Charging-rate-based Battery Energy Storage System in Wind Farm and Battery Storage Cooperation Bidding Problem

Zihang Qiu, Wang Zhang*, Shuai Lu, Chaojie Li, Jingbo Wang, Ke Meng, Zhaoyang Dong

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

18 Citations (Scopus)

Abstract

Wind power has been proven to have the ability to participate in the frequency modulation (FM) market. Using batteries to improve wind power stability can better aid wind farms participating in the FM market. Battery energy storage system (BESS) has a promising future in applying regulation and load management in the power grid. For regulation services, normally, the regulation power prediction is estimated based on the required maximum regulation capacity; the power needed for the specific regulation service is unknown to the BESS owner. However, this information is needed in the regulation model when formulating the linearised BESS model with a constraint on the state of charge (SoC). This compromises the accuracy of the model greatly when it is applied for regulation service. Moreover, different control strategies can be employed by BESS. However, the current depth of discharge (DoD) based models have difficulties in being used in a linearization problem. Due to the consideration of the control strategy, the model becomes highly nonlinear and cannot be solved. In this paper, a charging rate (C-rate) based model is introduced, which can consider different control strategies of a BESS for cooperation with wind farms to participate in wind farm estimation error compensation, load management, energy bid, and regulation bid. First, the limitation of conventional BESS models are listed, and a new C-rate-based model is introduced. Then the C-rate-based BESS model is adopted in a wind farm and BESS cooperation scheme. Finally, experimental studies are carried out, and the DoD model and C-rate model optimization results are compared to prove the rationality of the C-rate model. © 2021 CSEE.
Original languageEnglish
Pages (from-to)659-668
JournalCSEE Journal of Power and Energy Systems
Volume8
Issue number3
Online published5 Jan 2022
DOIs
Publication statusPublished - May 2022
Externally publishedYes

Research Keywords

  • Bidding
  • C-rate based battery energy storage system model
  • wind farm estimation error compensation

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