A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets

Saeed Mohammadi, M. Hesamzadeh
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Abstract

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.
即日电力市场中产消管理的机器学习方法
在考虑不确定性的同时,消费者运营商正在应对参与短期电力市场的广泛挑战。需求变化、太阳能、风能、电价以及日内电力市场更快的响应时间等挑战。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。为了解决这些挑战,本文提出了一个马尔可夫决策过程,并使用一种强化学习算法来解决这个过程,该算法采用表格q学习来进行适当的观察和行动。经过训练的智能体收敛到一个类似于全局最优解的策略。与知名的随机优化方法相比,该方法使产消者的利润提高了13.39%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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