Abstract
This paper proposes a new optimization tool based on the cross-entropy (CE) method to assess security-constrained optimal power flow (SCOPF) solutions. First, the corresponding SCOPF stochastic problem is defined so that the optimum solution is interpreted as a rare event to be reached by a random search. Second, the CE method solves this new problem efficiently by making adaptive changes to the probability density function according to the Kullback-Leibler distance, creating a sequence of density functions that guides the search in the direction of the theoretically degenerate density at the optimal point. Different types of density functions are tested in order to cope with discrete variables present in the SCOPF problem. Two test systems, namely the IEEE 57 bus and the IEEE 300 bus, are used to evaluate the effectiveness of the proposed method in terms of solution quality and computational effort. Comparisons carried out with reference algorithms in the literature demonstrate that the CE method is capable of finding better solutions for the SCOPF problem with fewer evaluations.
Original language | English |
---|---|
Article number | 8386709 |
Pages (from-to) | 6621-6629 |
Number of pages | 9 |
Journal | IEEE Transactions on Power Systems |
Volume | 33 |
Issue number | 6 |
DOIs | |
Publication status | Published - Nov 2018 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 1969-2012 IEEE.
Keywords
- Cross-entropy method
- Monte Carlo simulation
- rare event simulation
- security-constrained optimal power flow