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Microgrid Energy Management 2021

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (20 May 2021) | Viewed by 4453

Special Issue Editor


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Guest Editor
Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, Italy
Interests: power systems; analysis of the transmission and distribution unbalanced systems; power quality issues, with particular attention to the voltage sags; development of the distribution networks towards the smart grids
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Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to a Special Issue of Energies on the subject area of “Microgrid Energy Management”. The microgrids must meet several needs and expectations of customers and of the various stakeholders involved in the electrical energy chain. This heterogeneity requires identifying energy management so that it is able to address a variety of targets related to efficiency, power quality, resiliency, and affordability. Energy management should carefully take into account the presence of distributed energy resources (i.e., wind and solar energy sources) and of loads (e.g., plug-in electric vehicles) which are, for example, responsible for line overloading and critical voltage profiles.

This Special Issue will deal with innovative strategies for the management of microgrids. Topics of interest for publication include but are not limited to the following:

  • Distributed energy resources;
  • Energy storage systems;
  • Active demand;
  • Optimization methods;
  • Resiliency and affordability of power systems;
  • Power quality;
  • Control strategies.

Prof. Dr. Pietro Varilone
Guest Editor

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. Energies is an international peer-reviewed open access semimonthly 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 2600 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

  • distributed energy resources
  • energy storage systems
  • microgrids
  • dispersed storage and generation
  • optimization methods
  • power quality
  • control strategies
  • scheduling

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Published Papers (1 paper)

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Research

15 pages, 2573 KiB  
Article
Optimal Scheduling of Microgrid Based on Deep Deterministic Policy Gradient and Transfer Learning
by Luqin Fan, Jing Zhang, Yu He, Ying Liu, Tao Hu and Heng Zhang
Energies 2021, 14(3), 584; https://doi.org/10.3390/en14030584 - 23 Jan 2021
Cited by 28 | Viewed by 3968
Abstract
Microgrid has flexible composition, a complex operation mechanism, and a large amount of data while operating. However, optimization methods of microgrid scheduling do not effectively accumulate and utilize the scheduling knowledge at present. This paper puts forward a microgrid optimal scheduling method based [...] Read more.
Microgrid has flexible composition, a complex operation mechanism, and a large amount of data while operating. However, optimization methods of microgrid scheduling do not effectively accumulate and utilize the scheduling knowledge at present. This paper puts forward a microgrid optimal scheduling method based on Deep Deterministic Policy Gradient (DDPG) and Transfer Learning (TL). This method uses Reinforcement Learning (RL) to learn the scheduling strategy and accumulates the corresponding scheduling knowledge. Meanwhile, the DDPG model is introduced to extend the microgrid scheduling strategy action from the discrete action space to the continuous action space. On this basis, this paper holds that a microgrid optimal scheduling TL algorithm on the strength of the actual supply and demand similarity is proposed with a purpose of making use of the existing scheduling knowledge effectively. The simulation results indicate that this paper can provide optimal scheduling strategy for microgrid with complex operation mechanism flexibly and efficiently through the effective accumulation of scheduling knowledge and the utilization of scheduling knowledge through TL. Full article
(This article belongs to the Special Issue Microgrid Energy Management 2021)
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