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Artificial Intelligence Applications to Energy Systems
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Artificial Intelligence Applications to Energy Systems

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

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 19626

Special Issue Editors

Prof. Dr. Enrico Gerding

E-Mail Website
Guest Editor
Department of Electronics and Computer Science (ECS), University of Southampton, Southampton SO17 1BJ, UK
Interests: multi-agent systems; mechanism design; energy; electric vehicles; privacy
Dr. Sebastian Stein

E-Mail Website
Guest Editor
Department of Electronics and Computer Science (ECS), University of Southampton, Southampton SO17 1BJ, UK
Interests: Artificial Intelligence; multi-agent systems; mechanism design; crowdsourcing; smart transportation

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue on the application of Artificial Intelligence techniques to energy systems. The aim of this Issue is to bring together research that addresses pressing societal issues related to energy sustainability and climate change using AI, for example, by improving the efficiency of energy systems and making better use of renewable energy and storage. We welcome research using a range of AI techniques, including machine learning and multi-agent systems, as well as research on the interaction between humans and intelligent energy systems. Topics include but are not limited to:

  • Machine learning;
  • Multi-agent systems;
  • Decentralised optimisation;
  • Decision making under uncertainty;
  • Community energy markets;
  • Storage and renewable energy;
  • Mechanism design and incentive engineering;
  • Electric vehicles;
  • Human–system interaction;
  • Smart energy systems.

Dr. Enrico Gerding
Dr. Sebastian Stein
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

  • Machine learning
  • Multi-agent systems
  • Decentralised optimisation
  • Decision making under uncertainty
  • Community energy markets
  • Storage and renewable Energy
  • Mechanism design and incentive engineering
  • Electric vehicles
  • Human–system interaction
  • Smart energy systems

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Published Papers (5 papers)

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Research

20 pages, 4347 KiB  
Article
A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation
by Rahman Azis Prasojo, Harry Gumilang, Suwarno, Nur Ulfa Maulidevi and Bambang Anggoro Soedjarno
Energies 2020, 13(4), 1009; https://doi.org/10.3390/en13041009 - 24 Feb 2020
Cited by 37 | Viewed by 5498
Abstract
In determining the severity of power transformer faults, several approaches have been previously proposed; however, most published studies do not accommodate gas level, gas rate, and Dissolved Gas Analysis (DGA) interpretation in a single approach. To increase the reliability of the faults’ severity [...] Read more.
In determining the severity of power transformer faults, several approaches have been previously proposed; however, most published studies do not accommodate gas level, gas rate, and Dissolved Gas Analysis (DGA) interpretation in a single approach. To increase the reliability of the faults’ severity assessment of power transformers, a novel approach in the form of fuzzy logic has been proposed as a new solution to determine faults’ severity using the combination of gas level, gas rate, and DGA interpretation from the Duval Pentagon Method (DPM). A four-level typical concentration and rate were established based on the local population. To simplify the assessment of hundreds of power transformer data, a Support Vector Machine (SVM)-based DPM with high agreements to the graphical DPM has been developed. The proposed approach has been implemented to 448 power transformers and further implementation was done to evaluate faults’ severity of power transformers from historical DGA data. This new approach yields in high agreement with the previous methods, but with better sensitivity due to the incorporation of gas level, gas rate, and DGA interpretation results in one approach. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications to Energy Systems)
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13 pages, 3346 KiB  
Article
Wavelet Energy Fuzzy Neural Network-Based Fault Protection System for Microgrid
by Cheng-I Chen, Chien-Kai Lan, Yeong-Chin Chen, Chung-Hsien Chen and Yung-Ruei Chang
Energies 2020, 13(4), 1007; https://doi.org/10.3390/en13041007 - 24 Feb 2020
Cited by 13 | Viewed by 2675
Abstract
To perform the fault protection for the microgrid in grid-connected mode, the wavelet energy fuzzy neural network-based technique (WEFNNBT) is proposed in this paper. Through the accurate activation of protective relay, the microgrid can be effectively isolated from the utility power system to [...] Read more.
To perform the fault protection for the microgrid in grid-connected mode, the wavelet energy fuzzy neural network-based technique (WEFNNBT) is proposed in this paper. Through the accurate activation of protective relay, the microgrid can be effectively isolated from the utility power system to prevent serious voltage fluctuation when the power quality of power system is disturbed. The proposed WEFNNBT can be divided into three stages—feature extraction (FE), feature condensation (FC), and disturbance identification (DI). In the FE stage, the feature of power signal at the point of common coupling (PCC) between microgrid and utility power system would be extracted with discrete wavelet transform (DWT). Then, the wavelet energy and variation of singular power signal can be obtained according to Parseval Theorem. To determine the dominant wavelet energy and enhance the robustness to the noise, the feature information is integrated in the FC stage. The feature information then would be processed in the DI stage to perform the fault identification and activate the protective relay if necessary. From the experimental results, it is realized that the proposed WEFNNBT can effectively perform the fault protection of microgrid. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications to Energy Systems)
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31 pages, 732 KiB  
Article
Market Design and Trading Strategies for Community Energy Markets with Storage and Renewable Supply
by Abdullah M. Alabdullatif, Enrico H. Gerding and Alvaro Perez-Diaz
Energies 2020, 13(4), 972; https://doi.org/10.3390/en13040972 - 21 Feb 2020
Cited by 26 | Viewed by 4042
Abstract
Community Energy Markets (CEMs) enable trading opportunities between participants in a community to achieve savings and profits. However, the market design and the behaviour of participants are key factors that determine the success of such markets. To this end, this research presents a [...] Read more.
Community Energy Markets (CEMs) enable trading opportunities between participants in a community to achieve savings and profits. However, the market design and the behaviour of participants are key factors that determine the success of such markets. To this end, this research presents a CEM model and conducts agent-based simulations to study the benefits of the CEM to consumers and prosumers. The proposed market structure is an hour-ahead periodic double auction. In particular, market rules are proposed that incentivise the provision of energy supply to the community and the investment in energy storage. Furthermore, a trading strategy is introduced that leverages energy flexibility created by the storage devices. Finally, as well as the hour-ahead market, we include a minute-by-minute balancing as part of the CEM’s energy exchange mechanism. The balancing approach is introduced to account for a community budget deficit caused by the time difference between supply and demand. The proposed market results in cost savings for consumers and profit for prosumers similar to existing approaches, while increasing the energy suppliers’ percentage of financial benefits from 50% to a range between 60–96% depending on the community configuration. Moreover, the market model accounts for uncertainties in supply and demand and suggests a methodology to overcome the community budget deficit. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications to Energy Systems)
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Graphical abstract

20 pages, 2125 KiB  
Article
A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction
by Jun Peng, Zhiyong Zheng, Xiaoyong Zhang, Kunyuan Deng, Kai Gao, Heng Li, Bin Chen, Yingze Yang and Zhiwu Huang
Energies 2020, 13(3), 752; https://doi.org/10.3390/en13030752 - 8 Feb 2020
Cited by 21 | Viewed by 3278
Abstract
Data-driven methods are widely applied to predict the remaining useful life (RUL) of lithium-ion batteries, but they generally suffer from two limitations: (i) the potentials of features are not fully exploited, and (ii) the parameters of the prediction model are difficult to determine. [...] Read more.
Data-driven methods are widely applied to predict the remaining useful life (RUL) of lithium-ion batteries, but they generally suffer from two limitations: (i) the potentials of features are not fully exploited, and (ii) the parameters of the prediction model are difficult to determine. To address this challenge, this paper proposes a new data-driven method using feature enhancement and adaptive optimization. First, the features of battery aging are extracted online. Then, the feature enhancement technologies, including the box-cox transformation and the time window processing, are used to fully exploit the potential of features. The box-cox transformation can improve the correlation between the features and the aging status of the battery, and the time window processing can effectively exploit the time information hidden in the historical features sequence. Based on this, gradient boosting decision trees are used to establish the RUL prediction model, and the particle swarm optimization is used to adaptively optimize the model parameters. This method was applied on actual lithium-ion battery degradation data, and the experimental results show that the proposed model is superior to traditional prediction methods in terms of accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications to Energy Systems)
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Graphical abstract

17 pages, 2512 KiB  
Article
Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction
by Khikmafaris Yudantaka, Jung-Su Kim and Hwachang Song
Energies 2020, 13(1), 148; https://doi.org/10.3390/en13010148 - 27 Dec 2019
Cited by 16 | Viewed by 3298
Abstract
Load power forecast is one of most important tasks in power systems operation and maintenance. Enhancing its accuracy can be helpful to power systems scheduling. This paper presents how to use partial real-time temperature information in forecasting load power, which is usually done [...] Read more.
Load power forecast is one of most important tasks in power systems operation and maintenance. Enhancing its accuracy can be helpful to power systems scheduling. This paper presents how to use partial real-time temperature information in forecasting load power, which is usually done using past load power and temperature data. The partial real-time temperature information means temperature information for only part of the entire prediction time interval. To this end, a long short-term memory (LSTM) network is trained using past temperature and load power data in order to forecast load power, where forecasted load power depends on the temperature prediction implicitly. Then, in order to deal with the case where nontrivial temperature prediction errors happen, a multi-layer perceptron (MLP) network is trained using the past data describing the relation between temperature variation and load power variation. Then, the temperature is measured at the beginning of the prediction time-interval and compensated load forecast is computed by adding the output of the LSTM and that of the MLP whose input is the temperature prediction error. It is shown that the proposed compensation using the real-time temperature information indeed improves performance of load power forecast. This improved load forecast is used to predict system marginal price (SMP). The proposed method is validated using the real temperature and load power data of South Korea. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications to Energy Systems)
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