Collaborative Research: Computational Intelligence Methods for Dynamic Stochastic Optimization of Smart Grid Operation with High Penetration of Renewable Energy

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The objective of this research is to develop advanced computational intelligence methods to monitor, optimize and control large or so-called wide areas of a power network that will include solar farms (SFs) and wind farms (WFs), and controllable network transformers (CNTs), in order to ensure optimum usage of all these resources both during slow changing semi-steady state conditions, as well as during transient conditions. The research will be carried out in off-line simulations and then implemented on a real-time simulator.Intellectual meritThe behavior of renewable energy sources is uncertain and variable, and it is difficult for static optimization methods to optimize uncertain non-stationary distributed energy resources in a smart grid for maximum utilization. A novel ACD controller is proposed for the development of a real- time dynamic stochastic optimization smart grid engine. Advanced intelligent methods such as the biologically inspired artificial neural network and smart devices (CNTs) provide better identification and control capabilities for implementation of optimal power flows.Broader ImpactsEconomically operated reliable and secure power systems that can accommodate high penetration of renewable energy are of national interest. Being able to route power through underutilized lines will have major economic and environmental benefits due to avoiding the need for new lines. This project will have several dissemination channels, including software, websites, new contents added to existing courses, special sessions and tutorials at conferences, and journal publications.