arXiv subjects

Priya Donti

Publications and source records attributed to Priya Donti.

3 recordsLinked to original sources

arXiv2026

Optimization Under Uncertainty for Energy Infrastructure Planning: A Synthesis of Methods, Tools, and Open Challenges

Energy infrastructure planning under uncertainty has become increasingly complex as electrification, interdependence between energy carriers, decarbonization, and extreme weather events reshape long-term investment decisions. This paper surveys recent advances at the intersection of generation and transmission expansion, and optimization under uncertainty, with a focus on stochastic programming, robust optimization, and distributionally robust optimization. We then categorize modeling needs along the axes of modeling fidelity, uncertainty characterization, and solution methods to identify dominant modeling features and trace research gaps. We further examine emerging directions at the interface of optimization and machine learning, including surrogate modeling, learning uncertainty sets, probabilistic forecasting, and synthetic scenarios, and discuss how these tools can be embedded within infrastructure planning models.

eess.SY↗

arXiv2025

PF$\Delta$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations

Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces). Running these calculations at operational timescales or across large evaluation spaces remains a major computational bottleneck. Additionally, growing uncertainty in power system operations from the integration of renewables and climate-induced extreme weather also calls for tools that can accurately and efficiently simulate a wide range of scenarios and operating conditions. Machine learning methods offer a potential speedup over traditional solvers, but their performance has not been systematically assessed on benchmarks that capture real-world variability. This paper introduces PF$\Delta$, a benchmark dataset for power flow that captures diverse variations in load, generation, and topology. PF$\Delta$ contains 859,800 solved power flow instances spanning six different bus system sizes, capturing three types of contingency scenarios (N , N -1, and N -2), and including close-to-infeasible cases near steady-state voltage stability limits. We evaluate traditional solvers and GNN-based methods, highlighting key areas where existing approaches struggle, and identifying open problems for future research. Our dataset is available at https://huggingface.co/datasets/pfdelta/pfdelta/tree/main and our code with data generation scripts and model implementations is at https://github.com/MOSSLab-MIT/pfdelta.

cs.LG↗

arXiv2021

Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization

While reinforcement learning (RL) is gaining popularity in energy systems control, its real-world applications are limited due to the fact that the actions from learned policies may not satisfy functional requirements or be feasible for the underlying physical system. In this work, we propose PROjected Feasibility (PROF), a method to enforce convex operational constraints within neural policies. Specifically, we incorporate a differentiable projection layer within a neural network-based policy to enforce that all learned actions are feasible. We then update the policy end-to-end by propagating gradients through this differentiable projection layer, making the policy cognizant of the operational constraints. We demonstrate our method on two applications: energy-efficient building operation and inverter control. In the building operation setting, we show that PROF maintains thermal comfort requirements while improving energy efficiency by 4% over state-of-the-art methods. In the inverter control setting, PROF perfectly satisfies voltage constraints on the IEEE 37-bus feeder system, as it learns to curtail as little renewable energy as possible within its safety set.

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