Probabilistic analysis for optimal power flow under uncertainty

Abstract

This study presents a probabilistic analysis to consider the impact of uncertain system parameters on optimal power flow (OPF). A general OPF under uncertainty is formulated as a chance-constrained programming model and its stochastic features are investigated. The impact of the uncertain variables is addressed by the probabilistic analysis with the method of Monte-Carlo simulation (MCS) combined with a deterministic optimisation. In this way, relations between the optimality and reliability in power systems under uncertainty can be achieved. The information derived from the statistical results can be applied in the system risk assessment and operation performance evaluation, which is helpful for making decisions in power system optimal dispatch under uncertainty. Case studies of a 5-bus system and the IEEE 30-bus system are presented to illustrate the probabilistic analysis.