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Found 8 papers, 0 papers with code
Power-Flow-Embedded Projection Conic Matrix Completion for Low-Observable Distribution Systems
A low-observable distribution system has insufficient measurements for conventional weighted least square state estimators.
Outlier-immune Data-driven Linear Power Flow Model Construction via Mixed-Integer Programming
The common approaches to construct a data-driven linear power flow (DD-LPF) model cannot completely eliminate the adverse impacts of outliers in a training dataset.
Distributionally Robust Evaluation for Real-Time Flexibility of Electric Vehicles Considering Uncertain Departure Behavior and State-of-Charge
Accurately evaluating the real-time flexibility of electric vehicles (EVs) is necessary for EV aggregators to offer ancillary services.
Coordinated Frequency-Constrained Stochastic Economic Dispatch for Integrated Transmission and Distribution System via Distributed Optimization
TPS and ADNs can deliver base point power bidirectionally and provide frequency regulation support bidirectionally, which extend the existing reserve assumption in ITD dispatch and enhance the operational security of the ITD system.
Dynamic Security Region of Natural Gas Systems in Integrated Electricity-Gas Systems
In an integrated electricity-gas system (IEGS), the tight coupling of power and natural gas systems is embodied by frequent changes in gas withdrawal from gas-fired units to provide regulation services for the power system to handle uncertainty, which may in turn endanger the secure operation of the natural gas system and ultimately affect the safety of the whole IEGS.
Joint Chance-Constrained Economic Dispatch Involving Joint Optimization of Frequency-related Inverter Control and Regulation Reserve Allocation
The issues of uncertainty and frequency security could become significantly serious in power systems with the high penetration of volatile inverter-based renewables (IBRs).
Robust Data-Driven Linear Power Flow Model with Probability Constrained Worst-Case Errors
To limit the probability of unacceptable worst-case linearization errors that might yield risks for power system operations, this letter proposes a robust data-driven linear power flow (RD-LPF) model.
A Physics-based and Data-driven Linear Three-Phase Power Flow Model for Distribution Power Systems
Case studies have demonstrated that our model generally has 2 to over 10-fold smaller average errors than other linear power flow models, enjoys a satisfying accuracy against bad data, and facilitates a faster solution to DPS analysis and optimization problems.
