Power systems are undergoing a rapid change due to the proliferation of renewable-based generation and distributed energy resources (DERs). Current state-of-the-art methods for network monitoring and control need to adapt this emerging setting, and in particular they must be able to cope with an increased number of controllable points and faster network-level dynamics that can change in less than a second. This project advances a program to design new algorithms that will run faster, and therefore be able to optimize the power grid at fast time scales, and that will also be able to handle uncertainty and errors in the model. The algorithms will apply to other critical infrastructures beyond energy, including transportation and industrial automation. The direct impact to the power and energy sector, such as the AC power grid and the operation of wind farms, will be a more efficient use of power and more reliable enforcement of voltage and current constraints. An indirect impact of the project is the training of a new generation of students through undergraduate student involvement, graduate student mentoring and curriculum development, outreach activities targeted at middle and high school Science, Technology, Engineering and Mathematics (STEM) camps, broad dissemination activities, and industrial outreach.
Existing approaches are not adequate for data-processing and decision-making in complex power systems operating in highly dynamic environments because of the following drawbacks: i) batch solution approaches for optimization problems associated with monitoring and control tasks may fail to provide solutions at a time scale that match variability of distribution-level energy resources and non-controllable assets; ii) model-based optimization approaches require accurate knowledge of the network; and, iii) a naive online implementation of algorithms that are designed for a batch/static solution may not provide optimality and convergence guarantees. The proposed work in this project will significantly extend theory and application of accelerated methods -- successfully applied in batch optimization -- to time-varying optimization of networks and networked systems where costs, constraints and problem inputs evolve during the execution of the algorithmic steps. The project further investigates the development of online derivative-free strategies to deal with unknown gradients of the cost function, which apply when the network model is unknown or difficult to estimate accurately. Major efforts are devoted to the challenging optimization settings that involve constraints and non-differentiable terms in the time-varying objective. The proposed efforts focus on rigorous convergence analysis; the aim is to derive results in terms of error in the tracking of an optimal solution during the execution of the algorithms (rather than asymptotic bounds), and enable linear convergence rates in time-varying settings. Within the power systems area, the proposed research revisits classical monitoring and optimization tasks -- state estimation, AC optimal power flow, and yaw control in wind farms just to mention few -- under dynamic operating conditions; the proposed theoretical approach enables a real-time execution of these tasks.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
