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This paper presents two machine learning-based constraint management approaches based on Reference Governors (RGs). The first approach, termed NN-DTC, uses regression neural networks to approximate the distance to constraints. The second, termed NN-NL-RG, uses regression neural networks to approximate the input-output map of a nonlinear RG. Both approaches are shown to enforce constraints for a nonlinear second order system. NN-NL-RG requires a smaller dataset size as compared to NN-DTC for well-trained neural networks. For systems with multiple constraints, NN-NL-RG is also more computationally efficient than NN-DTC. Finally, promising results are reported by having both approaches implemented on a more complex spacecraft proximity maneuvering and docking application, through simulations. 

Renewable portfolio standards are targeting high levels of variable solar photovoltaics (PV) in electric distribution systems, which makes reliability more challenging to maintain for distribution system operators (DSOs). Distributed energy resources (DERs), including smart, connected appliances and PV inverters, represent responsive grid resources that can provide flexibility to support the DSO in actively managing their networks to facilitate reliability under extreme levels of solar PV. This flexibility can also be used to optimize system operations with respect to economic signals from wholesale energy and ancillary service markets. Here, we present a novel hierarchical scheme that actively controls behind-the-meter DERs to reliably manage each unbalanced distribution feeder and exploits the available flexibility to ensure reliable operation and economically optimizes the entire distribution network. Each layer of the scheme employs advanced optimization methods at different timescales to ensure that the system operates within both grid and device limits. The hierarchy is validated in a large-scale realistic simulation based on data from the industry. Simulation results show that coordination of flexibility improves both system reliability and economics, and enables greater penetration of solar PV. Discussion is also provided on the practical viability of the required communications and controls to implement the presented scheme within a large DSO.