Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Energy Management and Optimization for New Power Systems
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Energy Management and Optimization for New Power Systems

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

Deadline for manuscript submissions: closed (30 March 2024) | Viewed by 1388

Special Issue Editors

Prof. Dr. Tianguang Lu

E-Mail Website
Guest Editor
Department of Electrical Engineering, Shandong University, Jinan 250061, China
Interests: optimization for low-carbon power systems; microgrids; artificial intelligence and data-driven analytics in smart grids; electricity market
Dr. Changsen Feng

E-Mail Website
Guest Editor
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Interests: optimization and control in power systems; game theory; reinforcement learning

Special Issue Information

Dear Colleagues,

Driven by the goals of clean energy and zero carbon emissions, the power industry is undergoing significant transformations. The government of China plans to build a new type of power system featuring a gradual increase in the proportion of new energy sources and the large-scale deployment of clean power resources nationwide. The ever-increasing penetration of uncertain inverter-based distributed energy resources (DERs), such as wind and rooftop PV, will inevitably exert a considerable influence on the power system, generating reliability, economic and resiliency concerns. Hence, it is essential to improve the comprehensive regulation capability of the power system, accelerate construction of flexible regulation power, and guide self-supplied power plants, traditional high-energy industrial loads, industrial and commercial interruptible loads, electric vehicle charging networks and virtual power plants to participate in system regulation in order to build a strong smart grid and improve grid security.

Within this context, the new control methods, new management strategies as well as new market mechanisms are highly desirable. Research into the new power system is strongly interdisciplinary, involving the state of the art in control theory, economics, artificial intelligence (AI) and information technology. This Special Issue aims to compile innovative ideas and interdisciplinary research in this direction to address the forthcoming technical challenges.

Prof. Dr. Tianguang Lu
Dr. Changsen Feng
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

  • grid protection, reliability, energy/power quality, and maintenance
  • smart metering, measurement, instrumentation, and control
  • renewable energy, wind, solar, fuel cells, and distributed generation within microgrids
  • scalable optimization and control approaches for the new power system
  • novel market architecture, valuation mechanisms for der integration
  • business models and energy policies in the new power system
  • peer-to-peer energy and service trading
  • distributed ledger technology including blockchain
  • advanced information and computing technologies (ICT)
  • big data technology and AI for renewable energies
  • energy efficiency, conservation, and savings

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 3555 KiB  
Article
Artificial Intelligence for Energy Theft Detection in Distribution Networks
by Mileta Žarković and Goran Dobrić
Energies 2024, 17(7), 1580; https://doi.org/10.3390/en17071580 - 26 Mar 2024
Viewed by 464
Abstract
The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) [...] Read more.
The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks. Full article
(This article belongs to the Special Issue Energy Management and Optimization for New Power Systems)
►▼ Show Figures

Figure 1

18 pages, 2804 KiB  
Article
Efficiency Improvement of Permanent Magnet Synchronous Motors Using Model Predictive Control Considering Core Loss
by Lian Hou, Youguang Guo, Xin Ba, Gang Lei and Jianguo Zhu
Energies 2024, 17(4), 773; https://doi.org/10.3390/en17040773 - 06 Feb 2024
Cited by 1 | Viewed by 587
Abstract
The highway cycle is an important consideration in the EV’s new European driving cycles (NEDCs) range, as the steady-state efficiency improvement in such conditions can be greatly beneficial. In the model predictive control (MPC) of the permanent magnet synchronous motors (PMSMs), the predicted [...] Read more.
The highway cycle is an important consideration in the EV’s new European driving cycles (NEDCs) range, as the steady-state efficiency improvement in such conditions can be greatly beneficial. In the model predictive control (MPC) of the permanent magnet synchronous motors (PMSMs), the predicted next-step feedback reference generated by the equivalent circuit model (ECM) will contribute directly to the voltage vector selection, therefore influencing the performance of the motor control. In the current MPC scheme, when the conventional ECM is applied, it only considers copper loss, and the core loss is usually disregarded. In some circumstances, such as the highway cycle of EVs, the motors are at high speed, the torque is low, and the core loss can be significant in the losses, thus affecting the accuracy of control and the efficiency of the system; hence, the introduction of core loss ECM into the MPC would be beneficial. This paper aims to investigate the steady-state efficiency improvement of a novel ECM of PMSM considering core loss ECM, and the comparison will be based on model predictive direct torque control (MPDTC) using the core loss ECM, which will be compared to MPDTC with the conventional ECM of the PMSM. The results demonstrate the proposed ECM’s efficiency improvement in various conditions, the limitations of the model and the simulation are discussed, and future work is proposed. Full article
(This article belongs to the Special Issue Energy Management and Optimization for New Power Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop