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Distributionally robust chance constrained optimization for economic dispatch in renewable energy integrated systems

Author

Listed:
  • Xiaojiao Tong

    (Hunan First Normal University)

  • Hailin Sun

    (Nanjing University of Science and Technology)

  • Xiao Luo

    (Hunan Electric Power Research Institute)

  • Quanguo Zheng

    (Hunan Province Key Laboratory of Smart Grids Operation)

Abstract

Distributionally robust optimization (DRO) has become a popular research topic since it can solve stochastic programs with ambiguous distribution information. In this paper, as the background of economic dispatch (ED) in renewable integration systems, we present a new DRO-based ED optimization framework (DRED). The new DRED is addressed with a coupled format of distribution uncertainty for objective and chance constraints, which is different from most existing DRO frameworks. Some approximation strategies are adopted to handle the complicated DRED: the data-driven approach, the approximation of chance constraints by conditional value-at-risk, and the discrete scheme. The approximate reformulations are solvable nonconvex nonlinear programming problems. The approximation error analysis and convergence analysis are also established. Numerical results using an IEEE-30 buses system are presented to demonstrate the approach proposed in this paper.

Suggested Citation

  • Xiaojiao Tong & Hailin Sun & Xiao Luo & Quanguo Zheng, 2018. "Distributionally robust chance constrained optimization for economic dispatch in renewable energy integrated systems," Journal of Global Optimization, Springer, vol. 70(1), pages 131-158, January.
    • Handle: RePEc:spr:jglopt:v:70:y:2018:i:1:d:10.1007_s10898-017-0572-3
    DOI: 10.1007/s10898-017-0572-3
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    References listed on IDEAS

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