Probabilistic Guarantees for the N-1 Security of Systems with Wind Power Generation

  • Maria Vrakopoulou
  • Kostas Margellos
  • John Lygeros
  • Göran Andersson
Part of the Reliable and Sustainable Electric Power and Energy Systems Management book series (RSEPESM)


We propose a novel framework for designing an N-1 secure generation day-ahead dispatch for power systems with a high penetration of fluctuating power sources, e.g., wind or PV power. To achieve this, we integrate the security constraints in a DC optimal power flow optimization and formulate a stochastic program with chance constraints, which encode the probability of satisfying the transmission capacity constraints of the lines and the generation limits. To solve the resulting problem numerically, we transform the initial problem to a tractable one by using the so-called scenario approach, which is based on sampling the uncertain parameter while keeping the desired probabilistic guarantees. To generate wind power scenarios a Markov chain-based model is employed. To illustrate the effectiveness of the proposed technique we apply it to the IEEE 30-bus network, and compare it with the solution of a deterministic variant of the problem, where the operator determines a secure generation dispatch based only on the available wind power forecast. A Monte Carlo simulation study is conducted to collect statistical results regarding the performance of our method.


Wind Power Chance Constraint Wind Power Generator Scenario Approach Unit Commitment Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer India 2013

Authors and Affiliations

  • Maria Vrakopoulou
    • 1
  • Kostas Margellos
    • 2
  • John Lygeros
    • 2
  • Göran Andersson
    • 1
  1. 1.Power Systems Laboratory, Department of Electrical EngineeringETH ZürichZürichSwitzerland
  2. 2.Automatic Control Laboratory, Department of Electrical EngineeringETH ZürichZürichSwitzerland

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