论文标题
长期水电生产计划的深度加固学习
Deep Reinforcement Learning for Long Term Hydropower Production Scheduling
论文作者
论文摘要
我们探讨了深入增强学习的使用，以提供水力发电生产的长期计划的策略。我们考虑一个用例，目的是优化给出每周一次的年度收入，以供水库和电价流入。面临的挑战是要以电力现货价格立即释放，并以系统的限制以未知的价格存储水以以后的发电量。我们成功地在简化的场景上培训了软性演员批评算法，并具有来自北欧电力市场的历史数据。提出的模型还没有准备好替代传统优化工具，而是在水力发电调度的数据丰富的水平领域中展示了增强学习的互补潜力。
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.
