Benchmarking a Decentralized Reinforcement Learning Control Strategy for an Energy Community
Ebell N, Pruckner M (2021)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2021
Event location: Aachen
DOI: 10.1109/smartgridcomm51999.2021.9632323
Abstract
The energy transition towards a more sustainable, secure and affordable electrical power system consisting of high shares of renewable energy sources increases the energy system’s complexity. It creates an energy system in a more decentralized pattern with many more stakeholders involved. In this context, new data-driven operation control strategies play an important role in order to provide fast decision support and a better coordination of electrical assets in the distribution grid. In this paper, we evaluate a novel Multi-Agent Reinforcement Learning approach which focuses on cooperative agents with only local state information and aim to balance the electricity generation and consumption of an energy community consisting of ten households. This approach is compared to a rule-based and an optimal control policy. Results show that independent Q-learner achieve performance 35 % better than rule-based control and compensate high computational effort with adaptability, simplicity in communication requirements and respect of data-privacy.
Authors with CRIS profile
Niklas Ebell
Computer Science 7 (Computer Networks and Communication Systems)
Marco Pruckner
Department Informatik
Related research project(s)
How to cite
APA:
Ebell, N., & Pruckner, M. (2021). Benchmarking a Decentralized Reinforcement Learning Control Strategy for an Energy Community. In Proceedings of the 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). Aachen, DE.
MLA:
Ebell, Niklas, and Marco Pruckner. "Benchmarking a Decentralized Reinforcement Learning Control Strategy for an Energy Community." Proceedings of the 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Aachen 2021.
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