Optimization and Control for Smart Grids

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A complete conference program can be viewed here.

Summary

The power grid, arguably the largest, most advanced, and most expansive engineered system ever built, operates according to laws of physics. The system is controlled, but only to a degree. Moreover, growth and diversification of demand along with increasing integration of time-intermittent renewable generation is making analysis, control, and optimization more challenging. Specifically, research is required to improve understanding of:

Spatio-temporal scales associated with power grids.Appropriate separation of spatio-temporal scales enables rigorous formulation of accurate, properly coarse-grained universal models capable of abstracting out qualitatively distinct phenomena, e.g.transient stability, cascades, voltage collapse, and electro-mechanical frequency waves. Proper formulation of these reduced models captures the basic qualitative grid phenomena which guide development of improved optimization and control methods.

Statistical characterization of power grids.Electrical grid operators routinely estimate the grid’s steady state from a limited set of measurements, e.g. voltage magnitude and power flows, however, fluctuations due to time-intermittent generation and deferable loads will become increasingly important in the future. New statistical methods and models must be developed to assess the impact of these new sources of fluctuation on system reliability, stability, and economic operation.

Fast, reliable algorithms to estimate the current and future state of the grid.The grid is a large, complex system that must be analyzed and controlled in real time, and without dynamical estimation of the future grid state, control actions taken at the current time may lead to unreliable or unstable future operation. Accurate reduced models are necessary for this estimation because detailed dynamical models are too slow for real-time control.

Our conference will discuss these and other related challenges in Smart Grid Research by bringing together leading experts from power engineering, control and optimization theory, applied mathematics, network science and statistical physics.