论文标题
最小化长期碳排放和电力成本的发电投资中的深入增强学习投资
Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs
论文作者
论文摘要
从高碳发射电力系统改为基于可再生能源的变化将有助于缓解气候变化。电网格的脱碳将允许低碳加热，冷却和运输。必须长期进行可再生能源的投资，以最大程度地提高这些长寿发电机的投资回报。在这些长时间的视野中，存在多种不确定性，例如未来的电力需求以及对消费者和投资者的成本。 为了减轻未来的不完善信息，我们使用深层确定性的政策梯度（DDPG）深度加固学习方法来优化低成本，低碳电力供应，使用FTT：Power Model的修改版本。在这项工作中，我们对英国和爱尔兰的电力市场进行建模。 DDPG算法能够通过经验来学习最佳的电力组合，并在2017年至2050年之间实现这一目标。我们发现，基于风，太阳能和波浪，从化石燃料和核能到可再生能源的变化将提供廉价和低碳替代品化石燃料的替代品。
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.
