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Artificial Intelligence and Optimization for Smart Grids
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Artificial Intelligence and Optimization for Smart Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 28 August 2024 | Viewed by 5614

Special Issue Editors

Prof. Dr. Chun-Cheng Lin

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Interests: metaheuristic algorithms; machine learning; internet of things; wireless networks; computational management science
Special Issues, Collections and Topics in MDPI journals
Dr. Tony Huang

E-Mail Website
Guest Editor
Faculty of Transdisciplinary Innovation, University of Technology Sydney, Ultimo 2007, Australia
Interests: data science; network analysis and visualisation; human–computer interactions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart grids are deployed by the installation of smart measures and monitoring equipment and systems in power generation, transmission, and distribution. Through the bi-directional communications of these devices, the data of power supply and power consumption can be digitized and visualized, and the real-time and big data can be integrated and further analysed to achieve the best allocation of power resources. Furthermore, the integration of smart grids and renewable energy sources increase enormous applications and help to achieve energy sustainability. In order to efficiently address the data and further develop related applications, recent techniques in artificial intelligence (AI), machine learning, deep learning, deep reinforcement learning, and optimization have received considerable successful energy-related applications in various industries, including smart city, smart transportation, smart healthcare, and smart manufacturing. However, there is still a lack of research on the future development of these techniques in large-scale smart grids and novel energy-related applications, for example, how to improve the stability of renewable energy, and how to improve the quality of power supply for users and further save energy.

Therefore, this Special Issue encourages new thinking and discussion about how AI and optimization techniques addresses the numerous critical issues arising from smart grids and renewable energy. Topics of interest for publication include, but are not limited to:

  • Machine learning, deep learning, reinforcement learning, transfer learning, and federated learning for applications in smart grids;
  • Optimization techniques, mathematical programming methods, and metaheuristics for applications in smart grids;
  • Interoperation among electric vehicles, unmanned aerial vehicles, and smart grids;
  • AI and optimization techniques for smart grids;
  • AI and optimization techniques for internet of energy;
  • AI and optimization techniques for sharing energy and energy trading;
  • AI and optimization techniques for distributed energy;
  • AI and optimization techniques for energy storage systems;
  • AI and optimization techniques for renewable energy;
  • AI and optimization techniques for green energy and carbon footprint;
  • Novel applications of smart grids in smart city, smart transportation, smart healthcare, and smart manufacturing.

Prof. Dr. Chun-Cheng Lin
Dr. Tony Huang
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, deep learning, reinforcement learning, transfer learning, and federated learning for applications in smart grids
  • optimization techniques, mathematic programming methods, and metaheuristics for applications in smart grids
  • Interoperation among electric vehicles, unmanned aerial vehicles, and smart grids
  • AI and optimization techniques for smart grids
  • AI and optimization techniques for Internet of energy
  • AI and optimization techniques for sharing energy and energy trading
  • AI and optimization techniques for distributed energy
  • AI and optimization techniques for energy storage systems
  • AI and optimization techniques for renewable energy
  • AI and optimization techniques for green energy and carbon footprint
  • novel applications of smart grids in smart city

Published Papers (3 papers)

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Research

30 pages, 14423 KiB  
Article
Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing
by Guilherme Henrique Alves, Geraldo Caixeta Guimarães and Fabricio Augusto Matheus Moura
Energies 2023, 16(14), 5262; https://0-doi-org.brum.beds.ac.uk/10.3390/en16145262 - 09 Jul 2023
Cited by 1 | Viewed by 1479
Abstract
The current microgrid (MG) needs alternatives to raise the management level and avoid waste. This approach is important for developing the modern electrical system, as it allows for better integration of distributed generation (DG) and battery energy storage systems (BESSs). Using algorithms based [...] Read more.
The current microgrid (MG) needs alternatives to raise the management level and avoid waste. This approach is important for developing the modern electrical system, as it allows for better integration of distributed generation (DG) and battery energy storage systems (BESSs). Using algorithms based on artificial intelligence (AI) for the energy management system (EMS) can help improve the MG operation to achieve the lowest possible cost in buying and selling electricity and, consequently, increase energy conservation levels. With this, the research proposes two strategies for managing energy in the MG to determine the instants of charge and discharge of the BESS. A heuristic method is employed as a reference point for comparison purposes with the fuzzy logic (FL) operation developed. Furthermore, other algorithms based on artificial neural networks (ANNs) are proposed using the non-linear autoregressive technique to predict the MG variables. During the research, the developed algorithms were evaluated through extensive case studies, with simulations that used data from the PV system, load demands, and electricity prices. For all cases, the AI algorithms for predictions and actions managed to reduce the cost and daily consumption of electricity in the main electricity grids compared with the heuristic method or with the MG without using BESSs. This indicates that the developed power management strategies can be applied to reduce the costs of grid-connected MG operations. It is important to highlight that the simulations were executed in an adequate time, allowing the use of the proposed algorithms in dynamic real-time situations to contribute to developing more efficient and sustainable electrical systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization for Smart Grids)
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13 pages, 3395 KiB  
Article
A Novel Agent-Based Power Management Scheme for Smart Multiple-Microgrid Distribution Systems
by Zagros Shahooei, Lane Martin, Hashem Nehrir and Maryam Bahramipanah
Energies 2022, 15(5), 1774; https://doi.org/10.3390/en15051774 - 28 Feb 2022
Cited by 2 | Viewed by 1393
Abstract
In this work, a novel agent-based day-ahead power management scheme is proposed for multiple-microgrid distribution systems with the intent of reducing operational costs and improving system resilience. The proposed power sharing algorithm executes within each microgrid (MG) locally, and the neighboring MGs cooperate [...] Read more.
In this work, a novel agent-based day-ahead power management scheme is proposed for multiple-microgrid distribution systems with the intent of reducing operational costs and improving system resilience. The proposed power sharing algorithm executes within each microgrid (MG) locally, and the neighboring MGs cooperate via a multi-agent system cooperation scheme, established to model the communication among the agents. The power management for each agent is modeled as a multi-objective optimization problem (MOP) including two objectives: maximizing load coverage and minimizing the operating costs. The proposed MOP is solved using the Nondominated Sorting Genetic Algorithm (NSGA-II), where a set of Pareto optimal solutions is obtained for each agent through the NSGA-II. The final solution is obtained using an Analytical Hierarchical Process. The effectiveness of the proposed scheme is evaluated using a benchmark 4-MG distribution system. It is shown that the proposed power management scheme and the cooperation of agents lead to a higher overall system resilience and lower operation costs during extreme events. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization for Smart Grids)
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20 pages, 4505 KiB  
Article
Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation
by Jianlong Zhou, Weidong Huang and Fang Chen
Energies 2021, 14(21), 7049; https://0-doi-org.brum.beds.ac.uk/10.3390/en14217049 - 28 Oct 2021
Cited by 4 | Viewed by 1551
Abstract
Building an effective Machine Learning (ML) model for a data set is a difficult task involving various steps. One of the most important steps is to compare a substantial amount of generated ML models to find the optimal one for deployment. It is [...] Read more.
Building an effective Machine Learning (ML) model for a data set is a difficult task involving various steps. One of the most important steps is to compare a substantial amount of generated ML models to find the optimal one for deployment. It is challenging to compare such models with a dynamic number of features. Comparison is more than only finding differences of ML model performance, as users are also interested in the relations between features and model performance such as feature importance for ML explanations. This paper proposes RadialNet Chart, a novel visualisation approach, to compare ML models trained with a different number of features of a given data set while revealing implicit dependent relations. In RadialNet Chart, ML models and features are represented by lines and arcs, respectively. These lines are generated effectively using a recursive function. The dependence of ML models with a dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections. ML model performance information is encoded with colour and line width in RadialNet Chart. Taken together with the structure of visualisation, feature importance can be directly discerned in RadialNet Chart for ML explanations. Compared with other commonly used visualisation approaches, RadialNet Chart can help to simplify the ML model comparison process with different benefits such as the following: more efficient in terms of helping users to focus their attention to find visual elements of interest and easier to compare ML performance to find optimal ML model and discern important features visually and directly instead of through complex algorithmic calculations for ML explanations. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization for Smart Grids)
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