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Decentralized Management of Flexible Energy Resources in Smart Grid
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Decentralized Management of Flexible Energy Resources in Smart Grid

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 10557

Special Issue Editors

Dr. Tudor Cioara

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Guest Editor
Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu street, Cluj-Napoca, Romania
Interests: blockchain; smart environments; complex distributed systems; machine learning; energy efficiency and smart grid
Special Issues, Collections and Topics in MDPI journals
Dr. Massimo Bertoncini

E-Mail Website
Guest Editor
Director of European R&D Project Innovation Strategic Advisor for Smart Energy Systems, Engineering-Ingegneria Informatica SPA, Via San Martino Della Battaglia 56, Roma 00185, Italy
Interests: smart energy systems optimization, blockchain, energy efficiency, demand response
Dr. Ionut Anghel

E-Mail Website
Guest Editor
Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu Street, Cluj-Napoca, Romania
Interests: ambient assistive living; adaptive systems; blockchain; decentralized distributed systems
Special Issues, Collections and Topics in MDPI journals
Dr. Diego Arnone

E-Mail Website
Guest Editor
Head of Smart Energy Projects Research Area, Engineering-Ingegneria Informatica SPA, Viale Regione Siciliana Nord-Ovest 7275 - 90146 Palermo, Itay
Interests: data center management; energy efficiency optimization; energy projects management

Special Issue Information

Dear Colleagues,

Due to the rapid growth in the deployment of distributed energy resources, the need for decentralized Information and Communication Technology management solutions exploiting smart grid flexibility is widely recognized. Variations in energy production of such resources, either surplus or deficit, may threaten the security of supply due to the lack of energy storage capabilities, leading to energy distribution system overload and culminating with power outage or service disruptions. Demand response (DR) programs offer several benefits to the energy systems, including increasing efficiency of asset utilization, supporting greater penetration of renewables, easing capacity issues on distribution networks, reducing the costs of calling on traditional reserve, and finally reducing emissions. Energy flexibility is a key element in the development of the future optimized smart grid management solutions, incorporating a wide range of both centralized and distributed renewable and conventional power sources. Energy players such as data centers (DCs) are characterized by large, flexible energy load profiles which may enact as a potential solution for cost reduction for their voluntary participation in DR programs or offering ancillary services for the distribution system operator. Unfortunately, they are usually operated in an uncoordinated manner and lack novel technologies to allow them to elucidate their flexibility. This is also relevant in the context of next-generation virtual power plants (VPPs), energy aggregators, and decentralized microgrids.

This Special Issue aims to explore new and innovative solutions in this emerging context, contributions being expected that address, but are not restricted to, the following topics:

  • Smart grid flexibility management;
  • Blockchain for decentralized management of DR programs;
  • Flexibility assessment and forecasting;
  • DC energy management for optimal smart grid integration;
  • DCs as aggregators of themselves;
  • VPP construction and management;
  • Blockchain-enabled infrastructures for decentralized energy grid control;
  • Energy assets decentralized coordination;
  • Energy markets operation and coordinated clearance mechanisms

Assoc. Prof. Tudor Cioara
Dr. Massimo Bertoncini
Assoc. Prof. Ionut Anghel
Dr. Diego Arnone
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. Sustainability 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

  • Smart energy grids
  • Data centers
  • Decentralized coordination
  • Blockchain
  • Virtual power plant
  • Energy market operation
  • Flexibility management

Published Papers (3 papers)

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Research

19 pages, 4568 KiB  
Article
A Permissioned Blockchain-Based Energy Management System for Renewable Energy Microgrids
by Longze Wang, Shucen Jiao, Yu Xie, Saif Mubaarak, Delong Zhang, Jinxin Liu, Siyu Jiang, Yan Zhang and Meicheng Li
Sustainability 2021, 13(3), 1317; https://doi.org/10.3390/su13031317 - 27 Jan 2021
Cited by 22 | Viewed by 4086
Abstract
Peer-to-peer (P2P) energy management is one of the most viable solutions to incentivize prosumers in renewable energy microgrids. As the application of blockchain expends from the finance field to energy field, blockchain technology provides a new opportunity for distributed energy systems. However, a [...] Read more.
Peer-to-peer (P2P) energy management is one of the most viable solutions to incentivize prosumers in renewable energy microgrids. As the application of blockchain expends from the finance field to energy field, blockchain technology provides a new opportunity for distributed energy systems. However, a distributed energy system based on blockchains allows any node in the whole network to read data. In many application scenarios, user privacy cannot be effectively protected, and there is a security problem that the attack cannot be traced. In this paper, we propose an energy management mode based on a permissioned blockchain for a renewable energy microgrid. The novel permissioned blockchain framework uses entity mapping with a unique identity for each enterprise, natural person, or device, in order to avoid ineligible participants to join the microgrid. Each peer entity keeps the transaction information index of the whole network, but only keeps its own specific transaction information, so they can retrieve the transaction information of other peer entities but cannot obtain the details without permission. Moreover, this model could avoid communication delays and promote plug-and-play due to the distributed nature of the permissioned blockchain. The performance of the proposed method is evaluated with a demonstration program which is designed and deployed on a Hyperledger Fabric permissioned blockchain. Simulation results show the feasibility of the proposed method, and the model is conducive to the protection privacy and P2P energy management for decentralized energy systems. Full article
(This article belongs to the Special Issue Decentralized Management of Flexible Energy Resources in Smart Grid)
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23 pages, 7081 KiB  
Article
Data Centers Optimized Integration with Multi-Energy Grids: Test Cases and Results in Operational Environment
by Tudor Cioara, Marcel Antal, Claudia Daniela Antal (Pop), Ionut Anghel, Massimo Bertoncini, Diego Arnone, Marilena Lazzaro, Marzia Mammina, Terpsichori-Helen Velivassaki, Artemis Voulkidis, Yoann Ricordel, Nicolas Sainthérant, Ariel Oleksiak and Wojciech Piatek
Sustainability 2020, 12(23), 9893; https://doi.org/10.3390/su12239893 - 26 Nov 2020
Cited by 5 | Viewed by 2289
Abstract
In this paper, we address the management of Data Centers (DCs) by considering their optimal integration with the electrical, thermal, and IT (Information Technology) networks helping them to meet sustainability objectives and gain primary energy savings. Innovative scenarios are defined for exploiting the [...] Read more.
In this paper, we address the management of Data Centers (DCs) by considering their optimal integration with the electrical, thermal, and IT (Information Technology) networks helping them to meet sustainability objectives and gain primary energy savings. Innovative scenarios are defined for exploiting the DCs electrical, thermal, and workload flexibility as a commodity and Information and Communication Technologies (ICT) are proposed and used as enablers for the scenarios’ implementation. The technology and scenarios were evaluated in the context of two operational DCs: a micro DC in Poznan which has on-site renewable sources and a DC in Point Saint Martin. The test cases’ results validate the possibility of using renewable energy sources (RES) for exploiting DCs’ energy flexibility and the potential of combining IT load migration with the availability of RES to increase the amount of energy flexibility by finding a trade-off between the flexibility level, IT load Quality of Service (QoS), and the RES production level. Moreover, the experiments conducted show that the DCs can successfully adapt their thermal energy profile for heat re-use as well as the combined electrical and thermal energy profiles to match specific flexibility requests. Full article
(This article belongs to the Special Issue Decentralized Management of Flexible Energy Resources in Smart Grid)
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23 pages, 5966 KiB  
Article
Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs
by Andreea Valeria Vesa, Tudor Cioara, Ionut Anghel, Marcel Antal, Claudia Pop, Bogdan Iancu, Ioan Salomie and Vasile Teodor Dadarlat
Sustainability 2020, 12(4), 1417; https://doi.org/10.3390/su12041417 - 14 Feb 2020
Cited by 20 | Viewed by 3418
Abstract
In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such [...] Read more.
In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs. Full article
(This article belongs to the Special Issue Decentralized Management of Flexible Energy Resources in Smart Grid)
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