Accessibility navigation


The real-time optimisation of DNO owned storage devices on the LV network for peak reduction

Rowe, M., Yunusov, T. ORCID: https://orcid.org/0000-0003-2318-3009, Haben, S., Holderbaum, W. ORCID: https://orcid.org/0000-0002-1677-9624 and Potter, B. (2014) The real-time optimisation of DNO owned storage devices on the LV network for peak reduction. Energies, 7 (6). pp. 3537-3560. ISSN 1996-1073

[img]
Preview
Text - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.

313kB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.3390/en7063537

Abstract/Summary

Energy storage is a potential alternative to conventional network reinforcementof the low voltage (LV) distribution network to ensure the grid’s infrastructure remainswithin its operating constraints. This paper presents a study on the control of such storagedevices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, wherethe objective is to achieve the greatest peak reduction in demand, for a given storagedevice specification, taking into account the high level of uncertainty in the prediction of LV demand. The algorithms presented in this paper are compared to a standard set-pointcontroller and bench marked against a control algorithm with a perfect forecast. A specificcase study, using storage on the LV network, is presented, and the results of each algorithmare compared. A comprehensive analysis is then carried out simulating a large number of LV networks of varying numbers of households. The results show that the performance of each algorithm is dependent on the number of aggregated households. However, on a typical aggregation, the novel SRHC algorithm presented in this paper is shown to outperform each of the comparable storage control techniques.

Item Type:Article
Refereed:Yes
Divisions:Science > School of the Built Environment > Construction Management and Engineering
Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Interdisciplinary centres and themes > Energy Research
Science > School of the Built Environment > Energy and Environmental Engineering group
ID Code:36756
Publisher:MDPI Publishing, Basel

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation