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Special Issue "Energy Digitalisation and Data"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J: State-of-the-Art Energy Related Technologies".

Deadline for manuscript submissions: 12 September 2022 | Viewed by 2184

Special Issue Editors

Dr. Andrea Michiorri
E-Mail Website
Guest Editor
Centre for Processes, Renewable Energies and Energy Systems (PERSEE), MINES ParisTech, PSL University, Sophia Antipolis, France
Interests: renewables integration; forecasting; thermal aspects in power systems; smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Christina N. Papadimitriou
E-Mail Website
Guest Editor
FOSS Research Centre for Sustainable Energy, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus
Interests: smart grids; sustainable energy
Special Issues, Collections and Topics in MDPI journals
Carsten Hoyer-Klick
E-Mail Website
Guest Editor Assistant
German Aerospace Center (DLR), Department of Systems Analysis and Technology Assessment, Stuttgart, Germany
Interests: solar radiation assessment from satellites and ground measurements; solar energy meteorology; energy systems modelling in energy and transport systems; research data management in the domain of energy system analysis

Special Issue Information

Dear Colleagues,

Digitalisation is an ongoing trend shaping the energy sector, with the potential to guarantee better assets utilisation, facilitate the integration of variable renewable energy sources and multiple energy carriers, and create growth and jobs. In summary, it is a vital asset of the energy transition. However, it is also associated with new challenges such as IT energy consumption, data access, protection and privacy, as well as cybersecurity.

It is important for the research community to contribute to the debate on this issue, highlighting possible paths, benefits and challenges of this trend. How can new energy technologies harvest the potential benefits of digitalisation, expected in both energy systems planning and operation? What are the boundaries of these benefits, and are they matched by drawbacks? Which policies are necessary to allow societies to reap these benefits? Researchers are welcomed to submit original research manuscripts to this Special Issue on the topic of ‘Energy digitalisation’ and in particular on the keywords below. 

This Special Issue will particularly prize works whose results will be replicable though the sharing of data and methods. Authors are also encouraged to share their work through preprints repositories.

The works accepted by this Special Issue will be invited for presentation at the 62th EUREC general meeting in December 2022 during a workshop with the same title. 

Dr. Andrea Michiorri
Dr. Christina N. Papadimitriou
Carsten Hoyer-Klick
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 2200 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

  • energy and data policy
  • data sharing, including but not limited to governance, privacy, open data and other access solutions, pricing, etc.
  • digitalisation benefits for energy customers and other stakeholders
  • innovative IT technologies in the energy sector, including but not limited to high-performance computing, 5G, artificial intelligence, cloud and edge computing
  • cybersecurity
  • energy consumption of the IT sector
  • business models for digitalisation in the energy sector

Published Papers (2 papers)

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Research

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Article
Shapelets to Classify Energy Demand Time Series
Energies 2022, 15(8), 2960; https://doi.org/10.3390/en15082960 - 18 Apr 2022
Viewed by 429
Abstract
Data are an important asset that the electric power industry have available today to support management decisions, excel in operational efficiency, and be more competitive. The advent of smart grids has increased power grid sensorization and so, too, the data availability. However, the [...] Read more.
Data are an important asset that the electric power industry have available today to support management decisions, excel in operational efficiency, and be more competitive. The advent of smart grids has increased power grid sensorization and so, too, the data availability. However, the inability to recognize the value of data beyond the siloed application in which data are collected is seen as a barrier. Power load time series are one of the most important types of data collected by utilities, because of the inherent information in them (e.g., power load time series comprehend human behavior, economic momentum, and other trends). The area of time series analysis in the energy domain is attracting considerable interest because of growing available data as more sensorization is deployed in power grids. This study considers the shapelet technique to create interpretable classifiers for four use cases. The study systematically applied the shapelet technique to data from different hierarchical power levels (national, primary power substations, and secondary power substations). The study has experimentally shown shapelets as a technique that embraces the interpretability and accuracy of the learning models, the ability to extract interpretable patterns and knowledge, and the ability to recognize and monetize the value of the data, important subjects to reinforce the importance of data-driven services within the energy sector. Full article
(This article belongs to the Special Issue Energy Digitalisation and Data)
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Review
European Energy Regulatory, Socioeconomic, and Organizational Aspects: An Analysis of Barriers Related to Data-Driven Services across Electricity Sectors
Energies 2022, 15(6), 2197; https://doi.org/10.3390/en15062197 - 17 Mar 2022
Cited by 1 | Viewed by 445
Abstract
Data-driven services offer a major shift away from traditional monitoring and control approaches that have been applied exclusively over the transmission and distribution networks. These services assist the electricity value chain stakeholders to enhance their data reach and improve their internal intelligence on [...] Read more.
Data-driven services offer a major shift away from traditional monitoring and control approaches that have been applied exclusively over the transmission and distribution networks. These services assist the electricity value chain stakeholders to enhance their data reach and improve their internal intelligence on electricity-related optimization functions. However, the penetration of data-driven services within the energy sector poses challenges across the regulatory, socioeconomic, and organizational (RSEO) domains that are specific to such business models. The present review examines the existence and importance of various obstacles across these domains regarding innovative energy services, new business models, data exchanges, and other actors’ synergies across the electricity data value chain. This research is centered around the European landscape, with a particular focus on the five demonstration countries (Greece, Spain, Austria, Finland, and Croatia) of the SYNERGY consortium. A state-of-the-art analysis on the regulatory, socioeconomic, and organizational aspects related to innovative energy services (IESs) revealed a plethora of such potential obstacles that could affect, in various degrees, the realization of such services, both at a prototyping and a market replication level. More specifically, 13 barriers were identified in the regulatory domain, 19 barriers were identified in the socioeconomic domain, and 16 barriers were identified in the organizational domain. Then, a comprehensive, survey-based data gathering exercise was designed, formulated, and conducted at a national level as well as at a stakeholder type level. To ensure that our analysis encompassed business-wide perspectives and was validated from the whole electricity data value chain, we utilized a trilevel analysis (i.e., partner, stakeholder type, demo country) to formulate qualitative interviews with business experts from each stakeholder type (namely TSOs, DSOs, aggregators/ESCOs, facility managers/urban planners, and RES Operators). By combining the quantitative data with the qualitative interviews, further recommendations on identifying and facilitating ways to overcome the identified barriers are provided. For the regulatory domain, it is recommended to treat nationally missing regulations by conforming to the provisions of the relevant EU directives, as well as to provide a flexibility-related regulation. For the socioeconomic domain, recommendations were made to increase consumer awareness and thus alleviate the three more impactful barriers identified in this domain. All organizational barriers can be alleviated by taking complex big-data-related issues away from the hands of the organizations and offering them data-as-a-service mechanisms that safeguard data confidentiality and increase data quality. Full article
(This article belongs to the Special Issue Energy Digitalisation and Data)
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