  A change from a high-carbon emitting electricity power system to one based on
renewables would aid in the mitigation of climate change. Decarbonization of
the electricity grid would allow for low-carbon heating, cooling and transport.
Investments in renewable energy must be made over a long time horizon to
maximise return of investment of these long life power generators. Over these
long time horizons, there exist multiple uncertainties, for example in future
electricity demand and costs to consumers and investors.
  To mitigate for imperfect information of the future, we use the deep
deterministic policy gradient (DDPG) deep reinforcement learning approach to
optimize for a low-cost, low-carbon electricity supply using a modified version
of the FTT:Power model. In this work, we model the UK and Ireland electricity
markets. The DDPG algorithm is able to learn the optimum electricity mix
through experience and achieves this between the years of 2017 and 2050. We
find that a change from fossil fuels and nuclear power to renewables, based
upon wind, solar and wave would provide a cheap and low-carbon alternative to
fossil fuels.
