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Stochastic reactive power market with volatility of wind power considering voltage security

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  • Kargarian, A.
  • Raoofat, M.

Abstract

While wind power generation is growing rapidly around the globe; its stochastic nature affects the system operation in many different aspects. In this paper, the impact of wind power volatility on the reactive power market is taken into account. The paper presents a novel stochastic method for optimal reactive power market clearing considering voltage security and volatile nature of the wind. The proposed optimization algorithm uses a multiobjective nonlinear programming technique to minimize market payment and simultaneously maximize voltage security margin. Considering a set of probable wind speeds, in the first stage, the proposed algorithm seeks to minimize expected system payment which is summation of reactive power payment and transmission loss cost. The object of the second stage is maximization of expected voltage security margin to increase the system loadability and security. Finally, in the last stage, a multiobjective function is presented to schedule the stochastic reactive power market using results of two previous stages. The proposed algorithm is applied to IEEE 14-bus test system. As a benchmark, Monte Carlo Simulation method is utilized to simulate the actual market of given period of time to evaluate results of the proposed algorithm, and satisfactory results are achieved.

Suggested Citation

  • Kargarian, A. & Raoofat, M., 2011. "Stochastic reactive power market with volatility of wind power considering voltage security," Energy, Elsevier, vol. 36(5), pages 2565-2571.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:5:p:2565-2571
    DOI: 10.1016/j.energy.2011.01.051
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Dylan F. Jones & Graham Wall, 2016. "An extended goal programming model for site selection in the offshore wind farm sector," Annals of Operations Research, Springer, vol. 245(1), pages 121-135, October.
    2. Amin Kargarian & Fahime Ebrahimi & Mehdi Sarikhani, 2011. "An Economic Based Approach for Optimal and Secure Reactive Power Provision in Deregulated Environments," Modern Applied Science, Canadian Center of Science and Education, vol. 5(3), pages 111-111, June.
    3. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    4. Khorramdel, Benyamin & Raoofat, Mahdi, 2012. "Optimal stochastic reactive power scheduling in a microgrid considering voltage droop scheme of DGs and uncertainty of wind farms," Energy, Elsevier, vol. 45(1), pages 994-1006.
    5. Aouss Gabash & Pu Li, 2016. "On Variable Reverse Power Flow-Part I: Active-Reactive Optimal Power Flow with Reactive Power of Wind Stations," Energies, MDPI, vol. 9(3), pages 1-12, February.
    6. Saraswat, Amit & Saini, Ashish & Saxena, Ajay Kumar, 2013. "A novel multi-zone reactive power market settlement model: A pareto-optimization approach," Energy, Elsevier, vol. 51(C), pages 85-100.

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