Special Issue "Modern Computational Methods for Flexibility Control"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: closed (30 April 2021).

Special Issue Editors

Dr. Steffen Finck
E-Mail Website
Guest Editor
Research Center Business Informatics, Vorarlberg University of Applied Sciences, 6850 Dornbirn, Austria
Interests: evolutionary computation; artificial intelligence; algorithm design; benchmarking of algorithms; optimization and simulation
Dr. Peter Kepplinger
E-Mail Website
Guest Editor
Research Center Energy, Vorarlberg University of Applied Sciences, 6850 Dornbirn, Austria
Interests: demand side management; integration of renewables; system dynamics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite your contributions to the Energies Special Issue “Modern Computational Methods for Flexibility Control”. In the past decade, load and demand side management for energy systems has increased in complexity due to the growing importance of elements with uncertainties. Thermal or electrothermal storage systems provide potential flexibilities to shift the loads. At the same time, smart devices and smart grids provide access to more data than before, which fosters utilization of these flexibilities to schedule their deployment.

In other fields, such dynamic and uncertain systems are successfully tackled with methods from the field of Evolutionary Computation and Machine Learning. These methods may be able to provide robust solutions, more adaptive systems, and can work in large data-driven environments.

This Special Issue therefore invites contributions that investigate the use of such methods for dealing with flexibility control problems within energy and power systems. Topics of interest include but are not limited to:

  • Optimization and control of flexibilities;
  • Demand side management;
  • Load and power grid management;
  • Prediction for prices or demands;
  • Application of Evolutionary Computation or Machine Learning.

Dr. Steffen Finck
Dr. Peter Kepplinger
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 2000 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

  • Energy storage systems
  • Smart grid
  • Evolutionary computation
  • Prediction
  • Optimization
  • Demand-side management
  • Control
  • Scheduling
  • Machine learning

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Autonomous Operation of Stationary Battery Energy Storage Systems—Optimal Storage Design and Economic Potential
Energies 2021, 14(5), 1333; https://doi.org/10.3390/en14051333 - 01 Mar 2021
Cited by 1 | Viewed by 438
Abstract
Global warming requires a changeover from fossil fuel based to renewable energy sources on the electrical supply side and electrification of the demand side. Due to the transient nature of renewables and fluctuating demand, buffer capacities are necessary to compensate for supply/demand imbalances. [...] Read more.
Global warming requires a changeover from fossil fuel based to renewable energy sources on the electrical supply side and electrification of the demand side. Due to the transient nature of renewables and fluctuating demand, buffer capacities are necessary to compensate for supply/demand imbalances. Battery energy storage systems are promising. However, the initial costs are high. Repurposing electric vehicle batteries can reduce initial costs. Further, storage design optimization could significantly improve costs. Therefore, a battery control algorithm was developed, and a simulation study was performed to identify the optimal storage design and its economic potential. The algorithm used is based on autonomous (on-site) optimization, which relies on an incentive determining the operation mode (charge, discharge, or idle). The incentive used was the historic day-ahead stock market price for electricity, and the resulting potential economic gains for different European countries were compared for the years 2015–2019. This showed that there is a correlation between economic gain, optimal storage design (capacity-to-power ratio), and the mean standard deviation, as well as the mean relative change of the different day-ahead prices. Low yearly mean standard deviations of about 0.5 Euro Cents per kWh battery capacity lead to yearly earnings of about 1 €/kWh, deviations of 1 Euro Cent to 10 €/kWh, and deviations of 2 Euro Cents to 20 €/kWh. Small yearly mean relative changes, lower than 5%, lead to capacity-to-power ratios greater than 3, relative changes around 10% to an optimal capacity-to-power between 1.5 and 3, and for relative changes greater than 10% to an optimal capacity-to-power ratios of 1. While in countries like the United Kingdom, high potential earnings are expected, the economic prospective in countries like Norway is low due to limited day-ahead price performance. Full article
(This article belongs to the Special Issue Modern Computational Methods for Flexibility Control)
Show Figures

Figure 1

Back to TopTop