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Lookup NU author(s): Alex Kell, Dr Matthew ForshawORCiD, Dr Stephen McGough
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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.
Author(s): Kell AJM, Salas P, Mercure JF, Forshaw M, McGough AS
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Tackling Climate Change with Machine Learning workshop at NeurIPS 2020
Year of Conference: 2020
Acceptance date: 02/04/2020
Date deposited: 31/10/2020
Publisher: Climate Change AI
URL: https://www.climatechange.ai/papers/neurips2020/5
