Distributed Intelligent Microgrid Control Using Multi-Agent Systems

Chung, Ilyong; Yoo, Cheol-Hee; Oh, Sang-Jin

doi:10.4236/eng.2013.51b001

Cited by 23 publications

(5 citation statements)

References 8 publications

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“…SOC=Pbat/BC (8) Where Bcapacity represents the capacity of the battery and Pbat represents its power. Maintain the battery charge state (SOC) between SOCminimum and SOCmaximum.…”

Section: Workings Of the Systemmentioning

confidence: 99%

Enhancing hybrid renewable energy performance through deep Q-learning networks improved by fuzzy reward control

Ameur

Faquir²,

Yahyaouy

et al. 2022

IJECE

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<p>In a stand-alone system, the use of renewable energies, load changes, and interruptions to transmission lines can cause voltage drops, impacting its reliability. A way to offset a change in the nature of hybrid renewable energy immediately is to utilize energy storage without needing to turn on other plants. Photovoltaic panels, a wind turbine, and a wallbox unit (responsible for providing the vehicle’s electrical need) are the components of the proposed system; in addition to being a power source, batteries also serve as a storage unit. Taking advantage of deep learning, particularly convolutional neural networks, and this new system will take advantage of recent advances in machine learning. By employing algorithms for deep Q-learning, the agent learns from the data of the various elements of the system to create the optimal policy for enhancing performance. To increase the learning efficiency, the reward function is implemented using a fuzzy Mamdani system. Our proposed experimental results shows that the new system with fuzzy reward using deep Q-learning networks (DQN) keeps the battery and the wallbox unit optimally charged and less discharged. Moreover confirms the economic advantages of the proposed approach performs better approximate to +25% Moreover, it has dynamic response capabilities and is more efficient over the existing optimization approach using deep learning without fuzzy logic.</p>

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“…SOC=Pbat/BC (8) Where Bcapacity represents the capacity of the battery and Pbat represents its power. Maintain the battery charge state (SOC) between SOCminimum and SOCmaximum.…”

Section: Workings Of the Systemmentioning

confidence: 99%

Enhancing hybrid renewable energy performance through deep Q-learning networks improved by fuzzy reward control

Ameur

Faquir²,

Yahyaouy

et al. 2022

IJECE

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show abstract

“…Besides, research on microgrid central controller (MGCC) discussed its various applications including load shedding, smooth transition, RESs assessment, etc 20 . The various other benefits offered by MGCC include cost benefits, 21‐24 efficient, and optimized energy balance between the load demand and the energy generated in the MG while maintaining its stability 25‐27 . But, with an increase in the complexity of MG, the centralized controller fails to cope up with the system.…”

Section: Microgrid: Issues and Controllermentioning

confidence: 99%

A comprehensive review on control techniques for stability improvement in microgrids

Mehta

Basak

2021

Int Trans Electr Energ Syst

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In recent times, with the increase in the penetration of various renewable energy sources (RESs) into power systems, the complications related to the stability issues have increased. The main contribution of this paper is an in‐depth analysis of research in microgrid based on small‐signal, transient, and voltage stability. The small‐signal stability has been discussed based on uncertain load, limitation in power generation capacity, and nature of sluggish feedback observed in few microgrid systems. Instability caused by a transient phenomenon in microgrid has been thoroughly analyzed for losses in distributed energy resources (DERs), islanding and transition modes of operation, load shedding, and faults causing instability. The voltage fluctuations are duly considered in the perspective of load shedding, type of loads, power unbalancing, and different types of faults in microgrid systems. The existing controllers have been compared based on steady‐state error, response time, and robustness etc. The voltage, frequency, and active/reactive power control are analyzed based on centralized, decentralized, hierarchal/distributed control schemes aiming stabilization of microgrid systems. Finally, stability improvement features have been explored in detail, finding the implementation of microgrid stabilizers, energy storage devices, FACTS devices, load balancing, resource forecasting including the adaptive controller. Hence, the uniqueness of this paper can be considered as a state‐of‐the‐art review of the abovementioned stability components in the microgrid system which is not available in the published literature with a similar amount of vastness.

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“…Minimally intelligent agents require little configuration and maintenance, making them ideally suited to Makerspaces (Giunta et al, 2022(Giunta et al, , 2023. Their computational requirements are also minimal, enabling them to be placed on computationally constrained resources, such as the spare computational resource available on AM machine microcontrollers (Chung and Cheol-HeeYoo, 2013;Purusothaman et al, 2013;Pantoja et al, 2018). An unanswered research question lies in understanding how the configuration of agents across a set of AM machines experiencing an a-typical Makerspace demand profile affects the performance of the Makerspace to deliver components to its customers.…”

Section: Introductionmentioning

confidence: 99%

Optimal configurations of Minimally Intelligent additive manufacturing machines for Makerspace production environments

Gopsill,

Goudswaard,

Snider

et al. 2024

AIEDAM

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Additive manufacturing (AM) has transformed job shop production and catalysed the growth of Makerspaces, FabLabs, Hackspaces, and Repair Cafés. AM has enabled the handling and manufacturing of a wide variety of components, and its accessibility has enabled more individuals to make. While smaller than their production-scale counterparts, the objectives of minimizing technician overhead, capital expenditure, and job response time remain the same. The typical First-Come First-Serve (FCFS) operating model, while functional, is not necessarily the most efficient and makes responding to a-typical or urgent demand profiles difficult. This article reports a study that investigated how AM machines configured with Minimally Intelligent agents can support production in these environments. An agent-based model that simulated 5, 10, 15, and 20 AM machines operating a 9 am−5 pm pattern and experiencing a diverse non-repeating demand profile was developed. Machines were configured with minimal intelligence – FCFS, First-Response First-Serve (FRFS), Longest Print Time (LPT), Shortest Print Time (SPT), and Random Selection logics – that governed the selection of jobs from the job pool. A full factorial simulation totaling 15,629 configurations was run until convergence to a ranked list of production performance – min Job Time-in-System. Performance changed as much as 200%. Performant configurations featured a variety of logics, while the least performant were dominated by FCFS and LPT. All FCFS (a proxy for today’s operations) was one of the least performant configurations. The results provide an optimal set of logics and performance bands that can be used to justify capital expenditure and AM operations in Makerspaces.

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Distributed Intelligent Microgrid Control Using Multi-Agent Systems

Cited by 23 publications

References 8 publications

Enhancing hybrid renewable energy performance through deep Q-learning networks improved by fuzzy reward control

Enhancing hybrid renewable energy performance through deep Q-learning networks improved by fuzzy reward control

A comprehensive review on control techniques for stability improvement in microgrids

Optimal configurations of Minimally Intelligent additive manufacturing machines for Makerspace production environments

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