Lessons Learned: Gaming for Novel Optimization of Managing Energy Systems for Homes (GNOMES for Homes)

2026-01-13

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment

arXiv (Cornell University) · 2026-04-23

Maintaining instantaneous balance between electricity supply and demand is critical for reliability and grid instability. System operators achieve this through solving the task of Unit Commitment (UC),ca high dimensional large-scale Mixed-integer Linear Programming (MILP) problem that is strictly and heavily governed by the grid physical constraints. As grid integrate variable renewable sources, and new technologies such as long duration storage in the grid, UC must be optimally solved for multi-day horizons and potentially with greater frequency. Therefore, traditional MILP solvers increasingly struggle to compute solutions within these tightening operational time limits. To bypass these computational bottlenecks, this paper proposes a novel framework utilizing a transformer-based architecture to predict generator commitment schedules over a 72-hour horizon. Also, because raw predictions in highly dimensional spaces often yield physically infeasible results, the pipeline integrates the self-attention network with deterministic post-processing heuristics that systematically enforce minimum up/down times and minimize excess capacity. Finally, these refined predictions are utilized as a warm start for a downstream MILP solver, while employing a confidence-based variable fixation strategy to drastically reduce the combinatorial search space. Validated on a single-bus test system, the complete multi-stage pipeline achieves 100\% feasibility and significantly accelerates computation times. Notably, in approximately 20\% of test instances, the proposed model reached a feasible operational schedule with a lower overall system cost than relying solely on the solver.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment

arXiv (Cornell University) · 2026-04-23

Maintaining instantaneous balance between electricity supply and demand is critical for reliability and grid instability. System operators achieve this through solving the task of Unit Commitment (UC),ca high dimensional large-scale Mixed-integer Linear Programming (MILP) problem that is strictly and heavily governed by the grid physical constraints. As grid integrate variable renewable sources, and new technologies such as long duration storage in the grid, UC must be optimally solved for multi-day horizons and potentially with greater frequency. Therefore, traditional MILP solvers increasingly struggle to compute solutions within these tightening operational time limits. To bypass these computational bottlenecks, this paper proposes a novel framework utilizing a transformer-based architecture to predict generator commitment schedules over a 72-hour horizon. Also, because raw predictions in highly dimensional spaces often yield physically infeasible results, the pipeline integrates the self-attention network with deterministic post-processing heuristics that systematically enforce minimum up/down times and minimize excess capacity. Finally, these refined predictions are utilized as a warm start for a downstream MILP solver, while employing a confidence-based variable fixation strategy to drastically reduce the combinatorial search space. Validated on a single-bus test system, the complete multi-stage pipeline achieves 100\% feasibility and significantly accelerates computation times. Notably, in approximately 20\% of test instances, the proposed model reached a feasible operational schedule with a lower overall system cost than relying solely on the solver.

Learning to optimize meets neural-ODE: Real-time, stability-constrained AC OPF

Electric Power Systems Research · 2026-04-20

Optimal County-Level Siting of Data Centers in the United States

arXiv (Cornell University) · 2026-01-22

Data centers are growing rapidly, creating the pressing need for the development of critical infrastructure build out to support these resource-intensive large loads. Their immense consumption of electricity and, often, freshwater, continues to stress an already constrained and aging power grid and water resources. This paper presents a comprehensive modeling approach to determine the optimal locations to construct such facilities by quantifying their resource use and minimizing associated costs. The interdisciplinary modeling approach incorporates a number of factors including the power grid, telecommunications, climate, water use, and collocated generation potential. This work establishes the base model whose functionality is shown through several test cases focusing on carbon-free generation collocation on a county-level in the United States. The results suggest that while capital costs are the biggest driver, having a longer future outlook and allowing more variable generation collocation influences the model to choose sites with higher renewable potential.

Planning and Operation of Carbon, Cost, and Location Aware Electrified Chemical Plants

2026-03-25

The industrial sector of the U.S. is a large consumer of energy and responsible for a sizable fraction of nationwide emissions. Electrification has been proposed as one potential pathway to assist in the goal of decarbonizing heavy industry; however, it can prove challenging from a cost perspective. In this paper, we develop an optimization framework with the goal of minimizing operational costs, carbon costs, and the cost of on-site resources such as renewable energy and storage for an electrified ethylene plant. Our framework incorporates wind and solar energy generation, battery storage, chemical storage, and carbon costs, while considering hypothetical electrified plants located in ERCOT, MISO, and PJM. Results indicate that the plant is generally not profitable when time-varying costs for chemical feedstock and production are considered, though different regions produced different results. For renewable generation, the framework prioritized wind generation, and was less likely to choose solar. Future work can consider different price structures for electricity and chemicals, as well as different mixes of on-site resource availability across a wider variety of grid regions.

Learning energy burden indicators for data-driven policy using self-organizing maps

Environmental Research Energy · 2026-03-09

Abstract Energy burden, the ratio of energy expenditure to household income, is a critical yet often overlooked measure of economic and environmental inequality in the United States. A high energy burden, 6% or greater, is not just a financial issue; it is a public health and environmental justice concern, as frontline communities often experience greater exposure to pollution, poorer housing efficiency, and heightened vulnerability to extreme weather events. This study uses self-organizing maps (SOMs), an unsupervised neural network, to identify contributing factors and inform policy interventions for energy-burdened communities in the North, South, Midwest, and West census regions, a novel use of this method. It is also among the first to integrate environmental justice indicators, including outdoor air quality metrics and health disparities, as determinants of energy burden. In addition to environmental justice indicators, socioeconomic status, building characteristics, and power outages are explored to assist policymakers, engineers, and advocates working within the energy transition. Results revealed statistically significant ( p < 0.05) differences in these indicators across SOM-defined energy-burden regimes. For the Midwest and South regions, all 45 indicators showed statistical significance, while 44 were significant in the Northeast, and 41 were significant for the West. These findings suggest that high energy-burden regimes tend to coincide with elevated environmental and health risk indicators, which may intensify under climate change.

Optimal County-Level Siting of Data Centers in the United States

ArXiv.org · 2026-01-22

Data centers are growing rapidly, creating the pressing need for the development of critical infrastructure build out to support these resource-intensive large loads. Their immense consumption of electricity and, often, freshwater, continues to stress an already constrained and aging power grid and water resources. This paper presents a comprehensive modeling approach to determine the optimal locations to construct such facilities by quantifying their resource use and minimizing associated costs. The interdisciplinary modeling approach incorporates a number of factors including the power grid, telecommunications, climate, water use, and collocated generation potential. This work establishes the base model whose functionality is shown through several test cases focusing on carbon-free generation collocation on a county-level in the United States. The results suggest that while capital costs are the biggest driver, having a longer future outlook and allowing more variable generation collocation influences the model to choose sites with higher renewable potential.

Optimal Co-Design of Integrated Thermal-Electrical Networks and Control Systems for Grid-interactive Efficient District (GED) Energy Systems

2026-01-08

This project advances a unified, open-source framework for the optimal co-design of thermal, electrical, and control systems in grid-interactive efficient districts (GEDs). As communities integrate growing levels of distributed energy resources, traditional approaches that model thermal and electrical networks independently lead to reduced efficiency, limited flexibility, and missed opportunities for coordinated operation. To address these challenges, the research team developed a comprehensive suite of physics-based models, control algorithms, and software tools that enable holistic simulation, optimization, and demonstration of district-scale energy systems.

Bus Type Switching to Reduce Bound Violations in AC Power Flow

ArXiv.org · 2025-11-10

Wholesale power markets often use linear approximations of power system constraints. Because it does not consider inequality constraints, using AC power flow for feasibility post-processing can violate bounds on reactive power, voltage magnitudes, or thermal limits. There remains a need for a streamlined analytical approach that can guarantee AC feasibility while adhering to variable bounds. This paper suggests an augmented implementation of AC power flow that uses an additional two bus types (PQV and P) to help resolve voltage bound violations present in the traditional approach. The proposed method sacrifices the voltage setpoint at a generator in exchange for fixing the voltage at a load bus, thereby moving a degree of freedom around the network. Results on the IEEE 14-bus, 57-bus, and 300-bus test cases demonstrate how switching bus types can reduce overall network violations and help find feasible power system setpoints.