Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Integration of Distributed Energy Resources in Smart Grids
share announcement

Integration of Distributed Energy Resources in Smart Grids

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 1753

Special Issue Editors

Dr. Zihang Dong

E-Mail Website
Guest Editor
Department of Electrical Engineering, Tongji University, Shanghai 200092, China
Interests: robust economic model predictive control; flexible demand response and the design of control strategies for the smart grids
Prof. Dr. Yujian Ye

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: energy system economics; transportation electrification; artificial intelligence in power systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Increased urban electric loads driven by the electrification of the transport sector, cooling/heating technologies, various forms of distributed energy storage, advancements in distributed generations, demand side responses, etc., will fundamentally transform the operational paradigm of future urban energy infrastructure. In particular, in the future, flexibility and resilience will not necessarily be delivered through asset redundancy at the national level, but through the smart control of multi-energy systems at the local district level, by making use of local backup generation, energy storage, demand side response technologies, and the control of local urban energy infrastructure. Distributed energy resources (DERs) will constitute the cornerstone of the future inner-city smart grids, in which the security of supply will be delivered by local resources at the district level. To support such a paradigm shift, the large-scale integration of DERs through intelligent and sophisticated coordinative  actions are required. In this context, it is important to fully understand the interactions between different DERs, find out how to intensify the synergies across different energy vectors, enable them to support one another, and investigate the mechanism based on how different energy vectors can co-support the operation of smart grids. Additionally, since smart control is the core used to arouse the synergies between various DERs, it is significant to explore effective control strategies to realize the optimal dispatch of DERs aimed at enhancing the efficiency of urban energy systems.

Dr. Zihang Dong
Prof. Dr. Yujian Ye
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. Electronics 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

  • energy system flexibility and resilience enhancement through Distributed Energy Resources (DER)
  • demand side response and energy management of DERs
  • advanced Information and communication technologies supporting the integration of DERs in smart grids
  • prediction algorithms, communication approaches, control strategies and business model in Virtual Power Plant (VPP)
  • generation planning and market design for integrated energy systems
  • multi-energy system integration, covering electricity, gas, heat, cold, transport, information and hydrogen systems, etc.
  • Artificial Intelligence-based approaches for local energy system modelling
  • advanced control strategies for the coordination of numerous/heterogeneous DERs
  • smart scheduling and routing of Electric Vehicles (EV) for improving power system operation
  • modelling of local energy systems, including smart buildings, micro-grids, industrial parks, etc.

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

15 pages, 2222 KiB  
Article
Feature Extraction Approach for Distributed Wind Power Generation Based on Power System Flexibility Planning Analysis
by Sile Hu, Jiaqiang Yang, Yuan Wang, Chao Chen, Jianan Nan, Yucan Zhao and Yue Bi
Electronics 2024, 13(5), 966; https://doi.org/10.3390/electronics13050966 - 02 Mar 2024
Viewed by 481
Abstract
This study addresses the integral role of typical wind power generation curves in the analysis of power system flexibility planning. A novel method is introduced for extracting these curves, integrating an enhanced K-means clustering algorithm with advanced optimization techniques. The process commences [...] Read more.
This study addresses the integral role of typical wind power generation curves in the analysis of power system flexibility planning. A novel method is introduced for extracting these curves, integrating an enhanced K-means clustering algorithm with advanced optimization techniques. The process commences with thorough data cleaning, filtering, and smoothing. Subsequently, the refined K-means algorithm, augmented by the Pearson correlation coefficient and a greedy algorithm, clusters the wind power curves. The optimal number of clusters is ascertained through the silhouette coefficient. The final stage employs particle swarm and whale optimization algorithms for the extraction of quintessential wind power output curves, essential for flexibility planning in power systems. This methodology is validated through a case study involving wind power output data from a new energy-rich provincial power grid in North China, spanning from 1 January 2019, to 31 December 2022. The resultant curves proficiently mirror wind power fluctuations, thereby laying a foundational framework for power system flexibility planning analysis. Full article
(This article belongs to the Special Issue Integration of Distributed Energy Resources in Smart Grids)
►▼ Show Figures

Figure 1

22 pages, 4372 KiB  
Article
A Multi-Agent Deep-Reinforcement-Learning-Based Strategy for Safe Distributed Energy Resource Scheduling in Energy Hubs
by Xi Zhang, Qiong Wang, Jie Yu, Qinghe Sun, Heng Hu and Ximu Liu
Electronics 2023, 12(23), 4763; https://doi.org/10.3390/electronics12234763 - 24 Nov 2023
Viewed by 889
Abstract
An energy hub (EH) provides an effective solution to the management of local integrated energy systems (IES), supporting the optimal dispatch and mutual conversion of distributed energy resources (DER) in multi-energy forms. However, the intrinsic stochasticity of renewable generation intensifies fluctuations in the [...] Read more.
An energy hub (EH) provides an effective solution to the management of local integrated energy systems (IES), supporting the optimal dispatch and mutual conversion of distributed energy resources (DER) in multi-energy forms. However, the intrinsic stochasticity of renewable generation intensifies fluctuations in the system’s energy production when integrated into large-scale grids and increases peak-to-valley differences in large-scale grid integration, leading to a significant reduction in the stability of the power grid. A distributed privacy-preserving energy scheduling method based on multi-agent deep reinforcement learning is presented for the EH cluster with renewable energy generation. Firstly, each EH is treated as an agent, transforming the energy scheduling problem into a Markov decision process. Secondly, the objective function is defined as minimizing the total economic cost while considering carbon trading costs, guiding the agents to make low-carbon decisions. Lastly, differential privacy protection is applied to sensitive data within the EH, where noise is introduced using energy storage systems to maintain the same gas and electricity purchases while blurring the original data. The experimental simulation results demonstrate that the agents are able to train and learn from environmental information, generating real-time optimized strategies to effectively handle the uncertainty of renewable energy. Furthermore, after the noise injection, the validity of the original data is compromised while ensuring the protection of sensitive information. Full article
(This article belongs to the Special Issue Integration of Distributed Energy Resources in Smart Grids)
►▼ 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