Special Issue "The Artificial Intelligence Technologies for Electric Power Systems"
Deadline for manuscript submissions: 30 November 2021.
Interests: cyber-physical systems, industrial IoT, energy management, energy internet, wireless networks, applied artificial intelligence
Interests: power electronics; photovoltaic systems; control of power converters; reliability; power quality
Special Issues and Collections in MDPI journals
Special Issue in Applied Sciences: Planning, Operation, and Control of Power Systems with Large-Scale Renewable Energy
Special Issue in Energies: Emerging Converter Topologies and Control for Grid Connected Photovoltaic Systems
Special Issue in Applied Sciences: Advancing Grid-Connected Renewable Generation Systems 2019
Special Issue in Electronics: Advances in Photovoltaic Microinverter Technologies
Special Issue in Applied Sciences: Advancing Grid-Connected Renewable Generation Systems 2021
Artificial Intelligence (AI) is a dynamic topic of research and development in a wide range of fields. Electric power system is no different. This special session is dedicated to the advances of AI techniques applied in this engineering domain. We expect contributions that apply recent advances of AI for tasks related to classification, detection, prediction, diagnosis, analytics, control, planning and management of processes in power systems. Papers dealing with emerging topics such as explainable AI (XAI), deep learning (DL), cyber-security, event-driven/triggered approaches, communication-dependent solutions with low latency enabled by 5G/IoT/edge-computing and software-defined networks are especially welcome. Application domains can range from nano/micro-grids to high-voltage systems, including also topics related to investment plan, demand dispatching, load flexibility, distributed energy resource management and long-term energy storage. In general, all topics that apply cutting-edge AI technologies in electric power systems are equally supported.
We expect to have a session with high-quality publications that Energies readers will enjoy and learn more about the state-of-the-art in the field to move beyond it. We are very happy to answer specific questions of prospective authors.
Prof. Pedro NARDELLI
Prof. Dr. Yongheng Yang
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 papers will be 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 2000 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.
- Artificial intelligence in power system
- Fault classification, detection, identification, prediction, and diagnosis
- AI-based demand-side management and flexibility
- Deep learning
- Cyber-physical energy systems
- Communications to support AI in power systems
- AI-based learning for system optimization
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Production line optimization to minimize energy cost and participate in demand response events
Authors: Bruno Mota, Luis Gomes, Pedro Faria, Carlos Ramos, and Zita Vale
Affiliation: Knowledge Engineering and Decision-Support Research Center, Portugal
Abstract: The scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines, and machine limits. The main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost of the production line. For that, a genetic-algorithm based method is proposed. Currently, the use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers will allow the user to manage its consumption according to energy prices and energy availability. The proposed solution will take into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response programs by shifting its production, enabling the use of flexibility in production lines. This paper presents a case study using real production data that represents a textile industry where the tasks for six days are scheduled. During the week, a demand response program will be launched and the proposed algorithm will shift the consumption by changing tasks orders and machine usage.