Chen Shuai, Li Jinglin, Jiang Chengpeng, Xiao Wendong
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Entropy (Basel). 2022 Apr 29;24(5):630. doi: 10.3390/e24050630.
Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.
储能是提高电力系统经济性和可靠性的重要调节手段。由于微电网中电源、负荷、储能等元件耦合关系的复杂性，在经济运行和高效调度方面存在性能不足的问题。鉴于此，本文提出一种基于强化学习和电池健康状态评估的储能配置优化模型。首先，基于深度学习对电池健康寿命损失进行了定量评估。其次，在考虑综合能源互补性的基础上，设计了一种双层优化配置模型，对容量配置和调度运行进行优化。最后，通过实验验证了所提方法在微电网储能规划与运行中的可行性。通过融合强化学习和传统优化方法，所提方法不依赖于电源和负荷的精确预测，仅根据微电网的实时信息进行决策。本文分析了所提方法与现有方法的优缺点，结果表明所提方法能有效提高微电网储能动态规划的性能。
