Wide-Area Measurement Based Dynamic Stochastic Optimal Power Flow Control for Smart Grids With High Variability and Uncertainty

Abstract

To achieve a high penetration level of intermittent renewable energy, the operation and control of power systems need to account for the associated high variability and uncertainty. Power system stability and security need to be ensured dynamically as the system operating condition continuously changes. A wide-area measurement based dynamic stochastic optimal power flow (DSOPF) control algorithm using the adaptive critic designs (ACDs) is presented in this paper. The proposed DSOPF control replaces the traditional AGC and secondary voltage control, and provides a coordinated AC power flow control solution to the smart grid operation in an environment with high short-term uncertainty and variability. The ACD technique, specifically the dual heuristic dynamic programming (DHP), is used to provide nonlinear optimal control, where the control objective is explicitly formulated to incorporate power system economy, stability and security considerations. The proposed DSOPF controller dynamically drives the power system to its optimal operating point by continuously adjusting the steady-state set points sent by the traditional OPF algorithm. A 12-bus test power system is used to demonstrate the development and effectiveness of the proposed DSOPF controller.