  We explore the use of deep reinforcement learning to provide strategies for
long term scheduling of hydropower production. We consider a use-case where the
aim is to optimise the yearly revenue given week-by-week inflows to the
reservoir and electricity prices. The challenge is to decide between immediate
water release at the spot price of electricity and storing the water for later
power production at an unknown price, given constraints on the system. We
successfully train a soft actor-critic algorithm on a simplified scenario with
historical data from the Nordic power market. The presented model is not ready
to substitute traditional optimisation tools but demonstrates the complementary
potential of reinforcement learning in the data-rich field of hydropower
scheduling.
