energies-logo

Journal Browser

Journal Browser

Selected Papers from 10th International Conference on Smart Energy Grid Engineering (SEGE 2022)

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 (25 November 2022) | Viewed by 5667

Special Issue Editor


E-Mail Website
Guest Editor
Department of Energy and Nuclear Engineering, Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON L1G 0C5, Canada
Interests: esilient smart energy grid and micro-energy grid planning, control, and protection; advanced plasma generation and applications in fusion energy; advanced safety and control systems for nuclear power plants; safety engineering, fault diagnosis, and real-time simulation; risk-based energy conservation; smart green buildings; process systems engineering of the energy and nuclear facilities and oil and gas production plants
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 10th International Conference on Smart Energy Grid Engineering (SEGE 2022) will be held at Ontario Tech University, Oshawa, Canada, from August 10 to 12, 2022. SEGE provides participants with the opportunity to discuss various engineering challenges presented by smart energy grid design and operation and their applications. Researchers from academia and professionals from the industry, as well as government regulators, will be able to exchange knowledge and the best practices for smart energy grids.

Authors of papers related to energy and presented at the conference are invited to submit extended versions of their work to this Special Issue for publication.

Prof. Dr. Hossam A. Gaber
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

  • smart energy grids
  • energy efficiency
  • clean energy
  • renewable energy

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 3372 KiB  
Article
Energy Hub Gas: A Modular Setup for the Evaluation of Local Flexibility and Renewable Energy Carriers Provision
by Rafael Poppenborg, Malte Chlosta, Johannes Ruf, Christian Hotz, Clemens Düpmeier, Thomas Kolb and Veit Hagenmeyer
Energies 2023, 16(6), 2720; https://doi.org/10.3390/en16062720 - 14 Mar 2023
Viewed by 1703
Abstract
The ambitious targets for the reduction of Greenhouse Gas (GHG) emissions force the enhanced integration and installation of Renewable Energy Sources (RESs). Furthermore, the increased reliance of multiple sectors on electrical energy additionally challenges the electricity grid with high volatility from the demand [...] Read more.
The ambitious targets for the reduction of Greenhouse Gas (GHG) emissions force the enhanced integration and installation of Renewable Energy Sources (RESs). Furthermore, the increased reliance of multiple sectors on electrical energy additionally challenges the electricity grid with high volatility from the demand side. In order to keep the transmission system operation stable and secure, the present approach adds local flexibility into the distribution system using the modular Energy Hub Gas (EHG) concept. For this concept, two different test cases are configured and evaluated. The two configured EHGs demonstrate the ability to provide flexibility and adaptability by reducing the difference between maximal and minimal load in the surrounding grid infrastructure by 30% in certain time periods. Furthermore, the average energy exchange is reduced by 8%. Therefore, by relieving the grid infrastructure in the local surroundings, the additional potential of RES is enabled and the curtailment of existing ones can be reduced. Full article
Show Figures

Figure 1

21 pages, 738 KiB  
Article
A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies
by Jan Schweikert, Karl-Uwe Stucky, Wolfgang Süß and Veit Hagenmeyer
Energies 2023, 16(3), 1444; https://doi.org/10.3390/en16031444 - 1 Feb 2023
Cited by 1 | Viewed by 1500
Abstract
Smart grids of the future will create and provide huge data volumes, which are subject to FAIR (Findable, Accessible, Interoperable, and Reusable) data management solutions when used within the scientific domain and for operation. FAIR Digital Objects (FDOs) provide access to (meta)data, and [...] Read more.
Smart grids of the future will create and provide huge data volumes, which are subject to FAIR (Findable, Accessible, Interoperable, and Reusable) data management solutions when used within the scientific domain and for operation. FAIR Digital Objects (FDOs) provide access to (meta)data, and ontologies explicitly describe metadata as well as application data objects and domains. The present paper proposes a novel approach to integrate FAIR digital objects and ontologies as metadata models in order to support data access for energy researchers, energy research applications, operational applications and energy information systems. As the first example domain to be modeled using an ontology and to get integrated with FAIR digital objects, a photovoltaic (PV) system model is selected. For the given purpose, a discussion of existing energy ontologies shows the necessity to develop a new PV ontology. By integration of FDOs, this new PV ontology is introduced in the present paper. Furthermore, the concept of FDOs is integrated with the PV ontology in such a way that it allows for generalization. By this, the present paper contributes to a sustainable data management for smart grid operation, especially for interoperability, by using ontologies and, hence, unambiguous semantics. An information system application that visualizes the PV system, its describing data and collected sensor data, is proposed. As a proof of concept the details of the use case implementation are presented. Full article
Show Figures

Figure 1

14 pages, 1946 KiB  
Article
Electrical Load Classification with Open-Set Recognition
by Dániel István Németh and Kálmán Tornai
Energies 2023, 16(2), 800; https://doi.org/10.3390/en16020800 - 10 Jan 2023
Viewed by 1434
Abstract
Full utilization of renewable energy resources is a difficult task due to the constantly changing demand-side load of the electrical grid. Demand-side management would solve this crucial problem. To enable demand-side management, knowledge about the composition of the grid load is required, as [...] Read more.
Full utilization of renewable energy resources is a difficult task due to the constantly changing demand-side load of the electrical grid. Demand-side management would solve this crucial problem. To enable demand-side management, knowledge about the composition of the grid load is required, as well as the ability to schedule individual loads. There are proposed Smart Plugs presented in the literature capable of such tasks. The problem, however, is that these methods lack the ability to detect if a previously unseen electrical load is connected. Misclassification of such loads presents a problem for load estimation and scheduling. Open-set recognition methods solve this problem by providing a way to detect samples not belonging to any class used during the training of the classifier. This paper evaluates the novel application of open-set recognition methods to the problem of load classification. Two approaches were examined, and both offer promising results. A Support Vector Machine based approach was first evaluated. The second, more robust method used a modified OpenMax-based algorithm to detect unseen loads. Full article
Show Figures

Figure 1

Back to TopTop