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Timothy McNamara

Publications and source records attributed to Timothy McNamara.

3 recordsLinked to original sources

Unifying Power Flow and Electromagnetic Transient Modeling

Grid tools are separated by timescales: steady-state analysis is performed by power flow (PF), whereas the fastest dynamics are captured by electromagnetic transient (EMT) simulation. Although operating at varying timescales, different tools should produce consistent results when analyzing the same grid conditions. However, the PF steady-state solution does not match the time-to-infinity EMT response. This mismatch exists because simplified device models in PF are inconsistent with the "ground truth" EMT models derived from first principles. As inverter-based resources (IBRs) and data centers strip away inertia and operating reserves, approximations used in PF risk grid security by missing violations. To address this, we introduce a steady-state framework that uses full-physics EMT models for high-fidelity steady-state grid analysis. The challenge lies in systematically formulating algebraic frequency-domain representations of dynamic power devices that are naturally described in state-space form. Our tool, SALT (Steady-state After Last Transients), constructs steady-state representations that exactly capture grid device physics when embedded into transmission networks. The approach exploits the structural properties of power device models and the single-harmonic, balanced nature of the transmission system. Results demonstrate SALT achieves EMT steady-state accuracy while delivering a 1x10^6 times speedup. We demonstrate that SALT can capture security threats in contingency scenarios without exception --- whereas PF-based analyses reported 75% fewer rated line violations, 18% fewer Q-limit violations, and 7% fewer voltage violations.

eess.SY

Actionable Three-Phase Infeasibility Optimization with Varying Slack Sources

Modern distribution grids that include numerous distributed energy resources (DERs) and battery electric vehicles (BEVs) will require simulation and optimization methods that can capture behavior under infeasible operating scenarios to assess reliability. A three-phase infeasibility analysis (TPIA) localizes and identifies power deficient areas in distribution feeders via a non-convex optimization that injects and subsequently minimizes slack sources, subject to AC network constraints. In this paper, we extend the TPIA framework by introducing operational bounds to ensure realistic, actionable solutions. We incorporate current, reactive power, and susceptance slack sources to model real-world assets, and discuss their potential use cases. We show that the voltage-bounded TPIA formulations provide actionable solutions for realistic networks of up to 5360 nodes where power flow simulations either fail or return low-voltage solutions. We demonstrate reactive power compensation using the slack susceptance formulation on an infeasible test case.

math.OC

Two-Stage Homotopy Method to Incorporate Discrete Control Variables into AC-OPF

Alternating-Current Optimal Power Flow (AC-OPF) is an optimization problem critical for planning and operating the power grid. The problem is traditionally formulated using only continuous variables. Typically, control devices with discrete-valued settings, which provide valuable flexibility to the network and improve resilience, are omitted from AC-OPF formulations due to the difficulty of integrality constraints. We propose a two-stage homotopy algorithm to solve the AC-OPF problem with discrete-valued control settings. This method does not rely on prior knowledge of control settings or other initial conditions. The first stage relaxes the discrete settings to continuous variables and solves the optimization using a robust homotopy technique. Once the solution has been obtained using relaxed models, second homotopy problem gradually transforms the relaxed settings to their nearest feasible discrete values. We test the proposed algorithm on several large networks with switched shunts and adjustable transformers and show it can outperform a similar state-of-the-art solver.

math.OC