Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Special Issues
Advances in Energy Market and Distributed Generation
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Energy Market and Distributed Generation

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2986

Special Issue Editor

Dr. Thair Mahmoud

E-Mail Website
Guest Editor
School of Engineering, Edith Cowan University, 270 Joondalup Dr., Joondalup, WA 6027, Australia
Interests: artificial intelligence; resource adequacy; electricity market modelling; forecasting modelling and network planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Existing power systems are undergoing a significant transformation towards achieving a lower emission, more reliable, and more affordable operation. This objective is complicated, multi level, and practically challenging to achieve due to the highly dynamic operation issues at the generation, transmission, and distribution levels. With a specific focus on the distribution level, there is huge potential for transforming the challenging operation issues into opportunistic solutions for the whole system, including providing voltage and frequency support. By investigating the issue further, managing the distribution networks through a deregulated energy market would further promote a highly efficient and more reliable distribution of the power, which, in turn, can be of great support to the transmission networks. This problem requires comprehensive research in the field of smart grids, demand management, electric vehicles (EV) fleet management, optimal sizing, and the planning of distributed energy resources (DER). Considering the above topics, this Special Issue is targeting the most recent advances related to the management of modern distribution networks, which integrate a high level of DER. The main objective is to investigate the design and implementation of a distributed energy market and a distribution system operation concept.

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

  • Distributed energy resources (DER)
  • Electric vehicles (EV)
  • Battery energy storage system (BESS)
  • Demand management
  • Distribution energy market
  • Peer-to-peer energy trading
  • Multi-agent systems
  • Distribution network service provider
  • Distribution independent system operation (DISO)
  • Distribution network planning
  • Distribution feeders voltage control
  • Distribution network operation
  • DER forecasting
  • Load forecasting
  • Smart metering
  • Smart grid

Dr. Thair Mahmoud
Guest Editor

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

  • distributed energy resources
  • demand management
  • distribution energy market
  • distribution network
  • smart grid

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

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

29 pages, 11112 KiB  
Article
Master–Slave Game Optimization Scheduling of Multi-Microgrid Integrated Energy System Considering Comprehensive Demand Response and Wind and Storage Combination
by Hongbin Sun, Hongyu Zou, Jianfeng Jia, Qiuzhen Shen, Zhenyu Duan and Xi Tang
Energies 2024, 17(22), 5762; https://doi.org/10.3390/en17225762 - 18 Nov 2024
Cited by 1 | Viewed by 653
Abstract
This paper addresses the critical challenge of scheduling optimization in regional integrated energy systems, characterized by the coupling of multiple physical energy streams (electricity, heat, and cooling) and the participation of various stakeholders. To tackle this, a novel multi-load and multi-type integrated demand [...] Read more.
This paper addresses the critical challenge of scheduling optimization in regional integrated energy systems, characterized by the coupling of multiple physical energy streams (electricity, heat, and cooling) and the participation of various stakeholders. To tackle this, a novel multi-load and multi-type integrated demand response model is proposed, which fully accounts for the heterogeneous characteristics of energy demands in different campus environments. A leader–follower two-layer game equilibrium model is introduced, where the system operator acts as the leader, and campus load aggregators, energy storage plants, and wind farm operators serve as followers. The layer employs an enhanced particle swarm optimization (PSO) algorithm to iteratively adjust energy sales prices and response compensation unit prices, influencing the user response plan through the demand response model. In the lower layer, the charging and discharging schedules of energy storage plants, wind farm energy supply, and outputs of energy conversion devices are optimized to guide system operation. The novelty of this approach lies in the integration of a game-theoretic framework with advanced optimization techniques to balance the interests of all participants and enhance system coordination. A case study is conducted to evaluate the effectiveness of the proposed strategy, demonstrating significant economic benefits. The results show that the model encourages stakeholders to invest in energy infrastructure and actively participate in coordinated dispatch, leading to improved overall system efficiency and comprehensive revenue enhancement for the multi-agent energy system. Full article
(This article belongs to the Special Issue Advances in Energy Market and Distributed Generation)
►▼ Show Figures

Figure 1

27 pages, 15476 KiB  
Article
Explainable AI-Based Ensemble Clustering for Load Profiling and Demand Response
by Elissaios Sarmas, Afroditi Fragkiadaki and Vangelis Marinakis
Energies 2024, 17(22), 5559; https://doi.org/10.3390/en17225559 - 7 Nov 2024
Viewed by 741
Abstract
Smart meter data provide an in-depth perspective on household energy usage. This research leverages on such data to enhance demand response (DR) programs through a novel application of ensemble clustering. Despite its promising capabilities, our literature review identified a notable under-utilization of ensemble [...] Read more.
Smart meter data provide an in-depth perspective on household energy usage. This research leverages on such data to enhance demand response (DR) programs through a novel application of ensemble clustering. Despite its promising capabilities, our literature review identified a notable under-utilization of ensemble clustering in this domain. To address this shortcoming, we applied an advanced ensemble clustering method and compared its performance with traditional algorithms, namely, K-Means++, fuzzy K-Means, Hierarchical Agglomerative Clustering, Spectral Clustering, Gaussian Mixture Models (GMMs), BIRCH, and Self-Organizing Maps (SOMs), across a dataset of 5567 households for a range of cluster counts from three to nine. The performance of these algorithms was assessed using an extensive set of evaluation metrics, including the Silhouette Score, the Davies–Bouldin Score, the Calinski–Harabasz Score, and the Dunn Index. Notably, while ensemble clustering often ranked among the top performers, it did not consistently surpass all individual algorithms, indicating its potential for further optimization. Unlike approaches that seek the algorithmically optimal number of clusters, our method proposes a practical six-cluster solution designed to meet the operational needs of utility providers. For this case, the best performing algorithm according to the evaluation metrics was ensemble clustering. This study is further enhanced by integrating Explainable AI (xAI) techniques, which improve the interpretability and transparency of our clustering results. Full article
(This article belongs to the Special Issue Advances in Energy Market and Distributed Generation)
►▼ 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 pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy