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Special Issue "Demand Response in Electricity Markets"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: 31 October 2018

Special Issue Editors

Guest Editor
Prof. Dr. Henrik Madsen

Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Lyngby, Denmark
Website | E-Mail
Interests: demand response; electricity market; smart grid; power system operation; ancillary services; grey-box modelling; forecasting; control theory; bidding strategies
Guest Editor
Dr. Seyyed Ali Pourmousavi Kani

Global Change Institute, The University of Quensland, Australia
Website | E-Mail
Interests: bulk power system operation and electricity market; battery characterization, optimal sizing and operation in different applications; aggregation of small storage and demand response resources; demand response at the device level

Special Issue Information

Dear Colleagues,

Future power system will host significant amount of renewable generation inevitably. These energy resources are naturally undispatchable and unpredictable, and do not necessarily follow the load demand. Therefore, safe and secure operation of the future power system will require extra flexibility in real-time operation to compensate the varying generation. This will not be possible by large synchronous rotating machines, as they are slow, less economically efficient and polluting. In this regard, Demand Response Programs (DRP) are attracting a lot of attention. Preliminary studies on Demand Response (DR) resources in integrated energy systems have already projected incredible potential to act as flexibility resources for power systems operations. Nevertheless, there are still many questions and concerns related to DR resources involvement into the electricity and energy markets, which have to be properly addressed. This Special Issue is an attempt to encourage researchers from different discipline to offer solutions and algorithms to effectively incorporate DR resources in electricity and energy markets.  These include the conventional day-ahead and real-time wholesale markets as well as P2P electricity trading considering stochasticity, unpredictability, and non-linearity of the phenomenon. In this framework, physical and virtual energy storages and electric vehicles are also considered as DR resources. A special focus will be on how to model, forecast and control flexible resources in intelligent and integrated energy systems.

Prof. Dr. Henrik Madsen
Dr. Seyyed Ali Pourmousavi Kani
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 monthly 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 1600 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

  • demand response aggregation
  • electric vehicle
  • energy storages
  • market bidding mechanism
  • demand response in P2P trading
  • ancillary services
  • forecasting and control of flexibility
  • stochastic demand response
  • market operation with demand response
  • dynamic flexibility modelling and control
  • integrated energy systems
  • ICT solutions for demand response

Published Papers (2 papers)

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Research

Open AccessFeature PaperArticle A Decentralized Local Flexibility Market Considering the Uncertainty of Demand
Energies 2018, 11(8), 2078; https://doi.org/10.3390/en11082078
Received: 9 July 2018 / Revised: 2 August 2018 / Accepted: 7 August 2018 / Published: 9 August 2018
PDF Full-text (7393 KB) | HTML Full-text | XML Full-text
Abstract
The role of the distribution system operator (DSO) is evolving with the increasing possibilities of demand management and flexibility. Rather than implementing conventional approaches to mitigate network congestions, such as upgrading existing assets, demand flexibility services have been gaining much attention lately as
[...] Read more.
The role of the distribution system operator (DSO) is evolving with the increasing possibilities of demand management and flexibility. Rather than implementing conventional approaches to mitigate network congestions, such as upgrading existing assets, demand flexibility services have been gaining much attention lately as a solution to defer the need for network reinforcements. In this paper, a framework for a decentralized local market that enables flexibility services trading at the distribution level is introduced. This market operates on two timeframes, day-ahead and real-time and it allows the DSO to procure flexibility services which can help in its congestion management process. The contribution of this work lies in considering the uncertainty of demand during the day-ahead period. As a result, we introduce a probabilistic process that supports the DSO in assessing the true need of obtaining flexibility services based on the probability of congestion occurrence in the following day of operation. Besides being able to procure firm flexibility for high probable congestions, a new option is introduced, called the right-to-use option, which enables the DSO to reserve a specific amount of flexibility, to be called upon later if necessary, for congestions that have medium probabilities of taking place. In addition, a real-time market for flexibility trading is presented, which allows the DSO to procure flexibility services for unforeseen congestions with short notice. Also, the effect of the penetration level of flexibility on the DSO’s total cost is discussed and assessed. Finally, a case study is carried out for a real distribution network feeder in Spain to illustrate the impact of the proposed flexibility framework on the DSO’s congestion management process. Full article
(This article belongs to the Special Issue Demand Response in Electricity Markets)
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Open AccessArticle Stochastic Unit Commitment Based on Multi-Scenario Tree Method Considering Uncertainty
Energies 2018, 11(4), 740; https://doi.org/10.3390/en11040740
Received: 13 February 2018 / Revised: 7 March 2018 / Accepted: 21 March 2018 / Published: 24 March 2018
Cited by 1 | PDF Full-text (5288 KB) | HTML Full-text | XML Full-text
Abstract
With the increasing penetration of renewable energy, it is difficult to schedule unit commitment (UC) in a power system because of the uncertainty associated with various factors. In this paper, a new solution procedure based on a multi-scenario tree method (MSTM) is presented
[...] Read more.
With the increasing penetration of renewable energy, it is difficult to schedule unit commitment (UC) in a power system because of the uncertainty associated with various factors. In this paper, a new solution procedure based on a multi-scenario tree method (MSTM) is presented and applied to the proposed stochastic UC problem. In this process, the initial input data of load and wind power are modeled as different levels using the mean absolute percentage error (MAPE). The load and wind scenarios are generated using Monte Carlo simulation (MCS) that considers forecasting errors. These multiple scenarios are applied in the MSTM for solving the stochastic UC problem, including not only the load and wind power uncertainties, but also sudden outages of the thermal unit. When the UC problem has been formulated, the simulation is conducted for 24-h period by using the short-term UC model, and the operating costs and additional reserve requirements are thus obtained. The effectiveness of the proposed solution approach is demonstrated through a case study based on a modified IEEE-118 bus test system. Full article
(This article belongs to the Special Issue Demand Response in Electricity Markets)
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