Distributed Probabilistic ATC Assessment by Optimality Conditions Decomposition and LHS Considering Intermittent Wind Power Generation

Abstract

This paper investigates a probabilistic assessment of available transfer capability (ATC) by optimality condition decomposition (OCD) techniques and latin hypercube sampling (LHS) method in power systems with penetration of wind energy resources. First, the ATC assessment is mathematically formulated as a non-linear optimal power flow problem. Then, an iterative decomposition-coordination methodology based on OCD techniques is conducted for distributed assessment of the ATC. In order to estimate the probability density function and empirical cumulative density function under wind power fluctuations, LHS method is utilized for obtaining samples of the integrated wind power sources. Next, wind power samples are appended into the proposed decomposition-coordination approach to provide a fast LHS-based Monte Carlo (MC) simulation of the ATC at the current system state. In order to provide a preliminary approximation of the range variation of the ATC before executing the MC technique, OCD-based distributed computations of the average-case, best-case, and worst-case scenarios subjected to wind power variations are developed by following three approaches: first, assuming that the average-case of the ATC occurs at the mean power output of integrated wind farms, second, adding box-constraints related to the power output of each wind farm in order to estimate the best-case scenario, and third, developing an iterative sensitivity-based scheme to estimate the worst-case scenario. Numerical experiments in the standard IEEE 118-bus system demonstrate the correctness of the proposed distributed probabilistic ATC assessment.