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Energies
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Digital Solutions for Energy Management and Power Generation
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Digital Solutions for Energy Management and Power Generation

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

Deadline for manuscript submissions: closed (22 May 2019) | Viewed by 15125

Special Issue Editor

Dr. Ricardo J. Bessa

E-Mail Website
Guest Editor
Center for Power and Energy Systems, INESC TEC, Campus da FEUPRua Dr Roberto Frias, 4200-465 Porto, Portugal
Interests: renewable energy; energy analytics; smart grids and electricity markets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The digital revolution in the energy sector is producing large volumes of data with relevant impacts on the business and functional processes of system operators, energy utilities and grid users. The main challenge is to develop advanced data-driven methods, integrating domain knowledge, which extract value from data for different domains: Descriptive, predictive and prescriptive. This Special Issue aims at encouraging researchers to address the following topics of interest, but the Special Issue is not limited to only this list:

- Frequency and non-frequency system services from distributed energy resources (DER)

- Energy efficiency, smart homes and buildings

- Flexibility modelling and control of DER: standalone or combined with renewable energy sources

- Optimal combination of different energy carriers

- New business models: local energy communities, transactive energy, federated/virtual power plants, etc.

- Wholesale and retail energy markets

- Monitoring and predictive maintenance of DER

- Data privacy and economy in the energy sector

Dr. Ricardo J. Bessa
Guest Editor

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

  • data-driven
  • artificial intelligence
  • energy management
  • renewable energy
  • digitalization
  • smart grids
  • electricity market
  • distributed energy resources
  • energy efficiency

Published Papers (5 papers)

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Research

20 pages, 9311 KiB  
Article
On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs
by Kamalanathan Ganesan, João Tomé Saraiva and Ricardo J. Bessa
Energies 2019, 12(14), 2666; https://doi.org/10.3390/en12142666 - 11 Jul 2019
Cited by 4 | Viewed by 2635
Abstract
Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the [...] Read more.
Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers’ consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers’ usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers’ elasticity is effectively utilized. Full article
(This article belongs to the Special Issue Digital Solutions for Energy Management and Power Generation)
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29 pages, 1920 KiB  
Article
A Generic Data Model for Describing Flexibility in Power Markets
by Paul Schott, Johannes Sedlmeir, Nina Strobel, Thomas Weber, Gilbert Fridgen and Eberhard Abele
Energies 2019, 12(10), 1893; https://doi.org/10.3390/en12101893 - 18 May 2019
Cited by 39 | Viewed by 4017
Abstract
In this article, we present a new descriptive model for industrial flexibility with respect to power consumption. The advancing digitization in the energy sector opens up new possibilities for utilizing and automatizing the marketing of flexibility potentials and therefore facilitates a more advanced [...] Read more.
In this article, we present a new descriptive model for industrial flexibility with respect to power consumption. The advancing digitization in the energy sector opens up new possibilities for utilizing and automatizing the marketing of flexibility potentials and therefore facilitates a more advanced energy management. This requires a standardized description and modeling of power-related flexibility. The data model in this work has been developed in close collaboration with several partners from different industries in the context of a major German research project. A suitable set of key figures allows for also describing complex production processes that exhibit interdependencies and storage-like properties. The data model can be applied to other areas as well, e.g., power plants, plug-in electric vehicles, or power-related flexibility of households. Full article
(This article belongs to the Special Issue Digital Solutions for Energy Management and Power Generation)
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26 pages, 1595 KiB  
Article
Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks
by Ferhat Ucar, Jose Cordova, Omer F. Alcin, Besir Dandil, Fikret Ata and Reza Arghandeh
Energies 2019, 12(8), 1449; https://doi.org/10.3390/en12081449 - 16 Apr 2019
Cited by 9 | Viewed by 2566
Abstract
This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response [...] Read more.
This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine. Full article
(This article belongs to the Special Issue Digital Solutions for Energy Management and Power Generation)
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19 pages, 673 KiB  
Article
Distributed Reconciliation in Day-Ahead Wind Power Forecasting
by Li Bai and Pierre Pinson
Energies 2019, 12(6), 1112; https://doi.org/10.3390/en12061112 - 21 Mar 2019
Cited by 9 | Viewed by 2704
Abstract
With increasing renewable energy generation capacities connected to the power grid, a number of decision-making problems require some form of consistency in the forecasts that are being used as input. In everyday words, one expects that the sum of the power generation forecasts [...] Read more.
With increasing renewable energy generation capacities connected to the power grid, a number of decision-making problems require some form of consistency in the forecasts that are being used as input. In everyday words, one expects that the sum of the power generation forecasts for a set of wind farms is equal to the forecast made directly for the power generation of that portfolio. This forecast reconciliation problem has attracted increased attention in the energy forecasting literature over the last few years. Here, we review the state of the art and its applicability to day-ahead forecasting of wind power generation, in the context of spatial reconciliation. After gathering some observations on the properties of the game-theoretical optimal projection reconciliation approach, we propose to readily rethink it in a distributed setup by using the Alternating Direction Method of Multipliers (ADMM). Three case studies are considered for illustrating the interest and performance of the approach, based on simulated data, the National Renewable Energy Labaratory (NREL) Wind Toolkit dataset, and a dataset for a number of geographically distributed wind farms in Sardinia, Italy. Full article
(This article belongs to the Special Issue Digital Solutions for Energy Management and Power Generation)
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27 pages, 865 KiB  
Article
Comparative Analysis of Adjustable Robust Optimization Alternatives for the Participation of Aggregated Residential Prosumers in Electricity Markets
by Carlos Adrian Correa-Florez, Andrea Michiorri and Georges Kariniotakis
Energies 2019, 12(6), 1019; https://doi.org/10.3390/en12061019 - 15 Mar 2019
Cited by 18 | Viewed by 2601
Abstract
Active participation of end users in energy markets is identified as one of the major challenges in the energy transition context. One option to bridge the gap between customers and the market is aggregators of smart homes or buildings. This paper presents an [...] Read more.
Active participation of end users in energy markets is identified as one of the major challenges in the energy transition context. One option to bridge the gap between customers and the market is aggregators of smart homes or buildings. This paper presents an optimization model from the standpoint of an aggregator of residential prosumers who have PV panels, electric water heaters, and batteries installed at home level. This aggregator participates in the day-ahead energy market to minimize operation costs by controlling the settings of flexible devices. Given that energy prices, PV production, and demand have uncertain behavior, appropriate models should be used to include these effects. In the present work, Adjustable Robust Optimization (ARO) is used to include uncertainty in the optimization model, and a comparative study of modifications to this formulation is carried out to determine its potential and limitations. The comparative analysis is performed from the point of view of average cost and risk, after performing Monte Carlo simulation. Simulations show the advantages of using an ARO framework when compared to deterministic approaches and also allow us to conclude about the advantages of using the proposed alternative formulation to find more attractive solutions for an aggregator. Full article
(This article belongs to the Special Issue Digital Solutions for Energy Management and Power Generation)
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