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Resilience Engineering for Smart Energy Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 10146

Special Issue Editors

Information Technology Group, Wageningen University, 6708 PB Wageningen, The Netherlands
Interests: software engineering; software architecture; systems engineering; smart systems; critical infrastructures; software ecosystems; system of systems
Special Issues, Collections and Topics in MDPI journals
Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
Interests: Energy Informatics; Smart Energy Systems; Distributed Energy Systems; Smart Grids; Renewable Energy Integration; Modeling and Simulation
Special Issues, Collections and Topics in MDPI journals
Information Technology Group, Wageningen University and Research, Building No. 201 (Leeuwenborch), Hollandseweg 1, 6706 KN Wageningen, The Netherlands
Interests: data science; information technology; critical infrastructures; creative technologies
Special Issues, Collections and Topics in MDPI journals
Department of Computer Engineering, Bahcesehir University, 34349 Beşiktaş/Istanbul, Turkey
Interests: smart systems; cyber security; machine learning; deep learning; software architecture; software testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few decades, the planning and operation of energy production and energy consumption have been a growing concern globally. Smart energy management is the systematic coordination of the procurement, conversion, transmission, distribution, and use of energy to meet user, environmental, and economic requirements. In this context, the key drivers include resource conservation, climate protection, and cost savings, while the users have permanent and affordable access to the energy they need. Traditionally, smart energy management has been tackled separately in different sectors (e.g., transportation, electricity generation, built environment, industry, agriculture), each with its own specific constraints, requirements, and design solutions. In contrast to these single-sector solutions, smart energy systems adopt a holistic, systemic approach that aims to include, integrate, and coordinate technologies and stakeholders from/within multiple sectors to provide a feasible solution for each sector, as well as for the overall energy system. Smart energy systems are playing an increasingly broad and critical role in many countries to support sustainable development. One requirement of these smart energy systems is that they are resilient to any imposed damage or irregularities and continue to function at the required performance level. Resilience engineering is a holistic systems engineering approach that provides the models and methods to design and analyze systems for resilience.

The aim of this Special Issue is to bring together innovative developments and applications of resilience engineering for smart energy systems. We welcome both research papers addressing new insights and experience papers discussing the lessons learned in practice. Articles may include, but are not limited to, the following topics:

  • Modelling approaches for the resilience of smart energy systems;
  • System architecture design for resilience engineering of smart energy systems;
  • Application of system of systems for resilient smart energy systems;
  • Tools for resilient smart energy systems;
  • Obstacles to resilience of smart energy systems;
  • Methods and process models for resilience engineering of smart energy systems;
  • Empirical evaluation approaches for resilience engineering of smart energy systems;
  • Resilient engineering of smart grids and microgrids;
  • Critical infrastructures and smart energy systems;
  • Cross-sectoral integration of smart energy systems;
  • Workflow patterns for resilient smart energy systems;
  • Business modelling approaches for resilient smart energy systems;
  • Metrics for resilient smart energy systems.

Prof. Dr. Bedir Tekinerdogan
Dr. Tarek AlSkaif
Dr. William Hurst
Dr. Cagatay Catal
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. Sensors 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

  • smart energy systems: smart energy infrastructure and storage options
  • smart energy system analyses
  • tools and methodologies
  • integrated energy systems and smart grids
  • resilience engineering
  • systems engineering for smart energy
  • system of systems
  • smart grids and microgrids
  • smart energy critical infrastructures

Published Papers (3 papers)

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Research

19 pages, 5831 KiB  
Article
Blockchain-Based Peer-to-Peer Transactive Energy Management Scheme for Smart Grid System
by Aparna Kumari, Urvi Chintukumar Sukharamwala, Sudeep Tanwar, Maria Simona Raboaca, Fayez Alqahtani, Amr Tolba, Ravi Sharma, Ioan Aschilean and Traian Candin Mihaltan
Sensors 2022, 22(13), 4826; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134826 - 26 Jun 2022
Cited by 25 | Viewed by 3433
Abstract
In Smart Grid (SG), Transactive Energy Management (TEM) is one of the most promising approaches to boost consumer participation in energy generation, energy management, and establishing decentralized energy market models using Peer-to-Peer (P2P). In P2P, a prosumer produces electric energy at their place [...] Read more.
In Smart Grid (SG), Transactive Energy Management (TEM) is one of the most promising approaches to boost consumer participation in energy generation, energy management, and establishing decentralized energy market models using Peer-to-Peer (P2P). In P2P, a prosumer produces electric energy at their place using Renewable Energy Resources (RES) such as solar energy, wind energy, etc. Then, this generated energy is traded with consumers (who need the energy) in a nearby locality. P2P facilitates energy exchange in localized micro-energy markets of the TEM system. Such decentralized P2P energy management could cater to diverse prosumers and utility business models. However, the existing P2P approaches suffer from several issues such as single-point-of-failure, network bandwidth, scalability, trust, and security issues. To handle the aforementioned issues, this paper proposes a Decentralized and Transparent P2P Energy Trading (DT-P2PET) scheme using blockchain. The proposed DT-P2PET scheme aims to reduce the grid’s energy generation and management burden while also increasing profit for both consumers and prosumers through a dynamic pricing mechanism. The DT-P2PET scheme uses Ethereum-blockchain-based Smart Contracts (SCs) and InterPlanetary File System (IPFS) for the P2P energy trading. Furthermore, a recommender mechanism is also introduced in this study to increase the number of prosumers. The Ethereum SCs are designed and deployed to perform P2P in real time in the proposed DT-P2PET scheme. The DT-P2PET scheme is evaluated based on the various parameters such as profit generation (for prosumer and consumer both), data storage cost, network bandwidth, and data transfer rate in contrast to the existing approaches. Full article
(This article belongs to the Special Issue Resilience Engineering for Smart Energy Systems)
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29 pages, 2860 KiB  
Article
Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs
by Madiah Binti Omar, Rosdiazli Ibrahim, Rhea Mantri, Jhanavi Chaudhary, Kaushik Ram Selvaraj and Kishore Bingi
Sensors 2022, 22(12), 4342; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124342 - 08 Jun 2022
Cited by 6 | Viewed by 2786
Abstract
A smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of [...] Read more.
A smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of the electrical supply. Due to sensor or system failures, missing input data can often occur. It is worth noting that there has been no work conducted to predict the missing input variables in the past. Thus, this paper aims to develop an enhanced forecasting model to predict smart grid stability using neural networks to handle the missing data. Four case studies with missing input data are conducted. The missing data is predicted for each case, and then a model is prepared to predict the stability. The Levenberg–Marquardt algorithm is used to train all the models and the transfer functions used are tansig and purelin in the hidden and output layers, respectively. The model’s performance is evaluated on a four-node star network and is measured in terms of the MSE and R2 values. The four stability prediction models demonstrate good performances and depict the best training and prediction ability. Full article
(This article belongs to the Special Issue Resilience Engineering for Smart Energy Systems)
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15 pages, 505 KiB  
Article
Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network
by Alper Ozcan, Cagatay Catal and Ahmet Kasif
Sensors 2021, 21(21), 7115; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217115 - 27 Oct 2021
Cited by 11 | Viewed by 2568
Abstract
Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window [...] Read more.
Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures are crucial for service providers. Electrical load forecasting can be modeled as a time series prediction problem. One solution is to capture spatial correlations, spatial-temporal relations, and time-dependency of such temporal networks in the time series. Previously, different machine learning methods have been used for time series prediction tasks; however, there is still a need for new research to improve the performance of short-term load forecasting models. In this article, we propose a novel deep learning model to predict electric load consumption using Dual-Stage Attention-Based Recurrent Neural Networks in which the attention mechanism is used in both encoder and decoder stages. The encoder attention layer identifies important features from the input vector, whereas the decoder attention layer is used to overcome the limitations of using a fixed context vector and provides a much longer memory capacity. The proposed model improves the performance for short-term load forecasting (STLF) in terms of the Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE) scores. To evaluate the predictive performance of the proposed model, the UCI household electric power consumption (HEPC) dataset has been used during the experiments. Experimental results demonstrate that the proposed approach outperforms the previously adopted techniques. Full article
(This article belongs to the Special Issue Resilience Engineering for Smart Energy Systems)
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