基于Q-learning算法的多智能体微电网能量管理策略

Energy Management Strategy of Multi-agent Microgrid Based on Q-learning Algorithm

针对微电网电力市场的能量交易和利益分配问题,提出了一种基于Q-learning算法的多智能体微电网能量管理方法。构建基于电力市场的微电网系统及交易过程,明确各单元的职责;考虑风速、光照强度和环境温度的变化情况,以及各发电单元的输出功率上下限约束,建立分布式电源数学模型。在此基础上,将分布式电源和用户负载视为智能体,基于Q-learning算法设计它们的马尔可夫决策过程,以最大化分布式电源收益和最小化用户负载成本为目标,提出了基于Qlearning算法的微电网能量管理方案。研究结果表明,在不同场景下所提方法既可以增加分布式电源的收益,还可以降低用户负载的成本。

This paper proposes a multi-agent microgrid energy management method for the energy trading and benefit distribution in the microgrid power market based on the Q-learning algorithm.Based on the electricity market,microgrid system and transaction process are constructed to clarify the responsibilities of each unit.The mathematical models of distributed power generations are established by considering the changes in wind speed,light intensity and ambient temperature,as well as the upper and lower limit constraints of the output power of each power generation unit.On this basis,the distributed power generations and user loads are regarded as agents,and the Markov decision-making process is designed based on the Q-learning algorithm.Aiming at maximizing the benefits of distributed power generations and minimizing the costs of user loads,a microgrid energy management scheme based on Q-learning algorithm is proposed.The results show that the proposed method can not only increase the benefits of distributed power generations but also reduce the costs of user loads in different scenarios.