Integrated Bidding and Operating Strategies for Wind-Storage Systems

Huajie Ding, Pierre Pinson, Zechun Hu, Yonghua Song

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Due to their flexible charging and discharging capabilities, energy storage systems (ESS) are considered a promising complement to wind farms (WFs) participating in electricity markets. This paper presents integrated day-ahead bidding and real-time operation strategies for a wind-storage system to perform arbitrage and to alleviate wind power deviations from day-ahead contracts. The strategy is developed with two-price balancing markets in mind. A mixed integer nonlinear optimization formulation is built to determine optimal offers by taking into account expected wind power forecasting errors and the power balancing capability of the ESS. A modified gradient descent algorithm is designed to solve this nonlinear problem. A number of case studies validate the computational efficiency and optimality of the algorithm. Compared to the existing strategies, the proposed strategies yield increased economic profit, regardless of the temporal dependence of wind power forecasting errors.
Original languageEnglish
JournalIEEE Transactions on Sustainable Energy
Volume7
Issue number1
Pages (from-to)163-172
ISSN1949-3029
DOIs
Publication statusPublished - 2016

Keywords

  • Power, Energy and Industry Applications
  • Bidding strategy
  • electricity markets
  • Electricity supply industry
  • Energy storage
  • energy storage system (ESS)
  • Optimization
  • Probabilistic logic
  • real-time operation
  • Real-time systems
  • wind farm (WF)
  • Wind forecasting
  • Wind power generation

Cite this

Ding, Huajie ; Pinson, Pierre ; Hu, Zechun ; Song, Yonghua. / Integrated Bidding and Operating Strategies for Wind-Storage Systems. In: IEEE Transactions on Sustainable Energy. 2016 ; Vol. 7, No. 1. pp. 163-172.
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title = "Integrated Bidding and Operating Strategies for Wind-Storage Systems",
abstract = "Due to their flexible charging and discharging capabilities, energy storage systems (ESS) are considered a promising complement to wind farms (WFs) participating in electricity markets. This paper presents integrated day-ahead bidding and real-time operation strategies for a wind-storage system to perform arbitrage and to alleviate wind power deviations from day-ahead contracts. The strategy is developed with two-price balancing markets in mind. A mixed integer nonlinear optimization formulation is built to determine optimal offers by taking into account expected wind power forecasting errors and the power balancing capability of the ESS. A modified gradient descent algorithm is designed to solve this nonlinear problem. A number of case studies validate the computational efficiency and optimality of the algorithm. Compared to the existing strategies, the proposed strategies yield increased economic profit, regardless of the temporal dependence of wind power forecasting errors.",
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Integrated Bidding and Operating Strategies for Wind-Storage Systems. / Ding, Huajie; Pinson, Pierre; Hu, Zechun; Song, Yonghua.

In: IEEE Transactions on Sustainable Energy, Vol. 7, No. 1, 2016, p. 163-172.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Integrated Bidding and Operating Strategies for Wind-Storage Systems

AU - Ding, Huajie

AU - Pinson, Pierre

AU - Hu, Zechun

AU - Song, Yonghua

PY - 2016

Y1 - 2016

N2 - Due to their flexible charging and discharging capabilities, energy storage systems (ESS) are considered a promising complement to wind farms (WFs) participating in electricity markets. This paper presents integrated day-ahead bidding and real-time operation strategies for a wind-storage system to perform arbitrage and to alleviate wind power deviations from day-ahead contracts. The strategy is developed with two-price balancing markets in mind. A mixed integer nonlinear optimization formulation is built to determine optimal offers by taking into account expected wind power forecasting errors and the power balancing capability of the ESS. A modified gradient descent algorithm is designed to solve this nonlinear problem. A number of case studies validate the computational efficiency and optimality of the algorithm. Compared to the existing strategies, the proposed strategies yield increased economic profit, regardless of the temporal dependence of wind power forecasting errors.

AB - Due to their flexible charging and discharging capabilities, energy storage systems (ESS) are considered a promising complement to wind farms (WFs) participating in electricity markets. This paper presents integrated day-ahead bidding and real-time operation strategies for a wind-storage system to perform arbitrage and to alleviate wind power deviations from day-ahead contracts. The strategy is developed with two-price balancing markets in mind. A mixed integer nonlinear optimization formulation is built to determine optimal offers by taking into account expected wind power forecasting errors and the power balancing capability of the ESS. A modified gradient descent algorithm is designed to solve this nonlinear problem. A number of case studies validate the computational efficiency and optimality of the algorithm. Compared to the existing strategies, the proposed strategies yield increased economic profit, regardless of the temporal dependence of wind power forecasting errors.

KW - Power, Energy and Industry Applications

KW - Bidding strategy

KW - electricity markets

KW - Electricity supply industry

KW - Energy storage

KW - energy storage system (ESS)

KW - Optimization

KW - Probabilistic logic

KW - real-time operation

KW - Real-time systems

KW - wind farm (WF)

KW - Wind forecasting

KW - Wind power generation

U2 - 10.1109/TSTE.2015.2472576

DO - 10.1109/TSTE.2015.2472576

M3 - Journal article

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EP - 172

JO - I E E E Transactions on Sustainable Energy

JF - I E E E Transactions on Sustainable Energy

SN - 1949-3029

IS - 1

ER -