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Selected Papers from the 7th International Conference on Smart Energy...
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Selected Papers from the 7th International Conference on Smart Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 6558

Special Issue Editors

Dr. Iva Ridjan Skov

E-Mail Website
Guest Editor
The Technical Faculty of IT and Design Sustainable Energy Planning Research Group, Aalborg University, 2450 Copenhagen, Denmark
Interests: electrofuels; power-to-x; carbon capture and utilization; smart energy systems; renewable energy sources
Special Issues, Collections and Topics in MDPI journals
Dr. Steffen Nielsen

E-Mail Website
Guest Editor
Department of Planning, The Technical Faculty of IT and Design,Sustainable Energy Planning Research Group, Aalborg University, Aalborg, Denmark
Interests: GIS; energy; district heating; heat planning; energy planning; energy system analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of the conference is to establish a venue for presenting and discussing scientific findings and industrial experiences related to the subject of Smart Energy Systems based on renewable energy, 4th Generation District Heating Technologies and Systems (4GDH), electrification of heating and transportation sectors, electrofuels and energy efficiency. Authors of approved abstracts will be invited to submit papers to this Special Issue in Energies.

The 6th conference in the series cements it as a main venue for presentations and fruitful debates on subjects that are pertinent to the development and implementation of smart energy systems to fulfill national and international objectives.

More information on the conference: https://smartenergysystems.eu/about/

Call for abstracts: https://smartenergysystems.eu/abstract-submission/

Dr. Iva Ridjan Skov
Dr. Steffen Nielsen
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. 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 system analyses, tools and methodologies
  • smart energy infrastructure and storage options
  • integrated energy systems and smart grids
  • institutional and organisational change for smart energy systems and radical technological change
  • energy savings, in the electricity sector, in buildings and transport as well as within industry
  • 4th generation district heating concepts, future district heating production and systems
  • electrification of transport, heating and industry
  • the production, technologies for and use of electrofuels in future energy systems
  • planning and organisational challenges for smart energy systems and district heating
  • geographical informationsystems (GIS) for energy systems, heat planning and district heating
  • components and systems for district heating, energy efficiency, electrification and electrofuels
  • renewable energy sources and waste heat sources for district heating

Published Papers (3 papers)

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Research

23 pages, 4127 KiB  
Article
Forecasting Charging Point Occupancy Using Supervised Learning Algorithms
by Adrian Ostermann, Yann Fabel, Kim Ouan and Hyein Koo
Energies 2022, 15(9), 3409; https://doi.org/10.3390/en15093409 - 06 May 2022
Cited by 8 | Viewed by 1788
Abstract
The prediction of charging point occupancy enables electric vehicle users to better plan their charging processes and thus promotes the acceptance of electromobility. The study uses Adaptive Charging Network data to investigate a public and a workplace site for predicting individual charging station [...] Read more.
The prediction of charging point occupancy enables electric vehicle users to better plan their charging processes and thus promotes the acceptance of electromobility. The study uses Adaptive Charging Network data to investigate a public and a workplace site for predicting individual charging station occupancy as well as overall site occupancy. Predicting individual charging point occupancy is formulated as a classification problem, while predicting total occupancy is formulated as a regression problem. The effects of different feature sets on the predictions are investigated, as well as whether a model trained on data of all charging points per site performs better than one trained on the data of a specific charging point. Reviewed studies so far, however, have failed to compare these two approaches to benchmarks, to use more than one algorithm, or to consider more than one site. Therefore, the following supervised machine-learning algorithms were applied for both tasks: linear and logistic regression, k-nearest neighbor, random forest, and XGBoost. Further, the model results are compared to three different naïve approaches which provide a robust benchmark, and the two training approaches were applied to two different sites. By adding features, the prediction quality can be increased considerably, which resulted in some models performing better than the naïve approaches. In general, models trained on data of all charging points of a site perform slightly better on median than models trained on individual charging points. In certain cases, however, individually trained models achieve the best results, while charging points with very low relative charging point occupancy can benefit from a model that has been trained on all data. Full article
(This article belongs to the Special Issue Selected Papers from the 7th International Conference on Smart Energy Systems)
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16 pages, 5309 KiB  
Article
A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems
by EunJi Ahn and Jin Hur
Energies 2022, 15(7), 2676; https://doi.org/10.3390/en15072676 - 06 Apr 2022
Cited by 5 | Viewed by 1751
Abstract
The energy industry, primarily based on the use of fossil fuels (e.g., coal and oil) is rapidly shifting toward renewable energy for securing sustainable resources. Thus, preparing for large wind power ramp events is essential to retain reliable and secure power systems. This [...] Read more.
The energy industry, primarily based on the use of fossil fuels (e.g., coal and oil) is rapidly shifting toward renewable energy for securing sustainable resources. Thus, preparing for large wind power ramp events is essential to retain reliable and secure power systems. This study proposed a new statistical approach to predict wind power ramp events, and evaluated the performance of prediction. The empirical data, which is the observed wind power output data and wind speed data from Taebaek (South Korea) were used for analyzing ramp events and for evaluation. Based on the data analysis, a practical metric for evaluating the performance of wind power ramp events forecasting was developed and presented in detail. Notably, the accuracy of forecasting was evaluated through various metrics, whereas the normalized mean absolute error (NMAE) analysis demonstrated ≤ 10% values for all the analyzed months. In addition, a system review was conducted to check if the methodology suggested in this study has helped enhance the security of power systems. The results show that evaluating and considering the ramp events can improve the accuracy of wind power output forecasting which can secure the smart energy systems. Full article
(This article belongs to the Special Issue Selected Papers from the 7th International Conference on Smart Energy Systems)
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31 pages, 9779 KiB  
Article
Analyzing Intersectoral Benefits of District Heating in an Integrated Generation and Transmission Expansion Planning Model
by Henrik Schwaeppe, Luis Böttcher, Klemens Schumann, Lukas Hein, Philipp Hälsig, Simon Thams, Paula Baquero Lozano and Albert Moser
Energies 2022, 15(7), 2314; https://doi.org/10.3390/en15072314 - 22 Mar 2022
Cited by 10 | Viewed by 2481
Abstract
In the field of sector integration, the expansion of district heating (DH) is traditionally discussed with regard to the efficient integration of renewable energy sources (RES) and excess heat. But does DH exclusively benefit from other sectors or does it offer advantages in [...] Read more.
In the field of sector integration, the expansion of district heating (DH) is traditionally discussed with regard to the efficient integration of renewable energy sources (RES) and excess heat. But does DH exclusively benefit from other sectors or does it offer advantages in return? So far, studies have investigated DH only as a closed system or determined intersectoral benefits in a highly aggregated approach. We use and expand an integrated generation and transmission expansion planning model to analyze how the flexibility of DH benefits the energy system and the power transmission grid in particular. First of all, the results confirm former investigations that show DH can be used for efficient RES integration. Total annual system cost can be decreased by expanding DH, due to low investment cost and added flexibility, especially from large-scale heat storage. The high short-term efficiency of heat storage—in combination with electric heating technologies—can be exploited to shift heat demand temporally and, using multiple distributed units, locally to solve electric grid congestion. Although it is unclear whether these results can be replicated in the real world, due to the aggregation and detail of the model, further research in this direction is justified. Full article
(This article belongs to the Special Issue Selected Papers from the 7th International Conference on Smart Energy Systems)
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