research-article

Scheduling Fast Local Rule-Based Controllers for Optimal Operation of Energy Storage

Authors:

Julian de Hoog,

Khalid Abdulla,

Ramachandra Rao Kolluri,

Paras KarkiAuthors Info & Claims

e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems

Pages 168 - 172

https://doi.org/10.1145/3208903.3208917

Published: 12 June 2018 Publication History

Abstract

In most applications involving energy storage, multiple different opportunities to generate revenue or reduce cost must be stacked to optimise economic return. This requires repeated scheduling of the energy storage charging schedule in advance, across a finite future horizon. When forecasts and degradation models are incorporated, optimal solutions can become expensive to compute. Local conditions, however, can change instantly, and require fast response by local controllers to truly maximise the energy storage system's returned value. This work proposes a method for optimally operating energy storage in response to multiple value streams that finds the optimal solution over a future horizon, and then breaks this down into a schedule for a small set of simple, local, rule-based controllers. Each local controller is set up to maximise one particular value stream, and can respond to changing conditions on a timescale of milliseconds. The method is specifically designed for implementation on real systems, and successful at ensuring a near-optimal value return by the energy storage system.

References

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Digital Library

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Cited By

Tajalli-Ardekani EDelibra GPizzuti ICorsini A(2024)A Rome district transition towards optimal and sustainable heat and power generationApplied Thermal Engineering10.1016/j.applthermaleng.2024.124001255(124001)Online publication date: Oct-2024
https://doi.org/10.1016/j.applthermaleng.2024.124001
Vamvakas DMichailidis PKorkas CKosmatopoulos E(2023)Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid ApplicationsEnergies10.3390/en1614532616:14(5326)Online publication date: 12-Jul-2023
https://doi.org/10.3390/en16145326
Kell AStephen McGough AForshaw M(2022)Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10021028(4495-4502)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10021028
Show More Cited By

Index Terms

Scheduling Fast Local Rule-Based Controllers for Optimal Operation of Energy Storage
1. Applied computing
  1. Operations research
    1. Forecasting
2. Hardware
  1. Power and energy
    1. Energy generation and storage
      1. Batteries
      2. Renewable energy

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cover image ACM Conferences

e-Energy '18: Proceedings of the Ninth International Conference on Future Energy Systems

June 2018

657 pages

ISBN:9781450357678

DOI:10.1145/3208903

Copyright © 2018 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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Publication History

Published: 12 June 2018

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e-Energy '18

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e-Energy '18: The Ninth International Conference on Future Energy Systems

June 12 - 15, 2018

Karlsruhe, Germany

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Overall Acceptance Rate 160 of 446 submissions, 36%

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The 16th ACM International Conference on Future and Sustainable Energy Systems

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Cited By

Tajalli-Ardekani EDelibra GPizzuti ICorsini A(2024)A Rome district transition towards optimal and sustainable heat and power generationApplied Thermal Engineering10.1016/j.applthermaleng.2024.124001255(124001)Online publication date: Oct-2024
https://doi.org/10.1016/j.applthermaleng.2024.124001
Vamvakas DMichailidis PKorkas CKosmatopoulos E(2023)Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid ApplicationsEnergies10.3390/en1614532616:14(5326)Online publication date: 12-Jul-2023
https://doi.org/10.3390/en16145326
Kell AStephen McGough AForshaw M(2022)Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10021028(4495-4502)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10021028
Mohamed ABest RLiu XMorrow D(2021)Domestic Battery Power Management Strategies to Maximize the Profitability and Support the Network2021 IEEE Power & Energy Society General Meeting (PESGM)10.1109/PESGM46819.2021.9638038(1-5)Online publication date: 26-Jul-2021
https://doi.org/10.1109/PESGM46819.2021.9638038
Yan LLiu WJiang WLi YLi RHu S(2021)Deep Reinforcement Learning based Optimization of Battery Charging and Discharging Management for Data Center2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533476(1-9)Online publication date: 2021
https://doi.org/10.1109/IJCNN52387.2021.9533476
Kolluri Rde Hoog J(2020)Adaptive Control Using Machine Learning for Distributed Storage in MicrogridsProceedings of the Eleventh ACM International Conference on Future Energy Systems10.1145/3396851.3402122(509-515)Online publication date: 12-Jun-2020
https://dl.acm.org/doi/10.1145/3396851.3402122
Hoog JKolluri RIlfrich P(2020)Continuous Near-Optimal Control of Energy Storage SystemsIFAC-PapersOnLine10.1016/j.ifacol.2020.12.133453:2(12471-12478)Online publication date: 2020
https://doi.org/10.1016/j.ifacol.2020.12.1334
Hviid JJohansen ASangogboye FKjærgaard M(2019)Enabling Auto-Configuring Building ServicesProceedings of the Tenth ACM International Conference on Future Energy Systems10.1145/3307772.3328288(68-77)Online publication date: 15-Jun-2019
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Qi BRashedi MArdakanian O(2019)EnergyBoostProceedings of the Tenth ACM International Conference on Future Energy Systems10.1145/3307772.3328279(239-250)Online publication date: 15-Jun-2019
https://dl.acm.org/doi/10.1145/3307772.3328279

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