Probabilistic Optimisation of Generation Scheduling Considering Wind Power Output and Stochastic Line Capacity
|dc.contributor.editor||Mochamad Ashari (General Chair)|
|dc.identifier.citation||Banerjee, B. and Jayaweera, D. and Islam, S. 2012. Probabilistic Optimisation of Generation Scheduling Considering Wind Power Output and Stochastic Line Capacity, in Mochamad Ashari (General Chair) (ed), 22nd Australasian Universities Power Engineering Conference, Sep 26 2012. Bali, Indonesia: Institut Teknologi Sepuluh Nopember.|
Optimising the power flow in a system has been a challenge for decades. Due to the complexities that are introduced by new technologies, this problem is evolving. Lately, the effect of integrating wind turbines into the system has been taken into account when solving optimal power flow. However, transmission system constraints are usually modeled as fixed constraints using deterministic methods. Deterministic transmission line ratings have been shown to significantly underestimate the capability of the network. However, probabilistic line ratings are not used in optimization studies. In this paper, stochastic optimisation is used to consider the integration of wind turbines as well as probabilistic real time line capacities. It is shown that optimization considering probabilistic line ratings that lead to dynamic constraints in the OPF problem, represents the operational situation more accurately. This approach further reduces the optimum cost of system operation.
|dc.publisher||Institut Teknologi Sepuluh Nopember|
|dc.title||Probabilistic Optimisation of Generation Scheduling Considering Wind Power Output and Stochastic Line Capacity|
|dcterms.source.title||Proceedings of the 22nd Australasian Universities Power Engineering Conference|
|dcterms.source.series||Proceedings of the 22nd Australasian Universities Power Engineering Conference|
|dcterms.source.conference||22nd Australasian Universities Power Engineering Conference|
|dcterms.source.conference-start-date||Sep 26 2012|
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|curtin.department||Department of Electrical and Computer Engineering|