Grid Optimization Competition on Synthetic and Industrial Power Systems

Safdarian, Farnaz; Snodgrass, Jonathan; Yeo, Ju Hee; Birchfield, Adam B.; Coffrin, Carleton; DeMarco, Christopher L.; Elbert, Stephen T.; Eldridge, Brent; Elgindy, Tarek; Greene, Scott; Guo, Nongchao; Holzer, Jesse; Lesieutre, Bernard C.; Mittelmann, Hans D.; O’Neill, Richard P.; Overbye, Thomas J.; Palmintier, Bryan; Hentenryck, Pascal Van; Veeramany, Arun; Mak, Terrence W. K.; Wert, Jessica L.

doi:10.1109/naps56150.2022.10012138

Cited by 8 publications

(1 citation statement)

References 18 publications

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“…As the share of renewable energies significantly increases in the generation mix, it becomes increasingly important to solve AC-OPF problems, not their linearized versions. This is best exemplified by the ARPA-E GO competitions [2] designed to stimulate progress on AC-OPF. In recent years, machinelearning (ML) approaches to the OPF have received increasing attention.…”

Section: Introductionmentioning

confidence: 99%

Compact Optimization Learning for AC Optimal Power Flow

Park¹,

Chen²,

Mak³

et al. 2023

Preprint

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This paper reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability issues due to the high dimensionality of the output space. This paper first shows that the space of optimal solutions can be significantly compressed using principal component analysis (PCA). It then proposes COMPACT LEARNING, a new method that learns in a subspace of the principal components before translating the vectors into the original output space. This compression reduces the number of trainable parameters substantially, improving scalability and effectiveness. COMPACT LEARNING is evaluated on a variety of test cases from the PGLib [1] with up to 30,000 buses. The paper also shows that the output of COMPACT LEARNING can be used to warm-start an exact AC solver to restore feasibility, while bringing significant speed-ups.

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Section: Introductionmentioning

confidence: 99%

Compact Optimization Learning for AC Optimal Power Flow

Park¹,

Chen²,

Mak³

et al. 2023

Preprint

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A Review on Providing Realistic Electric Grid Simulations for Academia and Industry

Birchfield

Overbye

2023

Curr Sustainable Renewable Energy Rep

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Purpose of Review Engineering analysis and design for large-scale electric power grids require advanced modeling and simulation capabilities for a variety of studies, with two of the key study types being steady-state power flow and time-domain stability. In order to promote innovation in this area, during a time of rapid change, much recent work has been done on enhancing the availability of grid models and simulation datasets for the benefit of both academia and industry. The purpose of this paper is to review these new developments. Recent Findings Over the last several years, there have been many different developments in electric grid power flow and stability analysis. In power flow, key new changes include (1) the inclusion of geographic coordinates, (2) the addition of geomagnetic disturbance analysis, (3) the direct inclusion of weather data, and (4) new optimal power flow (OPF) and security-constrained OPF algorithms, some of which utilize machine learning. Key developments in stability are (1) many new models particularly for inverter-based resources, (2) wider availability of interactive stability simulations, and (3) greater use of wide-area visualization in both power flow and stability. Summary The paper shows the range of software platforms available for large-scale electric grid for power flow and stability simulations, along with associated data formats. It also considers modeling enhancements, including the ability to capture more detailed dynamics and coupling to inter-related infrastructure. The paper also summarizes the availability of test case datasets, both real and synthetic.

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Security constrained optimal power shutoff for wildfire risk mitigation

Rhodes,

Coffrin,

Roald

2024

IET Generation Trans & Dist

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Electric grid faults are increasingly the source of ignition for major wildfires. To reduce the likelihood of such ignitions in high risk situations, utilities use preemptive de‐energization of power lines, commonly referred to as Public Safety Power Shutoffs (PSPS). Besides raising challenging trade‐offs between power outages and wildfire safety, PSPS removes redundancy from the network at a time when component faults are likely to happen. This may leave the network particularly vulnerable to unexpected line faults that may occur while the PSPS is in place. Previous works have not explicitly considered the impacts of these outages. To address this gap, the Security Constrained Optimal Power Shutoff problem is proposed which uses post‐contingency security constraints to model the impact of unexpected line faults when planning a PSPS. This model enables, for the first time, the exploration of a wide range of trade‐offs between both wildfire risk and pre‐ and post‐contingency load shedding when designing PSPS plans, providing useful insights for utilities and policy makers considering different approaches to PSPS. The efficacy of the model is demonstrated using the EPRI 39‐bus system as a case study. The results highlight the potential risks of not considering security constraints when planning PSPS and show that incorporating security constraints into the PSPS design process improves the resilience of current PSPS plans.

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Grid Optimization Competition on Synthetic and Industrial Power Systems

Cited by 8 publications

References 18 publications

Compact Optimization Learning for AC Optimal Power Flow

Compact Optimization Learning for AC Optimal Power Flow

A Review on Providing Realistic Electric Grid Simulations for Academia and Industry

Security constrained optimal power shutoff for wildfire risk mitigation

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