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Digital Transformation in the Energy Sector: Data-Driven Analytics, Services and Business Models

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 (24 May 2023) | Viewed by 21310

Special Issue Editors


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Guest Editor
1. NET2GRID BV, 54630 Thessaloniki, Greece
2. School of Electrical &Computer Engineering, Aristotle University of Thessaloniki, 15451 Thessaloniki, Greece
Interests: data-driven business models; energy consumption data sets; energy data acquisition; energy data analytics; energy disaggregation; energy informatics; energy insights; non-intrusive load monitoring; smart grids; smart cities

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Guest Editor
European Climate, Infrastructure and Environment Executive Agency (CINEA), European Commission, B-1049 Brussels, Belgium
Interests: distributed generation; electricity distribution; energy markets; energy storage; renewables integration; smart grids

Special Issue Information

Dear Colleagues,

Digital transformation is nowadays a trend for energy utilities and companies globally, where energy data are becoming more and more important. Digital and “smart” hardware components replace old-generation equipment starting from the transmission level and going down to the energy end-users, giving access to more data from thousands of IoT endpoints within the smart grid. Most of the new components are able to connect with the internet or with each other, provide more frequent or even continuous access to data and allow third parties to build use cases and deliver data-driven solutions to either energy companies (TSOs, DSOs, energy retailers) or directly to the end users. In this context, most of the energy utilities have already dedicated digital transformation or innovation teams in place, having realized that Big (Energy) Data is the pillar of innovation and unlocks variable opportunities for them to become digital, divergent and decentralized.

A fundamental research challenge, still partially resolved as of today, is how to transform Big (Energy) Data into meaningful information or, in other words, turn them into valuable services and use-cases. Although a relatively new area of applied research, AI-based energy data analytics services are addressing market needs and enable several opportunities for energy companies. Thus, for this Special Issue we solicit research articles that cover the entire lifecycle of data-driven analytics, services and business models focused on the energy distribution and the end users. We welcome papers covering the whole range from methodological data collection, to the design and evaluation of data analytics solutions and finally to the actual implementation of data-driven use cases for different stakeholders.

Dr. Dimitrios I. Doukas
Dr. Antonios Marinopoulos
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 data acquisition hardware and methodologies
  • Big energy data analytics
  • Energy disaggregation (non-intrusive load monitoring) techniques
  • Behavioral or automated demand response programs
  • AI applications for the energy distribution and utilities sector
  • Data-driven business models for various energy stakeholders (TSOs, DSOs, retailers, aggregators, energy market participants, etc.)

Published Papers (9 papers)

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Research

19 pages, 1411 KiB  
Article
Digital Architecture for Monitoring and Operational Analytics of Multi-Vector Microgrids Utilizing Cloud Computing, Advanced Virtualization Techniques, and Data Analytics Methods
by Angelos Patsidis, Adam Dyśko, Campbell Booth, Anastasios Oulis Rousis, Polyxeni Kalliga and Dimitrios Tzelepis
Energies 2023, 16(16), 5908; https://doi.org/10.3390/en16165908 - 10 Aug 2023
Cited by 1 | Viewed by 937
Abstract
Microgrids are considered a viable solution for achieving net-zero targets and increasing renewable energy integration. However, there is a lack of conceptual work focusing on practical data analytics deployment schemes and case-specific insights. This paper presents a scalable and flexible physical and digital [...] Read more.
Microgrids are considered a viable solution for achieving net-zero targets and increasing renewable energy integration. However, there is a lack of conceptual work focusing on practical data analytics deployment schemes and case-specific insights. This paper presents a scalable and flexible physical and digital architecture for extracting data-driven insights from microgrids, with a real-world microgrid utilized as a test-bed. The proposed architecture includes edge monitoring and intelligence, data-processing mechanisms, and edge–cloud communication. Cloud-hosted data analytics have been developed in AWS, considering market arrangements between the microgrid and the utility. The analysis involves time-series data processing, followed by the exploration of statistical relationships utilizing cloud-hosted tools. Insights from one year of operation highlight the potential for significant operational cost reduction through the real-time optimization and control of microgrid assets. By addressing the real-world applicability, end-to-end architectures, and extraction of case-specific insights, this work contributes to advancing microgrid design, operation, and adoption. Full article
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27 pages, 539 KiB  
Article
HeartDIS: A Generalizable End-to-End Energy Disaggregation Pipeline
by Ilias Dimitriadis, Nikolaos Virtsionis Gkalinikis, Nikolaos Gkiouzelis, Athena Vakali, Christos Athanasiadis and Costas Baslis
Energies 2023, 16(13), 5115; https://doi.org/10.3390/en16135115 - 2 Jul 2023
Cited by 1 | Viewed by 1240
Abstract
The need for a more energy-efficient future is now more evident than ever. Energy disagreggation (NILM) methodologies have been proposed as an effective solution for the reduction in energy consumption. However, there is a wide range of challenges that NILM faces that still [...] Read more.
The need for a more energy-efficient future is now more evident than ever. Energy disagreggation (NILM) methodologies have been proposed as an effective solution for the reduction in energy consumption. However, there is a wide range of challenges that NILM faces that still have not been addressed. Herein, we propose HeartDIS, a generalizable energy disaggregation pipeline backed by an extensive set of experiments, whose aim is to tackle the performance and efficiency of NILM models with respect to the available data. Our research (i) shows that personalized machine learning models can outperform more generic models; (ii) evaluates the generalization capabilities of these models through a wide range of experiments, highlighting the fact that the combination of synthetic data, the decreased volume of real data, and fine-tuning can provide comparable results; (iii) introduces a more realistic synthetic data generation pipeline based on other state-of-the-art methods; and, finally, (iv) facilitates further research in the field by publicly sharing synthetic and real data for the energy consumption of two households and their appliances. Full article
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21 pages, 3991 KiB  
Article
Escope: An Energy Efficiency Simulator for Internet Data Centers
by Jun Liu, Longchuan Yan, Chengxu Yan, Yeliang Qiu, Congfeng Jiang, Yang Li, Yan Li and Christophe Cérin
Energies 2023, 16(7), 3187; https://doi.org/10.3390/en16073187 - 31 Mar 2023
Cited by 1 | Viewed by 1279
Abstract
Contemporary megawatt-scale data centers have emerged to meet the increasing demand for online cloud services and big data analytics. However, in such large-scale data centers, servers of different generations are installed gradually year by year, making the data center heterogeneous in computing capability [...] Read more.
Contemporary megawatt-scale data centers have emerged to meet the increasing demand for online cloud services and big data analytics. However, in such large-scale data centers, servers of different generations are installed gradually year by year, making the data center heterogeneous in computing capability and energy efficiency. Furthermore, due to different processor architectures, complex and diverse load dynamic changing, business coupling, and other reasons, operators pay great attention to processor hardware power consumption and server aggregation energy efficiency. Therefore, the simulation and analysis of the energy efficiency characteristics of data center servers under different processor architectures can help operators understand the energy efficiency characteristics of data centers and make the optimal task scheduling strategy. This is very beneficial for improving the energy efficiency of the production system and the entire data center. The Escope simulator designed in this study can simulate the online quantity (placement strategy) of different types of servers in the data center and the optimal operating range of the servers. The purpose of this is to analyze the energy efficiency characteristics of all servers in the data center and provide data center operators with the energy efficiency and energy proportionality characteristics of different servers, improve server utilization, and perform reasonable scheduling. Through the simulation experiment of Escope, it can be proved that running the server at the highest energy efficiency point or running the server under full load cannot improve the energy efficiency of the entire data center. The simulation algorithm provided by Escope can select the optimal set of servers and their corresponding utilization. Escope can set up a variety of simulation strategies, and data center operators can simulate data center energy efficiency according to their own needs. Escope can also calculate the power cost savings of introducing new servers in the data center, which provides an essential reference for operators to purchase servers and design data centers. Full article
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20 pages, 691 KiB  
Article
Torch-NILM: An Effective Deep Learning Toolkit for Non-Intrusive Load Monitoring in Pytorch
by Nikolaos Virtsionis Gkalinikis, Christoforos Nalmpantis and Dimitris Vrakas
Energies 2022, 15(7), 2647; https://doi.org/10.3390/en15072647 - 4 Apr 2022
Cited by 10 | Viewed by 3491
Abstract
Non-intrusive load monitoring is a blind source separation task that has been attracting significant interest from researchers working in the field of energy informatics. However, despite the considerable progress, there are a very limited number of tools and libraries dedicated to the problem [...] Read more.
Non-intrusive load monitoring is a blind source separation task that has been attracting significant interest from researchers working in the field of energy informatics. However, despite the considerable progress, there are a very limited number of tools and libraries dedicated to the problem of energy disaggregation. Herein, we report the development of a novel open-source framework named Torch-NILM in order to help researchers and engineers take advantage of the benefits of Pytorch. The aim of this research is to tackle the comparability and reproducibility issues often reported in NILM research by standardising the experimental setup, while providing solid baseline models by writing only a few lines of code. Torch-NILM offers a suite of tools particularly useful for training deep neural networks in the task of energy disaggregation. The basic features include: (i) easy-to-use APIs for running new experiments, (ii) a benchmark framework for evaluation, (iii) the implementation of popular architectures, (iv) custom data loaders for efficient training and (v) automated generation of reports. Full article
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24 pages, 2274 KiB  
Article
Optimal Management for EV Charging Stations: A Win–Win Strategy for Different Stakeholders Using Constrained Deep Q-Learning
by Athanasios Paraskevas, Dimitrios Aletras, Antonios Chrysopoulos, Antonios Marinopoulos and Dimitrios I. Doukas
Energies 2022, 15(7), 2323; https://doi.org/10.3390/en15072323 - 23 Mar 2022
Cited by 15 | Viewed by 2050
Abstract
Given the additional awareness of the increasing energy demand and gas emissions’ effects, the decarbonization of the transportation sector is of great significance. In particular, the adoption of electric vehicles (EVs) seems a promising option, under the condition that public charging infrastructure is [...] Read more.
Given the additional awareness of the increasing energy demand and gas emissions’ effects, the decarbonization of the transportation sector is of great significance. In particular, the adoption of electric vehicles (EVs) seems a promising option, under the condition that public charging infrastructure is available. However, devising a pricing and scheduling strategy for public EV charging stations is a non-trivial albeit important task. The reason is that a sub-optimal decision could lead to high waiting times or extreme changes to the power load profile. In addition, in the context of the problem of optimal pricing and scheduling for EV charging stations, the interests of different stakeholders ought to be taken into account (such as those of the station owner and the EV owners). This work proposes a deep reinforcement learning-based (DRL) agent that can optimize pricing and charging control in a public EV charging station under a real-time varying electricity price. The primary goal is to maximize the station’s profits while simultaneously ensuring that the customers’ charging demands are also satisfied. Moreover, the DRL approach is data-driven; it can operate under uncertainties without requiring explicit models of the environment. Variants of scheduling and DRL training algorithms from the literature are also proposed to ensure that both the conflicting objectives are achieved. Experimental results validate the effectiveness of the proposed approach. Full article
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27 pages, 489 KiB  
Article
Low-Frequency Non-Intrusive Load Monitoring of Electric Vehicles in Houses with Solar Generation: Generalisability and Transferability
by Apostolos Vavouris, Benjamin Garside, Lina Stankovic and Vladimir Stankovic
Energies 2022, 15(6), 2200; https://doi.org/10.3390/en15062200 - 17 Mar 2022
Cited by 10 | Viewed by 3113
Abstract
Electrification of transportation is gaining traction as a viable alternative to vehicles that use fossil-fuelled internal combustion engines, which are responsible for a major part of carbon dioxide emissions. This global turn towards electrification of transportation is leading to an exponential energy and [...] Read more.
Electrification of transportation is gaining traction as a viable alternative to vehicles that use fossil-fuelled internal combustion engines, which are responsible for a major part of carbon dioxide emissions. This global turn towards electrification of transportation is leading to an exponential energy and power demand, especially during late-afternoon and early-evening hours, that can lead to great challenges that electricity grids need to face. Therefore, accurate estimation of Electric Vehicle (EV) charging loads and time of use is of utmost importance for different participants in the electricity markets. In this paper, a scalable methodology for detecting, from smart meter data, household EV charging events and their load consumption with robust evaluation, is proposed. This is achieved via a classifier based on Random Decision Forests (RF) with load reconstruction via novel post-processing and a regression approach based on sequence-to-subsequence Deep Neural Network (DNN) with conditional Generative Adversarial Network (GAN). Emphasis is placed on the generalisability of the approaches over similar houses and cross-domain transferability to different geographical regions and different EV charging profiles, as this is a requirement of any real-case scenario. Lastly, the effectiveness of different performance and generalisation loss metrics is discussed. Both the RF classifier with load reconstruction and the DNN, based on the sequence-to-subsequence model, can accurately estimate the energy consumption of EV charging events in unseen houses at scale solely from household aggregate smart meter measurements at 1–15 min resolutions. Full article
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28 pages, 21216 KiB  
Article
Business Models for Demand Response: Exploring the Economic Limits for Small- and Medium-Sized Prosumers
by Guntram Pressmair, Christof Amann and Klemens Leutgöb
Energies 2021, 14(21), 7085; https://doi.org/10.3390/en14217085 - 29 Oct 2021
Cited by 5 | Viewed by 2447
Abstract
The European energy transition increasingly requires flexibility to ensure reliable operation of the electricity system, making use of demand response, a promising concept. With technological advances in the fields of big data analysis and the internet of things, small- and medium-sized prosumers could [...] Read more.
The European energy transition increasingly requires flexibility to ensure reliable operation of the electricity system, making use of demand response, a promising concept. With technological advances in the fields of big data analysis and the internet of things, small- and medium-sized prosumers could also provide flexibility services through aggregators. A lot of conceptual work has been conducted recently to formulate business models in this context, but their viability still remains unclear. In this paper, a quantitative validation is conducted of two business models that are frequently proposed in the scientific discussion. The aim of this work is to explore the economic limits of these business models and show under which conditions they can be profitable for small- and medium-sized prosumers. For this purpose, a multi-level contribution margin calculation for several scenarios, customer segments and target markets is conducted. The results show that the profitability for the participation of small loads is still very low under current market conditions. Especially for household consumers, transaction costs are too high to be covered by the revenues. Considering the quantitative results, in the future profitable business cases can only be expected for medium-sized tertiary consumers. Full article
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14 pages, 591 KiB  
Article
MC-NILM: A Multi-Chain Disaggregation Method for NILM
by Hao Ma, Juncheng Jia, Xinhao Yang, Weipeng Zhu and Hong Zhang
Energies 2021, 14(14), 4331; https://doi.org/10.3390/en14144331 - 18 Jul 2021
Cited by 8 | Viewed by 2074
Abstract
Non-intrusive load monitoring (NILM) is an approach that helps residents obtain detailed information about household electricity consumption and has gradually become a research focus in recent years. Most of the existing algorithms on NILM build energy disaggregation models independently for an individual appliance [...] Read more.
Non-intrusive load monitoring (NILM) is an approach that helps residents obtain detailed information about household electricity consumption and has gradually become a research focus in recent years. Most of the existing algorithms on NILM build energy disaggregation models independently for an individual appliance while neglecting the relation among them. For this situation, this article proposes a multi-chain disaggregation method for NILM (MC-NILM). MC-NILM integrates the models generated by existing algorithms and considers the relation among these models to improve the performance of energy disaggregation. Given the high time complexity of searching for the optimal MC-NILM structure, this article proposes two methods to reduce the time complexity, the k-length chain method and the graph-based chain generation method. Finally, we use the Dataport and UK-DALE datasets to evaluate the feasibility, effectiveness, and generality of the MC-NILM. Full article
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17 pages, 4277 KiB  
Article
Blockchain Technology Applied to Energy Demand Response Service Tracking and Data Sharing
by Alexandre Lucas, Dimitrios Geneiatakis, Yannis Soupionis, Igor Nai-Fovino and Evangelos Kotsakis
Energies 2021, 14(7), 1881; https://doi.org/10.3390/en14071881 - 29 Mar 2021
Cited by 25 | Viewed by 2927
Abstract
Demand response (DR) services have the potential to enable large penetration of renewable energy by adjusting load consumption, thus providing balancing support to the grid. The success of such load flexibility provided by industry, communities, or prosumers and its integration in electricity markets, [...] Read more.
Demand response (DR) services have the potential to enable large penetration of renewable energy by adjusting load consumption, thus providing balancing support to the grid. The success of such load flexibility provided by industry, communities, or prosumers and its integration in electricity markets, will depend on a redesign and adaptation of the current interactions between participants. New challenges are, however, bound to appear with the large scale contribution of smaller assets to flexibility, including, among others, the dispatch coordination, the validation of delivery of the DR provision, and the corresponding settlement of contracts, while assuring secured data access among interested parties. In this study we applied distributed ledger (DLT)/blockchain technology to securely track DR provision, focusing on the validation aspect, assuring data integrity, origin, fast registry, and sharing within a permissioned system, between all relevant parties (including transmission system operators (TSOs), aggregators, distribution system operators (DSOs), balance responsible parties (BRP), and prosumers). We propose a framework for DR registry and implemented it as a proof of concept on Hyperledger Fabric, using real assets in a laboratory environment, in order to study its feasibility and performance. The lab set up includes a 450 kW energy storage system, scheduled to provide DR services, upon a system operator request and the corresponding validations and verifications are done, followed by the publication on a blockchain. Results show the end to end execution time remained below 1 s, when below 32 requests/sec. The smart contract memory utilization did not surpass 1% for both active and passive nodes and the peer CPU utilization, remained below 5% in all cases simulated (3, 10, and 28 nodes). Smart Contract CPU utilization remained stable, below 1% in all cases. The performance of the implementation showed scalable results, which enables real world adoption of DLT in supporting the development of flexibility markets, with the advantages of blockchain technology. Full article
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