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Load Frequency Control: A Deep Multi-Agent Reinforcement Learning Approach

Rozada, S., Apostolopoulou, D. ORCID: 0000-0002-9012-9910 and Alonso, E. ORCID: 0000-0002-3306-695X (2020). Load Frequency Control: A Deep Multi-Agent Reinforcement Learning Approach. Paper presented at the 2020 IEEE PES General Meeting, 2-6 Aug 2020, Montreal, Canada.

Abstract

The paradigm shift in energy generation towards microgrid-based architectures is changing the landscape of the energy control structure heavily in distribution systems. More specifically, distributed generation is deployed in the network demanding decentralised control mechanisms to ensure reliable power system operations. In this work, a Multi-Agent Reinforcement Learning approach is proposed to deliver an agentbased solution to implement load frequency control without the need of a centralised authority. Multi-Agent Deep Deterministic Policy Gradient is used to approximate the frequency control at the primary and the secondary levels. Each generation unit is represented as an agent that is modelled by a Recurrent Neural Network. Agents learn the optimal way of acting and interacting with the environment to maximise their long term performance and to balance generation and load, thus restoring frequency. In this paper we prove using three test systems, with two, four and eight generators, that our Multi-Agent Reinforcement Learning approach can efficiently be used to perform frequency control in a decentralised way.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Reinforcement Learning, MADDPG, Droop Control, ACG, Load Frequency Control
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
Date Deposited: 21 Feb 2020 14:14
URI: https://openaccess.city.ac.uk/id/eprint/23763
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Official URL: http://pes-gm.org/2020/

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