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Advanced Optimization and Forecasting Methods in Power Engineering
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Advanced Optimization and Forecasting Methods in Power Engineering

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

Deadline for manuscript submissions: closed (17 August 2023) | Viewed by 13138

Special Issue Editors

Prof. Dr. Paweł Pijarski

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka St. 38D, 20–618 Lublin, Poland
Interests: power system analysis; electrical power engineering; heuristic optimization; metaheuristic; distributed generation; renewable energy systems; short-circuit calculations in the power system; OPF; SCOPF, artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Piotr Kacejko

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka St. 38D, 20–618 Lublin, Poland
Interests: power system analysis; optimization; power system control; renewable energy systems
Prof. Dr. Piotr Miller

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka St. 38D, 20–618 Lublin, Poland
Interests: power systems; short-circuit calculations in the power system; algorithms for technical analysis of the power system; voltage regulations

Special Issue Information

Dear Colleagues,

Modern power engineering, like every field of science, is struggling with various problems that must be solved on an ongoing basis. Some of them can be eliminated on the basis of engineering logic (engineering reasoning) and experience. Some, however, require the use of advanced methods due to the complexity and dimension of the issue.

The purpose of the research subject under consideration is to identify the possibilities and determine the desirability of using various optimization methods to solve problems of maintenance, planning and operation in the field of electrical power systems.

The purpose of this Special Issue is to consider various real and, above all, current problems related to power systems. Today’s power systems face both technical and economic problems. They appear not only at the stage of operation of the power grid, but also during its planning and maintenance. Practically at every voltage level of the grid, operators have to deal with various emergency conditions that contribute to the emergence of current and voltage exceedances, problems with power balance, stability issues, etc. These situations often require the use of advanced methods that allow them to be solved. A good choice seems to be the use of optimization methods or, for example, combining optimization with selected machine learning methods. Each issue should be thoroughly investigated, so that the adopted objective function is appropriately selected according to the nature of the problem under consideration. In this Special Issue, preference is given to works that deal with the above subject and describe it in detail.

We invite you to submit your original works for the Special Issue "Advanced Optimization and Forecasting Methods in Power Engineering".

All submitted articles will be reviewed and checked for compliance with the scope of this Special Issue as well as scientific quality.

The theme of the Special Issue includes but is not limited to the following selected topics:

  1. Optimization methods in electrical power engineering, such as:
    • Optimization of reactive power flow;
    • Minimization of active power losses in the system;
    • Determination of power system connection possibilities (hosting capacity);
    • Dynamic adjustment of the generation level to the transmission capacity of power lines and transformers;
    • Optimal selection of partition points in the MV network;
    • Minimizing the difference in voltage phasor angles when power lines are switched on;
    • Optimization of phase shifter settings;
    • Optimal network structure planning from the point of view of branch overload elimination;
    • Optimal planning of network structure from the point of view of minimizing short-circuit powers;
    • Optimal location and power of synchronous compensators;
    • Optimal location of wind turbines;
    • Maximizing the network infrastructure utilization rate (the network infrastructure utilization indicator should be understood as the sum of differences between the allowable current of the network branches and their real current);
    • Optimal management of inverters of photovoltaic installations;
    • Optimal selection of reactive power compensation devices;
    • Optimal management of the operation of the power grid with renewable energy sources;
    • Optimal selection of energy storage in the power grid;
    • Optimization of the voltage quality indicator in the distribution networks;
    • Optimal redispatching of power with RES installations;
    • Cable pooling—optimal use of common network infrastructure by various types of renewable energy sources.
  2. Selected methods of machine learning in electrical power engineering in combination with optimization.
  3. Metaheuristics.
  4. Application of probability and statistical analysis of data and results of calculations.

Prof. Dr. Paweł Pijarski
Prof. Dr. Piotr Kacejko
Prof. Dr. Piotr Miller
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

  • power engineering
  • optimization
  • metaheuristics
  • renewable energy sources
  • machine learning
  • probability, statistics

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Published Papers (5 papers)

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Research

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23 pages, 1261 KiB  
Article
Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique
by Hasnain Iftikhar, Josue E. Turpo-Chaparro, Paulo Canas Rodrigues and Javier Linkolk López-Gonzales
Energies 2023, 16(18), 6669; https://doi.org/10.3390/en16186669 - 18 Sep 2023
Cited by 15 | Viewed by 2591
Abstract
Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this [...] Read more.
Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this work presents a new decomposition–combination technique that employs several nonparametric regression methods and various time-series models to enhance the accuracy and efficiency of day-ahead electricity price forecasting. For this purpose, first, the time-series of the original electricity prices deals with the treatment of extreme values. Second, the filtered series of the electricity prices is decomposed into three new subseries, namely the long-term trend, a seasonal series, and a residual series, using two new proposed decomposition methods. Third, we forecast each subseries using different univariate and multivariate time-series models and all possible combinations. Finally, the individual forecasting models are combined directly to obtain the final one-day-ahead price forecast. The proposed decomposition–combination forecasting technique is applied to hourly spot electricity prices from the Italian electricity-market data from 1 January 2014 to 31 December 2019. Hence, four different accuracy mean errors—mean absolute error, mean squared absolute percent error, root mean squared error, and mean absolute percent error; a statistical test, the Diebold–Marino test; and graphical analysis—are determined to check the performance of the proposed decomposition–combination forecasting method. The experimental findings (mean errors, statistical test, and graphical analysis) show that the proposed forecasting method is effective and accurate in day-ahead electricity price forecasting. Additionally, our forecasting outcomes are comparable to those described in the literature and are regarded as standard benchmark models. Finally, the authors recommended that the proposed decomposition–combination forecasting technique in this research work be applied to other complicated energy market forecasting challenges. Full article
(This article belongs to the Special Issue Advanced Optimization and Forecasting Methods in Power Engineering)
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12 pages, 1960 KiB  
Article
Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches
by Agbessi Akuété Pierre, Salami Adekunlé Akim, Agbosse Kodjovi Semenyo and Birregah Babiga
Energies 2023, 16(12), 4739; https://doi.org/10.3390/en16124739 - 15 Jun 2023
Cited by 23 | Viewed by 4231
Abstract
Forecasting peak electrical energy consumption is important because it allows utilities to properly plan for the production and distribution of electrical energy. This reduces operating costs and avoids power outages. In addition, it can help reduce environmental impact by allowing for more efficient [...] Read more.
Forecasting peak electrical energy consumption is important because it allows utilities to properly plan for the production and distribution of electrical energy. This reduces operating costs and avoids power outages. In addition, it can help reduce environmental impact by allowing for more efficient power generation and reducing the need for additional fossil fuels during periods of high demand. In the current work, electric power consumption data from “Compagnie Electrique du Benin (CEB)” was used to deduce the peak electric power consumption at peak hours. The peak consumption of electric power was predicted using hybrid approaches based on traditional time series prediction methods (autoregressive integrated moving average (ARIMA)) and deep learning methods (long short-term memory (LSTM), gated recurrent unit (GRU)). The ARIMA approach was used to model the trend term, while deep learning approaches were employed to interpret the fluctuation term, and the outputs from these models were combined to provide the final result. The hybrid approach, ARIMA-LSTM, provided the best prediction performance with root mean square error (RMSE) of 7.35, while for the ARIMA-GRU hybrid approach, the RMSE was 9.60. Overall, the hybrid approaches outperformed the single approaches, such as GRU, LSTM, and ARIMA, which exhibited RMSE values of 18.11, 18.74, and 49.90, respectively. Full article
(This article belongs to the Special Issue Advanced Optimization and Forecasting Methods in Power Engineering)
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19 pages, 2822 KiB  
Article
Elimination of Line Overloads in a Power System Saturated with Renewable Energy Sources
by Paweł Pijarski and Piotr Kacejko
Energies 2023, 16(9), 3751; https://doi.org/10.3390/en16093751 - 27 Apr 2023
Cited by 6 | Viewed by 1703
Abstract
The increasing number of renewable energy sources (RESs) connected to power grids contributes to the emergence of not only balancing problems but also technical ones, such as the overloading of power lines. If renewable sources with a high generation level are planned to [...] Read more.
The increasing number of renewable energy sources (RESs) connected to power grids contributes to the emergence of not only balancing problems but also technical ones, such as the overloading of power lines. If renewable sources with a high generation level are planned to be connected in the area under consideration, then a large number of significant overloads should be expected, especially during contingency analysis. As a rule, high-voltage networks have a mesh topology, which is why the concept of using advanced mathematical algorithms was developed, with the help of which the resulting threats can be eliminated. This article presents a proposal for a new method of eliminating line overloads and determining the currently available nodal generation levels. Its innovation is a new method of eliminating problems related to the capacity of power grids. The high efficiency of the method results from the appropriately defined response of properly selected RES sources to the state of network congestion. The problem under consideration is illustrated with the example of a modified IEEE 118-bus test network. In order to eliminate line overloads, the article proposes a two-stage approach. In the first step, the sources that are most responsible for the occurring overloads are determined. In the second step, a metaheuristic algorithm is used to solve a nonlinear optimisation problem with constraints. This task involves reducing the power generated in the sources selected in the previous step in such a way that the resulting line overloads are eliminated, and, at the same time, the total value of the curtailed generation is minimal. Full article
(This article belongs to the Special Issue Advanced Optimization and Forecasting Methods in Power Engineering)
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16 pages, 4631 KiB  
Article
Multiple Load Forecasting of Integrated Energy System Based on Sequential-Parallel Hybrid Ensemble Learning
by Wenxia You, Daopeng Guo, Yonghua Wu and Wenwu Li
Energies 2023, 16(7), 3268; https://doi.org/10.3390/en16073268 - 6 Apr 2023
Cited by 5 | Viewed by 1852
Abstract
Accurate multivariate load forecasting plays an important role in the planning management and safe operation of integrated energy systems. In order to simultaneously reduce the prediction bias and variance, a hybrid ensemble learning method for load forecasting of an integrated energy system combining [...] Read more.
Accurate multivariate load forecasting plays an important role in the planning management and safe operation of integrated energy systems. In order to simultaneously reduce the prediction bias and variance, a hybrid ensemble learning method for load forecasting of an integrated energy system combining sequential ensemble learning and parallel ensemble learning is proposed. Firstly, the load correlation and the maximum information coefficient (MIC) are used for feature selection. Then the base learner uses the Boost algorithm of sequential ensemble learning and uses the Bagging algorithm of parallel ensemble learning for hybrid ensemble learning prediction. The grid search algorithm (GS) performs hyper-parameter optimization of hybrid ensemble learning. The comparative analysis of the example verification shows that compared with different types of single ensemble learning, hybrid ensemble learning can better balance the bias and variance and accurately predict multiple loads such as electricity, cold, and heat in the integrated energy system. Full article
(This article belongs to the Special Issue Advanced Optimization and Forecasting Methods in Power Engineering)
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Review

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20 pages, 1020 KiB  
Review
Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue
by Paweł Pijarski, Piotr Kacejko and Piotr Miller
Energies 2023, 16(6), 2804; https://doi.org/10.3390/en16062804 - 17 Mar 2023
Cited by 12 | Viewed by 2173
Abstract
Modern power engineering is struggling with various problems that have not been observed before or have occurred very rarely. The main cause of these problems results from the increasing number of connected distributed electricity sources, mainly renewable energy sources (RESs). Therefore, energy generation [...] Read more.
Modern power engineering is struggling with various problems that have not been observed before or have occurred very rarely. The main cause of these problems results from the increasing number of connected distributed electricity sources, mainly renewable energy sources (RESs). Therefore, energy generation is becoming more and more diverse, both in terms of technology and location. Grids that have so far worked as receiving networks change their original function and become generation networks. The directions of power flow have changed. In the case of distribution networks, this is manifested by power flows towards transformer stations and further to the network with a higher voltage level. As a result of a large number of RESs, their total share in the total generation increases. This has a significant impact on various aspects of the operation of the power system. Voltage profiles, branch loads, power flows and directions of power flows between areas change. As a result of the random nature of RES generation, there are problems with the quality of electricity, source stability issues, branch overloading, voltage exceedances and power balance. The occurrence of various types of problems requires the use of more and more advanced methods to solve them. This review paper, which is an introduction to the Special Issue Advanced Optimisation and Forecasting Methods in Power Engineering, describes and justifies the need to reach for effective and available mathematical and IT methods that are necessary to deal with the existing threats appearing in the operation of modern power systems. It indicates exemplary, current problems and advanced methods to solve them. This article is an introduction and justification for the use of advanced calculation methods and algorithms. Engineering intuition and experience are often not enough due to the size and complexity of power grid operation. Therefore, it becomes necessary to use methods based on artificial intelligence and other advanced solutions that will facilitate and support decision making in practice. Full article
(This article belongs to the Special Issue Advanced Optimization and Forecasting Methods in Power Engineering)
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