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Future Internet
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Multi-Agent Deep Reinforcement Learning for Distributed Operation and Control of...
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Multi-Agent Deep Reinforcement Learning for Distributed Operation and Control of Microgrids

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 3163

Special Issue Editors

Dr. Van-Hai Bui

E-Mail Website
Guest Editor
Department of Electrical Engineering, School of Engineering, State University of New York (SUNY), Maritime College, 6 Pennyfield Avenue, Throggs Neck, New York, NY 10465, USA
Interests: deep learning; deep reinforcement learning; distributed energy resource integration; energy management system; operation and control of microgrid; optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Akhtar Hussain

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G2, Canada
Interests: electric vehicles and energy storage systems; energy economics and policy; microgrid operation; distributed energy resource integration; resource allocation
Special Issues, Collections and Topics in MDPI journals
Dr. Wencong Su

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, USA
Interests: power systems; electrified transportation systems; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to invite submissions to the Special Issue on “Multi-agent Deep Reinforcement Learning for Distributed Operation and Control of Microgrids”.

With the increasing global concern over the crisis of energy and the environment, microgrids are gaining popularity in modern power systems due to allowing extensive utilization of distributed energy resources (DERs). However, due to the variety of owners of DERs, it is impossible to directly control and operate those DERs from a central authority. Distributed energy management and strategy-making frameworks are more appropriate for future microgrids. Besides, the integration of various DERs with high randomness also introduces challenges to traditional model-based management approaches. Recently, the applications of the multi-agent system and deep reinforcement learning have attracted much attention for developing the distributed operation and control frameworks as well as handling uncertainty factors. In this Special Issue, we are looking for novel methods, algorithms, and technologies using multi-agent deep reinforcement learning to enhance energy efficiency for distributed operation and control of microgrids. Review and survey articles on the following topics are also encouraging for submission.

Topics of interest for publication include, but are not limited to:

  • Applications of artificial intelligence in distributed operation and control of microgrids
  • Decentralized, and distributed operation and control of microgrids
  • Energy management systems for microgrids
  • Integration of renewables and EVs in microgrids
  • Multiagent systems for microgrids
  • Operation and control strategies with distributed energy storage systems
  • Peer-to-Peer energy trading in a microgrids
  • Power quality enhanced operation of distributed microgrids
  • Resilience enhancement through/for microgrids

Dr. Van-Hai Bui
Dr. Akhtar Hussain
Dr. Wencong Su
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • deep reinforcement learning
  • deep learning
  • distributed energy resources
  • distributed operation and control
  • double auction
  • game theory
  • energy management system
  • multi-agent reinforcement learning
  • microgrids
  • multiagent system
  • optimization
  • optimal equilibrium
  • peer-to-peer energy trading
  • smart energy

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Published Papers (1 paper)

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Research

19 pages, 892 KiB  
Article
Drifting Streaming Peaks-Over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast
by Yunchuan Liu, Amir Ghasemkhani and Lei Yang
Future Internet 2023, 15(1), 17; https://doi.org/10.3390/fi15010017 - 28 Dec 2022
Cited by 1 | Viewed by 1996
Abstract
This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of [...] Read more.
This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least 33.6% for the whole year data and at least 37% for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least 35.8% for the whole year data and at least 35.2% for the ramp events. Full article
(This article belongs to the Special Issue Multi-Agent Deep Reinforcement Learning for Distributed Operation and Control of Microgrids)
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