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Applied Sciences
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Data Science Applications in Medium/Low Voltage Smart Grids
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Data Science Applications in Medium/Low Voltage Smart Grids

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 8220

Special Issue Editors

Dr. Hartmann Balint

E-Mail Website
Guest Editor
1. Department of Electric Power Engineering, Budapest University of Technology and Economics, Budapest, Hungary
2. Centre for Energy Research, Budapest, Hungary
Interests: modeling and operation of distribution networks; energy storage; integration of renewable sources
Prof. Dr. Joao Miguel C. Sousa

E-Mail Website
Guest Editor
1. School of Technology and Management, Polytechnic Institute of Leiria, Leiria, Portugal
2. INESC Coimbra - Institute for Systems Engineering and Computers at Coimbra, Coimbra, Portugal
Interests: energy efficiency; machine learning; load forecasting; load profiling; energy in buildings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The topic of smart grids, and especially smart distribution networks, has received much attention in the past decade. As the transformation of the electricity sector advances, increased attention is given to the medium-voltage and low-voltage components of electricity supply. This transformation is driven by a number of technological advancements, including the increasing presence of distributed generation from renewable energy sources, the electrification of loads, and the integration of energy storage; however, the changes also affect regulatory activities and market development as well. A successful transition requires the combined effort of professionals of various backgrounds and the proper use of data that are available in unprecedented volumes.

Important theoretical and practical advances in the data science field contribute largely to the mentioned issues. Data mining, machine learning, and forecasting are just some of the inter-disciplinary areas that are being integrated into everyday power system operation with significant success. The Special Issue aims to present a selection of these recent advances, with emphasis on the results that are contributing to this field not only from a scientific perspective, but also on those that demonstrate a real-life application (pilot, demonstration, field test or mature phase) of these tools.

Dr. Hartmann Balint
Prof. Dr. Joao Miguel C. Sousa
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. Applied Sciences 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 2400 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

  • load and generation forecasting
  • load profiling
  • smart meter data analytics
  • state estimation on medium-voltage and low-voltage level
  • participation of smart grids and energy communities in electricity markets
  • energy management supervisory systems

Published Papers (4 papers)

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Research

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18 pages, 7378 KiB  
Article
Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data
by João C. Sousa and Hermano Bernardo
Appl. Sci. 2022, 12(19), 9844; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199844 - 30 Sep 2022
Cited by 4 | Viewed by 1180
Abstract
As the access to consumption data available in household smart meters is now very common in several developed countries, this kind of information is assuming a providential role for different players in the energy sector. The proposed study was applied to data available [...] Read more.
As the access to consumption data available in household smart meters is now very common in several developed countries, this kind of information is assuming a providential role for different players in the energy sector. The proposed study was applied to data available from the Smart Meter Energy Consumption Data in the London Households dataset, provided by UK Power Networks, containing half-hourly readings from an original sample of 5567 households (71 households were hereby carefully selected after a justified filtering process). The main aim is to forecast the day—ahead load profile, based only on previous load values and some auxiliary variables. During this research different forecasting models are applied, tested and compared to allow comprehensive analyses integrating forecasting accuracy, processing times and the interpretation of the most influential features in each case. The selected models are based on Multivariate Adaptive Regression Splines, Random Forests and Artificial Neural Networks, and the accuracies resulted from each model are compared and confronted with a baseline (Naïve model). The different forecasting approaches being evaluated have been revealed to be effective, ensuring a mean reduction of 15% in Mean Absolute Error when compared to the baseline. Artificial Neural Networks proved to be the most accurate model for a major part of the residential consumers. Full article
(This article belongs to the Special Issue Data Science Applications in Medium/Low Voltage Smart Grids)
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16 pages, 6975 KiB  
Article
Identification of Typical and Anomalous Patterns in Electricity Consumption
by José Nuno Fidalgo and Pedro Macedo
Appl. Sci. 2022, 12(7), 3317; https://doi.org/10.3390/app12073317 - 24 Mar 2022
Viewed by 1649
Abstract
Nontechnical losses in electricity distribution networks are often associated with a countries’ socioeconomic situation. Although the amount of global losses is usually known, the separation between technical and commercial (nontechnical) losses will remain one of the main challenges for DSO until smart grids [...] Read more.
Nontechnical losses in electricity distribution networks are often associated with a countries’ socioeconomic situation. Although the amount of global losses is usually known, the separation between technical and commercial (nontechnical) losses will remain one of the main challenges for DSO until smart grids become fully implemented and operational. The most common origins of commercial losses are energy theft and deliberate or accidental failures of energy measuring equipment. In any case, the consequences can be regarded as consumption anomalies. The work described in this paper aims to answer a request from a DSO, for the development of tools to detect consumption anomalies at end-customer facilities (HV, MV and LV), invoking two types of assessment. The first consists of the identification of typical patterns in the set of consumption profiles of a given group or zone and the detection of atypical consumers (outliers) within it. The second assessment involves the exploration of the load diagram evolution of each specific consumer to detect changes in the consumption pattern that could represent situations of probable irregularities. After a representative period, typically 12 months, these assessments are repeated, and the results are compared to the initial ones. The eventual changes in the typical classes or consumption scales are used to build a classifier indicating the risk of anomaly. Full article
(This article belongs to the Special Issue Data Science Applications in Medium/Low Voltage Smart Grids)
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18 pages, 4887 KiB  
Article
Benchmarking Various Pseudo-Measurement Data Generation Techniques in a Low-Voltage State Estimation Pilot Environment
by Gergő Bendegúz Békési, Lilla Barancsuk, István Táczi and Bálint Hartmann
Appl. Sci. 2022, 12(6), 3187; https://0-doi-org.brum.beds.ac.uk/10.3390/app12063187 - 21 Mar 2022
Cited by 2 | Viewed by 1799
Abstract
Distribution system state estimation (DSSE) is a valuable step for DSOs toward tackling the challenges of transitioning to a more sustainable energy system and the evolution and proliferation of electric cars and power electronic devices. However, on the LV level, implementation has only [...] Read more.
Distribution system state estimation (DSSE) is a valuable step for DSOs toward tackling the challenges of transitioning to a more sustainable energy system and the evolution and proliferation of electric cars and power electronic devices. However, on the LV level, implementation has only taken place in a few pilot projects. In this paper, an LV DSSE method is presented and implemented in four real Hungarian LV supply areas, according to well-defined scenarios. Pseudo-measurement datasets are generated from AACs and SLPs, which have been used in different combinations on networks built with different accuracies in terms of load placement. The paper focuses on the critical aspects of finding accurate and coherent information on network topology with automated management of information systems, real LV network implementation for power flow calculation and managing portions of the network characterized by uncertain or inconsistent line lengths. A refining algorithm is implemented for the integrated network information system (INIS) models. The published method estimates node voltages with a relative error of less than 1% when using AACs, and a meter-placement method to reduce the maximum value of relative errors in future scenarios is also presented. It is shown that the observation of node voltages can be improved with the usage of AACs and SLPs, and with optimal meter placement. Full article
(This article belongs to the Special Issue Data Science Applications in Medium/Low Voltage Smart Grids)
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Review

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28 pages, 2344 KiB  
Review
Applications of Probabilistic Forecasting in Smart Grids: A Review
by Hosna Khajeh and Hannu Laaksonen
Appl. Sci. 2022, 12(4), 1823; https://0-doi-org.brum.beds.ac.uk/10.3390/app12041823 - 10 Feb 2022
Cited by 8 | Viewed by 2731
Abstract
This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it [...] Read more.
This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future. Full article
(This article belongs to the Special Issue Data Science Applications in Medium/Low Voltage Smart Grids)
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