Reinforcement Learning Approach with Deep Deterministic Policy Gradient DDPG-Controlled Virtual Synchronous Generator for an Islanded Microgrid

Afifi, Mohamed A.; Marei, Mostafa I.; Mohamad, Ahmed M.I.

doi:10.1109/mepcon58725.2023.10462333

2023 24th International Middle East Power System Conference (MEPCON) 2023

DOI: 10.1109/mepcon58725.2023.10462333

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Reinforcement Learning Approach with Deep Deterministic Policy Gradient DDPG-Controlled Virtual Synchronous Generator for an Islanded Microgrid

Mohamed A. Afifi,

Mostafa I. Marei,

Ahmed M.I. Mohamad

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2024

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Cited by 2 publications

References 19 publications

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Deep and Reinforcement Learning in Virtual Synchronous Generator: A Comprehensive Review

Ding,

Cao

2024

Energies

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The virtual synchronous generator (VSG) is an important concept and primary control method in modern power systems. The penetration of power-electronics-based distributed generators in the power grid provides uncertainty and reduces the inertia of the system, thus increasing the risk of instability when disturbance occurs. The VSG produces virtual inertia by introducing the dynamic characteristics of the synchronous generator, which provides inertia and becomes a grid-forming control method. The disadvantages of the VSG are that there are many parameters to be adjusted and its operation process is complicated. However, with the rapid development of artificial intelligence (AI) technology, the powerful adaptive learning capability of AI algorithms provides potential solutions to this issue. Two research hotspots are deep learning (DL) and reinforcement learning (RL). This paper presents a comprehensive review of these two techniques combined with VSG control in the energy internet (EI). Firstly, the basic principle and classification of the VSG are introduced. Next, the development of DL and RL algorithms is briefly reviewed. Then, recent research on VSG control based on DL and RL algorithms are summarized. Finally, some main challenges and study trends are discussed.

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Deep and Reinforcement Learning in Virtual Synchronous Generator: A Comprehensive Review

Ding,

Cao

2024

Energies

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Reinforcement-Learning-Based Virtual Inertia Controller for Frequency Support in Islanded Microgrids

Afifi,

Marei,

Mohamad

2024

Technologies

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As the world grapples with the energy crisis, integrating renewable energy sources into the power grid has become increasingly crucial. Microgrids have emerged as a vital solution to this challenge. However, the reliance on renewable energy sources in microgrids often leads to low inertia. Renewable energy sources interfaced with the network through interlinking converters lack the inertia of conventional synchronous generators, and hence, need to provide frequency support through virtual inertia techniques. This paper presents a new control algorithm that utilizes the reinforcement learning agents Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG) to support the frequency in low-inertia microgrids. The RL agents are trained using the system-linearized model and then extended to the nonlinear model to reduce the computational burden. The proposed system consists of an AC–DC microgrid comprising a renewable energy source on the DC microgrid, along with constant and resistive loads. On the AC microgrid side, a synchronous generator is utilized to represent the low inertia of the grid, which is accompanied by dynamic and static loads. The model of the system is developed and verified using Matlab/Simulink and the reinforcement learning toolbox. The system performance with the proposed AI-based methods is compared to conventional low-pass and high-pass filter (LPF and HPF) controllers.

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Reinforcement Learning Approach with Deep Deterministic Policy Gradient DDPG-Controlled Virtual Synchronous Generator for an Islanded Microgrid

Cited by 2 publications

References 19 publications

Deep and Reinforcement Learning in Virtual Synchronous Generator: A Comprehensive Review

Deep and Reinforcement Learning in Virtual Synchronous Generator: A Comprehensive Review

Reinforcement-Learning-Based Virtual Inertia Controller for Frequency Support in Islanded Microgrids

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