  The global ambitions of a carbon-neutral society necessitate a stable and
robust smart grid that capitalises on frequency reserves of renewable energy.
Frequency reserves are resources that adjust power production or consumption in
real time to react to a power grid frequency deviation. Revenue generation
motivates the availability of these resources for managing such deviations.
However, limited research has been conducted on data-driven decisions and
optimal bidding strategies for trading such capacities in multiple frequency
reserves markets. We address this limitation by making the following research
contributions. Firstly, a generalised model is designed based on an extensive
study of critical characteristics of global frequency reserves markets.
Secondly, three bidding strategies are proposed, based on this market model, to
capitalise on price peaks in multi-stage markets. Two strategies are proposed
for non-reschedulable loads, in which case the bidding strategy aims to select
the market with the highest anticipated price, and the third bidding strategy
focuses on rescheduling loads to hours on which highest reserve market prices
are anticipated. The third research contribution is an Artificial Intelligence
(AI) based bidding optimization framework that implements these three
strategies, with novel uncertainty metrics that supplement data-driven price
prediction. Finally, the framework is evaluated empirically using a case study
of multiple frequency reserves markets in Finland. The results from this
evaluation confirm the effectiveness of the proposed bidding strategies and the
AI-based bidding optimization framework in terms of cumulative revenue
generation, leading to an increased availability of frequency reserves.
