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arXiv · 2510.03484

Contingency-Aware Nodal Optimal Power Investments with High Temporal Resolution

Thomas Lee·Andy Sun
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Abstract

We present CANOPI, a novel algorithmic framework, for solving the Contingency-Aware Nodal Power Investments problem, a large-scale nonlinear optimization problem that jointly optimizes investments in generation, storage, and transmission upgrades, including representations of unit commitment and long-duration storage. The problem's scale arises from the confluence of spatial and temporal resolutions, along with the large number of contingency constraints. Further, the underlying problem is nonlinear due to transmission upgrades' impact on impedances. We propose algorithmic approaches to address these computational challenges. We pose a linear approximation and develop a fixed-point algorithm to adjust for nonlinear impedance feedback. We solve the large-scale linear expansion model with a specialized level-bundle method leveraging a novel interleaved approach to contingency constraint generation. We introduce a minimum cycle basis algorithm that improves numerical sparsity and solve times of cycle-based DC power flow. CANOPI is demonstrated on a 1493-bus Western Interconnection test system built from realistic-geography network data, with hourly operations spanning 52 week-long scenarios and a total possible set of 20 billion individual transmission contingency constraints. Numerical experiments quantify reliability and economic benefits of incorporating transmission contingencies in integrated planning models and highlight the computational advantages of the proposed methods.

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Thomas Lee, Andy Sun. 2025-10-03. Contingency-Aware Nodal Optimal Power Investments with High Temporal Resolution. https://arxiv.org/abs/2510.03484

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