Dynamic, Stochastic, Computational and Scalable Technologies for Smart Grids
The smart electric power grid will evolve into a very complex adaptive system under semi-autonomous distributed control. Its spatial and temporal complexity, non-convexity, non-linearity, non-stationarity, variability and uncertainties exceed the characteristics found in today's traditional power system. The distributed integration of intermittent sources of energy and plug-in electric vehicles to a smart grid further adds complexity and challenges to its modeling, control and optimization. Innovative technologies are needed to handle the growing complexity of the smart grid and stochastic bidirectional optimal power flows, to maximize the penetration of renewable energy, and to provide maximum utilization of available energy storage, especially plug-in electric vehicles. Smart grids will need to be monitored continuously to maintain stability, reliability and efficiency under normal and abnormal operating conditions and disturbances. A combination of capabilities for system state prediction, dynamic stochastic power flow, system optimization, and solution checking will be necessary. The optimization and control systems for a smart-grid environment will require a computational systems thinking machine to handle the uncertainties and variability that exist. The importance and contributions of the computational intelligence field for developing the dynamic, stochastic, computational, and scalable technologies needed for sense-making, situational awareness, control and optimization in smart grids are presented in this paper.
smart grids distributed control power system dynamics power system stability stability criteria stochastic processes energy management