A new nonparametric density estimation for probabilistic security-constrained economic dispatch

What is it about?

Security-constrained economic dispatch (SCED) which is used to minimize the operation cost of the committed units with the constraints of power balance, ramp rate, and unit capacity is one of the routine challenges in power system operation. In this paper, a nonparametric estimation method based on kernel density and linear diffusion is proposed to obtain continuous probability density functions for probabilistic SCED outcomes. It is assumed that the probabilistic SCED problem is the second stage of a two-stage problem, while stochastic security-constrained unit commitment is the first stage. To evaluate the efficacy of the proposed method, a 6-bus test system and IEEE 118-bus system are used as case studies. Implementing the proposed method on these case studies demonstrates the accuracy of the proposed method for large scale power systems.

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Why is it important?

 Proposing a new PDF estimation method for probabilistic security-constrained economic dispatch problem using Kernel density and linear diffusion.  Load forecasting errors and wind forecasting errors are considered power system uncertainties.  Efficacy and capability of the proposed method are investigated.  A large number of uncertainties are considered in order to evaluate the proposed method in a large-scale system with multiple uncertainties.