Optimal bidding strategy of battery storage in power markets considering performance based regulation and battery cycle life

Guannan He, Qixin Chen, Chongqing Kang, Pierre Pinson, Qing Xia

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Large-scale battery storage will become an essential part of the future smart grid. This paper investigates the optimal bidding strategy for battery storage in power markets. Battery storage could increase its profitability by providing fast regulation service under a performance-based regulation mechanism, which better exploits a battery’s fast ramping capability. However, battery life might be decreased by frequent charge–discharge cycling, especially when providing fast regulation service. It is profitable for battery storage to extend its service life by limiting its operational strategy to some degree. Thus, we incorporate a battery cycle life model into a profit maximization model to determine the optimal bids in day-ahead energy, spinning reserve, and regulation markets. Then a decomposed online calculation method to compute cycle life under different operational strategies is proposed to reduce the complexity of the model. This novel bidding model would help investor-owned battery storages better decide their bidding and operational schedules and investors to estimate the battery storage’s economic viability. The validity of the proposed model is proven by case study results.
Original languageEnglish
JournalIEEE Transactions on Smart Grid
Volume7
Issue number5
Pages (from-to)2359 - 2367
Number of pages9
ISSN1949-3053
DOIs
Publication statusPublished - 2016

Bibliographical note

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Keywords

  • Battery cycle life
  • Battery storage
  • Optimal bidding strategy
  • Performance-based regulation (PBR)
  • Power markets

Cite this

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title = "Optimal bidding strategy of battery storage in power markets considering performance based regulation and battery cycle life",
abstract = "Large-scale battery storage will become an essential part of the future smart grid. This paper investigates the optimal bidding strategy for battery storage in power markets. Battery storage could increase its profitability by providing fast regulation service under a performance-based regulation mechanism, which better exploits a battery’s fast ramping capability. However, battery life might be decreased by frequent charge–discharge cycling, especially when providing fast regulation service. It is profitable for battery storage to extend its service life by limiting its operational strategy to some degree. Thus, we incorporate a battery cycle life model into a profit maximization model to determine the optimal bids in day-ahead energy, spinning reserve, and regulation markets. Then a decomposed online calculation method to compute cycle life under different operational strategies is proposed to reduce the complexity of the model. This novel bidding model would help investor-owned battery storages better decide their bidding and operational schedules and investors to estimate the battery storage’s economic viability. The validity of the proposed model is proven by case study results.",
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Optimal bidding strategy of battery storage in power markets considering performance based regulation and battery cycle life. / He, Guannan; Chen, Qixin; Kang, Chongqing; Pinson, Pierre; Xia, Qing.

In: IEEE Transactions on Smart Grid, Vol. 7, No. 5, 2016, p. 2359 - 2367.

Research output: Contribution to journalJournal articleResearchpeer-review

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AU - Chen, Qixin

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AU - Pinson, Pierre

AU - Xia, Qing

N1 - (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

PY - 2016

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