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Applied Sciences
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Artificial Intelligence for Power System Applications
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Artificial Intelligence for Power System Applications

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 October 2021) | Viewed by 9060

Special Issue Editors

Dr. Mònica Aragüés-Peñalba

E-Mail Website
Guest Editor
CITCEA-UPC, Department of Electrical Engineering, Universitat Politecnica de Catalunya, 08028 Barcelona, Spain
Interests: grid integration of renewable energy generation; wind power; solar power; energy storage systems; HVDC transmission; microgrids; smart grids; big data for electrical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Eduard Bullich-Massagué

E-Mail Website
Guest Editor
CITCEA-UPC, Department of Electrical Engineering, Universitat Politecnica de Catalunya, 08028 Barcelona, Spain
Interests: grid integration of renewable energy source; PV power plants, microgrids; multi-microgrids; active distribution networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, old and new metering devices coexist in electrical power systems. Thanks to the deployment of smart metering and monitoring systems such as smart meters, a significant increase in variety and volume of data is being observed in different power system domains: generation, transmission, distribution, and consumption. Large amounts of data are available, but often underused and, in fact, sometimes not used at all.

On the other hand, the evolution of electrical grids toward smart grids and the higher penetration of renewable energy sources implies a more challenging operation, control, and maintenance of power systems. The strategies to deal with them can require large computational times if addressed through traditional analysis tools. Additionally, a feasible solution might not always be found. In this sense, Artificial Intelligence (AI) techniques can contribute to the management of complex electrical networks, by treating and extracting value from large volumes of data, dealing with its variety and velocity through much faster computations.

We invite you to contribute to this Special Issue, which includes (but is not limited to) the following topics:

Application of artificial intelligence techniques for:

  • Forecasting of renewable power plants generation;
  • Demand forecasting;
  • Energy storage design and operation;
  • Market design and operation;
  • Distribution grids topology and observability;
  • Grid stability;
  • Reliability at transmission and distribution level.

Dr. Mònica Aragüés-Peñalba
Dr. Eduard Bullich-Massagué
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.

Published Papers (4 papers)

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Research

11 pages, 19287 KiB  
Article
Development of an Intelligent Voltage Control System for Bulk Power Systems
by Heungjae Lee, Wonkun Yu, Junghyun Oh, Hyungsuk Kim and Jinyoung Kim
Appl. Sci. 2021, 11(19), 9233; https://0-doi-org.brum.beds.ac.uk/10.3390/app11199233 - 04 Oct 2021
Cited by 1 | Viewed by 1463
Abstract
As modern power systems become large and complicated, an automated voltage and reactive power control system is required in most developed countries due to the remarkable recent progress in computer networks and information technology. To date, voltage control has depended on human operators [...] Read more.
As modern power systems become large and complicated, an automated voltage and reactive power control system is required in most developed countries due to the remarkable recent progress in computer networks and information technology. To date, voltage control has depended on human operators in the Korean power system. Accordingly, this paper proposes a universal intelligent voltage control system for bulk power systems based on sensitivity analysis and a main expert system. A detailed state space modeling technique is discussed, and an effective performance index is proposed to accelerate the searching performance of the expert system. As the searching strategy is an important factor for the speed of the expert system, the least-first search algorithm is applied using this performance index. The proposed system has been applied to the Korean power system, showing promising results. Full article
(This article belongs to the Special Issue Artificial Intelligence for Power System Applications)
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13 pages, 2071 KiB  
Article
Online Critical Unit Detection and Power System Security Control: An Instance-Level Feature Importance Analysis Approach
by Junyu Ren, Li Wang, Shaofan Zhang, Yanchun Cai and Jinfu Chen
Appl. Sci. 2021, 11(12), 5460; https://0-doi-org.brum.beds.ac.uk/10.3390/app11125460 - 12 Jun 2021
Cited by 4 | Viewed by 1625
Abstract
Rapid and accurate detection of critical units is crucial for the security control of power systems, ensuring reliable and continuous operation. Inspired by the advantages of data-driven techniques, this paper proposes an integrated deep learning framework of dynamic security assessment, critical unit detection, [...] Read more.
Rapid and accurate detection of critical units is crucial for the security control of power systems, ensuring reliable and continuous operation. Inspired by the advantages of data-driven techniques, this paper proposes an integrated deep learning framework of dynamic security assessment, critical unit detection, and security control. In the proposed framework, a black-box deep learning model is utilized to evaluate the dynamic security of power systems. Then, the predictions of the model for specific operating conditions are interpreted by instance-level feature importance analysis. Furthermore, the critical units are detected by reasonable local interpretation, and the security control scheme is extracted with a sequential adjustment strategy according to the results of interpretation. The numerical simulations on the CEPRI36 benchmark system and the IEEE 118-bus system verified that our proposed framework is fast and accurate for specific operating conditions and, thereby, is a viable approach for online security control of power systems. Full article
(This article belongs to the Special Issue Artificial Intelligence for Power System Applications)
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20 pages, 1385 KiB  
Article
A Very Short-Term Probabilistic Prediction Interval Forecaster for Reducing Load Uncertainty Level in Smart Grids
by Fermín Rodríguez, Najmeh Bazmohammadi, Josep M. Guerrero and Ainhoa Galarza
Appl. Sci. 2021, 11(6), 2538; https://0-doi-org.brum.beds.ac.uk/10.3390/app11062538 - 12 Mar 2021
Cited by 6 | Viewed by 1932
Abstract
Very short-term load demand forecasters are essential for power systems’ decision makers in real-time dispatching. These tools allow traditional network operators to maintain power systems’ safety and stability and provide customers energy with high reliability. Although research has traditionally focused on developing point [...] Read more.
Very short-term load demand forecasters are essential for power systems’ decision makers in real-time dispatching. These tools allow traditional network operators to maintain power systems’ safety and stability and provide customers energy with high reliability. Although research has traditionally focused on developing point forecasters, these tools do not provide complete information because they do not estimate the deviation between actual and predicted values. Therefore, the aim of this paper is to develop a very short-term probabilistic prediction interval forecaster to reduce decision makers’ uncertainty by computing the predicted value’s upper and lower bounds. The proposed forecaster combines an artificial intelligence-based point forecaster with a probabilistic prediction interval algorithm. First, the point forecaster predicts energy demand in the next 15 min and then the prediction interval algorithm calculates the upper and lower bounds with the user’s chosen confidence level. To examine the reliability of proposed forecaster model and resulting interval sharpness, different error metrics, such as prediction interval coverage percentage and a skill score, are computed for 95, 90, and 85% confidence intervals. Results show that the prediction interval coverage percentage is higher than the confidence level in each analysis, which means that the proposed model is valid for practical applications. Full article
(This article belongs to the Special Issue Artificial Intelligence for Power System Applications)
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10 pages, 2514 KiB  
Article
Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant
by Young Seo Kim, Han Young Joo, Jae Wook Kim, So Yun Jeong and Joo Hyun Moon
Appl. Sci. 2021, 11(4), 1776; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041776 - 17 Feb 2021
Cited by 12 | Viewed by 2988
Abstract
This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The [...] Read more.
This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time. Full article
(This article belongs to the Special Issue Artificial Intelligence for Power System Applications)
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