An Importance Sampling Technique for Probabilistic Security Assessment In Power Systems with Large Amounts of Wind Power
2016 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 131, 11-18 p.Article in journal (Refereed) Published
Larger amounts of variable renewable energy sources bring about larger amounts of uncertainty in the form of forecast errors. When taking operational and planning decisions under uncertainty, a trade-off between risk and costs must be made. Today's deterministic operational tools, such as N-1-based methods, cannot directly account for the underlying risk due to uncertainties. Instead, several definitions of operating risks, which are probabilistic indicators, have been proposed in the literature. Estimating these risks require estimating very low probabilities of violations of operating constraints. Crude Monte-Carlo simulations are very computationally demanding for estimating very low probabilities. In this paper, an importance sampling technique from mathematical finance is adapted to estimate very low operating risks in power systems given probabilistic forecasts for the wind power and the load. Case studies in the IEEE 39 and 118 bus systems show a decrease in computational demand of two to three orders of magnitude.
Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 131, 11-18 p.
Importance sampling, Monte-Carlo simulations, N-1 criterion, Risk-based operation, Stability boundary, Wind power
Research subject Electrical Engineering
IdentifiersURN: urn:nbn:se:kth:diva-166394DOI: 10.1016/j.epsr.2015.09.016ScopusID: 2-s2.0-84944351188OAI: oai:DiVA.org:kth-166394DiVA: diva2:810747
Updated from Manuscript to Article. QC 201601262015-05-082015-05-082016-01-28Bibliographically approved