Special Issue "Application of Artificial Intelligence and Data Mining in Energy System"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 20 February 2022.

Special Issue Editors

Prof. Dr. Sérgio Ramos
E-Mail Website
Guest Editor
Engineering Institute, Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: data mining; artificial intelligence; power systems; electricity markets; renewable energy resources management; shared PV generation; electricity communities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The power systems from today are very different from those of the past. Over the world, due to environmental concerns, the governments had been betting on the use of renewable energy sources to produce electricity. Power systems now face new challenges in order to integrate into this kind of generation. In fact, power systems are moving towards to the concept of smart grids. End-users are more aware of sustainability and energy efficiency issues having an active role in this sector. The quantity of data in power systems is growing rapidly due to huge database used by power systems engineers for various operations: power plants generation; transmission and distribution energy; electricity end-users (and prosumers).

Applications of data mining techniques play an important role in all knowledge discovery processes, and they have a widespread use in power systems for tasks such as helping power systems planner/operator to have smooth system planning/operation, load profile characterization, consumer classification, and electricity consumption/generation forecasting. Indeed, data mining techniques are useful for extracting useful information from the existing data bases. The application of artificial intelligence to power systems has been a great challenge in the last few decades and, gradually, has entered in our daily lives. Indeed, at present time, the use of artificial intelligence in power systems is a key point in many research fields and domains.

This Special Issue focuses on the application of artificial intelligence and data mining in power system. Topics of interest for publication include but are not limited to the following:

  • Data mining techniques applied to power systems (e.g., Typical load profile characterization, customers classification, identification of electricity failures)
  • Artificial intelligence applied to energy systems (e.g., use of methods such as knowledge-based (expert) systems, fuzzy logic, neural networks and genetic algorithms)
  • Renewable energy, demand-response, smart distribution grids
  • Energy management system in smart distribution grids and in residential buildings
  • Advanced flexibility strategies for electricity communities
  • Shared PV generation in residential building context
  • Electric vehicles planning and operation in smart grid (including behavior models for simulation and optimization of EVs in the grid)

Prof. Dr. Sérgio Ramos
Dr. João Soares
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 papers will be 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. Processes is an international peer-reviewed open access monthly 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 2000 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 mining techniques
  • Typical load profiles
  • Electricity consumers characterization
  • Optimization methods
  • Energy communities
  • Shared PV generation
  • Demand response
  • Renewable energy sources
  • Electric vehicles planning and operation

Published Papers (5 papers)

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Research

Article
Influence of Synchronous Condensers on Operation Characteristics of Double-Infeed LCC-HVDCs
Processes 2021, 9(10), 1704; https://doi.org/10.3390/pr9101704 - 23 Sep 2021
Viewed by 216
Abstract
Considering the advantages that dynamic reactive power (var) equipment (such as synchronous condensers (SCs), which can control var independently and improve voltage stability), SCs are widely used in AC/DC hybrid power grid to provide emergency var and voltage support. In order to evaluate [...] Read more.
Considering the advantages that dynamic reactive power (var) equipment (such as synchronous condensers (SCs), which can control var independently and improve voltage stability), SCs are widely used in AC/DC hybrid power grid to provide emergency var and voltage support. In order to evaluate the dynamic var reserve capacity of SCs and analyze the influence of SCs on the operation characteristics of power system, a model with double-infeed line-commutated converter-based high-voltage direct currents (LCC-HVDCs) and SCs is established. Through theoretical derivation and PSCAD/EMTDC simulation, the effects of SCs on the operation characteristics of double-infeed LCC-HVDCs networks are studied. Then, the non-smooth voltage waveform of electromagnetic transient simulation is approximately transformed into smooth waveform by data fitting method. Finally, the processed voltage waveform is searched step by step to explore the boundary of voltage safety region to determine the dynamic var reserve capacity of SCs. The numerical results show that SCs can enlarge the voltage security region of the direct current (DC) subsystem, thus effectively improving the steady-state and transient security level of the double-infeed LCC-HVDCs networks. Full article
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Article
Residential Demand Response Strategy Based on Deep Deterministic Policy Gradient
Processes 2021, 9(4), 660; https://doi.org/10.3390/pr9040660 - 09 Apr 2021
Viewed by 506
Abstract
With the continuous improvement of the power system and the deepening of electricity market reform, the trend of users’ active participation in power distribution is more and more significant. Demand response has become the promising focus of smart grid research. Providing reasonable incentive [...] Read more.
With the continuous improvement of the power system and the deepening of electricity market reform, the trend of users’ active participation in power distribution is more and more significant. Demand response has become the promising focus of smart grid research. Providing reasonable incentive strategies for power grid companies and demand response strategies for customers plays a crucial role in maximizing the benefits of different participants. To meet different expectations of multiple agents in the same environment, deep reinforcement learning was adopted. The generative model of residential demand response strategy under different incentive policies can be trained iteratively through real-time interactions with the environmental conditions. In this paper, a novel optimization model of residential demand response strategy, based on a deep deterministic policy gradient (DDPG) algorithm, was proposed. The proposed work was validated with the actual electricity consumption data of a certain area in China. The results showed that the DDPG model could optimize residential demand response strategy under certain incentive policies. In addition, the overall goal of peak load-cutting and valley filling can be achieved, which reflects promising prospects of the electricity market. Full article
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Article
The Impact of Attacks in LEM and Prevention Measures Based on Forecasting and Trust Models
Processes 2021, 9(2), 314; https://doi.org/10.3390/pr9020314 - 08 Feb 2021
Viewed by 502
Abstract
In recent years Local Energy Markets (LEM) have emerged as an innovative and versatile energy trade solution. They bring benefits when renewable energy sources are used and are more flexible for consumers. There are, however, security concerns that put the feasibility of the [...] Read more.
In recent years Local Energy Markets (LEM) have emerged as an innovative and versatile energy trade solution. They bring benefits when renewable energy sources are used and are more flexible for consumers. There are, however, security concerns that put the feasibility of the local energy market at risk. One of these security challenges is the integrity of data in the smart-grid that supports the local market. In this article the LEM and the types of attacks that can have a negative impact on it are presented, and a security mechanism based on a trust model is proposed. A case study is elaborated using a multi-agent system called Local Energy Market Multi-Agent System (LEMMAS), capable of simulating the LEM and testing the proposed security mechanism. Full article
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Article
Energy Management in Smart Building by a Multi-Objective Optimization Model and Pascoletti-Serafini Scalarization Approach
Processes 2021, 9(2), 257; https://doi.org/10.3390/pr9020257 - 29 Jan 2021
Cited by 6 | Viewed by 686
Abstract
Generally, energy management in smart buildings is formulated by mixed-integer linear programming, with different optimization goals. The most targeted goals are the minimization of the electricity consumption cost, the electricity consumption value from external power grid, and peak load smoothing. All of these [...] Read more.
Generally, energy management in smart buildings is formulated by mixed-integer linear programming, with different optimization goals. The most targeted goals are the minimization of the electricity consumption cost, the electricity consumption value from external power grid, and peak load smoothing. All of these objectives are desirable in a smart building, however, in most of the related works, just one of these mentioned goals is considered and investigated. In this work, authors aim to consider two goals via a multi-objective framework. In this regard, a multi-objective mixed-binary linear programming is presented to minimize the total energy consumption cost and peak load in collective residential buildings, considering the scheduling of the charging/discharging process for electric vehicles and battery energy storage system. Then, the Pascoletti-Serafini scalarization approach is used to obtain the Pareto front solutions of the presented multi-objective model. In the final, the performance of the proposed model is analyzed and reported by simulating the model under two different scenarios. The results show that the total consumption cost of the residential building has been reduced 35.56% and the peak load has a 45.52% reduction. Full article
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Article
Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network
Processes 2020, 8(12), 1631; https://doi.org/10.3390/pr8121631 - 11 Dec 2020
Cited by 2 | Viewed by 672
Abstract
With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed [...] Read more.
With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter. Full article
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