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Power flow convergence and reactive power planning in the creation of large synthetic grids
To encourage and support innovation, synthetic electric grids are fictional, designed systems
that mimic the complexity of actual electric grids but contain no confidential information …
that mimic the complexity of actual electric grids but contain no confidential information …
Spatio-temporal deep learning-assisted reduced security-constrained unit commitment
Security-constrained unit commitment (SCUC) is a computationally complex process utilized
in power system day-ahead scheduling and market clearing. SCUC is run daily and requires …
in power system day-ahead scheduling and market clearing. SCUC is run daily and requires …
Modeling, tuning, and validating system dynamics in synthetic electric grids
A synthetic network modeling methodology has been developed to generate completely
fictitious power system models with capability to represent characteristic features of actual …
fictitious power system models with capability to represent characteristic features of actual …
Feasibility layer aided machine learning approach for day-ahead operations
Day-ahead operation involves a complex and computationally intensive optimization
process to determine the generator commitment schedule and dispatch. The optimization …
process to determine the generator commitment schedule and dispatch. The optimization …
Deep sigma point processes-assisted chance-constrained power system transient stability preventive control
This paper proposes a deep sigma point processes (DSPP)-assisted chance-constrained
power system transient stability preventive control method to deal with uncertain renewable …
power system transient stability preventive control method to deal with uncertain renewable …
Learning without data: Physics-informed neural networks for fast time-domain simulation
In order to drastically reduce the heavy computational burden associated with time-domain
simulations, this paper introduces a Physics-Informed Neural Network (PINN) to directly …
simulations, this paper introduces a Physics-Informed Neural Network (PINN) to directly …
Stability implications of bulk power networks with large scale PVs
With the shift in renewable portfolio standards, conventional fossil-fuel based generators are
expected to be partially or fully replaced with renewable energy sources such as …
expected to be partially or fully replaced with renewable energy sources such as …
Educational applications of large synthetic power grids
This paper describes the use of large electric grids in university electric power system
courses. Since much actual power system information is not publicly available, the …
courses. Since much actual power system information is not publicly available, the …
[HTML][HTML] A framework for synthetic power system dynamics
We present a modular framework for generating synthetic power grids that consider the
heterogeneity of real power grid dynamics but remain simple and tractable. This enables the …
heterogeneity of real power grid dynamics but remain simple and tractable. This enables the …
Equitably allocating wildfire resilience investments for power grids: The curse of aggregation and vulnerability indices
Social vulnerability indices have increased traction for guiding infrastructure investment
decisions to prioritize communities that need these investments most. One such plan is the …
decisions to prioritize communities that need these investments most. One such plan is the …