  Wind energy has been increasingly adopted to mitigate climate change.
However, the variability of wind energy causes wind curtailment, resulting in
considerable economic losses for wind farm owners. Wind curtailment can be
reduced using battery energy storage systems (BESS) as onsite backup sources.
Yet, this auxiliary role may significantly weaken the economic potential of
BESS in energy trading. Ideal BESS scheduling should balance onsite wind
curtailment reduction and market bidding, but practical implementation is
challenging due to coordination complexity and the stochastic nature of energy
prices and wind generation. We investigate the joint-market bidding strategy of
a co-located wind-battery system in the spot and Regulation Frequency Control
Ancillary Service markets. We propose a novel deep reinforcement learning-based
approach that decouples the system's market participation into two related
Markov decision processes for each facility, enabling the BESS to absorb onsite
wind curtailment while performing joint-market bidding to maximize overall
operational revenues. Using realistic wind farm data, we validated the
coordinated bidding strategy, with outcomes surpassing the optimization-based
benchmark in terms of higher revenue by approximately 25\% and more wind
curtailment reduction by 2.3 times. Our results show that joint-market bidding
can significantly improve the financial performance of wind-battery systems
compared to participating in each market separately. Simulations also show that
using curtailed wind generation as a power source for charging the BESS can
lead to additional financial gains. The successful implementation of our
algorithm would encourage co-location of generation and storage assets to
unlock wider system benefits.
