Bidding strategy for trading wind energy and purchasing reserve of wind power producer–A DRL based approach

Di Cao, Weihao Hu*, Xiao Xu, Tomislav Dragicevic, Qi Huang, Zhou Liu, Zhe Chen, Frede Blaabjerg

*Kontaktforfatter

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

Wind power producers (WPP) are punished when take part in the short-term electricity market due to the inaccuracy of wind power prediction. The profit loss can be partially offset by strategic reserve purchasing. Due to the uncertainty of real-time wind power production, the reserve capacity price and the highly dynamic price regulation, it is difficult to determine the quantity of reserve to purchase, which might have a great impact on the profit of WPP. This paper investigates the possible influence on the revenue of WPP if they take part in both the energy and reserve market. This problem is first formulated as a Markov decision process (MDP). After that, the asynchronous advantage actor-critic (A3C) method is used to take care of this problem. Several agents are applied to explore the action space simultaneously. Neural networks are applied to fit the policy function and value function, which are trained against each other to learn the optimal dynamic bidding strategy from historical data by constantly trial and error. Simulation results of a wind farm in Denmark validate that the profit loss of the WPP can be significantly reduced when the A3C algorithm is used for bidding strategy formulation when WPP participate in both the energy and reserve market.
OriginalsprogEngelsk
TidsskriftInternational Journal of Electrical Power & Energy Systems
Vol/bind117
Antal sider10
ISSN0142-0615
DOI
StatusUdgivet - maj 2020

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Purchasing
Wind power
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title = "Bidding strategy for trading wind energy and purchasing reserve of wind power producer–A DRL based approach",
abstract = "Wind power producers (WPP) are punished when take part in the short-term electricity market due to the inaccuracy of wind power prediction. The profit loss can be partially offset by strategic reserve purchasing. Due to the uncertainty of real-time wind power production, the reserve capacity price and the highly dynamic price regulation, it is difficult to determine the quantity of reserve to purchase, which might have a great impact on the profit of WPP. This paper investigates the possible influence on the revenue of WPP if they take part in both the energy and reserve market. This problem is first formulated as a Markov decision process (MDP). After that, the asynchronous advantage actor-critic (A3C) method is used to take care of this problem. Several agents are applied to explore the action space simultaneously. Neural networks are applied to fit the policy function and value function, which are trained against each other to learn the optimal dynamic bidding strategy from historical data by constantly trial and error. Simulation results of a wind farm in Denmark validate that the profit loss of the WPP can be significantly reduced when the A3C algorithm is used for bidding strategy formulation when WPP participate in both the energy and reserve market.",
keywords = "Wind power bidding, Deep reinforcement learning, Data-driven, Agent-based, Uncertainty",
author = "Di Cao and Weihao Hu and Xiao Xu and Tomislav Dragicevic and Qi Huang and Zhou Liu and Zhe Chen and Frede Blaabjerg",
year = "2020",
month = "5",
doi = "10.1016/j.ijepes.2019.105648",
language = "English",
volume = "117",
journal = "International Journal of Electrical Power & Energy Systems",
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Bidding strategy for trading wind energy and purchasing reserve of wind power producer–A DRL based approach. / Cao, Di; Hu, Weihao; Xu, Xiao; Dragicevic, Tomislav; Huang, Qi; Liu, Zhou; Chen, Zhe; Blaabjerg, Frede.

I: International Journal of Electrical Power & Energy Systems, Bind 117, 05.2020.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Bidding strategy for trading wind energy and purchasing reserve of wind power producer–A DRL based approach

AU - Cao, Di

AU - Hu, Weihao

AU - Xu, Xiao

AU - Dragicevic, Tomislav

AU - Huang, Qi

AU - Liu, Zhou

AU - Chen, Zhe

AU - Blaabjerg, Frede

PY - 2020/5

Y1 - 2020/5

N2 - Wind power producers (WPP) are punished when take part in the short-term electricity market due to the inaccuracy of wind power prediction. The profit loss can be partially offset by strategic reserve purchasing. Due to the uncertainty of real-time wind power production, the reserve capacity price and the highly dynamic price regulation, it is difficult to determine the quantity of reserve to purchase, which might have a great impact on the profit of WPP. This paper investigates the possible influence on the revenue of WPP if they take part in both the energy and reserve market. This problem is first formulated as a Markov decision process (MDP). After that, the asynchronous advantage actor-critic (A3C) method is used to take care of this problem. Several agents are applied to explore the action space simultaneously. Neural networks are applied to fit the policy function and value function, which are trained against each other to learn the optimal dynamic bidding strategy from historical data by constantly trial and error. Simulation results of a wind farm in Denmark validate that the profit loss of the WPP can be significantly reduced when the A3C algorithm is used for bidding strategy formulation when WPP participate in both the energy and reserve market.

AB - Wind power producers (WPP) are punished when take part in the short-term electricity market due to the inaccuracy of wind power prediction. The profit loss can be partially offset by strategic reserve purchasing. Due to the uncertainty of real-time wind power production, the reserve capacity price and the highly dynamic price regulation, it is difficult to determine the quantity of reserve to purchase, which might have a great impact on the profit of WPP. This paper investigates the possible influence on the revenue of WPP if they take part in both the energy and reserve market. This problem is first formulated as a Markov decision process (MDP). After that, the asynchronous advantage actor-critic (A3C) method is used to take care of this problem. Several agents are applied to explore the action space simultaneously. Neural networks are applied to fit the policy function and value function, which are trained against each other to learn the optimal dynamic bidding strategy from historical data by constantly trial and error. Simulation results of a wind farm in Denmark validate that the profit loss of the WPP can be significantly reduced when the A3C algorithm is used for bidding strategy formulation when WPP participate in both the energy and reserve market.

KW - Wind power bidding

KW - Deep reinforcement learning

KW - Data-driven

KW - Agent-based

KW - Uncertainty

U2 - 10.1016/j.ijepes.2019.105648

DO - 10.1016/j.ijepes.2019.105648

M3 - Journal article

VL - 117

JO - International Journal of Electrical Power & Energy Systems

JF - International Journal of Electrical Power & Energy Systems

SN - 0142-0615

ER -