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Advanced Techniques for the Modeling and Simulation of Energy Networks

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

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 13000

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Special Issue Editors

Prof. Dr. Igor Simone Stievano

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Guest Editor

Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
Interests: modeling and simulation of circuits and systems; complex systems and graph-based approaches; uncertainty quantification; behavioral modeling; electromagnetic compatibility and signal integrity; compact modeling of electrical and gas networks; switching power converters; power line communication channels

Prof. Dr. Riccardo Trinchero

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Guest Editor

Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
Interests: surrogate modeling; machine learning; inverse models; uncertainty quantification; modeling and simulation of dynamical systems; switching power converters; electromagnetic compatibility

Special Issue Information

Dear Colleagues,

The need for a “smarter” energy grid infrastructure, with large-scale integration of renewables and a better demand-response mechanism, is leading to an ever-increasing complexity of next-generation energy networks. These networks are characterized by the increasing role of the intertwinement between different domains (e.g., electrical and gas) and a number of peculiar features, including large size, the interconnection of heterogeneous objects (e.g., distributed renewable sources), and a stochastic behavior due to the fluctuating customer demand and other external conditions affecting power generation and consumption. All the above features make the design, the optimization, and the monitoring of energy networks extremely challenging, thus motivating the demand for advanced modeling and simulation approaches able to accurately and inexpensively predict the network behavior for functional applications and reliability assessment. This Special Issue is tailored to contributions bringing together expertise from multiple domains, ranging from the generation of compact surrogates of electrical power networks to the application of machine learning and data-driven techniques in energy demand prediction or uncertainty quantification, as well as including possible novel concepts for high-level (e.g., graph-based) approaches for the analysis of a multi-energy scenario.

As the Guest Editors, we encourage scientists and application engineers to submit theoretical and applied contributions, as well as review articles, to this Special Issue of Energies on the subject “Advanced Techniques for the Modeling and Simulation of Energy Networks”. Topics of interest for publication include but are not limited to advanced simulation and modeling methods for complex energy problems, soft computing and artificial intelligence in energy systems, machine learning, multi-energy systems, co-simulation, distributed energy resources, and energy demand prediction.

Prof. Dr. Igor Simone Stievano
Dr. Riccardo Trinchero
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

Multi-energy networks
Distributed energy resources
Design and optimization in energy systems
Parametric Models
Co-simulation
Energy forecasting (production, consumption, and demand)
Machine learning
Uncertainty quantification.

Published Papers (6 papers)

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Editorial

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3 pages, 164 KiB

Open AccessEditorial

Advanced Techniques for the Modeling and Simulation of Energy Networks

by Igor Simone Stievano and Riccardo Trinchero

Energies 2023, 16(5), 2324; https://0-doi-org.brum.beds.ac.uk/10.3390/en16052324 - 28 Feb 2023

Viewed by 954

Abstract

The need for a “smarter” energy grid infrastructure, with the large-scale integration of renewables and a better demand–response mechanism, is leading to an ever-increasing complexity of next-generation energy networks [...] Full article

(This article belongs to the Special Issue Advanced Techniques for the Modeling and Simulation of Energy Networks)

Research

Jump to: Editorial

32 pages, 38827 KiB

Open AccessArticle

Modelling Long-Term Transition from Coal-Reliant to Low-Emission Power Grid and District Heating Systems in Poland

by Marcin Jaskólski and Paweł Bućko

Energies 2021, 14(24), 8389; https://0-doi-org.brum.beds.ac.uk/10.3390/en14248389 - 13 Dec 2021

Cited by 4 | Viewed by 2055

Abstract

Energy systems require technological changes towards climate neutrality. In Poland, where the power system is dominated by outdated coal-fired power plants, efforts to minimize the environmental impact are associated with high costs. Therefore, optimal paths for the development of the energy sector should be sought in order to achieve ambitious long-term strategic goals, while minimizing the negative impact on the consumers’ home budget. A methodology and a model for the development of the electricity and heat generation structure were developed and implemented in market allocation (MARKAL) modelling framework. Two scenarios were presented, i.e., business as usual (BAU) and withdrawal from coal (WFC) scenarios. The calculations showed a significant role of nuclear energy and offshore wind power in the pursuit of climate neutrality of electricity generation. In the BAU scenario, the model proposes to stay with coal technologies using carbon capture and storage systems. Withdrawal from coal (WFC scenario) makes it necessary to replace them by gas-fired power plants with CO₂ sequestration. Solar energy can be used both in electricity and district heating. In order to build on the latter technological option, appropriate energy storage techniques must be developed. Geothermal energy is expected to be the key option for district heat generation in the long-term horizon. The proposed development paths guarantee a significant reduction in greenhouse gases and industrial emissions. However, complete climate neutrality is uncertain, given the current degree and dynamics of technological development. Full article

(This article belongs to the Special Issue Advanced Techniques for the Modeling and Simulation of Energy Networks)

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17 pages, 3519 KiB

Open AccessArticle

A Statistical Assessment of Blending Hydrogen into Gas Networks

by Enrico Vaccariello, Riccardo Trinchero, Igor S. Stievano and Pierluigi Leone

Energies 2021, 14(16), 5055; https://0-doi-org.brum.beds.ac.uk/10.3390/en14165055 - 17 Aug 2021

Cited by 7 | Viewed by 2047

Abstract

The deployment of low-carbon hydrogen in gas grids comes with strategic benefits in terms of energy system integration and decarbonization. However, hydrogen thermophysical properties substantially differ from natural gas and pose concerns of technical and regulatory nature. The present study investigates the blending of hydrogen into distribution gas networks, focusing on the steady-state fluid dynamic response of the grids and gas quality compliance issues at increasing hydrogen admixture levels. Two blending strategies are analyzed, the first of which involves the supply of NG–H₂ blends at the city gate, while the latter addresses the injection of pure hydrogen in internal grid locations. In contrast with traditional case-specific analyses, results are derived from simulations executed over a large number (i.e., one thousand) of synthetic models of gas networks. The responses of the grids are therefore analyzed in a statistical fashion. The results highlight that lower probabilities of violating fluid dynamic and quality restrictions are obtained when hydrogen injection occurs close to or in correspondence with the system city gate. When pure hydrogen is injected in internal grid locations, even very low volumes (1% vol of the total) may determine gas quality violations, while fluid dynamic issues arise only in rare cases of significant hydrogen injection volumes (30% vol of the total). Full article

(This article belongs to the Special Issue Advanced Techniques for the Modeling and Simulation of Energy Networks)

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25 pages, 9992 KiB

Open AccessArticle

Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

by Khushwant Rai, Farnam Hojatpanah, Firouz Badrkhani Ajaei and Katarina Grolinger

Energies 2021, 14(12), 3623; https://0-doi-org.brum.beds.ac.uk/10.3390/en14123623 - 18 Jun 2021

Cited by 30 | Viewed by 2675

Abstract

High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD learns only from the HIF signals eliminating the need for presence of diverse non-HIF scenarios in the CAE training. CAE distinguishes HIFs from non-HIF operating conditions by employing cross-correlation. To discriminate HIFs from transient disturbances such as capacitor or load switching, CAE-HIFD uses kurtosis, a statistical measure of the probability distribution shape. The performance evaluation studies conducted using the IEEE 13-node test feeder indicate that the CAE-HIFD reliably detects HIFs, outperforms the state-of-the-art HIF detection techniques, and is robust against noise. Full article

(This article belongs to the Special Issue Advanced Techniques for the Modeling and Simulation of Energy Networks)

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21 pages, 1938 KiB

Open AccessArticle

Handling Initial Conditions in Vector Fitting for Real Time Modeling of Power System Dynamics

by Tommaso Bradde, Samuel Chevalier, Marco De Stefano, Stefano Grivet-Talocia and Luca Daniel

Energies 2021, 14(9), 2471; https://0-doi-org.brum.beds.ac.uk/10.3390/en14092471 - 26 Apr 2021

Cited by 5 | Viewed by 1913

Abstract

This paper develops a predictive modeling algorithm, denoted as Real-Time Vector Fitting (RTVF), which is capable of approximating the real-time linearized dynamics of multi-input multi-output (MIMO) dynamical systems via rational transfer function matrices. Based on a generalization of the well-known Time-Domain Vector Fitting (TDVF) algorithm, RTVF is suitable for online modeling of dynamical systems which experience both initial-state decay contributions in the measured output signals and concurrently active input signals. These adaptations were specifically contrived to meet the needs currently present in the electrical power systems community, where real-time modeling of low frequency power system dynamics is becoming an increasingly coveted tool by power system operators. After introducing and validating the RTVF scheme on synthetic test cases, this paper presents a series of numerical tests on high-order closed-loop generator systems in the IEEE 39-bus test system. Full article

(This article belongs to the Special Issue Advanced Techniques for the Modeling and Simulation of Energy Networks)

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16 pages, 2824 KiB

Open AccessArticle

Gaussian Copula Methodology to Model Photovoltaic Generation Uncertainty Correlation in Power Distribution Networks

by Harshavardhan Palahalli, Paolo Maffezzoni and Giambattista Gruosso

Energies 2021, 14(9), 2349; https://0-doi-org.brum.beds.ac.uk/10.3390/en14092349 - 21 Apr 2021

Cited by 13 | Viewed by 2132

Abstract

Deterministic load flow analyses of power grids do not include the uncertain factors that affect the network elements; hence, their predictions can be very unreliable for distribution system operators and for the decision makers who deal with the expansion planning of the power network. Adding uncertain probability parameters in the deterministic load flow is vital to capture the wide variability of the currents and voltages. This is achieved by probabilistic load flow studies. Photovoltaic systems represent a remarkable source of uncertainty in the distribution network. In this study, we used a Gaussian copula to model the uncertainty in correlated photovoltaic generators. Correlations among photovoltaic generators were also included by exploiting the Gaussian copula technique. The large sets of samples generated with a statistical method (Gaussian copula) were used as the inputs for Monte Carlo simulations. The proposed methodologies were tested on two different networks, i.e., the 13 node IEEE test feeder and the non-synthetic European low voltage test network. Node voltage uncertainty and network health, measured by the percentage voltage unbalance factor, were investigated. The importance of including correlations among photovoltaic generators is discussed. Full article

(This article belongs to the Special Issue Advanced Techniques for the Modeling and Simulation of Energy Networks)

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