Grid Forming Converters in Renewable Energy Sources Dominated Power Grid: Control Strategy, Stability, Application, and Challenges

Zhang, Haobo; Xiang, Wang; Lin, Weixing; Wen, Jinyu

doi:10.35833/mpce.2021.000257

“…Readers are encouraged to check Refs. [56] - [57] for some modified VSG and droop control techniques that provide more effective inertia support to microgrids.…”

Section: Microgrid Control Frameworkmentioning

confidence: 99%

Fusion of Microgrid Control With Model-Free Reinforcement Learning: Review and Vision

She¹,

Li²,

Cui³

et al. 2023

IEEE Trans. Smart Grid

17

0

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Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A highlevel research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of three fusing approaches, i.e., model identification and parameter tuning, supplementary signal generation, and controller substitution, with the existing control framework. Finally, the fundamental challenges associated with adopting MFRL in microgrid control and corresponding insights for addressing these concerns are fully discussed.

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“…2: Modularized control blocks of GFL and GFM inverters Readers are encouraged to check Refs. [44] - [45] for some modified VSG and droop control techniques that provide more effective inertia support to microgrids.…”

Section: Auxiliary Service (Supplementary Signal)mentioning

confidence: 99%

Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision

She¹,

Li²,

Cui³

et al. 2022

Preprint

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Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A highlevel research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of three fusing approaches, i.e., model identification and parameter tuning, supplementary signal generation, and controller substitution, with the existing control framework. Finally, the fundamental challenges associated with adopting MFRL in microgrid control and corresponding insights for addressing these concerns are fully discussed.

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“…are commonly designed based on the fault current characteristics of the SGs [9]. However, the fault current characteristics of the GFM inverters will be mainly determined by their current-limiting control methods [79], making the protection relays less reliable [80]. Proper coordination methods between the GFM inverters with current-limiting controls and the protection relays are required.…”

Section: Fault Current Injectionmentioning

confidence: 99%

A Review of Current-Limiting Control of Grid-Forming Inverters Under Symmetrical Disturbances

Fan¹,

Wang²

2022

Preprint

1

0

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<p>Grid-forming (GFM) inverters are recognized as a viable solution to increase the penetration of renewable energy in bulk power systems. However, GFM inverters are physically different from synchronous generators (SGs) in terms of fault current level. To protect the GFM inverters and support the power grid under faults or severe disturbances, various current-limiting control methods are developed. In this paper, an overview of these methods is presented, which can be classified as direct, indirect, and hybrid ones. An overall control diagram of GFM inverters is developed to demonstrate the implementation of different current-limiting controls. The advantages and disadvantages of different methods are also illustrated. Finally, open issues of current-limiting control methods for GFM inverters, including control stability, fault recovery, and fault current injection, are summarized.</p>

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Grid Forming Converters in Renewable Energy Sources Dominated Power Grid: Control Strategy, Stability, Application, and Challenges

Cited by 151 publications

References 104 publications

Fusion of Microgrid Control With Model-Free Reinforcement Learning: Review and Vision

Fusion of Microgrid Control With Model-Free Reinforcement Learning: Review and Vision

Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision

A Review of Current-Limiting Control of Grid-Forming Inverters Under Symmetrical Disturbances

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