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Advanced Artificial Intelligence/Machine Learning Techniques for Safe Operation and Control in Power and Sustainable Energy Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2072

Special Issue Editors

Dr. Van-Hai Bui

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, USA
Interests: energy management systems; applications of machine learning in smart grids; operation and control of power and energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Wencong Su

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, USA
Interests: power systems; electrified transportation systems; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Xuan Zhou

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48126, USA
Interests: battery design and manufacturing; battery modelling and control for electric vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Akhtar Hussain

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Laval University, 2325 Rue de l'Université, Québec, QC G1V 0A6, Canada
Interests: power system automation; smart grids; microgrid operation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing integration of distributed energy resources (DERs) into power distribution networks introduces numerous sources of uncertainty, significantly challenging the operation and control of power systems. These challenges may include grid stability, security risks, frequency instability, and voltage fluctuations. Conventional optimization methods often falter in handling such uncertainty, leading to increased operational costs and decreased service reliability. Recently, the rapid development of artificial intelligence/machine learning, especially deep reinforcement learning, has offered promising sustainable solutions for managing power system operations amidst these uncertainties. A key limitation of conventional deep reinforcement learning approaches, however, is their inability to ensure safety constraints during system operations, potentially resulting in electrical system instability or equipment failures.

Therefore, the safe operation of critical infrastructure, such as power and energy systems, has been attracting significant attention from the academic and industrial research communities. Integrating safety considerations into AI/ML is crucial for ensuring reliability, security, and efficiency across the generation, transmission, and distribution of electricity.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Advanced machine learning for power and energy systems;
  • Energy management system implementation;
  • Explainable AI (XAI) applications;
  • Human-in-the-loop ML applications;
  • Multiagent system-based management systems;
  • Sustainable energy systems;
  • Safe reinforcement learning in power system operation and control;
  • Uncertainty mitigation with extensive DER integration.
  • We look forward to receiving your contributions.
You may choose our Joint Special Issue in Algorithms

Dr. Van-Hai Bui
Dr. Wencong Su
Dr. Xuan Zhou
Dr. Akhtar Hussain
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. Sustainability 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.

Keywords

  • the applications of advanced machine learning in sustainable energy systems
  • energy management systems
  • microgrids
  • power system operation and control
  • reinforcement learning

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Published Papers (2 papers)

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Research

22 pages, 7765 KiB  
Article
Bayesian-Neural-Network-Based Approach for Probabilistic Prediction of Building-Energy Demands
by Akash Mahajan, Srijita Das, Wencong Su and Van-Hai Bui
Sustainability 2024, 16(22), 9943; https://doi.org/10.3390/su16229943 - 14 Nov 2024
Viewed by 486
Abstract
Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network [...] Read more.
Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network (BNN)-based probabilistic prediction model is proposed to tackle this challenge. By quantifying the uncertainty, BNNs provide probabilistic predictions that capture the variations in the energy demand. The proposed model is trained and evaluated on a subset of the building operations dataset of Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, which includes diverse attributes related to climate and key building-performance indicators. We have performed thorough hyperparameter tuning and used fixed-horizon validation to evaluate trained models on various test data to assess generalization ability. To validate the results, quantile random forest (QRF) was used as a benchmark. This study compared BNN with LSTM, showing that BNN outperformed LSTM in uncertainty quantification. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence/Machine Learning Techniques for Safe Operation and Control in Power and Sustainable Energy Systems)
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15 pages, 6431 KiB  
Article
Ensuring Sustainable Grid Stability through Effective EV Charging Management: A Time and Energy-Based Approach
by Saeed Alyami
Sustainability 2024, 16(14), 6149; https://doi.org/10.3390/su16146149 - 18 Jul 2024
Viewed by 1230
Abstract
The rise of electric vehicles (EVs) has significantly transformed transportation, offering environmental advantages by curbing greenhouse gas emissions and fossil fuel dependency. However, their increasing adoption poses challenges for power systems, especially distribution systems, due to the direct connection of EVs with them. [...] Read more.
The rise of electric vehicles (EVs) has significantly transformed transportation, offering environmental advantages by curbing greenhouse gas emissions and fossil fuel dependency. However, their increasing adoption poses challenges for power systems, especially distribution systems, due to the direct connection of EVs with them. It requires robust infrastructure development, smart grid integration, and effective charging solutions to mitigate issues like overloading and peak demand to ensure grid stability, reliability, and sustainability. To prevent local equipment overloading during peak load intervals, the management of EV charging demand is carried out in this study, considering both the time to deadline and the energy demand of EVs. Initially, EVs are prioritized based on these two factors (time and energy)—those with shorter deadlines and lower energy demands receive higher rankings. This prioritization aims to maximize the number of EVs with their energy demands met. Subsequently, energy allocation to EVs is determined by their rankings while adhering to the transformer’s capacity limits. The process begins with the highest-ranked EV and continues until the transformer nears its limit. To this end, an index is proposed to evaluate the performance of the proposed method in terms of unserved EVs during various peak load intervals. Comparative analysis against the earliest deadline first approach demonstrates the superior ability of the proposed method to fulfill the energy demand of a larger number of EVs. By ensuring sustainable energy management, the proposed method supports the widespread adoption of EVs and the transition to a cleaner, more sustainable transportation system. Comparative analysis shows that the proposed method fulfills the energy needs of up to 33% more EVs compared to the earliest deadline method, highlighting its superior performance in managing network loads. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence/Machine Learning Techniques for Safe Operation and Control in Power and Sustainable Energy Systems)
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