research-article

Adaptive Control Using Machine Learning for Distributed Storage in Microgrids

Authors:

Ramachandra Rao Kolluri,

Julian de HoogAuthors Info & Claims

e-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems

Pages 509 - 515

https://doi.org/10.1145/3396851.3402122

Published: 18 June 2020 Publication History

Abstract

The falling costs of solar photovoltaic systems and energy storage mean that these are being increasingly deployed in microgrids across the globe. Distributed storage can provide benefits for its owner, but can also play a key role in improving microgrid stability and resilience. However, most approaches to date assume that a central authority can control multiple nodes or households in the network. This introduces significant communication and control requirements, and may introduce points of failure. In this work we provide an initial exploration of how a machine learning model, trained on optimal control solutions, can be used locally at each node in the network to emulate a similar behaviour. The aim is for the trained model to provide benefits both for the individual energy storage owners, while also enabling community-level cooperative behaviour - all in a low communication-overhead, privacy-preserving manner. It is experimentally shown that a neural network trained on limited data from optimal schedules can learn node interactions and network characteristics, and can achieve partial voltage regulation for the entire microgrid. This can be done while still achieving a small (3%) network-wide cost savings compared to a scenario in which no distributed storage is present, can be implemented only locally, and does not introduce any significant requirements for central control and communication.

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Cited By

Shabbir NHusev ODaniel KJawad MRosin AMartins J(2024)Short-Term Residental DC Load Forecasting Using Extreme Gradient Boost (XgBoost) Algorithm2024 IEEE 18th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)10.1109/CPE-POWERENG60842.2024.10604392(1-6)Online publication date: 24-Jun-2024
https://doi.org/10.1109/CPE-POWERENG60842.2024.10604392
Wu JLu CWu C(2023)Learning-Aided Framework for Storage Control Facing Renewable EnergyIEEE Systems Journal10.1109/JSYST.2022.315438917:1(652-663)Online publication date: Mar-2023
https://doi.org/10.1109/JSYST.2022.3154389
Shabbir NAhmadiahangar RRosin AJawad MKilter JMartins J(2023)XgBoost based Short-term Electrical Load Forecasting Considering Trends & Periodicity in Historical Data2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG)10.1109/ETFG55873.2023.10407926(1-6)Online publication date: 3-Dec-2023
https://doi.org/10.1109/ETFG55873.2023.10407926
Show More Cited By

Index Terms

Adaptive Control Using Machine Learning for Distributed Storage in Microgrids
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Hardware
  1. Power and energy
    1. Energy generation and storage
      1. Batteries
      2. Renewable energy

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e-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems

June 2020

601 pages

ISBN:9781450380096

DOI:10.1145/3396851

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEnergy: ACM Special Interest Group on Energy Systems and Informatics

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Association for Computing Machinery

New York, NY, United States

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Published: 18 June 2020

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e-Energy '20

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e-Energy '20: The Eleventh ACM International Conference on Future Energy Systems

June 22 - 26, 2020

Virtual Event, Australia

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e-Energy '20 Paper Acceptance Rate 77 of 173 submissions, 45%;

Overall Acceptance Rate 160 of 446 submissions, 36%

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Cited By

Shabbir NHusev ODaniel KJawad MRosin AMartins J(2024)Short-Term Residental DC Load Forecasting Using Extreme Gradient Boost (XgBoost) Algorithm2024 IEEE 18th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)10.1109/CPE-POWERENG60842.2024.10604392(1-6)Online publication date: 24-Jun-2024
https://doi.org/10.1109/CPE-POWERENG60842.2024.10604392
Wu JLu CWu C(2023)Learning-Aided Framework for Storage Control Facing Renewable EnergyIEEE Systems Journal10.1109/JSYST.2022.315438917:1(652-663)Online publication date: Mar-2023
https://doi.org/10.1109/JSYST.2022.3154389
Shabbir NAhmadiahangar RRosin AJawad MKilter JMartins J(2023)XgBoost based Short-term Electrical Load Forecasting Considering Trends & Periodicity in Historical Data2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG)10.1109/ETFG55873.2023.10407926(1-6)Online publication date: 3-Dec-2023
https://doi.org/10.1109/ETFG55873.2023.10407926
Khan HNizami IQaisar SWaqar AKrichen MAlmaktoom A(2022)Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning ApproachesEnergies10.3390/en1521786515:21(7865)Online publication date: 24-Oct-2022
https://doi.org/10.3390/en15217865
Prasad PParimi A(2022)Recent advancements in hybrid AC/DC microgridsMicrogrids10.1016/B978-0-323-85463-4.00004-6(227-246)Online publication date: 2022
https://doi.org/10.1016/B978-0-323-85463-4.00004-6
Ndiaye MHancke GAbu-Mahfouz AZhang H(2021)Software-Defined Power Grids: A Survey on Opportunities and Taxonomy for MicrogridsIEEE Access10.1109/ACCESS.2021.30953179(98973-98991)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3095317
Noorazar HSrivastava APannala SK Sadanandan S(2021)Data‐driven operation of the resilient electric grid: A case of COVID‐19The Journal of Engineering10.1049/tje2.120652021:11(665-684)Online publication date: 9-Aug-2021
https://doi.org/10.1049/tje2.12065

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