  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.
