CyberSEES: Type 1: Data-driven approaches to managing uncertain load control in sustainable power systems

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Integrating high penetrations of renewable energy resources into electric power systems requires additional back-up capacity (reserves) to manage real-time power imbalance. Load control can provide reserves, possibly at lower cost and/or with less environmental impact than most power plants. However, scheduling load-based reserves is challenging because of uncertainty -- the availability of reserves is a function of stochastic factors including weather and load usage patterns. This research project is therefore investigating data-driven, distribution-free approaches to managing load control uncertainty in power system scheduling problems. By generalizing distributionally robust optimization algorithms, chance-constrained optimal power flow solution techniques are being developed to manage the time-varying, correlated, and complex uncertainty associated with load control. Furthermore, these new techniques can be used to determine conditions under which load control becomes a competitive option. Specifically, trade-offs between load control uncertainty and profitability are being quantified in order to assess the impact of uncertainty on environmental sustainability. The methods being developed through this work form a basis for quantifying the net environmental impact of using uncertain load control for reserves. In particular, they enable more informed utilization of load control for supporting the integration of renewable energy resources. The results will guide load control program design, power system market design, and, more broadly, energy policy that seeks to balance cost and environmental impact. Furthermore, the optimization approaches being developed in this work are applicable to other power system problems, and to stochastic problems in other fields.