  Amidst the climate crisis, the massive introduction of renewable energy
sources has brought tremendous challenges to both the power grid and its
surrounding markets. As datacenters have become ever-larger and more powerful,
they play an increasingly significant role in the energy arena. With their
unique characteristics, datacenters have been proved to be well-suited for
regulating the power grid yet currently provide little, if any, such active
response. This problem is due to issues such as unsuitability of the market
design, high complexity of the currently proposed solutions, as well as the
potential risks thereof. This work aims to provide individual datacenters with
insights on the feasibility and profitability of directly participating in the
energy market. By modelling the power system of datacenters, and by conducting
simulations on real-world datacenter traces, we demonstrate the substantial
financial incentive for individual datacenters to directly participate in both
the day-ahead and the balancing markets. In turn, we suggest a new short-term,
direct scheme of market participation for individual datacenters in place of
the current long-term, inactive participation. Furthermore, we develop a novel
proactive DVFS scheduling algorithm that can both reduce energy consumption and
save energy costs during the market participation of datacenters. Also, in
developing this scheduler, we propose an innovative combination of machine
learning methods and the DVFS technology that can provide the power grid with
indirect demand response (DR). Our experimental results strongly support that
individual datacenters can and should directly participate in the energy market
both to save their energy costs and to curb their energy consumption, whilst
providing the power grid with indirect DR.
