Abstract: A system and method for modeling, controlling and analyzing electrical grids for use by control room operators and automatic control provides a multi-dimensional, multi-layer cellular computational network (CCN) comprising an information layer; a knowledge layer; a decision-making layer; and an action layer; wherein each said layer of said CCN represents one of a variable in an electric power system. Situational awareness/situational intelligence is provided therefrom so that the operators and grid control systems can make the correct decision and take informed actions under difficult circumstances to maintain a high degree of grid integrity and reliability by analyzing multiple variables within a volume of time and space to provide an understanding of their meaning and predict their states in the near future where these multiple variables can have different timescales.

Abstract: A dynamic stochastic optimal power flow (DSOPF) control system is described for performing multi-objective optimal control capability in complex electrical power systems. The DSOPF system and method replaces the traditional adaptive critic designs (ACDs) 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 DSOPF system and method is used to provide nonlinear optimal control, where the control objective is explicitly formulated to incorporate power system economy, stability and security considerations. The system and method dynamically drives a power system to its optimal operating point by continuously adjusting the steady-state set points sent by a traditional optimal power flow algorithm.

Abstract: A system and method for modeling, controlling and analyzing electrical grids for use by control room operators and automatic control provides a multi-dimensional, multi-layer cellular computational network (CCN) comprising an information layer; a knowledge layer; a decision-making layer; and an action layer; wherein each said layer of said CCN represents one of a variable in an electric power system. Situational awareness/situational intelligence is provided therefrom so that the operators and grid control systems can make the correct decision and take informed actions under difficult circumstances to maintain a high degree of grid integrity and reliability by analyzing multiple variables within a volume of time and space to provide an understanding of their meaning and predict their states in the near future where these multiple variables can have different timescales.

Abstract: A dynamic stochastic optimal power flow (DSOPF) control system is described for performing multi-objective optimal control capability in complex electrical power systems. The DSOPF system and method replaces the traditional adaptive critic designs (ACDs) 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 DSOPF system and method is used to provide nonlinear optimal control, where the control objective is explicitly formulated to incorporate power system economy, stability and security considerations. The system and method dynamically drives a power system to its optimal operating point by continuously adjusting the steady-state set points sent by a traditional optimal power flow algorithm.