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

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.