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Building Power Grid 2.0: Deep Learning and Federated Computations for Energy Decarbonization and Edge Resilience

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Deep Learning Applications, Volume 3

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1395))

Abstract

Electrical grids are traditionally operated as multi-entity systems with each entity managing a geographical region. The current movement toward energy democratization and decarbonization is resulting in the growing penetration of controllable energy resources. This process in turn is increasing the number of grid entities (agents). The paradigm shift is also fueled by the increased adoption of intelligent sensors and actuators for advanced processing and computing. While collaboration among agents reduces costs and increases overall reliability, effective collaboration is challenging. The main challenges stem from the heterogeneity of agents and their data. Furthermore, while data collection is constantly increasing many grid entities have strict privacy requirements that limit data usage. Another challenge is the energy industry’s usual practice of keeping data in silos. Deep learning and federated computations are well suited to addressing these issues. Deep learning enables predicting energy needs and profiling agents’ energy profiles. Through federated computation, agents collaboratively solve learning and optimization problems while preserving privacy and overcoming cross-device and cross-organization data isolation. This chapter discusses the practical challenges of performing multi-agent data processing. It then addresses issues of coordinating connected distributed energy resources (DERs) in the Internet of Things (IoT). Finally, the chapter’s case studies demonstrate how these techniques can address load forecasting and coordination challenges.

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Author information

Authors and Affiliations

  1. Grid Fruit, LLC, Pittsburgh, PA, USA

    Carolyn Goodman, Jesse Thornburg, Shankar Koduvayur Ramaswami & Javad Mohammadi

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Goodman, Jesse Thornburg, Shankar Koduvayur Ramaswami & Javad Mohammadi

Authors
  1. Carolyn Goodman
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  2. Jesse Thornburg
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  3. Shankar Koduvayur Ramaswami
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  4. Javad Mohammadi
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Corresponding author

Correspondence to Jesse Thornburg .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Kashmir, Srinagar, India

    M. Arif Wani

  2. Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Bhiksha Raj

  3. Clemson University, Clemson, SC, USA

    Feng Luo

  4. University of Oregon, Eugene, OR, USA

    Dejing Dou

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Goodman, C., Thornburg, J., Ramaswami, S.K., Mohammadi, J. (2022). Building Power Grid 2.0: Deep Learning and Federated Computations for Energy Decarbonization and Edge Resilience. In: Wani, M.A., Raj, B., Luo, F., Dou, D. (eds) Deep Learning Applications, Volume 3. Advances in Intelligent Systems and Computing, vol 1395. Springer, Singapore. https://doi.org/10.1007/978-981-16-3357-7_11

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