Optimal energy storage scheduling for wind curtailment reduction and energy arbitrage: A deep reinforcement learning approach

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Abstract

Wind energy has been rapidly gaining popularity as a means for combating climate change. However, the variable nature of wind generation can undermine system reliability and lead to wind curtailment, causing substantial economic losses to wind power producers. Battery energy storage systems (BESS) that serve as onsite backup sources are among the solutions to mitigate wind curtailment. However, such an auxiliary role of the BESS might severely weaken its economic viability. This paper addresses the issue by proposing joint wind curtailment reduction and energy arbitrage for the BESS. We decouple the market participation of the co-located wind-battery system and develop a joint-bidding framework for the wind farm and BESS. It is challenging to optimize the joint-bidding because of the stochasticity of energy prices and wind generation. Therefore, we leverage deep reinforcement learning to maximize the overall revenue from the spot market while unlocking the BESS's potential in concurrently reducing wind curtailment and conducting energy arbitrage. We validate the proposed strategy using realistic wind farm data and demonstrate that our joint-bidding strategy responds better to wind curtailment and generates higher revenues than the optimization-based benchmark. Our simulations also reveal that the extra wind generation used to be curtailed can be an effective power source to charge the BESS, resulting in additional financial returns.

Original languageEnglish
Title of host publication2023 IEEE Power and Energy Society General Meeting, PESGM 2023
EditorsPraveen Kumar, P.E.
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781665464413
ISBN (Print)9781665464420
DOIs
Publication statusPublished - 2023
EventIEEE Power and Energy Society General Meeting 2023 - Orlando, United States of America
Duration: 16 Jul 202320 Jul 2023
https://ieeexplore.ieee.org/xpl/conhome/10252165/proceeding (Proceedings)
https://pes-gm.org/ (Website)

Conference

ConferenceIEEE Power and Energy Society General Meeting 2023
Abbreviated titlePESGM 2023
Country/TerritoryUnited States of America
CityOrlando
Period16/07/2320/07/23
Internet address

Keywords

  • Deep reinforcement learning
  • energy arbitrage
  • spot market
  • wind curtailment
  • wind-battery system

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