Mitigating load forecast errors for suppliers by utilizing energy storage at a substation level

Xiaohe Yan, Qiuyang Ma, Heather Wyman-Pain, Chenghong Gu, Ran Li, Furong Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Due to the growth of intermittent generation and flexible demand, the difference between real metered load profiles and predicted profiles has increased significantly. This has caused a higher cost to suppliers as they have to mitigate the errors by using costly fast response generators or buying expensive energy from other suppliers. Improving load forecast accuracy is an alternative to reduce the difference and consequently the costs, but it relies on large quantities of historical load data which is not necessarily available. This paper utilises a novel control strategy for energy storage systems to mitigate forecast errors for suppliers. This results in energy cost savings and the Use-of-System (UoS) savings. In order to test the charging/discharging strategies and quantify the economic benefits, a case study has been conducted by utilizing smart metering data. The case study has produced a 33.2% cost reduction in the energy cost savings.

Original languageEnglish
Title of host publicationProceedings of IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)9781538619537
DOIs
Publication statusPublished - 16 Jan 2018
Event2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2017 - Torino, Italy
Duration: 26 Sep 201729 Sep 2017
http://sites.ieee.org/isgt-europe-2017/

Conference

Conference2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2017
CountryItaly
CityTorino
Period26/09/1729/09/17
Internet address

Fingerprint

Energy storage
Costs
Cost reduction
Economics

Keywords

  • Charging/discharging methodology
  • Energy cost
  • Energy storage
  • Forecast uncertainty
  • UoS cost

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

Cite this

Yan, X., Ma, Q., Wyman-Pain, H., Gu, C., Li, R., & Li, F. (2018). Mitigating load forecast errors for suppliers by utilizing energy storage at a substation level. In Proceedings of IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 (pp. 1-5). IEEE. https://doi.org/10.1109/ISGTEurope.2017.8260117

Mitigating load forecast errors for suppliers by utilizing energy storage at a substation level. / Yan, Xiaohe; Ma, Qiuyang; Wyman-Pain, Heather; Gu, Chenghong; Li, Ran; Li, Furong.

Proceedings of IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 . IEEE, 2018. p. 1-5.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yan, X, Ma, Q, Wyman-Pain, H, Gu, C, Li, R & Li, F 2018, Mitigating load forecast errors for suppliers by utilizing energy storage at a substation level. in Proceedings of IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 . IEEE, pp. 1-5, 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2017, Torino, Italy, 26/09/17. https://doi.org/10.1109/ISGTEurope.2017.8260117
Yan X, Ma Q, Wyman-Pain H, Gu C, Li R, Li F. Mitigating load forecast errors for suppliers by utilizing energy storage at a substation level. In Proceedings of IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 . IEEE. 2018. p. 1-5 https://doi.org/10.1109/ISGTEurope.2017.8260117
Yan, Xiaohe ; Ma, Qiuyang ; Wyman-Pain, Heather ; Gu, Chenghong ; Li, Ran ; Li, Furong. / Mitigating load forecast errors for suppliers by utilizing energy storage at a substation level. Proceedings of IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 . IEEE, 2018. pp. 1-5
@inproceedings{f8d01be07edd43d0a31e059d41ff96ca,
title = "Mitigating load forecast errors for suppliers by utilizing energy storage at a substation level",
abstract = "Due to the growth of intermittent generation and flexible demand, the difference between real metered load profiles and predicted profiles has increased significantly. This has caused a higher cost to suppliers as they have to mitigate the errors by using costly fast response generators or buying expensive energy from other suppliers. Improving load forecast accuracy is an alternative to reduce the difference and consequently the costs, but it relies on large quantities of historical load data which is not necessarily available. This paper utilises a novel control strategy for energy storage systems to mitigate forecast errors for suppliers. This results in energy cost savings and the Use-of-System (UoS) savings. In order to test the charging/discharging strategies and quantify the economic benefits, a case study has been conducted by utilizing smart metering data. The case study has produced a 33.2{\%} cost reduction in the energy cost savings.",
keywords = "Charging/discharging methodology, Energy cost, Energy storage, Forecast uncertainty, UoS cost",
author = "Xiaohe Yan and Qiuyang Ma and Heather Wyman-Pain and Chenghong Gu and Ran Li and Furong Li",
year = "2018",
month = "1",
day = "16",
doi = "10.1109/ISGTEurope.2017.8260117",
language = "English",
pages = "1--5",
booktitle = "Proceedings of IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017",
publisher = "IEEE",
address = "USA United States",

}

TY - GEN

T1 - Mitigating load forecast errors for suppliers by utilizing energy storage at a substation level

AU - Yan, Xiaohe

AU - Ma, Qiuyang

AU - Wyman-Pain, Heather

AU - Gu, Chenghong

AU - Li, Ran

AU - Li, Furong

PY - 2018/1/16

Y1 - 2018/1/16

N2 - Due to the growth of intermittent generation and flexible demand, the difference between real metered load profiles and predicted profiles has increased significantly. This has caused a higher cost to suppliers as they have to mitigate the errors by using costly fast response generators or buying expensive energy from other suppliers. Improving load forecast accuracy is an alternative to reduce the difference and consequently the costs, but it relies on large quantities of historical load data which is not necessarily available. This paper utilises a novel control strategy for energy storage systems to mitigate forecast errors for suppliers. This results in energy cost savings and the Use-of-System (UoS) savings. In order to test the charging/discharging strategies and quantify the economic benefits, a case study has been conducted by utilizing smart metering data. The case study has produced a 33.2% cost reduction in the energy cost savings.

AB - Due to the growth of intermittent generation and flexible demand, the difference between real metered load profiles and predicted profiles has increased significantly. This has caused a higher cost to suppliers as they have to mitigate the errors by using costly fast response generators or buying expensive energy from other suppliers. Improving load forecast accuracy is an alternative to reduce the difference and consequently the costs, but it relies on large quantities of historical load data which is not necessarily available. This paper utilises a novel control strategy for energy storage systems to mitigate forecast errors for suppliers. This results in energy cost savings and the Use-of-System (UoS) savings. In order to test the charging/discharging strategies and quantify the economic benefits, a case study has been conducted by utilizing smart metering data. The case study has produced a 33.2% cost reduction in the energy cost savings.

KW - Charging/discharging methodology

KW - Energy cost

KW - Energy storage

KW - Forecast uncertainty

KW - UoS cost

UR - http://www.scopus.com/inward/record.url?scp=85046271150&partnerID=8YFLogxK

U2 - 10.1109/ISGTEurope.2017.8260117

DO - 10.1109/ISGTEurope.2017.8260117

M3 - Conference contribution

SP - 1

EP - 5

BT - Proceedings of IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017

PB - IEEE

ER -