Electricity grid operators routinely solve optimization problems to address core decision processes at various time-scales, ranging from 5 minutes to multiple decades. Historically, these problems are addressed in terms of deterministic optimization, with resources kept in reserve to address any potential uncertainty regarding the future. In the context of daily operations, this approach is becoming increasingly costly and unreliable with the introduction of significant quantities of renewables generation units, e.g., wind and solar farms, for which the electricity generation levels are both variable and uncertain. For planning, increasing volatile weather leads to disruptions caused by events that were not anticipated, e.g., "100 year" floods occurring multiple times in a decade. Thus, stochastic optimization — the ability to perform optimization while directly addressing system and environmental uncertainties — is becoming a significant algorithm driver for utilities and national planning agencies. The development of efficient algorithms for stochastic optimization remains a significant challenge, however, due to the complexity of the associated decision problems. This research is being conducted in the context of Sandia’s Coopr optimization package, through the modeling and solver functionality provided by the Pyomo and PySP libraries.
Beyond optimization, predictive simulation will likely play a significant role in the future electricity grid. Specifically, the ability to anticipate the consequences of and risk associated with specific control actions is critical in the operation of a resilient electricity grid. Examples of predictive simulation tools include advanced circuit simulators such as Xyce, which are capable of faster-than-real-time simulations of large-scale electricity networks. Similarly, network analysis tools can be leveraged to identify critical nodes in an electricity grid, which can inform both longer-term planning processes and shorter-term security concerns.