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Large-Scale Grid Optimization: the Workhorse of Future Grid Computations

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Abstract

Purpose of Review

The computation methods for modeling, controlling, and optimizing the transforming grid are evolving rapidly. We review and systemize knowledge for a special class of computation methods that solve large-scale power grid optimization problems.

Recent Findings

We find that while mechanistic physics-based methods are leading the science in solving large-scale grid optimizations, data-driven techniques, especially physics constrained ones, are emerging as an alternative to solve otherwise intractable problems. We also find observable gaps in the field and ascertain these gaps from the paper’s literature review and by collecting and synthesizing feedback from industry experts.

Summary

Large-scale grid optimizations are pertinent for, among other things, hedging against risk due to resource stochasticity, evaluating aggregated DERs’ impact on grid operation and design, and improving the overall efficiency of grid operation in terms of cost, reliability, and carbon footprint. We attribute the continual growth in scale and complexity of grid optimizations to a large influx of new spatial and temporal features in both transmission (T) and distribution (D) networks. Therefore, to systemize knowledge in the field, we discuss the recent advancements in T and D systems from the viewpoint of mechanistic physics-based and emerging data-driven methods.

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Notes

  1. Every inequality constraint, even if inactive, introduces a new dual variable into an optimization formulation, thus increasing problem complexity for primal-dual solvers.

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Acknowledgements

The authors like to thank industry members who helped shape the direction of this paper and provided expert feedback on observable gaps in the field of large-scale optimization from the industry’s viewpoint. These include Kwok Cheung from GE Grid Solutions, Xiaochuan Luo and Jinye Zhao from ISO New England, Dan Kopin from VELCO, Cyril Brunner from Vermont Electric Cooperative, and Marko Jereminov from Pearl Street Technologies, Inc.

Funding

M. Almassalkhi gratefully acknowledges support from National Science Foundation awards ECCS-2047306.

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  1. University of Vermont, VT, Burlington, USA

    Amritanshu Pandey, Mads R. Almassalkhi & Samuel Chevalier

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  1. Amritanshu Pandey
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Contributions

All authors contributed to the study conception and design. A.P. led the work on mechanistic physics-based methods for transmission and combined T &D networks, S.C. led the work on data-driven methods for both T and D networks, and M.A. led the work on physics-based methods for distribution grids. All authors read and approved the final manuscript.

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Correspondence to Amritanshu Pandey.

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A. Pandey owns equity and consults for clean-tech startup Pearl Street Technologies, Inc.

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Pandey, A., Almassalkhi, M.R. & Chevalier, S. Large-Scale Grid Optimization: the Workhorse of Future Grid Computations. Curr Sustainable Renewable Energy Rep 10, 139–153 (2023). https://doi.org/10.1007/s40518-023-00213-6

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