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Found 23 papers, 7 papers with code
Dual Pricing to Prioritize Renewable Energy and Consumer Preferences in Electricity Markets
Electricity markets currently fail to incorporate preferences of buyers, treating polluting and renewable energy sources as having equal social benefit under a system of uniform clearing prices.
Achieving the Tightest Relaxation of Sigmoids for Formal Verification
In the field of formal verification, Neural Networks (NNs) are typically reformulated into equivalent mathematical programs which are optimized over.
Enhancing Scalability of Optimal Kron-based Reduction of Networks (Opti-KRON) via Decomposition with Community Detection
Electrical networks contain thousands of interconnected nodes and edges, which leads to computational challenges in some power system studies.
GPU-Accelerated DCOPF using Gradient-Based Optimization
After solving the dual problem on both CPUs and GPUs to find tight lower bounds, we benchmark against Gurobi and MOSEK, comparing convergence speed and tightness on the IEEE 2000, 4601, and 10000 bus systems.
Towards Energysheds: A Technical Definition and Cooperative Framework for Future Power System Operations
There is growing interest in understanding how interactions between system-wide objectives and local community decision-making will impact the clean energy transition.
Towards Perturbation-Induced Static Pivoting on GPU-Based Linear Solvers
Linear system solving is a key tool for computational power system studies, e. g., optimal power flow, transmission switching, or unit commitment.
A Parallelized, Adam-Based Solver for Reserve and Security Constrained AC Unit Commitment
Power system optimization problems which include the nonlinear AC power flow equations require powerful and robust numerical solution algorithms.
GPU-Accelerated Verification of Machine Learning Models for Power Systems
Our contributions achieve a speedup that can exceed 100x and allow higher degrees of verification flexibility.
Scalable Bilevel Optimization for Generating Maximally Representative OPF Datasets
However, generating training datasets that accurately represent the many possible combinations of these active constraints is a particularly challenging task, especially within the realm of nonlinear AC Optimal Power Flow (OPF), since most active constraints cannot be enforced explicitly.
Global Performance Guarantees for Neural Network Models of AC Power Flow
This paper develops a tractable neural network verification procedure which incorporates the ground truth of the non-linear AC power flow equations to determine worst-case neural network prediction error.
Emission-Aware Optimization of Gas Networks: Input-Convex Neural Network Approach
Gas network planning optimization under emission constraints prioritizes gas supply with the least CO$_2$ intensity.
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations
To do so, we demonstrate that the Power Transfer Distribution Factors (PTDF) -- a physics-based linear sensitivity index -- can be derived from the SHAP values.
Optimization-Based Exploration of the Feasible Power Flow Space for Rapid Data Collection
This paper provides a systematic investigation into the various nonlinear objective functions which can be used to explore the feasible space associated with the optimal power flow problem.
Towards Optimal Kron-based Reduction Of Networks (Opti-KRON) for the Electric Power Grid
To overcome this challenge, this paper presents a novel network reduction methodology that leverages an efficient mixed-integer linear programming (MILP) formulation of a Kron-based reduction that is optimal in the sense that it balances the degree of the reduction with resulting modeling errors in the reduced network.
Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represents a challenging paradigm for the power system operators who are tasked with maintaining grid stability and security in the face of such changes.
Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment
Nonlinear power flow constraints render a variety of power system optimization problems computationally intractable.
Uncertainty Quantification in LV State Estimation Under High Shares of Flexible Resources
The ongoing electrification introduces new challenges to distribution system operators (DSOs).
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 learn the solutions of power system dynamics.
Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers
In the second approach, we model the Newton solver at the heart of an implicit Runge-Kutta integrator as a contracting map iteratively seeking a fixed point on a time domain trajectory.
Handling Initial Conditions in Vector Fitting for Real Time Modeling of Power System Dynamics
This paper develops a predictive modeling algorithm, denoted as Real-Time Vector Fitting (RTVF), which is capable of approximating the real-time linearized dynamics of multi-input multi-output (MIMO) dynamical systems via rational transfer function matrices.
Network Topology Invariant Stability Certificates for DC Microgrids with Arbitrary Load Dynamics
DC microgrids are prone to small-signal instabilities due to the presence of tightly regulated loads.
Accelerated Probabilistic Power Flow in Electrical Distribution Networks via Model Order Reduction and Neumann Series Expansion
This subspace is used to construct and update a reduced order model (ROM) of the full nonlinear system, resulting in a highly efficient simulation for future voltage profiles.
A Passivity Interpretation of Energy-Based Forced Oscillation Source Location Methods
The paper goes on to develop a simulation-free algorithm for predicting the performance of the DEF method in a generalized power system, and it analyzes the passivity of three non-classical load and generation components.
Systems and Control Systems and Control
