research-article

Flexibility Disaggregation under Forecast Conditions

Authors:

Dominik Danner,

Hermann de MeerAuthors Info & Claims

e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems

Pages 27 - 38

https://doi.org/10.1145/3447555.3464851

Published: 22 June 2021 Publication History

Abstract

Stationary battery energy storage systems and electric vehicles become more and more popular at households with local photovoltaic generation. Besides improving self-consumption and autarchy, these batteries can provide flexibility to an external utility. Thereby, generation and demand uncertainty, as well as cost optimality, need to be considered when utilizing distributed flexibility.

This paper discusses long short-term memory neural networks for photovoltaic generation forecast and persistence models for household load forecast with respect to their applicability in local energy management system optimization. Furthermore, a mixed-integer linear program is proposed to optimally utilize local flexible loads and storage systems. Its solution space yields the flexibility potential, which can be aggregated at flexibility pools. In order to disaggregate flexibility requests to a pool of distributed energy management systems, we propose a heuristic algorithm that can among others minimize the overall flexibility cost or maximize probability of flexibility delivery. The forecast models, the mixed integer linear program and the flexibility disaggregation are evaluated on realistic household photovoltaic and load profiles to demonstrate the full chain from local forecast to flexibility disaggregation under forecast conditions. Our experiments with flexibility disaggregation show that the probability to provide flexibility should not be neglected when it comes to distributed energy management optimization based on forecast models.

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Cited By

Lechl Mde Meer HFürmann T(2025)A stochastic flexibility calculus for uncertainty-aware energy flexibility managementApplied Energy10.1016/j.apenergy.2024.124907379(124907)Online publication date: Feb-2025
https://doi.org/10.1016/j.apenergy.2024.124907
Sales-Ortiz JArdakanian O(2024)Efficient Trading of Aggregate Bidirectional EV Charging Flexibility with Reinforcement LearningProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661949(134-146)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3661949
Lokhande SBichpuriya YSarangan V(2024)Real-time management of deviations in the demand of electric vehicle charging stations by utilizing EV flexibilityJournal of Energy Storage10.1016/j.est.2024.11271997(112719)Online publication date: Sep-2024
https://doi.org/10.1016/j.est.2024.112719
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Index Terms

Flexibility Disaggregation under Forecast Conditions
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
  2. Modeling and simulation
    1. Model development and analysis
      1. Uncertainty quantification
2. Hardware
  1. Power and energy
    1. Energy distribution
      1. Smart grid

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e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems

June 2021

528 pages

ISBN:9781450383332

DOI:10.1145/3447555

Copyright © 2021 ACM.

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Publication History

Published: 22 June 2021

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Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie
Zentrum Digitalisierung Bayern

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e-Energy '21

e-Energy '21: The Twelfth ACM International Conference on Future Energy Systems

June 28 - July 2, 2021

Virtual Event, Italy

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Overall Acceptance Rate 160 of 446 submissions, 36%

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Cited By

Lechl Mde Meer HFürmann T(2025)A stochastic flexibility calculus for uncertainty-aware energy flexibility managementApplied Energy10.1016/j.apenergy.2024.124907379(124907)Online publication date: Feb-2025
https://doi.org/10.1016/j.apenergy.2024.124907
Sales-Ortiz JArdakanian O(2024)Efficient Trading of Aggregate Bidirectional EV Charging Flexibility with Reinforcement LearningProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems10.1145/3632775.3661949(134-146)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3632775.3661949
Lokhande SBichpuriya YSarangan V(2024)Real-time management of deviations in the demand of electric vehicle charging stations by utilizing EV flexibilityJournal of Energy Storage10.1016/j.est.2024.11271997(112719)Online publication date: Sep-2024
https://doi.org/10.1016/j.est.2024.112719
Lechl MSchoppik Lde Meer H(2023)Aggregating multi-time-scale flexibility potentials of battery storages based on open data – a potential analysisEnergy Informatics10.1186/s42162-023-00273-46:S1Online publication date: 19-Oct-2023
https://doi.org/10.1186/s42162-023-00273-4
Brinkel NVisser Lvan Sark WAlSkaif T(2023)A novel forecasting approach to schedule aggregated electric vehicle chargingEnergy and AI10.1016/j.egyai.2023.10029714(100297)Online publication date: Oct-2023
https://doi.org/10.1016/j.egyai.2023.100297
Danner DHuwa RDe Meer H(2022)Multi-objective flexibility disaggregation to distributed energy management systemsACM SIGEnergy Energy Informatics Review10.1145/3555006.35550072:2(1-12)Online publication date: 4-Aug-2022
https://dl.acm.org/doi/10.1145/3555006.3555007
Danner Dde Meer H(2021)Quality of service and fairness for electric vehicle charging as a serviceEnergy Informatics10.1186/s42162-021-00175-34:S3Online publication date: 13-Sep-2021
https://doi.org/10.1186/s42162-021-00175-3

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