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Energies
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Grid Applications and Performance in Power Systems
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Grid Applications and Performance in Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 1522

Special Issue Editors

Prof. Dr. Qian Ai

E-Mail Website
Guest Editor
Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: virtual power plant; power quality; load modeling; smart grids; microgrid; intelligent algorithms
Dr. Xing He

E-Mail Website
Guest Editor
Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: big data analytics; intelligent algorithms; digital twins and their applications in power systems

Special Issue Information

Dear Colleagues,

The Guest Editor invites papers for a Special Issue of Energies on "Grid Applications and Performance in Power Systems." Modern power systems have experienced a fundamental revolution as a result of the advent of distributed energy resources (DERs), shifting from centralized to decentralized optimization. Such transition results in concepts such as virtual power plant (VPP) and microgrid, which may aggregate various DERs and have been acknowledged as a promising solution to improve grid dependability, security, and economy. Meanwhile, smart sensors, 5G, cloud platforms, and cutting-edge data sciences are coming together to offer digital twins (DT) in contemporary power systems, which can make full use of data collection while also enhancing situation awareness and grid performance decision-making.

This Special Issue will focus on cutting-edge theories, models, and applications to implement optimization approaches among VPPs or microgrids, as well as discuss the future of DT applications in current power systems.

Topics of interest for publication include, but are not limited to:

  • Power system control and optimization
  • Distributed energy resources aggregation
  • Energy management system of microgrids
  • Operation mode of virtual power plant
  • Digital twin technology in power grid
  • Big data analytics in distribution network
  • Healthy status assessment of smart meters
  • Anomaly detection of electrical device

Prof. Dr. Qian Ai
Dr. Xing He
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

  • virtual power plant
  • microgrid
  • distributed energy resources
  • optimization technique
  • distribution network
  • digital twin
  • big data analyzation
  • situation awareness

Published Papers (1 paper)

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Research

21 pages, 6003 KiB  
Article
Equivalent Modeling of LVRT Characteristics for Centralized DFIG Wind Farms Based on PSO and DBSCAN
by Ning Zhou, Huan Ma, Junchao Chen, Qiao Fang, Zhe Jiang and Changgang Li
Energies 2023, 16(6), 2551; https://0-doi-org.brum.beds.ac.uk/10.3390/en16062551 - 08 Mar 2023
Cited by 5 | Viewed by 1167
Abstract
As large-scale wind turbines are connected to the grid, modeling studies of wind farms are essential to the power system dynamic research. Due to the large number of wind turbines in the wind farm, detailed modeling of each wind turbine leads to high [...] Read more.
As large-scale wind turbines are connected to the grid, modeling studies of wind farms are essential to the power system dynamic research. Due to the large number of wind turbines in the wind farm, detailed modeling of each wind turbine leads to high model complexity and low simulation efficiency. An equivalent modeling method for the wind farm is needed to reduce the complexity. For wind farms with widely used doubly-fed induction generators (DFIGs), the existing equivalent studies mainly focus on such continuous control parts as electrical control. These methods are unsuitable for the low voltage ride through (LVRT) part which is discontinuous due to switching control. Based on particle swarm optimization (PSO) and density-based spatial clustering of applications (DBSCAN), this paper proposes an equivalent method for LVRT characteristics of wind farms. Firstly, the multi-turbine equivalent model of the wind farm is established. Each wind turbine in the model represents a cluster of wind turbines with similar voltage variation characteristics. A single equivalent transmission line connects all wind turbines to the power grid. By changing the terminal voltage threshold to enter LVRT, each equivalent turbine can be in different LVRT states. Secondly, an LVRT parameter optimization method based on PSO is used to obtain the dynamic parameters of the equivalent wind turbines. This method of parameter optimization is applicable to the equivalent of LVRT parameters. Thirdly, a clustering method based on DBSCAN is used to obtain suitable clusters of wind turbines. This clustering method can classify wind turbines with similar electrical distances into the same cluster. Finally, two examples are set up to verify the proposed method. Full article
(This article belongs to the Special Issue Grid Applications and Performance in Power Systems)
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