Special Issue "Demand Response in Smart Grids"

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

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Prof. Dr. Pedro Faria
E-Mail Website
Guest Editor
GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal
Interests: demand response; electricity markets; energy communities; renewable energy integration; real-time simulation; smart grids; virtual power players
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Special Issue Information

Dear Colleagues,

The concepts of demand response and smart grids are two rather wide-scope and key topics in the operation of power and energy systems. Although new demand response approaches appear every day, more work is needed to catch its full potential, bringing advantages for all the involved players. The successful implementation of smart grids requires the widespread use of demand response not only by gathering the flexibility of large and medium consumers but also targeting small-size consumers. Effective approaches are needed to put in place adequate strategies and methods to design and manage demand response. As part of the power and energy ecosystem, demand response is a very valuable resource which, when coordinated with the increasing penetration of renewable energy and market-driven business models, can significantly increase the system efficiency while keeping energy costs at reasonable levels.

This Special Issue will address all aspects related to demand flexibility, demand response, and their importance for efficient smart grids.

Prof. Dr. Pedro Faria
Prof. Dr. Zita Vale
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 papers will be 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 1800 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

  • active consumers
  • business models
  • consumer profiling
  • consumption baseline
  • demand flexibility
  • demand response
  • demand response programs
  • demand side management
  • distributed energy resources
  • distributed energy storage
  • distributed generation
  • electric and hybrid vehicles
  • energy efficiency
  • energy efficient buildings
  • energy management
  • energy markets
  • energy policy
  • energy resource optimization
  • energy tariffs
  • explicit and implicit demand response
  • incentive-based and price-based demand response
  • intelligent resource management
  • load balancing in smart grids
  • load flexibility
  • load forecasting
  • regulatory aspects
  • renewable energy
  • smart cities
  • smart grids
  • smart homes and smart buildings
  • transactive energy

Published Papers (2 papers)

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Research

Open AccessArticle
Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach
Energies 2019, 12(24), 4744; https://doi.org/10.3390/en12244744 - 12 Dec 2019
Abstract
This paper proposes a cyber–physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber–physical model, the prediction error of the physical model is [...] Read more.
This paper proposes a cyber–physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber–physical model, the prediction error of the physical model is used as an input of the cyber model to further calibrate the prediction error. Firstly, customers’ behavior characteristics are extracted by the integration of K-means and one-versus-one support vector machine. Secondly, based on the behavior characteristics and ambient temperature, the physical model is developed to predict daily electricity consumption. Finally, the error levels of physical model are classified, together with the temperature and prediction values of the physical model, are selected as the inputs of the cyber model using the back propagation (BP) neural network to calibrate the results of the physical model. The effectiveness of the proposed cyber–physical model (CPM) is verified by a 1 MW SETS system. The simulation results show that, compared with the physical model (PM) and cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and 4.8%, respectively. Full article
(This article belongs to the Special Issue Demand Response in Smart Grids)
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Open AccessArticle
Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior
Energies 2019, 12(19), 3727; https://doi.org/10.3390/en12193727 - 29 Sep 2019
Abstract
Demand response (DR) has been recognized as a powerful tool to relieve energy imbalance in the smart grid. Most previous works have ignored the irrational behavior of energy consumers in DR project implementation. Accordingly, in this paper, we focus on solving two questions [...] Read more.
Demand response (DR) has been recognized as a powerful tool to relieve energy imbalance in the smart grid. Most previous works have ignored the irrational behavior of energy consumers in DR project implementation. Accordingly, in this paper, we focus on solving two questions during the execution of DR. Firstly, considering the bounded rationality of residential users, a population dynamic model is proposed to describe the decision behavior on whether to participate in the DR project, and then the evolutionary process of consumers participating in DR is analyzed. Secondly, for the DR participants, they have to compete dispatching amounts for maximal profit in a day-ahead bidding market, hence, a non-cooperative game model is proposed to describe the competition behavior, and the uniqueness of the Nash equilibrium is analyzed with mathematical proof. Then, the distributed algorithm is designed to search the evolutionary result and the Nash equilibrium. Finally, a case study is performed to show the effectiveness of the formulated models. Full article
(This article belongs to the Special Issue Demand Response in Smart Grids)
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