Real-time optimal scheduling for active distribution networks: A graph reinforcement learning method

Xing, Qiang; Chen, Zhong; Zhang, Tian; Li, Xu; Sun, KeHui

doi:10.1016/j.ijepes.2022.108637

Cited by 12 publications

(3 citation statements)

References 39 publications

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“…The distribution network model is a program that was created using a graphical editor that contains a graphic field and a library of distribution network components with which it is possible to create a mnemonic diagram model that will completely duplicate the existing distribution network. The model can be run on any device with the Windows operating system, which is beneficial from an economic point of view [1,[4][5][6]. The model of distribution network 1254 (2023) 012045 IOP Publishing doi:10.1088/1755-1315/1254/1/012045 2 modes does not require the presence of a programmer at the task development stage, since all the processes that require the programmer's work (calculation of current or voltage values, mode switching, functions of the distribution network components) were done before the task development stage [7][8][9].…”

Section: Automatic Methods Of Preparation Of Datamentioning

confidence: 99%

Automatic method of preparation of data for setting the model of the electric network mode when constructing the mnemonic diagram of the object for the simulator

Liepatiev,

Samoylov

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The article is devoted to an automatic data preparation method for setting up a distribution network model, which is used to develop training tasks for distribution network personnel. The main goal in creating the method is to reduce the time for developing a simulator task by leveling the human error during the stage of filling in the structural data of the distribution network components. To interact with the automatic method and the already pre-created component library that was used in the previous method, it was necessary to change the internal structure of the double winding and three winding transformer components. Also, a new structural component was added to the library – Connection. The program algorithm of the automatic method consists of three cycles that go through all the components transferred from the library to the distribution network model. For a better understanding and perception of textual information about each cycle, a graphical representation of cycle process diagrams is presented. In conclusion, information is provided on the time spent when creating a task with manual data preparation and the developed automatic one, as well as suggestions to reduce computer resourced used during model run.

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Section: Automatic Methods Of Preparation Of Datamentioning

confidence: 99%

Automatic method of preparation of data for setting the model of the electric network mode when constructing the mnemonic diagram of the object for the simulator

Liepatiev,

Samoylov

2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…In this sense, some of the main DRL features, such as the adaptability and ability to generalise and extract information from past experiences, have already been demonstrated in a few sectors, as reflected in other reviews. Among them are robotics [103,120], scheduling [121,122], cyber-physical systems [123] and energy systems [124].…”

Section: Deep Reinforcement Learning In the Production Industrymentioning

confidence: 99%

A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework

et al. 2022

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In this review, the industry’s current issues regarding intelligent manufacture are presented. This work presents the status and the potential for the I4.0 and I5.0’s revolutionary technologies. AI and, in particular, the DRL algorithms, which are a perfect response to the unpredictability and volatility of modern demand, are studied in detail. Through the introduction of RL concepts and the development of those with ANNs towards DRL, the potential and variety of these kinds of algorithms are highlighted. Moreover, because these algorithms are data based, their modification to meet the requirements of industry operations is also included. In addition, this review covers the inclusion of new concepts, such as digital twins, in response to an absent environment model and how it can improve the performance and application of DRL algorithms even more. This work highlights that DRL applicability is demonstrated across all manufacturing industry operations, outperforming conventional methodologies and, most notably, enhancing the manufacturing process’s resilience and adaptability. It is stated that there is still considerable work to be carried out in both academia and industry to fully leverage the promise of these disruptive tools, begin their deployment in industry, and take a step closer to the I5.0 industrial revolution.

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“…GNNs are starting to gain widespread use in power systems, providing solutions to a wide range of prediction tasks, including fault location [13], stability assessment [14], power system parameter identification [15], and detecting false data injection attacks [16]. GNNs are also being used for control and optimisation tasks, such as optimal scheduling [17] and volt-var control [18]. Several studies have proposed the use of GNNs for power flow problems, including [19] and [20].…”

Section: Introductionmentioning

confidence: 99%

Graph neural networks on factor graphs for robust, fast, and scalable linear state estimation with PMUs

Ognjen¹,

Cosovic

Miskovic³

et al. 2023

Sustainable Energy, Grids and Networks

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Real-time optimal scheduling for active distribution networks: A graph reinforcement learning method

Cited by 12 publications

References 39 publications

Automatic method of preparation of data for setting the model of the electric network mode when constructing the mnemonic diagram of the object for the simulator

Automatic method of preparation of data for setting the model of the electric network mode when constructing the mnemonic diagram of the object for the simulator

A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework

Graph neural networks on factor graphs for robust, fast, and scalable linear state estimation with PMUs

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