A Distributed Frequency Regulation Architecture for Islanded Inertialess AC Microgrids

Cady, Stanton T.; Zholbaryssov, Madi; Domínguez-García, Alejandro D.; Hadjicostis, Christoforos N.

doi:10.1109/tcst.2016.2640940

Cited by 24 publications

(7 citation statements)

References 18 publications

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“…However, in this new paradigm where there are numerous generators participating in frequency control, the centralised approach shows important limitations in terms of computation and privacy concerns. In this regard, new distributed schemes are necessary to deal with the aforementioned challenges [3].…”

Section: Introductionmentioning

confidence: 99%

Deep multi‐agent Reinforcement Learning for cost‐efficient distributed load frequency control

Rozada

Apostolopoulou

Alonso

2021

IET Energy Syst Integration

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The rise of microgrid-based architectures is modifying significantly the energy control landscape in distribution systems, making distributed control mechanisms necessary to ensure reliable power system operations. In this article, the use of Reinforcement Learning techniques is proposed to implement load frequency control (LFC) without requiring a central authority. To this end, a detailed model of power system dynamic behaviour is formulated by representing individual generator dynamics, generator rate and network constraints, renewable-based generation, and realistic load realisations. The LFC problem is recast as a Markov Decision Process, and the Multi-Agent Deep Deterministic Policy Gradient algorithm is used to approximate the optimal solution of all LFC layers, that is, primary, secondary and tertiary. The proposed LFC framework operates through centralised learning and distributed implementation. In particular, there is no information interchange between generating units during operation. Thus, no communication infrastructure is necessary and information privacy between them is respected. The proposed framework is validated through numerical results and it is shown that it can be used to implement LFC in a distributed and cost-efficient manner.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Deep multi‐agent Reinforcement Learning for cost‐efficient distributed load frequency control

Rozada

Apostolopoulou

Alonso

2021

IET Energy Syst Integration

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“…However, the dynamics of power electronics systems are far too different from traditional systems. When dealing with power electronics systems, the problem of solving this frequency control in a central way is infeasible [3]. In addition, the deployment of distributed renewable generation, e.g., photovoltaic (PV) rooftops, requires new decentralised control designs that ensure frequency restoration in an efficient way.…”

Section: Introductionmentioning

confidence: 99%

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

Rozada

Apostolopoulou

Alonso

2020

2020 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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“…However, in this new paradigm where there are numerous generators participating in frequency control, the centralised approach has central limitations in terms of computation and privacy concerns. In this regard, new distributed schemes are necessary to deal with the aforementioned challenges [4].…”

Section: Introductionmentioning

confidence: 99%

“…Different approaches have attempted to tackle this problem by implementing the traditional hierarchical control in a distributed manner (see, e.g., [4], [5], [6]). In [7], the authors propose a methodology for primary control to mimic droop control strategies which are by nature decentralised algorithms that act upon each generator.…”

Section: Introductionmentioning

confidence: 99%

Deep Multi-Agent Reinforcement Learning for Cost Efficient Distributed Load Frequency Control

Rozada¹,

Apostolopoulou²,

Alonso³

2020

Preprint

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The rise of microgrid-based architectures is heavily modifying the energy control landscape in distribution systems making distributed control mechanisms necessary to ensure reliable power system operations. In this paper, we propose the use of Reinforcement Learning techniques to implement load frequency control without requiring a central authority. To this end, we approximate the optimal solution of the primary, secondary, and tertiary control with the use of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. Generation units are characterised as agents that learn how to maximise their long-term performance by acting and interacting with the environment to balance generation and load in a cost efficient way. Network effects are also modelled in our framework for the restoration of frequency to the nominal value. We validate our Reinforcement Learning methodology through numerical results and show that it can be used to implement the load frequency control in a distributed and cost efficient way.

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A Distributed Frequency Regulation Architecture for Islanded Inertialess AC Microgrids

Cited by 24 publications

References 18 publications

Deep multi‐agent Reinforcement Learning for cost‐efficient distributed load frequency control

Deep multi‐agent Reinforcement Learning for cost‐efficient distributed load frequency control

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

Deep Multi-Agent Reinforcement Learning for Cost Efficient Distributed Load Frequency Control

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