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Y. Fang, D. Zhao, M. Ke, X. Zhao, C. Herbert and K. Wong, “Quantum-Inspired Particle Swarm Optimization for Power System Operations Considering Wind Power Uncertainty and Carbon Tax in Australia,” IEEE Transactions on Industrial Informatics, Vol. 8, No. 4, 2012, pp. 880-888. doi:10.1109/TII.2012.2210431

has been cited by the following article:

• TITLE: A Bivariate Chance Constraint of Wind Sources for Multi-Objective Dispatching

  AUTHORS: Mostafa Elshahed, Hussein Zeineldin, Magdy Elmarsfawy

  KEYWORDS: Correlated Weibull Distribution; Economic Emission Dispatch; Stochastic Programming; Wind Power

  JOURNAL NAME: Smart Grid and Renewable Energy, Vol.4 No.4, July 4, 2013

  ABSTRACT: The economic emission dispatch (EED) problem minimizes two competing objective functions, fuel cost and emission, while satisfying several equality and inequality constraints. Since the availability of wind power (WP) is highly dependent on the weather conditions, the inclusion of a significant amount of WP into EED will result in additional constraints to accommodate the intermittent nature of the output. In this paper, a new correlated bivariate Weibull probability distribution model is proposed to analytically remove the assumption that the total WP is characterized by a single random variable. This probability distribution is used as chance constraint. The inclusion of the probability distribution of stochastic WP in the EED problem is defined as the here-and-now strategy. Non-dominated sorting genetic algorithm built in MATLAB is used to handle the EED problem as a multi-objective optimization problem. A 69-bus ten-unit test system with non-smooth cost function is used to test the effectiveness of the proposed model.