perspective of reliability. Existing optimal power
                flow (OPF) methods must therefore be extended to
                consider forecast errors to mitigate in an
                economic manner the uncertainty that renewable
                generation introduces. This paper presents an
                AC-QP OPF solution algorithm that has been
                modified to include wind generation uncertainty.
                We solve the resulting stochastic optimization
                problem using a scenario based algorithm that is
                based on randomized methods that provide
                probabilistic guarantees of the solution. The
                proposed method produces an AC-feasible solution
                while satisfying reasonable reliability criteria.
                Test cases are included for the IEEE 14-bus
                network that has been augmented with 2 wind
                generators. The scalability, optimality and
                reliability achieved by the proposed method are
                then assessed.},
      URL = {http://cps-forces.org/pubs/231.html}
  }
  

Posted by Ian Hiskens on 28 Feb 2017.
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