Browsing by Author "Fan, Xiaozhou"

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    Early warning mechanism for power system large cascading failures
    (IEEE, 2012) Ren, Hui; Fan, Xiaozhou; Watts Casimis, David; Lv, Xingchen
    As the interconnection of power grids, failure of single component could propagate out, and because of the scale of the power system, it is almost impossible to analysis all failure patterns. Traditional methods for online cascading analysis could face almost unsolvable obstacles on computational burden and accuracy. This paper discusses the mechanisms of early warning of cascading blackouts. Researches have shown, from the complex system point of view, the existence of early-warning signals to indicate for a wide class of systems whether a critical threshold is approaching. Instead of focusing on identifying the possible failure patterns that could lead to cascading blackouts, from the complex system point of view, early warning of cascading blackouts could be realized by identifying how far the system is to the critical state by simulation using fast computational techniques and a group of system indices, providing helpful aid for the operator.
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    Flexible transmission planning considering growing uncertainties from Renewable energy integration
    (IEEE, 2012) Ren, Hui; Fan, Xiaozhou; Watts Casimis, David; Lv, Xingchen
    Power systems are set to undergo dramatic changes driven by several factors ranging from climate change to technological developments. It is expected that networks will become more flexible to deal with increasing uncertainties, in those coming from future generation technologies and their locations as well as those from system operation practices. These developments will require fundamental changes in the way power systems are planned. This paper introduces a method of flexible transmission network planning with the traditional corrective control action assumed in the course of network planning. A two-stage solution algorithm is proposed combing Genetic Algorithm and Monte Carlo simulation. Candidate planning schemes are decided by Genetic Algorithm. Monte Carlo simulation and sensitivity method are used to find the most vulnerable part of the network, and then decides the amended control devices needed to be installed to achieve the optimum objective function and satisfy operational constraints (for each tested planning scheme). Discrete constraints associated with voltage control devices are included to make the simulation more realistic. The most flexible planning scheme is then defined as the one which needs the least investment on extra control devices. The proposed approach is implemented on an 18- bus test system and its feasibility is demonstrated.