论文标题
在日内电力市场中用于制造商管理的机器学习方法
A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets
论文作者
论文摘要
Prosumer运营商正在应对参与短期电力市场的广泛挑战，同时考虑不确定性。挑战，例如需求，太阳能，风能和电价的变化以及在盘中电力市场中的响应时间更快。机器学习方法可以解决这些挑战，因为它们能够持续学习复杂关系并提供实时响应。这种方法适用于高性能计算和大数据的存在。为了应对这些挑战，提出了马尔可夫决策过程，并通过使用表格Q学习的适当观察和行动来通过强化学习算法解决。受过训练的代理收敛到类似于全球最佳解决方案的策略。与众所周知的随机优化方法相比，它使生产商的利润增加了13.39％。
Prosumer operators are dealing with extensive challenges to participate in short-term electricity markets while taking uncertainties into account. Challenges such as variation in demand, solar energy, wind power, and electricity prices as well as faster response time in intraday electricity markets. Machine learning approaches could resolve these challenges due to their ability to continuous learning of complex relations and providing a real-time response. Such approaches are applicable with presence of the high performance computing and big data. To tackle these challenges, a Markov decision process is proposed and solved with a reinforcement learning algorithm with proper observations and actions employing tabular Q-learning. Trained agent converges to a policy which is similar to the global optimal solution. It increases the prosumer's profit by 13.39% compared to the well-known stochastic optimization approach.
