Analytics-based optimization for smart grid operations

Considering near-real-time data available on the smart grid, analytics can be used to determine the best-case scenario for optimal and reliable distribution of power. However, the distributed integration of renewable sources and demand response adds complexity to the modeling, control and optimization of smart grid operations. Latest concepts aim for using new model-based computational intelligence; that requires a combination of capabilities for system optimization, stochastic power flow, system state prediction, and solution checking. The statistical model-based optimization for developing dynamic, stochastic, computationally efficient, and scalable platforms is intended in this paper. Furthermore, an analytics-based optimization is proposed to the optimal reactive power dispatch considering load variations. This illustration uses analytics obtained from empirical modeling of recorded load data.