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

Xiaojiao Tong (), Hailin Sun (), Xiao Luo () and Quanguo Zheng ()
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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

Journal of Global Optimization, 2018, vol. 70, issue 1, No 7, 158 pages

Abstract: 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.

Keywords: Economic dispatch; Renewable energy integrated systems; Distributionally robust optimization; Chance constraint; CVaR approximation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10898-017-0572-3

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