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Stochastic Optimization for Power System Configuration with Renewable Energy in Remote Areas

Ludwig Kuznia(lkuznia***at***mail.usf.edu)
Bo Zeng(bzeng***at***usf.edu)
Grisselle Centeno(gcenteno***at***usf.edu)
Zhixin Miao(zmiao***at***usf.edu)

Abstract: This paper presents the first stochastic mixed integer programming model for a comprehensive hybrid power system design, including renewable energy generation, storage device, transmission network, and thermal generators, in remote areas. Given the computational complexity of the model, we developed a Benders' decomposition algorithm with Pareto-optimal cuts. Computational results show significant improvement in our ability to solve this type of problem in comparison to a state-of-the-art professional solver. This model and the solution algorithm provide an analytical decision support tool for the hybrid power system design problem.

Keywords: stochastic mixed integer programming, power system design, renewable energy, Benders' decomposition

Category 1: Applications -- OR and Management Sciences

Citation:

Download: [PDF]

Entry Submitted: 02/17/2011
Entry Accepted: 02/17/2011
Entry Last Modified: 02/17/2011

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