OMG: A Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control

Heid, Stefan; Weber, Daniel; Bode, Henrik; Hüllermeier, Eyke; Wallscheid, Oliver

doi:10.21105/joss.02435

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…Besides, the Gird2Op framework 10 is an open-source environment for training RL agents to operate power networks, which is the testbed for the Learning to Run a Power Network (L2RPN) challenge [157]. Other recently developed RL environments include RLGC [145] for power system control, gymgrid [158] and OMG [159] for microgrid simulation and control, and PowerGym [160] for voltage control in distribution systems, etc. A variety of test systems and test cases are available in these environments.…”

Section: E Numerical Implementationmentioning

confidence: 99%

Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

Chen

Tang

et al. 2022

IEEE Trans. Smart Grid

184

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With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. This paper provides a comprehensive review of various RL techniques and how they can be applied to decision-making and control in power systems. In particular, we select three key applications, i.e., frequency regulation, voltage control, and energy management, as examples to illustrate RL-based models and solutions. We then present the critical issues in the application of RL, i.e., safety, robustness, scalability, and data. Several potential future directions are discussed as well.

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Section: E Numerical Implementationmentioning

confidence: 99%

Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

Chen

Tang

et al. 2022

IEEE Trans. Smart Grid

184

View full text Add to dashboard Cite

show abstract

“…This simplifies the problem definition as also the reference signals become constants. This plant is simulated using the opensource OpenModelica Microgrid Gym (OMG) library [38], [39] for Python [40]. The transformation of the measured quantities in the fixed abc coordinate system to the rotating dq0 reference frame and inverse transformation of actions back to fixed abc frame is not shown in Fig.…”

Section: A Electrical Power Grid Applicationmentioning

confidence: 99%

Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control

Weber¹,

Schenke²,

Wallscheid³

2022

Preprint

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Reinforcement learning (RL) is a promising, upcoming topic in automatic control applications. Where classical control approaches require a priori system knowledge, data-driven control approaches like RL allow a modelfree controller design procedure, rendering them emergent techniques for systems with changing plant structures and varying parameters. While it was already shown in various applications that the transient control behavior for complex systems can be sufficiently handled by RL, the challenge of non-vanishing steady-state control errors remains, which arises from the usage of control policy approximations and finite training times. To overcome this issue, an integral action state augmentation (IASA) for actor-critic-based RL controllers is introduced that mimics an integrating feedback, which is inspired by the delta-input formulation within model predictive control. This augmentation does not require any expert knowledge, leaving the approach model free. As a result, the RL controller learns how to suppress steady-state control deviations much more effectively. Two exemplary applications from the domain of electrical energy engineering validate the benefit of the developed method both for reference tracking and disturbance rejection. In comparison to a standard deep deterministic policy gradient (DDPG) setup, the suggested IASA extension allows to reduce the steady-state error by up to 52 % within the considered validation scenarios.

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Deep Reinforcement Learning Techniques in Diversified Domains: A Survey

Gupta

Singal

Garg

2021

Arch Computat Methods Eng

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OMG: A Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control

Cited by 9 publications

References 6 publications

Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control

Deep Reinforcement Learning Techniques in Diversified Domains: A Survey

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