Title :
Probabilistic optimisation of generation scheduling considering wind power output and stochastic line capacity
Author :
Banerjee, B. ; Jayaweera, D. ; Islam, S.M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Curtin Univ., Perth, WA, Australia
Abstract :
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.
Keywords :
cost reduction; deterministic algorithms; load flow; optimisation; power generation economics; power generation scheduling; power system simulation; power transmission economics; power transmission lines; probability; stochastic programming; wind power plants; wind turbines; OPF; deterministic method; deterministic transmission line system; optimal power flow optimisation; optimum cost reduction; power generation scheduling; probabilistic optimisation; probabilistic real time line rating capacity; stochastic line capacity; stochastic optimisation; wind power output; wind turbine integration; Capacity planning; Generators; Load flow; Optimization; Power transmission lines; Probability distribution; Wind power generation; optimal generator scheduling; probabilistic line rating; stochastic optimisation; wind power;
Conference_Titel :
Universities Power Engineering Conference (AUPEC), 2012 22nd Australasian
Conference_Location :
Bali
Print_ISBN :
978-1-4673-2933-0