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 Scheduling and Trading in Microgrids and Local Energy Communities
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Energy Scheduling and Trading in Microgrids and Local Energy Communities

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 19728

Special Issue Editors

Dr. Barry Hayes

E-Mail Website
Guest Editor
School of Engineering, University College Cork, College Road, Cork T12 K8AF, Ireland
Interests: Power systems, power delivery and utilisation, smart grids, distribution system state estimation, distribution network energy management systems, demand side management
Dr. Vahid Hosseinnezhad

E-Mail Website
Guest Editor
School of Engineering, University College Cork, College Road, Cork T12 K8AF, Ireland
Interests: market; transactive energy management; smart grid; IOT; blockchain

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue on the topic "Energy Scheduling and Trading in Microgrids and Local Energy Communities".

Increasingly, the microgrid and local energy community concepts are being applied to integrate distributed energy resources into the power system and to achieve local balancing of generation and demand. Energy scheduling is particularly challenging in microgrids, since there is significant uncertainty on the supply side (due to the stochastic nature of renewable generation), and on the demand side (where local demands have higher levels of uncertainty than in large-scale grids). Recent research has also investigated local energy markets, which allow small-scale producers and consumers to trade energy directly with one another, in order to minimise their operational costs. Energy trading in microgrids and local energy communities presents many challenges, including the setting of fair trading prices, the allocation of network losses and charges, and the management of network constraints.

The aim of this Special Issue is to bring together new research that addresses issues related to energy scheduling and trading in microgrids and local energy communities. The scope of this Special Issue covers isolated microgrids (with no grid connection), embedded microgrids (can operate in grid-connected or island mode), and local energy communities (comprised of consumers cooperating to satisfy their energy needs using local production sources, and which are not designed to operate in an island mode).

We invite original and unpublished research work in the following areas including, but not limited to:

- Energy scheduling in microgrids and local energy communities

- Market models for energy trading in microgrids and local energy communities

- Energy exchange between multiple microgrids

- Optimisation techniques applied to microgrids and local energy communities

- Multi-agent systems applied to microgrids and local energy communities

- Field experiences from microgrids and local energy communities

Dr. Barry Hayes
Dr. Vahid Hosseinnezhad
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

  • Microgrid
  • Local energy communities
  • Energy scheduling
  • Local energy markets
  • Distributed energy resources
  • Smart grids
  • Energy management
  • Optimisation techniques
  • Multi-agent systems

Published Papers (6 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

Jump to: Review

19 pages, 5122 KiB  
Article
Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks
by Maher G. M. Abdolrasol, Mahammad Abdul Hannan, S. M. Suhail Hussain, Taha Selim Ustun, Mahidur R. Sarker and Pin Jern Ker
Energies 2021, 14(20), 6507; https://doi.org/10.3390/en14206507 - 11 Oct 2021
Cited by 28 | Viewed by 2584
Abstract
This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based [...] Read more.
This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algorithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers’ robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN’s performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance of these controllers under varying power system conditions. Additionally, a comparative study is performed to compare their performances with other solutions available in the literature based on several parameters. Results show the superiority of the ANN-based controllers in terms of cost reduction and efficiency. Full article
(This article belongs to the Special Issue Energy Scheduling and Trading in Microgrids and Local Energy Communities)
►▼ Show Figures

Figure 1

14 pages, 2973 KiB  
Article
Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids
by Seongwoo Lee, Joonho Seon, Chanuk Kyeong, Soohyun Kim, Youngghyu Sun and Jinyoung Kim
Energies 2021, 14(17), 5515; https://doi.org/10.3390/en14175515 - 3 Sep 2021
Cited by 10 | Viewed by 1887
Abstract
Inefficiencies in energy trading systems of microgrids are mainly caused by uncertainty in non-stationary operating environments. The problem of uncertainty can be mitigated by analyzing patterns of primary operation parameters and their corresponding actions. In this paper, a novel energy trading system based [...] Read more.
Inefficiencies in energy trading systems of microgrids are mainly caused by uncertainty in non-stationary operating environments. The problem of uncertainty can be mitigated by analyzing patterns of primary operation parameters and their corresponding actions. In this paper, a novel energy trading system based on a double deep Q-networks (DDQN) algorithm and a double Kelly strategy is proposed for improving profits while reducing dependence on the main grid in the microgrid systems. The DDQN algorithm is proposed in order to select optimized action for improving energy transactions. Additionally, the double Kelly strategy is employed to control the microgrid’s energy trading quantity for producing long-term profits. From the simulation results, it is confirmed that the proposed strategies can achieve a significant improvement in the total profits and independence from the main grid via optimized energy transactions. Full article
(This article belongs to the Special Issue Energy Scheduling and Trading in Microgrids and Local Energy Communities)
►▼ Show Figures

Figure 1

21 pages, 542 KiB  
Article
P2P, CSC and TE: A Survey on Hardware, Software and Data
by Brian O’Regan, Fabio Silva, Eoin O’Leidhin, Farah Tahir, Karen Mould, Barry Hayes, Vahid Hosseinnezhad, Ruzanna Chitchyan and Padraig Lyons
Energies 2021, 14(13), 3851; https://doi.org/10.3390/en14133851 - 26 Jun 2021
Cited by 5 | Viewed by 3990
Abstract
Peer-to-Peer (P2P), Transactive Energy (TE) and Community Self-Consumption (CSC) are exciting energy generation and use models, offering several opportunities for prosumers, micro-grids and services to the grid; however, they require numerous components to function efficiently. Various hardware devices are required to transmit data [...] Read more.
Peer-to-Peer (P2P), Transactive Energy (TE) and Community Self-Consumption (CSC) are exciting energy generation and use models, offering several opportunities for prosumers, micro-grids and services to the grid; however, they require numerous components to function efficiently. Various hardware devices are required to transmit data and control the generation and consumption equipment, whereas software is needed to use the gathered information to monitor and manage the hardware and energy trading. Data can be gathered from a variety of origins from within the grid and external sources; however, these data must be well-structured and consistent to be useful. This paper sets out to gather information regarding the hardware, software and data from the several archetypes available, focusing on existing projects and trials in these areas to see what the most-common hardware, software and data components are. The result presents a concise overview of the hardware, software and data-related topics and structures within the P2P, TE and CSC energy generation and use models. Full article
(This article belongs to the Special Issue Energy Scheduling and Trading in Microgrids and Local Energy Communities)
►▼ Show Figures

Figure 1

24 pages, 4366 KiB  
Article
Optimal Integration of Capacitor and Distributed Generation in Distribution System Considering Load Variation Using Bat Optimization Algorithm
by Thangaraj Yuvaraj, Kaliaperumal Rukmani Devabalaji, Natarajan Prabaharan, Hassan Haes Alhelou, Asokkumar Manju, Poushali Pal and Pierluigi Siano
Energies 2021, 14(12), 3548; https://doi.org/10.3390/en14123548 - 15 Jun 2021
Cited by 22 | Viewed by 2447
Abstract
In this article, an efficient long-term novel scheduling technique is proposed for allocating capacitors in a combined system involving distributed generation (DG) along with radial distribution systems (RDS). We introduce a unique multi-objective function that focuses on the reduction of power loss with [...] Read more.
In this article, an efficient long-term novel scheduling technique is proposed for allocating capacitors in a combined system involving distributed generation (DG) along with radial distribution systems (RDS). We introduce a unique multi-objective function that focuses on the reduction of power loss with the maximization of voltage stability index (VSI) subjected to constraints of equality and inequality systems. Loss sensitivity factor and VSI together are involved in pre-identifying the locations of capacitors and DG. Determination of the optimal size of capacitor and DG is performed by utilizing the Bat algorithm (BA) for all the loads in RDS. The conventional approach considers the medium load of (1.0) condition generally, but the proposed method changes the feeder loads linearly, ranging from light load (0.5) to peak load (1.6) with the value of step size as 1%. BA determines the optimal size of the capacitor and DG for each step load. The curve fitting technique is used for deducing the generalized equation of capacitor size and DG for all conditions of the load with the various loading condition sized by distributed network operators (DNOs). Further, various load models such as industrial, residential, and commercial loads have been considered to show the efficiency of the present approach. Validation of results is performed in different scenarios on a 69-bus test system and on a standard IEEE 33-bus system. The results exhibit improved accuracy with less power loss value, superior bus voltage, and stability of system voltage with a higher rate of convergence. Full article
(This article belongs to the Special Issue Energy Scheduling and Trading in Microgrids and Local Energy Communities)
►▼ Show Figures

Figure 1

25 pages, 3450 KiB  
Article
Smarter Together: Progressing Smart Data Platforms in Lyon, Munich, and Vienna
by Naomi Morishita-Steffen, Rémi Alberola, Baptiste Mougeot, Étienne Vignali, Camilla Wikström, Uwe Montag, Emmanuel Gastaud, Brigitte Lutz, Gerhard Hartmann, Franz Xaver Pfaffenbichler, Ali Hainoun, Bruno Gaiddon, Antonino Marvuglia and Maria Beatrice Andreucci
Energies 2021, 14(4), 1075; https://doi.org/10.3390/en14041075 - 18 Feb 2021
Cited by 8 | Viewed by 4536
Abstract
In a context where digital giants are increasingly influencing the actions decided by public policies, smart data platforms are a tool for collecting a great deal of information on the territory and a means of producing effective public policies to meet contemporary challenges, [...] Read more.
In a context where digital giants are increasingly influencing the actions decided by public policies, smart data platforms are a tool for collecting a great deal of information on the territory and a means of producing effective public policies to meet contemporary challenges, improve the quality of the city, and create new services. Within the framework of the Smarter Together project, the cities of Lyon (France), Munich (Germany), and Vienna (Austria) have integrated this tool into their city’s metabolism and use it at different scales. Nevertheless, the principle remains the same: the collection (or even dissemination) of internal and external data to the administration will enable the communities, companies, not-for-profit organizations, and civic administrations to “measure” the city and identify areas for improvement in the territory. Furthermore, through open data logics, public authorities can encourage external partners to become actors in territorial action by using findings from the data to produce services that will contribute to the development of the territory and increase the quality of the city and its infrastructure. Nevertheless, based on data that is relatively complex to extract and process, public data platforms raise many legal, technical, economic, and social issues. The cities either avoided collecting personal data or when dealing with sensitive data, use anonymized aggregated data. Cocreation activities with municipal, commercial, civil society stakeholders, and citizens adopted the strategies and tools of the intelligent data platforms to develop new urban mobility and government informational services for both citizens and public authorities. The data platforms are evolving for transparent alignment with 2030 climate-neutrality objectives while municipalities strive for greater agility to respond to disruptive events like the COVID-19 pandemic. Full article
(This article belongs to the Special Issue Energy Scheduling and Trading in Microgrids and Local Energy Communities)
►▼ Show Figures

Graphical abstract

Review

Jump to: Research

19 pages, 1231 KiB  
Review
Local Energy Trading in Future Distribution Systems
by Mohammad Esmaeil Honarmand, Vahid Hosseinnezhad, Barry Hayes and Pierluigi Siano
Energies 2021, 14(11), 3110; https://doi.org/10.3390/en14113110 - 26 May 2021
Cited by 28 | Viewed by 2836
Abstract
Today, the pace of development of decentralized transactive management systems has increased significantly due to growing renewable energy source technologies and communication infrastructure at the distribution system level. Such bilateral energy transactions have changed the structure of electricity markets and led to the [...] Read more.
Today, the pace of development of decentralized transactive management systems has increased significantly due to growing renewable energy source technologies and communication infrastructure at the distribution system level. Such bilateral energy transactions have changed the structure of electricity markets and led to the emergence of a local energy market in electricity distribution. While examining this change of attitude, this paper analyzes the effects of local market formation on the performance and performance of distribution companies. Accordingly, the technical requirements in the three areas of operation, network control, and ICT in the new workspace are thoroughly examined. The hardware requirements will be presented in two parts for the end-user and the distribution systems. Then, the proposed local distribution market framework will be introduced, and finally, the conclusion will be presented. Full article
(This article belongs to the Special Issue Energy Scheduling and Trading in Microgrids and Local Energy Communities)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop