智能电网中基于增强学习的动态价格优化算法

动态的电费价格是驱使消费者改变用电消费模式的有效手段，为此，提出基于增强学习的动态价格优化(RLODP)算法。RLODP算法结合电力服务商的利润和消费者的用电成本，对电网负载进行管理；利用增强学习算法，电力服务商自适应地决策零售价格，将动态价格问题转化为离散有限马尔可夫决策过程(MDP)，再利用Q-学习算法解决该决策过程。实验结果表明，提出的RLODP算法减少了消费者的用电成本，实现了电网市场中电力供应与需求之间的平衡。

Reinforcement Learning-based Optimizing Dynamic Pricing algorithm in smart grid

Dynamic pricing is one of the most effective ways to encourage customers to change their consumption pattern. Therefore, Reinforcement Learning-based Optimizing Dynamic Pricing(RLODP) algorithm is proposed for energy management in a hierarchical electricity market by considering both service provider''s profit and customers'' costs. Using Reinforcement Learning, the SP can adaptively determine the retail electricity price. Dynamic pricing problem is formulated as a discrete finite Markov Decision Process(MDP), and Q-learning is adopted to solve this decision-making problem. Simulation results show that the RLODP algorithm can reduce energy costs for customers, balance the energy supply and the demands in the electricity market.