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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 22245

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Special Issue Editors

Dr. Andrea Michiorri

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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

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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

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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 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

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 (11 papers)

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Research

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23 pages, 7154 KiB

Open AccessArticle

Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation

by Sarah Barber, Unai Izagirre, Oscar Serradilla, Jon Olaizola, Ekhi Zugasti, Jose Ignacio Aizpurua, Ali Eftekhari Milani, Frank Sehnke, Yoshiaki Sakagami and Charles Henderson

Energies 2023, 16(8), 3567; https://doi.org/10.3390/en16083567 - 20 Apr 2023

Cited by 2 | Viewed by 1602

Abstract

In this paper, a set of best practice data sharing guidelines for wind turbine fault detection model evaluation is developed, which can help practitioners overcome the main challenges of digitalisation. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration framework called WeDoWind was developed in recent work. The main innovation of this framework is the way it creates tangible incentives to motivate and empower different types of people from all over the world to share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA-data-based wind turbine fault detection models are investigated by carrying out a new case study, the “WinJi Gearbox Fault Detection Challenge”, based on the WeDoWind framework. A total of six new solutions were submitted to the challenge, and a comparison and evaluation of the results show that, in general, some of the approaches (Particle Swarm Optimisation algorithm for constructing health indicators, performance monitoring using Deep Neural Networks, Combined Ward Hierarchical Clustering and Novelty Detection with Local Outlier Factor and Time-to-failure prediction using Random Forest Regression) appear to exhibit high potential to reach the goals of the Challenge. However, there are a number of concrete things that would have to have been done by the Challenge providers and the Challenge moderators in order to ensure success. This includes enabling access to more details of the different failure types, access to multiple data sets from more wind turbines experiencing gearbox failure, provision of a model or rule relating fault detection times or a remaining useful lifetime to the estimated costs for repairs, replacements and inspections, provision of a clear strategy for training and test periods in advance, as well as provision of a pre-defined template or requirements for the results. These learning outcomes are used directly to define a set of best practice data sharing guidelines for wind turbine fault detection model evaluation. The guidelines can be used by researchers in the sector in order to improve model evaluation and data sharing in the future. Full article

(This article belongs to the Special Issue Energy Digitalisation and Data)

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14 pages, 4894 KiB

Open AccessArticle

Assessment of Conservation Voltage Reduction in Distribution Networks with Voltage Regulating Distribution Transformers

by Anthony Igiligi, Armin Vielhauer, Mathias Ehrenwirth, Christian Hurm, Thorsten Summ, Christoph Trinkl and Daniel Navarro Gevers

Energies 2023, 16(7), 3155; https://doi.org/10.3390/en16073155 - 31 Mar 2023

Cited by 3 | Viewed by 1029

Abstract

The application of voltage reduction in medium and low voltage grids to reduce peak power demand or energy consumption has been implemented since the 1980s using several approaches. Conservation Voltage Reduction (CVR), as one such approach, uses a voltage control device to reduce or increase the voltage setpoint on a busbar, thereby reducing or increasing the amount of active and reactive power supply in the network. Voltage regulation for CVR is always implemented according to established network planning standards in each country. Research in this field has proven that a CVR factor (

{C V R}_{f}

) of 0.7–1.5 for peak demand reduction can be achieved. This is an evaluation metric of CVR. The aim of this research is to determine and validate

{C V R}_{f}

for peak demand reduction by comparing actual results obtained during regular tap changes with other randomly distributed periods outside tap change operations, using a set of measurement data. It is important to understand CVR deployment capability by evaluating CVR potentials from historical random tap operations before a robust network-wide deployment is introduced. This research provides such guidance. It also provides a novel approach to determining tap changes from voltage measurements using a time-based algorithm. A

{C V R}_{f}

ranging from 0.95 to 1.61 was estimated using a measurement dataset from a test field. The result of the entire evaluation shows that the

{C V R}_{f}

are smaller during peak PV production and greater during peak demand periods. Further evaluation using statistical hypotheses testing and a control chart was used to validate the evaluation. Full article

(This article belongs to the Special Issue Energy Digitalisation and Data)

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23 pages, 567 KiB

Open AccessArticle

Topic Taxonomy and Metadata to Support Renewable Energy Digitalisation

by Andrea Michiorri, Anna Maria Sempreviva, Sean Philipp, Paula Perez-Lopez, Alain Ferriere and David Moser

Energies 2022, 15(24), 9531; https://doi.org/10.3390/en15249531 - 15 Dec 2022

Cited by 6 | Viewed by 1149

Abstract

Research and innovation in renewable energy, such as wind and solar, have been supporting the green transformation of energy systems, the backbone of a low-carbon climate-resilient society. The major challenge is to manage the complexity of the grid transformation to allow for higher shares of highly variable renewables while securing the safety of the stability of the grid and a stable energy supply. A great help comes from the ongoing digital transformation where digitisation of infrastructures and assets in research and industry generates multi-dimensional and multi-disciplinary digital data. However, a data user needs help to find the correct data to exploit. This has two significant facets: first, missing data management, i.e., datasets are neither findable because of missing community standard metadata and taxonomies, nor interoperable, i.e., missing standards for data formats; second, data owners having a negative perception of sharing data. To make data ready for data science exploitation, one of the necessary steps to map the existing data and their availability to facilitate their access is to create a taxonomy for the field’s topics. For this, a group of experts in different renewable technologies such as photovoltaics, wind and concentrated solar power and in transversal fields such as life cycle assessment and the EU taxonomy for sustainable activities have been gathered to propose a coherent and detailed taxonomy for renewable energy-related data. The result is a coherent classification of relevant data sources, considering both the general aspects applicable to electricity generation from selected renewable energy technologies and the specific aspects of each of them. It is based on previous relevant work and can be easily extended to other renewable resources not considered in this work and conventional energy technology. Full article

(This article belongs to the Special Issue Energy Digitalisation and Data)

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25 pages, 549 KiB

Open AccessArticle

Dynamic Capabilities in Electrical Energy Digitalization: A Case from the Norwegian Ecosystem

by Ahmed Idries, John Krogstie and Jayaprakash Rajasekharan

Energies 2022, 15(22), 8342; https://doi.org/10.3390/en15228342 - 08 Nov 2022

Cited by 7 | Viewed by 2326

Abstract

This paper aims to identify the dynamic capabilities required for electrical energy service providers to transform toward a digital and platform-based business models in the context of the current energy transition. The paper contributes to two fields: Information systems in the domain of platforms ecosystems and digital services innovation through the usage of dynamic capabilities theoretical lens and the field of energy informatics in the domain of digital business models and service innovation. Through the case study approach we investigate the case of Norwegian electrical energy provider TrønderEnergi and how the company is moving toward a fully digital business model and how the company build the dynamic capabilities required for the digitalization era. Through semi-structured interviews, the study managed to identify several activities related to each capability and then classified these activities under three main activities, which are: sensing, seizing, and transforming, and then classified them into sub-capabilities and identified activities related to each sup capability. The paper concludes with managerial implications for practitioners and initiates an empirical extension for the dynamic capabilities theoretical lens. Full article

(This article belongs to the Special Issue Energy Digitalisation and Data)

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19 pages, 6701 KiB

Open AccessArticle

Machine-Learning-Based Modeling of a Hydraulic Speed Governor for Anomaly Detection in Hydropower Plants

by Mehmet Akif Bütüner, İlhan Koşalay and Doğan Gezer

Energies 2022, 15(21), 7974; https://doi.org/10.3390/en15217974 - 27 Oct 2022

Cited by 2 | Viewed by 1272

Abstract

Hydroelectric power plants (HEPPs) are renewable energy power plants with the highest installed power in the world. The control systems are responsible for stopping the relevant unit safely in case of any malfunction while ensuring the desired operating point. Conventional control systems detect anomalies at certain limits or predefined threshold values by evaluating analog signals regardless of differences caused by operating conditions. In this study, using real data from a large hydro unit (>150 MW), a normal behavior model of a hydraulic governor’s oil circulation in an operational HEPP is created using several machine learning methods and historical data obtained from the HEPP’s SCADA system. Model outputs resulted in up to 96.45% success of prediction with less than 1% absolute deviation from actual measurements and an R² score of 0.985 with the random forest regression method. This novel approach makes the model outputs far more appropriate to use as an active threshold value changing for different operating conditions, eliminating insufficiency of the constant threshold, resulting in the detection of anomalies in early stages and taking precautions accordingly. The study fills the gap in the literature on anomaly detection in hydraulic power units, which is a demanding task with state-of-the-art methods. Full article

(This article belongs to the Special Issue Energy Digitalisation and Data)

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25 pages, 14936 KiB

Open AccessArticle

Context-Aware Policy Analysis for Distributed Usage Control

by Gonzalo Gil, Aitor Arnaiz, Mariví Higuero, Francisco Javier Diez and Eduardo Jacob

Energies 2022, 15(19), 7113; https://doi.org/10.3390/en15197113 - 27 Sep 2022

Viewed by 1199

Abstract

To boost data spaces and benefit from the great opportunities that they present, data sovereignty must be provided by Distributed Usage Control (DUC). Assuming that DUC will be managed by implementing and enforcing policies, notable efforts have already been undertaken in the context of Access Control (AC) regarding policy analysis due to the impact of low-quality policies on security. In this regard, this paper proposes that policy analysis in the DUC context should be understood as an extension of the AC, which is further affected by other challenging features, chief among which are context-aware control and extended control through action requirements. This paper presents a novel Context-Aware Policy Analysis (CAPA) algorithm for detecting inconsistencies and redundancies for DUC policies by supporting a large set of heterogeneous conditions. In this regard, the dependent relationship of conditions is formulated which will lead to more efficient conflict detection. By implementing this concept, a novel tree structure that combines a resource and a policy structure is presented to search for and compare relevant rules from policies. Built on the tree structure and through the formalization of rule conflicts, CAPA is developed and the security and performance it provides is tested in a wind energy use case. Full article

(This article belongs to the Special Issue Energy Digitalisation and Data)

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24 pages, 2170 KiB

Open AccessArticle

Remuneration Sensitivity Analysis in Prosumer and Aggregator Strategies by Controlling Electric Vehicle Chargers

by Cesar Diaz-Londono, José Vuelvas, Giambattista Gruosso and Carlos Adrian Correa-Florez

Energies 2022, 15(19), 6913; https://doi.org/10.3390/en15196913 - 21 Sep 2022

Cited by 9 | Viewed by 1814

Abstract

The efficient use of energy resources is profoundly changing power grid regulation and policy. New forms of power generation coupled with storage and the presence of new, increasingly flexible loads such as electric vehicles enable the development of multi-agent planning systems based on new forms of interaction. For instance, consumers can take advantage of flexibility by interacting directly with the grid or through aggregators that bridge the gap between these end-users and traditional centralised markets. This paper aims to provide insight into the benefits for aggregators and end-users from a financial perspective by proposing a methodology that can be applied to different scenarios. End-users may provide flexibility services related to private vehicle charging stations or battery storage systems. The paper will analyse different remuneration levels for end-users by highlighting the most beneficial scenarios for aggregators and end-users and providing evidence on potential conflict of interests. The numerical results show that some consumers may benefit more from aggregation. This is because if taken individually, consumption habits do not allow the same flexibility when considering clusters of consumers with different behaviour. It is also shown that there are cases in which consumers do not seem to benefit from the presence of intermediate parties. We provide extensive numerical results to gain insight for better decision making. Full article

(This article belongs to the Special Issue Energy Digitalisation and Data)

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18 pages, 2073 KiB

Open AccessArticle

Research on Evaluation Method of Digital Project Cloud Model Considering Weight Sensitivity

by Ye Zhu, Jinchao Li, Xinyi Lan, Shiqiang Lu and Jie Yu

Energies 2022, 15(15), 5738; https://doi.org/10.3390/en15155738 - 07 Aug 2022

Cited by 2 | Viewed by 1333

Abstract

Digitization is a driving force for social development and corporate innovation. Digital projects have become an indispensable part of the sustainable development of enterprises. However, due to the imperfect decision-making system of digital projects and the lack of experience of traditional enterprises’ digital projects, the decision-making of digital projects is an unavoidable challenge in the digital transformation of enterprises. For the digital project decision of the STATE GRID Corporation of China, this paper conducts a sensitivity analysis of digital project evaluation index weights based on cloud model theory, on top of historical successful project experience to support digital project decision-making. Firstly, this paper establishes a comprehensive evaluation index system for digitalization projects from five aspects: economic efficiency, interconnection, intelligent management, value release, and development innovation. The coefficient of variation method is used for index screening, and the weight intervals are formed by four subjective and objective assignment methods. Then, the LSOM model is established to generate the weight values in the interval, and, finally, the sensitivity of digital project comprehensive evaluation indexes is analyzed based on the cloud model to select the most robust index weights for project evaluation and choose the optimal project. The feasibility of the proposed method is verified by arithmetic examples. Full article

(This article belongs to the Special Issue Energy Digitalisation and Data)

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32 pages, 5903 KiB

Open AccessArticle

Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study

by Sarah Barber, Luiz Andre Moyses Lima, Yoshiaki Sakagami, Julian Quick, Effi Latiffianti, Yichao Liu, Riccardo Ferrari, Simon Letzgus, Xujie Zhang and Florian Hammer

Energies 2022, 15(15), 5638; https://doi.org/10.3390/en15155638 - 03 Aug 2022

Cited by 3 | Viewed by 2970

Abstract

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method based on a digital ecosystem is developed and demonstrated. The method is centred around specific “challenges”, which are defined by “challenge providers” within a topical “space” and made available to participants via a digital platform. The data required in order to solve a particular “challenge” are provided by the “challenge providers” under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the solutions perform significantly better than EDP’s existing solution in terms of Total Prediction Costs (saving up to €120,000). The digital ecosystem is found to be a promising solution for enabling co-innovation in wind energy in general, providing a number of tangible benefits for both challenge and solution providers. Full article

(This article belongs to the Special Issue Energy Digitalisation and Data)

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17 pages, 425 KiB

Open AccessArticle

Shapelets to Classify Energy Demand Time Series

by Marco G. Pinheiro, Sara C. Madeira and Alexandre P. Francisco

Energies 2022, 15(8), 2960; https://doi.org/10.3390/en15082960 - 18 Apr 2022

Cited by 2 | Viewed by 1992

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 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

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25 pages, 4388 KiB

Open AccessReview

European Energy Regulatory, Socioeconomic, and Organizational Aspects: An Analysis of Barriers Related to Data-Driven Services across Electricity Sectors

by Kyriaki Psara, Christina Papadimitriou, Marily Efstratiadi, Sotiris Tsakanikas, Panos Papadopoulos and Paul Tobin

Energies 2022, 15(6), 2197; https://doi.org/10.3390/en15062197 - 17 Mar 2022

Cited by 7 | Viewed by 2471

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 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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